From be5318fdb0fbb7a447e68fad6787e2b6e221a20d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 8 Jul 2020 21:16:36 -0500 Subject: [PATCH 0001/2572] Reorganizing some of the DAGMC-related code and using shared pointers instead. --- include/openmc/cell.h | 17 ++++++++++++++++- include/openmc/dagmc.h | 4 ++-- include/openmc/surface.h | 2 +- src/cell.cpp | 16 ++++++++++++++++ src/dagmc.cpp | 17 +++++++---------- src/finalize.cpp | 3 --- 6 files changed, 42 insertions(+), 17 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index c7ba0ab1374..282f00fff82 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -75,6 +75,20 @@ class Universe unique_ptr partitioner_; }; +#ifdef DAGMC + +class DAGUniverse : public Universe { + +public: + explicit DAGUniverse(std::string filename); + + + // Data Members + std::shared_ptr dag_instance_; //! DAGMC Instance for this universe +}; + +#endif + //============================================================================== //============================================================================== @@ -271,7 +285,7 @@ class DAGCell : public Cell void to_hdf5(hid_t group_id) const; - moab::DagMC* dagmc_ptr_; //!< Pointer to DagMC instance + std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of cell }; #endif @@ -343,6 +357,7 @@ struct CellInstanceHash { void read_cells(pugi::xml_node node); #ifdef DAGMC +void read_dagmc_universes(pugi::xml_node node); int32_t next_cell(DAGCell* cur_cell, DAGSurface* surf_xed); #endif diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 9f409d8730e..81aa163dc34 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -15,7 +15,7 @@ extern "C" const bool DAGMC_ENABLED; namespace openmc { namespace model { - extern moab::DagMC* DAG; + extern std::shared_ptr DAG; } //============================================================================== @@ -23,7 +23,7 @@ namespace model { //============================================================================== void load_dagmc_geometry(); -void free_memory_dagmc(); +void create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 5a8a88aa41d..b729514b9e6 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -168,7 +168,7 @@ class DAGSurface : public Surface void to_hdf5(hid_t group_id) const; - moab::DagMC* dagmc_ptr_; //!< Pointer to DagMC instance + std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of surface }; #endif diff --git a/src/cell.cpp b/src/cell.cpp index 6b88a67803e..733b70f0566 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -1013,6 +1013,11 @@ void read_cells(pugi::xml_node node) } } +#ifdef DAGMC + // read any DAGMC universes + read_dagmc_universes(node); +#endif + // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { int32_t uid = model::cells[i]->universe_; @@ -1034,6 +1039,17 @@ void read_cells(pugi::xml_node node) } } +void read_dagmc_universes(pugi::xml_node node) { + + // determine the max cell id + int32_t next_cell_id = 0; + for (const auto& c : model::cells) { + if (c->id_ > next_cell_id) next_cell_id = c->id_; + } + next_cell_id++; + +} + //============================================================================== // C-API functions //============================================================================== diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 0c2768834a5..5b8a61f7eac 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -41,7 +41,7 @@ const std::string DAGMC_FILENAME = "dagmc.h5m"; namespace model { -moab::DagMC* DAG; +std::shared_ptr DAG; } // namespace model @@ -151,14 +151,17 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) void load_dagmc_geometry() { + create_dagmc_universe(dagmc_file()); +} + +void create_dagmc_universe(const std::string& filename) { if (!model::DAG) { - model::DAG = new moab::DagMC(); + model::DAG = std::make_shared(); } // --- Materials --- // create uwuw instance - auto filename = dagmc_file(); UWUW uwuw(filename.c_str()); // check for uwuw material definitions @@ -180,7 +183,7 @@ void load_dagmc_geometry() MB_CHK_ERR_CONT(rval); // parse model metadata - dagmcMetaData DMD(model::DAG, false, false); + dagmcMetaData DMD(model::DAG.get(), false, false); DMD.load_property_data(); vector keywords {"temp"}; @@ -346,11 +349,5 @@ void read_geometry_dagmc() model::root_universe = find_root_universe(); } -void free_memory_dagmc() -{ - delete model::DAG; -} - - } #endif diff --git a/src/finalize.cpp b/src/finalize.cpp index 22dc6718360..54a8d681dca 100644 --- a/src/finalize.cpp +++ b/src/finalize.cpp @@ -51,9 +51,6 @@ void free_memory() if (settings::event_based) { free_event_queues(); } -#ifdef DAGMC - free_memory_dagmc(); -#endif } } From ef21ec493be10b4b68d5b303aaa0097be67ff244 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 8 Jul 2020 21:32:56 -0500 Subject: [PATCH 0002/2572] Returning universe index from the original dagmc function now. --- include/openmc/dagmc.h | 2 +- src/dagmc.cpp | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 81aa163dc34..1e66d8f1706 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -23,7 +23,7 @@ namespace model { //============================================================================== void load_dagmc_geometry(); -void create_dagmc_universe(const std::string& filename); +int32_t create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 5b8a61f7eac..a3567ebe1ee 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -151,10 +151,10 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) void load_dagmc_geometry() { - create_dagmc_universe(dagmc_file()); + int32_t univ_idx = create_dagmc_universe(dagmc_file()); } -void create_dagmc_universe(const std::string& filename) { +int32_t create_dagmc_universe(const std::string& filename) { if (!model::DAG) { model::DAG = std::make_shared(); } @@ -338,7 +338,7 @@ void create_dagmc_universe(const std::string& filename) { model::surface_map[s->id_] = i; } - return; + return model::universes.size() - 1; } void read_geometry_dagmc() From a16c0916dae5fd8f6137bb6ea1f96d3c2992248f Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 8 Jul 2020 22:38:06 -0500 Subject: [PATCH 0003/2572] Using the DAGUniverse constructor to setup the DAGMC instance. --- include/openmc/cell.h | 4 +- include/openmc/dagmc.h | 3 +- src/dagmc.cpp | 213 +++++++++++++++++- .../dagmc/refl/inputs_true.dat | 2 +- tests/regression_tests/dagmc/refl/test.py | 2 +- 5 files changed, 217 insertions(+), 7 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 282f00fff82..5b0b595608a 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -80,11 +80,11 @@ class Universe class DAGUniverse : public Universe { public: - explicit DAGUniverse(std::string filename); + explicit DAGUniverse(const std::string& filename); // Data Members - std::shared_ptr dag_instance_; //! DAGMC Instance for this universe + std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe }; #endif diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 1e66d8f1706..417cedebd75 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -9,8 +9,9 @@ extern "C" const bool DAGMC_ENABLED; #ifdef DAGMC #include "DagMC.hpp" -#include "openmc/xml_interface.h" + #include "openmc/position.h" +#include "openmc/xml_interface.h" namespace openmc { diff --git a/src/dagmc.cpp b/src/dagmc.cpp index a3567ebe1ee..37de2d0c6e9 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -151,7 +151,215 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) void load_dagmc_geometry() { - int32_t univ_idx = create_dagmc_universe(dagmc_file()); + model::universes.push_back(std::make_unique(dagmc_file())); + model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; +} + +DAGUniverse::DAGUniverse(const std::string& filename) { + + // determine the next cell id + int32_t next_cell_id = 0; + for (const auto& c : model::cells) { + if (c->id_ > next_cell_id) next_cell_id = c->id_; + } + next_cell_id++; + + // determine the next surface id + int32_t next_surf_id = 0; + for (const auto& s : model::surfaces) { + if (s->id_ > next_surf_id) next_surf_id = s->id_; + } + next_surf_id++; + + // determine the next universe id + int32_t next_univ_id = 0; + for (const auto& u : model::universes) { + if (u->id_ > next_univ_id) next_univ_id = u->id_; + } + next_univ_id++; + + // set the universe id + id_ = next_univ_id; + + // create a new DAGMC instance + dagmc_instance_ = std::make_shared(); + + // --- Materials --- + + // create uwuw instance + UWUW uwuw(filename.c_str()); + + // check for uwuw material definitions + bool using_uwuw = !uwuw.material_library.empty(); + + // notify user if UWUW materials are going to be used + if (using_uwuw) { + write_message("Found UWUW Materials in the DAGMC geometry file.", 6); + } + + // load the DAGMC geometry + moab::ErrorCode rval = dagmc_instance_->load_file(filename.c_str()); + MB_CHK_ERR_CONT(rval); + + // initialize acceleration data structures + rval = dagmc_instance_->init_OBBTree(); + MB_CHK_ERR_CONT(rval); + + // parse model metadata + dagmcMetaData DMD(dagmc_instance_.get(), false, false); + DMD.load_property_data(); + + std::vector keywords {"temp"}; + std::map dum; + std::string delimiters = ":/"; + rval = dagmc_instance_->parse_properties(keywords, dum, delimiters.c_str()); + MB_CHK_ERR_CONT(rval); + + // --- Cells (Volumes) --- + + // initialize cell objects + int n_cells = dagmc_instance_->num_entities(3); + moab::EntityHandle graveyard = 0; + for (int i = 0; i < n_cells; i++) { + moab::EntityHandle vol_handle = dagmc_instance_->entity_by_index(3, i + 1); + + // set cell ids using global IDs + DAGCell* c = new DAGCell(); + c->dag_index_ = i + 1; + std::cout << "Cell id: " << next_cell_id << std::endl; + c->id_ = next_cell_id++; + // c->id_ = model::DAG->id_by_index(3, c->dag_index_); + c->dagmc_ptr_ = dagmc_instance_; + c->universe_ = id_; // set to zero for now + c->fill_ = C_NONE; // no fill, single universe + + model::cells.emplace_back(c); + model::cell_map[c->id_] = model::cells.size() - 1; + cells_.push_back(model::cell_map[c->id_]); + + // Populate the Universe vector and dict + // auto it = model::universe_map.find(dagmc_univ_id); + // if (it == model::universe_map.end()) { + // model::universes.push_back(std::make_unique()); + // model::universes.back()->id_ = dagmc_univ_id; + // model::universes.back()->cells_.push_back(i); + // model::universe_map[id_] = model::universes.size() - 1; + // } else { + // model::universes[it->second]->cells_.push_back(i); + // } + + // MATERIALS + + // determine volume material assignment + std::string mat_str = DMD.get_volume_property("material", vol_handle); + + if (mat_str.empty()) { + fatal_error(fmt::format("Volume {} has no material assignment.", c->id_)); + } + + to_lower(mat_str); + + if (mat_str == "graveyard") { + graveyard = vol_handle; + } + + // material void checks + if (mat_str == "void" || mat_str == "vacuum" || mat_str == "graveyard") { + c->material_.push_back(MATERIAL_VOID); + } else { + if (using_uwuw) { + // lookup material in uwuw if present + std::string uwuw_mat = DMD.volume_material_property_data_eh[vol_handle]; + if (uwuw.material_library.count(uwuw_mat) != 0) { + // Note: material numbers are set by UWUW + int mat_number = uwuw.material_library[uwuw_mat].metadata["mat_number"].asInt(); + c->material_.push_back(mat_number); + } else { + fatal_error(fmt::format("Material with value {} not found in the " + "UWUW material library", mat_str)); + } + } else { + legacy_assign_material(mat_str, c); + } + } + + // check for temperature assignment + std::string temp_value; + + // no temperature if void + if (c->material_[0] == MATERIAL_VOID) continue; + + // assign cell temperature + const auto& mat = model::materials[model::material_map.at(c->material_[0])]; + if (dagmc_instance_->has_prop(vol_handle, "temp")) { + rval = dagmc_instance_->prop_value(vol_handle, "temp", temp_value); + MB_CHK_ERR_CONT(rval); + double temp = std::stod(temp_value); + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * temp)); + } else if (mat->temperature_ > 0.0) { + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * mat->temperature_)); + } else { + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); + } + + } + + // allocate the cell overlap count if necessary + if (settings::check_overlaps) { + model::overlap_check_count.resize(model::cells.size(), 0); + } + + if (!graveyard) { + warning("No graveyard volume found in the DagMC model." + "This may result in lost particles and rapid simulation failure."); + } + + // --- Surfaces --- + + // initialize surface objects + int n_surfaces = dagmc_instance_->num_entities(2); + + for (int i = 0; i < n_surfaces; i++) { + moab::EntityHandle surf_handle = dagmc_instance_->entity_by_index(2, i+1); + + // set cell ids using global IDs + DAGSurface* s = new DAGSurface(); + s->dag_index_ = i+1; + s->id_ = next_surf_id++; + s->dagmc_ptr_ = dagmc_instance_; + + // set BCs + std::string bc_value = DMD.get_surface_property("boundary", surf_handle); + to_lower(bc_value); + if (bc_value.empty() || bc_value == "transmit" || bc_value == "transmission") { + // set to transmission by default + s->bc_ = Surface::BoundaryType::TRANSMIT; + } else if (bc_value == "vacuum") { + s->bc_ = Surface::BoundaryType::VACUUM; + } else if (bc_value == "reflective" || bc_value == "reflect" || bc_value == "reflecting") { + s->bc_ = Surface::BoundaryType::REFLECT; + } else if (bc_value == "periodic") { + fatal_error("Periodic boundary condition not supported in DAGMC."); + } else { + fatal_error(fmt::format("Unknown boundary condition \"{}\" specified " + "on surface {}", bc_value, s->id_)); + } + + // graveyard check + moab::Range parent_vols; + rval = dagmc_instance_->moab_instance()->get_parent_meshsets(surf_handle, parent_vols); + MB_CHK_ERR_CONT(rval); + + // if this surface belongs to the graveyard + if (graveyard && parent_vols.find(graveyard) != parent_vols.end()) { + // set graveyard surface BC's to vacuum + s->bc_ = Surface::BoundaryType::VACUUM; + } + + // add to global array and map + model::surfaces.emplace_back(s); + model::surface_map[s->id_] = model::surfaces.size() - 1; + } } int32_t create_dagmc_universe(const std::string& filename) { @@ -204,6 +412,7 @@ int32_t create_dagmc_universe(const std::string& filename) { DAGCell* c = new DAGCell(); c->dag_index_ = i+1; c->id_ = model::DAG->id_by_index(3, c->dag_index_); + std::cout << "Cell id: " << c->id_ << std::endl; c->dagmc_ptr_ = model::DAG; c->universe_ = dagmc_univ_id; // set to zero for now c->fill_ = C_NONE; // no fill, single universe @@ -346,7 +555,7 @@ void read_geometry_dagmc() write_message("Reading DAGMC geometry...", 5); load_dagmc_geometry(); - model::root_universe = find_root_universe(); + model::root_universe = 0; } } diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index 87ceb944d81..27327300103 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -14,7 +14,7 @@ - 1 + 2 1 diff --git a/tests/regression_tests/dagmc/refl/test.py b/tests/regression_tests/dagmc/refl/test.py index c451b61250c..305e18ed5c8 100644 --- a/tests/regression_tests/dagmc/refl/test.py +++ b/tests/regression_tests/dagmc/refl/test.py @@ -30,7 +30,7 @@ def _build_inputs(self): # tally tally = openmc.Tally() tally.scores = ['total'] - tally.filters = [openmc.CellFilter(1)] + tally.filters = [openmc.CellFilter(2)] model.tallies = [tally] model.tallies.export_to_xml() From 249a5bb51142808774d736824473513c6ef68260 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 8 Jul 2020 23:18:12 -0500 Subject: [PATCH 0004/2572] Adding an XML node constructor for the DAGUniverse. --- include/openmc/cell.h | 3 +++ src/dagmc.cpp | 60 ++++++++++++++++++++++++------------------- 2 files changed, 37 insertions(+), 26 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 5b0b595608a..52d6e9fdc61 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -80,10 +80,13 @@ class Universe class DAGUniverse : public Universe { public: + explicit DAGUniverse(pugi::xml_node node); explicit DAGUniverse(const std::string& filename); + void initialize(); //!< Sets up the DAGMC instance and OpenMC internals // Data Members + std::string filename_; std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe }; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 37de2d0c6e9..395372a58d9 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -155,7 +155,37 @@ void load_dagmc_geometry() model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; } -DAGUniverse::DAGUniverse(const std::string& filename) { +DAGUniverse::DAGUniverse(pugi::xml_node node) { + if (check_for_node(node, "id")) { + id_ = std::stoi(get_node_value(node, "id")); + } else { + fatal_error("Must specify the id of the DAGMC universe"); + } + + if (check_for_node(node, "filename")) { + filename_ = get_node_value(node, "filename"); + } else { + fatal_error("Must specify a file for the DAGMC universe"); + } + + initialize(); +} + +DAGUniverse::DAGUniverse(const std::string& filename) : filename_(filename) { + // determine the next universe id + int32_t next_univ_id = 0; + for (const auto& u : model::universes) { + if (u->id_ > next_univ_id) next_univ_id = u->id_; + } + next_univ_id++; + + // set the universe id + id_ = next_univ_id; + + initialize(); +} + +void DAGUniverse::initialize() { // determine the next cell id int32_t next_cell_id = 0; @@ -171,23 +201,13 @@ DAGUniverse::DAGUniverse(const std::string& filename) { } next_surf_id++; - // determine the next universe id - int32_t next_univ_id = 0; - for (const auto& u : model::universes) { - if (u->id_ > next_univ_id) next_univ_id = u->id_; - } - next_univ_id++; - - // set the universe id - id_ = next_univ_id; - // create a new DAGMC instance dagmc_instance_ = std::make_shared(); // --- Materials --- // create uwuw instance - UWUW uwuw(filename.c_str()); + UWUW uwuw(filename_.c_str()); // check for uwuw material definitions bool using_uwuw = !uwuw.material_library.empty(); @@ -198,7 +218,7 @@ DAGUniverse::DAGUniverse(const std::string& filename) { } // load the DAGMC geometry - moab::ErrorCode rval = dagmc_instance_->load_file(filename.c_str()); + moab::ErrorCode rval = dagmc_instance_->load_file(filename_.c_str()); MB_CHK_ERR_CONT(rval); // initialize acceleration data structures @@ -237,17 +257,6 @@ DAGUniverse::DAGUniverse(const std::string& filename) { model::cell_map[c->id_] = model::cells.size() - 1; cells_.push_back(model::cell_map[c->id_]); - // Populate the Universe vector and dict - // auto it = model::universe_map.find(dagmc_univ_id); - // if (it == model::universe_map.end()) { - // model::universes.push_back(std::make_unique()); - // model::universes.back()->id_ = dagmc_univ_id; - // model::universes.back()->cells_.push_back(i); - // model::universe_map[id_] = model::universes.size() - 1; - // } else { - // model::universes[it->second]->cells_.push_back(i); - // } - // MATERIALS // determine volume material assignment @@ -554,8 +563,7 @@ void read_geometry_dagmc() { write_message("Reading DAGMC geometry...", 5); load_dagmc_geometry(); - - model::root_universe = 0; + model::root_universe = find_root_universe(); } } From c35c8dae43484e71231edb6372df6dbaa8a532ff Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 8 Jul 2020 23:39:06 -0500 Subject: [PATCH 0005/2572] Reading DAGMC universes from the geometry.xml now. --- include/openmc/cell.h | 1 + src/cell.cpp | 20 +++++++------------- src/dagmc.cpp | 2 +- src/geometry_aux.cpp | 9 +-------- src/surface.cpp | 7 ++++--- 5 files changed, 14 insertions(+), 25 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 52d6e9fdc61..0aa14e523c4 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -358,6 +358,7 @@ struct CellInstanceHash { //============================================================================== void read_cells(pugi::xml_node node); +void read_dagmc_universes(pugi::xml_node node); #ifdef DAGMC void read_dagmc_universes(pugi::xml_node node); diff --git a/src/cell.cpp b/src/cell.cpp index 733b70f0566..ffbb7dae7cd 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -992,9 +992,6 @@ void read_cells(pugi::xml_node node) // Count the number of cells. int n_cells = 0; for (pugi::xml_node cell_node: node.children("cell")) {n_cells++;} - if (n_cells == 0) { - fatal_error("No cells found in geometry.xml!"); - } // Loop over XML cell elements and populate the array. model::cells.reserve(n_cells); @@ -1013,10 +1010,7 @@ void read_cells(pugi::xml_node node) } } -#ifdef DAGMC - // read any DAGMC universes read_dagmc_universes(node); -#endif // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { @@ -1037,17 +1031,17 @@ void read_cells(pugi::xml_node node) if (settings::check_overlaps) { model::overlap_check_count.resize(model::cells.size(), 0); } + + if (model::cells.size() == 0) { + fatal_error("No cells were found in the geometry.xml file"); + } } void read_dagmc_universes(pugi::xml_node node) { - - // determine the max cell id - int32_t next_cell_id = 0; - for (const auto& c : model::cells) { - if (c->id_ > next_cell_id) next_cell_id = c->id_; + for (pugi::xml_node dag_node : node.children("dagmc")) { + model::universes.push_back(std::make_unique(dag_node)); + model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; } - next_cell_id++; - } //============================================================================== diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 395372a58d9..93d9b222a22 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -367,7 +367,7 @@ void DAGUniverse::initialize() { // add to global array and map model::surfaces.emplace_back(s); - model::surface_map[s->id_] = model::surfaces.size() - 1; +// model::surface_map[s->id_] = model::surfaces.size() - 1; } } diff --git a/src/geometry_aux.cpp b/src/geometry_aux.cpp index 17000129d35..94365428fdd 100644 --- a/src/geometry_aux.cpp +++ b/src/geometry_aux.cpp @@ -42,13 +42,6 @@ void update_universe_cell_count(int32_t a, int32_t b) { void read_geometry_xml() { -#ifdef DAGMC - if (settings::dagmc) { - read_geometry_dagmc(); - return; - } -#endif - // Display output message write_message("Reading geometry XML file...", 5); @@ -69,8 +62,8 @@ void read_geometry_xml() pugi::xml_node root = doc.document_element(); // Read surfaces, cells, lattice - read_surfaces(root); read_cells(root); + read_surfaces(root); read_lattices(root); // Allocate universes, universe cell arrays, and assign base universe diff --git a/src/surface.cpp b/src/surface.cpp index 76542fcd86e..caa3b9ba8ff 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -1036,9 +1036,6 @@ void read_surfaces(pugi::xml_node node) // Count the number of surfaces int n_surfaces = 0; for (pugi::xml_node surf_node : node.children("surface")) {n_surfaces++;} - if (n_surfaces == 0) { - fatal_error("No surfaces found in geometry.xml!"); - } // Loop over XML surface elements and populate the array. Keep track of // periodic surfaces. @@ -1196,6 +1193,10 @@ void read_surfaces(pugi::xml_node node) if (settings::run_mode != RunMode::PLOTTING && !boundary_exists) { fatal_error("No boundary conditions were applied to any surfaces!"); } + + if (model::surfaces.size() == 0) { + fatal_error("No surfaces found in geometry.xml!"); + } } void free_memory_surfaces() From a736756d9bbfcbccdf33cce90b593ed6881197a2 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 9 Jul 2020 00:10:36 -0500 Subject: [PATCH 0006/2572] Adjusting some geometry checks. Reading surfaces before cells. Combines with other CSG objects so far! --- src/geometry_aux.cpp | 2 +- src/surface.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/geometry_aux.cpp b/src/geometry_aux.cpp index 94365428fdd..d45cdcb23a0 100644 --- a/src/geometry_aux.cpp +++ b/src/geometry_aux.cpp @@ -62,8 +62,8 @@ void read_geometry_xml() pugi::xml_node root = doc.document_element(); // Read surfaces, cells, lattice - read_cells(root); read_surfaces(root); + read_cells(root); read_lattices(root); // Allocate universes, universe cell arrays, and assign base universe diff --git a/src/surface.cpp b/src/surface.cpp index caa3b9ba8ff..9114b65adb9 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -1190,13 +1190,13 @@ void read_surfaces(pugi::xml_node node) break; } } - if (settings::run_mode != RunMode::PLOTTING && !boundary_exists) { - fatal_error("No boundary conditions were applied to any surfaces!"); - } + // if (settings::run_mode != RunMode::PLOTTING && !boundary_exists) { + // fatal_error("No boundary conditions were applied to any surfaces!"); + // } - if (model::surfaces.size() == 0) { - fatal_error("No surfaces found in geometry.xml!"); - } + // if (model::surfaces.size() == 0) { + // fatal_error("No surfaces found in geometry.xml!"); + // } } void free_memory_surfaces() From 108c2a2f35c846c32f3d29afd28d42e77f85d53e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 9 Jul 2020 18:26:21 -0500 Subject: [PATCH 0007/2572] Adding surface and cell offsets to the DAGUniverse to recover surface and cell indices. --- include/openmc/cell.h | 3 +++ src/cell.cpp | 7 ++++++- src/dagmc.cpp | 6 ++++-- src/particle.cpp | 36 ++++++++++++++++++------------------ 4 files changed, 31 insertions(+), 21 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 0aa14e523c4..f87e594eaaf 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -63,6 +63,7 @@ namespace model { class Universe { public: + int32_t id_; //!< Unique ID vector cells_; //!< Cells within this universe @@ -88,6 +89,8 @@ class DAGUniverse : public Universe { // Data Members std::string filename_; std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe + int32_t cell_idx_offset_; + int32_t surf_idx_offset_; }; #endif diff --git a/src/cell.cpp b/src/cell.cpp index ffbb7dae7cd..3fb2050d19e 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -793,6 +793,11 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons p->last_dir() = u; } + const auto& univ = model::universes[p->coord_[p->n_coord_ - 1].universe]; + + DAGUniverse* dag_univ = static_cast(univ.get()); + if (!dag_univ) fatal_error("DAGMC call made for particle in a non-DAGMC universe"); + moab::ErrorCode rval; moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); moab::EntityHandle hit_surf; @@ -803,7 +808,7 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons MB_CHK_ERR_CONT(rval); int surf_idx; if (hit_surf != 0) { - surf_idx = dagmc_ptr_->index_by_handle(hit_surf); + surf_idx = dag_univ->surf_idx_offset_ + dagmc_ptr_->index_by_handle(hit_surf); } else { // indicate that particle is lost surf_idx = -1; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 93d9b222a22..f79215c1162 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -171,7 +171,7 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { initialize(); } -DAGUniverse::DAGUniverse(const std::string& filename) : filename_(filename) { +DAGUniverse::DAGUniverse(const std::string& filename): filename_(filename) { // determine the next universe id int32_t next_univ_id = 0; for (const auto& u : model::universes) { @@ -192,6 +192,7 @@ void DAGUniverse::initialize() { for (const auto& c : model::cells) { if (c->id_ > next_cell_id) next_cell_id = c->id_; } + cell_idx_offset_ = model::cells.size(); next_cell_id++; // determine the next surface id @@ -199,6 +200,7 @@ void DAGUniverse::initialize() { for (const auto& s : model::surfaces) { if (s->id_ > next_surf_id) next_surf_id = s->id_; } + surf_idx_offset_ = model::surfaces.size(); next_surf_id++; // create a new DAGMC instance @@ -367,7 +369,7 @@ void DAGUniverse::initialize() { // add to global array and map model::surfaces.emplace_back(s); -// model::surface_map[s->id_] = model::surfaces.size() - 1; + model::surface_map[s->id_] = model::surfaces.size() - 1; } } diff --git a/src/particle.cpp b/src/particle.cpp index fe2579593e5..755330808fa 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -386,24 +386,24 @@ Particle::cross_surface() // ========================================================================== // SEARCH NEIGHBOR LISTS FOR NEXT CELL -#ifdef DAGMC - if (settings::dagmc) { - auto cellp = dynamic_cast(model::cells[cell_last(0)].get()); - // TODO: off-by-one - auto surfp = - dynamic_cast(model::surfaces[std::abs(surface()) - 1].get()); - int32_t i_cell = next_cell(cellp, surfp) - 1; - // save material and temp - material_last() = material(); - sqrtkT_last() = sqrtkT(); - // set new cell value - coord(0).cell = i_cell; - cell_instance() = 0; - material() = model::cells[i_cell]->material_[0]; - sqrtkT() = model::cells[i_cell]->sqrtkT_[0]; - return; - } -#endif + +// #ifdef DAGMC +// if (settings::dagmc) { +// auto cellp = dynamic_cast(model::cells[cell_last_[0]].get()); +// // TODO: off-by-one +// auto surfp = dynamic_cast(model::surfaces[std::abs(surface_) - 1].get()); +// int32_t i_cell = next_cell(cellp, surfp) - 1; +// // save material and temp +// material_last_ = material_; +// sqrtkT_last_ = sqrtkT_; +// // set new cell value +// coord_[0].cell = i_cell; +// cell_instance_ = 0; +// material_ = model::cells[i_cell]->material_[0]; +// sqrtkT_ = model::cells[i_cell]->sqrtkT_[0]; +// return; +// } +// #endif if (neighbor_list_find_cell(*this)) return; From 0a463be15e82443f7019cec103bf186e3da6c632 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 19 Jul 2020 22:34:20 -0500 Subject: [PATCH 0008/2572] Adding universe types again. --- include/openmc/cell.h | 18 ++++-- include/openmc/settings.h | 1 - include/openmc/surface.h | 8 +-- src/cell.cpp | 132 ++++++++++++++++++++++---------------- src/cross_sections.cpp | 15 ----- src/dagmc.cpp | 4 +- src/material.cpp | 22 ++----- src/particle.cpp | 43 +++++++------ src/summary.cpp | 8 --- src/surface.cpp | 17 ++--- 10 files changed, 135 insertions(+), 133 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index f87e594eaaf..bef55690a29 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -31,6 +31,11 @@ enum class Fill { LATTICE }; +enum class UniverseType { + CSG, + DAG +}; + // TODO: Convert to enum constexpr int32_t OP_LEFT_PAREN {std::numeric_limits::max()}; constexpr int32_t OP_RIGHT_PAREN {std::numeric_limits::max() - 1}; @@ -73,6 +78,7 @@ class Universe BoundingBox bounding_box() const; + UniverseType type_ = UniverseType::CSG; unique_ptr partitioner_; }; @@ -138,7 +144,9 @@ class Cell { //! Write all information needed to reconstruct the cell to an HDF5 group. //! \param group_id An HDF5 group id. - virtual void to_hdf5(hid_t group_id) const = 0; + virtual void to_hdf5(hid_t group_id) const; + + virtual void to_hdf5_inner(hid_t group_id) const = 0; //! Get the BoundingBox for this cell. virtual BoundingBox bounding_box() const = 0; @@ -169,6 +177,8 @@ class Cell { //! \param[in] name Cell name void set_name(const std::string& name) { name_ = name; }; + + //! Get all cell instances contained by this cell //! \return Map with cell indexes as keys and instances as values std::unordered_map> get_contained_cells() const; @@ -245,7 +255,7 @@ class CSGCell : public Cell std::pair distance(Position r, Direction u, int32_t on_surface, Particle* p) const; - void to_hdf5(hid_t group_id) const; + void to_hdf5_inner(hid_t group_id) const; BoundingBox bounding_box() const; @@ -289,7 +299,7 @@ class DAGCell : public Cell BoundingBox bounding_box() const; - void to_hdf5(hid_t group_id) const; + void to_hdf5_inner(hid_t group_id) const; std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of cell @@ -365,7 +375,7 @@ void read_dagmc_universes(pugi::xml_node node); #ifdef DAGMC void read_dagmc_universes(pugi::xml_node node); -int32_t next_cell(DAGCell* cur_cell, DAGSurface* surf_xed); +int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); #endif } // namespace openmc diff --git a/include/openmc/settings.h b/include/openmc/settings.h index 384f629b0f3..cc3887f8f80 100644 --- a/include/openmc/settings.h +++ b/include/openmc/settings.h @@ -29,7 +29,6 @@ extern bool check_overlaps; //!< check overlaps in geometry? extern bool confidence_intervals; //!< use confidence intervals for results? extern bool create_fission_neutrons; //!< create fission neutrons (fixed source)? extern "C" bool cmfd_run; //!< is a CMFD run? -extern "C" bool dagmc; //!< indicator of DAGMC geometry extern bool delayed_photon_scaling; //!< Scale fission photon yield to include delayed extern "C" bool entropy_on; //!< calculate Shannon entropy? extern bool event_based; //!< use event-based mode (instead of history-based) diff --git a/include/openmc/surface.h b/include/openmc/surface.h index b729514b9e6..8b4e416052b 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -138,6 +138,9 @@ class Surface //! Get the BoundingBox for this surface. virtual BoundingBox bounding_box(bool /*pos_side*/) const { return {}; } + + protected: + virtual void to_hdf5_inner(hid_t group_id) const = 0; }; class CSGSurface : public Surface @@ -147,9 +150,6 @@ class CSGSurface : public Surface CSGSurface(); void to_hdf5(hid_t group_id) const; - -protected: - virtual void to_hdf5_inner(hid_t group_id) const = 0; }; //============================================================================== @@ -166,7 +166,7 @@ class DAGSurface : public Surface Direction normal(Position r) const; Direction reflect(Position r, Direction u, Particle* p) const; - void to_hdf5(hid_t group_id) const; + void to_hdf5_inner(hid_t group_id) const override; std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of surface diff --git a/src/cell.cpp b/src/cell.cpp index 3fb2050d19e..ec8c5e0e7d9 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -194,6 +194,15 @@ Universe::to_hdf5(hid_t universes_group) const // Create a group for this universe. auto group = create_group(universes_group, fmt::format("universe {}", id_)); + switch(type_) { + case UniverseType::CSG : + write_string(group, "geom_type", "csg", false); + break; + case UniverseType::DAG : + write_string(group, "geom_type", "dagmc", false); + break; + } + // Write the contained cells. if (cells_.size() > 0) { vector cell_ids; @@ -282,6 +291,66 @@ Cell::set_temperature(double T, int32_t instance, bool set_contained) } } +void +Cell::to_hdf5(hid_t cell_group) const { + + // Create a group for this cell. + auto group = create_group(cell_group, fmt::format("cell {}", id_)); + + if (!name_.empty()) { + write_string(group, "name", name_, false); + } + + write_dataset(group, "universe", model::universes[universe_]->id_); + + + to_hdf5_inner(group); + + // Write fill information. + if (type_ == Fill::MATERIAL) { + write_dataset(group, "fill_type", "material"); + std::vector mat_ids; + for (auto i_mat : material_) { + if (i_mat != MATERIAL_VOID) { + mat_ids.push_back(model::materials[i_mat]->id_); + } else { + mat_ids.push_back(MATERIAL_VOID); + } + } + if (mat_ids.size() == 1) { + write_dataset(group, "material", mat_ids[0]); + } else { + write_dataset(group, "material", mat_ids); + } + + std::vector temps; + for (auto sqrtkT_val : sqrtkT_) + temps.push_back(sqrtkT_val * sqrtkT_val / K_BOLTZMANN); + write_dataset(group, "temperature", temps); + + } else if (type_ == Fill::UNIVERSE) { + write_dataset(group, "fill_type", "universe"); + write_dataset(group, "fill", model::universes[fill_]->id_); + if (translation_ != Position(0, 0, 0)) { + write_dataset(group, "translation", translation_); + } + if (!rotation_.empty()) { + if (rotation_.size() == 12) { + std::array rot {rotation_[9], rotation_[10], rotation_[11]}; + write_dataset(group, "rotation", rot); + } else { + write_dataset(group, "rotation", rotation_); + } + } + + } else if (type_ == Fill::LATTICE) { + write_dataset(group, "fill_type", "lattice"); + write_dataset(group, "lattice", model::lattices[fill_]->id_); + } + + close_group(group); +} + //============================================================================== // CSGCell implementation //============================================================================== @@ -526,16 +595,10 @@ CSGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons //============================================================================== void -CSGCell::to_hdf5(hid_t cell_group) const +CSGCell::to_hdf5_inner(hid_t group_id) const { - // Create a group for this cell. - auto group = create_group(cell_group, fmt::format("cell {}", id_)); - if (!name_.empty()) { - write_string(group, "name", name_, false); - } - - write_dataset(group, "universe", model::universes[universe_]->id_); + write_string(group_id, "geom_type", "csg", false); // Write the region specification. if (!region_.empty()) { @@ -556,52 +619,9 @@ CSGCell::to_hdf5(hid_t cell_group) const region_spec << " " << ((token > 0) ? surf_id : -surf_id); } } - write_string(group, "region", region_spec.str(), false); - } - - // Write fill information. - if (type_ == Fill::MATERIAL) { - write_dataset(group, "fill_type", "material"); - vector mat_ids; - for (auto i_mat : material_) { - if (i_mat != MATERIAL_VOID) { - mat_ids.push_back(model::materials[i_mat]->id_); - } else { - mat_ids.push_back(MATERIAL_VOID); - } - } - if (mat_ids.size() == 1) { - write_dataset(group, "material", mat_ids[0]); - } else { - write_dataset(group, "material", mat_ids); - } - - vector temps; - for (auto sqrtkT_val : sqrtkT_) - temps.push_back(sqrtkT_val * sqrtkT_val / K_BOLTZMANN); - write_dataset(group, "temperature", temps); - - } else if (type_ == Fill::UNIVERSE) { - write_dataset(group, "fill_type", "universe"); - write_dataset(group, "fill", model::universes[fill_]->id_); - if (translation_ != Position(0, 0, 0)) { - write_dataset(group, "translation", translation_); - } - if (!rotation_.empty()) { - if (rotation_.size() == 12) { - array rot {rotation_[9], rotation_[10], rotation_[11]}; - write_dataset(group, "rotation", rot); - } else { - write_dataset(group, "rotation", rotation_); - } - } - - } else if (type_ == Fill::LATTICE) { - write_dataset(group, "fill_type", "lattice"); - write_dataset(group, "lattice", model::lattices[fill_]->id_); + write_string(group_id, "region", region_spec.str(), false); } - close_group(group); } BoundingBox CSGCell::bounding_box_simple() const { @@ -831,7 +851,9 @@ bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const return result; } -void DAGCell::to_hdf5(hid_t group_id) const { return; } +void DAGCell::to_hdf5_inner(hid_t group_id) const { + write_string(group_id, "geom_type", "dagmc", false); +} BoundingBox DAGCell::bounding_box() const { @@ -1306,7 +1328,7 @@ openmc_extend_cells(int32_t n, int32_t* index_start, int32_t* index_end) } #ifdef DAGMC -int32_t next_cell(DAGCell* cur_cell, DAGSurface* surf_xed) +int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) { moab::EntityHandle surf = surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); @@ -1316,7 +1338,7 @@ int32_t next_cell(DAGCell* cur_cell, DAGSurface* surf_xed) moab::EntityHandle new_vol; cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); - return cur_cell->dagmc_ptr_->index_by_handle(new_vol); + return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; } #endif diff --git a/src/cross_sections.cpp b/src/cross_sections.cpp index ea8d0cfbbad..942b088d905 100644 --- a/src/cross_sections.cpp +++ b/src/cross_sections.cpp @@ -96,27 +96,12 @@ void read_cross_sections_xml() { pugi::xml_document doc; std::string filename = settings::path_input + "materials.xml"; -#ifdef DAGMC - std::string s; - bool found_uwuw_mats = false; - if (settings::dagmc) { - found_uwuw_mats = get_uwuw_materials_xml(s); - } - - if (found_uwuw_mats) { - // if we found uwuw materials, load those - doc.load_file(s.c_str()); - } else { -#endif // Check if materials.xml exists if (!file_exists(filename)) { fatal_error("Material XML file '" + filename + "' does not exist."); } // Parse materials.xml file doc.load_file(filename.c_str()); -#ifdef DAGMC - } -#endif auto root = doc.document_element(); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index f79215c1162..e9070b16465 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -186,6 +186,7 @@ DAGUniverse::DAGUniverse(const std::string& filename): filename_(filename) { } void DAGUniverse::initialize() { + type_ = UniverseType::DAG; // determine the next cell id int32_t next_cell_id = 0; @@ -336,7 +337,7 @@ void DAGUniverse::initialize() { // set cell ids using global IDs DAGSurface* s = new DAGSurface(); s->dag_index_ = i+1; - s->id_ = next_surf_id++; + s->id_ = dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; // set BCs @@ -564,7 +565,6 @@ int32_t create_dagmc_universe(const std::string& filename) { void read_geometry_dagmc() { write_message("Reading DAGMC geometry...", 5); - load_dagmc_geometry(); model::root_universe = find_root_universe(); } diff --git a/src/material.cpp b/src/material.cpp index 59d2f954e45..a1aa6ea5f86 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -1223,24 +1223,14 @@ void read_materials_xml() pugi::xml_document doc; - bool using_dagmc_mats = false; -#ifdef DAGMC - if (settings::dagmc) { - using_dagmc_mats = read_uwuw_materials(doc); + // Check if materials.xml exists + std::string filename = settings::path_input + "materials.xml"; + if (!file_exists(filename)) { + fatal_error("Material XML file '" + filename + "' does not exist!"); } -#endif - - if (!using_dagmc_mats) { - // Check if materials.xml exists - std::string filename = settings::path_input + "materials.xml"; - if (!file_exists(filename)) { - fatal_error("Material XML file '" + filename + "' does not exist!"); - } - - // Parse materials.xml file and get root element - doc.load_file(filename.c_str()); - } + // Parse materials.xml file and get root element + doc.load_file(filename.c_str()); // Loop over XML material elements and populate the array. pugi::xml_node root = doc.document_element(); diff --git a/src/particle.cpp b/src/particle.cpp index 755330808fa..73cdb3dd6a9 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -285,6 +285,8 @@ Particle::event_collide() // Score flux derivative accumulators for differential tallies. if (!model::active_tallies.empty()) score_collision_derivative(*this); + + history_.reset(); } void @@ -318,9 +320,8 @@ void Particle::event_death() { #ifdef DAGMC - if (settings::dagmc) - history().reset(); -#endif + history_.reset(); + #endif // Finish particle track output. if (write_track()) { @@ -387,23 +388,25 @@ Particle::cross_surface() // SEARCH NEIGHBOR LISTS FOR NEXT CELL -// #ifdef DAGMC -// if (settings::dagmc) { -// auto cellp = dynamic_cast(model::cells[cell_last_[0]].get()); -// // TODO: off-by-one -// auto surfp = dynamic_cast(model::surfaces[std::abs(surface_) - 1].get()); -// int32_t i_cell = next_cell(cellp, surfp) - 1; -// // save material and temp -// material_last_ = material_; -// sqrtkT_last_ = sqrtkT_; -// // set new cell value -// coord_[0].cell = i_cell; -// cell_instance_ = 0; -// material_ = model::cells[i_cell]->material_[0]; -// sqrtkT_ = model::cells[i_cell]->sqrtkT_[0]; -// return; -// } -// #endif +#ifdef DAGMC + auto surfp = dynamic_cast(model::surfaces[std::abs(surface_) - 1].get()); + if (surfp) { + auto cellp = dynamic_cast(model::cells[cell_last_[n_coord_ - 1]].get()); + // TODO: off-by-one + auto univp = static_cast(model::universes[coord_[n_coord_ - 1].universe].get()); + + int32_t i_cell = next_cell(univp, cellp, surfp) - 1; + // save material and temp + material_last_ = material_; + sqrtkT_last_ = sqrtkT_; + // set new cell value + coord_[n_coord_ - 1].cell = i_cell; + cell_instance_ = 0; + material_ = model::cells[i_cell]->material_[0]; + sqrtkT_ = model::cells[i_cell]->sqrtkT_[0]; + return; + } +#endif if (neighbor_list_find_cell(*this)) return; diff --git a/src/summary.cpp b/src/summary.cpp index 3d2c6816b42..e43982a6a30 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -89,14 +89,6 @@ void write_geometry(hid_t file) { auto geom_group = create_group(file, "geometry"); -#ifdef DAGMC - if (settings::dagmc) { - write_attribute(geom_group, "dagmc", 1); - close_group(geom_group); - return; - } -#endif - write_attribute(geom_group, "n_cells", model::cells.size()); write_attribute(geom_group, "n_surfaces", model::surfaces.size()); write_attribute(geom_group, "n_universes", model::universes.size()); diff --git a/src/surface.cpp b/src/surface.cpp index 9114b65adb9..c2c20d0bfa7 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -198,16 +198,12 @@ Surface::diffuse_reflect(Position r, Direction u, uint64_t* seed) const return u/u.norm(); } -CSGSurface::CSGSurface() : Surface{} {}; -CSGSurface::CSGSurface(pugi::xml_node surf_node) : Surface{surf_node} {}; - void -CSGSurface::to_hdf5(hid_t group_id) const +Surface::to_hdf5(hid_t group_id) const { - std::string group_name {"surface "}; - group_name += std::to_string(id_); + hid_t surf_group = create_group(group_id, fmt::format("surface {}", id_)); - hid_t surf_group = create_group(group_id, group_name); + write_string(surf_group, "geom_type", "csg", false); if (bc_) { write_string(surf_group, "boundary_type", bc_->type(), false); @@ -224,6 +220,9 @@ CSGSurface::to_hdf5(hid_t group_id) const close_group(surf_group); } +CSGSurface::CSGSurface() : Surface{} {}; +CSGSurface::CSGSurface(pugi::xml_node surf_node) : Surface{surf_node} {}; + //============================================================================== // DAGSurface implementation //============================================================================== @@ -275,7 +274,9 @@ Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const return p->last_dir(); } -void DAGSurface::to_hdf5(hid_t group_id) const {} +void DAGSurface::to_hdf5_inner(hid_t group_id) const { + write_string(group_id, "geom_type", "dagmc", false); +} #endif From 674327e4a7f7ed663dd49d03a0c645ab7e76c7ee Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 8 Oct 2020 22:30:02 -0500 Subject: [PATCH 0009/2572] Using temperature accessor for material temperatures. --- src/dagmc.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index e9070b16465..ad50d85d5a5 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -308,8 +308,8 @@ void DAGUniverse::initialize() { MB_CHK_ERR_CONT(rval); double temp = std::stod(temp_value); c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * temp)); - } else if (mat->temperature_ > 0.0) { - c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * mat->temperature_)); + } else if (mat->temperature() > 0.0) { + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * mat->temperature())); } else { c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); } From 0f216bc5354705b800e0bef9f6c3eebc3ade1628 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 8 Oct 2020 22:30:28 -0500 Subject: [PATCH 0010/2572] Removing old DAGMC variable reset during simulation finalize. --- src/finalize.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/finalize.cpp b/src/finalize.cpp index 54a8d681dca..6d207bd7a27 100644 --- a/src/finalize.cpp +++ b/src/finalize.cpp @@ -71,7 +71,6 @@ int openmc_finalize() settings::confidence_intervals = false; settings::create_fission_neutrons = true; settings::electron_treatment = ElectronTreatment::LED; - settings::dagmc = false; settings::delayed_photon_scaling = true; settings::energy_cutoff = {0.0, 1000.0, 0.0, 0.0}; settings::entropy_on = false; From d94710996f66951e197de5a94fb94b928c1c507a Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 9 Oct 2020 00:00:14 -0500 Subject: [PATCH 0011/2572] Abstracting the Python Universe class. Adding a DAGMC Universe. --- openmc/geometry.py | 3 +- openmc/universe.py | 266 +++++++++++++++++++++++++++++---------------- 2 files changed, 177 insertions(+), 92 deletions(-) diff --git a/openmc/geometry.py b/openmc/geometry.py index e7d981d7b8a..30decd9ff8f 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -50,7 +50,8 @@ def bounding_box(self): @root_universe.setter def root_universe(self, root_universe): - check_type('root universe', root_universe, openmc.Universe) + check_type('root universe', root_universe, + (openmc.Universe, openmc.DAGMCUniverse)) self._root_universe = root_universe def add_volume_information(self, volume_calc): diff --git a/openmc/universe.py b/openmc/universe.py index 2dfbf9cd750..be6aa00fd72 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -1,3 +1,4 @@ +from abc import ABC, abstractmethod from collections import OrderedDict from collections.abc import Iterable from copy import copy, deepcopy @@ -12,7 +13,119 @@ from .plots import _SVG_COLORS -class Universe(IDManagerMixin): +class UniverseBase(ABC, IDManagerMixin): + """A collection of cells that can be repeated. + + Attributes + ---------- + id : int + Unique identifier of the universe + name : str + Name of the universe + """ + + next_id = 1 + used_ids = set() + + def __init__(self, universe_id=None, name=''): + # Initialize Universe class attributes + self.id = universe_id + self.name = name + self._volume = None + self._atoms = {} + + def __repr__(self): + string = 'Universe\n' + string += '{: <16}=\t{}\n'.format('\tID', self._id) + string += '{: <16}=\t{}\n'.format('\tName', self._name) + return string + + @property + def name(self): + return self._name + + @property + def volume(self): + return self._volume + + @name.setter + def name(self, name): + if name is not None: + cv.check_type('universe name', name, str) + self._name = name + else: + self._name = '' + + @volume.setter + def volume(self, volume): + if volume is not None: + cv.check_type('universe volume', volume, Real) + self._volume = volume + + def add_volume_information(self, volume_calc): + """Add volume information to a universe. + + Parameters + ---------- + volume_calc : openmc.VolumeCalculation + Results from a stochastic volume calculation + + """ + if volume_calc.domain_type == 'universe': + if self.id in volume_calc.volumes: + self._volume = volume_calc.volumes[self.id].n + self._atoms = volume_calc.atoms[self.id] + else: + raise ValueError('No volume information found for this universe.') + else: + raise ValueError('No volume information found for this universe.') + + @abstractmethod + def create_xml_subelement(self, xml_element, memo=None): + """Add the universe xml representation to an incoming xml element + + Parameters + ---------- + xml_element : xml.etree.ElementTree.Element + XML element to be added to + + memo : set or None + A set of object id's representing geometry entities already + written to the xml_element. This parameter is used internally + and should not be specified by users. + + Returns + ------- + None + + """ + + @abstractmethod + def clone(self, clone_materials=True, clone_regions=True, memo=None): + """Create a copy of this universe with a new unique ID, and clones + all cells within this universe. + + Parameters + ---------- + clone_materials : bool + Whether to create separates copies of the materials filling cells + contained in this universe. + clone_regions : bool + Whether to create separates copies of the regions bounding cells + contained in this universe. + memo : dict or None + A nested dictionary of previously cloned objects. This parameter + is used internally and should not be specified by the user. + + Returns + ------- + clone : openmc.Universe + The clone of this universe + + """ + + +class Universe(UniverseBase): """A collection of cells that can be repeated. Parameters @@ -44,15 +157,8 @@ class Universe(IDManagerMixin): """ - next_id = 1 - used_ids = set() - def __init__(self, universe_id=None, name='', cells=None): - # Initialize Cell class attributes - self.id = universe_id - self.name = name - self._volume = None - self._atoms = {} + super().__init__(universe_id, name) # Keys - Cell IDs # Values - Cells @@ -62,24 +168,15 @@ def __init__(self, universe_id=None, name='', cells=None): self.add_cells(cells) def __repr__(self): - string = 'Universe\n' - string += '{: <16}=\t{}\n'.format('\tID', self._id) - string += '{: <16}=\t{}\n'.format('\tName', self._name) + string = super().__repr__() + string += '{: <16}=\t{}\n'.format('\tGeom', 'CSG') string += '{: <16}=\t{}\n'.format('\tCells', list(self._cells.keys())) return string - @property - def name(self): - return self._name - @property def cells(self): return self._cells - @property - def volume(self): - return self._volume - @property def bounding_box(self): regions = [c.region for c in self.cells.values() @@ -90,20 +187,6 @@ def bounding_box(self): # Infinite bounding box return openmc.Intersection([]).bounding_box - @name.setter - def name(self, name): - if name is not None: - cv.check_type('universe name', name, str) - self._name = name - else: - self._name = '' - - @volume.setter - def volume(self, volume): - if volume is not None: - cv.check_type('universe volume', volume, Real) - self._volume = volume - @classmethod def from_hdf5(cls, group, cells): """Create universe from HDF5 group @@ -133,24 +216,6 @@ def from_hdf5(cls, group, cells): return universe - def add_volume_information(self, volume_calc): - """Add volume information to a universe. - - Parameters - ---------- - volume_calc : openmc.VolumeCalculation - Results from a stochastic volume calculation - - """ - if volume_calc.domain_type == 'universe': - if self.id in volume_calc.volumes: - self._volume = volume_calc.volumes[self.id].n - self._atoms = volume_calc.atoms[self.id] - else: - raise ValueError('No volume information found for this universe.') - else: - raise ValueError('No volume information found for this universe.') - def find(self, point): """Find cells/universes/lattices which contain a given point @@ -481,28 +546,6 @@ def get_all_universes(self): return universes def clone(self, clone_materials=True, clone_regions=True, memo=None): - """Create a copy of this universe with a new unique ID, and clones - all cells within this universe. - - Parameters - ---------- - clone_materials : bool - Whether to create separates copies of the materials filling cells - contained in this universe. - clone_regions : bool - Whether to create separates copies of the regions bounding cells - contained in this universe. - memo : dict or None - A nested dictionary of previously cloned objects. This parameter - is used internally and should not be specified by the user. - - Returns - ------- - clone : openmc.Universe - The clone of this universe - - """ - if memo is None: memo = {} @@ -523,23 +566,6 @@ def clone(self, clone_materials=True, clone_regions=True, memo=None): return memo[self] def create_xml_subelement(self, xml_element, memo=None): - """Add the universe xml representation to an incoming xml element - - Parameters - ---------- - xml_element : xml.etree.ElementTree.Element - XML element to be added to - - memo : set or None - A set of object id's representing geometry entities already - written to the xml_element. This parameter is used internally - and should not be specified by users. - - Returns - ------- - None - - """ # Iterate over all Cells for cell in self._cells.values(): @@ -600,3 +626,61 @@ def _determine_paths(self, path='', instances_only=False): cell._num_instances += 1 if not instances_only: cell._paths.append(cell_path) + + +class DAGMCUniverse(UniverseBase): + """A reference to a DAGMC file to be used in the model. + + Parameters + ---------- + filename : str + Path to the DAGMC file used to represent this universe. + universe_id : int, optional + Unique identifier of the universe. If not specified, an identifier will + automatically be assigned + name : str, optional + Name of the universe. If not specified, the name is the empty string. + cells : Iterable of openmc.Cell, optional + Cells to add to the universe. By default no cells are added. + + Attributes + ---------- + id : int + Unique identifier of the universe + name : str + Name of the universe + filename : str + Path to the DAGMC file used to represent this universe. + """ + + def __init__(self, filename, universe_id=None, name=''): + super().__init__(universe_id, name) + # Initialize class attributes + self.filename = filename + + def __repr__(self): + fmt_str = '{: <16}=\t{}\n' + string = super().__repr__() + string += fmt_str.format('\tGeom', 'DAGMC') + string += fmt_str.format('\tFile', self.filename) + return string + + @property + def filename(self): + return self._filename + + @filename.setter + def filename(self, val): + cv.check_type('DAGMC file', val, str) + self._filename = val + + def clone(self, clone_materials=True, clone_regions=True, memo=None): + pass + + def create_xml_subelement(self, xml_element, memo=None): + # Set xml element values + dagmc_element = ET.Element('dagmc') + dagmc_element.set('id', self.id) + dagmc_element.set('name', self.name) + dagmc_element.set('filename', self.filename) + xml_element.append(dagmc_element) From afaf3b04bf82e37e36c0c3a0466d1fd2f36d8d20 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 13 Oct 2020 11:40:07 -0500 Subject: [PATCH 0012/2572] Updates to the Python API to support model generation with DAGMC universes. --- openmc/cell.py | 9 ++++----- openmc/filter.py | 10 +++++----- openmc/geometry.py | 9 ++++----- openmc/lattice.py | 2 +- openmc/universe.py | 13 ++++++++++++- 5 files changed, 26 insertions(+), 17 deletions(-) diff --git a/openmc/cell.py b/openmc/cell.py index c55238df860..af1a07f9a9b 100644 --- a/openmc/cell.py +++ b/openmc/cell.py @@ -27,7 +27,7 @@ class Cell(IDManagerMixin): automatically be assigned. name : str, optional Name of the cell. If not specified, the name is the empty string. - fill : openmc.Material or openmc.Universe or openmc.Lattice or None or iterable of openmc.Material, optional + fill : openmc.Material or openmc.UniverseBase or openmc.Lattice or None or iterable of openmc.Material, optional Indicates what the region of space is filled with region : openmc.Region, optional Region of space that is assigned to the cell. @@ -38,7 +38,7 @@ class Cell(IDManagerMixin): Unique identifier for the cell name : str Name of the cell - fill : openmc.Material or openmc.Universe or openmc.Lattice or None or iterable of openmc.Material + fill : openmc.Material or openmc.UniverseBase or openmc.Lattice or None or iterable of openmc.Material Indicates what the region of space is filled with. If None, the cell is treated as a void. An iterable of materials is used to fill repeated instances of a cell with different materials. @@ -156,7 +156,7 @@ def fill(self): def fill_type(self): if isinstance(self.fill, openmc.Material): return 'material' - elif isinstance(self.fill, openmc.Universe): + elif isinstance(self.fill, openmc.UniverseBase): return 'universe' elif isinstance(self.fill, openmc.Lattice): return 'lattice' @@ -278,11 +278,10 @@ def fill(self, fill): cv.check_type('cell.fill[i]', f, openmc.Material) elif not isinstance(fill, (openmc.Material, openmc.Lattice, - openmc.Universe)): + openmc.UniverseBase)): msg = ('Unable to set Cell ID="{0}" to use a non-Material or ' 'Universe fill "{1}"'.format(self._id, fill)) raise ValueError(msg) - self._fill = fill # Info about atom content can now be invalid diff --git a/openmc/filter.py b/openmc/filter.py index 8da3cf78f65..f0ea7d7215c 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -15,7 +15,7 @@ from .material import Material from .mixin import IDManagerMixin from .surface import Surface -from .universe import Universe +from .universe import UniverseBase _FILTER_TYPES = ( @@ -408,8 +408,8 @@ class UniverseFilter(WithIDFilter): Parameters ---------- - bins : openmc.Universe, int, or iterable thereof - The Universes to tally. Either openmc.Universe objects or their + bins : openmc.UniverseBase, int, or iterable thereof + The Universes to tally. Either openmc.UniverseBase objects or their Integral ID numbers can be used. filter_id : int Unique identifier for the filter @@ -417,14 +417,14 @@ class UniverseFilter(WithIDFilter): Attributes ---------- bins : Iterable of Integral - openmc.Universe IDs. + openmc.UniverseBase IDs. id : int Unique identifier for the filter num_bins : Integral The number of filter bins """ - expected_type = Universe + expected_type = UniverseBase class MaterialFilter(WithIDFilter): diff --git a/openmc/geometry.py b/openmc/geometry.py index 30decd9ff8f..36cff94e30d 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -14,13 +14,13 @@ class Geometry: Parameters ---------- - root : openmc.Universe or Iterable of openmc.Cell, optional + root : openmc.UniverseBase or Iterable of openmc.Cell, optional Root universe which contains all others, or an iterable of cells that should be used to create a root universe. Attributes ---------- - root_universe : openmc.Universe + root_universe : openmc.UniverseBase Root universe which contains all others bounding_box : 2-tuple of numpy.array Lower-left and upper-right coordinates of an axis-aligned bounding box @@ -32,7 +32,7 @@ def __init__(self, root=None): self._root_universe = None self._offsets = {} if root is not None: - if isinstance(root, openmc.Universe): + if isinstance(root, openmc.UniverseBase): self.root_universe = root else: univ = openmc.Universe() @@ -50,8 +50,7 @@ def bounding_box(self): @root_universe.setter def root_universe(self, root_universe): - check_type('root universe', root_universe, - (openmc.Universe, openmc.DAGMCUniverse)) + check_type('root universe', root_universe, openmc.UniverseBase) self._root_universe = root_universe def add_volume_information(self, volume_calc): diff --git a/openmc/lattice.py b/openmc/lattice.py index 252fbf32741..5112dde05af 100644 --- a/openmc/lattice.py +++ b/openmc/lattice.py @@ -488,7 +488,7 @@ def pitch(self, pitch): @Lattice.universes.setter def universes(self, universes): - cv.check_iterable_type('lattice universes', universes, openmc.Universe, + cv.check_iterable_type('lattice universes', universes, openmc.UniverseBase, min_depth=2, max_depth=3) self._universes = np.asarray(universes) diff --git a/openmc/universe.py b/openmc/universe.py index be6aa00fd72..f71aec664ad 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -4,6 +4,7 @@ from copy import copy, deepcopy from numbers import Real import random +from xml.etree import ElementTree as ET import numpy as np @@ -677,10 +678,20 @@ def filename(self, val): def clone(self, clone_materials=True, clone_regions=True, memo=None): pass + def get_all_cells(self, memo=None): + return OrderedDict() + def create_xml_subelement(self, xml_element, memo=None): + + if memo and self in memo: + return + + if memo is not None: + memo.add(self) + # Set xml element values dagmc_element = ET.Element('dagmc') - dagmc_element.set('id', self.id) + dagmc_element.set('id', str(self.id)) dagmc_element.set('name', self.name) dagmc_element.set('filename', self.filename) xml_element.append(dagmc_element) From 70ec716c1f9fb51401587f8a55e6f9d1b2fb9fb3 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 13 Oct 2020 11:40:29 -0500 Subject: [PATCH 0013/2572] Fixed to automatic ID generation for DAGMC universes. --- src/cell.cpp | 4 ++-- src/dagmc.cpp | 20 ++++++++++++++------ 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/src/cell.cpp b/src/cell.cpp index ec8c5e0e7d9..eba407ed9b7 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -1026,6 +1026,8 @@ void read_cells(pugi::xml_node node) model::cells.push_back(make_unique(cell_node)); } + read_dagmc_universes(node); + // Fill the cell map. for (int i = 0; i < model::cells.size(); i++) { int32_t id = model::cells[i]->id_; @@ -1037,8 +1039,6 @@ void read_cells(pugi::xml_node node) } } - read_dagmc_universes(node); - // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { int32_t uid = model::cells[i]->universe_; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index ad50d85d5a5..8768eac1b15 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -257,8 +257,6 @@ void DAGUniverse::initialize() { c->fill_ = C_NONE; // no fill, single universe model::cells.emplace_back(c); - model::cell_map[c->id_] = model::cells.size() - 1; - cells_.push_back(model::cell_map[c->id_]); // MATERIALS @@ -330,14 +328,14 @@ void DAGUniverse::initialize() { // initialize surface objects int n_surfaces = dagmc_instance_->num_entities(2); - for (int i = 0; i < n_surfaces; i++) { moab::EntityHandle surf_handle = dagmc_instance_->entity_by_index(2, i+1); // set cell ids using global IDs DAGSurface* s = new DAGSurface(); s->dag_index_ = i+1; - s->id_ = dagmc_instance_->id_by_index(2, i+1); + s->id_ = next_surf_id++; + // s->id_ = dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; // set BCs @@ -369,11 +367,21 @@ void DAGUniverse::initialize() { } // add to global array and map + + auto in_map = model::surface_map.find(s->id_); + if (in_map == model::surface_map.end()) { + model::surface_map[s->id_] = model::surfaces.size(); + } else { + fatal_error(fmt::format( + "Two or more surfaces use the same unique ID: {}", s->id_)); + } model::surfaces.emplace_back(s); - model::surface_map[s->id_] = model::surfaces.size() - 1; - } + + } // end surface loop + } + int32_t create_dagmc_universe(const std::string& filename) { if (!model::DAG) { model::DAG = std::make_shared(); From f63ba31b55c01883135fbdb78b0bfaa82c2df057 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 13 Oct 2020 19:54:00 -0500 Subject: [PATCH 0014/2572] Adding an option for reporting ID overlap between DAGMC and CSG geometry. --- include/openmc/cell.h | 3 +- openmc/universe.py | 19 ++++++++++-- src/cell.cpp | 3 +- src/dagmc.cpp | 71 +++++++++++++++++++++++++++++++++++++------ 4 files changed, 82 insertions(+), 14 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index bef55690a29..0738fe9aab8 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -88,7 +88,7 @@ class DAGUniverse : public Universe { public: explicit DAGUniverse(pugi::xml_node node); - explicit DAGUniverse(const std::string& filename); + explicit DAGUniverse(const std::string& filename, bool auto_ids = false); void initialize(); //!< Sets up the DAGMC instance and OpenMC internals @@ -97,6 +97,7 @@ class DAGUniverse : public Universe { std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe int32_t cell_idx_offset_; int32_t surf_idx_offset_; + bool adjust_ids_; }; #endif diff --git a/openmc/universe.py b/openmc/universe.py index f71aec664ad..f7ae0390aca 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -652,12 +652,16 @@ class DAGMCUniverse(UniverseBase): Name of the universe filename : str Path to the DAGMC file used to represent this universe. + auto_ids : bool + Set IDs automatically on initialization (True) or report overlaps + in ID space between CSG and DAGMC (False) """ - def __init__(self, filename, universe_id=None, name=''): + def __init__(self, filename, universe_id=None, name='', auto_ids=False): super().__init__(universe_id, name) # Initialize class attributes self.filename = filename + self.auto_ids = auto_ids def __repr__(self): fmt_str = '{: <16}=\t{}\n' @@ -672,9 +676,18 @@ def filename(self): @filename.setter def filename(self, val): - cv.check_type('DAGMC file', val, str) + cv.check_type('DAGMC filename', val, str) self._filename = val + @property + def auto_ids(self): + return self._auto_ids + + @auto_ids.setter + def auto_ids(self, val): + cv.check_type('DAGMC auto ids', val, bool) + self._auto_ids = val + def clone(self, clone_materials=True, clone_regions=True, memo=None): pass @@ -693,5 +706,7 @@ def create_xml_subelement(self, xml_element, memo=None): dagmc_element = ET.Element('dagmc') dagmc_element.set('id', str(self.id)) dagmc_element.set('name', self.name) + if self.auto_ids: + dagmc_element.set('auto_ids', 'true') dagmc_element.set('filename', self.filename) xml_element.append(dagmc_element) diff --git a/src/cell.cpp b/src/cell.cpp index eba407ed9b7..39f7361576e 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -1026,7 +1026,6 @@ void read_cells(pugi::xml_node node) model::cells.push_back(make_unique(cell_node)); } - read_dagmc_universes(node); // Fill the cell map. for (int i = 0; i < model::cells.size(); i++) { @@ -1039,6 +1038,8 @@ void read_cells(pugi::xml_node node) } } + read_dagmc_universes(node); + // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { int32_t uid = model::cells[i]->universe_; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 8768eac1b15..6e9aac2667b 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -54,6 +54,46 @@ std::string dagmc_file() { return filename; } +std::string dagmc_ids_for_dim(std::shared_ptr& dagmc_instance, + int dim) { + // generate a vector of ids + std::vector id_vec; + int n_cells = dagmc_instance->num_entities(dim); + for (int i = 1; i <= n_cells; i++) { + id_vec.push_back(dagmc_instance->id_by_index(dim, i)); + } + + // sort the vector of ids + std::sort(id_vec.begin(), id_vec.end()); + + // generate a string representation of the range + std::stringstream out; + + int i = 0; + int start_id = id_vec[0]; + int stop_id; + while (i < n_cells) { + stop_id = id_vec[i]; + + if (id_vec[i + 1] > stop_id + 1) { + if (start_id != stop_id) { + // there are several IDs in a row, print condensed version + out << start_id << "-" << stop_id; + } else { + // only one ID in this contiguous block + out << start_id; + } + if (i < n_cells - 1) { out << ", "; } + start_id = id_vec[++i]; + stop_id = start_id; + } + + i++; + } + + return out.str(); +} + bool get_uwuw_materials_xml(std::string& s) { std::string filename = dagmc_file(); UWUW uwuw(filename.c_str()); @@ -168,10 +208,15 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { fatal_error("Must specify a file for the DAGMC universe"); } + adjust_ids_ = false; + if (check_for_node(node, "auto_ids")) { + adjust_ids_ = get_node_value_bool(node, "auto_ids"); + } initialize(); } -DAGUniverse::DAGUniverse(const std::string& filename): filename_(filename) { +DAGUniverse::DAGUniverse(const std::string& filename, bool auto_ids) +: filename_(filename), adjust_ids_(auto_ids) { // determine the next universe id int32_t next_univ_id = 0; for (const auto& u : model::universes) { @@ -249,13 +294,21 @@ void DAGUniverse::initialize() { // set cell ids using global IDs DAGCell* c = new DAGCell(); c->dag_index_ = i + 1; - std::cout << "Cell id: " << next_cell_id << std::endl; - c->id_ = next_cell_id++; - // c->id_ = model::DAG->id_by_index(3, c->dag_index_); + c->id_ = adjust_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); c->dagmc_ptr_ = dagmc_instance_; c->universe_ = id_; // set to zero for now c->fill_ = C_NONE; // no fill, single universe + + auto in_map = model::cell_map.find(c->id_); + if (in_map == model::cell_map.end()) { + model::cell_map[c->id_] = model::cells.size(); + } else { + warning(fmt::format("DAGMC Cell IDs: {}", dagmc_ids_for_dim(dagmc_instance_, 3))); + fatal_error(fmt::format("Cell ID {} exists in both DAGMC Universe {} " + "and the CSG geometry.", c->id_, this->id_)); + } + model::cells.emplace_back(c); // MATERIALS @@ -334,8 +387,7 @@ void DAGUniverse::initialize() { // set cell ids using global IDs DAGSurface* s = new DAGSurface(); s->dag_index_ = i+1; - s->id_ = next_surf_id++; - // s->id_ = dagmc_instance_->id_by_index(2, i+1); + s->id_ = adjust_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; // set BCs @@ -372,8 +424,9 @@ void DAGUniverse::initialize() { if (in_map == model::surface_map.end()) { model::surface_map[s->id_] = model::surfaces.size(); } else { - fatal_error(fmt::format( - "Two or more surfaces use the same unique ID: {}", s->id_)); + warning(fmt::format("DAGMC Surface IDs: {}", dagmc_ids_for_dim(dagmc_instance_, 2))); + fatal_error(fmt::format("Surface ID {} exists in both Universe {} " + "and the CSG geometry.", s->id_, this->id_)); } model::surfaces.emplace_back(s); @@ -427,12 +480,10 @@ int32_t create_dagmc_universe(const std::string& filename) { moab::EntityHandle graveyard = 0; for (int i = 0; i < n_cells; i++) { moab::EntityHandle vol_handle = model::DAG->entity_by_index(3, i+1); - // set cell ids using global IDs DAGCell* c = new DAGCell(); c->dag_index_ = i+1; c->id_ = model::DAG->id_by_index(3, c->dag_index_); - std::cout << "Cell id: " << c->id_ << std::endl; c->dagmc_ptr_ = model::DAG; c->universe_ = dagmc_univ_id; // set to zero for now c->fill_ = C_NONE; // no fill, single universe From 28153780a7cea321249b115dd9b58170a52ce8b0 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Nov 2020 08:07:16 -0600 Subject: [PATCH 0015/2572] Moving the read_dagmc_universes function into the dagmc files. --- include/openmc/cell.h | 3 +-- include/openmc/dagmc.h | 1 + src/cell.cpp | 9 ++------- src/dagmc.cpp | 10 ++++++++++ 4 files changed, 14 insertions(+), 9 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 0738fe9aab8..d8de4838675 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -372,10 +372,9 @@ struct CellInstanceHash { //============================================================================== void read_cells(pugi::xml_node node); -void read_dagmc_universes(pugi::xml_node node); + #ifdef DAGMC -void read_dagmc_universes(pugi::xml_node node); int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); #endif diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 417cedebd75..91755ed146e 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -26,6 +26,7 @@ namespace model { void load_dagmc_geometry(); int32_t create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); +void read_dagmc_universes(pugi::xml_node node); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); diff --git a/src/cell.cpp b/src/cell.cpp index 39f7361576e..10c1669cba7 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -1038,7 +1038,9 @@ void read_cells(pugi::xml_node node) } } + #ifdef DAGMC read_dagmc_universes(node); + #endif // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { @@ -1065,13 +1067,6 @@ void read_cells(pugi::xml_node node) } } -void read_dagmc_universes(pugi::xml_node node) { - for (pugi::xml_node dag_node : node.children("dagmc")) { - model::universes.push_back(std::make_unique(dag_node)); - model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; - } -} - //============================================================================== // C-API functions //============================================================================== diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 6e9aac2667b..e0bc322eea6 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -195,6 +195,16 @@ void load_dagmc_geometry() model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; } +#ifdef DAGMC +void read_dagmc_universes(pugi::xml_node node) { + for (pugi::xml_node dag_node : node.children("dagmc")) { + model::universes.push_back(std::make_unique(dag_node)); + model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; + } +} +#endif + + DAGUniverse::DAGUniverse(pugi::xml_node node) { if (check_for_node(node, "id")) { id_ = std::stoi(get_node_value(node, "id")); From 0f08cfe3d01cf8443f2cc9a60bb11be0870b5f4b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Nov 2020 08:22:01 -0600 Subject: [PATCH 0016/2572] Now writing a geometry file regardless CSG/CAD geometry use. --- openmc/model/model.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 7d6acb281ce..0501f03f3a0 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -179,8 +179,7 @@ def export_to_xml(self, directory='.'): d.mkdir(parents=True) self.settings.export_to_xml(d) - if not self.settings.dagmc: - self.geometry.export_to_xml(d) + self.geometry.export_to_xml(d) # If a materials collection was specified, export it. Otherwise, look # for all materials in the geometry and use that to automatically build From 4b5aa24a1e8c4f9e2dc021e37d6a4abde80d3d4e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Nov 2020 08:34:02 -0600 Subject: [PATCH 0017/2572] Removing dagmc property and related methods from the settings class. --- openmc/settings.py | 25 ------------------------- 1 file changed, 25 deletions(-) diff --git a/openmc/settings.py b/openmc/settings.py index 615e6365aac..7696aee9fc5 100644 --- a/openmc/settings.py +++ b/openmc/settings.py @@ -44,8 +44,6 @@ class Settings: weight assigned to particles that are not killed after Russian roulette. Value of energy should be a float indicating energy in eV below which particle type will be killed. - dagmc : bool - Indicate that a CAD-based DAGMC geometry will be used. delayed_photon_scaling : bool Indicate whether to scale the fission photon yield by (EGP + EGD)/EGP where EGP is the energy release of prompt photons and EGD is the energy @@ -269,8 +267,6 @@ def __init__(self): self._material_cell_offsets = None self._log_grid_bins = None - self._dagmc = False - self._event_based = None self._max_particles_in_flight = None @@ -434,10 +430,6 @@ def material_cell_offsets(self): def log_grid_bins(self): return self._log_grid_bins - @property - def dagmc(self): - return self._dagmc - @property def event_based(self): return self._event_based @@ -633,11 +625,6 @@ def photon_transport(self, photon_transport): cv.check_type('photon transport', photon_transport, bool) self._photon_transport = photon_transport - @dagmc.setter - def dagmc(self, dagmc): - cv.check_type('dagmc geometry', dagmc, bool) - self._dagmc = dagmc - @ptables.setter def ptables(self, ptables): cv.check_type('probability tables', ptables, bool) @@ -1125,11 +1112,6 @@ def _create_log_grid_bins_subelement(self, root): elem = ET.SubElement(root, "log_grid_bins") elem.text = str(self._log_grid_bins) - def _create_dagmc_subelement(self, root): - if self._dagmc: - elem = ET.SubElement(root, "dagmc") - elem.text = str(self._dagmc).lower() - def _eigenvalue_from_xml_element(self, root): elem = root.find('eigenvalue') if elem is not None: @@ -1407,11 +1389,6 @@ def _log_grid_bins_from_xml_element(self, root): if text is not None: self.log_grid_bins = int(text) - def _dagmc_from_xml_element(self, root): - text = get_text(root, 'dagmc') - if text is not None: - self.dagmc = text in ('true', '1') - def export_to_xml(self, path='settings.xml'): """Export simulation settings to an XML file. @@ -1465,7 +1442,6 @@ def export_to_xml(self, path='settings.xml'): self._create_max_particles_in_flight_subelement(root_element) self._create_material_cell_offsets_subelement(root_element) self._create_log_grid_bins_subelement(root_element) - self._create_dagmc_subelement(root_element) # Clean the indentation in the file to be user-readable clean_indentation(root_element) @@ -1539,7 +1515,6 @@ def from_xml(cls, path='settings.xml'): settings._max_particles_in_flight_from_xml_element(root) settings._material_cell_offsets_from_xml_element(root) settings._log_grid_bins_from_xml_element(root) - settings._dagmc_from_xml_element(root) # TODO: Get volume calculations From 956cf08481c1d712ecd05494fb57eff6a686a561 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 6 Nov 2020 10:56:59 -0600 Subject: [PATCH 0018/2572] Relying on the same enum to describe the Geometry type of different classes. --- include/openmc/cell.h | 12 +++--------- include/openmc/surface.h | 13 +++++++++++-- src/cell.cpp | 16 ++++++++++++---- src/dagmc.cpp | 2 +- src/surface.cpp | 31 +++++++++++++++++++------------ 5 files changed, 46 insertions(+), 28 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index d8de4838675..21ba3ddcd41 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -31,11 +31,6 @@ enum class Fill { LATTICE }; -enum class UniverseType { - CSG, - DAG -}; - // TODO: Convert to enum constexpr int32_t OP_LEFT_PAREN {std::numeric_limits::max()}; constexpr int32_t OP_RIGHT_PAREN {std::numeric_limits::max() - 1}; @@ -78,7 +73,7 @@ class Universe BoundingBox bounding_box() const; - UniverseType type_ = UniverseType::CSG; + GeometryType type_ = GeometryType::CSG; unique_ptr partitioner_; }; @@ -178,8 +173,6 @@ class Cell { //! \param[in] name Cell name void set_name(const std::string& name) { name_ = name; }; - - //! Get all cell instances contained by this cell //! \return Map with cell indexes as keys and instances as values std::unordered_map> get_contained_cells() const; @@ -195,10 +188,11 @@ class Cell { int32_t id_; //!< Unique ID std::string name_; //!< User-defined name - Fill type_; //!< Material, universe, or lattice + Fill type_; //!< Material, universe, or lattice int32_t universe_; //!< Universe # this cell is in int32_t fill_; //!< Universe # filling this cell int32_t n_instances_{0}; //!< Number of instances of this cell + GeometryType geom_type_; //!< Geometric representation type (CSG, DAGMC) //! \brief Index corresponding to this cell in distribcell arrays int distribcell_index_{C_NONE}; diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 8b4e416052b..28590b44935 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -30,7 +30,16 @@ namespace model { } // namespace model //============================================================================== -//! Coordinates for an axis-aligned cuboid bounding a geometric object. +// Constants +//============================================================================== + +enum class GeometryType { + CSG, + DAG +}; + +//============================================================================== +//! Coordinates for an axis-aligned cube that bounds a geometric object. //============================================================================== struct BoundingBox @@ -166,7 +175,7 @@ class DAGSurface : public Surface Direction normal(Position r) const; Direction reflect(Position r, Direction u, Particle* p) const; - void to_hdf5_inner(hid_t group_id) const override; + inline void to_hdf5_inner(hid_t group_id) const override {}; std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of surface diff --git a/src/cell.cpp b/src/cell.cpp index 10c1669cba7..c5d53dda4a9 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -195,10 +195,10 @@ Universe::to_hdf5(hid_t universes_group) const auto group = create_group(universes_group, fmt::format("universe {}", id_)); switch(type_) { - case UniverseType::CSG : + case GeometryType::CSG : write_string(group, "geom_type", "csg", false); break; - case UniverseType::DAG : + case GeometryType::DAG : write_string(group, "geom_type", "dagmc", false); break; } @@ -355,10 +355,15 @@ Cell::to_hdf5(hid_t cell_group) const { // CSGCell implementation //============================================================================== -CSGCell::CSGCell() {} // empty constructor +// default constructor +CSGCell::CSGCell() { + geom_type_ = GeometryType::CSG; +} CSGCell::CSGCell(pugi::xml_node cell_node) { + geom_type_ = GeometryType::CSG; + if (check_for_node(cell_node, "id")) { id_ = std::stoi(get_node_value(cell_node, "id")); } else { @@ -800,7 +805,10 @@ CSGCell::contains_complex(Position r, Direction u, int32_t on_surface) const // DAGMC Cell implementation //============================================================================== #ifdef DAGMC -DAGCell::DAGCell() : Cell{} { simple_ = true; }; +DAGCell::DAGCell() : Cell{} { + geom_type_ = GeometryType::DAG; + simple_ = true; +}; std::pair DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) const diff --git a/src/dagmc.cpp b/src/dagmc.cpp index e0bc322eea6..97398374c00 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -241,7 +241,7 @@ DAGUniverse::DAGUniverse(const std::string& filename, bool auto_ids) } void DAGUniverse::initialize() { - type_ = UniverseType::DAG; + type_ = GeometryType::DAG; // determine the next cell id int32_t next_cell_id = 0; diff --git a/src/surface.cpp b/src/surface.cpp index c2c20d0bfa7..92e50a76bb7 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -203,12 +203,17 @@ Surface::to_hdf5(hid_t group_id) const { hid_t surf_group = create_group(group_id, fmt::format("surface {}", id_)); - write_string(surf_group, "geom_type", "csg", false); + if (geom_type_ == GeometryType::DAG) { + write_string(surf_group, "geom_type", "dagmc", false); + } + else if (geom_type_ == GeometryType::CSG) { + write_string(surf_group, "geom_type", "csg", false); - if (bc_) { - write_string(surf_group, "boundary_type", bc_->type(), false); - } else { - write_string(surf_group, "boundary_type", "transmission", false); + if (bc_) { + write_string(surf_group, "boundary_type", bc_->type(), false); + } else { + write_string(surf_group, "boundary_type", "transmission", false); + } } if (!name_.empty()) { @@ -220,14 +225,20 @@ Surface::to_hdf5(hid_t group_id) const close_group(surf_group); } -CSGSurface::CSGSurface() : Surface{} {}; -CSGSurface::CSGSurface(pugi::xml_node surf_node) : Surface{surf_node} {}; +CSGSurface::CSGSurface() : Surface{} { + geom_type_ = GeometryType::CSG; +}; +CSGSurface::CSGSurface(pugi::xml_node surf_node) : Surface{surf_node} { + geom_type_ = GeometryType::CSG; +}; //============================================================================== // DAGSurface implementation //============================================================================== #ifdef DAGMC -DAGSurface::DAGSurface() : Surface{} {} // empty constructor +DAGSurface::DAGSurface() : Surface{} { + geom_type_ = GeometryType::DAG; +} // empty constructor double DAGSurface::evaluate(Position r) const { @@ -274,10 +285,6 @@ Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const return p->last_dir(); } -void DAGSurface::to_hdf5_inner(hid_t group_id) const { - write_string(group_id, "geom_type", "dagmc", false); -} - #endif //============================================================================== From 67aca58079d4c68cfd6f1d6e9e781232f74131f2 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 12 Nov 2020 10:12:01 -0600 Subject: [PATCH 0019/2572] Adding ifdefs to fix builds without DAGMC. --- src/particle.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/particle.cpp b/src/particle.cpp index 73cdb3dd6a9..5c6d0d466ae 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -286,7 +286,9 @@ Particle::event_collide() // Score flux derivative accumulators for differential tallies. if (!model::active_tallies.empty()) score_collision_derivative(*this); + #ifdef DAGMC history_.reset(); + #endif } void @@ -502,6 +504,10 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) coord(0).cell = cell_last(n_coord_last() - 1); surface() = -surface(); + #ifdef DAGMC + if (surf->geom_type_ != GeometryType::DAGMC) history_.reset(); + #endif + // If a reflective surface is coincident with a lattice or universe // boundary, it is necessary to redetermine the particle's coordinates in // the lower universes. From 3e93b814a94d4a2b6a6613c75b7f2d0f3c8d969c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 12 Nov 2020 10:26:44 -0600 Subject: [PATCH 0020/2572] Updating python module to enable summary file reads from DAGMC simulations. Updating legacy DAGMC test as well. --- openmc/summary.py | 3 ++- openmc/surface.py | 4 +++- openmc/universe.py | 3 +++ tests/regression_tests/dagmc/legacy/inputs_true.dat | 5 ++++- tests/regression_tests/dagmc/legacy/test.py | 4 ++++ 5 files changed, 16 insertions(+), 3 deletions(-) diff --git a/openmc/summary.py b/openmc/summary.py index 63ed6651695..a1ee2dcd660 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -123,7 +123,8 @@ def _read_materials(self): def _read_surfaces(self): for group in self._f['geometry/surfaces'].values(): surface = openmc.Surface.from_hdf5(group) - self._fast_surfaces[surface.id] = surface + if surface: + self._fast_surfaces[surface.id] = surface def _read_cells(self): diff --git a/openmc/surface.py b/openmc/surface.py index d71e73788ef..3c2c550101b 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -455,7 +455,9 @@ def from_hdf5(group): surface_id = int(group.name.split('/')[-1].lstrip('surface ')) name = group['name'][()].decode() if 'name' in group else '' - surf_type = group['type'][()].decode() + surf_type = group['geom_type'][()].decode() + if surf_type == 'dagmc': + return bc = group['boundary_type'][()].decode() coeffs = group['coefficients'][...] kwargs = {'boundary_type': bc, 'name': name, 'surface_id': surface_id} diff --git a/openmc/universe.py b/openmc/universe.py index f7ae0390aca..c3f4e36ceda 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -694,6 +694,9 @@ def clone(self, clone_materials=True, clone_regions=True, memo=None): def get_all_cells(self, memo=None): return OrderedDict() + def get_all_materials(self, memo=None): + return OrderedDict() + def create_xml_subelement(self, xml_element, memo=None): if memo and self in memo: diff --git a/tests/regression_tests/dagmc/legacy/inputs_true.dat b/tests/regression_tests/dagmc/legacy/inputs_true.dat index 769a3384c3c..ecc5d7373a0 100644 --- a/tests/regression_tests/dagmc/legacy/inputs_true.dat +++ b/tests/regression_tests/dagmc/legacy/inputs_true.dat @@ -1,4 +1,8 @@ + + + + @@ -22,7 +26,6 @@ -4 -4 -4 4 4 4 - true diff --git a/tests/regression_tests/dagmc/legacy/test.py b/tests/regression_tests/dagmc/legacy/test.py index 5af18e5c8cf..76b76fbda09 100644 --- a/tests/regression_tests/dagmc/legacy/test.py +++ b/tests/regression_tests/dagmc/legacy/test.py @@ -26,6 +26,10 @@ def model(): model.settings.dagmc = True + # geometry + dag_univ = openmc.DAGMCUniverse("dagmc.h5m") + model.geometry = openmc.Geometry(root=dag_univ) + # tally tally = openmc.Tally() tally.scores = ['total'] From 0bd2f9263b132b7091bd0f85821bf3fb6a9d66e5 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 6 Dec 2020 00:20:28 -0600 Subject: [PATCH 0021/2572] Always checking DAGMC models for additional material definitions. --- include/openmc/dagmc.h | 1 + src/dagmc.cpp | 44 +++++++++++++++++++ src/material.cpp | 4 ++ .../dagmc/uwuw/inputs_true.dat | 8 +++- tests/regression_tests/dagmc/uwuw/test.py | 6 ++- 5 files changed, 61 insertions(+), 2 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 91755ed146e..f0c6d47186f 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -27,6 +27,7 @@ void load_dagmc_geometry(); int32_t create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); void read_dagmc_universes(pugi::xml_node node); +void read_dagmc_uwuw_materials(); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 97398374c00..c162096c231 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -128,6 +128,50 @@ bool read_uwuw_materials(pugi::xml_document& doc) { return found_uwuw_mats; } +void read_dagmc_uwuw_materials() { + std::string filename = settings::path_input + "geometry.xml"; + if (!file_exists(filename)) { + fatal_error(fmt::format("Geometry XML file '{}' does not exist!", filename)); + } + + pugi::xml_document doc; + auto result = doc.load_file(filename.c_str()); + if (!result) { + fatal_error("Error processing geometry.xml file."); + } + pugi::xml_node root = doc.document_element(); + // Loop over DAGMC elements + for (pugi::xml_node dagmc_node : root.children("dagmc")) { + if (!check_for_node(dagmc_node, "filename")) { + fatal_error("No filename specified on a DAGMC universe element"); + } + std::string dagmc_filename = get_node_value(dagmc_node, "filename"); + // Load any existing UWUW materials + UWUW uwuw(dagmc_filename.c_str()); + const auto& mat_lib = uwuw.material_library; + if (mat_lib.size() == 0) continue; + + std::stringstream ss; + ss << "\n"; + ss << "\n"; + for (auto mat : mat_lib) { ss << mat.second.openmc("atom"); } + ss << ""; + std::string mat_xml_string = ss.str(); + + // create a pugi XML document from this string + pugi::xml_document doc; + auto result = doc.load_string(mat_xml_string.c_str()); + if (!result) { + fatal_error("Error processing XML created using DAGMC UWUW materials."); + } + pugi::xml_node root = doc.document_element(); + for (pugi::xml_node material_node : root.children("material")) { + model::materials.push_back(std::make_unique(material_node)); + } + } +} + + bool write_uwuw_materials_xml() { std::string s; bool found_uwuw_mats = get_uwuw_materials_xml(s); diff --git a/src/material.cpp b/src/material.cpp index a1aa6ea5f86..9010e8b01c4 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -1237,6 +1237,10 @@ void read_materials_xml() for (pugi::xml_node material_node : root.children("material")) { model::materials.push_back(make_unique(material_node)); } + + // Search for materials defined on DAGMC models + read_dagmc_uwuw_materials(); + model::materials.shrink_to_fit(); } diff --git a/tests/regression_tests/dagmc/uwuw/inputs_true.dat b/tests/regression_tests/dagmc/uwuw/inputs_true.dat index 87ceb944d81..901547fa29e 100644 --- a/tests/regression_tests/dagmc/uwuw/inputs_true.dat +++ b/tests/regression_tests/dagmc/uwuw/inputs_true.dat @@ -1,4 +1,11 @@ + + + + + + + eigenvalue 100 @@ -9,7 +16,6 @@ -4 -4 -4 4 4 4 - true diff --git a/tests/regression_tests/dagmc/uwuw/test.py b/tests/regression_tests/dagmc/uwuw/test.py index b4391d8e77e..5c9777d481e 100644 --- a/tests/regression_tests/dagmc/uwuw/test.py +++ b/tests/regression_tests/dagmc/uwuw/test.py @@ -27,13 +27,17 @@ def _build_inputs(self): model.settings.export_to_xml() + # geometry + dag_univ = openmc.DAGMCUniverse("dagmc.h5m") + model.geometry = openmc.Geometry(root=dag_univ) + # tally tally = openmc.Tally() tally.scores = ['total'] tally.filters = [openmc.CellFilter(1)] model.tallies = [tally] - model.tallies.export_to_xml() + model.export_to_xml() def test_uwuw(): harness = UWUWTest('statepoint.5.h5') From 9c2f9b523bbe8c08740dced743bcdbce6eaffdf7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 6 Dec 2020 11:36:23 -0600 Subject: [PATCH 0022/2572] Updating history reset to account for reflective boundaries. Adding mark as lost warning when no intersection is found. --- src/cell.cpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/cell.cpp b/src/cell.cpp index c5d53dda4a9..ae121d2ba1a 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -816,10 +816,8 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons Expects(p); // if we've changed direction or we're not on a surface, // reset the history and update last direction - if (u != p->last_dir() || on_surface == 0) { - p->history().reset(); - p->last_dir() = u; - } + if (u != p->last_dir()) { p->last_dir() = u; p->history().reset(); } + if (on_surface == 0) { p->history().reset(); } const auto& univ = model::universes[p->coord_[p->n_coord_ - 1].universe]; @@ -841,6 +839,7 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons // indicate that particle is lost surf_idx = -1; dist = INFINITY; + p->mark_as_lost(fmt::format("No intersection found with DAGMC cell {}", id_)); } return {dist, surf_idx}; From bac09e62959b8294fdaea75a1f6bb16ddcb19b79 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 6 Dec 2020 13:43:11 -0600 Subject: [PATCH 0023/2572] Adding geometry output to the reflecting boundary DAGMC test. --- tests/regression_tests/dagmc/refl/test.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/regression_tests/dagmc/refl/test.py b/tests/regression_tests/dagmc/refl/test.py index 305e18ed5c8..1920a572d92 100644 --- a/tests/regression_tests/dagmc/refl/test.py +++ b/tests/regression_tests/dagmc/refl/test.py @@ -27,6 +27,10 @@ def _build_inputs(self): model.settings.export_to_xml() + # geometry + dag_univ = openmc.DAGMCUniverse("dagmc.h5m") + model.geometry = openmc.Geometry(root=dag_univ) + # tally tally = openmc.Tally() tally.scores = ['total'] @@ -34,6 +38,7 @@ def _build_inputs(self): model.tallies = [tally] model.tallies.export_to_xml() + model.export_to_xml() def test_refl(): harness = UWUWTest('statepoint.5.h5') From d12eed03a23dd853dec87abb732eafe7deaa0c52 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sun, 6 Dec 2020 21:28:38 -0600 Subject: [PATCH 0024/2572] Updates to the reflection test inputs to ensure the correct cell is tallied. --- tests/regression_tests/dagmc/refl/inputs_true.dat | 8 +++++++- tests/regression_tests/dagmc/refl/test.py | 1 + 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index 27327300103..fdd68002d68 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -1,4 +1,11 @@ + + + + + + + eigenvalue 100 @@ -9,7 +16,6 @@ -4 -4 -4 4 4 4 - true diff --git a/tests/regression_tests/dagmc/refl/test.py b/tests/regression_tests/dagmc/refl/test.py index 1920a572d92..2b43caf89ed 100644 --- a/tests/regression_tests/dagmc/refl/test.py +++ b/tests/regression_tests/dagmc/refl/test.py @@ -29,6 +29,7 @@ def _build_inputs(self): # geometry dag_univ = openmc.DAGMCUniverse("dagmc.h5m") + dag_univ.auto_ids = True model.geometry = openmc.Geometry(root=dag_univ) # tally From 49e22c9091acfe51939cb17c7b87690f54a89f76 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 7 Dec 2020 10:09:42 -0600 Subject: [PATCH 0025/2572] Correcting extraction of surface type from hdf5 formats. --- openmc/surface.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/openmc/surface.py b/openmc/surface.py index 3c2c550101b..70fd0083189 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -453,15 +453,19 @@ def from_hdf5(group): """ + # If this is a DAGMC surface, do nothing for now + geom_type = group['geom_type'][()].decode() + if geom_type == 'dagmc': + return + surface_id = int(group.name.split('/')[-1].lstrip('surface ')) name = group['name'][()].decode() if 'name' in group else '' - surf_type = group['geom_type'][()].decode() - if surf_type == 'dagmc': - return + bc = group['boundary_type'][()].decode() coeffs = group['coefficients'][...] kwargs = {'boundary_type': bc, 'name': name, 'surface_id': surface_id} + surf_type = group['type'][()].decode() cls = _SURFACE_CLASSES[surf_type] return cls(*coeffs, **kwargs) From 41c462a0b17364fdd0b6c7feb6f7d43b32542011 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 7 Dec 2020 10:10:14 -0600 Subject: [PATCH 0026/2572] Using a Boolean type instead of the object pointer to dectect the surface type. --- src/particle.cpp | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/src/particle.cpp b/src/particle.cpp index 5c6d0d466ae..817f7f60ba1 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -389,8 +389,8 @@ Particle::cross_surface() // ========================================================================== // SEARCH NEIGHBOR LISTS FOR NEXT CELL - #ifdef DAGMC + // in DAGMC, we know what the next cell should be auto surfp = dynamic_cast(model::surfaces[std::abs(surface_) - 1].get()); if (surfp) { auto cellp = dynamic_cast(model::cells[cell_last_[n_coord_ - 1]].get()); @@ -512,13 +512,11 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) // boundary, it is necessary to redetermine the particle's coordinates in // the lower universes. // (unless we're using a dagmc model, which has exactly one universe) - if (!settings::dagmc) { - n_coord() = 1; - if (!neighbor_list_find_cell(*this)) { - mark_as_lost("Couldn't find particle after reflecting from surface " + - std::to_string(surf.id_) + "."); - return; - } + n_coord() = 1; + if (surf->geom_type_ != GeometryType::DAGMC && !neighbor_list_find_cell(*this, true)) { + this->mark_as_lost("Couldn't find particle after reflecting from surface " + + std::to_string(surf.id_) + "."); + return; } // Set previous coordinate going slightly past surface crossing From a09c468faf773bb1fc5c496c4ced226ebcc4072b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 7 Dec 2020 15:49:40 -0600 Subject: [PATCH 0027/2572] Updates to the dagmc test and settings test from the Python API. --- tests/unit_tests/dagmc/test.py | 4 +++- tests/unit_tests/test_settings.py | 2 -- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/unit_tests/dagmc/test.py b/tests/unit_tests/dagmc/test.py index e5d7725551e..b3e2c390f43 100644 --- a/tests/unit_tests/dagmc/test.py +++ b/tests/unit_tests/dagmc/test.py @@ -29,7 +29,9 @@ def dagmc_model(request): source = openmc.Source(space=source_box) model.settings.source = source - model.settings.dagmc = True + # geometry + dagmc_universe = openmc.DAGMCUniverse('dagmc.h5m') + model.geometry = openmc.Geometry(dagmc_universe) # tally tally = openmc.Tally() diff --git a/tests/unit_tests/test_settings.py b/tests/unit_tests/test_settings.py index 7a3357d4392..fdaf5c2db84 100644 --- a/tests/unit_tests/test_settings.py +++ b/tests/unit_tests/test_settings.py @@ -54,7 +54,6 @@ def test_export_to_xml(run_in_tmpdir): s.log_grid_bins = 2000 s.photon_transport = False s.electron_treatment = 'led' - s.dagmc = False # Make sure exporting XML works s.export_to_xml() @@ -111,4 +110,3 @@ def test_export_to_xml(run_in_tmpdir): assert s.log_grid_bins == 2000 assert not s.photon_transport assert s.electron_treatment == 'led' - assert not s.dagmc From c732e2ae88bb1ac58c3247c46677c24a7e468172 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 7 Dec 2020 16:05:52 -0600 Subject: [PATCH 0028/2572] Adding empty definition for dagmc materials function. --- include/openmc/dagmc.h | 8 ++++++-- src/dagmc.cpp | 2 +- src/material.cpp | 2 +- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index f0c6d47186f..d18b7057613 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -27,12 +27,16 @@ void load_dagmc_geometry(); int32_t create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); void read_dagmc_universes(pugi::xml_node node); -void read_dagmc_uwuw_materials(); +void read_dagmc_materials(); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); } // namespace openmc -#endif // DAGMC +#else + +inline void read_dagmc_materials() {}; + +#endif #endif // OPENMC_DAGMC_H diff --git a/src/dagmc.cpp b/src/dagmc.cpp index c162096c231..f459580d649 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -128,7 +128,7 @@ bool read_uwuw_materials(pugi::xml_document& doc) { return found_uwuw_mats; } -void read_dagmc_uwuw_materials() { +void read_dagmc_materials() { std::string filename = settings::path_input + "geometry.xml"; if (!file_exists(filename)) { fatal_error(fmt::format("Geometry XML file '{}' does not exist!", filename)); diff --git a/src/material.cpp b/src/material.cpp index 9010e8b01c4..80999abae98 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -1239,7 +1239,7 @@ void read_materials_xml() } // Search for materials defined on DAGMC models - read_dagmc_uwuw_materials(); + read_dagmc_materials(); model::materials.shrink_to_fit(); } From 70cb36b474e8a31a71cc2d453658818403a89aa6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 17 Feb 2021 23:08:51 -0600 Subject: [PATCH 0029/2572] Corrections after rebasing. --- include/openmc/surface.h | 8 +++++--- src/dagmc.cpp | 13 ++++++------- src/particle.cpp | 2 +- 3 files changed, 12 insertions(+), 11 deletions(-) diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 28590b44935..3d386e8768a 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -97,6 +97,7 @@ class Surface int id_; //!< Unique ID std::string name_; //!< User-defined name std::shared_ptr bc_ {nullptr}; //!< Boundary condition + GeometryType geom_type_; //!< Geometry type indicator (CSG or DAGMC) bool surf_source_ {false}; //!< Activate source banking for the surface? explicit Surface(pugi::xml_node surf_node); @@ -143,13 +144,13 @@ class Surface //! Write all information needed to reconstruct the surface to an HDF5 group. //! \param group_id An HDF5 group id. - virtual void to_hdf5(hid_t group_id) const = 0; + void to_hdf5(hid_t group_id) const; //! Get the BoundingBox for this surface. virtual BoundingBox bounding_box(bool /*pos_side*/) const { return {}; } protected: - virtual void to_hdf5_inner(hid_t group_id) const = 0; + virtual void to_hdf5_inner(hid_t group_id) const = 0; }; class CSGSurface : public Surface @@ -158,7 +159,8 @@ class CSGSurface : public Surface explicit CSGSurface(pugi::xml_node surf_node); CSGSurface(); - void to_hdf5(hid_t group_id) const; +protected: + virtual void to_hdf5_inner(hid_t group_id) const = 0; }; //============================================================================== diff --git a/src/dagmc.cpp b/src/dagmc.cpp index f459580d649..91e2ffb0127 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -154,7 +154,7 @@ void read_dagmc_materials() { std::stringstream ss; ss << "\n"; ss << "\n"; - for (auto mat : mat_lib) { ss << mat.second.openmc("atom"); } + for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } ss << ""; std::string mat_xml_string = ss.str(); @@ -389,7 +389,7 @@ void DAGUniverse::initialize() { std::string uwuw_mat = DMD.volume_material_property_data_eh[vol_handle]; if (uwuw.material_library.count(uwuw_mat) != 0) { // Note: material numbers are set by UWUW - int mat_number = uwuw.material_library[uwuw_mat].metadata["mat_number"].asInt(); + int mat_number = uwuw.material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); c->material_.push_back(mat_number); } else { fatal_error(fmt::format("Material with value {} not found in the " @@ -448,12 +448,11 @@ void DAGUniverse::initialize() { std::string bc_value = DMD.get_surface_property("boundary", surf_handle); to_lower(bc_value); if (bc_value.empty() || bc_value == "transmit" || bc_value == "transmission") { - // set to transmission by default - s->bc_ = Surface::BoundaryType::TRANSMIT; + // set to transmission by default (nullptr) } else if (bc_value == "vacuum") { - s->bc_ = Surface::BoundaryType::VACUUM; + s->bc_ = std::make_shared(); } else if (bc_value == "reflective" || bc_value == "reflect" || bc_value == "reflecting") { - s->bc_ = Surface::BoundaryType::REFLECT; + s->bc_ = std::make_shared(); } else if (bc_value == "periodic") { fatal_error("Periodic boundary condition not supported in DAGMC."); } else { @@ -469,7 +468,7 @@ void DAGUniverse::initialize() { // if this surface belongs to the graveyard if (graveyard && parent_vols.find(graveyard) != parent_vols.end()) { // set graveyard surface BC's to vacuum - s->bc_ = Surface::BoundaryType::VACUUM; + s->bc_ = std::make_shared(); } // add to global array and map diff --git a/src/particle.cpp b/src/particle.cpp index 817f7f60ba1..5b2105a36e1 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -505,7 +505,7 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) surface() = -surface(); #ifdef DAGMC - if (surf->geom_type_ != GeometryType::DAGMC) history_.reset(); + if (surf.geom_type_ != GeometryType::DAG) history_.reset(); #endif // If a reflective surface is coincident with a lattice or universe From 2eac03cee1edb56cf1a53b2409d662f8527473a1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 17 Feb 2021 23:11:24 -0600 Subject: [PATCH 0030/2572] Adding basic test for dagmc universes. --- .../dagmc/universes/__init__.py | 0 .../dagmc/universes/dagmc.h5m | Bin 0 -> 1233488 bytes .../dagmc/universes/geometry.xml | 27 ++++++++++++++++++ .../dagmc/universes/materials.xml | 26 +++++++++++++++++ .../dagmc/universes/settings.xml | 10 +++++++ .../regression_tests/dagmc/universes/test.py | 4 +++ 6 files changed, 67 insertions(+) create mode 100644 tests/regression_tests/dagmc/universes/__init__.py create mode 100644 tests/regression_tests/dagmc/universes/dagmc.h5m create mode 100644 tests/regression_tests/dagmc/universes/geometry.xml create mode 100644 tests/regression_tests/dagmc/universes/materials.xml create mode 100644 tests/regression_tests/dagmc/universes/settings.xml create mode 100644 tests/regression_tests/dagmc/universes/test.py diff --git a/tests/regression_tests/dagmc/universes/__init__.py b/tests/regression_tests/dagmc/universes/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/regression_tests/dagmc/universes/dagmc.h5m b/tests/regression_tests/dagmc/universes/dagmc.h5m new file mode 100644 index 0000000000000000000000000000000000000000..d5f884db27448dec82e80c18b1bea02833926b92 GIT binary patch literal 1233488 zcmeF430%zE|No~|Qc3&XOf@7V3EApcqAZm}Wo;2f3Wdm$ETt@2B764i*|W}vERiK! zb`pwgAr#?1(>dpT%v@d9{oecg-|IH_ar?~YIcGl0ne#d4ectc)>zL`Y4@-!>o(m!9x%k#2UC8GQEbAR$ij%*XiHeDXSk>g4J zNsmvrg6i#mss>zL+IdQD*q7WjZKc_yozcpOij0bqyr=Eam45Iiw{koM zsrIW$+Hb5eK49fViQoRH{XVIQ%U8GGKlwxa%fBYuerdZ*ApIJ>y{Ld8k&^epVZp%D!L?x4a9ugWE6)|cYw0rt|^mO;=DCvP|yW=ZJN`Lcw zko^#u>%gU||I!+$Y8>;dflG`a&o}M|6sjD@Ts!0P3FN?KQ}CNivY)UCzbPU6)tg_d z253LvPX?$mdV7iZ4IN)J!|^^HU)0IC)1jTWOJ@%+Kl(K}p1k-!W?w0YpLEHitSkPX zFaDpta6h07)}37~{0SW=eaU{bI_7rdi@9W+qsNimA_DwPWIvJK=abzMa(sGT0}cFv zBk8cLNx2NNPs`mP`(@Y+h5pQ%Jb$#D?DI$t_m^#|>IY=&???7PY!iG*#IJ{ zzoQ0dKS1qS19I7^CNp}9ktE%=Z`yBqL{o3i_lT*9m zPh3j%SN_E{AlrWVHqqU^GZ2@nZojx5{%e0_+b?ZrH%R-T_0hSTNBef&2D)`5 zfVN9S3;g63&m-hv9+A=L_YXkZ=Lpii>G@^b`|ls+*ZZ`pejrUB-z#T`w?FC!d_!?L zTF<{;O;z8~@6|w6{XjlxKlHnQPd}iWuh$hb!?SEvSGg|)tdQ0PBt}=dDHJ)ANrO5GW@YKZKj^P2(149S#D*Ii0 zPl$YuuQ+WL-b(rv$)RHLuK0a)r9a3(9O0APyQF?`9jNdmb4y7v@!V3f;q@3DAU*?+ z=NS<;Zb(Ep&pWu%#B5<z{7Bd$f~!qLXV&N>{(CYoNLYs%xOS2C8eIx(2Fipt=UCYoNLYs%xOS z2C8eIx(5C)YCz%-s+{waiN752z{$yDknDl4g3j}0_o;uS4&Ryh1`yAo7SG`;`2zE= z-~%%MO2}?}9sRb*7asHjTM75=uS*dc*^5~vbT{{`Lh zz@v!E|LAX3`MJ5x@i$$`0e<91PiTeKs(Q=+-e?+Svo$=TF zysCIwi6nVS{L&=P_mBF4^g*~h{XOtr_22(W4aoKb2IR)=Nc*An@b|>iSX|wp1wGUE zx3qm`llD#f0onHc{cr!el*ZF!`%N`sSIEn$^Hjwa2+>yzlxQf#!6cgIOsi2Go%(${ zE1kqcXq-iMUqKw8ksM4Czrw#2s;u;??W-r&xLoYV%7Wz2Uu9p-BX%K=#1+3i#~*oL zm7P`Mgum@d4)VkPwJk14+wUJacXgRxrv_yES3bG5WEs2VueP(QvJW&{;|s7O?ea(M zmp25LuWrBOhx?ChWZN&b4?Icxq1Ews+Xs%$^i1F1()Rg;JXdsJlWp(c|Mss-seK?j zU;0V=Kcg8H zjpmog?n8Zu8!m3UsPY?C+e@jo65C3g=_JlFVz0ldKDv{9sy}Zp{eIq)CLdoj-NOBg z@gKW?16yo@#)9ubazxqtXPIF5eE+}x%JF``K4y~(quZaakBs`bK0;^{t32X=w?15j z|E~JL{V5$!sSY;T6*r6!(msAY2LgWOfoQ$4EzXms{f%V2k>ujaeh>eyZ2gQf z*UH0H|M|%ppmujgNBqTH@;oxTTcy%3Hz;^;!050ju}hBDH2uXSD?;(azp-`Y0p#Us zRF)qS77-W_R%LPV^ZosCC9ONYju3JmKN?r;+;Ks1v#Ngm2Wz0JamAIiBU&YY^|(U& z!)P*2$o7ZsF1@<9b9blRq4@d0^EZjTEzynq-Tk2oc`oSvS8xAV4bc8jr7JFPP1+Ib z-(>qkTGM~9Km302D*M9>8GGYL{UPd!ue-WG{A0iOuU4w6{?M1SBYMAoSAUp597wYL zVMjMFw+^n|T)at-_kJSL}4`t^+RriOqrvG4n`2FHl_J^DgzV1J2 zAA0)Y@^n7&$L_ki)UQ$lvi%{S+*k-{NAx~ve;7JwVC9k`V*t6VdTuD2sx<8~fNL$hm=F8tNE^ddv@298cKHdJjpWdJ)ZW|+L z>P+Pg{#*U@v>xbIQXi6-pS(P+VEp@49mh&&jqAga{&4^B`uM&-rDOoU&p$d|N|gAL z7k&QmUiIJqN)5=4mvp>HCGCjT1065v!{JIl_>)^YUS|8_1N>&Zlvyw7XU9w0sY&vU zBsvK?ZvFmv$^6T{)!)!9 z8z1Na@qwxyAGkL7*+5S8N+sX;r;eAh@e&k3Dyn+(Q#J6Tc181z>GtQ_6)&bvnG$%~PY>pKn)b2XMRcggLF`)c=-tRZ_VkR&{gv*&6s! zyQ2Aibo=w|s^oxHRJ9iL){!KRC6csr{aelVL&4@}qc_ ze>kp4dYu1wwf^UBUv|F9CpDllT)HageDkmJ&(e8GNIX7I!c4rS`Aic3tc$mo-#{;y zp6%NA{Ga@@lDQ`zA71Q(GN{aFs!Hciiv4CouU9r-?Puq~w3Ej30ZDIEi3{)o!4ByY4P_m6z~m$D^P7_p;cyT)D)DRazkl5F|BsAY(k-j%aVzB47`LY4 z=irZyTQ^Ag>T&DW>OcPI-BdMhrA?7O!e2dZVZWRrFO{4I5&tB$J7xWqj`VU!J|ZnA zo1d6Y%4N^NZ)mv^vadN0^Gs6CaglU6f3hD-FN~D?r*5dfR8~Jr$Nx_4I=FbdxpyAu z?a@tg4}X6Am&{!MxPMpH@5-P4`!xu50O^maDs4bMu$xg;iC>HV{dPdYf5i@vYFSm= z0m=We`uP6*)t|(b{-_<`pM;+q`h5LbPf_*V{HO-1vIEjdypVqXueJl|ab?$4kku8- z>gr^5C9>N!)#Gs5zpOsKuS-cF^9}l(|0w=xJs&?m^!fTV zpQh@&s;UO68i(yjJEGtFtH)uQ*EV7SK0`V=5uHbVGp<#RfB)+|(wZD!fyb}R6S5KS zOL9dd{RK?``8Ib%b{sQUNDffNhK^$;GW8}~oIYpPDYBpZn)}pWAnOld|89zE<+?4Q zkpn{l!Uu)XbY$6esc64Ww`x4s$_kSxiTL9G={ib3`d#VwWo!FK1 z(9@}tO!IHXTdDcw|2po{b}s23l*Fh0c8rx=3H8fMxa2ir8Qxa)myWX`M?Uu{glsL+7Gu;77V!Qn%qLVmM; zBpqMb_V>pTGg7g1`}5;!+F3k~lt}$V|JHGp)&t!_eTW-AZu_Y6KC2#AlXHHh`oMR~ z3v=*Pimz?OkcE&gU>c8C15OYShpOECbM1uh$Ki7~2s0aMY^2qI@Wad$!(V|_k`@L>?9}ri#K#Umc4Fu zGFzm10Ds=TlCPXN(Rxr2A6MeXk@$W2-`tCAyd-VwHy?iIE&ZMBNdNPlB9(}qH)C5> zC4Mdbqd0^zxm~)2abL`FoubMTzcbEV!sDDBITGIdbH};g_aD)Epj+l2kZxI3*GDp` z5AI)5AKxGMV@dzwPwwJJ<34{EE=Zq4shh9f`Q>XscHGw_cWf;)PSJ6nj*qVYW6jX< zN_JeN;}y+U``Ph|#_;LCdrNya1@XV1BN?wGap73;|C0Nl_xioHLhv}o)~C#usSokj zt6zV(23jIq6t-Ly`rlDO*vl$NPEP=#fE$KI`b-D4#?Q$@_Jh_A5l+p!mZsWfp3~Lr`-CB0NG~Qep=Wm z7r1hv`{sic0;I37r_Q}y*MN6D8q7WXC_t;0*DgypyA51>#q>_=$^u$YccCBPjkqBc&v zD?sL_qsN;sehs`%`u?sJ*9C|>cC^vLS8sriY5CPg`?3J_?x1yiY3EYl58G@Ray&ZW5ha9w|?i>f?tTS`Ng;)U^rS-Ay zv2%Z#sZe*tp6F}xt= zB;yA|k4KvK5}>#KmkJ(*DM0x`-HnPZ+ytn+_M~Tbv=xEhy?b4wxQzfsb&PD{e~@uB z;_gjHI{{J*^m#tiUkS?Vj*Z`8X(>SMb7%K1S5gK(wYd29W?cblQ`~J^jV+A#@9n?s zLJa|$e0R6qe2*GXK0zxkYHm3nb&SrpOnJ+=W4O(<7sY&JRj7DgZLtcJH@`5l$ z^rX+k(eGN;1U~!7)+=%6_~^^#$Dv*CFuw3j=!t`e`RGlq?MKx~s!+bU%ZP#Vw(`;P zfqTZ;nb!ioamiu%8q4`8Fu#X$rigLw$Yc$ZBtEh+jb6WUpc<5)-_qo9?l?Xg+N-^4 z{ujnOYFj-MhKSGGKj~uh3Uw&&Fr#<8ac@4lw^_b#aBB_VBet!u*wulL+&Z+=TXTc) zMFKVJVmmQkc`v-d=-N=;Ch?Zy2~$4mvZwvCNh+GagDrxskt!c`Fr4;4wKwCX#ab{#j!%Gew=%$1+r#as)HlhK^-ruWJW-Ag@K zVK9{OhQsE$dLK7KS>eZeP1wVD_p&k>9Mtxy?-jbTzG6$QXNtezkeCn(X z$KSEzLfr=~%#ca3=2xwGjO#YK@W@Za3>{tD-0r|l#`S~3KUv-|MGa4JCC>ypaGZ2+ z^{Hs1DH<7ZY5As5#`AqXF3uQjiZ-lhx}YS3@wBvvKBGIBBCAbel!(BfsJ37X$E zdQ@EvJvh#j$hFzSoJ^4F<y8^ZBVMCkCgB^jcS z8^V+|{TUw<@YKD9yCK?8<9>&?OBwH@k{@ZSX^8ge#i;JM&UmQuq3a7S8KC(?vg5C6 z8^Q6ft(;XDGT#6#@cr69(1Y=dBevJx;bnkkH$Q9dlgM~m^8+V`Xc?eKM%{PpJi++H z$1#%@UeZV7_cn?h@rCg@hNZijC+j1D{IXv2TNuN6w=e5>-@{!WEqm(yP^| zP7j4o-IDVZ52ksHdOz>moDH_2EzUGX7=v#*uNObkW3O zvj?S58DICQ%sH{4F6w4{w2_~wDco1Zi|)_#3U$z$E9>g(crt!*bRTt_RXS+Y=JVUE z;u!Dbn$rHHj}CHA)>=GsH{;D_*xD4=)ONBp)~8#^%G@}70X+Gg5l>UO7cuThLQF?65(^_dph+QPbb_cX@iqI<4> zv04kAds8Dd$7H(fv zFj^hS>n~fiB#d$8nniuhudAUz*N@v9PGUSM=G?$NE!0pg`LL@4=Q2Ly{pc?17uQ0{ zMNhZbr!g+Nvghc#_o`@f`id*-G8k{U_-o*%UaBb7`Qfv@9OK1%`aQ_nT@w}ScU>Hu z%lP(o-HTl7)I`a4%g67&&v@C~{lYfkD(GgTyf&*|GMC6DNAx zTnW9}p)xbPDdSoXTEw4CRzz_%)%PkR#!tlV+*`j`0iAa{Ketsk#^vPSY3p`SKuO2F zF4y#B+@{y2;mOP75qIiUi>M&R9d7aFt$Hhm>bGh)XM<;_>;$#*sQ*0UKOyCCQ8omF3)pOyGcUbKku4i_``NbbQ@rM*bGzh;hA|n8H#5u%^y^;k zJp87Il}9GysqUjYHT&?v`R=^_7KcTQPdHaIXL{Ou=lyfXJ~?)Z@om}x2fW z;&A<&>l_efe*3Qab{PhDYA}&`vyGr@Uf|#+O?C3PqXRgyH()`8wtD@6N_I(|ju0 zHrRc}w3m#37}nnNeAYA3+zMl#nZ=CDX-!|9HRh#ga+2ByVIkvB-fkE)ZDEP%dX4py z`#fO$xzUbUonE{aWgR}PR__ku(+50{OmlfF(%-!Ma8e%QDnl-GPFztc3csq5k&w&y z%r%~l6{YV))8e<+G&{@q(cSf;Uv~WMdhDWqG(xgxgnjMGD-W(uizZFUF7O8DC3itH_mQYc*-qiE#^?k6teyDsmQew^^NT$#~3GQ}0_d zl{kmhe$OfzG9DSVT-&X&GB?qG+{Fv#jE_+YzS#V#GFN-pF4MMJj4!eA&R!K=gZo(g z!pc>V@nEh?`&bhdE-bKj^OARL{IVb3VcJ!Z3YUJR*ZO8fj0blPxse)JlY4c0!D)jV zjN67<&pM;1$_+U7wsc80<4z;HmjrH5g_MRnCV6Ki|3h|!GCsCTNh;z@OG zih?@Y8pQY@KSBMIfg0TGI)++_Js2;n=@xM9s|FW%>g;C~XU3l;UH59Tyf)Wj+|ls* z4H>_6);Y7-Sd*LnX7A-^28>5XB^wM3)#OAkQkHlsG2U|I%NkMpG&wud(?c@~m_4>Q zTK7~vg*x2x1ydcA4>JB`-1~*m9(6eF>31irO=kSo(PNo|met|h?c*F~4rV;@YsuK+ z!a7`8Ko`%<){JWo>tU>7uf^G2bU4#Qlkt|T)n6W(pv9e?;=la;LuRj*tlZyL@w65< zK%+)Zjs1+DTJF{7zNR+kdvr{n!7~|Gzu#|B*FM^uUz(b?c3;M~kGRT`%Ihbf3BmbUj?(<#~7;&E^Mm9?ef{<^C^e%_Z~ZvX2j@laXL3PK3~fC zV%Jj(b{yB?oVR{F*Cdqj#NpBT18eGX_DY^la+)*#vBQqM6c1f4J>#X>VFku7oG`xU zk)q2*w?DH)A(zcxYOi$;m~~5+Adw^5HvbJBaV`Xif{CA8d7KDJnod($y-?T#$QTPrFg)^DWGT~QnJ z#CR6tCyJcCyd(5E(R7WEMLij}e>U&Z`wV@qTZe|`y2gwvukVyt`;9&~LbKzVbr0Bl z^G)G7xfS*X+^*pRFD>54_yC_H+=@{KT-rySFR#KFUsP7$rMb(1n|(HR;`Szt`>g2D z`E7{-cW+&r^sDdKJbFV4oTrLetG|*_NOe2xXrqzDS5}3e?X|7UDjxT5hptM z`gD98;{%t+`BbDDacdeyjNIIw@gSSCDt1?lIJ3A4MI_I7o?P97r`3!(u2Zvew`0t| z5w|woSM~CH9A;_4<;9$cnG(hLtNS_2+WDDq`i+#$g6+QHH6D!~Imd)6 zy==Pg^fTs1ifGZ(>Cb7{$0ci)?EOSk7LYX&g>xIzAPeH&A5pMs#U z%!u*cJ^Xs4_cP@-JFZe0a+UeJCfs{bbIu%7?$+51m434sKe%?p{nH{-?%I}^sW)91 z|59uDZtoIPZuh-A1`j?nzg+AJgRrK2GcHYO;IOE@j2{h}`Xem~*>4miKVcV?1tyZ}Y`B%(+?Hl7ySjFn?_Qms*Z}d3;XiRif{< z35<_#b>x(~4xig6@3q;pG2?RrR$dLP$LBI{&N|Q_i}{U%J>6dWwc>Lo<^@AO*JV7) zC35?lZhWp&PSG>NMCO04<@J75!-0G*CFtw5Y0sG-+x6m=zBNXQucyO;gYDcIAAhR( z__#Rnan8uud+%WW@b}%uq^wxL=MqOI+z{1f-0O;d7vFV!&iAUVdqM>Bx91PJ)O^<- zKIb=oXY-vGnBPA7c~ZsZ6MQbO{_vJgjTj&A9PHHoDxYh<-&L+@67&DR_1U$0>_a|x zIW4Wtf+sAF@YGxGLYFsuF5R}tia-~}w@x-5euyXFrfSIDSht$RBb?gRXmCbDz&$?E zb$$QOEUvLdRdrvaiGT~N6~-m|GVW=&`RKv=0`8c}siJnfS=>Y;GvmXG<^nGD)y_Ln z>Wq6EHPAEdAmCyK1_w17!s0Jw@4_y%@DOl^T<*1x;8+~z&b~SiU-S`h1yjy-bTVYz zwec5a=fMIl{!L7?l_TY09#LT#_{nCZ`1%JNeK`M&9Pn3X55(?_5pa)sT;uZvjBjmo z(eLtf0hd?Umvf8eLHVvX4t1S9SHQKc6JS?wX=*r?> za)Wf1=B^fSn-*zqu>HW|XcJp?o#ne(z;WwW-PpP26C9_@$k)fm?Go4LzBuoN-B~75To7dduRBS3Vc))VU_$UK%{eT)*rs9Os;A$I>&m#K$>%&1gsm7O&J; z*)KppU%<6^%qQVATWQPgd{Lxl}IT`rLo-)a&Y9;2)N0x!(CC;0`MUceoaE1Ni4I*B?*% zEZ~L zWIRp*r{mgsJgxyRCF9@)JPrb<N)Xvezb`J2l#4g&6?IPgRj@pgw zDBw24?y7_BF5uIMotB90G~m>(%fNOW@C0H9im)9BoZ5|H*lq;giP)KQu$>8<+NDFW zT?(Aqv6HYJ3*3{~z1^|h3w#u@lLN7x44fl&^*wA?1CJwi_)2Vt1Lvt>yL~6N+kqb@ zcK#J?=L4tng6nu*0G!Sv((yb3IGuOw#`6x~$H_b;70*+Ek0kS&Q+Qqj{1ur8y}|P! z#>u?N2+x~<(|OhvJkJ6yGcN`vHKb62Czb_8S2ACw_*N*v|mGl=vkwuwMeW3h`rvV?PFP2jchW zg8d%Ak4yVWfICY2Re(>I_QL?Dew#JeZv(uIw4VpKj9&-xy0`@2K$|W zer3o+1>Qp1PYZmEv|kta4dMr$f&IY13yI&@5&MmS7fAb=f%{AQrGd-% zv4P9@y@AX4$$`uG)qyKZ`{99iAb$Is*l!QqUfRzOd?<+vyu@(<;11Gp1mK}0?l26; z9e}SOaf)&rrvN^g#5FWDNNyklq+epV*fS)6AnGZNF1KdP9jsrYH zI_?AfHHi}`;y4j-dFi+k@P5*9DBv84Th+mFE8w%F<6OY4NnC6xj*9`8iK79RiMs)p ziPHg(l8);E=cMC+z>}oohQM{CuaKh%}V(*S-=IB#+1%=Me$7mCic?u1xZj5^Ei^QH4Jfk#T`Z336c^8_9%ofitcv2-3OaGAVQ;9(?B zwI|L~1wNnTweH7xt-$R`9&94cg9YA^#l#x(PL`n*ctV=qwguods}9qRR-t zx(wj1M90wq>o|b>65WS6)_nk9L3AS4SSJF!faprrVO!g5FT~#-%s{&4SSe{si1w4Z2wmh+J3pmwz zJ;pjO;8Yh@j&)(c=Mf!QB-W7u7ZKf=1J<1Z-#~O~!>~>bcr&7F+kka#z!wl5+!w5a z13ri7=E|{d4mj1>6=Izo@RmfEXO4Awz^RU}J=XC7r@FrhSoa5<>I7S2ogi=-T_JE8 z9U^e5TO5ORi@>SQu>$KHfgd2c$ah#537qOEyI>t9@RLM$nTBr8=DUFvJBO9f7KtmCnc6*$$sDw|j8UV&4c zY&6!%0;js#K3G=^ynyI%7hxSPaH`u4$GTnMROh<~>wJMzU9bVx1p}u#;&7}Z22ORy z*Rk#xIMpfhu}&E{)iuw-x@O>12VEcQpn=Qirh!wPbvV{p1E;#|%~+QWoa(siVI4Pc zs{8JQb>G0LPJ9d2i36v)@}*c;4%~Of!KFE0g{b$v>$5M(WjgaJU*f!rEf0s;ig`)% zTmGqX*}#h|L&jKt5uz=J6uvKdBSaob>U_ax zmbcI_sh>jlDsC>W;xT9JMA2G1QiLLSQ`VtYVdFv(1C#kD?yiVx}A#(6-l+|z;(~l2} zovv`{h!73vu|80_h~=wfj`|$jX`c{9E?IncU?9tTxpqspzxQ?_s(nFMXn&XK$G^C5 z)cCYshV8>JnzRS~!#CHFfJYu+ceJ zA&Na)?_T{5EI&$P=Af66ZG@=iiA|a(Dj3&3;GVFru@H4sS%0ErFU!Bm-*T?SLJJ{E zx?Cq=t5{z}uRlD0cx!cCAxi8i|LkpDmiJ}Tcl8xP4I!GXxqEWfJ;qfd_q4xUW`(9W zF1w|_h~AvL2sp57;0E7W<=61N*u7~gz; zk86gp6}o()n|}69##^;NzFSGf3Oy)LzZckqu*UQt^)wb{JmiI6rU8NX9p;%2hVh5g_-a`=*UM%=n~^o%Obg zbuYK32KC8OW_kPl7WC@>GLMhOH~E+m@5%V_hautl`^D=&=$h0&xrFhs6=8SdQu%0u z`itQ41&nVRd)hfKnvZ(kF;w4RV+zmX>7Bj}Z~O95#>#uAI*np{SjW+==R5Mz{l@A} zeGf4Hq1I#N3VlAB5vR>B_{zA+4N*_~m*yzxg`DXv7pAiqcSASs>K1ck88)w|T@vH# zmK9&UA7YNeFV$~mkjuD1^9D)1gyu-?Rnd{YI!q_>e8B9&<`2wJ>Grj4YWXrgKXPV+ z>C4Sf7ykQjp?EzB+Rro(_g>x3#|+7jb=KWzCdt%W{ z@f)3Q?5V>;4K9k;RaiPKc)FSZt}~N&wg0eb#>lGY82`OCjPF{I;ncdNF>-zoy=Z|e z^OcW7wOe9 zHAHihf)>YXTEKnr^Ipz;cisRQ7!DVnXu$Z<^5=P>lMIlWk6hi&ofxlh9TOdDYkLat)PbZIvVf=OP`8zac>!X*RckgelJ(SfVWr1Qg;}-`hm7QFyhdw4>DZi1=c+AaKg8D9cDAq_Ps%JUlb2p4_ zZt+GJ-7`BnJF<=?;#IyJP;hy(tX(U0(Wc;{D~|OU&wKj7`%V{KbftIiYikhWopdjc zPkXO}6YZ8kyr!hWiv&eY)2W_;i z@bR3TOBwH8I1SsbW!0@S8Y_UK&5-)Nyc0C)j99{P77H-KCm?O z7UQ8hwr)dLYM~obGJJ|(GJf|?rf!?gTBzI64i;((R!|RRvp-zg_o@!s^kB}?3wn%C z+M8o`XK@{LzfELd6C1{#T-mmAn{yqc{A`J-LtDoA3)a7DSfq*S&m286&x7&YckRyf zoui3XJ?@}n5XiXS6>5SLgwl1cDVr^7- z?aIT2ix@8$VryU5Q@k!gqY_Kcjf|go(QSJ2GIiAZqPN1P{fr0dG`KXpP;BSfTRE&c z&G_7r3!;wMsi9>zdJna~&iL8x`MGn(*Frgm4)+Z#WV~JCGq;;rswi;F7m@8d#`!T{ zT-K?nqT&s!JQpj7=PRVW8SFdO=&g56G^Ej&>}7Qrzj>`;*qP-jsNJ+)g{{pQceEc9 zZu6)H>iX{Jkg*LJe==KsoneC-=&?dm4fmFe?>^u?Y3WF1)Otzca*+$;+Z%lzk+EM1 z#n+3yp3{Tz8L2a05BRKzhMdTlKB_(Aoz=b{cFdh>YUhApLC+DzJO*P97F;RJ&+`e%eCZs2r$KsLG2^F~ z&knkB@R{>K^G)`XUoxJjQNPc@!%v)-&-&=1EuP2Ic38`B(u4*_A3MjV>s)dY&-*EF zy#0oHGf{!_`uw{mGW@JlVsAQMo_l$Bcd;LW za_yqkc7?H5oL^+mSeo~l@%LlR*6)$aaXzO|!MzvzGidqC%MK1-S?Jponuoc=Cv*sRqX27 zt#=9IPy03NxTEC@QQ5@?pJqR2yrEIa&`HK6qV>$9Q3U%}+B^N=2m?Hn#D*$@tRr`pvIsy%!B1_9QL-3gg*1 z+TC~0{UEA2V}yqLdB!(QLeQ;avyPu_2w@KKa(m_Ko?i1A&%mXA{u zDntRb^EvxW#wT<-z9w;%jg= z{>}QlvS<8Yujl;}kP6o{W2#f3knz9<{TjR|Q{hSitcFAzF#f?QTj_R2O)h_rhRz06 z#@Ec&pW+^(%B3V2#79)H@$26Eu}wRg*WwyjzEimNg7F8F!uMugtHou#Y?_;QkMSAP zi&m#6sc|iBotuun$oSXSofc`fQ|F$g_ZgERVtk!fwEc^B>fEL7YaS)+VEjVK7lYQD zHMq^^RdT+pWW4VUrC0C!*XAZQ>>aH&hw%naTWp`BqR9>DQn&4i35-wQ^mh8S08MUi zP|+}*VT_|jV|!gVsL9#H-|$uNWBjt^hp4gIb+|q?RQk;8#CX;POQ(y&>Tpl*_BegC zDdXs_o!Zr`I$VP4fR9Oh#-q^P*3*o&xJ5-P-OJS&pIjYamUn!ywm5j zxlu{_4-?lj-o8&sgr9{Dcf8HO0&XtjyN?%tNE)ldwSJbkXURCm@0snE?|)8*Yr~mm z=>{>bZs=BVmaogjbN$9O>&E!2Hq*SWM(J`v=N&tiJ1}k*X*_ytwl245h5!5jQ^t=( zZoRh4SdVks?r=3+iE$gdtfFb*dfb!-r(*;!ETI4I)ARxVIj6@tU%FW_?keNA%BGbS zYUy+7S+~zcA7Xq;t0qq(2kCQ<1{MXHtzkUCA!^vXz53iqMXtrL1jd!`zx>)(*?^0? za`EKAaK;~)_Y+3T&7;tFz$gk~MGTvs)hK6(B8F26KE2i6- zF&<)i$RM<%A$NYvd4)6bj4xj7y!q^WL+(SgcS@}yHZMbWZw=d(Z^+fyc__c^n{`*} znMW6!*&1=F`iF+i6R&T|t9&`2kK4NM)rc|T`t)9TGmEB@YCdrK#=p54#C_xgKmex0{geQI3133uAja>y1D?vTkCCDqPcjTiX<=5HpW|CYSc&Xyg7H)(Ze(?gz;4O9hM7g@VQG3v@}aB7%#Z> zLF8%8=d#+I7?W{_`Gsm8k{isq^0}Oua%lxAjMv`XEv6uZ&z0<}bvNFfagWbu2JcG{ z>!vqmO|;_fU}r*;Nuh?<8FehHMAxOxWo=mZbj^2e!{)aRO8i_2)NHD#@N-G^T^Vm$mngXv0_1zhVIoj-Lr&-|9QpZ1o@zZ7sYGIQ6ooy>T>E3Ydy z*Rw3#E$W&gDj0cTUl@e9*(Si>YH^eQ?#wmmN{E+uO2Nr?jv4Tl0Lr~FQR9y z=wrd%80I%D>YH^cLhCdtD2}q=N|ewH(>KiDTeD_G_xw2)od4zz^SoCxe$*U!PfWMq z%9nPlZS2YTob6@fj~%n%>|0w-8TCaE&KnhQ&TZ>03ohjDh_HTJ8Q&OHt4rIr7M#uu z2j5$Mj6Z$4Q#q=ZB^S0n;`|F`#vknsRr9p8e;^UVaYAtI^%9;EyklxejFwWvE&Ae?v^*%&;0hQpG;`paIz(rxF9doHIVU* zL+)C%NwMTIyA&S#tj74YdrH|aH(PSHQ#@09?q_j?YmUp0cHk_z1xL-NcMV{?c&Xy* zR@W@K5%)|jic}f@>Kat*%2V-iMx0Z%{$|};G-c&7wJ(;OLhb%{YWHXHjv*fYuM6u~ zad|s8HA_=se6f$g_5h(3*Z6J2{kOKUILo=z&ruT`t+?A%;G!SZyW7T?r+5&K z9%mG%#+&}yinH?%JvRR}i{JT=)t{xz6LRr27Oagj{0wlgGtzEDqSZ zo@v*+#zL+{ermv^`Cf0&|Ju6TCVRA;>Y5WVM8bG z+uB~p6(lQ;P)uWS%mu4NT`#+fk8^7M8HYk8c#dK`LIUUa5pvfuQm<4rV7#%@61M0#NnC=i>C{@ zv-(3ic{npJoTI1cH&@76YtO#lZmJxV4_an@VRx#Kn-}{~{lO_7aI3ZZ4{Tg5uFqL} zyzEpNKb2v&++nkjyQeWHVDM75&O`guwcYyc5^_#{%Y1K{v$%GH%(3eX4hp%KC$^?! ztz+vvB<{+b6md+***+Onw|;#VH=kiLGIKz-`1)toG1l5v=FF>nIiNRIOY90S2)RKO zpQlf^V{!SVP4u&st_eBEh`TX~yV?5i!y~zXBe#Uy_@+VH*3DVmfAC1Z0=0Z0mtSTZ z9GuD4bLf<~$gcE}kekWdlH}Hk@6L)vH4jONHFWHa5{WCl!nSct2b20vvx=!vQ9VUxnN$yRO~3${h#Z<WJ?PIIV{cxE_GhdOLvY z4LGgmy||u%)89oC|1QAk@0f~zN8t2#Ux0si;Iy3#!R-Wi2x(VGaJvE?LE51YZim2^ zk#?(#+bwX~&i!yZ2Tq@hP58M0oZClf(TO@cE?Q)5HB9a68gZZovH{ zaN4h)!u=|6+7C~`{V;IaZ(qj!Ht^P@pWlf4dEmFnxDboS1>kMSIFgUY5#aa9xHBG) zJHP|TI5ifJQ@~BgxMq&WHQ+^L9K4UmLExXrxM_sPP2go@oIQ@mS>WbmT$aP*GVtSM z9CyRxIPelO?q9&;KJfm;PKd{L0`Qf@uGog{3gA149WoW$A;2#ayJY~jTYz66cFtgI z=Kx;A@CoIu+Zcz{e0fb^x|xfv+NVZ!)%ffiEF;axAu!ffo|H zIs)6(z>A3;9)#_1;G2ovu7T}#;A4rMABgRI;Pc45pb?%I0O!a&;x(Q}0Ix;n9ewb; z1NcxfPkDmpDZqD=dCg@!uK~W8%!3N?JP7!6GH>dQ=S{$E$ULh)o@W7XOy*@%@VpFo z1ewP*!}B=c;bh)-4bS_4?<4cXcX*x%yc?NUPQmj^;7iFo)CkW*fqx+LR!=-{1%92( zbEEJ)7kDC>7k9z)V&FMs9{mWGUAuW!F~zgPQ;IK9Q!eVn-jlBL+tke9!2~leXySd z_+jE#*^d1xz*i7IOd0mW052wfn}yhK1AG?o^Q^>v9>$4Z=q~mP0aqn{BwOr90)CYE zoeHtv33wv$Q`ulY74W0Puk{T3wSYe&ez4=%4+gve@ta-7ely?&#Lu^BDfnE093VLvnQ{!Ou8+6numf!j&@v4L~M@7)Iby@6X0KY0rF zlLId!e)T}?R|l?3{P5GUA0GH4;RT!*M0xgGn4}0**rgzenO$n{eC; z_)Ze%%EfUm;F@o6T40aFxZXk>*8{HY zjN^cJaU2kM42c`Q!f`|3BI!6I@D9>(N#HL?98-wnn84Ge-Q<(U9q|3E4)1$f|6sXP|oPSSZV!1onO<;ehlwnHkf z26!v!JRIQ9wn^pf06%Sj^L!F;o)7Rz(s@C^BS;>RKh7fpK8@rZ&BS>}!1JHuJf&Qm zrv$vFbY2tiCDM6NzW_k-W_nIByfU2g&nPzyufdeykB>m_X|9bN#62moVN@-gycEzzo_kOco@l}zJ&9rfwv-g*Gf3=8u(d~r=5rM zw1EebyzVVHuN(L*k_VoT^T2_RA$j9daNaoZGbGRa6V5XSK8fU|pTc?Rz#Ef1_IEgs z9rzQH_g;eY-hq3PJb3}mlL!8m&cg?;PxAJSao#>~carD70O$Dw zHzm3NcdQEl&LcVkf2<<_{xt*Z4id2L0C)n?Dac)@)F}X8Lv#(-u&x1kA<;qX!a4}x z8ALbH4(ld>?#ocd_2)*ti!qt;7&xx@d4{NfHx<)kHJ{?0bGyhM4Dio2=Mbn zSK^9wCBW+u9ZCh(p#UFAbSn$7ZUuM_(Yd_9Iv3zAh%Uw!>tcZ05FL#=*3kfuC%T(n zSa$>bDbeYC#5x_|xwthq65UXJtQ!JuM07@Tu+9khd!kF4g>^~5 zLx_&)G1f5wKS6X)`>^f__qM z>r#O`5FM)@*0BO_O?0mjSoaG24bjOaVVx}S*F;yl0qbgk`w<;(3fAEQZ$Wgs#aOos zd?V5M-o-j!;1NU@?1go~z}pZVaW||Z22ORyd$8^pIMpeaW1TW^s%zH5x@O>12OWcT z(7?A6-Lxy#O#@FLI_oi5XANAP=&~PST{iGiqT@b`b=-^--S+aVi4J`&)}aF*PIT*OSho)R9?`k4#yWT4T0|G$AM4_QQyskq*3ko}y8G!^ zcMqH+I{ltlrw_c8==ycAt{=D&SqI=FUIzd;T{oZ=UN-CYw$V@!0EaT$#~rc z;8tXvhY-BZ18}-7g#C!hbs>P$btK&IIugL?x)ZhWx)Z?ZIu$SRIu*d_x)w2bT?^oJ z9gI@E4hC?#Zbo0cZU%6=&c+?Q&IWM0F2_8)E(h?-WF3!Cyp9KOy6#6dUiSkyT_>d9 zh01k8fL|u-id?|!iU6nUkPJOhxef_%x^BsI$I5j}fYWtOHb1Cb=LC41-|{w#zZ#=Q zt+tm`2zNX4Dqjw$z;DXCeqW4H^M_lz?iFSMzjX3m(;gMZ=%azb^*j5|0atygP$R0` z7;(2=9a&^`1^C4_1E-yQC;s=%hn&gX@%%dQNhuRgySz3=Mz)%Fbobl=K59nIH>J;v z(X(wabG(kQ{2a~hXB%93Y>e{vOn7F(XZbuPTi2|~zh{i(PmHwjje88o>AP@lqUm*G zWF1-(_2wa4FJ;cE4W@|~jM2F@?aOa=Wb36|=&!T9?nz^m+2VO|#X6RE)OEB`x3>q4 z(bl5IQ{5CIy}_3w z{k+e8uB+?%e9k$q_xttwxHd{WccsrWt@{_X9m(vQK~>MJ6#g{7w_nwYvYhq0EVIrx zPYXYr`{L5t=N^{juj8AnFU}IrW2xP*#l`3(2U2JARo~6?gfFg^(`B1AaSp__pocxR z6#lwSmxsK(ALBqSw>wgI*dD5crmp6PZ( zM@zflbFSbgr@BV;J!(fz?(@1G6fAt9>pmFId>Ca%@|(SWZBit7V$Pczn}^$x+tw3X z6_n+7;dila*(v&<%8r!&`QJcmOPx|tbe$I z@Vgc!jM%iP?0Fn-UoPLMiW0p0E#nbSw%d}*PYo1B#==kAqGiGN`*-Z&dw%3U`I2pd zH90pUd)2LY!E?qZTL;azCMMJDOWwW{yhH0p5%Yx>H@cTaghvpWf=zQt+Z3*|*%Btw@d2XN~vG6x?m9*RG{`mPGZ`aM93Xf;Z>? z9sYOt9^n5E^8ttVgZBsS!9O2-Uf}Tez~2WP{(ksAfW!B-l7D}|;roT}8@L(&JzntN z2lxg4`@!!C+>Za=@cRQd=GO;YFTiW_>j|ze;BfuH^#~lU*SGxo1^$~~-*fr(4jk?W zxIchz=l72%zn_4^{q~sOf583u{R#Iga4UX4!~G5XJ-`18_<8{RF<&oW{QwT@3#>Q5 z*?*c4Sf7BW@%8HzU(bLS@bzvDU;lu^`UvYKa6P`B!ukpv)?ZkUfq&uaHLTyj+w=7u z)_dT-d_OqJ_Xpsxf53hMd<5TbVE+M5`Thj^6>!+kV1EN1!S_Gd4}rse3HvATVSImu z{T4Xv$FM&GZ^!p<*w2B(eh>RU@DzSNfb#-yI8VU&0(f11{;0*zBf#Oj0_PXtvHX0~ zn4fom!+9uxpO1jQ;^(J<{5%ERiJ!ON{000TKcB&Q4fuV2o`drp@F;%%gYzKp;rzS^ z=SSdK{Co-LP2lbLc@)m4z~TG~=UL$0_<0x3zraWH^D&&4fyeXnG@P%2!}%M|{eFOXo*FT_#fP30A zy#)FRcnhwtKyLwW*o5gZ&}YDnb_~tDlylLK{dbY?i)Q*wEsytL^Q-0g%g$E+({F0| z^PFOzUoC(BF!uMV<-gaQeLrgX`zc`Gr&|6#Gnsx<%illOZ)*AXQZW6dmVaNa-_-K; zaGdEkwS4_>{ic?$x8dyiRLj?ACw4unwhz*->AG_aQ#N*{pBaq zZ&cooxPGJZ{?(G{H!AOUT)$Cye;mg4Q!4ML&6s|p^8V}1_G=1y4!$?8->AI5bNxo; z^FU9g-{?86=U|@FKU)9OZ)Le@rssg4VET=o=6Vh|*KZW`9B_XAq4N2sgy}bG!}T1D z_h$Ny%I7Pt->7^Zbd5`wwdJg7SvGXBq$@Ls?L#E#-=sDnAzfsV0z^5_& zMxD5x176I|w-odoaDE=9pyz<|^D~vt&o$V2o7Uob4#so+Mptt^2b}9S8o~7(aIW8| zeEv6LdV#)@^ul?j-)JYU=U^VLM=0ny;9S2@S-)_-LuI|g^&2hbdJg6p&h#6V^%U1% zRMuZyuTfdAxiEc4LC?YbUzi@GwIn^r-;aWxgYjIyQCV*qGJQ(hOZt@m{S@>Z%)|8? z1w99x>o*E|4mj6u6!aYM45r5^=sDnAzfsV0z`1^-vfk(Vjs7Rq2OL;Efr6fcdANR~ zpyz<|>zm5;3a;NMzds0!=lYF;o&(PH8wEWFoa;9#*IT%LqjG(Q-~aTZRL|k-1qD3^ z^YitEf}R7;^&6GzOMLyJccpq1*KZW`9L&S@8$B)6yZCxa<@y-cZ*+iEPvh$~1w9Az z^YxvAo&(PJ11i_|v{*fm`b+gdGgd#OW2E{a-=8SxIhfy_)h8+FIpBQ%qjEhnmeoJ0 zPCL!|C*NNw=sB2&@5dDM9B{sWQ-7%*TY=SYsrD4j`Yk^nP|$NQ4?j;(&~w1~`Gd;! zWPV6cmpY!t>y)V_<`FV~$km~bXzfrlK zpUdk1G+3(t^YbOmmHGwzJWAV3{RMu0r72QBf}eM3N2!0o&&PDK)bHTuX?jBHkMQ$1 zJumfBRl4~s>NjycLsv`v zDXxF$ZK_43-({s1Ep7SLz{t4G}FdpX5$|>*1 zse!}iImh~O$l>!J;d&0n!{0lF>p9@?{kU^I2OPf7$6U_=hwndw>p9@?dzB`a_v7&M z!0%g=>p2(?*Fz$^9&kKdKS#KpgYj^^?Pu2;j)&`$_v4Vm_1ucK;0z~O#r z#qJlJ2ktN4k3-J&8|};W9L&S@8=b@T9B{bbZ*x5d9M%WkkHh)7exv4G&%tN8o*E|4!Bloxqd4<&oDg)oa;9VdJZ_( zZxr+#a5(>fo&(PH8wEWF9L`su=YYd`4D=juIKP3O1J3mu1w99x>o*E|4mj6u6!aW$ zuHPu=IpAEs!FiPehx0AyIpAEs!Fd?RbNvSAXXISJQP6WR57%#SKF9G~zfsV0FrMo- z3VIGW*KZW`9B{7RDCjxhT)%-Hp}-~mf}HC&3VII4bNvST2*-n-0zC)gxqhRd=YVtl z26_$WX~Ft&6!aX7=lYF;o&(PH8y(O3fjAHE$5GI8FrMo-3VIH>A?rs{AJ(75d1|x% zB?UbP1&(^en0p9JiH%Ac|Q*MOkUrxXY~!_aR0#l1oKzo^%Kx@z}xeB3t{yZoQLZ- z3VII4bNvSO92^hp#Vpp(N8W(fi*B&~Kk^FY6Pi#b*E#p;;eB-jJU^cs~xgRDVX!^&8ZykuT@zp9mhY^tj{A~%g=wH=U^W0--7x-j(@A!FF@XxpGV<*3iDjk z>__1EewzIY63ClBJ{J68*28ykjHBF%aE_t?5`oeo@nG@QRCrx zt@4)>@~4e7`gh2iYWDk(ALf2a&~xR}m91A3HT#J;emvK!pyyzGPHpC=1U(1*vu1x2 z=efuAGw3-O|D63^D*5-4`mH$6Nv`KX&%rz?T>pcf1HMzU|BLhVQ zV4m~5e&NOZl{nsq*E?P^en zN6FuU^Kkt}ba_1$=8^m}IG*b_@{0K@k>_)N&PC?0L@xPxkpE`-jewrhDu4NsL%hD6 zD4kE>yzpOX74aULV?zXCnS z-p`jvek~l&^&0^_2jkaqKUh`f2gC7_e+)U-Z)866n<4k$e#-aKdDx5lDM8P{`*Qt8 zyjVXD=aKw!$iYtudJg7E1tcKX^Y4<^ew?=sDnAzY)-Lz@eWf>p`yHz|V=F z=Ns=ADw)3$IrJB0eaiJ40X+xryPW$e|KoZNIQS_+&jIK9jewp54*gWnbHKq*33?7V z*KY*$9B}Ycf}R7;^&0^_2b}9S0(uTO_$fip0q6RSfSv;m{cO;4z`;)mdJZ_(Zv^xl zaPU)to&yg3c+hjexqbsbHU-Z08v#8Boa;9NdJeec565}HPYHSs#&i9KdJZ`F8@Rs` z=Sk##2+(sd9{dv@xt;?KehbiZz`;-1SgP-VpM(1=@qM{|BcSJC9gV8>O67VE<^g|I zrc|E?KjnIQIhY^(Vg0540{F>pNc{-#pAC@u7vNXp{z`mb@V8x* z`Xk_n%aQsi;GerI^q661+z`^er$Mqa=@JF7M`fI>>4h}vKg?V@m4mrFp?*k%-&&hLe z$iX+sb8yK2@jX!Z-gpiU$HVu`eXPjgcL5(I|9dI;Cb@3I@$kEYj)(DZop2v6j)&_C ze3URAu0yHsM&Y{Ux((-n>zw@o$AfQD>bs$DQrdUWHwivU_?+OIDG`EcEaeAP0x<3%*I{gTXxi&{^o4@J&j6H~Npd3YBtj_`XsO4mtQHp>GfG3%*I{ z^8;u9`Ty!jRLa5O{NS6E`fgOp!Qpr*2Z#J0bu;u$f^UL-K40`rN_{u_k2)Tea&Y** z|ELq9ZxVbW@V?-il2sr!8Zv$7nmP>lTsa(N;x>3=O1-i^i6_K2j&Og zq*Mo{|EL>NDF=t|E9KyjOF1~?QVtHel!HSq<=~J@IXL7}4i34LgG2t0z5tbSa5!Gd z!6BD&aLA<`9C9fKhg{0RA(wJ+$p6uop;8VG$4falIOI|e4*5U&l2ppU;dm(rhaB{q)OVv&4i3kI ze&c!$xs-!L4*E^%^HM1XhvTIj9C9fKhaB{q)TgFW4i3jlIXL7}4i34LgF`Om;E+o> zIOL$;q`n*aCZ)a``X<3Au2uf>rT_3DfbSgi9KJ93Cgt@O^qagsOF1~42kLZE-;GK+ zI2;f9P2OMr;R6BRKj=A}N6Nt=2mL1Rk5Uc}$N$6kLZuuWjtBiF@9&`B35GO@faSIQS;z^A+ef`8)=`N$^p^Jm8y@ z`fli(1Ro`g2j8UBcSGN#0_Po0Rnw^vPuX zCFS679`H@V{|@tmZxVczz~TL5{RqBE_`EP4e3Rg#1P*^cd>_C;zsdR+^qbUoqoCh- z4vxY+QVtF|=r^hF26|ui*`jZf=iqQ2@J+(?2=5E}4X$6{aDB`53MmJN^MiilIXL8? z-{ks(EhvOmV!gFxQ!AHe&aLBy zA&2uQ&%q&w91zdJAqSrs&%q&woDt8#A&2uc&%q%F-y6@tAqSrv&%q%F-z3k$AqPFc zb8yJPx5smE$RW4Ib8yJP7szvP$iYX*b8yHZN5*q-$ib&5_rD;g#&d8u9&&9w2Zx+p zFPMYlK1yE-<9QBF1=khK&vS6d;q&ku9CG;lJO_sy{$8GgLk`~$&%q&w?~~`?ki+-S zb8yJv_u@G?4aL8eOke*9Pr5qfNhxLc&;E;oU<2g9w;G2}5 zOG#lp=px>nDHo$XCDEKI09?);nb0MG~2RS&L zAM_jeC}AGZZ_;xr;5jCcgTr}1zwv$?a?o#F&mjl>20lu7U(j#RUji=a7n}$58`pEl zr5qe`&{N=}g!w_gaXp9Qq2C7mHy97TN$^nu2mQwN9L@v!4SbX^UdqAYc+hWL&mjlj zB={&{9w`Th<3Ybc{}aZ8ZxVczz@eWCK1$%A-=H509DI}DqXZ7VN$B?i2j3+4D1n1- z5`2`v!8Zv$O5os|1Ro`E@J&L08#wqT!AA)ke3Rg#1P=NQ`t86$zk!buIMjc@M+qE! zli;HS4!%k7Q3988a0EE`Cc#GuT*|@Wc+hX)cYyKWn*@IZa483e^MG%XulLBM92|0~ z9*7)#li;J&Du4M>@J)h`5;*uK!AA*P%E1xfQvDM-_$K*&iyVBD;G=|jqAm-Am-?;9r5qe`DF=sK%E2L*a&X9{92{~f2ZvnB!6BFW-N>aJ z9C9fKhg{0RA(wJ+$fX<{aw!LgT*|>AmvV5(r5qe`$TZ{V-g zDu4M>DF=t+r5qe`DF=sK@{=G3{RaL@t@4*Im2z-6Uh=mfmvV5(CI1X^&~H2khg{0R zA(#9-$fX<{aw!LgT*|>AmvV5(waQ=UUqTN0jpyKyOF1~?QVtHel!HSq`L&RPegl7{ zR{6`9N;xp2_``c1BffPUk84#$Ij<9ZG`=r^wCkb{2XdJZ}0H@W@; z`i<*391r@9>pA374h}i!H?HT9OF1~?px?k>sa5{+rBV(K$Acf9=ircoe&c!$xs-!L zF6H2mgMQoY#8^ z&%q&=a&X9{92{~f4}$e)&~H2khg{0RAqV{i{z|R#moMg7cn%K7L;i*5;E+RJhUegr zL%xRR;E;oU1AisFFX%U}=WsmaeRvKIIpl+Q4h}iwiFghUIpmMPUkUFEc_p5M!|{-B z;yF0vkcZ+qIOI|e4msqlcn%IZb65<=~J*zKrMKkV77g=irb- zevRkgkVD>$=ircg%&2xC^^byBPCUEbKH;PqzE8*2_x5;|@rhN< zZAi6z*XjjwRjs6UB}EEy{A#S3$Il$(U$dez-+fk)nkD9k-yeC6{Ax@>g^&*lvU+C+ zpN2!^{XPAcgSOX+sCxcQ;?jBJ|;Fd3Xr!})OA^(qaah~jSFkl z>?87qB~$jz&QcK9tD!Sp*MCA@dH?ASB@Y#ZCS9z(=EG;?(;EG$VR=tMdbhN=6x-%2 z@|S)GM_x)*kl72bj;Oh<2>Ip0sa1@wC`hZ#FFG~*_6>Q?@Q~&C7ZhYzX5&Mt9e*Hq zjyJzP^pt{pbvb_~b!Rd1dD#{^(~c;}D7Wxq^?&?Ce%X56NX1?SdA?Hro?fRC zYK+*TAel}34e;9W8+p(bzgex;D2SDHr;P`{mLj(+XukSzq#eFmsxMM2%}%cf?d}9G}v<>cOP33bNVc_31s%I>^ma-rDRM ztRQV4jn4_YBKXcXM8i)5Y<**8B7tv{R7cN^{;lwbVnNmG#gp-%mlN zv0Y^d5Zpb_E5Qn{~oFrb`7Jzt|#T%8Ft)(kk?Y$J#G~Hw><^pdjCkc;@QeGFfkk z<6Uo0s`K`V8+p@r_SB+A6_GEya40?ErW+}Zdp)|tQ^D7K9)0=jc{lPo&FiPp93vcG zzxBjHs}8x5&4UtVc)J=S-?#C+PKC{GBmU;xAJfR` zb=nL!GNxAxqZg%ux3ctnryN!Gz5}A~gl;j#@xF_CM>_U)BhTV=`VDVlhJ51TE$+u# zx{-D*o7?PsB=|aoNws|MGQRa$aLuWeaD0uZ$9k8Y-AMa{7VqX5nj;_XKD-(+aw9G6 z7i1gt7QEij?uHlNyON5!O&&JbC3r~T0^1gsUCFyi>y7n33jWpV?r76Zu4KPuiF&+W zWt@L=zej~`lU&KMQHBje77OlLYjo^ve^)Z<>CQ72_XTg-Va6~|M^{oSO=nqqR|}jc zzP?*Kn-4DJ*@m+vc4GvuHFj0oo|jxma&S_wSqXv%$JmT~zQ%=&=`uTSa;e~}HdZ{G zIKqX@xqDOhR~t*5|LD=%Rk9nn5T|_eKNhP5x2}ErRiL2@xwxyo_nAz=ZHET`@_6J- zYNgPR?-W)z&;FjK^T|GEGHJry%?Cyc{vzP#`uM5NWbc-`t3SpIzJ2?IzEfK|6VC&~ z>Z!jA-ajF_aD}-uaX7jq&$5{{&fn>3`&rsqPNdoUr{)Fo1y|~Z|ChSoiS)1V!J*xC z!DpVn-JmeUiNtgWom|Dt2IqMbvg_K|W=_QD-tR^8y9?gkq_%x)Lnrbhw(!Qlb%Ixq z>@_Uxt|QsG_Rxt_nSxtAz%KI7pGO`7)5mtFq^-~VSx7C)EMecSuzvqX=qxOe6 zkoip$Gva~-kBM@uyNo!HbJMTcG~X%so1g{VGxZ!uY#Tl6w%LNO81_xE{F*%(L?-7J1*&0q1|Pb!qOX7(24M&z}K7T?D@~@kphk-R;P-`Zs(6q6Ba1 zdgjVV3p?`4q0`Z0mj$2wI&99GyS8NJ$=cx)O9fwU|MhtNm9|8ov#D2114n${Bb!=1 z@6y?pY0YvR&% zPw<Tr09OeRp-M zu7cm0+SjzkHY+kY?$%+?2*Lf^u5EE8z>0KUX|aCE3Bl_v@~V+v$%-rvNSepQphVS>Nt(r91*>Xk{N z%e&06djucmZd?8FQ}eQQ?|AU!+k&Uno)BBK!kmmY>S0X32~LNOI~(7^oD6S!GN*ba zH~c(lN7L6hoNkrwt|$1tu10Hz9y24S1I-8aYAd)- z$deU6EzL;8>Ijp4eFZMtty)iM!dwbAlyWr~dgo{Prj7Z%6E$O@C1#h(ePtbv0Mr4EP z<-3zq@ce}S*~up=l6>0^>xZWaex!M)ytb7ql9k?@XP$U2_>UE*m43m7B(rvg-?l=* zQxorY-EpG=`7^L?WVPRd536kAHmgbna_3YH{Vv7|{GMAc|J5mNo&jlgdeSfpd%-VR zw%V}csXi&`QrdHJ6~SL6M5w>i*C!v28ZHU0E4W2=gUA~z^+-fT(~|~7@XO)HPFBy? zCAZq$TG60gJuQZPXGKzH8;!5t=cld zr>{=Udb+Jt_3mTf+;!^&Us;fJzi!9hDvMsf)^6P;QhUt z_RZh;@Krp@C}`(`q%sZU6r+Jfcts1;InR4OkKGB zo9gt6>2H!!1V3y!L4Vf(=K|IAt9uR( zdn5Sv#=EvzjmcLv4DI%9^?Siperx{JIhCjC`jF`L`zUxyY1qsw+OJg`^k%8gd=cFL zplj;0jybA=JEN`QzX^W)to1<0|DLO=)lB=;v{>+5pHVlHlQUJlm)~zY^q1hiV_FQ) z2)M5@>0Dys_FM1;H@ybG&bXzj_^Ouglv2Ts0o(&yZw_Pyfse0L>uSW*_ z6x^uq+2t)hzfiZiSJ6`Mhv3zGI*l8=@1=Tt$g}Uwiv%w@XjW?6KUbYdXN|Z0EVxe5 zjmUcDZ`FrKbY8TeQ1I_#TXemZ{7${1#4%t=zTnyx3wI<>D^$;oHuv( zoEJRas+ZdLN3lBOO~UU<34%90*}ln)PQTPs0>;lCb4YO4x?Ue$c9y8Sw2!V=vP*F9 zam8*f-+rq%*0c$zx=HZ0n}e+;wELsB{xjP8{VKteckeXP@2o}58`R(1ezD+#qiUY{ z@=c3Yn>DL>%Q=D@_>EA{-KI^~ja;F~2oe0%3D0(W+UQUN@5=LaM+?5QVCjOvA9QF@ z+t0gG2MBKI{NK*Q>vUy4up~dPa0$(wDCruL<64Qm2nWdyVMhkeJ9biGu5VG!Goo#+Vj| zUw$%uzu>vFj!ROWF}43?+4aGC!F9V-+%zZBgs$q*to6M`f*W4h?zOIpDRrvm<1%5Y z;ESrJG|{_aO6TdCl0!oU9~P*nekI6^E~{*B5!Frb+=}gnJosZqgHmq%HdG1zF8X2j zI-4ue1~V=OSFa`b;~T2Pd`EM-@bk%g@9YE*owCwy&}eh2E?B#|@~ zJ6|0WpCis=>qD(ms_Isz?^n5Ok{Lc3I^mJ73H?OG}LoqZyF zmy8fR>d(jN`MH(pk0I^5CN>e=d~7#ILmvz3eaH8D9dp4O?JzAoH_L+Fm^XOyx0m9) z`f=;&rg~Q`=s>dysTEEOp0c@n-{bshRz(!lK|JuUkQeq?&4 zf16#Fw8A{EE0t;q-mW+>IqjV#9dNUix_^l{w@-649FbesiXIP02>6vM__r=cqZfr) z(Fm(Y``*V2zP@eB>Z6yesOr$qn{`GD9yLDn#h{AT)JMPTo7DP(|7>|QV{I2}x<9_q z<-D%ow=X+B>$29GhPJrAQ8!KKmrA=-LzX_arfxC&7Vg?4xFTZOt#+<9bn9zZ%Pk`W zZ|3+XX3SVBT+9z+p+jQ4cHs5DMw>PtSv*V}GWs4i_EuNllLqE5Q+I2Kp@Fse? zQPpeP(sYw)Zyc8jemPIoz2^j5s$OXJGp~o>KJQlD{T6RaJGQLlYV9bv!5;spN}p}% zMDtcVcV`Q|`8oHd_7)#IdVKt#yX*G}J}}?{-7?vZZvSaj`eB^l>we^PH$QGim)!`T zeWZ@y0b5$O`SQ_@KHKfL|9+9s(d*h*^KSj0JvFG)=kd@pg129!+@&|(o(7n83mGs= zaI@7OjlARRX~joI8JC&~K4Zg(Jm|+M|1?w(5`a!s)-|| z{`HF6C5ie*#IF5Lms&g0K04V~ug?|SGU)p&=Q)n_&({0`+Ej3>x7|`RPdd`)mjd2- z{}OeWk|P_VzrJ^*g*K$6W1`?j-2-2b^KhaWVVA?^O%eRV^VChv1D&XCZ3CC#-hc55 zucl5~?nJ-db3S$Tov0&CXwakCuS-t!T7*S<++M-y_La$5-<)Xdku(GIA%efI`Qo8% z4QF~vSCRX}LGa$)0=w<+?@Z$wZZjNtU(~y1Ju9fVe7Q4yd?Vhl|1!bP?wa`gs@j=8 zI2blRv$f!*#+y&{{OC+iJbP-F{ae)K!nfFstLx@Mx9blYJLRO{7l+RO9Mr{yzIg8K zwsxH06I)F2&6({&hb1~bZ{;C)=Q;~K;t#seu2*JsUzjQChg%2tIGOs$h0Y4CRkhCw z!CU&Tcv)B9m5xh_F;TY?yw&O-2lKpL=?XG8;ast(bH-O5ZJyiDl?IRVO7C(+@P%VC zZM+t_(xm-Jr>C(f|%DC&I9$UT8qG3NR zH){1Es?XtBg3oMxA;r|njh@o!9@q6h!Iux&dOxVD8%@kydZuQws2dOO+U`?eV>jyL znltiu6~U*p4mt94&3k7bCF;+{-M?+CHOP&w9s1|Ng7>11-S$p;zY3GeK2OWl zXPb8xd}d1hnGq3X^IX^V>3LMt!@qT$wsy;EHySl*@gsF5!Mms1w(qmsjrO_k)p_v* zQEz`S@^1a(32rnn=2-n>w?*AP^nG+m++{bKQGI-)X0-*MsT$s_#eFwg|8!gJy3wNk z|0Uq~j_EJm=)LXR{Z_vbeT26?wQslo>_+!{)!7o%TJS@29mk*3QqcKk+K+bc5dDZ| z%`4QrZl<8GFL&58;I{$l^n*r5r$U?*G{|@yUD-$QuHJDM&sJB^B&U?T<|jno#4IuX z`fvlbJm=;J&+RrzH> zUj@yXce7P9d%@fOS8AXdp`ekU!|H9FB>F)m9znlqOe*{Q124Xexvq_$|HF+l;m5)h z^i{V9Zf**}5Bc2*ythz6GjjXUcA;80zQdz)9hR+7(597#cvs!}N3B)<@})%^-#X`P zP|(3`E@yOLk zx$(eZWJ}SnG~3#Li0umnZSdNy&Bc7t_gprv_v4aR3ff_66VDMIA8;OD?Zg%{-zw;z znOAqmPJEAip6>AQ6$J{Kno%dI`h~a12j*-I^ZTTraRH0HJSyfP_a3P4I`6B38njjI zS=aLw^8fVL2MqYGpyApXFII2MM*e2UF{`^}`n>P+Z_Rq$&qDrvlSSL7W%~TQ?(mim zMm<9QyZyu0bINq@_%eh2)slP2Pc@nSDzQu_uX#4-(^|JwS1e~X8iG| z$dmiG&i`D-U(`P}uzJFt|HpULE9WY>eqbIeHV<-m-`4DXk;CU~#Xcu;_`BAyzY95h z4?gUBKn~yAO7^`Whwu4woAU1&Is7hb+3$ipg8z=E+3$!Pe)o6mcSjD_NdtDBAcyO! zHoLBn!*#fiU5Ciwx_!m2TjUx1Iv>ZbbL4Pe>}B@_a=4G|*?oi@?z`#izC#Z8=~#B3 zB8U6>47;z9!#c2?tpmtm-3Vao269+u>a%qQIjl=}*}6pF`uWJ$u@`I|Lk{a+4qNw- z!#X*it&_;J__~_K)>Y)N4xeW0FmhP8r?GV#xhY@gx3F~{IqVB5Y+pbQ`^Z4Hk04j^ zedh(+caR_E`_x>vPa%hWZ4cYmkbmX-;BB@KB8Pp`hwYolVV}Lr_F3exFZW{mGV-2$ zAMeffapXVvzQ2g=`^e#((1M*4ki)scj-4xz!#QLnJBJ{Lb4zn}Zb1&`99wqILB4{Y zi%zg}5pp<3onYrE%xAz#4HX;JK)h8)gy@$6iOd@(-^$0F~_&%K@4xfl6leohWz=VauRpR1p-b2ah^eh%Nt z&f&Fk`39CX1$rVEgRj@Zw11ainKX4uNndui|z#9RT?TUN?wlbpzysd7WV^t1}?~%IgyGtS*7vkk>JSSseqp zFRy#FXLS$cmo)1n$Qx?bRgf>#tivFOy3I~jw?XcwS?576)rF8(=5-_&R!2gf#_LYg zSltQvZ(gU`%j#6fmul9vkZ9aZ>a&uny z3t)9WXyh?@jB->R_8>X&FiA=SzQ#l z{!CU!HDh&D z4Y|~(Lq0{buZNs!_5qPcYxWJ1TWR(gk+0P3OCtZK*~diwQM2!f+)}eoirjddMqd@V zm1Z9nx%(lFzAf_3ntfj6-n=jD&-%j1b2R(N$oFgZoskdK>{BDJXsFTGMt*C*Mjssc zl!h98bL96m`|QZuY4+ujCu{cck^5-&{gLNr`UH^2Yx)Y1x6Aiu2XTR`4a)8~L( z@O`9)3N1@iAzG<+J!A87hIko#-;K#=d$^o<}NqvkEj~+5h3@|^c^8L;69}&=2Jp0`I?YRJ}BgpZwk5OvqCQUvXDzYF61eizAxlo zHGN{p4{7?!kmqXp(2)CT`qq%2<36{8%;$zYLem$Ae1WEq4*4HL=DQope0RvVbDy3b z^XVZko~+^PLw=q6027%H5P1Uk4JI?+Ao45RXBftOhRDxyU*ZVnOGKWo>0?AblKURd zG2bKdY)zjea>-YTJVw)pi9AHpw~1Wxc_I(j^o1h-Pt!+=T=JbFAIE*FJ(y1wc?|cp zo@TyQqE9hjwBX!Fy^Z;( zkwkelY`A(wQJ<&uz3;yI>5mSaK=xhG?m zdqNI5DSwueLJqmAjx1M&9CBD)Sq=;N1fJXK%5q!CA?Nj)<-CwXF07d4!jP}xIkFIz zBSWs{xiepuJ43#g=hVisoEq|aJlD3D<=T+1<~g`hmV-mSoag3>S#AzFfke8?g9H;d)|kV8(e5z7f8mvV*3r5qx1$SqD|xkcoVb1Y#wN91RC zF0zQ_B9TLmvOUXDBEQ0Om)lwH5;^2FSF)TYa>#W~W4TV`kOLjYa-hf|H>zN{QS5VB z@|@{gmNP{TxztZAmx>&6tTS1T6*=Ty4P47}ugD=M8_IIB$RStTm*r}a=kOfvI+nvl z4!PZ6mfJ-RIo|}9^F{Njr;HqO&C6M?89C&j ztFs(5aw#{B9CFsdEN6`za@lb#myH~9+*MhQ8#&~@{aNlCIpo9-vYa?_$d$*kTsiVS zi_XTT{!xs7`-)_**BgZ4G#c^;#tRV!7JgUN+y`C6in zpMIjM`b>$EIBv0uKcOZ1{wJM^I_CUTl2f-oPZCj1pw?zLLAEJsO(_i(KhR4j7kb9&vW zGC8Ykyz#Ie-KPuxhF;R}b%hU=MC;i5q}N}C-^2BIho3t4l%$Ed_dx5(!XL70Ktk}* zR3#~$5N`gsQ20rz7+tFT^|F%q_Nkp*YplqRj}2d_d-sBp4D7Zi$RJPnRT3xv9`1ii zNkTTR&l(gYd@m0kTMy`YL`f>$wpRLNiTrqJ=Y3|s_9#i8=##ak1qfeFY^P~E8*fpP zVSoBejTt3;ICtmzw6I*GB=nTQAxEX~?G%5WYQHc_Nw)S5Y}g}P@VvH{d#wytl1pt; zH`d!Cd_mQpFR4;*nv!^J8`n+UNBD@G3LGwkj8&2km%n7c|0^e6wBdNij)RrN*z)$B zv$us$DM@#-k-Ddnv>)faLs{lShwHOr$3eAkwpEhw8&#iGZ)t$sY{{^~5I-fUc=>?& zOUoEWw__^$CJW;_2S^o25a1vB>G-N)pvW=iQeo z!uM69-;Q)e1tnQ#eqwI&Gr^5Q5?b8*;YsE--1OLXo$!yHU3>SYOTH)RQ8+86pri1i zeb@POy22ArGGy^o(p*>g){5phcU+e0NzPw34X<=YaI=!!dq>ZCl8jI7KJ*M0zPP9+ z1?wjq^dw`A&w8182!Gw-;TzuEjP)c9(OCDZM>r=xp%pv?X^t78@IT0Lf_DnWapSZ z3#uc0lKs0pNci=|gG8TMQ?hrl;9q+UIT-WYgKRadpC0pE@GoPw{so?caKJrv-_aNQ19?$p95j?Mo=^a&+2l2I!a6Q^U_)GHw z28__2;z15AtsZ=Rso>eR7kZ8u;6a8Pg*={@E%^JGO#KP|9^`9ydRj(3;dhPeYMbZy zp9j&|zEPdBL~!4lD@TsE^&oq@71?yn55hsSgCV8|)lihf^*k$mH*Jn1llUheQ z-nbGfc=&MZEg$E(6SIxl^{-wR+&}Mftve&#iBH40?g0+M&z#okk9qG-?qsXclI>H* z2wpTL>*_)ucd}-T?oQ{kf{#9Idv~C%JL$S_|C6p2?eV_uqoaEy`tc( zOP6Pxy-*N$v%^+h_X_^nr{%}oWCf{K)TEmISHZ7SzrkbnDab9Sp_@lE7k=_>nKjRB zEPI~Dn)vrx4VDYut5LTFQ^zaF+hyPK7C#m|WJ6fT(k=?}*`oM$-zviQ{=r6ntFMoO z*yY?>b#Rj4d$*+-*jp(`=h#yVW}Fv%POCOHhlGqnKtWpM)2>(uMJ9U-N>Q{OShaq zf;&A@_wXrnCD8@i&W~G*oW+bs))Ds)x)P6ZtMZyh3%+|({{82pTuJcV>h1h=bS zGrE`3m6&|UyU@={_XbReG67@61;wJ&mGMJ zT!_wemGyxv!BuxMSB$k-x#RmYPPQ{jbtZ4;Y|9PtcEj&y=KR`r_6wZJ z%C>p+)4B@o{k>+FO@7X#II&{dvx$OdL^o_1^VNy8e%#Z3+e*P*I#2HK=b#huIu{*g z9xu3Gvn5F{gPh24)!V^7cLZ;heOkM@y%Q;V<6ckiubgjq%K0&Ncgmit5Ic7GLK6jk z&P1*I1I8|JB%VE{4L(^z@Z+oFn>A_VNL1OO>sGfF{QZNeAxjDzNc>8#8Lnl1-tx;q z%a`s;i60iQ!-3qt^Ej-}e8FS-bep}mrvv$YtAk~;?SdN}+f^g9f&*D#k#+LUUpYJD z{_URLI%7}zo6LM-k}1Y3r~2p}7;8^D#BTrnwajMet`g@jcuCu7p`*R*$hECn_Kk)J-e=vWcSTvY z#O2f5xf8<#|I|C?sQEHmQrI;stIJx!edir+eXfZu`D(rObjA_E=MU*!=lfe5lH6eJ zYQ1ZM-x;R=dyXVvP)+D`m?*}`H;QrS4W^VsxMRaTyxE~7;e3~~& z(b{Q6?yTL~X=t$E!7H2JDD7xP9_)Wbiv`&r#m6Y z3kA=5nrQ9U#)5Rb*wWoZ*Au^oAIrYqJ@uh7Igq_P_O^}Sb55qZJY8RzJogI;s#8Po zH|dAB9#&N*2JbdH`!*HaZS|g_T6yNA`jV-WGP($!R@D4@zvbp++v}G4c0qy%&Rs>D z)H5fy_g0Gd5h8fR0bj!{rz?@5x5r|pFBH7$;oV_1^(v9v2k9@@tP?zEq?b>Xu4T_9 zsQuBS>psCR7j#^>a+4|PeW$1HfzyHqS=GEdKG%e3`FQ$nyDIpKNvo$Md7F?;k9v>s zc_{dePA}4y&om~f=g#*F$`!nM)Vp?>$wnmTV5!=xNN~5X($>38j7a|8ZC%&vD)D=^ zJC#)XOV5gAWbM*xn<@*Q`JmRg>zfTp^98+fo45+z&}Uk3jaL;&hoXxkr`Hnv%`%8VX-qivr6zUFLE25U#~@ug>IVgGgk14 z?Sd5_PySJjO}n}I{w~3rxsFOsYf`G3v0=ukHircN8ge&CkyN7EKQ(Agj|9QP#swR{ zZT(9%E~T#dk8^^5T(u(A>qfC^cHkoYddY(SuJ`doai<@u>W(kE8Ql=Ped?NrUGIKV zZ9kK6e|nnW1M42>(4=RPs!%mz!t4ye)3gUwr5RsTPxiIecYG>1Ih8s;x6fzQqM+4b zU9$x@DM$%B`S_#i&cnH9oO1<#UTIapsKi25oGWv*+KaHZT3z+VZpD#`be&)x7vx6}jK)ZnGl6|2y)?w4ORgwdY0FW$hn=Uv_)7 z_r~1kstxI0R{ehpJ~=+H+S>3;)x8z>PIM{}+%j*6cW!vPs^Hq9*o@zTf1B>ICqX+^ zbyK&5ek&FH-lnrr4RtQ6g3mlq9Qh;oxV-OUUuthq1y77SQ}P!dncRKsQ0?RD&bM?U zbN=F6w&;DIqJ2d@dgH*TL4WamuRN^t!_(AzXpNMRzutGMe}qM9c!oNyb!s!$zdmQB zGf$tB@Tcme+UtG}`6WJ2g~f?e*Q>JCRfpWy_A3^=I9t2N`DU-wRtu8`9{whH(C-sT z;Z5_@&2B_HpZX%WRnq*~8BOxlCC7Vo?EO*jxBct2I@+i}{o_u}U(4PLUd!R*m^qFg z)q6th{06)cTs3Ip%tsqPsmJuM;5%zQIq}T)i#n#)_bZp437#8S@z;{IU)5i4 z@AC`H6g+l+_4?@+-_+yBzS$m`F8H-n%T6a&d{l<6R@Ihyx><{ z2fh6Kqg36T>@suQFL=*Zt!@u(q(v{TG#xQzo8W$htzT-D<@%dePrW!|o#3v$I(T(D zu1%}1`qlFDQo%P>9_Q6MSclfI-|ucaU+~1H-G;rcu1inrB`(ZdO zDsi=g1+Plm&NAPwN9#tfIpsQ3@QE87My=_qPoED?czG~D@GYj@wj8%Ipe62>qwaMO zJV$55o{_f=Xcb-eS*uloH|u@PI6krhtueS>-w!^5pY8R2z+z%Z>&DM-ma7yzsAm6~ z1wRbw$0430L+u3r-t3zGllY4CMS_{tUL(PGF0-B2d6W@dyVx#rLW$VFo_(8Mw^iA5 z^lEw(={_hBJbO;?$;1c7G`X;DTE;WM7cI=&u|L{`Hu6%{oq9*`KcD>9nKw73@Amhd z7Oxh3clS`Af+ACTx6{s7i;oI^`(vqHlQ=UPcgrxfbgSU~9_fGhHlPxnQ>%BV#d5)G zzHM-1dD-*w2DY!#^ztmh7asVs@WBvsI$~(vSgWyuliJgJ-9BqhYeYWkqZ=srJ&*5G zrdw8~eJdFDUF9!$@@Iyb-b_p^dG+^|OL>P<^JIokGR)E>cG^!+#?(A|n&@*9*xR|tOM zQvUbo=~lGKyQqYXGX#I;azbaoO)Kh0U6ZYc3T|rOuH=TBHI1bGXVmK`_y@lQ-S1Da zrbBNvY*p+lxJ!uR)alo(>AEe0V}>{jej()0gX4}iwAm5g`@#Bx*YHlxTM%qR=heI# zrYI2S_P%wq-QH6hs=Aw5GULABkAE!qk!xW~_a{HO8G26ed5!D52^nTfUk%C&a@i^P z5Z@_dSDmz_SM+Fuv5N&acwYFYsev7hNWXJsP_W?HuKksvz3u3sQOYGHJp_;Wuzo^r=oAjr7Bf%6x-Ws#dQH+1E&!Jz_ua5L!qnG)e|9bwF+sSYrk7i|g_Q1it_dFB& zI%AjV{D?>=dbOd)$b)LZ6YX1l2rPU4!iQg{&b0RRD%Y-si}9x{ zhaMX{*qOf7`qVOeu;6;Ol>&F|bEe*t%+4Pqf-e~ML*1#+nLeMFf4!QU;E^7S){gdd zq5Do;GCy7_>J>RFQ=*GzxX_J1XH=beUhuW`rUrbx=t4_x46{5qL-14P+VOpKUFj+J z3l{$M1rJV$=~BCmD;?w-IilldQEzeRZSiGXlxx{@Dx$lq4-4MtZtcD{w_NFshFzSu zj}m-C=c68LD!9?RH7(3Px(lB3_`AAmH8+~O8rN8XE%e_!_8nXbA+Djv-i^|$-VwFY%O z6m-_86F=P73tluybCx^0l)`$lHD`>pk$iS2WfewzsX;FQ)-?w9)|4?-c&r(cLnwiNtl%%C^D(>-am z`F^b$4HSK}+-~W|w&i%zf=H9>s{2Jh&LP4?YvCtP>OFXLQp_jOzZ*Q=cBz4ul15h0 z=`g#o;LnadG50i6(x_{1Ugt-MK46oo&K%O`A$iupf3X18gqz~dZq?gnb{J&;%FTEeGq^E43=?$KwhvQcr{8%r0qLNl|3CI|7 zN*8%S&*h6Jgehs|#Dq)wzjTmCjW=JsexZ`yupQ~&MJ2d$xs6`n3MH*(x$JrK`Pw*s z=%#A7Pi#=qRpBp9vs1K?d+s`YX5Wso?{jHFcW)!XQ{r7V`^G8hGqdGGM#PHeJha%b zt7G5eO4=-NQ=iAKqOV;uar$n%vr1Zc`OwBeOIW%4z{&-1&l0NNbx~la|@jQp86WWM%KrWC^d}j8zbk1;tv>62&3TDj-DYgpJ7x8P#g#i|(z)U} z|2BH>ot~7{7j8RLtT;hS{JQ}C&9?c>DXT~HuUnAxMC$?0GpBCilEkw5MaggF^K06- zksq%$&?%~{-jU+np<@T_OUT=wi_867#t&8U%zUoB|Nrq(#^qcEpC9H)_^*5(SF`!|!6wei!8MJ8odV zBXaoNSF_(8Ib0_r*>!?^6u+)6u1b!U`u+n3b4kL$k`yN}j zkvHM%{64nMBY(p8g>benAot_@$P2cQAb-yHotbRkK|X}ME-;CvzORDi`>P;v|L8jJFsX`e>yL;?&O!1F z%m9vLM^p&w3NC-d3V$)7o<*6{x#~F15(#0pN=}{oYXOST1s1t8Wov6Gn>dKK)S1O;0Iy8^ep~`Qg zZfziStMXq^=k}L6SNV9<#dV}ER=yi`^ckt6m8VDDokQww`_=Sf|! zJS%j7OQHiP&j8(Ehv){%&q8O|Bszog&!J1~7F|Ml73dgSM8{BG5V}Wk(LI#+hfdN` zbQ0wop{uMET}Am9&|&U~4x{`Mbek!n+bEv^ooBY_JYf!9=!ED(%F{weDk(aW@=eg4 z&WY}%d^~ijSkbAJZ-TD%m*`r`&p-#;B08Az=b)P%5Zz4qAJEwriO!~c4s^LRqRT0d zhmN;QbUfupp!UGgkm46Q1^(E0=m0yHTyF_$a<=LU@4ijBhd2i^zn?(m!{tF8kI+Ya1m%t3I~)_=L3sjvihsqYP~H^2 zMoRHDl+T6_@~QYB%16OBnIgW4^1AR@-V>ii`40Fp7sQuQei%MZ3-NK3SBLNOtoT04 z#|M2P<)z^(wH9AVc^CLlL&S$teiFXb67j8+uYu3CUwkg*nXid2c2RsW<)6VvD;oHWGZ>xMceBNT>^C~Y5U$}_) z!pa*4ePrd`gTAx!t3jVy`C|Cm>BZMpei1%+d-1`Q_knNzvH0f7*T82_DL%XML-6HO zi!ZNyb0=XLH6@xh(<$tXRbvuNcri@l2h6*IVI(3g1IK;p9OPJ${Pi9Q_4fZoR#wP$YsS!E=&2*V2(?9N94YG zN$yMe$zV=QdH!IoO!=$Gq4kg)n({x9Tl-paYs%k7&g~t^xha1dxwv$ai&I`7Il7~g zqf_pYyXz&nJLL_L)2k>sJ>}hz>+2@DKIKb-IY8wrgSkQFCxSUc<;8=!MCCsRbBxL} zBlp-&mx2gP9?AIaTF#k!$@~a;?gL3g%#y7eH<{R&uk-&md>} zwB&4+4@NHcwB&M?Uqg;}spNQ-|BBpieaZbQ?}(i6Z;}&MUKY9HXC+sxyfJdf#UzIu z=EyD2mE5xOuE;sBlAN>h&d5doBDrYgJ&>dRS#s3MDaed!BO9)UiB z_R>e7{PAk(I~Xf{2g=8yPvMEZ$$bjS=b^9Ru=F)3KZibuwbBQnd^P$eUY5QIv$tZn2%Gn3hU;2QQzlFY`XQgjQc^>o`eI$KG%5R}BX@c}6DesCtrnAz=q5VcnNLEalD7w>3)owv@k%KCeHd&rA6{^o6yQ zzA)u~p^q$5`pA^`M&H>(={r;27=3D&rB6-yZ1lBFlfE|PJJAQXU;5ybpF!W;bJ91b zye<0dib|iI@+RoZt1W$b%DbYEuZi^WDZhcfzb~ZkPx(*i6Pz!7g37C*udt%@6)GQ! zKE#~Thp0R+`WEA)Z&7&;^f_LYK1b!-&=*-&`XZI*K_BI0>7!K6zRSJRcd7h&^l2`Z zK27CI(ASwt`Z|^Wg+94+^N!stNcau?Ou|;UFBb)&-aA%`6};? zzTk$^7p%N8`iSdFAF*=w9j}wVW995qzAJso%GuYPRr;Ehvk&@R>4R3j9DUQZq;FdJ zSoB#Bls;?a8PJz~M*6aq-#{PtF6rYAbM$@pmA-G~-=a^vi}Z;re;R$|)1|Lmc`@{% z&zC-Qocj#ymwg75-@(2Ft7KmS<=n^Mfb3(SockUO zmVFPDKg2!>BW0fi^{EeGUxiAtuY&UR*oR@B?8Bg(`!;+c`!*<##y$^SWuFJ-+!vy3 zpX7ZZlye`6+Om&?a_&2kN%oyk&V4Gb$UYUyxv#~$vaf}5?t^hd_Q6ojeKT6gz8T87 z&&F}tXG1yn<@i+gICw#zy6r+H{X8pTv*%X*an|+;-*7zOEBL>rqmF_rdA$BmTEXdF7{nJ$>k5lzW|X^d zsc);1J^oZa_jrRfqpwG~7fuvixUJM#<#7??v$nVz<(8zZyZGR>b74L@wq%NbqTJ_q z{!X{_n+wYO zE8X~26<)rf{P^+yxgz&Qxeg`T56K#JQ~4+B%49zCW0Z@pa=ys=h+E3v$gw7R$o8c9 zyVU<6=5&~+d+I>D3qH!N-c)B`tHj$n{-cCbdx~w0a*1E%`>E;dJ7GTR(?Kt-k8-KA zPklYd(7Vd3&P&^)$l9dq;g>vlrnb7Ld|HL0@qaFla;>kf+J3OYedUFt?{^-MwC}<0 zqqnN<%k@Bc>~9B0ADbKH4(@)j?|V1Hd|aJ!Q8{NtxuYq&#NIgYP{+^O((aX*X;Cg! z_LzFPS3gpoZ}XkZX+BB1{&VkLe{I5J<#&!`AGvv4lzVebLUdwWgx+uSjx4*Z{^%&T z;pVM>k0+hm9FhEr^KmIE%~?Gp%B{+HarMucpHM!y(x{&22SmBy+p9L2H7v}VjM-7- zdao#V;%5GRl`e;Q{*k4RAABRqb$<9L)2bFJbe@De4d;w&ALUXs`z6iI9bsN`N$jZX zEu!52UP!$vecY4b@p%hO@BdnqTbTFtYd5|O^S%Qg{=1}hlv^;c+z-n$q}1{I>!+B% z?&YNGvux$4=idqQ-EZ&nQz}HcGYwbYc>i3O4?fnXZJjbv?)K=~<-Th8l+H8#g;t*& zE*9m6ta<6!q)lO-^X8jxmWzyXuTHQ2TdmksI)3J(3n_c%igH^T&MdNXYM4KIdqlst zGe)_#*Pcko^u*IT{+C&`cC~sc$~8;5b9{jwVSeMS#6sC0B<&+nc>5>UkA(T@WxFQL zyB6tw9o>H0#Fta+JRO>^FZ1-pNY~}vNd+!13-db(6>?QQ73mVwpP!K-XBr*C#SWtOxr2A;tnESIQhI!qm#+GS1 zEz(urF!#{in_<54)2N?od>H9oD-yRlQ`@Be?BrLRulD@>*%?Pfx=lB`O+2+d%*$;4 z?wb|;BVDC*1$uNXkwM4*`2Mi6(cL0lDMOGZ9adr^xXSlK5u;W zebZlybZLrZ%JXW+j5<&0(jA|j`oBomd~E)7Yj=kE(zB??7cPPmnQ8i5Rv?f z^OvjUTh}g6K{s*73(w4o%&Ppog=Ikd+Fc$>33cT^Y_nY>{+}-e)mShgz@n?bLjiIvj3~N3+eK^7EgDH z=vzI^i`}`}p!KDEuHx*W4{r4h^Boz#&OUBuJ{J|aGGnbdVIF&+{lQ*~^10G6MStkD zC(J*s6#09GG5Os47e0@#eK*X9tll=MONV@JU(4#f?#ATQ_tUh@zL(yvlFu!D{^am} z4a5AS_j{JFkvE@P`NYbh>xPB-xi{bb?Wfy$U6BeS?rmBc=EV{Q4bFThulw}nrQ<$5 z66T?gr}xabI zR_~r{@;t?zTo+6sj}o|W5OJ5zYTw7Owl zqwuTGukD{S9{-Ij?77SZ8qEL4FVG60TFoXJH+42J0H~(OCCz9YlT| z>m;t5$RA@}#dQ|>>sW`?!n%xn5Y}y6$B}2oIxiE}edIr2U3eYqL~^bpxvnH{iFGH} zq2%eXPUX6loao)R6sOwngk+(!0$hwex6Y56Rk>qtyXR_`je+P9b>s0a^sAE~zlC$n*9ZY@_bu#N_ za@N(Xv&mV9C!j7TuZg;ybv!xieAfNs)CH&$ke`H(KwW`+ByiZbBV}JQH*l>MrDeLYJXVLrxusx(@kX=swhe$U8tMqHaW<6}nP& z=uG4(p+kKHU5Y#bx)pUS@|DoJsC$tYhAu{(jC>t*H0o;P)ZM7VkyEFmZbx1ex*l~t z@=?$MsSA>S2i=f5BKcY9jMN>;D?*o~PD%a=bWG}+bT^Oq4QGrC7%Xem^v}}73j#+mC1`iccut zotwNHba3k8h$F6pyN~5C;#C5z`K`@Zmlf;cOgffUB)Nc z@nvNGL_7c2qWdS>_ftULf1-W=P2}I3X#c%kCcJe$qyC z|3rKLSuOXgM0>v~AosUKdw*Lh_rpYcKa7|AXW}RD0r>em&X%nEC))dM2Dv{c+WRwf z|3rH~|55J$iT3`#S9JeG`@A?Q&zD5|d`TzIqeS~WY9!CEMEm^eAjCKg-mV|!iSF<1dSkBW{@$)n zj>~$++x5&JqWgQh{<$XWC2!YDgJpf??fR;gtjD}vk8PCoo44z?=Ca=NcD>g?)`#A% z525>eyPiBOy1%#U&yKQQ^>)1q-QU~wEp&fRAAsL4bboKx&pBkh?d^ISy1%#U^E{&a zd%K>8?(gmTAG*J<2Oog%r@rX^-qsghbbs{$I3BvcxAhC^9dGNMoud2uSKtG1o~%+& z`MK}`$XiSOP=4{fb%?(`qbO{ z^oG>4-qy3Dr2h4`{{2kqWltY~^WT>G+S~dX^|-h7cxtKNJ$(SqQ$%!spB6pzqk4Z^bl|LkTIhBd#j&}7QMwk1s{O(uM*wg zuY(Uj{I0CU65Zeb3Lk*{H_`olfA|38n??8c4n6=m^eAujs24=P^8MihaC|w@ zyOQn?;scO(65ZdAGx}Il(bN1@_y8PVPV_e)3mH+%r{;-dTe;zr*aCAz<# z3?G2wcZq)JhZ+5FvFMGyIeY+)UoN`8{{%h&d9>*M{sMde@{dIK_p$H+$XAK(@2y_i zM)Xxb13m!9Lyz@VkA;5gt$qvL-_r-+JkW=|)rX-c`!4VSI3D`5xBBx8(fvJr0FH;g z?XAB3p6KD;>fzAOz17bFd7^U(AC|IzbB_xD!+_u?1$8^$ldyq-P) z|6TASy!9jSzWos6UmO$N-{aryI1fG-?~OkK-QUv(;P`o>`+NET)6u=;CSf%o<0CMbbn7DfE>ENrw>35 z-QQb34!>LflkxBHJNILZ--r8xFKPV2opK-X`K+HP_Z?r}_>Z_xdFxl=zUE&y{$@M5 z5Bl=P4~6dU%Nzd`_gPOLfS&_&e~w2M z_xJA^KN`<1PalBei;3Uu>l?ot&qYrkfaCET^)rp1j_0nY55VzwPW$%8uZQmM=>u>) zbbnvWa^NF7fmMI3DX5KhxwHu>=( zJam8G+2k>>Zu9g3I3DXfpWWm=p!@q>CLgjy@+6)<0O!HF)3-DE6RcBxc9U1Zy4LS9 z`4+5$eT>P&l$QLAuWs@)(Ea@ulefXT+&41$9O(YOj>+?2-S6oG@ZW_x!J}?)QVZP`CJcCclI_$J@LU>LQUxd|Q*JLfz$2hw1x7o#yES@O`4L^G)Cb zkfRRts0(!-)Q#So{21y?|C-60p)U3G0XRSESdY3^=SSV^=>u^5KFQ1ZKTKW@b+un) z@^z@gJ?e6uA9cH@55W01NZ!w%0N+1!7N5uDEuqW!vnHPj9mgLwc~0m){x_5VgihoKn7kfJY3Qnct;ttI zhxIE=9vix?k2U#i=)C@_$$LW=_Jd4596GXJWAfzCoqcALKZj23)58bgbqihF+k888 zaQ~{w!$UXsElhqMI=e65H<-7FF7Fqcd_Hu1|L3V-o?qTqAum3_LHGbk`5(#u`$Z>{ z$0zdrbNd;oG@KRMt7kn?)`3_gHz@umD1@BzqqJ@+ydQe_0OY)X?tl-VTzpoaO76EhKkv`^;RA3y@8><_ey-zr z|KATEfaCeR*aIJcoX?lz@BzsAJjx0mfSk{-2JivM`Mi4rJ^(p%e~fP9AJ|NTJt0OVYc)q@W}&h;C80P@F@|M!jH1CVolNFRWl z>&XR@EAiyNOa9;g3?G2JqvZd63-|!!cP0Pt<7IuT^KdC z!w2B}T%XeiAm@5MCwu^MuKy=U4oK%`y@1@1a@H600XPrq5#*9|JnI)H^^0=WJM;lK z59^~H@BzqKPtgY;XZ?j7md+2|-_r-+cPnIrSX+0OZtv=mU^L_xJPx z$f5gFU-INek5UfZ-_r-+c zqz^z&{g6HYIdp$dAAlUXzo!pC4&C3=2Ox*;@96`OQ!k|tKn~sC(+40odaNfm`mJ*4 z{+>Pn$3yq`^a04B`+NET9J;@!55W1+cj3{8q2tk~;k&~J;COzo{GQ2q9q_s!pD+1;-&XSfI)4i3Gw~Os z??icX_*1jQpHjXQepYqqYf;YYp1*^L-Ywu z`+NET9M9(>pOfUs|9j;Bbsp&cKBe^GDTnUwM@!$H@+s2S=Pya0pK|2?J$-jtHeU(0) z^jYe7=>DEQ0LKpw<~MYF&S2g{Ir>7G4^h4Vc@p{noFBTsFC~4bIzDGGucG`Q*0psc z-=ZA4Kl3okq5FHTqa%`Eaeg=QHdUWb?z`3T(EXXuQC?4Uf6sM2=b4Fh|3K*j*73zr zC$yBlVdc{$|L+@1pRw`+s6(1aU$XLcs9Rn}9TSoKit~S=&gm-oe;vOSbrF34j_-py zsy*r|^7E*>PN5DXPe7e^UHZ0l{$9v;9hN?C<=s&SK15x}c@7{y_L=mN>-bKnGqX$I zx$@D$e46rlk44sI&QAF}9 z&OZZPi8>Q`7U)p)0mu(Rx1x?kZt||4yjUr#><;|fBUz2@elqUr9^vUN)HLSN=+?vIE=iJh3q%`Bg}HW6}LX^a14mALqBsmVJNd1CSrazCZK< z$Zs8xeShc!kQac@K_7s8R3Pb?o>1Yp@^0 zb9@%~M)U#5`@m;-+pOoKMXp!s#=hMEU^a)!{pylzo8o@0xxk<N!uN&-N^W=kq{cg&Aus=@u8u;k+0XWY@`0n%p$j`y2x6kXY@b&2faC~;; z0GJCPZy4+!^yDkC?~h$?Bp{d2$E;8OMUH{F2F`O0`~KMV&!b?!r2gGu!Tw6+t&zjX zVb){2k=yt`>$hOPr_R$2xsW4feYg(&q;@@d8vFj(^=CKaRP1_nW3azf|L&P!KdkcR z$j#XGv+1|h@srS>+tjSji(ucMR%Sgv8U4R@{crk(b$;mnZk6lKl3#H?Gjde)0mx1Nwazme`~J`e;P{Qmb={QyZXIv>!IeYzck}@` zPkZFd=mU^HA@!-V^{MG+*ZH~c4}Ad6GXps}`T*qD(O-Yd)Yshihduzur$YZeeE{-k zLGMUpPhwksL8a?MY_Wd~~`G1`sy1%0jzCpA0Ox`3@8|=NGZ%|}fOH<{{_eWu|CK}ccg2yzCpA0OwhN{YF|z{$IyK_jmLGI3BvcqYpqn9{c_jm;ArZvl#pS)G&H!Q|zxY z$LOmO*!Sm>(PL+0|CaVfzg>j=UW&_pFZy?@BNyHgIdQ(v=E#xvG*?1ys(xp}&Y zKlf`=&b|)%0GuZl`~EDE>mocJ`~J`e;CSf%jy?c6bbm)5fE>ENqYpsNz8Crcf~#kh3p~J^(p%e@7pHocsRJ2Ox*;@8|=NL-%*|0m!-U z4}Ab~_N~zeAcyYn=mU_mFOEI{Irsgc4?qsx-_Zvkhwks_1CT@ack}_sx$h5s0CMj8 zLmz+~y1%0jKn~sC(FY*szCZK<$l3Qe27Qp^(ES~K0CKFWTuS%=w3nFO)t2Idp$VAAlUXzw3ZLRC4yI(gz@i?(gUWkh2fAsO0}WIo8dthRM%h zo$dC*2jF=2+Zc9FMxo(FY($o#wtbc`fMvN&8MG@9*k49=gAy4?sQx`^VA; zAcyYn=mU^X!~V4InS2^_e@7pH<5BlI`T*q6{T+P(a_Ih!J^(rDaJSy%@u2&=Rwlm( z-QUp%;5^X%U0?VB%mCV$I)I#^jB;FG&iMPv$-*-A$gE`<^T_`DgBvGRfqnxvxrs$yak9mMJEW&3#+2f4hDT z+~?(i$$N8OnARpA&V6LInmjr8ohbw#fZq%EsVM*-fSmi<{9*F#+y|$#$-{HsoKYq} z&wX~O4+a>OqXVb933q4=?_vy!OR`HTK?%ah39X5H+&mN#!-)?Ns;0yD{#gF&lDB z``_=%GZkNxr~B;~_vVx{4}8x*l^414^EX{?CcV#TN3VZ3^{n#gy}vJ1;(Cm0vf``7 zBfmedd~t@WUB3S}#;xsi?8o$HFDh^SL+`P%+( z#vCQBx%~g6-TqsQJDMSN@uP!pC_kP5^9&0Q$GAMh zryOhY;Z5b~|L#%x)t_Trjs9PDob|;m`eOn zW4Cu6zWI*w;a8jdkalZ~TfP0s895K$Req`a`J*Skk8uy?=E>IU%su5Z%hjpMQ_fGaW&ujE3xVPFyD~rUfZ3M zV_eaBW0yRNeL~0IzntReKNDhH(_XjB4OtuJy;e^z_Q!iM?skfcx%XB`q2q6_E7*F@ zuo!o~+&d|+`!L_z=Dqie4vKM;kCa<-vF4LH{*_BFB)ro*>2o-m@4MA|!+c<^sI;HG z5##dCp7VE&CMk7%=-h^JquM2X?^%}JSb8kXCy%^Q_r+#0uH)53V~%xrO2=otU9;=@ z`Y~?S^|u@Taz4zbW?J!YiW)I)--8L+zUY}s$M>x}`+n}1V%)iUdxt)EJ2&clo(% zo*R>*UE6xsCd`PO$+nL&HW10X%y|=%0A=3heyKvQlV3y z-u_>-D}VLbPhQKJMdu&Bcjn`M<)hvFg`Y0E7kpdVV>sek3V|-P+@oVh~B!aVNRpW2rgRM-uvoV)Fo5n(_pEtAD(Nr}GyKM7Qc6;r`9Qt?P=yjUs2C zDC{a8NnP<+(VRMe`?pqSe&gRl?o6*z0|s^q^WE=`3?-aQ;srnHcXW1`Z|#}#)X6=C zT=vyp&PjDN%#SatmG^K$Ay@c|_TBGg&!zKE$gn^1i^YZ9rO_>4{^r#%Z{PQ$d@oKb zeZEPUH~FxAmai^E zxi_X4J(ASJqvPWj_gFXVP?YPKsY2tPJHmWY`7AABebTu_FSoDo+M_Uk&|^@UrOT4e z-?{hA{Tnaj(RrR87uV-e(z!G7)vjh~-ZRV}cB);g=%^^S;=8Wb-(C>rqyLKfJGy7m zIVpc;d#2^FF#mqRlu5UnMY)H)CfvE5C9lrkuJ|84=2VSx?@q~-u~Drs-}%kD)%i*! zog>q1_<`=j!aV)Bf|GkEowt(pP$yqyU6?-@KD+jeCz8&`8F#ti-iu-W!l)c+r~j37 zUdimMPvtI{PycS=i2Z4MB%Oz{qWH}w1saEW`ko_3U_Yhk`6bLXh|RFUqBgemoQMi)7GC zR*vi5;dh`MzY~YwjdJ|19DZlYhsp2I;diM#qIdG|)@_vEv2y&*9e(%9abIu`ex#S2^y> z4)Y!#YU$Fj*%#tecegk#&{BI!pP_vJP|SWL>8G$rqE? zZ4T==I?yyc*UP0FJ4(od517zLrPzNYSose|?S90B;{It{+ z4t0j|SgAuC>JsG}rEYPkW0ZH0I>&XEx<~ofQWv@JrA|_wM(QYsx=K0fE{8fy`C6&d z9O^dZ#ig!usPmNfk~+|#E>!-K)Qt{xr1HU1XFAlK%J)iL>QJXDKO%LkLtU#Jb+1Dm ztUN*LWQV$0IqGVMI$JsFaEH2F`B|yk9qM@HsPi4_e&x^w9CQNZUx<$2perbUU33Qr z9YXmn(J35s3*`kx*Kqws=TPoN2XW9vlz%3=iGz-!{BO}&9CR1u4@8%7&}o!I$8peg zlou1-$3X{DUPp8y2i-_{FVU4;L(!R(uM!<91YJt`GSRI<(6N-qip~{+?xlQ#=wc!0 zWXkV~juwKhrX0Fk2s)f{=yW0IcFNxqT`vTkPkC+80YlIQl@}1*Fa#Y@`64|U{j2Y+9FhDgqC7oxe>*+} zbbmX4H|YNM{j`McZ{L3@=>GQKdlB$nH2}K5U5{0S?r+y`Z$kIC>%Ca${&syx-QTV!_dxfz z>(5ls{q1^{y1!lDQul`spx--nf4hEu7P`M(Z&UZT>+{#4``h(Ab$`45r|y4Qd;onv z^Pv0N`r=FI{W?r-ZC>i)LgISJiAmG}Vqchf=lZzMi|@*kl4+xqKY=>E1|bI|?a z1L!>Ip!?f;Foo2Q-qw$Mp!>rI(0O`8_qX-waOnQFo~;et-`2n1LHCCbpz~*i?r-aB z)Z^aP<87h)!w1lL-hl2uPkaF7)cvhKK;7T!3Do_q{*V#6|2FXfbpA-_{#M_h?r-&w zy3qZtezFm|e-`lpbp8?0{fCGTpu8n?f2-$YhVCCFK7fvI3*G-C@d1=igYG|6d;sOt z{jDB#8M^;-;sfaTcF_Hsiw~f@HFW>=Mjty4-G8$906M-ZbpMCq11Mh!-9N4P0Ls6D z?*G{6d$pnaj}ae0$KQeOpJ?>M!O;CXh!3FSJ3#l}B|d=iOwj!^iVvVXC3OFX;sYoj z0o~u~rAwjvM~e@jb{2jl>_&e17 z;RATi!|R6E5jn3jUU%fw{jER6>)85P)cvjhMcv=}Wz_wxzee5P`f>c--ZTCkf9G|L z-^csHQsWP%!F^f#Mjewk;JO4pfR5)nW~0e7aNPqR zK*v8Xc?mz#66=89L)`gshb)z%+G1i$QOx}!jDSQC^yR2hb*K&T=y*B@LTJmzH#KS zr**i=U)z%rS8AY zW1Y^ewaGr z-zINNUGl!kCsW64X7bF`{V$sQGj-A>CNE80b)(5wQ-?id^4QdE7nuAub>8+S?@e8J zlF5ftM{Z#9dq#wPF>sP+o^*WGkJLG=EF>Wo;rKF7lL_v>hg6=KA$@N zm3_fHzubq4c<}*#-N^eA#}}9JA#%=tu~>5cUpe2;GVuYF^ZnO@kHLBP@4fRpNgE<^DOvUR{R5wkoWIA@@a4$)`;CQT zy~(NjBmeKn`8>T1U!9!K-}dm~$@#pV0^gpT&v*C$j-0yxMOhChA0YXEe;PRgj^}#g z-m2vMzmDhn1U`Ur>i)?8E9d$LK7jJUlK=NVAvY0`{EG7llK=NRk+UG@dh8wKGRV1p zgAd@yA4~qqqEaQr5$KX-@^pyLNi{@>q^PR{=;--KMtLgZXH z57)yjkc%PzQ1btNC2}<6TyGaa?uMM}bNB#`oa^~c;sYq>`oAA?Kpf9{fw>`a))(*r zbRO0t%q4L=>z9VeF_E+0VeW~X_0e&uk92<4Q}6+lv;JZZi}O(ThYz6Rsr$nRP|kV~ zK7ex8k53>+#`&rH!w1mutWV(sD5vfZA3!i+NnbUbx`_yEd{KA@a>0&|7Q(?=xjH!Jyn44tpqzRRd;sOtf8YZsr|u6QKsj}P=u66t9;KYRKYRe?)cxTDD5pLK zA3!pq%<4d;sOt{ow;Br|u6QKsj}P_yEeO z`@;uNPQ4U9fO6{o@Bx$?JyyBVZgUYSb3Ao__y9Vd`aFC9<<$L|>*qX`B>(T(2S85UA3lK2L*4%} z`V2UpxT>Bp1MDL03FZ13-)1fJo_|ypl^fR_&Yj} z@q?7-L*Gb#^pS9$C(&n;5q&4*3*k={MV|`!yYREhqpyYB_+O5kx^s7JNI7+X_APP#&d~jl|JU)Iq5H!JP|m(7 z_EB-3<><4*eOJd%3Hr0jsr$nRP)^;SePEowJ^bQN&^Jat7XI=(=rbcn{@=4Njhwna zo@0*uU+Dhu0hAkmTsiyb*jLASsQdqjK0I>j{%z5>M_v_ueIKLGkDPsg>e{3_n(4&0?1cE z_s@cT1;~d&_n(A)2*@7=^NNnV1ayD+0Ll*`4_OEMB5?eyV181^-wft0m8X4D_GRda zeHu7V%V3^U$Crlg|1I``;P__H{h=G__Q0-Ugf2Od0^!kp!@%UeMBOXUvb{#jdlEiU_M!S$zYyY z`H^7$S^4r{URwEF*9JPb^0;6gUitW7eqMPrbbsjX%D)y}J`p;-@&ds;zw+~smUnLU{2$Q$ zizvUpH`ym(zku>6=>B&8Nznc6`*|6d;sO!p!;(l zporvGoX-c{A3lJN_t5_ik-wECSJoW)1zdtN_-=C2E{pN@64G5lluv-}KLYyz@$Z^`B^|#Ex<7mXdX z<@JO8T*|TU5BtBAzXshOK7jHR@_b=`nexlf{ow;BpA_t0Q@$(M@20$Pus=@uP3Zpc z0hIR-_TMT03cA02ULTg{JNx@|`~&Fz$OS0B9_$}fomO&=Kc|EJk~;oSu)k9IN6`Hrn)O(H^l#er+uOl@Po1Y0`a_>G>%&Co{&qci z6uQ4%e`Z9#s$H+H4EDF`-|Z3XhgH4>y1!jNn|@mze*yiuUCsKu2Xy}lW<5U;y1!lj zQ}?&^0(Jip*as*g`4#6ENIl|gJ<pax`ZT*`d zIXP$R<@D&UUtsEM?EAxhe4W1y`uDjH5Z`AP$@w|>06P99(FeFcfO6{o@Bx(n5!`=3 zId%UX*as*g`4#8$O8!4-zdP9nh@84V_W>fO?%x6X0FhJohY#S$&HfC^sryehdQL{k z|GPri2Z-}f_lFOl^L#4$k{gD7fH;1U=uz%X>;ptj-5)-H&O_ZFK0ug@KIXU&5a*%p zKLz^$kyH17jD3K}sr$nR(D~yJ?P=RQCY$*-jInDDtb#rHr?-5)-H&Tsa2Qr;Wa!CqV! zoQJwUd;lF!-5)-Ha_au@0hCkshYz3}{+F}<7j=K`0~C?`iu2U{xepLI{5WU*IO_iJ z0rc-u_vb!9oCp4(v;H7;fA|184|RX|0LrQR!v|1K-5)-Ha_au@0hCkshYz5fx<7mX zvfFhD#ah|$Ad;lF!-5)-Ha_auv2Z-}f_vb!9P z$DQ@Zsrz#uAkIVGpZfrjQ}>4tpz~ArhYz5fxi+NnlvDSI51<_T z{%}7-QTK-rpz~1oKMkFL z^H4`vZSsWF{fC?Uq3RS_llLpu`KkMtgwDbFsrw%`c}V0Z-4`Z5c?i+4W z({O(3IIT^dle&LdlmDdd-@@cYsr&ag`BLgoTTC96y44<&Uq#;46*YNR>SCKsK9;(F zCh-9@PmBDmn`iR3)aken5I+a%dJ|2)mpb6{CJ&7Ku*+id!^j)ElO}JBe6l-Y^2yXO z%bGkhbu$O+K7Dax0T3 zr|vw}Mgh8;tRe=L;N{bA1!Zc^*Oi;CTd&=NI$|o?qa2-og99^9~%( zN4O;C`s(~VPa$XUJO#(|7fuGxUvNCHVOa3I2FLRqE(FhaaQ|^0gyZ=UOM>S|IG#80 zdGNdm$MY%11ka~%JkMfn@H`90^Do8(&%bayFQa7eybQ z$MaAUgXf_*o}ZFBcz#OKxsPdk6j>YPat6;^aXg=;b?|%^hjV=0?%;VYj_1D|44(hu zaE`A#8ayw?;T&I=DtNw(<9RgogXht>J92Kldoy@`jmtZ-?6Uf!quhqzc{h&d>#OtZub*Q6x|gHeX*t(d`R=#(`6(5m z+!;C7SNY&$ecIM36XkBpxxUJ$iC&Bk7#O|H+avnD zoiWO_m2-WS|03u3x@N)ip4^SM5({O25a|{L&xdlSm+hJ~?^>k$RnGO*c{(&-U*_qH zk*#LmSRV4<`t8#Cq?%AqQ(*D)I$+^BdzG{hsIV11? zer+P%W;xea`MmMf_f3B-(xs7eeU+Ck-SOF}|BH0ZgXgI^oa5`}$+^Bd{y)xZbD6t# z-#sfb(xp57W7$mi^gIQgJ9kyi@pX6PTwfiJb9^1n^;M2@ zd|etj*H`&}oZshgj<1_2=lbgS_ZDuwocOSS!@0i7Q_b4A;^655?vkAAtNcICA9Osg z(95~LIzBe%>3?dBE#Ppjuk!ym57F`b#PLUSzh1tdp1*8Yd^ShV+t)G79h4B11$Jf2lFkyUrPCb9Y{m1#3u9%$T>&$tYZb!zivya=E z&qYP9%vftqc%E1}$JdpXbA9!F{>S;Aj^}~yYgxV5-55Qu!8Mh0eBDwx*H`EHkMl;| zf1FS1ipe>??o&C}SLX@IIlgX#Mvq&SPCD=lUuy zg7aHf1kZ1E|8d@{>ni8?+Vf%EBAh2%Hh7*a&+&EU{8{%O=heE?|NrxC-8ebN*Wp}W zegFS)ey-bw^LA^@m%3^Mmf&$<#S0n&hd44jw%0-^Xna+ zi+oP%c%0+w@LW}nb9^11!^-iTc6e?p$8(*}dF43A*I`|tJgux79M1JsUis|k=xP(Q zIIKIA-;;F-*D1$amvxfEx=HzCSywr%vy{Is>oA9N zeU%TAbsN`l$}`J4&*5BO!$hw*9XysUEbKR{R>vD&6y7C&bj_0~w`6sgOcc=rDqfTJm zp!_|lD_CbJ&n0z;LtUc0vD7UNb&T?3Qs=ntQuip|EOn7Xous_8)KLy~m2%WwtizNq zmO71foAO3d*E!UA%3Deu=uj6b-z0UTLmjESj?|f~JC(m9b*V$0s{Dr3u?}^ua@4&J zb+Gc2QYSmq&B{?%v(8qII-GU6@|se&JJj*YQRlPnR}Ni(I)U<&q9agOP(D(02kH>Y z*NRR--9q_B(KQ^-^;O^`DaSd!A?S9>i;Aung3hOWl<0sV=z_|>6WuTb9Z~sNImb5y-BEc( z(IrFBDV2XBI%WvErt(D5Jws;}#4O);F-rLq(MdzlO_eVbT{Q%qRrzuM0D*C zbZ+I{L_I??e%(DjvnaDL$3OGmd>j!1qL zQl1^Uza3u&y1$+OYv}&={S<)iZ{L3t=>GQK+ZDRM{d_J%_qU&CcIf{0^FIpR-+o^c zp!?hJ_ZD=2dp$V0eiH5VvlhC)z1|i;_qW&QhtU1)^&Ah~A3lJ7{vSa1x4)M%(EaW2 zi@Lx4J+6oDZ-2i-q5Iq4dm-rl_Wn>U-S|}%UcQlN?H*y!K7fAj3DEuF11Mh&-5)-Ha_au@0hAwy?hhY8c}M8} z@Bx%ZK=+3apq#qDT|dl&?hhY8$IpfC4%9ih{q6dYx<7mXo&PX&fA|2(J3{xj>s9Lhc703TA3lK2 zL*3u5pL0O>x9e@{{_p{Go;=X~;R7hA?r+!s)cxx{HMUICY2iAkK6HOuUwG*L@Bwsw z>i+NnlvDSI51@P}bpKbx2T-0By8m490hG6f?r-a_FQNOx2hj1`rM~m<0hDKj?r-bC zfzbVP*I7~ZUbgV(&=I;nd;p#25p;iBpWcA(Z|m7nQvZ5e|9&R*vWE|#^WTQ<4Tz%D@zl`$;RERSBKOveU;0lLpZ03Z_iM`)R8HL=K7exS{#H+*?r-&n8JlK~>bN$m z|Db8-DxYuFb4A@I=>G5lbbjjo@Bx&MSvPyrxT@K_)lWu4_kU{o);il?3D3U@y8pUN z=gMR+5$1nG_qTe^sTaGl12hjJk8M?m{A3!;Ef2&8m zAo`W>f4TmY6Hy0m3=U@KB z%AxCq<@BGt-?My;yy1DGD~!0eX=zS>;mx;y`{}lx=j%R#?jI{YfX=fDy1&&++d%i9 zAwGbPr|xg{Sm?Lj>bKPW;REPA(1$&I0Oin=eHZZoltX{^R)3xmxn$qTrqB5Ku0qEV z@8{9+(6_zSx8D;z+*>`Iy1&)WJBi-ziyOVYI&^>d0Qz^S`@;uNPTimXjYxjQIWK;J zzj3DUfN6EZdOqj1{sR06|2p0m=i&Pva=?f79?)~K-7)C?{C9IaKNl}PfG4N!Z~c^c z(Ea&6aC~QcPaVVu(0N+l+dMq&ojm>(fBVdVU-cYtN8KMjfR5*N!|RCi^E%^oM^4?} z`cu4)t)B(|%Uk~oewl|4pznveKYRe?)cvg=$KUNw#=qn5d<=f~eE+;Jlr;X}PTWWG zSw9i?o$|O3aUR~MTHwA#&imTy#@}p*`(SzFhf?=1kNYU+;eD3(U2^!dp7&|;ez=bx zGkz}b`|trg$MZR1{bKmb-ulb%qy2lvkLGg=K7h_&O#E(N|MbU`mSqk5(R?mif1JB@-5)-H&ck)aOX344=eh(w zfO4*5W@25#`MK_a51`|tB`@J`n!E(pRqz3HJazxhCXc~&8+-sA&vjmQllP$RzYFU` zzRx9f+qUUmA=2AC3D=$NO#X!H)a)j&!gcK~lW*ZVIA-9{x=W^H(Dzwd@-x1=$m4zYP8)-Cl+eu;Gs>mJU} zx+uZqqgY3^HF+x5U97`659>7e0Qx>z*EK<%$MLKKSr?MCZuBNU#yayglQ&~s3LilK zF6&sO=!fUKIIKU&Q1~sY7Klc~t8Ddrf|oI@gaT?@C?lMU#)Ej^<6Cmb%+OlfR`- z_d0YtUJumuDw%vQb-;-x4@})~zsV0%XZ%-u0R3L5OI9)YWa^lUOrDv#|2C6aZ(J9-F#tEOcCc4%B(Cn!Go4;Xx)JP91rT$&*ue&TR7M)Tz^p51^k9 zb#0q(rw;zA$-`4OZ(;KD)Y;4TZ8y11+;*Lxy8L33&!>+6=c(rll&Y~@$IItY$cqnf za3en-j$d>#d3=bR^QVT-K+gAbQG5U$&-b4eJ_g70-&+j62RT2VHtE=;zG)PY=2ODChkO`G4iSzmu2A65CNPY1oUKFX(p z?@G@5bAI@=h?2+e%zJESnj>Bi>cs`G^!j~rJ^Q!@TY;r#D z-hl5-PTe2*e@D*e>DTbp$@%;p1|OcB&+8-b?aBFkhY#S$sr#pt^?>qPlK=Nrkt5LY zlK=O!#Rt&wT%W)PP)^++`G4hH|G)=O{Slb3M5Lxe|{5UGo3_XYm1ad`HRu`xeNpaQxkb3c0GD ziuCcwxsY=`dnZpE%2|IghsAlQ`@;v&@znj{ z11M)b2p>Q>>&F4ek#Tv87t zB9dQmj=Dd503A==A3lI`qYo&jp1@pT^7IjLnmdFKpyR1mzz0xHeFHv#a_aufMRI=X zC-4DuJavEg0LrQR!v|1KJqJF3a_T?u0hCkshYz5fx8Msr$c=J_d5+|LJ$={PaiIC&BU5{ow=Xc=lbe4};^|K=<#C zz72BY@8~?n4^loKeIsqrN5XkhpwHw2`cBB3!=IXkJ{9t%@UyC;uZ7(BUyhu*Kl^6L zsr$3fhTQmZIuCV!_VI8$^8cQFKjg+A)Oo1;!v|2#z9Za+lvDR--xBBV4&5L5e;r>5 zx<7mX;vQcSK$|5M&B5DJpAR=;xFqw z$p3rxrExrUe>}%@d;{qI@Bx$?e_T28|DJtyoS(XXO7!88Q}-W@zCH3O=agMb5)MM)oz5Q}@R@Naw!= z-5)-H@+VJAUu8P-5=|89bXT+Kl^$)ekS^U z2ci#{yg2egEzvhjJ{^6H+Up4u88`)5br zIr->dKFyJ*98T8p9O4E4`@;uNJ`8!t+t?R@^S2J>Cv|-OVBS*sJ?Q>p>y(SiIV=2rjt29bI{p}R|3cCC zI}i9OJbo~Af9OU!{u${0+-D*p`4#7~K=fUgf=ld0^!$q5Ge|IBaYG!F;vykAit@?hhY8`K(}ngz}@%{q5&@ z61qQp03AO9x<7mXfbRcf%~ywhdpgYLK=-%5-?q^G z;RERW`Jns52T)!Px<7mX<<+74pFG|_SLELC-!=V8I{q2x{_p{m&xG!u{;31)F8J^~ zTcP{g`)7(^zm?9D8oEF7|H``t`?-{3-yimWDc=R%A3lKc%h3Jd11Qe`-5)-H^18wP zHRXeX{cg&Aus=@u8tDGq2Ph)>73U@f`|ou8Iq3fOdEHf>@9gi>@!6sKBNw2&VX%Kt z`AX>icD<1x>l4?TTzxjXG zZ^3>~ou`|u58V;7K3oUg->xT5L-)7q&u-BD?Rs@%u)kIR?wMdetn%j2{q6eM^xNwA zN$AgQYS!mPp!>Hn>-ovh{q6dny1%U#sQa(_H0tLXA4clm{XyyxXX}wF(EV-wLfzli zJA0t}!w1m$GozmxK7exS{_p{m&z5?P{o2YmqQCp5^mi*a{ou-}`@;uN-X8tt+y^Kk z`4#7$kowfw`qcEZ>pax`ZT&j~{qnY6zK;I-Tc*CozCY~8*ZEVSf1mpR@qI>1&dKsj~)agzU6zCrT;?$#5B&gEQGKsj}P_yEeO`&XTp zYyNZH!{e#@!w1muW`73d)cvm-J?FUO|J^ak|Lb__{_p{mZxnsWRl`0&5y`JO_oe7j zuJGP$zt{XYzmBKw48Bm5XV#ZhYz6R zi%PE7mBBth98cYUs^tH5JavEg0Lm9&zmZnh2Z-}f_lFOlj=UW#KMApYIzk_&eoB`2=$vpM#Y z>1p)j=dl0GQtSi7d8qrt2he#oV1Jv{vcHY;A=nS+4E6!yJk%8Notj?}IBC!i9N;@D%z!nS9QL&yKJ3{OzDjHPmiVY1|i3Jgiv0^k>utw}1 z5uF{o*rK8$DmJXKft?~Bzq8l1=9~Nbn1B4e*L!E5Vdk0V*?X;fuO0MxbboSy7$4nV zK7hwT_m>Y~{*1YY-9bE^v>AGMXbbt8(u1EKm4`7b&FCV}h-CsU{x%@Bo!I-1_lLJ&~c~!~% zN#p>*<;SJy$D#Yn2k`jl{^S5Lj{LzC{Xukp`2ZdV-CsU{Il8}m0CRMI`2gnV{_+9L z(f#EEn4|m42QZgEo8$wSqx;JTFh}<%2dL8WswQ-Q`2em*_m>Y~j_ywm5aXcxlLG`t z_m>agamdGu{y4fnIY5kq?oSR79Nk|&fX7Gomk(f$?oSR7v&C>sG&b)bt&_BaG`(@3$TmJ0Ac>=nB z|I+{OahS);2QX*eKQ8qDp!>@Qa6P*JRa$58chUXj1Gt`bi+licbbt8(=IH*lUp|2OSmo267y8rC z{pAC=o^`K$0CRMI`2gnV{_+9LS%*Iy`ti{H_YeJh=>GBnJPx}53DOBFEw5@qNBAc6 z6QcW%4gH7c6q7=~BD(+c(m5C(-G4;rhgAP$IxzH4{_Baq^zAwJ_o-Xxw?y}!7y2{N zao!F6oap}Vh5k=;|5HQ1D7t^=&|iw~zjEkDMYnn*^slPlHQf>VUD3tX3H`C^r%lf& z2Z+y2{kLiF(0_|gM-CA6=z8mi{$6yz<3m5N`iIlKp?_HY#%X5gH%9kw75bCWF^>xU z%;=uChyG`D(n~|XG`i}f&|i%XdwJ-`Mz`HK^lz)*JIxFI-s%re14Dl}I`WgDpB&wJ zb>#r@IipjrsvID=`rFe-p}!p+d~oQ8SO0uEJM_<^vkxjhfcult{og7+fH^w;2;~6r ze#rm-b7TI0;=F)Ato`-@X~+L~9L^WGTIc#Q4(Ad4wedWH#Q6ogHJ)FPIPc(>A6w2n z_V}ESutnqf2#NC)E^0haA#wh~(T(RXB+hHNy79b*#Q6^W8_#!0i#QJ=aelG z;{1*s8qe=YocD2d<9Q!x5$A&>&J($(@jQ{l`6KhjcWyUi<{ZBt&MTSRcwR~3e3SJW z&o@b&hjLHjc_@kVQyy$QKP7SA$_b6$M@$} zE$7B3>N&^vv&Qpf66eue)_5LG>Z^0>CpVs7lTO*`_sd*$c+b?-c-~Foe4N=j*O$M0 z+24NXx9L7T)4`4B>7I<9R^o_RGf3ny^mK)T;6Pp!9*x@jX%J`ZCT(+jgrQ-JxfCOy~N-M>hZa zf2S|sGu@(dec`8EUHxRg#e1e{I@cFIQRgMS+IU`4TA*`$&(OKP)KAuVOxx;QU-s`w&h>>asdIccY&`ENJ@WUL&f5Kn9_jAJ^P$pXryqI$GY|DhFY8=i#yMrw zyeUiF-XkribA92QS9MS0c~$AKSC8oY!=*jafjZZh`U{@EFnOIG>AJ@A#nOONKiPF^mmX=?#`DM$ z=a+G=FYkZJr@!oWT%|{vu5*3idwtaTxY@J1rzLc*FPw9HpK3f$&FA>e(z(9WFXFtm z)a$Hke_8pV?rF*YUU~MFquV9wImdUwCVMP>%yV_lIleFJTwm%>eWmLI8=T)g4QV`Y zE>)+!G-$oQc2B2o+3CP1My%}dInR!BeHn*yd>_}jzVI7#j&D1i>kB{p;4k*ue&g_kh}+FYVvEqw5!O zo?{yG%}Wz6+;sIcxOexw^X>Ke({zsSRXW#~@i|}eHl6DWU&MKossETkE8hC~@6+K| zuKDJ|Jv@Ftoa1}2&h=%SMVybB=II>YaGqvb@`CE|zb#w`J+$qt6$c#K+2hYYq@3e> zr_S|d{6(Dax$t*-`K3Sa^ZPV^$c4ZByke8b`HRl+Jx%BOGR`8-8%>KipES+WIlfC= zP|o#boQ-vk?>Rcx7v5dx_}-~=ec{XL9N#5I^vLgx>XP7HbdGN~o$CufM(4LqZ9Km< zE#kb_bd=8V4d=tA=XIX!zK!R}CeHB<=g+1^oL8Hk`2U}8n}+BdU(WUA{V(GD-1M!^ z+wHG&eW~XhU(WS~bDl5%g;U4>4$ipz9r(ew>`?h+;W-`WZIT{px~tyz%lqcvLH+eQ zzc@FZUz|9{_lCyvjuW3F=lU|vKz;7dHJ+!O?$r6qd~ej>rSqEkp5brmIyk!VeCM>f zuA9z{=Rqf~Gp;+vxkuL}*D0LqnClwObtr+IF}d9Gjl`VVV-vRL;7=Nw<=1@MD3 zZ{S>Cc%S$CzV+lut0d+f@Rc<$VV(l7X&%G5zVN;}$Cr5!{7lW0m^Z-=fW zJDG zPGH>t|3K>s)*0}1v<_ii0-vmP3+ov8L0abw*189Nzt%;pli*8h9mTo|&bo_r82mo1 z(^$8`*VVd?bsl_()`6@G;ah3l$T|`}OzTY6o$!~nE@hnxe^cvN*0pffy{v=bleA7| z-3(`4%{m*-I-GSm{1dI)S;xa!=dI0{>b%3%Uz@o^+WZ_guNwN9WbwKROP&4)ts49N$OU z&N=Y8%j)$zOD94%qJD^Ur9GZG^ZK{1uh-u#9SU8F`rD;jp<}_#5?5&o-mNpwp1&eAc_ zHQ}F1_q^cw>jr%|v;KMBBb^l8l==sxtD>{QXG(`fmxaG@$UDz3_sJK{=(zCXrSqcu z!XK6{j7|(cSvoShGW;s(&gjtay`@v5Tf@JRu8qzO-$^<+x;Xqq>E`I@aCCNbcld_V z<%&L=w#CSy+da2s`M=jl@a0ND6Uz5ni{I~|<$9>a>mirxrxvfD zx6Acbi`Uy7<@&6}>vKZ6o@?=X{;*vCHGBX*|Lw}}r51lL-y-Inj`Kt5{yEMkUo5(Rj`PnY z%6v7)`KomP9Ot*v{WCrQ-@A1G9Out#m-%*%^KI$=InK`yExLb>^Lgq1InMv3`{%D# zfB%x7x9^d7KQ|QJKgadO^+os3aXlj4Kgacp);l?_cV-mbKQAF4fWNzJSx@CXW4sVEmQJ`Z~w;wbtV~uE!57>-UTgz&MA$yZ&BFf3-@!;fS95 zRNK|pv(g80)CZ&|y(Xmii?*DW!L68l^3Q_pkk> z=v{e$d;rE7SakpV@V}3F;LNG@`ge<-mM`nQ;=b$rU;E&;Df(NUFCTz$?kc)}UPe9u z{MMrT=LJFE>+^BDj{CjSIiLH#-+$fX*!ti7rRe^-7WBhmMQ_Xp$p>KkgZ4S=wWH2# z%2OVE;GQn))yHW&^^m;v15LTj1>G(?u0x%-F1ml7FCTz$MikvYN4<1H(N}X1`2f^Q zkIhk!m42I}ekf1LI zJv>J}T>5#A`uV`3x98D8Z{MTn{yFON((`lF^QHUesQ(|(Zk@9({d2ec$Zk9BJ@tR} z{WTgl;{)*Tk{^-(A|C**_njZ;y3{K3_O6dpE4qKyzdKQ{&n0*NXiCqWXLmK1?w|1i zsDGyD{uv(tenk16@;&ka;9WlG`|j$et(NbV4*-|$pYZ|Ux^6N)09@Bu#s`2)_s`Lv z(si8i0jQVmpYZ|U()}|&09?9%#s`2)_s`Lf)9*IV3I3ga=eb|`-6zJ;eIZW>{@~K( zK9cW^eqy=rn?`qfld-eSF zIs5(T!CmYAwVsO^AAov2NArs1xtid5?q+-dxSrGb(;*w2e99s1U9abQ#s{EYx_>^Z z%nMvE-9O_4P_KDL-dR2XT=SBQ4*=IZCQlCi44U_3d;sbxzsIK)u!>IrbxI-I5;&{YzTs+%Mnzf0@DtaYKs(Yi6G&_AYiW*!mx&9pAf_yGJ}tz)y+wH{yV-i!}G{RgFA zF6YoMr*(BcIrP_Q9iFu=_xM`3XM6z0Kfd() zP&!4P7y1>Y`{&g{f1`Ae9Qz@qn`C?d-oJE~{6pxslrEEh3;mhWaq@uB&nev}Zy+Cl z@ud^xy+gmK`b+a^p}$l*RDLY0G&Q=y#PamR}3~vC`4<5;rvV(@J;C z&xQV5>2&!oMYnVRt#rM7ZRqcn4w&}|{lL-<^CqExSUO|=T0Q`un{>%MH}oe<$ISbL zerD7tLDj}zgjwMo*MeGrQ7D4CSm{=s%ZEod<`0b?MqU_P0w1&ufK#cLzvI2GFQ3O6x%dFnQzxS7|((L~Ke8c1L`8+EhfOi^Gho~J*{2Y~bZ9aNsbuIG7uxqJZXdA{QVz@_`=FXRKj zPcHrc`Ge9&;PIJn%(;7;wXT`Jq&f2wd;rFg?w{5F?|SAx_yE)oEB*iZCHVmGNu~ck zKPMjm&V1}t`2cX{Z};g4(n`HXx3_^{IdpXc^C z^{bQDY*Vj4@vM(KT-PJtT;{jt%!l_aeKF=^O8!A@Tv>%>Pd;eLxw`dO>|d=BzL90jOs^qP`^8vwqp5tY6Go@8AP44(p?LHZlX0M7bX z{r?%xdKn)8&iWc30M2?G9{?`hKjQ;`G8{bt9{`U2fDZsiufPX@qi^5? zz@__Vd;mE52|fT^x_`z8fJ^t!_yBP99DD#c`VT$;T)Kb82Y^fWM_yH037`g9{?`hKjQ$PK4*-|$pYZ|U(*4n& zJ-&4Rj1NFP`ZhiQT)IDcxW_?1#|NNZx_`z8fTPdj1Hh&GXM6zo7PG&3@$D|%(El@B zx_`z8fJ^t!UzL3Zu9xng@d2op?w^k-`xsoW{(t-qb3PaClQ5U=pYZ`0U;8ey_F=eQ z`!w<~@&Tyld*yqEa~*J9z`K(7L8awYP15}{J^)<0f5r!Z^E>2s3777_&}VPi$CKge|Ih0GH|M^i@5E`Jwm#j6X~MDLw%Fj%S|yf5r!(p64RZNx1s|v-61bpL!< z*>~u1R#1P!jaLtuzQhjQ&9#q_eg<>t{`5bXpRIlge1J;JtC~(xf5ot}&(ihM{WCrQ z^}lQE-*A1K#(od;tsDD8%)eJZ2|fVhOZU%Rpa0_g?LMymd)qYjt9YE5n%A~2{VnFw z{pp7>m+qgLNAq`YRliNgvhUXQ(*5brG2g1_{+W3_wa#Fi4YUp!U-l)teu~yDqqL5peyG+thm`(**FU6n5k3I*?bT0pves4bkF@T3 zRO>MKb6Tf8T=s2y{6DL|t99AuZT>f{1LtX7$T)kef9%n+kKFZRwa%PZ_MMx*-PoUI z{)N`DCu?2H_>Zgq?cA~t-u2VeFZaHD0P4r8zwW)V&))Uz)sJ^u*_UrVSL^l@wT@?; z*1NoX#vi_4A`g)d0Dn*YftQ#5e~)vwbc7X4u7LR&(j6`>IRxgvHTEl-uc!V-e1J;J ztD2@t2idvgBDj8BWB;W2kB$A7=F2SJlFN{fE&c!I`!)7+n)j0KGqLplo9`o?2;HdC z@~Wm2r7NK`!E<9js>gX=x)nMW^`YO@^^Z69$C@u!bbtD3&97_hzcugO*sp6oy0O34 zyjNpCu=(=R4L|DoujOxkqo;Z3H#UE*u|L^-{lB4J$^Xh|#{aF9^_HXPbcl|#a`_IjfYwTAyANW&Ct_}U|=GDf2 zc=OSX{qyEMitbOpz4`sp<T4?Y0n>@J@e9{}E}JYUEG@;G0~hsFn>{?f+%Yp$QtxZlmZ zd*l8%^B?7-;{!1M(8m3DuD?}2eSBWOAzvRKfN|!j4}iV^`1g(b2R;7s%KeG+jf>Qm zFeA)Q{;NI)`WhJjA?5zW`OgPszLe;jpnhuO{z|`}i`0iPKg`E=QQtu+U$x9h`xaC7PY2_JxQUQnMIJ^=hA z?N5*E)3Bf2u(C{YjS_^0jNJf`}gqy;D@Qt4<7)&m2!XZ z0pKA|Aj3aW?hifyT)KaHru6^2zDc=1Gj89h>5WNsT_WTmxL&${I_QLpx@C2=~6E599T^975Hl_bRtzP>7{dcANCwu_LxkR}?XO{kd*WaStpQAfpxb#UY z)ZeFc|AY^~IMV$SK7hIUVwD5r@umBx%S->ixpe=uKz+E3Bi%pY1Hf-kUvI0yEjd6Q zN4kF+S^EFYrTZs*0LD2{c_Rmx{(sj?_fPl$)JykI_yF+VMwQ&31*QMr;|x>o&t^d{ zy+QdZYXyC^qTHXhL67aA{FYUNe*2>GUXCw$FaEo?sV{sr^@;O7Z&n}qu|ZGXUHLOF zst=ud>Hg#ZdHg2zwclLwZOpG!9?nzhi)S42b502Qxpe=84?z8&mHYE!(C0&*kN+-x z|M&omBi%pY1Hfl1Unu$uA&fFGyapO?#Z;c=w|4VJfJ^sJ z_yBPB#o+_M$^F3xfJ^sJ_yBO}{s|udF5N%j1Hj4s!3Tho`-2Yvm+qhN0pQa86FvZ( z+#h@ZIQt&^X&)q9x_`n4fNNfr{wf~;PVNsr0DSKkU%%zXTe>HF066z0HM0GIBc@B!fLi=G(zqqL4n z_yE*v-IeeG;994pDWP9Wx_{bDJ^=O7{S!U_e3J5G@d4n{{S!U_{9)zOz8m_}r28j) z0P3~wP51zC>HY~H0507>;RC?64o@BB1Hh&Gr{6;Vo^=0&4?w+i|MY=;0JwC7bWi9f zlu^VMqBv+{JY5AxFPi4lGC9aAot&r>+$!{-%Ae2Mxh^=+>jMR|1dct zGeW;Hxg>9f{$z4YHVpmDg3uSDIb9EksO>q$p?Uwn=>}_&r4@dTgeBY zUb=jm68iJW@!3T_0QKpScZa^WpeYSr?dlo#&A+)>JfF4NbFZJgkazlKo5!x4|DgFX zpZ%xn^S?EvbMHKCX4^Bf`B57U-RrISP3g^F@9O=B`A?exm<_eBJ!z=4Ea@;OC~)x$9RO z4xjsm`SExB>&}&aY)Y?AKH~T-CQUOx<;Hg>o;|xMUEJl733=E%=KXH&_R5{#Hl+@` z59`uu(;4O)?Q_u2mwnZg9^QNCh|^E~z`Wy$J8ib{7fosCmaTT)_3aPMZ@s(cHJ5zS zlIy{0t&jGJcO`@<*ZD^Iy* z*3xe`r6Ij?bKc`K^V?_Dw%T)QQ`-B>32m;p=X3KdKREFHeO_rwQ(x#kd)+o)nom3W z>5n>0X-YS2H+9UpNB_^f|0AF6b=gx*X~3Z)&-mwaUzrc+a>%w*9&bwH)?0Aop*_Db zfAYsOCQqK&l>R;Knjb#<+qdQ?UA*QiN8Q(ymbqf`pf<;SXMWtgpGQx~Pumy?;~MeNz8R`pu~Gw|1Jg$s4;jrL)glzw1h)e{ube=YRH( z6?SS${RiIDGSrxO#g0-&rQga zzUOa3J86@-9%sGoqo*9O>ca8AJLk&uVV&>v$>IO#)2=BkG4I2UXOEfZ z`o(r`J974tP3hpgur+bx`R3Jh+LQtFI;RtldwAVm@7MXIJN6xZ`gfhvgf?x5Up4AC z*SA{d$-57j**U#=)-yvM{^J7k;rCA&Gw-d=X`NAHj~O++&buD*^&j?qp>x{ke{+sF z?@yJ)eP(v={q8>L@y=xZk~<(+0brH{qf$>U_X<^WXpI#?I-F zmp<9K|5l56oUfj$y}aqz&S{t7moGN!;yRz(cV_p#qdKQO*1zN8gJ#xw-#*VQ*7elR z>EbDC&$)TiRvzcT2M!){=h2dv$)rZZmr= zyG!Tv+^Z9IJGj^49_P>{KEGnx)}7O9+g$nkZ_cjsch3B%_n{kfP6u7o=H)(b)cN;o zf8Ts>x6bLudDBM^U9+{vS?Qx`uTE~;InB7D`R~ULuk&**oVCiR1)b9V?dB{~eWA|x znz&iD-M5|6us62b{MjzQ^El(S=-2Sir}US_w(qe03QKsLY3(mO;y)8QrJYB1+IaDU>U_l&dTiR}s!r*nS2k)I zeQ%vlc=f4wo6hf)?tJN^R&Ou1q{r!c+(YMmIkZ!H@5%)|zumpgXB_(Ofu|naDXlc% z+s9uRU*~79|KJrb?Aj@9@W-2%8ZxWS$G*7lfEinMN`rgewDlWlDUbi(foHsN-`bth z(sL`1%RY1cdP{qpz22B{{^V~u zrthwMaoLqmuk)UV{`}Ha(>tc_OSV6Lrzv$lbMEi{v)MBp)6`36{c)>~%XpkQH}7`Y zxO+RMVI7vZWY(c|K5W|u{`$nYj%oG|SG_d&{yHDO%uVBOKfhy|c)=xWjjb%}ao%5Q z?d=Dg*f9-y^RFMD`{z18=Y|i*J~yyqI_&)6sn^wY{?ZDIy*O`&j_KtMjyn6wPwV`l z8M}1;u~*0R{IO4e^~i?HdHgr>yQQwAN}mM zx7T?-Xr<-9xV1w%{mBhpf8@tH|MZdvx^8!IhjjICSIz#UxsAs^=IR5MIdoWu^z?$K zhdg^holpGy`{nx$>X4Rr`iM&oc)iYty!6!}zwX!}9k}VNn{MsW*5ge1qVG<}ZqOl3 z`0|1uZaTEiNB!%DpKop7A-(=i=NI?7tIm%*yzSk$&2OK2uh)0jLqFB|r3Y_y$B8rB zr?Zd$(~X0>`FxGM;?;+&xA+U~(~5mg*=C1r>-_rR+nuogz3tPGE4KL0g2U>3Qm^$M zKJCi(>GcHrvnt<<2@EvF!YvR#}+uFlydW%l3Y?&Ii7` ze*1sy)IQyF(I;2``G-1hx_;hguddfVJ^pp~{+o61xgPoS4udK)R%)NF+hN|vb2h8< z@0OlE{{Ej=Nz-1tW7Z`D>wNV16WZ?i?kZ`{?eDJj+nIHK-_!SBI61G9HotaUwRwD< z?>F?m1HQXymDG98;d!qo>%3#<*3W)3YLzs7aL-{|e^TdPeg4Gq6OUOXE%)y=mzuYP z^APd_>s|QQe!H!bT8&u0>)+R^^RsU{Yv(&QS|zQ0#ySfweRt*5_V9E6*yheUpS*9^t-IG&PD|}N zclpUL)%jXSAKGuk_?6QVPpx>~7T?r)`_|o0nsCm_X}^R1xxZ( zKWf}@`aAF=^*&zI`+{$y_kFYe9q>W=cP9P2;rr-w>8Q^M-ddmI+WK7KztiXbxV{JY za{8VY=zD{Ipzn30zGwK^x(*ictNAQZSvxjuu!I#l>`L(W7_%vO|opoKq z+v~a?sNVs+w|*xN=ywDEMZc>H^*e)4)$eeYewXlT^}9V&zhn3${m#4TcMrc)_l3Xc zJ^>%8`^evPUxBZq`_4e!hv2X2KJ}RHTkxZGU%Nr~Id~i02ixhs2tP~r&C7Kkh5u3a z*%Nf%h5uLg%qt$W}vYF)%S z3I3$kQLL-rpKIO4It>1AtLgVwe1^|kJ09SmPh>*Q0lZic_8b#-g4v*F{k4rg5sAFOrzCR)eC z2WXwYxYqsfU*;BFU}xzB@IKNJ&=ugjNOyQtIs|;WbP99}_(bU%=p67JrGxAwT?9T< zx(PZ8d|T-(Bc!{)r%IREK{^e5_N1cYSl4O!e^hfH={}vM15saYUGWhY*(a_c4^DZyC+sV@5;1^1#L$`yEkgkW$ z2j5aUAi5xYq;$jS(h=bkr890V-4VXIbjdrUQ^NO7?kU@XMsD zqO-yWNQXt2h5t*sEjljz{oRVri|z~GU%K!x>BR7H(vi`X;h%OXx-&X7{37Yp=+^Ka z(zVgK;n!bYbZ~TW_)*f$(b3@@q_d;D!?*dm=;K5|d#T0W%kJg(Rl^71dwiw*9&7jj@Nwn$TZ_Nn{^j>xi@*1)%Kf1h?+-VZ z`$;X{PnIgWe+?gizk7PQU)AFM>f&;LtHt}<`sIFD!v|oT_sadV7Vn>jl>2Qh-f#Pr z`*RH+fN@f}pV#93ynng>*YE+T|E)YPYVmn7qdZ?~_yE-JTAoL>_&gd>o?o^2{MxfT z?`rq}jPrDPKGx#%@ze4=t;Of*e&zXF!v|oTJ>`{(~>{!`{l86SZ9^~(Gz$N80X|BMeny>$N^ z=Wo*eGd=+I7nS*8#s`4kRpyg9&L^e&=Q#hA?w{j)_0=-J&2fG!-9O_4@OP#A=Qw|s z?w|1isNc8D&oe#%T)Kb82Y_!~=Knd)|E2rqxL!D*tS>S?0ORy2>yaGSBOjFYOOET8 z+sk?<$MueM|BMg7_|pAzTu-f6)?XPPfO_fv86N;H-9O_4z@_`=xE_@5pX2&bx_`z8 zV4T&<`ZUM&sdWF04?z7XW&NAu`d7Mtj_YOV{uv*Daisfad;qv~|BMd+m+qhO0pQa8 zGd=)Zx_`z8fJ^t!_yBO}{uv(tF5N%l1Hh&GXM6y-bpMPG0GIBc@d4n{{WCrQT)Kb8 z2Y^fW&-egv>HZlX0507>;{(8@`)7OrxOD%F4*-|$Zyx|I-9O_4z@__Vd;qv~|BMd+ zm+qhO0pQa8Gd=)Zx_`z8fJ^tc4*-|$pYZ|U()}|&09?9%#s`2)_s{qMaOwUT9{?`h zKjQ$PK4**}L=+Ajl(4VFIXM6zarTb@m z0Qg}=56_ziJzTnf#s{EYx_`z8fNxUt`8+o0^V0n@J^=O7{WCrQT)Mw~0Qf)4xHO=SBC=ZG%6xO40rEd%@4rb)TdECEY*c1Mq&N z`)7OrxPFH@`f<|zGd=+IU5oCY@d4nvFXZSCO83wB0MzTglkoxIj~3lO;{(8NEV_R_ zGWeU){WCrQ_0s(_J^)i zNx@&1?w|1isMm8V;{(8@`)7OrxSoqS`s3#n-9O_4P%qs-zZd*>>HZlXfO_fv`SIZI zOZU&uhkgL*{`sKLKOo&d9}xNtt}D8Ko)r2MG>^&OgnkCidon%%?^C*e{%`1)ShMt3 z?e+$QvUXkM6?4gDd~{qvyEPa@qv;{$m7(yx-+gnkw2 z{`s}g-=cYN#s^>=&71SPp?_w2>9@&ShkhH)%QHRz<7ghA@d4o1l>VPw4gEj+6x}}` z7W#$GFS>v36Z(s!`{${lAL*ykzmz`?{Y&o`-9PUZ`kf9d{ZaXu&>!{B(odBi4*gV1 zm;S5#PUycHRQk2@xX`b)OVR!Fu+ZQ2ThaaVpF%&_cSZNlTZaCzfkpSv-D1C4(f#x4 zp+D`XqWkAIp`Wc=>3_?shyJ(Ii|(J73jK0R6x~1H68h^dFa3D=+|ZBLyXgM8Z|L7^ zU3CAPLcgDM|NOOl0IrkuOFv=m75WLK`{#{A|Dkk>JR|fgo>lrA^If68QM!L#KlDRN zH_5|7|D<%5yhG@>lFjyU&~GnYKBv&1FC9P6mJh)A<-c9>GUE?<`-zWUmJd+P z)PK>f#W$!?&-jze_~yKy!SVs9=lwq{9{|q3_iOn8a6X@vV_d;mDt=PmL9;9SpHJ^*|~>Hp6U$OnM) zd%0FV0G!{~NcjM8evhBa2Y~bYeN{dHoZtIM`2cY45BJFjfO9{2Q$7Hk`_Jm~0pQ%P zhRFwjbAP)=J^-Bi;g0eF;M_l_%LjmSzdcAk0G#{t-tqz9+|Qfk1Hife?ZCGXHJ9$6r^p9j9OlD&mie&jf6)B-voe1+XTJSrnQxmjKkq9a zfbp5nA64e_u4n$=TRs5wtQS@-eM7EieSr@^J?oKekj~`gp9Q2KerO(k^x_=&6`XbHIPw)X4U%G!D zTly|tufEKzK23A%ioWD=f*xfq-9L{geXQo{|IaIw zzE^YfG4;usqo?5mR9aqDbpO0=>BDusbpMPGKt1~2eWlOW_2_|bpMPG&@z0bn$fohmOgjaZ&q}F z^l)?Zb9?~Ck?x=I0pRHK_yBO}{uv(tF5MsfKf~4kpYZ|U()}}k2QJ+|;{(8@`)B?g zaOwW|9T|>4!si5+?w|1i;L`mwJ^)<0f5r!Z2Y)BSgCArr-9K|3Q7_#;bKSwE`)7Or zxOD%_bqx>xSB6XX&-egv>HZlX03Q4}k0aeb^E;+qx_{<(4-fvJ$C2)z@d2op?(h2$ zT)Kbez6F==pSjP$rTb@m0JwDjj1K^p?vLM^;nMx_XU(PiXM6zarTb@|190j7ndb&v zx_>@iJ^)<0f5r!ZOZUgW&T#4e86N;1{BhSy_s=|6sh95W=P+Elf9AOjm+qf=&cmhq zXM6y-bpMPG0GIBcnRmdY`)B4UaOwW^Gi3PC#{LI$>HZlXfO_fvo@c?O`)7Or_*Bi? zn8(4T`_u1{;nMx-4>6bSpYZ{xUswGn%tPVQ{pnZnI8!yRWu8mDbbtC`T>rNEXWEs0 zwB||kZ06mJBi*0=9M^Z$JbwSOuh)DN&HGsgFb?Yk)(vp!{+V?K{4K3RSeL-R)VhUr z44ici>mK+^jr~y>{Cvo444qIEm#czAEE^YH=T(*0}b z1n|wIBcLn5H2Z-sW#h7e*(pw7jbMqsD%6*RRmne{Mdmv0vSMUeW#O zZ#Vy`u^--ibYuU#`TRRta(3vqH~))td35?p%d47KXzb^AebN0N*ZIC(%r8{#4?Y0n zjFYbr$M4d(AHn0?Cf@=dfN|O@_Xi&UzFgz}2#<5Sd=z{D>Q^hDe=|M+e71ZVd;s_t z@^#|(+gZ6k_yE*DE8hqo0Dg&lCVT++c==NJ0PyGJW8nk9@0IU`4*AAtHxKgIdv2kJYC^Uth4l{jC0MSU&!0KEVHjr(E!e!f!QOq@T5 z{kE=`?w{iP{22A|#QD5*{}ku{^a%s?MoUk9NCR!U>xcG z2_FC+_Op9@>HY~Hfco#%C#M`B*GuC>+_5b_*OZQLs0Mye58TAlyfA9gQC-(;*03Pxg{CB1Mr>N(U`=kDU*WabQ zi04XPg!y#!mEr^Ncj-gL2Y{ce{0e*k_$tb~Xj}3wJpQrji&g)>`5Vg9Q2)RA2Fl+U zQ1Um-$0)Dk%#zn({($m529$gcb8>&w|8IVy@ovGi?6{Z_hv!Utd+`oifGhto$sPd)&gzVoO* zlly}YKs|l!QQubof1)p*div(0etxX-cAhVJJDGav{weD7AdVNpiHFIU~&pr@* z0Qd;y{`{fz|9c!=X9*vGdR><(`ct}&Q}nZR-KXe(u}>!YW$deo{+fP=Df)5bca1N< zW50j)`LOSY_rITVe{`R4z3wAP_Z4&9cgPZm982Oj{g=XAmcfa|%Q@B!fL17u$yoZO#7LjQo~8A&-nnR?Aj(xah2 zfqjhZYowlikLv&TIOP7k6#6AJuSy+4e+9We-jsabbw$Dlpq|{H*pH-jOTq`BUb=t62Y|CL8Xo|zbyRvH z^iyfwm9!4a)N7rV@B!dj*QGN;e;2twJBNNStsB!ep?^&4%=G)vZ^pj#wL*Uyxj(Cf zem1RpQ|y1!IytHT-~Do0S0}Br&9x3sT9=!X`-2a_=b&|d(z@UE=Cfb^Ldw z`y}N6nUfQN55PF&O6(l^OUa=a82VAkt#~Q)uaa|74gId0J2$)RTL1OXzAADi43<+uBDAm`g3wQ{&sS3Rx7%=-w(MtcZL3Wa(1M}Nj%K10veJbbQoPU>c{>}Lul=E-S=cb&0b3SL~{G0Q=DCggt?@>Ab=6vtU`8Qw0 zb&|NQl=JU;u0!Sgn{(YN=ii*`Tsi;d{4SL9Z_e*XIsfMT?v(Ry&hJz?|K|LzmGf`T zeLy+?=8L#*B1{pdcB$oEmszw62OQO>_P`99?RFb?@X`_Ds1G^+9-n-l(C44X z_fgKj>&f>a?}zbO=PKvl^&#iqoO~bhei(;*AM$?SW6*A2|6w>l<_a&B^y6 z?}u?h&cEx)_X)amT7-_BLe9U(A>SwT`KLwb>M7*>dmQq8LZ5#^=O^!nf7c>>ffRE7 zJwEw9q0b+EKPl(m^&#iqJmmbFlkXGy{8PyJcYVnDHxD`g<{{_bJmmbFhn#=&kn?XI za{kRj&c8YNKB3P)k?#}w{8PyJ_c-MHgg*a7zK?SLT_1A(&B^x(eg27jALabJo_rtW z{F{fIe{=GELZ5#k-zW6>r;zjSaYD|&Ir%=J&p(Bnf7g@mqnv;9Mfk{xe4o(gpUC%7 z&cDZ5gs+`K&cEx)_fgKjdC2)UUxY88Le9VIL(ac>$oV%9IsfJ%=ifZ!{F{^S6Z#y| zBKjgy$ocm;A?M$me4o&#krvU{kwVVD#|b(A=8NbvNg?Oo^&#iqd=Y&wDdhaSKIHtH zlkYRB$oV%9IsfJ%=ifZ!{F{fI zfAf&@Zys{~%|p(=dC2)U4>|wlA?M#b^KTw<{>?+qzj?^{HxD`g<{{_b zJmmbFhn#=&kn?XIa{kS?{r~q>rjYaR`jGQ)9&-N8L(ac>$oV%9IsfJ%=ifZ!{F{fI zfAdB3&8Cp^@A{DQZys{~%|p(=dC2)U4>|wlA?M#b^KTw<{>?+qzj?^{ zHxD`g<{{_bJmmbFhn#=&kn?XIa{kRj&cAuc`8N+a|K=g*-#p~}n}?i#^N{mz9&-N8 zL(ac>$oV%9IsfJ%=ifZ!{F{fIfAf&@Zys{~%@?unBZZuQ*N2>c^N{mz9&-N8L(ac> z$oV%9IsfMPZK2OUg`9uahn#=&kn?XIa{kRj&cAuc`8N+a|K^L>_mo1;zw1NJzj?^{ zHxD`g<{{_bJmmbFhn#=&kn?XIa{kRj&cAuc`8N+a|K=g*-#p~}n}?i#b98_C0Old* z-#p~}o1^>72QWwXmk(f$?k^v}9Nk|&fO*LIH%IrE4`3d0{>{<-^KXvs zFCV}h-CsU{dC2)UNB5TxV2lUp|0&$oV%%_m>Y~9&-N8(fvc8eE9yH6^`yt-VZ$F z{Cj+K|Ip{3Le9VI(f!H$VSMs^;`*0(Tw?1DJ=Le{*zy`2gnhbCd60X?ayS-a+e=>DP4pYwgm`FDND z`8P-Rmk(f0KRtOrm6li4{rAfGcRjj4c|X*n`-eXNM7~ex^Y{J%@_rZx-JiT4IQtVq zpTGArkoQA9`yWD|e?s>s?}vK!SCIDuC*Oy>A2|CrLZ5%)I^epXp8X-@{lM8z68iiT z`99?RP|to9@_yi4_xujv=>FvWz|sB5`+@U2A2|C7$@_t`|B$>NIP)0te&Eb|LZ5#k--o;(>e)X@-VdC7AM$?S=>GBn zGMsrHc|UOGh2;If*)K}o51jp_@&PiOc`A87aPobW^Y41*!J*GTF>hARzw6mg8~Xec z^K$Zj_`A&G1{V+ax0m}JzJ^2F4`8OwzKso>B{m=P|m+O`4`IhHzzkc^!d|&OU}RR$>UJYzd89G%K0}Z??XBN=H!DY=ieNi zQ91wS<@}q6oPTrjP?Ym;PJW7V{>{l-QO>_P`7Fx$Hz&_UIsfM5zbNP5 zoV*z2{F{?6qnv+p@@SOvZ%&TAa{kT9yHU=+Ir%us`8Ow5UpfEg}OKD(Byv&qw}M4D(Byv->-81&H246=ii+BgL3}Oxt}QK-<*6O<@}p-zf#V>Irlf^{F`$>RL;LS z_fO^gn{&Ta&c8YLXXX5xb3a$kzd84R<@}rTyim@+InNj6{G0PUQqI3Q&oAlkcOPe{-Iv%K10v`Kz3NbDr1A`8VhJuAG1Kkn?X&z7Kgnynp5!$`^D!^AqL# zn}?i#bLKzf{V+axh{{JyaPob~`++kbQ_jEZnZGIL-<&*0O3SO7n9m>8cy5>T zeaQQvp7nxq{yjeH3;6)%tVfjdZ_fHfK7cvv9r*y}tdErQZ_av3IsfLYzvKg$qx+Nh zQ)zis6Zt;M`FB0*LFN3LvwkG+hjGyT{;I$opX&^dItm;34PVDYM-Noazd8D$`v1+z_ff8hIl8}m0CVzv$or|Z zysFOkA@2u{UP|5%oO~bUs3dsMW6gtpYfiooc|VL3a{gT(a{kRj&c8XjKY2flkG`#( zf7gece{=M6`2gnV{_+9L$@d}er_%DOCUk%K0Io;(XCDCd=>FvWz|sB5`+=kT%LnlI z=>FvWP>=2(&Na3_qR+|Wp!<{e!#L>v@&R0r?k^v}9NnM1AI3rVC+`Q2?yu{}_eg+-Cy@D*Q5LEK4*^ZFCV}h-CsU{Il4dlt|~3BY6?03u1EKm4`7b& zujha{x<7e8EyFMLRnh(B1GpaDUp|02x-ydQYT`S&>J{(7#Oqx+Nh!#L>v z1{V-0*`FB0K zKl^m4C*Mao|E?$BhrA!^wNA+7{lL-v$@_taoPUo`z7Kgn)RXT+-VdC7ANEPZL(ad) z4>|wlvKu?|O89@_wif zIsdLF--o;(>O;=I>(Twm`=LJM{JWlfAM$>v4>|v?4>|wlA?M#b^KTw< z{>?+qzd89n?+qzj^3)H4i!e<{{_bJmmbFhn#=&kn?XIa{kRj z&cAu+H#QGB|K=g*-#p~}n}?i#^N{mz9&-N8L(ac>==U}cIsfJ%=ifZ!{F{fIfAf&@ zZys{~%|p(=dC2)U4>|wlA?M#b^KTw<{>?+qzj?^{HxD`g<{{_bJmmbF zhn#=&kn?XIa{kRj&cAuc`8N+a|K=g*-#p~}n}?i#^N{mz9&-N8L(ac>$oV%9IsfJ% z=ifZ!{F{gUO6DQw-#p~}n}?i#^N{mz9&-N8L(ac>$oV%9IsfJ%=ifZ!{F|fulLJ&~ zc~w)$`FDND`8N+a|K=g*-#p~}n}?i#^N{mzj_x1l4c^N{mz9&-N8(f#9mGUWWbKIHtHhn#=&kn?XIa{kT3ep_>N z|2RJnIsdLl_mA`cuwU5qA?M#b(kL%O0pWXH7{_+9LL(acBx_?|>hn#=cqx+KsRB3rt>HkmU z0Kr4fzsCuA0_N!cQGW(C(oF_8}blbkM1A!laTZ8`jF3Hj_x1z9CUwjfOtP4 z=ilRmoPTraQTPDnA?M#b^KTw<{>?+qzj?^{HxD`g<{{_bJmmbFqx+Ks zRB3rFeBNC?+qzj?^{ zH%Ip;2dL8Ws-kbFsBedye~%M#{>?+qzd5>p)aOH`ERA=Ri*zw@xOyJE`J9e z@`w@~-JgF4JmmblKI9{rqx|wl=>A-Hj1zMH zUC(vQbxl3jJ--8Z$jkCL{I2+&Q6F;tT~B_Oa{kRj-j{jE`8N+a|K=g*-#p}%nTMQz z^N{mz9`e)7(fzsaR$5-w6mtGuA9DW9x$pBFU>u$kJU8GW=ilRyN2i>BbDmo~#~26Q zpXVN&=OWKZc*xW9_&j&z1DK=x^W0_}bbp@naOMG={{u(&SI)o3NB1WOh{<-nHRSVztC?&_m>ag`jGQ)j_%JqopH$Xln>x~=Kb;k%+dW>H!u$C z3i$x84>|wl=>GBn%+dX0zZ1H@d;r&noPYC>^KZ^NO+J7*y1#P%%|p(=Il8}c{>?+q zzd7qt<@}q6oPTrHy{v;PEw5?{IsfjLL-%K$O?}AucRlNN`2gl2=ieONADw{lL(aeJ z(f#EEn3GqmoPYC>^KTw<{>{-%l=E*Ma{kRj&c8W%&geS)yF$*t>(Tw?1DKO9t(<>z z@~D;bZ%%%-a{kT9yH?J>Ir-Si`8Ov|TmAp${m^kNxfB;Vb9g_2lO(=ii*XeROxeck=m_^Y41{ z{FU>+C|=VzIgDc-{;qj=pXTA;Wgb2U^YFQuhtJtOd@ttVdo&N)bs2F3iL4$UOY+%){^0Jp8WB!+pR!+&9d_ea1Z8m(0U`%skxp%)@=sJlt2! z!+qF1+_%lcecn7g7tF(R#5_EA%)@iaJUrLT!*kF)JU7k5bJjdOm(9a-+&nz@&BHvw zJj^T1!#u=1%v;REJSTIX3G*WJFpn}1^Dgr+PcskmI`c3OG!OGe^Dxge5A#ykjj1X zu#Phi>pt_aPBaheO7pM|H4p1n^RUh}59?y{u#Pql>u&R~PB#zhdh?(ImkRdC>jLgHC83bVc)^Lz)NO(md##=0O)V4?3!O&|S@gPHP@?UGty=n+M(4 zJm}2kL6DA&FK63uj|qG$@`%meP4ga^}%;A zN8e|^0^M1}ee!-7C-^?D4?dAO`ab(h7$^8pu1DY3 zb#0Emuit@r@Wss0_t{TVX?az1@ZDS=d^+>s>zM~1&^-8t=D}w)55A;%@G;GU?`a-< zQgifu_E%L}Uez3YSl0*N)*O95IQl;O|KOodz~hI$0`t&^U>^Dw%tN1pdFYEU4}BEo zq3^;x^l6xfz7F%y2V#!CuX&|;=rb`7eJSRlkHs8)U-M$~&?jRa`fAM4_t}qIX?az1 z=-Y99=<_j0-)Da?G@% zQ#B8Lt>&Q*);#phnxpTNw^3<%RdeXeb$#gLH4lBi=Alp6JoFWthdyNU(6?+J`kc)} zU$lAXqc#tH*XE&5+dTAjn}Pu?7TpM0H4%d470 zAHM5D-@bY1^EVIs0?flc0`su%z&z|zFc140%)>qi^RREiJnXYD5BoCA!#)o4u z>=Q8$`%28iJ{0q?Z^b<9b1@J5V$8!n8uPI4#ysrPF%SEC%)>q)^RREoJnSM>tJ}mRFZ_7OF^D+r7^RREu zJnXYG5Bu`W!#+Oqu=QH(`wGp&K1B1dZ_zyLb2Jb8BF)1-O7pPq(md?bG!Oea z&BH!W^RRE!JnS=QQ+`^wG3K6LZ2Z{0lXb2ktB z;?2W8dh@XF-aPEnHxK*z%|i}=dB_bg4><$oA(y~B0dB}}14>>dDA(zIS{Ce_!DlM;S z4!Jk34>>vJAy>yd4aj_$9VBJ+@IWX|WJ z93*pefB69BA!o@vmRb?X}*+r(M}Tzy9LU&t5!w1@n+&W*&0S z%tKC^Il4dh-Ac==nnMno>qBmvdB}M)NB5TxU>Gx(LCfjnui=n^N<^99&#qlLoTIx$gwm>_viepO3SO7Lr$jaL$0Pd zxuZkguN+|WkQ;0sa)!-AF0pyYF*Xmm$L1j?**xSbn}-}`b98^@Hk*f>XY-H?Z60!@ z%|q_AdB~|Y54qOnAqU$$Oa>C6+uDE%~AvX`X<>n#h z+&tu>n}-~A^N_o49&*~vL$14d$bmNxx$)*9XWl&I(wm1Id-ITcZys{;&C&gpt8X51 z_{~FZzj>wQ<(&V?<^Qf`>UAzaGo0~tj(|DuN9PWh^Zs>CfjR$Pooisu=c98F%=tWZ zZh|?Vzs^}O=ljyR4CZ{lI>*7B>p|x}m~;K;oCtHSH=QeC&h@EtD9pK@b#8@uIOoEg z-;2)0Fz5HBb2QBPJ?h*IbAG=%r^B4zyUz76=l-B`K+L(H=-d!ao?G9 zzY6=3GWR!~W8!-5hdTGfocpKFNipYst8-P%xj*Y17IW_BI=97~`@hb4G3R-qb79PR zzUUkobDl>!cgCFOm(Hm%=Xs}dZOp?tIOaT0b#9J1&tILhW6twh=kl2Ie3uVk9?tzS z59b7#GvCm;Lgvg*cZ=FGQsu9i9T^RN$>yw0q1yIjxwU*~+8 zvtFQYsM7MPX4V(-0bI{|guW!|S-M z58(Q6j+;5_LHPjYtRHnwoOw7`&YbnBd;s%sZk;*n->}a;vtE`D;Cj~A@&U|QkJFb| zX?az1IH%9`;aorSpbwa%C(u{eGW^26AoPcjGmz0MeOC-MPY zAI?=Y59ctNqvyy6Fh~E94`3e7g*4}U-;nd~^L^<{t+c$VIh<4Jal*Nl=ICSc0nE|U zuR4`7adC?CK)oGWS`&LK4q=a!m>b570COXUNYhjUcT zgC1)h^jq_AuB$nH;q-}DT3*#0&W&|_IA_*8oJ(ttzD=Kd%kT?-N6Nd$2XH<5Ieqlh zhjVpZk3KISz#QG5zJA6D=lr@poC|D@?yqx%&BM9F=IH)9r`SB4Yiy1`!aj*g%d48v z{X=e24(BX;oNz9)dGL44gCArb&WSb;=SrK0bEwV3xz*;u|1wwqKj&hbqx-YZrqc4N z=HSPcJm2eUhuGyBoId=z4VjK7-eocyH%AfBx8~zj|cD&NH*dD;B_QfqHN;Kcek6EC=At+AD5^Y~?M8h`uwb*_9L<~4qw>i^H| zgXHf{y>!+ex9Ye|mJi_i)t794{7zGr&OHzP`K7C-*T>g+OIbdE>!*!b_L=L~TRO9k zlkrztZSbIPXDpRN&VN?EPiCJe^@DrfwDlWlsVpDBeD?YeUh%@Noy=Dn@a^L- zj9)T`od5jZl?!@)yZe&4>v0dA_vO%fJ-WZ<#U4lJW8njs_Zit~@*BJ7 zZ|i*Dc5{}gzR)_KbK$I2MlGoG8CNv_{n+8H^GY90dv$W#&VHXi&YM1Z=$ftb_iKOO zd~dfpKj@-1FZX$4@%+x2AN4+TgF1h8n=60+&Do1*_DSHhQe@q)q15CVknh%LZ%L`KRr7nf~3FIW=+% z_#B?TdfexqeDG^cK7jx3o&)dc@~@45t?hXJXa87Xr}{V}&Rf6hN~3?Nk)yykyH4tV zNxvCC*T`Lf&scoUYW+6+xwifDw{3L!;QBbfO&T$DrIA0?$aSE;{|WC_$G`n!t>X#5 zys_OW_4+hz)UtEd{jo-F1oi!H9eKm5BYvonGXX#3Z~NS|!t3AH$fbb4_tvkEzS{Hq z8aWp5(C?by7q@Ty&a+cy)yT)RjJ$Yp`wxA)Kyr=R*kjT{$v==V#aFnZfnU^0jYMLt{3pfvPvz4Ug+Aqw<`jq)wiu1QWm-${I2Z-^vKy4_RY{RlN-2~?b5h3ig_3!3(&o1fxR+&H!5aZ8G>+3Mlt*^=3 zB?pN5tJC^DkOKt&xYhx8@HA3*fTrM<)>_!^lI&j;r1n)ul?)XELZ=3aOXnC|9+eM*`-q}hKHV;_~)zD zv-K^K9u;oCzT&jk_f@xlSN;FNol_ar{~z4BmXY&)$pPYfK==Po>Z`T!(fzwu-_fS` zJ=uR({r|z8%NgGs{qwjBwpR*QuWnlX(etwP=>A8&xOc-BO|l%_-<=a0zg#wD%VXV- z3|o#^HQ}v4tJ&vK|9^1jkVf_Y2X}61tiJpDci-Yq0P%Oc{F-OljjB*3)GT?-KPC^&*7q#2 zyu{R|RYI7vZO?>0S&r`i^8PK&uRk}-)&C#dxw^5%jVB(m^N}iH{3RDG>Q*LOkM2(n z5TA$qANc_G`>OvxxO0KyIF0N51(!!;bhW@A-Znl16chmU1#zEjBnO;DIdUiOd-p7VXZKdAoyARoZ$Ip0@4fN^wx z`2faW9MtyqZZoO}`2fb%{~zQ77rcg4`5vWY>*FNT>bw+K7es_e{z6wihd5{ z@aR1uAHeEw8P}sspMf=kd;sI*(UAkhIOzWJ0jysA|3N;0aq{uJKaTEC4iMv``;!9% zNB5TxVB@3v%Lg!y?oSR7y9^GF)fN|zk$7r6FQ}k0{y1#q?tDp5~`TtBPQ7gy?FplmoAHX=ezsvbI-CsU{ z)ua12Yw>dFzFQ7A&OB86-?k^v}IJ&=l0OP-$ z{L@JtXXJ)vTBkBTy1#q?t7qLSAHX=ezkC4emqYiL4`7^i_++ii`FGL%@6b9Pj_xlX zz{WxMFO_@%u85aW)v3~1;pnjarOU$6ZQqfO3rFXz zadFap;pD@QkxmRpNB%;(G92AGH~9eecR{CqU%EB*=-O9G=Z2$$mw&Rz2e5I_&A*b4 zPCYvNFzN1abopAp75M-*4my7C}))T5^VhEY@WIxaZ?9SOz40wa)jQ(SKl4NPvCq}7@cIwN>rQBR*pMCh zVeh8fyWM}wPsS&#zG%kKZTaD(?~Z->!)8AlFUT2L`HC(1;fazRUt7L)eU=Yx+_c2+ z`C;y^U&~H@d!zAwwRRnG-{$-jR5@5*HR?brWu z?|ogr%MUXy%2XQl;ufp_ws*f&>^jkjTqV0pX8n4=Z-Gx=Z$`4I1oxQQ?HQS7T z^X1{qn=i``=an5ft<{;^jjvwazec^!^TTCLdfr_*e~0lgvszSI_i27u*k(h+cXD$qE_;4g zmJfOC&NJT04^^u^*{9k)e;Ge(VwqbS&dd+FU+3mN*=@J+r&_I={nM2EaMzY;A1!aS z$N16p_w@eftNG!(RXfjKT4S&A#@{R-`t5}LuzK++w?D8W%OC7;a(?yk2d+^TEj@obh&#$W9|K39f?yq5U z>t_mb0{3a|{U^TBX=r}Z7x|jK4R_FCDh9;kLf{;kO-imY%*j%WL1?eD(6{^TX}?{;oLfiV`-?oL!w?c&KN7 z$h-XOBX%sz@^-H`9#Zv+{Lt;B($mTn9BTFN=GA?=|Hb*?oxDCw+-0$$LsC$ z!+Qfx{&-6Hl2-qDrxGvCIx|1~d1CAQmUm_OqCrdJ6Rq;YU!A9KAHF`z@BFskRUKO7 zhmynEpZsd)!)%G7#9lIdQtM0h@=9BB?hijj1`^|Zcv-8_${@z&f z)*AU?S?6&LKY23C_YE3+$Dj%a?ys$f%&qu;=G804pSP&nVF&JulAnyMdrNk{z3m^F zqpR+%7yfh1M`N~qmF3^Rv1shXt@Xl&p*=r*LoHMZ{NH?n-! z+*UQt`o3ORSZ>2J<*OfI^*5Bhwc8~t>V^N^I=IP}`?CDZCd;dzzPMhvdUUO?pZq1u zyF7MjufJlwPF7;d2hejzg}opw(c!AG%0WOpANtG#QHbZ3&##v z)$#Qwvb@9Jy;|JSqh46PVO8@9d$N4O$hJ$LzPMhf-l$^kg}o}+IL(^(I_}AH>V<0_ ztW$R8Ct3b_$E$kW)T&;XbZMW%KW$pk>bGCe=Ajx*>V*%^I;6_uPh@$;8*W-WzTScH z*M53p#obw6v%{Nb?XFTU4E+1}lb>%^$;LTvS<4E|53d(`KYZv<<$7g#*&(%_yLZq`@#6$n;w=m1ETQU8@Pcuh_VVj2D)_Yf$uY2M7!*@?Q zDm?e*@xz)nKGOc(Z7+m3RzGxLoVQ+S^Le){U$Oq4dv5Q0RA}?_(E4XTlI2TA?0auc z=c7W|ujkDhwIIt6-_qbeGfzG$9JhPM$jQHF`KGgK&$>GIsPM$XlWLBySK0pEp)Y^1 zIkT^B=(&FA|9W1WW ztG`k=EI93m%b))-%Ntc+`+K_w>xPTgK05Y|%2n*&4HIj;bo@yzEHCl& z2OT~-zit?@s#c#<@5u7$71y_@+N5rnJ^RW^?@Y+@&Cj%&RI@_eu%%Lq^AGzX%fI?( z^WT>JRww*$(z+Lq&dKVccl@-i``JtDgm0@C{JTQ4EDu*++vcWeb;5!huKeti9$B9M zd6gf|eY8%P^j_I{7mdpDJ2rQEVs+m-;k360?tFP}mS5eq@@tJc)d{En(W%@g8?$`a z&lPSxp-G)^Q|GxOXa67l`M3YRTClNfop8nB*X7*aHmgfF-nHeT?we{KI4}2}y*qEu z@=q$fRrR4yYKPkOrdBxbg)Bc|S#6PXYKN(ZOuc8; zy;;8g=0V>q`7`j>6r>N+*cuiE|IePwp#hW7E#b<0*}`Asi(TlJ4+xuM7DlM5>! zn(Z%r?XWqeo|}>znvbm8t$LFze=)S`HF$V#7+3H0rE4zB@~LZo>QKLDZdfp(#mP4h z&hj5O_c&@qtK2YnP0L2}UeEI3`+sfodga`3_p(^{`LZlOuG?KlJp8{}q0bBV)tIy= z%NOn&@%;%O)(UM$OzqIRe%8l~Gg}?~==fS;`?_QP`BcX&Z+FavEoUC+H$6}Pn^W{t z5VxxCef@XvLXG>0{vG(W`Wz?dbHVS@=YCkf2mEgRp6m2`!{62SGF{&j{26_ZFX?-Q zAFuCyvi=V6uKGLOs=ph&t^TgP^mm58qU+!UT^I1@b=~yTbp+4Rb+$>@9sDv~moMl# zg}!CP<{j{DnwRXd z&%7S~xaR$=1KonGF@Ef(R`%3FP__bOG{;hQ(e6iMz&uSeB@2+)bHLW}0 z_iA1GwAQKczlWxE>_Dw+;aj!tWgQIvL+j+FS~tVzYF*t=>umT`t;1QD!#iu;ey-N> z@TywpzpHgW{08X)b)*x(*Gor0SAahv-C>|~2>1-?6zCT4C|v`c1AeD;kX_P6;2ou# zprgRcOJ^A`-37i`y39k;Y2b^b_k!1yE{0A9pCug)T@Bt=y4xD*aPX6*)1lkJYfINd=YwA*9S~g* z{(^MF4blpH zheel#A1U1y9T&b*Ixo5}{50vpTcs1j2TMmrSB9?{n{;P%X!xr2NvB4)hL4x7jm{0P zD;*qN9KLf|(#_G);Vb$jogLjB{{Hhxmq(|EU%Dpg_~`oZ^Shlmpywywsn7rD06#J5 z{uy6CBkBGbKYqug`)B;;xi#tj8UOjOOuui&|Gr&-m}NR?_`5{`;Ss z{$3gX_v)Jdz8U}b-I=b3jK3b5rt2r;ubG_uN&$r3xd6>ZmV4UUY`I+(0&m+_GHshbSHPiDs zgAc$s1Jm<7rssbKAAtH*>3xy$?~C2({gS~4puSFeA7%XesC#;UW&Hc=-t@kU z_yCOaNqRp<|9*Toy-%ZmpFWx1zY!mRaW<#-b@cD++tT|z;sa1G-9O?3z^_R2hlmdV zpOxkt(a$%e`$v2L>ZSWfd;qv~|LEsG()}Yo0QE!D{3`nSm304z4?w+i|LEs$()}Yo z0QG&-{4nAJz~`pHg8rKc)LeKVSVI&2OWh-%9t7_yGL7(*2{KKTG$I_yE*D zo#y8e9{?`hKjH(x$EEpy^z(n|{?V@&E=lW)h!4Oxi_>}}`t?ZLw0?Hg8LAEo<8d;rEdG_6mg zU!O|%kN5!84^8Xe1NlqI2Y^fWkAA%@-9O?3P%qs-;sd~?`$v2LxOD%B4*-|$AMpX; z()}Yo09?9%#0P*&_mB7haOwUL9{?`hKjH(xrTa&G0JwDjhz|gl?jP|1;L`mgJ^)<0 zf5ZoXOZSiX0C4I45g!09-9O?3z@_^~d;qv~|A-F&m+l|&0pQa8BR&9Jx_`t6fJ^s} z_yBO}{t+JlF5N%k1Hh&GM|=RdbpMDC0GI9`@d4n{{UbgAT)Kb62Y^fWkN5y^>HZNP z0507>;sd~?`$v2L_?t=ZjSZdNE8Rch15hvBKjH(xPfYrAe8uU{()}Yo0QJ)SBR&9p zNYcaO8mEU#_mB7h)Jyk|_yF*BNuQ7JJAGcdf5Zo%Ub=t82Y^fWHy;50VH!7je?hu` z#0Q}Mp``mqd;qw9&-j4zJEkPvKYD*ex_`t6V4O+mdyfUqf4MH{{_$$(*XZvWy}zUD zAnLjZjDKa){o||7KU$x3|5(!bP0u9VKlX6`RAJKn<22`I>AH{J|B~(>@d5Ze()}Yo z09^NB^nRRl|A-Gj{o_gZkN5y^Jr|<)2c`Q*d;se8+==)A@GVLAkN5!ahm!6euXX;W zbpMDCK)rPThz|hQb2d(Neyg6#@jd6yO81ZW0F1LT>HhJ1=l}kebpMDCK>e(w`^Ra{ zUzYA4@d2pUdn@7tz@_^~d;qxKi_!bzBa-eP@d2op?jJWf|6RI�Q{Wx_^Ac`TNrS zV?);uAl*NfcKrj={bOs_Z?Gll{_#)OpP+e6eAe|dXx(}}^>HhH|`2c(m`;+b;FLC`~Ph#H7x;d&r2EGK zu0L&1(*5Ia@&WjF7pMNW_@nE8`)|_y<674*cYD(P;{?}VH#ha;#phi=-g!y)k7v35 zy}3#Ek0o8dpLGA&%k>9#N&STJE7wmb-9O&z`VXa3#J60(VqWTRjPJVsM(O_Xde;vr z-6US*`X{Bc#LBMUQo4U!?fNsNiT1)qs7^-pH{kCyv6n3N~eq6UB9k$y?CPQ@0AW1hq-=W z^$*97T>r3i#<?obPA2d;mD#XF2%*aK8Ua@&Vxdy=KS< zfb;jgQ$7Hk>tTz0065oAWBCAZuD3Vk0~k-fRGcXv0M7ONpnL%My43$4Ka>vu=YDxb zJ^-Bit4+GU?EB|_{6jtf_1wSzl@9>tet$$h0G#K;>GA>KJWu+_2Y~bZ86Y13&hskD z2Y~Z@J4HSKoaf=E@&VvHKR=fbU_AM(v7&qcIM3&X@&VvH&;KbO0M7IO8~Ffm-WMz6 z1HgH|{45^;&ig1=J^-Be*X8m7;Joi{mJa}z?tfrjSJeL>;k-|0%Ljn-{v9G80M7e* zm3#m=@AvEE1Hjb>5RXXnfe2UMKs;AI0G#>8MEL-4<|nVD`H79AK8E;Xn$H+#{1K=kJvTM_lz??>>?kaX!rxU ztjs50lMeu2tNG`OH2<`5uF-t;sx)6UF5N%Ql@Gu;%!j{8^I@w$OY`Ro()`&t^X z&iZIkS|3F?>#3?~J!PEr7d`;>(*5Jav|h9NZKRKWaiRYsN&^Kc0b2Kj9KXyue zk;c(a@BtWKx_^8i^<7%M`ZA;XG>xO@{GR$cjidkI12DdH|A-F&ZY%-G4;tBM^D2C$SL|MK=(h8qf^xXZ}rmsBR&B2=zC35pRd)U z2M$Yp!N$=K@c|g;veaiBC#Sw+tC#K{@d2op?jP|1;Eht>b9^lILEAX!r8AOVYWykb zt5+v|)wt7RjXV9;_%TWMkJIG?#|MB*_mB7haOwW&{}HbK|A-F& zm+l|=ci__fBR&9Jx_{*NfJ^ts?}%{x5xyt5bpMDC0GI9`@d4n{{UbgA-1$2Z?))I* z()}aX5%tpjBi9{Vx_`t6fJ^s}T-R{te?_=-|A-F&m+l|&0pQM$vvH*RNA6?lrTa(j zd${ulZ5-+T5g&kh>HhW{f=l<0Jh$M|{UgsgxOD%B4*-|$AMpX;(*5yUBV4*a{;YB7 z{t+L5dg=a=_W)eFf8@OZm+l|&0pQa8BR&9Jx0y#K+tbpMDC zK)rN-n`gnL`$v2L_yEn@n8(4T`_u0c;nMx-4>2y?KjH&W-(CGD%tPVQ{pnY+aX!(! zmU%Aq(*5a&vHH{1Khq-Zqct8i&t~4uIMV&;&$0Tpn#W&|_VpSct$9D|0LEdRz`6l0 z-9NI=-I+k@UT)KZ`9SmQsbuvBxT)KZ`oegIl&bl0a zj@Iq0vwrTa&80{Cj_2r3k@qSLL|5UmUIuP~Ui}#CKeR=6h>i;*sze-UK1^uYTXG^yl zlX5JKyM9;Wb&L1M8lPUgpVoMf;{CVA&o17tYy9Zq{k_KTDc%ok{2l3r=!iK*KLyeC z8(Vz~>5}M_)Q>3M&usOxi}ybpf4F$RwDC#B`>T!5EZ&c8JQVNWHtzbpjgOKpj82?W z^ivR574Ijv`l`kI&y8-p_CKN%#Mtb*eks_$=lA-~*)pzaS2lui(dDU3@=+jdQMi3w!{^xluj`J^=h~ z`6BoL@I#dQgAV|2q}(5T0Qhh6Y48EyPs!Kuf8YJZ_k%?0+sik?2Y}C$&x8*Ee?z_$ zJ{9~4`B?Y>@Qi#fd;s_u`DFM2@YCe0`TOf<`Ed9E)X$S|=kMPk^7-%qs6SS|AU*(` z+#h@Z_%-q!@d4m&zfy!BBVQ9A06tYdC_Vsuu6$E`0C>sb`>i6pvV2+pe7;6LE$OVd;s`u^11!{ZoYhRd;sca%16fsfZs3Q z-M@d=$fx)3>)yrp_eI7jp*{dVACT@J{QTi%^%?m2#w7J6`1#2i^)dMQjCB9t=Rezv z@0YaCDcwKd1MvAfs1L)>#}=q>!_VKYEWY2<#=k^;A%1=+-9PyGU-*I!3W^;e_MP%tbLwK)i>kk&u+i1)l2seetuqCeLQ|XFWo=*`9FO^_yBy)i?zQP z9{^4tl3$OsQs0taztHD|55PF@sV}Ny+8=GdS6TH@O-wmJ#_7BA>o4j4!LQdoR$muB z0ROJr4{qa2_YZ#k*hhV4_yCL}-9O+1z}DJ^4Wro2)RG_0MwKF zgAV|A`3&~&O7{<5&ms3m{r^_~o$@03rMw8^AE>VsAAo`-2ZaJ$>z7-&X&Bpf8?!`sTfUe!cQ`j!k(x zk$UO=!Rzxb&nH{2+#h@Z#v%6y9{{d#gZCHMhv5APeeQq{z&LA*=N;MSVP6D30QKbl zct3@G7wp5JUVkU@nrwXbb>IU~&pr@*0C;!h{=A&}|7{#yX8|98dR>>n`%}7(gZHy^ z-3RY~u}{YPW$df*{+jN?;QcuE?JQ0Av3-8_`LOSY&)-uaX>&$SW>o;Rx`rWQSjohCdF0)PGDR*Lio=06H0do_yCMgPQf(SuPEI=sQ=%_mkttqKP0&c zom~GUISZv+zom4Uu*&sklH(v<$NFZa`vm0x87C(KAAsMBT#4$gzmyz`(ykws+=`6r zUnS>ay6bl(7o&mek0nP#{r~nk$=#3+XPlf4>2}7+^=RPwd&vPA?D~PF8-_Wqf0&#R zd;q>5>5`$T>rW=fWSHw`Cii5f>whLEWti)iCRgP-*I!Kz%j>Qmo7@)Vx7+tX&dWyE z?=4+8-0S+o$&p#(`pLo<9tr#{2S-@QqI3|z6a&}8|Qmd&cAWK zXXX4G=kKDNf8+cemGf_$zq@k&jUVJX30zmo`L}wmL*@J%=ekwSzj3Z}<@_7xzEIA; zaqc7K{2S-KQ_jC}?o;Lb8|S`O&cAV<1Iqa~evs!z;5nn5f2-%Yq?~`_JjaytZ=C0z za{i6;oK((#mKV?YH_mfdIse9aZY$^CIQc%x`8UpcK{@}%$@fvtzj2rIZ=Cm(a{i5z z@1vamsQWIE@1vZ5_{@_oqr$tn6N2;}=H=ilnd z_fgKjaq@l0`(YgNeaQQPlkY>`51f1-@_yhh=Rd;9_aW~G?sEREo_rtjeyAtkM>+pi zPri?G{*9CGL*5VLkncm@51e_da{g@`@_oqrp`Lsn@_yjt`;hkoC*Mao|297PKIHvS zPri?G{;i&M0eL^vvySMZydOCEKFayGame>k&cAWiHOl!nPQDL$Ka5Yl4|zXu@_m%^ zZ{v{fL*5Vd2U*7j@_oqrq2A^E+xX=BxIX_tzK?SLt)6@z@_rbfb*^&$t={GQ8z`5BwlH zNkCU2?}vKweaQQPlkY>`5BwlHPaxmN_4x<#eaQP^9P)k0`+*-srwZizkoQA9`99?R zzz?FE1@e8!`=OqEAJ^v})aOsmf295(x?eyibbbDTd>{IY8RsB6WH^X!8OZmc-RryiahLON zoO~bG=O0|ozty{(f8#Fa-?+>9H|}!&jk}zG<1XjlxXbxB?sEQ(lkemD`~&$uuFpTX zoPQgKd>_~6AISGn&cD^WoPXow`?x;;K)#Q1{;i&TALaZTcRByY$@g)6{(*cS*XJKx z&cBW0a{i5z@8kOXgUk81dh&gg^KbkhK5`)6$MyLK@_m%^Z{r-q*A6b{-|ETtQO>_{ zm-BD@AijKXIsaDga{i6GoPXmk=ij)?`8V!z{*9CG^=R_}8Djg#-AoPXm7>Dvh|=ilmG z&cAV&^KbkheMiCN{9C=t`8V!z{*Akwf8#Fa-?+>9H|}!&jk}zG<1XjlxXbxB?sEQ( zyPSXHF6ZC4%lS9%a{i6GoPXmk=ij)?`8V!z{*Akwf8#Fa-?+>9H|}!&jk}zG<1Xjl zxXbxB?sEQ(yPSXHF6ZC4%lS9%a{i6GoPXmk=ij)?`8V!z{*51`Z#KA`f2(&n|HfU; zzj2rIZ`|el8+SSX#$C?8ahLON+~xcmcRByYUCzI8m-BDj<@_6WIse98&cAV&^Kabc z{2O;U|HfU;zj2rIZ`|el8+SSX#$C?8ahLON+~xcmcRByYUCzI8m-BDj<@_6WIseAL z`v32P2rlQ}>RryiahLON+~xcmKghn1;Bx-0-sSuocRByYUCzI8m-BDj<@_6WIseA- z+gzW2a5?{0?{fZ)yPSXHF6ZC4%lS9%a{i6GoPXm7+4mG&&cD^WoPXmk=ij)?`8V!z z{*Akwf8#Fa-?+>9H|}!&jk}zG<1XjlxXbxB?sEQ(yPSXHF6ZAky1#q?<1XjlxXbxB zj_xlXz&N_Ud;sI<{_+8gqx;JTFz#~xjidX^2Qcn({*9yi%Lg#-a{i6GoPXo!{_+8g zqx;JTFz#~xjidX^2QZHAFCV}-y1#q?<1XjlIJ&=l0OKy_-#EIz>+=uf`?x;;;Bx+L z9P)k0`^hQ#DG22IxITZ*_a*1w#zFTd?}u^7_mK}^_2~ZO{ZLQ;7J064bbs=G;4bIi z#z*&eeg47a{98S`KY2flPri>||I!af-VgQY{_+8Ad~|>L0LIb%U7tVa`;zl-_2~Zc z0gR*j%Lg!y?k^v}IQ@vO&);-^*XPgqzLC72oT8rs&iC~>37qds&cFS;=>GBnjHCO@ z2QW^5r|a|Qd|z_@tsdQfB68$(f!F6=ijCO*!B5yzAridHV(Q!dFPCS z?k^v}>e2n>0~km5mk(eZ-CsU{ar(K*ch4#MDd2oxa{jFz-CsU{ar(>EZ*Cmj-}U)( zzAridR_}8DjidX^2QW@QJ$XMlML!4p`VmFv`;zl-_2~ZO{ZNnY@A~`$`97}C-}VoX z_ro~o{^b3@*`MJ0{B1u2c|X*%|H1Y72Xue(eyC@E1$jSk@_oqrfwOrld>`_D z;PMB_`HygPfAW6dJa@?Zfs^k;-VdC7AM$?S=>FvWz|sB5`+>8c%Juo<&-xs;fbLJ; z595&UL*5UZ{b1z%z{&R^?+1?VPu>rl{b}U=z}e5{`uy?7$@!1elkY>`51jpVuFpTP zAJ6sq2lnr|KL0?z4|zX~PreU%KXCRFlJ^5={~>ulaON@O{lJ;`xIX_tz7Kgn)U$t* zydOCEKIHwt(f#EEL^$(2@_yjV3(5O|vtN|FA2|C<<=gJhkEvtE9c+ppDCX6 zZ`|el8)ttzc|VNLet6gCAJ{)n-VgQcw^z=;jqh^)jkBL$IseAV_aX0x@yQEN&cD@@ zFQA-%+`4omYjd9Cyzrp|HjGh zP|m+`@;;RFZ=8G(<@_5*XH?F=aq>r$^KYEI66O3GcRByY$wN`jzj5+Yl=GkE>AY2P z{*9B*qMU!@8-#Ga%%K0}=UW{`7jgv2^#4cY{2S-pqzi>TtCYBH%`8fa{jZt zc+S6Zu4m=^8z^KYEzgL3|j^E^?` zzj5+?l=E+#=aq8)jq`j{&cAVw{~?dE&}&cAW;eU$TW zocXZ!y&5OqM>+q-nQv=ft#Rh(fN|C% z%K10W`b9o~an?KX0gSUgQqI3|)>F#)H_rM?K7es_fAW5Eihc?L`98||w|drt%K10W z`jNaJ#zFU&4`B7IPs#hC9^GF#|5nfXm%Jb9SuZQ+-|AUkllMbC>v84$TRplzc|X*X z@1vZ5t9SZd{Zg`=OqEALaa8z03JG zj-Er_596T!koN<3IsY~e`9AI($>8)Tt4H@&&cAV&^KTq|j6T_%qMw3*o~GOkt4DuR z&cAV&^KTq|PdWd_(F2w9ZyfzlK7euZeUvL=9Nk|&fN}DD$ot7D`YEt{AM$?S=%wWS zz{&Sfj!J+#J=VC>Z;g}hL*5VLxSW5hcRByYUCzI8bbs=G7$1FGIsaDga{i5@pUVd@ zj_xlXz&QCnGBnjHCOrFNbl^{dFH(J-WZ{d*jX@G>+~sAHX=eKl_k! zihc?Ly1$-VR*&wl=bUkLfB68$(f#EE7)SSK-&Ic0PeE`w|5lIgFCV}-y1(87#?k%B z`zad!z`PXQUp|1Y~^)Bb%IJ&>ytH#m&$@^h^bbs=G;OPG3 z{lL-vFvWP>=3U-VYqzpS&M9x<7e8aF_ER;OPG3{lL-v$@_t$`;+$rcRBw7 zj_%LCPdK_i`$XaB{^b3@UCw`iqx-XO74CBWtsdQ`5B22xkoN;8--mtDaF_FM5A`nR-|Ah?zj2rIZyep9ydTDQIsaCV?oZwi^)Bb%>dE&Z z?}vJq^KbR&{^b2o?{faFo_rtjeyDdj|5opE{*Akwf8#Fa-?+>9H|}!&jk}zG<1Xjl zIQc&0M&uOz6a<&^Z}l$c-?+>9H}3jfjk}zG<1XjlxXbxB?sEQ(yPSXHF6ZC4%lS9% z`i+gdoPXmk=ij)?`8V!z{*Akwf8#Fa-?+>9H}3kqjk}zG<1XjlxXbxB?sEQ(yPSXH zF6ZC4%lS9%a{i6GoPXmk=ij)?`8V!z{*Akwf8#Fa-?+>9H|}!&jk}zG<1XjlxXbxB z?sEQ(yPSXHF6ZC4%lS9%a{i6GoPXmk=ij)?`8V!z{*Akwf8#Fa-?+>9H|}!&jk}zG z<1XjlxXbxB?)EDgcRByYUCzI8m-BDj<@_6WIse98&cAV&^Kabc{2O;U|HfU;zj1Vb za)5G*ehPxi`L}wP^Kabc{2O;U|HfU;zj2rIZ`|el8%Ou|^9PsnZ}l$c-?+>9H;(S_ z=RYpz-|Er*$y3ZJ`Y8x5=ilmG&cAV&^Kabc{2NF2_wz}Y^KbPo=ij)?`8V!z{*Akw zf8%byt#NdJKRGBnjJuqF-135r&m-BDqxI6*l=>A@Ra5?{0kM2*NG5@a1L$G>uf3KgooPVo#`3%O< z{k@)p?oSR7pU36=+c+-g-?+>9H|}!&jk}zG<1XjlxXbxB?sEQ(yPSXHF6ZC4%lS9% za{i6GoPXo!{^S7V6#W#?|8F@!aCCq2&f)0(UN3by|ABg!^Kabc{2NF2mk(gv<@_6W zIse98&cAVVe{z6wihc^vw}aQWUCzIa<8uCuyPSXH=>A@xcX>Y9`rY~^)Bb% zIJ!UA9pkv1f2-#@=DMby>z?}n?((v19PTUbGwNN=ztxl9rJR4`F7L~@%lS9%a{i6G zoPXmkugtj1`8V!z{*Al*G~?+0Ja==7ehPxi`L}wP^KYEzKJNj>;XT271MYJEZ5;CG zl=E+#_ZIIl#zFVzy$9#L$a@m*^7L$c-n;SvjHCPW-ew$hf8O(O<^i1l14s8)&cBV1 z?oSR7^~_6@^KbR&{@T}QoOus9K#YU#ubh9YXI`b8f8*%>%K10WyiGa(#$C?8addy} z6E)5}l6hrL(N95eIsaDga{i6GoPXo!{>+PuhCkppq5I1RuzHvCZyep9c{<~e=P4h+ z>Y4Y;2Qcn({*AM)kPl$o<@_5*_m>Y~9NpjdJE8l_2e5jV^Kabc{2OPTCLh2!y1#P% zjk}zGN^;No@f8#Fa-#B^B=sGz? zKLx?%{98S`zkC4W9&+^p&AC&WNoV;u0{2M19TRH#6$L zTwb@;lkcsZf8*qVE9c)h`Qgg>H%{KTd;sI*lPl-nICQFZ=Ae(<@_5b-`@APlZUUI zf2$`yUpfEA$=gSF=kHEFzjFSqo;-i${2LGNuI~Tc{=Cqu-ncb0_q_~{_tpDk`9BZv zCw@EYKTq#_+xRUT-mCNJ-+7_-o7b(YcyBa*Q_KFBe!VX*O#N%>smJg8$oQaVTi*5J z-n>xn$*0e+boXN8{c7%itp1+7Pg#MianPO>#`{fq?#;?O^1}N0H}pGw?n>hWURph8$kx2@VeNM($ARA(@A&dj zOW*uGFYLN#VC@p8tTEp5@@xKld~;rS=d%8T@4joT@tSv>*XpES^1{&8B|2RA%@4+3 zoz~#rkNun%p1knfBX*whlkvrGZ#(|=AM(QdX-D?B^{I8n@6WmX_kVqt7hbt%(z+Sj ze>Prq(Z9DG@l9SB);t!*&Kr!su_bfHC12)+yEaX(@Z|K3##^tw>if%==7pt;Pu+S# zg-ym+-u%&zH5TQCDQ&-ec-YPVGv4Lh4VOOtVP3es&+vPm`DC;4uC=c{d(nctFuKY9 z8~Zf)&G-j9?wLP-PF{Fz<-fOYxc_(K{}^-Z(wk=Hg(^?Z?^)rtKaAhD_s@}EzL6L1 z`1Y;Sa_Vd`e*MN~IlCw2g-5HG{PyETS^mPHIfdW;J1-P0S@*=X1Gidz%cGC!Ht?yu zu;lB%=6%s%oALP1*L#04Ixlp;|ME$dmS_3(dr!D+{qVfd;nm?2>ki&-^@Sx*8U4<^ zdEtbW50>41!VcrPx9?ifc0gWefBWi!3EyP-tdZ@zjlMN6{55xQ|H{L6T7A1uUvK$D z@4T?^(B1Vqp7^Kn!7mp6^RjMvq0QVbk9Ay=jCiJWn*;aDxbYi*UYq3?wH|ome_G~+ud8=j^~b}zt$yC7 zqiQ!hE-#$Y@;~R#J$aAurpJt2)T3_Rfjo(!Plq3}yu;7eKia-ZUif3r4>gB8yw~bW zcBnXf>*0B!b&SW~bJ9NJCx?G6>bf^Kbh_=G6PkaY#;63ynf%pzVEcyZ~X4J7CpT8>)cTK!KZF{a8;Hcb;Ga6U$Hnh)c@b^8y+|_ zC+K+C2X9YvwA)J=FPd``AeVe_n(njUV8TB>-Kif4QuD^X!z=PS^m^T>zbGClpEgv zV)8}3njdQ8^eMIR$(3z#!;-U}&i`#lmVbZmkEix&mK!b^UE%ZgE3$n1aoY-K9F-e3 z?Oipp|FI=)oT@*r{9=B^-0;!Q@_Vgt z%Zj$AeO&u68|UArckFW2np)xHFIQgNuy2-MvGkgWH!i6aYL5B+!HY6k{_g7+opaLs zTA|w^=hQf-QYjl}RrOIfyf?X4XfwRlNrzsO!)W_;7XJ zh+5&bB|nz<=8(f}oO-v-eqdAoT4Cwa`y2dzah9*`v-+yLdesVtcl~|A;t5$ktm)fN zF21l56(tWpN`O}|W(REGhTA^2iNo`hy(l-8oyWg{7=5e*c5qom}oiirO*G{;l zb^GcEc)i{|e_xa3vmY)0p9v*uh5tTWcHxUnj<9hqTd`)u{NHMZ-(UEwY}LE7{MbH! zE_rrU%~0d;>iy1Jl;uC~Iqbbw3u}fYW4E+8qh=W!XZOn&JwAR$&G3&Jr5@YTC(G|T zd)A%rkFOcFwtIF-@3*r2g));SyfLC?nD@|Qjh@OWYvZgfecU-+@2DBNeRbzg!!F43 z2PXdT)F<6*hHFO*3eCr5`I1V9e73h;&G31%n}$5SKFjB<>6E*pdCjok){i#7dtx~o z|NPz!=gi2h8K%yg{=rlCX8C|HVD*V5YliO%_nqHlQI-$;BC}xNuQkGXjYge>!KKbSR^KNZcBTU`&(Dq4vvV8b+6aRd*dX2E^+uYAC zotovhU0-q9>-(yQf+pt=oV_#4$M!m7${p*fhavsWe5vv!A{@(uUvV3myChz>~>FQzmXE%R5W@MIkt-hm8?|Z6;re~eA zZ`Yeyes9@*=T*D5dU$y6O=VC0BFnE_-L(3n=T#50M*lqSg6&zJ_u}3SUo@#67W{fl zmsT~7w9k2Wjh;DcDpwB^+U@;mcdIP_?TA$q-ukmzShZ}*mdCnh`N#>kSG;6(wXpk* z)s6nXH_K;!^wy~PvD$%iKgSmoPRR1E{b%<0V^Xz{xBL2d=?7U}Ew|*yzdcwjeBZml zz&1Z;`TC9Tm!ES>wQ%HX$Cln(s508A{^xv|)ddXw{F?nd!aOE}6 zv>R2SihcgpbGGf7(5GtXU1E8OsZF!|rTtr)Uw>}ZaMp-HH@(;?%NO4_tNZRoRm08C z%xQXU-zx}1Ad);&(rjK!}(sS>U)CoJvP$!3NNSceUAPP z@FVqi;_n9M@5x}CTUPad>*D0LqnClwObHxC&ms5IL~!B&wbtlaNZNVH{iTic+bGc={>}I z3C??q_ZXb_9Pd3i??v8|aNeW5SK({*-sL?E=RM7P8_s*3_dJ|=0P_NPZ_OKqY90aa zp?L=L4)}jGFJYbn-==xYK+S9512pep9t2;ic@py`c&_GE%(LKIH4nQ~^D_8Xnzu2J zgO}4hZ;j@C@ba1$F4a5{&ODNNCA_iboyxVBG*eU+W6i8Srnk4q;sa->h{D>lkmt@k@TheZ>nb?wF4kf27qw1f-3A||bsg(Gcss2FSr@|FXx+#<5?)v9OxB(7 zhFX`hPK8(1I+k@UoOLhjV0cNblUX;zSy!{phO-W5T@LT8bvx^LIO}}Y{cv;vbOLy5 z=?Lfw@YAF_phLh{OQ%4$fWIYO;~wc8@YAG&po_rok#2&H0zXGO3%Uz@iF6rs8aO%* zx(>X9bRTpecr)ol=tl5%(v@zM&IIo&9SU6vzDT+iIu`sy>0Ibu@JFPJp_9S;Nk>Ch zgQL5l!@<$%(Cy%3rR$;d!TU=GL>Gk5lx~QQ2>(htBf2Aeh;&JGO86-0nCP1D8PYum zO9zF2BApc76h1+^Dmp9tG3l`Avha_k+oI#bb1zOhFS;-M7wN+2#PF@skP8UOirK2fCmXZ-KGcTthE-Gyd;eE?o~9e?7!>{bc<0GcsLo8GpTvPuFM0U!U)# z>pA1E=gH~%&)@^_{XdZImyEw({*~^pjK9C6`)B<9I62+F8GrxwP4|1o-|r8n=R?Ln zA0{Ty-4q z%lP-*^z?qr-~;gcu1xRKjDMd>_s{tEZ>#ja&iMEBuSxgM`1kv%N%znA`G9o)jGsR| zoOJ(;pKrX9bpMQ>pX8+ZOvcY=j!wFN^z)y}X})AW0N+FXG{1^|epNfo$D*H)wNCT5 z=;v>3(|j-b`QE@ZKa75UDBVB$`Q-OW_m6)5xggC~qo1!z_m6&lE8Rch1MvGw_m6)5 z{B@ddM?c?|?jQa9{HCP)M?asJ?jQa9U%G$nCLe&$b7j)~qhDWyr29v|9+B=J{rW}g zo#@v)Unbo@9wi@uf44$fPsKIz0pKU3^;h)kubxTwkAA&YHm&a>J^)jedRl-?W~Mem&bit$(9m|NfTN%Ml-d@#m)Xb@c0Nt;eHZ zk6)A4?-3t>aXwADfBalN09^V&^!kAGgy{7I>Hg8{4`(LbKRzHIfbri>x_|WghV+o= z^^k!{_m5sb8JqN$xIsPub|63#V6zgP+uYGU9pOM0Qikb_m3|*eQa6M)8ah&0Ms{5`dhqG zJ^*}p(*5K8@&Vw}lkOk8JAH3%(*0u{`2f^EpY+3cuhS1#CcQEKAs>MHT}k(kqvQj? zPffaioFyLso||<4c%^&*_;*S7k6tgWnDo`SQ9c0m(qp67W2N6luir}dkN5zLBYik} zeOP*O+$|q~dg;&6>(3>V?jP|1sF%JSy}o^Y(!-o?H6B;o^5uX#-T!}T+0 z-V^Zws9%@*CE_yIFQIu=#0Q{Wx_|t`^7V!b7*E}y)a{V6C{bL2!AM#Y{CyDp~ zjH7vHY~uP)G*69{T)&Fuwec(0-=cYNe8cs_9GUuO;#$`~qj`2b%k|r6ULJe6{v7H4 zv99aq(Y!z61Mqukoe;Hd2ym?{B0d0I>yYUCk+g1!8(jaA);ZDlJ84}M7rFi@t)t>x z*H5K&SJXPpKBv}c5g&lhsdZiam+SA+IxuQoXya(z7=!B{(>gOA-HzSg}FAAtJ1Qomd*>-y!iu8vb&f1TFhQR{N+yVJTo;sY@Lp{d_5wsZY{(gosn z*B>YyA)ey;38g#4)~^3hIz{}+^(#vEk8ivFM(H5Y_d`lIiTD6~e(5Z+lk2yXE)$o^ z2cTX$PTb@AIi>r=OI-h_bfQ@3`bE`W8ZUGGrP86|HLf33x_?~d`d6iM#dBT1t8}sW zZ`U6y9qmBNa`Apz>2C38*MBRWE`H_ub*1Yam?szS@0AW1hr51Y>4xzO*FP+sF>ZAI z#?mF@aMzzK9Wy>JAArA$bpJTZ^*>7|jnVZ>OIMArxc+MCuyKUz$Chpzr@H=a>AbO& zd;mU=bm2JH^@mGGjvu&wa_P>ooa;ZAP90~tes$^E(f7AY2amrdUEJ

wkLx>UjFS5M%&(ie>y?>UjUs&&C>3Zkwy!X!zXAa=sTPt5r zW#gFxnD@@tUs<~TdL-}tvvj@oX1>15oCA2CTKRe~OV@*!=j+ETT|b_luQxO20G{Wc ze0`dw>(h1ldNxbfvqSUsZ+vjIk8ho?-!ta`o~K#f z`)AuS2Qa4}$kKj*J|Ro{1bY80?H}&Td;e@Ca{$jjC-42UwBMi)$- z?_07hm;-qJ=kne^o6a1-{Kvfa&(c2Ui@f*GmM{nK_)qiRKO4v#!2H#`_s;@z0CW1N zEbXKA&ikuuAaekZ-zV?8{(FDO=K$ts1{0 ze>R^vfO*Tj_s?1;{oc^L_s{NP4&d?g^8PTpHt7$a%KOHw2Xg?ApOp9h+3m~$%y-Fq z|Li;F0Oq6f-al)>9Kig!y!X%2zO+x?uV(i#2k?0M*eva1>2I^NzoqxjoCA0s`r$0? zhv}2EvzP;TJpFT)_Rsg_y?^E$z~kw+v$Ws7A@9Spv=660&(i+Pv$%H{+V+C&%^5`a}Hq6>nw8) zU{3F!rSmCX$C+~gkEi#~oCBEC`)AGp%<26z=K$vP{#iPY` zWUZ6=;M@88NY*f&C+6=v*?!6Vk@u-AomcX{mYtc*H&4yq2ebW>c__VqwqG(o<$X4D z4&e8J-aq4gI+$OQzmI31CG%X~_cP}J9?$1Q<{ZGB-am5=U{3F!-H^}TM*Cvy(q@w?>vC9>a={SsVPWzGRSp58y}m+Z&j zx-D}K;PG7NWwn$29`yd%ykvjK)A@dq%sGJP;kq+BHQ9f{b!t{S*{{NNZ8k62-@?C0USKXVS?zl(K3#=0Sxv#!XT z1DLZ8$-vkuHy7sm6jZp^Y|{}}7c?3iS~8SB!_Ie_P99hh#Y_yzfXx$LWC zzZ~o8?1^N59qaInb$L8L>-Nk!faiZH-|v@glkE4S7s$>^_6O13&Fhlgv4QUq3xdR-Ej&q?gIQN%m*b<7A7I{hahZ*{8|= zPkN&4vShy~`%ANC$^KG$sH{e^AC=xedq3H~O3#(Olk9h;7t8id_Q%qrWm&SHmfkJ9 zJlTIsPnVsU?AN8&%MM8P_tFDqwAeq(yctYfl2nI1EHBH7PO z@1MPq?0=>w&2CKgOVg`n(~|ww^sw2p$$o5l+pI;hf192+Ta)bfrWekxNcM-*BWF{S z{p9q{S-OSw06?#2nzi{XhBs|Llq7n~tyW>-YRyGY2sD>-nBJfVp4)w#)&{ z{r5I!4q)#0rw?-gbHAS}nFE;n{r~pPP5b}j*XhseCguPh@6Y!P<^blt9zJIdVD9Uu z4s!r=UvKv_2Z(t-m&zVw4q)!mIpzT7{=VGF9KhV)uNKS!%>8{_mH$4* z?}NX;Z!!n)cz@rAGY2sD{b4WW0Or1*^kxoV?)%RL%mK`OzheJ?{Ca$UYt06c)Zsq&H>En{WJFe$Mbmo;~c=_2j{(i z_7ige^ZWAs|Jmit0nEJ~JAyfYxz}&b0nGo+_y1?dF$XaB`p`Lmx!02u@_i+l`Iq_r z|LjBN0OqIX`~S0(nFE;rneYG4X5`nm@jPA+f0XZwiTT0#{{O6Ne*GMCueWP52k`t} zpF0OI_j^8q>HRb303J{8pE(CGcRlDFz})rYWy}H0>HRb30OqbwodcND z`)AGp%w7Mo|35Q#z3d#o-1W6{0CU&l&H>En{WIqP=JfuVa{%+CAIQw@6PyE>+dnu5 zFt@L84q$G-;T*u6-am5=U~Yfn9Kf93KXVRXPVb*N2QatKaSmW^|Kl9MoZdfk4q#62 zZ@-k8Cw)}R>HRb303J{8pE(CGw;yv3U~Zr09Kf93KXVRXPVb*N2Qataa}Ho`ALtyw z-2TuxfH}Q?<{ZGB-am5=U{3F!IR`MO_s^UInA?{+2Qa7i&zu98Cw**Yp7ggdr}xjC z19&{Wf94#(oZdfk4q#62Z~q+6Pw$^O2k?0NZRY^y^#1nY@jUkD&H+50-am5=U~WI} z9Kf93KXVRXepu!HKQpKI&zu98)B9(y<@XuHHRb30G^-wE;8=Jh{toEMs^-^0FU?Q>d)ES*MYAK^T+c2|Jf<|{{MLXD*1gT z*?0MUCo%8Ad}>@ipNjcJ=2@Nd`&we|>)zjkqD_CwyiX=`4q#62pE(CG_xI4>OLKbv zjQe;pbN2sd?EjCs?+d<9cs#v-<{ZGB`;MGH#`DnoXYBuv`FYGY+5aE&1DJ<82Z-lq ze(D^+d_sPomGjnko;j80voWXl&zu8z9(wdGDVs&hI;n=lPxe3BTs|DaM@p7~Rhhb9#UG zKg9fZ_DeViDBAR=ENs4F(|wg$wfsKIcs#v-<{ZG|2UXs`5s$B1dA~=@xi8fHAu*r8 zeiG*Zo}b=7+cUopH6CBL@_v<=FXFnkTfV;~=JfvVhlx48f97>`(WXCTVLtn9IF+U>j{WGuYJY=;vhMnVb(r}q)@i@y z_ie}X_h*0C;`~1En4iZw@Nd?Io@XKZ$L`PXBag?Q!8)^ce&2b_hgII67W1mZH{Hjc zUBSB6^Z&{Iw|V(}@bUOR*e~}!a{!NjmHl-W<@edgqczhcbaW`Cn|fTB%*%EGnu zAb;e~MTp1uuDpLT=8Y=vw~YA)dYK#Z=QPB8Y32Q#G5?I-XQ%u*5HTM_Ph@XYwCPV- zs86qC&t$#>J(P0*^F{Pl_E_f0e%H*rdFB1FF@K}-e%hEHUwQv+%)33eD(`=e`K^`rOUHah<^9z$ zA6-oG95WWRUJd(aE7&7Tt!^I4VmlgGS4<^AU|uao!w?pKfb$CdZD$Gm6d z{qQlru=4)-n0L>6fA`zReCorSp3CF@{Fv`mc|U*55AU_t<$d4&e0I^MKRd>g%sGJh^8EL!{E7TIK$-bM=5U+m&jE`0!_4i{-``W1^En6bJPnx(ItMV{ zhdH8i0Q1hw9hc|N0g8Wba=%i{>oeDM4&ZslG6&r*e-2PQ{&nW2>HBAu%J*Bv>R*+HFIU>0Os2>hjtEN-o5huYnk~KmG5_p zc~<%UxR_66j_w@5^W4hZ-8q2yD(3X*=k+HFd~NmtxG%u`*vj`0X6BQ5?oWEX zF^hc(1Cr~LU)aasz6Q^;mgoMY*FXPMzF#u_z1LR0zcS{%*@salxgML(zK#EP{Z{#Y z&v>45*%$J0a(y_R`$^O5$rU{JC%yhWmwhVf_3G@(_qWEscV*@KVPoEdeKYCxb8^3J zJpNAZ&pk1@K5xcze|ja?^LKIoZ+iWo+%Fu@PwyX|%bx=j^S9WCl&(iQvTrF}za;lN z$Md|)z9{DaMVtPVg_`W6at>gg+B~Md=K$uLZcJb}#o+se-$h&jD~xGCTNAM=-Z?$7V}bAV!=JP#q}^!}k! z{v4o~)B6YK07aYrl*Q-%xKGoZ-ao8K`kZAv_vf>G|9?C`y?<~H;CW{A+@FK<{r~a! zM|kef&iQkIVovWLoCA0sdjH@Yz}$VYJO?PAhu%N@n(zOQIlX^q$v#}qL+>A)1DNl| zzTUR^{{MI$djD`wzW+bw^!~v)fajUO^G15*`~Tzd^!~v)fXCDO2j>9hqj>I5%Y6TT zJkL`+_oqwJm!8P;RUS_I)gqqz^Fz|dKE(4|`X>GD6Fl#wW&XUE`1f{ZU-;?l6Zh-v z!9MZ}lRkM5ogT=Kvn>zJKQc=Jfu-Ie__Go-dTn7n0`@WgbuOADjc2e^vQ;NAdXN z`A9MMxj)VUJkL`+_oq$%bBM>&`}@46n0sG`a{$lNg6IA`oxd&$$Mf7D=Kvm0?;o54 znA7_Q=K$vP{=qqbx%a&|2Qc@!KkWa{%zf^Ua{zPi!*LE^?(@5x1DN~VALjt(^!~v) zfH}Q?a1LPZeMim#%zf^Ua{zOn`{NwIoZde;2Qc@!KRiDzGxt6#=K$v3m*pJ5oZde; z2Qc@!Kh6Qn>HULq0CRf(;2gl*=l(bcF!#PS=K$vP{=qqbx%b662Qc@!Kh6Qn>HULq z0CRf(;2gl5-aj}8F!#AX&H>DQ?vHZ-b9(>a9Kf93KR5?4_qjjL0nEMcaX9xun$!CS z=K$thSB0v~0nB~wk8=R?`aHkUIe@wMc{&F$_r6f)0Os`m!8w3Ay?;23`%uljPt`eq zIlX^y4q)zmu)F2^|1)#0n?sjm{|wjJ;eF-+9`Aj*CvcyxxzGJ^4q(o8e;AkS|DpE} z&H+50-ap*W9Kf93KR5?4XWbH<1DMnM$2owx_eIZ5_D8Xf3eEvMo^@An4q(nYExeNK z*P{3T@43^Pp6{A@JiUK#4q$#C&yRHuU{3EJoCBDT;rX;TB>U6o{eyD=k7wN*oCBEC z`v>O$=Jfu-Iec-{FLk`r1uYFlKqE1 zr(jmHU(x6Oyu%#8^V9o>A<2G7pPO(>vVZacp10g2*>6ejA2ufYGkuQ3vSdFey?^*T z+5hQtBCbsKi~4-&;$(lR&!MQD>_?@y3X7Bdt3KyqQnKIG=VG)-_Q(1hjk(ML{J#0z zjT4jow?3zX=K#h1u0GeJb+W(L=YR}I_5=Iekg3W3VV^UyGTCqJb4jWs`;&c+$$80s zW}kcVOtSyk=cL@3?3ebrDzlRP)jo&i?qol<&u!uP?eY8Ib6z$k`@MZGOz&iWxX+P! zJ=st0b7yv94&cwl=hQT24q)zcZN5tOxBDENe#w4#pPMr@*+1`dcKR|0@ccfP=M&}t z=03+~2y+1Q&GY}GXd74+ZVk7ETf(hi`v2$uzD4r?=lMLp|GVe$@9})P$9Z0l_v`Vz zHTnDMC9IFj2DmK7WeF}D;<6Dg8{@JGE}P=A6qh^Ua%Wubg3DcT*$kJv;j%d{cgJN5 zT<(F(mbh$%%htHu6PImp*%p_3;j$er%W%0jF89IZzPQ{Em+f)6KQ0fzB{#wp$piV{ zWk+0=<1*m#AY69B<-xe@jLSoC*#(z};_@(DcEx2kTpo_g?zlVxmq+6AC|n+m%VThP zEH013xa^I~KDayum#5;gFD_5R<>|OQ1DE}9c_uE; z!sXexJO`KO;_^IPo{!55aCspv`{VK=Tn@nH#kjl#mzUylATBS%<>k1%0+)kuIT)8$ z;_@n7UX9CZaCt2*ufydKTwagMp|~7|%i*}Z0hc3iITDvQ;_@b3j>6^5xV#0Ix8ia% zE^ou-?YO)Hmv`dwE?nM?%X@G+2AB8Z@;+SNkIS*Rd;pi@aQPrEAHwCsxO@bc<8k>Y zE+50?1YACj%O`O8Brc!A<kIMzP`~a69;&LG_7vb_FTrS4t$GH3imrHQ@DK3}d@-tj6!{z6=T#m~x zaJd4PU*hsBTz-wqmAL!{m#c93EiS*q<@dP!0hj;7aoGTu#kefN zWkXyx!ewJzHo;|6T$bW;CtU7~%Uy7}D=wSiayMKy$K~$0Y=O%?aM==FSbskrQm%hPaqIxf$^Wj|b=iOaKac{VQ3!R5KQJP()WZ^7lQxEzhk+i-b1F7LqQow&RUmv`gx z9$b#W<-NGP5104jax5+%z~wkxK8VYQaQQGUAHn5#Tt14+$8b3TmyhG}30yvj%cpSp zG%hFN@)=xC!sWBLoQ%uoa5)8+Q*k*Bm(SyJIxb(pxcms0i*fleEg&BKXADomw)1N z11|r<xImpkCH7A|+hWo=y6!DU@s*286eTsFXEF)mAR*$|hFaM>7_O>o&1m!-Je z370$Lau;0gipyrW+zpq_ak)D#Ti|jJT(-nzD_pk5<({}~gUhzK+zXfOa9M`Sy>Yn@ zF89Udez|J}*$UxV!+D7vi!%E-%9609;;-%S&*1 zDJ}=%@-kdrj>{`>IS7}7ad{;!ufpZkxV#3J*W&UzTn@qI^|&00%VD@2j>{WxIRcj> zad{&yZ^Gp$T;7b!TX18<=wcv2bW`Tc`q*S!{zR<~0+&zX@+n+CjmwF+dFAWroXFarqi9U&rMexO@|rb8-0=F5kxGJGh*O%lWu`7nkqh@_k$`z~u+H{1BH5 zak&VWAK`K_Egg3-*Nc|F4yDoPh4)m z~dk$xNM5cQe5tY z%bjt#3oduXWiwpvhRf!-+#Q!KaJdIATjH`6E?eVrPh7UaWm{bCh0AuhEW_pAxZDSq z`{HsxT(-yM{9k{#`mv`avZd~4j%Q3jT7nk?p@_t;7#pMII z9EZyXarqD~AI9Y)xEznmM{)TWE+^pfaa=xu%O`R96fU2}FAWroXFarqi9U&rMe zxO@|rb8-0=F5kxGJGh*O%lWu`7nkqh@_k$`z~u+H{1BH5ak&VWAK`K_Egg3-*Nc|F4yDoPh4)matmB;iOa2Uxiv1g!R5BNtcJ_&aJfA$tK+f;E^Fd)2VB;|<&LpCRhs)z}c>*p^#N|o2?19UZaoH1>y>QtZmwj+~3NBB@WnWyLhRf4&c?K@~;qpve zo`uV^ad{3d&&B0=xI7=17vS^v-;Bq7`Z^Y$IxEzJcn{jyyE^o!< zXk6Zg%iD2z2QKf#@w!aQOi)Kg8uiTrR@pN4Q*!%a3vS2`-o5@>5(c#pP$XT!zcfak(6q zU*K{DF2BU(SGfEdmn(7k4K7#V@>^Vfhs*DA`2#Nhhs)Kt{1KNw;qqr({({RjxLk|N zUvaq(m%riicU=B~%k{YY6PFus`4=uX;_`1?{)5YZaaptl>wmae!Ya7j9G6vbxdkq_ z#N}4F+!~kL;Bs4BR>S3XxZEC>)p1z^mo;&@11@Xfaz|X&#$_E`*2QH#T-L{B16&s4 zvILh6aoGr$jd9romrZe5ip!mFxicJ}yW_G2F89D?OI)_XWoumS ziOV*)Y>UgiaM=!*Ww_iMm;2yyUtI2o%l5e3AD0K50+&bP@+e#$jmu+jc`PoE!{za~JOP&{;_@V1 z_Q2)Ixa^6`UbyUy%Raa~1(&DdvM(-A!{zC?JOh{gaCs&!&%))|xI71!=i>4_T%M21 z3vhWMF8ky1B3ur@<;A$X1ecfMav&}*!{z0;yaJbla5)&4SK{(2TwaaKYjAljF0aGo z5L{l5%b~a&hRfl&yaAUZa5)l}H{$XpT#mx!&A7Y;m$%|_G%jz$C@(El%iOZ*O`7|yk;_?|>PQvB0xSWj3=Wsa%ms4>$4VTa3ayl+wz~u~FzKF}2 zxO@qhvvBz`E@$KN6{2mtW&@B`&|g{*B9jaQQDTi?-y04{nyQ3NAOtWmQ~mfy*s%xfL$A z#^pA++!mMBaJd~Wx5s65T-Ly4ONt%m&Ld&!DT~S zHo|3NTsFaFQ(TtfawlBwjLThcxhpQ4;c_=zHpk`exNL#TJ#g6)m#uKw8kc+GvJEcV z;&Lxsw!>u^F89XeKDgW$m;2$eJudghF2$!93c`zs$4=zu^<*B&ri_6n+c{(o7z-2#No{7t|aCtT^&%x!nxI7P+=i~AMTwaLF z{u@;)m)GNR zC@zQLayTw;z~u;Bj>P4SxV#CMqi}gME^oo*t+*VG%iC~yJ1+0Q<(;^^3zv7}@*Z4{ z!R5WUybqW6<8mx6AHd}}Tt0})hj95YE+4_=cw9b;%g1mz0hf>C@(El%iOZ*O`7|yk z;_?|>PQvB0xSWj3=Wsa%ms4>$4VTa3ayl+wz~u~FzKF}2xO@qhvvBz`E@$KN6{2mtW&@B`&|g{*B9jaQQDTi?(9@4>wC#1(%!SvMMgOz~z>>+zOXl<8m8ZZi~xmxZDnx+vBo2 zE^FYjCN6itWi4Fph|Aiztb@zCxU7fE`nYU>%VJ!X;Ibhu8{x7sE}P)8DK1NKxf3pT z#^o-!+!dG2aJd^Uo8xkKT(-dF9=L3Y%T~B-jmtf8*#?(wak&>R+u^bdmwV%KA6)K> z%l&ZK9+&&$@&H_Rz~zCs?1;;9Tn1bogv(C2JQ$aqad`+XyWsLrTpotYuK53Pbyne8 z6x%xT-L;8EnL>dWgT4B#brHQ*2iT7TsFjIBV0Dd zWfNRB#bq;GHpgWPT(-nzD_pk5WgA?!#brBOw#Q`$Tz14|CtP;MWfxp_#bq~KcE@E8 zT=v9eFI@J zoQunOxSWs61-M*@%SE_cjLRjsT#CzOxLl6Q6}Vi9%T>7i5tl#Vay2g3;BqZ4*WvPK zT>gT~^|;)C%Z<3)gv-si+=9!kxZH-z?YP{5%bmE~h0EQz+=I)#xZH=!{kS}U%Y(Q) zgiHUsr3d+K++pVO2riG}@)$0UGj~3@*>&@>g7*!{vEgUclu=TwcQE zWn5mtkEnNPE%iFlTgUi2h`42At#pPXGCb)+m zNHAewB3vfMWfEK_#bq*FCdcJHxJ-e|dvTc(m-pfFeq26)%T%~bjmrmdnFg0>ahVR6 z58?7*T&Bn6Be;ANmyhA{aa=xu%O`R96fU2}H6)wNVWd>Yk#APO2X2xX}TxP{(He6=MWe!~C#APmA=Eh|nT;|1PK3wLox&Tvo+p zHC$H5Wer@`#APj9*2ZNWT-L>9JzUnuWdmF`#APE~HpXQWTsFmJGh8;uWeZ%k#APd7 zw#H=}T(-q!J6yKMWd~e##APR3cE)8FTz17}H(YkdWe;5T#APpB_Qqu&T=vCfKV0_5 zhE}T#m)%H@N&3m*3&?dtCm2%W=3I zkIM{RioQca>xSWm4Ik=pQ%Xzq*kIMzPT!_m>xLl0O zCAeIQ%VoG+j>{FeT#3t7xcm{9KjCsUF4y34EiTvL@@HKBg3I-|+x^@)|C$gQ} zKXG{rmw(~%HZJer@^4)JgUf$$c^8)nQtUDqN<<<%76PgUhtIOoz*daQQGU)8q0HTt14+$8h;LE}y{Vlel~e zmrvvJ8C*V#%ja{2mtW&D11>Y-G7~N{<1z~_v*I!v zF0*BH=F6-m6 z0WKTjvJoyD11>w_vJ);l~&TrS1sGF&dl_O z<$hcqz~w<)9>V2eTpq#YQCuFw<#Ak|z~xC?p2Fp6T%N(@SzP{#%X7FqkIM_Vyok$7 zxV((ZE4aLh%inN$4VTw(`8zIe;PNIe|G?#+xV(kSzi@dQmv?aaH!lCd<-fSRi^~M} z^80_7Ffb7=6XP-oE|cOi87`CK@*Z5Kz~#NTOo_|;aCtv2AHZcQT&Bk5gSbqC%e1&m zhs%d>`7kciaqi@1CVm;QH4 zY4~C1dzt*)UVA#`3B%sQxbJb|<1Z7$yOiv`%pYIAg3DKN`5G=?$K@Nid=r;%;qq-< zzJtqmarquD-^b+#xcm^8AK~(2Tz-PfPjUGfE%c3kGbWlmh?!ewq;=D}rNT;{`Neq0v7WkFmP!ewDx7QtmvTo%J+aa@+b zWl3C?!ewb(mceCNT$aOSd0bY&Wkp<8!ewP#R>5UeTvo$nbzIiKWldbx!ewn-*1=_6 zT-L*7eOxxcWkXyx!ewJzHo;|6TsFgHb6mE-WlLPP!ewh*w!vjvT(-kydt7$FWk+0g z!ewV%cEM#=Tz11{cU<{dm+={9cJc-LwxIB%^Gq^m9%U^MM4wvU~ zc>$Lfad`=smvMOomsfH58!oTm@;WYm$K?%N-o)h}xcn2Bw{ZCvE^p)V4le)3mu zOSpU)m#^URRb0M?%hz%F1}@*karp%wP*mz8i? z8JAUXSrwPna9JIfHE>xIm$h(N8<%x(Sr?b}a9JOh4RF~ImyK}Q7?(|O*%X(}aM>J} zEpXWqm#uKw8kcQw*%p`WaM>Q09dOwZmz{9g8JAsf*%g=FaM>M~J#g6*m%VV=8<%}> z*%z1naM>T118_MImxFLQ7?(qEITV+}a5)^8BXBtqm!ohw8kb{mITn}S;PP8seuvBN zarpx-$Ki53E+^n}A}%N4axyNb;BqQ1r{QusE@$9!CN5{;ayBmK;Bqc5=izcbE*Ic( zAubo;axpHK;BqN0m*H|bE?3}kB`#Ot@<&|$gv-^qT!YKCxLk+JpKk> zauY5$<8liwx8iaeF1O=y2QGKwau+Ul<8lu!_u_INF8AZ|04@*W@(?Z$gg3Yq-3Q%inQ% z1D7{(`3Ell#N{nq{)NlixV(eQzj65wF8{^lU0f#c|G0$-0~6sgF)owfGAS;T;W9Zc z@4;mXT;7Yzl(@VPm-plH0bHiSWoleLh|4s%OpD8OxO@ng592aDE+4_=qquwwmyhG} z30yvj%cpSpG%laP<+Hea4wuj4@&#PJh|8C7`7$nF!R4#Cd<~bcJ1%qJGAAx`;W9Ta^WZWsF7x3sKQ0U4vLG%C;j%C;i{P>-E{ox^I4(=z zvLr4`;j%O?%iyvsF3aJvJT5EXvLY@k;j%I=tKhOKF00|PIxcJAvL-HT;j%U^>)^63 zF6-g4J}w*JvLP-T;j%FyWp}bF1z8fJ1%?RvL`Nk;j%X_`{1%KF8krKKQ0I0av&}T;c_r8hv0H3E{EZA zI4(!vawINC;c_%C$KY}-F2BL$x48Tcm*3;^2V9QB<#=39z~w|-PQvA6Tu#B|R9sHO z<#b%mz~xL_&cfwvT+YGeTwKn><$PQ&z~w?*F2dzvTrR=oQd};><#JrEz~xF@uEOPy zxa7A72_E9V*ZhgOT#d^$xLk|Nb-4T)m%re0JuWxkaw9G`;c_!Bx8QOsF1O)wJ1%$N zawjf#;c_=F_uz6bF8ASbKQ0g8@*pk`;qovpkKpnsE|1~zI4)1%@+2-#;qo*t&*1Vb zE`P=4Ib5E{xO9hWz7c@vj^;POvg-ooWyxV(+a zJGlHCm;d1MUtHeBWdc8-7bXl$gv-RZOoGd#xJ-u21ecHE@-bXKj>{)-`6Mo%!sXMrd6aQPxGU&7_fxO@eduj2AGT)vLWH*onTF5klC+qirOm+#{8JzTzz%MWn*Aud0{ z<;S@E1ec%U@-tk1j>|7_`6VvD!sXYv%z(>`xXgsh%(%>g%dEJ}hRf`@%z?|CxXgvi z+_=nx%e=VEhs*r9EP%^`xGaRr!niDg%c8g}hRfo(EP>0CxGaUs(zq;x%d)sEhs*M~ ztbogkxU7WB%DAk8%c{7nhRf=>tbxm#xU7ZC+PJKP%euI%hs*l7Y=Fy#xNL;W#<*;P z%ci(&hRf!-Y=O&`xNL>X*0^kg%eJ^|hs*Z3?10OTxa@?>&baJ?%dWWWhRg1_?19Uk zxa@_?-ni_8%f7hmhs*xB9DvJ#xEzGb!MGfP%b~a&hRfl&9D&P`xEzJc(YPFg%dxop z2AALB@;h99kINr$IS!ZOaXA5(6LC2Smy>Zh1(#ECISrT7aXAB*GjTZ!m$Pv>2bXhk zIS-ffak&7O3vsy!my2<^1eZ&3xeS-fak&DQD{;9Bmp|h2CtR+^eT>g&B8@RlQ%Rg}WCoXT{@-JN8#^oJc{*B9jaQQDT@8U9n-vAC11}4H~Vq7M{ zWl~%w!)0<@-h;~&xV#saDRFrpF7LRTt0%! zM{)TWE+5C`6S#a5mrvpHX7%c3kGbWlmh?!ewq;=D}rNT;{`Neq0v7WkFmP!ewDx7Qtmv zTo%J+aa@+bWl3C?!ewb(mceCNT$aOSd0bY&Wkp<8!ewP#R>5UeTvo$nbzIiKWldbx z!ewn-*1=_6T-L*7eOxxcWkXyx!ewJzHo;|6TsFgHb6mE-WlLPP!ewh*w!vjvT(-ky zdt7$FWk+0g!ewV%cEM#=Tz11{cU<{dm z+={9cJc-LwxIB%^Gq^m9 z%U^MM4wvU~c>$Lfad`=smvMOomsfH58!oTm@;WYm$K?%N-o)h}xcn2Bw{ZCvE^p)V z4le)3V5drtGIj(m#^dU4P3s7%eQd(HZI@6<-53i50~%b@&jCch|7;~ z`7tg(!R4p8{0x_$MkaQQVZGvG2KE;HdWGcL2>GAk~#;W9febKo*3E_2~B zH!kzwGA}Ok;W9ri3*fRKE(_tZFfNPWvM4T#;j%a`OW?93E=%FEG%m~FvMesk;j%m~ zE8wysE-T@(GA^s&vMMgC;j%g|Yv8gbE^FbkHZJSnvMw&`;j%t18{o1bE*s&pF)o|n zvMDZ`;j%d{Ti~)KE?eQUH7?uWvMny#;j%q0JK(Y-E<53}GcLQ}vMVmT;j%j}d*HGs zE_>m!H!l0&vM(VG%m;Bax5;t z!R5EO{0^7?ci_Gc*!TbXo?qYpD}7IJ68`&fUN@D1kGcAf_N443hI|}$0$z*#AMMH5 zlRhWz+x_$K-anrA((avqKK^-oFYVs>$M=uty|jB5|N8mYRmQ)b-|{yg{`dLc(VhGE z%l=K6+rOtfm-eyDrTqu`7^siKM?-x)J__m+@R3lTh>w8!Bz!p3C*#AQJ_R2N^{My} zs87QOLw!0v2I?9mP+y4m zfchf5JJc8B-Jre%?+W#$co(QI!#hKLIo=8CEAWm`Ux{~s`YOCV)PKa=LH#GZE!0=z zZJ@peZw>Xecq^!{!&^fAXS@Z}f5DqWeLda`>KpK;P~V6*f%+!AG1NEXji9~-ZwU3R zcmt?!!|OwRJ6;d!JMg+t--*|O`YyaS)OX{xpuPvM3H7~r4XE$Kt3!Q1UJdF8@TyQh zh*yF7A-pow595`fegv-w^`m$Ns2{`2L;W~j4(ccHvQR&Xmx1~zyfoBL)p|B4rf`Z>H9)X(EZp?(1`0`-e{VW?li3qk!dUJ&Y6@B&c3isy&=Z+JeaU&Hf4 z{W_ip>c8W;p?(9;1@)VFPN@HZ=Yaa3cy_4Y!m~mBFFY&MZ{t~@eh1GC^}q2x}K2!C0$QM|AMY3rhiV?lh8k->q+UK()DEYPw4u`xZOMF zzw_(%yy^e{3*_s#{YIF(XV1f(wBO_|H*@_DdM>*DC;e8K$DWfr@7@24JLl4#gF9)z z&0Tip`W^b;VIF%n?!0&ZAMTt>dsgnG{a@~~FxT(W6QutC-zVp>XXehi`w6*oF727P zllDa1Wn``=rY8yW*fVhFz57YIb1v;)b0_V|xJwS5$Np8gcmE#loJ;$c+(~;1?!I8I z-%C#!=COayo%inF$DMO&|BO3nzn{AYp!3*24fpP+;?B9Wf5M%#r{?Zs=K6#5G+`e5 zN8EYuep>FFOZ$i1Nqai(9)iwe{~+AE|1fvXrTu;Gq&+=%k3i?KzZdS^f0R4t(*7=Y z(*78Ck3;9NzZ34=e}X&b(*8Dg(*7iOPeJFgzZLG?f0{ez(*7oQ(*6v0&qC+1zY*@; ze~vrn(*8Pk(*8VmFF@z9zZUM@e~~-q(*7!U(*6>6FGJ_CzY^}u=IupzCkZ-wyNGpXbhd_ut{pxwOB_{5kIR_vp{k z_4nx?gn8`GaOb`IA9Ckh+CO6cH23<)^rz_hC-hIlJoYEK^WOc>xN|P;pEG}gd;JUg zmth|JNm-ffFllHH<%K)9n{%E*&KO=X}r9Bh#N4VEB)3bzm?CH7l-u2Y0t+z4flF} zdVw&Hyya4-G+4s)9I4{Wl752SzFU||Gf0=#n+>7(V>|bKvJNM$e2>Tb=_s+dI zFUtM}_Puj2&Wo{so_+7!i}T{_pJU%U_u{++`@gd9oqKU!lKr#nd*@!9mty}6``)=1 z=cU;{&AxZ;#d#U_PqFWvdvRWt{gdo_=U$wbWB&yE-nkd&<=H>ZzIX1$c?I^5vG1LG zabA)AqwIUr?4N==wDJV7fk?K8UW*pbw<$GwB29`Yd{Xx;~rUkFL+5_oeG|>3!(>JbG`s-V3*T z=iDC`-?8^C&$0ja<0{O*$G>6!zdL%I|3DwhzIX1$`8f8+uKYUYt*0 ze-!)PxfkaX*&oTickadcB=$$J@11*bKAHXD?0e^)^ZCp_XWze%_61??o_!(nMPV=Y z#bNGq>`Rz04STUK3v-`i|BU|~(!QL#70`L?E5qD9`zq!?hP~K-3Ui-hU(I|?*o%E_ znEM?2I_979zgz#A{!5t0zCO%-j(r33jbShLO=0eH?4R)GC+(ZL+X9`(zBSC>u&Rm-ao}?S;-`-xucY*+1m@(!QU&1JHTw z2gBSw`yu8Z@W;^)(~pFC>_@}g=h)xpxzc`&yW`M#>?gw9J^On+U)oP{cM3X>{oQcy z{%P)@RWWz5D-g=Um$VW&R@f`dxa02mk-ygE^1= z1@4@?pO8D}(w>O<^W5u+>Ce&iB=n?V9{aQ0dGCHQ?wm_|a^}x)uiryY5$3T!&7Jq| z-^-nIX@814X-~=Beb9OAPlkK<@8{0Bv_HW73GVe&^vCIXYWjm=9{Xe5dGCH2?wm_| zTIP>(ucxCwLf0RnKOE+!oB-XbLU*zALLHjpW*IV=sfn+;okk{xN|P;skoE&=ec_UI*SMopWiw&YiU9 z;qDr9Juf|9n8*Gbciy|7pF8K$ew90EFTmXu=6XT;Wx8I7eu=IZreCD%Md%mkdQtj$ zx?YTaj;Tw9Q`<5FHb*4*DKJE z()Eh;BXqqI{V-jxOg}`|tI!Y9^{Vs(biEpVKV7d*-$&PL(D%~yn)E$%y%v2pU9U~w zMc3=lchdE`^c{4)9(_AquTS4b*Bj8c()EV)Ep)vReKTEeOy5M;o6tAX^``UN2Yn%3?@3=k*L%_D)Aio;d33!G zeJ)+^OP@p6`_X69_5So(bbSDQCS4y$pF!6L(Wle(!Src#eF%LjT^~xHLf41UC)4%e z^htDm1brf1A4#7;*GJLE)AiBxaddqQ{Rg@}mi|3m|Azh@UH_K;EnWYP{taFKp8i9a z$3E6S4s?GUch04K4ExePp1aY^^$GM*bbTUyBwe3GA3@hA(}&abDfD47|eJQ;wU0+7;Lf4nmJJa zy)j+iLT^OZx6&KZ^=LOVagU=_Tm;IeKxrex6>8u3w-RrRx{zMdsRRo==yK;{B->qJs(}aPR~o%f2Zf6>o@4R>H1B2F1r2)JttlNlb(aF z-=b%y>wnR+(e>N(taSYjJqum`o1U4j|3lA2*Z-wwr0aL-8R&X~H2?qq`M##>3F%+a z^+fb9>3U-N7j!)d{d2mWl>QlAPe%Wgt|zB|Lf7x1rwH@dKjzMR_wVJ-xwL=8owTRq z?nCDKef0aoJoXQ`^WOajxN|P;?{g>ZsknQOxt^N-V3^1LE_dF$pN2c<(*6#2(w>&P zx0&nd=nsW?>~C@Bz55Sy=Um#~w{So@3VIKSI+ZV)o}0rliWF%_E)%*_NTaenYsQn{h2V2{Uz?acmG-LoJ;$Q+)4X$+&vGS$NoaN zcmDikNwGT@BUldIhXb)xRds`xqAmXkNxp*@BX{oIhXdwxRdtxxO*Qu zkNweb@BRneIhXcFxRdq|x%&t@k3D_3cmHGVoJ;$|+)4W<+sr9CNk(q4qSqR@HlNy5GR#kg}W z?ZufV=3XyBFB#^sC*sa~_e*i-T-r-BPsqJqhF&(zV^6@H_wJYD&bhRgXMUIW6zUb| z6~jFCf4TGC{Yu<9m-foc|KVP*La!R;u~!RopJV@<=Sq8Z?rK2ivEK>z?$_kbxwO|} zew%x}HoZ=m$6hzgeUAMvo-6J3xT_DH$9^l^yWfC2=hEJgd84owd*d+oIrb*Zn})sE zn}xa0u{USlBJ9Q9GR%FBy%qD;VK4SJVeWJ6ZJD|Mj$=h(Y3?;iGI?-Axc$KI29udo+;?=bf{_CCz}hP~MPg}KkM_h&vJ?8QDX z%zci15c9!dFZLl}?sM!znGXwlu@4V(pJN}vd}P>*eN>qH9Q$bIW5QnSW5e9%*uP=^ zZP<(byD;}T_H6vT^(WM`<9|Rs2YwUkIq@4%&xQXE_1yS%sOQ12K|L@28`SgRSD~IC zzXJ6F_+_XU#4ka;5PlKrh4BkeFM^+kdQtow)QjQ2LcKVC7V0JNGf*#ypN4uV{1ntn z<0qkB20sDyviNbRm&1=iy*z#t>J{)KP_Kv|hI%FZ5Y#K<2ccdCKLGWr_ zLA^IV7wUcRIZ*G5&xU$Gd=}LE<1?W?0G|Q%f%tT&55lKGeK0;1>O=4;P#=mAe|0V3jzCO%-j(r33jbShLO=0eH z?3l67JpK&7E^;-@|-w*o!?g z&-dQ_ecU;h_WjH=ajzes9}M%@Gjiv>`-ix5F71bz9|?Q0XW;qXyML5B=hA+R`Pba* z$LS}+Joc}+^WOcF+&P!_Q_R2QUO!Dg6Xvmh!JYT+pXJWEwExQdbMEzX^z&gJ`)AyF z@BRhuoJ;#f=AUw}U!q?Q^VmP(&U^Q-aOYgwKju!_uX6VrbRPRh;okjg+&P!_>&!po zUjLnbBg|v}fIIKqzsa3*X@8$PY5#+}KcVy3-wXHd-{Q`>wExBYUGDYU^mpj`9s1v4 z9{bzedGG!|+&P!_f0@6@S9U_wVP< zxwOB)owPr|T`K52_UFUB`>DBeF73~8C+!b%mj*hI{n>Euep>FFOZzk2Nqai(9)iwe ze>&W||1fvXrTr=Hq&+=%k3i?KKN;@bf0R4t(*6W@(*78Ck3;9NKOXM?+xMRNe|z-D z=<*5rlkidI`cw3$!#ws!xbxoqXSj1N?diFb_Gh_!4mywh;c)N%^V~U?_J_EW_7}KI z$6SAr{!*C7o|ZfB-G7-o=hB{rJ86G~yH}y}*dGk{?!U&Jb7@b_owUEsT`K1K8}v8B zJoX2;^WOcpxN|P;_j4!hZ*zAabNwCqyI~%CO76UO|2^)UOZ&atN&EZUrC_dqK>skz zW50(x@7@21JLl4#oI7dv^BXTtx1dS?7@sAs|NKs_sd8|vBczo4ESzXkOi_@7YEiT?rh zT=-3>=f-b9JrDjn)brxkp`H)F2KD^-Z%{9QUxj)>{0h_y;g_La7{3JdBKSq97sW3? zy%>HT>c#PMP%nZ13iXosS*Vx7&p^F2ej4g!@KaDPi=TvgIs63F%j3tPUI9M_^@{jW zs8_;|K)o`480uBUHp4P_K*c zgnB)E2h{81+o9e7-v;%D_*SSl!nZ)ZF}@kK*V^Q16JZgnB1@1=Ks^%c0%{Uk3HA z_)@5M!n^ z)BQS-UuW{`NxEN`^6O829m}ti`Smc}udDg>Gr#`l*BAXdqF)E~>#Tk~)vwF?bzi@} z>(`0>I<#MJ_UqOe_;sXT|IUPGhJL;OJN$b%Huw+t7^siKM?-x)J__m+@R3lTh>w8! zBz!p3C*#AQJ_R2N^{My}s87QOLw!0v2I?9mP+y4mfchf5JJc8B-Jre%?+W#$co(QI!#hKLIo=8CEAWm`Ux{~s z`YOCV)PKa=LH#GZE!0=zZJ@peZw>Xecq^!{!&^fAXS@Z}f5DqWeLda`>KpK;P~V6* zf%+!AG1NEXji9~-ZwU3Rcmt?!!|OwRJ6;d!JMg+t--*|O`YyaS)OX{xpuPvM3H7~r z4XE$Kt3!Q1UJdF8@TyQhh*yF7A-pow595`fegv-w^`m$Ns2{`2L;W~j4(ccHvQR&X zmx1~zyfoBL)p|B4rf`Z>H9)X(EZp?(1`0`-e{VW?li3qk!dUJ&Y6 z@B&c3isy&=Z+JeaU&Hf4{W_ip>c8W;p?(9;1@)VFPN@HZ=Yaa3cy_4Y!n48K@D8lS z{~z}c{x7Udzl-~QQRV4=AC=#yRhsVib@_c`#p!;3o8LcIfbRF(`Tcx(=(^wkmz(bQ z3;O+t+3C99znG2g_dELil$q$d-+!5r{v6*Y=J#{INca2EUt#_f-S5};eE{j{zE{Ba z7Nn*7{sZ5ykc#em8hj5#O1kfp@O>4@>ALT?NJiIvA4XET?)x*6&~@Lpk(jRgevU+R z-S>GUr0W^@>yv=4XQcaH5Iqy!_lW43>ArVF&qDV-C3;r6?={i0(R~k!o}KP{Q}i5k z-?O6Ur2AeLJr~{gxahg*zV}7XL-#!~dS1HkmC^IjeGiSEpYD5W^a6C>bE6le`(7Nq z5Z(9a=!NONcSkQm_dPv&QM&K-(TmZ250GA*?t6pu5_I1)q?e@oULw5|-S-&jrRl!+ zNH0V8JxO|5y6;ue%h7!glU|x&SET!1D7_Nh_eklL>ArVLuR`}dReDvr z@3qpa(R~k=UY+iHv-BEt-?OFHr2Aejy%yc~c`(8A?5#9Hw>5b{WcTI0X_dRWTQ@ZbU)0@$K51ihd?tA0(7Ifb; zr?;g0UOK%M-S^n(t?9n^PH#i^J$ZUty6@G~+tGawpWdGCd;9bbbl>x*ccic4JyCim z`bxUqnZAOqccCw*>s{%~=z2H$Qo7!qzJ#v#pf9HDJ?V?+dN2Ayy55_GSA%Kl)s{-k&~)t`DHkrt1Ugv*`LD`b@e$m_CE951~(|>qF_&==w1FRJuN#K83E2 zpiidjBk7aq`Y8HDx;~mdfv%6CkEefwe+$2(|L4x>-{IfGZ|M3D^s#h(9DNL3A5R}m z*C)_N(e;V+k#v0$eFR;fOdn3yr_hJd^{MosbbT6q2wk5}A57P0&5leHMKH zU7t^`-Q# zbbT4U3teAM?@ZTM&^yufmGq8ueHFa}UH_5Zp058yZ%5Zx)7#SZHS{)geJ#B;U0+9U zMc03(x1{U8&|A>;_4MX+eFME2UEfG=O4m2ho6z;m^u~033%wCt-%4*t*SFCd(Dm)~ z`gDB#O4kq3 ztI+ks^vZPo2)zwZpMTDqTW=jZ07qU(On-UD<$575sWOiuUn3H|)T#B|-yOH4%f^A-L4Mn9*~&wKRq zB>mh-KY!BCxAb!?{hUod&(qKC^m9f1{8B%s)X#zS^Je{ASwBbkGyXdJ`Nn?k^A~hK zw_4i2Wd2pyi~Z{`_c`_q%rl0)*fWK>&#`A_o+a$Xo;A#Ujy)Um>|rnV9AWNr>^Yg| z3VX5V4s)Ml&%-=#*o!@1nEM=ie&z+jUhD@~yO=h$m8uO0ScuM_4z$6l9ty|5R1{V?}A_6E!whP~Jug}KkM zH)h@>?8V+R%zci%8T000FZLE;?sM!dnYRjiv9}I$pJQ*sylvQvy;33+=z4$p zY`Q*xK8vmoq|c=5gXlBp`e6EWx;}(Hjjj)+Po?X_=u_zWaQbArK7u}pu8*Wor0b*T z6X^PA`gpoNhCYry8J_}&)1`eFUD~H|Hoe$s==x0hK)ODQK7g*z zruV1obLjo(`doTnx;~FSKg?ra5avF|zL5E%uowH{F!wq3CCrzGz1Ww9xzDjLXTBor z#lAAkeU5z<^B==r>_3IM&#|v&z9#I&zBbH#j(r{TpTl14zl6EZv9D*oA?(GzG0c6A zeG~J|VK4SAVeWJ6TbXYQd$Dg1bDv}1!F*@fi+xv^`yBgj=6k|k?0du9=h*i#-yim3 zKM>|V$9|Ccp|BVG;V}0(_9M)XhP~L2g}KkMA7_3d?8SaE%zcji6!X(zFZMHG?sM#C zng1I0Vm}w=KF5BZ`Gv3-`^7N7f?o~Y=iB@6^RKl3#@#jOJof8h?w^H;Q z=h**X{%6>W{Z^R!9Q$9)Z->3u?}WL}vH#8dpRgDEzhUlk?018bG4uqZtZo)+e#r^6qDx#$n$>0vhdBlx2*6a5MNN%%Sa zDg0^pFAL?nN6P>Iw35KN-93AJt{(q9^!Vqk$N!yr{Oh8}zm9tR z>#oPYPkQ|Os>dG(di-&t#~){U{BfzrAIEz9aj(aJPW1TCl^*{&)Z;(5di>{Hk3TQy z@#hge{=B2dpQrTr^O_!i9@OK{n|l0tR*yd~>+$DtJ^s9}$A3@g@!u<=uixX>Q}p=t7d?KxMvq_L(c{;H^!W87J$}7Ok6)kC z{d(57{Cqa%>Hq)4#V?l<*uM*N_w1v&llJeq8^v7zfj*M1kE4&E>*MLe>G}lvFuFdG zK9sIcq7R|#lj(!$`V{&gx;~XYkgiXo51{MQ>HX>Y40=DhK9k;;uFs+|W|>G}eCH@d!%-j%K|qIaR|i|L)|`Vx94y1ta&k*+VJccAOb z>Fw$I3VJ)bzLMUSuCJoEq3b`=ThsNQ=&k7bYI;k$zJ}g{uCJvxr|awJ&FK2i^rm$E z7kU%AzMkHgu5X|>qU#&!4e9zOdIP$?nO>i+Z=u(t>s#q{>H0Q$9lE}qUYoA(px2`7 zJLxs)`Yw76y1tuUov!bpSEK8D=~e0aK6(|pzMo#1t{qqHj>H0Bx8M=O)UYf3-pqHZSC+Q{W`YCz|x_+8ooUWgt7o+QE=|$=Kuk<2x z{T#h8T|ZARMAt9S3)1zA^a6DK56HU#I7! z>%Y@;(DfVi>~#GmJsVyBgPxVH|4Gk6*Kg4?)AhgTndtg$dPcf_hn|71|4sjzuKz>- zimv}l|B|lXrGG)!6Fl<&UpIYD*Avn|qw9(2pVIZj^iSw|68gt65_nGVW(o=?c?C){sz5DlZ=Um#~rk8<}IbRPT5;okknxpOY0#`;+0`{a3kjF6~cnC+)9s_d0YQ`{Uu>{WrLCF71zTC+%-?_ZD;>`=jCB z{kOSuF71zSC++WW_bzlEd-`zi{(Iaxm-dIbllJ$y`v5wR{h@I0{)gN-m-ckrN&83K zeGHw)o;KXO{|R@_r9BOI(*7xTpF!ubKN#-a|C~GL(w>?-Y5#(|FQN0;Q-yo?zv9lh zw13U~0q*q-^!w?0MtY_&kNrOGymvn{ch03f3-grR>sjgd()Dcg>|q{z3humjKL>Zt zr9CI}d$`wg(Ua5l-1IzQ9(ywGymvn@ch03fAM>Q#>-p&g!aVjQ+ch04~BJ(@k>y_x0!#wsXVeWJ6w|TC# zSLLo6bRPR(;okk~+&P!_8q9BTuh*p43iH@&hq=$O|H*Tuy$*MEq4U`P2>0&S*xNC0ANFGJ5avF|-jR8yuorvhF!wq3F3h`zz1X{jxzDk8XWk?1#ojZ_eU7~s z^WI@E_C8_mbL@SY_X~Tm_YZTQV;{hLVAzX&P?-B1`(Wln!d~n{!`$cChcO=>_F^9q z=03+hlKH5x7yIZi_c``4%*Tek*uM#LpJUI$&s#U4o)y0V^=$a>P|uEEhk6eD8q{;* zzd=10eiiDu@heczgI|VvUi=c&^Whhvo*%ye^#b^Ls29Y~LA?p=eh}&v@B>h< zi0_AbC43*$E8}~iUIpI+^{V)8s8_>xLA^S@6Y4ea9Z;`{Z-;shnj<6EI#2j2qq zy7*?O*TXkKy*|DX>J9J>P;ZE@hk7IY7pOPJe};M!d>z!A;%lMa3||BF=J;x;x4?gb zdQ1FAsJFsbLA^D;66$U66;N-BFNbPc+<4d950bc_3j`(7zclsY)XBo9+!F5{{ zP%u$M5eo$ru~0w(3keA&B%~W8lm-bUq`O<`?(UQh=|(yvq$C6bIAeV8oc`EjobS&X zbFRHEEI9AsdAM$P0V%hG=aX`KcpfQtfaj8OM|ch?cY2hjG4wu1KZ$;FxQl*D=zWfUD*d!@7yb0m`yBlY`kCP_`dOj( zIr`c3bHZKpb3^ZQ^z-QFhr8$(gx=@q7t${ZchN5nz0c7vpd^Zf{TlkU;V$}hq4zoZ_4FIUUGy76?{oB<=r@PE=zj^l&(Uw8 z-x}_s-xhkGqtC^!OQwE1yB(x`^f|-c`MydpMA}E6Iqbc^%+B7_U!l*$Uj74rHT2QH!_K|;*Vx&c`nTDc z`s?g&koM7M414eYWM^;cZ_>ZTUcQBY6EFXT|2y>2zroJE_y4f7H}(J0zs_F1jeiX< z-@)Gvee@aFx%WQ7)BpRrkiDtDhyGRe^1b-u=oB!cJ`+JC3dF%A$AXw_R+r>_TE3j&fe6g zW@qXjWtWV!kN$?<_NM-McBcMucF9Tm=${LF@1J02Z|a|AXX;b1dy=$|{+Y1% z{wa3$ral!rQ=gLE)1-a$Plx?~_wMQcyThl%o2l^6kWbOe&*Gm8ee_SVbMO80?Cec_ z3U;Rc1$L=P`{Sy`sg2H=id8t?Cec_GIpl^Wp=NS z_R&8Y_THywXK(5sVQ1=JW%n?>oB{t@=%as#oqO+JXJ>EfA7p3h-(dFuz5FKrtB%OB!zU=|KjD3@&DlE9QeQS z@+bJe@N!Q4Exeoye-kg~#{Y?z^WbmbROL(~;{vuv3guj573*&#s%SG_#@$wh=b9lKZ{w!WDhChRsi{pR8%O&uq@$#4W zQ+T-~{v=*5g+GCpOXH8@u8aQ#FW19w#>@5boA7c2{6@Uo5WfL0H^Q&S%Z>5t@NyIUTD<%%ehpr3 zieHVFo8edC<>vUG@p23NO1%6Xeg$6s9={wfx5O{Q%dPNB@p5bY61?07zZft7fM0}{ z+u|4E<#zZ5c)2}(K3?vCpNE$_;^*S!PWU-^xifw?UhaaQg_pbHXX52<_!)S)JAOJ| z?t!0%mwV!;;^kiWDR{XzellL}gP(+#`{F0!<$m}Hc)34*JYF7vABUF*;>Y6ULHIFv zc`$x7ULJxUg_nooN8;rl@gwl^Px#?@c^G~eULNjqLLdE4?#L0`k0k9){g2$6`cdqL z(#xaqL-6t#{9wF17C#6tkHZhd%j59_@bUzFf4n>q-w!WO!uQ3?lkt7<@)Ue;ygU`( z3olQ@_r%N7@jdYJ419OIJQLpyFVDhv#mlqtUGVZ8d}q8o7vBjl&%<}b%k%La@bUtD zd%V05-wrP?!neiCi}63;013%tAv z-yAQm#y7*uYw%6+@>=}2czGSZ30_{0Z;Y2W;2YuPjrfLmc@w??UfztakC%VJ*Tc(O z@OAO>R(u`2ybb>iUfz!X8ZYm_*T%~`@wM>sE__YAyc=HwFYm#Bg_rl@tK;Q;_-c50 zKfWqnK7g-+mk;7A?Cec_E_SB=Uv@d^<=gl>p^yF(cJ95u%g)}^ z=U`{*6Qugz@2`DKFW-Z|H}uhe#Lm6<3EA13`VZNe`b6v!llIYn5cb~R$Ijl=zt7Ip z-_I^Py_^J}H1yGDW9Q!c2iV!0`mF3s{e$ctBJHDpFYLX4n4P_;f0v!9e}r8Ydiha& zvd~ALnVoy@A7f{4>NBx3^^dblPTEKRPS|_@1Uq|E|28{QpMu?!qh!Z_%e_FK5KRfS2FK zzZ3fCpJ(UZ`%LWYO?_ti=h(|x@XzAqck%CqKKf_ax%WORJ9|@~jXo87IXnLS&`19? zJNMpyz|P*(r(|d9KV#w&9Q{K)*VLC}SBkWc{=u;K zzBD^~Q(uO@Y`Ba50iN&P`*Q5;O?`R#r0nGi_==&AzEbFYjy?&`HT9L*RUz%8uNr#q z^!M|8Q(ui;b<#fiuR`ygz6Sk${5Wz=e67$&Upw?ZN1vGIn)Fd%b z;>VHe;p>M!`Uau*Ir@h5jlx~@3HkBed*7Iyy{T_P|82O7zG>)vj=mXv^Kcjaz5I2$ z_r3)?dsF`%{rBN6`j(;hIr>)gt;1dPZ9?yJ^gqzI4R_JE3%$?L-@`u-Q{SFl2hu+J zj-mHX--*6+xQo6^=zWg9D}4g~dgZ&%{_pos^zQ6`r@ugU<2mO8pM&-8y#I-Q7&)Bh zj3Di!|B-up??#4?uT&i&b!!;;eIgp z?!1frSndaL@6Nl}kK=wI_wKxl{dn#NaPQ7L``Pr_xcA?qeopAU)6b=!7w)2;A9|mo zUqHVw+(o}A^gc(QmA?;DznI+;(mwj7q4!R|jDC5zi+)AueU5%5{m?{oC)={JPC=r@Mm=jh+%ug}zPVz-&JkN%g?d#B$*zct)NpC$Zt zc)yLEy{X?$za!j5zcchcN1vG=-_-A7x0|$&eoyGV(`VxOrhYHGeWZQ#`$O-Y{s8?u z{5bMK{I8*p{!r+Bj{a?)Yw8cPJ3`t=e>C*o=`-?tQ-6%zane5ex5D206YT6w{Ym;$ z;V$|&dA@t^PqVW(^}o@-!CpRtKO6e!&xPLS=wIi#rv5y;-%0!EUkiKhFR-&W^%v^j=lUB{_oI7pO&3_@Bd+EZ|eW0Ps3imjlUE6=wD*z-ut`k>`i@wXa4v9Z+elv zd=EY~UcMKfF!a&Cz|Ot*iP+hj`o#3lvzPC~-yizupJV6V`y}k_P5ra%Onp*z50LiJ zKNI%eKgiDB)IUU@ioN_W{%O4Y2>#K~N1u|Nd+(F6vp4mR(LcprejNWKUQUjGBJ|Ox zVCUZZ6zuH(OP}ZS-NR3j$? zP5q`eU| z?A|2pqfZp}-oM4p-qa^#XX-PuyO&;m8~;w|qrZopd+#%`vp4k#*qQpw?6Q#d(ccZd z_wVBEP5m8uQ~w_RHYsO?vxPqTe?#wmcD%i*|A*exzmNYQ^wIwvdhb8P+nf5o=uQ1c z_*F`Ogx(ccWc_n+YHP5qzrramYB1}W!)bB8|q>!J5P58mF?U!ynmpW?5Qa$Y!J z=%fE5^xo&k+nf3;^rpT5{xT_l27ey<=r4ud`+|6TQ-6`()EB~EAmzgF@1$G=K2OSD zz~@N0D14Tbi@|3|xj6hADVKmxlk%7FDN-&8pCsi{@Ci~b4Id}vGVn1{E(;$e<#O;5 zQZ5f4Cglq7AyTdg|4Pc0;De-G89qSDRp9-kTov9&%GKb#q+A`|L&{&lyGgkQyo;1; z!aGU17QBO$Ys1?~`D=I^DSrcRCFMHs7E-PY|3b?3;LW65AKpaD4d9KW+z{SC%8lUl zq}&)@N6Jm$wWRzlyoQvU!mCNS8N7;=o5MeoatnARDSrpAAm#7j<)qvaUPj8T;H9M8 z8eT%mZQ#YE`~$p*l-t4!Nx2=ofRx+A^GUe_Jdcz+!gEQv6Fi5MJHxX{xeGjtl)J(+ zNx2(5gOt0&(@D7pJdKol!c$4P7d(ZOd&84Sxeq*vl>5RHNx2_9ft35h<4Jh{JdTtH z!edE!5IlyI2g9RDc?dj;l!wA2N%=>31S$Un4=3ee@WXr=UiNdtf8z6U?DewGb0_=b z`{T&|_4?N(`}gPHhwMKO|Gs7a`T5UD_Mf-^TxI|L`0qjX->3iHWdHs9?^*W07yrAE z{qM{Fj%5FP^uIgV|9<`NRQA7j|GSp``Qgt4*`H7TypjF+=g%|QpRfMBl>Pbb&tutN z5BzyA`|F3lPRRaxz}{Q$^LrjuZyz3zWVE^?61fEx-0wZ zx4%xy{(A4P>+&*wp7{R`$jk99NqGhQJt?n*za!Sgzai!A@Ykfg z1FlWVJKL%Ddqjq`U|Iij?=l)k%3DT#c0X!&OQ709=KX55kp6`B%6SDIbC> zlJa4=0x2JX%aig^xEv`TgUgcgakvaApMXn~@=3T9DW8H%lJaTzOH%#~E;OSlz)edkn#n%FezVz3z3(}D`WxkDtV3kl)ORyN#-PPk$;gNk+;b^ z;z{so)~^Hm^lNml+j`+YHfzfg9(-|uAV->3f|+(rLk=zWgu`qAwbHpQA5EUp(AJUn2BANB<>#$#55asnGizeQEkK;V$~Jq4zoZ za`ffHUGx<~?{oAO=_`f1=qrcb=jf}@R}FX3R|~z*(O0McD%?e1BlJE;Uz5I8xQo7a z=zWgFb5N=IHw<^tHwwMa(Kn`V67Hh^HuOG6-;};t zxQo7d=zWg91^sv7F8c37?{oAm>05=n=v#;0=jhwe{}Aq?ZyS1_qi;vwKHNp$A@n{+ z-;uskxQo7X=)1z*$eDbfj@%vZ_o2%@@YA`Md*Y|zx`bDAlIr_!)OTu0BOGEE-^vmd%hr8%kgx=@qSJM9+ z?xJ56dY_|TO}{4GMZY%mK1aWfeto!$enaSej(#Kkrf?Vi=Fs~b{V()e!d>)RL+^9+ z+vvB4yXbd>-sk9d((ej)(eDnu&(ZIp-y80t-xqqHqu)<|AlyZNF!Vl0|115Wa2NgI z(EA+y5&EOyF8X7k_c{9G^e4hy^e02_bM&X^Plvnce+#|O(Vw9|8}6b%7y1kE#o&Fu zzAL}}n)*xZE|d1rUkSZ;`akHehP&vmh2H1ruhZWMchUbDdY_}eNq;NcMgLdmeUAQb z`hUV*^#6w5=jdWz@3oa1;(ZUU+z9V`bLGZ(-?J+>!TVlb`CGj2@s*q6eebW_4DWk_<>q+bD=fFb z`yOKXJG}2LmcPgQo@2Qseg*HLlw0AK;Si+RXWB9Uo`8d7|UOs^@jh9d2OX1~H_>y?}H2zDx{2RUmUOs~_j+f8k zi{a&S_@a3EJpK#3{5!q~UcP`YjF&Is3*qHU_=0%(GX8VCdl$m94A;g86}_#E&j z2P;ao&k3w<(Y6-Ql15OA?4X{XHuR6cOvDva7R*} z2X`Rl`EYwuUI4cv<%MutQeFiAK+22ZHl(}+ZcWNd;Z~%)3~ouv%i-@yc?J9(DX)ZE zkn+!Pb5dRfHzVcMa8puV1Aj}(YvCrOybf+m%Io1qq`U!cNXi@G2Bf?Ru20IF;d-R} z3tX3!x4?Bsc`N)4DQ|u1v}Y;Yy_ZD_oJ355W~k`7m6bl#jsWNckvSmXwddWk~rrT$+?mz@sN%<1| zIVoR;KO^NUZ~;>O1I|y%SK)l5d=1V^%GcpfN%;nxhm`+>bCb8ozsQ>W?}~rmf62=D z+wdK-JpL}6fIt6N8h;ObFIfVg7`~4zgufq7LKeWwN%8sd55NzSdGPW>_}uu1;YY|E zc==KM$N1Fni)0#nI{0PsS$qcgH8KVM4fsv+F?>e&ZSrA!CO9*h6#p*#9+?;~XT>MN z%h~V=@p5+jy?FV3{5^R21AGF!{2~7CQ~&#eq4G!gJ9znH{B69P1OG2x{sjLIUe1aC z8!zX=|Am)x<8R^RJouY<`BVI#csVcr242pGzmAvlbu6}(&! ze;F?q!e7G6h4B~hauNImy!-|Jcf4E_e;zLv!=J;;#qnqHatZtyy!<8pH@sXDe;O~B z!k@y+rST{6avA&yyj&K4950u{AH&P#@kjA;1^f}bToHd5FIU1J!poKMzvAU8_=9-4 zD*gaou7=-_m#gFV;pMOJd+~A&{2shq6TcfT*TV0@%eC=4@$%RB9eDX0{C2!t2fqz3 z*Trwe%k}VE@N#|pFL=2DeluQfh~I>l8{s$N<;M68c)1CFJzoA6zYZ@q#jnN7&G2jR za&!D@yxao63NL?${~0fTk6($GTjE#XO=C;U9T+!;R?FL%Mu!OLCov+;5_{4BiO9X}H<_rTA<%RTYa z@p3QxG`!p!KNTqO7ygUxy7cY;;_rc4(VZA$ha{_zsP5ngrUOZ2pgzt%$C*ynI~<seoAKY^<*#AA zJA3mN_THQN+C0b9Z(+BUw2!`4*n7W?oxQ2A$-huxLFIR{4?(EH-?7cVj zyXdR&Jb5?1Dqh}$uY#BN;w$6jO0eFYy}6IQ_ojY7eMO!pAHY|@%Lno0@$#?ua(KBc ztaoQ`9%ApksV~EGO#NYYM@akVONYJpN7>n%`cmvn{V{eW>E+}2FY$5-Sntl>Ji*?3 zQ-6}aIM0($;fvwr)A*uz`8WI*c)19ycV};&Veh@EKTBVj=gH^rh4Auud_lbYJN|RL z{28owXK!9$@4cxnz;jIfMRu1+`{?tBz4w>d*_--&>`eU?c6sULKk%R8r2gqNShzlfJp!+Ll2=2PswH}xs$U*LK2 z)A;A{aw`0Dc=;Lpvv~O#Snuw?-W}<^`7Az_Kc3ISKSw?v?xKG>?7e@1oxQ0~$aB?W0c`_TIn6&fe4~ zVQ18!x|){}(U+1MA({n;)?E-qioib4>k*>^>sxqyH=H zz5ke6za&G)JyqpJr6)%5^{{t^y zf%Wd}&AjZrH}(1GFY`P(KmHP4E`Yy?mp{W_z{|hGdUy8b=j^>V_2+qxsV~T`5NRL% zxv=-XFgtrwf0mu8FT(B&z5E6KH@tir*1NMei?a9L)EA>a#q;Fi_>*|K1pWkG{t|y2 zFCT;T?(EHy?7cVjrRb0HJh?Rf2wpCOKa7{l;t%2FUtzsFd$SyS?@j$do@46Ev#UVb zM}Hvfy|2j5-qi1BXX-1l+ea@~#_z?;dtkjgd$S6A?@fJG`rSNFu7=-*m#gD<;^nXK zJMi*$Sntl>tij%UQ(u#Q8_$z#;kV-D+W0Ma`D^?yczH9dcV};Y!`^#SzlrCV`a10D zlJ?PW414eEv9mYz8`zop`s~)z%MI}B@bX$%@6O(A$liNX--vz<&yySDSL5X-_*HoM zTl~*>c_plOXKyxT@4cySM!$mR$<6W0@p23NGQ9j9ekopF0_)w`o8Pnd-qbJVIi|iP zyH=!q^ozpY`_}C2P5nZ4roIik1@!U{`1yEw9;|m~Z?qO7 zygUxy7cY;;_rc4(VZA$ha{_zsP5ngrUOZ2pgzt%$C*ynI~<seoAKY^<*#AAJA3mN z_THQN+C0b9Z(+BUw2!`4*n7W?oxQ2A$-huxLFIR{4?(EH-?7cVjyXdR& zJb5?1Dqh}$uY#BN;w$6jO0eFYy}6IQ_ojY7eMO!pAHY|@%Lno0@$#?ua(KBctaoQ` z9%ApksV~EGO#NYYM@akVONYJpN7>n%`cmvn{V{eW>E+}2FY$5-Sntl>Ji*?3Q-6}a zIM0($;fvwr)A*uz`8WI*c)19ycV};&Veh@EKTBVj=gH^rh4Auud_lbYJN|RL{28ow zXK!9$@4cxnz;jIfMRu1+`{?tBz4w>d*_--&>`eU?c6sULKk%R8r2gqNShzlfJp!+Ll2=2PswH}xs$U*LK2)A;A{ zaw`0Dc=;Lpvv~O#Snuw?-W}<^`7Az_Kc3ISKSw?v?xKG>?7e@1oxQ0~$aB?W0c`_TIn6&fe4~VQ1?;H|HaGyz98>=xyN^iw=>H0P?>}Z| zZ|ZNcGxa&x-K3X4!T*VuZ@_wY_GV7@-kbVd^w)WwoEv`)FXzEu#mk@K|G>*vV7)tg zGcSAZO?^K4%REockH3VM3*ax}<I?)A<(H{tV z?<=yiH}(73nfglX_R-6g@q6*|9$4?r-mJpjdsAPPemBpPtKoOy*4!pb_ z*1NMeYq0m;)Yqin#`EM__^o)kHhv3U{u=)aUfvAr-PxPpu=n27Z{j(oz7D&(qtKD+hwas&K2yu23HyR$bNviIK9H=0loYKem-8F2kYJ0n{C;9Z|d98&*gb?d;A={+yOruFL%Vx z!pk#Zy*qoe6MOGXeP{X^JWuX|pN^Ni;-}%|ZuqHqc?zs|XK!|A@4cy?%yUeA4|Y9C z`{*Zyz4yJ?*_--_>`Z-cb`$93KKSu?c^s^FXK(gp@4cz-M?aS5$^G$T@bUosXuLcS zKMF68g!S(1%|YzFH}!+*NANs(2!1$T9*Q4^mw&|ngqMGW_3r-b-I3m#KjDY^oWS0DQ$LZu7tfO?;d|oc$@m_4c?!NeUhW3#-PxN{*?Vv5yYd`U zKaJgV(mwhwVekD6cJ`*eGdoj1lU*lzc^1ASUhV+v-PxP7*?Vv5=g_z3dGcI*JG?v( z-xe>=$Nzwr+rWBv_T~ci-kbV`^sRZGya?Y4FE7To#LG+Y-{a-)V7)tgb18f8O??ZV zW9pZ&TTa?X-#qNSU%}4a)Hh>i>Q}OBN-zJ6{}wMdf%Wd}%~kBZH}$LO8}mGQ4Zaax zUW;#tm)GGN;N|+T-krU`egl0yo+od_*Tu`5@OAL=X8bpJ`D60CP;Z|-C7y{X?%Uy)qL#huC{> z>dWvPQ-7G<5z;>T(qZrYQFivGz7#uCe~ev8digm1OT1hH*1NMePq6pi)Ssj;&hz9` z_+ohZG`=Wa{tf>HUM>Rb-PxOG*n4m4&(as>dGa}YA-sGZUl1?J!puW-lkgXTr;g@$cZ}`|xk$<&3c2oxOQKd+$ws68g7zo}3i_CSHC3{{~)u5dS(} zeht>Uvo{}N@4czdz;jIf!|Wa*?W2D+?7e@KoxQ0~&(72*WA_TZ{22aayqpf!yR$bR zXYakKPfnkf=gCjt)8OS4_?PhVllT|$a%x!b&fa{Az4xX*CH)ILPktKzJYG(Pe-1A{ zgMSt;KLhLC{nxuAy*Ho5r}D@1dHCnZ=fhp}PlvttFR-&W^(onz`qb>6qL*L9KZ%!9 zz7+y{W>)qL#udw&t)TgI^l;_E>;vd1w z8SoF|<=5~J;pGQmy*qpJb@twy`UiN9segmro1}g8NyFazx7gX6`Xua3eMWZo)5~w; z@59T9VZA$h^BwlyoBB-jiFlrz8J`d@XTjf#m*2(TgO?M)dUy8bd+fb8^;zlfrseyu z;B4d_yqq0>8!x|){}(U+1MA({n;)?E-qioib4>k*>^>sxqyH=Hz5ke6za&G)JyqpJr6)%5^{{t^yf%Wd}&AjZrH}(1G zFY`P(KmHP4E`Yy?mp{W_z{|hGdUy8b=j^>V_2+qxsV~T`5NRL%xv=-XFgtrwf0mu8 zFT(B&z5E6KH@tir*1NMei?a9L)EA>a#q;Fi_>*|K1pWkG{t|y2FCT;T?(EHy?7cVj zrRb0HJh?Rf2wpCOKa7{l;t%2FUtzsFd$SyS?@j$do@46Ev#UVbM}Hvfy|2j5-qi1B zXX-1l+ea@~#_z?;dtkjgd$S6A?@fJG`rSNFu7=-*m#gD<;^nXKJMi*$Sntl>tij%U zQ(u#Q8_$z#;kV-D+W0Ma`D^?yczH9dcV};Y!`^#SzlrCV`a10DlJ?PW414eEv9mYz z8`zop`s~)z%MI}B@bX$%@6O(A$liNX--vz<&yySDSL5X-_*HoMTl~*>c_plOXKyxT z@4cySM!$mR$<6W0@p23NGQ9j9ekopF0_)w`o8Pnd-qbJVIi|iPyH=!q^ozpY`_}C2 zP5nZ4roIik1@!U{`1yEw9;|m~Z?qO7ygUxy7cY;;_rc4( zVZA$ha{_zsP5ngrUOZ2pgzt%$C*ynI~<seoAKY^<*#AAJA3mN_THQN+C0b9Z(+BU zw2!`4*n7W?oxQ2A$-huxLFIR{4?(EH-?7cVjyXdR&Jb5?1Dqh}$uY#BN z;w$6jO0eFYy}6IQ_ojY7eMO!pAHY|@%Lno0@$#?ua(KBctaoQ`9%ApksV~EGO#NYY zM@akVONYJpN7>n%`cmvn{V{eW>E+}2FY$5-Sntl>Ji*?3Q-6}aIM0($;fvwr)A*uz z`8WI*c)19ycV};&Veh@EKTBVj=gH^rh4Auud_lbYJN|RL{28owXK!9$@4cxnz;jIf zMRu1+`{?tBz4w>d*_--&>`eU?c6sULKk%R8%ILdT&09Pvwv2^YG7+&xgC{pALKPUtni%>Qk~a^{LrCMK8aIe-baJfc5U|&6n7F zZ|c*~Kf&|lwD{zBIUW9Sy!+HQZ^$+kIQ~w6LH%a^GlZL(bZ?UsC^-0*7`i$)Er|+&Vs)eFTabw2QMdp_3rG=_t<-H>a)_{CFN}J9a7E?-zMev z;eSc_pWxlun;&rJy{Z43-qe4H|0wj){}p=gKgQde`djp-J_r6LDSrb0Ny;~ZcV}K&3QZ50XAmuONKl5%PI2q~9=50i3P_z)@o8oWDu zvmAHcoBD(FroKGBLg=GE5PI(`;_XfSetJ`13BQk&E5mz9c~9`}?9D3Nd2i~g((fka zYVa;nt`6@c<*(o!q`WZH{5w| z>NnAw`a1Z!p^tuJ=)JFpw>R}0=uLfn{CZMu0IwtEwZXfyHyd*2y{T_RzlM|>!>dWT z3A~DwzlDD$<(0v^vp1V^=e?L=2h`rh~nq}&G{Ps-zhcV}<*<<5Ik-;aJQDffrRkn#X{G${{+N0IW# z;N97qgShkF)DNZ~LCQnm;iNni9!APP!atGnqyPJD=>&Rr_U2>Ud2i~6zVg2V_x^SH zyzxSbN+hfuaEw^+lJ>XBbSpc0B@PvGlt zZc@Gh=OS;Cx5ymiZSoG8J@8#P8!7wyURm+}ewn`ymjy5T`*xY}vcJ!l3GeR@`umO< z@&5j)zb~5sFZ=trui|BY-#0zp-!Jy}q0`}If8RPSUiSC7)8PI6aev=EHQuiu_;ra? zc-gOGJdKxA^ZSn}@qWF;ufwFk%YNPF3A~(+=OoAb^&!9Rlnn3J&-}XF19;i5<0Zw* z8F_vZyk9T$>yU}@vR}7MgqO4MoP>D4KI+$96X5;&v0s<=>(8=Z$M);hat@yF*SB-R zxk$g>F6YMk^?5lD-mmB9h4Ybqe?dXG5b5_{$c6EKzlK}{@Ar2Ug^Q7XzlmHN@As$3 zCGdVfOG&sC>G#)^hbxeN|B+k~@AoUomGFLlQx&)>>Gxa7)$o3QmRue0_jA>NYm$C{ zSsl18>G$8s_3(bbo?IXA_xCk~8&%kHNBKV8&C9(iszKqY0m#^US z;jhBi$UJ!YIzBgEzJbq$zX{(WbKq~ocgXB``7S;iUiQ5gS@FIv!}oAx!OOn4BQsw1 zJs+9yz8}Q*j%399{uJNKk^wLK9+y|~vhRIKkN15szK13qUiQ5;Y4Nh}xk-cf{W!jN zCpF&p2l-y2RCw9<7(I=bQ}h0jlz88l;AP+2^aNf`$8(b7eLs}%ol1uH{aL=3 z>jAv%d%Tk3<%~Q(3EuaG`5v;wc-i-sCBn;DcuqpR??>~!YYFhaKhF2k`TjZC_t^Qq zIync=_x*M`;asHe+mmzSeLtU^2k-m*^1}H@-!E7YE=2nNMY%BE_ch8z@V?)%C|r#6 zeUox=yzi%!OW=KH9Uy!xc#1KPp$m`@T}S65jWlR)MRMzHe2ohWGufa&^4# zbFBf_Bz?bZ9k?#(`*-Dfc;DA6*T?&Q--d7_()SI^jq$#pSZ;#%ea21UW~A>|ZV9&{ z7xR8Wxix+fUT%Y5h;IwGBj@4e_V~GYxdVO*B@N}{(UY>#Pf|qCFJL6}=bI1;Oc`m*^UY>_?qfvk_;0&gX22@JX@+UOt5{j+am4i{a0}XUQV?i|{3~0A9X~ z&ySa{;Pc_H!q><=c=v{k%*+hcgRa z_H#Qk<7GeRGZWs=2laDDGvfXHR6mzB177xXTwle@e(q~}yq_2A=g_9Z%YJTcTD$bkyr2q;X>qjyj&Q6 z4lftMpT!r2i;<`Ca&i1Ayj%i*5?>N7MIOVKhbxc=@p47{0lZuZzaL)(u1fB~%hm9^ z@p5(iE_@BRCb=D72d+zQ!prsW8}V{|{04kOxDmM)FE_@o!OKnXtMN_YX5>nIOSl!e z7%#WRFT%@h@C)&6;dbOayxbl?7cY0f&%t+sJCifZ_?WW3x9KM61Q#!tleh5M1? z@N$3rSiC#{KL$St9!!qJ4}*u3L-Fzm{1Ci654POf{Z^zff?}T@e)$#Ifd^Nnh2VWJx z58h8!!XJi@kY(`lQG989oym+|@W z@)dkO{8jiGnFlXl$LGe&H}JXeH{n}k4*YHS4w)S<-^FLc%L({?oviqK;e=!syqpN1 z880WsXTskPCm}Q9ABG$Km8;I=uV@J}q8Oflq^f3QkF;#yMet|wMd4!PX}nw5K{BFEl9lr}-1FlJK$Jc@DlAG{yJ^V(zTpzyy-w!p^(cPvWj? z*m)QG$=r1bJMUsYg}csS=Uwcla@Q&Byo>!b?mC8@cd?(&U5BvqF7`9HYae#r#eODt z?ZVEx*w5mwZP!B?&^h| zcd=i~UEQ$rF81rVs}pwK#eO|^--MlavERVm*J0;f>^E{(JM6rR{U+{eg`Ib?-^^Xj zu=6hVzi?M0?7WNp7Vf?ZJMUt@mAmR;=Uwc#aaS$uyo>#I?y82Jcd_5WU6ru&F7`XQ zs~mRT#eNremBP-u*ze}9V%T{X`#s!M2s`g$zn8o6Vdq`!_i6@xwRh37Z9S0UK*UU+_sd*y>Y?}g{LxmPaO^ImvaKUC4)Whh35~sS0dQ+UU>e9d&Pr2?}g`& zxmPUM^ImxVgnLDUJ@19*Pq|kl*z;a^{)~HtgFWws=g+xUDA@B}c>aQW1%o~Bh39{9 zuRyTpz3}{R?&S~myceFoaca zxr06Lh3EfrFITYVz3}`k_i_e%-V4tor2qf-y>bM5-V4v);eK}RyXU>|JRd)^Drqj5hy_ucbecpjbmpK{+l?}g_vxSx*u?s+dfkIDVC+;`7= z;dw0Xr{TVP-V4t^;C^cEyXU>|JT~`Jao;`fh39d&pOX9Tc`rPV%l#DGch7s_c|7hX z=e~R13(r5~elqU6=e_X!Bkm{VzI)ya&*O7H3HROeUU>d7_Y-s9J@19*3Amq#`|f!! zJWt5|Pq^=%_rmi}xSx>w?)`Vq$Me?_+;=Y#o`A=3>WMl3*nbZ?2{S%!C&fR)?PU0e zxSbr2hubOexVW7XkAvH(@YuMW8vg*d)8MgiJ1rg)x6|PV*fAK`Wh{2^|a#2?^xDf~Wem&Whmb{YIGZkNUH;C4CuHg1>4Z{cE&M!g*T&D`b{+gI zZr8=n;C4OyG;Y_&PvLe0{3LES#82RMBm6jSH^z_Qb`$(4Za2mM#O-GIAGqBdKZ4sW z@ZWK}C4LyUTj7UryET3gx7*+caJwzOAGh1#`*6EGz8AMU;CpbpBfcBAJK?|Kc4vGS zZg;_V;&xa3SKRJ~@4)Tu_;%dxfp5d@p7>VW?uBo`?cVri-0p*K!tK8JM%?a)Z@}&T z_83uf*+d@D;fIExsJL2jRcq_F#M&ZV$ni z;`UH{32qO=7vuJDd=YMsz!&27Nc?Bq9)&N!?a}yr+#ZAfgxh2BdAL0epNre$@j19X z0iTWA6Y*KNJqe$Q+mrDbxIG1*j@wi5A94FTd>U?lkH`7{w?QJ++pF=faC;3t0JqoTU*h&U{0rP(kN3yz4R}A?-iY_b?M-+e+}@1$#_cV5FWlaW z_r&dOcn{p(j(5lH9e6j~{uS?v+dJ_txV;PSjN8BAop5_M-VwL=;2m&#FWw%v_u=hu zdq3V5w-4ZLaQh(M8n+MOt#JD=-V(Qe$6Mg`5xhBW|A9Bd?LYCRxP26Fg4@UN#<+bP zZ-mj`kJT-2|##7;T96Tj%$Hi0Nc04>eZhwd;!|jjoq_`a)PlDSYHSQ*9leifx2N|q?GE%FrrnX=&9pnwzcKC3^e(2|h2F`uyVAch?QZlArrn+1 z&a`{b+n9DwdMnfJMQ>r+z3I(NyAQpIY4@c!GVOl!2BzJgUeC0@pw}_&FX^>RdjP$L zX@5npX4+rVtC;pcdL`5ThF-z6zonNm?LqV}OnWfBjA;*{mon|4^b)2$j9$#NhtrFg z_6T|*(;i9x%(O?*3z+t3dOp)0L;u9I$I|nd_BeVj(;iRHVcHYu*-U#PJ&S2iqGvMg z$@C1SJ%ygmw5QTPGVSl^X-xZj`iJ0L_r8zkp6x&1e~#_1*I$?Izn}jew!a_#d)xm0 z`TJ!1`|a!R(~SHF(hem(ZX@{Fzp?5cc%R--HmDQq`NZhU33?w{TtnxY44^xG3`BcN2a}(?!dJ7 z(e0V`e!3mgK0vo++6U=2O#2YsnrR=VTQTk5>6T3U2;G8d|3Nor+JDl`nD$Y+Dbqei zH(}bx>Bda^1l@>fpQIZy?Nf9ErhS^O&$Q3b^_ccqx-Qc`N7rH6=jqx^`vP5yXCg{bOok;n=a3^@6hF# z_FcLx)4oTSVcPfU(oFjSU5aTxq)RgGM|26M{g^J!w4cz$nD$e;DARsM7h&4Z>B3C= z1zm_~|3w#M+JDmpnD$FLKhu6if6lc3q4P2A*K}T{{f5rNwEv}ZGwru@E~f4G!*b%b z-#^QN+kU?-J8t{^xoo)Y_w%yiw%`BDg4=$-Ff(rZ{l(94+wVta!fn5QnGv`BerE>U z_WPshaog{weu~?E|1}+M`~BLqxb63M)8Mw>4^EBSe*ZWXZu|Y_l(_Bpr&Hjz-_K5t z+kXE$8E*Ui@}#)!_t%r)w%?CWjN5+yJ`ryF{r*pI+vf)o;0_J|{MA`~1%bxb5>rv2fexmtx|!&qu|; zZJ)o2j@v%p6%Dt2ek>|(`+Qmy-1hmm_i@|j>muW}&+om5+dd!oE^hn$VIoV-h zxUb`|tKh!w!>)?^IuW}X?(0hI>bS2%v1{PIZpE&N`#Kl97Vhg}?Ao}mqp|DYzV61Z zi~BkqyB_ZAdhGhRuLH6h;J$9iZixFjBfAmq>yqroxUXZfo8Z3g$!?1KIw`vu?(3@T z=D4rJvRmN3Zp&_o`#LYX74GZ8?AExiBeUD!zV6I!i~Bk?yB+T9+U)kYuY+847Z2kOL2P`z67_2ve zdwd#h`+EN$aNp0HnD%|V&Lp_+<8>y*eIKtg8SeXdoyl?E$Lmai`#xT0O5FGHI#c1k zkJp(R_kFz1G`R2Mb*9CAAFneV?)!M1pW?oc*O?yoeZ0;LxbNe2X2g9TuQLm1<>nwu% zK3->0-1qT1i{ZYH*I69*eZ0;RxbNe2mc)G@ud@{H`*@wDao@-5EQ9+#UT0a{_whQ* z;l7X8SswR&yv_=^@8fk=#C;#Hvl8z6c%7AT-^c5$g8M#RXI0$y@j9#FzK_>g9rt~_ z&Kmed-e={kiC@5-wea(}vo?MXch+Fd8K3-=h-1qT1JLA5O*VzU4eZ0=D zxbNe2cEf!iud_Su`*@u_@NNA0boRuz;?7?97Tnnz-;6u^;G1w~Uwk9(?1yi_o&E9k zxbq8q9q#-RUyC~j;A?Q_SNLk&`8B=@cMil?;?8gI6}aS+caFyA~P@m$NsJb9)8;HEyrOzryWR_yF8qjem*TYw#~{ zdoA7{x7XqQaC<%87q|P+x_h2GH?Z%0r@oQ%-aMYY3Gap5oAI8wy#?=q+gtJOxV;VU zhTGfmuDHDe?}FRE;+=7OC*BFSci|mz`!~D;Znvj(_dIv*X5afxeGljDcszSA-WIp_ z;cakxKi(R*58$nE`yk#Dw-4bhaQiUc9JhbRo8k5myeV$~fj7bJ#pF1Kd7=*T?OXcs<-ch1bRH(|8@+K7-fB?X!3-+&+ib#O?EV4cxwf zSI6yYwCF~Xyef}pU&gE8_7%J`ZePVK;r2DWB5q&DE8zAGygY8-#LMCK zExasl-^R<}_8q)5Zr{aA;dV({ch7U@J@&ot)bDd%g2%HT;KgzKAzlo(AK^uD`!QYw zx1ZpJar-G=2)Cc%1#$a1UI4dW;Q4X;FZ^@d&PVI+dG7q1eeXNfik zr^M~pcnaK(gD1!BWVG&{=gzq7d*7+Y<2)&kXMcz%!R?Rm#JC+FPlVeaEG ziGP6Gsqk31of?mc+iCC^xSbY{j@#+*Xt@0;9u>FK<56%s1O7g4N2Yc6Ja=Yf-}_EI z6X);oc=l)bySSYhkA&M<@QApb6@Le}v*8hNJ3Ib1`~Sc0+BxulaXTme2DfwJuW>s! z{ts@yqILH?cjjT=`%e8OkK@$yvX_tPdFX!!``-VYJn z#_!;E8T>YGm&I@4b~*eeZkNYz;C2Q4I&N3Quio&#;@RZ75p-8U!ryQJa<-Q z-}_Fz8s`^zJi9u60k>=5=W)9xeh#;5;b(EXHhu=T>)@wxyDokTx9j02al1Z#0=FCB z$8q}@t-I&BvmyK5cj`xZ9H-uhy~a$>L;o|__kI)hJa_7Uu;3wa!gu0!XZ%;(?t<^Y?XLKC-0p^N!|m?)R^0A^Z^7-J_-5Shg>S;`jkNBb=g!{j zd*7+|;d}#+XZOX|<90uM9d7r>*W&gU_!`{)5?_tm1MpS2{T03vx4*_$;Pya#Ic|T0 z|AO1gXx%-}o!_$WeWyN%^QAnVJs4kt+e7fhxIGkKgxkaLg}6N&{~5PO;0thjBt9Rv zN8vx=_Go+_ZjZs|;`Ug44sOq;b@x1Xj$_~ZPJKM*vv@pv0zMPBC*m`3dlEh!w4tNwn^s=g#Tud*7+g;Cv#F zXV1hZ;PxzhJZ{g%$Km!Id@OFy#mC_GJbW~6|Addi?fLjf++Ki>!0n&$;kZ4F*4^{m zxsZMDJM~4J59RUf#rP21UV;zC?WOo2++K!%i`&28-{AIgd?0SGz`w@rmH1bD0$H{gA6dn4W(w>ROvaC%cyECo3=ehG&_Py`aJMlP9eJ6Xnn4X8;G1&M1Z|r&Q z)H|@})OWMjo^yK--VV3-;%#wzAKnJH_v5W``vBeww-4eiar+S70=Ez2&2hUKt-I&B z^LO^W@6?ZQ-jv6)|G=Bz_MdoT+&+po!tG;tL)<=&H^A)^czxVHiPyvJQ+Qq6K8@GG z?K607+&+uf!tI*0?w;q)bL@NHsh{V(29IZ7z^mi-MZ6krU&5>6_GP>ZZePJG3Qg-gMIJcWzTb` zUWz@ZeviG9oZI*D61e>UFOJ&}@nX3B2rr7;kMSb7{RA(J+fVUAxcv+-h}#8d-968p z&)N6BQ-8sEejd;M3;!Iq|Hkv-_Dei3Zok6w;PyXwZrpy2=fdqbcuw5@7tev)Z}IH7 z9pN*c-^A^A@T|CVZ?#qAh)8r+VFr^fA6wCIkMah&=G?8Rn!9(syk-}`ac z^W3Q?XV0m}WiJ`$c04>OZhwd;!R?Rm#JC+FPlVeaQ{o@sb}Bp;Zl}g$ z;&vK525zUtqvLiuJQ{9CrFHi_cYeyg_nmrr&ZF>nb_V=?+|Gzc#_de_d$|1>{w{83 z#v|c&7Ca(uXT{&a?QD1i+|G`_&BpVjbPndfxc!FK-Sga;lYQ?y^<134=JD*@_&>Ov z2Y-dzdGVLHoe%#Tw?D`K!tMO{3*0V%KgaEY_%qxtgg?dY!uS*1E`mSC?MJlkp6AY@ z?0es-7vuaPk7pOhAK-Qg{621%#P8vDDf}*Om&Whlb{YIOZkNSx;dVLvCT^FCVmdL zYvE^cyEc9Xx9i}ial0;l3b#+vx_h2G>#^^Br(U1)6Fi>X06&h~4e?{R-3UL5+l}!* zak~lr2W~gTkKlGQ{CC`LjvvPD7Wg6DZiyen?N;~!+}=;??s@KP&A#`YdK=F7@pyJy zd@pXd!}s8Jdwe%;cffzc?T+{^-0p<$#O==buejX>-+|j*@$I_hxS+=XM`_18(=l*W-3Sd>wB0$JgTa z7x)_7{t{n}+XL`bxcwEr61P{-x_h2Gzh>Y2PJJNf%XvKe8~hjC{uW<`+k^0>xIGwO zg4;vz#kf5bUxeGk@P)WN9RC@&N8k%^dn7&|w@2YW;r2XQch7U@X!gDD)W>i>m&dcm z;&X6&96lSj$K$hbdjdWawU>~#ecx<@9^(&`#V~9@4s{R znBI4Ok5BcV=i}i&FsB9gLZ1@sd;drFJa_7o*>mdC*_*__@iDkP4G-yah&>g_I5Bm54}sU@BLrd^W3R-X3webWUmwF_Ab05ZvTdN!0p|5d)(fG zx5Mqdcw5}whquA){djBKK7hBv?UuCep6AYk?0es-AL6_Pk7pmoo8$KHcr)BSf;YwO zKkz2F{U_cSw~ykDaQhhE5Vw!x4RHGeULUtl;`MO*6kZp%>(IJ;o;y#o?|r9!hV$Ay zo_!Xth1=)wnz(%)uYuba@anjI5wC{Zm+-2%eHpKU+gI?)xP29`gxlBfinv{Y*4^{m zd7XXlJM|l!m*?^9n|L|gzJ-^??b~=6+`faC#_hX!Dcrt?m&EP+cnREofEUN@hj=mE zeuNjr?Z8x&xqUa;~8)}3Z5RfqvD_9b~HR4Zl|Sn_dIt-XW#oyJqG7# zcsx5Mo*K7f;i+)@13V>e$Hr6Ob{sr8ZpX!w;dVScDQ_z3=PLD^y?F{()xSbJ?jN6&;_i+0&{9WA6j7P%lEOPitKsr)UUDU z)GM)fm2^W52meeXN#y8@2AAAFD_r=%ac0YU_ZuiI6;`SH#8r)t@>+X5({E~g| zJM{sauj29Suke+){WZPfvv7L? zJ`=Ym;xll25jC?lY(2KhV>d zQ*ir7d@^oN$0yJ)f)B>+rT8G+UWR{*+rQx7;PyaT zch7U@a`wIN)K_r+HIHYn#J|GrRrmnhUX6c=+iUPIaC$3oULLn^;^lDr7G4&&Z{uZf z`wm_jx9{SmaQhx!61VT;C2;!zUL3cJ(Ykw{J0G&|eW(72^P)VS{TMHT+fVSqxcw9_ zgxk;Xg1G%0FM!)G@cg*_7ydbJ|BdIv?U#67+PCzUUtsy2w8ZZ6t~~Ov*LC{JPU3|!ZYLcyZC3g{T`kP zwfik zr^M~pcnaK(gD1!BxOg($j)y13?GN!JxSg2R-Sgb}5&PbE>hU>G#N*i?FK<56%s1O7g4XT&4pb|(Bi-2M!I z7q=tPx_h2GGqdk~r=ErLh&-O16@Le}v*8hNJ3IcCY3HE-W!gFEH%vPh{hDd#rvG8u zdFWS6J1_l`Y3HN=X4;?Ae=+SBfxG9qGe7sd@6-!${+wwSq@OYELiAInU6_8tw2RP> znRZe75z{V4KV;g)=?6@^1bv@rm!$78?NanzrhO-H_dIu&=AQSRdKu1dGwrhUEv8+L zzR9%9(>IuQ1^PPEu1H^F+Lh?5OuI6Dg=trzFEj0`^d+WUjlRgVtJ4>l_W8iw^W0g3 zd){~IH90@Wv}@64nRadZ4AZVdpJv*1=~GO*9(|H&*QZY~?FRI5rrnS}#>eSm4VruQ@LHuOHG z-Im_VwA;~pn09-5H`DGw|Hia;1@4~b&W_ykzEkhS`A(+Ynf{e&ccFJM?XL88rrnL+ z#kj9QWL*|IE2lAAv7m+9T=tOnVgl6Vo0|&tuwS=($XL zEIo&5kE3TZ?eX+1ragh4$+Tw#?w;q)iQMzPQ=i27bf!I-{*h@eo@*ug3$}v;F$**J<0Y z_kLZs{oe=wcVGjLw~^T==uLEQroEZ&#k9B3J(>1ax(CzVMt5i0+v#phdk5W>Y5z)h zVcI+C&P;n3-HExIxrf<4=)H71roE4D%e42?ZJ71}x;4{2NVj6zhv=3}`!L;tY5z_) zXWB>TX3RgCN12U-K1MfU+Q;dJO#1}gfN7tk>oe_BbUmhhny$;V&(L+4_F1|%(>_Pn zV%q2Fn#_yLOU!CPU#6=v?JIN@rhS#J%(So3m6-N*x+2rQL04efH|g?B`xae}Y2T*H zGVMEb8Rk9aeP+p^AJ8S3_CvZj(|$x3W7?1DqD=bbPlHN_w%yjw%`BDhTDF>Fe`5R z{lzS}-ANP3_pPz|`+diKY7q?UJedRc~?ej&kaXS_FKfrCDkBWucK7SPx zw|%}V25$TOSajU>`Lt-b?elL@ai6#I`M}7y?emB4;kM5=zKh#FKN$(Pv-0~85x2A9 z@8Gu2mqx&CpI`NPRG%mI`DdSJwtc?Z=cR3*-}ZTIy8yo)pZB(X{@mxuZJ%%Vd3D?8 z=Y1aDF2dvcyuDo%_j!K181Cx=O3)>lzD~g|h5Nb&yEN|WAnY=@ubZ&T;=azpE{FTN z47)t;>p1KRxUc)LE8@OR#IA(h?(4Sfrns;3vYX+)F3fI@`#Lha1%8-c zr`;0wb!v7i+}E|)t#MxmXScz9-CR4mJ=53m*&T3S_h)y+eVw4)3HNn{c4yqzA=+JV zU$ar-NL6>fixuf*+v_zL`6dJuCNZV$$n;`R`H32qO?7vuIYd=YLB#~0%E z2>fT<9*Hl&?NRuA+#Zepgxh2AdH6VbJaaZ~Przs4_C$OpZcoBz;PzyEI&M$Ff5h#n z_%z)94*vnSzsJAF?H};(aC;g)6`xMeU{1pAnfOH9o`p}q?b-Nv+@6Dv!|l2FSlph6 zkHPJq@X@$EA0LI=3-FQnLV6K%7;Z1dhvN1Ud8}ESId+_$Sy%%qX+xzggxV;~5gWCu2*0_BTZ-v{3@Rs=R^buw= z-2MY^iratUO>p}t-Wa!!;f-+nINlJqPv8x3`y^f;w@=~qaQie~7q`#gb?|fad1g)A zzJS-j?TdJI+`fcY!|ls>RouRUSHbP8cxBwahF8LG(|4F3mbzwrFH{WtzOZokCy;r1&$FK+*X z=fUmQcy8Q&gXhBSfAO5S{T9!G+kT!$cHGYa@$*Hp;I^Mfk{P%C{F2Xb+s`}6gxh{T zN=DrF^Heh6wx7R}9=H9xmQQg%cgD}NNr~Hj{!I$p_VaR*{0^Q&Uwwx4$u1NU>X{QRw`xb5e4MZs-9-|Kzc z_Vd6ZrPMhuLz4^Ir zwx18@=fK&1o}8Z>XXoSBR1yBhB2V%pVlKS$H9fnVTr{_L8# zpVMjA!u?!NyEg9UfZBC%KR2`l;(pGq-46G2dF}SNpW|zH z!2R4`yCd%B1lyhPUHm@SopC>hxCh;nxrO_7FMKm@_r^Ejb{~8rZuiAE;C4TJJ#P2M z*WvaT_*&fl5?_Pc1Mt=O*YrT<3f%q%Uyj?~;=kbbAbc5a55||`_7HptZV$y5#_?LByV+}?|~!|i=|Tio7{x4{q6hnOvK`!L=Dw|~c*i*RlG8OoxZ`WfZI3m^0<8qFNfQ=@v^vm2QP!$ck$A=eGe~% z+xPL3xcvYxf!h!9;<)_?FNQy%pE3*M_A|T?Za>Eh;`R%?0B-+<=f~~8@y~JlC7utr zU*UOi`yV_HZokHJSgxmgn)Qq_8&r{8S+y4C3^teB_)t?8O7PtNRv1xGIpEsKtxBdCFsc_q$XPXkY z{rR^kaND1kn;f_O`MSw)+n>jq6u15Py-9G}pZA*>|L^Au^K)Di;{H5he_n9{-1g@i ze~jDyJmmPe?axpC2)F%t%OB#lKc6`sZu|3`}jA z;@5Gz5`GQ0E8|yjy9#~01Gk&uNAMPOOXeZmZiOGj?bi4K z+-`&K$L+TGKHP4H@5Sx*_#WKufbYh;(%qQbal1Rd4YzyXTXDN5z6G~?;hS;0H@*qC z``{aKyDz>0xBKDial1dh4*!xKz+8>nU*W58`)hn9ZV$v);PyB8a@_tF{{^=P;mdG) zFuoMGhu}+adnmpbw};`2@DcP#<^tRvh0n+B(fCieJqDkL+hg&$xIGS^gWKcr*|&ap{FvZ;r4g<54in3{ylF0fPaVE)9|Ud{UbgFx2NNiaeD?n3AbnB z6LEVMJ^{C9{zr-hlVT z?TvUJd^5d;*%P<7;yrMC8{Qqax8vP#dk5YXw|~XE;Py_uGj8v~JK^?kct_mcjd#H9 zJ$QS3AHAR12DcC3t#SJx-U_!5;Vp6dFx~>Uf5)5S_7S`pZvTNd#ZS;Dne}n|6kZRv zPvdoQ`wU(Ox6k6War+!z3%AeXHF5g_UIVu;;?;5c5?&3zLSJQ8#_el(CEUJ_SH$fb zcm>?PiI>OiTX;F#zKxf~?K^lG+`fyK#_fA}Dcrt~m&6~^kC?@9`!QY=x1ZoeaQi7< z7`LC{g>d^hUJ$om;018|FFZf~ntsE~h1>t)IdS_fo&&cd@Hq$Bar+%S8*WF$v*LCn zJPU5Wi)Y5|_wdhfJ2IXLk3vUfrpN7Q_@}rX9Z!ebG4Qmw9TQK3+p+M}xcvd13b$k9 zDRDavo&vYy;>mG49-a*Uh>p)pjN2dMiEujs{t0d;#1rE7CwKzfPK1Aq+lle`xSa(5 z2)C2sAL4d0JRY8cPRWdo+o|vma62^~3%AqYF>yOB9s{@2;n8vXQ#=}Or^ln>b_P5O zZfC^b$L&mbWIQvSg&7IAv*HnPI~)EEZfD0M;C2rDEuVj4=fwZT?Oga9+|G@^#_c@# zKe(M2e}&um@RxXgx&ZS9ZWqL#<8~qZ8EzNGpW=2A{0VLs#UJB#G5is37snssb_x6e zZkNRGkKuM> z{3za(ZpJ)<+s*Ocak~Y67`I#Ehj6xt+?G2--6q{@Xfg08{dT6eejKVKe|72 z9d3Vtuf^>z@in+T0AG#UU*W58`)hn9ZV$v);PyB8a(pN~jJXK6hvN%zdj$S7ZjZzl z;PxndK5mc3f5PoC_&nSmi_gXFarhj33O$wS=ZD#``5ZDoZ){4Q|3jSM^U0>-o=4|g zpB$X)-{GD+^+}vN_3!bCO#26V0@I#Gk7wFH(&L!+bb2h)o6d>^a!Rsmmbcv=h4HM_D}Rsrahk?!n7CAgPHcv^dP3akp7lwFQUI;+KcIdOnV9a zHPc>7f5o(y(F2(FFZ7p8dpZ3D(_TUMXWA?2eoT86-Ir;vru#7MHFR&Ly_W99wAax+ znf7|R2h-j_cW2rg>26GW6Wx_*Z>GC2?Jaa?roEN!#I(239hvrax&zbRLAPhxztZiP z_D;Gj)80k5VcNgZt(o?2x)syjL$_qwd+8QTdmr7LY44|-G3^6%Q>J~8Zo;$=(T$n* zVY(61{+({fw2#mYnD!rZeWv{FP}T3|)-}~3t^W3RdV9%*vXYU5n^U%u&``*9Fp65=z9D7dv7JFqmw{PQj zg7eVJu;;$_@3QB)Q!mY)Q@_XFeWvH3mkRd1|A0Nuoq9?3occrdN^ovJ!XF3cp%-V* zeeXYE&vU0BJ4T!7wrAT^gQ&!!M^wZX3uk{UWh%X z{*t{{OwU6v80>rhAND+V>IK+y>aW>*!}L7#{K3BW|7Fi}r~Wy6PW>%=5pw?j>#XOY z=L`0|{|PC1y~s?@L(diLd;fj*Ja_6j z*>mbq*o(^aJoFsFzW1ZC=ebkQ&Yn|`&Rz_r=b>i{_PrmIJm6fpRnh-Q%}VCr|jE_@pQPI1Wy{Ahn|)__r0HtJPXh<913sRd619 zD)!v}@4ow-|91~h!%WG!ofc0QoQIx*J@>u;DSMtf_4J%4XW!0%C&TTGc&6Yy^rY;$ z@BPo%^W3Rt<~#}eb{0Hqa2|SM_T2Y=HugMs>e)F@#J-&a&l#MD{t0{Tdp{R@o;&s2 zoF`=8&V%O-&O=YYp8MX<$DZd-{d3MgX5Y?_7YNQnkI$a_-Y>|W=T5y4=Y@lNp?}2V zyYKxX?0N3gi*o)U`*tzBcyJziJoeo8ehKzGcj_fMFBRMiJuZ*$zV}PB=ebia!+9L` z?Xq~e;5_v5!MTs4$L4XJdIk0>GCdFdgJ9qLmDuy#saNK_N^mdqSUkS_-ml7@=T5yE z=hcIIp~vL$-S>VC_B?m$H94;p+zUMhkMF+sYqRIMQ?J8$-QZs6(RqCLyEfIQO33l=G@V;y zgY(b_1m`}E{uSq62lqlB7@Ye!`Zt_^8{7;1UH(1X_kJYYb3G#Nx&99BxgG)cTz~sH zPyBMO|BHLBzrj7%U*n$Z|KOhMuW--xm$>Kp-?-=cU%2P`3*2-4Iqtds4EJ1rihHg< z!9CX>sUOCj`XSt@AH<#d0oX--SE%ow!r~6?f`8aHqZRWK9z8QDwn{cPT5qIhv zaHqZ=ck1hKr@j_<>T7VPz8ZJxt8k~j5_jq=aHqZ;cj~|3PJJ2f)R*E;eF^T=7voNS z5$@C%;!b@q|NHhc=k^eM0d5b)=i~M;{3qNVj?csG5%^r(9*NJv?NRt_+#Ze3!tF8m zOxzxe&%o_*_;lPJkN=3<6Yy!cJrVx_w|yean&a^F4gh3Cz|3(s3}e=qml^Imw~iu-%G@1FO<^VZzo&3*U07oNA_{%_oO&wJr{Tkh}TzI)ya z&)ac-C->d+UU=S~`@eGEJ@19*9k{=P`|f!!JnzW;?c8_Id*OK}?r-D1d)^DrJ9B?4 z_ucbec;1EkTe$C@_rmk8+~3T7_q-RLcjNvh?z`u`@Vq?s?C1 z|99oP;D2BI-x+-p|2yNiFi+Th&B z(bsXlKDZb9hTz=C(Km9wDYzH<=HT4N(YJ8EHMkdg8h$_A_x?8aJa_8bIo}c73;oyN z+{e*(a=t6L7kX-bo$h=8H}*Vt>bp7L6Wj}ZZ*cD8==(TN#jnHOj~@umLq8at`#Aa` z&QtRE_F??@;5_sr!MTs4|G{|*9^d{GKN_5eek?fmarEPyC+G3)6ZpyCJoHn+xsRhK z<8ht(Y4*-AJrDhCaPB=lDUa{e&#`x&>3QfEf^+ZbNqBsxev!RPOwU8V9GrVkPt4;x z^(*XMWqKZZqF~?q*VyyisbA;(MsP3mPk4Oyy?>KE&z<@$&J(h4-^T9*=b_&X&V3v` z0gvm{@3D8E>3QfM2m9WCz@Fz${UPV^*|#6zkAw5jp9JSVj{Xsk>(rmJ_l)Uz=pP3A z-ha-X=T7|v=keIL|HA(c&O?vOp8MW^$)4v<{T1hN*th?|UkB%*$7auc@4sQsbEp0< z=O3_dzr`cu`v3EGo`)WbJ^b$w z?7h$QJoIS6zW1ZB=ebjl%6U}w?P&h3y+N3AKS@_a$Mih(zk+@5f6AWcPW=UYPCY$)8JM1j{yf^b%9 z?B!s39{R&z-}^b)^W3RFV9%-NVlOw-^U&`H``*vPp65>e9(ztbFMIizo`-%n*!TYD z?0N3g@3808^Rrig>3Qh4gMIH8WY2S_ev3V)UWmQIOwU8V8SHz%2z#D8^&9Lt^`h)u z=iDxa7Z1)uzs8>X-Y>zP=T7}9drrM1d!?A3hkhm4_kL;iJa_7s*>mb;*t^8JT^27F zoQHmqJ@>s|o;}Z<`UUo!dIk0>GCdFde6a8RO6+;=)X%Z!)GM=hmUFubUNtxm{S15V zd%qfco;&r^>^b%7?A2g;9{QU5j9exbA+v7)Zy90g%w>#o*;C3hc zFm89o58-wf{2*?3#Sh?iH+(;CcgOePb`N|nZui9Z;C3&3H*WXFcj0y)d?#-A#dqL# zKYTlG_s6&4_5geZcoIQ;PxbZF>X)B7vc64d?9X6 z#TVfAG<-g8Psius_6&S3ZqLN$;PxzhHg3O;zmD6p@maV%2cH|(qtEoO!*u^V&bjW? zXRzO?&*$89*6juOG~8Z@PsQy;_!Qh;j8DexCHN%VUW!k|?Pd4`++L24$L$sPINV-| zkHzg(_!!(?jgQ9dHTWppUW<>!?REGF++L3l$L$UHFx=jV55?_G_z>LQj1R``E%+eZ z-ii;z?QQq~+}@7&$L$?>KiuAl_r>j9cpu!}jrYdwJ$NtN-i!Cd?R|I;+}@9O$L#}n zH{3pmcg5{Pco*D0jCaQEH}FoleFX1_+eh&ZxP1(7kK4!bcDVf}-WIpt!rS2X+jwi- zeg|)b+wbBnar-^I1#X|fo8$ILycuqv!kgmuX}k$;zmGS@?GNxqxcwpC5Vz0Z4RHG` zULUtV!t3Go$9P@bK8M%A?elnT-2Mcwh1;LvHF5hhyasN6j#tO+3wSl${sOOx+h5{U zaQiE~GH!p3SHkU!ctza)2CsnI-{R$Q`#ZcGZePO7;`aA=8QlH>FOAzj;-zr=C%hzX z|BRQw?O*WXxcw_$47Y#7i{ke0coE#bj2Fi3EBMv09=#CfJn#N%oO9i&7v!8%|ATXX zGF^{eAe?vqUz~H@spsdMQ@_r+e5~7lJCXAI}v|1jrV zcj_59=hPqJ+@nm_qo)t&-Jh6qt~>Q~oO9|)IQJOS_2{pK^X`9~bFMq}S2^d@lX5N@ z)Ai`Dg!Aryf^)7r^_My4)RS{A1=IEDFNO2&f0A>qJM|Yi=hRbj?kT40(bIQKoO9~Wa4r?o_2@5z^X^a0IoF-~^PF?)&vNcLrt8t43+Mm0-}9{hZy)|F?tC79 zfte=ki=H~1cYj*Wx$e|can7l~$hnu8u19|+oOl1roO9i&zry;{oVQ=apTh0e@N{84 zdP>fD-u>x0=ekqR!1|M%w=?1?a61#8Ijl!d&Nua6!5_x$qIj{e9z7A~Jn#PEoO9i&mtg%N&f6vNQei#%gPilc z`%81qb*Em2^#?d_m&MD4_2~C=&hzds&pFqfdIi?+RXthWmLqPGs~zK-68^|oPO^mbw0*U{Uv-XZLZ-Z8BEI(jG8 zJBNMIyM%RLNAJpdx3DjI_pt8k=sj5P8TLi*71n(ny*KN9!oKKz!@94d_hY?(*cW|3 zSod}GfvgV-`=SpH>%NXYg!Q3eU-V&N-Ph5Fvpyp1i#{@}`#Sn4)<=hZ(Z_^!Uq>Iy z`na$!`uMQ!>*y0$pBVN%NY@ zh4rmrU-WHZ-Ph5#v%Vwji@r0g`#SnA)^~?}(f5RPUq|1|`o6F)`u?!)>*xnqKN$8! zKNQw|9sMxtZ-jl(kA!t!M?cE?v9K@t@v!de=x?(AR@fK)?Xd3a=S9tY_uC{XYIdSdac;Sod}GEWEB$Kf}4ROxL4l4(Hwf z5$9ZY>L0UyF6@h*iP!hM`_FUEb*KIb>lrz3e~Nz=)}v?Ooaf#DIpK9o5BJ7Kv zp4a!h`@iIz>rVYE*3)s`{u;j+)}z11InTTQ8_v1z)W2o@RnFVr;g`aC^jA3NdG~+M zIoF-~53Ik;dHYBFr?4LVCC+)?{XcWgb*KId>o0QN{uTc%tVd7FInTTQch0%))GxE1 zhV%9n`~}>;ieC%s(Vyp>=iUDY=UjK{f3p4@=k34n>tQ|mvz+t1`~T*g>rVY2)>Cud z{ufV$+c)r=VLkdYob$ZW{MiIOpxe_+z-8M0c_tJqhml$NcLsU3Wf?C-$$$)RVHFEbNQ^XgKfwCphQ2Q-6eW zPCYs2QZQYQ{%|<&{wF!-x>HZYIj5eIb5Ai{kN!|N@BXJb=ekpWkaJG`8P26*x*q+3 zaNhl?Ip?}lzn^nX{aMaE$8rVYP>rOo#o<6KczZKTq zp8so!MXsb|D*Fzrlq=CB_9->~lfEV%1V{U6qydR9DJSdad9Sa*MR+;yjZopq<4 z1OJO@=cIFm_2_?wb@%7SU3cn#uc6vIgjtmNTj*l+uS~l*{R`7BLI2FOOVU3v?NaoQOuIDw1Jf=;f6uhb z(wCTaIr=-MU7r4yX;+}XVcHexi%h!`{Wa6BOn=3+tI%IE?W*(_OuHI=foWH#KWExC z=+BsTP5M)&U5oyNY1gLDGwnL`Ii_8g{+Makqd#KW_35)ry8(TMX*Z-lWZI4B514ji z`hBL|gg(u*o6@J4b~E}U({4_mVA?I{_n3A|`dy~oihhS_x2E4_+HL5!n08zGO{U$B zKF+k;)5n;02l^<}?nobD+MVb(n09CSFw^crA7a{F>4QwW8-0Ljcc=F=?H=?#rrnd? z%d~sZdzf}_dNwV%lTrl}vjay@F|vr4i*t8ohvNPp9WI?HTkurahCM%e=z8%FNIF zi+P=ygL#8_lbMm}|DX2%uP4TB|Ns4?xSt33dBa1v?dKB@;(ngt=Oy>zwx6#g#Qi+R z&wKn_$M*9fKL_&jBtNh6b1B=;xBMK-&%^w@&Ck{Re9q7R{G8AB^Flut^z%hOzw~oT zKkxMOR6jTM^H)FL^>bW55BBqBKUeniZ$Dr6b9ApGWaIzGd0k=_J)1d`slWdJS)YT? z2%TaL#>keFOW3gmdnT>l@iOIGl4|T;IgLLE)VH;`(Ox4Gibp7uUD2Z$LQbzPP@X zef`5Z_r>*X?CTfKxi7A7XJ6lN&V6xx2mAVjbMA}lJK5JeoO54X-^IRO;hg*8`fm31 z4CmYz*Y~imM>yxcxW1Qt-NQNe#r1vc>lV(rFRt%rU)ONXeR2H&`?`d4?u+XO+1ELo zb6;FP#J*19ocrSXVfJ+l=iC?9-(X*daL#>k{RsQohjZ?W>qptwE}U~;TtCLXw&9%n z;`(v+wF&3k7uVlpU+ZwreR2IQ_O%M<+!xp1W?##2&V6zH9rm>d=iKMI^WCuDb9!^u zo%(xtGp2olZpySz(oLB5DY`M!K20}b+V9g1nf3>C1E&2UU7u;6q3bd2vvggi{SjS< zX@5-DX4>cIT1@*qU6W~lLf2s0pVHNt_Gff8ru{ixm1$p~t1#^^=*mp{OS%%%{)(>1 zw7;e+Fzt(Ud8YjhU5;sgOP6KZ-_d25_9ePB)Bc_=#k7B*OET>r=@Lx)C%QP({+TYu zw11(CGVNdKB24=?x-irJoi4<*FVh8?_7%DS)4odQXWG~3d`$ZfIxo}ylg`7m|Dtm< z?dxa{13Yv?(;(I^0?0zu`A#{kHoHs`}`8S67KU(?8>;$N3pBmK2OE2iu?Q( zyBhBETI}k$&v&tF;64w=u8I5n7`qnk^JeVYxX-7t>)<}m#;%L|{2RL-?(=f&`nb>6 zu^Zq%kH>C^`}`ie5$^MT?8dmy2eO;sK2OMQiu?Q_yBY5DitOgN&o{DL;64w@Zi)N+ zB)b*v^Oo$^xX)*@+u%OW$!?4L{3p8|?(?GT_PEcNvOC~DkIL?d`}``q6Yle_?9RB) z$FjTNhk0I!-4#EC+uiVkxZNE;fZILr{kYu|--p}1@V&U*8{dQ5eem75-51}5+x_sJ zxZNM$f!hP{?YKP<--g?R@U6H#7~g{1L-5VGJrv)B+r#jUxIG--fZHSR^|(C}Ux(YH z@U^%-8efCkWAN3uJr-Yu+vD(+xIG?Uf!h=C<+wc&UxwS0@TIst8DE0iQ}D&OJr!Ss z+tcucxIGjQb>?jRf9*Oxo1Vj*h1+xSnYcXxV;7+h1+ZK zk+{7MAA#HJ@!`0=0Uw6j8}XsIy$K(J+ne#hxV;4*gxg#3fw;X5AAsB2@&35I1Mi31 zJMq4_y$kPy+q?1JxV;DOh1+}ap18da?}6L<@$R^N0Plv|2l1}BeF*P@+lTSaxcvs+ z3Ac~n9dY|8-T}9d;q7tzINlDo-^APE_FH%x+l@0rE$8}vSod}G zEUae@`=VzH>%NYjo%I}HU-X<|-Ph4`v7S5Zi=HQ}`#O4F*7JpZ(esCOUq>&%dcm+S zdZDoH>*$49FB0}eFB;Z;9laRq#lybnCBnL|qnBj8RM;22bXfOw^fIiM4f~>(3+uj) zUY_*|VPEu$Vcpl!E3sZV?2BF{tou58Ro1J8ebK9jbzeuX!FtWGFM6%8?(68aS+5iJ zMXwvyeI30X>-EFF=ncZUucJ3)y;0Z~y>VFgb@V2zHx2uuHw){&j^3R07GYoXmSNr3 z(Oa?JI_!(yCan89dRx}pg?-W6hjm{^@4$M;urGS2utoIH3qW25yzK-6X^#NgD^nqdB*U<;DJ~-@)J|wLB zI{Hx7hlPFm2z(@SEkD=Q9)+*L?a}yZ+#Z9k!tJs6O57fYufXl`_;TEyfG@-CiTG07 zo`f&K?aBCJ+@69j!tJT}LfoE)FTm~T_LZZF2C;Pw)HGHx%$C*k%od?Icy$0y+S3Vb|nuf)gU_9}cV zZm-72;Px7PG;XiON8$Gu`0u%e4=@Md57CL3t#JEcycwR9PR1;YC#O>|i{UBhrlW@a+n&Vnb!OVXv7mvj90&$}F5p7|qQnXbaTh*zVlGrz!l z(!H2_ai_iqcj~=4x0`jl555bx`{FxsyC1#-xBKJUaeDy14Yvp4TXB03z6G}jb?-CuenRgrbb3Fb_ZK?7pRft{_c*HUP>U+DCHLhmnhdOu+i{{A_= zpV0dYo!(FA{e@2NCoIV8IK7|H`wN}kPne(m&TDkOFuk8JFYfey!aTUs`w4U7&g-=I z7drpOy}!`;5AOYiPVXo5{z2cTd|!(1Yj%7ewB!4x9p7i|_`Yn%_i;PE@7wX;6L$Rf ziXHzwWXFGR+40|VcKr9E9sfOQ$A9nI@!!*S{P(&Y{~Tb)KR4L%&lz_7bBP`Q9An2n z_t^2zNp}2ml^y>aX2(Cb+40YLcKmap9se9@$3J)4{yFst{=J-x`DmDW65RP@*ylMt zA?uz`9?ttZy661wb5A($>*$`FS@6G&uQO*b^;!R)_1XCJaL(6>>vPyQEu3>-T%XIn zso|Xa;`%)HO$q1R7uV;rZ*n;2zPP@CeUrjD_r>*v?3)e?u+Yd*f%Vkb6;Fv%f6xEocrSXI`$0-=iC?9*RyYMIOo2& zzJYy%!a4WF^^NQs7|yvbu5V)BfN;)zaeXuU`iFDwi|bq1*DsuNUtHhHzP{m{`{MdG z_Vo$p+!xokv#)nJ=f1eUgMGciIrqi&o$Tuw&bcqH?_yt%aL#>keK-5MhjZ?W>wDPO zEu3>-T;I#SuHl^f;`%=JbqVL(7uWZ*uX8x(zPNsXeVxKN_r>*t?CTiLxi79CVqb@F z&V6zHF#FnvbMA}lZ?LajIOo2&euRB(!#VfG^`q=-6VACWt{-Dx>u}C}as4>^T7`4& zi|cQ)uVpyrzPSDt`&xu^?u+Yhv#)tL=f1f94*QygbMA}l@3OCHIOo2&{vP|9gmdnT z>nGUPIGl4|TtCUaM&X?M;`%A}H4Nw67uQd-uR%EHzPSEA`|5{t?u+Xmu&-V?=f1f9 zA^YlvbMA}lXV_OKoO54XKg+(_;hg*8`bX@m70$UYu7AwFn&F)L;`%xE)d=U@7uV0T zuX;G=zPSDg`>KU=?u+Z6vaf15=f1f98T+b)bMA}lpR=!WIOo2&et~_J!a4WF^)J|0 zF`RQ>T>p}N6~a09#r3b)S3aC`UtIs1edWSA_r>*#>?<42xi7AN!@e@%ocrSXx9lq& z&bcqHf5*O3;hg*8`X%<24CmYz*S}|9iEz$+as3DO6%Xg!7uSDeU$Jn`eR2IK_7x51 z+!xn>W?zwT&V6zH7xonn=iC?9e`Q~xaL#>k{Wta%4CmYz*MDbUfpE@!as4v;@`rQo zi|bd|moJ=iUtGV+zP#a_`{MdF_T>rZ+!xpXU|;TV&V6zHPxj>s=iC?9|6*UxaL#>k z{W|+{gmdnT>wmK^dpPI5xc(3OvW0W*i|hZgFKamGzPNsaeObae_r>*_?8_X^xi7BY zVqd0k&V6zHHv2M$bMA}lci5L9oO54XzstV#;hg*8dV)g#ea@FIoO54XzlZ&=vEOs< zi|Yy5|0?@E=f1dpFZ*9%zvtW+*Y9Kh%k1}@`{Mfj?0<>xtO^0{cDZzPSD{`=4jO=iC?9A7TGLBu_niCUdJ^`hV!!9y7uO$S|1<3OocrSX*O z>`%#l&$%zIKf(Sd+3z{`#r5RuPr-iAxi7A#V1IJjIq|=6I~V>ZZs*4T!0kNvHQdgN zU&ZZw_!ZpFk6*^^0{HK^T@e2bw+rFF;&x&D7u+s_|BTy3@t<(J82%$}7sr3V?GpI+ zxLp#zgxjU??{K>`{w;2o!N0-nviL>ZE{A`O+vV}EaJvHjC2m*5zrgKE_yydqjDL>X zRq)SnyDI)EZdb!U!R_k!dEBmnpTq5%_{X?i3;zhWYvX5eyAFN^x9j2`;&wg!1Kh5U zzmMAu@YA^65I=?6jqsDW-55WC+fDHIaJwn~E^ar&-@)zX_}jSM0)Gp)TjFoxb}Rfi zZnwsd;dUGRC~mjKkKlGY{0-c0j~~YE4)`J5?uZ}6?N0as-0qC;$L%ioKHTn#@5SwI z_#WKuj_=0p9{4WY?uqZj?Oyl}-0qEU$L&7&Hr(!uZ^i9?_!ivmk8j590r)1|9*A$m z?Lqhk+#Zat$L%5bI@}(Luf^?Q_!`_Ej<3e;5%?E5w{oN z6L5PmJ|4H1;Nx(6DLxjrm*Hb@dpSNDw^!h!aC;>_61P|3BXD~)J{-5#;KOixEj|>t z*Wp8Odp$lFw>RK}aC;*@5Vtqs18{pY-XFKO;QerWE8Z8kx8Z$odpq76w|C&ZaC;}- z6SsHaJ#c$B-W|91;N5V0FWwcm_u*Y|dq3V8w-4Z*aQh(M5w{QF9dP?F-X6E#z}w;W z5xgyKAI00?_A$IQZXd^6;r5$&OWb}7Z-Luy~+&+WX!R@nnZQT9{uZ7zm<27;n99{#r&*Rl` z`xCqxZhwkb#qH1VD!BbQUKzJ9;FWOu3%nw3e~DMX?XU3ixcxO=4!1AjWpVo(ybNxC ziJr!1ckX?gxmMvuiq}@55il z?fdbUaQgxLMcjT6Pm9|R;c0L?5&i;hKa4+*+mGPS;r65Wv$&lYPmSA2@Km_{82$`y zKaM|*+ez`Ka61{E61ShgpTzCtcnbV~jVH%bc%JzL{v@4}nGCm|!jt0m)A-}K{S5vX zZl}VN;C5;}F>XJLKZ@JW;g8_<^Z3KK{Q{l{x6|Mc;dWa5LEL^3e*m{%!tclJm+|{> z`xX3N+_uzIqJOOT}$L})j4D=nQosquHv@_ASn099RCezMB-(cEV>3^Aa zHu@i?ot^%hY3HD?Gwq!8Uraj}{U_7TP5;5P^U&9rc3%1_)6Pdza?ZWggOuGpEGt(|g|HQP5(LXZn;`9$py9E6`(=JJ0V%nwX@0fOJ`dg-5 zhW>_Wm!&Tg^ckjIm;R7x*P}mR+V$!8nRWyEG}CTKpJLjL z=#xylF@1t*H=*BS+D++qnRYY!9j4u!ew%5xpx4W7=)#qfEOk zeS~SZqu*fK?diizy90fQX?LU#GVM^bV%oo8Hc}`_S8%c3*lc)9y!aVcPxa%}jd$y@_cLq&G6{LG%WuJ(yn4 zw1?2^nD$V5Ez=%GuVLE5>D5eo1igxBkEB;J?NRg!rahWo&a}tS%b509dMVQ$M=xR8 z&kF0h z`+Ysn+5YwY>)8JH^}m_Z#0=Y~P=JAF_Qv^L@+q{m=I~+xJV~7j55PeIK=bKlXjs z_Wj%UY1{XE-`DM3{66T(w0F}znD!pJJJa4vcVpW7=&nqAKi!3CAD}xk?SphDrhSO+ z$g~gB9hmkTbbF?Kgl@;QkJ4?K_A$B*(>_kOX4-Gkt(f*(bW5iFHr;}0ze6`?+V9fM znD%>gQ>J}_Zo;%r(v6w+DY_BUK20}d+V9g1nDz&BeWv{(U5{y>q3bg3vveJ%{SjT8 zX@5-DV%q2EnoRpVU4vLmc=t@lcOS&S{{)(=^ zw7;gyGwq9XIi~#$U6yHoOP68V-_fO+_9eO$)Bc_=$+UlEcZLC%PEZ{+TYy zw11(CFzsLI!c6-&x)9U;oi50_7yrm)4odQW7^l~yiEHKIuFzSlg`bw|Dtm- z?dx<-ru{dagK7UmXJ^{~(%G2y4LU2+zDZ|c+PCP;O#3#SiD}=VGcxVFbOxsF=YQ#O z+s_Nr;kKVIzJ}X=9{DP6`}yT7xb5eiFXOhKkG_Q4exCXwZu|LbTHN;Y+BCTB=esZ9 zwx0(-kK2BJ{2XrkdGoWl?dQ{}aof+cQ{lFse?NoUeqR1GZu|NAQ@HKt@hNfJ&+nha zZ9nf%f!kgmNRHcHPj~{iz5b94x4mAG6t}&;@i=aKJ>)Um_WDT@-1d4)V%+xn%%iyN z^_)j=+v`6M!weGqmT-1{c%vbgtI*yV8V%dpGi-p65Az`gIou84b|h+PTyz7o4K?tLhB72Nw) z?5eo;x!Bck?~Adk*Lm72k*3-SEA*-5uY9+dc5zxZM-qh1 zmAE|`UxC|W@a4EY7GH+j^z8V(K0AI7(2n05wBz>-?fAV!JARMRj^BH<@O zJO^(7jc3R0fADO${V$#sw{PHCaQh~n8MklYnQ;3yo)Ne2;2CiHE}kB@<8wgl_}maX zK4-*^&n2Rk7o9SnT-R7CS!Y#g5N~vEy@O?D*UnJ3gnzj?cBR z<8yHA_}m;jK4-`FxjeJ@bAMvK*PDW=PcQQyxURp>IoF-~G|oBo*_@lox;+Pp~NyfJRShd09Q6L>@1K8ZKL?NfMt+&+!h!|nI+y14xTUI({7#B1aB8N3#5 zpT%q9_D6UP-2ND^j@#$(YPfwKuZr8B;8k$@Q@k>6e}-4W?a%RwxP1YyfZJc-<#GE< zyc}+Sg_p(cukkXteGxB>+uz`&aQj=lByN9)m%!~ycyZkR9xsO5Kj1}i`$xP8ZvTWA z#_gZ+Lb&}4UJ$o`#S7r}Z+L#({vFSU+n4dYxP1lBgWFf}+_-%W&xPB6;5l*oPdo>1 z|AlAA?dy0p-2NNSirfF-S#bMbJTq?Jz%$|YO*|uR-@-HC_H8^pZr{Pv;r3npHQY{6 z^uIqRzKYxT;IH6zLi}aiz88N9x9`JW#O?d>w7C5Mo(8uc#9zSehw$fdI}!dIZa<7a zi`$Rjsd4*JJQZ#y#-G9MB>2;~{TTifZaPl4M{;K}j-HU0#i-1E$2 zcnbPSW>VZvi9e3pPvMW@_S1M0+I{qx4Yvzak~e;1GjtP+i|-Wz74l~<6CjN555Jr`{J8%yC1#@xBKH8 zaeDy10k;R@>v4M!z7Dqs<7;ty2)+ilhvKVodla zZcoQ&;r0xCCjP(1XW%nE&zz3WqF-lD!|mDlRNS6}Pr>cE_+;Fkhfl)o`S?WKUVu-) z?S=Sw++Ku_!|lcRSlnKMkHPJw_-Nc-hL6JS<@iY4UV)Fm?Und&++Kwb!|m1hP~2XF z55euV_+Z>#hY!N-_4q*C-hdCl?TvVU+}?!u!|ly@U) z_6fWpZlA;(;PxrJK5n1J>*4nMcwOB70I!4FAL6xf`wU(Sx6k4=ar+~@25x_hSI6yh zcs1NUk5|R*Pw*vsUJ|#z!%N`yCA>Ipe~%Z#?H}->xcwtu1h;>}3*+|Bcp=>W1uux(zv2aO z`!_s4ZvT$w!|ls>UfjNd=fUl(cy8RjhUdcVKk%Hm{U@FSxBtSk2Uil{u*v4D3;*={_`qs--Ew`+X?ZP zar<8UCEUIbe-XFu$J65W19%$Teh_~Fw;#fv$L&P;bGZF5{w!`kf~UsqNAXm+ofv-x zx0B#cLU!nfgeZ+t6m_rbT|c3*rmZui4C;dXy~BW@4CH{kX_d_8Ut!q?&U zV0q_E3B^ZV$s(;r4KRC2o(vSK#(Yd^v89!k6LpXnZMdkHMGV_E>x|ZjZwk z;r4iZA#P8=7vT0pd_HbZ!sp@kWPC1ePr>Kl_EdZ}ZcoEs$L;C(EZm-f&&2=N_zZle z=b6*-S@i47X}CQbpNiXa@F}=G7oUvV^YBTyJs+Ql+Y9gsxV;b`kK2pzak#x0AB)>d z@G-c(6d#S-%kWXSy&NBj+bi%9xV;h|j@zs7VYs~-ABx*+@FBRp79Wh;>+nIiy&fNk z+Z*r!xV;hYkK3E@ez?6E?~B`8@IJV`74MDP+wfkvy&dm~+dJ?cxV;naj@!HNZn(W0 z?~2=d@GiK$7w?SQ`|wV@us-_4&DT}-^Cl__Ir3E+&+Of#O;%K1Kd7^*T?PC zcs<;HAFqqsAK-Ox`$N1oZlA$x;r3a)CT@R(*TC(M@#?sJ4zGsW=kcnz{Rv(Lw?D-z z^*YI4p{Rf^CxBtX*;Pzj5cHF*>XT$Bk@vON051s|L|HU)o_6$sgA z{|mQs;D6$FPW%tt&V^sY?cDfP+|Gkv!R@^GW!%n(|Bl=F@!xQ}0RAg(7sP+T?Lzp^ zxLp|k3Ac;jKjL;#{0H1FhJTOS#qmqHT>}3Ow@c#R;&v(g8{969U&QS)_}92y7XJ#j z%i&+*c6s~@+^&FM!0n3o=eS)7{|vV)?P~Zr+^&v)jN3Kvk8ryt zeipZD;b(BWHvS=Q*TFx)?Yj8;xLprFjobC{Q@Gs#KZ)B7@e{b+2!9W^8{_Zdb`$&^ z+-{1$joZ!ew{W{T{w8j>z>nj0OZ*sax5AI&c5D0yZnweT!0op9Vcc$qAHwbS_(9z6 zfFHo^j`)7u?u75d?augK-0p(!!R@a2ZrtvM@51fw_)gsJf$zZWp7?g$?uBo|?cVrS z-0p*K!R@~IX58+FZ^G^V_(t3wfN#L>rk@#}l9)&N%?a}yB+#Z83!R@j5V%#2wFT(Bd_(I&CfG@!9iTHfno`lcC z?aBCD+@6BZ!R@K|Y}}rPzmD6}@maV%1D}ciukji9OwTi?x3}TFaC~-X6CP;q7qyFy0oo-@x18_7S`_ZXd;4;r21SC2k+bTj2Jacyru-3vY(o zZ{tmI`yIRqZoi8+#_jj;M!0;-zu>8@v>5e~XvI?eFjsxP1vPj@#ek#c=xvyeMw}h!?@_ zpYX!C{WD$&w|~J4;`Xn20o?u#&yU-`XTWKZQSr+fU<3aQhiNF>a^AAI0s|_#?ReEdDTVKZhs6?dS1_aQg-P zLEKJ*KY-h5@%wT6Mf^V8ehI%9w_nB+;`S@}J-GcUo&dLB!|yWfbo3pjou0nUv@_7R zn07|`CezMD-(cFA>3^Aa7WyBiot6HZX=kIaGwtm3Uraj({U_7TN&msLbJ5qBc5eDA z)6PR*VcL1=%S<~T{X5gnPyfcW3(&tZ?Sk|#OuG>MGt(|i|HQP5&_6QmqVx|;yBPgF z(=JY5V%jC>@0fN;`dg-5ivEUam!>Z=?K1S&OuH=o71J(9f627V(_b*{3iJi0U6KBr zX;-2@W7?JJPnmWV`V*#Il|Ik3tI_9}c6It=rd@;nh-ufP&ob>=^ckjIoBoh#*P%aP z+I8vonRY$;G}Eq6pJLh#=#xylA$@{rH=^HT+KuUVnRXNU9j4usew%4Gqu*lM&FMFp zb_@DA({4#0W7@6gqfEOseS~SZq2FNIZRx{IyB&RqX}6~jGVKoZ0jAxN-p{l<(fgQo zXL>Kw?n3Wj+Fj}0OuHMsi)nYKcQWlB^bV%olitp>d(qpNc5iws)9yoWVcLD^%}l!= zy@_e}r#CX~0rUo@J&<0{vD5eo7`=*V52sf$?Gf|}rah8g z&a_9-%b50PdMVQ$LoZ?4W9h|AdmO!pX^*EDGVKZU0;WBYp3k%=(es%0WO^>so$>ghdCvB)?_bCEzpwwjZ2$N9zr*(L zhyS~6|Ni;+$@cHJe_w6?`S{Pl_MfN!+-(2(`_I|-*NeX{Y=3?E>&W)kqrdKKfBpLF z)b`iAzpib6fB5^r_V<&&Z)|`6`TNZF_p84zZGV6J``GsVz~A?_?;pNT*uLNRzGD0S zQnQ8B*J2CA8bVsIrknX^=57F(J_F=jm(|&_)%e0Ts zZJ72^x;4{2Mz>H19jeYzgg{(!E_v_GWlFzqvRZKi#euEn%JqH8kkkLemr`y5@JX`iR7 zG3`(2s!aP+x(d_&jIPYIKc_1(?F)28ru_w7foXq9muK2v(dC%-*K}E?eUUE1w7;QC zGwpBbQcU|hx+K%SM3-RN-_ymJ_78M1ru`#blxhD&7h&2z(}kJ#FLWWM{VQFNY5ztS zVA{Xa`I+`*Iv>-%Lg!`LSLr-V`x>2_Y5zgzV%mSwIhpofbPlF{ozBj*|E9As?SJU3 zO#5Ft3)8+qXJ*Oa>eDo#U_Vd&iaof*d)8e+D*QUX3Ki_=;xBWc$dEEB%miTfw%1RR;I`LW663blXCB3Eujf31+g|^97`MG%lnA%IzVr}odp+tw z-1hp_1Gw$=uKRJ@>tpxfw%60{#ci*@CB*Glxejm-ZhL(%0d9Le(CdD-*AKl;XnVcU z>x#D5C%q17dp*$*}`G=e-VZdp+Ol_O{pm zz0PlYzkv4z*hTqu<$VNpG2HtO?BclhDcB`&?`yD2;@$^gm%_bo!Y++_pM_lp_r46f zEbe_Ab~)VpKJ4Rd!LJ41^2!fyDIK|GG~_jeDPwT?hBRB)cx|eN1*e-20yF`ndN=*$r^-tFjy7 z-iKv3!o6?HZj5`Mm)!*SzA(Ef?tNr-Gu->m?B=-lso5=X?`yML;@$^mx5B+|&Tfr+ zpPk(X_r5&4E$)4Mc02qq_kq~$@k6-X0Y8Y_9q|LW-3i~1+nw=!xZMTci`!lCJ-FQs z-;LYd@m;vx1K)|;J@Fm5-3#B2+r9B^xZMZeiranhEx6qe-;CS+@lCir0N;q)1Mv;G zJqTZq+k^3SxIF}4i`zr-HMl(tUya+t@m07z0$+*SBk>iuJqllr+oSPixIG46irZuH zCAd8fUyR%1@kO{j0bhvQ6Y&MOJqe$W+mrEmxIG1*i`!H2Ik-IypN-qo@z-&C20jb7 zXW}z)dlo(ew_nGnerQ_C|aFZg0Z-O=sydG|UfY-(C5AiyE8_MScm>@45-*S2U*Y9&`)j-`ZePU9;Py9oY25x6FNNFR z;U#hV5?%tgzsHN?_78Y5-2M?SirYWoMR5CPyfAM6f)~Q=U-5#t{Tp5Yw|~d;IA9ya@{u9rM+kfFXaQix*9k>6+v*GqXcvjs07tey*H}K53 zeG|`w+qdwHxP2SXfZKQQ^tgQ&Plww+U*t91_IV_);%Y59?bK&?ek-v!)>28^DJ)re45m_?elC>;kM7ec?P$A zUe43F?ele>!fl_&lM=Ume$SJ*?el(8;I_{PN{-t;Pv{BU_W47}aNFkHy&|MW0!`@E<`xb5?$9>Q&(NA)0X`~0d0 zaNFly-H+QoAL~Bc_IXnmybqu5{rtRdpYHwtyw9KR{Q|u&pq`swSKdcZ&qMdVgL+=N_bJr#(Y>#s zo}cc05cL9d@0+L>qZR%4M^i6D_r9BYS-SV>)XUMmucuy~?tMV@3Uu!qs#m0YpHaOM z-TRX2mFeEcRIft!zNdOsy7x)dtI@r$s$QM$eOUDxbnn}$*Q9%&SG^Y9`@-tA>E1_H zuS55~vwB^+_o>zE(Y>#&UZ3uLaP5Pc3^A55Q3*N4z&(e_eH?uPT^~;$PuC~V$Ib!Z%o(s&>PY9 zz4V53eILC6UEfcyPuCC7>(TXt^tyEY5WNmvKTNMp*N@O^(edjIR59)TDIX=c#^0*M0tK61wj5S`*WCpYNK8uKPULFX_6^kNtwK`@GqNblvCE zCZOv+&-Qb=?(=Uyqw78|_fxv=^L0O=>pqY7W4iA1dq1M8Nzw=TS$Y>ps8wJ-Y7muHU8WJ|8pnl+=Y;D%Z`|jK>pq{{=aB0@&)nyh>puV7=bY<4FWu*&>poxI=cwyGkKO04 z>ps8T=d|lS@7?FR>pmae=fLYePu}Oo>pp+p=gjLquiod<>ptJ!=h*96cy6=Lz1OqS zf6o2iU(naH(SM@r+37#h^&IqPbUi2iDP7M+e?r%D(;w6IJoHC&Jum$sUC&2u^%C@JbiE|~DqSx{ zze3kb(=XHYGW1Jyy)6AAT`xz!K-bID&(rk^^mBB*BK<5~uS7pX*DKRc)AcI!Q*^y5 z{UlwlMn6H1hZ$#fi*BjG!)Ac6wU39%EeJ5RSM&Ci#o71<`^%nGPbiE~gD_w6z-$K`0 z(>K%gHuOz&y)AtsU2jL(+AV_IrKqveJ*_v4kHS|t& zeJ#BsU0+A(liE^m=stAiXYKKSZxX z*ALTc)Ab|tT6Fy=y(V2hMz2BFkJGEu^%L}Jbp0f~DqTNCuR_;P(<{^UGxSPy{VcsA zT|Y;!K-bUH%hUA>^m26lBE2kKzeF!X*Dupc)AcL#Qgr<)y(C?~MlV6vuhWau^&9kJ zbp0m1C|$orFGAOE(+kt}JM=!K-cfn^V9VQ^n7&vAw4f$e?-qi*B{e! z)Ac9xTy*^@Jttj%M$bXlf23!p>p#)6(e(S_6()IW0U(odr=n3h1bb1219)tclU5`osjIPI`e@fS5(?6l>ap)h@^|9OegXY`nK{d0N@x}Ja@ovtUO ze?Zs2pubPozobW_>xt-5>3U*%6uO>-{vKWbivBKLPfCwW*OSpB(e>o?h;%&#{T;fV zk{*Gsr=q{j72*H>qo=0-OV`uT-_Z56^nd7jI{It6o}T_UUH_W?7hTUle?`|b(*LCE z-_T#u^>67f=z1plA9Ou4{dc)Gi) z()AqlXLLO${V83~MSnuqbJHKw^*r=PbUiQqAzjZ$e?Zss)9=&u0`z-yy&(NAT`xqx zL)Qz_Z`1W6^jmbjDE%f~FGjyX*NfAy)AbVcYjnLN{VH8AMZZGVOVcmY^)mEJbiFM7 zB3&;>zd+Z^)6diO3iNYyy(0ZAU9UtxL)RXVdi| z^jUO$D19bfA4Z=+*N4-m)AbSbX>@%geJWiaMV~^~N7ES@iyNeKx%xU7thmOV{Vp`_T1y^xkxRKD`%RUqJ6k*B8=z z(Dg<1?sR=Iy&GL$Lhnk~m(sh?^=0(VbbUF!6J1|H??~5I(mT-gRrL0BeKoxuU0*|Q zOV`)Z+tBrO^wxBJJ-roO-#~9k*EiBz(DhC9=5&2Cy%}BKLT^gfx6+%?^=(KRm^xAZNKfM-RKR~ZZ*ALQb(Dg&~ z>U8}uy&7FVLa$2KkJ78q^<(tPbp1HJ5?wz*uSnNV(ksyQQ}pt5{WQHCT|YxFOV`iR z%h2_6^wM~#Gx zJsVwrLeEOqpVG6?^=I@SgL&A0V9$Ne|HPhiY5$%*Y5$qMU!e1_e;4d~{#W*#OM7Pa zr2RR2nV9Rp(SHx-VgHsr_dWjyd(Nf(8}_9A1$!@{^RQwnSz4(4G`&z}39f6bnAX-~(VwEx528|XajX@h;w|I40pX-~tRw7+F9HFG^e;s5@J z`yJ>!?5Wsu?)ix9IhXd7>`8kh_98>)VNVh4d;VSaoJ)Ih_N4ti_M$-NVNVw9dp;_A z&ZRvmd(s|_z4xK>uzwZod;SCVoJ)HW_M|;JdoiH%uqO`oJs*=j=hB{tJ!y}{UTo+* z>|X}^o{z(xb7}vAJ!y~2UOebL>NOCt}aJv?pf%A^Um~dOW)R6+LM%4|`np-1mGk_MA(5a^`W^*Hh4A)Af|}RKYy# zvDkCp^QqZ$F70WU$7EkmOOHX<)6vrh^RP!}&wbB-&7N~<&%pcx_VtYP_v!jK^lyWC z*rT!MzUMQs=Umz|Gmpx?{vG}MU>^1;?78pxAJ}s)?OB+=$G)DG{w`h5M$aD1!ycJE z_dTD3J?GM%lX)ce^<4Da!945{*>m6XdDwF^YbAqRij0uNR{i59VS2hduW_UxGd7(q58zso-4L zUvqu;JzttV=h9w=`QPm8W$ERDdD#DA&wbCAXV1B`S72T-I2ZO;T;F}qS7Oh(v{z>S zC;NI8devYa_G-c0>)2m%U1_h*UJd9x>@R|S&(~znxwO|}UOPA!_Bz4b>)7iuuNRyP zd;MVUb?gn8Hw?~&y-_gtI`+oQn*`^=-ZYqd9eXq8&4Y7cZxPJBj=d%GR>8Tjw+`lB z$KHl{+u&T-+XZv4V{gyALvSwa9fP^ov3FwLIXD;gF2UUE*t;_C7Mu%v_h9aI>^+$G z49!3c4K0cU-eL^tzI`)aoCk5xi zJ~^0s9s3mKQ-gD1pBBu$j(s}w8Ns=*&kW{X$3Bbs?BHD3=LB=FW1q`>UT`k#^Mkq9 zu`gi0FgO?XMZw(b*cUTj5}XVB(qQg&?8}%h56*>sMKJd|_La<61?R%PI+%MM`x@qJ zgL7eD7tFnmeLeFH!MU(+4CY?PzKQwf;9S_Z1aq%r-^zSja4ziIgSpqS?_j<&I2ZO^ z!QAWEcQfA;oD2KjVD5G7`?ygv`<_3`o^xqG$2^45?78pxi|jd<_Djq!2j{|`jO)Aa`77)>m-eg7ld`X0qhAl^VgHIf_dS1u zJ?GMXlX(*M^;`7Y!947V*>m6Xci3|-?RS|cVqd>UzaPxQ{v~_vd;S4?&ZYe!^Do%f zAJHEN^ROpm&wbB7Vb8g=KV_bPef=5zbGrT`{ik3a_RrXJ-}67S=Um!`8lE_ToY3VUHB-d;UZA zoJ)H|_M|;NdmlmPVSgvs_x#7~IhXbb>`D74?0pKIhy85{zEPL!c>Xi?oJ;$^>`D9Q z>?MHC!~Q1N_k2S3oJ;#Z>`D6?MKD!~R#W@A?MQF!~SQm@A>5HIhXdA>`8kH_Fgd8Q_@of^RWNHp8K9p&7N~<|D8Q) zPs3hX=sfJd1^b>)$DVU(f6kt?r)Te1=K9z448c6?zp&@N=QFbBT-twTPujm>?_20R z>^}wjp3lUdb7}vPJ!#L(-ZSRcuIHdXpzAs5_vv~r`aQayn|_zB=b_)B>v`$7>3Tl;ExMkcev_^j zpx>bD1?ku6dLjBXx?Y%mm97_|U!m(o>6htxG5RICUYvfBu9u)+pz9^+=jnPW`Z>B@ zntqn9m!Y4b>t*Su>3TW(DY{;sev+4)ih zHTogCUY&lBuGgR+pzAg1`{{Zu`aZf|o4%K>*P-vB>vie7>3Ti-F1lWyzLTyupzomT z4e8tIdL#Nay55++m996TZ=vf=>6__#Gx{dF-kiRXuD76XpzAH^>*;zc`Z~Jan!c8< zx1q10>uu?)>3Tc*D!SgDzLKtYps%3o9qG&IdMElay55<-l&*K7FQMyQ>5J)lH~J#F z-krXXuJ@oXpzA&9^XYmo`aHVcn?9GW_o2_B>wW37>3To67XDF#06AKAb+0u8*Kkpz9;)tnr6Fc14^_ux3rkB82seH7=VeFA$Und=kjBk1}h`f$2FnLdoJPoWQ`>r?4N==wDJ zV7fk?K8UW*pbw<$GwB29`Yd{Xx;~rUkFL+5_oeG|>3!(>JbG`sKA+x;t}meXr0Wam zJ?Q!(dUv|MnBI-9FQIp(>r3ff==w5xXS%+e-ifZSpm(I}E9o8R`YL*Ry1ts;j;^nv zx25ZA>22uxI(lomzMkHSu5X~Xr0W~$E$I3tdULwIncj@9Z=pA(>s#qf==wH#W4gYb z-iWU6pf{xJJLwJR`Yw8Xy1tuUkFM{b*QM)w>2>J(K6-7szMo!;t{w zdUd*fm|l&pAE8&J>qqHT==w2wWx9TxUWu-spjV{pC+QXF`YC#Ox_+8oj;^1fm!<1x z>1F8pIeKZjex6>6u3w;+r0W;ymx6iNOR(p@=P$G8T-uAXC+%0*y9%9$y;!jC`D^Ss zm-eFUN&9v7iZIu2&~FCwuoq^}eb3)w&$+Z0Vo%y{vv&tN4|~C2-}86bb1v-#*pv2q z?B!>!-={wa=3&prp8KAE$ewd)&&!^)KVt7ObRPCR!M^98u;*ObbF(MyPua`GTz^LY zF_?!vCwuOD{wMaFOM4FXr2S|1eu2)zo;}$2{IBdem-cMzN&9p5euK`#o;BF_{O{~J zm-Z~|N&6q{y@1Zc{zI_u`IqcDm-g@3llDK^dj*|`{kve_^MA4DT-r0UC+&Z;_Zm76 zd!}ID^Z&5tT-v{7PukzG_b+rF_HTlH&%b5QxwL0wPue3CM`ABBbRPEf!M^9;WzV^^r(;jr-(xQdbRPD!!M^9Cvgcgd)37J)(b#(*IuCp5 zVBhl}u;*Obqccy%z8-^~lCH<3#|q|QPr;u1o{!C*b7_ymJURP%TzWFP9*_QEFb{iD z_T2Y;eD<76`$x>bVqgE5o`kM{LjN?FhdnWS?tA_-_MA)m=gbqauP2~?N!Jt7zX;}G z|AIaD|99Sf=KniKPXrS(*Avr|1oN;bV9$Nef5o12X-~@hbN2OQ^v~#ea(aqj9`;Y! zbKmnR*>f)KshEGlzMh(%CYXo)WA@zld|LLLOM5!zAF;2er+*#H!ycbK_dTD1J?GM% zk@<)0>)+774d!8w$DaG1&%~Z{Y0u0&F8lg-^zVat*yFJ0zUO~n&$+Z`VV*TO7xvg( z-+j+#W6!y?XJ;OZeLV*~XD|L z^V16i^RO2T=3dAC0oRrGLhKcW&cptGu^YbAqRfj0=fWP1>$~sy;_Nw>_7coX z2Is;amFv6j`BLmTm-f=k%LM1b9);_>@A^YbAM$8)r=fd73n0p<2Q|8Trb76mnAE*1CZ_b`` zX>Y;2WpFO+t%AANvA1U4CO8-Nw!z%%*xNC0ADjz&hhXk?>>Zg$;O9l}MDHBT!`>yB zdmVdM=G}sGVecNyy^g&H^Pa)Eu)i(N_c!wICWxH(4E_VraQO)M0alg zk?!2yi~s)4rTrOu(*Be#?N8{^{+KT9kLc3=kS^^H=+b_lF75Z|(tejN?RV(Xew!}s zx9HM-lP>Ky=+b_jF74Oo(tedL?N{j1ewi-qm*~=dkuL2Q=+b_kF74;&(tegM?PuuH zewr@rr|8mtk}mBh=+b_iF73zY(teaK?MLX+ewZ%phv?FNkS^^9=+eHQF75m1(!Q52 z?R)6bzMC%XyXexslP>K$=+eHOF74as(!P~0?OW*5zL_rVo9NQMkuL2U=+eHPF74~+ z(!Q21?Q7`LzM3xWtLW0ck}mBl=+eHNF73x1ai>H1*$G`c>7K9#Ny zrB9*j!|0Rg`f&Orx;}zFk*<%VPoV3g=;P`7X!(l82>G}-% z0J=Vt-k+||qW7cgv*~^5`W$*6x;~fQo378J_oD0b={@QC0(uX+-W|8Q=X@c1NrFB5 zqG0Yh`(oxxf^%VC8qB?peHruR!MU)n2}!I#*Rii)1Ci-x!<=`=(&-b?lp&ZwbzYJrO@2?t6YKd(NeO8}seKxv=jD=3d9XlliXT zT-d+l$LYT3ceCeQ+V?Qu8=MRKzF_Wk?E9I2!H+{fKtCAF!+t23dmZ~><_WpJeuREB zn1}sXF!wt4Tm$J)f5DT-u*8m-ck@^uavrPlLJVzot8v_9x7xJp(;sFc16VVD9;E=+346 z5p!w(mYyk?hy7tN_k3o$b7_CTT-v{*e;>@lem|Id{s+2qX}`x@+OyEJ2J^7r4d$ND zMt3glcbH3icKU6o=fHCY^RV9v=AO?*cP{NWnM-?adY)h&_8Y<6^LgpcrTsc{Y0pQ$ z2KD@SfnXl?tHIp!1?kSE{R(qwFGMdK%)@>;n0vkm-MO@1VlM4P=@+413@;wc!+s%{ zd%gtSxwM~WF6|}hrGk0b&joYOm!>W%PiP;ZQHg?bZw3)Gw9o1xwe-vssM_(rI=z&Aj> zCB7c&t?+eFZ;h{odK-KV)Z5~#q23N(1@-p$N~m|hS3tcZz8vbE@MTc%j4y?H7kmlS zyW)$X-VI*__3rpWsQ17ZK)okEAL_mEc~I|-&xLv)d=Aw6;VxoUP#=s>h58VD3e<<$#`<@%=cuKVE-ay8nLud+7dr`0uUz^XJc#?$5VBue$$z{O_Rq z-_!qYy8r$C@2val#a|b?zrOr+r2Fg9Uw68{e*JZ-`|I6b*Sfzy{C%MN`^n!oy1)PY zeWv^S)!&!8zrX!`towT4?|a?X4__yAUvGR}(S3dLbx8O1%-1d5*FRt9bYCxhUDSPj z^>tMD_1M>4-Pdnlr*&WNeO=er@cYEi1NvHe8>p|tTSI+4-U{j)@Rm^Dh_`_HCcHV+ zH{;Eqz6Eaz^{sdlsBgm?Lw!5m2Id+eP(O&*fchc4I@Ay2)u4U^uL|{}conE0!z)AmI9>_rC-90;KZ#d>`YF6T z)KBB(pne7~3-z;j8F&$1f<@sqcpc`0x8WU_9ln4sVZy-w#1p_*@GtmD;D6&E!`JX1 z7%%WQcwG1|d<$a+?w^0hp!?_L{&{>fy6&IfN2UAq0>7RRneNv;{JM%?Kk@4@ex1j! z=lFFYzwYGMm;5@FUkCH+U4A{zulxD+J-`0w*E#)qreE*&>*{{}T=(nwe%)U8`w0Ag z0p0Ik@cR_}{tmw{#_xa8zvAl=e*cfOCuN>2I2ZQh!QAWEQ!q~%oC|xZVD5G7shOt< z&V@Z~F!wt4bj;HS=feJVF!wt449qhI=feI?F!wt4Z<%Kb&V@a5F!wt4@0fodoD2I8 z!QAWEvoOyZoC|xlVD5G7*_r1E&V@Z^F!wt4T+DL^=fa*Rn0p<2Ugr6Nb79XP%)O4i z0P}*uxv&=s=3d8Mn0b-lT-b{SbFX7B#=LlNF6?se?tnO6wTg}q`h_d51U%qs`y!d@kqdmVdK=GB68VXq#{y^g&G^P0iAu-6La zUdLXWd7a=~*y{#!uVb&rynb*l>)4wxZyKBnd$VBfb?nWV zw+POKy=5@>I`&q~TL8Ny${{*gVy`f=Wt%{N1sjC`_pI9^#SymbbTOw z23;RSpH9~Y)2Gq(A@r$qeJFhjT^~lDOxK6gC(-p0^oewRBz*#1A4MNeAB&HJW9a&L z`e?d7fj)|^Po$5e>yzjs==x;(aJoK)K8&tUr4Oa+)96F!`gHnWx;}$Gh_27152Wj} z=mY5bY<51=~T`&W3M zjC^$O+u?mdveUg!iT6RtO!vMi-j^jK-TSzBpO|!X? z_v!IIK#AzyHz*N*?>YfpPfY)uo&^62eoWVs(m$dn!;{0fbUg(<4m~BF3dW%8sp-+_ z-sdkj-TMXR#|uF3r&t6p3ccTCNxT&Few=z~y7%wY%h0{wr(Txs{Xz9|bnhpsm#2IG zQN04)`<3by>E7Q|uSEBLsCs3(_fOTU(7oTPUX||sS@mjk@8_ylr+fcby$0R;#p*Tb z-d|R)MfZNRdTqM*uhr|&z2B`~m+t*>^?G#gr>obed;eX%0eu(u+tVA;chdDn^c{4) zF?~B-Z$jTj*PGI}()DKaEp)v(eKTEeLEl8zThceu^;Yx^biFlwJzZ}@Uq{#5($~`U zcJwuLy*+(3UGG3&Mb|shSJL%P^c8fyGkrN-??PWj*Spe}()DiiC3L+zeKB3{L0?4I zd(s!u^BH#y6#7uQK9xR%u1}*6rt8z`gXsDU`arrqlRkj1&!YFI>$B4cc<%%>D}o15_(s#OPQ==vIZTe`lM-iEHPqqnB(>*=lN`UZMSy1tR# zg063(H>c~H>CNc+7J5^$~ao==vUd zUAn%PUWcylqt~YE`{}jl`T=@Px_*#egRUQDB1^5qed+ew1E?t{!<1E==vFYS-O6fUWTrpqnD=Z=jo;B`UQGPx_*&fg05ep z7pLo&>BZ>!6?##+ewAK?u3w`Urt8<~h3NVXdO^B=lU{(X-=gQI>$mCo==vRcUb=pl zo`prjB=W_deZ=WCTbHaVzxX&~9x#hagKleH3J}=$pvHRS0pWp8D;e8Ig&y)9g^*)#0 z=i4XadAe75{&fmGB|Jw@gQtZ@=%ewma1^wU`G3sE(MJY*UMHN7=iG>3&vW5?0_TPY zd!7sD6FE05*z;UCpTxPL!Jg;B`DD%w3HCe}&Zls0aIojOa6XlDgMvNJh4X2g8yM_) zE}T#2+<;)ubK!gj=lTbGo(tzQIoB`P^ISNe#ks!0p69~(Y|ixw_B8SHs3oX_W6k6_Pp;d}w-x(9on3+D?t*DcudTsU9Exvs&U=fe47 z&UFd)JQvQFaISN(=ecmclyjYeJwF&k-7tYsku63~Ixp2OgbFG3s&xP}KoNF2Ec`lr<=Uj_m z&vW5?1Lv9td!7sD8#&i3*z;UC-^975!Jg;B`DV^F3HCe}&bM%`aj@sPaK4puje(^ISOJ#ksn{p69~(ZqC&S_Bo27_S2L zBY0(~AH^#{{TN;m>c{a4P(Oi}hx$pp9Mn(YWubl=F9Y>6cxkAg#Y;i`99|OY=kXFy zzknBq`bE4L)Gy&hp?(=J0`)6+VW?ll3qk!FUJ&Zn@d8l4f#-+%O*|jeZ{c~NejCpN z^*eZOsNcnNLH!<{6YBTz98iCNXNUSjJR8&>;aQ>n7|#OrC-@Ige~Nz(^=J5ZQ2!Cn z4E3MzOi=$B{}$@M;NL*~S3D!spW_*z{u}-^)PKj*L;Vjt9n@doV|l;m-2Nx?SFkeY z{>ER!GW0k2zpyC%EgpftFIABK4jvKarANXe!<_VY@%La>x*mm|g&q};1~b#&$3K7> z>3Vc}26_xUCQM7$W6{&lW8-mP3c4Pbo}3;J{}3jj>+$J{>8bHF@NJ?0{zBTaQv`z+=z3oIUAmr+ zeuu8-r{AXQ1?acvdO`Y4x?YHWgRU2*U#IIu=-23aQTkQ7UW|T)t{10Yrt2l>m*{#) z`bD~4ihhBvm!_Ym>t*QY=z3ZDS-M`1eul1>r=Ovib+=z3lHUbY zr|+ig4d}b*dPDk7y55MsgRVEGZ>Q@`=-cReQ~FlA-i*G5t~aM|rt2-}o9KE=`bN6m zioSuax2CVB>uu=k=z3fFTDsnjzJ{*1r>~~#9q6m*dPn+7y55Ptg06R_FQ@BW=*#GO zSNc-A-i^M5u6L&|rt3ZEi|Be!`a-(ii@t!a_omOM>wW0+=z3rJT)N(mK8LRNr_ZMA z1L(8p`at?jx;}_LgRT#zPp9ic=+o%>Q2JE5K8!wvt`DbArt2fqCrSC(laZdU^v?^WtKGgT6()Xd2IWK)5T8S=wAL{#3>HE+MoR=r?@&Qlc zxqP~VqI--r6XRQf*D z_odSJp}sGbz7O?%sq}qlHhw;&??bcFrSC(1Un+ec`UB^s??b<*OW%k3zEt`?G&ASr zPk5$)z7PGDE`1;R4PE*^G$UO;$9-Qaf1~@pRQ^u)eW~<)sP8-L&-(9uOas^uHiC^| z6WA2CfbD|b4DSG&LwkGNedlfIEnyqzb?kn;R?OYgTL=4oJbOpxyAKd$HPeVO-z1EIY?J_rth1EA-J20c7Kn7QZlVf4{Z z9~Jn>z(?T2q5FC`ci*|!_ndRx>)AaoosZ$;PlWooz{dwZ0Urz9*TcE{&b_{Lu6sSZ z*OSgC@$n}^T}}z+o|Dc)PG#@^7fW*eQm`~E1Ixm4uso~~up%xi;nKaz^eO?X;<6ep z-Sc{$^SWNokMEv8PWP*G-5RhawATu{=j^qa*MW7RywPW&-i)89)3RY^M~Ef7k>Wn^N61>d|mhTJAB;^U$4X0(eeEJO@I^OJKQI1 z1|A2G1wVk^FDyPD14f7OU|bjzdi{v_NAN=!8AgTg!*^jM7!5{&??HdO*BRV^H{mUK z8{UC;;XQaCK7bG5Bls9TfluKx_#^xY{tSPCzryG6H~2gJ1HOPS;h*po{0sgKU&DXk z8~87L3nTCo{0@u=Bf-eJUL zz_zd*Y!5rYj<6H#47<a2Om8N5GMA z6dVo5z_D-~91kbJiEt8}45z@Ua2lKrXTX_o7Mu;|z`1Z9oDUbkg>VsE441&Aa2Z?< zSHP8U646nee@EW`hZ@`=I7Q7Abz`O7sybmA1hwu@6 z44=TK@EQCO{se!9zrbJNbNCzl9sU7dz?bk(_zM07|Aw#OKkyCw7ruoN_#dcuU_=-R zMuzXg_h1wl6-I;a!w+C|7z4(Hv0!W%2gZf*;D<0i{0M#wKY^dZ&*0}U0Za(LfM3Ey zFfmL5zk*3&GMF5ufGJ@rm>Q;mX<<5;9)1loz>M%4_$|x?GsExT_wWap1!jfWV0M@T z=7hOmZkPw=h52B9SO6A;gy*SOHdqm0)F91y+UC zV0Bmn)`YcSZCD4^h4o;4*Z?+!jbLNg1U7}uU~||4wuG%6h4qrj*z8hjsq z0HebgFeZ!zW5YNwE{q31gz@1=@MHK1{1ko$KZglmLih#z5+;I)VG{TiObV01Db= z3+KW4Z~3H^I$t3)~8~!R>Gd+zEHV z-Ea@w3-`hO@Blmr55dFm2s{dp!Q=1*JPA+1)9?&D3(vvx@B+LDFTu<33cL!h!Rzn_ zya{i?+wcy&3-7`E@Bw@XAHm1)3498l!5`sI@Mri7{1rZjzro+(AMgcy3IBwz;9u}> z_!|BL-@t$2TNt78f3IKfz=$vsj11p}@4+ZADvSo-habS`Fb0eXW5L)k4vY)q!4F}4 z_!0aVegZ#*pTW;z0+Db=3+KW4Z~3H^I$t3)~8~!R>Gd+zEHV-Ea@w3-`hO@Blmr z55dFm2s{dp!Q=1*JPA+1)9?&D3(vvx@B+LDFTu<33cL!h!Rzn_ya{i?+wcy&3-7`E z@Bw@XAHm1)3498l!5`sI@Mri7{1rZjzro+(AMgcy3IBwz;9u}>_!|BL-@t$2TNt4V zpZ{S*7zsv(@51+B6c`mogYUx+V00J*#)Pq8Y#0Z|h4J8rFh2YUehfc>pTf`J=P&_G z2)}?|!bC7JOai}xNntXW9HxLNVJes!rh#c;I+z}Q4Ku)u@EiCo%mg#T@8I|F2bcwB zh1pg7xnORX2j+$OV18Ht7KDXhVORtfg~ecTSOS)WrC@1T29|~8V0l;pR)m#c zWmpAPh1FnnSOeCCwP0;n2iAr4V13vCHiV5}W7q^Xh0S1d*aEhMtzc`|2DXLmV0+jB zc7&Z^XV?XHh23Cx*aP;2yiV1GCO4upf?U^oO0g~Q-*I0BA@qu^*b29AZ} z;CMIzPK1--WH<#*h11}4I0Mdvv*2tv2hN4_;C#3ME`*EVVz>k@h0EY_xB{+(tKe$5 z2CjwcpxDb=3+KW4Z~3H^I$t3)~8~!R>Gd+zEHV-Ea@w3-`hO@Blmr z55dFm2s{dp!Q=1*JPA+1)9?&D3(vvx@B+LDFTu<33cL!h!Rzn_ya{i?+wcy&3-7`E z@Bw@XAHm1)3498l!5`sI@Mri7{1rZjzro+(AMgcy3IBwz;9u}>_!|BL-@t$2TNr_F zBzy-(gppum_%3`8MuAabH26OJ07i!~U`!Yb#)ffVTo@022;;+#;K%S2_$mAhehw4B zgzyXaB}@bp!zA!4m=q?1$zckZ5~hNwVH%hgri1C>*DwRj2)}{f!b~tT{0@E(e}GwF zR+tTDhdE$Qm<#5Hd0<|c59WsjU_n?27KTM&QCJKXhb3T1SPGVgWnfuY4wi=%U`1F7 zR)$qzRagyHhc#eLSPRyMbzogs57vhbU_;mlHik`LQ`ihPhb>@B*b26WZD3p24z`CK zU`N;qc7|PGSJ(}9hdp3V*bDZCePCbM5B7%x;6OMC4u(VEP&f<@ha=!fI0}x2W8hdg z4vvQt;6ykHPKHz9R5%Szhcn;6-=|UWQlTRd@|vhd1C&cnjW!ci>%k58j6l;6wNbK88==Q}_)2 z2!DbEKP&(X!a}exECP$dVz4+Y0ZYPCurw?K%ffQ7JgfjK!b-3*tOBdTYOp%20c*lq zur{m%>%w}lK5PIR!bY$$Yyz9YX0SPI0b9aWur+K0+roCRJ?sEG!cMR=>;k*OZm>J- z0eiw;us7@j`@(*(KO6uD!a;B_90G^JVQ@Gc0Y}17a5NkP$HH-NJe&Y0!bxy4oC2r9 zX>dB60cXNla5kI+=fZh#K3o77!bNZ~TmqNEWpFuM0awCRa5Y>5*TQvhJ=_2{!cA~9 z+yb}4ZE!o>0e8Y(a5vlo_riT}KRf^r!b9*dJOYoxWAHdU0Z+nH@H9LF&%$%?JiGue z!b|WnyaKPnYcN5D|Ni_xA^ZY<2@}D@FbVt$CWXmha+m_9gsEU^m0o;JHOv4r z!f)WWFcZuSzk}byA7B=k6=s9kVGfuR=7PCl9+(&AgZW_rSP&M1g<%m`6c&TUVF_3g zmV%{W8CVvUgXLibSP@o&m0=ZF6;^}QVGURl)`GQR9atCEgY{tp*bp{?jbRhm6gGp+ zVGGz2wt}r;8`u`MgY97l*b#PuonaT)6?TK&VGr07_JX}(AJ`Z6gZ<$EI1mnkgW(W3 z6b^&K;RrYqj)J4%7&sP=gX7@@I1x^Qli?IN6;6ZG;S4wv&VsYy95@%wgY)46xDYOa zi{TQu6fT3y;R?7Cu7a!K8n_m&gX`f2xDjrGo8cC?6>fvu;SRVH?t;7F9=I3ogZtqD zcn}_fhv5-;6dr@e;R$#Wo`R?08F&_+gXiG|coANLm*Ew76<&ka;SG2b-h#K`9e5Ys zgZJSB_z*sVkKq&e6h4DL!k^&J@E7z6Ybgs4yCQAASI%!x%6oj0I!EI4~}Z2S0@I;YaXe_zC z31C9_1^f~wf{9@g_!UeFlfmRL1xyK3!PGDfObgS&^zdt#0cM2Xz;9tDm>GTtzlT4- zEHEp~2D8H)Fel6fbHh9^FU$w?!ve4%ECdU~BCsed28+WIup}%6OT#j-EG!4h!wRq> ztOP5=DzGZ72CKswuqLbpYr{IQF02RZ!v?S+Yy=y_Ca@`N2AjhcuqA8-Tf;W6Eo=wd z!w#?`>;yZ*F0d=?2D`%^uqW&Vd&54kFYE{V!vSz090Ui$A#f-h28Y8Da3mZBN5e62 zEF1^N!wGOAoCGJsDR3&B2B*Ura3-7uXTv#gE}RGF!v%05Tm%=xC2%QR2A9JXa3x#? zSHm@MEnElJ!wqmF+ypnnEpRK`2Dif;CHxbJVJ?^( z=7D)(KA0aCfCXV8SQr+8MPV^m9F~A3VJTP|mVsqqIanT6fE8gSSQ%D4Hm@b~3X!c;IdOas%xbTB>q8fJhQ;WzMGm{DhzKGoIz={DSBBCBNc%e$8+AEidpQFY!BG z=J))8Kk^EH;?Mkrzw#=t@i+d?KlmsA;&tBOO~&&B?Qe|F1Wd?8Ow1%q%4AH=6imrf zOwBY*%XCc749v((%*-sz%52Qe9L&jF%*{N^%Y4kw0xZZvEX*P-%3>_e5-iD5EX^`3 z%W^Ew3arRVtjsE`%4)368m!4$tj#*SgLQc)>+vquX9G55BQ|CeHf1w5XA8DuE4F4E zwq-lEX9spn2?E>m`RwF$(Woen3AcOnrWDp>6o4wn30*7nOT^X*_fR< zn3K7fn|YX*`Iw&tSdfKSm_=BW#aNsrSdyh!nq^p)+(+4<6W%J25iViY|JKX%4TfN7Hr8@Y|S=o%XVzf4(!NI?949g%5Ln=9_-0p z?9D#x%YN+70UXFd9Lymc%3&PN5gf@;9L+Ht%W)jf37p7DoXjbl%4wX=8Jx*koXt6$ z%Xys71zgBQT+Ah0%4J;66yCzQ))21`qKt-{cV<7d1<&zIe#P_rn&0qSUf@Mu;&;5v@A(6N%766 zjMv4VBVl|dU_vHhVkTiyCS!7@U`nQ9YNlaYrek_$U`A$QW@celW@C2dU{2;@ZsuWL z=3{;qU_lmQVHROg7GrUiU`du@X_jGGmScHVU`1A9WmaKTR%3P6U`^IyZPwu(tjjxD zk9V;?8?Yf8u`!#lDVwo5Td*Ztu{GPUE!(j@JFp`=u`|1{E4#5fd$1>au{Zm$FZ;1S z2XG( z!bkZSALkQ%l27qzKEr4E9C!11?%`hU;|qL|`+0yb@nyckgM5{*@pZnzLp;nkd4xxK zjK_I`Z}Dxu!*}@}Px5`9;%R=s4|#?k@ne3%Px%?o@^gN{bNrHD@jSogH~f|tc#)U* z9WV2H{=gr3g+K9U{=#2*mDl(if9D_klYj9#Z}2ALb@lU~@tJ@LnTUy*gh`o<$(e#F znTn~IhH06O>6w8UnTeU1g;|-6*_nemnTxrZhk2Qg`B{JkS%`&Mghg45#aV(SS&F4u zhGkifNj_kzF?82_>#_sIFp6tcm?8Cn7$Nn6^fgHra9KxX-#^D^nksQU*9K*33 z$MKxNiJZjAoWiM`#_62FnViMhoWr@C$N5~qgWxA_j=<$FBI_j!t^`2j!V z8Ggi%`3XPeXFSW#`32AMOMb=k{F>kJTVCKrUgCGW%QWilpb3Z`T#re+$ZWjdy324-X?W@Z*`QSvjH2j5gW4!o3a_3vjtnS6TA$KK<`{4=rfVqVTf zPiyhIJ5CSwWH0t+ANFNG_U8Z&?yQj^_kUZs!i} zC%t!brALHYEf=}`(KFw$NET7|UKF>Ye%YA%-FLFN*@Fl*? zS9p-G@-@EBH+YDL`6iF>D39?tPw*|i&3E`N-{VQX&r>|j5BMR^@FRZAPxvW6<5_;r zFL;h$@++R_*ZhXx@&YgN62IeRe$OBHBd_o${>)$aE3fhzf8+1`gMacbUgr(oWW2xK z{@`zn&jd`!L`=*iOv+?T&J;|^R7}k@Ov`jk&kW4SOw7zI%*t%c&K%6iT+Gcp%*%Yt z&jKvSLM+T8EXram&JrxiQY_6fEX#5%&kC%_O03K(tjcPv&Kj)ATCB}Fyn}UlC+qPp z)@K7YWFt0a6E?yQj^_kUZs!i}C%t!br zALHYEf=}`(KFw$NET7|UKF>Ye%YA%-FLFN*@Fl*?S9p-G@-@EBH+YDL`6iF>D39?t zPw*|i&3E`N-{VQX&r>|j5BMR^@FRZAPxvW6<5_;rFL;h$@++R_*ZhXx@&YgN62IeR ze$OBHBd_o${>)$aE3fhzf8+1`gMacbUgr(oWW4x({xd!kFd-8$F_SPUlQB6{FeOtl zHPbLH(=k0WFe5WDGqW%&voSk!Feh^{H}fzr^D#dQupkSuFpID#i?KLMup~>dG|R9o z%dtEwup%q5GOMsEtFbz3uqJDba4+{DhzKGoIz={DSBBCBNc%e$8+AEidpQFY!BG=J))8Kk^EH z;?Mkrzw#=t@i+d?KlmsA;&tBOO~z~O=Re~!0TVJ26Eg{uG8vOI1yeE=Q!@?IG9A-1 z12ZxcGcyabG8?lq2XitPb2AU~G9UA^01L7Z3$qA|vKWiA1WU3MOS25ivK-5^0xPl- zE3*o#vKp(i25YhwYqJjTU|rtHdc2GE*?h8VP1%gi*@7+Eimlm(ZP||P*?}F| ziJjSnUD=J@*@HdVi@n*0ec6xwIe-H>h=VzVLphAYIf5fOilaG(V>yoFIe`;7iIX{n zQ#p;(IfFAfi?cb0b2*Rmxqu6~h>N*|OSz28xq>UXimSPXYq^f=xq%zGiJQ5FTe*$f zxq~~oi+A%L-pl)VKOf+Oe25S85kAVt_&A^7lYEL#^BF$N=eV2Ca}W1&A79{$+|L7i zi7)dN9^|Whjj!_!9^zrX$s;_B=Z?|7Nt^9TOOEBuK+^B4ZgtGveF_&fjLpZts0d4o3@ zuZ^GojL!s2$V5!cBuvU=OwJTc$y7|uG)&8MOwSC=$V|-4EX>Mm%+4Il$z06MJj}~{ z%+CTW$U-d4A}q>cEY1=v$xm`RwF z$(Woen3AcOnrWDp>6o4wn30*7nOT^X*_fR+(+4<6W%J25iViY|JKX%4TfN z7Hr8@Y|S=o%XVzf4(!NI?949g%5Ln=9_-0p?9D#x%YN+70UXFd9Lymc%3&PN5gf@; z9L+Ht%W)jf37p7DoXjbl%4wX=8Jx*koXt6$%Xys71zgBQT+Ah0%4J;66-$ju|EfJAO~?Uhj1u|aX3eCBu8;H$8apiaXcq*A}4V&r*JB#aXM#k zCTDRr=Ws6PaXuGtAs2BmmvAYUaXD9TC0B7Z*KjS@aXmM1BR6p~w{R=BaXWW#CwK8~ z-otx&AMfV_e2@?EVLrk~`4}JP6MT|S@o7H8XZaj=^Lg&!Uhd-ye3AQkfG_c7zQTii zm9O!2zQIF0%r|+2M|q6Ld4g~8ZN9^I`5sU5eV*cJe!vfTh9B``e!@@r8PD=_e!+A6 zl3(#WzvegmmKS)Dm-rno^LzflA9;m8@n`JnVE%InT^?*gE^UtxtWJ~nUDEdfCX8I zg;|6}S&YS5f+bmsrCEk$S&rpdffZSam05*VS&h|MgEd);wONOEurBXpJ>JFoY`}(W z#KvsGrfkOMY{8an#nx=Ywrt1t?7)uf#Ln!(uI$F{?7^Pw#op}0zU;^T9KeAb#K9cG zp&Z8H9Kn$s#nBwYu^h+ooWO~k#L1k(shq~?oWYr##o3(0xtz!OT)>4~#Kl~~rCi44 zT)~xG#noKHwOq&b+`x_8#Le8ot=z`#+`*mP#rEB9KM!_bM|NUoc41d`V|VsoPxfMO z_F-T4V}B0dKn~(y4&hJ^<8Y4PNRHxYj^S92<9JTsL{8#lPT^Ee<8;p8OwQtL&f#3n z<9sgQLN4NBF5yxx<8rRxO0ME+uHjm)<9cr3MsDI}ZsAsL<96=gPVVB}yodMlKHkp< z_#hwR!+eB~@-aTnC-@|v;?sPF&+<9$=JVXcz1+ta_#*f70AJ$Ee1!-3DqrL4e1nI0 zm~Zk3kMbCg^90}G+kA)b@;#p9`#i{DhzKGoIz={DSBBCBNc%e$8+A zEidpQFY!BG=J))8Kk^EH;?Mkrzw#=t@i+d?KlmsA;&tBOO-BE)$KSf&etwP51Wd?8 zOw1%q%4AH=6imrfOwBY*%XCc749v((%*-sz%52Qe9L&jF%*{N^%Y4kw0xZZvEX*P- z%3>_e5-iD5EX^`3%W^Ew3arRVtjsE`%4)368m!4$tj#*SgLQc)>+vquX9G55BQ|Ce zHf1w5XA8DuE4F4Ewq-lEX9spE zR$*0EV|CVGP1a&<*5Mtj%R5<*cdLM zGrO=WyRkcauqS)5H~X+J`>{U)0*Ks{Ja3eQyGq-Rnw{bgn za3^>1Zr;Otc^~iR1ALGV@nJr~NBI~Z=M#LAPw{C!!)N&%ck_Af;a=|J3w)9Ld4MnR zWxm3Le3h^9b-uwvJj^$FghzRd$9aNp@om1tcljPq@_nA-X@0;Dd4?bHV}8O<`5Djh zbAG{d{E}bsJiq2Q{FWDZk(c-#FY|l;z#n;qKk;Y&!e4on*Z3QM=O6r&fAKnR@FwH+ z^z)zbnScqIh>4kmNtukvnSv>qim91~X_=1cnSmLZiJ6&&S(%O5nS(i*i@BMHd6|#- zS%3vuh=o~%MOlo+S%M{5ilteGWm%5pS%DQDZ`3ryLRbJz7{GEUBPyWU0 zyuq7{m%#G{<1+yhG7%Fq36nAzlQRWVG8I!Z4bw6m(=!7zG7~d13$rpCvoi;CG8c0* z5A!k~^Roa8vJeZi2#c~9i?akvvJ^|R49l_{%d-M2vJxw^3ahdjtFs1cvKDKz4)0)H z-pP8ri}l%n4cUl|*@R8mjLq4CE!m2#*@kV|j_uij9odPU*@a!%josOUJ=u%B*@u1E zkNr7-138F;IfO$wjKevCBRPtrIfi37j^jCj6FG^KIfYX>jng@UGdYX1IfrvOkMp^J z3%Q7kxr9r(jLW%#E4hlRxrS@Gj_bLB8@Y*_xrJM~joZ0{JGqN@^B&&I`*=Se;DdaK z5AzW|%E$OPpWu^xicj+yKFjC0o6mC(_i`U!;EUYP1AK`u^A#TCt9*^G^9>&2VZO;D zJj!D{&J%o#Z}T0#%lCMa@ADK-^8Ur*i9hof{>rPo#^3lm|KOkei`RLBHyJOXpZ|={1Wd?8Ow1%q%4AH= z6imrfOwBY*%XCc749v((%*-sz%52Qe9L&jF%*{N^%Y4kw0xZZvEX*P-%3>_e5-iD5 zEX^`3%W^Ew3arRVtjsE`%4)368m!4$tj#*SgLQc)>+vquX9G55BQ|CeHf1w5XA8Du zE4F4Ewq-lEX9speAArmn%lQ1chF*#E(B~vjq(=aX5F+DRdBQr5GvoI^O zF*|cGCv!13^Dr;-F+U5iAPccDi?Aq*u{cYxBulY0%djlVu{+lZN<(;g@yI7wM*pQ9bm`&J}&DfkR*pjW-nr+yY?bx0j*pZ#snO)eG-PoNy z*pt23n|;`q{n(!aIFN%lm_s;}!#JEHIFh3{nqxSY<2arZIFXY$nNv8G(>R?oIFqwD zn{zmq^EjUixR8sum`k{n%eb5?xRR?_-M^1ggEd);wONOEurBXpJ>JFoY`}(W#KvsG zrfkOMY{8an#nx=Ywrt1t?7)uf#Ln!(uI$F{?7^Pw#op}0zU;^T9KeAb#K9cGp&Z8H z9Kn$s#nBwYu^h+ooWO~k#L1k(shq~?oWYr##o3(0xtz!OT)>4~#Kl~~rCi44T)~xG z#noKHwOq&b+`x_8#Le8ot=z`#+`*mP#k+YA@8x~GpAYasKE#Ll2p{ERe4J14Nj}A= z`3#@sbKK46xrckXk1y~=?&kr%#FzOB5As#M#@G1<5AiVHDZ z`3ryLRbJz7{GEUBPyWU0yuq7{*VgZ?jL!s2$V5!cBuvU=OwJTc$y7|uG)&8MOwSC= z$V|-4EX>Mm%+4Il$z06MJj}~{%+CTW$U-d4A}q>cEY1=v$xC?DhFe1cE%DL&0-_$;5}Za&XF+{=A@fiH4D5AY?v z%vX4juktm%&Nq07hxsOt@F|F5lxxzRy!U%@6n?&+sFD%uo0!KjT?` z&M$b5U-B!S=hys(-|_-4@)Ez}Wq!{e_#?0IC;rS|_$#mS8h_*O{DXh;FJ9*j-ekOX ze*QB)6EGnYF)@=cDU&fdQ!ph{F*VaLEz>bQGcY4FF*CC;E3+{>b1)}!F*oxtFY_@! z3$P#yu`r9UD2uT;ORywMu{6uDEX%PxE3hIfu`;W$Dyy+NYp^D3u{P`Q4%X$JtjD`p zpAFcMjo6q?*p$uKoGsXrt=O7v*p}_so*meco!FUO*p=Pbojur-z1W+5*q8m-p946M zgE*K&IF!RUoFh1rqd1ylIF{o$o)b8clQ@}EIF-{loijL-vpAb`IG6J{p9{E@m!JjUZZ z!MFG}-{HG_k0<#)Pw_NA;Di_1oi})s@!I?O&-hHhgiOT5Ov0p0#^g-FluX6c zOvAKH$Mnp=jLgK$%)+e9#_Wv#ndl6+zr1+Wyx$S6r?-7~=i47d>(M_i^_=a|dUU)T zjvw=%xs6xV@uNQuoZ0cK`19$}dPdu;+8(V($9q2J^G5gEV|(;CqR+E8=J7|z+h=?9 z^+w09?DIzdw`Ed~XHCo7A0)RP&9~X^|6Yg}%{SN{-9MUdu|4`@QOO){we3l)NAq>I zC$>GBZ?!$Te>7ijd-OP>?Q3n19(Q!VjkZURFFO7v+oQ)HJ)X_Br}22AZs}syx97RN z;f4b?9BBs!jNN~0k=yg=&x9%&?C0B1PjP!SUiA3V#;k8CVSkUW z{!}}7e6jbBeY~40#N2=GKi@xkywQ2Mw&6eT-+bdg?;rjBjOg)ZGLF{Q*ZJrBH(mM9 z_m6$NvG>2*1N!^-!+9Rh*Bkr!V|(q)^3R{Y;lO|X{Lyt6Js(8R7t!@qf9dW08!-C1 zqCU}e8C}oyJ-!Bv_KW>I(f$n`w-I9>N9^$%+pkH?$BX7s&uAWPkBmA*^XPcdJo-LF^XPcdJn9$CqvJ*M=7G+p{@dRUbs>Q9~y{bl&=L7%!Sn_YH`?U$tZI|EvJfd0RJT zK0e#+4bk!H#muYZxxFDeZ|ld*tD8sXdBd1_E%WF+ZX7ee!#p}qo5sxRnMdbg^O$)9 z^XNQl88dHe9-T+6W9H4wqw}O~%)F&}bRM*inYS^IzRw+F=Iza8j_23WzkmNandfl7 zt}*xTY93uT(Lc)(eZP8`Bl_#Qe?NY2gM5zLJLdTP%%kh7Z_Iq4c_G{T$IOSA7qNX{ z%zU_cblnY(nU69rVf)aS`B?K(whxb)PcV!y3A00EFW?sSeu`%1u%nzDJ&kM6+=7-Ir>wdO*^n7)~Ji4yu#LQ2cN7wD#nE4s==(?O2 zGe2t{U3c@%qwmjo^XR%-5Oe=a=FxStFlK(mJi0Cx#mujnN9XEf33aZ8T4C1>1{D{jdhx8#Xi^2ROs;+Fhz%m3Ts zW{A6Q#<(R@+>$wNDG;|5j9UuDErsKjB5_O6xTRR!Qao-c5x10#TS~<(rQ??9^CpP< zIQp8B#C`n#duyqBIWL{7oV`x8zWnm;UDpmhRr`O}-{Yg->!U2O z>#@J@N9)o4vFp+6f@uC9tw*mPqW%AOTmL`p_3EwnWp3RP-6mdA-g@(vi^e^~4;x8@DU^q$ava_=$! v-eGRd`wr{^-jkn4Wz{4*z$4f&IS#v3cVT literal 0 HcmV?d00001 diff --git a/tests/regression_tests/dagmc/universes/geometry.xml b/tests/regression_tests/dagmc/universes/geometry.xml new file mode 100644 index 00000000000..556637f65de --- /dev/null +++ b/tests/regression_tests/dagmc/universes/geometry.xml @@ -0,0 +1,27 @@ + + + + + + 48.0 48.0 48.0 + 3 3 3 + -24.0 -24.0 -24.0 + + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + 1 1 1 + + + + + + + + + diff --git a/tests/regression_tests/dagmc/universes/materials.xml b/tests/regression_tests/dagmc/universes/materials.xml new file mode 100644 index 00000000000..47e740ba0e6 --- /dev/null +++ b/tests/regression_tests/dagmc/universes/materials.xml @@ -0,0 +1,26 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/regression_tests/dagmc/universes/settings.xml b/tests/regression_tests/dagmc/universes/settings.xml new file mode 100644 index 00000000000..770ec7c355d --- /dev/null +++ b/tests/regression_tests/dagmc/universes/settings.xml @@ -0,0 +1,10 @@ + + + eigenvalue + 100 + 10 + 5 + +

false + + diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py new file mode 100644 index 00000000000..0ed20254629 --- /dev/null +++ b/tests/regression_tests/dagmc/universes/test.py @@ -0,0 +1,4 @@ +import openmc + +def test_dagmc_universes(): + openmc.run() \ No newline at end of file From b3ad45f9a8a0098828da1b81396a057826093d59 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 3 Mar 2021 11:55:18 -0600 Subject: [PATCH 0031/2572] Formatting update to surface header. --- include/openmc/surface.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 3d386e8768a..5c729f8202d 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -149,8 +149,8 @@ class Surface //! Get the BoundingBox for this surface. virtual BoundingBox bounding_box(bool /*pos_side*/) const { return {}; } - protected: - virtual void to_hdf5_inner(hid_t group_id) const = 0; +protected: + virtual void to_hdf5_inner(hid_t group_id) const = 0; }; class CSGSurface : public Surface From 41e3179167c06244ca3af1d332bccaaa07217d3b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 3 Mar 2021 20:01:45 -0600 Subject: [PATCH 0032/2572] Updating the DAGMC universes test. --- .../dagmc/universes/inputs_true.dat | 47 +++++++++++++ .../dagmc/universes/results_true.dat | 2 + .../regression_tests/dagmc/universes/test.py | 69 ++++++++++++++++++- 3 files changed, 117 insertions(+), 1 deletion(-) create mode 100644 tests/regression_tests/dagmc/universes/inputs_true.dat create mode 100644 tests/regression_tests/dagmc/universes/results_true.dat diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat new file mode 100644 index 00000000000..f8acb45ccc3 --- /dev/null +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -0,0 +1,47 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + eigenvalue + 100 + 10 + 2 + + false + + diff --git a/tests/regression_tests/dagmc/universes/results_true.dat b/tests/regression_tests/dagmc/universes/results_true.dat new file mode 100644 index 00000000000..203d35efeb8 --- /dev/null +++ b/tests/regression_tests/dagmc/universes/results_true.dat @@ -0,0 +1,2 @@ +k-combined: +7.531408E-01 1.260163E-02 diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index 0ed20254629..0144a0c3a80 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -1,4 +1,71 @@ import openmc +import openmc.lib + +import pytest +from tests.testing_harness import PyAPITestHarness + +pytestmark = pytest.mark.skipif( + not openmc.lib._dagmc_enabled(), + reason="DAGMC CAD geometry is not enabled.") + + +class DAGMCUniverseTest(PyAPITestHarness): + + def _build_inputs(self): + model = openmc.model.Model() + + ### MATERIALS ### + fuel = openmc.Material(name='no-void fuel') + fuel.set_density('g/cc', 10.29769) + fuel.add_nuclide('U234', 0.93120485) + fuel.add_nuclide('U235', 0.00055815) + fuel.add_nuclide('U238', 0.022408) + fuel.add_nuclide('O16', 0.045829) + + cladding = openmc.Material(name='clad') + cladding.set_density('g/cc', 6.55) + cladding.add_nuclide('Zr90', 0.021827) + cladding.add_nuclide('Zr91', 0.00476) + cladding.add_nuclide('Zr92', 0.0072758) + cladding.add_nuclide('Zr94', 0.0073734) + cladding.add_nuclide('Zr96', 0.0011879) + + water = openmc.Material(name='water') + water.set_density('g/cc', 0.740582) + water.add_nuclide('H1', 0.049457) + water.add_nuclide('O16', 0.024672) + water.add_nuclide('B10', 8.0042e-06) + water.add_nuclide('B11', 3.2218e-05) + water.add_s_alpha_beta('c_H_in_H2O') + + model.materials = openmc.Materials([fuel, cladding, water]) + + ### GEOMETRY ### + # create the DAGMC universe + pincell_univ = openmc.DAGMCUniverse(filename='dagmc.h5m', auto_ids=True) + + left = openmc.XPlane(x0=-24.0, name='left', boundary_type='reflective') + right = openmc.XPlane(x0=24.0, name='right', boundary_type='reflective') + front = openmc.YPlane(y0=-24.0, name='front', boundary_type='reflective') + back = openmc.YPlane(y0=24.0, name='back', boundary_type='reflective') + bottom = openmc.ZPlane(z0=-20.0, name='bottom', boundary_type='vacuum') + top = openmc.ZPlane(z0=20.0, name='top', boundary_type='vacuum') + + box = +left & -right & +front & -back & +bottom & -top + + bounding_cell = openmc.Cell(fill=pincell_univ, region=box) + + model.geometry = openmc.Geometry(root=[bounding_cell]) + + # settings + model.settings.particles = 100 + model.settings.batches = 10 + model.settings.inactive = 2 + model.settings.output = {'summary' : False} + + model.export_to_xml() + def test_dagmc_universes(): - openmc.run() \ No newline at end of file + harness = DAGMCUniverseTest('statepoint.10.h5') + harness.main() \ No newline at end of file From 30e42489cd0cd62a56a6b20c54dac00e65db402d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 3 Mar 2021 20:02:09 -0600 Subject: [PATCH 0033/2572] Removing XML files. --- .../dagmc/universes/geometry.xml | 27 ------------------- .../dagmc/universes/materials.xml | 26 ------------------ .../dagmc/universes/settings.xml | 10 ------- 3 files changed, 63 deletions(-) delete mode 100644 tests/regression_tests/dagmc/universes/geometry.xml delete mode 100644 tests/regression_tests/dagmc/universes/materials.xml delete mode 100644 tests/regression_tests/dagmc/universes/settings.xml diff --git a/tests/regression_tests/dagmc/universes/geometry.xml b/tests/regression_tests/dagmc/universes/geometry.xml deleted file mode 100644 index 556637f65de..00000000000 --- a/tests/regression_tests/dagmc/universes/geometry.xml +++ /dev/null @@ -1,27 +0,0 @@ - - - - - - 48.0 48.0 48.0 - 3 3 3 - -24.0 -24.0 -24.0 - - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - 1 1 1 - - - - - - - - - diff --git a/tests/regression_tests/dagmc/universes/materials.xml b/tests/regression_tests/dagmc/universes/materials.xml deleted file mode 100644 index 47e740ba0e6..00000000000 --- a/tests/regression_tests/dagmc/universes/materials.xml +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/tests/regression_tests/dagmc/universes/settings.xml b/tests/regression_tests/dagmc/universes/settings.xml deleted file mode 100644 index 770ec7c355d..00000000000 --- a/tests/regression_tests/dagmc/universes/settings.xml +++ /dev/null @@ -1,10 +0,0 @@ - - - eigenvalue - 100 - 10 - 5 - - false - - From a06b906da6a3d4f6d7c150e5350e34527c552217 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 4 Mar 2021 15:29:19 -0600 Subject: [PATCH 0034/2572] Updating example notebooks. --- examples/jupyter/cad-based-geometry.ipynb | 159 ++++++++++-------- .../jupyter/unstructured-mesh-part-ii.ipynb | 43 ++--- 2 files changed, 114 insertions(+), 88 deletions(-) diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb index 9fa56f6fbdf..4c950b744ba 100644 --- a/examples/jupyter/cad-based-geometry.ipynb +++ b/examples/jupyter/cad-based-geometry.ipynb @@ -141,7 +141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "OpenMC expects that the model has the name \"dagmc.h5m\" so we'll name the file that and indicate to OpenMC that a DAGMC geometry is being used by setting the `settings.dagmc` attribute to `True`." + "To create a geometry where DAGMC represents the entire model, we'll make a DAGMC universe and use it as the root universe of the model." ] }, { @@ -149,9 +149,19 @@ "execution_count": 6, "metadata": {}, "outputs": [], + "source": [ + "dagmc_univ = openmc.DAGMCUniverse(filename=\"dagmc.h5m\")\n", + "geometry = openmc.Geometry(root=dagmc_univ)\n", + "geometry.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], "source": [ "settings = openmc.Settings()\n", - "settings.dagmc = True\n", "settings.batches = 10\n", "settings.inactive = 2\n", "settings.particles = 5000\n", @@ -169,12 +179,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -201,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -280,28 +290,35 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:17\n", - " OpenMP Threads | 2\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n", + " Date/Time | 2021-03-04 13:23:02\n", + " MPI Processes | 1\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", + " Reading geometry XML file...\n", + "Set overlap thickness = 0\n", + "Set numerical precision = 0.001\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.0001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -322,20 +339,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.4575e-01 seconds\n", - " Reading cross sections = 1.3129e-01 seconds\n", - " Total time in simulation = 1.6897e+00 seconds\n", - " Time in transport only = 1.6829e+00 seconds\n", - " Time in inactive batches = 3.1605e-01 seconds\n", - " Time in active batches = 1.3737e+00 seconds\n", - " Time synchronizing fission bank = 2.8739e-03 seconds\n", - " Sampling source sites = 2.5550e-03 seconds\n", - " SEND/RECV source sites = 2.8512e-04 seconds\n", - " Time accumulating tallies = 7.8450e-06 seconds\n", - " Total time for finalization = 1.7404e-04 seconds\n", - " Total time elapsed = 1.9588e+00 seconds\n", - " Calculation Rate (inactive) = 31640.8 particles/second\n", - " Calculation Rate (active) = 29119.1 particles/second\n", + " Total time for initialization = 1.1835e+00 seconds\n", + " Reading cross sections = 1.3664e-01 seconds\n", + " Total time in simulation = 1.0779e+01 seconds\n", + " Time in transport only = 1.0771e+01 seconds\n", + " Time in inactive batches = 1.6833e+00 seconds\n", + " Time in active batches = 9.0961e+00 seconds\n", + " Time synchronizing fission bank = 4.0641e-03 seconds\n", + " Sampling source sites = 3.6309e-03 seconds\n", + " SEND/RECV source sites = 3.4735e-04 seconds\n", + " Time accumulating tallies = 4.9789e-05 seconds\n", + " Time writing statepoints = 2.9456e-03 seconds\n", + " Total time for finalization = 6.8913e-05 seconds\n", + " Total time elapsed = 1.2048e+01 seconds\n", + " Calculation Rate (inactive) = 5940.54 particles/second\n", + " Calculation Rate (active) = 4397.49 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -368,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -387,7 +405,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": { "image/jpeg": { "width": 600 @@ -409,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -432,12 +450,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -466,12 +484,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAADICAAAAAAR9MBqAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAAmJLR0QA/4ePzL8AAAAHdElNRQflAwQHGRohsovxAAAFv0lEQVR42u3d0XKiQBBG4Zb4YPtmFk+evRAUFBWY/qW7c76LTbYqm0L3OAyIzOnXAH/d0RuAmggLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFB4tzfvr0cvS0o5Hz/tjfigpfz/K89bcHF8xyr73f8GmBuafJOWmi2fFRIWWh0+rXljphqocVpcnPbeV+UhQan+V2Tp21RFvZ7mGNdqAkuTs/3eb+NWkSG3RaOCukJ7ZZON1AWmi2ex6IstFoMi/OjaMX1WJBYfK/w6I1Cfm9GLGZa2G8hLAYstFu4Hmv4yoCFBkzeIfEUFgMWPDyGRVdw8RAWE3f4mIfFhQ1wMguLruBlGhZdwQ2XzUBiEhYTd/i5h8WOEI5uYdEVPI1h0RVcDWHRFXxdw6IrOOvM6Ar+OqMrCExPkNIV3HScGIVCx44QCj+n8Tu6gqPbHIuu4GkMi67gagiLruDrGhZdwVlnRlfw1xldQYBPQkOiY8CCQkdXUFi4HTfQjjkWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkPg5/Tt6E1BRx71todCZ9aQFd9eVKUgLzsZFmkgLru7rFZIWHJ3v3/bcKgtuzrO/0RacnH6fd4G0hWan38VVmmgLba539FuauNMWGoy3ilw+JiQu7DS5B+mL8w3EhR3mN7d9dS6LuLDR012TX54nJS5ssHQ77tfn4IkLK724z/u793eoC5+9WUDg7ZuH1IW33q9M8eGNaerCK5+XPPl41QN54dm6tXRWXFJDXpjasEjTqgu26Atmtnn1r7VXA9LXX7dnWbkN15oS2F+1e73CjVcyU9gf07gQ5uYL5Qnsj3BZYXXP5zAorDbHpXt3fcyHvoryXxOa4QsmXGx8e1/UVYl+FftNhRFXFfqwRqsDI64KvhfWzZrCiCu7A8IacU1OZQeGNXjTF23ldXxYZsZl9vUECcvM+PBZKZHCMuNTs2VEC8toq4aAYZlxK4n8goZltJVc3LDsRVuklULosMy4K1xWW9fSiXBv5QjbgA+Cj1gcIWbF6l+QyBkWO8PwYofFmzxpxQ4LaaUMiwErvtdHhf3x/33jnvDCZTXphD7dMOR04RZd+Zzbf0UALC4VToI51ucBy4z1FqOJPGJ9TuUy/bGeUSuQyGGtdLExLtKKI/LkfeWINf9h0oohcFgPx4SfiqGsUBJM3le6DEUxiQ8hTVgrBiLKCiR8WOtONgw/e/1CWQHEDavhBm6Udby4Ye1CWVGEP4+18pjw8V/VOjbstz8Dh/3WQfQRa+sDrxXUoL/94flLJb/1Jm5Yl9sfexTaGUreBZW/tRr4BOn1Cbh+2RBYtROltwIcH9CkKtXTFHOO1fJy2nJ+IgHBo/nKExQyLJdHXmL+LhhavvS6izvH2m38Hygwcsm7ushefSFHrCcVBp8d/Lt6yEq47TnC2macZWXfGd4rSJdVyV1hlQFO25VuJ3gV9HRD42urwikH767mWck3P2hYrfa9ERSJ9+mraVffeFpK7gpv8h4YOnfVf7urqmElHqrMzL+ryffqydWg4lHhRNIDQ9+uvj5amZUdsXIPWQW6qj5ipRyyXLs6Jqu6I1biIatEV3XDGqU7MFR19aVJ+6huWEmHLFlXX34c1edYOWdZ5hLCgVlVHrFyDlmOb0Yd2lXlsEaZZlmO23psV6XDSjlkeW365D3sQ56HymGN8gxZfjtC92tutiodVrYhq1BXtcMaJRmy/Dbz+K6Kh5VtyHLa6gBdFQ9rlGLIctsR3s+xHvjCKh5WziGrjeKj09sVD2uUYMjyGrBidFU+rDRDVrGuyoc1SjBk+T7Oo19R5cM6+gleyWnACtOV/QcPWfsa5qh78gAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMS0wMy0wNFQxMzoyNToyNi0wNjowMCCUuacAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjEtMDMtMDRUMTM6MjU6MjYtMDY6MDBRyQEbAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] @@ -498,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -537,12 +555,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "...and setup a couple mesh tallies. One for the kettle, and one for the water inside." + "...and setup a couple of mesh tallies. One for the kettle, and one for the water inside." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -570,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -602,21 +620,25 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:35\n", - " OpenMP Threads | 2\n", + " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.12.1-dev\n", + " Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n", + " Date/Time | 2021-03-04 13:25:29\n", + " MPI Processes | 1\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", + " Reading geometry XML file...\n", + "Set overlap thickness = 0\n", + "Set numerical precision = 0.001\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", @@ -624,10 +646,12 @@ " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for Fe58\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Initializing source particles...\n", + " Maximum neutron transport energy: 20000000.0 eV for Fe58\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", @@ -645,15 +669,16 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.9659e+00 seconds\n", - " Reading cross sections = 6.3783e-01 seconds\n", - " Total time in simulation = 1.5112e+01 seconds\n", - " Time in transport only = 1.4102e+01 seconds\n", - " Time in active batches = 1.5112e+01 seconds\n", - " Time accumulating tallies = 2.8790e-04 seconds\n", - " Total time for finalization = 3.2375e-02 seconds\n", - " Total time elapsed = 2.0224e+01 seconds\n", - " Calculation Rate (active) = 3308.59 particles/second\n", + " Total time for initialization = 5.6350e+01 seconds\n", + " Reading cross sections = 6.7418e-01 seconds\n", + " Total time in simulation = 1.0014e+02 seconds\n", + " Time in transport only = 1.0013e+02 seconds\n", + " Time in active batches = 1.0014e+02 seconds\n", + " Time accumulating tallies = 4.0980e-04 seconds\n", + " Time writing statepoints = 5.3643e-03 seconds\n", + " Total time for finalization = 1.9142e-02 seconds\n", + " Total time elapsed = 1.5651e+02 seconds\n", + " Calculation Rate (active) = 499.320 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -675,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -696,22 +721,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -750,7 +775,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb index 478417970ed..3dcc9d418a0 100644 --- a/examples/jupyter/unstructured-mesh-part-ii.ipynb +++ b/examples/jupyter/unstructured-mesh-part-ii.ipynb @@ -181,14 +181,15 @@ "\n", "settings.run_mode = \"fixed source\"\n", "settings.batches = 10\n", - "settings.particles = 100" + "settings.particles = 100\n", + "settings.export_to_xml()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "And we'll indicate that we're using a CAD-based geometry." + "Next we'll apply the DAGMC model as the root universe of the geometry." ] }, { @@ -197,9 +198,9 @@ "metadata": {}, "outputs": [], "source": [ - "settings.dagmc = True\n", - "\n", - "settings.export_to_xml()" + "dagmc_univ = openmc.DAGMCUniverse(filename='dagmc.h5m')\n", + "geometry = openmc.Geometry(root=dagmc_univ)\n", + "geometry.export_to_xml()" ] }, { @@ -246,15 +247,15 @@ " Copyright | 2011-2020 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.12.1-dev\n", - " Git SHA1 | 8ae407c90e927af62bfdc8f150e96bfd5d7b2ec2\n", - " Date/Time | 2021-01-06 09:17:05\n", + " Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n", + " Date/Time | 2021-03-04 13:34:25\n", " MPI Processes | 1\n", - " OpenMP Threads | 2\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", - " Reading DAGMC geometry...\n", + " Reading geometry XML file...\n", "Set overlap thickness = 0\n", "Set numerical precision = 0.001\n", "Loading file dagmc.h5m\n", @@ -317,16 +318,16 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.4196e+01 seconds\n", - " Reading cross sections = 2.1428e+00 seconds\n", - " Total time in simulation = 1.9512e-01 seconds\n", - " Time in transport only = 1.9269e-01 seconds\n", - " Time in active batches = 1.9512e-01 seconds\n", - " Time accumulating tallies = 2.0220e-06 seconds\n", - " Time writing statepoints = 2.2461e-03 seconds\n", - " Total time for finalization = 1.8940e-06 seconds\n", - " Total time elapsed = 2.4401e+01 seconds\n", - " Calculation Rate (active) = 5125.02 particles/second\n", + " Total time for initialization = 1.7970e+02 seconds\n", + " Reading cross sections = 1.8083e+00 seconds\n", + " Total time in simulation = 7.2061e-01 seconds\n", + " Time in transport only = 7.1887e-01 seconds\n", + " Time in active batches = 7.2061e-01 seconds\n", + " Time accumulating tallies = 2.7050e-06 seconds\n", + " Time writing statepoints = 1.5381e-03 seconds\n", + " Total time for finalization = 1.1780e-06 seconds\n", + " Total time elapsed = 1.8043e+02 seconds\n", + " Calculation Rate (active) = 1387.71 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -352,7 +353,7 @@ "metadata": {}, "outputs": [], "source": [ - "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\", library='moab')\n", + "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\")\n", "\n", "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n", "\n", @@ -496,7 +497,7 @@ "text": [ "\r\n", "\r\n", - " \r\n", + " \r\n", " manifold.h5m\r\n", " \r\n", " \r\n", From 58222a7ded7bf4c8cf2c7cd8d2bae54e8fbb8500 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 4 Mar 2021 15:30:00 -0600 Subject: [PATCH 0035/2572] Correcting docstrings on the DAGMC Universe class. --- openmc/universe.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index c3f4e36ceda..59a41a44f12 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -641,8 +641,9 @@ class DAGMCUniverse(UniverseBase): automatically be assigned name : str, optional Name of the universe. If not specified, the name is the empty string. - cells : Iterable of openmc.Cell, optional - Cells to add to the universe. By default no cells are added. + auto_ids : bool + Set IDs automatically on initialization (True) or report overlaps + in ID space between CSG and DAGMC (False) Attributes ---------- From b35158d44ad2363bc15f529fcb514fdcb076a40a Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 12 Mar 2021 11:20:13 -0600 Subject: [PATCH 0036/2572] Updating documentation on DAGMC geometry. --- docs/source/usersguide/geometry.rst | 83 ++++++++++++++++++++--------- 1 file changed, 59 insertions(+), 24 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index c8e5fd99f3b..a271490717c 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -433,30 +433,65 @@ will handle creating the unverse:: Using CAD-based Geometry -------------------------- -OpenMC relies on the Direct Accelerated Geometry Monte Carlo toolkit (`DAGMC -`_) to represent CAD-based geometry in a -surface mesh format. A DAGMC run can be enabled in OpenMC by setting the -``dagmc`` property to ``True`` in the model Settings either via the Python -:class:`openmc.settings` Python class:: - - settings = openmc.Settings() - settings.dagmc = True - -or in the :ref:`settings.xml ` file:: - - true - -With ``dagmc`` set to true, OpenMC will load the DAGMC model (from a local file -named ``dagmc.h5m``) when initializing a simulation. If a `geometry.xml -<../io_formats/geometry.html>`_ is present as well, it will be ignored. - - **Note:** DAGMC geometries used in OpenMC are currently required to be clean, - meaning that all surfaces have been `imprinted and merged - `_ - successfully and that the model is `watertight - `_. Future - implementations of DAGMC geometry will support small volume overlaps and - un-merged surfaces. +Defining Geometry +----------------- + +OpenMC relies on the `Direct Accelerated Geometry Monte Carlo`_ (DAGMC) +to represent CAD-based geometry in a surface mesh format. DAGMC geometries are +applied as universes in the OpenMC geometry file. A geometry represented +entirely by a DAGMC geometry will contain only the DAGMC universe. Using a +:class:`openmc.DAGMCUniverse` looks like the following:: + + dag_univ = openmc.DAGMCUniverse(filename='dagmc.h5m') + geometry = openmc.Geometry(root=dag_univ) + geometry.export_to_xml() + +The resulting ``geometry.xml`` file will be: + +.. code-block:: xml + + + + + + +DAGMC universes can also be used to fill CSG cells or lattice cells in a geometry:: + + cell.fill = dagmc_univ + +It is important in these cases to understand the DAGMC model's position +with respect to the CSG geometry. DAGMC geometries can be plotted with +OpenMC to verify that the model matches one's expectations. + +**Note:** DAGMC geometries used in OpenMC are currently required to be clean, +meaning that all surfaces have been `imprinted and merged +`_ +successfully and that the model is `watertight +`_. Future +implementations of DAGMC geometry will support small volume overlaps and +un-merged surfaces. + + +Cell and Material IDs +--------------------- + +By default, DAGMC applies cell IDs defined by the CAD engine that the model +originated in. If these IDs overlap with ID's in the CSG cell ID space, this +will result in an error. However, the ``auto_ids`` property of a DAGMC universe +can be set to set DAGMC cell IDs by appending to the existing CSG cell ID space +in the OpenMC model. + +Similar options exist for the material IDs of DAGMC models. If DAGMC material +assignments are based on natively defined OpenMC materials, no further work is +required. If DAGMC materials are assigned using the `University of Wisconsin +Unified Workflow`_ (UWUW), however, material IDs in the UWUW material library +may overlap with those used in the CSG geometry. In this case, overlaps in the +UWUW and OpenMC material ID space will cause an error. To automatically resolved +these ID overlaps, ``auto_ids`` can be set to ``True`` to append the UWUW +material IDs to the OpenMC material ID space. + +.. _Direct Accelerated Geometry Monte Carlo: https://svalinn.github.io/DAGMC/ +.. _University of Wisconsin Unified Workflow: https://svalinn.github.io/DAGMC/usersguide/uw2.html ------------------------- Calculating Atoms Content From 6af978efb9357640ac0b85d50d73297c1025b6f9 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 12 Mar 2021 23:05:42 -0600 Subject: [PATCH 0037/2572] Moving UWUW material read into the DAGUniverse class. --- include/openmc/cell.h | 3 ++ include/openmc/dagmc.h | 1 - src/dagmc.cpp | 80 ++++++++++++++++++------------------------ src/material.cpp | 4 --- 4 files changed, 37 insertions(+), 51 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 21ba3ddcd41..d1e3dc50ecb 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -87,12 +87,15 @@ class DAGUniverse : public Universe { void initialize(); //!< Sets up the DAGMC instance and OpenMC internals + std::map read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + // Data Members std::string filename_; std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe int32_t cell_idx_offset_; int32_t surf_idx_offset_; bool adjust_ids_; + }; #endif diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index d18b7057613..0c76d6b1df4 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -27,7 +27,6 @@ void load_dagmc_geometry(); int32_t create_dagmc_universe(const std::string& filename); void read_geometry_dagmc(); void read_dagmc_universes(pugi::xml_node node); -void read_dagmc_materials(); bool read_uwuw_materials(pugi::xml_document& doc); bool get_uwuw_materials_xml(std::string& s); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 91e2ffb0127..02bd2338e3d 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -55,7 +55,8 @@ std::string dagmc_file() { } std::string dagmc_ids_for_dim(std::shared_ptr& dagmc_instance, - int dim) { + int dim) +{ // generate a vector of ids std::vector id_vec; int n_cells = dagmc_instance->num_entities(dim); @@ -128,50 +129,9 @@ bool read_uwuw_materials(pugi::xml_document& doc) { return found_uwuw_mats; } -void read_dagmc_materials() { - std::string filename = settings::path_input + "geometry.xml"; - if (!file_exists(filename)) { - fatal_error(fmt::format("Geometry XML file '{}' does not exist!", filename)); - } - - pugi::xml_document doc; - auto result = doc.load_file(filename.c_str()); - if (!result) { - fatal_error("Error processing geometry.xml file."); - } - pugi::xml_node root = doc.document_element(); - // Loop over DAGMC elements - for (pugi::xml_node dagmc_node : root.children("dagmc")) { - if (!check_for_node(dagmc_node, "filename")) { - fatal_error("No filename specified on a DAGMC universe element"); - } - std::string dagmc_filename = get_node_value(dagmc_node, "filename"); - // Load any existing UWUW materials - UWUW uwuw(dagmc_filename.c_str()); - const auto& mat_lib = uwuw.material_library; - if (mat_lib.size() == 0) continue; - - std::stringstream ss; - ss << "\n"; - ss << "\n"; - for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } - ss << ""; - std::string mat_xml_string = ss.str(); - - // create a pugi XML document from this string - pugi::xml_document doc; - auto result = doc.load_string(mat_xml_string.c_str()); - if (!result) { - fatal_error("Error processing XML created using DAGMC UWUW materials."); - } - pugi::xml_node root = doc.document_element(); - for (pugi::xml_node material_node : root.children("material")) { - model::materials.push_back(std::make_unique(material_node)); - } - } +void read_single_file_dagmc_materials(std::string dagmc_filename) { } - bool write_uwuw_materials_xml() { std::string s; bool found_uwuw_mats = get_uwuw_materials_xml(s); @@ -308,6 +268,9 @@ void DAGUniverse::initialize() { // --- Materials --- + // read any UWUW materials from the file + auto uwuw_id_map = read_uwuw_materials(); + // create uwuw instance UWUW uwuw(filename_.c_str()); @@ -317,6 +280,8 @@ void DAGUniverse::initialize() { // notify user if UWUW materials are going to be used if (using_uwuw) { write_message("Found UWUW Materials in the DAGMC geometry file.", 6); + + } // load the DAGMC geometry @@ -353,7 +318,6 @@ void DAGUniverse::initialize() { c->universe_ = id_; // set to zero for now c->fill_ = C_NONE; // no fill, single universe - auto in_map = model::cell_map.find(c->id_); if (in_map == model::cell_map.end()) { model::cell_map[c->id_] = model::cells.size(); @@ -418,7 +382,6 @@ void DAGUniverse::initialize() { } else { c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); } - } // allocate the cell overlap count if necessary @@ -484,9 +447,34 @@ void DAGUniverse::initialize() { model::surfaces.emplace_back(s); } // end surface loop - } +std::map +DAGUniverse::read_uwuw_materials() { + + UWUW uwuw(filename_.c_str()); + const auto& mat_lib = uwuw.material_library; + if (mat_lib.size() == 0) return {}; + + std::stringstream ss; + ss << "\n"; + ss << "\n"; + for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } + ss << ""; + std::string mat_xml_string = ss.str(); + + // create a pugi XML document from this string + pugi::xml_document doc; + auto result = doc.load_string(mat_xml_string.c_str()); + if (!result) { + fatal_error("Error processing XML created using DAGMC UWUW materials."); + } + pugi::xml_node root = doc.document_element(); + for (pugi::xml_node material_node : root.children("material")) { + model::materials.push_back(std::make_unique(material_node)); + } + return {}; +} int32_t create_dagmc_universe(const std::string& filename) { if (!model::DAG) { diff --git a/src/material.cpp b/src/material.cpp index 80999abae98..3ef8c70c269 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -14,7 +14,6 @@ #include "openmc/capi.h" #include "openmc/cross_sections.h" #include "openmc/container_util.h" -#include "openmc/dagmc.h" #include "openmc/error.h" #include "openmc/file_utils.h" #include "openmc/hdf5_interface.h" @@ -1238,9 +1237,6 @@ void read_materials_xml() model::materials.push_back(make_unique(material_node)); } - // Search for materials defined on DAGMC models - read_dagmc_materials(); - model::materials.shrink_to_fit(); } From 9fca1f690dd13752f73214627faf63778bba6df5 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 12 Mar 2021 23:42:09 -0600 Subject: [PATCH 0038/2572] Moving the DAGUniverse into the DAGMC header file. --- docs/source/usersguide/geometry.rst | 17 ++++++++--------- include/openmc/cell.h | 25 +------------------------ include/openmc/constants.h | 9 +++++++++ include/openmc/dagmc.h | 22 +++++++++++++++++++--- include/openmc/surface.h | 9 --------- src/cell.cpp | 1 - src/particle.cpp | 1 + 7 files changed, 38 insertions(+), 46 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index a271490717c..4df3681a536 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -471,15 +471,14 @@ successfully and that the model is `watertight implementations of DAGMC geometry will support small volume overlaps and un-merged surfaces. - -Cell and Material IDs ---------------------- - -By default, DAGMC applies cell IDs defined by the CAD engine that the model -originated in. If these IDs overlap with ID's in the CSG cell ID space, this -will result in an error. However, the ``auto_ids`` property of a DAGMC universe -can be set to set DAGMC cell IDs by appending to the existing CSG cell ID space -in the OpenMC model. +Cell, Surface, and Material IDs +------------------------------- + +By default, DAGMC applies cell and surface IDs defined by the CAD engine that +the model originated in. If these IDs overlap with ID's in the CSG ID space, +this will result in an error. However, the ``auto_ids`` property of a DAGMC +universe can be set to set DAGMC cell and surface IDs by appending to the +existing CSG cell ID space in the OpenMC model. Similar options exist for the material IDs of DAGMC models. If DAGMC material assignments are based on natively defined OpenMC materials, no further work is diff --git a/include/openmc/cell.h b/include/openmc/cell.h index d1e3dc50ecb..b7f18a116ff 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -10,7 +10,6 @@ #include #include "hdf5.h" #include "pugixml.hpp" -#include "dagmc.h" #include "openmc/constants.h" #include "openmc/memory.h" // for unique_ptr @@ -77,29 +76,6 @@ class Universe unique_ptr partitioner_; }; -#ifdef DAGMC - -class DAGUniverse : public Universe { - -public: - explicit DAGUniverse(pugi::xml_node node); - explicit DAGUniverse(const std::string& filename, bool auto_ids = false); - - void initialize(); //!< Sets up the DAGMC instance and OpenMC internals - - std::map read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map - - // Data Members - std::string filename_; - std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe - int32_t cell_idx_offset_; - int32_t surf_idx_offset_; - bool adjust_ids_; - -}; - -#endif - //============================================================================== //============================================================================== @@ -372,6 +348,7 @@ void read_cells(pugi::xml_node node); #ifdef DAGMC +class DAGUniverse; int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); #endif diff --git a/include/openmc/constants.h b/include/openmc/constants.h index aac3a52a832..89afadeb436 100644 --- a/include/openmc/constants.h +++ b/include/openmc/constants.h @@ -379,6 +379,15 @@ enum class RunMode { // For non-accelerated regions on coarse mesh overlay constexpr int CMFD_NOACCEL {-1}; +//============================================================================== +// Geometry Constants + +enum class GeometryType { + CSG, + DAG +}; + + } // namespace openmc #endif // OPENMC_CONSTANTS_H diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 0c76d6b1df4..69b3f1610e6 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -10,6 +10,7 @@ extern "C" const bool DAGMC_ENABLED; #include "DagMC.hpp" +#include "openmc/cell.h" #include "openmc/position.h" #include "openmc/xml_interface.h" @@ -19,9 +20,24 @@ namespace model { extern std::shared_ptr DAG; } -//============================================================================== -// Non-member functions -//============================================================================== +class DAGUniverse : public Universe { + +public: + explicit DAGUniverse(pugi::xml_node node); + explicit DAGUniverse(const std::string& filename, bool auto_ids = false); + + void initialize(); //!< Sets up the DAGMC instance and OpenMC internals + + std::map read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + + // Data Members + std::string filename_; + std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe + int32_t cell_idx_offset_; + int32_t surf_idx_offset_; + bool adjust_ids_; + +}; void load_dagmc_geometry(); int32_t create_dagmc_universe(const std::string& filename); diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 5c729f8202d..2bebdc8ce62 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -29,15 +29,6 @@ namespace model { extern vector> surfaces; } // namespace model -//============================================================================== -// Constants -//============================================================================== - -enum class GeometryType { - CSG, - DAG -}; - //============================================================================== //! Coordinates for an axis-aligned cube that bounds a geometric object. //============================================================================== diff --git a/src/cell.cpp b/src/cell.cpp index ae121d2ba1a..63edf644be9 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -22,7 +22,6 @@ #include "openmc/material.h" #include "openmc/nuclide.h" #include "openmc/settings.h" -#include "openmc/surface.h" #include "openmc/xml_interface.h" namespace openmc { diff --git a/src/particle.cpp b/src/particle.cpp index 5b2105a36e1..b623b291788 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -9,6 +9,7 @@ #include "openmc/capi.h" #include "openmc/cell.h" #include "openmc/constants.h" +#include "openmc/dagmc.h" #include "openmc/error.h" #include "openmc/geometry.h" #include "openmc/hdf5_interface.h" From fdcc1ddb7ba2a0a86a1e7089069a6c41fd4eccfe Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 13 Mar 2021 00:05:41 -0600 Subject: [PATCH 0039/2572] Creating a new data member for auto-assigning material IDs. --- include/openmc/dagmc.h | 6 +++--- src/dagmc.cpp | 12 ++++++------ 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 69b3f1610e6..cf399c9364c 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -24,7 +24,7 @@ class DAGUniverse : public Universe { public: explicit DAGUniverse(pugi::xml_node node); - explicit DAGUniverse(const std::string& filename, bool auto_ids = false); + explicit DAGUniverse(const std::string& filename, bool auto_geom_ids = false); void initialize(); //!< Sets up the DAGMC instance and OpenMC internals @@ -35,8 +35,8 @@ class DAGUniverse : public Universe { std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe int32_t cell_idx_offset_; int32_t surf_idx_offset_; - bool adjust_ids_; - + bool adjust_geometry_ids_; + bool adjust_material_ids_; }; void load_dagmc_geometry(); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 02bd2338e3d..e6d0d56a2ca 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -222,15 +222,15 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { fatal_error("Must specify a file for the DAGMC universe"); } - adjust_ids_ = false; + adjust_geometry_ids_ = false; if (check_for_node(node, "auto_ids")) { - adjust_ids_ = get_node_value_bool(node, "auto_ids"); + adjust_geometry_ids_ = get_node_value_bool(node, "auto_ids"); } initialize(); } -DAGUniverse::DAGUniverse(const std::string& filename, bool auto_ids) -: filename_(filename), adjust_ids_(auto_ids) { +DAGUniverse::DAGUniverse(const std::string& filename, bool auto_geom_ids) +: filename_(filename), adjust_geometry_ids_(auto_geom_ids) { // determine the next universe id int32_t next_univ_id = 0; for (const auto& u : model::universes) { @@ -313,7 +313,7 @@ void DAGUniverse::initialize() { // set cell ids using global IDs DAGCell* c = new DAGCell(); c->dag_index_ = i + 1; - c->id_ = adjust_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); + c->id_ = adjust_geometry_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); c->dagmc_ptr_ = dagmc_instance_; c->universe_ = id_; // set to zero for now c->fill_ = C_NONE; // no fill, single universe @@ -404,7 +404,7 @@ void DAGUniverse::initialize() { // set cell ids using global IDs DAGSurface* s = new DAGSurface(); s->dag_index_ = i+1; - s->id_ = adjust_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); + s->id_ = adjust_geometry_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; // set BCs From cdcc9181f9396a446d50d59de421bfa68c56135e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 13 Mar 2021 15:43:58 -0600 Subject: [PATCH 0040/2572] Differentiating between geometry and material ID updates in the Python API. --- openmc/universe.py | 42 ++++++++++++++----- src/dagmc.cpp | 12 ++++-- .../dagmc/refl/inputs_true.dat | 2 +- tests/regression_tests/dagmc/refl/test.py | 3 +- .../dagmc/universes/inputs_true.dat | 2 +- .../regression_tests/dagmc/universes/test.py | 2 +- 6 files changed, 43 insertions(+), 20 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index 59a41a44f12..c246e33bb02 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -653,16 +653,25 @@ class DAGMCUniverse(UniverseBase): Name of the universe filename : str Path to the DAGMC file used to represent this universe. - auto_ids : bool + auto_geom_ids : bool Set IDs automatically on initialization (True) or report overlaps in ID space between CSG and DAGMC (False) + auto_mat_ids : bool + Set IDs automatically on initialization (True) or report overlaps + in ID space between OpenMC and UWUW materials (False) """ - def __init__(self, filename, universe_id=None, name='', auto_ids=False): + def __init__(self, + filename, + universe_id=None, + name='', + auto_geom_ids=False, + auto_mat_ids=False): super().__init__(universe_id, name) # Initialize class attributes self.filename = filename - self.auto_ids = auto_ids + self.auto_geom_ids = auto_geom_ids + self.auto_mat_ids = auto_mat_ids def __repr__(self): fmt_str = '{: <16}=\t{}\n' @@ -681,13 +690,22 @@ def filename(self, val): self._filename = val @property - def auto_ids(self): - return self._auto_ids + def auto_geom_ids(self): + return self._auto_geom_ids + + @auto_geom_ids.setter + def auto_geom_ids(self, val): + cv.check_type('DAGMC automatic geometry ids', val, bool) + self._auto_geom_ids = val + + @property + def auto_mat_ids(self): + return self._auto_mat_ids - @auto_ids.setter - def auto_ids(self, val): - cv.check_type('DAGMC auto ids', val, bool) - self._auto_ids = val + @auto_geom_ids.setter + def auto_mat_ids(self, val): + cv.check_type('DAGMC automatic material ids', val, bool) + self._auto_mat_ids = val def clone(self, clone_materials=True, clone_regions=True, memo=None): pass @@ -710,7 +728,9 @@ def create_xml_subelement(self, xml_element, memo=None): dagmc_element = ET.Element('dagmc') dagmc_element.set('id', str(self.id)) dagmc_element.set('name', self.name) - if self.auto_ids: - dagmc_element.set('auto_ids', 'true') + if self.auto_geom_ids: + dagmc_element.set('auto_geom_ids', 'true') + if self.auto_mat_ids: + dagmc_element.set('auto_mat_ids', 'true') dagmc_element.set('filename', self.filename) xml_element.append(dagmc_element) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index e6d0d56a2ca..8a54b1546f3 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -223,9 +223,15 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { } adjust_geometry_ids_ = false; - if (check_for_node(node, "auto_ids")) { - adjust_geometry_ids_ = get_node_value_bool(node, "auto_ids"); + if (check_for_node(node, "auto_geom_ids")) { + adjust_geometry_ids_ = get_node_value_bool(node, "auto_geom_ids"); } + + adjust_material_ids_ = false; + if (check_for_node(node, "auto_mat_ids")) { + adjust_geometry_ids_ = get_node_value_bool(node, "auto_mat_ids"); + } + initialize(); } @@ -280,8 +286,6 @@ void DAGUniverse::initialize() { // notify user if UWUW materials are going to be used if (using_uwuw) { write_message("Found UWUW Materials in the DAGMC geometry file.", 6); - - } // load the DAGMC geometry diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index fdd68002d68..ca3cba5f040 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/refl/test.py b/tests/regression_tests/dagmc/refl/test.py index 2b43caf89ed..2f4162b16aa 100644 --- a/tests/regression_tests/dagmc/refl/test.py +++ b/tests/regression_tests/dagmc/refl/test.py @@ -28,8 +28,7 @@ def _build_inputs(self): model.settings.export_to_xml() # geometry - dag_univ = openmc.DAGMCUniverse("dagmc.h5m") - dag_univ.auto_ids = True + dag_univ = openmc.DAGMCUniverse("dagmc.h5m", auto_geom_ids=True) model.geometry = openmc.Geometry(root=dag_univ) # tally diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat index f8acb45ccc3..3113159393a 100644 --- a/tests/regression_tests/dagmc/universes/inputs_true.dat +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -1,7 +1,7 @@ - + diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index 0144a0c3a80..2ae2b0e9f2b 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -42,7 +42,7 @@ def _build_inputs(self): ### GEOMETRY ### # create the DAGMC universe - pincell_univ = openmc.DAGMCUniverse(filename='dagmc.h5m', auto_ids=True) + pincell_univ = openmc.DAGMCUniverse(filename='dagmc.h5m', auto_geom_ids=True) left = openmc.XPlane(x0=-24.0, name='left', boundary_type='reflective') right = openmc.XPlane(x0=24.0, name='right', boundary_type='reflective') From 4d58990957f5536e7aff16bbe76c3fe3a23112bc Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 13 Mar 2021 17:03:14 -0600 Subject: [PATCH 0041/2572] Adjusting UWUW material IDs if needed. --- include/openmc/dagmc.h | 6 +++++- src/dagmc.cpp | 34 ++++++++++++++++++++++------------ 2 files changed, 27 insertions(+), 13 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index cf399c9364c..a908f9f6c63 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -14,12 +14,15 @@ extern "C" const bool DAGMC_ENABLED; #include "openmc/position.h" #include "openmc/xml_interface.h" +class UWUW; + namespace openmc { namespace model { extern std::shared_ptr DAG; } + class DAGUniverse : public Universe { public: @@ -28,7 +31,8 @@ class DAGUniverse : public Universe { void initialize(); //!< Sets up the DAGMC instance and OpenMC internals - std::map read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + std::shared_ptr + read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map // Data Members std::string filename_; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 8a54b1546f3..9f29912684a 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -275,13 +275,10 @@ void DAGUniverse::initialize() { // --- Materials --- // read any UWUW materials from the file - auto uwuw_id_map = read_uwuw_materials(); - - // create uwuw instance - UWUW uwuw(filename_.c_str()); + std::shared_ptr uwuw = read_uwuw_materials(); // check for uwuw material definitions - bool using_uwuw = !uwuw.material_library.empty(); + bool using_uwuw = !uwuw->material_library.empty(); // notify user if UWUW materials are going to be used if (using_uwuw) { @@ -355,9 +352,9 @@ void DAGUniverse::initialize() { if (using_uwuw) { // lookup material in uwuw if present std::string uwuw_mat = DMD.volume_material_property_data_eh[vol_handle]; - if (uwuw.material_library.count(uwuw_mat) != 0) { + if (uwuw->material_library.count(uwuw_mat) != 0) { // Note: material numbers are set by UWUW - int mat_number = uwuw.material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); + int mat_number = uwuw->material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); c->material_.push_back(mat_number); } else { fatal_error(fmt::format("Material with value {} not found in the " @@ -453,12 +450,25 @@ void DAGUniverse::initialize() { } // end surface loop } -std::map +std::shared_ptr DAGUniverse::read_uwuw_materials() { - UWUW uwuw(filename_.c_str()); - const auto& mat_lib = uwuw.material_library; - if (mat_lib.size() == 0) return {}; + int32_t next_material_id = 0; + for (const auto& m : model::materials) { + next_material_id = std::max(m->id_, next_material_id); + } + next_material_id++; + + auto uwuw = std::make_shared(filename_.c_str()); + const auto& mat_lib = uwuw->material_library; + if (mat_lib.size() == 0) return uwuw; + + // if we're using automatic IDs, update the UWUW material metadata + if (adjust_material_ids_) { + for (auto& mat : uwuw->material_library) { + mat.second->metadata["mat_number"] = std::to_string(next_material_id++); + } + } std::stringstream ss; ss << "\n"; @@ -477,7 +487,7 @@ DAGUniverse::read_uwuw_materials() { for (pugi::xml_node material_node : root.children("material")) { model::materials.push_back(std::make_unique(material_node)); } - return {}; + return uwuw; } int32_t create_dagmc_universe(const std::string& filename) { From 4819e02f227394b7f687aa0e456d934245bbb2e4 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 13 Mar 2021 22:24:27 -0600 Subject: [PATCH 0042/2572] Removing some unused DAGMC functions. --- include/openmc/dagmc.h | 10 -- src/dagmc.cpp | 211 ----------------------------------------- 2 files changed, 221 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index a908f9f6c63..650829fa475 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -18,11 +18,6 @@ class UWUW; namespace openmc { -namespace model { - extern std::shared_ptr DAG; -} - - class DAGUniverse : public Universe { public: @@ -43,12 +38,7 @@ class DAGUniverse : public Universe { bool adjust_material_ids_; }; -void load_dagmc_geometry(); -int32_t create_dagmc_universe(const std::string& filename); -void read_geometry_dagmc(); void read_dagmc_universes(pugi::xml_node node); -bool read_uwuw_materials(pugi::xml_document& doc); -bool get_uwuw_materials_xml(std::string& s); } // namespace openmc diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 9f29912684a..d04ec4fc00f 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -39,13 +39,6 @@ namespace openmc { const std::string DAGMC_FILENAME = "dagmc.h5m"; -namespace model { - -std::shared_ptr DAG; - -} // namespace model - - std::string dagmc_file() { std::string filename = settings::path_input + DAGMC_FILENAME; if (!file_exists(filename)) { @@ -129,9 +122,6 @@ bool read_uwuw_materials(pugi::xml_document& doc) { return found_uwuw_mats; } -void read_single_file_dagmc_materials(std::string dagmc_filename) { -} - bool write_uwuw_materials_xml() { std::string s; bool found_uwuw_mats = get_uwuw_materials_xml(s); @@ -193,21 +183,12 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) } } -void load_dagmc_geometry() -{ - model::universes.push_back(std::make_unique(dagmc_file())); - model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; -} - -#ifdef DAGMC void read_dagmc_universes(pugi::xml_node node) { for (pugi::xml_node dag_node : node.children("dagmc")) { model::universes.push_back(std::make_unique(dag_node)); model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; } } -#endif - DAGUniverse::DAGUniverse(pugi::xml_node node) { if (check_for_node(node, "id")) { @@ -490,197 +471,5 @@ DAGUniverse::read_uwuw_materials() { return uwuw; } -int32_t create_dagmc_universe(const std::string& filename) { - if (!model::DAG) { - model::DAG = std::make_shared(); - } - - // --- Materials --- - - // create uwuw instance - UWUW uwuw(filename.c_str()); - - // check for uwuw material definitions - bool using_uwuw = !uwuw.material_library.empty(); - - // notify user if UWUW materials are going to be used - if (using_uwuw) { - write_message("Found UWUW Materials in the DAGMC geometry file.", 6); - } - - int32_t dagmc_univ_id = 0; // universe is always 0 for DAGMC runs - - // load the DAGMC geometry - moab::ErrorCode rval = model::DAG->load_file(filename.c_str()); - MB_CHK_ERR_CONT(rval); - - // initialize acceleration data structures - rval = model::DAG->init_OBBTree(); - MB_CHK_ERR_CONT(rval); - - // parse model metadata - dagmcMetaData DMD(model::DAG.get(), false, false); - DMD.load_property_data(); - - vector keywords {"temp"}; - std::map dum; - std::string delimiters = ":/"; - rval = model::DAG->parse_properties(keywords, dum, delimiters.c_str()); - MB_CHK_ERR_CONT(rval); - - // --- Cells (Volumes) --- - - // initialize cell objects - int n_cells = model::DAG->num_entities(3); - moab::EntityHandle graveyard = 0; - for (int i = 0; i < n_cells; i++) { - moab::EntityHandle vol_handle = model::DAG->entity_by_index(3, i+1); - // set cell ids using global IDs - DAGCell* c = new DAGCell(); - c->dag_index_ = i+1; - c->id_ = model::DAG->id_by_index(3, c->dag_index_); - c->dagmc_ptr_ = model::DAG; - c->universe_ = dagmc_univ_id; // set to zero for now - c->fill_ = C_NONE; // no fill, single universe - - model::cells.emplace_back(c); - model::cell_map[c->id_] = i; - - // Populate the Universe vector and dict - auto it = model::universe_map.find(dagmc_univ_id); - if (it == model::universe_map.end()) { - model::universes.push_back(make_unique()); - model::universes.back()->id_ = dagmc_univ_id; - model::universes.back()->cells_.push_back(i); - model::universe_map[dagmc_univ_id] = model::universes.size() - 1; - } else { - model::universes[it->second]->cells_.push_back(i); - } - - // MATERIALS - - // determine volume material assignment - std::string mat_str = DMD.get_volume_property("material", vol_handle); - - if (mat_str.empty()) { - fatal_error(fmt::format("Volume {} has no material assignment.", c->id_)); - } - - to_lower(mat_str); - - if (mat_str == "graveyard") { - graveyard = vol_handle; - } - - // material void checks - if (mat_str == "void" || mat_str == "vacuum" || mat_str == "graveyard") { - c->material_.push_back(MATERIAL_VOID); - } else { - if (using_uwuw) { - // lookup material in uwuw if present - std::string uwuw_mat = DMD.volume_material_property_data_eh[vol_handle]; - if (uwuw.material_library.count(uwuw_mat) != 0) { - // Note: material numbers are set by UWUW - int mat_number = uwuw.material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); - c->material_.push_back(mat_number); - } else { - fatal_error(fmt::format("Material with value {} not found in the " - "UWUW material library", mat_str)); - } - } else { - legacy_assign_material(mat_str, c); - } - } - - // check for temperature assignment - std::string temp_value; - - // no temperature if void - if (c->material_[0] == MATERIAL_VOID) continue; - - // assign cell temperature - const auto& mat = model::materials[model::material_map.at(c->material_[0])]; - if (model::DAG->has_prop(vol_handle, "temp")) { - rval = model::DAG->prop_value(vol_handle, "temp", temp_value); - MB_CHK_ERR_CONT(rval); - double temp = std::stod(temp_value); - c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * temp)); - } else { - c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * mat->temperature())); - } - - } - - // allocate the cell overlap count if necessary - if (settings::check_overlaps) { - model::overlap_check_count.resize(model::cells.size(), 0); - } - - if (!graveyard) { - warning("No graveyard volume found in the DagMC model." - "This may result in lost particles and rapid simulation failure."); - } - - // --- Surfaces --- - - // initialize surface objects - int n_surfaces = model::DAG->num_entities(2); - - for (int i = 0; i < n_surfaces; i++) { - moab::EntityHandle surf_handle = model::DAG->entity_by_index(2, i+1); - - // set cell ids using global IDs - DAGSurface* s = new DAGSurface(); - s->dag_index_ = i+1; - s->id_ = model::DAG->id_by_index(2, s->dag_index_); - s->dagmc_ptr_ = model::DAG; - - if (contains(settings::source_write_surf_id, s->id_)) { - s->surf_source_ = true; - } - - // set BCs - std::string bc_value = DMD.get_surface_property("boundary", surf_handle); - to_lower(bc_value); - if (bc_value.empty() || bc_value == "transmit" || bc_value == "transmission") { - // Leave the bc_ a nullptr - } else if (bc_value == "vacuum") { - s->bc_ = std::make_shared(); - } else if (bc_value == "reflective" || bc_value == "reflect" || bc_value == "reflecting") { - s->bc_ = std::make_shared(); - } else if (bc_value == "white") { - fatal_error("White boundary condition not supported in DAGMC."); - } else if (bc_value == "periodic") { - fatal_error("Periodic boundary condition not supported in DAGMC."); - } else { - fatal_error(fmt::format("Unknown boundary condition \"{}\" specified " - "on surface {}", bc_value, s->id_)); - } - - // graveyard check - moab::Range parent_vols; - rval = model::DAG->moab_instance()->get_parent_meshsets(surf_handle, parent_vols); - MB_CHK_ERR_CONT(rval); - - // if this surface belongs to the graveyard - if (graveyard && parent_vols.find(graveyard) != parent_vols.end()) { - // set graveyard surface BC's to vacuum - s->bc_ = std::make_shared(); - } - - // add to global array and map - model::surfaces.emplace_back(s); - model::surface_map[s->id_] = i; - } - - return model::universes.size() - 1; -} - -void read_geometry_dagmc() -{ - write_message("Reading DAGMC geometry...", 5); - model::root_universe = find_root_universe(); -} - } #endif From da6a369d1fa04b13fa40618059cae52f9d467fe6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 15 Mar 2021 14:28:44 -0500 Subject: [PATCH 0043/2572] Refactoring all of the openmc namespace functions into the DAGUniverse class. --- include/openmc/dagmc.h | 15 ++++- src/dagmc.cpp | 126 ++++++++++++++++++++--------------------- 2 files changed, 75 insertions(+), 66 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 650829fa475..8eb7f272bd5 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -26,12 +26,23 @@ class DAGUniverse : public Universe { void initialize(); //!< Sets up the DAGMC instance and OpenMC internals - std::shared_ptr - read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + void read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + + bool uses_uwuw() const; + + std::string get_uwuw_materials_xml() const; + + void write_uwuw_materials_xml(const std::string& outfile = "uwuw_materials.xml") const; + + void legacy_assign_material(std::string mat_string, + std::unique_ptr& c) const; + + std::string dagmc_ids_for_dim(int dim) const; // Data Members std::string filename_; std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe + std::shared_ptr uwuw_; int32_t cell_idx_offset_; int32_t surf_idx_offset_; bool adjust_geometry_ids_; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index d04ec4fc00f..947f48a647a 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -47,14 +47,14 @@ std::string dagmc_file() { return filename; } -std::string dagmc_ids_for_dim(std::shared_ptr& dagmc_instance, - int dim) +std::string +DAGUniverse::dagmc_ids_for_dim(int dim) const { // generate a vector of ids std::vector id_vec; - int n_cells = dagmc_instance->num_entities(dim); + int n_cells = dagmc_instance_->num_entities(dim); for (int i = 1; i <= n_cells; i++) { - id_vec.push_back(dagmc_instance->id_by_index(dim, i)); + id_vec.push_back(dagmc_instance_->id_by_index(dim, i)); } // sort the vector of ids @@ -88,55 +88,50 @@ std::string dagmc_ids_for_dim(std::shared_ptr& dagmc_instance, return out.str(); } -bool get_uwuw_materials_xml(std::string& s) { - std::string filename = dagmc_file(); - UWUW uwuw(filename.c_str()); +bool +DAGUniverse::uses_uwuw() const +{ + return uwuw_ && !uwuw_->material_library.empty(); +} - std::stringstream ss; - bool uwuw_mats_present = false; - if (uwuw.material_library.size() != 0) { - uwuw_mats_present = true; - // write header - ss << "\n"; - ss << "\n"; - const auto& mat_lib = uwuw.material_library; - // write materials - for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } - // write footer - ss << ""; - s = ss.str(); +std::string +DAGUniverse::get_uwuw_materials_xml() const +{ + + if (!uses_uwuw()) { + throw std::runtime_error("This DAGMC Universe does not use UWUW materials"); } - return uwuw_mats_present; -} + std::stringstream ss; + // write header + ss << "\n"; + ss << "\n"; + const auto& mat_lib = uwuw_->material_library; + // write materials + for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } + // write footer + ss << ""; -bool read_uwuw_materials(pugi::xml_document& doc) { - std::string s; - bool found_uwuw_mats = get_uwuw_materials_xml(s); - if (found_uwuw_mats) { - pugi::xml_parse_result result = doc.load_string(s.c_str()); - if (!result) { - throw std::runtime_error{"Error reading UWUW materials"}; - } - } - return found_uwuw_mats; + return ss.str(); } -bool write_uwuw_materials_xml() { - std::string s; - bool found_uwuw_mats = get_uwuw_materials_xml(s); - // if there is a material library in the file - if (found_uwuw_mats) { - // write a material.xml file - std::ofstream mats_xml("materials.xml"); - mats_xml << s; - mats_xml.close(); +void +DAGUniverse::write_uwuw_materials_xml(const std::string& outfile) const +{ + if (!uses_uwuw()) { + throw std::runtime_error("This DAGMC universe does not use UWUW materials."); } - return found_uwuw_mats; + std::string xml_str = get_uwuw_materials_xml(); + // if there is a material library in the file + std::ofstream mats_xml(outfile); + mats_xml << xml_str; + mats_xml.close(); } -void legacy_assign_material(std::string mat_string, DAGCell* c) +void +DAGUniverse::legacy_assign_material(std::string mat_string, + std::unique_ptr& c) const { bool mat_found_by_name = false; // attempt to find a material with a matching name @@ -152,7 +147,7 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) // report error if more than one material is found } else { fatal_error(fmt::format( - "More than one material found with name {}. Please ensure materials " + "More than one material found with name '{}'. Please ensure materials " "have unique names if using this property to assign materials.", mat_string)); } @@ -166,7 +161,7 @@ void legacy_assign_material(std::string mat_string, DAGCell* c) c->material_.emplace_back(id); } catch (const std::invalid_argument&) { fatal_error(fmt::format( - "No material {} found for volume (cell) {}", mat_string, c->id_)); + "No material '{}' found for volume (cell) {}", mat_string, c->id_)); } } @@ -256,10 +251,10 @@ void DAGUniverse::initialize() { // --- Materials --- // read any UWUW materials from the file - std::shared_ptr uwuw = read_uwuw_materials(); + read_uwuw_materials(); // check for uwuw material definitions - bool using_uwuw = !uwuw->material_library.empty(); + bool using_uwuw = uses_uwuw(); // notify user if UWUW materials are going to be used if (using_uwuw) { @@ -293,7 +288,7 @@ void DAGUniverse::initialize() { moab::EntityHandle vol_handle = dagmc_instance_->entity_by_index(3, i + 1); // set cell ids using global IDs - DAGCell* c = new DAGCell(); + auto c = std::make_unique(); c->dag_index_ = i + 1; c->id_ = adjust_geometry_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); c->dagmc_ptr_ = dagmc_instance_; @@ -304,13 +299,11 @@ void DAGUniverse::initialize() { if (in_map == model::cell_map.end()) { model::cell_map[c->id_] = model::cells.size(); } else { - warning(fmt::format("DAGMC Cell IDs: {}", dagmc_ids_for_dim(dagmc_instance_, 3))); + warning(fmt::format("DAGMC Cell IDs: {}", dagmc_ids_for_dim(3))); fatal_error(fmt::format("Cell ID {} exists in both DAGMC Universe {} " "and the CSG geometry.", c->id_, this->id_)); } - model::cells.emplace_back(c); - // MATERIALS // determine volume material assignment @@ -333,12 +326,12 @@ void DAGUniverse::initialize() { if (using_uwuw) { // lookup material in uwuw if present std::string uwuw_mat = DMD.volume_material_property_data_eh[vol_handle]; - if (uwuw->material_library.count(uwuw_mat) != 0) { + if (uwuw_->material_library.count(uwuw_mat) != 0) { // Note: material numbers are set by UWUW - int mat_number = uwuw->material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); + int mat_number = uwuw_->material_library.get_material(uwuw_mat).metadata["mat_number"].asInt(); c->material_.push_back(mat_number); } else { - fatal_error(fmt::format("Material with value {} not found in the " + fatal_error(fmt::format("Material with value '{}' not found in the " "UWUW material library", mat_str)); } } else { @@ -350,7 +343,10 @@ void DAGUniverse::initialize() { std::string temp_value; // no temperature if void - if (c->material_[0] == MATERIAL_VOID) continue; + if (c->material_[0] == MATERIAL_VOID) { + model::cells.emplace_back(std::move(c)); + continue; + } // assign cell temperature const auto& mat = model::materials[model::material_map.at(c->material_[0])]; @@ -364,6 +360,8 @@ void DAGUniverse::initialize() { } else { c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); } + + model::cells.emplace_back(std::move(c)); } // allocate the cell overlap count if necessary @@ -384,7 +382,7 @@ void DAGUniverse::initialize() { moab::EntityHandle surf_handle = dagmc_instance_->entity_by_index(2, i+1); // set cell ids using global IDs - DAGSurface* s = new DAGSurface(); + auto s = std::make_unique(); s->dag_index_ = i+1; s->id_ = adjust_geometry_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; @@ -422,16 +420,17 @@ void DAGUniverse::initialize() { if (in_map == model::surface_map.end()) { model::surface_map[s->id_] = model::surfaces.size(); } else { - warning(fmt::format("DAGMC Surface IDs: {}", dagmc_ids_for_dim(dagmc_instance_, 2))); + warning(fmt::format("DAGMC Surface IDs: {}", dagmc_ids_for_dim(2))); fatal_error(fmt::format("Surface ID {} exists in both Universe {} " "and the CSG geometry.", s->id_, this->id_)); } - model::surfaces.emplace_back(s); + + model::surfaces.emplace_back(std::move(s)); } // end surface loop } -std::shared_ptr +void DAGUniverse::read_uwuw_materials() { int32_t next_material_id = 0; @@ -440,13 +439,13 @@ DAGUniverse::read_uwuw_materials() { } next_material_id++; - auto uwuw = std::make_shared(filename_.c_str()); - const auto& mat_lib = uwuw->material_library; - if (mat_lib.size() == 0) return uwuw; + uwuw_ = std::make_shared(filename_.c_str()); + const auto& mat_lib = uwuw_->material_library; + if (mat_lib.size() == 0) return; // if we're using automatic IDs, update the UWUW material metadata if (adjust_material_ids_) { - for (auto& mat : uwuw->material_library) { + for (auto& mat : uwuw_->material_library) { mat.second->metadata["mat_number"] = std::to_string(next_material_id++); } } @@ -468,7 +467,6 @@ DAGUniverse::read_uwuw_materials() { for (pugi::xml_node material_node : root.children("material")) { model::materials.push_back(std::make_unique(material_node)); } - return uwuw; } } From 8658dbaed7f03de103f090d62b060fcc2440bd7d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 15 Mar 2021 14:32:17 -0500 Subject: [PATCH 0044/2572] Removing another now unused function. --- include/openmc/dagmc.h | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 8eb7f272bd5..8da6898d36c 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -1,4 +1,3 @@ - #ifndef OPENMC_DAGMC_H #define OPENMC_DAGMC_H @@ -53,10 +52,6 @@ void read_dagmc_universes(pugi::xml_node node); } // namespace openmc -#else - -inline void read_dagmc_materials() {}; - -#endif +#endif // DAGMC #endif // OPENMC_DAGMC_H From 8f9788742a23d5a6901a2d86add0037a28429dc6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 26 Mar 2021 20:10:10 -0500 Subject: [PATCH 0045/2572] A couple of cosmetic fixes. --- src/dagmc.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 947f48a647a..880a42ebc1c 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -292,7 +292,7 @@ void DAGUniverse::initialize() { c->dag_index_ = i + 1; c->id_ = adjust_geometry_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); c->dagmc_ptr_ = dagmc_instance_; - c->universe_ = id_; // set to zero for now + c->universe_ = id_; c->fill_ = C_NONE; // no fill, single universe auto in_map = model::cell_map.find(c->id_); @@ -370,7 +370,7 @@ void DAGUniverse::initialize() { } if (!graveyard) { - warning("No graveyard volume found in the DagMC model." + warning("No graveyard volume found in the DagMC model. " "This may result in lost particles and rapid simulation failure."); } From 329394824893e63170139b6785affa3d5f68a79e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 29 Mar 2021 14:58:18 -0500 Subject: [PATCH 0046/2572] 65;6003;1cAdding a find_cell method to universes. --- include/openmc/cell.h | 2 ++ src/cell.cpp | 25 +++++++++++++++++++++++++ src/geometry.cpp | 27 +++------------------------ 3 files changed, 30 insertions(+), 24 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index b7f18a116ff..54e06f782ec 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -70,6 +70,8 @@ class Universe //! \param group_id An HDF5 group id. void to_hdf5(hid_t group_id) const; + virtual bool find_cell(Particle &p) const; + BoundingBox bounding_box() const; GeometryType type_ = GeometryType::CSG; diff --git a/src/cell.cpp b/src/cell.cpp index 63edf644be9..4dee4730f00 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -212,6 +212,31 @@ Universe::to_hdf5(hid_t universes_group) const close_group(group); } +bool +Universe::find_cell(Particle& p) const { + const auto& cells { + !partitioner_ + ? cells_ + : partitioner_->get_cells(p.r_local(), p.u_local()) + }; + + for (auto it = cells.begin(); it != cells.end(); it++) { + int32_t i_cell = *it; + int32_t i_univ = p.coord_[p.n_coord_-1].universe; + if (model::cells[i_cell]->universe_ != i_univ) continue; + + // Check if this cell contains the particle; + Position r {p.r_local()}; + Direction u {p.u_local()}; + auto surf = p.surface_; + if (model::cells[i_cell]->contains(r, u, surf)) { + p.coord_[p.n_coord_-1].cell = i_cell; + return true; + } + } + return false; +} + BoundingBox Universe::bounding_box() const { BoundingBox bbox = {INFTY, -INFTY, INFTY, -INFTY, INFTY, -INFTY}; if (cells_.size() == 0) { diff --git a/src/geometry.cpp b/src/geometry.cpp index 59607f637a5..7e7b3b17291 100644 --- a/src/geometry.cpp +++ b/src/geometry.cpp @@ -112,34 +112,13 @@ find_cell_inner(Particle& p, const NeighborList* neighbor_list) // that. if (i_cell == C_NONE) { int i_universe = p.coord(p.n_coord() - 1).universe; - const auto& univ {*model::universes[i_universe]}; - const auto& cells { - !univ.partitioner_ - ? model::universes[i_universe]->cells_ - : univ.partitioner_->get_cells(p.r_local(), p.u_local()) - }; - - for (auto it = cells.cbegin(); it != cells.cend(); it++) { - i_cell = *it; - - // Make sure the search cell is in the same universe. - int i_universe = p.coord(p.n_coord() - 1).universe; - if (model::cells[i_cell]->universe_ != i_universe) continue; - - // Check if this cell contains the particle. - Position r {p.r_local()}; - Direction u {p.u_local()}; - auto surf = p.surface(); - if (model::cells[i_cell]->contains(r, u, surf)) { - p.coord(p.n_coord() - 1).cell = i_cell; - found = true; - break; - } - } + const auto& univ {model::universes[i_universe]}; + found = univ->find_cell(p); } if (!found) { return found; } + i_cell = p.coord_[p.n_coord_ - 1].cell; // Announce the cell that the particle is entering. if (found && (settings::verbosity >= 10 || p.trace())) { From 7190f6685f224f52f80a2a99cd204fd992774f19 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 29 Mar 2021 18:19:02 -0500 Subject: [PATCH 0047/2572] Fix after rebase. --- src/particle.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/particle.cpp b/src/particle.cpp index b623b291788..6fc27ebfa81 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -514,7 +514,7 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) // the lower universes. // (unless we're using a dagmc model, which has exactly one universe) n_coord() = 1; - if (surf->geom_type_ != GeometryType::DAGMC && !neighbor_list_find_cell(*this, true)) { + if (surf.geom_type_ != GeometryType::DAG && !neighbor_list_find_cell(*this)) { this->mark_as_lost("Couldn't find particle after reflecting from surface " + std::to_string(surf.id_) + "."); return; From 97691874f62c0872c9b0b03db9eef7ddd9544c2f Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 30 Mar 2021 01:31:15 -0500 Subject: [PATCH 0048/2572] Improved handling of lower level DAGMC universes. --- include/openmc/dagmc.h | 4 ++++ src/cell.cpp | 4 +++- src/dagmc.cpp | 20 ++++++++++++++++++++ 3 files changed, 27 insertions(+), 1 deletion(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 8da6898d36c..76bd0a4ec3e 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -29,6 +29,8 @@ class DAGUniverse : public Universe { bool uses_uwuw() const; + int32_t implicit_complement_idx() const; + std::string get_uwuw_materials_xml() const; void write_uwuw_materials_xml(const std::string& outfile = "uwuw_materials.xml") const; @@ -38,6 +40,8 @@ class DAGUniverse : public Universe { std::string dagmc_ids_for_dim(int dim) const; + virtual bool find_cell(Particle &p) const override; + // Data Members std::string filename_; std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe diff --git a/src/cell.cpp b/src/cell.cpp index 4dee4730f00..ad711573b24 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -863,7 +863,9 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons // indicate that particle is lost surf_idx = -1; dist = INFINITY; - p->mark_as_lost(fmt::format("No intersection found with DAGMC cell {}", id_)); + if (!dagmc_ptr_->is_implicit_complement(vol) || model::universe_map[dag_univ->id_] == model::root_universe) { + p->mark_as_lost(fmt::format("No intersection found with DAGMC cell {}", id_)); + } } return {dist, surf_idx}; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 880a42ebc1c..635f05bd886 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -88,6 +88,26 @@ DAGUniverse::dagmc_ids_for_dim(int dim) const return out.str(); } +int32_t +DAGUniverse::implicit_complement_idx() const { + moab::EntityHandle ic; + moab::ErrorCode rval = dagmc_instance_->geom_tool()->get_implicit_complement(ic); + MB_CHK_SET_ERR_CONT(rval, "Failed to get implicit complement"); + return cell_idx_offset_ + dagmc_instance_->index_by_handle(ic) - 1; +} + +bool +DAGUniverse::find_cell(Particle &p) const { + // if the particle isn't in any of the other DagMC + // cells, place it in the implicit complement + bool found = Universe::find_cell(p); + if (!found && model::universe_map[this->id_] != model::root_universe) { + p.coord_[p.n_coord_ - 1].cell = implicit_complement_idx(); + found = true; + } + return found; +} + bool DAGUniverse::uses_uwuw() const { From 5978d78e1caceb5b0bd30f5fb126353935e34ae0 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 31 Mar 2021 18:07:08 -0500 Subject: [PATCH 0049/2572] Creating a more complicated test --- tests/regression_tests/rotation/test.py | 44 +++++++++++++++++++++++-- 1 file changed, 42 insertions(+), 2 deletions(-) diff --git a/tests/regression_tests/rotation/test.py b/tests/regression_tests/rotation/test.py index 31c3d8d655e..4a4526342e6 100644 --- a/tests/regression_tests/rotation/test.py +++ b/tests/regression_tests/rotation/test.py @@ -1,6 +1,46 @@ from tests.testing_harness import TestHarness -def test_rotation(): - harness = TestHarness('statepoint.10.h5') +@pytest.fixture +def model(): + model = openmc.model.Model() + + fuel = openmc.Material() + fuel.set_density('g/cc', 10.0) + fuel.add_nuclide('U235', 1.0) + + h1 = openmc.Material() + h1.set_density('g/cc', 0.1) + h1.add_nuclide('H1', 0.1) + + inner_sphere = openmc.Sphere(x0=1.0, r=5.0) + + fuel_cell = openmc.Cell(fill=fuel, region=-inner_sphere) + hydrogen_cell = openmc.Cell(fill=h1, region=+inner_sphere) + univ = openmc.Universe(cells=[fuel_cell, hydrogen_cell]) + + + box = openmc.rectangular_prism(15., 15., 'z', boundary_type='vacuum') + lower_z = openmc.ZPlane(-7.5, boundary_type='vacuum') + upper_z = openmc.ZPlane(22.5, boundary_type='vacuum') + middle_z = openmc.ZPlane(7.5) + + lower_cell = openmc.Cell(fill=univ, region=box & +lower_z & -middle_z) + lower_cell.rotation = (10, 20, 30) + upper_cell = openmc.Cell(fill=univ, region=box & +middle_z & -upper_z) + upper_cell.translation = (0, 0, 15) + + model.geometry = openmc.Geometry(root=[lower_cell, upper_cell]) + + model.settings.particles = 10000 + model.settings.inactive = 5 + model.settings.batches = 10 + source_box = openmc.stats.Box((-4., -4., -4.), (4., 4., 4.)) + model.settings.source = openmc.Source(space=source_box) + + return model + + +def test_rotation(model): + harness = PyAPITestHarness('statepoint.10.h5', model) harness.main() From 67ae0806297d127f5237f31ce39859c8b84615c1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 31 Mar 2021 18:26:34 -0500 Subject: [PATCH 0050/2572] Updating test results for new problem. --- tests/regression_tests/rotation/geometry.xml | 11 ------ .../regression_tests/rotation/inputs_true.dat | 38 +++++++++++++++++++ tests/regression_tests/rotation/materials.xml | 14 ------- .../rotation/results_true.dat | 4 ++ tests/regression_tests/rotation/settings.xml | 15 -------- tests/regression_tests/rotation/test.py | 5 ++- 6 files changed, 46 insertions(+), 41 deletions(-) delete mode 100644 tests/regression_tests/rotation/geometry.xml create mode 100644 tests/regression_tests/rotation/inputs_true.dat delete mode 100644 tests/regression_tests/rotation/materials.xml delete mode 100644 tests/regression_tests/rotation/settings.xml diff --git a/tests/regression_tests/rotation/geometry.xml b/tests/regression_tests/rotation/geometry.xml deleted file mode 100644 index f3246170654..00000000000 --- a/tests/regression_tests/rotation/geometry.xml +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - - diff --git a/tests/regression_tests/rotation/inputs_true.dat b/tests/regression_tests/rotation/inputs_true.dat new file mode 100644 index 00000000000..24b00199beb --- /dev/null +++ b/tests/regression_tests/rotation/inputs_true.dat @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + eigenvalue + 10000 + 10 + 5 + + + -4.0 -4.0 -4.0 4.0 4.0 4.0 + + + diff --git a/tests/regression_tests/rotation/materials.xml b/tests/regression_tests/rotation/materials.xml deleted file mode 100644 index 160c9c67899..00000000000 --- a/tests/regression_tests/rotation/materials.xml +++ /dev/null @@ -1,14 +0,0 @@ - - - - - - - - - - - - - - diff --git a/tests/regression_tests/rotation/results_true.dat b/tests/regression_tests/rotation/results_true.dat index f4a3cfcccbd..f0e7868a011 100644 --- a/tests/regression_tests/rotation/results_true.dat +++ b/tests/regression_tests/rotation/results_true.dat @@ -1,2 +1,6 @@ k-combined: +<<<<<<< HEAD 4.132181E-01 1.130226E-02 +======= +4.453328E-01 5.918369E-03 +>>>>>>> Updating test results for new problem. diff --git a/tests/regression_tests/rotation/settings.xml b/tests/regression_tests/rotation/settings.xml deleted file mode 100644 index 70b4e802f83..00000000000 --- a/tests/regression_tests/rotation/settings.xml +++ /dev/null @@ -1,15 +0,0 @@ - - - - eigenvalue - 10 - 5 - 1000 - - - - -4 -4 -4 4 4 4 - - - - diff --git a/tests/regression_tests/rotation/test.py b/tests/regression_tests/rotation/test.py index 4a4526342e6..7a59ce2000e 100644 --- a/tests/regression_tests/rotation/test.py +++ b/tests/regression_tests/rotation/test.py @@ -1,4 +1,7 @@ -from tests.testing_harness import TestHarness +import pytest +import openmc + +from tests.testing_harness import PyAPITestHarness @pytest.fixture From e455c3d58e83f3ecff41c22b314ecfa034901a22 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 31 Mar 2021 18:27:37 -0500 Subject: [PATCH 0051/2572] Adding a quick comment. --- tests/regression_tests/rotation/test.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/regression_tests/rotation/test.py b/tests/regression_tests/rotation/test.py index 7a59ce2000e..25aada5aada 100644 --- a/tests/regression_tests/rotation/test.py +++ b/tests/regression_tests/rotation/test.py @@ -22,7 +22,8 @@ def model(): hydrogen_cell = openmc.Cell(fill=h1, region=+inner_sphere) univ = openmc.Universe(cells=[fuel_cell, hydrogen_cell]) - + # Create one cell on top of the other. Only one + # has a rotation box = openmc.rectangular_prism(15., 15., 'z', boundary_type='vacuum') lower_z = openmc.ZPlane(-7.5, boundary_type='vacuum') upper_z = openmc.ZPlane(22.5, boundary_type='vacuum') From e0aed3d2b3a1c938b0346cefa9f97bd83ab6cdf0 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 31 Mar 2021 22:56:03 -0500 Subject: [PATCH 0052/2572] Restoring old test (but using PyAPITestHarnes). Moving the new test into its own directory. --- .../regression_tests/rotation/inputs_true.dat | 19 +++++---------- .../rotation/results_true.dat | 4 ---- tests/regression_tests/rotation/test.py | 24 ++++++------------- 3 files changed, 13 insertions(+), 34 deletions(-) diff --git a/tests/regression_tests/rotation/inputs_true.dat b/tests/regression_tests/rotation/inputs_true.dat index 24b00199beb..d90d498bf62 100644 --- a/tests/regression_tests/rotation/inputs_true.dat +++ b/tests/regression_tests/rotation/inputs_true.dat @@ -1,17 +1,10 @@ - - - - - - - - - - - - + + + + + @@ -27,7 +20,7 @@ eigenvalue - 10000 + 1000 10 5 diff --git a/tests/regression_tests/rotation/results_true.dat b/tests/regression_tests/rotation/results_true.dat index f0e7868a011..a58aa1c8ecc 100644 --- a/tests/regression_tests/rotation/results_true.dat +++ b/tests/regression_tests/rotation/results_true.dat @@ -1,6 +1,2 @@ k-combined: -<<<<<<< HEAD -4.132181E-01 1.130226E-02 -======= 4.453328E-01 5.918369E-03 ->>>>>>> Updating test results for new problem. diff --git a/tests/regression_tests/rotation/test.py b/tests/regression_tests/rotation/test.py index 25aada5aada..6b68367b40b 100644 --- a/tests/regression_tests/rotation/test.py +++ b/tests/regression_tests/rotation/test.py @@ -1,8 +1,7 @@ -import pytest import openmc +import pytest -from tests.testing_harness import PyAPITestHarness - +from tests.testing_harness import TestHarness, PyAPITestHarness @pytest.fixture def model(): @@ -16,27 +15,18 @@ def model(): h1.set_density('g/cc', 0.1) h1.add_nuclide('H1', 0.1) + outer_sphere = openmc.Sphere(r=10.0, boundary_type='vacuum') inner_sphere = openmc.Sphere(x0=1.0, r=5.0) fuel_cell = openmc.Cell(fill=fuel, region=-inner_sphere) hydrogen_cell = openmc.Cell(fill=h1, region=+inner_sphere) univ = openmc.Universe(cells=[fuel_cell, hydrogen_cell]) + outer_cell = openmc.Cell(fill=univ, region=-outer_sphere) + outer_cell.rotation = (10, 20, 30) - # Create one cell on top of the other. Only one - # has a rotation - box = openmc.rectangular_prism(15., 15., 'z', boundary_type='vacuum') - lower_z = openmc.ZPlane(-7.5, boundary_type='vacuum') - upper_z = openmc.ZPlane(22.5, boundary_type='vacuum') - middle_z = openmc.ZPlane(7.5) - - lower_cell = openmc.Cell(fill=univ, region=box & +lower_z & -middle_z) - lower_cell.rotation = (10, 20, 30) - upper_cell = openmc.Cell(fill=univ, region=box & +middle_z & -upper_z) - upper_cell.translation = (0, 0, 15) - - model.geometry = openmc.Geometry(root=[lower_cell, upper_cell]) + model.geometry = openmc.Geometry(root=[outer_cell]) - model.settings.particles = 10000 + model.settings.particles = 1000 model.settings.inactive = 5 model.settings.batches = 10 source_box = openmc.stats.Box((-4., -4., -4.), (4., 4., 4.)) From 230e054ec39134369fd908a1c516293eb449b8e4 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 1 Apr 2021 23:24:06 -0500 Subject: [PATCH 0053/2572] Move DAGCell implementation into the dagmc files. --- include/openmc/cell.h | 23 ----------- include/openmc/dagmc.h | 22 ++++++++++- src/cell.cpp | 90 ------------------------------------------ src/dagmc.cpp | 90 ++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 111 insertions(+), 114 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 54e06f782ec..ad17b6f78ba 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -260,28 +260,6 @@ class CSGCell : public Cell vector::iterator start, const vector& rpn); }; -//============================================================================== - -#ifdef DAGMC -class DAGCell : public Cell -{ -public: - DAGCell(); - - bool contains(Position r, Direction u, int32_t on_surface) const; - - std::pair - distance(Position r, Direction u, int32_t on_surface, Particle* p) const; - - BoundingBox bounding_box() const; - - void to_hdf5_inner(hid_t group_id) const; - - std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance - int32_t dag_index_; //!< DagMC index of cell -}; -#endif - //============================================================================== //! Speeds up geometry searches by grouping cells in a search tree. // @@ -351,7 +329,6 @@ void read_cells(pugi::xml_node node); #ifdef DAGMC class DAGUniverse; -int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); #endif } // namespace openmc diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 76bd0a4ec3e..1dc1c2bc375 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -10,13 +10,32 @@ extern "C" const bool DAGMC_ENABLED; #include "DagMC.hpp" #include "openmc/cell.h" +#include "openmc/particle.h" #include "openmc/position.h" +#include "openmc/surface.h" #include "openmc/xml_interface.h" class UWUW; namespace openmc { +class DAGCell : public Cell { +public: + DAGCell(); + + bool contains(Position r, Direction u, int32_t on_surface) const; + + std::pair + distance(Position r, Direction u, int32_t on_surface, Particle* p) const; + + BoundingBox bounding_box() const; + + void to_hdf5_inner(hid_t group_id) const; + + std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance + int32_t dag_index_; //!< DagMC index of cell +}; + class DAGUniverse : public Universe { public: @@ -26,7 +45,6 @@ class DAGUniverse : public Universe { void initialize(); //!< Sets up the DAGMC instance and OpenMC internals void read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map - bool uses_uwuw() const; int32_t implicit_complement_idx() const; @@ -52,7 +70,9 @@ class DAGUniverse : public Universe { bool adjust_material_ids_; }; +// DAGMC Functions void read_dagmc_universes(pugi::xml_node node); +int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); } // namespace openmc diff --git a/src/cell.cpp b/src/cell.cpp index ad711573b24..23786727f14 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -825,81 +825,6 @@ CSGCell::contains_complex(Position r, Direction u, int32_t on_surface) const } } -//============================================================================== -// DAGMC Cell implementation -//============================================================================== -#ifdef DAGMC -DAGCell::DAGCell() : Cell{} { - geom_type_ = GeometryType::DAG; - simple_ = true; -}; - -std::pair -DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) const -{ - Expects(p); - // if we've changed direction or we're not on a surface, - // reset the history and update last direction - if (u != p->last_dir()) { p->last_dir() = u; p->history().reset(); } - if (on_surface == 0) { p->history().reset(); } - - const auto& univ = model::universes[p->coord_[p->n_coord_ - 1].universe]; - - DAGUniverse* dag_univ = static_cast(univ.get()); - if (!dag_univ) fatal_error("DAGMC call made for particle in a non-DAGMC universe"); - - moab::ErrorCode rval; - moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); - moab::EntityHandle hit_surf; - double dist; - double pnt[3] = {r.x, r.y, r.z}; - double dir[3] = {u.x, u.y, u.z}; - rval = dagmc_ptr_->ray_fire(vol, pnt, dir, hit_surf, dist, &p->history()); - MB_CHK_ERR_CONT(rval); - int surf_idx; - if (hit_surf != 0) { - surf_idx = dag_univ->surf_idx_offset_ + dagmc_ptr_->index_by_handle(hit_surf); - } else { - // indicate that particle is lost - surf_idx = -1; - dist = INFINITY; - if (!dagmc_ptr_->is_implicit_complement(vol) || model::universe_map[dag_univ->id_] == model::root_universe) { - p->mark_as_lost(fmt::format("No intersection found with DAGMC cell {}", id_)); - } - } - - return {dist, surf_idx}; -} - -bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const -{ - moab::ErrorCode rval; - moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); - - int result = 0; - double pnt[3] = {r.x, r.y, r.z}; - double dir[3] = {u.x, u.y, u.z}; - rval = dagmc_ptr_->point_in_volume(vol, pnt, result, dir); - MB_CHK_ERR_CONT(rval); - return result; -} - -void DAGCell::to_hdf5_inner(hid_t group_id) const { - write_string(group_id, "geom_type", "dagmc", false); -} - -BoundingBox DAGCell::bounding_box() const -{ - moab::ErrorCode rval; - moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); - double min[3], max[3]; - rval = dagmc_ptr_->getobb(vol, min, max); - MB_CHK_ERR_CONT(rval); - return {min[0], max[0], min[1], max[1], min[2], max[2]}; -} - -#endif - //============================================================================== // UniversePartitioner implementation //============================================================================== @@ -1356,21 +1281,6 @@ openmc_extend_cells(int32_t n, int32_t* index_start, int32_t* index_end) return 0; } -#ifdef DAGMC -int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) -{ - moab::EntityHandle surf = - surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); - moab::EntityHandle vol = - cur_cell->dagmc_ptr_->entity_by_index(3, cur_cell->dag_index_); - - moab::EntityHandle new_vol; - cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); - - return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; -} -#endif - extern "C" int cells_size() { return model::cells.size(); } } // namespace openmc diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 635f05bd886..10d620d7d04 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -7,6 +7,7 @@ #include "openmc/file_utils.h" #include "openmc/geometry.h" #include "openmc/geometry_aux.h" +#include "openmc/hdf5_interface.h" #include "openmc/material.h" #include "openmc/string_utils.h" #include "openmc/settings.h" @@ -489,5 +490,94 @@ DAGUniverse::read_uwuw_materials() { } } + +//============================================================================== +// DAGMC Cell implementation +//============================================================================== + +DAGCell::DAGCell() : Cell{} { + geom_type_ = GeometryType::DAG; + simple_ = true; +}; + +std::pair +DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) const +{ + Expects(p); + // if we've changed direction or we're not on a surface, + // reset the history and update last direction + if (u != p->last_dir_) { p->last_dir_ = u; p->history_.reset(); } + if (on_surface == 0) { p->history_.reset(); } + + const auto& univ = model::universes[p->coord_[p->n_coord_ - 1].universe]; + + DAGUniverse* dag_univ = static_cast(univ.get()); + if (!dag_univ) fatal_error("DAGMC call made for particle in a non-DAGMC universe"); + + moab::ErrorCode rval; + moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); + moab::EntityHandle hit_surf; + double dist; + double pnt[3] = {r.x, r.y, r.z}; + double dir[3] = {u.x, u.y, u.z}; + rval = dagmc_ptr_->ray_fire(vol, pnt, dir, hit_surf, dist, &p->history_); + MB_CHK_ERR_CONT(rval); + int surf_idx; + if (hit_surf != 0) { + surf_idx = dag_univ->surf_idx_offset_ + dagmc_ptr_->index_by_handle(hit_surf); + } else { + // indicate that particle is lost + surf_idx = -1; + dist = INFINITY; + if (!dagmc_ptr_->is_implicit_complement(vol) || model::universe_map[dag_univ->id_] == model::root_universe) { + p->mark_as_lost(fmt::format("No intersection found with DAGMC cell {}", id_)); + } + } + + return {dist, surf_idx}; +} + +bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const +{ + moab::ErrorCode rval; + moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); + + int result = 0; + double pnt[3] = {r.x, r.y, r.z}; + double dir[3] = {u.x, u.y, u.z}; + rval = dagmc_ptr_->point_in_volume(vol, pnt, result, dir); + MB_CHK_ERR_CONT(rval); + return result; +} + +void DAGCell::to_hdf5_inner(hid_t group_id) const { + write_string(group_id, "geom_type", "dagmc", false); +} + +BoundingBox DAGCell::bounding_box() const +{ + moab::ErrorCode rval; + moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); + double min[3], max[3]; + rval = dagmc_ptr_->getobb(vol, min, max); + MB_CHK_ERR_CONT(rval); + return {min[0], max[0], min[1], max[1], min[2], max[2]}; +} + +// DAGMC Functions +int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) +{ + moab::EntityHandle surf = + surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); + moab::EntityHandle vol = + cur_cell->dagmc_ptr_->entity_by_index(3, cur_cell->dag_index_); + + moab::EntityHandle new_vol; + cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); + + return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; +} + + } #endif From 9874004e946527969fc5b683f64fd5839804ae98 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 1 Apr 2021 23:27:56 -0500 Subject: [PATCH 0054/2572] Moving the DAGMC surface implementation into the DAGMC files as well. --- include/openmc/dagmc.h | 16 ++++++++++++ include/openmc/surface.h | 21 --------------- src/dagmc.cpp | 51 +++++++++++++++++++++++++++++++++++++ src/surface.cpp | 55 ---------------------------------------- 4 files changed, 67 insertions(+), 76 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 1dc1c2bc375..c062467c4a6 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -19,6 +19,22 @@ class UWUW; namespace openmc { +class DAGSurface : public Surface +{ +public: + DAGSurface(); + + double evaluate(Position r) const; + double distance(Position r, Direction u, bool coincident) const; + Direction normal(Position r) const; + Direction reflect(Position r, Direction u, Particle* p) const; + + inline void to_hdf5_inner(hid_t group_id) const override {}; + + std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance + int32_t dag_index_; //!< DagMC index of surface +}; + class DAGCell : public Cell { public: DAGCell(); diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 2bebdc8ce62..d29bbee0868 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -154,27 +154,6 @@ class CSGSurface : public Surface virtual void to_hdf5_inner(hid_t group_id) const = 0; }; -//============================================================================== -//! A `Surface` representing a DAGMC-based surface in DAGMC. -//============================================================================== -#ifdef DAGMC -class DAGSurface : public Surface -{ -public: - DAGSurface(); - - double evaluate(Position r) const; - double distance(Position r, Direction u, bool coincident) const; - Direction normal(Position r) const; - Direction reflect(Position r, Direction u, Particle* p) const; - - inline void to_hdf5_inner(hid_t group_id) const override {}; - - std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance - int32_t dag_index_; //!< DagMC index of surface -}; -#endif - //============================================================================== //! A plane perpendicular to the x-axis. // diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 10d620d7d04..6a81ebd5f72 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -578,6 +578,57 @@ int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; } +//============================================================================== +// DAGSurface implementation +//============================================================================== +DAGSurface::DAGSurface() : Surface{} { + geom_type_ = GeometryType::DAG; +} // empty constructor + +double DAGSurface::evaluate(Position r) const +{ + return 0.0; +} + +double +DAGSurface::distance(Position r, Direction u, bool coincident) const +{ + moab::ErrorCode rval; + moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); + moab::EntityHandle hit_surf; + double dist; + double pnt[3] = {r.x, r.y, r.z}; + double dir[3] = {u.x, u.y, u.z}; + rval = dagmc_ptr_->ray_fire(surf, pnt, dir, hit_surf, dist, NULL, 0, 0); + MB_CHK_ERR_CONT(rval); + if (dist < 0.0) dist = INFTY; + return dist; +} + +Direction DAGSurface::normal(Position r) const +{ + moab::ErrorCode rval; + moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); + double pnt[3] = {r.x, r.y, r.z}; + double dir[3]; + rval = dagmc_ptr_->get_angle(surf, pnt, dir); + MB_CHK_ERR_CONT(rval); + return dir; +} + +Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const +{ + Expects(p); + p->history_.reset_to_last_intersection(); + moab::ErrorCode rval; + moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); + double pnt[3] = {r.x, r.y, r.z}; + double dir[3]; + rval = dagmc_ptr_->get_angle(surf, pnt, dir, &p->history_); + MB_CHK_ERR_CONT(rval); + p->last_dir_ = u.reflect(dir); + return p->last_dir_; +} } #endif diff --git a/src/surface.cpp b/src/surface.cpp index 92e50a76bb7..e0b88d24a5b 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -232,61 +232,6 @@ CSGSurface::CSGSurface(pugi::xml_node surf_node) : Surface{surf_node} { geom_type_ = GeometryType::CSG; }; -//============================================================================== -// DAGSurface implementation -//============================================================================== -#ifdef DAGMC -DAGSurface::DAGSurface() : Surface{} { - geom_type_ = GeometryType::DAG; -} // empty constructor - -double DAGSurface::evaluate(Position r) const -{ - return 0.0; -} - -double -DAGSurface::distance(Position r, Direction u, bool coincident) const -{ - moab::ErrorCode rval; - moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); - moab::EntityHandle hit_surf; - double dist; - double pnt[3] = {r.x, r.y, r.z}; - double dir[3] = {u.x, u.y, u.z}; - rval = dagmc_ptr_->ray_fire(surf, pnt, dir, hit_surf, dist, NULL, 0, 0); - MB_CHK_ERR_CONT(rval); - if (dist < 0.0) dist = INFTY; - return dist; -} - -Direction DAGSurface::normal(Position r) const -{ - moab::ErrorCode rval; - moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); - double pnt[3] = {r.x, r.y, r.z}; - double dir[3]; - rval = dagmc_ptr_->get_angle(surf, pnt, dir); - MB_CHK_ERR_CONT(rval); - return dir; -} - -Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const -{ - Expects(p); - p->history().reset_to_last_intersection(); - moab::ErrorCode rval; - moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); - double pnt[3] = {r.x, r.y, r.z}; - double dir[3]; - rval = dagmc_ptr_->get_angle(surf, pnt, dir, &p->history()); - MB_CHK_ERR_CONT(rval); - p->last_dir() = u.reflect(dir); - return p->last_dir(); -} - -#endif - //============================================================================== // Generic functions for x-, y-, and z-, planes. //============================================================================== From 3d8c6a9f4b7200ada54d30b7575624a154fb1a15 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 23 Apr 2021 11:08:48 -0500 Subject: [PATCH 0055/2572] Some updates from self-review. --- openmc/summary.py | 1 + openmc/universe.py | 2 +- src/cell.cpp | 4 ++-- src/geometry.cpp | 5 ++--- src/material.cpp | 1 - src/particle.cpp | 10 ++++------ tests/regression_tests/dagmc/universes/test.py | 2 +- 7 files changed, 11 insertions(+), 14 deletions(-) diff --git a/openmc/summary.py b/openmc/summary.py index a1ee2dcd660..0af45b77014 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -123,6 +123,7 @@ def _read_materials(self): def _read_surfaces(self): for group in self._f['geometry/surfaces'].values(): surface = openmc.Surface.from_hdf5(group) + # surface may be None for DAGMC surfaces if surface: self._fast_surfaces[surface.id] = surface diff --git a/openmc/universe.py b/openmc/universe.py index c246e33bb02..40b7ca70f48 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -638,7 +638,7 @@ class DAGMCUniverse(UniverseBase): Path to the DAGMC file used to represent this universe. universe_id : int, optional Unique identifier of the universe. If not specified, an identifier will - automatically be assigned + automatically be assigned. name : str, optional Name of the universe. If not specified, the name is the empty string. auto_ids : bool diff --git a/src/cell.cpp b/src/cell.cpp index 23786727f14..501fef49578 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -193,6 +193,7 @@ Universe::to_hdf5(hid_t universes_group) const // Create a group for this universe. auto group = create_group(universes_group, fmt::format("universe {}", id_)); + // Write the geometry representation type. switch(type_) { case GeometryType::CSG : write_string(group, "geom_type", "csg", false); @@ -318,7 +319,7 @@ Cell::set_temperature(double T, int32_t instance, bool set_contained) void Cell::to_hdf5(hid_t cell_group) const { - // Create a group for this cell. + // Create a group for this cell. auto group = create_group(cell_group, fmt::format("cell {}", id_)); if (!name_.empty()) { @@ -984,7 +985,6 @@ void read_cells(pugi::xml_node node) model::cells.push_back(make_unique(cell_node)); } - // Fill the cell map. for (int i = 0; i < model::cells.size(); i++) { int32_t id = model::cells[i]->id_; diff --git a/src/geometry.cpp b/src/geometry.cpp index 7e7b3b17291..efa993b061f 100644 --- a/src/geometry.cpp +++ b/src/geometry.cpp @@ -115,9 +115,8 @@ find_cell_inner(Particle& p, const NeighborList* neighbor_list) const auto& univ {model::universes[i_universe]}; found = univ->find_cell(p); } - if (!found) { - return found; - } + + if (!found) { return found; } i_cell = p.coord_[p.n_coord_ - 1].cell; // Announce the cell that the particle is entering. diff --git a/src/material.cpp b/src/material.cpp index 3ef8c70c269..4e6dfbdf1b4 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -1236,7 +1236,6 @@ void read_materials_xml() for (pugi::xml_node material_node : root.children("material")) { model::materials.push_back(make_unique(material_node)); } - model::materials.shrink_to_fit(); } diff --git a/src/particle.cpp b/src/particle.cpp index 6fc27ebfa81..136963d5789 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -392,12 +392,11 @@ Particle::cross_surface() #ifdef DAGMC // in DAGMC, we know what the next cell should be - auto surfp = dynamic_cast(model::surfaces[std::abs(surface_) - 1].get()); - if (surfp) { + if (surf->geom_type_ == GeometryType::DAG) { + auto surfp = dynamic_cast(surf); auto cellp = dynamic_cast(model::cells[cell_last_[n_coord_ - 1]].get()); - // TODO: off-by-one auto univp = static_cast(model::universes[coord_[n_coord_ - 1].universe].get()); - + // determine the next cell for this crossing int32_t i_cell = next_cell(univp, cellp, surfp) - 1; // save material and temp material_last_ = material_; @@ -505,9 +504,8 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) coord(0).cell = cell_last(n_coord_last() - 1); surface() = -surface(); - #ifdef DAGMC + // if we're crossing a CSG surface, make sure the DAG history is reset if (surf.geom_type_ != GeometryType::DAG) history_.reset(); - #endif // If a reflective surface is coincident with a lattice or universe // boundary, it is necessary to redetermine the particle's coordinates in diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index 2ae2b0e9f2b..d94f6fe0114 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -66,6 +66,6 @@ def _build_inputs(self): model.export_to_xml() -def test_dagmc_universes(): +def test_univ(): harness = DAGMCUniverseTest('statepoint.10.h5') harness.main() \ No newline at end of file From c38b30221d030e8cdb633c88a94e96450f9b639e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 23 Apr 2021 11:18:47 -0500 Subject: [PATCH 0056/2572] Re-adding ifdef cause I'm a dummy. --- src/particle.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/particle.cpp b/src/particle.cpp index 136963d5789..2f0f52e63d8 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -505,7 +505,9 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) surface() = -surface(); // if we're crossing a CSG surface, make sure the DAG history is reset + #ifdef DAGMC if (surf.geom_type_ != GeometryType::DAG) history_.reset(); + #endif // If a reflective surface is coincident with a lattice or universe // boundary, it is necessary to redetermine the particle's coordinates in From 400f3e9334780ea527a7a9320a6830e41d1f5e64 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 23 Apr 2021 12:20:42 -0500 Subject: [PATCH 0057/2572] More updates from self review. Moving check for BCs into geometry_aux. --- src/geometry_aux.cpp | 15 +++++++++++++++ src/surface.cpp | 17 ----------------- tests/regression_tests/dagmc/universes/test.py | 2 +- 3 files changed, 16 insertions(+), 18 deletions(-) diff --git a/src/geometry_aux.cpp b/src/geometry_aux.cpp index d45cdcb23a0..80741b3e780 100644 --- a/src/geometry_aux.cpp +++ b/src/geometry_aux.cpp @@ -66,6 +66,21 @@ void read_geometry_xml() read_cells(root); read_lattices(root); + // Check to make sure a boundary condition was applied to at least one + // surface + bool boundary_exists = false; + for (const auto& surf : model::surfaces) { + if (surf->bc_) { + boundary_exists = true; + break; + } + } + + if (settings::run_mode != RunMode::PLOTTING && !boundary_exists) { + fatal_error("No boundary conditions were applied to any surfaces!"); + } + + // Allocate universes, universe cell arrays, and assign base universe model::root_universe = find_root_universe(); } diff --git a/src/surface.cpp b/src/surface.cpp index e0b88d24a5b..8c1ed51d198 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -1133,23 +1133,6 @@ void read_surfaces(pugi::xml_node node) surf2.bc_ = surf1.bc_; } } - - // Check to make sure a boundary condition was applied to at least one - // surface - bool boundary_exists = false; - for (const auto& surf : model::surfaces) { - if (surf->bc_) { - boundary_exists = true; - break; - } - } - // if (settings::run_mode != RunMode::PLOTTING && !boundary_exists) { - // fatal_error("No boundary conditions were applied to any surfaces!"); - // } - - // if (model::surfaces.size() == 0) { - // fatal_error("No surfaces found in geometry.xml!"); - // } } void free_memory_surfaces() diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index d94f6fe0114..cc407aa5ec6 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -68,4 +68,4 @@ def _build_inputs(self): def test_univ(): harness = DAGMCUniverseTest('statepoint.10.h5') - harness.main() \ No newline at end of file + harness.main() From ec7595261fc592742c6af127e58d90b24c97903d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 23 Apr 2021 12:42:13 -0500 Subject: [PATCH 0058/2572] Adding doc strings to the dagmc header. --- include/openmc/dagmc.h | 37 +++++++++++++++++++++++++++---------- 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index c062467c4a6..5f58fb902ab 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -58,35 +58,52 @@ class DAGUniverse : public Universe { explicit DAGUniverse(pugi::xml_node node); explicit DAGUniverse(const std::string& filename, bool auto_geom_ids = false); - void initialize(); //!< Sets up the DAGMC instance and OpenMC internals + //! Initialize the DAGMC accel. data structures, indices, material assignments, etc. + void initialize(); - void read_uwuw_materials(); //!< Reads UWUW materials and returns an ID map + //! Reads UWUW materials and returns an ID map + void read_uwuw_materials(); + //! Indicates whether or not UWUW materials are present + //! \return True if UWUW materials are present, False if not bool uses_uwuw() const; + //! Returns the index to the implicit complement's index in OpenMC for this DAGMC universe int32_t implicit_complement_idx() const; + //! Transform UWUW materials into an OpenMC-readable XML format + //! \return A string representing a materials.xml file of the UWUW materials in this universe std::string get_uwuw_materials_xml() const; + //! Writes the UWUW material file to XML (for debugging purposes) void write_uwuw_materials_xml(const std::string& outfile = "uwuw_materials.xml") const; + //! Assign a material to a cell based + //! \param[in] mat_string The DAGMC material assignment string + //! \param[in] c The OpenMC cell to which the material is assigned void legacy_assign_material(std::string mat_string, std::unique_ptr& c) const; + //! Generate a string representing the ranges of IDs present in the DAGMC model + //! \param[in] dim Dimension of the entities + //! \return A string of the ID ranges for entities of dimension \p dim std::string dagmc_ids_for_dim(int dim) const; virtual bool find_cell(Particle &p) const override; // Data Members - std::string filename_; - std::shared_ptr dagmc_instance_; //! DAGMC Instance for this universe - std::shared_ptr uwuw_; - int32_t cell_idx_offset_; - int32_t surf_idx_offset_; - bool adjust_geometry_ids_; - bool adjust_material_ids_; + std::string filename_; //!< Name of the DAGMC file used to create this universe + std::shared_ptr dagmc_instance_; //!< DAGMC Instance for this universe + std::shared_ptr uwuw_; //!< Pointer to the UWUW instance for this universe + int32_t cell_idx_offset_; //!< An offset to the start of the cells in this universe in OpenMC's cell vector + int32_t surf_idx_offset_; //!< An offset to the start of the surfaces in this universe in OpenMC's surface vector + bool adjust_geometry_ids_; //!< Indicates whether or not to automatically generate new cell and surface IDs for the universe + bool adjust_material_ids_; //!< Indicates whether or not to automatically generate new material IDs for the universe }; -// DAGMC Functions +//============================================================================== +// Non-member functions +//============================================================================== + void read_dagmc_universes(pugi::xml_node node); int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); From d0f32b80e0711f9cd24ad633a50027cf305a4502 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 23 Apr 2021 14:48:48 -0500 Subject: [PATCH 0059/2572] Updating formatting and a small refactor for the dagmc_filename function. --- include/openmc/dagmc.h | 10 +- src/dagmc.cpp | 398 +++++++++++++++++++++-------------------- 2 files changed, 212 insertions(+), 196 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 5f58fb902ab..6d44f9e1b58 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -55,8 +55,16 @@ class DAGCell : public Cell { class DAGUniverse : public Universe { public: + explicit DAGUniverse(pugi::xml_node node); - explicit DAGUniverse(const std::string& filename, bool auto_geom_ids = false); + + //! Create a new DAGMC universe + //! \param[in] filename Name of the DAGMC file + //! \param[in] auto_geom_ids Whether or not to automatically assign cell and surface IDs + //! \param[in] auto_mat_ids Whether or not to automatically assign material IDs + explicit DAGUniverse(const std::string& filename, + bool auto_geom_ids = false, + bool auto_mat_ids = false); //! Initialize the DAGMC accel. data structures, indices, material assignments, etc. void initialize(); diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 6a81ebd5f72..ce750a4e6dc 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -1,6 +1,5 @@ #include "openmc/dagmc.h" -#include "openmc/cell.h" #include "openmc/constants.h" #include "openmc/container_util.h" #include "openmc/error.h" @@ -11,7 +10,6 @@ #include "openmc/material.h" #include "openmc/string_utils.h" #include "openmc/settings.h" -#include "openmc/surface.h" #ifdef DAGMC #include "uwuw.hpp" @@ -38,173 +36,9 @@ const bool DAGMC_ENABLED = false; namespace openmc { -const std::string DAGMC_FILENAME = "dagmc.h5m"; - -std::string dagmc_file() { - std::string filename = settings::path_input + DAGMC_FILENAME; - if (!file_exists(filename)) { - fatal_error("Geometry DAGMC file '" + filename + "' does not exist!"); - } - return filename; -} - -std::string -DAGUniverse::dagmc_ids_for_dim(int dim) const -{ - // generate a vector of ids - std::vector id_vec; - int n_cells = dagmc_instance_->num_entities(dim); - for (int i = 1; i <= n_cells; i++) { - id_vec.push_back(dagmc_instance_->id_by_index(dim, i)); - } - - // sort the vector of ids - std::sort(id_vec.begin(), id_vec.end()); - - // generate a string representation of the range - std::stringstream out; - - int i = 0; - int start_id = id_vec[0]; - int stop_id; - while (i < n_cells) { - stop_id = id_vec[i]; - - if (id_vec[i + 1] > stop_id + 1) { - if (start_id != stop_id) { - // there are several IDs in a row, print condensed version - out << start_id << "-" << stop_id; - } else { - // only one ID in this contiguous block - out << start_id; - } - if (i < n_cells - 1) { out << ", "; } - start_id = id_vec[++i]; - stop_id = start_id; - } - - i++; - } - - return out.str(); -} - -int32_t -DAGUniverse::implicit_complement_idx() const { - moab::EntityHandle ic; - moab::ErrorCode rval = dagmc_instance_->geom_tool()->get_implicit_complement(ic); - MB_CHK_SET_ERR_CONT(rval, "Failed to get implicit complement"); - return cell_idx_offset_ + dagmc_instance_->index_by_handle(ic) - 1; -} - -bool -DAGUniverse::find_cell(Particle &p) const { - // if the particle isn't in any of the other DagMC - // cells, place it in the implicit complement - bool found = Universe::find_cell(p); - if (!found && model::universe_map[this->id_] != model::root_universe) { - p.coord_[p.n_coord_ - 1].cell = implicit_complement_idx(); - found = true; - } - return found; -} - -bool -DAGUniverse::uses_uwuw() const -{ - return uwuw_ && !uwuw_->material_library.empty(); -} - -std::string -DAGUniverse::get_uwuw_materials_xml() const -{ - - if (!uses_uwuw()) { - throw std::runtime_error("This DAGMC Universe does not use UWUW materials"); - } - - std::stringstream ss; - // write header - ss << "\n"; - ss << "\n"; - const auto& mat_lib = uwuw_->material_library; - // write materials - for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } - // write footer - ss << ""; - - return ss.str(); -} - -void -DAGUniverse::write_uwuw_materials_xml(const std::string& outfile) const -{ - if (!uses_uwuw()) { - throw std::runtime_error("This DAGMC universe does not use UWUW materials."); - } - - std::string xml_str = get_uwuw_materials_xml(); - // if there is a material library in the file - std::ofstream mats_xml(outfile); - mats_xml << xml_str; - mats_xml.close(); -} - -void -DAGUniverse::legacy_assign_material(std::string mat_string, - std::unique_ptr& c) const -{ - bool mat_found_by_name = false; - // attempt to find a material with a matching name - to_lower(mat_string); - for (const auto& m : model::materials) { - std::string m_name = m->name(); - to_lower(m_name); - if (mat_string == m_name) { - // assign the material with that name - if (!mat_found_by_name) { - mat_found_by_name = true; - c->material_.push_back(m->id_); - // report error if more than one material is found - } else { - fatal_error(fmt::format( - "More than one material found with name '{}'. Please ensure materials " - "have unique names if using this property to assign materials.", - mat_string)); - } - } - } - - // if no material was set using a name, assign by id - if (!mat_found_by_name) { - try { - auto id = std::stoi(mat_string); - c->material_.emplace_back(id); - } catch (const std::invalid_argument&) { - fatal_error(fmt::format( - "No material '{}' found for volume (cell) {}", mat_string, c->id_)); - } - } - - if (settings::verbosity >= 10) { - const auto& m = model::materials[model::material_map.at(c->material_[0])]; - std::stringstream msg; - msg << "DAGMC material " << mat_string << " was assigned"; - if (mat_found_by_name) { - msg << " using material name: " << m->name_; - } else { - msg << " using material id: " << m->id_; - } - write_message(msg.str(), 10); - } -} - -void read_dagmc_universes(pugi::xml_node node) { - for (pugi::xml_node dag_node : node.children("dagmc")) { - model::universes.push_back(std::make_unique(dag_node)); - model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; - } -} +//============================================================================== +// DAGMC Universe implementation +//============================================================================== DAGUniverse::DAGUniverse(pugi::xml_node node) { if (check_for_node(node, "id")) { @@ -232,8 +66,10 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { initialize(); } -DAGUniverse::DAGUniverse(const std::string& filename, bool auto_geom_ids) -: filename_(filename), adjust_geometry_ids_(auto_geom_ids) { +DAGUniverse::DAGUniverse(const std::string& filename, + bool auto_geom_ids, + bool auto_mat_ids) +: filename_(filename), adjust_geometry_ids_(auto_geom_ids), adjust_material_ids_(auto_mat_ids) { // determine the next universe id int32_t next_univ_id = 0; for (const auto& u : model::universes) { @@ -247,7 +83,8 @@ DAGUniverse::DAGUniverse(const std::string& filename, bool auto_geom_ids) initialize(); } -void DAGUniverse::initialize() { +void +DAGUniverse::initialize() { type_ = GeometryType::DAG; // determine the next cell id @@ -283,6 +120,10 @@ void DAGUniverse::initialize() { } // load the DAGMC geometry + filename_ = settings::path_input + filename_; + if (!file_exists(filename_)) { + fatal_error("Geometry DAGMC file '" + filename_ + "' does not exist!"); + } moab::ErrorCode rval = dagmc_instance_->load_file(filename_.c_str()); MB_CHK_ERR_CONT(rval); @@ -325,7 +166,7 @@ void DAGUniverse::initialize() { "and the CSG geometry.", c->id_, this->id_)); } - // MATERIALS + // --- Materials --- // determine volume material assignment std::string mat_str = DMD.get_volume_property("material", vol_handle); @@ -447,10 +288,160 @@ void DAGUniverse::initialize() { } model::surfaces.emplace_back(std::move(s)); - } // end surface loop } +std::string +DAGUniverse::dagmc_ids_for_dim(int dim) const +{ + // generate a vector of ids + std::vector id_vec; + int n_cells = dagmc_instance_->num_entities(dim); + for (int i = 1; i <= n_cells; i++) { + id_vec.push_back(dagmc_instance_->id_by_index(dim, i)); + } + + // sort the vector of ids + std::sort(id_vec.begin(), id_vec.end()); + + // generate a string representation of the ID range(s) + std::stringstream out; + + int i = 0; + int start_id = id_vec[0]; + int stop_id; + while (i < n_cells) { + stop_id = id_vec[i]; + + if (id_vec[i + 1] > stop_id + 1) { + if (start_id != stop_id) { + // there are several IDs in a row, print condensed version + out << start_id << "-" << stop_id; + } else { + // only one ID in this contiguous block + out << start_id; + } + if (i < n_cells - 1) { out << ", "; } + start_id = id_vec[++i]; + stop_id = start_id; + } + + i++; + } + + return out.str(); +} + +int32_t +DAGUniverse::implicit_complement_idx() const { + moab::EntityHandle ic; + moab::ErrorCode rval = dagmc_instance_->geom_tool()->get_implicit_complement(ic); + MB_CHK_SET_ERR_CONT(rval, "Failed to get implicit complement"); + // off-by-one: DAGMC indices start at one + return cell_idx_offset_ + dagmc_instance_->index_by_handle(ic) - 1; +} + +bool +DAGUniverse::find_cell(Particle &p) const { + // if the particle isn't in any of the other DagMC + // cells, place it in the implicit complement + bool found = Universe::find_cell(p); + if (!found && model::universe_map[this->id_] != model::root_universe) { + p.coord_[p.n_coord_ - 1].cell = implicit_complement_idx(); + found = true; + } + return found; +} + +bool +DAGUniverse::uses_uwuw() const +{ + return !uwuw_->material_library.empty(); +} + +std::string +DAGUniverse::get_uwuw_materials_xml() const +{ + if (!uses_uwuw()) { + throw std::runtime_error("This DAGMC Universe does not use UWUW materials"); + } + + std::stringstream ss; + // write header + ss << "\n"; + ss << "\n"; + const auto& mat_lib = uwuw_->material_library; + // write materials + for (auto mat : mat_lib) { ss << mat.second->openmc("atom"); } + // write footer + ss << ""; + + return ss.str(); +} + +void +DAGUniverse::write_uwuw_materials_xml(const std::string& outfile) const +{ + if (!uses_uwuw()) { + throw std::runtime_error("This DAGMC universe does not use UWUW materials."); + } + + std::string xml_str = get_uwuw_materials_xml(); + // if there is a material library in the file + std::ofstream mats_xml(outfile); + mats_xml << xml_str; + mats_xml.close(); +} + +void +DAGUniverse::legacy_assign_material(std::string mat_string, + std::unique_ptr& c) const +{ + bool mat_found_by_name = false; + // attempt to find a material with a matching name + to_lower(mat_string); + for (const auto& m : model::materials) { + std::string m_name = m->name(); + to_lower(m_name); + if (mat_string == m_name) { + // assign the material with that name + if (!mat_found_by_name) { + mat_found_by_name = true; + c->material_.push_back(m->id_); + // report error if more than one material is found + } else { + fatal_error(fmt::format( + "More than one material found with name '{}'. Please ensure materials " + "have unique names if using this property to assign materials.", + mat_string)); + } + } + } + + // if no material was set using a name, assign by id + if (!mat_found_by_name) { + try { + auto id = std::stoi(mat_string); + c->material_.emplace_back(id); + } catch (const std::invalid_argument&) { + fatal_error(fmt::format( + "No material '{}' found for volume (cell) {}", mat_string, c->id_)); + } + } + + if (settings::verbosity >= 10) { + const auto& m = model::materials[model::material_map.at(c->material_[0])]; + std::stringstream msg; + msg << "DAGMC material " << mat_string << " was assigned"; + if (mat_found_by_name) { + msg << " using material name: " << m->name_; + } else { + msg << " using material id: " << m->id_; + } + write_message(msg.str(), 10); + } +} + void DAGUniverse::read_uwuw_materials() { @@ -490,7 +481,6 @@ DAGUniverse::read_uwuw_materials() { } } - //============================================================================== // DAGMC Cell implementation //============================================================================== @@ -537,7 +527,8 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons return {dist, surf_idx}; } -bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const +bool +DAGCell::contains(Position r, Direction u, int32_t on_surface) const { moab::ErrorCode rval; moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); @@ -550,11 +541,13 @@ bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const return result; } -void DAGCell::to_hdf5_inner(hid_t group_id) const { +void +DAGCell::to_hdf5_inner(hid_t group_id) const { write_string(group_id, "geom_type", "dagmc", false); } -BoundingBox DAGCell::bounding_box() const +BoundingBox +DAGCell::bounding_box() const { moab::ErrorCode rval; moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); @@ -564,28 +557,16 @@ BoundingBox DAGCell::bounding_box() const return {min[0], max[0], min[1], max[1], min[2], max[2]}; } -// DAGMC Functions -int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) -{ - moab::EntityHandle surf = - surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); - moab::EntityHandle vol = - cur_cell->dagmc_ptr_->entity_by_index(3, cur_cell->dag_index_); - - moab::EntityHandle new_vol; - cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); - - return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; -} - //============================================================================== // DAGSurface implementation //============================================================================== + DAGSurface::DAGSurface() : Surface{} { geom_type_ = GeometryType::DAG; } // empty constructor -double DAGSurface::evaluate(Position r) const +double +DAGSurface::evaluate(Position r) const { return 0.0; } @@ -605,7 +586,8 @@ DAGSurface::distance(Position r, Direction u, bool coincident) const return dist; } -Direction DAGSurface::normal(Position r) const +Direction +DAGSurface::normal(Position r) const { moab::ErrorCode rval; moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); @@ -616,7 +598,8 @@ Direction DAGSurface::normal(Position r) const return dir; } -Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const +Direction +DAGSurface::reflect(Position r, Direction u, Particle* p) const { Expects(p); p->history_.reset_to_last_intersection(); @@ -630,5 +613,30 @@ Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const return p->last_dir_; } +//============================================================================== +// Non-member functions +//============================================================================== + +void read_dagmc_universes(pugi::xml_node node) { + for (pugi::xml_node dag_node : node.children("dagmc")) { + model::universes.push_back(std::make_unique(dag_node)); + model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; + } +} + +int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) +{ + moab::EntityHandle surf = + surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); + moab::EntityHandle vol = + cur_cell->dagmc_ptr_->entity_by_index(3, cur_cell->dag_index_); + + moab::EntityHandle new_vol; + cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); + + return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; +} + + } #endif From b466941b78cd39969747db87779278dfd78137fe Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 27 Apr 2021 15:57:01 -0500 Subject: [PATCH 0060/2572] Correction to attribute setting in constructor. --- src/dagmc.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index ce750a4e6dc..ac3edb7da94 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -60,7 +60,7 @@ DAGUniverse::DAGUniverse(pugi::xml_node node) { adjust_material_ids_ = false; if (check_for_node(node, "auto_mat_ids")) { - adjust_geometry_ids_ = get_node_value_bool(node, "auto_mat_ids"); + adjust_material_ids_ = get_node_value_bool(node, "auto_mat_ids"); } initialize(); From bf1a2435ea4f83b93b1a9098f9577c79ad956824 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 28 Apr 2021 12:36:42 -0500 Subject: [PATCH 0061/2572] Assign material IDs as int instead of string. --- src/dagmc.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index ac3edb7da94..34917a562ad 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -458,7 +458,7 @@ DAGUniverse::read_uwuw_materials() { // if we're using automatic IDs, update the UWUW material metadata if (adjust_material_ids_) { for (auto& mat : uwuw_->material_library) { - mat.second->metadata["mat_number"] = std::to_string(next_material_id++); + mat.second->metadata["mat_number"] = next_material_id++; } } From 3b9f9fa1bd4ea544b63f2ac00e22d52db9fdcf20 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 28 Apr 2021 12:36:58 -0500 Subject: [PATCH 0062/2572] Correction to doc strings and auto_mat_ids setter. --- openmc/universe.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index 40b7ca70f48..333441a8e5c 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -641,9 +641,12 @@ class DAGMCUniverse(UniverseBase): automatically be assigned. name : str, optional Name of the universe. If not specified, the name is the empty string. - auto_ids : bool + auto_geom_ids : bool Set IDs automatically on initialization (True) or report overlaps in ID space between CSG and DAGMC (False) + auto_mat_ids : bool + Set IDs automatically on initialization (True) or report overlaps + in ID space between OpenMC and UWUW materials (False) Attributes ---------- @@ -702,7 +705,7 @@ def auto_geom_ids(self, val): def auto_mat_ids(self): return self._auto_mat_ids - @auto_geom_ids.setter + @auto_mat_ids.setter def auto_mat_ids(self, val): cv.check_type('DAGMC automatic material ids', val, bool) self._auto_mat_ids = val From d1a24339dad1852339c579b123faa437b9647569 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 28 Apr 2021 12:37:49 -0500 Subject: [PATCH 0063/2572] Updating expected inputs for the DAGMC reflecting BC and universe tests. --- tests/regression_tests/dagmc/refl/inputs_true.dat | 2 +- tests/regression_tests/dagmc/universes/inputs_true.dat | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index ca3cba5f040..4ac926e5ec7 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat index 3113159393a..6c0fa8ccd91 100644 --- a/tests/regression_tests/dagmc/universes/inputs_true.dat +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -1,7 +1,7 @@ - + From 9ffbf02f4028027567567683a8e0b61696292c13 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 5 May 2021 23:15:49 -0500 Subject: [PATCH 0064/2572] Addressing comments from @makeclean. --- include/openmc/dagmc.h | 4 +++- include/openmc/surface.h | 2 +- openmc/universe.py | 1 - src/cell.cpp | 1 - src/dagmc.cpp | 24 ++++++++++++++++-------- 5 files changed, 20 insertions(+), 12 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 6d44f9e1b58..6e292488a0e 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -91,7 +91,9 @@ class DAGUniverse : public Universe { void legacy_assign_material(std::string mat_string, std::unique_ptr& c) const; - //! Generate a string representing the ranges of IDs present in the DAGMC model + //! Generate a string representing the ranges of IDs present in the DAGMC model. + //! Contiguous chunks of IDs are represented as a range (i.e. 1-10). If there is + //! a single ID a chunk, it will be represented as a single number (i.e. 2, 4, 6, 8). //! \param[in] dim Dimension of the entities //! \return A string of the ID ranges for entities of dimension \p dim std::string dagmc_ids_for_dim(int dim) const; diff --git a/include/openmc/surface.h b/include/openmc/surface.h index d29bbee0868..5d747b48948 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -30,7 +30,7 @@ namespace model { } // namespace model //============================================================================== -//! Coordinates for an axis-aligned cube that bounds a geometric object. +//! Coordinates for an axis-aligned cuboid that bounds a geometric object. //============================================================================== struct BoundingBox diff --git a/openmc/universe.py b/openmc/universe.py index 333441a8e5c..8a12d1160fb 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -720,7 +720,6 @@ def get_all_materials(self, memo=None): return OrderedDict() def create_xml_subelement(self, xml_element, memo=None): - if memo and self in memo: return diff --git a/src/cell.cpp b/src/cell.cpp index 501fef49578..b85e4fd569d 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -328,7 +328,6 @@ Cell::to_hdf5(hid_t cell_group) const { write_dataset(group, "universe", model::universes[universe_]->id_); - to_hdf5_inner(group); // Write fill information. diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 34917a562ad..5254035794a 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -296,8 +296,8 @@ DAGUniverse::dagmc_ids_for_dim(int dim) const { // generate a vector of ids std::vector id_vec; - int n_cells = dagmc_instance_->num_entities(dim); - for (int i = 1; i <= n_cells; i++) { + int n_ents = dagmc_instance_->num_entities(dim); + for (int i = 1; i <= n_ents; i++) { id_vec.push_back(dagmc_instance_->id_by_index(dim, i)); } @@ -308,22 +308,30 @@ DAGUniverse::dagmc_ids_for_dim(int dim) const std::stringstream out; int i = 0; - int start_id = id_vec[0]; + int start_id = id_vec[0]; // initialize with first ID int stop_id; - while (i < n_cells) { + // loop over all cells in the universe + while (i < n_ents) { + stop_id = id_vec[i]; + // if the next ID is not in this contiguous set of IDS, + // figure out how to write the string representing this set if (id_vec[i + 1] > stop_id + 1) { + if (start_id != stop_id) { - // there are several IDs in a row, print condensed version + // there are several IDs in a row, print condensed version (i.e. 1-10, 12-20) out << start_id << "-" << stop_id; } else { - // only one ID in this contiguous block + // only one ID in this contiguous block (i.e. 3, 5, 7, 9) out << start_id; } - if (i < n_cells - 1) { out << ", "; } + // insert a comma as long as we aren't in the last ID set + if (i < n_ents - 1) { out << ", "; } + + // if we are at the end of a set, set the start ID to the first value + // in the next set. start_id = id_vec[++i]; - stop_id = start_id; } i++; From a4fa21eea54ca66879978a6a3cdfe5cb325772b6 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 5 May 2021 23:17:28 -0500 Subject: [PATCH 0065/2572] Fixing rotation test. --- tests/regression_tests/rotation/geometry.xml | 11 +++++ tests/regression_tests/rotation/materials.xml | 14 +++++++ tests/regression_tests/rotation/settings.xml | 15 +++++++ tests/regression_tests/rotation/test.py | 40 ++----------------- 4 files changed, 43 insertions(+), 37 deletions(-) create mode 100644 tests/regression_tests/rotation/geometry.xml create mode 100644 tests/regression_tests/rotation/materials.xml create mode 100644 tests/regression_tests/rotation/settings.xml diff --git a/tests/regression_tests/rotation/geometry.xml b/tests/regression_tests/rotation/geometry.xml new file mode 100644 index 00000000000..f3246170654 --- /dev/null +++ b/tests/regression_tests/rotation/geometry.xml @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/tests/regression_tests/rotation/materials.xml b/tests/regression_tests/rotation/materials.xml new file mode 100644 index 00000000000..160c9c67899 --- /dev/null +++ b/tests/regression_tests/rotation/materials.xml @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/tests/regression_tests/rotation/settings.xml b/tests/regression_tests/rotation/settings.xml new file mode 100644 index 00000000000..70b4e802f83 --- /dev/null +++ b/tests/regression_tests/rotation/settings.xml @@ -0,0 +1,15 @@ + + + + eigenvalue + 10 + 5 + 1000 + + + + -4 -4 -4 4 4 4 + + + + diff --git a/tests/regression_tests/rotation/test.py b/tests/regression_tests/rotation/test.py index 6b68367b40b..31c3d8d655e 100644 --- a/tests/regression_tests/rotation/test.py +++ b/tests/regression_tests/rotation/test.py @@ -1,40 +1,6 @@ -import openmc -import pytest +from tests.testing_harness import TestHarness -from tests.testing_harness import TestHarness, PyAPITestHarness -@pytest.fixture -def model(): - model = openmc.model.Model() - - fuel = openmc.Material() - fuel.set_density('g/cc', 10.0) - fuel.add_nuclide('U235', 1.0) - - h1 = openmc.Material() - h1.set_density('g/cc', 0.1) - h1.add_nuclide('H1', 0.1) - - outer_sphere = openmc.Sphere(r=10.0, boundary_type='vacuum') - inner_sphere = openmc.Sphere(x0=1.0, r=5.0) - - fuel_cell = openmc.Cell(fill=fuel, region=-inner_sphere) - hydrogen_cell = openmc.Cell(fill=h1, region=+inner_sphere) - univ = openmc.Universe(cells=[fuel_cell, hydrogen_cell]) - outer_cell = openmc.Cell(fill=univ, region=-outer_sphere) - outer_cell.rotation = (10, 20, 30) - - model.geometry = openmc.Geometry(root=[outer_cell]) - - model.settings.particles = 1000 - model.settings.inactive = 5 - model.settings.batches = 10 - source_box = openmc.stats.Box((-4., -4., -4.), (4., 4., 4.)) - model.settings.source = openmc.Source(space=source_box) - - return model - - -def test_rotation(model): - harness = PyAPITestHarness('statepoint.10.h5', model) +def test_rotation(): + harness = TestHarness('statepoint.10.h5') harness.main() From a754f34e606db9ba4eb4b79dfdc2dd8510fa2e9c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Thu, 6 May 2021 11:48:08 -0500 Subject: [PATCH 0066/2572] Updating the CAD notebook to remove . --- examples/jupyter/cad-based-geometry.ipynb | 139 +++++++++++----------- 1 file changed, 69 insertions(+), 70 deletions(-) diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb index 4c950b744ba..bfd03c7745b 100644 --- a/examples/jupyter/cad-based-geometry.ipynb +++ b/examples/jupyter/cad-based-geometry.ipynb @@ -184,7 +184,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -290,13 +290,13 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.1-dev\n", - " Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n", - " Date/Time | 2021-03-04 13:23:02\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | c3d81b5ac372c1412d112070c0f9120b7e3c9162\n", + " Date/Time | 2021-05-06 11:37:07\n", " MPI Processes | 1\n", - " OpenMP Threads | 4\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -309,59 +309,59 @@ "Using faceting tolerance: 0.0001\n", "Building acceleration data structures...\n", "Implicit Complement assumed to be Vacuum\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/U235.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/H1.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/O16.h5\n", + " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/endfb80_hdf5/c_H_in_H2O.h5\n", " Minimum neutron data temperature: 294.0 K\n", " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for U235\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.15789\n", - " 2/1 1.05169\n", - " 3/1 1.00736\n", - " 4/1 0.97863 0.99300 +/- 0.01436\n", - " 5/1 0.95316 0.97972 +/- 0.01566\n", - " 6/1 0.95079 0.97248 +/- 0.01322\n", - " 7/1 0.96879 0.97175 +/- 0.01027\n", - " 8/1 0.94253 0.96688 +/- 0.00970\n", - " 9/1 0.97406 0.96790 +/- 0.00826\n", - " 10/1 0.97362 0.96862 +/- 0.00719\n", + " 1/1 1.16335\n", + " 2/1 1.03688\n", + " 3/1 0.99328\n", + " 4/1 0.97507 0.98417 +/- 0.00911\n", + " 5/1 0.98174 0.98336 +/- 0.00532\n", + " 6/1 0.97979 0.98247 +/- 0.00387\n", + " 7/1 0.97215 0.98040 +/- 0.00364\n", + " 8/1 0.96031 0.97705 +/- 0.00448\n", + " 9/1 0.95999 0.97462 +/- 0.00450\n", + " 10/1 0.95588 0.97227 +/- 0.00455\n", " Creating state point statepoint.10.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.1835e+00 seconds\n", - " Reading cross sections = 1.3664e-01 seconds\n", - " Total time in simulation = 1.0779e+01 seconds\n", - " Time in transport only = 1.0771e+01 seconds\n", - " Time in inactive batches = 1.6833e+00 seconds\n", - " Time in active batches = 9.0961e+00 seconds\n", - " Time synchronizing fission bank = 4.0641e-03 seconds\n", - " Sampling source sites = 3.6309e-03 seconds\n", - " SEND/RECV source sites = 3.4735e-04 seconds\n", - " Time accumulating tallies = 4.9789e-05 seconds\n", - " Time writing statepoints = 2.9456e-03 seconds\n", - " Total time for finalization = 6.8913e-05 seconds\n", - " Total time elapsed = 1.2048e+01 seconds\n", - " Calculation Rate (inactive) = 5940.54 particles/second\n", - " Calculation Rate (active) = 4397.49 particles/second\n", + " Total time for initialization = 5.5283e+00 seconds\n", + " Reading cross sections = 5.4196e+00 seconds\n", + " Total time in simulation = 2.5960e+00 seconds\n", + " Time in transport only = 2.5805e+00 seconds\n", + " Time in inactive batches = 4.9064e-01 seconds\n", + " Time in active batches = 2.1054e+00 seconds\n", + " Time synchronizing fission bank = 9.9395e-03 seconds\n", + " Sampling source sites = 5.0143e-03 seconds\n", + " SEND/RECV source sites = 7.6818e-04 seconds\n", + " Time accumulating tallies = 4.0849e-05 seconds\n", + " Time writing statepoints = 2.5422e-03 seconds\n", + " Total time for finalization = 9.8980e-05 seconds\n", + " Total time elapsed = 8.1568e+00 seconds\n", + " Calculation Rate (inactive) = 20381.7 particles/second\n", + " Calculation Rate (active) = 18998.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.97020 +/- 0.00750\n", - " k-effective (Track-length) = 0.96862 +/- 0.00719\n", - " k-effective (Absorption) = 0.95742 +/- 0.00791\n", - " Combined k-effective = 0.96203 +/- 0.00898\n", - " Leakage Fraction = 0.57677 +/- 0.00317\n", + " k-effective (Collision) = 0.97074 +/- 0.00589\n", + " k-effective (Track-length) = 0.97227 +/- 0.00455\n", + " k-effective (Absorption) = 0.96869 +/- 0.00491\n", + " Combined k-effective = 0.97175 +/- 0.00373\n", + " Leakage Fraction = 0.57322 +/- 0.00242\n", "\n" ] } @@ -455,7 +455,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -489,7 +489,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAADICAAAAAAR9MBqAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAAmJLR0QA/4ePzL8AAAAHdElNRQflBQYGJSMpzy0aAAAFv0lEQVR42u3d0XKiQBBG4Zb4YPtmFk+evRAUFBWY/qW7c76LTbYqm0L3OAyIzOnXAH/d0RuAmggLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkCAsSBAWJAgLEoQFCcKCBGFB4tzfvr0cvS0o5Hz/tjfigpfz/K89bcHF8xyr73f8GmBuafJOWmi2fFRIWWh0+rXljphqocVpcnPbeV+UhQan+V2Tp21RFvZ7mGNdqAkuTs/3eb+NWkSG3RaOCukJ7ZZON1AWmi2ex6IstFoMi/OjaMX1WJBYfK/w6I1Cfm9GLGZa2G8hLAYstFu4Hmv4yoCFBkzeIfEUFgMWPDyGRVdw8RAWE3f4mIfFhQ1wMguLruBlGhZdwQ2XzUBiEhYTd/i5h8WOEI5uYdEVPI1h0RVcDWHRFXxdw6IrOOvM6Ar+OqMrCExPkNIV3HScGIVCx44QCj+n8Tu6gqPbHIuu4GkMi67gagiLruDrGhZdwVlnRlfw1xldQYBPQkOiY8CCQkdXUFi4HTfQjjkWJAgLEoQFCcKCBGFBgrAgQViQICxIEBYkCAsShAUJwoIEYUGCsCBBWJAgLEgQFiQICxKEBQnCggRhQYKwIEFYkPg5/Tt6E1BRx71todCZ9aQFd9eVKUgLzsZFmkgLru7rFZIWHJ3v3/bcKgtuzrO/0RacnH6fd4G0hWan38VVmmgLba539FuauNMWGoy3ilw+JiQu7DS5B+mL8w3EhR3mN7d9dS6LuLDR012TX54nJS5ssHQ77tfn4IkLK724z/u793eoC5+9WUDg7ZuH1IW33q9M8eGNaerCK5+XPPl41QN54dm6tXRWXFJDXpjasEjTqgu26Atmtnn1r7VXA9LXX7dnWbkN15oS2F+1e73CjVcyU9gf07gQ5uYL5Qnsj3BZYXXP5zAorDbHpXt3fcyHvoryXxOa4QsmXGx8e1/UVYl+FftNhRFXFfqwRqsDI64KvhfWzZrCiCu7A8IacU1OZQeGNXjTF23ldXxYZsZl9vUECcvM+PBZKZHCMuNTs2VEC8toq4aAYZlxK4n8goZltJVc3LDsRVuklULosMy4K1xWW9fSiXBv5QjbgA+Cj1gcIWbF6l+QyBkWO8PwYofFmzxpxQ4LaaUMiwErvtdHhf3x/33jnvDCZTXphD7dMOR04RZd+Zzbf0UALC4VToI51ucBy4z1FqOJPGJ9TuUy/bGeUSuQyGGtdLExLtKKI/LkfeWINf9h0oohcFgPx4SfiqGsUBJM3le6DEUxiQ8hTVgrBiLKCiR8WOtONgw/e/1CWQHEDavhBm6Udby4Ye1CWVGEP4+18pjw8V/VOjbstz8Dh/3WQfQRa+sDrxXUoL/94flLJb/1Jm5Yl9sfexTaGUreBZW/tRr4BOn1Cbh+2RBYtROltwIcH9CkKtXTFHOO1fJy2nJ+IgHBo/nKExQyLJdHXmL+LhhavvS6izvH2m38Hygwcsm7ushefSFHrCcVBp8d/Lt6yEq47TnC2macZWXfGd4rSJdVyV1hlQFO25VuJ3gV9HRD42urwikH767mWck3P2hYrfa9ERSJ9+mraVffeFpK7gpv8h4YOnfVf7urqmElHqrMzL+ryffqydWg4lHhRNIDQ9+uvj5amZUdsXIPWQW6qj5ipRyyXLs6Jqu6I1biIatEV3XDGqU7MFR19aVJ+6huWEmHLFlXX34c1edYOWdZ5hLCgVlVHrFyDlmOb0Yd2lXlsEaZZlmO23psV6XDSjlkeW365D3sQ56HymGN8gxZfjtC92tutiodVrYhq1BXtcMaJRmy/Dbz+K6Kh5VtyHLa6gBdFQ9rlGLIctsR3s+xHvjCKh5WziGrjeKj09sVD2uUYMjyGrBidFU+rDRDVrGuyoc1SjBk+T7Oo19R5cM6+gleyWnACtOV/QcPWfsa5qh78gAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMS0wNS0wNlQxMTozNzozNS0wNTowMHA4mIwAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjEtMDUtMDZUMTE6Mzc6MzUtMDU6MDABZSAwAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] @@ -521,7 +521,6 @@ "outputs": [], "source": [ "settings = openmc.Settings()\n", - "settings.dagmc = True\n", "settings.batches = 10\n", "settings.particles = 5000\n", "settings.run_mode = \"fixed source\"\n", @@ -620,13 +619,13 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.1-dev\n", - " Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n", - " Date/Time | 2021-03-04 13:25:29\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | c3d81b5ac372c1412d112070c0f9120b7e3c9162\n", + " Date/Time | 2021-05-06 11:37:35\n", " MPI Processes | 1\n", - " OpenMP Threads | 4\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -639,19 +638,19 @@ "Using faceting tolerance: 0.001\n", "Building acceleration data structures...\n", "Implicit Complement assumed to be Vacuum\n", - " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", + " Reading Fe54 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/Fe54.h5\n", + " Reading Fe56 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/Fe56.h5\n", + " Reading Fe57 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/Fe57.h5\n", + " Reading Fe58 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/Fe58.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/H1.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/endfb80_hdf5/O16.h5\n", + " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/endfb80_hdf5/c_H_in_H2O.h5\n", " Minimum neutron data temperature: 294.0 K\n", " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for Fe58\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", @@ -669,20 +668,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.6350e+01 seconds\n", - " Reading cross sections = 6.7418e-01 seconds\n", - " Total time in simulation = 1.0014e+02 seconds\n", - " Time in transport only = 1.0013e+02 seconds\n", - " Time in active batches = 1.0014e+02 seconds\n", - " Time accumulating tallies = 4.0980e-04 seconds\n", - " Time writing statepoints = 5.3643e-03 seconds\n", - " Total time for finalization = 1.9142e-02 seconds\n", - " Total time elapsed = 1.5651e+02 seconds\n", - " Calculation Rate (active) = 499.320 particles/second\n", + " Total time for initialization = 7.8856e+00 seconds\n", + " Reading cross sections = 3.6158e+00 seconds\n", + " Total time in simulation = 3.9350e+01 seconds\n", + " Time in transport only = 3.9334e+01 seconds\n", + " Time in active batches = 3.9350e+01 seconds\n", + " Time accumulating tallies = 1.1706e-02 seconds\n", + " Time writing statepoints = 3.4045e-03 seconds\n", + " Total time for finalization = 1.0902e-01 seconds\n", + " Total time elapsed = 4.7351e+01 seconds\n", + " Calculation Rate (active) = 1270.66 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.62594 +/- 0.00117\n", + " Leakage Fraction = 0.62666 +/- 0.00245\n", "\n" ] } @@ -727,7 +726,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -736,7 +735,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] From ff69f5a07a1437afb763f77b4c125263e39f570e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 May 2021 20:33:34 -0500 Subject: [PATCH 0067/2572] Fixing dagmc header includes after rebase. --- include/openmc/surface.h | 1 - src/surface.cpp | 1 - 2 files changed, 2 deletions(-) diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 5d747b48948..b3f2bdfad68 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -8,7 +8,6 @@ #include "hdf5.h" #include "pugixml.hpp" -#include "dagmc.h" #include "openmc/boundary_condition.h" #include "openmc/constants.h" #include "openmc/memory.h" // for unique_ptr diff --git a/src/surface.cpp b/src/surface.cpp index 8c1ed51d198..20aadc6e83e 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -9,7 +9,6 @@ #include "openmc/array.h" #include "openmc/container_util.h" -#include "openmc/dagmc.h" #include "openmc/error.h" #include "openmc/hdf5_interface.h" #include "openmc/math_functions.h" From 618efbe9a777f2835a78d7a0c36e0e32d3981af0 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 May 2021 20:33:59 -0500 Subject: [PATCH 0068/2572] Using ParticleData accessors where needed. --- src/cell.cpp | 6 +++--- src/dagmc.cpp | 18 +++++++++--------- src/geometry.cpp | 2 +- src/particle.cpp | 22 +++++++++++----------- 4 files changed, 24 insertions(+), 24 deletions(-) diff --git a/src/cell.cpp b/src/cell.cpp index b85e4fd569d..c58fb176790 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -223,15 +223,15 @@ Universe::find_cell(Particle& p) const { for (auto it = cells.begin(); it != cells.end(); it++) { int32_t i_cell = *it; - int32_t i_univ = p.coord_[p.n_coord_-1].universe; + int32_t i_univ = p.coord(p.n_coord()-1).universe; if (model::cells[i_cell]->universe_ != i_univ) continue; // Check if this cell contains the particle; Position r {p.r_local()}; Direction u {p.u_local()}; - auto surf = p.surface_; + auto surf = p.surface(); if (model::cells[i_cell]->contains(r, u, surf)) { - p.coord_[p.n_coord_-1].cell = i_cell; + p.coord(p.n_coord()-1).cell = i_cell; return true; } } diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 5254035794a..5932e04a12c 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -355,7 +355,7 @@ DAGUniverse::find_cell(Particle &p) const { // cells, place it in the implicit complement bool found = Universe::find_cell(p); if (!found && model::universe_map[this->id_] != model::root_universe) { - p.coord_[p.n_coord_ - 1].cell = implicit_complement_idx(); + p.coord(p.n_coord() - 1).cell = implicit_complement_idx(); found = true; } return found; @@ -504,10 +504,10 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons Expects(p); // if we've changed direction or we're not on a surface, // reset the history and update last direction - if (u != p->last_dir_) { p->last_dir_ = u; p->history_.reset(); } - if (on_surface == 0) { p->history_.reset(); } + if (u != p->last_dir()) { p->last_dir() = u; p->history().reset(); } + if (on_surface == 0) { p->history().reset(); } - const auto& univ = model::universes[p->coord_[p->n_coord_ - 1].universe]; + const auto& univ = model::universes[p->coord(p->n_coord() - 1).universe]; DAGUniverse* dag_univ = static_cast(univ.get()); if (!dag_univ) fatal_error("DAGMC call made for particle in a non-DAGMC universe"); @@ -518,7 +518,7 @@ DAGCell::distance(Position r, Direction u, int32_t on_surface, Particle* p) cons double dist; double pnt[3] = {r.x, r.y, r.z}; double dir[3] = {u.x, u.y, u.z}; - rval = dagmc_ptr_->ray_fire(vol, pnt, dir, hit_surf, dist, &p->history_); + rval = dagmc_ptr_->ray_fire(vol, pnt, dir, hit_surf, dist, &p->history()); MB_CHK_ERR_CONT(rval); int surf_idx; if (hit_surf != 0) { @@ -610,15 +610,15 @@ Direction DAGSurface::reflect(Position r, Direction u, Particle* p) const { Expects(p); - p->history_.reset_to_last_intersection(); + p->history().reset_to_last_intersection(); moab::ErrorCode rval; moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); double pnt[3] = {r.x, r.y, r.z}; double dir[3]; - rval = dagmc_ptr_->get_angle(surf, pnt, dir, &p->history_); + rval = dagmc_ptr_->get_angle(surf, pnt, dir, &p->history()); MB_CHK_ERR_CONT(rval); - p->last_dir_ = u.reflect(dir); - return p->last_dir_; + p->last_dir() = u.reflect(dir); + return p->last_dir(); } //============================================================================== diff --git a/src/geometry.cpp b/src/geometry.cpp index efa993b061f..c93e1940b68 100644 --- a/src/geometry.cpp +++ b/src/geometry.cpp @@ -117,7 +117,7 @@ find_cell_inner(Particle& p, const NeighborList* neighbor_list) } if (!found) { return found; } - i_cell = p.coord_[p.n_coord_ - 1].cell; + i_cell = p.coord(p.n_coord() - 1).cell; // Announce the cell that the particle is entering. if (found && (settings::verbosity >= 10 || p.trace())) { diff --git a/src/particle.cpp b/src/particle.cpp index 2f0f52e63d8..de1b2431fe3 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -288,7 +288,7 @@ Particle::event_collide() if (!model::active_tallies.empty()) score_collision_derivative(*this); #ifdef DAGMC - history_.reset(); + history().reset(); #endif } @@ -323,7 +323,7 @@ void Particle::event_death() { #ifdef DAGMC - history_.reset(); + history().reset(); #endif // Finish particle track output. @@ -394,18 +394,18 @@ Particle::cross_surface() // in DAGMC, we know what the next cell should be if (surf->geom_type_ == GeometryType::DAG) { auto surfp = dynamic_cast(surf); - auto cellp = dynamic_cast(model::cells[cell_last_[n_coord_ - 1]].get()); - auto univp = static_cast(model::universes[coord_[n_coord_ - 1].universe].get()); + auto cellp = dynamic_cast(model::cells[cell_last(n_coord() - 1)].get()); + auto univp = static_cast(model::universes[coord(n_coord() - 1).universe].get()); // determine the next cell for this crossing int32_t i_cell = next_cell(univp, cellp, surfp) - 1; // save material and temp - material_last_ = material_; - sqrtkT_last_ = sqrtkT_; + material_last() = material(); + sqrtkT_last() = sqrtkT(); // set new cell value - coord_[n_coord_ - 1].cell = i_cell; - cell_instance_ = 0; - material_ = model::cells[i_cell]->material_[0]; - sqrtkT_ = model::cells[i_cell]->sqrtkT_[0]; + coord(n_coord() - 1).cell = i_cell; + cell_instance() = 0; + material() = model::cells[i_cell]->material_[0]; + sqrtkT() = model::cells[i_cell]->sqrtkT_[0]; return; } #endif @@ -506,7 +506,7 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) // if we're crossing a CSG surface, make sure the DAG history is reset #ifdef DAGMC - if (surf.geom_type_ != GeometryType::DAG) history_.reset(); + if (surf.geom_type_ != GeometryType::DAG) history().reset(); #endif // If a reflective surface is coincident with a lattice or universe From e244b8f0cf64635204ca0bd5945752a7c699dd2c Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 May 2021 20:34:43 -0500 Subject: [PATCH 0069/2572] Resetting rotation test results to match upstream after PRN update. --- tests/regression_tests/rotation/results_true.dat | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/regression_tests/rotation/results_true.dat b/tests/regression_tests/rotation/results_true.dat index a58aa1c8ecc..f4a3cfcccbd 100644 --- a/tests/regression_tests/rotation/results_true.dat +++ b/tests/regression_tests/rotation/results_true.dat @@ -1,2 +1,2 @@ k-combined: -4.453328E-01 5.918369E-03 +4.132181E-01 1.130226E-02 From 9779906d0188265412a3ed74c6287c4a95cbb041 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 19 May 2021 20:35:40 -0500 Subject: [PATCH 0070/2572] Updating the DAGMC universes test result for the PRN update. --- tests/regression_tests/dagmc/universes/results_true.dat | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/regression_tests/dagmc/universes/results_true.dat b/tests/regression_tests/dagmc/universes/results_true.dat index 203d35efeb8..c9e1bc8fa5d 100644 --- a/tests/regression_tests/dagmc/universes/results_true.dat +++ b/tests/regression_tests/dagmc/universes/results_true.dat @@ -1,2 +1,2 @@ k-combined: -7.531408E-01 1.260163E-02 +7.687742E-01 3.415061E-02 From 0d6bc863311c0092fc0a07d2ed50177909bc35ab Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 25 May 2021 09:21:00 -0500 Subject: [PATCH 0071/2572] Moving history reset on CSG surface crossing to correct location after rebase. --- src/particle.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/particle.cpp b/src/particle.cpp index de1b2431fe3..8abe2d9c68e 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -381,6 +381,12 @@ Particle::cross_surface() int64_t idx = simulation::surf_source_bank.thread_safe_append(site); } + // if we're crossing a CSG surface, make sure the DAG history is reset + #ifdef DAGMC + if (surf->geom_type_ != GeometryType::DAG) history().reset(); + #endif + + // Handle any applicable boundary conditions. if (surf->bc_ && settings::run_mode != RunMode::PLOTTING) { surf->bc_->handle_particle(*this, *surf); @@ -504,11 +510,6 @@ Particle::cross_reflective_bc(const Surface& surf, Direction new_u) coord(0).cell = cell_last(n_coord_last() - 1); surface() = -surface(); - // if we're crossing a CSG surface, make sure the DAG history is reset - #ifdef DAGMC - if (surf.geom_type_ != GeometryType::DAG) history().reset(); - #endif - // If a reflective surface is coincident with a lattice or universe // boundary, it is necessary to redetermine the particle's coordinates in // the lower universes. From 1b6b73affa2fd90f7714fcd1339ed85b197d4ca8 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 15 Jun 2021 09:41:29 +0700 Subject: [PATCH 0072/2572] Changed version number of 0.13.0-dev --- CMakeLists.txt | 4 ++-- docs/source/conf.py | 4 ++-- include/openmc/version.h.in | 2 +- openmc/__init__.py | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index dcd0a0bef55..7326e7f3525 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -3,8 +3,8 @@ project(openmc C CXX) # Set version numbers set(OPENMC_VERSION_MAJOR 0) -set(OPENMC_VERSION_MINOR 12) -set(OPENMC_VERSION_RELEASE 2) +set(OPENMC_VERSION_MINOR 13) +set(OPENMC_VERSION_RELEASE 0) set(OPENMC_VERSION ${OPENMC_VERSION_MAJOR}.${OPENMC_VERSION_MINOR}.${OPENMC_VERSION_RELEASE}) configure_file(include/openmc/version.h.in "${CMAKE_BINARY_DIR}/include/openmc/version.h" @ONLY) diff --git a/docs/source/conf.py b/docs/source/conf.py index 55cfef35218..ed790a2e27f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -70,9 +70,9 @@ # built documents. # # The short X.Y version. -version = "0.12" +version = "0.13" # The full version, including alpha/beta/rc tags. -release = "0.12.2" +release = "0.13.0-dev" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/include/openmc/version.h.in b/include/openmc/version.h.in index fe282be7c01..995f20c3b18 100644 --- a/include/openmc/version.h.in +++ b/include/openmc/version.h.in @@ -6,7 +6,7 @@ namespace openmc { constexpr int VERSION_MAJOR {@OPENMC_VERSION_MAJOR@}; constexpr int VERSION_MINOR {@OPENMC_VERSION_MINOR@}; constexpr int VERSION_RELEASE {@OPENMC_VERSION_RELEASE@}; -constexpr bool VERSION_DEV {false}; +constexpr bool VERSION_DEV {true}; constexpr std::array VERSION {VERSION_MAJOR, VERSION_MINOR, VERSION_RELEASE}; } diff --git a/openmc/__init__.py b/openmc/__init__.py index bd27ab308ed..50246cdd8a2 100644 --- a/openmc/__init__.py +++ b/openmc/__init__.py @@ -34,4 +34,4 @@ # Import a few convencience functions that used to be here from openmc.model import rectangular_prism, hexagonal_prism -__version__ = '0.12.2' +__version__ = '0.13.0-dev' From c8db88dff2ef61656a371f1e73b10fed0f8bc4aa Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 4 Jun 2021 11:55:48 +0100 Subject: [PATCH 0073/2572] Add a cpp driver test for comparing mesh tallies with internal and external moab ptrs: currently fails because feature not implemented --- .../external_moab/__init__.py | 0 .../external_moab/inputs_true.dat | 79 +++++ tests/regression_tests/external_moab/main.cpp | 50 ++++ tests/regression_tests/external_moab/test.py | 281 ++++++++++++++++++ .../external_moab/test_mesh_tets.h5m | Bin 0 -> 481444 bytes 5 files changed, 410 insertions(+) create mode 100644 tests/regression_tests/external_moab/__init__.py create mode 100644 tests/regression_tests/external_moab/inputs_true.dat create mode 100644 tests/regression_tests/external_moab/main.cpp create mode 100644 tests/regression_tests/external_moab/test.py create mode 100644 tests/regression_tests/external_moab/test_mesh_tets.h5m diff --git a/tests/regression_tests/external_moab/__init__.py b/tests/regression_tests/external_moab/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/regression_tests/external_moab/inputs_true.dat b/tests/regression_tests/external_moab/inputs_true.dat new file mode 100644 index 00000000000..78081163514 --- /dev/null +++ b/tests/regression_tests/external_moab/inputs_true.dat @@ -0,0 +1,79 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + fixed source + 100 + 10 + + + + + 0.0 1.0 + + + 0.0 1.0 + + + + + 15000000.0 1.0 + + + + + + + test_mesh_tets.h5m + + + 1 + + + 1 + flux + tracklength + + diff --git a/tests/regression_tests/external_moab/main.cpp b/tests/regression_tests/external_moab/main.cpp new file mode 100644 index 00000000000..ddcd2999262 --- /dev/null +++ b/tests/regression_tests/external_moab/main.cpp @@ -0,0 +1,50 @@ +#include "openmc/capi.h" +#include "openmc/error.h" +#include "openmc/mesh.h" +#include +#include "moab/Core.hpp" + +int main(int argc, char * argv[]){ + + using namespace openmc; + int openmc_err; + + // Create MOAB interface + std::shared_ptr moabPtrLocal = + std::make_shared(); + + // Load unstructured mesh file + std::string filename = "test_mesh_tets.h5m"; + moab::ErrorCode rval = moabPtrLocal->load_file(filename.c_str()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to load the unstructured mesh file: " + filename); + } + else{ + std::cout<<"Loaded external mesh from file "<)BcJoL7^R{}u?7Z7-D471#`on>WFR1v6quL)1Ond(~ zO~(~~$MVm-b02%kDbsEyf3eD@8xFQV9fR19?^YK$^^}v&-dp&*+SZ-jzdq18^cn9Nw>FC4WzsJ`5_jz|~hMS>w|G4!2ee-Qw{{H*-9sEGNi^9FK*nPe${iIwc+;kOD?_gsw!2TW$pj33yi*wo!!1HPS2{p-%Fndj(Ko% zyoB+7e7CxQ&jZ)i)>l_tKcW4>*OzBKv0-0d?oeNM9(LAw=bwDq8Rt~l?%(WNAMxwc z2^X{s@3Z!K;MV=SyA$4{>Ap^$SNQ5XH2#EY@VfdsS8x_q#XR-yGwCp5tI%7@U*F^2 zvk52GC#>Tu@7luht}p!7JGBI#&yKIZfAaG0W7)ma!szor-f!vi!1L~P_#W@H0r!pG zB`)CeKz=`aP5u3_#JLw=b)UUIdw-uwcn9O?{hNOem)^f8yl)%8-@|usoc*$Q(F^4J zcdPa|uRc#(%lESvUU9{3~tAC}&~C*HL++<*Vx%){Tp z((?U#eC>DKdmp~2_wQx9&sTfOg;!jD)s3IaypK;fyljX@@84^x?-sJ&E`1$4`^hcOoo{)U+TZA=E|8xGj;rJw>iwg3{&Rz-tR%dhv3(vmy8mA1 z#0T88fo}_sU-*X4`}pemd*Jg${=K;AZtps(--n$VKZm{j)V`xQFx_Ul{q9e1pLxO6 z=U;l!wE4W-ZnWAg=|2B{|F-kq=as!*i9D~t-QQkZJ?;Hy_qg5P@a^U9eH>cNG1Dm* zJ>%lNv~!<*>0X!~^PCHIJ51BrS6ueoXIydlwEOJ22dA^2`|Jyzx!e5gXJ2~dGk5W$ zFTeEZdxfW7eD$8Z$-m^{i=Mq(vUwdi>gZb?y!CC4x$UNB`}vdy*aP*BIdGs&ajRCc z`+LXs+IvUsDR&a9FHl#qYpgm{rB`~T5bN<|J3jU3WkoORN}_H?fo zzn4Gl$NdZJUts?N`xn^1!2SjHFR*`s{R`}0VE+R97udhR{ssQ4FR=IXpv^OXJ@_A( zj^DIAa8x7iR{svobADn!U%BJfL+fV-tLZ-PG2Q2FRdDF%E6wmmKVP}9?e>D#-+0IT z`O595_5;2H(``3Dx4QDG3!Z(|<|kLJ>e$^MNVK0xtTu$7Ux-ufbK@R2_2<#Q&wbtf zTGgply!3PQ7u>fcw%Kl~dOeXEu{zW$w-o&7#|_xHe;Uwq+Jmwxi4S3P%6 z^Zs4<@iv_5P-kJ>{{dopk#7=bUoxo^Nj@f))b)r4IqpvH+)ce9o-q~MQykAeOuM7G4@XS*lcg9I)p6S!!fBWad*Tnwrupe*w0zMz+ z*V99v-}3XJOW(}Theu8yem;EGscqhy`}uG>t>y23KD_CV^DR{!eLg&{-beQSuljs= zYJLBkpASzy?Xjml{M6G=Ik!F|{&#o<072=z`u!3qLEE<-+1@^Ki~2%kY6u-y||=)U+4n9Uiz$dD&apF^Y?1`bLITGN32ttfu_@b4~p{*-hDhy&V_ueM+ z&%fk?%P+d@;_|-n_ZWNB-p6V0B{sCPK62a|@W&1KV+Z^+;-~h?{?qEn{ILW6xB&^4(#^*Bxej&7ZtGB<~K%Zw-9h zA@g%y*d6SFcZcNpCJ&2z>I~D2ulbXAhveNM`K^J^&WK-6hrP_XHQ@Uls!D#pL-6L) zL_REbeuZff-|tW|zTYAEtyK}h_d6uNE@IUp|9*$y`yGPscL=`UA^E9Z$rql2?{^5k z-y!&Zhv54il3!1omp*xiqZ2Y&M1A^BGk(>C0q$|&voqq?t?y{R{eut9r z{SLwRI|RS8DkAxH5vvyYi&OCZ4#D?31V62(_Da6!^oK=r^gD!4zeDg_h0|we#IL8# zOCNp3bjgCBn}d3Q+O9g?30 z{;lio*NavAlYZ`yyt$KyMLzD3{7&_o@#~tS^zJj@-9aDu?vVU6@M&Flzki6n6D+Q$ z&C4A!zwZfkr-W`&6hve;+z%L4yjM;w%3erbpO7ZKGmGC=nim?gM4@B zdF$5D9cFy#xraL>uReKLl;;jJzKu1__*D~>Xm%qng4+AcL<+;hv54il3!1omwVgO z9g?Rf?+(ek!;Eij`}fs+?VtIuDBm5DcZcNNVaC@T`0;m#EjMFzWjO}EQY^3WWGBj?+!D5YOfhz{gQWw;nvy>5ki0u2?+(fD z4E){2e&$i1JS@r=lb_aYuNmLy`S%|+XXd*@^6rqlJ0$N8Grs1uhv*KlnDzKkbcfXI z4E_6Ry7vRzW8J6Yn%fDPlZVBg=MdGu^Q#hq_kKtpcSznH zlHVEl?^M6)-akHD1HRv(3d}F2k2z*MB7D3b$b9=9BER1u_$za`F@Ae`S&{nzcujbcZmF*^|X2E!`~f}cZcNFB|i=PTh~KB zuU6mpU$qQ;_YK}1y1s68?vT7Y%=oJ7etZVJJ7m6jlXr(1-^T4fuU794nQ!joVUdqJ zB<~J0-rlP=Soaz5?x2r+F?n~G@vU9|dA0hf=E!_^NZvll!yt&-m7+e_t)n9Wvh?l6QyX-68p%y2FgGz3pM1tpTq-^I?&XIOFRM zGrs0b-W`&6hveNM`JI8k_k(%Gt-1p&x&tg`Km0J(9cFsXnY=qB?+(ekL-IQV|9*#< zYio_DSh(MTTztPn@O_TtVR2o5SRpE|TkLlr7vJv?e7{5R{SL{O-?S>BaKA(F{SLwR zI|SeFko;7yJF-yM>7hvauwt?`Vnz4`Iq8u0Fr`REBn&bBD|~ck-~v#~qU2S+&M9 zzV=nueFnTcWWGBj-~FmT+@?A7??5_VdFkU0$=fG+SmeJm@ZVXtiRkjT2E03DzB?q( zZ^pN_{ohy1n^s4j#~sqg9g??y@~}ABGh&G5z};!l&OM z_E8cL)2`c>VkS zikoWEKy8r3s{b6`yGPscL=`UA^CL?tHXt@0pITs z`TY*T_m4;NQ@xUh#Y*dU2)^GT_C zCyF>EsFyq(FvEQ{CYz=sK$b5H5-W_Iq^_lUjCM0j}vpJQEpD^9D1w+inB-|rB7zeDiTz-Ovg`s<&3zeD)uK|{pM1W_ z!=ik5NPZglx2}i&yG1@?`nW^#?vVV}s&z=-9cFyZX%BO_L-OjAhebZ_FyreGGrs0e z-W`&6hveNMd3TucbqDjB-yM>7hvd62zuKqgB|p_G`PGqmcj$hV=MKrcL-IQV|DAR9 z6)JCQ!1p^ue!oNTeU9W|aaDg1gYS0;zTYAEeuv=u9g<%cu{u2VI|SeF5PZKw@cjBqS{50^H>ZQNFITv2c{CEsko?ZT-(BpN{gQ`8`R_Y3f^4F z_d7&>zeDi-4#}^l%}XEA9bnNN;2sA)?$GmAt)Ks$@uep}!tRi|J0$N8Gk)DBNhE5uI7c^A@g?z{{CK=tNWC1^8F4a$nSRu-W=%zi+rXPrbT?eL&s4w2vQkoe z?f{E9H+~e|A$50{@ik}GxkK{a56Qbj^3%ZI9cKBfrX=qU$=fG+Sd`}uGrs1e%Xf$5 z-645*NPZglw{36q{|{tUOvUtZhvdzhygieL#a_Q|{dX#=-x~1!4w2vQ5d1Xond+5& zr&SY@?{`=?-JH1xUQ8eR&v?G#s`|iU@$TSG`nf~$?l9w9oBn+@pQ-v}zB?rE4$1RN zerMpnvu*?R{UR@USk%uQlAi|tt?QwG2f}Bn{^{cm$-6`H{E~Nv8DDeSL%lmDuReKL zki4gL-OvBy#C3%!;G&xm{+|!B<~K%^GzNW`PUix_tl-RJ7m5)B<~K% zcfZ;X-WmAsRNhoC`C9|N-y!n*9fCJs(f!|d!lJ&@3ezIK-=SoDzeDhJZg9Ut-y!&Zhv54ig70@oeyUgUg{R>A9fI$72;Ls))9(=ZJL_rl(nq~JB<~K%yF>Es zFymX>{(ZH+=FWUrlpicSwDw?l98_ygOvRJ0w31 ze5QKM@>fkr-W`&6hve;@%;|L_d5hX!QyHE8cZdFQj=bby zk*_->KdswdGrrON-+}O%s(fkr z-W`&6hveNMd2`M9bq!Qe-qwJ32m8tAn>;Maude<3>dvo9ss4C(NFR4d-W`(XH{+{c zbwBjq?F{&ShbrLP?-0Cx=?{y1{JluN-=PHg{SLwRI|Q#T{dd;ItXh<}HQ@UlBER1u z_-WuXd1ZcK_b^YtL*(~61mEuvJiqjJ2Yuvit*6Z^d3Q+O9g?30KF$5m|DQd5&6hr~ zDBm5DcZcNF&G?#=pS-OB?+%&o4#`ggA9tALubPs)xs!**KL5anp84Jn?k8_+-R4Q& z9g=s4E!NgfvYi!*+G_#8cNz`KJy(H&rM8u+xXhkjn& z^H)uQMR$NjcYwv&o&#U`+w0ah&eC#{pYv1JUo4kFKw{PgYWwWub=&9`$m4>H~7A9@O|Il`@YH7zWSKQ zzRBA+dHW`B-x*(XBoB+`v2XJBP2RpUzV_wE-@eJ)H+lOeZ{HbT`zCL``jdv)Z!Qipwf)#2@%ynU0m?~M2N zYBg%;TxDXNG2zEy|s`v%|lP2Rr2_kDx!`v%|l4Ssty zZC*KlV)KupeN(q@^7fta-p}hc7u~+FXkWO;)#3H{?Ge9j`J+9OheiJOP2RpUzV=Pt zzRBA+c^}{8?K|UZU-Oz@OkRERu&Ca?Grsms-oDA(H+lOeZ{HbT`;M`eb&?FWpDeWju$h(?^}NI`o78AH~7A9@O|Il`@X?%ucpl_ z=Y;KNPFTc?$=f%1`_6dp=XINluYEJ$zRBA+`R!Hno$>3&>nCO&eiZGSx_xJS?VG%P zlecg3_Dz0!b>zK#d~;s=CeJr{Sd?eq8DIM*Z{OtYo4kFKx9^N!w>y~2oc2wgo;=^= zVX@cOzRBA+dHW`B-{kE(H7xX_YJ=9n|$r7 zkG}Rzo}RpYleh1TuQ`&3Mg8oXynU0m?~Jc~`SG`J^7Q2Go4kEzyt}Wa&5LiJFZf;` zynVaA-q=ms^2GJDdHHx#E;^5#n3zRBBn#@D{d+c$apBoB+`+g@$f>pz$4eC+oXw^nJs}=c&x^`$m4> zH~7A9@Y}0t^Gbi%e&&WnynU0WCvV>w@BO@PbE&s)=G!-U`zF7=YQ8gm-FSQGD=&Fi zlrJW4-x=@oRrObI-^{mf^7c)Bdv)Z!e0+Pq>TloV(>L|R_MP#yZ}Rp{-oDA(H+lQc z_}VY$vTySCO`dP^u-Nlz-{kF^ynU0mZ}Rq?@%D{-_kDx!`v#wTbbQ^kdHMX*`KnL8 z?;HMo-{AYc!S{WWuYL6~kA0K3Z}RGr@AqA8)}G|`J%jJ{!P~d%>*iuK}y z_qY2MZ{OtYo4h@ex9^OvyC-k%9&!eFnUJ^^q?oZ{HbT`zCMS zZ^o}TWAvZr$@lS9hqrI?_Dx>hjIWPx^7c*MzRBAsd06aotf$S( z*QI{<0pIs6U4Gv;`0OKJ%)Wi!@ag*o-}eoEdo^ud=@0vOngbT`_D$Zt$=i3v*T*+` z`zCMS-`8CFuFEI*zHjh32mev*`-V^7H~7A9@Y|~+&uQQ6W8dU`e3Q3t z^7ftaHDB_u=q~n6-oDA(cgC;V9r*FLZ}Rp{-oDA(cgEMg$(t{ESnPAIjy(VNh+nsS zcj`U^-oBY{-{kE(<7?mK?VG$klZQp~Z?BHLm-jPWzK?J6_D$Zt$=i3v*S^WyH+lOe zZ_ngm(O!Mu^|X2E*Y^#+?;CvIH~7A9^7aj0e{;co-^lO#2EVg}66 z-{fJDzkQSMddy?r%(rjy+cocuUr(Et{`&QO>wSms`v%|l4PL+WheiHk@_pa%@B0S7 zy*lz<{rz0Je)fe$`@%i0n}5gUXWtng^QE5HzRBA+`R(dIbw z6EiP(`}SD*_D$ZtGrsms-oDA(H+lOezg=_9_}Zt>TY2u1y!zx}(R}uu@wIRA_D!B& z^7c*MzB9h|H5Xs|CU4*5`6dsG{Cz)>ynU1Jb=OUs7i{0mx9^O%ulLt#W7psJ=HlP? zjr_iE@cN|>Eb{OBh7UdS`@WIi_f5X`)rYT-Z}Rp{-oDA(cgEKo$-|;~#N_RpynSbU z?aNQJFWloG-@ZL>HEmw@o$+yZdd`O*Mf;|{UHxbLdNbCce+OQ9_MQ0+^6i`X_MP#y zZ}Rp{-rUKuUejcyI#C~leh1T_kIq4 z`)0mfAaR7@#|^xGOv1fNnU;O zu*k>0Grsms-oDA(H+g-Mx9^Ovea)rbzRBA+dA`ZRB7fgEByZp3?VEh}yRLoQVEbmi z_p|=;`@a63t(sroH~7A9@cN}cEb80$4WGVm@aod1?;H8{)knR3lecg3_D$ZtGrs0X z9v01G-{k4Z+jqvV>*Ka+QN4Zj!P_@^`zCMS8DIM*Z@%PV(L7@E+qKV(U&o%6XJ3Az zePPkQuxMXc%=aJOzOa~c<41AZ9ud8tXZm{Dypp$X^7c*MzB9f)zRBA+dHW`B-{fJj z=dGvBYqs}*@B7kMoAUnOoup6SH}ZXaGr#W}`F-EuXM3!s&1+j+Z(#rTbozP!%7aC` zeUta`P2RpU-sjDj$G(|w-{kF^ym@DQ^|MFjB@c`8?VG%PXS~l>)xZ0!rp+t!?VG%P zljk?%*UjI&^4ukP^~u8`AN$Vu+BbP0-{kF^ynU0`f5xw;&C6W!?3=uOljoZ}Eb{mF zH+lOeZ{OtYn|$wc-DbVn-(UUZ^?mE(fbaVT-}epP9O(~>{QJJ))AtR&?;AY7^!NEn zA9?mo-oDA(H+lQc_?jSp|U+PwJ5vv2bDP2RrA+jqv-zR8;}d06ao zt~TTM|4tAuX1+PvtZePPkQuxMXc zv@a~q{$8!7&C9;>MEkdYF%V z_3Cb4bda+MI?P8tT^&CCVROq@&)ZLDa*1dmh{by#2d>g{DdeduuBkZ(N}oQn?nj45_3DnTx)(-0%ttQjgHm9O4j=F^7`=N;*M^^t#K z2{3>8bokbFx6i6bhe!45?pSqS81*n8`TW%_&Cf@^dh@GK9oARAdV85qA3B_Or1RBB z{sXEJ%wIkozD?cjc@^pKs9xQjs_u)T9_AyTzq+OQ`N&sqe)Xxt`pQ>tFZ1a`hx3kf zzWT_2VBH_)FP{z{Q+NC9igb8XukO98?nO}#^O4VA-O~Jg^-5=&JpAO%)?)Etq>F}st-JPrMi=!UqBcH#zrTO{DS8smxsl)opS8p%# z=|hL}jI$bV4XALcKg4&Scs_PG`5@Tgwh`&8YRL_N$$K7Vyf^Yf9f-u&uQhxL`O z-d^U@hYsf*>3sE(|KPem%wIkoevi7_=T)S`qk47kTXkO=^)Mg#{M9YZ&quy`^Q%uC z)>poIdznukI-GZ;-=X%seZ}Wjd`R6N{`}(Q)8XSv|FWouNA>F7uj*bL^)Mg#`m0-- zpO1X?>hS3g+f%;!ypK8Qu>SJt?^*lucc+Ke{oyaDJo$8W=G2c4^N~;IcbD@2@``kL z)L-3Fe?Id0n@4@>usP+cx0m_!p~HCx`g_G)`K;>x@CzzWKHXgA)Q=AHkx%D$*Yf|0 zigb9?U)@rFKJxjSM}6wBIpwRjm-+Oe!+8h#v2j;EC)NGo7gnBpy1C4$A06f+pU&@Y z<^Pox>F}t(x~2YnHO|q{$Ev*4v+e)Tk6k8K7aG5PaQU=eD(G+pFVUr??As(+?CJ6>;CW;Ri1phxy-2_ z9p)pS&hP!p|Env~;Zc8eOa1xC=WibMsl(=!uijqf(}xb{9q8{Jcja?(-5-8Y<;ka; z%bfbrVLtNd{63)kzosG`9`#qZ)Sr)h{^n7iI&4n)>g{DdeduuBfqv(>E1y&9{_q!9 zo_xBw%&8w8<|CiZ?*q&Knu>II)L-3Fe?Id0n@4@>usP+cx0m_!p~HCx`uoIP`8=ZT z4}VGJ$)}skochsWKJw}Ow#xr&E7IXne|1a!`N-#Q9`&ii=9I7AUgpz>4(A={?>n^@ ze?E__`@>&adGhJ%%&8w8<|CiZ?}N(!>nhUWQGazy{rSk}Zyxoj!{(H)-d^U@hYsf* z=*QK*?^p4~6;G}E!(Udsd^-HWrGG=z!=rk2+g11VQ4jNxufMvb`T59KuMVI7us!9g z&-<8@4(l(UewW&pzdM~)_lLi{^5oOinNvSH%tt<*-%j~|V?{bV>aT98KOg!0&7(eb z*qrj!+sl0V(BZrT{jPCWK98#V!(UN(^6BO>r+##pk9<164=Mj|sz`@N{nah?=OdrL zdDN#4n^V4edznukI-GZ)-!1OS=g|k70DooW$)}skochsWKJw}OKD7M5xgs4N^;fsl zpO1Y0=24$IY)<*=?PWfF=y2YFe)qU5pVRC9@K;rye7d>JsUIEYBcIOi!^;0973uJ( zzq+OVeB|>tkNVVMbIMn5FZ1a`hw~2f_n+E}Kc6$|{_s~usP+cx0m_!p~HCx`Ul2c`J7evhp(wT`E+xcQ$IS) zM?Rh3N0k4!R;0tD{_2+c^O4WrJnB=2%_(2Kz09W%9nL$@x8kmR&aV5zUt4+d>E<%0 zesq|Rd^*36EdQ5Pq{E~B>X!QRkQjf!DPO(4%%=|>&O6XQsP^U0=P`AE`0FZ9 zK3$zT^`palaT98KOg!0&7(eb*qrj!+sl0V z(BZrT{X^oee9o==!{1bS^6BO>r+##pk9<166UzTPD$?Ome|1a!`N-#Q9`&ii=9I7A zUgpz>4(A={9~yV%^SHV{{LPgopKdO5>PLt9$fxtWSNVTuMLIm{uWqS7ANl;vqds-m zobuJ%%Y6FK;k*O=!{V-dKJH)>;FnaMe7d>JsUIEYBcIOi-sS&Y73uJ(zq+OVeB|>t zkNVVMbIMn5FZ1a`hw~2f<7;33d>&u-hhJKG^6Bc#sUIEYBcIOiW6J-#E7IXne|1a! z`N-#Q9`&ii=9I7AUgpz>4(A={A71JsUIEYBcIOizUBYD z73uJ(zq+OVeB|>tkNVVMbIMn5FZ1a`hw~2fkBqzWd1Bokep%(or<=>1`q5!N^6C8U zSN`8ukq(dgt6S>NM?Qb^s81a>r+oGHGM_$lIPXCJsJJVikFWc~FRwiLbaRQX-BN!(^7)%bed@3|<*T=s`ShW~c?bGEYTu8p_-z%RRQHF!y?FU_ z_yMK=K-9yddUYpO-SXzo`BVWBbeEP%ol&?PTV@^7(zkK>VYhV8E^yIof z{EEtxPgiG7{pc_s`E-5{EdL*@NQX!L)h+esBcH!{)Ta)cQ@(n8nNJ@&oOhs~5O?MC zl)69s9hE1aZZ31`M~C^yr}O*R^8cZVba>QX-BN!(^7)%bed@3|<*T=s`ShW~c?bHv z;;wwoulvK_S$Xp5<}#;#beNBPI==^%{|{HB!=wJ{miqIN&)+=iQ-{qdU%kD|rw<*@ zJJ9bPcjfccx<@2<JsUIEYBcIN1RsKI#kq(dgt6S>NM?Qb^ zs81a>r+oGHGM_$lIPXBe-_&0G`CL@@hrh4#tkNVVMbIMn5FZ1a`hw~2f``5nw`CMH0hp(+X`E+&W)Q=AHkx%FMu=4+jigb9? zU)@rFKJxjSM}6wBIpwRjm-+Oe!+8h#18UzBD}H~)r`P@AA1Gcv9X`4ApNe{TRIl#g zRrixo5A%_)zq+OQ`N&tV4xj$8J>{#<`o1@Fz}lC;J3XWB5C350$)~F`r+##p zk9<16Q_BCRE7IXne|1a!`N-#Q9`&ii=9I7AUgpz>4(A={9~*b&bIHLbz&})Z^6BO> zr+##pk9<16N0k3{73uJ(zq+OVeB|>tkNVVMbIMn5FZ1a`hw~2f2gP0aTw3>sf4K7G z)6Hd0{pc_s`E-7dEdO;yIy~yHZmB;X`TWhJK6Tif^3~hReEQJgyaWBgQ+x5}^9gl- z_(v*FK3$zT^`palPLt9$fxr=t^9wcA{`#}SGUxkk9_{-QJ*?&PWkHXWj=lAaNdFb z(6}p~%j*8{D=SYv-CX9>j}G&ZPv`fj^8eY2ba>QX-BN!(^7)%bed@3|<*T=s`ShW~ zc?bF`?#kzxb$|HBDo;M$T;|k|4)c*u=lAIH|GA2Ec+_9rQhz@3`I|?5>aaQGtGAc= z^r6Ff2l`31FMmFl*ZtuiuRQs5b>`HM4)c*u=XZMfzq%qF9`#qZ)Sr)h{^n7iI&4n) z>g{DdeduuBf&Q@Cmp`8?4mJV)iOQ2tS7%QB=rAAobbe=)|Ib&X!=wJ{miqIN&)+=i zQ-{qdU%kD|rw<*@JJ3(AeIH)&Co4Xy?hpS|@$%{LS*5=w>fuqnx-+Zp7or~KBVT`Y zOY`%QuU;KK{b75`SD*JWCmq&bKK+#1m%lrGQr#c^>B^H&S7%QB=rAAobbe=-|1Vag z!=wJ{miqIN&)+=iQ-{qdU%kD|rw<*@JJ25ycjfc!x<7nf<;ka;%bfbrVLtNd{2o*O zzf_S9kNT@y>d!|$fAgqM9X6+Y_4YEKK6E(mK!0T1mCuz2n*gsXPd?pT=G2c4^N~;I z_t^6P<%)E8)L-3Fe?Id0n@4@>usP+cx0m_!p~HCx`l+=qe?C{${oz+to_xAGbLvNj z`N*g9JE#0#TagZr`m0;&&qqFg^Qcc9Hm7{`_A;M7bU5!oKdtuV&*wRHfB0uAPd;6p zIrXE%eB{&lom>9DQjrdi`m0;&&qqFg^Qcc9Hm7{`_A;M7bU5!oe^lI+&nMUY;h(KM z`E+xcQ$IS)M?Rh34(A={kB+PLt9$fxuBxbpwCigb9?U)@rFKJxjSM}6wBIpwRjm-+Oe!+8h#>2X&+ zSJ(aFS67~Vy1C4$A06f+pU&^`<^Q^hba>QX-BN!(^7)%bed@3|<*T=s`ShW~c?bF# zaaTUiJ=g^J=POS>-CX9>j}G&ZPv>`D`Tu%FIy~yHZmB;X`TWhJK6Tif^3~hReEQJg zyaWBL+V{+gzfkd0>;CX-ikDA^pIG{DMm;>LSNDXf`;DlF`N-E_-O~JgX!QRkQjf!DPO(4%%=|>&O6W_6L;nF>2-hjmnu&_-CX9>j}G&ZPv`ff^8f9Mba>QX-BN!( z^7)%bed@3|<*T=s`ShW~c?bGqHMBt{=ZX^4v+e) zTk6k8K7aG5PaQU=eD(G+pFVUr??68%?#kyg>;CX-D^EV%T;|k|4)c*u=l7KI|J{mo zc+_9rQhz@3`I|?5>aaQGtGAc=^r6Ff2l}~jS3aLr_lJL_^5oOaWlsI*FdzAJe&?6} z^%d#xsK2_U{(R)~H;?+%VROn?Z!h!dLx=MY^vA_r`8==g5C3ZA$)}skochsWKJw}O zo?8CDSCI~n`m0;&&qqFg^Qcc9Hm7{`_A;M7bU5!o|G25W`1ASfxvs>^5^q8b$|GEl_#IB z&Yb$uVLtNd{GL|+zh99KkNT@y>d!|$fAgqM9X6+Y_4YEKK6E(mKtC_;%I9aT98KOg!0&7(eb*qrj!+sl0V(BZrT{fV{j6Ds~j z#m}qz!@pU)d^&t_>3ddJh9p)pS&hP2v|3?++@TkAKrT%>6^EZ$B)M0bVS8p%# z=|hL}4)iC*UHN=|-5>t#%9BqwmpS#L!+hk^`8}ijZ>UIzNBz|;_2(m>zj@TB4x3ZH zdV85qA3B_Opg(zPFaCVKpzaU6^EZ$B z)M0bVS8p%#=|hL}4)mwgzWn*TpzaUzj@TB4x3ZHdV85qA3B_Opg%S4%I6E~{_yWro_xBw%&8w8<|CiZ?-R@aPb<>l zQGazy{rSk}Zyxoj!{(H)-d^U@hYsf*=oi$!{P}!Q-5-8^<;kb3GpBxZn2&rqzst)1 zO%>_zsK2_U{(R)~H;?+%VROn?Z!h!dLx=MY^rzLn{Q11-U=!fquRQs5b>`HM4)c*u z=l9I=|Fep8c+_9rQhz@3`I|?5>aaQGtGAc=^r6Ff2l|C^S3X}{_lMt5dGhJzGN*oY zn2&rqzst-2&nwd5QGazy{rSk}Zyxoj!{(H)-d^U@hYsf*=oi<%7ghX&ieFOqhySp6 z`E>YMrT<0L!=rk2S5)1bqaNlXUw?H=^Yf9fUL8LDVSCC~pZ75*9oAny{pqzYe|P%Q zgH3?{sPg2~)tOU2I?P8to!=*w|6f+5!=wJ{miqIN&)+=iQ-{qdU%kD|rw<*@JJ6pI zcjfcqgH3>Ms66>}bD2{=I?P8to!_&||F0_2;Zc8eOa1xC=WibMsl(=!uijqf(}xb{ z9q5i+PbRGxggxy-2_9p)pS&hI(p|F;$C@TkAKrT%>6^EZ$B z)M0bVS8p%#=|hL}4)jlqyYl(UxaaQGtGAc=^r6Ff2l{1kS3X}=_lMt9dGhJzGN*oYn2&rqzfURuw^XFVqyFla z`ty;`-#qG5hs`Npy}it*4;{`s(4QH1<@41Cn*je=<;ka;%bfbrVLtNd{H`wlzpqG# zNBz|;_2(m>zj@TB4x3ZHdV85qA3B_OpkE$$usP+cx0m_!p~HCx`lr^upH}hnD_&Fgho4`(d^)WDpQ0ZA z$3eY1ecl%JFdzB)t25t!L_N$$zIt``Fee@6BR_TX=|hLr^P}76(`)W8sQ9%9+aF+a z%csM1{phgyRzH;k*OgJfBf}zo6pR z)%{_8<KtPlxsY*XR$A>eZR^sJcIYn2&tAx~2K~$X9PJ{_^N>-hpnO z&#Jw@u;Mq={b7CO(_y-Pbl80I>9GE9jsEbcUY$9QuKV+c`N*fMTbiGbeD&tyFOLrA z9q8tHUhVxw6~D3W59=$R4%79c!{(DuhxPxr=ns$T)tU2Fb$|XaANh23OY`%Quijkz z<SvNYA-rGs#j;ugLQxPFdzAJbxZT} zk+0rd{N>T%yaU}lpHq8(am8=0`@{Omr^9sp=&jTYzKg>ry zUER|BeB`S)7k_zlIPX9=&*#?OUsCZUb$?i2`E;1BA00NId^)WEzej&~RIkpQx2gN{ zhxy2-t6Q3%k9_s!;xCU5=N;(g`MlcuODn##?horLpAOUYqr>KtPlxsYf6*Tv)vGh- zF?E0bFdzAJbxZT}k+0rd{N>T%yaWCDwXf%oFDv{lb$|H9#mlFw!~aLr!*7c|S+CFS z>V9;1RIko_w~cz3k9_{>mgeUpUw!VU4;@x7pYDF1Kfb)~^sRM&*xd5z>hSJFhdqDD zr>o;{UOLQ2KAoRA-=pqNhe!R@E%oOkpTD{I%cH}&2i^RhKfa>w|FXJ2tgn2!I=ngP zu;&l?banjAONaT$r}H!C?d$$@c+_9rQhz@3`J0QsJUX0v(9Q4p<16d_FR%N<`pT!P z!<&N+d;XA5SI6JHbeNBPIzMyXq3%zINBz|;_2(m>zq$C!qriCi%?i)L-3Fe?Id0n~T3ZI-GmZ&F}f+ zYwG^5sQbhE%BQQtn}ZH}{*X^s$KSkkn2&rqKXV>i_ou_7{_2+c^O4WrT>RzH;oO66 ze$OA*)cwDs?horLpRNvX4m#}lLq1&{fAi8|KJw}O%z4MUKOG+RSGUxkk9_{-;xCU5 z=N@$Pd;a*^y8n09{b7CO)79b4L5Dqm$fv90Z(cggM?RgOIqy{Wr^BQE>X!QRkT%+=Fg@&vjo{_y4ZCKdi5Ox;nf$=&F}t(x~2Yn z>8^ZfA*b^q_K`@>&fynMPkyyrYR?D<1J9oFZ4>V9;1RIko_caD0P zk9_{>mgeUpUw!VU4;@x7pKgE8AKzGa{+_x&Y;O5|K7Sktgn2!I=ngPu;&l?banjAONaT$ zr}H!C`_=vF@TkAKrT%>6^EVfNd2~4UpqtnoqG4sQ-R?D<1JT^)b( z(qTUG>HN%jT-~1zkNT@y>d!|$e{=DdM~8C{y7@hSyrk}bZQUQ%S3X@G-W+t;^M`!8 zI{xOR!+hk^`I+-Bb$>cM>aT98KOg!0&Bb3H9nL-I=J)*Z(y6_^zwQs~E1#|oZw@-_ z`9nTk9e?xEVLtNd{LFdRx<4Hr^;fslpO1Y0=Hf4p4(A?p^LzgImUjN>2kQQ?zVhkn z@aCYyod!|$e{=DdM~8C{y7@iV zy}a)K!*zdHU-@)(cyrKU&w=vk>iCiCRzH;oO66e$OA@S@-|3x<9P1e7ZWkIq0zG5BYR;{LM>;`N*g9Gv^1_ z{ps+izq+OVeB|>t7k_zlIQO8N|0Q+z@2dFYb$?i2`E+%7bI@VWAM)wy_?wpw^N~;I zXU?6vKOG+RSGUxkk9_{-;xCU5=N@$PzjSJ^@2>b0b$?i2`E+%7bI@VWAM)wy_?wpw z^N~;IXU-2fFl}CRc+_9rQhz@3`J0QsJUX0v(9Q4p<9pip(@)m@VSVM()#1%ShdqDD zr>o;{UOLQ2KAoRAKlDIbfDVuPt6S>NM?QaZ@s~%3a}T=tJ%4;}-T$ZR{;FV(2 zpu?U&X!QRkT%+=Fg@&mZ4c_y6g-Kdi5O zx;nf$=&o;{UOLQ2KAoRAKfLZwhe!R@E%oOkpTD{I%cH}&2i^0W=Z_z# z`>*T%@b?!lpRNw?Igbu|{*X_H_4&xU9~~amt25t6L_N$$K7Vyf^Yf9fKKIjy4y%_> zx4-9)AFMmSs_qY)TRvSK-ks>M=MVXGb^Ogshxy2-^E2m1)&1%4sK2_U{(R)~Hy3|- zbU62*o8R-t57qsDrtS~xE1#|oZw@-_`9nTk9e?xEVLtNd{LJ~$b$>cM>aT98KOg!0 z&Bb3H9nL-I=J)*Z!*&0kt^33J%BQQtn}ZH}{*X^s$KSkkn2&rqKXcxr?oWqD{nah? z=OdrLx%kVY!?_3D{GLC4r0)N7b$?i2`E+%7bI@VWAM)wy_?wpw^N~;IXU==p{ps+i zzq+OVeB|>t7k_zlIQO8N-}A?h*8N{y_lNbBPgjRG2OakOA)l^}zj^5}ANh2C<~*V9 zPlreS)h+esBcH#y_{*cixd+|+oQX-BN!(^7)&KzdSmed(h4A`Qyjx{=ZQ7hxL_DSBEzT9rpYopRSI-dFe18 z`E-8fym#H74v+e)Tk6k8K7VuZmq&+l54!n1fBbme|21`gSYP>cb$D~oVb34(>FW5K zmk#rhPv>XOkE#39;Zc8eOa1xC=Wj0l^5}5xK{vnWx}T`~|6<)A)>l4V9o`&t*mIzK zx;p;mrNeyW)A^b6K6QUOJnFA*sXrh2{LRH*9v#j-=$_|1fBaP4|Cj3i@J|*mpRNw? zIgbu|{*X_H^|@c&j}DLO)tT?UQ4jNx&tKir{CwoA&;9hF!|LVJ?eF>Hr|ZtYT=$2~ zEuXFq?@n~s^M`!8I{xOR!+hk^`I+cM>aT98KOg!0&Bb3H9nL-I=J)(@UETk+ zb$?i2`E+%7bI@VWAM)wy_?wpw^N~;IXU-Gr{&aZMU)@rFKJxjSi@!WNoO{sC@A+e0 z_y3i;Kdi5Ox;nf$=&QX-BN!(^7)&KzdSmed(h4A z`Quend;MzNAJ$hsT^-&WblCHUe7ZXR=B2}Yt7k_zlIQO8N-}A@M)%}0H?horLpRNvX4m#}lLq1&{fAi8|KJw}O%=zHDKOG+R zSGUxkk9_{-;xCU5=N@$Pd;WNJ-Tyc0{;FV(2pu?U&?m;)d=enP-`~T*__6JyB`E+%7bI@VWf%56<_?wpw^N~;I zXU>P#{ps+izq+OVeB|>t7k_zlIQO7?p7Z?in!5jQ)&1dLC|*8Y9o};u9rpYopAPGD zQr(XZkLuN#Zx!`0ANl;%EzQqIzWUrxA3CgFKHdJFKYp?9{M&VZ*xd5z>hSJFhdqDD zr>o;{UOLQ2KAoRAA6ECL!=wJ{miqIN&);19<^<->v(@`pT!P z!<&N+d;XA5SI6JHbeNBPIzMxsT=%ELqyFla`ty;`-(39V(c#>KZvIzI?e*G<*Vp}F zedW{D;mtvZJ%7ljtK)B8I?P8tou4^RIWTQrba>QX-BN!(^7)&KzdSmed(h4A`QumG z_|xyz{b7CO)79b4L5Dqm$fv90Z(cggM?RgOIUjMLEkK7y{nah?=OdrLx%kVY!?_3D z{GLC4weJ7=x<9P1e7ZWkIq0zG5BYR;{LM>;`N*g9Gv_1g{&aZMU)@rFKJxjSi@!WN zoO{sC@A>1`>i)l9_lNbBPgjRG2OakOA)l^}zj^5}ANh2C<~+6TPlreS)h+esBcH#y z_{*cixd+|+udchluHqZ&{;FV(2pu?U&F}t(x~2Yn z2ia{LM>;`N*g9Gv}k~{&aZM zU)@rFKJxjSi@!WNoO{qc&w2j%&AR^|9&CSrf1`N$bai;od34zGhkQD$&*^nPIy|aZ zXTC>AJ1m>;5;?{b7CO)79b4L5Dqm$fv90 zZ(cggM?RgOInS*7)8SEnbxZyE$mee^{_^N>?m;)d=a1j1`@ga759=$Rt`2VwI_&vF zK3yGu^U`5H^6C7{c~;$@4v+e)Tk6k8K7VuZmq&+l54!n1fBbIU|Bvhbu)gx?>hR{E z!=69n)79}eFCFG1pU%&mXV?Ad@TkAKrT%>6^EVfNd2~4Upqtzq$C!qriC+Ak+ zs{6zG%BQQtn}ZH}{*X^s$KSkkn2&rqKXaZ__ou_7{_2+c^O4WrT>RzH;oO66e$OAj zU-$pBx<9P1e7ZWkIq0zG5BYR;{LM>;`N*g9Gv~Q=e>yzsuWqS7ANl;v#a|vB&OPYn z_gwdey8oZo{b7CO)79b4L5DpD%BQR2Z(cggM?RgOIUiT|r^BQE>X!QRkT% z+=K3U&hy6~*8SgH_lN(Wc=>d7c+Yus*z<>cI;_v*>wa{2RIko_9~bp7ANl;%EzQqI zzWUrxA3CgFKHdJFKmMrh{1hSJFhdqDDr>o;{UOLQ2KAoRA&#U{>;Zc8e zOa1xC=Wj0l^5}5xK{vnWj~nX#e_8j3^_5Rohc^cu_WU8Au8zNX=`bJpbbjW1LfxMZ zkNT@y>d!|$e{=DdM~8C{y7@hSym4x;zpDGg`pT!P!<&N+d;XA5SI6JHbeNBPIzMwh zvF=ZYNBz|;_2(m>zq$C!qr?m;)d=Z`>F}t(x~2Yn>8^ZfA_ zb?<+u`@?T8UOrtN-g6!u_WU8A4(s!@x*r`L)vGh#1yK+4kcx;p;mrNeyW)A^b6!n!{l9`#qZ)Sr)h{^sH@ zj}GS^bo1X(cmJ!3|5W#f^_5Rohc^cu_WU8Au8zNX=`bJpbbjW%sP0dPNBz|;_2(m> zzq$C!qrl4V9o`&t*z<>cx;p;mrNeyW)A^b6;sev>MTbZI z)h+esBcH#y_{*cixd+|+o7FdzAJ ze&&2e-JcGR`m0;&&qqFgbMco)hjS0Q`8|L9UETk!b$?i2`E+%7bI@VWAM)wy_?wpw z^N~;IXUcM>aT98KOg!0&Bb3H9nL-I=J#Cp4|V_ld$9ch)>l4V z9o`&t*mIzKx;p;mrNeyW)A^b66YKtTc+_9rQhz@3`J0QsJUX0v(EqXS{ZAF&R`K8K z{_uYkFP{#pe`f7Zhc7E$K3$zYS4Ta}N4|P>=KGYWhxy1?ug+ZNq{DpVr*1xd=&*Wz zbo=~s-FK?_|6ko7Hn)5_OxKSNn@>I+*8lS850C2Ane(|(5A%_)zq+OQ`N&sqF8=c9 zaNdD#o`0$PAE^2NqwWvuE1wS2^`pb)lTU~Bzasj>qk47b{M4w2`N-E_-O~Jg?8q?V|?|+eeRw_3u~u;m*%z=i$W<%c0lL zH`*_Up5HF{ba=Sl0dLPMx&NfuKhYHb{{gI*9uMR7;bHse@v#2SlzzDL^VxYs@xyZH z_4AGP%c1ADOFkVQu6MxO^J?xtS@uty`@?$a@i1N=9=4Aj59|MI>4!T%pPfe*KP-n{ zKi_D-9D07cSo^Kiv8G>^!RYVL9~r z`9}NY(DU0RpAHY#JK*hkJ@=m?`zOu)VZHQt7_Scx+eeRw^&e3B;m*%z=h4Lv%c0lL zH`*_Up5HF{ba=Sl0dLP6x&M^eKUwY%>!ruTczt--K6*T?|G?4@cYZ!Qk12jw4!wT9 z(SAAf{C3Hw!^8CsczfQ={in+Q$#Z{LFFhW{>%+tL(c@wL2bF%f^YhtxZ1KZ#==JlB z_RFE?w@W@995>L?&$gij9UiXlfwzB>?4CZqQ|JD$UV1#A=XT&>-yigNKKb_IVL9}8d3K&u z_QPF2->6>>y?(po)8XOz9(emF&F&fUJ5BBn>!ru@d2Run5T|eKbUk<%~yX4d1;rbqU`=`wAS@JtW?hosw$Mbn^2Od5qx%7BG`S#*rIrMmW zcAj4L!(Bh$s9z4fe!Jw;;oAZy?pgCYW9|>@rN{GmZU-JdHo5e8KKb_IVL9}8 zd3K&r_QPF2->6>>y?(po)8XOz9{8!Vcbfdpp5K{re|WY$r^oYoeq8ax$LBeH_3JsS z_~FjaXWyB{56hv~&o|mHhn~N_pB_BSPmgzxX|sEd{LY;F!*G6E> z?Zv}#=<)LGJiF|NyMDe=zZ`n~cFCv1!}UGz_D`4HbLMxJ+#l9UkLUB;4m^Bfa_RAW z^6kaLa_I5$>^!IJhr52hQNJ8|{dUQx!^8DG@b*ui-E-x4*4!V~OONOC+zvc^QgZ3> zeDdwZ!*b~H^6Wgf?1#I4zEQs%di{3Er^CbbJ@EF=kll0VcedOg)=Q7)^V|+Rd~$N> z@qF^_#lv#w@$&3Euk44re!fw^9D4nB$*04^^*!+R&zRlwG6E>?Zv}#=<)LGJiqLRyMDe=zZ`n~cFCv1!}UGz_Ro~v^X7Ms+#l9UkLUB;4m^Bn za_RAW^6kaLa_I5$?7X1thr52hQNJ8|{dUQx!^8DG@b=G~-Sg#l&fFi?OONOC+zvc^ zT5{>}eDdwZ!*b~H^6b2@?1#I4zEQs%di{3Er^CbbJ@EF=lHK#?cdpzY)=Q7)^V|+R ze0p-}@qF^_#lv#w@$&4vsO*Qke!fw^9D4nB$*04^^*!+R&zju}G6E>?Zv}#=<)LGytwR#yMDe=zZ`n~cFCv1!}UGzvt{q>`CTZ#^W^^Uf_YAl z=kxr`;)l=5bNcGnb7}FzouALXONt+sL$9B2v|kQAe|wDnspDVi;&F}oV zKdhG?&*!-vc=+7p(&PE$+lzwDnspD(+Y z%|5cl~^$emV5|?UGN2hwFRb?Vmrp zm&)%Vxj(Fz9?$2w9eDVn$Meaz7Z1y!$IG+xma-r2`uRrva_IHj zC7%ut*Z08Nzj$`9l;5Rue^@U)p3ieT@bFd1rN{Hhw-*n~p~uU!^VYH-?)v#g{c`B_ z+a;e457+m=+rLD1ubkgya(`GaJ)X~VJMi$;$)(5h$+s5|%b~~1v-7sHAMX13M*VW= z_1h(%4iDG&z}vrMcCV7(WpjU6FFl^mb35?xHOZyN^U1dt56hv)%d_+LvLEjH`9}S6 z==IwrpAHY#_rTk~RCceL-{o?DST8-E&vQHQ@U_XM$Meaz7Z1y!$IG+xjMEFVD`q z%YL})=Nt9Qq1SJhd^$W_-ve*|^4Yysepkx*pKw%c0kAmwY-rT;Bt4{|ec?c79jR{b9ZIcs|eVz{58ummbe2-(EZ{haNA_ z&U?##xa;Q|^~<5xZA+58swtdOV+ed-1Rw zdb~V4zgYIeT|eKbUk<%~yX4d1;rbqU`&Y^C_4B)W?hosw$Mbn^2OhpXx%7BG`S#*r zIrMmWc7Cbshr52hQNJ8|{dUQx!^8DG@b<5o-5cb0jocsBOONOC+zvc^M{?=$eDdwZ z!*b~H^6b2??1#I4zEQs%di{3Er^CbbJ@EFgmfaiXcg@@%)=Q7)^V|+Rd}ng$@qF^_ z#lv#w@$&4vzwC#*e!fw^9D4nB$*04^^*!*bXYU&M-8jE%<^J$Sc}|b#^Zc&jhd-C+ z^wqED!QzKIKc9UM6hAD7UO(SxzZ`o0`hI%wFh4!sJ=V{AMX13M*VW=_1h(%4iDG&z}vr8c5j;Bb#i}LFFl^mb35?x zJ;|lV^U1dt56hv)%d_+0vLEjH`9}S6==IwrpAHY#_rTk~c6M);-*t0;ST8-E&vQHQ z@V&{U$Meaz7Z1y!$IG+xk+L7|`uRrva_IHjC7%ut*Z08NzfN{dOV-! zcHrSJB$pn~C*NK?EQcO1&(1HG{czXMH|m!|uiq~Dba=SF2j2emvU|(?Zjk%Kdg<|e zp4)+kznENlJfD1f@vt0vygWO)^1ET~59_7J z^LcIu9{y5t>G6E>?Zv}#=<)LGe5~w;yMDe=zZ`n~cFCv1!}UGz_HU5gTjzJ9+#l9U zkLUB;4m^Bca_RAW^6kaLa_I5$?EGrk4|n~1qkcK``t6cWhllHX;O*ZqySK^j#<@SN zmmbgOxgB`;{^Zi*`Q+P+hvm@Y<=Odo*$;R9e4~Cj^!n|RPlt!=d*C;+H^1BEcaz*7 z-Zsza@f(-t4-`NAV4l-gzn&+HAMX5o_B~PjupD~*eBR4mJS>O4^7iS$!~F8_6Yi1S z+vj)F+#j}^9=}QSp4)+kA99zH&nMqrepn7YUca4Bm3+AC=Nt9Qq1SJhd^$W_-ve*| zrrEtiemBegVZHQtKF{sI!w)=AFF!1Y9$mf3B_Hnk`9}S6==IwrpAHY# z_rTk~Wp?k9->q_gST8-E&vQHQuS-SWFl?hosw$Mbn^2Oj>ayOexB`S$X|a_I5;?fhoRhr52hQNJ8|{dUQx z!^8DG@b+($-Mi;^+uR@4OONOC+zveaxVw~mKKb_Y!*b~H`tAHy$%ngszEQs%di{3E zr^CbbJ@DIR?{@j!1J3>7cX+SD^T~Ol@bHtK`%ON-p6?VM?)-fAeY^N!IrQ75pKr8Z z4n2Q;KRtMupC0f1x6kf(=6Cz-gD31RJf9qQ;)kE&^ED+)ba+?}J>LEuvin{6-68jZC-fGcPmUe@@Y8&Llg}sLUVc~(Jzl?^-z)iW z*UvZVmqV}LF8OqLxV{J8{vES>&;0I~`@j==3(qIV4u1F3B85qlVb-z{4Af}UKJbLz!t=?ogCF+&L67H?Z!aE}Lywnd z=MT$1xa;Q|^~<5xZgUGnMhaD5NF{kvuNzWLoP_kkz$7M@Rz9sKY&`TQoIPrkkU zupD~4emj3s^5L$ZZ`3b`UcX)P>F{uU54`=mXZL&ZyL;{fPv|W?pBy{*;cxNzO+KG| zd--8G^mzSt{@?pE__XyAD z`Gh-#hrgTL3HtDS^6ixm%b~~1v-1~aKiu{6jr!%#>$gij9Ugw$dzAaVE4x47PT^s_ z-uIp1`8>A+4}UMY6ZEBDzPcK)*Lhr52hQNJ8|{dUQx!^3ZTk8-~~v-^YY z6du;=ecu(H&vQHQ@b{BDL0|gi+shBj(Sw&~=da3sxa;Q|^~<5xZ`qD4oUVd1P9=tp|e_i&&T|eKbUk<%~yX4d1;kUg< zx!>N|{b6?s59{^5dxhuo+zveK`(uK>^vk!GAC{vBFVD{3l>Knm&o}CqL$BX1`E+>r zZSPU;w@-F|B)@xyhxK}2JfG)w;9=h%6ZEBDzPcK){Phr52hQNJ8|{dUQx z!^3ZTkG#MA@6PUz=69d)uwHsRpXYYq;U6b=g1+?2x0fH5qX#d~&fk^&aM#Z_>X$>W z-!A!dc=&Dak@vTM-|YUFJB5e!(!V=ApXYYq;h!XTg1+?2x0fH5qX#d~&fk~)aM#Z_ z>X$>W-!A!dc=&DaQSSGi?Ebhrg@^Te-+jaLd2R{6pCf zcl~^$emV5|?UGN2hu`)ddH+vj?#vJ(%>p|8Asdhjs6JiPb$RCXVl-}mOeu-)`{7_Scx+eeRw_5Zo_!=0bc z&KHUwmP4F{v91KyrbXZK!ruTczt--K6*T?|1YHJN-G}G*{kcD^mmUw}_2FUr=<%@rzm|Tu^YhvH zQt`ub==JlB_RFE?w@W@99;GrzhdV!?ov#!> zEQek{-)O%adVag))8XNI2fRH8X7@4q{YdT)>!ruTczt--K6*T?|6ipa?)-dqzFPdS z9D4nHqy2K|`R$TVhllGO@b(;(-N)wlqq#qA+55JgPdOV+ed-1Rw zdb~V4|5x_IT|eKbUk<%~yX4d1;rbqU`w!3Vlk)q?+#l9UkLUB;4m|u)a_RAW^6kaL za_I5$?0l>2hr52hQNJ8|{dUQx!^8DG@b({(-6!YwQ@KB^mmbgOxgB`;Kgp%X^U1dt z56hv)%d>N$NhbQgKRn#^^NsrD(95?=J{=ye?}4}f$lT|Y{C+z3hxO9q`8>A+5C1p0 z^msn`_TphV^muu8PMrJW;jW)=)Gvo#zFqR^@Nj((y!}VzKBwk)zuX_zOONOC+zvea za&qbMeDdwZ!*b~H^6Z=>_s7FsKi{Zd4!wN4A7|%I z2j>2;-Sl`q&)o?RznNTmJfD1f@vt0vygWOn%KhMJlys3jr!%#%ePBD9UiXlfw$lH z$GN%F!MQ)ImmbgOxgB`;t>n_<`Q+P+hvm@Y<=Ht+?vIDNe!fw^9D4b7$*04^^*!+R z`~EmDcRD2ZhxO9q`8>A+5BvV0$Meaz7Z1y!$IG*G+T0%xcl~^$emV5=?UGN2hwFRb z?f3n0e(ry0?hosw$Mbn^2OjqQL67H?Z!aE}Lywnd=XAM09`5@2M*VW=<=Z8n4iDG& zz}xTp~jv^NsrD(95?=J{=ye z?}4}9_s4~~|KYhmtd}0o=eZqt*!KrLo=?8LcvucSUY?yZ*pKw%b}NVmwY-r zT;Bt4zweKWa{nW8e^@U)p3ieT@UZU>dOV+ed-1Rwdb~V4XUzTaaM#Z_>X$<=-!A!d zc(}d?-hSV87w7&*=Kio=dOV-!cHm*(f%JGj`S#*rIrMmWcFvUhF{uU54`U=-yfIe{zv8h@FjUpkLUB;cOD-0{Xvh1^~{p{;o;8DXWz`l56hvK&o|mH zhn~N_pB_BSPmlNhzCSL@osZ7_VY}(^e4e`#9`^k~kLQzbFCLadkC$iXthqlP?)v#g z{c`B#+a;e457+m=+wc41^4$NJ+#l9UkLUB;4m|ApgC5T(-(EZ{haNA_&e?K*Jlys3 zjr!%#%ePBD9UiXlfw$lH#}&E%vAI92mmbgOxgB`e_Xj6>>y?ndm)8XOz9(enGe_WIMpP2i@dg<|ep4)+keSgs7`Q+P+hvm@Y<=HuR z?vIDNe!fw^9D4b7$*04^^*!+R`~J8#_dhB3hxO9q`8>A+5BvV0$Meaz7Z1y!$IG*G zp4=Y~cl~^$emV5=?UGN2hwFRb?e|@GUG9H!?hosw$Mbn^2OjnvNRQ`}Z!aE}Lywnd z=e)T;9`5@2M*VW=<=Z8n4iDG&!26!_{c%I?e@gBTU!Ujncs|d4=iy=BAM|)w&-}R` z9`5{n_RUxPupD~%e53tx==tmW>A}PN^my;@`{Tyk`PAGWwwoT$=eaxKVc#G0cs}{| z;$b=TczJd%ko)7|uAgtzFNa>fUGnMhaD5NF{k}hL%KcBv{b9ZIcs|eVz{9>j=<$5= z?Zv}#=<)LGTrl^?!(Bh$s9z4fe7oe+;o8^S+?@NLp8Lak>G6D?+kuCDf6(Lk zOg+#e5j{d}W-IrQ@Fl23<+>wDns_x*8e?tf&;9Xm*UvZVmqRb#F8OqLxV{J8e&2O>=KkmA{;*zpJfG)w z;9=i^^msn`_TphV^muu8E|L4=;jW)=)Gvo#zFqR^@Nj((yze>RAD_$p&&&PcyYieK z&*!=CJUs0CgB}m-St|F#!=0bcz9owvmP0R}Z?saJ$RU(9`F5qf83orpP&1~ zcGKheJa;EN?E8Zr&nMqrJS>MEFVD`UbALSC_4AGT<A+5BvV0$Meaz7Z1y!$IG*G`P?55cl~^$emV5=?UGN2hwFRb?f3oh z#oYgr+#l9UkLUB;4m|ApgC5T(-(EZ{haNA_&J}WhJlys3jr!%#%ePBD9UiXlfw$lH z$Cq;dOLKo%FFl^mb35>`?+6i1uAgtzFNa>fUGnMhaD5NF z{l4q&&;2jY{b9ZIcs|eVz{9=+>G6E>?Zv}#=<)LGTsim0!(Bh$s9z4fe7oe+;oa zJ$RU(9`F5qe>{{sUzz*EcGKheJa;EN?E8Zr&nMqrJS>MEFVD`^a(_JB_4AGT<-yigNKKb_IVL9}8d3LUu`{UuRpKsJJ zhhDy2^6Bt!eGk0-zCXU4`(K;;!+PoQe4g8Zhkbw037yMDe= zzZ`n`cFCv1!}UGz_WS<$O74GM?hosw$Mbn^2OjqQL67H?Z!aE}Lywnd=i0eH9`5@2 zM*VW=<=Z8n4iDG&z}xTp~jv z^NsrD(95?=J{=ye?}4}9_s3Ur{~L0DST8-E&vQHQuRA5Z4~H|75D6M0UL=kwfm9v=4nL63*^Y>@lm;m*%z z-}=Q5%b}OgH`*_Up1;1I9z4uXkN5t*Kc32+Z_fQ;yXo`?+yO_lNb;~jv^NsrD(95?=J{=ye?}4}9_s2JL|GRR3ST8-E&vQHQut!^8DG@V@7Ke|#tRzdQGbzn$mwcs|d4 z=iy=BAM|)w&(^sg9`5{n_H9-CupD~%e53tx==tmW>A}PN^my;@`{TR0^F6sgY&Sig z&vSRe!@fW0@qF^_#lv#w@$&54Cilm~T|eKbUk<%|yX4d1;rbqU`+a|WFZaJU_lNb; z4|n~1qkcK`^6ipOhllHX;O+PQ@%`NY^SM8) zmmbgOxgB`e_XjF!%pr z?hosw$Mbn^2OjqQL67H?Z!aE}Lywnd=MK3)9`5@2M*VW=<=Z8n4iDG&z}xTp<43vw zmvVntFFl^mb35>`?+ zB=^5R_lNb;~jv^NsrD(95?=J{=ye?}7I{ z=lkR5x&MQ?Km4;ir^oYo?mG_;`~INE!+Lhh{qS(-=d*9u;)mtX%jX;ImqX8A-%k%7 z=BLMdf8QU!$eka`{b9T5@qC`U6CU>cL67H?Z!aE}Lywnd=kB>b9`5@2M*VW=<=Z8n z4iDG&z}xTpI`@Ax_lNb;`?+A+5BvV0$Meaz7Z1y!$IG*G&)gpmcl~^$ zemV5=?UGN2hwFRb?f3ohyWIb=+#l9UkLUB;4m|ApgC5T(-(EZ{haNA_&b@MfJlys3 zjr!%#%ePBD9UiXlfw$lH$M19hujc-+UV1#A=XT&>-yigNKKb_IVL9}8d3Nrd`{UuR zpKsJJhhDy2^6Bt!eGk0-zU%&w`#+xh!+PoQe4g8ZhkXaqIrn`bzfb1=u-)`{7_Scx+eeRw^}i?g z!^54Q&(4n*KP-n{KHq4+9D07cSYv zet5X^^V#`{;)mtX%jX;ImqX8QmwY-rT%+tL(c@wL zAISaiaOdZ<^V7u-%b}OgH`*_Up5HF{ba=Sl0dLPga`%_>`?cI3)=Q6v@%r$vee`%( z{|9qFJly&D?A)*TVL9~j`9}NY(DU0RpAHY#JK*j4XYT$=e!rgk!+PoQFkT-XwvQeU z>;F*hhle{qpPip6epn8@e7@0sIrRK?$*04^^$vJ@{*}AGn%{5a{;*zpJdD?ehwY=s z!}>p*`{Civ&u8aniyxLlFQ0F;Uk*LLUGnMhaJ>WGo`2`=ujTifxj(Fz9uMR7;bHse z@v#1n`?+F{uU54`=pKPJxp@8|xo zUV1#A=XT&>-yigNKKb_IVL9}8d3GLF_QPF2->6>>y?(po)8XOz9(enGe@v47Kgj)I zz4UlK&+Wj&zCY;keDdwZ!*b~H^6Wgk?1#I4zEQs%di{3Er^CbbJ@EGX{+KlTf0+Bj zdg<|ep4)+keSgs7`Q+P+hvm@Y<=J^e*$;R9e4~Cj^!n|RPlt!=d*JQ&{V`eg|0wr| z_0r?{JhuZ6`~INE^U1dt56hv)%d_*yvLEjH`9}S6==IwrpAHY#_rTllyKeIA|8edQ z>!ru@d2RA}PN^my;@`(vu? z|7q?I+f9$>^W2^Au5>K z?iup?>)apKOONOC+zveK`-2|OC*NK?EQcO1&(4#}ez@!B8}-Yf*Ke16Iy_w818@Ii z**#-^f0O&edg<|ep4)+keSgs7`Q+P+hvm@Y<=J^k*$;R9e4~Cj^!n|RPlt!=d*JP# zJiBMg?{9N|ST8-E&vQHQu*pKomqX8A-%k%7 z=BLMd|EaQj*8KiH_lNDK$MbpaPI&m(G6D?+kuBqOfEg1Prki)SPng2o}K5E{czXMH|m!| zuiq~Dba=SF2j2eavwN=m{w4Q^_0r?{JhuZ6pOjpBJfD1f@vt0vygWP4E&JiFpKsJJ zhhD#3^6Bt!eGk0-Gi3MN`Tc9|59_7J^LcIu9zHp_^msn`_TphV^muu8o>%t6T|eKb zUk<%~yX4d1;rbqU`)ADVdGh^#5hhr52h zQNJ8|{dUQx!^8DG@b=G?-Sg)6@3}v$mmbgOxgB`;)a26R`Q+P+hvm@Y<=J^b*$;R9 ze4~Cj^!n|RPlt!=d*Elz-dXZHe}4aw`@{3)IX#}w^V5nSK0VLrt6$GW#SeFWKKm{# zepn8@e!kIuIrRMX{q*2retNun%$nT`G6D?+kuDAOfEg1Prki)SPng2 zo}HJJ{czXMH|m!|uiq~Dba=SF2j2eKvwNZZ{yX=F_0r?{JhuZ6pOsvCJfD1f@vt0v zygWNEE&JiFpKsJJhhD#3^6Bt!eGk0-b7c3z`F$?;hxO9q`8>A+51*Y}dOV+ed-1Rw zdb~V4FDv`uuAgtzFNa>gUGnMhaD5NF{c~pbBKdtj_lNb;F{uU54`A+51*f0dOV+e zd-1Rwdb~V4uPXcDuAgtzFNa>gUGnMhaD5NF{qtt`68Zg4?hosw$Mbn^2Ohp4x%7BG z`S#*rIrMmWc3xff!(Bh$s9z4fe!Jw;;okLUCJ!s3T7 z%5(ba*K=+0!=0bczH5pfmP4aJ$RU(9`7CtWcSkfeL44s?WV``dG1bl z_~PW!wDnsUog9u$?q$HyO+!FYq>wHmmbgO zxgB`;vgFd^`Q+P+hvm@Y<=J^-*$;R9e4~Cj^!n|RPlt!=d*JO~B)gZ-@9Vigtd}0o z=eZqt`10h^wDnsUo^W{$nP7uKdhG? z&*!-vc=(Fs(&PE$+lzLyI0EZ|8jp= zFFl^mb35?xRmr8t^U1dt56hv)%d_*=vLEjH`9}S6==IwrpAHY#_rTk~M0T&7-?wsq zST8-E&vQHQ@YTts$Meaz7Z1y!$IG+xwz41Y`uRrva_IHjC7%ut*Z06LnY~Npch&q( zH04D9_Xn?%=k$0!&#x(d_}VX$>W-!A!dc(}d? z-u`8>d-eQIlKaDY>G6D?+kuC#PcA*4Prki)SPng2o}G7<{czXMH|m!|uiq~Dba=SF z2j2c=vwMyFPMZ6}dg<|ep4)+kZ%8gZo=?8LcvucSUY?zwEBoQDpKsJJhhD#3^6Bt! zeGk0-%VqbP`JF8HhxO9q`8>A+58s$vdOV+ed-1Rwdb~V4?=Jh{uAgtzFNa>gUGnMh zaD5NF{mW&(&PC&w*wE~oLqW5pL~1qupD~4JUj0#`{AyiZ`3b`UcX)P z>F{uU54`;=X7@Vzoig`__0r?{JhuZ6-;!K*aUq+#l9UkLUB;4m^BYa_RAW^6kaLa_I5$?EGTc4|n~1qkcK` z`t6cWhllHX;8)4sRr9++ey7R(;q~*J9?$3b?Zpq@k>~W)ujjtvhdV!?eP1emSPs2@ zzR`X;^!)Yx^x$EBdc1qAmfaiXciP+^wwoT$=eaxK;X9K{kLQzbFCLadkC$iX{bfJg z_4AGT<dOV-!cHrU9C6^x0C*NK?EQcO1&&~(S zez@!B8}-Yf*Ke16Iy_w818@JD*}X}AXUP3wz4UlK&+Wj&cPEz~&nMqrJS>MEFVD`0 z%6_=(=Nt9Qq1SJhd^$W_-ve*|TG_p6erL@6VZHQtKF{sI!}lbY9?vJ=UOX&^9xu<% zhs%Dr>*pKw%c0kAmwY-rT;Bt4|JvETS$=2A{b9ZIcs|eVz{B?@mmbe2-(EZ{haNA_ z&PU3Axa;Q|^~<5xZky?K6T&i!G%^msnc?ZCsIPcA*4Prki)SPng2 zo}G`D{czXMH|m!|uiq~Dba=SF2j2d5vwMsD&XW7Xdg<|ep4)+kzmQydJfD1f@vt0v zygWOG6E>?Zv}# z=<)LG{7Tsmcl~^$emV5|?UGN2hwFRb*U#P!^1F3@XUqNJt@4~6&*%AMEFVD`$%YL})=Nt9Qq1SJhd^$W_-ve*|M%le>e&@*jVZHQtKF{sI!w)2v z9?vJ=UOX&^9xu<%C(3@f>*pKw%c0kAmwY-rT;Bt4|Hj$9U4G}x{b9ZIcs|eVz{3wF zmmbe2-(EZ{haNA_&L_)$xa;Q|^~<5xZMEFVD`W%YL})=Nt9Qq1SJhd^$W_-ve*|X4$=Ce&@;kVZHQtKF{sI z!;d7F9?vJ=UOX&^9xu<%XUcxK>*pKw%c0kAmwY-rT;Bt4|K{1fQ-0^o{b9ZIcs|eV zz{8Iwmmbe2-(EZ{haNA_&S%Shxa;Q|^~<5xZou&%l%=!^msnc z?ZCrdPA)y3Prki)SPng2o}FJS`{AyiZ`3b`UcX)P>F{uU54`=7_0r?{ zJhuZ6emhrgQV^wqEDo5c@zem?uYQT(tRdi{K({c`B}>-*`!!~FDk_t+-8chB#F zxj$?-J)X~Vcf!MuCzl@2C*NK?EQcO1&(3d^{czXMH|m!|uiq~Dba=SF2j2c|v-=(S zT`2d5_0r?{JhuZ6KapH|JfD1f@vt0vygWOE59_7J^LcIu9)2>p^msn`_TphV^muu8ey8k*yMDe=zZ`n~cFCv1!}UGz_HUov z@67Kaxj(Fz9?$2w9eDVu*UvZVmqV}LF8OqLxV{J8 z{++UWulz2a`@?$a@qC`!frp<>E&(&PC&w*wD*urYcf}9Oq37rG9(Lkk zIrNpcPY)jEmxuR0@5%0u=XdGcAGVtw599UWVf*Ouu>PNxez^1V+4+a!hvm@g=Ns*p zL(gxQd^$W_?|`@Gz1jVV{4SIG!+PoQFkT-XwvQeU>;GBlhdV!?oqsHTSPs2@zR`X; z^!#?or^Cbb4tRUsm))Pt@3Of+td|}Semdg<{nULPK|j~)-}|3&GCJ3pVDe=dGl4!wT9(SAAf z{C3Hw!^8CsczZsO-Jj0y^0_~(mmUw}_2FUr=<%@rUzUEj^YhvHm*R)z(Cg&(6OWKP-n{Ki_D-9D07c zy&uc(f%#n}_lFP2b9y|V=f5j{`1g5EU;TRiRs3-0=d$gij9UiXlfw%t?*?n++SIhljz4UlK&+Wj&e@reto=?8LcvucSUY?!LmHlwn z&o}CqL$BX1`E+==z6aj^PiFTa`CUEthxO9q`8>A+5C18-^msn`_TphV^muu8K4133 zT|eKbUk<%~yX4d1;rbqU`#+W4hvs*U+#l9UkLUB;4m|wl*pKw%c0kAmwY-rT;Bt4|7WuMi2Sad`@?$a@qC`!frtN=TzWj8e0%Y*9D2Mw zJO5Mm!(Bh$s9z4fe!Jw;;o6z_-ACqko!lSROONOC+zvea_vF&!`Q+P+hvm@Y z<=Oe)vLEjH`9}S6==IwrpAHY#_rTk~e|8_0-*t0;ST8-E&vQHQ@IR7EkLQzbFCLad zkC$iX%Vj^@_4AGT<JGu0D zKKb_IVL9}8d3L^5_QPF2->6>>y?(po)8XOz9(el?&hF##yFu;`>!ru@d2R52_?i2I7aqbW6rN{GmZU-KI zF}d`3KKb_IVL9}8d3OG!@n%~WH ze^@U)p3ieT@bJsYrN{Hhw-*n~p~uU!bCTR24|n~1qkcK`^6ipOhllHX;E&E-e1DuC zyhZL0pO)wJcs|cxDSr6X(o_9WsaOdZH$Ge~J zk27+oEpva^ZhAbQ=kA1uUrR1Mo=?8LcvucSUY?zk=l*!O>*pKw%b}NVmwY-rT;Bt4 zzweJTbEmCxe^@U)p3ieT@bK%&rN{Hhw-*n~p~uU!bBf#_4|n~1qkcK`^6ipOhllHX z;O+PQaaQiMb?y)ArN{GmZU-KIBf0c=KKb_IVL9}8d3H{j`{UuRpKsJJhhDy2^6Bt! zeGk0-zCX^+owmvSVZHQtKF{sI!*3>+9?vJ=UOX&^9xu<%sd9fj-1YN~`sL8ew@W@9 z9!ru@d2Rf zUGnMhaD5NF{k}iW%bm8*{b9ZIcs|eVz{9>j=<$5=?Zv}#=<)LGoHqBz!(Bh$s9z4f ze7oe+;o8^SoS*yeko&`W>G6D?+kuCDf6(LkO(+#e5j{d}W- zIrQ@Fl23<+>wDmR&-wniDEHqf_lGacb9y|V=f3msul4|+VGe0%Y*9D2MwJ7>!M z@o?AAH|m!|FW)Ztba=SF2i|_)AD86*yX5|`UV1#A=XT&>-yigNKKb_IVL9}8d3Mg6 z`{UuRpKsJJhhDy2^6Bt!eGk0-zCSL_{ddj%VZHQtKF{sI!@fW0@qF^_#lv#w@$&4P zCHKd}T|eKbUk<%|yX4d1;rbqU`+a|0mizCP`@?$a@qC`!frov6(Bt{!+lzt!^8DG@b>%uxIFjYJ@<$8(&PC&w*wFR{-DS6$+s5|%b~~1 zvvaoG9}jo^e4~Cj^z!YJPlt!=d*JQ&{c%O^|Bl=r)=Q7)^V|+R?E8Zr&nMqrJS>ME zFVD`|bALSC_4AGT<F{uU54`=p>#olI-)y>ow9FFl^mb35>` z?+fUGnMhaD5NF{k}hL%>Cb;`@?$a@qC`! zfrov6(Bt{!+lzt!^8DG@b>%uxGDGFH}{A2(&PC& zw*wFR{-DS6$+s5|%b~~1vva}R9}jo^e4~Cj^z!YJPlt!=d*JQ&{c&^d|DN0*)=Q7) z^V|+R?E8Zr&nMqrJS>MEFVD_}a(_JB_4AGT<dOV+ed-1Rwdb~V47ta0haM#Z_>X$<=-!A!dc(}d?-hSU7x90xu%l%=! z^msnc?ZCsnKj`s%^6kaLa_I5$>|7-G$HQGe->6>>y?ndm)8XOz9(enG*WH%;zd!ef z_0r?{JhuZ6`wpbX^U1dt56hv)%d>OQ+#e5j{d}W-IrQ@Fl23<+>wDmR&-wniBlrJ6 z?hoId=k$0!&wc0NVc#G0cv#QkxgQ?x{CxH;R{XFWdii{#{c`B}>-*`!!~FDk@9+EL z&fNKfxj$?-J)X~Vcf!NIKj`s%^6kaLa_I5$>|7%E$HQGe->6>>y?ndm)8XOz9(enG zf83S(e<=5d_0r?{JhuZ6`~INE^U1dt56k&~gxXc`##Qw-a7sXwR6wM=J4Cu{Dd|)& zC{aR??(XjH?kw$;8Kj`s%^7X~Ta_I5$^jtLi$HU!x zzR`R+^z!wRPlt!=J@EQ_f83t^?~?t)=F;Q&oa=#yy+7#jeDd|h!*b~H^7LFR`^UrG ze7@0qIrQ@Nl23<+>pk%Ldw<-K{qLIn!{*ZC`JC&4hrK`O@qF_2#lv#w@$&RsJp0GP z-F&{$d^z;;^^#AAhwDA?`g?!enf>pU{ln(clGf6(LkA7U~ zkB7VYe53ht=;iArpAHY#d*Jo=uDd(?-#z<>&85flIoAUZdk50v`Q+=1hvm@Y<>|Rp z_K%0V`Fx}Ka_HsjC7%ut*L&c-=e$4e&HlfT{loXTRU?$7@B$o^q->G6Ed^}xg4AM|)W`TF8v zIrMmWdM=m!T-)O!Zdii?Er^Cbb9(et|KOV^b_ssrbbLsJX&h@~<-XHXMKKc6M zVL9}8d3r9N{o~hCcs}|1 z;$b=TczJrRkp1J~Za&{=z8re_dda85!}T6`{k=aP%KrDx{$X?J@qEtpz{B1j^msn` z`r=_Z^muuCu9*Gf;chTRU9?t&1l>Niz(&PD@>w$;8Kj`s% z^7X~Ta_I5$^js{@?@00z*=F;Q&oa=#yy+7#j zeDd|h!*b~H^7MRP_K%0V`Fx}Ka_HsjC7%ut*L&dg_x|`w_P=lT51UJm=X0(H9`^pA z$Meb87Z1y!$IH`m+fCn)$ISv**|P9J)Y0G9(dS0 zkRHz`Utc^dhaN9a&-Z8lc(|L-H<~YpUcO%P>F{v92i|+m`{V1`|9;s&{I#6Z z=iy=R4|+Uo&Z^lT9`5{n`mIv@upD~%e53wy==tmY%)!I_^mzC8{&+Nd-aq??^`^)3 zIkzV~?EOKH=aa859+pFom#61y**_lc=JSo_%b}OAmwY-rTe)XY?&kB2=F6d%ua|r}JY4UA*Wdf&@$CP=>>oCl z9?$1o4?OJsL67H?uP+{!LywoI=Nj2R9`5Gzjpoatm#>$6Iy_wOf!E*rFoc| z>>oCl9?$1o4?OJsL67H?uP+{!LywoI=Q`Ox9`5Gzjpoatm#>$6Iy_wOf!E*r>Wst=aa859+pFom#61?**_lc=JSo_%b}OAmwY-rTdpvUvc*B1}Vp~uV9^F!G`9`5Gzjpoatm#>$6Iy_wO zf!E*r~jH=NrwJLoZ)1`E+== z-UF|{_s5Ib|Iyh$Y%V>X&$%9W*!zPX&nI7BJS>MEFHg^pWdC@$o6k3zFNa>fUh?Vi zaJ>g!fA5c%vj1bUf7o1lJfCwt@UZs>J)Td#zIa#;Jzk!kAI<*pa5tZCG+z$Ae7)q; z;o*7@y#C%FFK7S9X8*9c^msn!df;L24|+VGe0}k-9D2MwJwKNHT-)O!Zdii?E zr^Cbb9(et|KVHfHkIVjHbLsJX&h@~<-XHXMKKc6MVL9}8d3t_4`^UrGe7@0qIrQ@N zl23<+>pk%Ldw;x|{U4wG!{*ZC`JC&4hrK`O@qF_2#lv#w@$&RsKl{hS-F&{$d^z;; z^^#AAhwDA?`g?!8mi?cQ{ln(cw$;81L^U6^7X~Ta_I5$^xQD}$HU!xzR`R+ z^z!wRPlt!=J@9X2-*4voqkK=w{^1|yoE{JJZ=Cz%;f-=mkLNRI+v11i(DU=@w@vZG za_IT_^wJX#%b~Ble&*m|etCHJ`DXU}<9tuf{$ai8@i5*zJggr*9yb3I*&iP6{Cs+D zSNyOXdii{#{&MK~^^#AAhwC2j`g|*U|4F{5WdE?a^mrI=9v;?@9uJ$pN%n__J3pVE z+ZR79hh9G4sJ|R~e!b+=;o-UmyguK~-hZ0!so6hlE(>qsPPMZ<_t#;m*&e z=O>FFmP0R}Z`5B7J-=S^>F{vf174rEviG0mds_Ann@f*}@#f)S{pj(q`I}{bc)0WP z>G`SRhvm@A=Nt8xL(i|5d^$W__kh>uJK6is^F2NLhs~wO!+7)XuzvJ-*!<12KRn#| z`SjeO_+dHp^7%&n<{$X?J@i5*zJggr*9yWiA><wF0dda85!*vgMeZH5y|1#e*vwzrJdOVCb4-e}{kB80QGW)~B zou5z7PZvKdhh9G4sJ|R~e!b+=;o-UmyguL0-hY+vS=m2qE(>qsPPMZF{vf174pWWbePu_w4K+HkTd`G6Edz4P#}_Xj;5 zHfN{o4-a>KKK(vd{IDE)`Fx}Pa_IT%{mj9`{PcMD_x|`@_Iz&k59>{j=W}jPc-Z@c z9?vIVUpy>_9xqSNowI*D+|B13&6h(jUoZJ|c(~pJufO-l@3a5&vVYiIdOV+VJ@Byi z2R)uozP@-^4n1C;p1WlKc(|L-H<~YpUcO%P>F{v92VQ^gk3VGp=V$-0x%7BG=X&5_ z?+VRPy6e9ra2 z!`>hCcs}|1;$b=TczJs6mi^=5Za&{=z8re_dda85!}T6`{k=c_l>J|r{ln(c&F34J)Td#zIa#;Jzk!kU(EjTa5tZCG+z$Ae7)q;;o*7@y#C&Gf6M+a&HiC? z>G6Ed^}xg4f%JGj`TF8vIrMmWdhU_^T-)O!Zdii?Er^Cbb9(eCL?~i|E|CeR| z@ZWPzkLPplorj0LKj`tWIeTS)c)0WP>9=R`!*b~5^Nsq;q35snGY1dz)8pOW`{SS4 z^X1t;tT#QL&$&I}Veb!mJfD1h@vt0vygWVk&i?UmH=l1bUk<%|z2wv3;d&3e{@x$| z%Kop&{$X?J@qEtpz{B1j^msn``r=_Z^muuCekuFM!`*zo(R?}d^7WEWhllGu@cMgy z{5$)lGf6(LkA6q#kB7VYe53ht=;iArpAHY#d*Jo= z{`gP!e^vGmn@f-9bFK#-_Wq#9^U2p256hv)%hPk;>>m$z^Z7>e<F{v9 z2VQ^gk4e)1+Uy@TmmbgOTn{|#{XviCldmrxmP3!1r|15qKitjd8_kzPZ@ymg>F{v9 z2VQ^gk4e-2y6hh|mmbgOTn{|#{XviCldmrxmP3!1r{@8sKitjd8_kzPZ@ymg>F{v9 z2VQ^gy2;Z2`s^PzmmbgOTn{|#9Y~MoldmrxmP3!1r{{sCKitjd8_kzPZ@ymg>F{v9 z2i|+m`(ujqzajgFC(k)Op3k{=9v=4opvS}J99-tZou5y?gNh%PLvKFcsJ|R~{(3)i z@Gw6;-u=Bl-j@D1X8*9>^msn!_JoJMKj`s%^7X~Ta_I5$^gN{Whr9WFqxo{^&DTpl z9UiXt!0Ye*F=hJSl>Niz(&PD@>w$;8Kj`s%^7X~Ta_I5$^gOimhr9WFqxo{^&DTpl z9UiXt!0Ye*F;)8Coc+V*(&PD@>w$;8Kj`s%^7X~Ta_I5$^gOKehr9WFqxo{^&DTpl z9UiXt!0Ye*F?IUilKsQx(&PD@>w$;8Kj`s%^7X~Ta_I5$^gO)uhr9WFqxo{^&DTpl z9UiXt!0Ye*@%Hq;HT#FnrN{F**8>lGf6(Lk3Kxy4|ntVM)T#+o3EFA zIy_wOf!E*rW1958E&GSfrN{F**8>lGf6(Lk3L-74|ntVM)T#+o3EFA zIy_wOf!BYM^nORaw`c#bx%7BG=X&5_?+oj%{YvVVBGoYUj^oO|ctVeb!mJZ#Q!Wj@^b`Sd%s_+dHp=JSpE z%c1A5_cI3%^V8$q|840#L%w%s|FGWlcs}R$gonL9=<$5=^~J+-=<)LOJihdYyZL;h z`EuyZ*GoPf9px|B&zSE$**|P9J)Y0G9(dUMgC5T(Utc^dhaN9a&l5_2xSP*6 znlFdme7)q;;o*7@y#7w$+4OD;X0Prkl*SPng2o}MR`{%|*+ zZ!}*Hz4>~{r^Cbb9(etyPVaZ+dtdesn@f-9bFK#-K0LYfcs}|1;$b=TczJrBRQki+ ze7@0qIrQf1C7%ut*L&dge|vhrJKy`Wf7o1lJfCwt@bD4IrN{Hh*B1}Vp~uV9^W@SW z?&kB2=F6csUoZJ|c(~pJum3dZJ#)SfWdE?a^msn!df?$BlS_~1ldmrxmP3!1r{^i9 zKitjd8_kzPZ@ymg>F{v92VVbor1vcOKA8Q(=F;Q&oa=#yk4i2*o=?8McvucSUY?$( zmi}-zpKml@4!!w$$*04^^&WWr-uz9-F&{$d^z;y>m{EK57&F(^`ADq-;?jd**|P9J)Y0G9(eee3MqT4|ntVM)T#+o3EFAIy_wOfuAmYr_cAj`96~U!?Wd_9?$3e*y4we%Q=1Zn{#II z!=0Z`zcY#-mP2no->APFdj5JpbMP=fJ>EWMNblM6{Yv%^>rId6b8b&~`1s_~e<w{+|B13&6h)OzFzX_@Nm5cUjLcWd(M2nmi@!#(&PD@>w$+)OfEg1 zPrkl*SPng2o}TBF{%|*+Z!}*Hz4>~{r^Cbb9(euVmELpZ`}OP}HkTgH=Ufjwd{T1h z@qF_2#lv#w@$&RMxAcd*`Fx}Ka_G(1OFkVQuJ^#}|L*jjJKsmMf7o1lJfCwt@bJmW zrN{Hh*B1}Vp~uV9^Ssg@?&kB2=F6csUoZJ|c(~pJum8;HJx{)mW&g0b^msn!df?$x zl1q>0ldmrxmP3!1r|0>lKitjd8_kzPZ@ymg>F{v92VVbK(tF-~AJ6__bLsJX&h@~< zrzV#k&nI7BJS>MEFHg@4N`JVU&o`Pchu(a>x%7BG`TF8vIrMmWdR|oe!`*zo(R?}d=IbS&4iDFR;AczU_vX7`zE5TU@B%rf z$MZQqqxj)7b539V=3G+zaOdaK@8aTz<pk%L&yn5>=le|d z51UJm=X0(H9zHv{^msn``r=_Z^muuCURL_U-F&{$d^z;y>m{EK57&F(^`A4n7s>b8 z>>oCl9?$1o4?KKMa_RAW^7X~Ta_I5$^t``4v=?{1F`9|~Q(3`K9d^$W_?}68U?(|+P z-{-S`*j#!%pL0F%@OjCl$Meb87Z1y!$IH|6%F-Y1=JSo_%b_=4FZpzMxZVS=|2*lv zc)s7r{$X?J@qEtpz{BS!mmbe2Utc^dhaN9a&#Ov*xSP*6nlFdme7)q;;o*7@y#Dj1 z_Y(QOkp08v(&PD@>w$+aNG?5|Prkl*SPng2o}O2i{%|*+Z!}*Hz4>~{r^Cbb9(eud zOYbG~eKGro&85flIoAUZUzl8aJfD1h@vt0vygWUxDgEJYKHq4*9D4Khl23<+>pk%L z&!65)<@-|h51UJm=X0(H9=<5K^msn``r=_Z^muuCUR(OZ-F&{$d^z;y>m{EK57&F( z7f9a)^Iaz2m$QF(>73K!`J7)|{O~0?r>}lU+-rQ z9_FXV+s8uby==a(WdE?<^msn!_JoHoO)fp2Prkl*SPng2o}M?9{%|*+Z!}*Hz4>~{ zr^Cbb9(es1PVeRNeKq@s&85flIoAUZUzS{YJfD1h@vt0vygWT`EdAkbKHq4*9D4Kh zl23<+>pk%LFOuHN=lfdr51UJm=X0(H9=<%e^msn``r=_Z^muuC-cm{EK57&F(^>oCl9?$1o4?KKDa_RAW^7X~Ta_I5$^t`$Bhr9WFqxo{^ z&DTpl9UiXt!0W$Qdas!88`(c>E`4#=?{1F`9|~Q z(3`K9d^$W_?}68U@$_CP-#4>=*j#!%pL0F%@KwpB$Meb87Z1y!$IH|6*3uvD=JSo_ z%b_=4FZpzMxZVS={}So_zI?x#{ln(cR}5@0IiYR`w5@OONMst_L2zCb{%@KKc6MVL9}8d3xSn`orCP zzR`R+^ycd&pAHY#d*Jn7D!t#I@3*sm*j#!%pL0F%@U_XM$Meb87Z1y!$IH|6j?y3Q z=JSo_%b_=4FZpzMxZVT5bowrn@2dH}mHor33MhQ4|ntVM)T#+o3EFAIy_wOf!BYz^j`5U=?{1F`9|~Q(3`K9d^$W_?}68UrSx7q-ydfGu(|YjKIeMi;aihSkLQ!G zFCLadkC&(C1EoLQ&F34g!|M#W$I{E%6`-jb?$MZSY0}tPpTzWj8 ze0}k-9D2MwJs&Ln;chh9mGoUT-w)>d)9fGqK+fs$e9rGI ze)z7O(^tPaUnzdL^YiKVNb$pR=*{OF^_N4>U+-rQ9_FXV+sA6@{h@q+mi@ze)8qM^ z+Y=tXJGu0DKKc6MVL9}8d3t`e^oP6oe53ht=*`zlJ{=ye_rUAFdU}62-=Amyu(|Yj zKIeMi;d_!xkLQ!GFCLadkC&(C*Ghl5o6k3zFNfZIz2wv3;d&3e{%fT7NAmqe_79s& zkLPo)2OhpRx%7BG`TF8vIrMmWdVan1hr9WFqxo{^&DTpl9UiXt!0W$edVe(EUuOTX zx%7BG=X&7b`;tqK=aa859+pFom#62Wr9a%w=NrwJLvOxb^6Bt!y$4?ZwbJ`z`Ti>V zhs~wO^EuZ858t0$dOV+eeetjydb~V6A1nRgZa&{=z8re<^^#AAhwDA?`mdedAJ6yK z**|P9J)Y0G9(eeHG^o+4|ntVM)T#+o3EFAIy_wOf!BYX^j<&T z-(>%=x%7BG=X&7b2a`*W=aa859+pFom#60wr9a%w=NrwJLvOxb^6Bt!y$4?Zb<=x; ze1Dt$!{*ZC`JC&4haXBVJ)Td#zIa#;Jzk!kPnQ00H=l1bUk<(bdda85!}T6`{ntzH z4fFk7_79s&kLPo)2OfSnx%7BG`TF8vIrMmWdOlV9!`*zo(R?}d=IbS&4iDFR;5SO& zjq}|$-`{8d@HRQ8$HV;36hHiQ~6(3{Vv->bzB%c1Aza}Pc7upIiz>t_xg z=9hl23<+ z>mKm>Y?|Jm%=b^(KWr{N9>$x8hxMb!!{$F<=EI$zPtP}tAC^OJKHsRn9D07eIo2B=s^8Iu651UJmhwIo2T~<`TiyQhs~wO!+7)XuzvJ-*!&mDe7N)T>G`eVhvm?l&o}BXhn`M#RSPs4Ue53wy==t@MPlt!= z9`O2XmENDp_wU(1Y%V<>#+!$S^`pnb=D%F#!=0Z`&+imJEQj8FzEOWU^!$3sr^CZ_ z4|sjHPVdj=`;Y7&HkTd`APFdVam+)8XN| z2mChayKTNZ<@?XG6EdUoC$4wVcyezd7G8ez^1V>G!?jhvm?l&o}BXhn~OQ z&m26=Pmi~c?b3VaeE*gG!+O)>`JCGm9)3N!^msn``r=_Z^muuC{-E@SyZL;h`EuyZ z*GoPf9%V<^?~?DovwzrJdOV+VJ@D`w$)(5h$=4ST%b~~1)ANU=Kitjd8_kzP zZ@ymg>F{v92VVbAruVM-{wMo~&85flIoAUZznNTmJfD1h@vt0vygWUBRQki+e7@0q zIrQf1C7%ut*L&dg|5SSKmhXSFf7o1lJfCwt@bEX2OONN1uP+{!LywoI=Z{N&xSP*6 znlFdme7)q;;o*7@y#70+_viEdU-l21OONMst_L3eR&wd_eDd|h!*b~H^7Q;k=?{1F z`9|~Q(3`K9d^$W_?}68U$MoJk-$|yPpk%Le>%Otkng0~KWr{Np3k`+c=)a4(&PE$>x+lw(BtLl z`Log=?&kB2=F6csUoZJ|c(~pJum5M#`-}Nbmi@!#(&PD@>w$;AlU#Z{pL~7supD~4 zJUxG2`orCPzR`R+^ycd&pAHY#d*Jo|YX&$%9W_`Aua$Meb87Z1y! z$IH|67o|Vk&F34iqbLqQNzI)|6MfMNxnR9wPpY!h(Km7fi(^tPa zzbbyX^YiKV%i@RS(3{UU>Mw_$zuwOrJj_pzw~w9Ed+&VTmi@ze)8qM^+Y=uCL2~Kw zeDd|h!*b~H^7Q<5=?{1F`9|~Q(3`K9d^$W_?}68Um-PNpzEfuZu(|YjKIeMi;U6ZK z9?vIVUpy>_9xqSN-<1AvH=l1bUk<(bdda85!}T6`{dZ08ee#_u`-jb?$MZSY0}uZw zx%7BG`TF8vIrMmWdj7Wbhr9WFqxo{^&DTpl9UiXt!0W$TdheU>)Y(65EG6E>^~J+-=<)LO{C(*Uck}s1^X1T+ua|r}JY4UA*MIl)-Y?&2vVYiIdOV+V zJ@D{PlS_~1ldmrxmP3!1r{^C^f4G~^H<~Yp-h92})8XNI54`?gNbmjgeMj~Wn@f-9 zbFK#-{#kPA@qF_2#lv#w@$&TiW9bif^Z7>e<$Qrf0c9k>Nn@F#SeFWKK=eu{IDE)^Z7>o<`RVcYv3GhO zlJE4{Kdd)Bp3k{G;o)B=mmbe2Utc^dhaN9a&%c%aa5tZCG+z$A`FhEx!^8C+c>TYW z-iPKpL-r4wOONMst_L3eO>*h+eDd|h!*b~H^7Q%V_`AC>RS**|P9J)Y0G9(eeV$)(5h$=4ST%b~~1)AK*2Kitjd8_kzPZ@ymg z>F{v92VVaJ();LqXUYCybLsJX&h@~pk%LADG_9G6E>^~J+-=<)LO{9ox0ck}s1^X1T+ zua|r}JY4UAKPY_<&iA-{-;@2r$L5?K&*%Iv#Si~A=k(QY&ZJZP|NkA~;m*&e-z3Ek z%b}OgH|j5kp1m{EK57&F(^*=QGIU(QoX8*9c^msn!df?%|Czl@2CtqJYEQcO1 zPtVD-e>~jH=NrwJLoZ)1`E+==-UF|{_s5CZ)9l$lY%V>X&$%9W_#ero$Meb87Z1y! z$IH`mitHZ`ck}s1^X1UX*GoPf9+k(>QuZ`Q_79s&kLPo)2Oj=sa_RAW^7X~T za_I5$^n6?PkB7VYe53ht=;iArpAHY#d*Jo={x~^%nlt-{&85flIoAUZ|0}ulcs}|1 z;$b=TczJqGnf>G8Za&{=z8re_dda85!}T6`{k=a<$)4uQ{$X?J@qEtpz{CGeEOK2c(|L-H<~YpUcO%P>F{v92VQ^gk5jX!xwC)RTzWj8b3O3zf09d& z=aa859+pFom#62{**_lc=JSo_%b}OAmwY-rT}j6tA2ydB&*xkZJpA9} z(&PE$>x+lw(BtLl`S$D|4|ntVM)T#+%hyXj9UiXt!0Yc_cY5|TZ}tzHOONMst_L3e zUvla3eDd|h!*b~H^7Nc0`^UrGe7@0qIrQ@Nl23<+>pk$^bKW0kW>52F|L_?(r^oX- z_s+w^-XHXM*qnD}e|Wg_^Xd1F;)mtX%jX;QmqX8A?`IAk=BLNIzxT&k+4KC_Kdd)B zp3k{G;bHF&dOV+eeetjydb~V6r_KKHa5tZCG+z$Ae7)q;;o*7@y#C%FXJ`KlWdE?a z^msn!df;L24|+VGe0}k-9D2MwJ*Uh5@o+bvZ!}*Hy?njo)8XNI54`@~ALnHM3ugbY zx%7BG=X&5_?+6%dOV+eeetjydb~V6XUzWba5tZCG+z$Ae7)q;;o*7@y#C%F=V$+m zWdE?a^msn!df;L24|+VGe0}k-9D2MwJ!i`P@o+bvZ!}*Hy?njo)8XNI54`@~9~Wf* zi)R0@x%7BG=X&5_?+hCcs}|1;$b=TczJrhJNw7O-F&{$d^z;;^^#AAhwDA?`g_-1 zl>INB{ln(cG6E>^~J+-=<)LOoH_f)!`*zo(R?}d^7WEWhllGu@ZNLY zAD3kROJx7>#W|CgNOO) z@$T>aacTCvWcCm1O^@euZcljF`-2|OCtqJYEQcO1PtW&c|9H5Y&o`PchhDy3^6Bt! zy$4=@?~lu}|E02j*j#!%pL0F%u=fW&o=?8McvucSUY?$_W&e1%o6k3zFNa>fUh?Vi zaJ>g!fA5dWv;U>Df7o1lJfCwt@UZs>J)Td#zIa#;Jzk!k@6G=4a5tZCG+z$Ae7)q; z;o*7@y#C%FS7iUoWdE?a^msn!df;L24|+VGe0}k-9D2MwJ!jAU@o+bvZ!}*Hy?njo z)8XNI54`@~A6I7o%Vz(ux%7BG=X&5_?+g{_$`(pKml@4!wN6 zMCkH=l1bUk<%| zz2wv3;d&3e{@x!~XaCD*|FF6Acs}QP;9>6%dOV+eeetjydb~V6=gR)^a5tZCG+z$A ze7)q;;o*7@y#C%F*JS@IWdE?a^msn!df;L24|+VGe0}k-9D2MwJ?GB;@o+bvZ!}*H zy?njo)8XNI54`@~b=PM9D`x+&x%7BG=X&5_??8GypL~7supD~4JU!>h{_$`(pKml@ z4!wN6G6Edz4P#}_Xj;5HfO%<4-a>KKK>oCl9?$1o4?OJsL67H?uP+{!LywoI=K|S3 z9`5Gzjpoatm#>$6Iy_wOf!E*r~jH=NrwJLoZ)1`E+==-UF|{_s7lI|0>x(Y%V>X&$%9W*!zPX&nI7BJS>MEFHg^f zvVT0>&F34_9xqSN zg|mM=+|B13&6h(jUoZJ|c(~pJufO-lt=a!-**|P9J)Y0G9(dUMgC5T(Utc^dhaN9a z&qcC-JlxIa8_kzPFJCYDba=Sl1FygL$8FjF>e)YRE~jH=NrwJLoZ)1`E+==-UF|{_s8wo{~FmpY%V>X&$%9W*!zPX&nI7BJS>ME zFHg_KvVT0>&F34_ z9xqSN#j}4r+|B13&6h(jUoZJ|c(~pJ?>*=JaaZ=gR`w6ynR9wPpL6d#Jna2JkB7}! zGW)~Bou5y?C5j)GLoc6i)L#xgf4!eMc$l9a@BZE&cW2LQXaBI?^msn!_JoJMKj`s% z^7X~Ta_I5$^js?Y$HU!xzR`R+^z!wRPlt!=J@EQ_f83M(uao`5=F;Q&oa=#yy+7#j zeDd|h!*b~H^7LFf`^UrGe7@0qIrQ@Nl23<+>pk%Ldw<-U{jZz-!{*ZC`JC&4hrK`O z@qF_2#lv#w@$&RsCi}<3-F&{$d^z;;^^#AAhwDA?`g?!em;JAo{ln(clG zf6(LkA76?kB7VYe53ht=;iArpAHY#d*Jo={&*n!|6ukHn@f-9bFK#- z_Wq#9^U2p256hv)%hPlD>>m$z^Z7>e<`RVcQ@BQ)B?D=Ea zKdd)Bp3k{G;bHF&dOV+eeetjydb~V6-=F>C;chTRUzLx!e zJo|^urN{F**8>lGf6(LkA6bwkB7VYe53ht=;iArpAHY#d*Jo={`h+K zzkc=)n@f-9bFK#-_Wq#9^U2p256hv)%hPk!>>m$z^Z7>e<+k*X zSoXhR_79s&kLPo)2OjqRpvUvc*B1}Vp~uV9bM@>W4|ntVM)T#+%hyXj9UiXt!0Ye* z@p$&XQT7j;OONMst_L3W{-DS6$=4ST%b~~1({qjN9}jo)`9|~Q(973LJ{=ye_rUA# z{qaQhzj5{tn@f-9bFK#-_Wq#9^U2p256hv)%hPks>>m$z^Z7>e<Ay?&kB2=F6d%ua|r}JY4UA z*WbJDsqBA~>>oCl9?$1o4?OH0NRQ`}uP+{!LywoI=i1pn9`5Gzjpoatm#>$6Iy_wO zfqy#tekR}7^4&E1hhNP(Js##?H}}WG>*Sms&u7jC#ShD&=jYRJ{o;q^(DU=@r6(Si zLtlCQ%)!I_^6>8SZ1($lzMEzLu-^1|7;hdP){hI&t>m#P*cdii{# z{&MK~^^#AAhwC2j`g|jM|7N~hX8*9c^mrI=9v;?@9uJ%Uq3jP2cYZ!SKT-U!9D4bD zqyBQ}`Sp@dhllGP@cO)vy?-m;t+IdETzWi=HxCc%M~{ci|8Vw)hdV!?o|_auEQek` z->APFdVam+)8XN|2fRKnX7At5ckApQHkTd`GSU-9^Z2m{HKRn#|`SjeZ_+dHp z^7%&n<<wF0dda85!*vgMeO}4lznkxN**|P9Js!rJhlll}$HV4-Jp04Lou5z7Es7tO zLoc6i)L#xgzh3g`@NnG&-h0mbhCcs}|1;$b=T zczJqmll|l2Za&{=z8re_dda85!}T6`{k=bal>P6J{ln(clGf6(LkA79@kB7VYe53ht=;iArpAHY#d*Jo={`g7u|LN=>HkTgH=Ufjw?EOKH=aa85 z9+pFom#63U**_lc=JSo_%b}OAmwY-rTJ)Td# zzIa#;Jzk!kpUnR8a5tZCG+z$Ae7)q;;o*7@y#C%FKg<3w$;8Kj`s% z^7X~Ta_I5$^!!xzkB7VYe53ht=;iArpAHY#d*Jo={`h(J|GDfRHkTgH=Ufjw?EOKH z=aa859+pFom#60r**_lc=JSo_%b}OAmwY-rT_9xqSN9kYKt+|B13&6h(jUoZJ|c(~pJ?>*=J@vH2A=jJsvjaGua;=?)-fEeY*HzIrQ@RM*Zc`^Vj>CgNOO)@$T>a@$2k)m+T+bn;y^S z+@A2T_XjhCcs}|1;$b=TczJq$F8jyB-F&{$d^z;;^^#AAhwDA?`g?!;Hv8W#`-jb? z$MZSY0}p$D(Bt{!>x+lw(BtLlxl{I!hr9WFqxo{^X&$%9W*!zPX&nI7BJS>MEFHg^1vVT0>&F346%dOV+eeetjydb~V6cg_Csa5tZCG+z$Ae7)q;;o*7@y#C%Ff6V^B znEk`%(&PD@>w$;8Kj`s%^7X~Ta_I5$^xQ4`$HU!xzR`R+^z!wRPlt!=J@EQ_fBY%? z-y{2n&85flIoAUZdw~qP>>m$z^Z7>e<~jH=NrwJLoZ)1`E+== z-UF|{_s8F}|1V|#u(|YjKIeMiVeb!mJfD1h@vt0vygWVk%>MCkH=l1bUk<%|z2wv3 z;d&3e{@x$|$o}`q{$X?J@qEtpz{B1j^msn``r=_Z^muuC?v?%H;chTRU{+a#noBhM)(&PD@>w$;8Kj`s%^7X~Ta_I5$^xQl9$HU!xzR`R+^z!wR zPlt!=J@EQ_fBY-^|8n*Zn@f-9bFK#-_Wq#9^U2p256hv)%hU5q**_lc=JSo_%b}OA zmwY-rT_9xqSNeX@T%+|B13&6h(j zUoZJ|c(~pJufO-lf3pAmvwzrJdOV+VJ@Byi2R)uozP@-^4n1C;p8ID1c(|L-H<~Yp zUcO%P>F{v92VQ^gkN;->2W0=Sx%7BG=X&5_?+o<`RVcQ@BJ}Z`X8MA!+O)>`JCGm9`^pA$Meb87Z1y!$IH|6z|tS? z=JSo_%b_=4FZpzMxZVS=zxT)F>3>M}51UJm=X0(H9`^pA$Meb87Z1y!$IH|6pwb`i z=JSo_%b_=4FZpzMxZVS=zxT%!>3?YU51UJm=X0(H9`^pA$Meb87Z1y!$IH|6;L;!N z=JSo_%b_=4FZpzMxZVS=zxT)6(*LmRA2ydB&*xkZJna2JkLQ!GFCLadkC&(CA*Dau z&F34g!fA5be)Bo`7A2ydB&*xkZJna2JkLQ!GFCLadkC&(Cp`}0E z&F34g!fA5c}(*KC;A2ydB&*xkZJna2JkLQ!GFCLadkC&(CVWmIZ z&F34g!fA5c})BniqA2ydB&*xkZJna2JkLQ!GFCLadkC&(C;iW&^ z&F34g!fA5dCr~gseKWr{Np3k`+c-Z@c9?vIVUpy>_9xqSNBT9d` zo6k3zFNfZIz2wv3;d&3e{@!)dr2o;`KWr{Np3k`+c-T9T9?vIVUpy>_9xqSNBTIj{ zo6k3zFNfZIz2wv3;d&4JBJsvja=rSMf{CxTy zRs66Vdh_{4{pHZ}*ZY}+hxzI8?mt<2Pn++t**~l|J)Y0GJ>g;R4|+VGe0}k-9D2Mw zJ&!5<;chpHkTgH=Ufjw?EOKH=aa859+pFo zm#63Pr9a%w=NrwJLvOxb^6Bt!y$4?ZDbst#d{4~&VRPy6e9ra2!`>hCcs}|1;$b=T zczJrBQ2N8&e7@0qIrQf1C7%ut*L&dgpDMj)%J-z~A2ydB&*xkZJbYMk>G6E>^~J+- z=<)LOJhAkLyZL;h`EuyZ*GoPf9pyjRzboI9vwzrJdOV+VJ@D}1$)(5h$=4ST z%b~~1)AOX#AMWP!jpoatH(xLLba=Sl1F!$v)BD}|o|65;=F;Q&oa=#yk4P>(o=?8M zcvucSUY?#Om;P`!pKml@4!!w$$*04^^&WWrr%CUb^F1~Dhs~wO^EuZ84`Fx}Ka_G(1OFkVQuJ^#}KV5pymhTzaKWr{Np3k`+ zc=*`l(&PE$>x+lw(BtLlc}D3Eck}s1^X1T+ua|r}JY4UA*MIu-es8{KX8*9c^msn! zdf?&Xl1q>0ldmrxmP3!1r{|faKitjd8_kzPZ@ymg>F{v92VVaf(tGxN&&vK`bLsJX z&h@~<$0wH_&nI7BJS>MEFHg_2N`JVU&o`Pchu(a>eDd|h!*b~H^7K5n^oP6oe53ht=*`zlJ{=ye_rUA_?)08J-}AD6 z*j#!%pL0F%@X5)g$Meb87Z1y!$IH|6ywV@;=JSo_%b_=4FZpzMxZVS=|IFzloi_rmNS)|(#B=iHv~@af5=$Meb87Z1y!$IH|6qS7Dk=JSo_%b_=4FZp!%37lT^ zc>QM!zd*hhW&g0b^msn!6FuUG&qyvkKc9Sk@vt0vygWTGF8$$dKHq4*9D4Khl23=9 z!0APg*WdeN!RRi|{$O+I@qErFdc+T(nOu5)KKc6MVL9}8d3s(_`orCPzR`R+^ycd& zpAJ8P(~BOj|Loxx%J-7&4>p$`&*yxiNBrF^Ucz3B1!&k=s%d@s%ZU~}p5e9k9EgHTzY;!`TF8vIrMmWdR|ug!`*zo z(R?}d=IbS&4nKj@iyp84oZ%PA_pm{EKKY`PW9H0=>G6EdCwjyWpPyWMem?p7 z;$b=TczJqWRrAga}H|GAZx%7BG=X&7bE0Rl(=aWCtH}hdR^!)Plyt(v;yZL;h`EuyZ*GoPf9%UlfubA&mxj$?!J)Y0G9(ef5pk%LFP`2j<$H7P51UJm=X0(H9=(nS+P<>GAflRC>QZ-&?bPSZ{hfpL2V{!`CL49?vIVUpy>_9xqSNJ4%1Jo6k3z zFNfZIz2wv3;d&3e{!6F#D*4`){ln(cR}2?^W}?J^P2vrN{F**8>kUHZe_ ze7@0qIrQf1C7%ut*L&dgUoO2@&-c#kA2ydB&*xkZJbYtv>G6E>^~J+-=<)LOyr=Ys zyZL;h`EuyZ*GoPf9%V+@uaWOv**|P9J)Y0G9(ees3MJI z4|ntVM)T#+o3EFAIy_wOf!BY9^j+5^U2p256hv)%hU6| z(jV^T^Nr@qp*LSI`E+==-UF}yis`*pzV~GRu(|YjKIeMi;aieRkLQ!GFCLadkC&(C z{iQ$L&F34g!|CQ2v?R@Xe{$X?J@qEtpz{9sDmmbe2Utc^dhaN9a z&j(6>xSP*6nlFdme7)q;;o*7@{5t8oZoV7jdtdesub*>zJk0-4@xu=`em--aEPhxH zz4?6lJyHCy9D05}_s|m$%b~Ble&*m|etCHJSuede%=iB6AJ&^5597_l!}`(VVe=m@ z^Wo0Vr{`0}56hu9pKsJ(4n4nK^6Bt!-2+~q52W`-`96^S!{*ZCVZ3>GSU-9^Z2lu< zKHT~F^nAMbVL9~X^Nsq;q3732J{=yed%)}S!SvoZ-v_gQ*j#!%j5iMt>qn1=&HqZ7 z4|je(J)bFlSPs4Ue53wy==t@MPlt!=9`O2nD7`#+!$S^`pnb=6|)! zhdV!?p3fFPEQj8FzEOWU^!$3sr^CZ_4|sh(oZg${`*8LTn@f*}@#f)S{pj(q`ClvZ z;m*&e=X1pm%b_=)Z`5B7J-=S^>F{vf174qxr1z%zK9c>z=F;O~ym@$7KYBcD{@2TV zxbySr`F!!ia_G(H8}*k%&##w!Iy_wWfY;}v>AhLLU&;PqbLsIg-aI_4A3Yv6|IsoZ z?)-duexvwdIrQf9jrz->=hsU<9UiWG!0Yp|^xiz*uV(+Sx%7A#Zyp}jj~)-3|5%w1 zcYZ!SUnqW94!!w&qyBQ}`Sp@dhllGP@cMi_y|>8sYuP_+E(>qsPPMKVIg; zou5z77mFX3LvKFcsJ|R~e!b+=;o-Um{QBv;LB3n%`}OP}-ZJO(cs}P(6hHiA&grY) zoR^Cq?)-fEy;S_L9D4KlM*Zc`^Vj>CgNOO)@%FJ{dT*WYquD>KH$9%uxjo_Gr;_mHVRPy6e9ra2!%rud z9?vIVUpy>_9xqSNS4)4mo6k3zFNfZIz2wv3;d&3e{u`(Fw)sAu{ln(cO<--rME-MD`DxOONMst_L1| zHo5e8KKc6MVL9}8d3wHH`orCPzR`R+^ycd&pAHY#d*JooB)zxK_sQ%ZHkTgH=Ufjw z{9JPB@qF_2#lv#w@$&S1qx6Tn`Fx}Ka_G(1OFkVQuJ^#}ziE1ZGT*1Nf7o1lJfCwt z@bL4=rN{Hh*B1}Vp~uV9^Ucy9?&kB2=F6csUoZJ|c(~pJum5K0{i%GP&i-L@>G6Ed z^}xg5NG?5|Prkl*SPng2o}S+<{o!st-)O!Zdh_*?Plt!=J@EQ(p58m;`%Lx^n@f-9 zbFK#-ej&N^cs}|1;$b=TczJq$tMrGv`Fx}Ka_G(1OFkVQuJ^#}zeRfQnD4XMKWr{N zp3k`+c=*NS(&PE$>x+lw(BtLl`R&pl?&kB2=F6csUoZJ|c(~pJzh(MvmG5WreJ=Zl zKb>=WJfHKIiXVPC=k(QY&UcC*?)-fEy;c0M9D4KlM*Zc`^Vj>CgNOO)@%FKGdVe12bzjk83}U{n)SbW<6`2`aG)n=%@3O z^YTG{^{D;k#_i{$-+sCH>!YiO??IP;m2$5Y&+Pow_Ufmb^IQ(P`rLf=)6Mahm#*fc zpUzLtD+c-1qxPE{x1W!G`{m-VkFFlR2VMSE%e{8|N9V7$S3ljH=W@{1=jE%PZjQga zbTuFSbbfMPImoXbwcp&h{e1M>FBgA(boKB(=<=^t?seinJAbvk`swC8mxHc8KVSWH zbNuC{tNG}s^ON(cL4Nh9{pQB)=cC_#x%lg&tB3DFm;Y1cUN@fA`K#^KPdDee9CY;s z`Rb>e<1a5=%|}0-pPW|@@~cPfH#crSAN}^r#a|y?J$w(k{GTrOdhzVeUu~~`x;f9~ zpsO#;S3lhxe|hO@KKkkWQVd6joZ&hzx{IY*GE?m--9my>gC=bp4<7W z?bS~==eZno^~L$>r<>z1FI~+?Kb@bP*A4QkN9{K^Za*LW_RGazA6-3s54!wolzYQ? zUgxj2S3ljH=W@{1m*lITZjQgabTuFSbbfMPKgh2hwcp&h{e1M>FBgA(boKB(=xdhu zvvK2ie&?^=sOS3W<~+Z2Ft5I>=lX~9_S`s_SC5)EC*KW&c{LyX_M01*pO1d?!}qg? zt~Rfq?mRwM?oHwaoxfUc{d9AlJ14sO@_hBv&GDC)uI8hk&QH#p2Km*a_M02GpO1d~ z<>If8t{%PzUH-Moy=lC#^H!YiO??IP;-EwacFX{Z%_Ufmb^IQ(P`s#f3)6Mahm#*fcpUzLt z+Xng7qxPE{x1W!G`{m-VkFFlR2VMU4%DrX0wDVWntDkPpb2;ehYx31kH^*OIx|)xE zIzKsYALLh$+HY>$em?r`my5qXx_bB?botjW_g3+;&R=b>e!4l&<)Evt%~wC&9DjM~ zYCihu{N%i2kY7D&zqxVy`RKP_F8=!H>fw9P<=>#(TgS^gf3>~(>E=9_gRZ_VU;T7* z{N<&q`RJ$flk?6&e)XvR=Em*kqu+kH`0Jyqhwnj`f5UQb6R+s})%NPAoAX=_y88Nj z_0!GqmzS>Qqo2-C&btQr)uZ;C8@Hd2e*5L(uaB-Cz6X7y@@^cri&u93>TP?jpKi|c z8wT_08+)#QIB(BAgL(C+d2{mJJ(ySX(Qm)Garyb^H$Qwod+2KO`svPNlX7n#uj>5O za_gs?^V~Vn)i>pe<1a5=%|}0-pPUa2@~cPfH#crSAN}^r#a|y? zJ$w(k{9BZJ=XhP`ueMh|-JIug(ABr)tDkO;zr1ucAN_QGay~f7uO7AE+_?RG^xH2N ze|>cI@IC1AZ&~hL;`N=s+Ft#1bDqmVSKpqme!4mS^3v6O^wast`OqN0denY% zZ@*mp_0iSC_n^zaRk?SKH+24Ld-c=Jc`gTCeMi3f>E`&$OIP#JPv7ou8bK4Dzc- z?Kd}WKOg<}%f(+GT|Imcy8PRed-r%#=dZR`Ki!u``JTRo7Ycw9^04u^YNC>UoE$Ox;f9C6J338zWV9r z_{&RI^U+V|C+8D`{OVEr&5hg7N5B1Y@z+OJ58s0>{|@E;LcF!}SKF(fZq9Q#=<56O z)lWCaUtYSJkA6BoIVVj$>HqyhSC86nZrpx8`uWSnUmsmPd=I+(J9a)_jJI|EYJ2t5 z&3P^dU44JP`swEQ%S%`D(NE_m=W{xLx_Z=pbK~~&(a&El{`%LKYQoOzM zSKF(fZq9Q#=;{aZ)lWCaUtYSJkA6BoIj89S>FQDY&5hg7M?ZhL`0Jyqhwnj`f9KBU z%khrRUu~~`x;f9~psOFuS3lhxe|hO@KKkkWjTpH#crSAN~C0;;)ac9=-=% ze)o^BbWV46{%U*m)6ID<2VMP8zWV9r_{&RI^U+V|C+Ae1KV3a)zqxVy`RL~_7k_

e<1a5=%|}0-pPW;7{&e-I{pQB)=cAv$ zT>SOX)x-Co%kTcNcjt6>=dZR`Ki!-_M02G zpO1e2a`D$kR}bHVF28%-S39SBI)Amj`swC8mxHc;G++I6bNuC{tNG}s^OJMh&Y!Lx zwcp&h{e1NEmy5qXx_bB?boV*;kFRx3_jdm3eR{5+ZqD<^2J`C2d#-;tZ_o6dA6-3a z-kf~X4d&H+^z%12Ez1FI~+?Kb@bPGj;xS^{D;k#_i{$pTAuE_0iSC_n^z~{;_}O|6u2@wpTyh zoab`T)$Skq>E`&$OIP#JPv7ou8bub^dhqsQu=~?dPMPzg+zF(bdEE zpv&+6ad7AVXy>oCS3ljH=W@{1?jQQ;=J?A?SM$+N=O^dvoj+YYYQMR0`}yeSFBgA( zboKB(=<>VQ9n$$f*7>XL)lWC)xg2!0d!T;0IsWp})qM2R`N=s)=TBFU+HY>$em?s7 z%f(+GT|Imcy8E2_$2U9w$2))ZH+rt0Zq9S}Ji6NbLqA<@&s?1!T|H{voP2W*=GA=k z^EWpxKOg<(hwo<(U2R@J-TS+Le5-SQqVreFt)FhrbLT`?yMO4Xo8vDpUCl>7ou8a@ zcm8zssQu=~?dPMPzg+zF(bdEEpv&+6acJj1Y5FJscT}~#`swC8mxHc$|Ikl2$6sE$ znvZ@uKRM^={ORgZ`^}Bp&qqIhx%lg&tB3DFm*4&4u+INEoxj>%{d9Al%RyJWf9R*1 z<1a5=%|}0-pPch{{&e-I{pQB)=cAv$T>SOX)x-Co%kTbic;`Pw=dZR`Ki!fKV3a)zqxVy`RL~_7k_zx&6xJO8OW zf3>~(>E=9_gRXY}&`&qVUtYSJkA6BoIT!5w>FQDY&5hg7M?ZhL`0Jyqhwnj`-~HpL z&VQQDUu~~`x;f9~psU?K^wZ7pmzS>Qqo2-C&V@REx_Z=pbK~~&(a&El{`%!+LZ+&R(J?jQQ;=J?A?SM$+N=O^cLJAb-* z)P8g0_VdxtUoQUo=<4Bn(B*gk_)h0PL+7uyS3ljH=W@{1?jQQ;=J?A?SM$+N=O^c4 zoj+YYYQMR0`}yeSFBgA(boKB(=<>UNe7EzTvGZ5ktDkPpb2;d0_YeJabNuC{tNG}s z^ON&=oj+YYYQMR0`}yeSFBgA(boKB(=<>UN9N+oR)cLFJ)lWC)xg2!0`-gtIIsWp} z)qM2R`N_F>=TBFU+HY>$em?s7%f(+GT|Imcy8P}RCv^TZcm8U7_0!FHE(cxh{-K|4 zj=#KgH6Q(SesV6+`P0>-_M02GpO1e2a`D$kR}bHVF2DQ7iJku}oxj>%{d9Al%RyJW zf9R*1<1a5=%|}0-pPbL{{ORgZ`^}Bp&qqIhx%lg&tB3DFm*4&4d!7HRoxj>%{d9Al z%RyJWf9R*1<1a5=%|}0-pPWl}{&e-I{pQB)=cAv$T>SOX)x-Co%kTd2{my^3&R=b> ze!4l&<)EwGKlIbh@t2pb=A)m^PtF%~{&e-I{pQB)=cAv$T>SOX)x-Co%kN%yQs+N= z=dZR`Ki!= zF8=!H>fw9P<#+!$t@EF&^HjTpH#crSAN~C0;;)ac9=-=%e)o?bcK-8r{%U*m)6ID<2VL#{p`UJ!zr1ucAN_QG za=xhZr>jTpH#crSAN~C0;;)ac9=-=%e)o?bb^h~p{%U*m)6ID<2VL#{p`UJ!zr1uc zAN_QGa<0(%)77K)n;W;EkAD7g@z+OJ58s0>zx&4-o&WruzuI2?baS4|L07wf=%<_G zFE3rqM?amPoGW(zboHqH=Em*kqo2QA{Poe*!}p-e@BZ=Q&VPZQqo2-C&XqcUx_Z=pbK~~&(a&El{`%cI@IC0ydGgylC+Sl^G5BYO z`sh&Nx?`JI)j5jq}C% z;{tKPxKLa;E)o}w&y9=4=f%b267l(Q$@qe}R9reP6PJxIjLXI4nPsh*1)#Dm*&G^~)xwuwbJFXMg zjqAnr;|6iVxKZ3VZW1?*o5juJ7IDkCRopsm6Ss}q#qHw`amTn*+&S(Nca6Kn-Qyl{ z&-nTHh4{tzrTFFemAF^jJAO6p6TcR}9`}v=#r@*}@xXXcJUAW_zY)I~zZDOShsDF= z5%I|Q?RZo?Ivx{`jmO3B#P7!A;|cM^_`Uf3cv3t$o)S-ur^VCb58@BwkK!5e$MGle zr}1a;=kXWum+@Ee*YP*;xAAxJ_wf($kMU3O&+#wuukmm3@A1s|kND4cRy;eN6VHw3 z#q;9@@xpjfyf|JGFO8SQ%i|UC%6L`0I$jg6jn~EN;|=k~cvHMN-V$$(x5eAz9r4b1 zSG+sk6Yq`p#rxv}@xk~|d^kQ5AB~U2$Kw-m(sQ2t56_8H#3|!caq2itoHkAur;jtl z8RJZG<~U27HO>}ik8{L1<6Lp>I8U56&KKv83&aKELUG}^NL(~NH!c>R7Z;C9#OKE) z;|ton)urIy7>C| zhWN(#rugRgmiX5Aw)pn=j`+^_uK4cwp7`GQzWDz5f%w7rq4?qWk@(U0vH0=$iTKI5 zN?bLr7C#j~9X}ISk88v=<7eaN;#zU-xK3O*t{2yj8^jIcMsee~N!&DU7B`Pu#4Y1i zaqGBE+%|3(w~sr-9pg@M=eSGUHSQL7k9)*D>i{962a z+&AtQ_m2m}1LHyQ;CM*{Ry;Hw77vd{#3SRk<5BVGcuYJt9v8n8zZ;K_C&Uxu z_u}{CN%7=(N<1~57Eg~qh(C-!if6#$Ux>$KS-?#^1%?$3MhB z#y`bB$G^nC#=phC$1~$U;y>eA@$7g`JU5;f&yN?x3*$xc;&@5CG+q`jk5|Mi<5ltM zcul-EUKg*AH^dv`P4VVkvKe0)({A+8u#iYvz# z$Ct#H#+SvH$5+Hx##hBx$JfNy#@EHy$2Y_`#y7<`$G60{#<#_{$9Kec#&^Yc$M?kd z#`ned#}C90#t+30$B)F1#*f91$4|sh##Q2~akcoV`04nWxO!Y8t{FcYKNr`EYsYos zx^ca@e%v5#7&nR=$4%mR*WH;|y`e zI8&TC&Jt&hv&Grt9C6M#SDZV}6X%Wd#rfj`alyDyTsSTg7md%2i^b=~#p4q3`Ekkk zg1A&%IxZ8JjW3ML#pUCR;tFxaxKdm>zBs-lzBIlpzC6AnzB0ZlzB;}pzBaxtzCOMo zzA?TjzB#@nzBRrrzCFGpzB9fnzB|4rzBj%vzCV5-elUJ0emH(4el&h8ems66elo5S zSB(HSQC?7QY_% zjr+y@;{ox&cu+hz9umJ1zZt(34~>V#!{ZV0$oTDeR6IH!6OWC@#qY%L#^d7&@x=JO z`2BcNJUN~cPmQO=)8h}~595#G8S%&QC-JB8XYuFp7x9@Njem=Ok7vez#DB)K;@R<>cy2r| zo*yrW7siX?#qpAOX}m069o8ry!mUwHtE#4mQh$XnZU_9-oN5vF|xkKl%57DdLoIsyKC=CQciti_^y$ z;*4>oICGpO&KhTnv&T8&oN@U712X*oMHv2^`0(duhd)Q?=hTKjUp4%Bli~N@55FII z`2CrF4$jYG`MDrJpW^2ve80Z$8~6RozK_=Tb3XO^HYfjAHIx6kHu)o)d@R#U{F-*+ z*K`xVrl0sV!^E!{Cw@KMzL_R^pN`+;zL_U}joYhlmWkefm+z^4vrhDmdXHh>Y!kiz z>V5xJ-|Q2;!*dzcH^)TpQ_t_;_02ia>zv2+%{9?G`SW->eREIrJ^Q&$n)HA1b9=g< z?>rNq@w30*CQbTJ`Th1xKDWtV|0aK3oBZ`^^4Fos|DH~MkI7#TCjUO4{QGzE=R5Dj zuV=qbK6x(xTVE&tw?4P0`}xi{@fkn+=i8J2-anH+-~JOmx2N;_ck<^u|HQ9n|6cOs z*R}s^pWC>v&pzJ;CO+f;=D$z=H-B#b?$6(+e!dG%e8&IR?g7t!Zj&ZW{@h0W{c8C6 zE;P~mpKyPB_H%pYpWDCs`}4p0d>5YhjQ^YeKKV@kUh+)-e0wHeC!fjhw`cOX4ev*z lKHo(qKI8x9zfb-*e{N5`|NgtrchQN@_^JO){ypv2{{SV^R=5BF literal 0 HcmV?d00001 From 9ab4ae4755b8056759043cd9e28631e6ca10de3f Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 4 Jun 2021 12:34:16 +0100 Subject: [PATCH 0074/2572] Small refactor of MOAB construction into its own method --- include/openmc/mesh.h | 3 +++ src/mesh.cpp | 26 +++++++++++++++++++------- 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 260ee2ad780..1bac011e63c 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -412,6 +412,9 @@ class MOABMesh : public UnstructuredMesh { // Methods + //! Create the MOAB interface pointer + void create_interface(); + //! Find all intersections with faces of the mesh. // //! \param[in] start Staring location diff --git a/src/mesh.cpp b/src/mesh.cpp index 09ae82c707b..e81f2c82dac 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1583,13 +1583,12 @@ MOABMesh::MOABMesh(const std::string& filename) { } void MOABMesh::initialize() { - // create MOAB instance - mbi_ = make_unique(); - // load unstructured mesh file - moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to load the unstructured mesh file: " + filename_); - } + + // Create the MOAB interface and load data from file + create_interface(); + + // Initialise MOAB error code + moab::ErrorCode rval = moab::MB_SUCCESS; // set member range of tetrahedral entities rval = mbi_->get_entities_by_dimension(0, n_dimension_, ehs_); @@ -1619,6 +1618,19 @@ void MOABMesh::initialize() { build_kdtree(ehs_); } +void +MOABMesh::create_interface() +{ + // create MOAB instance + mbi_ = make_unique(); + + // load unstructured mesh file + moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to load the unstructured mesh file: " + filename_); + } +} + void MOABMesh::build_kdtree(const moab::Range& all_tets) { From 12bd84712975ea4e2f9bb42decea06d62faa550a Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 4 Jun 2021 12:46:04 +0100 Subject: [PATCH 0075/2572] Promote moab instance from unique to shared ptr and check null before calling constructor --- include/openmc/mesh.h | 2 +- src/mesh.cpp | 15 +++++++++------ 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 1bac011e63c..49d9dd87296 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -506,7 +506,7 @@ class MOABMesh : public UnstructuredMesh { moab::Range ehs_; //!< Range of tetrahedra EntityHandle's in the mesh moab::EntityHandle tetset_; //!< EntitySet containing all tetrahedra moab::EntityHandle kdtree_root_; //!< Root of the MOAB KDTree - unique_ptr mbi_; //!< MOAB instance + std::shared_ptr mbi_; //!< MOAB instance unique_ptr kdtree_; //!< MOAB KDTree instance vector baryc_data_; //!< Barycentric data for tetrahedra vector tag_names_; //!< Names of score tags added to the mesh diff --git a/src/mesh.cpp b/src/mesh.cpp index e81f2c82dac..62358911537 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1621,13 +1621,16 @@ void MOABMesh::initialize() { void MOABMesh::create_interface() { - // create MOAB instance - mbi_ = make_unique(); + if(mbi_ == nullptr){ + // create MOAB instance + mbi_ = std::make_shared(); + + // load unstructured mesh file + moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to load the unstructured mesh file: " + filename_); + } - // load unstructured mesh file - moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to load the unstructured mesh file: " + filename_); } } From b3b325d02f70db17cd65953cae359c09894d70da Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 4 Jun 2021 18:06:07 +0100 Subject: [PATCH 0076/2572] Add new constructor for MOAB Unstructured Mesh class, taking a pointer to an external MOAB instance as an argument --- include/openmc/mesh.h | 1 + src/mesh.cpp | 11 +++++- tests/regression_tests/external_moab/main.cpp | 37 +++++++++++++------ tests/regression_tests/external_moab/test.py | 4 +- 4 files changed, 39 insertions(+), 14 deletions(-) diff --git a/include/openmc/mesh.h b/include/openmc/mesh.h index 49d9dd87296..5eb56a3795f 100644 --- a/include/openmc/mesh.h +++ b/include/openmc/mesh.h @@ -366,6 +366,7 @@ class MOABMesh : public UnstructuredMesh { MOABMesh() = default; MOABMesh(pugi::xml_node); MOABMesh(const std::string& filename); + MOABMesh(std::shared_ptr external_mbi); // Overridden Methods diff --git a/src/mesh.cpp b/src/mesh.cpp index 62358911537..f9f50f95c4a 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -160,7 +160,6 @@ StructuredMesh::bin_label(int bin) const { //============================================================================== UnstructuredMesh::UnstructuredMesh(pugi::xml_node node) : Mesh(node) { - n_dimension_ = 3; // check the mesh type if (check_for_node(node, "type")) { @@ -1582,6 +1581,12 @@ MOABMesh::MOABMesh(const std::string& filename) { initialize(); } +MOABMesh::MOABMesh(std::shared_ptr external_mbi) { + mbi_ = external_mbi; + filename_ = "unknown (external file)"; + initialize(); +} + void MOABMesh::initialize() { // Create the MOAB interface and load data from file @@ -1590,6 +1595,9 @@ void MOABMesh::initialize() { // Initialise MOAB error code moab::ErrorCode rval = moab::MB_SUCCESS; + // Set the dimension + n_dimension_ = 3; + // set member range of tetrahedral entities rval = mbi_->get_entities_by_dimension(0, n_dimension_, ehs_); if (rval != moab::MB_SUCCESS) { @@ -1621,6 +1629,7 @@ void MOABMesh::initialize() { void MOABMesh::create_interface() { + // Do not create a MOAB instance if one is already in memory if(mbi_ == nullptr){ // create MOAB instance mbi_ = std::make_shared(); diff --git a/tests/regression_tests/external_moab/main.cpp b/tests/regression_tests/external_moab/main.cpp index ddcd2999262..270bbc133e7 100644 --- a/tests/regression_tests/external_moab/main.cpp +++ b/tests/regression_tests/external_moab/main.cpp @@ -9,6 +9,15 @@ int main(int argc, char * argv[]){ using namespace openmc; int openmc_err; + // Initialise OpenMC + openmc_err = openmc_init(argc, argv, nullptr); + if (openmc_err == -1) { + // This happens for the -h and -v flags + return EXIT_SUCCESS; + } else if (openmc_err) { + fatal_error(openmc_err_msg); + } + // Create MOAB interface std::shared_ptr moabPtrLocal = std::make_shared(); @@ -20,23 +29,29 @@ int main(int argc, char * argv[]){ fatal_error("Failed to load the unstructured mesh file: " + filename); } else{ - std::cout<<"Loaded external mesh from file "<(moabPtrLocal)); - if (openmc_err == -1) { - // This happens for the -h and -v flags - return EXIT_SUCCESS; - } else if (openmc_err) { - fatal_error(openmc_err_msg); + // Check we now have 2 copies of the shared ptr + if(moabPtrLocal.use_count()!=2){ + fatal_error("Incorrect number of MOAB shared pointers"); } - // Create a new unstructured mesh tally using new constructor + // Set mesh ID (-1 signifies auto-assign) + model::meshes.back()->set_id(-1); + int mesh_id = model::meshes.back()->id_; + + // Check we now have 2 meshes and id was correctly set + if(model::meshes.size()!=2) + fatal_error("Wrong number of meshes."); + else if(mesh_id!=2) + fatal_error("Mesh ID is incorrect"); + + // Add a new mesh filter tally - // Add unstructured mesh tally to openmc - // Run OpenMC openmc_err = openmc_run(); if (openmc_err) fatal_error(openmc_err_msg); diff --git a/tests/regression_tests/external_moab/test.py b/tests/regression_tests/external_moab/test.py index b9926715d48..113b6789e91 100644 --- a/tests/regression_tests/external_moab/test.py +++ b/tests/regression_tests/external_moab/test.py @@ -39,7 +39,7 @@ def cpp_driver(request): target_link_libraries(main OpenMC::libopenmc) set_target_properties(main PROPERTIES CXX_STANDARD 14 CXX_STANDARD_REQUIRED YES CXX_EXTENSIONS NO) set(CMAKE_CXX_FLAGS "-pedantic-errors") - + add_compile_definitions(DAGMC=1) """.format(openmc_dir))) # Create temporary build directory and change to there @@ -93,7 +93,7 @@ def _overwrite_results(self): def _test_output_created(self): pass - + # Directly compare results of unstructured mesh with internal and external moab def _compare_results(self): From 6f63bee2e2c282bf8d06764d88e6eb60d917760c Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Tue, 8 Jun 2021 09:24:54 +0100 Subject: [PATCH 0077/2572] External moab test now passes --- tests/regression_tests/external_moab/main.cpp | 37 +++++++++++++++++-- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/tests/regression_tests/external_moab/main.cpp b/tests/regression_tests/external_moab/main.cpp index 270bbc133e7..813592e6a75 100644 --- a/tests/regression_tests/external_moab/main.cpp +++ b/tests/regression_tests/external_moab/main.cpp @@ -1,6 +1,8 @@ #include "openmc/capi.h" #include "openmc/error.h" #include "openmc/mesh.h" +#include "openmc/tallies/filter_mesh.h" +#include "openmc/tallies/tally.h" #include #include "moab/Core.hpp" @@ -40,8 +42,8 @@ int main(int argc, char * argv[]){ fatal_error("Incorrect number of MOAB shared pointers"); } - // Set mesh ID (-1 signifies auto-assign) - model::meshes.back()->set_id(-1); + // Auto-assign mesh ID + model::meshes.back()->set_id(C_NONE); int mesh_id = model::meshes.back()->id_; // Check we now have 2 meshes and id was correctly set @@ -50,7 +52,36 @@ int main(int argc, char * argv[]){ else if(mesh_id!=2) fatal_error("Mesh ID is incorrect"); - // Add a new mesh filter tally + // Add a new mesh filter with auto-assigned ID + Filter* filter_ptr = Filter::create("mesh",C_NONE); + + // Upcast pointer type + MeshFilter* mesh_filter = dynamic_cast(filter_ptr); + + if(mesh_filter == nullptr){ + fatal_error("Failed to create mesh filter"); + } + + // Pass in the index of our mesh to the filter + int32_t mesh_idx = model::meshes.size() -1; + mesh_filter->set_mesh(mesh_idx); + + // Create a tally with auto-assigned ID + model::tallies.push_back(make_unique(C_NONE)); + + // Set tally name - matches that in test.py + model::tallies.back()->name_ = "external mesh tally"; + + // Set tally filter to our mesh filter + std::vector filters(1,filter_ptr); + model::tallies.back()->set_filters(filters); + + // Set tally estimator + model::tallies.back()->estimator_ = TallyEstimator::TRACKLENGTH; + + // Set tally score + std::vector score_names(1,"flux"); + model::tallies.back()->set_scores(score_names); // Run OpenMC openmc_err = openmc_run(); From 130724e3f7505e806c131d0f6409cfb7506f1db0 Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Tue, 15 Jun 2021 16:05:32 +0100 Subject: [PATCH 0078/2572] Small refactor of Tally::set_filters method: create new public method set_strides. --- include/openmc/tallies/tally.h | 3 +++ src/tallies/tally.cpp | 9 +++++++++ 2 files changed, 12 insertions(+) diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h index aaaff0e700e..25208f6fb89 100644 --- a/include/openmc/tallies/tally.h +++ b/include/openmc/tallies/tally.h @@ -53,6 +53,9 @@ class Tally { void set_filters(gsl::span filters); + //! Given already-set filters, set the stride lengths + void set_strides(); + int32_t strides(int i) const {return strides_[i];} int32_t n_filter_bins() const {return n_filter_bins_;} diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index b159f43b03b..153440c5064 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -366,9 +366,18 @@ Tally::set_filters(gsl::span filters) } } + // Set the strides. + set_strides(); + +} + +void +Tally::set_strides() +{ // Set the strides. Filters are traversed in reverse so that the last filter // has the shortest stride in memory and the first filter has the longest // stride. + auto n = filters_.size(); strides_.resize(n, 0); int stride = 1; for (int i = n-1; i >= 0; --i) { From 6c106433e66329316dd01a2890b42164998fb80a Mon Sep 17 00:00:00 2001 From: helen-brooks <67463845+helen-brooks@users.noreply.github.com> Date: Thu, 17 Jun 2021 09:15:27 +0100 Subject: [PATCH 0079/2572] c++ style changes: use this-> for calling object's methods Co-authored-by: Paul Romano --- src/mesh.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index f9f50f95c4a..a1818ed8450 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1584,13 +1584,13 @@ MOABMesh::MOABMesh(const std::string& filename) { MOABMesh::MOABMesh(std::shared_ptr external_mbi) { mbi_ = external_mbi; filename_ = "unknown (external file)"; - initialize(); + this->initialize(); } void MOABMesh::initialize() { // Create the MOAB interface and load data from file - create_interface(); + this->create_interface(); // Initialise MOAB error code moab::ErrorCode rval = moab::MB_SUCCESS; From c487c9ad2d1e812ed98ff50545a8839d663b3647 Mon Sep 17 00:00:00 2001 From: helen-brooks <67463845+helen-brooks@users.noreply.github.com> Date: Thu, 17 Jun 2021 09:21:12 +0100 Subject: [PATCH 0080/2572] C++ style changes: change how pointers are compared to null Co-authored-by: Paul Romano --- src/mesh.cpp | 2 +- tests/regression_tests/external_moab/main.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index a1818ed8450..773c5bf369c 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1630,7 +1630,7 @@ void MOABMesh::create_interface() { // Do not create a MOAB instance if one is already in memory - if(mbi_ == nullptr){ + if (!mbi_) { // create MOAB instance mbi_ = std::make_shared(); diff --git a/tests/regression_tests/external_moab/main.cpp b/tests/regression_tests/external_moab/main.cpp index 813592e6a75..3d867622d86 100644 --- a/tests/regression_tests/external_moab/main.cpp +++ b/tests/regression_tests/external_moab/main.cpp @@ -58,7 +58,7 @@ int main(int argc, char * argv[]){ // Upcast pointer type MeshFilter* mesh_filter = dynamic_cast(filter_ptr); - if(mesh_filter == nullptr){ + if (!mesh_filter) { fatal_error("Failed to create mesh filter"); } From 9dbbf82d257f473a884c2385f9b346386cfd2de9 Mon Sep 17 00:00:00 2001 From: helen-brooks <67463845+helen-brooks@users.noreply.github.com> Date: Fri, 18 Jun 2021 09:01:38 +0100 Subject: [PATCH 0081/2572] Some whitespace and bracketing changes suggested in code review Co-authored-by: Paul Romano Co-authored-by: Patrick Shriwise --- src/mesh.cpp | 1 - tests/regression_tests/external_moab/test.py | 16 +++++++--------- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 773c5bf369c..866d12a28db 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1639,7 +1639,6 @@ MOABMesh::create_interface() if (rval != moab::MB_SUCCESS) { fatal_error("Failed to load the unstructured mesh file: " + filename_); } - } } diff --git a/tests/regression_tests/external_moab/test.py b/tests/regression_tests/external_moab/test.py index 113b6789e91..c6d33254031 100644 --- a/tests/regression_tests/external_moab/test.py +++ b/tests/regression_tests/external_moab/test.py @@ -64,8 +64,8 @@ def cpp_driver(request): shutil.rmtree('build') os.remove('CMakeLists.txt') -class ExternalMoabTest(PyAPITestHarness): +class ExternalMoabTest(PyAPITestHarness): def __init__(self, executable, statepoint_name, model): super().__init__(statepoint_name, model) self.executable = executable @@ -80,7 +80,6 @@ def _run_openmc(self): openmc.run(openmc_exec=self.executable, event_based=config['event']) - # Override some methods to do nothing def _get_results(self): pass @@ -100,8 +99,8 @@ def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: # loop over the tallies and get data - ext_data=[] - unstr_data=[] + ext_data = [] + unstr_data = [] for tally in sp.tallies.values(): @@ -111,17 +110,17 @@ def _compare_results(self): if isinstance(flt.mesh, openmc.UnstructuredMesh): - if(tally.name == "external mesh tally"): + if tally.name == "external mesh tally": ext_data = tally.get_reshaped_data(value='mean') - elif (tally.name == "unstructured mesh tally"): + elif tally.name == "unstructured mesh tally": unstr_data = tally.get_reshaped_data(value='mean') # we expect these results to be the same to within at 8 # decimal places decimals = 8 np.testing.assert_array_almost_equal(unstr_data, - ext_data,decimals) + ext_data, decimals) @staticmethod def get_mesh_tally_data(tally): @@ -131,7 +130,6 @@ def get_mesh_tally_data(tally): std_dev.shape = (std_dev.size, 1) return np.sum(data, axis=1), np.sum(std_dev, axis=1) - def _cleanup(self): super()._cleanup() output = glob.glob('tally*.vtk') @@ -238,7 +236,7 @@ def test_external_mesh(cpp_driver): mesh_filename = "test_mesh_tets.h5m" # Create a normal unstructured mesh to compare to - uscd_mesh = openmc.UnstructuredMesh(mesh_filename,'moab') + uscd_mesh = openmc.UnstructuredMesh(mesh_filename, 'moab') # Create filters uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) From 32933587b9bcade0d53e03927404d8487a229e3f Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 18 Jun 2021 09:08:56 +0100 Subject: [PATCH 0082/2572] Run external-moab test main.cpp file through clang-format --- tests/regression_tests/external_moab/main.cpp | 38 ++++++++++--------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/tests/regression_tests/external_moab/main.cpp b/tests/regression_tests/external_moab/main.cpp index 3d867622d86..6e3709b6a10 100644 --- a/tests/regression_tests/external_moab/main.cpp +++ b/tests/regression_tests/external_moab/main.cpp @@ -1,12 +1,13 @@ +#include "moab/Core.hpp" #include "openmc/capi.h" #include "openmc/error.h" #include "openmc/mesh.h" #include "openmc/tallies/filter_mesh.h" #include "openmc/tallies/tally.h" #include -#include "moab/Core.hpp" -int main(int argc, char * argv[]){ +int main(int argc, char* argv[]) +{ using namespace openmc; int openmc_err; @@ -29,17 +30,17 @@ int main(int argc, char * argv[]){ moab::ErrorCode rval = moabPtrLocal->load_file(filename.c_str()); if (rval != moab::MB_SUCCESS) { fatal_error("Failed to load the unstructured mesh file: " + filename); - } - else{ - std::cout<<"Loaded external MOAB mesh from file "<(moabPtrLocal)); // Check we now have 2 copies of the shared ptr - if(moabPtrLocal.use_count()!=2){ - fatal_error("Incorrect number of MOAB shared pointers"); + if (moabPtrLocal.use_count() != 2) { + fatal_error("Incorrect number of MOAB shared pointers"); } // Auto-assign mesh ID @@ -47,13 +48,13 @@ int main(int argc, char * argv[]){ int mesh_id = model::meshes.back()->id_; // Check we now have 2 meshes and id was correctly set - if(model::meshes.size()!=2) - fatal_error("Wrong number of meshes."); - else if(mesh_id!=2) - fatal_error("Mesh ID is incorrect"); + if (model::meshes.size() != 2) + fatal_error("Wrong number of meshes."); + else if (mesh_id != 2) + fatal_error("Mesh ID is incorrect"); // Add a new mesh filter with auto-assigned ID - Filter* filter_ptr = Filter::create("mesh",C_NONE); + Filter* filter_ptr = Filter::create("mesh", C_NONE); // Upcast pointer type MeshFilter* mesh_filter = dynamic_cast(filter_ptr); @@ -63,7 +64,7 @@ int main(int argc, char * argv[]){ } // Pass in the index of our mesh to the filter - int32_t mesh_idx = model::meshes.size() -1; + int32_t mesh_idx = model::meshes.size() - 1; mesh_filter->set_mesh(mesh_idx); // Create a tally with auto-assigned ID @@ -73,24 +74,25 @@ int main(int argc, char * argv[]){ model::tallies.back()->name_ = "external mesh tally"; // Set tally filter to our mesh filter - std::vector filters(1,filter_ptr); + std::vector filters(1, filter_ptr); model::tallies.back()->set_filters(filters); // Set tally estimator model::tallies.back()->estimator_ = TallyEstimator::TRACKLENGTH; // Set tally score - std::vector score_names(1,"flux"); + std::vector score_names(1, "flux"); model::tallies.back()->set_scores(score_names); // Run OpenMC openmc_err = openmc_run(); - if (openmc_err) fatal_error(openmc_err_msg); + if (openmc_err) + fatal_error(openmc_err_msg); // Deallocate memory openmc_err = openmc_finalize(); - if (openmc_err) fatal_error(openmc_err_msg); + if (openmc_err) + fatal_error(openmc_err_msg); return EXIT_SUCCESS; - } From d4bf1c9b6f19c33a540a360513454dd36e845e32 Mon Sep 17 00:00:00 2001 From: helen-brooks <67463845+helen-brooks@users.noreply.github.com> Date: Fri, 18 Jun 2021 09:15:47 +0100 Subject: [PATCH 0083/2572] Reorder python imports in accordance with PEP8 in external moab test file Co-authored-by: Paul Romano --- tests/regression_tests/external_moab/test.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/regression_tests/external_moab/test.py b/tests/regression_tests/external_moab/test.py index c6d33254031..8680effcb8f 100644 --- a/tests/regression_tests/external_moab/test.py +++ b/tests/regression_tests/external_moab/test.py @@ -2,20 +2,19 @@ import os import shutil import subprocess +from subprocess import CalledProcessError import textwrap import glob from itertools import product + import openmc import openmc.lib import numpy as np - import pytest from tests.regression_tests import config from tests.testing_harness import PyAPITestHarness -from subprocess import CalledProcessError - pytestmark = pytest.mark.skipif( not openmc.lib._dagmc_enabled(), reason="DAGMC is not enabled.") From fa588b373f2f52c3e68d97b0223834afd1a636cf Mon Sep 17 00:00:00 2001 From: helen-brooks <67463845+helen-brooks@users.noreply.github.com> Date: Fri, 18 Jun 2021 09:17:48 +0100 Subject: [PATCH 0084/2572] Remove redundant code for setting source in test file as suggested in code review Co-authored-by: Paul Romano --- tests/regression_tests/external_moab/inputs_true.dat | 10 ++-------- tests/regression_tests/external_moab/test.py | 6 +----- 2 files changed, 3 insertions(+), 13 deletions(-) diff --git a/tests/regression_tests/external_moab/inputs_true.dat b/tests/regression_tests/external_moab/inputs_true.dat index 78081163514..af259eee471 100644 --- a/tests/regression_tests/external_moab/inputs_true.dat +++ b/tests/regression_tests/external_moab/inputs_true.dat @@ -48,14 +48,8 @@ 100 10 - - - - 0.0 1.0 - - - 0.0 1.0 - + + 0.0 0.0 0.0 diff --git a/tests/regression_tests/external_moab/test.py b/tests/regression_tests/external_moab/test.py index 8680effcb8f..29d402f4a70 100644 --- a/tests/regression_tests/external_moab/test.py +++ b/tests/regression_tests/external_moab/test.py @@ -255,11 +255,7 @@ def test_external_mesh(cpp_driver): settings.batches = 10 # Source setup - r = openmc.stats.Uniform(a=0.0, b=0.0) - theta = openmc.stats.Discrete(x=[0.0], p=[1.0]) - phi = openmc.stats.Discrete(x=[0.0], p=[1.0]) - - space = openmc.stats.SphericalIndependent(r, theta, phi) + space = openmc.stats.Point() angle = openmc.stats.Monodirectional((-1.0, 0.0, 0.0)) energy = openmc.stats.Discrete(x=[15.e+06], p=[1.0]) source = openmc.Source(space=space, energy=energy, angle=angle) From ffeed77d5d4437d0dc6aadbf42843a9b565208dc Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 18 Jun 2021 10:04:38 +0100 Subject: [PATCH 0085/2572] Reduce nested code in create_interface by changing if statement following @pshriwise suggestion --- src/mesh.cpp | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/mesh.cpp b/src/mesh.cpp index 866d12a28db..4f85c58c800 100644 --- a/src/mesh.cpp +++ b/src/mesh.cpp @@ -1630,15 +1630,15 @@ void MOABMesh::create_interface() { // Do not create a MOAB instance if one is already in memory - if (!mbi_) { - // create MOAB instance - mbi_ = std::make_shared(); + if (mbi_) return; - // load unstructured mesh file - moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); - if (rval != moab::MB_SUCCESS) { - fatal_error("Failed to load the unstructured mesh file: " + filename_); - } + // create MOAB instance + mbi_ = std::make_shared(); + + // load unstructured mesh file + moab::ErrorCode rval = mbi_->load_file(filename_.c_str()); + if (rval != moab::MB_SUCCESS) { + fatal_error("Failed to load the unstructured mesh file: " + filename_); } } From b8583eed83d8a00dc8ebba5034d2890930469d88 Mon Sep 17 00:00:00 2001 From: helen-brooks Date: Fri, 18 Jun 2021 10:26:06 +0100 Subject: [PATCH 0086/2572] external moab test now adheres to pep8 style standard --- tests/regression_tests/external_moab/test.py | 31 ++++++++++---------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/tests/regression_tests/external_moab/test.py b/tests/regression_tests/external_moab/test.py index 29d402f4a70..caff6b5dbd5 100644 --- a/tests/regression_tests/external_moab/test.py +++ b/tests/regression_tests/external_moab/test.py @@ -23,6 +23,7 @@ # Test that an external moab instance can be passed in through the C API + @pytest.fixture def cpp_driver(request): """Compile the external source""" @@ -36,7 +37,8 @@ def cpp_driver(request): add_executable(main main.cpp) find_package(OpenMC REQUIRED HINTS {}) target_link_libraries(main OpenMC::libopenmc) - set_target_properties(main PROPERTIES CXX_STANDARD 14 CXX_STANDARD_REQUIRED YES CXX_EXTENSIONS NO) + set_target_properties(main PROPERTIES CXX_STANDARD + 14 CXX_STANDARD_REQUIRED YES CXX_EXTENSIONS NO) set(CMAKE_CXX_FLAGS "-pedantic-errors") add_compile_definitions(DAGMC=1) """.format(openmc_dir))) @@ -83,7 +85,7 @@ def _run_openmc(self): def _get_results(self): pass - def _write_results(self,results_string): + def _write_results(self, results_string): pass def _overwrite_results(self): @@ -92,7 +94,8 @@ def _overwrite_results(self): def _test_output_created(self): pass - # Directly compare results of unstructured mesh with internal and external moab + # Directly compare results of unstructured mesh with internal and + # external moab def _compare_results(self): with openmc.StatePoint(self._sp_name) as sp: @@ -139,7 +142,7 @@ def _cleanup(self): def test_external_mesh(cpp_driver): - ### Materials ### + # Materials materials = openmc.Materials() fuel_mat = openmc.Material(name="fuel") @@ -160,7 +163,7 @@ def test_external_mesh(cpp_driver): materials.export_to_xml() - ### Geometry ### + # Geometry fuel_min_x = openmc.XPlane(-5.0, name="minimum x") fuel_max_x = openmc.XPlane(5.0, name="maximum x") @@ -189,9 +192,9 @@ def test_external_mesh(cpp_driver): clad_cell.region = (-fuel_min_x | +fuel_max_x | -fuel_min_y | +fuel_max_y | -fuel_min_z | +fuel_max_z) & \ - (+clad_min_x & -clad_max_x & - +clad_min_y & -clad_max_y & - +clad_min_z & -clad_max_z) + (+clad_min_x & -clad_max_x & + +clad_min_y & -clad_max_y & + +clad_min_z & -clad_max_z) clad_cell.fill = zirc_mat bounds = (10, 10, 10) @@ -221,16 +224,14 @@ def test_external_mesh(cpp_driver): water_cell.region = (-clad_min_x | +clad_max_x | -clad_min_y | +clad_max_y | -clad_min_z | +clad_max_z) & \ - (+water_min_x & -water_max_x & - +water_min_y & -water_max_y & - +water_min_z & -water_max_z) + (+water_min_x & -water_max_x & + +water_min_y & -water_max_y & + +water_min_z & -water_max_z) water_cell.fill = water_mat # create a containing universe geometry = openmc.Geometry([fuel_cell, clad_cell, water_cell]) - ### Tallies ### - # Meshes mesh_filename = "test_mesh_tets.h5m" @@ -240,7 +241,7 @@ def test_external_mesh(cpp_driver): # Create filters uscd_filter = openmc.MeshFilter(mesh=uscd_mesh) - # Create tallies + # Tallies tallies = openmc.Tallies() uscd_tally = openmc.Tally(name="unstructured mesh tally") uscd_tally.filters = [uscd_filter] @@ -248,7 +249,7 @@ def test_external_mesh(cpp_driver): uscd_tally.estimator = 'tracklength' tallies.append(uscd_tally) - ### Settings ### + # Settings settings = openmc.Settings() settings.run_mode = 'fixed source' settings.particles = 100 From 94fc050fec82b94ba98658baa2ae9512dd60c03e Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 23 Jun 2021 10:23:32 +0700 Subject: [PATCH 0087/2572] Add open_group that works with std::string --- include/openmc/hdf5_interface.h | 2 +- src/hdf5_interface.cpp | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/include/openmc/hdf5_interface.h b/include/openmc/hdf5_interface.h index f6016730411..ae91ce2ae9d 100644 --- a/include/openmc/hdf5_interface.h +++ b/include/openmc/hdf5_interface.h @@ -51,7 +51,7 @@ inline hid_t create_group(hid_t parent_id, const std::stringstream& name) hid_t file_open(const std::string& filename, char mode, bool parallel=false); - +hid_t open_group(hid_t group_id, const std::string& name); void write_string(hid_t group_id, const char* name, const std::string& buffer, bool indep); diff --git a/src/hdf5_interface.cpp b/src/hdf5_interface.cpp index 0f8db2dc5fe..522c9c26257 100644 --- a/src/hdf5_interface.cpp +++ b/src/hdf5_interface.cpp @@ -225,6 +225,11 @@ file_open(const std::string& filename, char mode, bool parallel) return file_open(filename.c_str(), mode, parallel); } +hid_t open_group(hid_t group_id, const std::string& name) +{ + return open_group(group_id, name.c_str()); +} + void file_close(hid_t file_id) { H5Fclose(file_id); From 482848cadb5f96b0bc902204715c120556b8de06 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 23 Jun 2021 10:23:05 +0700 Subject: [PATCH 0088/2572] Add openmc_properties_export/import to C API (incomplete) --- include/openmc/capi.h | 1 + include/openmc/cell.h | 8 ++++++ include/openmc/summary.h | 10 ++++++++ openmc/lib/cell.py | 18 ++++++++++++++ openmc/lib/core.py | 34 +++++++++++++++++++++++++- src/cell.cpp | 43 ++++++++++++++++++++++++++++++++ src/summary.cpp | 53 ++++++++++++++++++++++++++++++++++++++++ 7 files changed, 166 insertions(+), 1 deletion(-) diff --git a/include/openmc/capi.h b/include/openmc/capi.h index 2ca9fc430fc..43c33dd91aa 100644 --- a/include/openmc/capi.h +++ b/include/openmc/capi.h @@ -15,6 +15,7 @@ extern "C" { int openmc_cell_get_id(int32_t index, int32_t* id); int openmc_cell_get_temperature(int32_t index, const int32_t* instance, double* T); int openmc_cell_get_name(int32_t index, const char** name); + int openmc_cell_get_num_instances(int32_t index, int32_t* num_instances); int openmc_cell_set_name(int32_t index, const char* name); int openmc_cell_set_fill(int32_t index, int type, int32_t n, const int32_t* indices); int openmc_cell_set_id(int32_t index, int32_t id); diff --git a/include/openmc/cell.h b/include/openmc/cell.h index c7ba0ab1374..6976a8ee2ab 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -153,6 +153,14 @@ class Cell { //! \return Map with cell indexes as keys and instances as values std::unordered_map> get_contained_cells() const; + //! Export physical properties to HDF5 + //! \param[in] group HDF5 group to read from + void export_properties_hdf5(hid_t group) const; + + //! Import physical properties from HDF5 + //! \param[in] group HDF5 group to write to + void import_properties_hdf5(hid_t group); + protected: void get_contained_cells_inner( std::unordered_map>& contained_cells, diff --git a/include/openmc/summary.h b/include/openmc/summary.h index ff2ab71a897..95fb0be1678 100644 --- a/include/openmc/summary.h +++ b/include/openmc/summary.h @@ -11,6 +11,16 @@ void write_nuclides(hid_t file); void write_geometry(hid_t file); void write_materials(hid_t file); +//! Export physical properties for model +//! \param[in] filename Filename to write to +//! \return Error code +extern "C" int openmc_properties_export(const char* filename); + +//! Import physical properties for model +//! \param[in] filename Filename to read from +// \return Error code +extern "C" int openmc_properties_import(const char* filename); + } #endif // OPENMC_SUMMARY_H diff --git a/openmc/lib/cell.py b/openmc/lib/cell.py index c69d1e2a402..7fdc4542dbb 100644 --- a/openmc/lib/cell.py +++ b/openmc/lib/cell.py @@ -25,6 +25,9 @@ c_int32, POINTER(c_int), POINTER(POINTER(c_int32)), POINTER(c_int32)] _dll.openmc_cell_get_fill.restype = c_int _dll.openmc_cell_get_fill.errcheck = _error_handler +_dll.openmc_cell_get_num_instances.argtypes = [c_int32, POINTER(c_int32)] +_dll.openmc_cell_get_num_instances.restype = c_int +_dll.openmc_cell_get_num_instances.errcheck = _error_handler _dll.openmc_cell_get_temperature.argtypes = [ c_int32, POINTER(c_int32), POINTER(c_double)] _dll.openmc_cell_get_temperature.restype = c_int @@ -78,6 +81,14 @@ class Cell(_FortranObjectWithID): ---------- id : int ID of the cell + fill : openmc.lib.Material or list of openmc.lib.Material + Indicates what the region of space is filled with + name : str + Name of the cell + num_instances : int + Number of unique cell instances + bounding_box : 2-tuple of numpy.ndarray + Lower-left and upper-right coordinates of bounding box """ __instances = WeakValueDictionary() @@ -159,6 +170,12 @@ def fill(self, fill): indices = (c_int32*1)(-1) _dll.openmc_cell_set_fill(self._index, 0, 1, indices) + @property + def num_instances(self): + n = c_int32() + _dll.openmc_cell_get_num_instances(self._index, n) + return n.value + def get_temperature(self, instance=None): """Get the temperature of a cell @@ -211,6 +228,7 @@ def bounding_box(self): return llc, urc + class _CellMapping(Mapping): def __getitem__(self, key): index = c_int32() diff --git a/openmc/lib/core.py b/openmc/lib/core.py index e740e28699a..4dd01ebda59 100644 --- a/openmc/lib/core.py +++ b/openmc/lib/core.py @@ -62,7 +62,13 @@ class _SourceSite(Structure): _dll.openmc_next_batch.restype = c_int _dll.openmc_next_batch.errcheck = _error_handler _dll.openmc_plot_geometry.restype = c_int -_dll.openmc_plot_geometry.restype = _error_handler +_dll.openmc_plot_geometry.errcheck = _error_handler +_dll.openmc_properties_export.argtypes = [c_char_p] +_dll.openmc_properties_export.restype = c_int +_dll.openmc_properties_export.errcheck = _error_handler +_dll.openmc_properties_import.argtypes = [c_char_p] +_dll.openmc_properties_import.restype = c_int +_dll.openmc_properties_import.errcheck = _error_handler _dll.openmc_run.restype = c_int _dll.openmc_run.errcheck = _error_handler _dll.openmc_reset.restype = c_int @@ -307,6 +313,32 @@ def plot_geometry(): _dll.openmc_plot_geometry() +def properties_export(filename=None): + """Export physical properties. + + Parameters + ---------- + filename : str or None + Filename to export properties to (defaults to "properties.h5") + + """ + if filename is not None: + filename = c_char_p(filename.encode()) + _dll.openmc_properties_export(filename) + + +def properties_import(filename): + """Import physical properties. + + Parameters + ---------- + filename : str + Filename to import properties from + + """ + _dll.openmc_properties_import(filename.encode()) + + def reset(): """Reset tally results""" _dll.openmc_reset() diff --git a/src/cell.cpp b/src/cell.cpp index 6b88a67803e..37550e9f502 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -282,6 +282,38 @@ Cell::set_temperature(double T, int32_t instance, bool set_contained) } } +void Cell::export_properties_hdf5(hid_t group) const +{ + // Create a group for this cell. + auto cell_group = create_group(group, fmt::format("cell {}", id_)); + + // Write temperature in [K] for one or more cell instances + vector temps; + for (auto sqrtkT_val : sqrtkT_) + temps.push_back(sqrtkT_val * sqrtkT_val / K_BOLTZMANN); + write_dataset(cell_group, "temperature", temps); + + close_group(cell_group); +} + +void Cell::import_properties_hdf5(hid_t group) +{ + auto cell_group = open_group(group, fmt::format("cell {}", id_)); + + // Read temperatures from file + vector temps; + read_dataset(cell_group, "temperature", temps); + + // Modify temperatures for the cell + sqrtkT_.resize(0); + sqrtkT_.reserve(temps.size()); + for (auto T : temps) { + sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * T)); + } + + close_group(cell_group); +} + //============================================================================== // CSGCell implementation //============================================================================== @@ -1278,6 +1310,17 @@ openmc_cell_set_id(int32_t index, int32_t id) } } +extern "C" int +openmc_cell_get_num_instances(int32_t index, int32_t* num_instances) +{ + if (index < 0 || index >= model::cells.size()) { + set_errmsg("Index in cells array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + *num_instances = model::cells[index]->n_instances_; + return 0; +} + //! Extend the cells array by n elements extern "C" int openmc_extend_cells(int32_t n, int32_t* index_start, int32_t* index_end) diff --git a/src/summary.cpp b/src/summary.cpp index 3d2c6816b42..730a53a50b4 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -1,6 +1,9 @@ #include "openmc/summary.h" +#include + #include "openmc/cell.h" +#include "openmc/file_utils.h" #include "openmc/hdf5_interface.h" #include "openmc/lattice.h" #include "openmc/material.h" @@ -133,4 +136,54 @@ void write_materials(hid_t file) close_group(materials_group); } +extern "C" int openmc_properties_export(const char* filename) +{ + // Set a default filename if none was passed + std::string name = filename ? filename : "properties.h5"; + + // Display output message + auto msg = fmt::format("Exporting properties to {}...", name); + write_message(msg, 5); + + // Create a new file using default properties. + hid_t file = file_open(name, 'w'); + + // Write cell properties + auto geom_group = create_group(file, "geometry"); + write_attribute(geom_group, "n_cells", model::cells.size()); + auto cells_group = create_group(geom_group, "cells"); + for (const auto& c : model::cells) c->export_properties_hdf5(cells_group); + close_group(cells_group); + close_group(geom_group); + + // Terminate access to the file. + file_close(file); + return 0; +} + +extern "C" int openmc_properties_import(const char* filename) +{ + // Display output message + auto msg = fmt::format("Importing properties from {}...", filename); + write_message(msg, 5); + + // Create a new file using default properties. + if (!file_exists(filename)) { + set_errmsg(fmt::format("File '{}' does not exist.", filename)); + return OPENMC_E_INVALID_ARGUMENT; + } + hid_t file = file_open(filename, 'r'); + + // Read cell properties + auto geom_group = open_group(file, "geometry"); + auto cells_group = open_group(geom_group, "cells"); + for (const auto& c : model::cells) c->import_properties_hdf5(cells_group); + close_group(cells_group); + close_group(geom_group); + + // Terminate access to the file. + file_close(file); + return 0; +} + } // namespace openmc From ef7b43d986de99e7a6c87d7079b904523ff36b63 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 23 Jun 2021 11:39:22 +0700 Subject: [PATCH 0089/2572] Add material density to properties.h5 --- include/openmc/material.h | 8 ++++++++ src/material.cpp | 17 +++++++++++++++++ src/summary.cpp | 23 +++++++++++++++++++++-- 3 files changed, 46 insertions(+), 2 deletions(-) diff --git a/include/openmc/material.h b/include/openmc/material.h index 7e975a1bb66..f0d81037077 100644 --- a/include/openmc/material.h +++ b/include/openmc/material.h @@ -69,6 +69,14 @@ class Material //! Write material data to HDF5 void to_hdf5(hid_t group) const; + //! Export physical properties to HDF5 + //! \param[in] group HDF5 group to write to + void export_properties_hdf5(hid_t group) const; + + //! Import physical properties from HDF5 + //! \param[in] group HDF5 group to read from + void import_properties_hdf5(hid_t group); + //! Add nuclide to the material // //! \param[in] nuclide Name of the nuclide diff --git a/src/material.cpp b/src/material.cpp index 59d2f954e45..c2240c99bbd 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -1053,6 +1053,23 @@ void Material::to_hdf5(hid_t group) const close_group(material_group); } +void Material::export_properties_hdf5(hid_t group) const +{ + hid_t material_group = create_group(group, "material " + std::to_string(id_)); + write_attribute(material_group, "atom_density", density_); + write_attribute(material_group, "mass_density", density_gpcc_); + close_group(material_group); +} + +void Material::import_properties_hdf5(hid_t group) +{ + hid_t material_group = open_group(group, "material " + std::to_string(id_)); + double density; + read_attribute(material_group, "atom_density", density); + this->set_density(density, "atom/b-cm"); + close_group(material_group); +} + void Material::add_nuclide(const std::string& name, double density) { // Check if nuclide is already in material diff --git a/src/summary.cpp b/src/summary.cpp index 730a53a50b4..df694488478 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -152,10 +152,20 @@ extern "C" int openmc_properties_export(const char* filename) auto geom_group = create_group(file, "geometry"); write_attribute(geom_group, "n_cells", model::cells.size()); auto cells_group = create_group(geom_group, "cells"); - for (const auto& c : model::cells) c->export_properties_hdf5(cells_group); + for (const auto& c : model::cells) { + c->export_properties_hdf5(cells_group); + } close_group(cells_group); close_group(geom_group); + // Write material properties + hid_t materials_group = create_group(file, "materials"); + write_attribute(materials_group, "n_materials", model::materials.size()); + for (const auto& mat : model::materials) { + mat->export_properties_hdf5(materials_group); + } + close_group(materials_group); + // Terminate access to the file. file_close(file); return 0; @@ -177,10 +187,19 @@ extern "C" int openmc_properties_import(const char* filename) // Read cell properties auto geom_group = open_group(file, "geometry"); auto cells_group = open_group(geom_group, "cells"); - for (const auto& c : model::cells) c->import_properties_hdf5(cells_group); + for (const auto& c : model::cells) { + c->import_properties_hdf5(cells_group); + } close_group(cells_group); close_group(geom_group); + // Read material properties + auto materials_group = open_group(file, "materials"); + for (const auto& mat : model::materials) { + mat->import_properties_hdf5(materials_group); + } + close_group(materials_group); + // Terminate access to the file. file_close(file); return 0; From 7a4e32f7d51974e03c7c40fd9c6ca794f3e5f809 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 23 Jun 2021 11:48:45 +0700 Subject: [PATCH 0090/2572] Add filetype and other metadata to properties.h5 --- include/openmc/constants.h | 1 + src/summary.cpp | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/include/openmc/constants.h b/include/openmc/constants.h index aac3a52a832..2083f33b423 100644 --- a/include/openmc/constants.h +++ b/include/openmc/constants.h @@ -31,6 +31,7 @@ constexpr array VERSION_SUMMARY {6, 0}; constexpr array VERSION_VOLUME {1, 0}; constexpr array VERSION_VOXEL {2, 0}; constexpr array VERSION_MGXS_LIBRARY {1, 0}; +constexpr array VERSION_PROPERTIES {1, 0}; // ============================================================================ // ADJUSTABLE PARAMETERS diff --git a/src/summary.cpp b/src/summary.cpp index df694488478..29d37009b18 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -148,6 +148,17 @@ extern "C" int openmc_properties_export(const char* filename) // Create a new file using default properties. hid_t file = file_open(name, 'w'); + // Write metadata + write_attribute(file, "filetype", "properties"); + write_attribute(file, "version", VERSION_STATEPOINT); + write_attribute(file, "openmc_version", VERSION); +#ifdef GIT_SHA1 + write_attribute(file, "git_sha1", GIT_SHA1); +#endif + write_attribute(file, "date_and_time", time_stamp()); + write_attribute(file, "path", settings::path_input); + + // Write cell properties auto geom_group = create_group(file, "geometry"); write_attribute(geom_group, "n_cells", model::cells.size()); @@ -184,6 +195,14 @@ extern "C" int openmc_properties_import(const char* filename) } hid_t file = file_open(filename, 'r'); + // Ensure the filetype is correct + std::string filetype; + read_attribute(file, "filetype", filetype); + if (filetype != "properties") { + set_errmsg(fmt::format("File '{}' is not a properties file.", filename)); + return OPENMC_E_INVALID_ARGUMENT; + } + // Read cell properties auto geom_group = open_group(file, "geometry"); auto cells_group = open_group(geom_group, "cells"); From d6f483d36fbfb16a7912dcec302d6f062428b81d Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 23 Jun 2021 14:52:04 +0700 Subject: [PATCH 0091/2572] Make sure openmc_properties_export/import appear in global namespace --- include/openmc/capi.h | 26 ++++++++++++++++++-------- include/openmc/summary.h | 10 ---------- src/summary.cpp | 5 +++++ 3 files changed, 23 insertions(+), 18 deletions(-) diff --git a/include/openmc/capi.h b/include/openmc/capi.h index 43c33dd91aa..f08ec5c02f4 100644 --- a/include/openmc/capi.h +++ b/include/openmc/capi.h @@ -145,13 +145,13 @@ extern "C" { //! \param[in] meshtyally_id id of CMFD Mesh Tally //! \param[in] cmfd_indices indices storing spatial and energy dimensions of CMFD problem //! \param[in] norm CMFD normalization factor - extern "C" void openmc_initialize_mesh_egrid(const int meshtally_id, const int* cmfd_indices, - const double norm); + void openmc_initialize_mesh_egrid(const int meshtally_id, const int* cmfd_indices, + const double norm); //! Sets the mesh and energy grid for CMFD reweight //! \param[in] feedback whether or not to run CMFD feedback //! \param[in] cmfd_src computed CMFD source - extern "C" void openmc_cmfd_reweight(const bool feedback, const double* cmfd_src); + void openmc_cmfd_reweight(const bool feedback, const double* cmfd_src); //! Sets the fixed variables that are used for CMFD linear solver //! \param[in] indptr CSR format index pointer array of loss matrix @@ -162,10 +162,10 @@ extern "C" { //! \param[in] spectral spectral radius of CMFD matrices and tolerances //! \param[in] map coremap for problem, storing accelerated regions //! \param[in] use_all_threads whether to use all threads when running CMFD solver - extern "C" void openmc_initialize_linsolver(const int* indptr, int len_indptr, - const int* indices, int n_elements, - int dim, double spectral, - const int* map, bool use_all_threads); + void openmc_initialize_linsolver(const int* indptr, int len_indptr, + const int* indices, int n_elements, + int dim, double spectral, + const int* map, bool use_all_threads); //! Runs a Gauss Seidel linear solver to solve CMFD matrix equations //! linear solver @@ -174,9 +174,19 @@ extern "C" { //! \param[out] x unknown vector //! \param[in] tol tolerance on final error //! \return number of inner iterations required to reach convergence - extern "C" int openmc_run_linsolver(const double* A_data, const double* b, + int openmc_run_linsolver(const double* A_data, const double* b, double* x, double tol); + //! Export physical properties for model + //! \param[in] filename Filename to write to + //! \return Error code + int openmc_properties_export(const char* filename); + + //! Import physical properties for model + //! \param[in] filename Filename to read from + // \return Error code + int openmc_properties_import(const char* filename); + // Error codes extern int OPENMC_E_UNASSIGNED; extern int OPENMC_E_ALLOCATE; diff --git a/include/openmc/summary.h b/include/openmc/summary.h index 95fb0be1678..ff2ab71a897 100644 --- a/include/openmc/summary.h +++ b/include/openmc/summary.h @@ -11,16 +11,6 @@ void write_nuclides(hid_t file); void write_geometry(hid_t file); void write_materials(hid_t file); -//! Export physical properties for model -//! \param[in] filename Filename to write to -//! \return Error code -extern "C" int openmc_properties_export(const char* filename); - -//! Import physical properties for model -//! \param[in] filename Filename to read from -// \return Error code -extern "C" int openmc_properties_import(const char* filename); - } #endif // OPENMC_SUMMARY_H diff --git a/src/summary.cpp b/src/summary.cpp index 29d37009b18..c2276c063ee 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -2,6 +2,7 @@ #include +#include "openmc/capi.h" #include "openmc/cell.h" #include "openmc/file_utils.h" #include "openmc/hdf5_interface.h" @@ -136,6 +137,10 @@ void write_materials(hid_t file) close_group(materials_group); } +//============================================================================== +// C API +//============================================================================== + extern "C" int openmc_properties_export(const char* filename) { // Set a default filename if none was passed From 01b023b7ec7a4ad391afd293d514c1352d1292e3 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 12:09:59 +0700 Subject: [PATCH 0092/2572] Include cell instance in id_map --- openmc/lib/plot.py | 2 +- src/plot.cpp | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/openmc/lib/plot.py b/openmc/lib/plot.py index c51000e0de4..b029d675708 100644 --- a/openmc/lib/plot.py +++ b/openmc/lib/plot.py @@ -232,7 +232,7 @@ def id_map(plot): OpenMC property ids with dtype int32 """ - img_data = np.zeros((plot.v_res, plot.h_res, 2), + img_data = np.zeros((plot.v_res, plot.h_res, 3), dtype=np.dtype('int32')) _dll.openmc_id_map(plot, img_data.ctypes.data_as(POINTER(c_int32))) return img_data diff --git a/src/plot.cpp b/src/plot.cpp index 615701341e8..3e55d1d5f96 100644 --- a/src/plot.cpp +++ b/src/plot.cpp @@ -36,7 +36,7 @@ constexpr int32_t NOT_FOUND {-2}; constexpr int32_t OVERLAP {-3}; IdData::IdData(size_t h_res, size_t v_res) - : data_({v_res, h_res, 2}, NOT_FOUND) + : data_({v_res, h_res, 3}, NOT_FOUND) { } void @@ -44,18 +44,20 @@ IdData::set_value(size_t y, size_t x, const Particle& p, int level) { // set cell data if (p.n_coord() <= level) { data_(y, x, 0) = NOT_FOUND; + data_(y, x, 1) = NOT_FOUND; } else { data_(y, x, 0) = model::cells.at(p.coord(level).cell)->id_; + data_(y, x, 1) = p.cell_instance(); } // set material data Cell* c = model::cells.at(p.coord(p.n_coord() - 1).cell).get(); if (p.material() == MATERIAL_VOID) { - data_(y, x, 1) = MATERIAL_VOID; + data_(y, x, 2) = MATERIAL_VOID; return; } else if (c->type_ == Fill::MATERIAL) { Material* m = model::materials.at(p.material()).get(); - data_(y, x, 1) = m->id_; + data_(y, x, 2) = m->id_; } } From 1a1a5a1cf47270e43999a152db7e3b3c47ecec74 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:17:31 +0700 Subject: [PATCH 0093/2572] Add documentation for properties file format --- docs/source/io_formats/index.rst | 1 + docs/source/io_formats/properties.rst | 36 +++++++++++++++++++++++++++ 2 files changed, 37 insertions(+) create mode 100644 docs/source/io_formats/properties.rst diff --git a/docs/source/io_formats/index.rst b/docs/source/io_formats/index.rst index 7ae22c0a33f..8c89bd2a1d9 100644 --- a/docs/source/io_formats/index.rst +++ b/docs/source/io_formats/index.rst @@ -45,6 +45,7 @@ Output Files statepoint source summary + properties depletion_results particle_restart track diff --git a/docs/source/io_formats/properties.rst b/docs/source/io_formats/properties.rst new file mode 100644 index 00000000000..5030e78f35d --- /dev/null +++ b/docs/source/io_formats/properties.rst @@ -0,0 +1,36 @@ +.. _io_properties: + +====================== +Properties File Format +====================== + +The current version of the properties file format is 1.0. + +**/** + +:Attributes: - **filetype** (*char[]*) -- String indicating the type of file. + - **version** (*int[2]*) -- Major and minor version of the + statepoint file format. + - **openmc_version** (*int[3]*) -- Major, minor, and release + version number for OpenMC. + - **git_sha1** (*char[40]*) -- Git commit SHA-1 hash. + - **date_and_time** (*char[]*) -- Date and time the summary was + written. + - **path** (*char[]*) -- Path to directory containing input files. + +**/geometry/** + +:Attributes: - **n_cells** (*int*) -- Number of cells in the problem. + +**/geometry/cells/cell /** + +:Datasets: - **temperature** (*double[]*) -- Temperature of the cell in [K]. + +**/materials/** + +:Attributes: - **n_materials** (*int*) -- Number of materials in the problem. + +**/materials/material /** + +:Attributes: - **atom_density** (*double*) -- Total density in [atom/b-cm]. + - **mass_density** (*double*) -- Total density in [g/cm^3]. From d135cc272136500eac6d9cef769754935cd4ee58 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:23:54 +0700 Subject: [PATCH 0094/2572] Add consistency checks on number of cells/materials --- src/summary.cpp | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/src/summary.cpp b/src/summary.cpp index c2276c063ee..62f51c94de8 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -208,8 +208,16 @@ extern "C" int openmc_properties_import(const char* filename) return OPENMC_E_INVALID_ARGUMENT; } - // Read cell properties + // Make sure number of cells matches auto geom_group = open_group(file, "geometry"); + int32_t n; + read_attribute(geom_group, "n_cells", n); + if (n != openmc::model::cells.size()) { + set_errmsg(fmt::format("Number of cells in {} doesn't match current model.", filename)); + return OPENMC_E_GEOMETRY; + } + + // Read cell properties auto cells_group = open_group(geom_group, "cells"); for (const auto& c : model::cells) { c->import_properties_hdf5(cells_group); @@ -217,8 +225,15 @@ extern "C" int openmc_properties_import(const char* filename) close_group(cells_group); close_group(geom_group); - // Read material properties + // Make sure number of cells matches auto materials_group = open_group(file, "materials"); + read_attribute(materials_group, "n_materials", n); + if (n != openmc::model::materials.size()) { + set_errmsg(fmt::format("Number of materials in {} doesn't match current model.", filename)); + return OPENMC_E_GEOMETRY; + } + + // Read material properties for (const auto& mat : model::materials) { mat->import_properties_hdf5(materials_group); } From 33f7d09f715e196d1afa1e065543be0e8352c6f4 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:26:50 +0700 Subject: [PATCH 0095/2572] Use names import_properties and export_properties for Python API --- openmc/lib/core.py | 60 ++++++++++++++++++++++++++-------------------- 1 file changed, 34 insertions(+), 26 deletions(-) diff --git a/openmc/lib/core.py b/openmc/lib/core.py index 4dd01ebda59..0da24aab885 100644 --- a/openmc/lib/core.py +++ b/openmc/lib/core.py @@ -126,6 +126,24 @@ def current_batch(): return c_int.in_dll(_dll, 'current_batch').value +def export_properties(filename=None): + """Export physical properties. + + Parameters + ---------- + filename : str or None + Filename to export properties to (defaults to "properties.h5") + + See Also + -------- + openmc.lib.import_properties + + """ + if filename is not None: + filename = c_char_p(filename.encode()) + _dll.openmc_properties_export(filename) + + def finalize(): """Finalize simulation and free memory""" _dll.openmc_finalize() @@ -184,6 +202,22 @@ def hard_reset(): _dll.openmc_hard_reset() +def import_properties(filename): + """Import physical properties. + + Parameters + ---------- + filename : str + Filename to import properties from + + See Also + -------- + openmc.lib.export_properties + + """ + _dll.openmc_properties_import(filename.encode()) + + def init(args=None, intracomm=None): """Initialize OpenMC @@ -313,32 +347,6 @@ def plot_geometry(): _dll.openmc_plot_geometry() -def properties_export(filename=None): - """Export physical properties. - - Parameters - ---------- - filename : str or None - Filename to export properties to (defaults to "properties.h5") - - """ - if filename is not None: - filename = c_char_p(filename.encode()) - _dll.openmc_properties_export(filename) - - -def properties_import(filename): - """Import physical properties. - - Parameters - ---------- - filename : str - Filename to import properties from - - """ - _dll.openmc_properties_import(filename.encode()) - - def reset(): """Reset tally results""" _dll.openmc_reset() From b63030a04f3492d45fe88a11c926bf62ff6c9196 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:50:05 +0700 Subject: [PATCH 0096/2572] Add Model.import_properties method --- openmc/model/model.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/openmc/model/model.py b/openmc/model/model.py index 7d6acb281ce..e7a56bb19a0 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -2,6 +2,8 @@ from pathlib import Path import time +import h5py + import openmc from openmc.checkvalue import check_type, check_value @@ -197,6 +199,38 @@ def export_to_xml(self, directory='.'): if self.plots: self.plots.export_to_xml(d) + def import_properties(self, filename): + """Import physical properties + + Parameters + ---------- + filename : str + Path to properties HDF5 file + + See Also + -------- + openmc.lib.export_properties + + """ + cells = self.geometry.get_all_cells() + materials = self.geometry.get_all_materials() + + with h5py.File(filename, 'r') as fh: + # Update temperatures for cells filled with materials + cells_group = fh['geometry/cells'] + for name, group in cells_group.items(): + cell_id = int(name.split()[1]) + cell = cells[cell_id] + if cell.fill_type in ('material', 'distribmat'): + cell.temperature = group['temperature'][()] + + # Update material densities + mats_group = fh['materials'] + for name, group in mats_group.items(): + mat_id = int(name.split()[1]) + atom_density = group.attrs['atom_density'] + materials[mat_id].set_density('atom/b-cm', atom_density) + def run(self, **kwargs): """Creates the XML files, runs OpenMC, and returns the path to the last statepoint file generated. From fd2637cb485eca0f23ee2bdea0ff880795aff26b Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:56:38 +0700 Subject: [PATCH 0097/2572] Add Model.from_xml classmethod --- openmc/model/model.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/openmc/model/model.py b/openmc/model/model.py index e7a56bb19a0..de80bfa3a11 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -126,6 +126,31 @@ def plots(self, plots): for plot in plots: self._plots.append(plot) + @classmethod + def from_xml(cls, geometry='geometry.xml', materials='materials.xml', + settings='settings.xml'): + """Create model from existing XML files + + Parameters + ---------- + geometry : str + Path to geometry.xml file + materials : str + Path to materials.xml file + settings : str + Path to settings.xml file + + Returns + ------- + openmc.model.Model + Model created from XML files + + """ + materials = openmc.Materials.from_xml(materials) + geometry = openmc.Geometry.from_xml(geometry, materials) + settings = openmc.Settings.from_xml(settings) + return cls(geometry, materials, settings) + def deplete(self, timesteps, chain_file=None, method='cecm', fission_q=None, **kwargs): """Deplete model using specified timesteps/power From 87bd987ebb802200540c23cfccaba2c56a672ed5 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 14:58:49 +0700 Subject: [PATCH 0098/2572] Make Model available in main openmc namespace --- openmc/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/openmc/__init__.py b/openmc/__init__.py index 50246cdd8a2..8985099ba81 100644 --- a/openmc/__init__.py +++ b/openmc/__init__.py @@ -31,7 +31,7 @@ from openmc.polynomial import * from . import examples -# Import a few convencience functions that used to be here -from openmc.model import rectangular_prism, hexagonal_prism +# Import a few names from the model module +from openmc.model import rectangular_prism, hexagonal_prism, Model __version__ = '0.13.0-dev' From 8f26aeb47128e1b7707c47e8499e30e8f08115e2 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 15:02:32 +0700 Subject: [PATCH 0099/2572] Fix Model docstring --- openmc/model/model.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index de80bfa3a11..84650767afc 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -13,11 +13,11 @@ class Model: This class can be used to store instances of :class:`openmc.Geometry`, :class:`openmc.Materials`, :class:`openmc.Settings`, - :class:`openmc.Tallies`, :class:`openmc.Plots`, and :class:`openmc.CMFD`, - thus making a complete model. The :meth:`Model.export_to_xml` method will - export XML files for all attributes that have been set. If the - :meth:`Model.materials` attribute is not set, it will attempt to create a - ``materials.xml`` file based on all materials appearing in the geometry. + :class:`openmc.Tallies`, and :class:`openmc.Plots`, thus making a complete + model. The :meth:`Model.export_to_xml` method will export XML files for all + attributes that have been set. If the :attr:`Model.materials` attribute is + not set, it will attempt to create a ``materials.xml`` file based on all + materials appearing in the geometry. Parameters ---------- From 51e1b5f41c4bd0e16afa083f505bdda4406b04b8 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 15:14:00 +0700 Subject: [PATCH 0100/2572] Fix id_map test --- tests/unit_tests/test_lib.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/unit_tests/test_lib.py b/tests/unit_tests/test_lib.py index 87d4843c77f..9ed080a7bb0 100644 --- a/tests/unit_tests/test_lib.py +++ b/tests/unit_tests/test_lib.py @@ -116,6 +116,7 @@ def test_cell(lib_init): cell.name = "Not fuel" assert cell.name == "Not fuel" + def test_cell_temperature(lib_init): cell = openmc.lib.cells[1] cell.set_temperature(100.0, 0) @@ -558,9 +559,9 @@ def test_load_nuclide(lib_init): def test_id_map(lib_init): - expected_ids = np.array([[(3, 3), (2, 2), (3, 3)], - [(2, 2), (1, 1), (2, 2)], - [(3, 3), (2, 2), (3, 3)]], dtype='int32') + expected_ids = np.array([[(3, 0, 3), (2, 0, 2), (3, 0, 3)], + [(2, 0, 2), (1, 0, 1), (2, 0, 2)], + [(3, 0, 3), (2, 0, 2), (3, 0, 3)]], dtype='int32') # create a plot object s = openmc.lib.plot._PlotBase() From 4449cc42f2e57935cac23e54d2e6baad17f0aa31 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 15:35:54 +0700 Subject: [PATCH 0101/2572] Make sure properties.h5 gets closed properly upon error --- src/summary.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/summary.cpp b/src/summary.cpp index 62f51c94de8..64d9a63629a 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -204,6 +204,7 @@ extern "C" int openmc_properties_import(const char* filename) std::string filetype; read_attribute(file, "filetype", filetype); if (filetype != "properties") { + file_close(file); set_errmsg(fmt::format("File '{}' is not a properties file.", filename)); return OPENMC_E_INVALID_ARGUMENT; } @@ -213,6 +214,8 @@ extern "C" int openmc_properties_import(const char* filename) int32_t n; read_attribute(geom_group, "n_cells", n); if (n != openmc::model::cells.size()) { + close_group(geom_group); + file_close(file); set_errmsg(fmt::format("Number of cells in {} doesn't match current model.", filename)); return OPENMC_E_GEOMETRY; } @@ -229,6 +232,8 @@ extern "C" int openmc_properties_import(const char* filename) auto materials_group = open_group(file, "materials"); read_attribute(materials_group, "n_materials", n); if (n != openmc::model::materials.size()) { + close_group(materials_group); + file_close(file); set_errmsg(fmt::format("Number of materials in {} doesn't match current model.", filename)); return OPENMC_E_GEOMETRY; } From 329dff20189a3e8ec960a2eb1421664a2428924a Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 15:36:41 +0700 Subject: [PATCH 0102/2572] Add tests for export_properties and import_properties --- openmc/lib/material.py | 2 ++ tests/unit_tests/test_lib.py | 46 ++++++++++++++++++++++++++++++++++++ 2 files changed, 48 insertions(+) diff --git a/openmc/lib/material.py b/openmc/lib/material.py index 6f80abb5e29..fa27c4678b7 100644 --- a/openmc/lib/material.py +++ b/openmc/lib/material.py @@ -87,6 +87,8 @@ class Material(_FortranObjectWithID): ID of the material nuclides : list of str List of nuclides in the material + density : float + Density of the material in [g/cm^3] densities : numpy.ndarray Array of densities in atom/b-cm name : str diff --git a/tests/unit_tests/test_lib.py b/tests/unit_tests/test_lib.py index 9ed080a7bb0..3b6b02c393b 100644 --- a/tests/unit_tests/test_lib.py +++ b/tests/unit_tests/test_lib.py @@ -115,6 +115,7 @@ def test_cell(lib_init): assert cell.name == "Fuel" cell.name = "Not fuel" assert cell.name == "Not fuel" + assert cell.num_instances == 1 def test_cell_temperature(lib_init): @@ -125,6 +126,21 @@ def test_cell_temperature(lib_init): assert cell.get_temperature() == pytest.approx(200.0) +def test_properties_temperature(lib_init): + # Cell temperature should be 200 from above test + cell = openmc.lib.cells[1] + assert cell.get_temperature() == pytest.approx(200.0) + + # Export properties and change temperature + openmc.lib.export_properties('properties.h5') + cell.set_temperature(300.0) + assert cell.get_temperature() == pytest.approx(300.0) + + # Import properties and check that temperature is restored + openmc.lib.import_properties('properties.h5') + assert cell.get_temperature() == pytest.approx(200.0) + + def test_new_cell(lib_init): with pytest.raises(exc.AllocationError): openmc.lib.Cell(1) @@ -133,6 +149,13 @@ def test_new_cell(lib_init): assert len(openmc.lib.cells) == 5 +def test_properties_fail_cell(lib_init): + # The number of cells was changed in the previous test, so the properties + # file is no longer valid + with pytest.raises(exc.GeometryError, match="Number of cells"): + openmc.lib.import_properties("properties.h5") + + def test_material_mapping(lib_init): mats = openmc.lib.materials assert isinstance(mats, Mapping) @@ -168,6 +191,21 @@ def test_material(lib_init): m.name = "Not hot borated water" assert m.name == "Not hot borated water" + +def test_properties_density(lib_init): + m = openmc.lib.materials[1] + orig_density = m.density + + # Export properties and change density + openmc.lib.export_properties('properties.h5') + m.set_density(orig_density*2, 'g/cm3') + assert m.density == pytest.approx(orig_density*2) + + # Import properties and check that density was restored + openmc.lib.import_properties('properties.h5') + assert m.density == pytest.approx(orig_density) + + def test_material_add_nuclide(lib_init): m = openmc.lib.materials[3] m.add_nuclide('Xe135', 1e-12) @@ -183,6 +221,13 @@ def test_new_material(lib_init): assert len(openmc.lib.materials) == 5 +def test_properties_fail_material(lib_init): + # The number of materials was changed in the previous test, so the properties + # file is no longer valid + with pytest.raises(exc.GeometryError, match="Number of materials"): + openmc.lib.import_properties("properties.h5") + + def test_nuclide_mapping(lib_init): nucs = openmc.lib.nuclides assert isinstance(nucs, Mapping) @@ -576,6 +621,7 @@ def test_id_map(lib_init): ids = openmc.lib.plot.id_map(s) assert np.array_equal(expected_ids, ids) + def test_property_map(lib_init): expected_properties = np.array( [[(293.6, 0.740582), (293.6, 6.55), (293.6, 0.740582)], From 6190ff8ff3b6d687b9e2485f787cec64fab26394 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 15:50:04 +0700 Subject: [PATCH 0103/2572] Add test for Model.import_properties --- tests/unit_tests/test_model.py | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 tests/unit_tests/test_model.py diff --git a/tests/unit_tests/test_model.py b/tests/unit_tests/test_model.py new file mode 100644 index 00000000000..5ff3430eae4 --- /dev/null +++ b/tests/unit_tests/test_model.py @@ -0,0 +1,33 @@ +import pytest +import openmc +import openmc.lib + + +def test_import_properties(run_in_tmpdir, mpi_intracomm): + """Test importing properties on the Model class """ + + # Create PWR pin cell model and write XML files + model = openmc.examples.pwr_pin_cell() + model.export_to_xml() + + # Change fuel temperature and density and export properties + openmc.lib.init(intracomm=mpi_intracomm) + cell = openmc.lib.cells[1] + cell.set_temperature(600.0) + cell.fill.set_density(5.0, 'g/cm3') + openmc.lib.export_properties() + openmc.lib.finalize() + + # Import properties to existing model and re-export to new directory + model.import_properties("properties.h5") + model.export_to_xml("with_properties") + + # Load model with properties and confirm temperature/density has been changed + model_with_properties = openmc.Model.from_xml( + 'with_properties/geometry.xml', + 'with_properties/materials.xml', + 'with_properties/settings.xml' + ) + cell = model_with_properties.geometry.get_all_cells()[1] + assert cell.temperature == [600.0] + assert cell.fill.get_mass_density() == pytest.approx(5.0) From 803f17500c4e148b1b72b27f901bafc2676957db Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Thu, 24 Jun 2021 17:05:10 +0700 Subject: [PATCH 0104/2572] Fix use of get_map within plot.cpp and failing properties test --- src/plot.cpp | 5 +++-- tests/unit_tests/test_model.py | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/plot.cpp b/src/plot.cpp index 3e55d1d5f96..999cf3d294f 100644 --- a/src/plot.cpp +++ b/src/plot.cpp @@ -157,7 +157,8 @@ void create_ppm(Plot const& pl) // assign colors for (size_t y = 0; y < height; y++) { for (size_t x = 0; x < width; x++) { - auto id = ids.data_(y, x, pl.color_by_); + int idx = pl.color_by_ == PlotColorBy::cells ? 0 : 2; + auto id = ids.data_(y, x, idx); // no setting needed if not found if (id == NOT_FOUND) { continue; } if (id == OVERLAP) { @@ -840,7 +841,7 @@ void create_voxel(Plot const& pl) IdData ids = pltbase.get_map(); // select only cell/material ID data and flip the y-axis - int idx = pl.color_by_ == PlotColorBy::cells ? 0 : 1; + int idx = pl.color_by_ == PlotColorBy::cells ? 0 : 2; xt::xtensor data_slice = xt::view(ids.data_, xt::all(), xt::all(), idx); xt::xtensor data_flipped = xt::flip(data_slice, 0); diff --git a/tests/unit_tests/test_model.py b/tests/unit_tests/test_model.py index 5ff3430eae4..4b658d5ab07 100644 --- a/tests/unit_tests/test_model.py +++ b/tests/unit_tests/test_model.py @@ -7,6 +7,7 @@ def test_import_properties(run_in_tmpdir, mpi_intracomm): """Test importing properties on the Model class """ # Create PWR pin cell model and write XML files + openmc.reset_auto_ids() model = openmc.examples.pwr_pin_cell() model.export_to_xml() From a7dab4e68c022b039b0f4f8ca2b3e58c4b74aa59 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 06:39:49 -0500 Subject: [PATCH 0105/2572] Adding comment on DAGMC indices. --- src/dagmc.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 5932e04a12c..1132681a5b5 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -245,7 +245,7 @@ DAGUniverse::initialize() { // set cell ids using global IDs auto s = std::make_unique(); - s->dag_index_ = i+1; + s->dag_index_ = i+1; // DAGMC indices start at 1 s->id_ = adjust_geometry_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); s->dagmc_ptr_ = dagmc_instance_; From fbcaf5acd8db43707ea6b6f430d1f2ba240ca6d4 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:45:02 -0500 Subject: [PATCH 0106/2572] Apply PR suggestions from @paulromano Co-authored-by: Paul Romano --- docs/source/usersguide/geometry.rst | 6 +++--- include/openmc/cell.h | 2 +- include/openmc/dagmc.h | 21 +++++++++---------- openmc/universe.py | 5 ++--- src/particle.cpp | 1 - src/surface.cpp | 3 +-- tests/regression_tests/dagmc/legacy/test.py | 2 +- tests/regression_tests/dagmc/refl/test.py | 2 +- .../regression_tests/dagmc/universes/test.py | 2 +- 9 files changed, 20 insertions(+), 24 deletions(-) diff --git a/docs/source/usersguide/geometry.rst b/docs/source/usersguide/geometry.rst index 4df3681a536..2b6a6fef2f1 100644 --- a/docs/source/usersguide/geometry.rst +++ b/docs/source/usersguide/geometry.rst @@ -443,7 +443,7 @@ entirely by a DAGMC geometry will contain only the DAGMC universe. Using a :class:`openmc.DAGMCUniverse` looks like the following:: dag_univ = openmc.DAGMCUniverse(filename='dagmc.h5m') - geometry = openmc.Geometry(root=dag_univ) + geometry = openmc.Geometry(dag_univ) geometry.export_to_xml() The resulting ``geometry.xml`` file will be: @@ -475,7 +475,7 @@ Cell, Surface, and Material IDs ------------------------------- By default, DAGMC applies cell and surface IDs defined by the CAD engine that -the model originated in. If these IDs overlap with ID's in the CSG ID space, +the model originated in. If these IDs overlap with IDs in the CSG ID space, this will result in an error. However, the ``auto_ids`` property of a DAGMC universe can be set to set DAGMC cell and surface IDs by appending to the existing CSG cell ID space in the OpenMC model. @@ -485,7 +485,7 @@ assignments are based on natively defined OpenMC materials, no further work is required. If DAGMC materials are assigned using the `University of Wisconsin Unified Workflow`_ (UWUW), however, material IDs in the UWUW material library may overlap with those used in the CSG geometry. In this case, overlaps in the -UWUW and OpenMC material ID space will cause an error. To automatically resolved +UWUW and OpenMC material ID space will cause an error. To automatically resolve these ID overlaps, ``auto_ids`` can be set to ``True`` to append the UWUW material IDs to the OpenMC material ID space. diff --git a/include/openmc/cell.h b/include/openmc/cell.h index ad17b6f78ba..f04f9f6b769 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -231,7 +231,7 @@ class CSGCell : public Cell std::pair distance(Position r, Direction u, int32_t on_surface, Particle* p) const; - void to_hdf5_inner(hid_t group_id) const; + void to_hdf5_inner(hid_t group_id) const override; BoundingBox bounding_box() const; diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 6e292488a0e..1369424e737 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -19,15 +19,14 @@ class UWUW; namespace openmc { -class DAGSurface : public Surface -{ +class DAGSurface : public Surface { public: DAGSurface(); - double evaluate(Position r) const; - double distance(Position r, Direction u, bool coincident) const; - Direction normal(Position r) const; - Direction reflect(Position r, Direction u, Particle* p) const; + double evaluate(Position r) const override; + double distance(Position r, Direction u, bool coincident) const override; + Direction normal(Position r) const override; + Direction reflect(Position r, Direction u, Particle* p) const override; inline void to_hdf5_inner(hid_t group_id) const override {}; @@ -39,14 +38,14 @@ class DAGCell : public Cell { public: DAGCell(); - bool contains(Position r, Direction u, int32_t on_surface) const; + bool contains(Position r, Direction u, int32_t on_surface) const override; std::pair - distance(Position r, Direction u, int32_t on_surface, Particle* p) const; + distance(Position r, Direction u, int32_t on_surface, Particle* p) const override; - BoundingBox bounding_box() const; + BoundingBox bounding_box() const override; - void to_hdf5_inner(hid_t group_id) const; + void to_hdf5_inner(hid_t group_id) const override; std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance int32_t dag_index_; //!< DagMC index of cell @@ -98,7 +97,7 @@ class DAGUniverse : public Universe { //! \return A string of the ID ranges for entities of dimension \p dim std::string dagmc_ids_for_dim(int dim) const; - virtual bool find_cell(Particle &p) const override; + bool find_cell(Particle &p) const override; // Data Members std::string filename_; //!< Name of the DAGMC file used to create this universe diff --git a/openmc/universe.py b/openmc/universe.py index 8a12d1160fb..d5d015eba56 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -677,10 +677,9 @@ def __init__(self, self.auto_mat_ids = auto_mat_ids def __repr__(self): - fmt_str = '{: <16}=\t{}\n' string = super().__repr__() - string += fmt_str.format('\tGeom', 'DAGMC') - string += fmt_str.format('\tFile', self.filename) + string += '{: <16}=\t{}\n'.format('\tGeom', 'DAGMC') + string += '{: <16}=\t{}\n'.format('\tFile', self.filename) return string @property diff --git a/src/particle.cpp b/src/particle.cpp index 8abe2d9c68e..6acd989faa5 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -386,7 +386,6 @@ Particle::cross_surface() if (surf->geom_type_ != GeometryType::DAG) history().reset(); #endif - // Handle any applicable boundary conditions. if (surf->bc_ && settings::run_mode != RunMode::PLOTTING) { surf->bc_->handle_particle(*this, *surf); diff --git a/src/surface.cpp b/src/surface.cpp index 20aadc6e83e..1de606198ae 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -204,8 +204,7 @@ Surface::to_hdf5(hid_t group_id) const if (geom_type_ == GeometryType::DAG) { write_string(surf_group, "geom_type", "dagmc", false); - } - else if (geom_type_ == GeometryType::CSG) { + } else if (geom_type_ == GeometryType::CSG) { write_string(surf_group, "geom_type", "csg", false); if (bc_) { diff --git a/tests/regression_tests/dagmc/legacy/test.py b/tests/regression_tests/dagmc/legacy/test.py index 76b76fbda09..e0fccb9344c 100644 --- a/tests/regression_tests/dagmc/legacy/test.py +++ b/tests/regression_tests/dagmc/legacy/test.py @@ -28,7 +28,7 @@ def model(): # geometry dag_univ = openmc.DAGMCUniverse("dagmc.h5m") - model.geometry = openmc.Geometry(root=dag_univ) + model.geometry = openmc.Geometry(dag_univ) # tally tally = openmc.Tally() diff --git a/tests/regression_tests/dagmc/refl/test.py b/tests/regression_tests/dagmc/refl/test.py index 2f4162b16aa..3fe41345cf1 100644 --- a/tests/regression_tests/dagmc/refl/test.py +++ b/tests/regression_tests/dagmc/refl/test.py @@ -29,7 +29,7 @@ def _build_inputs(self): # geometry dag_univ = openmc.DAGMCUniverse("dagmc.h5m", auto_geom_ids=True) - model.geometry = openmc.Geometry(root=dag_univ) + model.geometry = openmc.Geometry(dag_univ) # tally tally = openmc.Tally() diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index cc407aa5ec6..1458462c48b 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -55,7 +55,7 @@ def _build_inputs(self): bounding_cell = openmc.Cell(fill=pincell_univ, region=box) - model.geometry = openmc.Geometry(root=[bounding_cell]) + model.geometry = openmc.Geometry([bounding_cell]) # settings model.settings.particles = 100 From 5e4aef6cb05eb87845cb30c68a19134d3fd7d732 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:21:33 -0500 Subject: [PATCH 0107/2572] Removing virtual on Cell::to_hdf5. --- include/openmc/cell.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index f04f9f6b769..8bcc1ea0675 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -121,7 +121,7 @@ class Cell { //! Write all information needed to reconstruct the cell to an HDF5 group. //! \param group_id An HDF5 group id. - virtual void to_hdf5(hid_t group_id) const; + void to_hdf5(hid_t group_id) const; virtual void to_hdf5_inner(hid_t group_id) const = 0; From 1a839061d4d7a758656af5b489a60e715cf13928 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:22:20 -0500 Subject: [PATCH 0108/2572] Making DAGMC geom class attrs private where possible and adding accessors. --- include/openmc/dagmc.h | 24 ++++++++++++++++++------ src/dagmc.cpp | 27 +++++++++++++-------------- 2 files changed, 31 insertions(+), 20 deletions(-) diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 1369424e737..591ea647486 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -21,7 +21,7 @@ namespace openmc { class DAGSurface : public Surface { public: - DAGSurface(); + DAGSurface(std::shared_ptr dag_ptr, int32_t dag_idx); double evaluate(Position r) const override; double distance(Position r, Direction u, bool coincident) const override; @@ -30,13 +30,18 @@ class DAGSurface : public Surface { inline void to_hdf5_inner(hid_t group_id) const override {}; + // Accessor methods + const std::shared_ptr& dagmc_ptr() const { return dagmc_ptr_; } + int32_t dag_index() const { return dag_index_; } + +private: std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance - int32_t dag_index_; //!< DagMC index of surface + int32_t dag_index_; //!< DagMC index of surface }; class DAGCell : public Cell { public: - DAGCell(); + DAGCell(std::shared_ptr dag_ptr, int32_t dag_idx); bool contains(Position r, Direction u, int32_t on_surface) const override; @@ -47,8 +52,13 @@ class DAGCell : public Cell { void to_hdf5_inner(hid_t group_id) const override; + // Accessor methods + const std::shared_ptr& dagmc_ptr() const { return dagmc_ptr_; } + int32_t dag_index() const { return dag_index_; } + +private: std::shared_ptr dagmc_ptr_; //!< Pointer to DagMC instance - int32_t dag_index_; //!< DagMC index of cell + int32_t dag_index_; //!< DagMC index of cell }; class DAGUniverse : public Universe { @@ -100,11 +110,13 @@ class DAGUniverse : public Universe { bool find_cell(Particle &p) const override; // Data Members - std::string filename_; //!< Name of the DAGMC file used to create this universe std::shared_ptr dagmc_instance_; //!< DAGMC Instance for this universe - std::shared_ptr uwuw_; //!< Pointer to the UWUW instance for this universe int32_t cell_idx_offset_; //!< An offset to the start of the cells in this universe in OpenMC's cell vector int32_t surf_idx_offset_; //!< An offset to the start of the surfaces in this universe in OpenMC's surface vector + +private: + std::string filename_; //!< Name of the DAGMC file used to create this universe + std::shared_ptr uwuw_; //!< Pointer to the UWUW instance for this universe bool adjust_geometry_ids_; //!< Indicates whether or not to automatically generate new cell and surface IDs for the universe bool adjust_material_ids_; //!< Indicates whether or not to automatically generate new material IDs for the universe }; diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 1132681a5b5..9f8d233d21c 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -150,11 +150,9 @@ DAGUniverse::initialize() { moab::EntityHandle vol_handle = dagmc_instance_->entity_by_index(3, i + 1); // set cell ids using global IDs - auto c = std::make_unique(); - c->dag_index_ = i + 1; - c->id_ = adjust_geometry_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index_); - c->dagmc_ptr_ = dagmc_instance_; - c->universe_ = id_; + auto c = std::make_unique(dagmc_instance_, i + 1); + c->id_ = adjust_geometry_ids_ ? next_cell_id++ : dagmc_instance_->id_by_index(3, c->dag_index()); + c->universe_ = this->id_; c->fill_ = C_NONE; // no fill, single universe auto in_map = model::cell_map.find(c->id_); @@ -244,10 +242,8 @@ DAGUniverse::initialize() { moab::EntityHandle surf_handle = dagmc_instance_->entity_by_index(2, i+1); // set cell ids using global IDs - auto s = std::make_unique(); - s->dag_index_ = i+1; // DAGMC indices start at 1 + auto s = std::make_unique(dagmc_instance_, i+1); s->id_ = adjust_geometry_ids_ ? next_surf_id++ : dagmc_instance_->id_by_index(2, i+1); - s->dagmc_ptr_ = dagmc_instance_; // set BCs std::string bc_value = DMD.get_surface_property("boundary", surf_handle); @@ -493,7 +489,8 @@ DAGUniverse::read_uwuw_materials() { // DAGMC Cell implementation //============================================================================== -DAGCell::DAGCell() : Cell{} { +DAGCell::DAGCell(std::shared_ptr dag_ptr, int32_t dag_idx) + : Cell{}, dagmc_ptr_(dag_ptr), dag_index_(dag_idx) { geom_type_ = GeometryType::DAG; simple_ = true; }; @@ -569,7 +566,9 @@ DAGCell::bounding_box() const // DAGSurface implementation //============================================================================== -DAGSurface::DAGSurface() : Surface{} { +DAGSurface::DAGSurface(std::shared_ptr dag_ptr, int32_t dag_idx) +: Surface{}, dagmc_ptr_(dag_ptr), dag_index_(dag_idx) +{ geom_type_ = GeometryType::DAG; } // empty constructor @@ -635,14 +634,14 @@ void read_dagmc_universes(pugi::xml_node node) { int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) { moab::EntityHandle surf = - surf_xed->dagmc_ptr_->entity_by_index(2, surf_xed->dag_index_); + surf_xed->dagmc_ptr()->entity_by_index(2, surf_xed->dag_index()); moab::EntityHandle vol = - cur_cell->dagmc_ptr_->entity_by_index(3, cur_cell->dag_index_); + cur_cell->dagmc_ptr()->entity_by_index(3, cur_cell->dag_index()); moab::EntityHandle new_vol; - cur_cell->dagmc_ptr_->next_vol(surf, vol, new_vol); + cur_cell->dagmc_ptr()->next_vol(surf, vol, new_vol); - return cur_cell->dagmc_ptr_->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; + return cur_cell->dagmc_ptr()->index_by_handle(new_vol) + dag_univ->cell_idx_offset_; } From 8942659fe0682cca23b2bb2571f22871e22f21f7 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:38:37 -0500 Subject: [PATCH 0109/2572] Adding support for cloning DAGMC universes in the Python API. --- openmc/universe.py | 50 ++++++++++++++++++++-------------------------- 1 file changed, 22 insertions(+), 28 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index d5d015eba56..fe08e27c98f 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -35,6 +35,10 @@ def __init__(self, universe_id=None, name=''): self._volume = None self._atoms = {} + # Keys - Cell IDs + # Values - Cells + self._cells = OrderedDict() + def __repr__(self): string = 'Universe\n' string += '{: <16}=\t{}\n'.format('\tID', self._id) @@ -101,7 +105,6 @@ def create_xml_subelement(self, xml_element, memo=None): """ - @abstractmethod def clone(self, clone_materials=True, clone_regions=True, memo=None): """Create a copy of this universe with a new unique ID, and clones all cells within this universe. @@ -124,6 +127,24 @@ def clone(self, clone_materials=True, clone_regions=True, memo=None): The clone of this universe """ + if memo is None: + memo = {} + + # If no memoize'd clone exists, instantiate one + if self not in memo: + clone = deepcopy(self) + clone.id = None + + # Clone all cells for the universe clone + clone._cells = OrderedDict() + for cell in self._cells.values(): + clone.add_cell(cell.clone(clone_materials, clone_regions, + memo)) + + # Memoize the clone + memo[self] = clone + + return memo[self] class Universe(UniverseBase): @@ -161,10 +182,6 @@ class Universe(UniverseBase): def __init__(self, universe_id=None, name='', cells=None): super().__init__(universe_id, name) - # Keys - Cell IDs - # Values - Cells - self._cells = OrderedDict() - if cells is not None: self.add_cells(cells) @@ -546,26 +563,6 @@ def get_all_universes(self): return universes - def clone(self, clone_materials=True, clone_regions=True, memo=None): - if memo is None: - memo = {} - - # If no nemoize'd clone exists, instantiate one - if self not in memo: - clone = deepcopy(self) - clone.id = None - - # Clone all cells for the universe clone - clone._cells = OrderedDict() - for cell in self._cells.values(): - clone.add_cell(cell.clone(clone_materials, clone_regions, - memo)) - - # Memoize the clone - memo[self] = clone - - return memo[self] - def create_xml_subelement(self, xml_element, memo=None): # Iterate over all Cells for cell in self._cells.values(): @@ -709,9 +706,6 @@ def auto_mat_ids(self, val): cv.check_type('DAGMC automatic material ids', val, bool) self._auto_mat_ids = val - def clone(self, clone_materials=True, clone_regions=True, memo=None): - pass - def get_all_cells(self, memo=None): return OrderedDict() From ff39f76a3dfb193a979a3f1e649bac2213e9d0ac Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:40:29 -0500 Subject: [PATCH 0110/2572] Changing name of XML element from 'dagmc' to 'dagmc_universe' --- openmc/universe.py | 2 +- src/dagmc.cpp | 2 +- tests/regression_tests/dagmc/legacy/inputs_true.dat | 2 +- tests/regression_tests/dagmc/refl/inputs_true.dat | 2 +- tests/regression_tests/dagmc/universes/inputs_true.dat | 2 +- tests/regression_tests/dagmc/uwuw/inputs_true.dat | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index fe08e27c98f..402609e2cb1 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -720,7 +720,7 @@ def create_xml_subelement(self, xml_element, memo=None): memo.add(self) # Set xml element values - dagmc_element = ET.Element('dagmc') + dagmc_element = ET.Element('dagmc_universe') dagmc_element.set('id', str(self.id)) dagmc_element.set('name', self.name) if self.auto_geom_ids: diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 9f8d233d21c..775c130f75c 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -625,7 +625,7 @@ DAGSurface::reflect(Position r, Direction u, Particle* p) const //============================================================================== void read_dagmc_universes(pugi::xml_node node) { - for (pugi::xml_node dag_node : node.children("dagmc")) { + for (pugi::xml_node dag_node : node.children("dagmc_universe")) { model::universes.push_back(std::make_unique(dag_node)); model::universe_map[model::universes.back()->id_] = model::universes.size() - 1; } diff --git a/tests/regression_tests/dagmc/legacy/inputs_true.dat b/tests/regression_tests/dagmc/legacy/inputs_true.dat index ecc5d7373a0..45c10a7aaad 100644 --- a/tests/regression_tests/dagmc/legacy/inputs_true.dat +++ b/tests/regression_tests/dagmc/legacy/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index 4ac926e5ec7..13bc9ecf52a 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat index 6c0fa8ccd91..fdc8b07bc96 100644 --- a/tests/regression_tests/dagmc/universes/inputs_true.dat +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -1,7 +1,7 @@ - + diff --git a/tests/regression_tests/dagmc/uwuw/inputs_true.dat b/tests/regression_tests/dagmc/uwuw/inputs_true.dat index 901547fa29e..54b7a9cb83f 100644 --- a/tests/regression_tests/dagmc/uwuw/inputs_true.dat +++ b/tests/regression_tests/dagmc/uwuw/inputs_true.dat @@ -1,6 +1,6 @@ - + From d1df19a21f127f3208c745a5a00245f3e9054f53 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Fri, 25 Jun 2021 07:44:40 -0500 Subject: [PATCH 0111/2572] Removing errant inputs true file from rotation test. --- .../regression_tests/rotation/inputs_true.dat | 31 ------------------- 1 file changed, 31 deletions(-) delete mode 100644 tests/regression_tests/rotation/inputs_true.dat diff --git a/tests/regression_tests/rotation/inputs_true.dat b/tests/regression_tests/rotation/inputs_true.dat deleted file mode 100644 index d90d498bf62..00000000000 --- a/tests/regression_tests/rotation/inputs_true.dat +++ /dev/null @@ -1,31 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - eigenvalue - 1000 - 10 - 5 - - - -4.0 -4.0 -4.0 4.0 4.0 4.0 - - - From 983470ed4ef099d63807ee9a7b5fa3336a7315b5 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 19 Apr 2021 09:34:17 -0500 Subject: [PATCH 0112/2572] Adding StatePoint.close(). Updating some examples. --- examples/jupyter/chain_simple.xml | 1 - examples/jupyter/mdgxs-part-i.ipynb | 223 +++++++++++++++------------ examples/jupyter/mdgxs-part-ii.ipynb | 81 +++++----- openmc/statepoint.py | 12 +- 4 files changed, 175 insertions(+), 142 deletions(-) delete mode 120000 examples/jupyter/chain_simple.xml diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml deleted file mode 120000 index 96f6fa8818e..00000000000 --- a/examples/jupyter/chain_simple.xml +++ /dev/null @@ -1 +0,0 @@ -../../tests/chain_simple.xml \ No newline at end of file diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index ea1dd7bcdfa..ae31859a4f5 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -363,17 +363,20 @@ { "data": { "text/plain": [ - "OrderedDict([('delayed-nu-fission', Tally\n", + "OrderedDict([('delayed-nu-fission',\n", + " Tally\n", " \tID =\t1\n", " \tName =\t\n", - " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", - " \tNuclides =\tU235 Pu239 \n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", " \tScores =\t['delayed-nu-fission']\n", - " \tEstimator =\ttracklength), ('decay-rate', Tally\n", + " \tEstimator =\ttracklength),\n", + " ('decay-rate',\n", + " Tally\n", " \tID =\t2\n", " \tName =\t\n", - " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", - " \tNuclides =\tU235 Pu239 \n", + " \tFilters =\tCellFilter, DelayedGroupFilter\n", + " \tNuclides =\tU235 Pu239\n", " \tScores =\t['decay-rate']\n", " \tEstimator =\ttracklength)])" ] @@ -403,15 +406,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=8.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=14.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } @@ -483,26 +486,29 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 06:56:34\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", + " Date/Time | 2021-04-18 13:10:08\n", + " MPI Processes | 1\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading Pu239 from /opt/data/hdf5/nndc_hdf5_v15/Pu239.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for H1\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading Pu239 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Pu239.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -563,20 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.7388e-01 seconds\n", - " Reading cross sections = 4.4709e-01 seconds\n", - " Total time in simulation = 3.9290e+01 seconds\n", - " Time in transport only = 3.9005e+01 seconds\n", - " Time in inactive batches = 1.4079e+00 seconds\n", - " Time in active batches = 3.7882e+01 seconds\n", - " Time synchronizing fission bank = 1.8814e-02 seconds\n", - " Sampling source sites = 1.6376e-02 seconds\n", - " SEND/RECV source sites = 2.3626e-03 seconds\n", - " Time accumulating tallies = 8.3299e-04 seconds\n", - " Total time for finalization = 1.1533e-02 seconds\n", - " Total time elapsed = 3.9783e+01 seconds\n", - " Calculation Rate (inactive) = 35514.2 particles/second\n", - " Calculation Rate (active) = 5279.54 particles/second\n", + " Total time for initialization = 1.1312e+00 seconds\n", + " Reading cross sections = 1.1046e+00 seconds\n", + " Total time in simulation = 2.7801e+02 seconds\n", + " Time in transport only = 2.7794e+02 seconds\n", + " Time in inactive batches = 1.0359e+01 seconds\n", + " Time in active batches = 2.6765e+02 seconds\n", + " Time synchronizing fission bank = 2.5796e-02 seconds\n", + " Sampling source sites = 2.3811e-02 seconds\n", + " SEND/RECV source sites = 1.7176e-03 seconds\n", + " Time accumulating tallies = 2.5310e-02 seconds\n", + " Time writing statepoints = 6.3601e-03 seconds\n", + " Total time for finalization = 2.2876e-02 seconds\n", + " Total time elapsed = 2.7919e+02 seconds\n", + " Calculation Rate (inactive) = 4826.80 particles/second\n", + " Calculation Rate (active) = 747.234 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -644,7 +651,9 @@ "chi_delayed.load_from_statepoint(sp)\n", "delayed_nu_fission.load_from_statepoint(sp)\n", "beta.load_from_statepoint(sp)\n", - "decay_rate.load_from_statepoint(sp)" + "decay_rate.load_from_statepoint(sp)\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" ] }, { @@ -888,7 +897,6 @@ " \n", " cell\n", " delayedgroup\n", - " group in\n", " nuclide\n", " mean\n", " std. dev.\n", @@ -899,7 +907,6 @@ " 0\n", " 1\n", " 1\n", - " 1\n", " U235\n", " 0.013336\n", " 0.000061\n", @@ -908,7 +915,6 @@ " 1\n", " 1\n", " 1\n", - " 1\n", " Pu239\n", " 0.013271\n", " 0.000053\n", @@ -917,7 +923,6 @@ " 2\n", " 1\n", " 2\n", - " 1\n", " U235\n", " 0.032739\n", " 0.000149\n", @@ -926,7 +931,6 @@ " 3\n", " 1\n", " 2\n", - " 1\n", " Pu239\n", " 0.030881\n", " 0.000123\n", @@ -935,25 +939,22 @@ " 4\n", " 1\n", " 3\n", - " 1\n", " U235\n", " 0.120780\n", - " 0.000549\n", + " 0.000550\n", " \n", " \n", " 5\n", " 1\n", " 3\n", - " 1\n", " Pu239\n", " 0.113370\n", - " 0.000451\n", + " 0.000452\n", " \n", " \n", " 6\n", " 1\n", " 4\n", - " 1\n", " U235\n", " 0.302780\n", " 0.001378\n", @@ -962,65 +963,60 @@ " 7\n", " 1\n", " 4\n", - " 1\n", " Pu239\n", " 0.292500\n", - " 0.001163\n", + " 0.001167\n", " \n", " \n", " 8\n", " 1\n", " 5\n", - " 1\n", " U235\n", " 0.849490\n", - " 0.003865\n", + " 0.003866\n", " \n", " \n", " 9\n", " 1\n", " 5\n", - " 1\n", " Pu239\n", " 0.857490\n", - " 0.003411\n", + " 0.003420\n", " \n", " \n", " 10\n", " 1\n", " 6\n", - " 1\n", " U235\n", " 2.853000\n", - " 0.012980\n", + " 0.012985\n", " \n", " \n", " 11\n", " 1\n", " 6\n", - " 1\n", " Pu239\n", " 2.729700\n", - " 0.010858\n", + " 0.010887\n", " \n", " \n", "\n", "" ], "text/plain": [ - " cell delayedgroup group in nuclide mean std. dev.\n", - "0 1 1 1 U235 0.013336 0.000061\n", - "1 1 1 1 Pu239 0.013271 0.000053\n", - "2 1 2 1 U235 0.032739 0.000149\n", - "3 1 2 1 Pu239 0.030881 0.000123\n", - "4 1 3 1 U235 0.120780 0.000549\n", - "5 1 3 1 Pu239 0.113370 0.000451\n", - "6 1 4 1 U235 0.302780 0.001378\n", - "7 1 4 1 Pu239 0.292500 0.001163\n", - "8 1 5 1 U235 0.849490 0.003865\n", - "9 1 5 1 Pu239 0.857490 0.003411\n", - "10 1 6 1 U235 2.853000 0.012980\n", - "11 1 6 1 Pu239 2.729700 0.010858" + " cell delayedgroup nuclide mean std. dev.\n", + "0 1 1 U235 0.013336 0.000061\n", + "1 1 1 Pu239 0.013271 0.000053\n", + "2 1 2 U235 0.032739 0.000149\n", + "3 1 2 Pu239 0.030881 0.000123\n", + "4 1 3 U235 0.120780 0.000550\n", + "5 1 3 Pu239 0.113370 0.000452\n", + "6 1 4 U235 0.302780 0.001378\n", + "7 1 4 Pu239 0.292500 0.001167\n", + "8 1 5 U235 0.849490 0.003866\n", + "9 1 5 Pu239 0.857490 0.003420\n", + "10 1 6 U235 2.853000 0.012985\n", + "11 1 6 Pu239 2.729700 0.010887" ] }, "execution_count": 18, @@ -1094,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1103,7 +1099,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "

" ] @@ -1141,6 +1137,31 @@ "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[CellFilter\n", + " \tBins =\t[1]\n", + " \tID =\t3,\n", + " DelayedGroupFilter\n", + " \tBins =\t[1 2 3 4 5 6]\n", + " \tID =\t13]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dr_tally.filters" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1150,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1190,7 +1211,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 8.779139e-08\n", - " 4.658590e-10\n", + " 4.659945e-10\n", " \n", " \n", " 1\n", @@ -1199,7 +1220,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 7.149814e-09\n", - " 3.559010e-11\n", + " 3.565137e-11\n", " \n", " \n", " 2\n", @@ -1208,7 +1229,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 9.527880e-07\n", - " 5.055905e-09\n", + " 5.057375e-09\n", " \n", " \n", " 3\n", @@ -1217,7 +1238,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 1.303159e-07\n", - " 6.486820e-10\n", + " 6.497988e-10\n", " \n", " \n", " 4\n", @@ -1226,7 +1247,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.353903e-07\n", - " 1.249083e-09\n", + " 1.249446e-09\n", " \n", " \n", " 5\n", @@ -1235,7 +1256,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.032895e-08\n", - " 1.011928e-10\n", + " 1.013670e-10\n", " \n", " \n", " 6\n", @@ -1244,7 +1265,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 4.720191e-07\n", - " 2.504737e-09\n", + " 2.505466e-09\n", " \n", " \n", " 7\n", @@ -1253,7 +1274,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.626309e-08\n", - " 1.307315e-10\n", + " 1.309566e-10\n", " \n", " \n", " 8\n", @@ -1262,7 +1283,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.827915e-08\n", - " 1.500614e-10\n", + " 1.501050e-10\n", " \n", " \n", " 9\n", @@ -1271,7 +1292,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 2.430587e-09\n", - " 1.209889e-11\n", + " 1.211972e-11\n", " \n", " \n", " 10\n", @@ -1280,7 +1301,7 @@ " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 1.477530e-09\n", - " 7.840413e-12\n", + " 7.842692e-12\n", " \n", " \n", " 11\n", @@ -1289,7 +1310,7 @@ " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", " 6.994312e-11\n", - " 3.481605e-13\n", + " 3.487599e-13\n", " \n", " \n", "\n", @@ -1312,20 +1333,20 @@ "\n", " score mean std. dev. \n", "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.66e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.56e-11 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.57e-11 \n", "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.49e-10 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.50e-10 \n", "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 1.25e-09 \n", "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.01e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.50e-09 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.31e-10 \n", "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.21e-11 \n", "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.48e-13 " + "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.49e-13 " ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1334,7 +1355,7 @@ "# Use tally arithmetic to compute the precursor concentrations\n", "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n", " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / \\\n", - " decay_rate.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n", + " decay_rate.xs_tally.summation()\n", "\n", "# Get the Pandas DataFrames for inspection\n", "precursor_conc.get_pandas_dataframe()" @@ -1349,7 +1370,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1363,16 +1384,16 @@ { "data": { "text/plain": [ - "(0, 7)" + "(0.0, 7.0)" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1420,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1429,13 +1450,13 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEaCAYAAAAL7cBuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABP7ElEQVR4nO2deXhU5fX4P4cdTRBFrQSBoIIiqyRIcSO4L4ALoixawCXYij+VaMVaZdC61BKXKvoFq6CWRQouQFFr1eCuJIgoKIgaBEJVFoUAEpbz++Pemdwks2WZzExyPs9zn9z7bvfMO5l77vue9z1HVBXDMAzDKE+DeAtgGIZhJCamIAzDMIygmIIwDMMwgmIKwjAMwwiKKQjDMAwjKKYgDMMwjKCYgjCMGkZEThaRr0WkWEQuiuF9XhWRkdWo/38icmdNymTULUxBJAEiUigiu9wHjv94PN5yRUJELhSRZSKyTUQ2ichbItIhxvdUETkmlveIgruBx1U1RVVfrm5jIjJdRErKff+Xq+p5qvpsVdtV1etU9Z7qylceEfG538NlnrRGblp6DbQ/XUT+Ut12jMg0ircARtQMVNX/xvIGItJIVffWUFvHAM8BlwBvASnA2cC+mmi/GnLV2GcMQ3tgRVUqhpHvQVX9c/XEqlW2ABNFZJ6q1up3Xkvfcb3ARhBJjoiMEpH3RGSSiGwVke9E5DxP/kEi8rSIbBSRDSLyFxFp6Kn7vog8LCKbAZ+ItBKRBe5b/xK3/Htu+ckiklvu/vNF5OYgovUEvlPVN9Vhu6rOU9Xv3Xo+EZkrIi+IyHYRWSoiPTztponIPBH5yf1M/8+T11BE/iQi37h1C0SkrYi84xb5zP+WLSJZIrJeRG4Tkf8B0/x9Vu5zBEYe7hvqE+4UTrHbR0eIyCNuH38lIieE+D6+AY4CFrh1m7qfZb6IbBGRNSJyrae8vx/+KSLbgFHhv/Ey98oTkWvc82NEZLGI/OKO1l5w08X9fn90v9PPRaSr53P+xdPeta58W1x508r1z3XiTJ397P4vSBjxXgNKgCtCyN7U/Z/9XkR+EGe6q7mbF/L7EZFsYATwR7d/F7j5he53vBzYIc6IZZCIrHDlzRORzp72CkXkFhFZ7vbZCyLSzM07VEQWuvW2iMi7IlIvn5X18kPXQfoAq4BDgQeBpz0/3unAXuAY4ASct/hrytX9FvgNcC8wGdgBHAGMdA8/zwLD/D8WETkUOBOYGUSmpcBx7sOpv4ikBClzIfAv4BC3jZdFpLHb/gLgM6ANcAZwk4ic49YbBwwDzgdaAFcBO1X1NDe/hzu984J7fYR7j/ZAdhA5gnEZ8GecPt0NfOh+pkOBucBDwSqp6tHA9zgjvhRV3Q3MBtYDacClwH0icnq5fpgLtARmRClfee4B/gMcDBwJPOamnw2cBnQCDnI/1+bylV157nfzWwNrXbm9DAB6A93dcucQGgXuBCaISOMg+Q+4MvXE+d9sA9wV/iOCqk7F6aMH3f4d6MkeBlyA049HAbOAm4DDgEU4SruJp/xlwLlAB/czjXLTc3C+r8Nwfhd/cj9PvcMURPLwsvtG4z+u9eStVdWn3KH8szg/8N+IyG9wHqI3qeoOVf0ReBgY6qlbpKqPuUPyEmAwMEFVd6rqSrc9AFT1E+AXnAc2bjt5qvpDeWFV9VsgC+eHPwfY5L6xehVFgarOVdU9OA/cZsBvcR5Ch6nq3apa4rb1lEfua4A/q+oqd3TymapWeOh52O9+pt2quitMOS8vqWqBqv4KvAT8qqrPuX38Ao6yjYiItAVOBm5T1V9VdRnwD+B3nmIfqurLqro/jHy3eL77TUHy9+AowDT3Pu950lOB4wBR1S9VdWOQ+iOAZ1R1qavUbgf6SlmbwQOq+rM7Cnwb5+EeElWdD/xE2RcS3JeXbOBmVd2iqtuB+yj7f1kV/q6q69w+vBz4t6q+4f5/TQKaAyeVK1+kqltwXkj8n2cPzm+ovaruUdV3tZ46rTMFkTxcpKotPcdTnrz/+U9Udad7moLzwGgMbPQ/XIApwOGeuus854fh2KXWhcgHR2H4pw2uAJ4PJbCqfqSql6nqYcCpOG+ydwRrW1X3U/qW3R5I8ypEnLe437jF2wLfhLpvEH5yH/SVwav0dgW5DjYiCkYa4H8I+lmLozj9lO/jYEzyfPeHBsn/IyDAJ+60ylUAqvoW8DjOyPBHEZkqIi1CyLnWf6GqxTgjDa+c//Oc7yS6PvgzznfezJN2GHAAUOD5fl9z06uDtx/Lf579bn40n+dvwBrgPyLyrYiMr6ZcSYspiLrNOpzpkUM9D5cWqtrFU8b7ZvQTznTUkZ60tuXa/CdwoTj2gs7Ay9EIoqpLgBeBrsHadqeVjgSKXLm/K6cQU1X1fM/nOjqa+/pvX+56B84Dyn/vIyrRVmUpAg4RkVRPWjtgg+e62m+nqvo/Vb1WVdOAMcAT4tpUVPXvqpoBHI8zrXNrCDnb+y9E5ECgVTk5qyLXGzgP2z94kjfhKNkunu/3IFX1P6AjfT+h+subXv7zCM7/W8TP49rLclT1KGAQME5EzohUry5iCqIO404l/AfIFZEWItJARI4WkX4hyu/DeYj7ROQAETmOslMhqOp6YAnOyGFeqCkRETnFNXoe7l4fh/Nj+8hTLENELhGRRjhzxbvd/E+A7a7Rsbk4RumuItLbrfcP4B4R6egaYbuLSCs37wec+edwfAZ0EZGermHSF6F8lVHVdcAHwP0i0kxEugNX4yjaGkNEhoiIX7FvxXlY7heR3iLSx7UD7AB+xZlyK88sYLTbJ01xpnw+VtXCGhDvDpwRDhB4m38KeNjz/9HGY2OK9P1E8x3PAS4QkTPcz56D8//1QSRhRWSAaxAXnCnVfQTvszqPKYjkwb8qxn+8FGW93wFNgJU4D465OPOroRiLY8z8H44SmIXzw/LyLNCNMNNLwM84CuFzESnGmUJ4CceI7ucVnLnircCVwCXunO8+HINoT+A7nDfOf7hygWOvmIOj/LYBT+PML4PzMHnWnboIrMP3oqqrcfYq/Bf4GngvWLkaZBiQjvNW+xKOPaSmlyz3Bj52+3o+cKNru2mB8zDeijPlshlnCqUMrjx3AvOAjTgjtOraBPxtv4+j9L3chjOy+Eic1Vv/BY51y0f6fp4Gjne/45dD3HMVzhToYzj/PwNxFg6URCFyR/fexTiLE55Q1bejqFfnkHpqezGiRET+ChyhqiM9aafhvAG3r6rxTkR8wDGqGnQZpGEY8cdGEEYZROQ4d8pGROREnOmQlzz5jYEbgX/U15UdhlFfMAVhlCcVxw6xA2c5Zy7OVBDibDT6GWeK6pH4iGcYRm1hU0yGYRhGUGwEYRiGYQTFFIRhGIYRlJh6cxWRc4FHgYY4Rs0HyuWfhjOX3R0Yqqpzy+W3wFme+bKqjg13r0MPPVTT09NrTnjDMIx6QEFBwSbX20EFYqYgxPEYOhk4C8eFwhIRme/69/HzPY6DrFtCNHMP8E6IvDKkp6eTn59fdYENwzDqISKyNlReLKeYTgTWqOq37uaU2TheKwOoaqGqLifILkURycDxvfOfGMpoGIZhhCCWCqINZZ1nraeso6yQuH55cgk9sjAMwzBiTKIaqf8ALHL9/oRERLJFJF9E8n/66adaEs0wDKN+EEsj9QbKegI9kug9Q/YFThWRP+C44G0iIsWqWsbtrjrBQ6YCZGZmVtjQsWfPHtavX8+vv1bW07ORKDRr1owjjzySxo2DxZwxDCOWxFJBLAE6ihOkfgOO46/h0VRU1RH+cxEZBWSWVw7RsH79elJTU0lPT0fCRkc0EhFVZfPmzaxfv54OHTrEWxzDqHfEbIpJnQhlY4HXgS+BOaq6QkTuFpFBAK4r4vXAEGCKiFQp0Hsofv31V1q1amXKIUkREVq1amUjQMOIEzHdB6Gqi3BiwXrT7vKcL6FscJpgbUzHiatcJUw5JDf2/dU8UwumMmbhmMB1SpMUfP185JyUE0epjEQkUY3UdYLCwkK6du1aJs3n8zFp0qQKZdetW0f//v05/vjj6dKlC48++mgg784776R79+707NmTs88+m6KiIgDy8vI46KCD6NmzJz179uTuu++OKNP06dMZOzbsnsOoylSX9PR0Nm0KFlrZqAy5H+SSen8qMlGCHumPpFP4c2HYNopLivEt9tWKvEZyYQoiQWjUqBG5ubmsXLmSjz76iMmTJ7NypbOn8NZbb2X58uUsW7aMAQMGlFEEp556KsuWLWPZsmXcddddoZo36ii+xT6KS4pD5m8v2c7gOYPJmp4Vtp1wbRj1F1MQCULr1q3p1asXAKmpqXTu3JkNG5xFXy1alMaY37FjR6WnXaZNm0anTp048cQTef/99wPpP/30E4MHD6Z379707t27TJ6fBQsW0KdPH0444QTOPPNMfvjhB/bv30/Hjh3xLy3ev38/xxxzDD/99FPINjdv3szZZ59Nly5duOaaazAvwjVDpAf7ll1bWL15NQM7DQykZWdkoxMUnWDfgRGeeqUgfD4Qie7Izq5YPzu7bBmfLzZyFhYW8umnn9KnT59A2h133EHbtm2ZMWNGmRHEhx9+SI8ePTjvvPNYsaKijX/jxo1MmDCB999/n/feey8wKgG48cYbufnmm1myZAnz5s3jmmuuqVD/lFNO4aOPPuLTTz9l6NChPPjggzRo0IArrriCGTNmAPDf//6XHj16cNhhh4Vsc+LEiZxyyimsWLGCiy++mO+//77G+stw8D/0yx/bb99u9gWjSsTUSF3fCfWmH24EUFxczODBg3nkkUfKjBzuvfde7r33Xu6//34ef/xxJk6cSK9evVi7di0pKSksWrSIiy66iK+//rpMex9//DFZWVkcdpjji+vyyy9n9erVgPNg9yqMbdu2UVxc9o10/fr1XH755WzcuJGSkpLActOrrrqKCy+8kJtuuolnnnmG0aNHh23znXfe4cUXXwTgggsu4OCDDw7feUZUzB86P94iGHWYejWCqG1atWrF1q1by6Rt2bKFQw89lHXr1gWMy//3f/8HOBv7Bg8ezIgRI7jkkkuCtjlixAjmzZsHOFNPKSkpAJx//vns2bOnUobf/fv389FHHwVsGBs2bAi05+eGG25g7NixfP7550yZMiWw5LRt27b85je/4a233uKTTz7hvPPOi7pNo+YYeOzAwGEYNU29UhA+H6hGd0ydWrH+1Klly0SaYkpJSaF169a89dZbgKMcXnvtNU455RTatm0beIhed911qCpXX301nTt3Zty4cWXa8Y4KXnnlFY477jgA/ve//wXm8j/55BP2799Pq1atytTt06cPixcvZvPmzezZs4d//etfgbyzzz6bxx57LHC9bNmyCp/hl19+oU0bx4XWs88+Wybvmmuu4YorrmDIkCE0bNgwbJunnXYaM2fOBODVV1+toDgNw0g8bIopxjz33HNcf/31gYf+hAkTOProoyuUe//993n++efp1q0bPXv2BOC+++7j/PPPZ/z48axatYoGDRrQvn37wIhj7ty5PPnkkzRq1IjmzZsze/bsCtNXrVu3xufz0bdvX1q2bBloG+Dvf/87119/Pd27d2fv3r2cdtppgbb9+Hw+hgwZwsEHH8zpp5/Od999F8gbNGgQo0ePDkwvhWtzwoQJDBs2jC5dunDSSSfRrl27avWrUTMM6DQg3iIYCUydiUmdmZmp5eNBfPnll3Tu3DlOEtV98vPzufnmm3n33Xdjeh/7Hg0jdohIgapmBsuzEYRRJR544AGefPLJwEomIz6k5aYFzotyiuIoiVEXMQVhVInx48czfnyl/ScaNczG4o3xFsGow9QrI7VhGIYRPTaCMIx6jC/PV3qe5QtZzqifmIIwjHrMxMUTA+emIIzy2BSTYRiGERRTEDGmYcOG9OzZk65duzJkyBB27twZdd1ly5bRt29funTpQvfu3XnhhRcCeVdffTU9evSge/fuXHrppQEXGWvXruWMM86ge/fuZGVlsX592LDeQGgX5JUtU11sx7VhJBamIGJM8+bNWbZsGV988QVNmjSpsBEtHAcccADPPfccK1as4LXXXuOmm27i559/BuDhhx/ms88+Y/ny5bRr147HH38cgFtuuYXf/e53LF++nLvuuovbb789Fh/LMIx6gCmIWuTUU09lzZo15OXlMWBA6Q7WsWPHMn369ArlO3XqRMeOHQFIS0vj8MMPD7jY9jvyU1V27doV2EG9cuVKTj/9dAD69+/PK6+8ElSWe++9l06dOnHKKaewatWqQPo333zDueeeS0ZGBqeeeipfffVVhbpPPfUUvXv3pkePHgwePJidO3eyfft2OnTowJ49ewDHSZ//OlSb3333HX379qVbt278+c9/rlRfGoYRe+qVgvDl+UJG3ip/ZC+o6O87e0F2mTLeFSCR2Lt3L6+++irdunWrkuyffPIJJSUlZdx0jB49miOOOIKvvvqKG264AYAePXoEvKa+9NJLbN++nc2bN5dpq6CggNmzZ7Ns2TIWLVrEkiVLSj9jdjaPPfYYBQUFTJo0iT/84Q8VZLnkkktYsmQJn332GZ07d+bpp58mNTWVrKws/v3vfwMwe/ZsLrnkEho3bhyyzRtvvJHf//73fP7557Ru3bpK/WIYRuyoVwoiHuzatYuePXuSmZlJu3btuPrqqyvdxsaNG7nyyiuZNm0aDRqUfmXTpk2jqKiIzp07B+wTkyZNYvHixZxwwgksXryYNm3aBBzp+Xn33Xe5+OKLOeCAA2jRogWDBg0CHFfjH3zwAUOGDKFnz56MGTOGjRsrbsT64osvOPXUU+nWrRszZswIxKG45pprmDZtWkC20aNHh23z/fffZ9iwYQBceeWVle4XwzBiS0yXuYrIucCjQEPgH6r6QLn804BHgO7AUFWd66b3BJ4EWgD7gHtV9QWSEL8NwkujRo3Yv39/4NrvQvvjjz9mzBgnmPzdd9/NoEGD2LZtGxdccAH33nsvv/3tbyu037Bhw0Agn9GjR5OWlhYYQRQXFzNv3jxatmwZlaz79++nZcuWQb26ehk1ahQvv/wyPXr0YPr06eTl5QFw8sknU1hYSF5eHvv27aNr165s27YtbJuVjY5nlCX/2vzIhQyjqqhqTA4cpfANcBTQBPgMOL5cmXQc5fAccKknvRPQ0T1PAzYCLcPdLyMjQ8uzcuXKCmm1zYEHHlgh7fvvv9f27dvrr7/+qlu3btX09HSdNm1ahXK7d+/W008/XR9++OEy6fv379evv/46cJ6Tk6M5OTmqqvrTTz/pvn37VFX1T3/6k955550V2i0oKNBu3brpzp07ddu2bXrMMcfo3/72N1VV7du3r86ZMyfQ9rJly1RVdcKECYEyrVq10h9++EFLSkr0zDPP1JEjRwbanjRpkrZu3VqfeOKJQFqoNgcOHKjPP/+8qqo+8cQTQftKNTG+x0Rl0iTVlBTHAX2vXhXzp0xx8idNCl4fH4HDqJ8A+RriuRrLKaYTgTWq+q2qlgCzgQvLKadCVV0O7C+XvlpVv3bPi4AfgcNiKGut0rZtWy677DK6du3KZZddxgknnBC03Jw5c3jnnXeYPn16ILjQsmXLUFVGjhxJt27d6NatGxs3buSuu+4CIC8vj2OPPZZOnTrxww8/cMcdd1Rot1evXlx++eWBUKW9e/cO5M2YMYOnn36aHj160KVLl6BG7nvuuYc+ffpw8sknB2JT+BkxYgRbt24NTB2Fa/PRRx9l8uTJdOvWLRB/26gcPh8UhwlLXVjo5McqPK5Rt4mZu28RuRQ4V1Wvca+vBPqo6tggZacDC9WdYiqXdyLwLNBFVfeXz/dj7r4Tg7lz5/LKK6/w/PPP11ib9j2GxjtD16sXFBSEzg/2U/cuxpg6MEiULKPOk7TuvkWkNfA8MDKYchCRbCAbsAA0CcANN9zAq6++yqJFi+ItSr2kvHKIBlMKRjhiqSA2AG0910e6aVEhIi2AfwN3qOpHwcqo6lRgKjgjiKqLatQE3lCjRi3h8xr57Sdg1CyxtEEsATqKSAcRaQIMBeZHU9Et/xLwXLBpJ8MwDCP2xExBqOpeYCzwOvAlMEdVV4jI3SIyCEBEeovIemAIMEVEVrjVLwNOA0aJyDL36BkrWQ3DMIyKVGqKSUQOBtq6K48ioqqLgEXl0u7ynC/BmXoqX++fwD8rI5thGJUnmYzUublwyy3O+bhxzrURWyIqCBHJAwa5ZQuAH0XkfVUdF2PZDMOIMU8tfSpwnugKwqh9opliOkhVtwGX4NgE+gBnxlasukFhYSFdu3YtkxbKbfa6devo378/xx9/PF26dOHRRx8N5N155510796dnj17cvbZZ1NU5ASnz8vL46CDDgrskbj77rtj+4GqyH333RdvEYy6RN9cHmrh+EPLeT0n3tLUaaJREI3c5aaXAQtjLE+9pVGjRuTm5rJy5Uo++ugjJk+ezMqVKwG49dZbWb58OcuWLWPAgAFlFMGpp57KsmXLWLZsWWCzXFXYu3dvtT9DKExBJAfBnFZWxiFlrMnJcfZyeN+vPvjQ2QRoGwFjQzQK4m4cQ/MaVV0iIkcBX8dWrPpH69at6dWrFwCpqal07tw5sLvY79obYMeOHZX2X5SSksLNN99Mly5dOOOMMwIuw7OysrjpppvIzMzk0Ucf5c033+SEE06gW7duXHXVVezevRuA9PR0br/99oDTwaVLl3LOOedw9NFHB+Jb5OXlcdppp3HBBRdw7LHHct1117F//37Gjx8fcFg4YsSIaveTUTkGDCg9gpHSJLGDNHX+Y3bgCMZHH8LEic5h1DwRFYSq/ktVu6vqH9zrb1V1cOxFq3l8PmdnqUjwN46cnNL8YAaw7OzS/KkxnK4tLCzk008/pU+fPoG0O+64g7Zt2zJjxowyI4gPP/ww4DLD71W1PDt27CAzM5MVK1bQr18/Jnp+TSUlJeTn53P99dczatQoXnjhBT7//HP27t3Lk08+GSjXrl07li1bxqmnnsqoUaOYO3cuH330ERMmTAiU+eSTT3jsscdYuXIl33zzDS+++CIPPPBAwGHhjBkzarKbjChYsKD0CIavny+hlcRXBz4VOPzknJSDTlB0gsJ/zFIdS6IxUh8GXIvjWC9QXlWvip1YdYNQb/rhRgDFxcUMHjyYRx55pMzI4d577+Xee+/l/vvv5/HHH2fixIn06tWLtWvXkpKSwqJFi7jooov4+uuKg7sGDRpw+eWXA3DFFVdwySWXBPL86atWraJDhw506tQJgJEjRzJ58mRuuukmgIBL8G7dulFcXExqaiqpqak0bdo0EOXuxBNP5KijjgJg2LBhvPfee1x66aXRdJURJ3JOyiHnpOSdx/e8nxgxIJoppleAg4D/4uxs9h9GBFq1asXWrVvLpG3ZsoVDDz2UdevWBYzL/mmaPXv2MHjwYEaMGFHmIe5lxIgRzJs3D3CmnvxxnM8//3z27NnDpk2bIsrlVVAHHnhgVJ+ladOmgKNs/Of+a7/9orziM1feRnUZcdCUwBGULF/pYdQ8ody8aqnr7WWRyiTCkajuvjMyMvTNN99UVdXNmzdrx44ddc2aNRXK7d+/X6+88kq98cYbK+StXr06cP73v/9dBw8erKqqGzdu1P3796uq6scff6xt27YNXHsBdNasWaqqes899+jYsWNVVbVfv366ZMkSVVXdtWuXtm3bNuBGfOTIkfrII4+oqmr79u31p59+UlXVadOm6fXXXx9o25/39ttva7NmzfTbb7/Vffv26dlnn61z585VVdWWLVtqSUlJtF1WgUT4HhOVw4/eEDhiwYCZAwJHrJkxw3FbDqrDhkVXx9yVVx/CuPuOZqPcQhE5X51Nb0Ylee6557j++usZN87ZNjJhwoQyYUP9vP/++zz//PN069aNnj17As7qn/PPP5/x48ezatUqGjRoQPv27QMjjrlz5/Lkk0/SqFEjmjdvzuzZs4O+tR944IF88skn/OUvf+Hwww8PRJ/z0qxZM6ZNm8aQIUPYu3cvvXv35rrrrqvUZ+3duzdjx45lzZo19O/fn4svvhhwwph2796dXr16mR2ihvnxm7Sw+V5bW1VW+ixcXcsLF7vNhMEjmAUwbxgzB8+s3fsbZYjo7ltEtgMHAiXAHjdZVbVF6Fq1j7n7Dk1KSgrF4YIG1AB5eXlMmjSJhQtr/oFi32NoIrnzjpQfsf2JpQ3ohNg6A5w5E0Y84CgIgGFdIysI7zJcnzvNlPN6Dg999BAAk86alNQ2ltqgWu6+VTW15kUyDMMoy/DhQDcY8WL0dbYv8JVeZNWwQEZ0vphc53qnuZd5qmob5pKIWI8ewNlTkZWVFfP7GOVILfJchJ9uSkR+c/PAwPkPDy9geLfhDO82POr6Dz1Ueh5safott4Dvc2d6LccGEpUmmmWuDwC9Af/k8Y0icrKq3h5TyQzDIPeDXHyLfRSXhFDyZR56yRcP4seW1XvXTElxQqqO9cSpzD0nl9xzcsnOhqc+hGJMQVSVaEYQ5wM91Y3oJiLPAp8CpiAMI8aEVQ5edifuZrdY4nezkZ4eusztt4fPN0ITrbvvlsAW9/yg2IhiGEZ5olYOCeQzqTL8+aioYoiFJCcn9Mhg6lRgYDabgE1ANuattrJEoyDuAz4VkbcBwbFFjI+pVIZhAJFXDiX7XsR7rhwYuVA1MHfm1SPsTmoRaQDsB34LvAjMA/qqasWF9EZQGjZsSM+ePenatStDhgxh586dUdddtmwZffv2pUuXLnTv3r3M/oWrr76aHj160L17dy699NKAIXrt2rWcccYZdO/enaysLNavX1/jn6m6/PzzzzzxxBPxFsMwjAiEVRCu3eGPqrpRVee7x/9qSbY6gd9R3RdffEGTJk0Cm9yi4YADDuC5555jxYoVvPbaa9x0000Bv0cPP/wwn332GcuXL6ddu3Y8/vjjANxyyy387ne/Y/ny5dx1113cfnvVTUX79u2rct1wmIIwaospA6YEDqPyROOL6b8icouItBWRQ/xHzCWrg5x66qmsWbOGvLw8Bnj8L48dO5bp06dXKN+pUyc6duwIQFpaGocffnjAVbffkZ+qsmvXrsAO6pUrV3L66acD0L9/f1555ZUK7RYWFnLccccxYsQIOnfuzKWXXhoY2aSnp3PbbbfRq1cv/vWvfzFr1iy6detG165due222wJtpKSkcOutt9KlSxfOPPNMPvnkE7KysjjqqKOYP9+ZV54+fToXXnghWVlZdOzYMeBFdvz48XzzzTf07NmTW2+9tVp9Wt/Jzy89YkGyP2DzcrMDh1EFQvng8B/Ad0GObyPVq+0jGl9ME96eEPDbMuHtCRXKj3ttXCB/0vuTKuRfO//aQP6U/CkV8oNx4IEHqqrqnj17dNCgQfrEE0/o22+/rRdccEGgzPXXX6/Tpk0L287HH3+sxx13nO7bty+QNmrUKD388MM1KytLd+zYoaqqw4YNC/hQmjdvngK6adOmMm199913Cuh7772nqqqjR4/Wv/3tb6rq+Fb661//qqqqGzZs0LZt2+qPP/6oe/bs0f79++tLL72kqo5/p0WLFqmq6kUXXaRnnXWWlpSU6LJly7RHjx6q6vhtOuKII3TTpk26c+dO7dKliy5ZskS/++477dKlS1T9p1q/fTHlb8gPHFXh2mtLj0TkoBv7BY5Y4PfthLlqCglhfDFFM4LorKodvAdwfDTKR0TOFZFVIrJGRCoYtkXkNBFZKiJ7ReTScnkjReRr9xgZzf0SEX+wnMzMTNq1a8fVV19d6TY2btzIlVdeybRp02jQoPQrmzZtGkVFRXTu3Dlgn5g0aRKLFy/mhBNOYPHixbRp04aGDRtWaLNt27acfPLJgOMC/L333gvk+V2AL1myhKysLA477DAaNWrEiBEjeOeddwBo0qQJ5557LuC4AO/Xrx+NGzemW7duFBYWBto666yzaNWqFc2bN+eSSy4pcx8jMplPZQaOqjB1aumRiPxy8OLAYSQe0axi+gDoFUVaGUSkITAZOAtYDywRkfmqutJT7HtgFHBLubqHABOATJzdPwVu3bK+s5MAvw3CS6NGjdi/f3/g+tdffwXg448/ZsyYMQDcfffdDBo0iG3btnHBBRdw77338tvf/rZC+w0bNmTo0KE8+OCDjB49mrS0NF580fFVUFxczLx582jZsmWFeuFcc0fjArxx48aBOl4X4F7335HuYxixxnxDVo+QCkJEjgDaAM1F5AScJa4ALYADomj7RJwwpd+67c0GLgQCCkJVC928/eXqngO8oapb3Pw3gHPBcfJYVXxZvoBDr2D4d2CGYurAqTWyVK59+/asXLmS3bt3s2vXLt58801OOeUU+vTpU0aZlJSUcPHFF/O73/2uTOAdVeWbb77hmGOOQVWZP38+xx13HACbNm3ikEMOoUGDBtx///1cdVXwuE7ff/89H374IX379mXmzJmccsopFcqceOKJ/L//9//YtGkTBx98MLNmzeKGG26o1Gd944032LJlC82bN+fll1/mmWeeITU1le3bt1eqHaNu8nCPt2Pa/sKmpW47hmOeYStLuBHEOThv90cCHo8nbAf+FEXbbYB1nuv1QJ8QZaOp2ybKuglP27Ztueyyy+jatSsdOnTghBNOCFpuzpw5vPPOO2zevDlgxJ4+fTrdu3dn5MiRbNu2DVWlR48egfCgeXl53H777YgIp512GpMnTw7a9rHHHsvkyZO56qqrOP744/n9739foUzr1q154IEH6N+/P6rKBRdcwIUXXlipz3riiScyePBg1q9fzxVXXEFmpjNVcvLJJ9O1a1fOO+88/va3v1WqTaOU6nprjUTG1IzAeUF2QY23f9NFWTXeppdZX5S+U5rr8MoTjbvvwao6r9INOzaFc1X1Gvf6SqCPqo4NUnY6sFBV57rXtwDNVPUv7vWdwC5VnVSuXjaQDdCuXbuMtWvXlmnX3EQHp7CwkAEDBvDFF1/E9D7Tp08nPz8/sAS3qtTn7zGSu+1ICiLbs3inKnaI2nT3HQuSXf7aoFruvoGuItKlfKKq3h2h3gagref6SDctGjZQ1nnvkUBeEBmmgrN/PjMz0759wyjHU6UbiRPGUF1QAO5Akl69nOtYMeMSM0JUh2gUhNcZTDNgAPBlFPWWAB1FpAPOA38oEK0f39eB+0TkYPf6bMw5YI2Rnp4e89EDwKhRoxg1alTM72MYoZj1J48NYkEcBUlSIi5zVdVcz3Evzpv9UVHU2wuMxXnYfwnMUdUVInK3G18CEektIuuBIcAUEVnh1t0C3IOjZJYAd/sN1oZhVA2RikdlwpAGq78gwkP3sEG5NPhzKk1yOlXMbF3A0kFCqwdblbF11CQLF5YeRuWJ1purlwNwpnwiok4c60Xl0u7ynC8J1ZaqPgM8UwX5yrdjSyuTmEg2MiM8/ngJ8WJTVx80LmZPg8JAWkaGYy8pKILMp2DLri2U7CuJm4xGaKIJGPQ5pZFIGgKHAZHsDwlBs2bN2Lx5M61atTIlkYSoKps3b6ZZs2bxFiWhKCqCNlGu6fPHS4ibkmjq3rjhnpBFUpqk4Ovni8nt51fPm3i9J5oRxADP+V7gB3f6KOE58sgjWb9+fcB/kZF8NGvWjCOPjGrAWif5zQGtoyqXEiJeULh4CZWlKoO51y5YFTIvIy0j5iuLphaXuhMfiBkhKktEBaGqa0XkFKCjqk4TkUNFJFVVv6sF+apF48aN6dChQ7zFMIwq88MfPTGnQ/g1TEmpnC2hNjknM4jtoRZZuNqMD9Uhmikmv8uLY4FpQBPgn8DJsRXNMIxgpKXV3Ka4gZ54PZEMzqFk8VNUFLpcopE1PYvFax3/T2+PfJus9Kz4CpSgRDPFdDFwArAUQFWLRCQ1plIZhlErVHd1z8aNNSNHrJg/1IwQ1SEaBVGiqioiCiAikT25GYZRJ0j2B2zuGI8NIi9+ciQr0SiIOSIyBWgpItcCVwFPRahjGEZN0Mk77xPb+M3BFvpNmTKwjLuOytLwj6VzUPserP05qMUhvIjnjcqrVTmSlWiM1JNE5CxgG44d4i5VfSPmkhmGAcMHeS5qfsVPrPdJ7D8wweegjLBEtVHOVQimFAyjjhH3fRIx5u0g3sQzMmDpUuc8P9+5NoITzSqmS4C/AofjxIQQnPh9LWIsm2EYMaYm90kEo2B4tP45Y0NWVvj8K97N4ADXWWAs3JknO9GMIB4EBqpqNA76DMOoQ6TlltoQinIqb0Po1TEtcqE48tUvS+GXeEuRuEQTk/oHUw6GUT/ZWLwxcNQVCgqcfSTm5isy0Ywg8kXkBeBlYLc/UVVfjJVQhmEYtUH+tfnxFiGhiUZBtAB24sRk8KOAKQjDMJKaYVmlFurVq+MoSIISzTLX0bUhiGEYyUd+hBdwuaPU6YLeuz3G0lSer7+OtwSJTTQ2CMMwYkTuB7mk3p+KTBSmFiRITNBKkJFRegSlSXHpYSQdVQkYZBhGDeFb7KO4JPTD85Dmh7Bl1xZSmoTw521Ui1WhvZEbmIIwjLgSTjkAHN4knV9LSrjmaF/tCFQNvK46/CuENozdFh9homTA66XuyFd3MiNEeUIqCBEZF66iqj5U8+IYRv1lTGY2YyqkOpu3HgEevqx25akJ0loltuPnr7eYESIc4UYQ/m/2WKA34HfrOBD4JJZCGYZRllAR4+KNRfKt24RUEKo6EUBE3gF6qep299oH/DuaxkXkXOBRnFjW/1DVB8rlNwWeAzKAzcDlqlooIo2BfwC9XBmfU9X7K/fRDKNukMgR47wk48azVWPNCBGOaGwQvwFKPNclblpYRKQhMBk4C1gPLBGR+aq60lPsamCrqh4jIkNxfD5dDgwBmqpqNxE5AFgpIrNUtTCaD2UYyUgiPmDr+kayrG6lNohkiohXW0SjIJ4DPhGRl9zri4Bno6h3IrBGVb8FEJHZwIWAV0FcCPjc87nA4yIiOBvxDhSRRkBzHKWU2NYuw6iDZKRVz9WpTCydg9IJiacBEz0iXryJuA9CVe8FRgNb3WO0qt4XRdttgHWe6/VuWtAyqroXx21WKxxlsQPYCHwPTFLVLVHc0zAMw6ghol3megCwTVWnichhItJBVb+LoVwnAvuANOBg4F0R+a9/NOJHRLKBbIB27drFUBzDMMJyeyoy0Vmyu238NlKbJvbqJT8b4uuNPOGJJh7EBCATZzXTNKAx8E/g5AhVNwBtPddHumnByqx3p5MOwjFWDwdeU9U9wI8i8r4rQxkFoapTgakAmZmZiTd+NYwI9G4xIN4ixJREnFbykjmreu7M6zrRjCAuBk4AlgKoapGIRPN6sAToKCIdcBTBUJwHv5f5wEjgQ+BS4C1VVRH5HjgdeF5EDgR+i7MU3DDqFEvGeWJO3xw/OUKR6DaE6lKX3JjHgmgURIn70FYA94EdEVXdKyJjgddxlrk+o6orRORuIF9V5wNP4yiBNcAWHCUCzuqnaSKyAieC3TRVXV6pT2YYRu1x//aEXIVlVI9oFMQcEZkCtBSRa4GrcPYoRERVFwGLyqXd5Tn/FWdJa/l6xcHSDcMwapIN48wIEY5o3H1PEpGzcJaZHgvcpapvxFwywzASn1TvvH1ihxcNxrFppTJvTzxv5HEnGiP1X1X1NuCNIGmGYVSHLJ/nwheiUAKT4125nnxzTMXmhTws0UwxnQWUVwbnBUkzDKOyZE30XPjiJYVhBCWcN9ffA38AjhYRr4E4Ffgg1oIZhpH4NNjROt4iVItt5p8hLOFGEDOBV4H7gfGe9O22q9kwDIB9Dyb33oG0x0tX7G+/3YwQ5QnnzfUX4BcR2auqa715IvK8ql4Zc+kMw0hovA7u0pLPRh0xYFN9JxobRBfvhbvjuXoevAzDSCoaN2hc5rpoexFtHnIN1LtTIM+HfpATB8mMWBLOBnE78CeguYj4Z+oEx7Nq8kVXNwyj0qQ0SaG4pJi8UXmhCzUtdldjJZ+C2DbejBDhCOnNVVXvV9VU4G+q2sI9UlW1lareXosyGoYRJ3z9fKQ0SSG9ZXrwAlvaO8eOVrUqV03Rollq4DAqIhpif7yIHKeqX4lIr2D5qro0ppJVkszMTM3Pr9vBTYy6R7L7Okp6+b0hU33J/VmqiogUqGpmsLxwNohxOK60c4PkKY4zPcMwDKOOEm4VU7b7t3/tiWMYRlKxNT3eElQL7wSKTKyYv3136dLXZIlxUZNE42qjIXABkO4tr6oPxU4sw6gfnNL82niLEDUZGbDUnVjOz3euObgwniLVKMGmlVo80CJsfl0nmmWuC4Bfgc+B/bEVxzDqF+/d5lkQ+Mf4yWEYwYhGQRypqt1jLolhGEaCkdIkJd4ixJVoFMSrInK2qv4n5tIYhpGwFBTEW4Kax+viOzWIiaG+u9+IRkF8BLwkIg2APTib5VRVW4SvZhhGspOXV3qelRWkwBTP0vIJMRYmBrTwPMUsIl5FQm6U8/AQ0Bc4wLNZzpSDYQC5uc6bp0jFw+erWH7gwLJlGJhdeiQg/f+US///pNJ/sZBXmFexwJjM0iOJad++7HVRUel3lJrqfM/1kWgUxDrgCw21o84w6jE+XzWDzmQ8VXokIlk+x5VGCA5pfgiQvHP1KSnO4R0plSc1FabWU+dC0SiIb4E8EbldRMb5j1gLZhjJQF2PSHbQzgya7mofMj+9ZTopTVK45mhf0FFUor+B+3yOfOnpwfPbHFfES/8t4u385HZrXlVCutoIFBAJOrOoqkG2lVSoey7wKNAQ+IeqPlAuvynwHI532M3A5apa6OZ1B6YALXCW1/ZW1V9D3ctcbRjxwOuqoSpj7GR3VVFQ5Fiuv/wSrjwztJPnlJTkjPmc7N9PNFTV1QZQqghE5ABV3VmJmzYEJuOELF0PLBGR+aq60lPsamCrqh4jIkOBvwKXuy7F/wlcqaqfiUgrHAO5YRgJROZT3udK6AdoXR9p1VUiTjGJSF8RWQl85V73EJEnomj7RGCNqn6rqiXAbODCcmUuBJ51z+cCZ4iIAGcDy1X1MwBV3ayq+6L6RIZhxAXVikey0zqldeCoj0SzzPUR4BxgPoD7Rn9aFPXa4Bi4/awH+oQqo6p7ReQXoBXQCVAReR04DJitqg9GcU/DqFWuTR5PGUYVyBtYP20PfqJREKjqOinjF5dYv803Ak4BegM7gTfdebI3vYVEJBvH4yzt2rWLsUiGUZG6vrrlzucXBM7vuXJgpeu3TvIX72OPLT2vCyOiyhKNglgnIifhvNE3Bm4Evoyi3gagref6SDctWJn1rt3hIBxj9XrgHVXdBCAii4BeQBkFoapTcaPbZWZm1sOvzzBiy1++HRQ4vyeMjSEURfX7BTzpiWaZ63XA9TjTQRuAnu51JJYAHUWkg4g0AYbiTlN5mA+MdM8vBd5y91u8DnQTkQNcxdEPWIlhGEYt0rFj6VEfiWYV0yZgRGUbdm0KY3Ee9g2BZ1R1hYjcDeSr6nzgaeB5EVkDbMFRIqjqVhF5CEfJKLBIVf9dWRkMI97kfpCLb7GP4hJnGc+ATgNYMGxBhFqJw+E/D4i3CHFl4YerPVed4iZHvIjKBlFVVHURsKhc2l2e81+BISHq/hNnqathJCzZHg8ZwewRXuUQjAn9EtuB0Q8PJ48yiwXHPl5qhKir+yDCEVMFYRh1nac8HjKCKYhwygHAl+WrWYESjAUe/TKw8jZuI86YgjCMWqI+voEOKrVxJ+UqoI6H1FPjg0s0IUebAoOpGHL07tiJZRhGMlDXN5DNOml15EJ1mGhGEK8AvwAFwO7YimMYRm2Sm+s4rMvNLWtPARg+HGZ9MZPLLoNDD4XJ1w2vUL8op26vY830eBJJxhFQdYk25Oi5MZfEMIxax++uvLAwRIHBI5izD/gBJlNRQRh1m2j2QXwgIt1iLolhGLWO34ne/ffHV45EpVev0qM+Es0I4hRglIh8hzPF5A852j2mkhmGEXPC+ZKaOROYN6zWZElEpi7wBuIO7c68rhKNgjgv5lIYRh1lyoAp8RYhLN6ludkLsnlqqbNud8qAKWRnZDNz8Myw9ResKl3HOvDYureO1evOvD6uQotmJ/VaEekBnOomvet3w20YyU75nc5e5g+dz+qFAwPz9AMGQEFWGhuLN5YW8gG7UyDPB+RUaCM7IzFjTdcUg2aXrmOtjw/Quk408SBuBGYAh7vHP0XkhlgLZhi1QaSdzlHFnG5aDP19NSmWkSD0at0rcNRHopliuhroo6o7AETkr8CHwGOxFMwwaoNIO52jjoTWJPlDpk0dOJWpA+u4//JKktupIHKhOkw0CkIoG/9hn5tmGHWKSFMkjtuIsuv+vTGLk5GB9+cGzhfcXnGKrLok++qf/v1Lz20fRHCmAR+LyEvu9UU4XlgNw4hAxtTSlS8F2Yn3Nrqw5BbPVc0riILE+8hGJYjGSP2QiOThLHcFGK2qn8ZUKsOoIyzduDTeIhjVoF+/eEsQX6INOboUsP90o85RX42PfjJ2j4u3CAmNb3qe5yorTlLED/PmatRrqjvtk39tfg1JEh/y78uNXKge0//ZUiNEfVzGawrCMKpBRlr9211bGcpsxKvbW0LqJKYgDMOIGWPGlJ4no4Lo175+GyGiiQdxCfBXnE1yQqkvphYxls0w4s6UxPaUEXfqug0np1VevEWIK9GMIB4EBqrql7EWxjBqm6kFpXMgwdxiJONbb2XI8vkC53me82hJxKW7NUmyR8SrLtEoiB9MORh1lTELS+dAquI3ybtRLhmNmItloufKFy8xEp9OC5CJjrYY0GkAC4YtiFChbhBNPIh8EXlBRIaJyCX+I5rGReRcEVklImtEZHyQ/KZu22tE5GMRSS+X305EikXklvJ1DcMwYs2AAc7Ru3e8JYkP0YwgWgA7gbM9aQq8GK6SiDQEJgNnAeuBJSIyX1VXeopdDWxV1WNEZCiOreNyT/5DwKtRyGgYRhXopxPiLUJCs8AdKCxYBYNmV8yf+XmpO/Th3epexL1odlKPrmLbJwJrVPVbABGZDVwIeBXEhZSOa+cCj4uIqKqKyEXAd8COKt7fMKpNhmcVa1XcRswfOr/mhKkmublwizsWHzfOua6K3cFLJBtOXWHgsQODTiGOeHFE4LxeKggRORLHc+vJbtK7wI2quj5C1TbAOs/1eqBPqDKquldEfgFaicivwG04o4+Q00sikg1kA7Rr1y7SRzGMsEgM/O7VxSA6XqprwzESm2id9c0EhrjXV7hpZ8VKKJxRxcOqWixhfrWqOhWYCpCZmZl8FkIjaUhJibcERiIyrGvdDskajYI4TFWnea6ni8hNUdTbALT1XB/ppgUrs15EGgEHAZtxRhqXisiDQEtgv4j8qqqPR3Ffw6hRUlKcwEHJTk6OcxjRM9MTcXV4kBmkSCFZk51oVjFtFpErRKShe1yB8xCPxBKgo4h0EJEmwFCg/ITsfGCke34p8JY6nKqq6aqaDjwC3GfKwYg1qsGP7duT98H623G5NJmQSuc/Vpz+yf0gF5koDJ4zmJzXY/MB/auABgyISfMxZ8SI0qM8U6c605KpqY49py4SzQjiKhwbxMM4q5c+ACIarl2bwljgdaAh8IyqrhCRu4F8VZ2PE1fieRFZA2zBUSKGUWdIy00LnBflFIUpGRs+buaDBsV89b/CkGVe/PJFUpqkkHtOzT/lFiT5doGUFCeqYN++ocsUFzsjzGR9iQhHNKuY1gKDIpULUXcRsKhc2l2e818ptW2EasNXlXsbRiKwsXhjfAVo6oZCPfqNkEVSmqTg6+er9q38mwbr0kYyn8850tNDlznrrPD5yUxIBSEif1TVB0XkMZyRQxlU9f/FVDLDqAUGdErSuY8oGXFQaGdSOSflkHNS9V57U5qkRIzrncyEs9tkZwMZ3hjedW8VV7gRhN+9RnI7vDeMMBTkeN50q7AgZcO48usuEot/3hTbh5avnw/fYl9IJeHL85WeZ/mClklm6voyX9EIHqhEZIiq/itSWrzJzMzU/HzTZUbl8K6ijoUztmT31VRd6vrnrwufT0QKVDUzWF40RurbgfLKIFiaYRhGveLaXtfGW4SYEs4GcR5wPtBGRP7uyWoB7I21YIZhGInOsaunRi6UxIQbQRTh2B8GAV4vNNuBm2MplGHUGmXmxX0hCoWmaHvp0tW01LQwJeNDek7p7q7C3Lq9qSse3OJxBFSvlrmq6mfAZyLyErBDVfdBwEtr01qSzzBiS1b14iG0eahN4DwR56DXtpjluTIFYVSOaGwQ/wHOBPzLFJq7aSfFSijDMKJnwYLSyGcDBiT/5rRkYty4eEsQW6JREM1UNbCGzXWgd0AMZTIMo4b4w29mxFuEOk3aYO/u87o3xxSNgtghIr1UdSmAiGQAu2IrlmHUDfKvje/S68nX1b0YBYnELW+UGiGqu+kwEYlGQdwE/EtEigABjqBs1DfDMCi7Jh4cm0RGWkaI0jXHwIGlezgWrEqe2MnZC7J5aulTAVcfdfEBm+xE44tpiYgcBxzrJq1S1T2xFcswkoO67mqi2uxOcfxBfVMxfMy77wIHQnFJMXe8kZwKYtxv67YRIhp33+Aoh+OBXsAwEfld7EQyjOTB189HShOLJhSKJh/6YHcKbQ5MD1tuN8mpZFM/zA0cdZFoXG1MALJwFMQi4DzgPVW9NObSVQJztWFUhWR3ldDypqzA+c+P5MVNjlDk5jreUIcNc+IneMnOhqfaJHf/x9pVS20QztVGNCOIS4EzgP+p6migB07kN8Mw4swvBy8OHIlITo4TcKm8coDgaUZiEY2Repeq7heRvSLSAviRsqFEDcMw6iUTJsRbgtgSjYLIF5GWwFM4LjeKgQ9jKZRh1BbJ7mzt4R5vx1uE+k01XbUkOmFtECIiwJGqus69TgdaqOry2hEveswGYVSFujCHnMwkuw0o2eWHarj7VlUVkUVAN/e6sObFMwzDMBKRaKaYlopIb1VdEnNpDMOISEEBZLrve716OddGfJjQr24bIaJREH2AK0SkENiBs5taVbV7pIoici7wKNAQ+IeqPlAuvynwHJABbAYuV9VCETkLeABoApQAt6rqW1F/KsOoI+R+kMvML2ZSkF1OC7QugDGZfLGzPYU/55HeMj0u8lWbPM8D1j0dOGsgC1cvDCQn8k7r7Qt8pRdZ8ZIidoQLGNROVb8HzqlKw65b8MnAWcB6YImIzFfVlZ5iVwNbVfUYERkK/BXHjccmYKCqFolIV+B1oA2GUdMM9MYRTrx1l7cu8tFoV2s6nFDId5+mV8jf12g7g+cMJrVJKnmj8mpdvuoyoZ8vYpnikmJ8ixNTQTz0kOfi7Bwe+shJmHTWpISUt7KEG0G8DPRS1bUiMk9VB1ey7ROBNar6LYCIzAYuBLwK4kJKTf9zgcdFRFT1U0+ZFUBzEWmqqrsrKYNhhCfjKc9F4ikIbVzMngaFFGZlAYUAZGRAfj5kPgX7mmxh9eYSfFE8aBMRny+6cubOJD6EUxBez2NHVaHtNsA6z/V6nOmqoGVUda+I/AK0whlB+BkMLA2mHEQkG8gGaNeuXRVENIwkoOEeaLm2TFJGWkbSrpqJhNfBYHkHiInGpEml50WhiyUt4RSEhjivNUSkC86009nB8lV1Ku5rX2ZmZt38tRj1mtcuWBVvEYwwlA0zmkvuOWV9MmUvKJ3CnDow8UaokQinIHqIyDackURz9xxKjdQtIrS9gbI7ro9004KVWS8ijXBceGwGEJEjgZeA36nqN9F8GMMoj98XUHExbNgAaZ6w0UVJ8Mp3TmaneIsQcwoLISsL1q4NkumrXVlqmqeWlk5hJqOCCOmLSVUbqmoLVU1V1Ubuuf86knIAWAJ0FJEOItIEGArML1dmPjDSPb8UeMvde9ES+DcwXlXfr/SnMgwXv3IwEpPhw+GGGyA1Nd6SVI/sbGfTpUjd8jEVzTLXKuHaFMbirEBqCDyjqitE5G4gX1XnA08Dz4vIGmALjhIBGAscA9wlIne5aWer6o+xkteom5hySGwyMuq2Ep8yYEq8RagWMVMQAKq6CMdFuDftLs/5r8CQIPX+AvwllrIZ9Q/v9FKw63iQ+0EuuR/mUpRTdr6raHsRbR5qQ/uD2pM3Kon3OUQgJ6f8PH5ZJNPzgE3CPWl5uaU2iOyZcRSkikSMB5EsmC8mIxiRfC3F25dOs4mp7C6BA6Z/zo4N6YF0v4IAYHtrGu5LZe/D9c9g7Z2uyc4OXS5RSQZfX1X2xWTUT/yxgv3Ecidr7ge5+Bb7Kqxz3zBuA2mpNfCK3zfX8bjZtJiBsxIvRvNuiqEJ7LwsC/8+hwo02U7DD5N/01VV8CqFjKkZLN24tEKZRN5pnexEG3LUqMf4d7LGgmDKoSbwGwz9yiEUI3s4ayTiHja03D6HtNQ0Nlyr4FNSHtvOfQPs4ReKWP5/VpcZM0qPZMRGEPWcaNdpx2ona6R2i7aXzs0HG1F4HdVlZARpIIxyAEhvmR54A40HG8ZuC5mXlpa40xKJRqLutF7YdHjgfDjJZ4QwG0Q9J9IcfKzn6Ctzf3zh7+/9Vw7M/fqS31+/EZp425AikejyQfVjUhtGwpNSboZI1d6+6wIZGaWHUfvYFJOR9KSkRO/0LVFIhtUticDSijbppGLGJUlqfHAxBWEkDcn4IL3v7Vzu/9DHqjFFpLUqt13YJ/BzksdziDPzh5Z3zpBYzPqTxwaRWAvoosIUhGHEkDve8EHTYtocX4j+0K1igZZr6fBoB1KapLD99u21Ll+yM/DYgfEWISwLF0Yuk8iYgjDCEuuQiolquKsx/KuoRmXh+qEEnNGQTHTO47mKyjDCYQrCCIsvyxdvEZKChn9MY/+BGwEoGL6BXh3dJbklrvW8UUmFOnVeOdYwXrvN/PkwMLEHD4AjZzJjCsKo08TbWZrea9NG1SElJbkd+U0tHkjhz4V88eMXzO80v8KUWNb0LAo2FiTsTnBTEAlOKFcUAK1TWldw8hYrygeSD8eUAVPIznA24HnjMYTCvwopnNO2quKXw0hOfL7w/z9puaWbJ2vrt1AZ8grzwm7iK/y5MKFjbts+iAQnVq4oaouIrpxbF1CcWsCdTxaEKZT47HuwCJ2g6AQtnV4yqk1ODmzfXrqvxX/4p5c2Fm8MHImIr58vrBuXtb84LlYS9TduCiLBqe4/Tm6uE4zF75uo/EHeBDoWTahgjPb5SsukpsI3lYjpt8CznK/h7zNodmMGZIfY6TQmE8ZksuvKoBs5E5YmOZ2QiYJMFF7PXx1vcYwEJeekHD4ftp3205RBxw0kL69s/tsj36bHp29zXbO34yJfJGyKKYmoilEz4ht8no9jU8CXFbpIcTGs++sCNMh0empqxfYzPLrmlwOXwoHAwaHcbYeRDcq614hDPIB/vlk6srniDNvOa1SOgQOdEVAo0smi4Tr455vw5G21J1e0mIJIchasKn1dD7YmvKYMfKHaqWkDYkZGze6ezZha+lAvyK44jTXw/lwWFvugSTEP93ibmy7KKpN/5XulI5srzrBVR0blyMoK/5KWleUokEQ1xJuCSHIGzR4UOI80wqjMTmS/cdC7tDASsdzpXN7XUrQEix/gxa8cQiG7DkGbbyldrupSkmvTSkZkIkXMW7s2dF4iYAoiQSgogMxg0/Ct82l+AIyp4mKcKckdEheoIV9LG3oHPGv++aj53HOlM9o6aEcGxbsL2Zca/JfabHc6uxqWMCClugIYscb7MpOfnxwO/hLdAbUpiERnYwa7gH/cAw//sfLVI4VpHDirdFoqEaKtFZSbBSoo8iaE/8UPHw6zZjnnM2Y415H4+ZG8sPk7H07u1VVGYpNdkEHhz4Vs2bWF/Nb5ZKSV/R/v9FgnNhZvjNs+iZiuYhKRc0VklYisEZHxQfKbisgLbv7HIpLuybvdTV8lIufEUs5kIFZzlAtXLwwckYhHTODMpzIDB0DnP2YHVg9d8UiQAEeDh4NPGPG1MPPz5AvQYtQvVm9ezZZdWwC4+JLSlYOr3RlM7z6JeBAzBSEiDYHJwHnA8cAwETm+XLGrga2qegzwMPBXt+7xwFCgC3Au8ITbXp0lI6PiWu9E8F4aae7/rLMqlht4f27gIU7haRXqZPl8pfnf9auQn/mnnLD1q0SbJYF9Cv7pJaNu4f3d+KeXyizpTkAi7ZPYs38PEL99ErGcYjoRWKOq3wKIyGzgQmClp8yFgM89nws8LiLips9W1d3AdyKyxm3vw1A3K/h+RdnoYwBb0+HgQuf8xemwfGTZ/FuOgJQfnPPth0Pqj2Xzd6eUOlv7x7uw/pSy+b4w/3W7D4SmO0rbeXwVbPdsoDp8GfzhBOe8+DelcvjZ0h4OWev0zuZjaD//6zLZ4yYV8PAO12jx03Fw2Feln7+oF/wnF0b1ByB1cz8mjRzOmIVjShtY3xuOXOKcb+gNbZaUaf/6/5vJEz+MgFtAvhiGzi37Nn7FI1OZ8csYpGt7mn2eh++W9NB94bJg1YJqed+UiQI/neIsmwVmfPY8b+X6AjtoZ84E5sGsL5z8ES+OqPK9jOQm8FvwedNCFM7dwIav0khzf55F24to81Cb6G/m0zIvcwVFBYERb0S2t6b1zO1sL4JOntARxz7WCQ51f/O/pMGLzyM+YFwatPBsCvxfVxpM/Zz9+53LbdvgsEebsXvf7jLylZ6Xe2Z9MSSseLFUEG2AdZ7r9UCfUGVUda+I/AK0ctM/Kle3wjcmItmAM/FxWLOakjvxaLCXTdemAlXz67NjJ0yaBBwXuky4t5h27aAwxGjm4P2dOPzK+5i1OZUccivkN5CG7I9CxlD3byiN2OdNSH+v3HnrMuVnDp7JsK7DyqzuCte+kdzkX5vgVt5K4J9WWrBqAYNmu8rhsa9gdyoUx2d3flLvpFbVqaqaGSqeal1iYn9flevu3wc9eoQrIVV2N73l4Df4qvEsWqeW/gMvuD0nMJ3z4DUXVHg45/l8gfzW3VZXcHedf19uIP+v15xf7Ye7udOuu2SkZQSOOknGVChJjVwuRojGaKJbRPoCPlU9x72+HUBV7/eUed0t86GINAL+BxwGjPeW9ZYLdb/MzEzNT/Q1Y4ZhGAmGiBSEesmO5QhiCdBRRDqISBMco3N57+jzAb9h4FLgLXU01nxgqLvKqQPQEfgkhrIahmEY5YiZDcK1KYwFXgcaAs+o6goRuRvIV9X5wNPA864ReguOEsEtNwfHoL0XuF5V9wW9kWEYhhETYjbFVNvYFJNhGEblidcUk2EYhpHEmIIwDMMwgmIKwjAMwwiKKQjDMAwjKHXGSC0i24FVMbzFQcAvMaoXqUyo/GDp0aR5rw8FNkWQrzpUpd+irWP9VrU6sey3SNex7LdY/kYjlatsXiL1W0dVPShojqrWiQNn6Wws258aq3qRyoTKD5YeTZr3OhH7Ldo61m+J129RXMes32L5G41UrrJ5ydJvNsUUPVUNlhBNvUhlQuUHS48mrTYDP1TlXtHWsX6rWp1Y9luy9Vll6oUrV9m8pOi3ujTFlK/1wCdTTWP9VjWs36qG9VvViFe/1aURRJDoMUYUWL9VDeu3qmH9VjXi0m91ZgRhGIZh1Cx1aQRhGIZh1CCmIAzDMIygmIIwDMMwglJnFYSIdBaR/xORuSLy+3jLk0yIyIEiki8iA+ItS7IgIlki8q77P5cVb3mSBRFpICL3ishjIjIycg1DRE51/8/+ISIfxPJeSaUgROQZEflRRL4ol36uiKwSkTUi4o9G96WqXgdcBpwcD3kThcr0m8ttwJzalTLxqGS/KVAMNMOJoV5vqWS/XQgcCeyhHvdbJZ9t77rPtoXAszEVLFa782K04+80oBfwhSetIfANcBTQBPgMON7NGwS8CgyPt+zJ0m/AWTiBm0YBA+ItexL1WwM3/zfAjHjLnkT9Nh4Y45aZG2/Zk6HPPPlzgNRYypVUIwhVfQcn8pyXE4E1qvqtqpYAs3HeSlDV+ap6HjCidiVNLCrZb1nAb4HhwLUiklT/IzVJZfpNVfe7+VuBprUoZsJRyf+39Th9BlBvo0ZW9tkmIu2AX1R1eyzlilnI0VqkDbDOc70e6OPOA1+C82NdVPtiJTxB+01VxwKIyChgk+fBZziE+n+7BDgHaAk8Hge5Ep2g/QY8CjwmIqcC78RDsAQmVJ8BXA1Mi7UAdUFBBEVV84C8OIuRtKjq9HjLkEyo6ovAi/GWI9lQ1Z04DzujEqjqhNq4T12YPtgAtPVcH+mmGeGxfqsa1m9Vw/qt8sS9z+qCglgCdBSRDiLSBMfAOj/OMiUD1m9Vw/qtali/VZ6491lSKQgRmQV8CBwrIutF5GpV3QuMBV4HvgTmqOqKeMqZaFi/VQ3rt6ph/VZ5ErXPzFmfYRiGEZSkGkEYhmEYtYcpCMMwDCMopiAMwzCMoJiCMAzDMIJiCsIwDMMIiikIwzAMIyimIIx6h4jsE5FlnmN85FqxRxzeEpEWYcpME5Ex5dIuEpFXRaSJiLwjInXWhY5Ru5iCMOoju1S1p+d4oLoN1tBD+XzgM1XdFqbMLJwdtV6GArNcj59vApfXgCyGYQrCMPyISKGITBSRpSLyuYgc56Yf6AZ0+UREPhURv8vlUSIyX0TeAt4UkQNEZI6IrBSRl0TkYxHJFJGrROQRz32uFZGHg4gwAnjFU+4K957LRGSKiDTEUQDHiUhrv2zAmcDLbrWXqefu7Y2awxSEUR9pXm6KyfvGvUlVewFPAre4aXcAb6nqiUB/4G/ugxmcIC+Xqmo/4A/AVlU9HrgTyHDLzAEGikhj93o08EwQuU4GCsAJmYszEjhZVXvixEoYoar7gHk4kRIBBgJ5nlHHF0DvyneJYVTE5iqN+sgu96EbDL/L7gKceCIAZwODRMSvMJoB7dzzN1TVH+jlFJz4BqjqFyKy3D0vdkcZA0TkS6Cxqn4e5N6HeALAnIGjYJaICEBz4Ec3bxYwyb3XUOB5fwOquk9ESkQkNdbBZIy6jykIwyjLbvfvPkp/HwIMVtVV3oIi0gfYEWW7/wD+BHxF6EAve0WkgRukSYBnVfX2IOU+AFqLSA/gJCraJJoCv0Ypl2GExKaYDCMyrwM3iPsqLyInhCj3Pu7Uj4gcD3TzZ6jqxzi+/YfjjACCsQon/jA4toZLReRwt71DRKS925YCL+AErH9VVQPKQERa4UyT7anC5zSMMpiCMOoj5W0QkVYx3QM0BpaLyAr3OhhPAIeJyErgL8AK4BdP/hzgfVXdGqwy8G+cmOCo6krgz8B/3KmqN4DWnrKzgB5UVDb93XYMo9qYu2/DqCHcVUaNVfVXETka+C9wrLv8FBFZCDysqm+GqN8aeE5Vz6qGDC8C41V1dVXbMAw/ZoMwjJrjAOBtd7WSAH9Q1RIRaQl8grPHIahyAFDVjSLylIi0iLAXIihu1LGXTTkYNYWNIAzDMIygmA3CMAzDCIopCMMwDCMopiAMwzCMoJiCMAzDMIJiCsIwDMMIiikIwzAMIyj/H27W5UNjB9g6AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -1487,7 +1508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb index 1c996c82a21..a7740fc2675 100644 --- a/examples/jupyter/mdgxs-part-ii.ipynb +++ b/examples/jupyter/mdgxs-part-ii.ipynb @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEVyEhLpgJFNv8T///98iBL0AAAAAWJLR0QDEQxM8gAAAAd0SU1FB+UEEgcdMS4wY+YAAAVuSURBVGje7Zs9buM6EIBHhuXClYu1Cx0hp+ARVNgLrPstVqfIEVR4+xQKYOmUS2pIzpCiYsmjh+RBYYDgMxLaMn8/DkmAz07HEuACkJ2HqGLMBwhQNZB3NRQtQ2Uw03hi2Gl873FHqH+1sO8aqDplcBdj7bCIsOtR/+r06/4/GW66ThUuv8G7w71+D4Ymv/mzfl1bBI+tfVeLbxuPjcM+/wvP30KAbyz/Lsj/WtDnNzDv898y+vyWfWmgL81Q7Tn6ojBZAcu0pkJ/0xkVlTRHoPKHClFX+k2/h9I1DXA1WMLOYDOGlUEFhUFd/5DrRvRDP0eJeAwQGJ4DzCyuPp1KyHRHzP8i/vIIBi+EvzWePR4RddU0ff2bmq48mu7pscVejZh1N8IG2w+2tFfCmnq176m+0TXwQuj7r2/0DTX6O3Xllrqyb/8T+m/Y/8b6rzA/RP139Pk3ieffhIU2qfw83sP664JKS2Nuu7KrP7jqRvNHt4l3gKImvHk8aaw8AkOd9Tvl0I9fZqTK0qgAtkk0WXHQVLB7R9TFu80sljEWiLluNwUOpX789iN1DQfwg3aIbtD2raIJ2p9DBQc3fyD284fFHZ8/7sn5C+DgUGdyjV6Zz0rNX4P50+c3CDvKPz5/Bv13NP9H/bei8cc/9D3CLByK9m7+LEJpUa5WbKG5mfIQ+ouy86ftv2hKOdZfgGhKDE+u/9bg9Sw/p5D72yWF3+movPTmDMsxPDLU/481Vfb+q7a96ZxQhSP/NdJj/EdXpPHfK/NfI12m/eg/Rv4b+Fc/K2z1j/Ev16qY/5lv89B/zT9x/2OTnh6ZHvvnQT8kmwp5/8X8D/wX8w/6r+DzZd+f+++c8if/bXylx/XP/Desf4v9I+fp9qeopamw0QX4nY7Wf5PjX34ZHf/OOP5l4fjrTakh6cWeWtCgeyIVjsf/PalwtH59IZUrEv5bBf676SJ/vIf+O3X9Os1/F16/TvXf4iP/HVu/Dv134D8hJqSH+w9c6166mH9xbEPpChCMf/WmZ5JC3OKrj/GAL7ef3Xe+QrriomObY6GbRUduS3qAJ1v+mV2KlLT+8aaLWHnEmr5x/7WYcf+1psaQpJdUDtsfU2Hf/kPpTfovV+GB/7L8D/1Vl9bAf+fnD/3XZ5rgv/b5A/9VFMpj0htjRypsxz83/lrp3ZHpsvrLCTOOug2kxv/LA+Tj/+qTk44yFX9j/hHF37x/sPgfU6FU/E9xFXLxv0H8MQ46plXMj9/S+Odg0pwWf114/hR9vij+nQy6K5KW17H4O/Nft/5JBP2TyNc/fP3F9h/G119s/6Fff6092UIbWfSPhgL4/k0Uf2D7N1aFo/0bHn9I7t+M+u+8/ZuU/87bv/kU/+1G/Pfx/s0k/32wf1OPj5/kv2z9eo32b1B6o62cCrGI9m90G3L7N26nZoiZ8ls5CVx9+ll66T2l8YabNgksQ//l/tSOYEYqHOzfKI4P/Jf728Afp/tv0H8F/rqs/858fmn5SetP2n5Wn6Tjl3D8lI7f0vlDPn8t4r/i/ddn/UHqL1J/Evvb2pN0/SBcv0jXTwudX/ofr18XPr80M34gjV+I4ydrT8L4nTR+KI1fSuOnS/ivPH78fPxaGj+Xxu+l+werT9L9K+H+mXT/Trp/KN2//Fr+O3//WLp/Ld0/l+7frz5Jz48Iz69Iz898n19a4vzS8+fHpOfXxOfn1p6k5zeF50el51el52cXOL+7wPnh588vS89PS89vS8+Pi8+vrz0J709I729I749I7698ufs3M+8PSe8vSe9PSe9vrT5J7w8K7y9K708uc35Jcn/0v1y/Tro/K4p/S+8PS+8vi+9Pf276B9XSZgbOTLmpAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDIxLTA0LTE4VDEyOjI5OjQ5LTA1OjAwrxbF7AAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyMS0wNC0xOFQxMjoyOTo0OS0wNTowMN5LfVAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] @@ -388,17 +388,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=17.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=23.\n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", " warn(msg, IDWarning)\n" ] } @@ -486,26 +486,29 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-18 22:07:58\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", + " Date/Time | 2021-04-18 12:29:49\n", + " MPI Processes | 1\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -566,20 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.2397e-01 seconds\n", - " Reading cross sections = 4.0321e-01 seconds\n", - " Total time in simulation = 2.0407e+01 seconds\n", - " Time in transport only = 2.0154e+01 seconds\n", - " Time in inactive batches = 1.0937e+00 seconds\n", - " Time in active batches = 1.9314e+01 seconds\n", - " Time synchronizing fission bank = 7.8056e-03 seconds\n", - " Sampling source sites = 6.7223e-03 seconds\n", - " SEND/RECV source sites = 9.5783e-04 seconds\n", - " Time accumulating tallies = 9.2006e-02 seconds\n", - " Total time for finalization = 1.0890e-02 seconds\n", - " Total time elapsed = 2.0869e+01 seconds\n", - " Calculation Rate (inactive) = 22858.4 particles/second\n", - " Calculation Rate (active) = 5177.70 particles/second\n", + " Total time for initialization = 9.3825e-01 seconds\n", + " Reading cross sections = 9.2114e-01 seconds\n", + " Total time in simulation = 1.5872e+02 seconds\n", + " Time in transport only = 1.5607e+02 seconds\n", + " Time in inactive batches = 8.3963e+00 seconds\n", + " Time in active batches = 1.5032e+02 seconds\n", + " Time synchronizing fission bank = 1.2433e-02 seconds\n", + " Sampling source sites = 1.1456e-02 seconds\n", + " SEND/RECV source sites = 7.1083e-04 seconds\n", + " Time accumulating tallies = 2.5981e+00 seconds\n", + " Time writing statepoints = 2.4262e-02 seconds\n", + " Total time for finalization = 1.7827e-05 seconds\n", + " Total time elapsed = 1.5971e+02 seconds\n", + " Calculation Rate (inactive) = 2977.49 particles/second\n", + " Calculation Rate (active) = 665.252 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -638,7 +642,10 @@ "mgxs_lib.load_from_statepoint(sp)\n", "\n", "# Extrack the current tally separately\n", - "current_tally = sp.get_tally(name='current tally')" + "current_tally = sp.get_tally(name='current tally')\n", + "\n", + "# Close statepoint file now that we have the info we need\n", + "sp.close()" ] }, { @@ -1104,7 +1111,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1171,7 +1178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/openmc/statepoint.py b/openmc/statepoint.py index 6e90bcaf7c6..0ce32065738 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -153,9 +153,7 @@ def __enter__(self): return self def __exit__(self, *exc): - self._f.close() - if self._summary is not None: - self._summary._f.close() + self.close() @property def cmfd_on(self): @@ -490,6 +488,14 @@ def sparse(self, sparse): for tally_id in self.tallies: self.tallies[tally_id].sparse = self.sparse + def close(self): + """Close the statepoint HDF5 file and the corresponding + summary HDF5 file if present. + """ + self._f.close() + if self._summary is not None: + self._summary._f.close() + def add_volume_information(self, volume_calc): """Add volume information to the geometry within the file From d3314107844fcdfaa4ccf4a0e04ab951d53b8928 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 26 Jun 2021 08:48:03 -0500 Subject: [PATCH 0113/2572] Updating jupyter notebooks to either use context managers or close SP files. --- examples/jupyter/cad-based-geometry.ipynb | 152 +- examples/jupyter/candu.ipynb | 701 +-------- examples/jupyter/mdgxs-part-i.ipynb | 352 ++--- examples/jupyter/mdgxs-part-ii.ipynb | 244 ++-- examples/jupyter/mg-mode-part-i.ipynb | 181 +-- examples/jupyter/mg-mode-part-ii.ipynb | 370 ++--- examples/jupyter/mg-mode-part-iii.ipynb | 115 +- examples/jupyter/mgxs-part-iii.ipynb | 764 +++++----- examples/jupyter/pandas-dataframes.ipynb | 1625 +++++++++++---------- examples/jupyter/post-processing.ipynb | 356 ++--- examples/jupyter/tally-arithmetic.ipynb | 336 +++-- 11 files changed, 2318 insertions(+), 2878 deletions(-) diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb index 9fa56f6fbdf..3699360a63b 100644 --- a/examples/jupyter/cad-based-geometry.ipynb +++ b/examples/jupyter/cad-based-geometry.ipynb @@ -174,7 +174,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -280,70 +280,76 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:17\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 11:58:36\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading DAGMC geometry...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.0001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.15789\n", - " 2/1 1.05169\n", - " 3/1 1.00736\n", - " 4/1 0.97863 0.99300 +/- 0.01436\n", - " 5/1 0.95316 0.97972 +/- 0.01566\n", - " 6/1 0.95079 0.97248 +/- 0.01322\n", - " 7/1 0.96879 0.97175 +/- 0.01027\n", - " 8/1 0.94253 0.96688 +/- 0.00970\n", - " 9/1 0.97406 0.96790 +/- 0.00826\n", - " 10/1 0.97362 0.96862 +/- 0.00719\n", + " 1/1 1.16613\n", + " 2/1 1.05158\n", + " 3/1 0.99824\n", + " 4/1 0.98119 0.98971 +/- 0.00853\n", + " 5/1 0.99111 0.99018 +/- 0.00494\n", + " 6/1 0.98946 0.99000 +/- 0.00350\n", + " 7/1 0.96858 0.98571 +/- 0.00507\n", + " 8/1 0.96709 0.98261 +/- 0.00518\n", + " 9/1 0.97415 0.98140 +/- 0.00454\n", + " 10/1 0.93691 0.97584 +/- 0.00681\n", " Creating state point statepoint.10.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.4575e-01 seconds\n", - " Reading cross sections = 1.3129e-01 seconds\n", - " Total time in simulation = 1.6897e+00 seconds\n", - " Time in transport only = 1.6829e+00 seconds\n", - " Time in inactive batches = 3.1605e-01 seconds\n", - " Time in active batches = 1.3737e+00 seconds\n", - " Time synchronizing fission bank = 2.8739e-03 seconds\n", - " Sampling source sites = 2.5550e-03 seconds\n", - " SEND/RECV source sites = 2.8512e-04 seconds\n", - " Time accumulating tallies = 7.8450e-06 seconds\n", - " Total time for finalization = 1.7404e-04 seconds\n", - " Total time elapsed = 1.9588e+00 seconds\n", - " Calculation Rate (inactive) = 31640.8 particles/second\n", - " Calculation Rate (active) = 29119.1 particles/second\n", + " Total time for initialization = 1.4751e-01 seconds\n", + " Reading cross sections = 1.0076e-01 seconds\n", + " Total time in simulation = 3.0461e-01 seconds\n", + " Time in transport only = 2.9733e-01 seconds\n", + " Time in inactive batches = 5.7728e-02 seconds\n", + " Time in active batches = 2.4689e-01 seconds\n", + " Time synchronizing fission bank = 2.9442e-03 seconds\n", + " Sampling source sites = 2.6400e-03 seconds\n", + " SEND/RECV source sites = 3.0032e-04 seconds\n", + " Time accumulating tallies = 4.2046e-05 seconds\n", + " Time writing statepoints = 3.0596e-03 seconds\n", + " Total time for finalization = 8.3508e-05 seconds\n", + " Total time elapsed = 4.5681e-01 seconds\n", + " Calculation Rate (inactive) = 173226.0 particles/second\n", + " Calculation Rate (active) = 162018.0 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.97020 +/- 0.00750\n", - " k-effective (Track-length) = 0.96862 +/- 0.00719\n", - " k-effective (Absorption) = 0.95742 +/- 0.00791\n", - " Combined k-effective = 0.96203 +/- 0.00898\n", - " Leakage Fraction = 0.57677 +/- 0.00317\n", + " k-effective (Collision) = 0.97672 +/- 0.00532\n", + " k-effective (Track-length) = 0.97584 +/- 0.00681\n", + " k-effective (Absorption) = 0.96793 +/- 0.00613\n", + " Combined k-effective = 0.97097 +/- 0.00582\n", + " Leakage Fraction = 0.57298 +/- 0.00284\n", "\n" ] } @@ -437,7 +443,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -471,7 +477,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -602,21 +608,23 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 2c0b16e73d5d81a2f849f4e2bfae5eb5319f2417\n", - " Date/Time | 2019-06-18 08:54:35\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 11:58:41\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading DAGMC geometry...\n", + "Using the DOUBLE-DOWN interface to Embree.\n", "Loading file dagmc.h5m\n", "Initializing the GeomQueryTool...\n", "Using faceting tolerance: 0.001\n", - "Building OBB Tree...\n", + "Building acceleration data structures...\n", + "Implicit Complement assumed to be Vacuum\n", " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", @@ -624,10 +632,12 @@ " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for Fe58\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Initializing source particles...\n", + " Maximum neutron transport energy: 20000000.0 eV for Fe58\n", "\n", " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", "\n", @@ -645,19 +655,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.9659e+00 seconds\n", - " Reading cross sections = 6.3783e-01 seconds\n", - " Total time in simulation = 1.5112e+01 seconds\n", - " Time in transport only = 1.4102e+01 seconds\n", - " Time in active batches = 1.5112e+01 seconds\n", - " Time accumulating tallies = 2.8790e-04 seconds\n", - " Total time for finalization = 3.2375e-02 seconds\n", - " Total time elapsed = 2.0224e+01 seconds\n", - " Calculation Rate (active) = 3308.59 particles/second\n", + " Total time for initialization = 9.0900e-01 seconds\n", + " Reading cross sections = 4.7368e-01 seconds\n", + " Total time in simulation = 4.5284e+00 seconds\n", + " Time in transport only = 4.5248e+00 seconds\n", + " Time in active batches = 4.5284e+00 seconds\n", + " Time accumulating tallies = 2.4557e-04 seconds\n", + " Time writing statepoints = 3.1711e-03 seconds\n", + " Total time for finalization = 1.4047e-02 seconds\n", + " Total time elapsed = 5.4540e+00 seconds\n", + " Calculation Rate (active) = 11041.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " Leakage Fraction = 0.62594 +/- 0.00117\n", + " Leakage Fraction = 0.63090 +/- 0.00246\n", "\n" ] } @@ -679,19 +690,16 @@ "metadata": {}, "outputs": [], "source": [ - "sp = openmc.StatePoint(\"statepoint.10.h5\")\n", - "\n", - "water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n", - "water_flux = water_tally.mean\n", - "water_flux.shape = (40, 120)\n", - "water_flux = water_flux[::-1, :]\n", - "\n", - "pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n", - "pot_flux = pot_tally.mean\n", - "pot_flux.shape = (40, 120)\n", - "pot_flux = pot_flux[::-1, :]\n", + "with openmc.StatePoint(\"statepoint.10.h5\") as sp:\n", + " water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n", + " water_flux = water_tally.mean\n", + " water_flux.shape = (40, 120)\n", + " water_flux = water_flux[::-1, :]\n", "\n", - "del sp" + " pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n", + " pot_flux = pot_tally.mean\n", + " pot_flux.shape = (40, 120)\n", + " pot_flux = pot_flux[::-1, :]" ] }, { @@ -702,7 +710,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -711,7 +719,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAADHCAYAAACZdhEvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3debSkV1nv8d9TVWfqOZ1OmkwkZAAMcBPmoEyXgERE411XASeCgnGpLMErSoB1Fa94RRQRBbyGKUG8zC7hKqKICXNCEghDJjKPnU6n08PpPn2mquf+UdWn3uepfqtOnalOd38/a2Wldr3Tfne9p87uffbzbHN3AQAAAAAAHEpl0BUAAAAAAACrFwMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHwBHMzJ5vZvctw3nvMrMXLvV5AQAA5svMrjSz1wy6HsDRgIEDYIDyP8DN7BVmtsvMnjePYzt+WZqZm9mZS1S3y8xs2sz2Ff57+VKcGwAADJ6ZvcnM/jW9d2vJe6+Yx/neamYfXeI6vtXMZlJ/5PeX8hoAemPgAFglzOwiSe+V9JPu/uVB16flHe6+rvDfJwZdIQAAsGS+IulHzawqSWZ2gqQhSU9O753Z2ndZmVmtZNMnUn/kHctdFwARAwfAKmBmvy7pnZJe7O7fKLx/npl9w8x2m9l3zez5rff/RNJzJL2nNfL+HjM7+Av9u2WzA8zsRDP7jJntMLM7zey3l6Dul5nZ2wrlufAIMzvDzB4xs6cUrr/j4H0AAICBukbNgYJzW+XnSLpC0i3pvdvd/QFJMrN3m9m9ZrbXzK4zs+e03r9A0pslvbzVD/lu6/2NZvZBM9tmZveb2dsKgxKvMrOvm9m7zGynpLcu9EbybAczO601E7NmZpvN7D4z+6nWtnVmdpuZvXKh1wOONgwcAIP3G5L+l6Tz3f3ag2+a2UmS/kXS2yRtlvQGSZ8xs+Pc/S2Svirpta2R99e6+3Nbh55zqNkBZlaR9P8kfVfSSZLOl/R6M3vxct2Yu98u6Y2SPmpmayR9WNLl7n7lcl0TAADMj7tPS7pa0sE+xHPV7F98Lb1XnG1wjZqDCpsl/V9JnzKzUXf/gqT/rfbsgHNa+18maVbNWQtPlvTjkoqhls+UdIekrZL+ZCnv7yB3f0TSr0p6v5kdL+ldkq53948sx/WAIxEDB8DgvUjSVZK+n97/JUmfd/fPu3vD3b8o6VpJL1ngdZ4u6Th3/1/uPu3ud0h6v6RuMYtvaM122G1mDy/kou7+fkm3qdkxOUHSWxZyHgAAsCy+rPYgwXPUHDj4anpvLoTS3T/q7jvdfdbd3ylpRNLjDnViM9uqZr/l9e6+390fUvMf7cW+xwPu/jet8x0oqePLCv2R3WZ2Yr836e7/LulTkr7UqtOv93sO4GjGwAEweL8h6bGSPmBmVnj/VEk/V/xFKenZav7jeyFOlXRiOt+b1RzhL/MX7r6p9d+WBV5Xag5QPFHS37j71CLOAwAAltZXJD3bzDar+QeGWyV9Q83cB5vV/P09N+PAzN5gZjeZ2Z5WX2KjpLI+wqlqhkJsK/Q9/k7S8YV97p1HHT9Z6I9sOhg2sQCXqnk/l7n7zgWeAzgqMXAADN52NcMGniPpfYX375X09+kX5Vp3f3tru/d5nXsl3ZnOt97dFzqD4aD9ktYUyo8qbjSzdZL+StIHJb211QkBAACrwzfV/Mf/r0n6uiS5+15JD7Tee8Dd75SkVj6D35f0MknHuPsmSXskHfzDR+6b3CtpStKWQt9jg7s/obBPv/2ZMr36I1U1Bw4+Iuk3l2oVKuBowcABsAq0Rs7Pl3SBmb2r9fZHJf2Umb3YzKpmNtpKPHhya/t2SaenUx3qvYO+JWnczN5oZmOtcz7RzJ6+yOpfL+klrcRDj5L0+rT93ZKudffXqJmz4f8s8noAAGCJtMIDrpX0P9QMUTjoa633ivkN1quZr2CHpJqZ/YGkDYXt2yWd1sqrJHffJunfJb3TzDaYWaWVOLnnstMLcL2k55rZo81so6Q3pe1vVnOQ4lcl/bmkjxxM0gigNwYOgFXC3e+R9AJJP2tmf+ru90q6UM1fdDvUHLX/PbV/bt/d2neXmf116723Srq8NR3wZen8dUkvVTOh0Z2SHpb0ATX/yrAYf69mwsW71OwczCVlNLMLJV2gZjiG1OyAPMXMfnGR1wQAAEvny2qGD3yt8N5XW+8VBw7+TdIXJP1Q0t2SJhVDDT7V+v9OM/t26/UrJQ1LulHSLkmf1sLDLku1ckF9QtL3JF0n6Z8PbjOzp6rZB3llqz/0Z2oOIlyy1PUAjlTmvlSzgwAAAAAAwJGGGQcAAAAAAKAUAwcAAAAAAKAUAwcAAAAAAKDUogYOzOwCM7vFzG4zM5KLAACAFUd/BACA5bXg5Iit5Ut+KOlFku6TdI2kn3f3G5euegAAAOXojwAAsPxqizj2GZJuc/c7JMnMPq7m0nGlv6iHbcRHtXYRlwQA4Mgzqf2a9ikbdD0OU/RHAABYAt36I4sZODhJcd3W+yQ9s9sBo1qrZ9r5i7gkAABHnqv9S4OuwuGM/ggAAEugW39kMQMH82JmF0u6WJJGtWa5LwcAANCB/ggAAAu3mIGD+yWdUiif3HovcPdLJV0qSRts88ISKgAlKmvanT9bMxa22chIKDd274nl/fuXr2I4LFRGR2P5uC1xh5QDxvftm3tdT88TgIGhPwIAwDJbzKoK10g6y8weY2bDkl4h6XNLUy0AAIB5oT8CAMAyW/CMA3efNbPXSvo3SVVJH3L3G5asZgAAAD3QHwEAYPktKseBu39e0ueXqC4AAAB9oz8CAMDyWvbkiMCiVKqhWP2RM0O5cdtd7V3XpaW1Uny6nbg1br/1jkVXD4e3xrmPDWXbuS/usHs8FL3emHtdOffsuO2m22N5amoJaggAAAAM3mJyHAAAAAAAgCMcAwcAAAAAAKAUoQpY1aqPPyOU/fa7Y7kwHXz2rnu6nsue+oR47h85K5TrN926kCriMFJ9XAx12b8lLsc4etX35n2uys0x1KVy2imhXL/ltj5rdwQyi2UvXwEvL41pw8OhXB+PYSPdzgUAAIClxYwDAAAAAABQioEDAAAAAABQioEDAAAAAABQihwHWFVqp58Wyj49E8qNyckFn9uvuyGU6ws+Ew5XOe/A6C0LP1d+Fmt741KOlfXr4/45Rv9oVFxetRF/AhvpZ115OUtL49zOTzAAAMBKYcYBAAAAAAAoxcABAAAAAAAoxcABAAAAAAAoRY6Dw1kxXliSvNF+neOBk+oxG+OhOb44bJsOZctrsyeWYrs1O9t+feymsKmxcU0oT6+La7dXvnJ912sBq8Xs9h2h3PjRJ4Vy5WtHwbPc47sh5zWY9zaJnAYAAAADxIwDAAAAAABQioEDAAAAAABQioEDAAAAAABQihwHq4iNjIRyZVPMQ6ADcd14d4/7r1vb3rY5Hmt798dzVWIsckfegkphTGlmNmzy0ZiHwPL2VE/btGHu9cSZW8K2A8fGPA3r70lrt6d7BFatFKM/fP+uUPYNG0K5vnfvsldpudnQcNftPjPddXv3k6fvJL4LAAAABoYZBwAAAAAAoBQDBwAAAAAAoBQDBwAAAAAAoBQ5DlZYNcU5a2x07qWtGQubfM1oKHfkKRgfj/uvLRw/FWOLG8esC+XK+IG4fX28dn1tO9+CV2Os8dAjE7Ee0zPxXCcfF6+1r523YHZNHKuqpKXZq1fdEMpENeNwNXv3faFcffwZcYcbD/8cBzmHQc7T0iHnLQgn8+5lAAAADAwzDgAAAAAAQCkGDgAAAAAAQCkGDgAAAAAAQClyHCyzymjMU6CTHxXLhfwAjXUxz4BNxdwBsydtjsc2jgnF2kN72oWZ2bjveMyP4I0YP2wTMedBrbh9Np2rFh+bHIlcuWcy1bOdyGD9V/eETfUzTojnWsy678Bq0ogJPGw85gax/HOUf85Wo0o1FK0Scxb41JS6KuYt6JbvAAAAAKsKMw4AAAAAAEApBg4AAAAAAEApQhWWWGX9+lDOSyzann2h3DhuU/vYB3fGbXvjcos2Hafx56nNh8FEZ1XOPTuUqzfeFcppdUbgiNHYHcN07OwzQ9m/d/NKVmfeKmvXtl9viN9v9Ud2xZ37CbdguUUAAIDDBjMOAAAAAABAKQYOAAAAAABAqZ4DB2b2ITN7yMx+UHhvs5l90cxubf3/mG7nAAAAWAz6IwAADM58chxcJuk9kj5SeO8SSV9y97eb2SWt8huXvnqHH095COzErXF7NY7V2LaH517Pbn9o+So2SIUl3LwW77+xd+9K1wYYiMZ4zFni60ZCuVJYnnE1Lc1YrEv94Ufitj6XT7WR9j3n70pyHmAeLhP9EQAABqLnjAN3/4qkR9LbF0q6vPX6ckk/s8T1AgAAmEN/BACAwVlojoOt7r6t9fpBSVu77QwAALAM6I8AALACFp0c0d1dUukcUzO72MyuNbNrZzS12MsBAAB0oD8CAMDymU+Og0PZbmYnuPs2MztBUmlwvrtfKulSSdpgm4/4INbKSIxbVsppkGP8bRXFMi+X2qknz71u3HJ32HbEPxBAidrt2+Ibjztj7mX9hltWuDblrFrIUdKI31fVTRtD2adnQrkxOZW2F/IakNMAS4P+CBbGLJXT39K8ETcXvgt7n7vH3+Uq8dqW6xL2Tefqtq/U+d3aKNxHPlcj3qN3HJvKhTbxvC2xSvd6Fo/P+3acO30WuX17XavbsR3yZ1Pr459KqX07cvl0U6/Hco/nsWv75/bqF7+fUWKhMw4+J+mi1uuLJH12aaoDAAAwb/RHAABYAfNZjvFjkr4p6XFmdp+ZvVrS2yW9yMxulfTCVhkAAGBZ0B8BAGBwes6/cfefL9l0/hLXBQAA4JDojwAAMDgLzXGAErZ+XXxj7/64PcUwze7as9xVWnkp/q6+ud0mfufdeW/gqFTfHkOxZ59wytzr2g0rXZu2jrwFM+28BpWx0bgt5TTo0C0u1VM8JwCsIBsejuWcOyDlNLCh2GX2enkcea9z5Th6pTj6cHyOsa/1yLUwm2PlC+dKses+E7/DO/JupT5r8Z5zf7Zf3bISWKNHjH5He6acB8U265EfoSOnwdBQ+bXSM6D8DORcDROTKtUjx5nn7alNQvvn9ujx2XQ8uzl/wlGQfw0Ls+hVFQAAAAAAwJGLgQMAAAAAAFCKgQMAAAAAAFCKHAdLbPb+B0K5uuXYULaxsVCurF0z97oxPr58FVtB1bMfG9/YMzH3kqhm4NBq+9rrPeefofqNP1y261bWrg1lS2XtL+RpyetG5zXAU1xkXve817rfALCcbKid1yB/P9lwim3vFQuftxfi6q2Wv/tSfHrOW5DzJxSPz9+jIykfQj3lLUj5FUK+gJn0HZ3vOeWt8anpUDYrHF9N95+/33O9+4ib76hXLznGv6g2Es9dyZ9rKufPoliXnOOgh45rFdrfc56L1H6Wt6fPzovnzjkh8nVzzo2U68Is3XOxLo1V1HPvkq8DK4MZBwAAAAAAoBQDBwAAAAAAoBQDBwAAAAAAoBQ5DpZYZc2a+MZxm0Mxx/ja5FS7cITkOLjvxTGvwwl/dfWAagIcPuzbN829fuTlTwvbNt64dNfJOQ101qmh6HfdH7eH9bDj91dj3/5Q7shpkNeSXk2xkgCOOsXY+fx91fvglDtgJMXhF78r874pXt17xcqPtHMxNMbidRrDsd71se7nqk62v3er+6bixumU8yDHvuc8BoUcCD4d8x/Iusec2+hofGOmcHyv/Dc5n0SuV5fP0sbSdVP8f8dnkfJTNEbb2+trhsM2r8bPtTITcw1Uh1IejUJ722Rqv5TDoCOHkMXPLly53iPfUM5xkKXf1VZoE58a4O/tbvXukbcBy4MZBwAAAAAAoBQDBwAAAAAAoBShCkusMRmnEtXSVCQfi8vC+Antaf2ViYmwrbE/TgNerarHxnAMS6vCMD0Z6K04tXB6Q5yCl5d1rT+8c97nrZ51eijvOfe4UN7ww73d67Wv/HsoT7+trI3Lzebpp/W93a8FAEupY9nD4jT1vNxdng6flgTsmO49EqetdyyBFw+O5RzWNZrCEQpLLjZSKMLUMXHfyY1pOnyasT2yp90HG6nFetR2T8Z65CUqU5+22AYdSw2me/TJeO6O9i5eK0ca5M8thzJ0LI1Z/tl0hCLkUIXc9ik0ZHZtuzxxfNzmqQlGd8f+7lBq78qB9o1W63kJxXRPsyl8IO4dwxHSs9sxwT+HIuTQjtRGVmjvRjq2n2U1+1bpHj5U7HN0hEISurAimHEAAAAAAABKMXAAAAAAAABKMXAAAAAAAABKkeNgqaV4ft+bllgcjTFxM5vbyzcO6ZS47/duXtKqLZe9//WsUH7UVfsGVBPgyLDp1pgb5YVX3hnKn3jHi0N572kpNrIQ2rfhjhhHuemHaQnFO+Pyi/Wc06BLjpKO+OGh+P3WIKcBgAHK31HF5Rg74ujzsUN5ucWUlyCVQ+x8jrfOyzEOx2sXcxpIMa/B1KZYj/ET074/viuUDxyI38Prr0jLhBfYdFo+8MBM3KEjj0GjfFteyjG1r+eY/i5L7XXkLKh2zxHR8dkUP+eUqyLLn0Uxp4Ek7T213Z47nxdzPlRq8Z43XRmXftxwd9xeKSxtmHMvVPJyjLl90z1acbnGWsrbkPNJzKZyzg+QFdrXRlJutn5zHBTzYuQcGt1ykOhQSzq329PSI9CxL5YFMw4AAAAAAEApBg4AAAAAAEApBg4AAAAAAEApchwss8aeGONbHYvrnA8XYn8mTt0Ytq3ZFtdbr+/YscS1Wxq7fyHmNFj3sltCmZVUgf4M/cd1ofyeK18Uyn5ejOU74ctxDPiYax5s75u+g+qPxHjY+iLWOu6IH14XY2l9lX5nAThCpdh4Wxu/k2y4EP+f46urKb465zDIcfe5XIxZb6R4/h719ByjXtjeqOV946n++AmfDeVRi3kK3vj1X2tfNoeMj8R7rMzGeltuEy/Evuf7zzHmFnMFmGK93MtzHHToyBmR/u6Zcx4UzabcYyPd/+lTH47nnlnbvvb7fuwfuh77pm+8OpTzZ1ds78pkatuR1F5TKd9ER16H8t/dlnMF5Gc9t2fuBxT2z89ANeVmKOYdkNSRz6OrdKznPBnp58iLVSGlwUAw4wAAAAAAAJRi4AAAAAAAAJRi4AAAAAAAAJQix8Eyy+ud+v6JUC6uKVydirE8s2eeGMrViXhsY39ab32FVLccG8pPP/GeUH6g3zVeAXR11muv7mv/lfoJtI0bVuhKANCb1VKceDGngSQV+lw+krZ1xHnHmPIcg96Rx6BYzPsmjdHY/W4Mx2vVR9p/16vMxnqN7Irlt/zdq0K5Oh2vNbKnvb/1ymmTY8pTXL3VC9vzueqpPTpyHqT8E8VCjt/vkU/CR9NnlxXq5ikmP99TbntLtzW2s31fl7z71fPeV5KsnnYoNl/Ka2EHUrx/yi1guX2LuQRynoGcf0jJTMqf0C0HQv6cq/39zdknp9qnzbkXUv4Jm4kPr9fTtYrPVH5mchs4SRCWAzMOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAKXIcrLD67t2hXJlqx/6MpJi4R54ScwlUT3lSKG+84vZ47hVaM338OWeG8s3XxO1nqb94bACHiRRnOvPYk0K5dt0tK1kbAAgqY6PxjdGRUCzGuzfWp31zHH1aj97ymvOVFGMdKpKOTbHunuP98/r1hf29GvcdGY+x29XpFDeeqjWyp72/5bQDsyluPucWSNcO8e459r0jFj5VJOc8KG5Pse8d50psJmXy6ZYDIf+JNN2T53QKqU1GdrVfD+2P2xpD8eDagbi9Oh3LlZlCG+T8Bzl3QCU/jznnQeFcub06Psd87pQjIrd/4ficI6JDPneuS/H4/LnlPCH5mammnBHFy+TnydO5sCyYcQAAAAAAAEr1HDgws1PM7Aozu9HMbjCz17Xe32xmXzSzW1v/P2b5qwsAAI5G9EcAABic+YQqzEr6XXf/tpmtl3SdmX1R0qskfcnd325ml0i6RNIbl6+qR4g0hcen20uPVHbtDds2fzseuu+xsS904KmnhfLYnRvnXtdvuW0Rlexuz0Wxnqe9Z82yXQvA6lHdtCmUPU3dbaQlY4ElRn8EXdna2B/pWIpvrD1FuzEaw0OnN6alHNNU8o5p59N5Ocb2/pU03T0v+Zenx+ewiErh2sO74/TuPD2+MpX6lbV88vbL6mRa/i5Pl0+6hTLkts1L7XWEJuQp7MX9K3nBwI4FBKO0jF9xmc3Oc3f/G2lug9qBeO5GcTnMyXhsx+eW2stmyp8R69U+Wdq/2P6WY1B6hS7kZRFz+xc/27RvYzgtJTqW/imZIzAmCkssTkyFbR3hP6lstfL78Bz20Kv9sCR6zjhw923u/u3W63FJN0k6SdKFki5v7Xa5pJ9ZrkoCAICjG/0RAAAGp68cB2Z2mqQnS7pa0lZ339ba9KCkrUtaMwAAgEOgPwIAwMqa98CBma2T9BlJr3f3MFfd3V0dk1PmjrvYzK41s2tnNHWoXQAAAOaF/ggAACtvXssxmtmQmr+k/8Hd/7H19nYzO8Hdt5nZCZIeOtSx7n6ppEslaYNtJgAl8dl2jM7sQw+HbbWhGLM1tn0slPc9OsbyTZ+zpb3viRviub5xQ7zu1MI7Tb985rdC+UtXxtwLfMjAEaSw5Ni+554VNq39z5tCmcWQsNzojyDIsdsjcZk5T3kMZte3l2ecPC4u1bh/a/e/pa15OH7DDe9NyyJOtPtzDXWPIc9LLOaOU1iOcbj70o6WV0VMceKVmULuhRxz35FaINVzKOZmCFtzjHmWcwt0+xdHPdUrx6vX0jJ9OadB1ke8e14K05VzSLQ/Z6+lz3UoL5kYy9WOHAflvyU72noqtW9eqrBwro58EzkHRL5WXkIxf+6FPAaechrMbIg/Y/sf1f2zWH9P+1pDKQeE52VIZ+M9eyPldZgufBY5RwRWxHxWVTBJH5R0k7v/ZWHT5yRd1Hp9kaTPLn31AAAA6I8AADBI85lx8GOSflnS983s+tZ7b5b0dkmfNLNXS7pb0suWp4oAAAD0RwAAGJSeAwfu/jWVr4ty/tJWBwAAoBP9EQAABmdeOQ6wQlIsz+z920K5OhnzEqyfOT6Ux09fN/d695kxdk9nPiUUj//6zlCu3/jDeVfz6WN3hvJ/zK6f97EADi8P/eYz514fc3P8DmqMj690dQBgjuW47x7r1xdj1CeOi9G6f/yGD4fypspEKL/2L18bT51DrAu5BTpi25Oca8DzcFghB0LOh9CopTj6qe55C6xQr3wdyzunGHSb6RJH3tG2KUZ/eqb82B7nyp+jp9wVPWP4i/kVUl4B65GaIeeQaBSeGUu5GCo5Rj/ll8ifndfa/+yq5lwVua1zHoKcU6J47ZxPIn82PXIadFyrsL0+Ej/Xia0xp8FL33hlKI/XR0P5629r9yEsPV+1R9I997gPFfMaNMhxMAh9LccIAAAAAACOLgwcAAAAAACAUgwcAAAAAACAUuQ4WM1S/E59x45Qrqb1TsfWP2bu9exozHEwuSXGCd3z01tC2S+M5dM+fHv7XA9uD9suffB5qaK7BODIMHv+U0N5Yms7jvL4912fdweAwelY2z6vC59yCVTafaFKCsF/3bdeEcojI3GH4ZxKIF+rGHJ+IPbPGiOxu51zDTRGcq6Gwrlmcox+jptP5Uoqe/H4+PdCy7kYOmLy0/6T5XkLeuUdyJ+NivXMuSmGYntZiu/3tL0jFj5cJ95DzjuglGtAuT1rffyNNadqyNcq5pvI5835EnLOg3yPxXLK49B5rtR+KR+FcrlwvvycKxU/+I3nxmtNx3qePNs+oDKZ8jTketfjPfuBybj7ZCxj5THjAAAAAAAAlGLgAAAAAAAAlGLgAAAAAAAAlCLHwWGsvivmFhj6VnuN9XWVHwnbRsZj/NLuM+JHP7M+nvvGt55aKJ0atv3hsf8Uyh/TifOpLoBVwIbieth3/c+Y02Bqa4xBfPzrvjP3usG6yQBWs7wefd5ciLFe+1Dct/ovcf15WSxbPcZjV2ZSHHmOBS9KMec5vr2R4uqL+RIaVr5N6syX0FHOcfbFc002SrdJnTkiQl6CFDffU46jL+ZEGB4Km3Kehtz2HTkRchsVYuc9H5qupVStjvYttF/+nCopP0J9KJ6seiD+zuz2jHTkk5hO7ZvvuVDOOQw68iHknAepPT3lpygeXZmK516zPea5ePS/xHvOz8zow+28BJX9MUeBTU6HcmP/RCxPxDIGjxkHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFDkOjiDFWKDaFd8O24b/y+NDeWbNxlDefmqMf3rWk26de3386HjYdlxtb7oyOQ6A1WrqJU8P5Xe+972h/IpPxRwHj/ut74RyY7bPOFYAWCk5ZnwqxkxbivUudnor0zGuuzYR8780Um6AxlCKQc9h9jON0m35WvUcz57PNdt+ozES9805DBpD8Y2cH0CFPAb5r4UduQRSe3XG3ZfnROi4biXFvufPqhh3n2L0reNcqR45Rj9XppDjQJX4uXqPPBj1kVjvYo6D+mjcNpPavnYg1qsxHK9VKdxzIyVXqE7EZ9erOXdAl3wJqX3C/UudOQ/y9lwufByVyXjs8NT+UB5KeR0s51PYd6DwOuUwGN8Xy/vjubH6MOMAAAAAAACUYuAAAAAAAACUIlThSJWmCjW+e1MoH3Pf5lAe2XNGKF9lj517fe45d4RtZ295OJStFh8jZ2ozsKyqx8af3x0//bhQfu8f/PXc63tn4s/vm1/+6lA+/VvfDOUuC4oBwKriszOpnKZz5yXsittmUqjC3qlQzlPaG2Opy5yW4qtOtc/XsSRins6djq3MxKnis4Up8Y2ReLJ6mv4+tT5ur8YZ76oWjh/ZHe+54x7TlPZKbr9CvTuWSOw1HT7/dukWMpCm6fecWp9DG4YKn1W6TH4mGik0YXYslddUCttS++TursWLDe0tD+3omNKfl77sOKB8Wc2e+/ZavjIv51gMfeiyVO1lzuIAABEcSURBVKOkzmU502fT2L1n7nU9L6/Y5ecTqxMzDgAAAAAAQCkGDgAAAAAAQCkGDgAAAAAAQClyHByl6jsfCeWRz+8K5cc+cPbc61tfeFbY9jsv/e+hPP7fTgvldZ+6eglqCOCgvKTilR94fyiff+O6UH7Lqy6ee135clxeUfr+ktYNAAYmxUj75GQo2/BQLO9vLw2nsZG4bTYte5iWIsxLKnbEZxfj//Of5fJyjGvjEoGaSfkSCjHoeVm+qS0xynzfKd3j7kd3tF8P7UvLLaYlJ3vF2Vsxfj3Fsncst5jzEHSLZ88x+T3i5lVLORDycoTF86VtHctG5ltOp5pe335j/LTueQbW35U+x8n0DBWW2bQDKT9HzjswGXNudCi253Q8V2cOiB7LNWbF/espL0bKY1Z/ZHc6d/o5wRGFGQcAAAAAAKAUAwcAAAAAAKAUAwcAAAAAAKAUOQ7QlOMEv3PD3OsTU4j0nu/HeOsHXxzjss78dI/1YwF0l2Idz/iDm0L5J5/+klCu3X9POkEuA8CRr7E/rhPfkeOgPlR4HeO8fd1Y3PdAirPPcfXpeFULf4vL4f3D3fMl1Efiub24ew7/XxvfuPnX3qduznnHb7avMxqPrU6m8kTKWzCd2qDYn7Me+RJynoKs2F6zKS4+xdV3xOhnHTH7hfZMeRsqM7E8Mxr/KdSoxfuY3Nwuf/FX3xG2HVeNeTLO+9PXhbI9GGtVvLbNxHu0qelY7pUzIrdZF55zIGR95CWo79qz4GNx+GPGAQAAAAAAKMXAAQAAAAAAKMXAAQAAAAAAKEWOA/Rt+AvXhPIpFnMe7HzNeaF87Pu/uex1Ao4k913yrFDe/b4Y67jpfn6mAKBDirdu7N0XypV1a9uFlH/J9qYY8pzTYCoWfahLF7ra/e9yHfHttbi/Fapi9VjP4fFYPuujv5EqFoub9rbfqO2P91g7kOqR8jbka6sQd285xj7ns8rtl3MeFMspX0K/cj6AYky/pc/JZ2O9KjlXheL20Z3tc7/go7+XLhyLx+xNbZDbpFDuaNtebZDzPHi7/frOYZDzJ+ScEsVdp9KDT06DoxozDgAAAAAAQKmeAwdmNmpm3zKz75rZDWb2R633H2NmV5vZbWb2CTMbXv7qAgCAoxH9EQAABmc+Mw6mJL3A3c+RdK6kC8zsPEl/Juld7n6mpF2SXr181QQAAEc5+iMAAAxIzxwH7u6SDgaJDbX+c0kvkPQLrfcvl/RWSX+79FXEajfyrzHnwczLYo4De/qT5l77Nd9fkToBh5vG854893rtAymnwUfIaQDQH0G/fGY6lg+0/16WI8o9xX13RJynGHRTitkvxMp7ynGQcwf46FAoV1LOAy/kPLAUJ79mR6zH0ER5fgRJqk2036hNxutUplOOg1QPpdj5cB+NnBugh5z3oXiunAsg8dnY1pbj/eupnoX8Cp4/t3SP1X3xGRlJt1WZbtdtZDxe11M1hsbjuYf2xXpXDhTKKeeD5RwQuX1zHodim+S8AzlngeX8COl5TO3rheM95zjAUW1eOQ7MrGpm10t6SNIXJd0uabf7XGaO+ySdtDxVBAAAoD8CAMCgzGvgwN3r7n6upJMlPUPS4+d7ATO72MyuNbNrZ3JKWgAAgHmiPwIAwGD0taqCu++WdIWkZ0naZGYHQx1OlnR/yTGXuvvT3P1pQxpZVGUBAADojwAAsLJ65jgws+Mkzbj7bjMbk/QiNRMRXSHpZyV9XNJFkj67nBXF4WPdp64O5R0Xt3MeHFs7J2yzb353ReoErDbVs04P5QOb2jGvx1xOTgMgoz+CxWpMTs69zrkDKiNxMMlz3HgtdpnNUxe6UOyIVy/E3DdPnmL6LV67Otk+vlGLf+OrpZj9ofEUn16J26tT7fuoTOacBd69nO9jMTkOOuLu2/X06Wl1le45729DMWdE11Ple0p/Qq3tnYybpwofbGrbnD+hMp3zFqQcErNd2i+XU36JrnkgUv6DjnwInj/HlFMjtUnODQIc1HPgQNIJki43s6qaP16fdPd/NrMbJX3czN4m6TuSPriM9QQAAEc3+iMAAAzIfFZV+J6kJx/i/TvUjC8EAABYVvRHAAAYnPnMOAD6k6ZTbbn0qrnX+37umWFbdWvs643907eWr17AADWefW4oT22MUyvHPsuzDwArJS8z18jLMXoMH8hT3L2awg8Kyw1aCmvI/aIcJpGXKrTClHcbjufKSzd2nDtvL0yn79iWlonsmMY/W77Mn+dt3iN0IV0r3HOeap/ljGy99i9cy+pp2n0KN7CptGTnyHAoV2fL78tr5Z/boeoZ2jeHbqT27GjfFDrjOZQh7JvqnJ6RHOqRl2MEyvSVHBEAAAAAABxdGDgAAAAAAAClGDgAAAAAAAClyHGA5VeIrVr3yavCpsq5Z4fyrl88L5Q3fuyaeK68PBKwWlRivOvUTzwllNfcuSfu/rUfLnuVAADzk5eg8xSDXhkbDeUYKa8Q4++W/i6XcwmkZfxs34G4fzEOfybmw7FKj7/5pXpbMc4+51boVc7L9hXj7lNcfI6Tt5S3wXMb1AvLMebrphh9G0r/XMn5JnJ+hUL753oo56ZIOnJZjLVzXVjO05CW2ezIEZHvq0tego6cBjnvQM6JUGyj/Dn1yGnQdWlHoAtmHAAAAAAAgFIMHAAAAAAAgFIMHAAAAAAAgFLkOMBANa6/MZQ333dsKN/9xmeG8il/Hte6Z+1ZrBrPeEIorrn27lCub39oJWsDAFiMlFOpMTERypWRkVAuxs6HvAJpmyR5pSNDQlSIWbd6zK2gHLOf5Vj5Qix8jqPviP9PfOJA1+3hXClvg3eJ55ckty5t0BGjn86Vj52K+xfvyxspp8FMOlet+z+FrNgEOX9EPrZXvYu5GHK+hJSnwXvkS+jYHrZNl24DFoMZBwAAAAAAoBQDBwAAAAAAoBQDBwAAAAAAoBQ5DrCq1B/eGcqn/Md4KPvTzg7lyvdum3ud4w+BpVaMZ6w+amvY5rfdH8r5WQYAHMZyjHnOsVSMWa+mPAMpx4GmYwy6DZV3x31yMr5h6W9+KTa+Q46lLx460/1YT/dczJfQ0R45/j/nPMjn6ibniOiRE8JyDolQl5RnIO1rqd6ZT02VHqv82fTgXT6LDqleuf3CPfZ6BoAlwowDAAAAAABQioEDAAAAAABQioEDAAAAAABQihwHWNX8mu+HcuWJj4/bzz597vXEKWvDtv2PiuNijVqMkRvbGWPCDmxp7z+2I24bfSTGMk4cP5TOHeu9blt7f0/Dc6PbUy6GStyhsidut7372udaH+9Ru1MOiBRvl2MMi3GCjf1xfebKunjujjWrx+Ja0rY21aV4rR6xjI1jNsTymtiexXqOn7ombKpNxc/Gypcyblal8NnkZ2BoPMYQTm+MH+TQ/rh9zW272ue694GwjRwbAHD06MhxUNyWwuqVfxfnPAUH8mYr3zfrFf9fuHZfeQakjlwDVm3XJcfr5xwHfV0px+jne67nzSl/Qrc8BflcM/Fz89x+Ob9CN30eW6xnz3vo97MCVgAzDgAAAAAAQCkGDgAAAAAAQCkGDgAAAAAAQClyHOCw0vjBzaXb1lybyn2ee9P69XOvc7x/NrZhXSj7SJc1mEdi/H59LMXz59jHRswlUC3E4/nIcDz0uGNieefudK4Un7iu3SqW4hMt3VMlxd9ZbpPZFM84NqIyNhnXrK5MpFwMjRTfWIjt23hDOnZ8f+l1JMkLOSEkyQvrZTf2dz92uOvWjjBLAAB6y/Hq3v23SQ75X9Sll+5UnbkblssKts+gHAn3gKMPMw4AAAAAAEApBg4AAAAAAEApQhWAlsb4+CFfH9K2hV+n12hdnlbYY7XB/uwo39Tznnc+spQ1WTBm9wEAAAArixkHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACgFAMHAAAAAACglLnnVeOX8WJmOyTdLWmLpIdX7MKHP9qrP7RXf2iv/tBe/aG95udUdz9u0JU4WtAfWTDaqz+0V39or/7QXv2hveantD+yogMHcxc1u9bdn7biFz5M0V79ob36Q3v1h/bqD+2F1Yznsz+0V39or/7QXv2hvfpDey0eoQoAAAAAAKAUAwcAAAAAAKDUoAYOLh3QdQ9XtFd/aK/+0F79ob36Q3thNeP57A/t1R/aqz+0V39or/7QXos0kBwHAAAAAADg8ECoAgAAAAAAKLWiAwdmdoGZ3WJmt5nZJSt57cOBmZ1iZleY2Y1mdoOZva71/mYz+6KZ3dr6/zGDrutqYmZVM/uOmf1zq/wYM7u69Zx9wsyGB13H1cLMNpnZp83sZjO7ycyexfNVzsx+p/Wz+AMz+5iZjfJ8RWb2ITN7yMx+UHjvkM+UNf11q+2+Z2ZPGVzNcTSjP9Id/ZGFoT8yf/RH+kN/pDf6I8tvxQYOzKwq6b2SfkLS2ZJ+3szOXqnrHyZmJf2uu58t6TxJv9Vqo0skfcndz5L0pVYZba+TdFOh/GeS3uXuZ0raJenVA6nV6vRuSV9w98dLOkfNduP5OgQzO0nSb0t6mrs/UVJV0ivE85VdJumC9F7ZM/UTks5q/XexpL9doToCc+iPzAv9kYWhPzJ/9Efmif7IvF0m+iPLaiVnHDxD0m3ufoe7T0v6uKQLV/D6q567b3P3b7dej6v5JXqSmu10eWu3yyX9zGBquPqY2cmSflLSB1plk/QCSZ9u7UJ7tZjZRknPlfRBSXL3aXffLZ6vbmqSxsysJmmNpG3i+Qrc/SuSHklvlz1TF0r6iDddJWmTmZ2wMjUF5tAf6YH+SP/oj8wf/ZEFoT/SA/2R5beSAwcnSbq3UL6v9R4OwcxOk/RkSVdL2uru21qbHpS0dUDVWo3+StLvS2q0ysdK2u3us60yz1nbYyTtkPTh1lTKD5jZWvF8HZK73y/pLyTdo+Yv6D2SrhPP13yUPVP8HsBqwHPYB/oj80Z/ZP7oj/SB/sii0B9ZQiRHXIXMbJ2kz0h6vbvvLW7z5jIYLIUhycxeKukhd79u0HU5TNQkPUXS37r7kyXtV5oGyPPV1oqDu1DNDs6JktaqcwoceuCZAg5f9Efmh/5I3+iP9IH+yNLgmVq8lRw4uF/SKYXyya33UGBmQ2r+kv4Hd//H1tvbD06faf3/oUHVb5X5MUk/bWZ3qTnV9AVqxsxtak3lknjOiu6TdJ+7X90qf1rNX9w8X4f2Qkl3uvsOd5+R9I9qPnM8X72VPVP8HsBqwHM4D/RH+kJ/pD/0R/pDf2Th6I8soZUcOLhG0lmtDKDDaib1+NwKXn/Va8XDfVDSTe7+l4VNn5N0Uev1RZI+u9J1W43c/U3ufrK7n6bm8/Sf7v6Lkq6Q9LOt3WivFnd/UNK9Zva41lvnS7pRPF9l7pF0npmtaf1sHmwvnq/eyp6pz0l6ZSub8XmS9hSmEAIrhf5ID/RH+kN/pD/0R/pGf2Th6I8sIWvO2lihi5m9RM0YsKqkD7n7n6zYxQ8DZvZsSV+V9H21Y+TerGZc4SclPVrS3ZJe5u45+cdRzcyeL+kN7v5SMztdzRH/zZK+I+mX3H1qkPVbLczsXDUTNw1LukPSr6g5gMjzdQhm9keSXq5mhvHvSHqNmjFwPF8tZvYxSc+XtEXSdkl/KOmfdIhnqtXheY+aUywnJP2Ku187iHrj6EZ/pDv6IwtHf2R+6I/0h/5Ib/RHlt+KDhwAAAAAAIDDC8kRAQAAAABAKQYOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAKQYOAAAAAABAqf8PQX3FyrAi+8wAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -750,7 +758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index eaf1e36d217..30cdd78bc62 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -121,7 +121,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -130,7 +130,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADnVJREFUeJzt3W+sXVWdxvHn8QJOlGrTFEJom2kb/6VaDeRKxpBBFGJQkb6ZF5hgBF/caLTBhIQU6rzzBRknKlFjcgP4xibMBB0xBMUSrWZeUCkIIq2aWh1pAwFDGqsmdjr85sU5N9xe7r3n9O611157r+8nIeGce+5a6+zu9Zzf2nvffRwRAlCv13U9AADdIgSAyhECQOUIAaByhABQOUIAqBwhAFSOEAAqRwgAlTuvi043rHNsuoj8Adpy4qVX9PKp8DSv7SQENl30Oj34xQu76Bqowq4v/GXq1/JxDFSOEAAqRwgAlSMEgMolCQHb620/YPvXto/Yfl+KdgG0L9XZgbsl/TAi/sX2BZLekKhdAC1rHAK23yzpKkk3S1JEnJZ0umm7APJIsRzYJuklSd+y/Qvb99h+Y4J2AWSQIgTOk3S5pG9GxGWS/ippz9IX2Z6zfcj2oZdPcV9DoBQpQuC4pOMRcXD8+AGNQuEsETEfEbMRMbth3VRXMwLIoHEIRMQLkp6z/fbxU9dIOty0XQB5pDo7sFvSvvGZgWOSbknULoCWJQmBiHhK0myKtgDkxRWDQOUIAaByhABQOUIAqBwhAFSOEAAqRwgAlSMEgMoRAkDlCAGgcoQAUDlCAKgcIQBUjhAAKkcIAJUjBIDKEQJA5QgBoHKEAFA5QgCoHCEAVI4QACpHCACVIwSAyiULAdsz428lfihVmwDal7ISuFXSkYTtAcggSQjY3izpo5LuSdEegHxSVQJflXS7pFcStQcgk8YhYPt6SS9GxBMTXjdn+5DtQy+fiqbdAkgkRSVwpaQbbP9B0v2SPmj720tfFBHzETEbEbMb1jlBtwBSaBwCEXFHRGyOiK2SbpT044i4qfHIAGTBdQJA5c5L2VhEHJB0IGWbANpFJQBULmklgGE4cHLbVK+7ev3vWx4JciAEKjXtRF9rGwREfxAClUgx6Zv0RyiUixAYqNyTfhJCoVyEwICUNvFXs3isBEK3CIEB6NPkX87C+AmDbnCKEKgclUCP9b0CWIqKoBuEQA8NbfIvRRjkRQj0xNAn/nI4eJgHxwR6oMYAWIpt0B4qgYKx45+NZUI7qAQKRQCsjG2TFiFQIHbyydhG6bAcKAg79rlheZAGlUAhCIC1Y9s1QwgUgJ24Obbh2rEc6BA7blosD9aGSgCoHCHQEaqA9rBtzw0h0AF20vaxjadHCGTGzpkP23o6hABQuRRfSLrF9k9sH7b9rO1bUwxsiPhkyo9tPlmKU4RnJN0WEU/aXifpCdv7I+JwgrYHow87411fO7qm39uz+y2JR5LWgZPbOG24isYhEBHPS3p+/P+nbB+RtEkSITBWagCsddJPaqfEUCAIVpb0YiHbWyVdJulgynaRTqqJP20fJQYCzpYsBGxfKOk7kj4fEX9e5udzkuYk6dKNTtVt8UqpAnJM/tX6LSEMqAaW54ho3oh9vqSHJD0SEV+e9Pqd22fiwS9e2Ljf0nUdAF1N/Em6DoQagmDXF/6iZ47931SftinODljSvZKOTBMAyKPUAJDKHluNUlwncKWkT0j6oO2nxv99JEG7ADJIcXbgvyXVs8ifUldLgb58ynZ5rIBjA2fjisEB6UsALNbHMQ8NIdCCLqqAPk+mLsbe9UHbkhACA9DnAFgwhPfQV4RAQgdObsv+CTOkyZP7vVANjBACPTakAFgwxPdUOkIgESqAdHK+ty6qt9IQAj005ABYUMN7LAUhAFSOW44nkKucrO3TMecFRTVfQEQl0BO1BcBiNb/3HAgBoHKEAFA5QqChHMcDKIfzbINaTxUSAkDlCIHCUQW8im3RDkIAqBzXCTQw5DXkz/5306o/v+r8E5lGkleN1wsQAgXLXf5OmvgrvTZnINz1taOd36h0aFgOQNK5BUDK30X3CAEkmcQEQX8RApVLOXkJgn4iBAqV43hAG5M2RxBwqjAtQgCoHCGwRkM+PVi72v5tk4SA7ets/8b2Udt7UrSJdrVZtnNsoF9SfBfhjKRvSPqwpB2SPm57R9N2AeSRohK4QtLRiDgWEacl3S9pV4J2AWSQIgQ2SXpu0ePj4+cA9EC2A4O252wfsn3o5VORq1sAE6QIgROStix6vHn83FkiYj4iZiNidsM6vsQYKEWKEHhc0lttb7N9gaQbJX0/QbsAMmj8V4QRccb25yQ9ImlG0n0R8WzjkQHIIsmfEkfEw5IeTtEW8rjq/BOtnc8f6r0GhoorBteothtP1KS2f1tCAKgcIVCoHHfPaaNsz7EU4M5CaREClUs5aTkW0E+EAJJMXgKgvwgBSGo2iQmAfuNuwwXbs/stWe+is3gyl3rLcY4HpEcINHD1+t8P9gYUtX6613Z6UGI5AFSPECgc5e+r2BbtIASAyhECDeVYQ/IJmGcb1Hg8QCIEgOoRAkDlCIGeqHlJUPN7z4HrBBLIdb3AwmSo5Wu4ck7+Wo8HSFQCQPUIgR6qoTyu4T2WghBIJHc5OeRJknsZUPNSQCIEem2IQTDE91Q6QiChLj5VhjRpcr+X2iuABYTAAAwhCIbwHvqKEGhBF58wfZ5EXYydKuBVhMCA9DEI+jjmoWl0sZDtL0n6mKTTkn4n6ZaIOJliYH3X1Q1H+nJBUZeTnyrgbE0rgf2S3hUR75b0W0l3NB8SgJwahUBE/CgizowfPqbRNxKjACWX2SWPrUYp/3bgU5L+I2F7vdf1PQgXT7aulwelTHyWAq81MQRsPyrpkmV+tDciHhy/Zq+kM5L2rdLOnKQ5Sbp0o9c02D7qOggWdHWsoJTJLxEAK5kYAhFx7Wo/t32zpOslXRMRsUo785LmJWnn9pkVX4d25agOSpr4mKzp2YHrJN0u6f0R8bc0QxqeUqqBpZZO1rWGQh8mPVXAypoeE/i6pNdL2m9bkh6LiE83HtUAlRoEi/VhMq8FAbC6RiEQEcPca1rShyAYGgJgMq4YBCpHCGTGJ1M+bOvpEAIdYOdsH9t4eoRAR9hJ28O2PTeEAFA5bjneoYVPLM4YpEEFsDZUAgVg522Obbh2hEAh2InXjm3XDMuBgrA8ODdM/jSoBArEzj0Z2ygdQqBQ7OQrY9ukxXKgYCwPzsbkbweVQA+w87MN2kQl0BOLJ0EtlQETPw9CoIeGvkxg8udFCPTY0MKAyd8NQmAA+h4GTP5ucWAQqByVwID06eAhn/7lIAQGaukk6zoUmPTlIgQqkTsUmPT9QQhUarVJOm1AMNGHgRDAazC568LZAaBySULA9m22w/bGFO0ByKdxCNjeIulDkv7YfDgAcktRCXxFoy8l5ZuGgR5qFAK2d0k6ERFPJxoPgMwmnh2w/aikS5b50V5Jd2q0FJjI9pykOUm6dKPPYYgA2jQxBCLi2uWet71T0jZJT4+/lnyzpCdtXxERLyzTzrykeUnauX2GpQNQiDVfJxARz0i6eOGx7T9Imo2IPyUYF4BMuE4AqFyyKwYjYmuqtgDkQyUAVI4QACpHCACVIwSAyhECQOUIAaByhABQOUIAqBwhAFSOEAAqRwgAlSMEgMoRAkDlCAGgcoQAUDlCAKgcIQBUjhAAKkcIAJUjBIDKEQJA5QgBoHKEAFA5QgCoXOMQsL3b9q9tP2v731IMCkA+jb6ByPYHJO2S9J6I+Lvtiyf9DoCyNK0EPiPproj4uyRFxIvNhwQgp6Yh8DZJ/2z7oO2f2n5vikEByGficsD2o5IuWeZHe8e/v0HSP0l6r6T/tL09ImKZduYkzUnSpRvdZMwAEpoYAhFx7Uo/s/0ZSd8dT/qf235F0kZJLy3TzrykeUnauX3mNSEBoBtNlwPfk/QBSbL9NkkXSPpT00EByKfR2QFJ90m6z/avJJ2W9MnllgIAytUoBCLitKSbEo0FQAe4YhCoHCEAVI4QACpHCACVIwSAyrmLM3q2X5L0P1O8dKO6v+6AMTCGPo7hHyPiomka6yQEpmX7UETMMgbGwBjaGwPLAaByhABQudJDYL7rAYgxLGAMI4MbQ9HHBAC0r/RKAEDLig+BUm5kavs222F7Ywd9f2m8DX5p+79sr8/Y93W2f2P7qO09ufpd1P8W2z+xfXi8D9yaewyLxjJj+xe2H+qo//W2HxjvC0dsvy9Fu0WHwJIbmb5T0r93NI4tkj4k6Y9d9C9pv6R3RcS7Jf1W0h05OrU9I+kbkj4saYekj9vekaPvRc5Iui0idmh0B6vPdjCGBbdKOtJR35J0t6QfRsQ7JL0n1ViKDgGVcyPTr0i6XVInB1Ai4kcRcWb88DFJmzN1fYWkoxFxbPxn4/drFMrZRMTzEfHk+P9PabTjb8o5BkmyvVnSRyXdk7vvcf9vlnSVpHul0Z/xR8TJFG2XHgKd38jU9i5JJyLi6dx9r+BTkn6Qqa9Nkp5b9Pi4OpiAC2xvlXSZpIMddP9VjT4IXumgb0naptFt+741XpLcY/uNKRpuemehxlLdyLTFMdyp0VKgVauNISIeHL9mr0bl8b62x1Ma2xdK+o6kz0fEnzP3fb2kFyPiCdtX5+x7kfMkXS5pd0QctH23pD2S/jVFw51KdSPTNsZge6dGCfy0bWlUhj9p+4qIeCHHGBaN5WZJ10u6JuMt3E5I2rLo8ebxc1nZPl+jANgXEd/N3b+kKyXdYPsjkv5B0ptsfzsict5V67ik4xGxUAU9oFEINFb6cqDTG5lGxDMRcXFEbI2IrRr9Q1yeOgAmsX2dRqXoDRHxt4xdPy7prba32b5A0o2Svp+xf3mUvvdKOhIRX87Z94KIuCMiNo/3gRsl/ThzAGi8zz1n++3jp66RdDhF251XAhNwI9ORr0t6vaT944rksYj4dNudRsQZ25+T9IikGUn3RcSzbfe7xJWSPiHpGdtPjZ+7MyIezjyOEuyWtG8cyMck3ZKiUa4YBCpX+nIAQMsIAaByhABQOUIAqBwhAFSOEAAqRwgAlSMEgMr9P9Ske2C/wpRUAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAD4CAYAAAAn+OBPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOXElEQVR4nO3db4wd1X3G8eepCa3spN5WlBrZVu22wZWbkII2qBFq2gQUSILgTaQ4yFFxXlhFDSIICQFWX7aqmioOUiKqFWCpspEbEfJHEYljlKZSXuBmIRAKJhFCaTACQaRuEu1KsSx+fXHvimXZf75z5syZOd+PZMn3zt1zfjM7z5wzd+7OdUQIQD1+q+sCAORF6IHKEHqgMoQeqAyhBypzQRedbt6yNbZObeuia6AKv5x7VQvzv/RKyzoJ/dapbTpwy31ddA1U4ch9t6y6jOk9UBlCD1SG0AOVIfRAZQg9UJkkobc9Zfth28/bPm37AynaBZBeqkt290r6TkR8wvaFkjYnahdAYo1Db3urpA9KulmSIuKspLNN2wXQjhTT+92SXpd0xPaPbN9ve8vyF9k+aHvW9uzC/FyCbgFMIkXoL5B0haT7IuJySfOS7lr+ooiYiYjpiJjevGUqQbcAJpEi9GcknYmIU+PHD2t0EABQoMahj4hXJb1ke8/4qaslPde0XQDtSPXu/a2Sjo3fuX9R0oFE7QJILEnoI+IpSdMp2gLQLj6RB1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UJlkobe9afyttd9K1SaA9FKO9LdJOp2wPQAtSBJ62zskfVzS/SnaA9CeVCP9FyXdKemN1V5g+6DtWduzC/NziboFcL4ah9729ZJei4gn1npdRMxExHRETG/eMtW0WwATSjHSXyXpBts/k3Rc0odtH03QLoAWNA59RNwdETsiYpekfZK+FxH7G1cGoBVcpwcqc0HKxiLi+5K+n7JNAGklDT2G4fD+fRt63e1Hj7dcCdpA6Cu10WBP2gYHhHIR+kqkCHmT/jgIlIPQD1TukK+Hg0A5CP2AlBb0tSytlQNAXoR+APoU9pUs1k/48+A6PVAZRvoe6/sIvxwjfh6EvoeGFvblCH+7CH1PDD3oK+HNvnZwTt8DNQZ+ObZBOoz0BWNHfyum/Wkw0heKwK+ObdMMoS8QO/X62EaTY3pfEHbk88N0fzKM9IUg8JNj250fQl8Adtrm2IYbx/S+Q+yoaTHd3xhGeqAyhL4jjPLtYduujdB3gJ2yfWzj1RH6zNgZ82Fbr4zQA5Uh9Bkx8uTHNn+7xpfsbO+U9O+S/lBSSJqJiHubtjs0fdj5Ttx0ZKKfu/ahA4krSevw/n1cxlsixXX6c5LuiIgnbb9L0hO2T0bEcwnaHoRSAz9pyNdrp8SDAMF/U+PQR8Qrkl4Z///Xtk9L2i6J0BcoVdA32keJB4DaJT2nt71L0uWSTq2w7KDtWduzC/NzKbstWimj/ImbjmQJfCn9rqSU30XXkn0M1/Y7JX1V0uci4lfLl0fEjKQZSbpk+55I1W/Jut7JSgmbVM7ozzQ/0Uhv+x0aBf5YRDySok00U1Lglyu5tho0Dr1tS3pA0umI+ELzkgC0KcVIf5WkT0v6sO2nxv8+lqDdXutqal/SOfRauqyz69OurjUOfUT8ICIcEZdFxF+M/z2aojicnz6Efbk+1tx3fCKvBV2MJH0OTxe11zzaE/oB6HPgFw1hHfqC0Cd0eP++7CPIkMKSe11qHe0JfY8NKfCLhrhOpSH0iTDCp5Nz3bqYnXWN0PfQkAO/qIZ17AqhBypD6BPINT3sywdvUsm5vjVN8Ql9T9QU9uVqXvc2EHqgMoQeqAyhbyjHuSDT2zzboJbzekIPVIbQF45R/k1sizQIPVAZvqq6gSGfA/7eJ/9pzeX/9x/3ZKokrxruoUfoC5Z7Orte0Fd7bc4DwImbjnBb7YaY3kPS+QU+5c8iP0KPJKEl+P1B6CuXMqwEvx8IfaFynM+3EdIcwefSXTOEHqgMoZ/QkC/X1W7ov1tCX6k2p+Gc25ct1XfZXWf7J7ZfsH1XijYBtCPFd9ltkvRlSR+VtFfSp2zvbdougHakGOmvlPRCRLwYEWclHZd0Y4J2AbQgRei3S3ppyeMz4+fewvZB27O2Zxfm5xJ0C2AS2d7Ii4iZiJiOiOnNW6ZydQtgmRShf1nSziWPd4yfA1CgFKH/oaR3295t+0JJ+yR9M0G7AFqQ4vvpz0n6rKQTkk5L+kpEPNu0XbSrzT+HHerf2g9FknP6iHg0Ii6NiD+JiH9M0Wbphn6jhZoN/XfLJ/KAyhD6QuW4O0wb0/AcU3vunNMMoa9cypByLt8PhB5Jwkrg+4PQQ1Kz0BL4fuFuuAW79qEDWe8SszS8pd4Cm/P55gh9A7cfPT7YGy7UOnoP/XKdxPQeqA6hLxzT2TexLdIg9EBlCH1DOc4BGeHybIMazuclQg9Uh9ADlSH0PVHzFL/mdW8DoU8g17ngtQ8dqCoAOde3lvN5idAD1SH0PVTDaF/DOnaF0CeSe3o45FDkXLfbjx6vamovEfpeG2Lwh7hOpSH0CXUxagwpJLnXpbYRfhGhH4AhBH8I69AXhL4FXYwgfQ5NF7XXOspLhH5Q+hj8Ptbcd4S+JV2NJH35AE+XddY8yksNQ2/787aft/1j21+zPZWoLgAtaTrSn5T0noi4TNJPJd3dvCSkUPJoX3JtNWh0j7yI+O6Sh49L+kSzcoal63voLQ1XzhtsrqSUoNc+tZfSntN/RtK3V1to+6DtWduzC/NzCbstWyk7WVfn0CW9x1DK76Jr6470th+TtG2FRYci4hvj1xySdE7SsdXaiYgZSTOSdMn2PTFRtWgsx+hfSsixsnVDHxHXrLXc9s2Srpd0dUQQ5hV0Pc1fzfJwTnoQ6EPIGeXf1Oic3vZ1ku6U9NcRsZCmpGEqNfhL9SG8kyDwb9X0nP5Lkt4l6aTtp2z/W4KaBoudLz+2+ds1fff+T1MVAiAPPpGXGSNPPmzrlRH6DrAzto9tvDpC3xF2yvawbddG6IHK8FXVHVockUq/lNcXjPAbw0hfAHbW5tiGG0foC8FOOzm23flhel8Qpvvnh7BPhpG+QOzM62MbTY7QF4qdenVsm2aY3heM6f5bEfY0GOl7gJ2dbZASI31PLN3paxn5CXo7CH0PDX3aT9jbReh7bGjhJ+x5EPoB6Hv4CXtevJEHVIaRfkD69GYfo3t3CP1ALQ9V1wcBQl4OQl+J3AcBQl4uQl+ptUK50QMCwe4nQo+3IczDxrv3QGUIPVCZJKG3fYftsH1RivYAtKdx6G3vlPQRST9vXg6AtqUY6Q9r9CWWfGMt0AONQm/7RkkvR8TTG3jtQduztmcX5ueadAuggXUv2dl+TNK2FRYdknSPRlP7dUXEjKQZSbpk+x5mBUBH1g19RFyz0vO23ytpt6SnbUvSDklP2r4yIl5NWiWAZCb+cE5EPCPp4sXHtn8maToifpGgLgAt4To9UJlkH8ONiF2p2gLQHkZ6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByqT4fvpbbT9v+1nb/5KiKADtafQNN7Y/JOlGSe+LiN/Yvni9nwHQraYj/S2S/jkifiNJEfFa85IAtKlp6C+V9Fe2T9n+L9vvX+2Ftg/anrU9uzA/17BbAJNad3pv+zFJ21ZYdGj8878v6S8lvV/SV2z/cUTE8hdHxIykGUm6ZPuety0HkMe6oY+Ia1ZbZvsWSY+MQ/7ftt+QdJGk19OVCCClptP7r0v6kCTZvlTShZJ+0bBNAC1q+v30D0p60Pb/SDor6W9XmtoDKEej0EfEWUn7E9UCIAM+kQdUhtADlSH0QGUIPVAZd/Fmu+3XJf3vBl56kbq/BEgN1NDHGv4oIv5gpQWdhH6jbM9GxDQ1UAM1pKuB6T1QGUIPVKb00M90XYCoYRE1jPS+hqLP6QGkV/pIDyAxQg9UpvjQl3LjTdt32A7bF3XQ9+fH2+DHtr9meypj39fZ/ontF2zflavfJf3vtP2ftp8b7wO35a5hSS2bbP/I9rc66n/K9sPjfeG07Q9M0k7RoV92480/l/SvHdWxU9JHJP28i/4lnZT0noi4TNJPJd2do1PbmyR9WdJHJe2V9Cnbe3P0vcQ5SXdExF6N7tD09x3UsOg2Sac76luS7pX0nYj4M0nvm7SWokOvcm68eVjSnZI6edczIr4bEefGDx+XtCNT11dKeiEiXhz/GfVxjQ7C2UTEKxHx5Pj/v9ZoR9+eswZJsr1D0scl3Z+773H/WyV9UNID0ujP2iNibpK2Sg/9hm+82RbbN0p6OSKezt33Kj4j6duZ+tou6aUlj8+og8Atsr1L0uWSTnXQ/Rc1OvC/0UHfkrRbo9vQHRmfYtxve8skDTW9c05jqW682WIN92g0tW/VWjVExDfGrzmk0XT3WNv1lMb2OyV9VdLnIuJXmfu+XtJrEfGE7b/J2fcSF0i6QtKtEXHK9r2S7pL0D5M01KkSbry5Wg2236vREfZp29JoWv2k7Ssj4tUcNSyp5WZJ10u6OuMtyV6WtHPJ4x3j57Ky/Q6NAn8sIh7J3b+kqyTdYPtjkn5H0u/aPhoROe8adUbSmYhYnOU8rFHoz1vp0/uvq8Mbb0bEMxFxcUTsiohdGm34K1IHfj22r9NoanlDRCxk7PqHkt5te7ftCyXtk/TNjP3Lo6PtA5JOR8QXcva9KCLujogd431gn6TvZQ68xvvcS7b3jJ+6WtJzk7TV+Ui/Dm68OfIlSb8t6eR4xvF4RPxd251GxDnbn5V0QtImSQ9GxLNt97vMVZI+LekZ20+Nn7snIh7NXEcJbpV0bHwAflHSgUka4WO4QGVKn94DSIzQA5Uh9EBlCD1QGUIPVIbQA5Uh9EBl/h8FjJsanv3x1AAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -176,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -185,7 +185,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -251,7 +251,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -399,688 +399,9 @@ "metadata": {}, "outputs": [], "source": [ - "sp = openmc.StatePoint('statepoint.{}.h5'.format(settings.batches))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
level 1level 2level 3distribcellnuclidescoremeanstd. dev.
univcellunivcellunivcell
idididididid
03441100250totalflux0.2078050.007037
13441200251totalflux0.1971970.005272
23441201252totalflux0.1903100.007816
33441202253totalflux0.1947360.006469
43441203254totalflux0.1910970.006431
53441204255totalflux0.1899100.004891
63441205256totalflux0.1822030.003851
73441300257totalflux0.1659220.005815
83441301258totalflux0.1689330.008300
93441302259totalflux0.1595870.003085
1034413032510totalflux0.1591580.005910
1134413042511totalflux0.1485370.005308
1234413052512totalflux0.1509450.006654
1334413062513totalflux0.1542370.003665
1434413072514totalflux0.1658880.004733
1534413082515totalflux0.1567770.006540
1634413092516totalflux0.1652770.005935
1734413102517totalflux0.1565280.005732
1834413112518totalflux0.1596100.004584
1934414002519totalflux0.0965970.004466
2034414012520totalflux0.1182140.005451
2134414022521totalflux0.1061670.004722
2234414032522totalflux0.1108140.004208
2334414042523totalflux0.1123190.005079
2434414052524totalflux0.1102320.004153
2534414062525totalflux0.0999670.005085
2634414072526totalflux0.0954440.003615
2734414082527totalflux0.0926200.003997
2834414092528totalflux0.0955170.004022
2934414102529totalflux0.1137370.009530
3034414112530totalflux0.1083680.007241
3134414122531totalflux0.1069900.005716
3234414132532totalflux0.1120500.005002
3334414142533totalflux0.1150540.006239
3434414152534totalflux0.1143940.004919
3534414162535totalflux0.1143520.005322
3634414172536totalflux0.1108900.005051
\n", - "
" - ], - "text/plain": [ - " level 1 level 2 level 3 distribcell nuclide score mean \\\n", - " univ cell univ cell univ cell \n", - " id id id id id id \n", - "0 3 44 1 100 2 5 0 total flux 2.08e-01 \n", - "1 3 44 1 200 2 5 1 total flux 1.97e-01 \n", - "2 3 44 1 201 2 5 2 total flux 1.90e-01 \n", - "3 3 44 1 202 2 5 3 total flux 1.95e-01 \n", - "4 3 44 1 203 2 5 4 total flux 1.91e-01 \n", - "5 3 44 1 204 2 5 5 total flux 1.90e-01 \n", - "6 3 44 1 205 2 5 6 total flux 1.82e-01 \n", - "7 3 44 1 300 2 5 7 total flux 1.66e-01 \n", - "8 3 44 1 301 2 5 8 total flux 1.69e-01 \n", - "9 3 44 1 302 2 5 9 total flux 1.60e-01 \n", - "10 3 44 1 303 2 5 10 total flux 1.59e-01 \n", - "11 3 44 1 304 2 5 11 total flux 1.49e-01 \n", - "12 3 44 1 305 2 5 12 total flux 1.51e-01 \n", - "13 3 44 1 306 2 5 13 total flux 1.54e-01 \n", - "14 3 44 1 307 2 5 14 total flux 1.66e-01 \n", - "15 3 44 1 308 2 5 15 total flux 1.57e-01 \n", - "16 3 44 1 309 2 5 16 total flux 1.65e-01 \n", - "17 3 44 1 310 2 5 17 total flux 1.57e-01 \n", - "18 3 44 1 311 2 5 18 total flux 1.60e-01 \n", - "19 3 44 1 400 2 5 19 total flux 9.66e-02 \n", - "20 3 44 1 401 2 5 20 total flux 1.18e-01 \n", - "21 3 44 1 402 2 5 21 total flux 1.06e-01 \n", - "22 3 44 1 403 2 5 22 total flux 1.11e-01 \n", - "23 3 44 1 404 2 5 23 total flux 1.12e-01 \n", - "24 3 44 1 405 2 5 24 total flux 1.10e-01 \n", - "25 3 44 1 406 2 5 25 total flux 1.00e-01 \n", - "26 3 44 1 407 2 5 26 total flux 9.54e-02 \n", - "27 3 44 1 408 2 5 27 total flux 9.26e-02 \n", - "28 3 44 1 409 2 5 28 total flux 9.55e-02 \n", - "29 3 44 1 410 2 5 29 total flux 1.14e-01 \n", - "30 3 44 1 411 2 5 30 total flux 1.08e-01 \n", - "31 3 44 1 412 2 5 31 total flux 1.07e-01 \n", - "32 3 44 1 413 2 5 32 total flux 1.12e-01 \n", - "33 3 44 1 414 2 5 33 total flux 1.15e-01 \n", - "34 3 44 1 415 2 5 34 total flux 1.14e-01 \n", - "35 3 44 1 416 2 5 35 total flux 1.14e-01 \n", - "36 3 44 1 417 2 5 36 total flux 1.11e-01 \n", - "\n", - " std. dev. \n", - " \n", - " \n", - "0 7.04e-03 \n", - "1 5.27e-03 \n", - "2 7.82e-03 \n", - "3 6.47e-03 \n", - "4 6.43e-03 \n", - "5 4.89e-03 \n", - "6 3.85e-03 \n", - "7 5.82e-03 \n", - "8 8.30e-03 \n", - "9 3.09e-03 \n", - "10 5.91e-03 \n", - "11 5.31e-03 \n", - "12 6.65e-03 \n", - "13 3.67e-03 \n", - "14 4.73e-03 \n", - "15 6.54e-03 \n", - "16 5.94e-03 \n", - "17 5.73e-03 \n", - "18 4.58e-03 \n", - "19 4.47e-03 \n", - "20 5.45e-03 \n", - "21 4.72e-03 \n", - "22 4.21e-03 \n", - "23 5.08e-03 \n", - "24 4.15e-03 \n", - "25 5.08e-03 \n", - "26 3.62e-03 \n", - "27 4.00e-03 \n", - "28 4.02e-03 \n", - "29 9.53e-03 \n", - "30 7.24e-03 \n", - "31 5.72e-03 \n", - "32 5.00e-03 \n", - "33 6.24e-03 \n", - "34 4.92e-03 \n", - "35 5.32e-03 \n", - "36 5.05e-03 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "output_tally = sp.get_tally()\n", - "output_tally.get_pandas_dataframe()" + "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n", + " output_tally = sp.get_tally()\n", + " output_tally.get_pandas_dataframe()" ] }, { @@ -1108,7 +429,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index ae31859a4f5..972dee54abb 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -406,15 +406,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n", " warn(msg, IDWarning)\n" ] } @@ -489,9 +489,9 @@ " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", - " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", - " Date/Time | 2021-04-18 13:10:08\n", - " MPI Processes | 1\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 12:02:59\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -515,82 +515,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.21670\n", - " 2/1 1.24155\n", - " 3/1 1.21924\n", - " 4/1 1.22486\n", - " 5/1 1.21719\n", - " 6/1 1.24330\n", - " 7/1 1.22322\n", - " 8/1 1.24133\n", - " 9/1 1.21840\n", - " 10/1 1.25141\n", - " 11/1 1.21217\n", - " 12/1 1.25625 1.23421 +/- 0.02204\n", - " 13/1 1.22056 1.22966 +/- 0.01351\n", - " 14/1 1.21757 1.22664 +/- 0.01002\n", - " 15/1 1.24571 1.23045 +/- 0.00865\n", - " 16/1 1.26489 1.23619 +/- 0.00910\n", - " 17/1 1.22323 1.23434 +/- 0.00791\n", - " 18/1 1.26108 1.23768 +/- 0.00762\n", - " 19/1 1.23145 1.23699 +/- 0.00676\n", - " 20/1 1.23548 1.23684 +/- 0.00605\n", - " 21/1 1.20446 1.23390 +/- 0.00621\n", - " 22/1 1.20533 1.23152 +/- 0.00615\n", - " 23/1 1.22520 1.23103 +/- 0.00568\n", - " 24/1 1.18367 1.22765 +/- 0.00625\n", - " 25/1 1.23614 1.22821 +/- 0.00585\n", - " 26/1 1.23746 1.22879 +/- 0.00550\n", - " 27/1 1.23626 1.22923 +/- 0.00518\n", - " 28/1 1.21334 1.22835 +/- 0.00497\n", - " 29/1 1.25169 1.22958 +/- 0.00486\n", - " 30/1 1.25579 1.23089 +/- 0.00479\n", - " 31/1 1.23828 1.23124 +/- 0.00457\n", - " 32/1 1.26911 1.23296 +/- 0.00468\n", - " 33/1 1.20090 1.23157 +/- 0.00469\n", - " 34/1 1.28606 1.23384 +/- 0.00503\n", - " 35/1 1.23129 1.23374 +/- 0.00483\n", - " 36/1 1.22535 1.23341 +/- 0.00465\n", - " 37/1 1.20367 1.23231 +/- 0.00461\n", - " 38/1 1.22886 1.23219 +/- 0.00444\n", - " 39/1 1.24056 1.23248 +/- 0.00429\n", - " 40/1 1.25038 1.23307 +/- 0.00419\n", - " 41/1 1.21504 1.23249 +/- 0.00410\n", - " 42/1 1.20762 1.23171 +/- 0.00404\n", - " 43/1 1.20597 1.23093 +/- 0.00399\n", - " 44/1 1.24424 1.23133 +/- 0.00389\n", - " 45/1 1.24767 1.23179 +/- 0.00381\n", - " 46/1 1.22998 1.23174 +/- 0.00370\n", - " 47/1 1.26195 1.23256 +/- 0.00369\n", - " 48/1 1.23146 1.23253 +/- 0.00359\n", - " 49/1 1.22059 1.23222 +/- 0.00351\n", - " 50/1 1.24724 1.23260 +/- 0.00345\n", + " 1/1 1.26527\n", + " 2/1 1.23398\n", + " 3/1 1.25383\n", + " 4/1 1.24224\n", + " 5/1 1.22254\n", + " 6/1 1.20745\n", + " 7/1 1.22674\n", + " 8/1 1.22724\n", + " 9/1 1.23185\n", + " 10/1 1.22019\n", + " 11/1 1.24342\n", + " 12/1 1.23258 1.23800 +/- 0.00542\n", + " 13/1 1.20626 1.22742 +/- 0.01103\n", + " 14/1 1.19410 1.21909 +/- 0.01141\n", + " 15/1 1.24556 1.22439 +/- 0.01030\n", + " 16/1 1.27632 1.23304 +/- 0.01207\n", + " 17/1 1.25083 1.23558 +/- 0.01051\n", + " 18/1 1.23155 1.23508 +/- 0.00912\n", + " 19/1 1.27501 1.23952 +/- 0.00918\n", + " 20/1 1.24863 1.24043 +/- 0.00827\n", + " 21/1 1.18837 1.23569 +/- 0.00885\n", + " 22/1 1.21978 1.23437 +/- 0.00819\n", + " 23/1 1.22815 1.23389 +/- 0.00754\n", + " 24/1 1.24244 1.23450 +/- 0.00701\n", + " 25/1 1.21128 1.23295 +/- 0.00671\n", + " 26/1 1.22836 1.23267 +/- 0.00628\n", + " 27/1 1.21573 1.23167 +/- 0.00598\n", + " 28/1 1.19115 1.22942 +/- 0.00607\n", + " 29/1 1.24854 1.23042 +/- 0.00583\n", + " 30/1 1.24486 1.23115 +/- 0.00558\n", + " 31/1 1.23967 1.23155 +/- 0.00532\n", + " 32/1 1.24406 1.23212 +/- 0.00511\n", + " 33/1 1.24808 1.23282 +/- 0.00493\n", + " 34/1 1.23056 1.23272 +/- 0.00472\n", + " 35/1 1.24209 1.23310 +/- 0.00454\n", + " 36/1 1.23203 1.23305 +/- 0.00437\n", + " 37/1 1.21629 1.23243 +/- 0.00425\n", + " 38/1 1.22928 1.23232 +/- 0.00409\n", + " 39/1 1.23665 1.23247 +/- 0.00395\n", + " 40/1 1.24100 1.23276 +/- 0.00383\n", + " 41/1 1.26373 1.23375 +/- 0.00384\n", + " 42/1 1.25002 1.23426 +/- 0.00375\n", + " 43/1 1.24100 1.23447 +/- 0.00364\n", + " 44/1 1.25701 1.23513 +/- 0.00359\n", + " 45/1 1.23027 1.23499 +/- 0.00349\n", + " 46/1 1.25747 1.23562 +/- 0.00345\n", + " 47/1 1.24960 1.23599 +/- 0.00338\n", + " 48/1 1.23535 1.23598 +/- 0.00329\n", + " 49/1 1.21318 1.23539 +/- 0.00325\n", + " 50/1 1.27184 1.23630 +/- 0.00330\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.1312e+00 seconds\n", - " Reading cross sections = 1.1046e+00 seconds\n", - " Total time in simulation = 2.7801e+02 seconds\n", - " Time in transport only = 2.7794e+02 seconds\n", - " Time in inactive batches = 1.0359e+01 seconds\n", - " Time in active batches = 2.6765e+02 seconds\n", - " Time synchronizing fission bank = 2.5796e-02 seconds\n", - " Sampling source sites = 2.3811e-02 seconds\n", - " SEND/RECV source sites = 1.7176e-03 seconds\n", - " Time accumulating tallies = 2.5310e-02 seconds\n", - " Time writing statepoints = 6.3601e-03 seconds\n", - " Total time for finalization = 2.2876e-02 seconds\n", - " Total time elapsed = 2.7919e+02 seconds\n", - " Calculation Rate (inactive) = 4826.80 particles/second\n", - " Calculation Rate (active) = 747.234 particles/second\n", + " Total time for initialization = 2.7487e-01 seconds\n", + " Reading cross sections = 2.6530e-01 seconds\n", + " Total time in simulation = 2.1335e+01 seconds\n", + " Time in transport only = 2.1311e+01 seconds\n", + " Time in inactive batches = 9.3940e-01 seconds\n", + " Time in active batches = 2.0396e+01 seconds\n", + " Time synchronizing fission bank = 9.9228e-03 seconds\n", + " Sampling source sites = 8.2374e-03 seconds\n", + " SEND/RECV source sites = 1.6638e-03 seconds\n", + " Time accumulating tallies = 8.4502e-04 seconds\n", + " Time writing statepoints = 7.4764e-03 seconds\n", + " Total time for finalization = 6.2220e-03 seconds\n", + " Total time elapsed = 2.1623e+01 seconds\n", + " Calculation Rate (inactive) = 53225.7 particles/second\n", + " Calculation Rate (active) = 9805.91 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.23256 +/- 0.00308\n", - " k-effective (Track-length) = 1.23260 +/- 0.00345\n", - " k-effective (Absorption) = 1.23111 +/- 0.00186\n", - " Combined k-effective = 1.23135 +/- 0.00184\n", + " k-effective (Collision) = 1.23688 +/- 0.00324\n", + " k-effective (Track-length) = 1.23630 +/- 0.00330\n", + " k-effective (Absorption) = 1.23291 +/- 0.00216\n", + " Combined k-effective = 1.23399 +/- 0.00202\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -685,17 +685,17 @@ { "data": { "text/plain": [ - "array([[[5.14223507e-06, 1.16426087e-06]],\n", + "array([[[5.15913423e-06, 1.16842422e-06]],\n", "\n", - " [[2.65426350e-05, 7.58220468e-06]],\n", + " [[2.66298632e-05, 7.60931827e-06]],\n", "\n", - " [[2.53399053e-05, 5.73796202e-06]],\n", + " [[2.54231809e-05, 5.75848069e-06]],\n", "\n", - " [[5.68141581e-05, 1.04757933e-05]],\n", + " [[5.70008690e-05, 1.05132542e-05]],\n", "\n", - " [[2.32930026e-05, 5.45658817e-06]],\n", + " [[2.33695515e-05, 5.47610066e-06]],\n", "\n", - " [[9.75735783e-06, 1.65150949e-06]]])" + " [[9.78942387e-06, 1.65741521e-06]]])" ] }, "execution_count": 16, @@ -757,8 +757,8 @@ " 1\n", " 1\n", " U235\n", - " 9.345817e-08\n", - " 6.616216e-08\n", + " 9.493755e-08\n", + " 9.395351e-08\n", " \n", " \n", " 199\n", @@ -766,8 +766,8 @@ " 1\n", " 1\n", " Pu239\n", - " 1.574816e-08\n", - " 1.114955e-08\n", + " 1.602797e-08\n", + " 1.586127e-08\n", " \n", " \n", " 398\n", @@ -775,8 +775,8 @@ " 2\n", " 1\n", " U235\n", - " 4.824023e-07\n", - " 3.415087e-07\n", + " 4.900384e-07\n", + " 4.849591e-07\n", " \n", " \n", " 399\n", @@ -784,8 +784,8 @@ " 2\n", " 1\n", " Pu239\n", - " 1.025593e-07\n", - " 7.261101e-08\n", + " 1.043816e-07\n", + " 1.032959e-07\n", " \n", " \n", " 598\n", @@ -793,8 +793,8 @@ " 3\n", " 1\n", " U235\n", - " 4.605432e-07\n", - " 3.260339e-07\n", + " 4.678332e-07\n", + " 4.629841e-07\n", " \n", " \n", " 599\n", @@ -802,8 +802,8 @@ " 3\n", " 1\n", " Pu239\n", - " 7.761348e-08\n", - " 5.494962e-08\n", + " 7.899251e-08\n", + " 7.817094e-08\n", " \n", " \n", " 798\n", @@ -811,8 +811,8 @@ " 4\n", " 1\n", " U235\n", - " 1.032576e-06\n", - " 7.309948e-07\n", + " 1.048921e-06\n", + " 1.038048e-06\n", " \n", " \n", " 799\n", @@ -820,8 +820,8 @@ " 4\n", " 1\n", " Pu239\n", - " 1.416989e-07\n", - " 1.003215e-07\n", + " 1.442166e-07\n", + " 1.427166e-07\n", " \n", " \n", " 998\n", @@ -829,8 +829,8 @@ " 5\n", " 1\n", " U235\n", - " 4.233415e-07\n", - " 2.996976e-07\n", + " 4.300427e-07\n", + " 4.255852e-07\n", " \n", " \n", " 999\n", @@ -838,8 +838,8 @@ " 5\n", " 1\n", " Pu239\n", - " 7.380753e-08\n", - " 5.225504e-08\n", + " 7.511894e-08\n", + " 7.433765e-08\n", " \n", " \n", "\n", @@ -847,16 +847,16 @@ ], "text/plain": [ " cell delayedgroup group in nuclide mean std. dev.\n", - "198 1 1 1 U235 9.345817e-08 6.616216e-08\n", - "199 1 1 1 Pu239 1.574816e-08 1.114955e-08\n", - "398 1 2 1 U235 4.824023e-07 3.415087e-07\n", - "399 1 2 1 Pu239 1.025593e-07 7.261101e-08\n", - "598 1 3 1 U235 4.605432e-07 3.260339e-07\n", - "599 1 3 1 Pu239 7.761348e-08 5.494962e-08\n", - "798 1 4 1 U235 1.032576e-06 7.309948e-07\n", - "799 1 4 1 Pu239 1.416989e-07 1.003215e-07\n", - "998 1 5 1 U235 4.233415e-07 2.996976e-07\n", - "999 1 5 1 Pu239 7.380753e-08 5.225504e-08" + "198 1 1 1 U235 9.493755e-08 9.395351e-08\n", + "199 1 1 1 Pu239 1.602797e-08 1.586127e-08\n", + "398 1 2 1 U235 4.900384e-07 4.849591e-07\n", + "399 1 2 1 Pu239 1.043816e-07 1.032959e-07\n", + "598 1 3 1 U235 4.678332e-07 4.629841e-07\n", + "599 1 3 1 Pu239 7.899251e-08 7.817094e-08\n", + "798 1 4 1 U235 1.048921e-06 1.038048e-06\n", + "799 1 4 1 Pu239 1.442166e-07 1.427166e-07\n", + "998 1 5 1 U235 4.300427e-07 4.255852e-07\n", + "999 1 5 1 Pu239 7.511894e-08 7.433765e-08" ] }, "execution_count": 17, @@ -909,7 +909,7 @@ " 1\n", " U235\n", " 0.013336\n", - " 0.000061\n", + " 0.000056\n", " \n", " \n", " 1\n", @@ -917,7 +917,7 @@ " 1\n", " Pu239\n", " 0.013271\n", - " 0.000053\n", + " 0.000050\n", " \n", " \n", " 2\n", @@ -925,7 +925,7 @@ " 2\n", " U235\n", " 0.032739\n", - " 0.000149\n", + " 0.000137\n", " \n", " \n", " 3\n", @@ -933,7 +933,7 @@ " 2\n", " Pu239\n", " 0.030881\n", - " 0.000123\n", + " 0.000116\n", " \n", " \n", " 4\n", @@ -941,7 +941,7 @@ " 3\n", " U235\n", " 0.120780\n", - " 0.000550\n", + " 0.000504\n", " \n", " \n", " 5\n", @@ -949,7 +949,7 @@ " 3\n", " Pu239\n", " 0.113370\n", - " 0.000452\n", + " 0.000427\n", " \n", " \n", " 6\n", @@ -957,7 +957,7 @@ " 4\n", " U235\n", " 0.302780\n", - " 0.001378\n", + " 0.001264\n", " \n", " \n", " 7\n", @@ -965,7 +965,7 @@ " 4\n", " Pu239\n", " 0.292500\n", - " 0.001167\n", + " 0.001101\n", " \n", " \n", " 8\n", @@ -973,7 +973,7 @@ " 5\n", " U235\n", " 0.849490\n", - " 0.003866\n", + " 0.003545\n", " \n", " \n", " 9\n", @@ -981,7 +981,7 @@ " 5\n", " Pu239\n", " 0.857490\n", - " 0.003420\n", + " 0.003228\n", " \n", " \n", " 10\n", @@ -989,7 +989,7 @@ " 6\n", " U235\n", " 2.853000\n", - " 0.012985\n", + " 0.011907\n", " \n", " \n", " 11\n", @@ -997,7 +997,7 @@ " 6\n", " Pu239\n", " 2.729700\n", - " 0.010887\n", + " 0.010276\n", " \n", " \n", "\n", @@ -1005,18 +1005,18 @@ ], "text/plain": [ " cell delayedgroup nuclide mean std. dev.\n", - "0 1 1 U235 0.013336 0.000061\n", - "1 1 1 Pu239 0.013271 0.000053\n", - "2 1 2 U235 0.032739 0.000149\n", - "3 1 2 Pu239 0.030881 0.000123\n", - "4 1 3 U235 0.120780 0.000550\n", - "5 1 3 Pu239 0.113370 0.000452\n", - "6 1 4 U235 0.302780 0.001378\n", - "7 1 4 Pu239 0.292500 0.001167\n", - "8 1 5 U235 0.849490 0.003866\n", - "9 1 5 Pu239 0.857490 0.003420\n", - "10 1 6 U235 2.853000 0.012985\n", - "11 1 6 Pu239 2.729700 0.010887" + "0 1 1 U235 0.013336 0.000056\n", + "1 1 1 Pu239 0.013271 0.000050\n", + "2 1 2 U235 0.032739 0.000137\n", + "3 1 2 Pu239 0.030881 0.000116\n", + "4 1 3 U235 0.120780 0.000504\n", + "5 1 3 Pu239 0.113370 0.000427\n", + "6 1 4 U235 0.302780 0.001264\n", + "7 1 4 Pu239 0.292500 0.001101\n", + "8 1 5 U235 0.849490 0.003545\n", + "9 1 5 Pu239 0.857490 0.003228\n", + "10 1 6 U235 2.853000 0.011907\n", + "11 1 6 Pu239 2.729700 0.010276" ] }, "execution_count": 18, @@ -1090,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1210,8 +1210,8 @@ " 1\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 8.779139e-08\n", - " 4.659945e-10\n", + " 8.808003e-08\n", + " 4.352878e-10\n", " \n", " \n", " 1\n", @@ -1219,8 +1219,8 @@ " 1\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.149814e-09\n", - " 3.565137e-11\n", + " 7.175417e-09\n", + " 3.446159e-11\n", " \n", " \n", " 2\n", @@ -1228,8 +1228,8 @@ " 2\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 9.527880e-07\n", - " 5.057375e-09\n", + " 9.559207e-07\n", + " 4.724119e-09\n", " \n", " \n", " 3\n", @@ -1237,8 +1237,8 @@ " 2\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.303159e-07\n", - " 6.497988e-10\n", + " 1.307826e-07\n", + " 6.281133e-10\n", " \n", " \n", " 4\n", @@ -1246,8 +1246,8 @@ " 3\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.353903e-07\n", - " 1.249446e-09\n", + " 2.361643e-07\n", + " 1.167114e-09\n", " \n", " \n", " 5\n", @@ -1255,8 +1255,8 @@ " 3\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.032895e-08\n", - " 1.013670e-10\n", + " 2.040175e-08\n", + " 9.798408e-11\n", " \n", " \n", " 6\n", @@ -1264,8 +1264,8 @@ " 4\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 4.720191e-07\n", - " 2.505466e-09\n", + " 4.735711e-07\n", + " 2.340368e-09\n", " \n", " \n", " 7\n", @@ -1273,8 +1273,8 @@ " 4\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.626309e-08\n", - " 1.309566e-10\n", + " 2.635713e-08\n", + " 1.265862e-10\n", " \n", " \n", " 8\n", @@ -1282,8 +1282,8 @@ " 5\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.827915e-08\n", - " 1.501050e-10\n", + " 2.837213e-08\n", + " 1.402138e-10\n", " \n", " \n", " 9\n", @@ -1291,8 +1291,8 @@ " 5\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.430587e-09\n", - " 1.211972e-11\n", + " 2.439290e-09\n", + " 1.171525e-11\n", " \n", " \n", " 10\n", @@ -1300,8 +1300,8 @@ " 6\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.477530e-09\n", - " 7.842692e-12\n", + " 1.482388e-09\n", + " 7.325898e-12\n", " \n", " \n", " 11\n", @@ -1309,8 +1309,8 @@ " 6\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 6.994312e-11\n", - " 3.487599e-13\n", + " 7.019358e-11\n", + " 3.371208e-13\n", " \n", " \n", "\n", @@ -1332,18 +1332,18 @@ "11 1 6 Pu239 \n", "\n", " score mean std. dev. \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.66e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.57e-11 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.50e-10 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 1.25e-09 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.01e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.31e-10 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.21e-11 \n", - "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.49e-13 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.81e-08 4.35e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.18e-09 3.45e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.56e-07 4.72e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.31e-07 6.28e-10 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07 1.17e-09 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.04e-08 9.80e-11 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.74e-07 2.34e-09 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.64e-08 1.27e-10 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.84e-08 1.40e-10 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.44e-09 1.17e-11 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.33e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11 3.37e-13 " ] }, "execution_count": 23, @@ -1377,7 +1377,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Beta (U-235) : 0.006504 +/- 0.000007\n", + "Beta (U-235) : 0.006504 +/- 0.000006\n", "Beta (Pu-239): 0.002245 +/- 0.000002\n" ] }, @@ -1456,7 +1456,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEaCAYAAAAL7cBuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABP7ElEQVR4nO2deXhU5fX4P4cdTRBFrQSBoIIiqyRIcSO4L4ALoixawCXYij+VaMVaZdC61BKXKvoFq6CWRQouQFFr1eCuJIgoKIgaBEJVFoUAEpbz++Pemdwks2WZzExyPs9zn9z7bvfMO5l77vue9z1HVBXDMAzDKE+DeAtgGIZhJCamIAzDMIygmIIwDMMwgmIKwjAMwwiKKQjDMAwjKKYgDMMwjKCYgjCMGkZEThaRr0WkWEQuiuF9XhWRkdWo/38icmdNymTULUxBJAEiUigiu9wHjv94PN5yRUJELhSRZSKyTUQ2ichbItIhxvdUETkmlveIgruBx1U1RVVfrm5jIjJdRErKff+Xq+p5qvpsVdtV1etU9Z7qylceEfG538NlnrRGblp6DbQ/XUT+Ut12jMg0ircARtQMVNX/xvIGItJIVffWUFvHAM8BlwBvASnA2cC+mmi/GnLV2GcMQ3tgRVUqhpHvQVX9c/XEqlW2ABNFZJ6q1up3Xkvfcb3ARhBJjoiMEpH3RGSSiGwVke9E5DxP/kEi8rSIbBSRDSLyFxFp6Kn7vog8LCKbAZ+ItBKRBe5b/xK3/Htu+ckiklvu/vNF5OYgovUEvlPVN9Vhu6rOU9Xv3Xo+EZkrIi+IyHYRWSoiPTztponIPBH5yf1M/8+T11BE/iQi37h1C0SkrYi84xb5zP+WLSJZIrJeRG4Tkf8B0/x9Vu5zBEYe7hvqE+4UTrHbR0eIyCNuH38lIieE+D6+AY4CFrh1m7qfZb6IbBGRNSJyrae8vx/+KSLbgFHhv/Ey98oTkWvc82NEZLGI/OKO1l5w08X9fn90v9PPRaSr53P+xdPeta58W1x508r1z3XiTJ397P4vSBjxXgNKgCtCyN7U/Z/9XkR+EGe6q7mbF/L7EZFsYATwR7d/F7j5he53vBzYIc6IZZCIrHDlzRORzp72CkXkFhFZ7vbZCyLSzM07VEQWuvW2iMi7IlIvn5X18kPXQfoAq4BDgQeBpz0/3unAXuAY4ASct/hrytX9FvgNcC8wGdgBHAGMdA8/zwLD/D8WETkUOBOYGUSmpcBx7sOpv4ikBClzIfAv4BC3jZdFpLHb/gLgM6ANcAZwk4ic49YbBwwDzgdaAFcBO1X1NDe/hzu984J7fYR7j/ZAdhA5gnEZ8GecPt0NfOh+pkOBucBDwSqp6tHA9zgjvhRV3Q3MBtYDacClwH0icnq5fpgLtARmRClfee4B/gMcDBwJPOamnw2cBnQCDnI/1+bylV157nfzWwNrXbm9DAB6A93dcucQGgXuBCaISOMg+Q+4MvXE+d9sA9wV/iOCqk7F6aMH3f4d6MkeBlyA049HAbOAm4DDgEU4SruJp/xlwLlAB/czjXLTc3C+r8Nwfhd/cj9PvcMURPLwsvtG4z+u9eStVdWn3KH8szg/8N+IyG9wHqI3qeoOVf0ReBgY6qlbpKqPuUPyEmAwMEFVd6rqSrc9AFT1E+AXnAc2bjt5qvpDeWFV9VsgC+eHPwfY5L6xehVFgarOVdU9OA/cZsBvcR5Ch6nq3apa4rb1lEfua4A/q+oqd3TymapWeOh52O9+pt2quitMOS8vqWqBqv4KvAT8qqrPuX38Ao6yjYiItAVOBm5T1V9VdRnwD+B3nmIfqurLqro/jHy3eL77TUHy9+AowDT3Pu950lOB4wBR1S9VdWOQ+iOAZ1R1qavUbgf6SlmbwQOq+rM7Cnwb5+EeElWdD/xE2RcS3JeXbOBmVd2iqtuB+yj7f1kV/q6q69w+vBz4t6q+4f5/TQKaAyeVK1+kqltwXkj8n2cPzm+ovaruUdV3tZ46rTMFkTxcpKotPcdTnrz/+U9Udad7moLzwGgMbPQ/XIApwOGeuus854fh2KXWhcgHR2H4pw2uAJ4PJbCqfqSql6nqYcCpOG+ydwRrW1X3U/qW3R5I8ypEnLe437jF2wLfhLpvEH5yH/SVwav0dgW5DjYiCkYa4H8I+lmLozj9lO/jYEzyfPeHBsn/IyDAJ+60ylUAqvoW8DjOyPBHEZkqIi1CyLnWf6GqxTgjDa+c//Oc7yS6PvgzznfezJN2GHAAUOD5fl9z06uDtx/Lf579bn40n+dvwBrgPyLyrYiMr6ZcSYspiLrNOpzpkUM9D5cWqtrFU8b7ZvQTznTUkZ60tuXa/CdwoTj2gs7Ay9EIoqpLgBeBrsHadqeVjgSKXLm/K6cQU1X1fM/nOjqa+/pvX+56B84Dyn/vIyrRVmUpAg4RkVRPWjtgg+e62m+nqvo/Vb1WVdOAMcAT4tpUVPXvqpoBHI8zrXNrCDnb+y9E5ECgVTk5qyLXGzgP2z94kjfhKNkunu/3IFX1P6AjfT+h+subXv7zCM7/W8TP49rLclT1KGAQME5EzohUry5iCqIO404l/AfIFZEWItJARI4WkX4hyu/DeYj7ROQAETmOslMhqOp6YAnOyGFeqCkRETnFNXoe7l4fh/Nj+8hTLENELhGRRjhzxbvd/E+A7a7Rsbk4RumuItLbrfcP4B4R6egaYbuLSCs37wec+edwfAZ0EZGermHSF6F8lVHVdcAHwP0i0kxEugNX4yjaGkNEhoiIX7FvxXlY7heR3iLSx7UD7AB+xZlyK88sYLTbJ01xpnw+VtXCGhDvDpwRDhB4m38KeNjz/9HGY2OK9P1E8x3PAS4QkTPcz56D8//1QSRhRWSAaxAXnCnVfQTvszqPKYjkwb8qxn+8FGW93wFNgJU4D465OPOroRiLY8z8H44SmIXzw/LyLNCNMNNLwM84CuFzESnGmUJ4CceI7ucVnLnircCVwCXunO8+HINoT+A7nDfOf7hygWOvmIOj/LYBT+PML4PzMHnWnboIrMP3oqqrcfYq/Bf4GngvWLkaZBiQjvNW+xKOPaSmlyz3Bj52+3o+cKNru2mB8zDeijPlshlnCqUMrjx3AvOAjTgjtOraBPxtv4+j9L3chjOy+Eic1Vv/BY51y0f6fp4Gjne/45dD3HMVzhToYzj/PwNxFg6URCFyR/fexTiLE55Q1bejqFfnkHpqezGiRET+ChyhqiM9aafhvAG3r6rxTkR8wDGqGnQZpGEY8cdGEEYZROQ4d8pGROREnOmQlzz5jYEbgX/U15UdhlFfMAVhlCcVxw6xA2c5Zy7OVBDibDT6GWeK6pH4iGcYRm1hU0yGYRhGUGwEYRiGYQTFFIRhGIYRlJh6cxWRc4FHgYY4Rs0HyuWfhjOX3R0Yqqpzy+W3wFme+bKqjg13r0MPPVTT09NrTnjDMIx6QEFBwSbX20EFYqYgxPEYOhk4C8eFwhIRme/69/HzPY6DrFtCNHMP8E6IvDKkp6eTn59fdYENwzDqISKyNlReLKeYTgTWqOq37uaU2TheKwOoaqGqLifILkURycDxvfOfGMpoGIZhhCCWCqINZZ1nraeso6yQuH55cgk9sjAMwzBiTKIaqf8ALHL9/oRERLJFJF9E8n/66adaEs0wDKN+EEsj9QbKegI9kug9Q/YFThWRP+C44G0iIsWqWsbtrjrBQ6YCZGZmVtjQsWfPHtavX8+vv1bW07ORKDRr1owjjzySxo2DxZwxDCOWxFJBLAE6ihOkfgOO46/h0VRU1RH+cxEZBWSWVw7RsH79elJTU0lPT0fCRkc0EhFVZfPmzaxfv54OHTrEWxzDqHfEbIpJnQhlY4HXgS+BOaq6QkTuFpFBAK4r4vXAEGCKiFQp0Hsofv31V1q1amXKIUkREVq1amUjQMOIEzHdB6Gqi3BiwXrT7vKcL6FscJpgbUzHiatcJUw5JDf2/dU8UwumMmbhmMB1SpMUfP185JyUE0epjEQkUY3UdYLCwkK6du1aJs3n8zFp0qQKZdetW0f//v05/vjj6dKlC48++mgg784776R79+707NmTs88+m6KiIgDy8vI46KCD6NmzJz179uTuu++OKNP06dMZOzbsnsOoylSX9PR0Nm0KFlrZqAy5H+SSen8qMlGCHumPpFP4c2HYNopLivEt9tWKvEZyYQoiQWjUqBG5ubmsXLmSjz76iMmTJ7NypbOn8NZbb2X58uUsW7aMAQMGlFEEp556KsuWLWPZsmXcddddoZo36ii+xT6KS4pD5m8v2c7gOYPJmp4Vtp1wbRj1F1MQCULr1q3p1asXAKmpqXTu3JkNG5xFXy1alMaY37FjR6WnXaZNm0anTp048cQTef/99wPpP/30E4MHD6Z379707t27TJ6fBQsW0KdPH0444QTOPPNMfvjhB/bv30/Hjh3xLy3ev38/xxxzDD/99FPINjdv3szZZ59Nly5duOaaazAvwjVDpAf7ll1bWL15NQM7DQykZWdkoxMUnWDfgRGeeqUgfD4Qie7Izq5YPzu7bBmfLzZyFhYW8umnn9KnT59A2h133EHbtm2ZMWNGmRHEhx9+SI8ePTjvvPNYsaKijX/jxo1MmDCB999/n/feey8wKgG48cYbufnmm1myZAnz5s3jmmuuqVD/lFNO4aOPPuLTTz9l6NChPPjggzRo0IArrriCGTNmAPDf//6XHj16cNhhh4Vsc+LEiZxyyimsWLGCiy++mO+//77G+stw8D/0yx/bb99u9gWjSsTUSF3fCfWmH24EUFxczODBg3nkkUfKjBzuvfde7r33Xu6//34ef/xxJk6cSK9evVi7di0pKSksWrSIiy66iK+//rpMex9//DFZWVkcdpjji+vyyy9n9erVgPNg9yqMbdu2UVxc9o10/fr1XH755WzcuJGSkpLActOrrrqKCy+8kJtuuolnnnmG0aNHh23znXfe4cUXXwTgggsu4OCDDw7feUZUzB86P94iGHWYejWCqG1atWrF1q1by6Rt2bKFQw89lHXr1gWMy//3f/8HOBv7Bg8ezIgRI7jkkkuCtjlixAjmzZsHOFNPKSkpAJx//vns2bOnUobf/fv389FHHwVsGBs2bAi05+eGG25g7NixfP7550yZMiWw5LRt27b85je/4a233uKTTz7hvPPOi7pNo+YYeOzAwGEYNU29UhA+H6hGd0ydWrH+1Klly0SaYkpJSaF169a89dZbgKMcXnvtNU455RTatm0beIhed911qCpXX301nTt3Zty4cWXa8Y4KXnnlFY477jgA/ve//wXm8j/55BP2799Pq1atytTt06cPixcvZvPmzezZs4d//etfgbyzzz6bxx57LHC9bNmyCp/hl19+oU0bx4XWs88+Wybvmmuu4YorrmDIkCE0bNgwbJunnXYaM2fOBODVV1+toDgNw0g8bIopxjz33HNcf/31gYf+hAkTOProoyuUe//993n++efp1q0bPXv2BOC+++7j/PPPZ/z48axatYoGDRrQvn37wIhj7ty5PPnkkzRq1IjmzZsze/bsCtNXrVu3xufz0bdvX1q2bBloG+Dvf/87119/Pd27d2fv3r2cdtppgbb9+Hw+hgwZwsEHH8zpp5/Od999F8gbNGgQo0ePDkwvhWtzwoQJDBs2jC5dunDSSSfRrl27avWrUTMM6DQg3iIYCUydiUmdmZmp5eNBfPnll3Tu3DlOEtV98vPzufnmm3n33Xdjeh/7Hg0jdohIgapmBsuzEYRRJR544AGefPLJwEomIz6k5aYFzotyiuIoiVEXMQVhVInx48czfnyl/ScaNczG4o3xFsGow9QrI7VhGIYRPTaCMIx6jC/PV3qe5QtZzqifmIIwjHrMxMUTA+emIIzy2BSTYRiGERRTEDGmYcOG9OzZk65duzJkyBB27twZdd1ly5bRt29funTpQvfu3XnhhRcCeVdffTU9evSge/fuXHrppQEXGWvXruWMM86ge/fuZGVlsX592LDeQGgX5JUtU11sx7VhJBamIGJM8+bNWbZsGV988QVNmjSpsBEtHAcccADPPfccK1as4LXXXuOmm27i559/BuDhhx/ms88+Y/ny5bRr147HH38cgFtuuYXf/e53LF++nLvuuovbb789Fh/LMIx6gCmIWuTUU09lzZo15OXlMWBA6Q7WsWPHMn369ArlO3XqRMeOHQFIS0vj8MMPD7jY9jvyU1V27doV2EG9cuVKTj/9dAD69+/PK6+8ElSWe++9l06dOnHKKaewatWqQPo333zDueeeS0ZGBqeeeipfffVVhbpPPfUUvXv3pkePHgwePJidO3eyfft2OnTowJ49ewDHSZ//OlSb3333HX379qVbt278+c9/rlRfGoYRe+qVgvDl+UJG3ip/ZC+o6O87e0F2mTLeFSCR2Lt3L6+++irdunWrkuyffPIJJSUlZdx0jB49miOOOIKvvvqKG264AYAePXoEvKa+9NJLbN++nc2bN5dpq6CggNmzZ7Ns2TIWLVrEkiVLSj9jdjaPPfYYBQUFTJo0iT/84Q8VZLnkkktYsmQJn332GZ07d+bpp58mNTWVrKws/v3vfwMwe/ZsLrnkEho3bhyyzRtvvJHf//73fP7557Ru3bpK/WIYRuyoVwoiHuzatYuePXuSmZlJu3btuPrqqyvdxsaNG7nyyiuZNm0aDRqUfmXTpk2jqKiIzp07B+wTkyZNYvHixZxwwgksXryYNm3aBBzp+Xn33Xe5+OKLOeCAA2jRogWDBg0CHFfjH3zwAUOGDKFnz56MGTOGjRsrbsT64osvOPXUU+nWrRszZswIxKG45pprmDZtWkC20aNHh23z/fffZ9iwYQBceeWVle4XwzBiS0yXuYrIucCjQEPgH6r6QLn804BHgO7AUFWd66b3BJ4EWgD7gHtV9QWSEL8NwkujRo3Yv39/4NrvQvvjjz9mzBgnmPzdd9/NoEGD2LZtGxdccAH33nsvv/3tbyu037Bhw0Agn9GjR5OWlhYYQRQXFzNv3jxatmwZlaz79++nZcuWQb26ehk1ahQvv/wyPXr0YPr06eTl5QFw8sknU1hYSF5eHvv27aNr165s27YtbJuVjY5nlCX/2vzIhQyjqqhqTA4cpfANcBTQBPgMOL5cmXQc5fAccKknvRPQ0T1PAzYCLcPdLyMjQ8uzcuXKCmm1zYEHHlgh7fvvv9f27dvrr7/+qlu3btX09HSdNm1ahXK7d+/W008/XR9++OEy6fv379evv/46cJ6Tk6M5OTmqqvrTTz/pvn37VFX1T3/6k955550V2i0oKNBu3brpzp07ddu2bXrMMcfo3/72N1VV7du3r86ZMyfQ9rJly1RVdcKECYEyrVq10h9++EFLSkr0zDPP1JEjRwbanjRpkrZu3VqfeOKJQFqoNgcOHKjPP/+8qqo+8cQTQftKNTG+x0Rl0iTVlBTHAX2vXhXzp0xx8idNCl4fH4HDqJ8A+RriuRrLKaYTgTWq+q2qlgCzgQvLKadCVV0O7C+XvlpVv3bPi4AfgcNiKGut0rZtWy677DK6du3KZZddxgknnBC03Jw5c3jnnXeYPn16ILjQsmXLUFVGjhxJt27d6NatGxs3buSuu+4CIC8vj2OPPZZOnTrxww8/cMcdd1Rot1evXlx++eWBUKW9e/cO5M2YMYOnn36aHj160KVLl6BG7nvuuYc+ffpw8sknB2JT+BkxYgRbt24NTB2Fa/PRRx9l8uTJdOvWLRB/26gcPh8UhwlLXVjo5McqPK5Rt4mZu28RuRQ4V1Wvca+vBPqo6tggZacDC9WdYiqXdyLwLNBFVfeXz/dj7r4Tg7lz5/LKK6/w/PPP11ib9j2GxjtD16sXFBSEzg/2U/cuxpg6MEiULKPOk7TuvkWkNfA8MDKYchCRbCAbsAA0CcANN9zAq6++yqJFi+ItSr2kvHKIBlMKRjhiqSA2AG0910e6aVEhIi2AfwN3qOpHwcqo6lRgKjgjiKqLatQE3lCjRi3h8xr57Sdg1CyxtEEsATqKSAcRaQIMBeZHU9Et/xLwXLBpJ8MwDCP2xExBqOpeYCzwOvAlMEdVV4jI3SIyCEBEeovIemAIMEVEVrjVLwNOA0aJyDL36BkrWQ3DMIyKVGqKSUQOBtq6K48ioqqLgEXl0u7ynC/BmXoqX++fwD8rI5thGJUnmYzUublwyy3O+bhxzrURWyIqCBHJAwa5ZQuAH0XkfVUdF2PZDMOIMU8tfSpwnugKwqh9opliOkhVtwGX4NgE+gBnxlasukFhYSFdu3YtkxbKbfa6devo378/xx9/PF26dOHRRx8N5N155510796dnj17cvbZZ1NU5ASnz8vL46CDDgrskbj77rtj+4GqyH333RdvEYy6RN9cHmrh+EPLeT0n3tLUaaJREI3c5aaXAQtjLE+9pVGjRuTm5rJy5Uo++ugjJk+ezMqVKwG49dZbWb58OcuWLWPAgAFlFMGpp57KsmXLWLZsWWCzXFXYu3dvtT9DKExBJAfBnFZWxiFlrMnJcfZyeN+vPvjQ2QRoGwFjQzQK4m4cQ/MaVV0iIkcBX8dWrPpH69at6dWrFwCpqal07tw5sLvY79obYMeOHZX2X5SSksLNN99Mly5dOOOMMwIuw7OysrjpppvIzMzk0Ucf5c033+SEE06gW7duXHXVVezevRuA9PR0br/99oDTwaVLl3LOOedw9NFHB+Jb5OXlcdppp3HBBRdw7LHHct1117F//37Gjx8fcFg4YsSIaveTUTkGDCg9gpHSJLGDNHX+Y3bgCMZHH8LEic5h1DwRFYSq/ktVu6vqH9zrb1V1cOxFq3l8PmdnqUjwN46cnNL8YAaw7OzS/KkxnK4tLCzk008/pU+fPoG0O+64g7Zt2zJjxowyI4gPP/ww4DLD71W1PDt27CAzM5MVK1bQr18/Jnp+TSUlJeTn53P99dczatQoXnjhBT7//HP27t3Lk08+GSjXrl07li1bxqmnnsqoUaOYO3cuH330ERMmTAiU+eSTT3jsscdYuXIl33zzDS+++CIPPPBAwGHhjBkzarKbjChYsKD0CIavny+hlcRXBz4VOPzknJSDTlB0gsJ/zFIdS6IxUh8GXIvjWC9QXlWvip1YdYNQb/rhRgDFxcUMHjyYRx55pMzI4d577+Xee+/l/vvv5/HHH2fixIn06tWLtWvXkpKSwqJFi7jooov4+uuKg7sGDRpw+eWXA3DFFVdwySWXBPL86atWraJDhw506tQJgJEjRzJ58mRuuukmgIBL8G7dulFcXExqaiqpqak0bdo0EOXuxBNP5KijjgJg2LBhvPfee1x66aXRdJURJ3JOyiHnpOSdx/e8nxgxIJoppleAg4D/4uxs9h9GBFq1asXWrVvLpG3ZsoVDDz2UdevWBYzL/mmaPXv2MHjwYEaMGFHmIe5lxIgRzJs3D3CmnvxxnM8//3z27NnDpk2bIsrlVVAHHnhgVJ+ladOmgKNs/Of+a7/9orziM1feRnUZcdCUwBGULF/pYdQ8ody8aqnr7WWRyiTCkajuvjMyMvTNN99UVdXNmzdrx44ddc2aNRXK7d+/X6+88kq98cYbK+StXr06cP73v/9dBw8erKqqGzdu1P3796uq6scff6xt27YNXHsBdNasWaqqes899+jYsWNVVbVfv366ZMkSVVXdtWuXtm3bNuBGfOTIkfrII4+oqmr79u31p59+UlXVadOm6fXXXx9o25/39ttva7NmzfTbb7/Vffv26dlnn61z585VVdWWLVtqSUlJtF1WgUT4HhOVw4/eEDhiwYCZAwJHrJkxw3FbDqrDhkVXx9yVVx/CuPuOZqPcQhE5X51Nb0Ylee6557j++usZN87ZNjJhwoQyYUP9vP/++zz//PN069aNnj17As7qn/PPP5/x48ezatUqGjRoQPv27QMjjrlz5/Lkk0/SqFEjmjdvzuzZs4O+tR944IF88skn/OUvf+Hwww8PRJ/z0qxZM6ZNm8aQIUPYu3cvvXv35rrrrqvUZ+3duzdjx45lzZo19O/fn4svvhhwwph2796dXr16mR2ihvnxm7Sw+V5bW1VW+ixcXcsLF7vNhMEjmAUwbxgzB8+s3fsbZYjo7ltEtgMHAiXAHjdZVbVF6Fq1j7n7Dk1KSgrF4YIG1AB5eXlMmjSJhQtr/oFi32NoIrnzjpQfsf2JpQ3ohNg6A5w5E0Y84CgIgGFdIysI7zJcnzvNlPN6Dg999BAAk86alNQ2ltqgWu6+VTW15kUyDMMoy/DhQDcY8WL0dbYv8JVeZNWwQEZ0vphc53qnuZd5qmob5pKIWI8ewNlTkZWVFfP7GOVILfJchJ9uSkR+c/PAwPkPDy9geLfhDO82POr6Dz1Ueh5safott4Dvc2d6LccGEpUmmmWuDwC9Af/k8Y0icrKq3h5TyQzDIPeDXHyLfRSXhFDyZR56yRcP4seW1XvXTElxQqqO9cSpzD0nl9xzcsnOhqc+hGJMQVSVaEYQ5wM91Y3oJiLPAp8CpiAMI8aEVQ5edifuZrdY4nezkZ4eusztt4fPN0ITrbvvlsAW9/yg2IhiGEZ5olYOCeQzqTL8+aioYoiFJCcn9Mhg6lRgYDabgE1ANuattrJEoyDuAz4VkbcBwbFFjI+pVIZhAJFXDiX7XsR7rhwYuVA1MHfm1SPsTmoRaQDsB34LvAjMA/qqasWF9EZQGjZsSM+ePenatStDhgxh586dUdddtmwZffv2pUuXLnTv3r3M/oWrr76aHj160L17dy699NKAIXrt2rWcccYZdO/enaysLNavX1/jn6m6/PzzzzzxxBPxFsMwjAiEVRCu3eGPqrpRVee7x/9qSbY6gd9R3RdffEGTJk0Cm9yi4YADDuC5555jxYoVvPbaa9x0000Bv0cPP/wwn332GcuXL6ddu3Y8/vjjANxyyy387ne/Y/ny5dx1113cfnvVTUX79u2rct1wmIIwaospA6YEDqPyROOL6b8icouItBWRQ/xHzCWrg5x66qmsWbOGvLw8Bnj8L48dO5bp06dXKN+pUyc6duwIQFpaGocffnjAVbffkZ+qsmvXrsAO6pUrV3L66acD0L9/f1555ZUK7RYWFnLccccxYsQIOnfuzKWXXhoY2aSnp3PbbbfRq1cv/vWvfzFr1iy6detG165due222wJtpKSkcOutt9KlSxfOPPNMPvnkE7KysjjqqKOYP9+ZV54+fToXXnghWVlZdOzYMeBFdvz48XzzzTf07NmTW2+9tVp9Wt/Jzy89YkGyP2DzcrMDh1EFQvng8B/Ad0GObyPVq+0jGl9ME96eEPDbMuHtCRXKj3ttXCB/0vuTKuRfO//aQP6U/CkV8oNx4IEHqqrqnj17dNCgQfrEE0/o22+/rRdccEGgzPXXX6/Tpk0L287HH3+sxx13nO7bty+QNmrUKD388MM1KytLd+zYoaqqw4YNC/hQmjdvngK6adOmMm199913Cuh7772nqqqjR4/Wv/3tb6rq+Fb661//qqqqGzZs0LZt2+qPP/6oe/bs0f79++tLL72kqo5/p0WLFqmq6kUXXaRnnXWWlpSU6LJly7RHjx6q6vhtOuKII3TTpk26c+dO7dKliy5ZskS/++477dKlS1T9p1q/fTHlb8gPHFXh2mtLj0TkoBv7BY5Y4PfthLlqCglhfDFFM4LorKodvAdwfDTKR0TOFZFVIrJGRCoYtkXkNBFZKiJ7ReTScnkjReRr9xgZzf0SEX+wnMzMTNq1a8fVV19d6TY2btzIlVdeybRp02jQoPQrmzZtGkVFRXTu3Dlgn5g0aRKLFy/mhBNOYPHixbRp04aGDRtWaLNt27acfPLJgOMC/L333gvk+V2AL1myhKysLA477DAaNWrEiBEjeOeddwBo0qQJ5557LuC4AO/Xrx+NGzemW7duFBYWBto666yzaNWqFc2bN+eSSy4pcx8jMplPZQaOqjB1aumRiPxy8OLAYSQe0axi+gDoFUVaGUSkITAZOAtYDywRkfmqutJT7HtgFHBLubqHABOATJzdPwVu3bK+s5MAvw3CS6NGjdi/f3/g+tdffwXg448/ZsyYMQDcfffdDBo0iG3btnHBBRdw77338tvf/rZC+w0bNmTo0KE8+OCDjB49mrS0NF580fFVUFxczLx582jZsmWFeuFcc0fjArxx48aBOl4X4F7335HuYxixxnxDVo+QCkJEjgDaAM1F5AScJa4ALYADomj7RJwwpd+67c0GLgQCCkJVC928/eXqngO8oapb3Pw3gHPBcfJYVXxZvoBDr2D4d2CGYurAqTWyVK59+/asXLmS3bt3s2vXLt58801OOeUU+vTpU0aZlJSUcPHFF/O73/2uTOAdVeWbb77hmGOOQVWZP38+xx13HACbNm3ikEMOoUGDBtx///1cdVXwuE7ff/89H374IX379mXmzJmccsopFcqceOKJ/L//9//YtGkTBx98MLNmzeKGG26o1Gd944032LJlC82bN+fll1/mmWeeITU1le3bt1eqHaNu8nCPt2Pa/sKmpW47hmOeYStLuBHEOThv90cCHo8nbAf+FEXbbYB1nuv1QJ8QZaOp2ybKuglP27Ztueyyy+jatSsdOnTghBNOCFpuzpw5vPPOO2zevDlgxJ4+fTrdu3dn5MiRbNu2DVWlR48egfCgeXl53H777YgIp512GpMnTw7a9rHHHsvkyZO56qqrOP744/n9739foUzr1q154IEH6N+/P6rKBRdcwIUXXlipz3riiScyePBg1q9fzxVXXEFmpjNVcvLJJ9O1a1fOO+88/va3v1WqTaOU6nprjUTG1IzAeUF2QY23f9NFWTXeppdZX5S+U5rr8MoTjbvvwao6r9INOzaFc1X1Gvf6SqCPqo4NUnY6sFBV57rXtwDNVPUv7vWdwC5VnVSuXjaQDdCuXbuMtWvXlmnX3EQHp7CwkAEDBvDFF1/E9D7Tp08nPz8/sAS3qtTn7zGSu+1ICiLbs3inKnaI2nT3HQuSXf7aoFruvoGuItKlfKKq3h2h3gagref6SDctGjZQ1nnvkUBeEBmmgrN/PjMz0759wyjHU6UbiRPGUF1QAO5Akl69nOtYMeMSM0JUh2gUhNcZTDNgAPBlFPWWAB1FpAPOA38oEK0f39eB+0TkYPf6bMw5YI2Rnp4e89EDwKhRoxg1alTM72MYoZj1J48NYkEcBUlSIi5zVdVcz3Evzpv9UVHU2wuMxXnYfwnMUdUVInK3G18CEektIuuBIcAUEVnh1t0C3IOjZJYAd/sN1oZhVA2RikdlwpAGq78gwkP3sEG5NPhzKk1yOlXMbF3A0kFCqwdblbF11CQLF5YeRuWJ1purlwNwpnwiok4c60Xl0u7ynC8J1ZaqPgM8UwX5yrdjSyuTmEg2MiM8/ngJ8WJTVx80LmZPg8JAWkaGYy8pKILMp2DLri2U7CuJm4xGaKIJGPQ5pZFIGgKHAZHsDwlBs2bN2Lx5M61atTIlkYSoKps3b6ZZs2bxFiWhKCqCNlGu6fPHS4ibkmjq3rjhnpBFUpqk4Ovni8nt51fPm3i9J5oRxADP+V7gB3f6KOE58sgjWb9+fcB/kZF8NGvWjCOPjGrAWif5zQGtoyqXEiJeULh4CZWlKoO51y5YFTIvIy0j5iuLphaXuhMfiBkhKktEBaGqa0XkFKCjqk4TkUNFJFVVv6sF+apF48aN6dChQ7zFMIwq88MfPTGnQ/g1TEmpnC2hNjknM4jtoRZZuNqMD9Uhmikmv8uLY4FpQBPgn8DJsRXNMIxgpKXV3Ka4gZ54PZEMzqFk8VNUFLpcopE1PYvFax3/T2+PfJus9Kz4CpSgRDPFdDFwArAUQFWLRCQ1plIZhlErVHd1z8aNNSNHrJg/1IwQ1SEaBVGiqioiCiAikT25GYZRJ0j2B2zuGI8NIi9+ciQr0SiIOSIyBWgpItcCVwFPRahjGEZN0Mk77xPb+M3BFvpNmTKwjLuOytLwj6VzUPserP05qMUhvIjnjcqrVTmSlWiM1JNE5CxgG44d4i5VfSPmkhmGAcMHeS5qfsVPrPdJ7D8wweegjLBEtVHOVQimFAyjjhH3fRIx5u0g3sQzMmDpUuc8P9+5NoITzSqmS4C/AofjxIQQnPh9LWIsm2EYMaYm90kEo2B4tP45Y0NWVvj8K97N4ADXWWAs3JknO9GMIB4EBqpqNA76DMOoQ6TlltoQinIqb0Po1TEtcqE48tUvS+GXeEuRuEQTk/oHUw6GUT/ZWLwxcNQVCgqcfSTm5isy0Ywg8kXkBeBlYLc/UVVfjJVQhmEYtUH+tfnxFiGhiUZBtAB24sRk8KOAKQjDMJKaYVmlFurVq+MoSIISzTLX0bUhiGEYyUd+hBdwuaPU6YLeuz3G0lSer7+OtwSJTTQ2CMMwYkTuB7mk3p+KTBSmFiRITNBKkJFRegSlSXHpYSQdVQkYZBhGDeFb7KO4JPTD85Dmh7Bl1xZSmoTw521Ui1WhvZEbmIIwjLgSTjkAHN4knV9LSrjmaF/tCFQNvK46/CuENozdFh9homTA66XuyFd3MiNEeUIqCBEZF66iqj5U8+IYRv1lTGY2YyqkOpu3HgEevqx25akJ0loltuPnr7eYESIc4UYQ/m/2WKA34HfrOBD4JJZCGYZRllAR4+KNRfKt24RUEKo6EUBE3gF6qep299oH/DuaxkXkXOBRnFjW/1DVB8rlNwWeAzKAzcDlqlooIo2BfwC9XBmfU9X7K/fRDKNukMgR47wk48azVWPNCBGOaGwQvwFKPNclblpYRKQhMBk4C1gPLBGR+aq60lPsamCrqh4jIkNxfD5dDgwBmqpqNxE5AFgpIrNUtTCaD2UYyUgiPmDr+kayrG6lNohkiohXW0SjIJ4DPhGRl9zri4Bno6h3IrBGVb8FEJHZwIWAV0FcCPjc87nA4yIiOBvxDhSRRkBzHKWU2NYuw6iDZKRVz9WpTCydg9IJiacBEz0iXryJuA9CVe8FRgNb3WO0qt4XRdttgHWe6/VuWtAyqroXx21WKxxlsQPYCHwPTFLVLVHc0zAMw6ghol3megCwTVWnichhItJBVb+LoVwnAvuANOBg4F0R+a9/NOJHRLKBbIB27drFUBzDMMJyeyoy0Vmyu238NlKbJvbqJT8b4uuNPOGJJh7EBCATZzXTNKAx8E/g5AhVNwBtPddHumnByqx3p5MOwjFWDwdeU9U9wI8i8r4rQxkFoapTgakAmZmZiTd+NYwI9G4xIN4ixJREnFbykjmreu7M6zrRjCAuBk4AlgKoapGIRPN6sAToKCIdcBTBUJwHv5f5wEjgQ+BS4C1VVRH5HjgdeF5EDgR+i7MU3DDqFEvGeWJO3xw/OUKR6DaE6lKX3JjHgmgURIn70FYA94EdEVXdKyJjgddxlrk+o6orRORuIF9V5wNP4yiBNcAWHCUCzuqnaSKyAieC3TRVXV6pT2YYRu1x//aEXIVlVI9oFMQcEZkCtBSRa4GrcPYoRERVFwGLyqXd5Tn/FWdJa/l6xcHSDcMwapIN48wIEY5o3H1PEpGzcJaZHgvcpapvxFwywzASn1TvvH1ihxcNxrFppTJvTzxv5HEnGiP1X1X1NuCNIGmGYVSHLJ/nwheiUAKT4125nnxzTMXmhTws0UwxnQWUVwbnBUkzDKOyZE30XPjiJYVhBCWcN9ffA38AjhYRr4E4Ffgg1oIZhpH4NNjROt4iVItt5p8hLOFGEDOBV4H7gfGe9O22q9kwDIB9Dyb33oG0x0tX7G+/3YwQ5QnnzfUX4BcR2auqa715IvK8ql4Zc+kMw0hovA7u0pLPRh0xYFN9JxobRBfvhbvjuXoevAzDSCoaN2hc5rpoexFtHnIN1LtTIM+HfpATB8mMWBLOBnE78CeguYj4Z+oEx7Nq8kVXNwyj0qQ0SaG4pJi8UXmhCzUtdldjJZ+C2DbejBDhCOnNVVXvV9VU4G+q2sI9UlW1lareXosyGoYRJ3z9fKQ0SSG9ZXrwAlvaO8eOVrUqV03Rollq4DAqIhpif7yIHKeqX4lIr2D5qro0ppJVkszMTM3Pr9vBTYy6R7L7Okp6+b0hU33J/VmqiogUqGpmsLxwNohxOK60c4PkKY4zPcMwDKOOEm4VU7b7t3/tiWMYRlKxNT3eElQL7wSKTKyYv3136dLXZIlxUZNE42qjIXABkO4tr6oPxU4sw6gfnNL82niLEDUZGbDUnVjOz3euObgwniLVKMGmlVo80CJsfl0nmmWuC4Bfgc+B/bEVxzDqF+/d5lkQ+Mf4yWEYwYhGQRypqt1jLolhGEaCkdIkJd4ixJVoFMSrInK2qv4n5tIYhpGwFBTEW4Kax+viOzWIiaG+u9+IRkF8BLwkIg2APTib5VRVW4SvZhhGspOXV3qelRWkwBTP0vIJMRYmBrTwPMUsIl5FQm6U8/AQ0Bc4wLNZzpSDYQC5uc6bp0jFw+erWH7gwLJlGJhdeiQg/f+US///pNJ/sZBXmFexwJjM0iOJad++7HVRUel3lJrqfM/1kWgUxDrgCw21o84w6jE+XzWDzmQ8VXokIlk+x5VGCA5pfgiQvHP1KSnO4R0plSc1FabWU+dC0SiIb4E8EbldRMb5j1gLZhjJQF2PSHbQzgya7mofMj+9ZTopTVK45mhf0FFUor+B+3yOfOnpwfPbHFfES/8t4u385HZrXlVCutoIFBAJOrOoqkG2lVSoey7wKNAQ+IeqPlAuvynwHI532M3A5apa6OZ1B6YALXCW1/ZW1V9D3ctcbRjxwOuqoSpj7GR3VVFQ5Fiuv/wSrjwztJPnlJTkjPmc7N9PNFTV1QZQqghE5ABV3VmJmzYEJuOELF0PLBGR+aq60lPsamCrqh4jIkOBvwKXuy7F/wlcqaqfiUgrHAO5YRgJROZT3udK6AdoXR9p1VUiTjGJSF8RWQl85V73EJEnomj7RGCNqn6rqiXAbODCcmUuBJ51z+cCZ4iIAGcDy1X1MwBV3ayq+6L6RIZhxAXVikey0zqldeCoj0SzzPUR4BxgPoD7Rn9aFPXa4Bi4/awH+oQqo6p7ReQXoBXQCVAReR04DJitqg9GcU/DqFWuTR5PGUYVyBtYP20PfqJREKjqOinjF5dYv803Ak4BegM7gTfdebI3vYVEJBvH4yzt2rWLsUiGUZG6vrrlzucXBM7vuXJgpeu3TvIX72OPLT2vCyOiyhKNglgnIifhvNE3Bm4Evoyi3gagref6SDctWJn1rt3hIBxj9XrgHVXdBCAii4BeQBkFoapTcaPbZWZm1sOvzzBiy1++HRQ4vyeMjSEURfX7BTzpiWaZ63XA9TjTQRuAnu51JJYAHUWkg4g0AYbiTlN5mA+MdM8vBd5y91u8DnQTkQNcxdEPWIlhGEYt0rFj6VEfiWYV0yZgRGUbdm0KY3Ee9g2BZ1R1hYjcDeSr6nzgaeB5EVkDbMFRIqjqVhF5CEfJKLBIVf9dWRkMI97kfpCLb7GP4hJnGc+ATgNYMGxBhFqJw+E/D4i3CHFl4YerPVed4iZHvIjKBlFVVHURsKhc2l2e81+BISHq/hNnqathJCzZHg8ZwewRXuUQjAn9EtuB0Q8PJ48yiwXHPl5qhKir+yDCEVMFYRh1nac8HjKCKYhwygHAl+WrWYESjAUe/TKw8jZuI86YgjCMWqI+voEOKrVxJ+UqoI6H1FPjg0s0IUebAoOpGHL07tiJZRhGMlDXN5DNOml15EJ1mGhGEK8AvwAFwO7YimMYRm2Sm+s4rMvNLWtPARg+HGZ9MZPLLoNDD4XJ1w2vUL8op26vY830eBJJxhFQdYk25Oi5MZfEMIxax++uvLAwRIHBI5izD/gBJlNRQRh1m2j2QXwgIt1iLolhGLWO34ne/ffHV45EpVev0qM+Es0I4hRglIh8hzPF5A852j2mkhmGEXPC+ZKaOROYN6zWZElEpi7wBuIO7c68rhKNgjgv5lIYRh1lyoAp8RYhLN6ludkLsnlqqbNud8qAKWRnZDNz8Myw9ResKl3HOvDYureO1evOvD6uQotmJ/VaEekBnOomvet3w20YyU75nc5e5g+dz+qFAwPz9AMGQEFWGhuLN5YW8gG7UyDPB+RUaCM7IzFjTdcUg2aXrmOtjw/Quk408SBuBGYAh7vHP0XkhlgLZhi1QaSdzlHFnG5aDP19NSmWkSD0at0rcNRHopliuhroo6o7AETkr8CHwGOxFMwwaoNIO52jjoTWJPlDpk0dOJWpA+u4//JKktupIHKhOkw0CkIoG/9hn5tmGHWKSFMkjtuIsuv+vTGLk5GB9+cGzhfcXnGKrLok++qf/v1Lz20fRHCmAR+LyEvu9UU4XlgNw4hAxtTSlS8F2Yn3Nrqw5BbPVc0riILE+8hGJYjGSP2QiOThLHcFGK2qn8ZUKsOoIyzduDTeIhjVoF+/eEsQX6INOboUsP90o85RX42PfjJ2j4u3CAmNb3qe5yorTlLED/PmatRrqjvtk39tfg1JEh/y78uNXKge0//ZUiNEfVzGawrCMKpBRlr9211bGcpsxKvbW0LqJKYgDMOIGWPGlJ4no4Lo175+GyGiiQdxCfBXnE1yQqkvphYxls0w4s6UxPaUEXfqug0np1VevEWIK9GMIB4EBqrql7EWxjBqm6kFpXMgwdxiJONbb2XI8vkC53me82hJxKW7NUmyR8SrLtEoiB9MORh1lTELS+dAquI3ybtRLhmNmItloufKFy8xEp9OC5CJjrYY0GkAC4YtiFChbhBNPIh8EXlBRIaJyCX+I5rGReRcEVklImtEZHyQ/KZu22tE5GMRSS+X305EikXklvJ1DcMwYs2AAc7Ru3e8JYkP0YwgWgA7gbM9aQq8GK6SiDQEJgNnAeuBJSIyX1VXeopdDWxV1WNEZCiOreNyT/5DwKtRyGgYRhXopxPiLUJCs8AdKCxYBYNmV8yf+XmpO/Th3epexL1odlKPrmLbJwJrVPVbABGZDVwIeBXEhZSOa+cCj4uIqKqKyEXAd8COKt7fMKpNhmcVa1XcRswfOr/mhKkmublwizsWHzfOua6K3cFLJBtOXWHgsQODTiGOeHFE4LxeKggRORLHc+vJbtK7wI2quj5C1TbAOs/1eqBPqDKquldEfgFaicivwG04o4+Q00sikg1kA7Rr1y7SRzGMsEgM/O7VxSA6XqprwzESm2id9c0EhrjXV7hpZ8VKKJxRxcOqWixhfrWqOhWYCpCZmZl8FkIjaUhJibcERiIyrGvdDskajYI4TFWnea6ni8hNUdTbALT1XB/ppgUrs15EGgEHAZtxRhqXisiDQEtgv4j8qqqPR3Ffw6hRUlKcwEHJTk6OcxjRM9MTcXV4kBmkSCFZk51oVjFtFpErRKShe1yB8xCPxBKgo4h0EJEmwFCg/ITsfGCke34p8JY6nKqq6aqaDjwC3GfKwYg1qsGP7duT98H623G5NJmQSuc/Vpz+yf0gF5koDJ4zmJzXY/MB/auABgyISfMxZ8SI0qM8U6c605KpqY49py4SzQjiKhwbxMM4q5c+ACIarl2bwljgdaAh8IyqrhCRu4F8VZ2PE1fieRFZA2zBUSKGUWdIy00LnBflFIUpGRs+buaDBsV89b/CkGVe/PJFUpqkkHtOzT/lFiT5doGUFCeqYN++ocsUFzsjzGR9iQhHNKuY1gKDIpULUXcRsKhc2l2e818ptW2EasNXlXsbRiKwsXhjfAVo6oZCPfqNkEVSmqTg6+er9q38mwbr0kYyn8850tNDlznrrPD5yUxIBSEif1TVB0XkMZyRQxlU9f/FVDLDqAUGdErSuY8oGXFQaGdSOSflkHNS9V57U5qkRIzrncyEs9tkZwMZ3hjedW8VV7gRhN+9RnI7vDeMMBTkeN50q7AgZcO48usuEot/3hTbh5avnw/fYl9IJeHL85WeZ/mClklm6voyX9EIHqhEZIiq/itSWrzJzMzU/HzTZUbl8K6ijoUztmT31VRd6vrnrwufT0QKVDUzWF40RurbgfLKIFiaYRhGveLaXtfGW4SYEs4GcR5wPtBGRP7uyWoB7I21YIZhGInOsaunRi6UxIQbQRTh2B8GAV4vNNuBm2MplGHUGmXmxX0hCoWmaHvp0tW01LQwJeNDek7p7q7C3Lq9qSse3OJxBFSvlrmq6mfAZyLyErBDVfdBwEtr01qSzzBiS1b14iG0eahN4DwR56DXtpjluTIFYVSOaGwQ/wHOBPzLFJq7aSfFSijDMKJnwYLSyGcDBiT/5rRkYty4eEsQW6JREM1UNbCGzXWgd0AMZTIMo4b4w29mxFuEOk3aYO/u87o3xxSNgtghIr1UdSmAiGQAu2IrlmHUDfKvje/S68nX1b0YBYnELW+UGiGqu+kwEYlGQdwE/EtEigABjqBs1DfDMCi7Jh4cm0RGWkaI0jXHwIGlezgWrEqe2MnZC7J5aulTAVcfdfEBm+xE44tpiYgcBxzrJq1S1T2xFcswkoO67mqi2uxOcfxBfVMxfMy77wIHQnFJMXe8kZwKYtxv67YRIhp33+Aoh+OBXsAwEfld7EQyjOTB189HShOLJhSKJh/6YHcKbQ5MD1tuN8mpZFM/zA0cdZFoXG1MALJwFMQi4DzgPVW9NObSVQJztWFUhWR3ldDypqzA+c+P5MVNjlDk5jreUIcNc+IneMnOhqfaJHf/x9pVS20QztVGNCOIS4EzgP+p6migB07kN8Mw4swvBy8OHIlITo4TcKm8coDgaUZiEY2Repeq7heRvSLSAviRsqFEDcMw6iUTJsRbgtgSjYLIF5GWwFM4LjeKgQ9jKZRh1BbJ7mzt4R5vx1uE+k01XbUkOmFtECIiwJGqus69TgdaqOry2hEveswGYVSFujCHnMwkuw0o2eWHarj7VlUVkUVAN/e6sObFMwzDMBKRaKaYlopIb1VdEnNpDMOISEEBZLrve716OddGfJjQr24bIaJREH2AK0SkENiBs5taVbV7pIoici7wKNAQ+IeqPlAuvynwHJABbAYuV9VCETkLeABoApQAt6rqW1F/KsOoI+R+kMvML2ZSkF1OC7QugDGZfLGzPYU/55HeMj0u8lWbPM8D1j0dOGsgC1cvDCQn8k7r7Qt8pRdZ8ZIidoQLGNROVb8HzqlKw65b8MnAWcB6YImIzFfVlZ5iVwNbVfUYERkK/BXHjccmYKCqFolIV+B1oA2GUdMM9MYRTrx1l7cu8tFoV2s6nFDId5+mV8jf12g7g+cMJrVJKnmj8mpdvuoyoZ8vYpnikmJ8ixNTQTz0kOfi7Bwe+shJmHTWpISUt7KEG0G8DPRS1bUiMk9VB1ey7ROBNar6LYCIzAYuBLwK4kJKTf9zgcdFRFT1U0+ZFUBzEWmqqrsrKYNhhCfjKc9F4ikIbVzMngaFFGZlAYUAZGRAfj5kPgX7mmxh9eYSfFE8aBMRny+6cubOJD6EUxBez2NHVaHtNsA6z/V6nOmqoGVUda+I/AK0whlB+BkMLA2mHEQkG8gGaNeuXRVENIwkoOEeaLm2TFJGWkbSrpqJhNfBYHkHiInGpEml50WhiyUt4RSEhjivNUSkC86009nB8lV1Ku5rX2ZmZt38tRj1mtcuWBVvEYwwlA0zmkvuOWV9MmUvKJ3CnDow8UaokQinIHqIyDackURz9xxKjdQtIrS9gbI7ro9004KVWS8ijXBceGwGEJEjgZeA36nqN9F8GMMoj98XUHExbNgAaZ6w0UVJ8Mp3TmaneIsQcwoLISsL1q4NkumrXVlqmqeWlk5hJqOCCOmLSVUbqmoLVU1V1Ubuuf86knIAWAJ0FJEOItIEGArML1dmPjDSPb8UeMvde9ES+DcwXlXfr/SnMgwXv3IwEpPhw+GGGyA1Nd6SVI/sbGfTpUjd8jEVzTLXKuHaFMbirEBqCDyjqitE5G4gX1XnA08Dz4vIGmALjhIBGAscA9wlIne5aWer6o+xkteom5hySGwyMuq2Ep8yYEq8RagWMVMQAKq6CMdFuDftLs/5r8CQIPX+AvwllrIZ9Q/v9FKw63iQ+0EuuR/mUpRTdr6raHsRbR5qQ/uD2pM3Kon3OUQgJ6f8PH5ZJNPzgE3CPWl5uaU2iOyZcRSkikSMB5EsmC8mIxiRfC3F25dOs4mp7C6BA6Z/zo4N6YF0v4IAYHtrGu5LZe/D9c9g7Z2uyc4OXS5RSQZfX1X2xWTUT/yxgv3Ecidr7ge5+Bb7Kqxz3zBuA2mpNfCK3zfX8bjZtJiBsxIvRvNuiqEJ7LwsC/8+hwo02U7DD5N/01VV8CqFjKkZLN24tEKZRN5pnexEG3LUqMf4d7LGgmDKoSbwGwz9yiEUI3s4ayTiHja03D6HtNQ0Nlyr4FNSHtvOfQPs4ReKWP5/VpcZM0qPZMRGEPWcaNdpx2ona6R2i7aXzs0HG1F4HdVlZARpIIxyAEhvmR54A40HG8ZuC5mXlpa40xKJRqLutF7YdHjgfDjJZ4QwG0Q9J9IcfKzn6Ctzf3zh7+/9Vw7M/fqS31+/EZp425AikejyQfVjUhtGwpNSboZI1d6+6wIZGaWHUfvYFJOR9KSkRO/0LVFIhtUticDSijbppGLGJUlqfHAxBWEkDcn4IL3v7Vzu/9DHqjFFpLUqt13YJ/BzksdziDPzh5Z3zpBYzPqTxwaRWAvoosIUhGHEkDve8EHTYtocX4j+0K1igZZr6fBoB1KapLD99u21Ll+yM/DYgfEWISwLF0Yuk8iYgjDCEuuQiolquKsx/KuoRmXh+qEEnNGQTHTO47mKyjDCYQrCCIsvyxdvEZKChn9MY/+BGwEoGL6BXh3dJbklrvW8UUmFOnVeOdYwXrvN/PkwMLEHD4AjZzJjCsKo08TbWZrea9NG1SElJbkd+U0tHkjhz4V88eMXzO80v8KUWNb0LAo2FiTsTnBTEAlOKFcUAK1TWldw8hYrygeSD8eUAVPIznA24HnjMYTCvwopnNO2quKXw0hOfL7w/z9puaWbJ2vrt1AZ8grzwm7iK/y5MKFjbts+iAQnVq4oaouIrpxbF1CcWsCdTxaEKZT47HuwCJ2g6AQtnV4yqk1ODmzfXrqvxX/4p5c2Fm8MHImIr58vrBuXtb84LlYS9TduCiLBqe4/Tm6uE4zF75uo/EHeBDoWTahgjPb5SsukpsI3lYjpt8CznK/h7zNodmMGZIfY6TQmE8ZksuvKoBs5E5YmOZ2QiYJMFF7PXx1vcYwEJeekHD4ftp3205RBxw0kL69s/tsj36bHp29zXbO34yJfJGyKKYmoilEz4ht8no9jU8CXFbpIcTGs++sCNMh0empqxfYzPLrmlwOXwoHAwaHcbYeRDcq614hDPIB/vlk6srniDNvOa1SOgQOdEVAo0smi4Tr455vw5G21J1e0mIJIchasKn1dD7YmvKYMfKHaqWkDYkZGze6ezZha+lAvyK44jTXw/lwWFvugSTEP93ibmy7KKpN/5XulI5srzrBVR0blyMoK/5KWleUokEQ1xJuCSHIGzR4UOI80wqjMTmS/cdC7tDASsdzpXN7XUrQEix/gxa8cQiG7DkGbbyldrupSkmvTSkZkIkXMW7s2dF4iYAoiQSgogMxg0/Ct82l+AIyp4mKcKckdEheoIV9LG3oHPGv++aj53HOlM9o6aEcGxbsL2Zca/JfabHc6uxqWMCClugIYscb7MpOfnxwO/hLdAbUpiERnYwa7gH/cAw//sfLVI4VpHDirdFoqEaKtFZSbBSoo8iaE/8UPHw6zZjnnM2Y415H4+ZG8sPk7H07u1VVGYpNdkEHhz4Vs2bWF/Nb5ZKSV/R/v9FgnNhZvjNs+iZiuYhKRc0VklYisEZHxQfKbisgLbv7HIpLuybvdTV8lIufEUs5kIFZzlAtXLwwckYhHTODMpzIDB0DnP2YHVg9d8UiQAEeDh4NPGPG1MPPz5AvQYtQvVm9ezZZdWwC4+JLSlYOr3RlM7z6JeBAzBSEiDYHJwHnA8cAwETm+XLGrga2qegzwMPBXt+7xwFCgC3Au8ITbXp0lI6PiWu9E8F4aae7/rLMqlht4f27gIU7haRXqZPl8pfnf9auQn/mnnLD1q0SbJYF9Cv7pJaNu4f3d+KeXyizpTkAi7ZPYs38PEL99ErGcYjoRWKOq3wKIyGzgQmClp8yFgM89nws8LiLips9W1d3AdyKyxm3vw1A3K/h+RdnoYwBb0+HgQuf8xemwfGTZ/FuOgJQfnPPth0Pqj2Xzd6eUOlv7x7uw/pSy+b4w/3W7D4SmO0rbeXwVbPdsoDp8GfzhBOe8+DelcvjZ0h4OWev0zuZjaD//6zLZ4yYV8PAO12jx03Fw2Feln7+oF/wnF0b1ByB1cz8mjRzOmIVjShtY3xuOXOKcb+gNbZaUaf/6/5vJEz+MgFtAvhiGzi37Nn7FI1OZ8csYpGt7mn2eh++W9NB94bJg1YJqed+UiQI/neIsmwVmfPY8b+X6AjtoZ84E5sGsL5z8ES+OqPK9jOQm8FvwedNCFM7dwIav0khzf55F24to81Cb6G/m0zIvcwVFBYERb0S2t6b1zO1sL4JOntARxz7WCQ51f/O/pMGLzyM+YFwatPBsCvxfVxpM/Zz9+53LbdvgsEebsXvf7jLylZ6Xe2Z9MSSseLFUEG2AdZ7r9UCfUGVUda+I/AK0ctM/Kle3wjcmItmAM/FxWLOakjvxaLCXTdemAlXz67NjJ0yaBBwXuky4t5h27aAwxGjm4P2dOPzK+5i1OZUccivkN5CG7I9CxlD3byiN2OdNSH+v3HnrMuVnDp7JsK7DyqzuCte+kdzkX5vgVt5K4J9WWrBqAYNmu8rhsa9gdyoUx2d3flLvpFbVqaqaGSqeal1iYn9flevu3wc9eoQrIVV2N73l4Df4qvEsWqeW/gMvuD0nMJ3z4DUXVHg45/l8gfzW3VZXcHedf19uIP+v15xf7Ye7udOuu2SkZQSOOknGVChJjVwuRojGaKJbRPoCPlU9x72+HUBV7/eUed0t86GINAL+BxwGjPeW9ZYLdb/MzEzNT/Q1Y4ZhGAmGiBSEesmO5QhiCdBRRDqISBMco3N57+jzAb9h4FLgLXU01nxgqLvKqQPQEfgkhrIahmEY5YiZDcK1KYwFXgcaAs+o6goRuRvIV9X5wNPA864ReguOEsEtNwfHoL0XuF5V9wW9kWEYhhETYjbFVNvYFJNhGEblidcUk2EYhpHEmIIwDMMwgmIKwjAMwwiKKQjDMAwjKHXGSC0i24FVMbzFQcAvMaoXqUyo/GDp0aR5rw8FNkWQrzpUpd+irWP9VrU6sey3SNex7LdY/kYjlatsXiL1W0dVPShojqrWiQNn6Wws258aq3qRyoTKD5YeTZr3OhH7Ldo61m+J129RXMes32L5G41UrrJ5ydJvNsUUPVUNlhBNvUhlQuUHS48mrTYDP1TlXtHWsX6rWp1Y9luy9Vll6oUrV9m8pOi3ujTFlK/1wCdTTWP9VjWs36qG9VvViFe/1aURRJDoMUYUWL9VDeu3qmH9VjXi0m91ZgRhGIZh1Cx1aQRhGIZh1CCmIAzDMIygmIIwDMMwglJnFYSIdBaR/xORuSLy+3jLk0yIyIEiki8iA+ItS7IgIlki8q77P5cVb3mSBRFpICL3ishjIjIycg1DRE51/8/+ISIfxPJeSaUgROQZEflRRL4ol36uiKwSkTUi4o9G96WqXgdcBpwcD3kThcr0m8ttwJzalTLxqGS/KVAMNMOJoV5vqWS/XQgcCeyhHvdbJZ9t77rPtoXAszEVLFa782K04+80oBfwhSetIfANcBTQBPgMON7NGwS8CgyPt+zJ0m/AWTiBm0YBA+ItexL1WwM3/zfAjHjLnkT9Nh4Y45aZG2/Zk6HPPPlzgNRYypVUIwhVfQcn8pyXE4E1qvqtqpYAs3HeSlDV+ap6HjCidiVNLCrZb1nAb4HhwLUiklT/IzVJZfpNVfe7+VuBprUoZsJRyf+39Th9BlBvo0ZW9tkmIu2AX1R1eyzlilnI0VqkDbDOc70e6OPOA1+C82NdVPtiJTxB+01VxwKIyChgk+fBZziE+n+7BDgHaAk8Hge5Ep2g/QY8CjwmIqcC78RDsAQmVJ8BXA1Mi7UAdUFBBEVV84C8OIuRtKjq9HjLkEyo6ovAi/GWI9lQ1Z04DzujEqjqhNq4T12YPtgAtPVcH+mmGeGxfqsa1m9Vw/qt8sS9z+qCglgCdBSRDiLSBMfAOj/OMiUD1m9Vw/qtali/VZ6491lSKQgRmQV8CBwrIutF5GpV3QuMBV4HvgTmqOqKeMqZaFi/VQ3rt6ph/VZ5ErXPzFmfYRiGEZSkGkEYhmEYtYcpCMMwDCMopiAMwzCMoJiCMAzDMIJiCsIwDMMIiikIwzAMIyimIIx6h4jsE5FlnmN85FqxRxzeEpEWYcpME5Ex5dIuEpFXRaSJiLwjInXWhY5Ru5iCMOoju1S1p+d4oLoN1tBD+XzgM1XdFqbMLJwdtV6GArNcj59vApfXgCyGYQrCMPyISKGITBSRpSLyuYgc56Yf6AZ0+UREPhURv8vlUSIyX0TeAt4UkQNEZI6IrBSRl0TkYxHJFJGrROQRz32uFZGHg4gwAnjFU+4K957LRGSKiDTEUQDHiUhrv2zAmcDLbrWXqefu7Y2awxSEUR9pXm6KyfvGvUlVewFPAre4aXcAb6nqiUB/4G/ugxmcIC+Xqmo/4A/AVlU9HrgTyHDLzAEGikhj93o08EwQuU4GCsAJmYszEjhZVXvixEoYoar7gHk4kRIBBgJ5nlHHF0DvyneJYVTE5iqN+sgu96EbDL/L7gKceCIAZwODRMSvMJoB7dzzN1TVH+jlFJz4BqjqFyKy3D0vdkcZA0TkS6Cxqn4e5N6HeALAnIGjYJaICEBz4Ec3bxYwyb3XUOB5fwOquk9ESkQkNdbBZIy6jykIwyjLbvfvPkp/HwIMVtVV3oIi0gfYEWW7/wD+BHxF6EAve0WkgRukSYBnVfX2IOU+AFqLSA/gJCraJJoCv0Ypl2GExKaYDCMyrwM3iPsqLyInhCj3Pu7Uj4gcD3TzZ6jqxzi+/YfjjACCsQon/jA4toZLReRwt71DRKS925YCL+AErH9VVQPKQERa4UyT7anC5zSMMpiCMOoj5W0QkVYx3QM0BpaLyAr3OhhPAIeJyErgL8AK4BdP/hzgfVXdGqwy8G+cmOCo6krgz8B/3KmqN4DWnrKzgB5UVDb93XYMo9qYu2/DqCHcVUaNVfVXETka+C9wrLv8FBFZCDysqm+GqN8aeE5Vz6qGDC8C41V1dVXbMAw/ZoMwjJrjAOBtd7WSAH9Q1RIRaQl8grPHIahyAFDVjSLylIi0iLAXIihu1LGXTTkYNYWNIAzDMIygmA3CMAzDCIopCMMwDCMopiAMwzCMoJiCMAzDMIJiCsIwDMMIiikIwzAMIyj/H27W5UNjB9g6AAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb index a7740fc2675..a9102dfb8fb 100644 --- a/examples/jupyter/mdgxs-part-ii.ipynb +++ b/examples/jupyter/mdgxs-part-ii.ipynb @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -388,17 +388,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n", " warn(msg, IDWarning)\n", - "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n", " warn(msg, IDWarning)\n" ] } @@ -489,9 +489,9 @@ " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", - " Git SHA1 | d4df243d920d12c3d268a4c20941950d2299d678\n", - " Date/Time | 2021-04-18 12:29:49\n", - " MPI Processes | 1\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 13:44:53\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", @@ -515,82 +515,82 @@ "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.03852\n", - " 2/1 0.99743\n", - " 3/1 1.02987\n", - " 4/1 1.04397\n", - " 5/1 1.06262\n", - " 6/1 1.06657\n", - " 7/1 0.98574\n", - " 8/1 1.04364\n", - " 9/1 1.01253\n", - " 10/1 1.02094\n", - " 11/1 0.99586\n", - " 12/1 1.00508 1.00047 +/- 0.00461\n", - " 13/1 1.05292 1.01795 +/- 0.01769\n", - " 14/1 1.04732 1.02530 +/- 0.01450\n", - " 15/1 1.04886 1.03001 +/- 0.01218\n", - " 16/1 1.00948 1.02659 +/- 0.01052\n", - " 17/1 1.02644 1.02657 +/- 0.00889\n", - " 18/1 1.03080 1.02710 +/- 0.00772\n", - " 19/1 1.00018 1.02411 +/- 0.00743\n", - " 20/1 1.05668 1.02736 +/- 0.00740\n", - " 21/1 1.01160 1.02593 +/- 0.00685\n", - " 22/1 1.04334 1.02738 +/- 0.00642\n", - " 23/1 1.03105 1.02766 +/- 0.00591\n", - " 24/1 1.01174 1.02653 +/- 0.00559\n", - " 25/1 0.99844 1.02465 +/- 0.00553\n", - " 26/1 1.02241 1.02451 +/- 0.00517\n", - " 27/1 1.02904 1.02478 +/- 0.00487\n", - " 28/1 1.02132 1.02459 +/- 0.00459\n", - " 29/1 1.01384 1.02402 +/- 0.00438\n", - " 30/1 1.03891 1.02477 +/- 0.00422\n", - " 31/1 1.04092 1.02553 +/- 0.00409\n", - " 32/1 1.00058 1.02440 +/- 0.00406\n", - " 33/1 0.99940 1.02331 +/- 0.00403\n", - " 34/1 0.98362 1.02166 +/- 0.00420\n", - " 35/1 1.05358 1.02294 +/- 0.00422\n", - " 36/1 0.99923 1.02202 +/- 0.00416\n", - " 37/1 1.08491 1.02435 +/- 0.00463\n", - " 38/1 1.01838 1.02414 +/- 0.00447\n", - " 39/1 0.98567 1.02281 +/- 0.00451\n", - " 40/1 1.05047 1.02374 +/- 0.00445\n", - " 41/1 1.01993 1.02361 +/- 0.00431\n", - " 42/1 1.01223 1.02326 +/- 0.00419\n", - " 43/1 1.06259 1.02445 +/- 0.00423\n", - " 44/1 1.01993 1.02432 +/- 0.00411\n", - " 45/1 0.99233 1.02340 +/- 0.00409\n", - " 46/1 0.98532 1.02234 +/- 0.00411\n", - " 47/1 1.02513 1.02242 +/- 0.00400\n", - " 48/1 1.01637 1.02226 +/- 0.00390\n", - " 49/1 1.03215 1.02251 +/- 0.00381\n", - " 50/1 1.01826 1.02241 +/- 0.00371\n", + " 1/1 1.03409\n", + " 2/1 1.02768\n", + " 3/1 1.01526\n", + " 4/1 1.03910\n", + " 5/1 1.00144\n", + " 6/1 1.00485\n", + " 7/1 1.03532\n", + " 8/1 1.01159\n", + " 9/1 0.97707\n", + " 10/1 1.06530\n", + " 11/1 1.08615\n", + " 12/1 1.00438 1.04527 +/- 0.04089\n", + " 13/1 1.03145 1.04066 +/- 0.02405\n", + " 14/1 1.04785 1.04246 +/- 0.01710\n", + " 15/1 1.04634 1.04323 +/- 0.01327\n", + " 16/1 1.02264 1.03980 +/- 0.01137\n", + " 17/1 1.00157 1.03434 +/- 0.01105\n", + " 18/1 1.09443 1.04185 +/- 0.01216\n", + " 19/1 1.02810 1.04032 +/- 0.01084\n", + " 20/1 1.01800 1.03809 +/- 0.00995\n", + " 21/1 1.02815 1.03719 +/- 0.00904\n", + " 22/1 1.00064 1.03414 +/- 0.00880\n", + " 23/1 0.99013 1.03076 +/- 0.00877\n", + " 24/1 1.01182 1.02940 +/- 0.00823\n", + " 25/1 1.07537 1.03247 +/- 0.00826\n", + " 26/1 1.02924 1.03227 +/- 0.00772\n", + " 27/1 1.01499 1.03125 +/- 0.00733\n", + " 28/1 1.01749 1.03049 +/- 0.00695\n", + " 29/1 1.04072 1.03102 +/- 0.00660\n", + " 30/1 0.99990 1.02947 +/- 0.00645\n", + " 31/1 1.03239 1.02961 +/- 0.00613\n", + " 32/1 1.02613 1.02945 +/- 0.00585\n", + " 33/1 1.04340 1.03006 +/- 0.00562\n", + " 34/1 1.05081 1.03092 +/- 0.00545\n", + " 35/1 1.02511 1.03069 +/- 0.00524\n", + " 36/1 0.99923 1.02948 +/- 0.00517\n", + " 37/1 0.97758 1.02756 +/- 0.00534\n", + " 38/1 0.99628 1.02644 +/- 0.00526\n", + " 39/1 1.06004 1.02760 +/- 0.00521\n", + " 40/1 1.08287 1.02944 +/- 0.00536\n", + " 41/1 1.02731 1.02937 +/- 0.00518\n", + " 42/1 1.02634 1.02928 +/- 0.00502\n", + " 43/1 1.04269 1.02968 +/- 0.00488\n", + " 44/1 1.05044 1.03029 +/- 0.00478\n", + " 45/1 1.05183 1.03091 +/- 0.00468\n", + " 46/1 1.02770 1.03082 +/- 0.00455\n", + " 47/1 1.07099 1.03191 +/- 0.00455\n", + " 48/1 1.04467 1.03224 +/- 0.00444\n", + " 49/1 1.02996 1.03218 +/- 0.00433\n", + " 50/1 1.03418 1.03223 +/- 0.00422\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 9.3825e-01 seconds\n", - " Reading cross sections = 9.2114e-01 seconds\n", - " Total time in simulation = 1.5872e+02 seconds\n", - " Time in transport only = 1.5607e+02 seconds\n", - " Time in inactive batches = 8.3963e+00 seconds\n", - " Time in active batches = 1.5032e+02 seconds\n", - " Time synchronizing fission bank = 1.2433e-02 seconds\n", - " Sampling source sites = 1.1456e-02 seconds\n", - " SEND/RECV source sites = 7.1083e-04 seconds\n", - " Time accumulating tallies = 2.5981e+00 seconds\n", - " Time writing statepoints = 2.4262e-02 seconds\n", - " Total time for finalization = 1.7827e-05 seconds\n", - " Total time elapsed = 1.5971e+02 seconds\n", - " Calculation Rate (inactive) = 2977.49 particles/second\n", - " Calculation Rate (active) = 665.252 particles/second\n", + " Total time for initialization = 2.5756e-01 seconds\n", + " Reading cross sections = 2.4233e-01 seconds\n", + " Total time in simulation = 1.2099e+01 seconds\n", + " Time in transport only = 1.2046e+01 seconds\n", + " Time in inactive batches = 8.7582e-01 seconds\n", + " Time in active batches = 1.1223e+01 seconds\n", + " Time synchronizing fission bank = 4.8051e-03 seconds\n", + " Sampling source sites = 4.1028e-03 seconds\n", + " SEND/RECV source sites = 6.7903e-04 seconds\n", + " Time accumulating tallies = 3.0247e-02 seconds\n", + " Time writing statepoints = 1.4253e-02 seconds\n", + " Total time for finalization = 5.3700e-07 seconds\n", + " Total time elapsed = 1.2369e+01 seconds\n", + " Calculation Rate (inactive) = 28544.8 particles/second\n", + " Calculation Rate (active) = 8910.20 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02207 +/- 0.00343\n", - " k-effective (Track-length) = 1.02241 +/- 0.00371\n", - " k-effective (Absorption) = 1.02408 +/- 0.00356\n", - " Combined k-effective = 1.02306 +/- 0.00307\n", + " k-effective (Collision) = 1.02658 +/- 0.00374\n", + " k-effective (Track-length) = 1.03223 +/- 0.00422\n", + " k-effective (Absorption) = 1.02640 +/- 0.00361\n", + " Combined k-effective = 1.02849 +/- 0.00321\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -720,8 +720,8 @@ " 1\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000099\n", - " 2.275247e-05\n", + " 0.000079\n", + " 1.583613e-05\n", " \n", " \n", " 1\n", @@ -731,8 +731,8 @@ " 2\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.001260\n", - " 2.852271e-04\n", + " 0.001001\n", + " 1.983614e-04\n", " \n", " \n", " 2\n", @@ -742,8 +742,8 @@ " 3\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000800\n", - " 1.795615e-04\n", + " 0.000636\n", + " 1.248704e-04\n", " \n", " \n", " 3\n", @@ -753,8 +753,8 @@ " 4\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000630\n", - " 1.397151e-04\n", + " 0.000503\n", + " 9.721681e-05\n", " \n", " \n", " 4\n", @@ -764,8 +764,8 @@ " 5\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000023\n", - " 4.861639e-06\n", + " 0.000018\n", + " 3.397815e-06\n", " \n", " \n", " 5\n", @@ -775,8 +775,8 @@ " 6\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000002\n", - " 3.879558e-07\n", + " 0.000001\n", + " 2.710107e-07\n", " \n", " \n", " 6\n", @@ -786,8 +786,8 @@ " 1\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000091\n", - " 2.062544e-05\n", + " 0.000092\n", + " 2.407283e-05\n", " \n", " \n", " 7\n", @@ -797,8 +797,8 @@ " 2\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.001162\n", - " 2.584797e-04\n", + " 0.001165\n", + " 3.017676e-04\n", " \n", " \n", " 8\n", @@ -808,8 +808,8 @@ " 3\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000737\n", - " 1.626991e-04\n", + " 0.000739\n", + " 1.899840e-04\n", " \n", " \n", " 9\n", @@ -819,8 +819,8 @@ " 4\n", " total\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 0.000581\n", - " 1.265708e-04\n", + " 0.000582\n", + " 1.478539e-04\n", " \n", " \n", "\n", @@ -842,16 +842,16 @@ "\n", " score mean std. dev. \n", " \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.000099 2.275247e-05 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.001260 2.852271e-04 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.000800 1.795615e-04 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.000630 1.397151e-04 \n", - "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.000023 4.861639e-06 \n", - "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.000002 3.879558e-07 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.000091 2.062544e-05 \n", - "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.001162 2.584797e-04 \n", - "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.000737 1.626991e-04 \n", - "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.000581 1.265708e-04 " + "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.000079 1.583613e-05 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.001001 1.983614e-04 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.000636 1.248704e-04 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.000503 9.721681e-05 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.000018 3.397815e-06 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.000001 2.710107e-07 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.000092 2.407283e-05 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.001165 3.017676e-04 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.000739 1.899840e-04 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.000582 1.478539e-04 " ] }, "execution_count": 18, @@ -972,8 +972,8 @@ " x-max out\n", " total\n", " current\n", - " 0.03245\n", - " 0.000677\n", + " 0.03154\n", + " 0.000643\n", " \n", " \n", " 3\n", @@ -983,8 +983,8 @@ " x-max in\n", " total\n", " current\n", - " 0.03180\n", - " 0.000659\n", + " 0.03030\n", + " 0.000662\n", " \n", " \n", " 4\n", @@ -1016,8 +1016,8 @@ " y-max out\n", " total\n", " current\n", - " 0.03072\n", - " 0.000677\n", + " 0.02981\n", + " 0.000595\n", " \n", " \n", " 7\n", @@ -1027,8 +1027,8 @@ " y-max in\n", " total\n", " current\n", - " 0.03104\n", - " 0.000652\n", + " 0.03083\n", + " 0.000732\n", " \n", " \n", " 8\n", @@ -1061,12 +1061,12 @@ " x y z surf \n", "0 1 1 1 x-min out total current 0.00000 0.000000\n", "1 1 1 1 x-min in total current 0.00000 0.000000\n", - "2 1 1 1 x-max out total current 0.03245 0.000677\n", - "3 1 1 1 x-max in total current 0.03180 0.000659\n", + "2 1 1 1 x-max out total current 0.03154 0.000643\n", + "3 1 1 1 x-max in total current 0.03030 0.000662\n", "4 1 1 1 y-min out total current 0.00000 0.000000\n", "5 1 1 1 y-min in total current 0.00000 0.000000\n", - "6 1 1 1 y-max out total current 0.03072 0.000677\n", - "7 1 1 1 y-max in total current 0.03104 0.000652\n", + "6 1 1 1 y-max out total current 0.02981 0.000595\n", + "7 1 1 1 y-max in total current 0.03083 0.000732\n", "8 1 1 1 z-min out total current 0.00000 0.000000\n", "9 1 1 1 z-min in total current 0.00000 0.000000" ] @@ -1111,7 +1111,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb index c393ecc417c..dfcb88aeca6 100644 --- a/examples/jupyter/mg-mode-part-i.ipynb +++ b/examples/jupyter/mg-mode-part-i.ipynb @@ -18,9 +18,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -48,9 +46,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create a 7-group structure with arbitrary boundaries (the specific boundaries are unimportant)\n", @@ -119,9 +115,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Initialize the library\n", @@ -178,9 +172,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection, register all Materials, and export to XML\n", @@ -205,7 +197,16 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", + " FutureWarning)\n" + ] + } + ], "source": [ "# Create the surface used for each pin\n", "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", @@ -391,12 +392,24 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAD8CAYAAAB6iWHJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ - "" + "" ] }, + "execution_count": 10, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -435,9 +448,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "tallies_file = openmc.Tallies()\n", @@ -523,44 +534,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 966169de084fcfda3a5aaca3edc0065c8caf6bbc\n", - " Date/Time | 2017-03-09 08:18:02\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:36:11\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections HDF5 file...\n", - " Reading tallies XML file...\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", " Loading cross section data...\n", " Loading uo2 data...\n", " Loading mox43 data...\n", @@ -569,7 +578,9 @@ " Loading fiss_chamber data...\n", " Loading guide_tube data...\n", " Loading water data...\n", - " Building neighboring cells lists for each surface...\n", + " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -578,40 +589,31 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.3630E-01 seconds\n", - " Reading cross sections = 3.0827E-02 seconds\n", - " Total time in simulation = 8.9648E+00 seconds\n", - " Time in transport only = 8.2752E+00 seconds\n", - " Time in inactive batches = 2.4798E+00 seconds\n", - " Time in active batches = 6.4849E+00 seconds\n", - " Time synchronizing fission bank = 1.4553E-02 seconds\n", - " Sampling source sites = 1.0318E-02 seconds\n", - " SEND/RECV source sites = 4.0840E-03 seconds\n", - " Time accumulating tallies = 4.2427E-04 seconds\n", - " Total time for finalization = 1.7081E-02 seconds\n", - " Total time elapsed = 9.1340E+00 seconds\n", - " Calculation Rate (inactive) = 1.00813E+05 neutrons/second\n", - " Calculation Rate (active) = 77101.8 neutrons/second\n", + " Total time for initialization = 2.1712e-02 seconds\n", + " Reading cross sections = 9.5683e-03 seconds\n", + " Total time in simulation = 1.6152e+01 seconds\n", + " Time in transport only = 1.6108e+01 seconds\n", + " Time in inactive batches = 3.8534e+00 seconds\n", + " Time in active batches = 1.2299e+01 seconds\n", + " Time synchronizing fission bank = 2.8962e-02 seconds\n", + " Sampling source sites = 2.3951e-02 seconds\n", + " SEND/RECV source sites = 4.9397e-03 seconds\n", + " Time accumulating tallies = 4.9472e-04 seconds\n", + " Time writing statepoints = 2.4687e-03 seconds\n", + " Total time for finalization = 1.4768e-03 seconds\n", + " Total time elapsed = 1.6182e+01 seconds\n", + " Calculation Rate (inactive) = 64877.2 particles/second\n", + " Calculation Rate (active) = 40654.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.18880 +/- 0.00179\n", - " k-effective (Track-length) = 1.18853 +/- 0.00244\n", - " k-effective (Absorption) = 1.18601 +/- 0.00111\n", - " Combined k-effective = 1.18628 +/- 0.00111\n", - " Leakage Fraction = 0.00175 +/- 0.00006\n", + " k-effective (Collision) = 1.18598 +/- 0.00158\n", + " k-effective (Track-length) = 1.18629 +/- 0.00194\n", + " k-effective (Absorption) = 1.18569 +/- 0.00111\n", + " Combined k-effective = 1.18574 +/- 0.00111\n", + " Leakage Fraction = 0.00184 +/- 0.00006\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -635,22 +637,23 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAAEICAYAAADhtRloAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnX2cnVV177+LyRDIC8S8EEISSIQoIGKkkcTiC4pWiC9A\nL7bY+6HoB4nlaquW+7ngywVsayutSq1vGAoVqxexVTTaUI1cXsK1IAGHt0Qk6GCGxIQACQl5ncm6\nf5xnYOZkrz3nzJw5c57h9/18ns+cs/az197Pc85Zs5+99lrb3B0hhCgTB4x0B4QQol5kuIQQpUOG\nSwhROmS4hBClQ4ZLCFE6ZLiEEKVDhqvEmNnNZnb+SPdDiGZjWsfV2phZJzAd6AGeA5YDf+7u21tR\nrxDNQCOucvBOd58AnAS8Bvhki+utCzMbMxLtivIiw1Ui3P0J4GbgBAAzu83M3l+8fq+Z3WlmnzWz\nZ8zsN2Z2xiD1HmFmy8zsaTNba2YXFvKDzGynmU0t3n/SzLrN7JDi/d+Y2T8Wr8cWffmtmW00s6vN\n7OCi7FQz6zKzS8zsd8C/mNlUM/uRmW0p2l1pZvp+iiT6YpQIM5sNLAZ+EZyyEHgEmAr8PXCtmdkg\n9N4AdAFHAOcAf2tmp7n7LuAe4I3FeW8AHgdO6fP+9uL1lcDLgPnAMcBM4LI+zR4OTAaOApYAFxdt\nTqPyCPtxQPMYIokMVzn4vpltAe6kYhj+NjjvcXe/xt17gOuBGVSMQM16CyP2OuASd9/l7h3APwPn\nFXVuB95YPN6dCPxT8f4gKo+bKwtjeSHwUXd/2t23FX0+t0/b+4DL3X23u+8E9hb9Pcrd97r7StcE\nrAjQ3EI5OMvdf1rDeb/rfeHuO4rB1oR69JrZEUCvsenlcWBB8fp24PNU5sUeBFYA1wKLgLXuvtnM\nDgPGAff2GfAZ0NZH55PFCK6XfwCuAH5S1Fnq7p8Z6ILFixONuEQ164HJZjaxj+xI4Ini9c+AlwNn\nA7e7++qi/O288Ji4GdgJvMLdJxXHoYUjoJd+oyl33+buF7v7S4F3An9pZqc1+uLE6ECGS/TD3ddR\nMU5/V0zGnwhcAHyrKN8B3At8kBcM1c+AD/S+d/d9wDXAVcXoCzObaWZvi9o1s3eY2THFY+azVJZp\n9AzDJYpRgAyXSPEeYA6V0ddNVOaiVvQpvx1oB37e5/1E4I4+51wCrAXuMrNngZ9SGalFzCvO2Q78\nF/AVd79tqBciRidagCqEKB0acQkhSocMlxCidMhwCSFKhwyXEKJ0NHUB6tQx5nPaEwVtCdlA5Bzl\nUZDLwYF8d0ZX1LecyZ8YyPdk6hwYyFP3K9cGsHPM2KR8N2k5wM7g5oyhOynfxGGhrp7gpvXsiz/o\nfc8FX8V08/nPP7rPka6ByuptZ18gz/nB9gbyXMDWIQnZc534rs0DhnnlOMbMd9R47gb4sbufPpT2\nBkNTDdecdlh1TKJgfKZS1MOnMnUOCuQnBPLfZHRFfcv1+Y2BfF2mzuxAfkRa7JmlmQ9MPjIp72Ru\nWOdBXpmUT2FzUv5F/iLUtS2wqluemxTW2X7XtHRBuvnKoomIrkAe6YI+MQdV5H4hnYF8V53ywbaf\nMhfLFySE9bGDyqK8WriiEhfbdBTyI4Toh9H6hqHV+yeEaDIHEM+qtAoyXEKIfhjx1GqrIMMlhOiH\nHhWrOYhKRFo1uYnunwyinUDf3d9MyxfGc9YwJS3e8f/iKuO2BgXp+e8KmwJ54PG0jljVyxc+mpTv\nHD8urPNT0rP90UT7x8OUYHAjf5yUt42PXXc/O+2UpHwSzyTlO4mvpev+1JcM2BJWiSfac0R1InnO\nOTArkOemvlO3swERfBpxCSFKh0ZcQojSoRGXEKJ0yKsohCgdGnEJIUpJqxuGVu+fEKLJaMRVTTfw\ndEJ+d1zl0WCZwG2DaP7CINvrnZmtB18XLK0Ytz32O++dlNbXvj7uG2sCfX8Y9C0Tq3lQsLyje24c\n5Dw2iBjeEcx2rOb4UFdUZyLbknKAhcGX4Ie8Oyk/nvtCXQcc/lxS3vOqeN2NRXGED4VV8MsDXRfF\ndUKi2MtcfGO0hGKIyKsohCgdmpwXQpQOPSoKIUqHHhWFEKVDIy4hROnQiKuag0lmIX3qzrjKE4E8\n8hAC3Bh4CW8O5GcEMblAGOTsU2JPZHuU1filmXYWBfqiFMXfy0TT/t+0rulzo0humBK4KSMPYY43\nBT7fT/LZsM55XJOUn8UNSfm6594S6po4Ke29nNKTibLunhmXBdiXg4IgA6//MKPr7KAgThqbDsCO\nUoDXwagYcZnZQVR2KB5bnP/v7n65mc0Fvg1MBu4DznP3XFZ1IUQJMFrfq1jLLj+7gTe7+6uA+cDp\nZrYIuBK4yt3nAc8AFwxfN4UQzcKA9jG1HQPqMpttZrea2Roze9jMPpw451Qz22pmHcVx2UB6B2za\n3Z0Xlse1F4cDbwb+pJBfD1wBfHXgSxFCtDJmMKbWSaSBd0fqBi529/vMbCJwr5mtcPfVVeetdPd3\n1NrHmvZVNLM2M+ugku5uBfAYsMXde7vdBSQnCcxsiZmtMrNVT+6stVtCiJHCDNrbajsGwt03uPt9\nxettwBoCW1EPNRkud+9x9/lUggxOBo5LnRbUXeruC9x9wbRWf3AWQjw/4qrlAKb2DkyKY0ms1+YA\nryYd5PdaM7vfzG42s1cM1Me6vIruvsXMbgMWAZPMbEwx6poF5CLxhBAlwQza472Dq9ns7gNu5mhm\nE4DvAh9x92eriu8DjnL37Wa2GPg+6STvz1OLV3EasLcwWgcDb6EyMX8rcA4Vz+L5wA8G0sVWYPn+\n4u7MrsRR/PVtmcDoKLX7Ubl+BTwVBDPnJiYPSadPjzeqzfVhUSD/RGaz4uBrlFvaEOV23xL44xew\nKtQVBWB/lL8L62xjTlIe5ZafND5e2rB5a3qjgJ7uzIc2mIVB0cazweds78noir4buX4NU875Ri/k\nMrN2KkbrW+7+veryvobM3Zeb2VfMbKq7h1n6a+neDOB6M2uj8mj5HXf/kZmtBr5tZn8D/AK4ts7r\nEUK0Ig00XGZmVGzDGnf/fHDO4cBGd3czO5mKncntVV+TV/EBKs+l1fJfU5nvEkKMNho34joFOA94\nsHDwAXwcOBLA3a+m8uR2kZl1AzuBc4vVDE3onhBidGBADR7DWnD3OwuNuXO+BHypHr0yXEKI/pQg\nWLHFuyeEaDpGJcCvhWmu4WoHjthf/Oxv4iqXBI+6n854FVOLzACOD3TtmBDreiLweJ7YHT+CdwV9\nm5XbMfvXgb7Ie5jTdXZa1+yMV3ELLwnqrEvKo6BsgMu5Min/HyTnZgGYG2z/fBUfS8pfyT2hro3d\n05PyPVMOCetYtAI8s5O0XxzoSnc5u8rc/y3Q9b64zrChEZcQonTIcAkhSkmDJueHCxkuIUR/NOIS\nQpQOGS4hROmQV7E25iU8jb1EHrrckv3IdxR5D8dlwjlPDJxnudTNsxYHBbl5g/cF+k4Lzk+kwO5l\n13NpXavHnxTW2RPk/N1IlIc6ZiWXJOWP8PpM++lfykJuT8o7n4s3pN17W/obYIsyu6tuDoIFM1Xs\na0FB5D3MeBUtCrCdE9dJ9m1v5vxa0YhLCFE6ZLiEEKWjgSE/w4UMlxCiPxpxCSFKhybnhRClQyMu\nIUTpkOGqog0Yn5BnlkNMfy4tn5Ubyk5Oiz3ayDk4H4Cgfcv0mYWB/KH620neL8i61g8KgtZ7Tog/\n7rUck5RvCpZDzKcjKQdYx+ykfEy4LTdsIJ1ueUeQunl717RQV7hUpCuTO3t7IO+Mq4QB2NFnk/u1\nRV3LpftOldW0/U0NyHAJIUqFvIpCiNKhR0UhROmQV1EIUTo04hJClA4ZriragEMT8kfjKu1bgpTG\nx8VBzr/5ZVo+dxCpm8cF3kO7+/Kwjh/6qXRBFEgL8NngOpcHfct9ciekde3JBDlHG8IeEWxQPoVw\nr06uJH1v3p/ZyOVA9iTlP+Td6faPeSLUtWVzehPbfcf+Q1iH265IyzNePb8gLbfg4w89l4AHmajt\n7LhO8juQ3U+nRkpguAZ0nprZbDO71czWmNnDZvbhQn6FmT1hZh3FEeVEEEKUjbYajxGiFrvaDVzs\n7veZ2UTgXjNbUZRd5e6fHb7uCSGaTglGXLXsZL0B2FC83mZma4CZw90xIcQIUQKvYl3rbM1sDvBq\n4O5C9CEze8DMrjOz5P5WZrbEzFaZ2aondw+pr0KIZtA74qrlGCFqNlxmNgH4LvARd38W+CpwNDCf\nyojsc6l67r7U3Re4+4JpLW7FhRCUwnDV1LSZtVMxWt9y9+8BuPvGPuXXAD8alh4KIZrLaAj5MTMD\nrgXWuPvn+8hnFPNfAGeTDyHOc2SuLPDvZurMPTYomBnknI+CooEg9hc/IfJ5E2+l/cZMOzfVt2P1\nc8fFg+UdTEzKt2WWQ0REOecnsSWs807S2zI/RZwnPgrynsGvk/KnV7001BUuYbjrirjOnYH8d3GV\ncNlDVyBfm9EVfTTplR0VUtf5IlkOUUv3TgHOAx40s96UAB8H3mNm8wGnEkP/gWHpoRCiuRj5rBQt\nQC1exTtJ2/Hlje+OEGLEaeCjopnNBr4BHA7sA5a6+xeqzjHgC8BiYAfwXne/L6e3xQeEQoim09hH\nxeQ6UHdf3eecM4B5xbGQiuMvN4HTsLRjQojRRIO8iu6+oXf05O7bgNQ60DOBb3iFu4BJZjZjoO4J\nIcQL1PeoONXMVvV5v9TdlybV7r8OtJeZwLo+77sK2QYCmmu4ohW5udTJ0Q3M7D4d+jcDDyHTM7oi\nD2U69rhCKpAcINgVGwg9kc++sj0pb+uO0yD3BDdtS8ZFtS3wRG4P5OuJ/yFGunI8tTH94bSNCa5z\nVmaL6Z8GM8s5D130S4jSM0OcojlyuB6e0bUqU1Zv+0OlvkfFze6+YECV+68DrW6xmiDrQAWNuIQQ\n/WlwyE9qHWgVXdBvo4JZ5IcGmuMSQlTRwJXz0TrQKpYBf2oVFgFb+6wRTaIRlxCiP431KkbrQI8E\ncPerqSytWkxlie4O4H0DKZXhEkL0p4GGK7MOtO85DnywHr0yXEKI/Sl7rKIQ4kXGKIlVbBzjgZTj\n9CeZOrcEXtG3Z0afkUdkMLrWBfJrMt7af64/MJz5aX2HLAt0vSxWNf7YtD/+YM4K6+wMdoyOcsHP\nDm8MfJH/lZS/jR+EdV4+/ZGkfDUnpfv1VLVH/QX2HpNeDuGnhFWwHwcFwf4FAP7hQNeFQYUo+Brw\nYN8Fe2dcJ6kv/XHVRwkSCba4XRVCNB2NuIQQpUOGSwhROmS4hBBlxOVVFEKUCT8A9pQ9kWDDW0tl\nAs7t1vtHg8hF+7ZAfmF96ZEBOC2Q/1WmX9GO1cGu2AB01OeJfHZeOvgaYBW/n5Q/FsghDsA+jE2B\nrnSqZYBz+GZSvjFTJyJK3by3o/7UzfYfmYaitMqZWG67MiiIvIdxtmvsNUFBzoBMSMiyocm14Qbd\nbbVGA+4beoODQCMuIUQ/3IyeMbWahkasv6gfGS4hxH70tLX2JJcMlxCiH46FOd1aBRkuIUQ/HKNb\nhksIUSYcY0+Lx/w013BNAF6XkAdxWkC818fFGffJ5YGHLvoszsu0vzWQR95GiL2HuzN1IoI6h7TF\nk6Ibg5jENuJ0z1G65d3BTZvK5lDXM4GHsoNFYZ1Xck9SPibqc+6bG8UXZjyEYRrkTKxiMu4WwnTP\nfnOsyl4bFGQ8kclU0A0YKJXhUXFAn6eZzTazW81sjZk9bGYfLuSTzWyFmT1a/H3J8HdXCNEMemir\n6Rgpalms0bsv2nHAIuCDZnY8cClwi7vPA24p3gshSk7vHFctx0hRy07WGyi2CXL3bWbWuy/amcCp\nxWnXA7cBlwxLL4UQTaPyqNja09919a5qX7TpvQnt3X2DmaXWxGNmS4AlAEdWbwMphGg5KpPzB450\nN7LUbLiq90WrbN4xMMXmkEsBFpxoDQhIEEIMJw6jYzlEsC/aRjObUYy2ZkAQ1CaEKBmj4FExsy/a\nMuB84DPF3zgvb8HeA9v43ez9I0PHHbEjrHPI5L3pguWZEd/8QH52MOC7O6Mr2pV6YTx4/CVzkvIp\nma2sp7EtKX88GZUOKzkn1LWaE5Pyxzg6rLM2CICexDNJeW5X7KjsZdwf1ukJUkevC7Yst9wygeBb\n7Zl9ZOwLQcGsuE6kz34ayN8a6wrJBVmnlnA0Isi6BMshajGr0b5onwG+Y2YXAL8F3j08XRRCNJvS\nG64B9kXLLcMUQpSQ0TLiEkK8iHAsjJhoFWS4hBD90IhLCFE6ZLiq2Es76xMRyDvaDg7rTJ+fXmXR\nNj+KioWngijXOUEg8bqFx4W6Ig/ZbGaHdTYHnsAHw5zOAOmdPzfyrqS8I3SdkrzHEAdSA+zZnV5w\nOGZsOsh5bCZifGLgIX3kqZfHdSal60zq/l26wjGpCOOCh9Ji+1pche2BPAiYBrCLgoJUSmWA4FKA\nvPewnjqDyHSeYlSs4xJCvHgoQ8hPrRnxhRAvEnofFRuRHcLMrjOzTWaWHAeb2almttXMOorjslr6\n2NpmVQjRdCpexYbFKn4d+BLwjcw5K939HfUoleESQvSjkY+K7n5HkZyhoehRUQixH01OJPhaM7vf\nzG42s1fUUkEjLiFEP+pcDjHVzFb1eb+0yAhTK/cBR7n7djNbDHwfggDVPjTVcG3hUH6YcPvvCAJs\nAY5gfVKec+1HN/0wNiblOzPtrwuWPeTcxdEwO9IF8XV2BgHbuSDnR3anlx3s3hXPW+zqnJwuODYt\n3rYlvv/7toxPF8Rp6nm645B0QbS0ILd8YBC7UtMZyKP2IVx2EeaJzwWGR8sx4o853bcGbCxdp+Ha\n7O5R9v2B23J/ts/r5Wb2FTOb6u6Zb4tGXEKIKpoZ8mNmhwMb3d3N7GQq01dxGpUCGS4hRD8auXLe\nzG6gkuJ9qpl1AZcD7QDufjVwDnCRmXUDO4Fz3X3A5DwyXEKI/WiU4XL39wxQ/iUqyyXqQoZLCNEP\n7WQthCgdZQj5aWrvxtDDpIRrZTATgbmhbPTf4rEgPXFOV7T7c+o6enmEtFcvF5gceQ+ja8l5YqeM\nTc9tjhsbp8je9qp0MPvjPw7cirlvTuQPynnVopj5KDA5pyvyHkaeu1z7uZ2sozpR33L3bDC/xNR9\njnMP1IWyQwghSsWo2p5MCPHiQHNcQojSoTkuIUQp0RyXEKJUKHVzFVuYxLJErOKfEefU3cyUpDzy\n9kH9MYk5XVFeouNZHdaJ9OXiC6M+dzI3Kd8QpGeGOHVyFA8JcOvWU9MFUZfr3agUspurZr2EKQYT\nd5iNfgvI9TnyeEZZpbsyutIO7zyptNIN+EWXYY5rwLQ2qQyGZnaFmT3RJ2vh4uHtphCiWVS8imNr\nOkaKWvJxfR04PSG/yt3nF8fyxnZLCDFSNDJ183BRy07Ww5LBUAjRurT6HNdQMqB+yMweKB4lXxKd\nZGZLzGyVma3a8+TWITQnhGgGvXNctRwjxWAN11eBo4H5wAbgc9GJ7r7U3Re4+4IDpx06yOaEEM2i\ndx1XLcdIMaiW3f15F5iZXQP8qGE9EkKMKKM25MfMZrj7huLt2cRJbPuxg3F09Lx6P/ndbQvDOlHA\n8kLuDutE3o6/5CtJ+VncEOo6njVJ+Tu4Jazzdb6ZlEc7TAP8jDcn5X/M9Un50TwW6lq5+/Xp9sfG\n7e9aFaRujsh9c6LlABMy+eGmprdg9mDza/tBpv0gLtzfHlexK4OCzriOrwh0nRFUGMwSktyyjxfx\ncogBLzPIYHiqmc0HnMpH+4Fh7KMQosmUPuQnyGB47TD0RQjRAmjlvBCidMhwCSFKSennuIQQLy72\nccCIhvPUQlMNV8/Odp7+5cz95MtesX/gdS8v51dJ+Sf5bFgn8hJu5LKk/IhM8PO9/F5S/oWMP2Iz\nZwfydMA4wNtIu8l2BJ7Ilbw11HX02IeT8i09md1Fo6Jc6uKIlLcL8KPTnkMAuzeQ3x5UyAVMB967\n0HMI8XVmUiFbtH9N0L7fnNGV/spkvZpJj2MDNoSF1l85rxGXEKIfmuMSQpQOR3NcQojSodTNQoiS\noUdFIUTpcCzM/NsqyHAJIfqhXX6qcZIu3JdkEo5HQ9bTMgkpxgZrUO7m5KQ8yusOcZ72H/O2sE6U\nW34LYdoy1rOnLl1zMusUDgx8+JPa4vv8dPf+y1SA+ndrhnBpRbTkIasvF2QcEe1YndMVBUDndr+O\n6gRLNSwd+15hQiDP/UJT7Q8lw14f9KgohCgVZZjjapB9FkKMFhyjZ19bTcdApDbbqSo3M/snM1tb\nZFQ+qZY+asQlhOiH7zN272pYyM/XgS8B3wjKzwDmFcdCKtmV4wR9BTJcQoh+uBs93Y15VKxhs50z\ngW+4uwN3mdmkqkSlSWS4hBD9ceoxXFPNbFWf90vdfWkdrc0E1vV531XIWshwdVP3bsLL+cOkPOdV\n3MbEpDwKTJ7PXaGuaFfoqF8Ax3NfUj6OHWGdB3lNUn406YDp6cHO1xCngbbHMqmTI69e9HkFgdQA\nno5Lx26M64SByWcOQldnoOvyuIp9NKMvwL8a6IoCpqOdrwFfGejKeSJTn1m8KXvNuBvde2s2XJvd\nfcEQmktF3me+qBU04hJCVGHs62maaegCZvd5PwuCNUh9kFdRCNEfB7rbajuGzjLgTwvv4iJg60Dz\nW6ARlxCimn0GuxpjGoLNdtoB3P1qYDmwGFgL7ADeV4teGS4hxP5kEijWQ7DZTt9yBz5Yr14ZLiFE\nfyoJuVqaWvZVvA54B7DJ3U8oZJOBG4E5VHw4f+TuzwzYWuBVfGR3sOsncPCup4OS3w/r7No+Limf\nOOnJpHzH9leGuu7vWpTu1zFRv2DX2vTi30NPiN1K0XXuWvuKpPzXk44PdVnUzOY4dXJIvd5GwP4j\nKMjFCgbt2NcGoSv40dmnM3WiX0KmnbrTLWfiHm3/fZIr5H6hKX0N8CqWwXDVMjn/deD0KtmlwC3u\nPg+4pXgvhBgNOLC3xmOEGNBwufsdQPVw4Ex4fm/464GzGtwvIcRI4cDuGo8RYrBzXNN7XZbuvsHM\nDmtgn4QQI0kJHhWHfXLezJYASwCYcuRwNyeEGColMFyDXYC60cxmABR/N0UnuvtSd1/g7gs4ZNog\nmxNCNI1ew1XLMUIM1nAtA84vXp8PwW6mQojyUQLDVctyiNTK188A3zGzC4DfAu+uqbU9VCKTqtja\nNb3W/r7Aroxrf0La3bEjWCaxr3N8rCtIQ7yra3JcJ3Dtb+04vO52Qhf6QZnrz6Ubjqg3mDq3w/UJ\ngTxKTwz1/wg6M2X13kuorNtOUe9yhBw5XdGyk1wbqa9Tozx9Lf6oOKDhyqx8Pa3BfRFCtAL7GFyu\n/yailfNCiP6UYHJehksI0R8ZLiFE6ZDhEkKUEhmuPkSpmzMeQk/HGHPw1kyQc0fa49fzxvak3DpC\nVUkvKADnXBHX+VFQdmw84+kz07mLLcoqnfnkPNir1r4b1wm9Wp2BPOMg9f8etP+tTPvB9fj5abn9\n74yuKMj86iviOqcHZbn7vCItt9cGFTKb6PpvAl3xXsVpL20jcvtpxCWEKB37gJ0j3Yk8MlxCiP44\njUmPM4zIcAkh9kePikKIUqE5LiFE6ZDhEkKUDoX8VBEEWed2+LUomLc7E+Qc6LPow+iMVYWBwdGS\nB4gDlm8Ltmsm07coyDaX8/3KoCD3aUfLPqL2c/nTvxAUPJRpP7jP1hmcn/nOhMsOcktYovufu89R\nnvio/Vz++mjZQ+46U/esUZPqGnEJIUqFHhWFEKWjd7OMFkaGSwjRH63jEkKUDj0qCiFKh6OQn370\nkPa4dGbq1OttgzgAuF7PWa79SFeuTu6/WJiieRDtR+3krrPee5O7lownLiRKnRy1k/vmRvc/5+LP\npZVuVDu5+xJdT+4+p35LjdrJWo+KQohSoUdFIUTpKIHhGuz2ZEKI0Urvcohajhows9PN7BEzW2tm\nlybK32tmT5pZR3G8fyCdGnEJIfanQXNcZtYGfBl4K5WZ1HvMbJm7r6469UZ3/1CtemW4hBD9aWys\n4snAWnf/NYCZfRs4E6g2XHUxJMNlZp3ANir2udvdFwxFnxCiBahv5fxUM1vV5/1Sd1/a5/1MYF2f\n913AwoSe/2ZmbwB+BXzU3dclznmeRoy43uTutTnAo+UQmUBS/0Rans1fHujziwNdUVAwhAGzfnlc\nxaJ9vY+N6/hVga5PBRUyk6f+14Gui+I6df+HzbV/TdD+hRl9cwJdXwx0vTOjK1jC4f8VV7HjgoLc\ndT4a6JoXVMh9zwNDYZkNy5PLKxoxqV7fcojNAwxYUlfgVe9/CNzg7rvN7M+A64E35xrV5LwQYn+6\nazwGpguY3ef9LGB93xPc/Sl33128vQb4vYGUDtVwOfATM7vXzJYMUZcQohXoXQ7RGMN1DzDPzOaa\n2YHAucCyvieY2Yw+b98FrBlI6VAfFU9x9/Vmdhiwwsx+6e53VHVqCVAxagceOcTmhBDDTgMn5929\n28w+BPyYyuZp17n7w2b2V8Aqd18G/IWZvYuKKXwaeO9AeodkuNx9ffF3k5ndRMWDcEfVOUuBpQA2\nYUH1s60QotVo8AJUd18OLK+SXdbn9ceAj9Wjc9CPimY23swm9r4G/oB8jkshRFlo3KPisDCUEdd0\n4CaruD3GAP/H3f8zW6OHtMcnl7r5fUHBpEw7wTDX/jw4v95AVsA+Wn+d3E7G4XVGQda5lMKRVzNH\nFEwd9TkTlGznBQW5x4/geuytwflRUDaE3+rQc5ipk/3MZgQFuXTLkS6L1h9kfqJjEg67PfW3vR+j\nOZFgsaDsVQ3sixCiFVB2CCFE6ShBkLUMlxCiP/tQIkEhRAnRo6IQonS0+MKl5hqufaS9VzkPUeC9\n8n+Jq4RerTmBvCPT/iA29wzr5CI66/Tq+cpYlb02005EI9MQR33OxQq+PiiIPHS5+x99q+P9eOPr\nybVT5zyQZ4xB7FXMuPe2j6uvA6MIxSoKIUqHDJcQonRojksIUUXruxVluIQQVbT+0nkZLiFEFa2/\nAlWGSwggJk2gAAAFY0lEQVRRhUZc/Yny/OR6UW/wbaaO/2ug600ZXYE73G+Kq9irg4LOuE60VMDO\nCOSD6HP2PmeCiZPkdsUOlrBkl2kESxU8SCkXBjhD/Jllspjb3KAgc50eTAOZPVuXfPCkOtCo3M0y\nXEKIUuFocl4IUTI0xyWEKB16VBRClA6NuIQQpUMjrv4cQNp7lEvDHHl1cv8QDk+Lw5TGmTTEUd/s\n7Prr5Ag3OI2CjHMBw1HZYP6JRrpy9yyq05WpMzUtDjdXzV1/Z6BrYqZOROaexZu1Rj/69kxDO2rr\nz4Dsa4AOjbiEEKVDIT9CiNKhR0UhRCnRo6IQolRoxCWEKB2tb7iGlEjQzE43s0fMbK2ZXdqoTgkh\nRpJer2LrbmU96BGXmbUBXwbeSsXRfY+ZLXP31WGlKcB7E/Kcm/yEQN6ZqTMrkA/GTR+5/XN3LqqT\n+5yjsjmBPBfkPJjlEAsCeZSLPacruv7oc4E4MDxYJpENCh/M9UdLWDozdcK8+1MylSIatRyiEQ9R\no9ureDKwttjRGjP7NnAmEBsuIUQJaP1HxaEYrplA30QhXcDCoXVHCDHyjO4FqKl1w/ttwGRmS4Al\nABx65BCaE0I0h9YfcQ1lcr4LmN3n/SxgffVJ7r7U3Re4+wLGTRtCc0KI5jCKJ+eBe4B5ZjYXeAI4\nF/iThvRKCDGCtP7kvHlue92BKpstBv4RaAOuc/dPD3D+k8Djxdup5PdDHm7Uvtofje0f5e5DerQx\ns/8k9udWs9ndTx9Ke4NhSIZrSA2brXL3yAmv9tW+2hch2slaCFE6ZLiEEKVjJA3X0hFsW+2r/Rd7\n+6VmxOa4hBBisOhRUQhROmS4hBClY0QM10inwzGzTjN70Mw6zGxVE9q7zsw2mdlDfWSTzWyFmT1a\n/H1Jk9u/wsyeKO5BR7Embzjanm1mt5rZGjN72Mw+XMibcv2Z9pt1/QeZ2c/N7P6i/U8V8rlmdndx\n/Tea2YHD0f6oxd2belBZrPoY8FLgQOB+4Pgm96ETmNrE9t4AnAQ81Ef298ClxetLgSub3P4VwP9s\nwrXPAE4qXk8EfgUc36zrz7TfrOs3YELxuh24G1gEfAc4t5BfDVzUrO/jaDhGYsT1fDocd98D9KbD\nGbW4+x3A01XiM4Hri9fXA2c1uf2m4O4b3P2+4vU2YA2VzCJNuf5M+03BK/RmT2svDgfeDPx7IR/W\nz380MhKGK5UOp2lfpAIHfmJm9xbZK0aC6e6+ASo/LuCwEejDh8zsgeJRctgeVXsxsznAq6mMOpp+\n/VXtQ5Ou38zazKwD2ASsoPLEscXde6OUR+I3UGpGwnDVlA5nmDnF3U8CzgA+aGZvaHL7rcBXgaOB\n+cAG4HPD2ZiZTQC+C3zE3Z8dzrZqbL9p1+/uPe4+n0oGlZOB41KnDVf7o5GRMFw1pcMZTtx9ffF3\nE3ATlS9Ts9loZjMAir+bmtm4u28sflD7gGsYxntgZu1UjMa33P17hbhp159qv5nX34u7bwFuozLH\nNcnMerOzNP03UHZGwnA9nw6n8KScCyxrVuNmNt6sshm7mY0H/gB4KF9rWFgGnF+8Ph/4QTMb7zUa\nBWczTPfAzAy4Fljj7p/vU9SU64/ab+L1TzOzScXrg4G3UJlnuxU4pzit6Z9/6RkJjwCwmIp35zHg\nE01u+6VUPJn3Aw83o33gBiqPI3upjDgvoLKjwi3Ao8XfyU1u/1+BB4EHqBiRGcPU9uuoPAY9AHQU\nx+JmXX+m/WZd/4nAL4p2HgIu6/M9/DmwFvg3YOxwfw9H06GQHyFE6dDKeSFE6ZDhEkKUDhkuIUTp\nkOESQpQOGS4hROmQ4RJClA4ZLiFE6fj/bGZR578SvHsAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# Load the last statepoint file and keff value\n", - "sp = openmc.StatePoint('statepoint.' + str(batches) + '.h5')\n", - "\n", - "# Get the OpenMC pin power tally data\n", - "mesh_tally = sp.get_tally(name='mesh tally')\n", - "fission_rates = mesh_tally.get_values(scores=['fission'])\n", + "with openmc.StatePoint('statepoint.' + str(batches) + '.h5') as sp:\n", + " # Get the OpenMC pin power tally data\n", + " mesh_tally = sp.get_tally(name='mesh tally')\n", + " fission_rates = mesh_tally.get_values(scores=['fission'])\n", "\n", "# Reshape array to 2D for plotting\n", "fission_rates.shape = mesh.dimension\n", @@ -694,7 +697,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mg-mode-part-ii.ipynb b/examples/jupyter/mg-mode-part-ii.ipynb index ef9e00017be..01ae6bf370f 100644 --- a/examples/jupyter/mg-mode-part-ii.ipynb +++ b/examples/jupyter/mg-mode-part-ii.ipynb @@ -102,7 +102,16 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", + " FutureWarning)\n" + ] + } + ], "source": [ "# Create cylinders for the fuel and clad\n", "# The x0 and y0 parameters (0. and 0.) are the default values for an\n", @@ -278,7 +287,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -287,12 +296,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAD4CAYAAAD2OrMWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAiDElEQVR4nO2dfewmVXXHv8dFNKG2K7Aib1ppqRFt2eJmrQk10DUKtHXVrQg21aotaiCttdWiJmowJLTE+F4sWioaBakrShR5jQkaY+tCQME3VoqFZYGtuIDVliyc/vHM6DB77517znPvnTvznE+y2d8zM2fOPXfuc57nuXO/c4iZYRiGEctjxm6AYRjTwpKGYRgiLGkYhiHCkoZhGCIsaRiGIWKfsRugYd+1a/jxB68ZuxmGMVv+d+fDeGj3w+TaN8mk8fiD12DDJ548djMMY7Zse+Xd3n2TTBp9du/aute2teu2ZLcZOl5jM1YspWxqbVcpmymPmRaa4uKuX33Gvtx+09h607nOYzYdcoSzE3bv2opr77ptcjZbjn6zc7vPpuZYarXxHQ+s3jjb9sq78cB3H3L+PEkyEUpEFxDRvUR0c2fb/kR0NRHd2vz/RI/tq5pjbiWiV0n8urJly7V33RbcL7HxdXBoX8hGei7AHWvo4o8dvwapn1D8PjR9NjTOpGjGTIn42/1DpLp78nEAJ/S2nQngWmY+EsC1zetHQUT7A3gngOcA2Ajgnb7kkgpNJ0uPGbLxDYDUuPykfJO3pEg0qxS/y0YzzobIESuQKGkw83UA7utt3gzgwubvCwG82GH6QgBXM/N9zPwTAFdj7+STjBwDU0stAyAHOd4AY5Ljg6YUOdqSc53GQcy8s/n7bgAHOY45FMAdndd3NtuyEDvRU8KPtC2bDjlC3A6NTQpyxJ+KEv1YU/w5/BRZ3MWL2dalZlyJ6DQi2kZE2x7a/UiUjavDNAMgZBOaPAvZuPyUIPWbxhf/kE2fmH6WUiJpauJ3MaVxljNp3ENEBwNA8/+9jmN2ADi88/qwZtteMPP5zLyBmTfsu3bR7FBH+7YP2aR684YGgM+HtF2aWErFHzpPSh9j91mobT5SJTPttVz2+ie75UpEvw7gi8z8rOb1uQB+zMznENGZAPZn5rf0bPYHcD2AY5pNNwB4NjP350ceRfeWK1DmXviUbGLflFIbi38a8aewCd1yTZI0iOgiAMcBOBDAPVjcEfk8gEsAPAXAjwCczMz3EdEGAK9n5r9obF8D4G3Nqc5m5n8d8tdPGoZhpCV70iiNJQ3DyEv2xV2GYawOs0oamnvSUpvdu7aqbKTMzaaEjznZ1DzOJv/zpA26uwAnZha4u5y2nU0O2Wj89G1i/fR9aGwk8cfYWPx1x9/3o4m/62fWcxpSIREwvshLKj7T2IwdP+C/Npr4U4rPSgjWNDZjxw8UFKyNhUawJhUsDQl8NCIvabs0Nqnib7f70Ii8NPFLBVsp49eOs5CNpF0+P6njb/cPMemkoaGEhkMjWMtBKcFWH22iTc1Y8cf4GSv+FKxU0pi7kMiog5qu7dQEa9WhWQ6s9RMi5XLtIT8x21JTi2BrzPiH/JSKf7KCtbHQdFjfRiskyqGMTGGjOWetgrVS8YdIKSST9vNYY2bSSSOHYE1iMzRgpDYlxEep+8zXLk2fhWKcU/w+NH02acFaSYYEa0AdD3zV2KSIRWOjiSWHH42PUn5WaZzNep2GYRjpme06DcMwymNJwzAMEbMolgSUeaBKKT8lHihTysbiLx9/Tj/ADOY0XKsPh0Q+PhuJkCiHjUsUFePHdx6Jn1zx9/e7tqXwIxF5DfVPrnbVYhMzzmY9p+FbKuxbQutb4hxa2lzKxtduqVZg7botWLtuS1B70D9njvh9tzxz93Mbny/+tn9c+3zUOmZKjbMuWZMGET2diG7s/HuAiN7YO+Y4Irq/c8w7Ys8/tERWuoRWK9iSbPehiWXoU1tKyvi1/iX7hjQuUkKJNtQuzTiTbPft08Sf4j2TNWkw8/eZeT0zrwfwbAA/A3Cp49Cvtscx81m52pOjk13HxAjWSugTXAklh4jJFX8ombnenDn6wxVrieXbMeNME3+qD8FlKfnzZBOAHzLzjwr6NAwjMSWTxikALvLsey4R3UREXyaiZ7oO0BRL6lPiUybWT6lPvD6rLlgb6xue5pgUTFawRkT7AngRgH9z7L4BwFOZ+WgAH8Si9MFe+IolhUghvsohWNOIr6STihq0sUgnFX2Tt6lFblJCk7ehdpUS7GnGpstPiJgkU+qbxokAbmDme/o7mPkBZv5p8/flAB5LRAfGntgn8EkpPitl42v3kMipz+5dW4NvAJefHPFLJ29T9XMbXyjRSnUktY6ZUuOsS5F1GkR0MYArXYWQiOjJAO5hZiaijQA+i8U3D2/DXNoTW9xVp43FP83FXaMK1ohoPwD/BeAIZr6/2fZ6AGDmjxDRGQDeAGAPgJ8DeBMzfz10ThOsGUZeQkkj+zJyZv4fAAf0tn2k8/eHAHwodzsMw0jD5FeEGoZRltkkDd/kltlMr12lbGptV+02sxSsAfJiMalFbj5RVOjuQapYStr4Ch/5bIbEV64CQ1OO33X9gfTjTGozFP/KCdZC20MCHx8aURAgX1shLXyjsQktcZb2WbtPYjMkvvK1a4rxS9d8aMeZRrAm2d5n0kkjhfimi7SKlc8GkK/E0yzSyrG60XXOEoWPYvrZZZOaEvoO39oaaT/HaFxcfpbZD0w8aUjRdLLWT4jUKzl9jFVhTJtoUzNm/EMfGqXin7pgbXRKrfePoaa2GGmp6dpOVntSEzUItkp8ygD1VhjTFBjSMFb8QNy3zdzkinXSSSOF+KZLSsFait+5Q/tTD7yUgjUpNQjW2nPGbOu2K8X8VQ7BmosU75lZ3HIFZM+7bO1am6HbrVo//dtrQ7fb+u2StG2Z+GNslolf2s9zi7/E9e+2TRt/18/KFEuKmYBa1ibmwo/RrlI22vhL2dTYZxqbseNfmaRhGEYaZr24yzCMsljSMAxDhFVYq9xmTg+UKWVjfWYV1vYih2AtZBNa4aix8YmcxhZfDd0+lvpxic8AXfyAbPn12H0G+OMvMc4mLVgjotuJ6NtNIaRtjv1ERB8gou1E9C0iOib23KnFRymX3Q4J4CQ2WvFVKptQ23yE+lGj7/CdZ+w+8yEVmGlIfS1jx3+pOY3jm0JIGxz7TgRwZPPvNADnpXIqFV/5zpFaR1FKfBXrexkbX/wa8ZnUZogSOhNN/C6mNM5qmAjdDOATvOAbANYS0cE5HJV6Y8b4GUuwVYJa4ndRg2Ax9pgUTFWwxgCuIqLrieg0x/5DAdzReX1ns+1RTKlYUgw1tSU1MbGtevylmKpg7VhmPgaLnyGnE9HzNCdxFUvSkHqtvuuYIRtNERsNpQRbGr1KzDmWpdb4XTY5Eu1kBWvMvKP5/14sij9v7B2yA8DhndeHNdsGSS0kSlUta8hGei7AL6SS2gz5TxW/hhKCNc2YGbKRohkzKeNfdsxkveXa1Dx5DDM/2Px9NYCzmPmKzjF/COAMACcBeA6ADzBzP7E8iv4y8u4twRiBT9emi9SmhA+JjSb+OfXZFOLX2IzRZ6NpT4joCCy+XQCLhWSfZuaze8WSCIu6JycA+BmAVzPzXrdmu5j2xDDyMlqxJGa+DcDRju3dYkkM4PSc7TAMIx013HI1DGNCWNIwDEPELARrJSabStnUOtlWyo/GRyk/qz7OWkywJrDp7ws9KamkYM0nZCoh2ApVGNNQqsJaqj4LxS8VrPnaUOpaViNYy0lJwZr0/n0pwZqvDWvXuWuraOLfetO5XhvfG0MjWPOd69q7bnPu04rPUvaZJv7Q2JCMs7kL1kYhlZAoxTFDbcihEXANthzaC+lX3ZD8PiWuWHOsPF21cTbrpNGnJpFQKnl4jdQk2EpBDQlAy1QFa9UwZSFRqSI/xt7k0CuVYqqCtdHQCIlc5xj6fV6rYM3V7tSaGF8RH02fpda4uGxSf/Jq9DqAW7Am7bOYtg351TDppBEaaL7tQzap3rwaUZBGsOYbaL5BVip+6ZyG1oc0Fm2fadrmI9W3Ru21XPb6T/6WK/DoWWSJ+EhTYa09pv86ZLNMJbfYalndY2I+lfoz77mqpXWPydlnmgpry/SZ9lrG2izbZxLBmsvGiiUZhiFitus0DMMojyUNwzBEWNIwDEPE5AVr/QkdIH4iqNSk1rKTehqb3BOhGhuLv9w408Q/5Kdl8hOhWlHQlCqsaWzGjh9IV2FN07axq6VpbMaOH/jltRllIpSIDieirxDRd4joFiL6a8cxxxHR/U31tRuJ6B0SHxpRkFbkIyWlYM1noxVslYhfI1jT+ChVYc1HaJxJbaSkFqy15xwi55zGHgB/y8xHAfg9LMoXHOU47qtN9bX1zHxWygakEhJJK18NdXwpwZrLT4kKa9o+K1FhrZRgb8hPij4bYnKCNWbeycw3NH8/COC7cBRBKklNIqk5C9bmRg7BWikmK1gjol8H8LsA/t2x+7lEdBMRfZmInhk4x2QqrMX4mbNgLUf8Y1KiwFYuJilYI6JfAbAVwBuZ+YHe7hsAPJWZjwbwQQCf951HU2EtlZBIKtgaIpWQSCOMk8Y/ZJOqwJJGsFYq/iEfqYSRGmHgUNtcfpYla9IgosdikTA+xcyf6+9n5geY+afN35cDeCwRHRh7/hyCNYlNalFQCZvUfeZrl6bPQjHOJX6Nn7HHTJ9st1ybIkgXAriPmd/oOebJAO5hZiaijQA+i8U3j2CjfBXWusQKdmKPL2WTIhaNjSaWHH40Pkr5WaVxNopgjYiOBfBVAN8G0E5CvA3AU4BfVFc7A8AbsLjT8nMAb2Lmrw+d2wRrhpGXUSqsMfPXADiddo75EBYlGQ3DmAimPTEMQ4QlDcMwRMxCsNZffDMkvvHZSERROWxcoqgYP32NQ0z8fT81xJ/KRhM/EC58NKX4h2y046xl8t80fEuFpdqT0DLdUjbt/phtXT+u44e0F/1z1hB/KhtN/O0+H1OKf8jGF3/sytdJJ42hJbKapdpSHUFIMCRhSHsgFV9pSBm/1r9kX6n4h5JJKklAifhTvGcmnTQ05NARaARrJfQJYwm2coj8NJSKv0/Mm1kTfy16pZVKGjUJiQyjBJMVrNVCTSKhEm0ZS+RWS/wuSvRJTfFPUrA2JmMJiYbIJSTS+l7GZkzB2hAlEsSYgrWYtrn8LMvkH/dnt1ztlqvdck0/zlaiWFL3t1suUVApP1IfNdtY/OXjT+FnJZKGYRjpsAprhmEkw5KGYRgiZpU0pPekd+/aWrWNFKmf2uPPbaPx0dpJj6/ZRsrk5zQ0dwJaO+ndA+DRE0a7d22Nsmn95KwwJmmXz48kFolNe4yrD2PapbGJiX+ZaxnjR3v9++2KsUkx/rt+Rp0IJaITALwfwBoAH2Pmc3r7HwfgEwCeDeDHAF7OzLeHzhlTYQ1w30ILLfENVbEK3b7y2Uj9SGMZwtU2bfy19pnUJiahuihVLa2WcTbaRCgRrQHwYQAnAjgKwKmOgkmvBfATZv5NAO8F8A+x508hvuni00SEBtnadVvEIi8XmlhSLxHWCtZStCNGryK10bYjZlu3XSk0IZpxpok/xXsm95zGRgDbmfk2Zn4IwMUANveO2YzFA4iBxYOFNzUPJc5CKcHSUBtK6GBcg7BU/JpEmxpXrCVW3sa8mceKPwW5k8ahAO7ovL4Te1dZ+8UxzLwHwP0ADuifKEWxJMMwlmcyd080xZJc1CBY0ugINLg+zUrFH/ok1c4rSHHFWuobXuqiTBpyXevcSWMHgMM7rw9rtjmPIaJ9APwaFhOigwx1vEbg07fRvgE0giXp/tQDTys+S9EOjWAt5s2paUfMtm67UgnJpONME3+K90zWuydNEvgBgE1YJIdvAngFM9/SOeZ0AL/NzK8nolMAvJSZTw6d12657m1jt1ztlmtsu3w2XT9j33I9CcD7sLjlegEzn01EZwHYxsyXEdHjAXwSiwLR9wE4hZmDMzg+7Yn0a2/MBRnTRvoJJvVTe/y5bTQ+WrvaYlnGxnW8CdYMwxBhgjXDMJJhScMwDBGTL5bU0p15zvVwlFJ+pD5qtrH4p/kQnhCTn9Nwrb6zx/3Z4/4Ae9zfMuNsthOhQ8t1pYI1wC9ySikKSmWTOhaNTWrBllR8NsX4gTRiSk0sQNw4W9mJ0FRCstSCLZ9gqQQaPYIm/tTis9TtToUmfhdTGmezThp9Sr0xY/xMWbA0RC3xu6hBsBh7TApy+FmppFFivb9h1ESOMb9SSQNIrwlxHVOLYM0Vaw4Rk1QT4dP4pKZU/H00gjXNOBtiqoK1rJQQrLXbQzaS7T6GBppPSJVyYKSMX+tfsq9U/CUEa6Htvn1jCdYmnTQA/6eJrzN9HR26+KVs2v0x27p+XMeH3gCu/qkh/lQ2mvjbfT6mFP+QjS/+2AQ06VuuhmHkYWVvuRqGkR5LGoZhiLCkYRiGiFkI1lwLWGKeXCQ5vpRNilg0NppYcvjR+CjlZ9XHWUuWpEFE5wL4YwAPAfghgFcz827HcbcDeBDAwwD2MPMGqS/f8ttNCBee2cvmrnMHRT57CXyifGhsloxFY6OIHwC2rHMe7rUJtQtwr9isuZ+l8U91nHXJ9fPkagDPYubfweIZoW8NHHs8M69PmTAA/3LhlDYhPUDIx5g2qfvM1y5Nn4VinEv8Gj9jj5k+WZIGM1/V1DABgG9g8RTy4miERH2bmoVEQza+ASg9Z62CtVLxD/kYS7CmiX8qgrXXAPiyZx8DuIqIriei00InSVEsqSaRUIpEUys1CbZSoEkAKY5JQVWCNSK6hohudvzb3Dnm7QD2APiU5zTHMvMxWNR6PZ2Inufzl6JYUk2CtVp0BMYwOfRKpahKsMbMz2fmZzn+fQEAiOjPAfwRgD9lz7JTZt7R/H8vgEuxqP2aDKmOwneO1OIr6RJmLakEW5r4c9hIGUuwF+MnRZ8NkWucZfl5QkQnAHgLgBcx8888x+xHRE9o/wbwAgA3S/xItQKtTUgwlOrNK9WLaGw0sZSKP3SelD5KxK8dZ1IbKdprqRmbXbJoT4hoO4DH4ZflFb/RVFA7BMDHmPkkIjoCi28XwOLW76eZ+eyY86eusNZ2Ysim7yenTakKY8tUC9PYWPzlxpkm/q6f2T4j1DCMPJhgzTCMZFjSMAxDhCUNwzBEzEawJpk40ti0k03tMf3XIZsSk1rdY/qvfTalJvVK9JlmInSZPss9qblsn8UK1iQ2LZOfCPWKbxTiK59NaED59oWWBfsqeWltNG2Wxi8tFqRB40d7/aV95iswFPIjtUk5zpYd/7OdCM0hWEu17Da09FgqJPLZhAbZ2nVbVIKtVPGHzpPSh0awpukzTdt8pJIEaK9llYK1WkglJBpaqFSLkKiPq92aAasRUmn6TCpYG8Jlk3rlrVawlqLPYto25FfDrJNGn5pEUnMWrM2NHIK1UlQlWJsipYREMX7mLFjLEf+Y5BCs1TQWpcw6aaQSrKU4ZqgNOS6u61OmlGBLOqdRSrBX6mfgnMfZpJPGkGBHYxOacY7ZNuS/bYPEJiSk8rXB9xtZE/+Wo9/stfHd1dEI1nzn2nTIEc59WvFZyj7TxB8aG5JxllqwFnsnbPK3XAHdJ1ffJqazStikiEVjo4klhx/tt5A59/MY8ZtgzTAMEbNdp2EYRnksaRiGIcKShmEYImYjWAMWi3AkgrU+NUzqLWOjiX9OfTaF+DU2tfRZS7aJUCJ6F4C/BLCr2fQ2Zr7ccdwJAN4PYA0WjwI8Z+jc3YlQjSgolSiqpE0qkVsNsYxtU0p8Vmv8MWNmzInQ9zbV09Z7EsYaAB/GooTBUQBOJaKjYk8+JArSLNWW6gikgrkh/z6k4jOfzZD/VPFrSF1gyYVmzKQWn2nGTMr4lx0zY89pbASwnZlvY+aHAFwMYPOAzVKUKHxTi2BNU2FMQ4pEs0rxu2w042yIXHql3EnjDCL6FhFdQERPdOw/FMAdndd3Ntv2YkoV1mKoqS2pyfEGmBI1xVadYG2gytp5AH4DwHoAOwG8ZxlfU6qwVotgaSyRWy3xuyjRJzXFX51gLVRljZnvYeaHmfkRAB+Fu3raDgCHd14f1mxLQioh0VQqX2l9L2Pji19aLS2mn6WUSBCa+F1MaZzlvHtyMDPvbP7+GwDPYeZTesfsA+AHADZhkSy+CeAVzHxL6NxDj/sbuoXkswl1aAmb7m0wiZ/+zP5U4y9l4+tn3x2qUu0qZRMzzkbRnhDRJ7H4acIAbgfwOmbe2a2y1hx3EoD3YXHL9YKYKmsu7Un3t1stoqAUNrGfDCVsrM9Wp89MsGYYhggTrBmGkQxLGoZhiJhV0tDck5ba7N61VWUjpVYbbfzWz/OJf/JzGq6Z4NgKU8tUy4rx09q0x/RfD7VL0rZl4o+xWSb+ZaulaWxqir/E9e+2TRt/18+sJ0J9QiJgfMFW6JaXREgF+G8HhmxcbatBFDWmzZD2QtrPparylRDsAXUI1rIy9NUqhWBraJD5RE7SRTSaWFIvEdYK1lK0I6afpTbadsRs67ZLGr/0ze/zk1qwF7MfmHjS0FCi6NBQx5da/VlKsNVHm2hTM1b8gO4ZGKmZqmCtKuYuJDLqoKZrW51gbWoMre9P6SdE6PduSlyxloo/tY5Cw5jx1/BtM1c/TzppDHWIRuDTt9G+AVL8zs1hozlniQSgEayVij+EJn7fHJi0nzUfgineM5NOGkC4WpQLX0cPVUTz2cTcCutv89mUqJYVGmjSPmv3SWxCsYRinGL80klV7TiT2kjj7zP5W64tMffA+6y6Ta3tKmVTa7tqsJn1Og3DMNIz23UahmGUx5KGYRgiZlEsCSjzcJRSfmp9oIzGxuIvH39OP0CmOQ0i+gyAp7ftAbCbmdc7jrsdwIMAHgawh5k3xJzfHvcXZmhCTCu+0sTv0n7k7ueY6x/an6tdtdjEjLPicxrM/PK2SBKArQA+Fzj8+ObYqITRx7dU2LeE1rfEObS0uZSNr93S4jZr120J3vJz+ckRv++WZ+5+buML3fKWrq2pdcyUGmddss5pEBEBOBnARTnOrxHfpBYFaUVeMW0d2p96ZWlKwVqoXVrBltRGimZtRcrrn2NsuvyEqEGw9vsA7mHmWz37GcBVRHQ9EZ0WOtGUiiXF+CnRFtebswbBXuwxy+KKtcTy7Vriz+VHPRFKRNcAcC2WeDszf6H5+1SEv2Ucy8w7iOhJAK4mou8x83WuA5n5fADnA4s5DW27DcNYDvU3jVChJOAXNU1eCuAzgXPsaP6/F8ClcBdUSkaOtfquY4ZsSgm2XJ8yOQRbrvhDn3Cun1U5+sMVa6lveEP9rIm/hC4mhpw/T54P4HvMfKdrJxHtR0RPaP8G8AIAN0sclBCstdtDNpLtPjSxxKgpJaSMX+tfsk/zIRAiNHkbapdmnEm2+/bNUbB2Cno/TYjoECK6vHl5EICvEdFNAP4DwJeY+QqpE59YRyNYSy2+0giJNGK6Prt3bQ2+AVx+csQvnbxN1c9tfKFEK528rXXMlBpnXWajPbHFXXXaWPzTXNxlgjXDMESYYM0wjGRY0jAMQ8QsBGvSSS2XTYnfp5p2lbLRxJLDj8ZHKT+rPs5aJp80fEtpNyFcrGYvm7vOHRT57CXwGfABOERB0naVslHEDwBb1jkP99qE2gW4V3HGxTJOP2viB4Riygri7zLpnyehtfdSwZrGZqh+h0ZI5NueyiZ1n6UWX/naNaf4fWjFZ77tKW26TDppDKFZ+FRCsBbjN5eN5pylBHupxVcapOeMfaP1bVx+NYI1KSn6bNZJo09qVWTITwjNQNOwjPx5GWoRbI0Z/5CfUvHn8LNSSaOE3iOWmtpipKWmazs17UmV1FBhrUQbfH6swtp4fd9lrPhTMOmkoREShQZ0ygprUr3IkJ8UsWhtNPGHro0mfqlgK2X82nEWspG0y+cndfzt/iEmv4zcdWsz5pOs+7tz6JmSWj99m1g/fR8aG0n8MTYWf93x9/1o4u/6WRntSUhB6UNq014gqU3udtVuI6XmWFZhnK1M0jAMIw0mWDMMIxmWNAzDELGU9oSIXgbgXQCeAWAjM2/r7HsrgNdiUQjpr5j5Sof90wBcDOAAANcD+DNmfkjajlpFQWPZ1PpAmVI2qx5/ThtgyTkNInoGgEcA/DOAv2uTBhEdhcWj/jYCOATANQB+i5kf7tlfAuBzzHwxEX0EwE3MfN6Q36EKa8BwhSmJTWiFn8Zmy9FvTtKuUjaa+AFg603nOrdL49e0bew+A/zxlxhny8aSbU6Dmb/LzN937NoM4GJm/j9m/k8A29F70nhTSOkPAHy22XQhgBdL/OcQrKVadjskTJLYaMVXqWw0hM6T0sfYfRZqm49US9m117JWwdqhAO7ovL6z2dblAAC7mXlP4JilSCUk0wi2QpQSX8X6XsZmTMHaECV0JikFi1MZZ4NJg4iuIaKbHf82L+1dwKpXWKv1TeOiFsGaixL9WFP8owjWhooiedgB4PDO68OabV1+DGBtU1TJd0y3Hecz8wZm3rDvWt0XpCkLiUppJlIQE1tN12KIHAW2SjElwdplAE4hosc1d0iOxKK2yS/gxQzsVwD8SbPpVQBCiSgJtVRY0/iRUkqwpdGrxJxjWWqN32WTI9FWKVgjopcQ0Z0AngvgS0R0JQAw8y0ALgHwHQBXADi9vXNCRJcT0SHNKf4ewJuIaDsWcxz/IvGvERKFSFlhTHPBSgjWhvynil9DSsGajxyCNSmaMVMi/nb/ELNYRu763RYj2FnWJse98LFiKWVTa7tK2UxlzJj2xDAMEaY9MQwjGZP8pkFEuwD8yLP7QAD/XbA5OZlLLHOJA1idWJ7KzM4CDZNMGiGIaBszbxi7HSmYSyxziQOwWAD7eWIYhhBLGoZhiJhj0jh/7AYkZC6xzCUOwGKZ35yGYRh5meM3DcMwMmJJwzAMEbNJGkT0MiK6hYgeIaINvX1vJaLtRPR9InrhWG2UQkTvIqIdRHRj8++ksdskhYhOaPp9OxGdOXZ7loGIbieibzfXYtuwRT0Q0QVEdC8R3dzZtj8RXU1Etzb/PzHmXLNJGgBuBvBSANd1NzaPHjwFwDMBnADgn4hoTfnmqXkvM69v/l0+dmMkNP38YQAnAjgKwKnN9ZgyxzfXYmprNT6OxfjvciaAa5n5SADXNq8HmU3SWObRg0Y2NgLYzsy3NQ+MvhiL62EUhpmvA3Bfb/NmLB6zCQgetzmbpBEg5tGDNXMGEX2r+XoZ9fWxIqbe930YwFVEdD0RnTZ2YxJwEDPvbP6+G8BBMUZLlTAoDRFdA8Alb337wJPEqiUUE4DzALwbi8H6bgDvAfCacq0zehzLzDuI6EkAriai7zWf4JOHmZmIotZfTCppMPPzFWYxjx4cjdiYiOijAL6YuTmpqbrvpTDzjub/e4noUix+fk05adxDRAcz804iOhjAvTFGq/DzZPDRg7XSXMiWl2Ax2TslvgngSCJ6GhHti8WE9GUjt0kFEe1HRE9o/wbwAkzvevS5DIvHbAKCx21O6ptGCCJ6CYAPAliHxaMHb2TmFzLzLU1Rpu8A2IPOowcnwD8S0Xosfp7cDuB1o7ZGCDPvIaIzAFwJYA2AC5pHQU6RgwBcuijXg30AfJqZrxi3SfEQ0UUAjgNwYPOIzncCOAfAJUT0WiweNXFy1LlsGblhGBJW4eeJYRgJsaRhGIYISxqGYYiwpGEYhghLGoZhiLCkYRiGCEsahmGI+H+G++jtQ1FPmgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -472,7 +483,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mgxs/library.py:412: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", + "/home/shriwise/opt/openmc/openmc/openmc/mgxs/library.py:418: RuntimeWarning: The P0 correction will be ignored since the scattering order 3 is greater than zero\n", " warn(msg, RuntimeWarning)\n" ] } @@ -554,11 +565,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=66.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=66.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n", " warn(msg, IDWarning)\n" ] } @@ -603,50 +614,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:02:43\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:38:16\n", + " OpenMP Threads | 2\n", "\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16513 +/- 0.00090\n", - " k-effective (Track-length) = 1.16337 +/- 0.00104\n", - " k-effective (Absorption) = 1.16479 +/- 0.00080\n", - " Combined k-effective = 1.16460 +/- 0.00068\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16510 +/- 0.00094\n", + " k-effective (Track-length) = 1.16422 +/- 0.00106\n", + " k-effective (Absorption) = 1.16519 +/- 0.00081\n", + " Combined k-effective = 1.16502 +/- 0.00071\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -671,7 +683,7 @@ "source": [ "# Move the statepoint File\n", "ce_spfile = './statepoint_ce.h5'\n", - "os.rename('statepoint.' + str(batches) + '.h5', ce_spfile)\n", + "os.rename('statepoint.{}.h5'.format(batches), ce_spfile)\n", "# Move the Summary file\n", "ce_sumfile = './summary_ce.h5'\n", "os.rename('summary.h5', ce_sumfile)" @@ -772,11 +784,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=3.\n", " warn(msg, IDWarning)\n" ] } @@ -876,32 +888,38 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -949,38 +967,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:04:03\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:41:06\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -988,11 +1005,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16541 +/- 0.00086\n", - " k-effective (Track-length) = 1.16590 +/- 0.00096\n", - " k-effective (Absorption) = 1.16469 +/- 0.00046\n", - " Combined k-effective = 1.16480 +/- 0.00045\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16517 +/- 0.00094\n", + " k-effective (Track-length) = 1.16588 +/- 0.00106\n", + " k-effective (Absorption) = 1.16485 +/- 0.00050\n", + " Combined k-effective = 1.16499 +/- 0.00048\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1068,9 +1085,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Continuous-Energy keff = 1.164600+/-0.000677\n", - "Multi-Group keff = 1.164805+/-0.000448\n", - "bias [pcm]: -20.4\n" + "Continuous-Energy keff = 1.165017+/-0.000706\n", + "Multi-Group keff = 1.164985+/-0.000485\n", + "bias [pcm]: 3.2\n" ] } ], @@ -1161,7 +1178,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'Multi-Group Fission Rates')" ] }, "execution_count": 38, @@ -1170,12 +1187,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADHCAYAAAAXg5iPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAltklEQVR4nO2deZwV1ZXHvz9ZRRAUEAVZ3KISjUQ7LhMTicaNMWoWHU00khi30dFMYtRsblk0TjLGqAnj9sFdSRTDjCTqmKAhk6CoKK5xQwFFUBBFRBbP/HFv6+vHe+9e+r2mu4vz/Xz60+9V/ereU1Xnnld16566MjMcx3Gc4rJeexvgOI7jtC0e6B3HcQqOB3rHcZyC44HecRyn4HigdxzHKTge6B3HcQpOpwv0kp6QNLq97VhXqff4S/qDpGMaZ9G6iSSTtHWN9YVsJ5K+IunuOrb/lKRnGmlTZ6AhgV7SlyVNl7RE0quxMe/ZgHLHS/px6TIz+6iZTam37LVF3Ifl8dg0/z3a3nalkDRF0rIyu/eo9/ib2YFmdm0DTQVWO84LJd0jabvMbUfEwNm10XZVqGtWtHNA2fJHog0jWlFmq9qJpCZJ/yNpkaQ3JT0p6SeSNlpTGxqNpNGS3i/zv/82sxvNbL/WlmtmfzGzbRtpK7TwoWZbZ0k6aw22X+0cNpK6A72kbwG/BH4KDAKGAb8GDqm37AJxkZn1LvnbqdEVtFGQOqXM7r+1QR2N5CIz6w0MAeYCV7ezPdV4ETiy+YukHYFea9MASf8ETAH+CmxnZv2AA4CVQEX/XBs/hGW8UuZ/n1vL9beGftEHvwT8UNK+7W0QAGbW6j+gL7AEOKyGpgfhh+CV+PdLoEdcNxqYA3wbmA+8CnwtrjseWAEsj3X8d1w+C/hs/HwuMAG4DngbeAJoKqnbgK1Lvo8Hflzy/TjgOWAhMAkYHJePiNt2LdFOAb4RP28N3AcsBl4Hbq2x/y3qLFvXXM8xwMuxrO+XrF8POAt4Hngj7uvGZdseG7e9H+gC/CKW8yJwSvN+AIcBD5XV/y3g91Vs+2B/y5aXHv9dgenAW8BrwH/G5T2BG6LNbwIPAoMqHMf1gB8AL8Xzfx3QN+fYpI4zMAZ4p+T7PwOPRFtnA+eWrHs51rUk/u0Rl38deApYBNwFDI/LBVwcbX4LmAnskNlmZsV9frBk2c+B70cbRlQ6/sBYYGq5b5PRTqrYMRW4NGHrWMIPwcXxXP6Y0OavAxbE8/YDYL2S9nhDBf/uWrJPFwAPxOP2e6I/V6h7NDCnik1TU+chnv8nCXFhLnB6pXKB7aNdbxLix8FlPnU5cGcsZxqwVaItl8aMB4DvlHz/LTCPEDfuBz6aiHWDgdvisX4ROLWkrIptr9pfvVf0exAa9cQamu8DuwOjCFcKuxKco5lNCc4zhBC0Lpe0kZldAdzIh1fD1X7NDwZuAfoRgvVlOYZL2pvgdIcDmxGc9pacbYEfAXcDGwGbA5dmbleNPYFtgX2AsyVtH5f/G3AosBfhpC8iOF4pexGcdX/CD9eBhGO9c9y2mUnAFiVlAxxNaLSt5RLgEjPbENiK8EMEITj3BYYC/YETgXcrbD82/n0G2BLozernr9qxqYqkDQhXzM+VLH4H+CrBT/4ZOEnSoXHdp+P/ftHX/ibpEOB7wBeAgcBfgJujbr+4zUfifh5OCIS5/B3YUNL2kroARxB+GNeYNWgnHxCPzx6EIJJiN+AFwt36Twi+3pdwvvYiHNOvrYHJXyX8gG5GuHv41RpsW06t83A1cIKZ9QF2AP5UvrGkbsB/E9ryJoT2dqOk0q6dI4DzCG39OcIxSCJp91hvqQ/+Adgm1vUw4bxVPIeS1ou2PUqIjfsA35S0fyyrWturSL2Bvj/wupmtrKH5CnC+mc03swWEg3Z0yfoVcf0KM5tM+EVbkz60qWY22cxWAddT5bazil3XmNnDZvYe8F1gj8w+0hXAcMIdwDIzm5rQnx77QJv/yvuozzOzd83sUcKJbd6HEwlXsXOijecCXyq7hT7XzN4xs3cJjn5J1C8CLmwWxe1vBY4CkPRRwlXI/9Sw+1clNj9c5ThsLWmAmS0xs7+XLO9PuJtaZWYPmdlbFbb/CuFK5AUzW0I4B0eU7V+1Y1OJ0yW9Sbj62pMSPzOzKWY208zeN7PHCEF7rxplnQhcYGZPRf/+KTBK0vC4f32A7QBFzas1yqrE9YSgty/hrmHuGm5fDxsR2v685gWSLorn+R1JpRdir5jZpfEYLCcEvu+a2dtmNotwB1nanlNcb2aPm9k7wA+Bw+OPXSUGl7Wbw8vW1zoPK4CRkjY0s0VmVsl/dydcXFxoZsvN7E+E9nBkiWaimT0Q9/9GwkVULV6X9C7wN0IX9h3NK8zsmnjcmtvyTpL6VinnE8BAMzs/2vYCcCXh+DfvX6W2V5F6A/0bwIBE391gwtVyMy/FZR+UUfZDsZRw8HOZV/J5KdAzsy+xhV0x0LxB+PVMcQbhtvGBOLrh6wCSvlfyMGZcif7nZtav5K981En5PjTv/3BgYrOjEwLCKsLVVTOzy/ZpdpV1ANcCX5YkQuOcEJ2uGqeW2LxzhfXHEq6mnpb0oKSD4vLrCV0dt0h6JQaRbhW2r+QbXcv2r9qxqcTPLfQ1jyDcQXxwwSBpN0l/lrRA0mJCIB9QsZTAcOCSkmO/kHDOh8SAcBnh7mq+pCskbVijrEpcD3yZcEdTz11VkrIHmsMId4bvE66qATCzM+Kxm0g4B82U+tAAoBurn7OcNlOpvJdiedXOwytl7abFVWviPHyR0H3zkqT7JO1RofzBwGwze7/G/qyJ/xH3pTehO3p03D8kdZF0oaTnJb1F6Fpr1ldiOGU/dIQ7zOa2Ua3tVaTeQP834D1adhGU80o0uplhcVkO9b5acyktH3JtWs2ueDvbn3Bl9U5cXHFbM5tnZseZ2WDgBODXkrY2s5/ahw+OTqzTdgiN4sAyZ+9pZqVXf6XH6FVCV1IzQ0sLi7/6y4FPEYLM9fUYZ2bPmtmRhFvRnwG/k7RBvDs7z8xGAv8EHES4ei2nkm+sJPQ51mPXy8BphEC9flx8E6H7aqiZ9QXGEQI3VPaz2YRb/9Jjv76Z/V+s41dmtgswktDgvrOGNr5E6HcdA9xeQfIO1X13teISdZU+0Hw5Xk1PI3RLJU0t+fw6H97NNjOMD+9Gcmwu9clhsbzXM+yobFyV82BmD5rZIQTfvIPKXRuvAENjN0mpTXXdXcW72P8ElgH/Ghd/mTBA5bOEbqYRcXk1H5wNvFjmf33MbEyso2Lbq2ZTXYHezBYDZxP61Q+V1EtSN0kHSrooym4GfiBpoMKQsrPJ7498jdAX2FpmEK5gu0g6gJa36jcDX5M0SlIPwq35NDObFbuY5gJHxW2/TugHA0DSYZKaA+oiwkkqvSpoFOOAn8TuAuIxrDWaaQJwmqQhkvoBZ1bQXEe4ClqR0eVUE0lHSRoYr4jejIvfl/QZSTvGW/K3CI250vG5Gfh3SVtI6k04B7da7a7ALMzsHkJDPj4u6gMsNLNlknYlNLxmFkT7Sn1tHPDd2MWFpL6SDoufPxHvELoRgtuyKvuX4lhg7xh4y5kBfCG2qa2jthqtaSdnAF+XdJakTQCiT29RbQML3aMTCD7ZJ/rlt/iwPc8APi1pWOyS+G6FYo6SNFJSL+B84Hex3DWm2nmQ1F1hvH1fM1tB8MFK52ca4WLwjBi3RgOfI/9ZXYoLY9k9Cf73HqHXoBfB10spP4cPAG9LOlPS+jEO7SDpE1C97VUzpO7hlWb2C8LJ/gGhwcwmjPa4I0p+THg6/BjhqfjDcVkOVxP62d6UdEdKXIHTCCfuTUJ/8AdlmNn/EvoIbyNcCW/Fh/1fEB5sfodwYj4K/F/Juk8A0yQtIVwlnhb70KpxRtntc+4VzCWx/LslvU14iLdbDf2VhAdLjxFGmEwmXCGXNqTrCQ+JWvXwr4wDgCficbgEOMLCs4JNgd8RGthThBFKle4eronL7ydc3S4jPBBrFP9BOPY9CFdW58fjeDYlV3hmtpTwkO2v0dd2N7OJhCulW+Kt9uOEB90AGxKO9SLCrf4bsa41wsyeN7PpVVZfTLj7eo3Q5XZjjaLWuJ3EH/m9CQ8z/xG7Bv5IGIFSa3DBvxGC6guEkTs3Ec5j84/rrQT/e4jKz3+uJ4xmmUcYyHFqjr1VqHUejgZmxXN3IqH9t8DMlhPiw4GEu4pfA181s6frsKmUO6NtxxEusF4iXEA+SWjLpbQ4h/HH7yDCM4EXo31XEe4GoHrbq4jM6u0dcToqkg4ExplZaRfV+oThaDub2bPtZpyzziFpCmH45VXtbcu6Rqd7BYJTnXiLN0ZSV0lDgHNYfejrSYQx3B7kHWcdYW1nujltiwjDV28ljDq5k9BNEVZKs6Lm0HawzXGcdsK7bhzHcQqOd904juMUHA/0juM4BadD9tFLvSy8kqQGPQfXXg9hsF6KPhmajdOSngOWJjW9SGsWLqqVrBnL2WhJUrP0vYzk4nQxrN+/0hDvlixb1SOpWW+9dBfhqmcqJc+WUXUAWQnvp4a0v4zZ60qIGo40wD7Mk6mHjIPQdf2kRB9ND1/v2jWd0rBicfr8D+s7K6npQrqudzNe8jnvvYzYsDwt6d4nHUCWr0jvO/MyXG1+WpLOM30Ts6UVK6sr0MckpEsIb028yswuLFvfgzB+dBfCGNd/ie/HSNCPD/NcqrD1ueliHk9Lao5Kb+aItGSLYyu9SqMlu/BQUnPDbcclNdt9MZ3n9PDzn0xqmJp2wI8cU/MVGgA8s/gjSU2PnumWtXh0reTPSM45XZL6QU1PldA2vj0CulYbNh/JShV7LC3p97GkpOu9lV4/1JJB/dNJynPu3CapOeufxyY1/T7I+6nOTNL7dcHz5yU1zEr7/mb7pIfTvzR3RLqun/dMa36Z86w0tV9XVF3T6q6bmPV4OSHZYCRwpKSRZbJjgUVmtjUhAeRnra3PcdYW7ttO0ainj35X4DkLbx5cTkgbLk/PP4SQ1QchU3IfSWv9ltlx1hD3badQ1BPoh9DyTXRzWP0tdh9o4vtLFhNeHLYako5XmI5wOhl92Y7ThjTMt1v69YI2MtdxatNhRt2Y2RVm1mRmTWt5VjXHaTNa+vXA9jbHWUepJ9DPpeUrRzdn9dd7fqBReEd8X9ZsJh7HaQ/ct51CUU+gfxDYJr5itjthbMqkMs0kwrRyECbL/ZN5Kq7T8XHfdgpFXa9AkDSGMNl3F8K0fD+RdD4w3cwmxfcwXw98nDBDzxGJ1/nGcne01dtVORW7+lsyOj3pT887FiY1/9L31qRm/Px/TWq+t8kPk5qRPJnUvEt6jPQzGbMx/pnRSc1Dsz+V1Nw+9MCk5ovPTE5qNt026RrM+1nGa9cTIxi5twlbOL3mg9O28G31bTL+KWHcH69MGA+MSw/B7Tt2XlJzX49PJzWDMgZ4v0J63PrO859Kap7eZHhSc13F+WtaMpSXk5qvvVM+m+fq7LXBlKTmgWm1ZqMM9N4h/WxmyaYZ3XpLXkwIDsZsZuPH0VuY43Vy2bKzSz4vAw6rpw7HaQ/ct50i0WEexjqO4zhtgwd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B0yIlHGNoDvr1Fbc2hK5LF7Dw8/d72hyalE4JIv5Yazkgnnv10fMbLDa/KqGtqRpLb5Iy6pmXUdV66ri9k1LV4/+5JzXGkk4UmbJ2RMHVWWtIudCE5nw6zxiaL2WH4g0nNzL/umrbn7rQk5/xvemaGr81OS7a7aVZS89PdM+p6Ly3hkfR+TZuYUdfQtEQ57XVEWsIBiZh4ffVJUPyK3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+DUMzn4UEl/lvSkpCcknVZBM1rSYkkz4t/ZlcpynI6E+7ZTNOoZXrkS+LaZPSypD/CQpHvMrPyF6n8xs4PqqMdx1jbu206haPUVvZm9amYPx89vA0+x+gTKjtPpcN92ikZDEqYkjSDMtFMpBWcPSY8CrwCnm9kTVco4HjgegA2GJWcJ6ntienrOh/6UkQx1cEYyQ0ai05VKa46bkK5qwdTeSc3AgzMSOU5JS/hchua3GXVlJN5sOGZ5UnMBmyU1E5q+nK5s826117+WLqKZen27hV8PGpY8L6cMvzhp06U3nJnUcFSGXx+ecW5fyNCcmJbwbIbm6Iy6vpFRTnpSLNgyo65hGeVMSR9nm5Su64SZv0xqfrfqSzXXL/5z9STSuh/GSuoN3AZ808zeKlv9MDDczHYCLgXuqFaOmV1hZk1m1kSPjGm1HKeNaYRvt/Drfu7XTvtQV6CX1I3QEG40s9vL15vZW2a2JH6eDHSTNKCeOh1nbeC+7RSJekbdCLgaeMrM/rOKZtOoQ9Kusb50n4vjtCPu207RqKeP/pPA0cBMSTPisu8Re7bMbBzwJeAkSSuBd4EjzCyj89Bx2hX3badQtDrQm9lUoOZTBjO7DListXU4Tnvgvu0UDc+MdRzHKTge6B3HcQqOB3rHcZyC0zFnmFoOzKotOb3Hz9Pl/CItuWGfdDLDFzZIlzM3LUkmgQH0229JWpQz49XMDM2RaYntn9bctzCteeyq9HE+9fp0OV886tak5raxR9UWXJ2upy3o1XsJ232y9qxn/XgzWc5DR6fr2uXbGQlBY9KS34xPa07qm9ZkzWZVfYKkD9k+Q/NOhmaTtOSh+9KamRmJkmPvzDEnncW3amWX2oIaQwH8it5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILjgd5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILTMROmNiE5E89o/pwu5870ywQ/lpHw8HZGAsa5GS8unJJR1+jB6br4R8ZLEnNm0Hk1LdEb6bqGZ+zX8HRVWbMi/W5+uq6RPxpZc/2Lf1iaY027cP7CC5Ia5bwk85sZ5z9j1qeTMup6MuP8jzwsXRcTMvZrx4z9WpxR18vpunbJmIHrvt9m1DUmXddZ7yWSoYCZPXasuX6K3q66rhEzTM2SNFPSDEmr5X4q8CtJz0l6TNLO9dbpOG2N+7VTJBp1Rf8ZM3u9yroDgW3i327Ab+J/x+nouF87hWBt9NEfAlxngb8D/SSlZ4F2nI6N+7XTaWhEoDfgbkkPxRnvyxkCzC75Picuc5yOjPu1Uxga0XWzp5nNlbQJcI+kp83s/jUtJDam0KAGDGuAWY5TFw336+7DBjXaRsfJou4rejObG//PByYCu5ZJ5gJDS75vToW3+prZFWbWZGZNbDiwXrMcpy7awq+7DuzXRtY6Tm3qCvSSNpDUp/kzsB/weJlsEvDVOEphd2CxmWUM7HOc9sH92ika9XbdDAImKoyj7QrcZGZ/lHQigJmNAyYTpjh4DlgKfK3OOh2nrXG/dgpFXYHezF4AdqqwfFzJZwNOXqOCuwMjakuO5JZkMbNvTic8fOypDHteTkuezUmGypjRJ+uM7J+ua+4LGyc1Qy5JTw31WMZ+fez2pASuytDcna5rzH63JTWvrardF76CbjXXt5VfL3u/J/94Z9uamt9tfFCynMMyZuvKsuyctGRSxvk/+BsZdX0+LbH+6bpef6N3UvM8WyU1uw/JOIZ7pyXfuiuteSjjGO6SEYfmb1fbr1fW8Gt/BYLjOE7B8UDvOI5TcDzQO47jFBwP9I7jOAXHA73jOE7B8UDvOI5TcDzQO47jFBwP9I7jOAWnQ84w1XeDRXxqt9pTt3yeiemCnk5LbP+0Rhkz1gzqkdbws7Rk3g59k5pNn00bNGRiOhmKTdKSj52W1pA2GZoyNBmza23Ca0nNmC6Ta66fnDUFUeN5/82uLLmj9nucPvKVZ9IF3ZdRWc45OSMtOTgjWfCFKzdNarY8bl5SMz/DZQeNX5LUDLz90XRB+6Ul/CRDkzFL1y4ZyWsLtksngp3Af9Vcfy4Lqq7zK3rHcZyC44HecRyn4HigdxzHKTge6B3HcQqOB3rHcZyC0+pAL2lbSTNK/t6S9M0yzWhJi0s0Z9dtseO0Me7bTtFo9fBKM3sGGAUgqQthGrVKYx7/Ymbpl2w7TgfBfdspGo3qutkHeN7MXmpQeY7TUXDfdjo9jUqYOgK4ucq6PSQ9CrwCnG5mT1QSSToeOB6AzYdy1xu1M5m+0T9jyqLzLCnRoIyZZjISizZclq7rnoyZZvadmpHM88t0XYzO2K+cGa8y6sqahSojGSrnfH0nY/agT6+6v+b6xVyWYcwH1OXbpX694bANOfUr36tZ2U4vZmTgXJ8+Tkt7p89Jr5xz8o90XVuek+Fru6Ulg67M8Ovd03WNn5YuZqw1xq/fTVfFbhl1DcyYNWzGN0bVXL+Uv1ZdV/cVvaTuwMFApVTWh4HhZrYTcClwR7VyzOwKM2sysyb171+vWY5TN43w7VK/3mBgrzaz1XFq0YiumwOBh81stdx0M3vLzJbEz5OBbpIGNKBOx1kbuG87haARgf5IqtzaStpUCvc/knaN9b3RgDodZ23gvu0Ugrr66CVtAOwLnFCy7EQAMxsHfAk4SdJKQnfWEWYZHVaO0864bztFoq5Ab2bvAP3Llo0r+XwZrNmTL8fpCLhvO0XCM2Mdx3EKjgd6x3GcguOB3nEcp+B0yBmmzNZjxbLuNTXTMjIwDrk7I5FjeoZBf8/Q7JmRDDUlo5yc2YO+lq7LMmxWRm4O389IhpqbLua+wbsmNXvNT9d18iZ/SGpGdHmx5vqneS9ZRlvQi6V8nBk1NS9skTFbU0ZyTa+jMgx6Jy2x/um6lDFLG2PTkqyEwm9nVLV9WsP2GX6dMbvaDb/8YlKz2yXpuq497fCkZiiza67vzvKq6/yK3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcgtMhE6ZYKXizZ03JXUP2Sxbz02U/Std1V4Y9GTPWvFZ9cpcP6NuU1vS8KK1hg7REMzLKyZnOOmPCKyanJSMeqZ3ElF3O2HQ51953Um3B273TFbUBi+nLXdTOLlpKenKSLSfdltQs/VPanl6fTGtWrkprZt6UzlDa+ftPJTX73p6uix4Zmoyo9la1OcNK2DBjVqyjMs4Fu6clq+iS1DzCx2uuX1qjAWVd0Uu6RtJ8SY+XLNtY0j2Sno3/N6qy7TFR86ykY3Lqc5y1gfu1s66Q23UzHjigbNlZwL1mtg1wb/zeAkkbA+cQZozcFTinWsNxnHZgPO7XzjpAVqA3s/uBhWWLDwGujZ+vBQ6tsOn+wD1mttDMFgH3sHrDcpx2wf3aWVeo52HsIDN7NX6eBwyqoBkCLd7EMycuc5yOivu1UzgaMuomTqFW1zRqko6XNF3SdBYtaIRZjlMXjfbrZQuWNMgyx1kz6gn0r0naDCD+n19BMxcYWvJ987hsNczsCjNrMrMmNhpYh1mOUxdt5tc9B7bPaB/HqSfQTwKaRxscA/y+guYuYD9JG8WHVfuRN6DRcdoL92uncOQOr7wZ+BuwraQ5ko4FLgT2lfQs8Nn4HUlNkq4CMLOFwI+AB+Pf+XGZ47Q77tfOuoJCN2THolvTjjZgeqULqQ+5kfQUOnvzf0nNpIxZbQ7eKymBKenjuLR3xsxA6TwwuD1d14sZ+/W/GVUdl+EfUzPq2vPkjMouy/DF2em6uveqneW1cp+9eH/GIxnTjzWWnZq62uTpfWtqhkzP+L1oSh+nnNma3k3XxMEZ539KRl2jc/z6rozzf1GDTtsZ6bre6pmua8OMpDPuzdivF9N1nb5F7QTQG5p+w7zpcysW5K9AcBzHKTge6B3HcQqOB3rHcZyC44HecRyn4HigdxzHKTge6B3HcQqOB3rHcZyC44HecRyn4HTIGaZWvt6DeVdvWVNz07FfTpaz9zczkqGGJiVwWFryZEbSyMjXMuqamKE5PV3XFhPSxRx3ZlqTlQyTcwx/m6HZPV3X9476YVKzYm732gJrn+ubBQzgKmrPUTK6aUqynL2OblAyVEYS04p+Gec/5+UP6QmmYP+MZKh9MsrZOy15o2u6ro1r57YBMD5jJq93c5IybeOk5hc//EFtwSt3VF3lV/SO4zgFxwO94zhOwfFA7ziOU3A80DuO4xQcD/SO4zgFJxnoJV0jab6kx0uW/YekpyU9JmmipH5Vtp0laaakGZKmN9Bux6kb921nXSHnin48q89wfw+wg5l9DPgH8N0a23/GzEaZWVPrTHScNmM87tvOOkAy0JvZ/cDCsmV3m9nK+PXvhDkzHadT4b7trCs0ImHq68CtVdYZcLckA/7LzK6oVoik44HjAdh4WLLSaeyWtmxGWsLLjZmtZ9+cmWZyGJOhmZShyTizVnsiJiAv8eY3s9OakzJmKnqJTZKaC847P13ZvMT6V7NnKarbt1v49UbDOPfyn9Ws8Ecnn540aq9VDyQ1OTNDMTp9HH6S4SPn5iRD7Z6heTFDk5G8yLNpSf+cxKsZacnYnON8Yvo4n8mpSc1BP6qddfiXPyyquq6uQC/p+8BK4MYqkj3NbK6kTYB7JD0dr6JWIzaUKwA0ImOuNMdpQxrl2y38epj7tdM+tHrUjaSxwEHAV6zKxLNmNjf+n09I7t+1tfU5ztrCfdspGq0K9JIOAM4ADjazpVU0G0jq0/wZ2A94vJLWcToK7ttOEckZXnkz8DdgW0lzJB0LXAb0IdyyzpA0LmoHS5ocNx0ETJX0KPAAcKeZ/bFN9sJxWoH7trOukOyjN7MjKyy+uor2FeLjRDN7AdipLuscpw1x33bWFTwz1nEcp+B4oHccxyk4Hugdx3EKToecYYqXgG/Uljze+xPJYsZO+XVSM35iRjLU2UkJnNeYxAk+nlHXyRl1ZcxClcOBGQkhz2YklHF4WnPGhPHpcnqnJYx7MiFYllFIG9AXOKD28fzhSxcki5l5045Jza0zMs5JxkxM507J8LWPZ9T117SECRl1bZ+ua8Wr6WK6vZlR13Hpun7L55Ka2eNOSmouuvqctD0rE+sXVE/G8yt6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjqq8brtdkZoMEvMtH5FR0KFpSc8DFiY1W/d9Pqk5muuSmllskdSsoktS8+9cnNQ8wqikZiSpxCLYaXZ6up7/GZrOvPk1Jyc1f/juF5IaLnwtraFPYv2emD3cmIyyNUA7NBm3Jfz6jnQ5O585Nan5fEZBQ0lPDXbMfRPSBm2Zljw8dPukZucb0lNV3XPUnklNH95Oanb/66NJzYOf3CGpOYXLkpoHpu2V1CSToQBSp/3SJmzO9Ip+nfOa4mskzZf0eMmycyXNja9xnSGp4gR4kg6Q9Iyk5ySdlarLcdYm7tvOukJO18144IAKyy82s1Hxb3L5SkldgMuBA4GRwJGSRtZjrOM0mPG4bzvrAMlAH+fBTPdvrM6uwHNm9oKZLQduAQ5pRTmO0ya4bzvrCvU8jD1F0mPx9nejCuuHQItOwDlxWUUkHS9puqTpsKAOsxynbhrm2y38epH7tdM+tDbQ/wbYChgFvAr8ol5DzOwKM2sysyYYWG9xjtNaGurbLfx6I/drp31oVaA3s9fMbJWZvQ9cSbiVLWcuMLTk++ZxmeN0WNy3nSLSqkAvabOSr58HHq8gexDYRtIWkroTBkROak19jrO2cN92ikhy4hFJNwOjgQGS5gDnAKMljQIMmAWcELWDgavMbIyZrZR0CnAX0AW4xsyeaIudcJzW4L7trCt00ISpwQbHJ1Q7pwv6wcFpzYwMgzJmNdr+5oeTmlfeG5zUrN9jaVIziPlpgzLowXtJzfq8m9Tc90ylEYpl/D3DoEQuEQCXrcgQdUusb8KscmJJW6JuTcaAxE5OTbfHnbaaltSM4pGk5lQuTWr+wqeSmtkterEq869cntRM4TNJzXNsndS8Sb+kZhDpxLuJfD6pefS83ZMaNk9LshKmvplY/14T9n4rE6Ycx3Gczo0HesdxnILjgd5xHKfgeKB3HMcpOB7oHcdxCo4HesdxnILjgd5xHKfgeKB3HMcpOB00YUoLgJdKFg0AXm8nc1qL29z2tNbe4Wa21t8wVsGvYd055u3JumJzVb/ukIG+HEnTw1stOw9uc9vT2eytRGfbh85mL7jN4F03juM4hccDveM4TsHpLIH+ivY2oBW4zW1PZ7O3Ep1tHzqbveA2d44+esdxHKf1dJYresdxHKeVdPhAL+kASc9Iek7SWe1tTwpJsyTNlDQjTHTe8YiTXs+X9HjJso0l3SPp2fi/0qTY7UYVm8+VNDce6xmSxrSnjWtCZ/NrcN9uK9aGb3foQC+pC3A5cCAwEjhS0sj2tSqLz5jZqA48pGs8UD5byFnAvWa2DXBv/N6RGM/qNgNcHI/1KDObvJZtahWd2K/BfbstGE8b+3aHDvSEiZmfM7MXzGw5cAtwSDvb1Okxs/uBhWWLDwGujZ+vBQ5dmzalqGJzZ8X9uo1w365MRw/0Q4DZJd/nxGUdGQPulvSQpNR8iB2JQWb2avw8DxjUnsasAadIeize/naoW/IadEa/BvfttU3DfLujB/rOyJ5mtjPhtvxkSZ9ub4PWFAtDsTrDcKzfAFsBo4BXgV+0qzXFx3177dFQ3+7ogX4utJh5ePO4rMNiZnPj//nARMJtemfgNUmbAcT/jZmBvA0xs9fMbJWZvQ9cSec51p3Or8F9e23SaN/u6IH+QWAbSVtI6g4cAUxqZ5uqImkDSX2aPwP7AY/X3qrDMAk4Jn4+Bvh9O9qSRXPjjXyeznOsO5Vfg/v22qbRvt21PnPaFjNbKekU4C6gC3CNmT3RzmbVYhAwURKEY3uTmf2xfU1aHUk3A6OBAZLmAOcAFwITJB1LeMPi4e1n4epUsXm0pFGEW/FZwAntZd+a0An9Gty324y14dueGes4jlNwOnrXjeM4jlMnHugdx3EKjgd6x3GcguOB3nEcp+B4oHccxyk4Hugdx3EKjgd6x3GcguOB3nEcp+D8P5G8dStRKxN0AAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1258,38 +1277,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:04:39\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:42:24\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1297,11 +1315,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16379 +/- 0.00090\n", - " k-effective (Track-length) = 1.16469 +/- 0.00101\n", - " k-effective (Absorption) = 1.16315 +/- 0.00052\n", - " Combined k-effective = 1.16335 +/- 0.00050\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16293 +/- 0.00093\n", + " k-effective (Track-length) = 1.16281 +/- 0.00107\n", + " k-effective (Absorption) = 1.16309 +/- 0.00049\n", + " Combined k-effective = 1.16306 +/- 0.00048\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1327,8 +1345,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "P3 bias [pcm]: -20.4\n", - "P0 bias [pcm]: 125.1\n" + "P3 bias [pcm]: 3.2\n", + "P0 bias [pcm]: 195.7\n" ] } ], @@ -1415,38 +1433,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-22 15:05:16\n", - " OpenMP Threads | 8\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 14:43:41\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1454,11 +1471,11 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.16471 +/- 0.00093\n", - " k-effective (Track-length) = 1.16412 +/- 0.00106\n", - " k-effective (Absorption) = 1.16449 +/- 0.00050\n", - " Combined k-effective = 1.16441 +/- 0.00049\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16377 +/- 0.00093\n", + " k-effective (Track-length) = 1.16368 +/- 0.00111\n", + " k-effective (Absorption) = 1.16357 +/- 0.00047\n", + " Combined k-effective = 1.16357 +/- 0.00047\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -1489,8 +1506,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "P3 bias [pcm]: -20.4\n", - "Mixed Scattering bias [pcm]: 19.5\n" + "P3 bias [pcm]: 3.2\n", + "Mixed Scattering bias [pcm]: 144.4\n" ] } ], @@ -1514,6 +1531,19 @@ "\n", "**NOTE**: The biases obtained above with P3, P0, and mixed representations do not necessarily reflect the inherent accuracies of the options. These cases were *not* run with a sufficient number of histories to truly differentiate methods improvement from statistical noise." ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Close all StatePoint files as a matter of best practice\n", + "sp.close()\n", + "mgsp_p0.close()\n", + "mgsp.close()\n", + "mgsp_mixed.close()" + ] } ], "metadata": { @@ -1532,7 +1562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mg-mode-part-iii.ipynb b/examples/jupyter/mg-mode-part-iii.ipynb index ea1e773f8df..52a6ff23912 100644 --- a/examples/jupyter/mg-mode-part-iii.ipynb +++ b/examples/jupyter/mg-mode-part-iii.ipynb @@ -362,7 +362,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -371,7 +371,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -642,7 +642,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mgxs/mgxs.py:4144: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", + "/home/shriwise/opt/openmc/openmc/openmc/mgxs/mgxs.py:4872: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n", " warnings.warn(msg)\n" ] } @@ -688,19 +688,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=11.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=22.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=22.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=12.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=12.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=18.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=18.\n", " warn(msg, IDWarning)\n" ] } @@ -764,25 +764,25 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:54:35\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:15:04\n", + " OpenMP Threads | 2\n", "\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83866 +/- 0.00102\n", - " k-effective (Track-length) = 0.83799 +/- 0.00118\n", - " k-effective (Absorption) = 0.83968 +/- 0.00103\n", - " Combined k-effective = 0.83900 +/- 0.00085\n", + " k-effective (Collision) = 0.83621 +/- 0.00102\n", + " k-effective (Track-length) = 0.83692 +/- 0.00114\n", + " k-effective (Absorption) = 0.83714 +/- 0.00103\n", + " Combined k-effective = 0.83686 +/- 0.00084\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -903,7 +903,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -980,7 +980,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 31, @@ -989,7 +989,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQqElEQVR4nO3de9Rd853H8fdHCEmEJEJckoXQmjHGJQ1Soy41CEXaRWdQRF3Si2uXoYy1RqfTTutWNW21E2SkqkFdWkspqRbTVUJEIiE0CUEiEpJIXCoR+c4fZydzPD0nz5OzfztO/T6vtZ71nOfs3/nub/Z5Ptnn7Gef/VNEYGYffet92A2Y2brhsJtlwmE3y4TDbpYJh90sEw67WSbW72yApDHAEcCCiNil7v6zgDOA94FfR8QFndXq118xcFuVaLex+YsGJa8JsGj+hslr9t16YfKaAFt261FJ3ZcWbllJ3Y3fer+Suq8uey55zQ36d0teE2Bwn/S/t68smMfiJYsbhqzTsAM3AD8EfrrqDkkHAiOA3SJimaQtutLIwG3FXX9MH6Crb7koeU2An31/cPKa/3zJjclrAnx9k106H9SCM8aeX0ndfR5ZWkndy2YckLzmFkdvmrwmwLgjr05e87hzj2+6rNOX8RHxMLCow91fAb4bEcuKMQvKNGhm1Wv1PfvHgU9JmiDpIUl7pmzKzNLrysv4Zo/rBwwD9gRulTQ4Gpx7K2kUMApg62reWptZF7S6Z58D3BE1jwErgf6NBkbE6IgYGhFDN9s8/cE5M+uaVsP+S+BAAEkfB7oDryfqycwq0JU/vY0DDgD6S5oDXAKMAcZImgYsB0Y2eglvZu2j07BHxHFNFp2QuBczq5DPoDPLhMNulgmH3SwTDrtZJhx2s0w47GaZcNjNMuGwm2XCYTfLhMNulgmH3SwTDrtZJhx2s0y0eqWaliycOpif73hl8rpvntHphW1bcumsB5LXnHxa8pIAnHhf90rqzjzkbyqpO3XDUZXUvfza55PX/M5e/528JsA7K3dMXnNl742aLvOe3SwTDrtZJhx2s0w47GaZ6DTsksZIWlBcb67jsvMkhaSGV5Y1s/bRlT37DcDwjndKGgQcAryUuCczq0Cr0z8BXAVcAPiqsmZ/BVp6zy5pBDA3IqYk7sfMKrLWJ9VI6gn8K7WX8F0Zv3r6p027bb62qzOzRFrZs+8AbA9MkTQbGAhMktRwIu/66Z96rbdJ652aWSlrvWePiKnA6vnYi8APjQhP/2TWxrryp7dxwCPATpLmSDq1+rbMLLUy0z+tWr5dsm7MrDI+g84sEw67WSYcdrNMOOxmmXDYzTLhsJtlwmE3y4TDbpaJdXp12fcHL+GN0fcmr3vYWTOS1wRYvl/6j+rvP/YzyWsCvHT20ZXUPXDeLyqp+5XXp1dS99DTBySved7Qx5PXBBh08BadD1pL3ee+3XSZ9+xmmXDYzTLhsJtlwmE3y4TDbpYJh90sEw67WSYcdrNMOOxmmWhp+idJl0t6VtJTku6U1KfSLs2stFanfxoP7BIRuwJ/Ai5K3JeZJdbS9E8RcX9ErCh+fJTatePNrI2leM9+CtD00y2SRkmaKGnin994N8HqzKwVpcIu6WJgBXBTszH1M8L06LNRmdWZWQktf8RV0snAEcBBEeGZXM3aXEthlzSc2nTN+0fEO2lbMrMqtDr90w+B3sB4SZMl/aTiPs2spFanf7q+gl7MrEI+g84sEw67WSbW7QUnnwuW7r8sed3bNt8veU2AR46/OXnNbZ5anLwmwK9PmF1J3bP/uHEldXucdmMldbd9aHbymiuPvDp5TYCDT/pW8povjH+l6TLv2c0y4bCbZcJhN8uEw26WCYfdLBMOu1kmHHazTDjsZplw2M0y4bCbZcJhN8uEw26WCYfdLBMOu1kmWp0Rpp+k8ZJmFN/7VtummZXV6owwFwIPRMTHgAeKn82sjbU0IwwwAhhb3B4LfDZtW2aWWqvv2QdExLzi9qvAgET9mFlFSh+gKyaIaDpJRP30T+/i6Z/MPiythn2+pK0Aiu8Lmg2sn/5pIzz9k9mHpdWw3wWMLG6PBH6Vph0zq0qnV5ctZoQ5AOgvaQ5wCfBd4NZidpgXgX/qysp6brKY3fe5rfVum/j5b55MXhNg0P/umrzmrbtdnrwmwCdeP6qSugd0u7aSulP+dlgldUfv9Z/Ja37+mnHJawJs3P+M5DVj/eeaLmt1RhiAg1ptyMzWPZ9BZ5YJh90sEw67WSYcdrNMOOxmmXDYzTLhsJtlwmE3y4TDbpYJh90sEw67WSYcdrNMOOxmmXDYzTLhsJtlwmE3y4TDbpYJh90sE6XCLulrkp6WNE3SOEm+fKxZm2o57JK2Ac4GhkbELkA34NhUjZlZWp1ecLILj+8h6T2gJ/DKmgb3792TUz+9e8lV/qXzJ/4heU2AYVt2S17zoZO+lLwmwN5fOr6Sulfs3aOSuu/cNa3zQS349yt3S17z6PGHJK8JcPJ/nJ+85hfeuLvpspb37BExF7gCeAmYByyJiPtbrWdm1SrzMr4vtQketwe2BnpJOqHBuNXTP73+9nutd2pmpZQ5QPePwAsR8VpEvAfcAezTcVD99E/9e21QYnVmVkaZsL8EDJPUU5KoTRoxPU1bZpZamffsE4DbgEnA1KLW6ER9mVlipY7GR8Ql1OZ+M7M25zPozDLhsJtlwmE3y4TDbpYJh90sEw67WSYcdrNMOOxmmXDYzTLhsJtlwmE3y4TDbpYJh90sEw67WSYcdrNMlL267NqJd9C7k5OX3fPYh5LXBNho8t8nr7nZ8NOT1wS45/Jqnsprrtu9krqHfvOqSupu99XPJ6/5yH5fTF4T4NRrZiavOfvNd5su857dLBMOu1kmyk7/1EfSbZKelTRd0idTNWZmaZV9o3c18JuIOEZSd2qzwphZG2o57JI2BfYDTgaIiOXA8jRtmVlqZV7Gbw+8BvyPpCclXSepV6K+zCyxMmFfHxgC/Dgi9gDeBi7sOOiD0z9FidWZWRllwj4HmFNMFgG1CSOGdBz0wemfVGJ1ZlZGmRlhXgVelrRTcddBwDNJujKz5MoejT8LuKk4Ev88UM2pRmZWWtnpnyYDQ9O0YmZV8hl0Zplw2M0y4bCbZcJhN8uEw26WCYfdLBMOu1kmHHazTDjsZplw2M0yoYh197HT3gN6xNDjd0he9/avXZe8JsDEnY9MXnPXJz+RvCbAKyvfqKTuvtfsXkndnn1XVFJ33En9ktd8dNRzyWsCHDf7xOQ1Pzfn60x9d1bDj5d6z26WCYfdLBMOu1kmHHazTDjsZplw2M0y4bCbZaJ02CV1K64bf3eKhsysGin27OcA0xPUMbMKlZ3YcSDwGaCaU9jMLJmye/bvAxcAK5sNqJ8R5r0/v19ydWbWqpbDLukIYEFEPLGmcfUzwmzQo1urqzOzksrs2f8BOErSbOBm4NOSfpakKzNLrsz0TxdFxMCI2A44FvhdRJyQrDMzS8p/ZzfLRNm53gCIiAeBB1PUMrNqeM9ulgmH3SwTDrtZJhx2s0wkOUDXVest35GeL9+ZvO7Oow9OXhPgsWWnJ685/tvpawLcevynKql7xcIbK6n74EkPV1L3oZcvTl7zyO1uSF4T4Lcv/Ch5zaWxsOky79nNMuGwm2XCYTfLhMNulgmH3SwTDrtZJhx2s0w47GaZcNjNMuGwm2XCYTfLhMNulokyV5cdJOn3kp6R9LSkc1I2ZmZplfnU2wrgvIiYJKk38ISk8RHxTKLezCyhMleXnRcRk4rbb1KbAmqbVI2ZWVpJ3rNL2g7YA5iQop6ZpZdiFteNgduBcyNiaYPlq6d/Wr5sUdnVmVmLyk7suAG1oN8UEXc0GlM//VP3DfuVWZ2ZlVDmaLyA64HpEfG9dC2ZWRXKzvV2IrU53iYXX4cn6svMEmv5T28R8QdACXsxswqt06vLLtu6GzMu6Zu87lVanLwmwL6bvJa85rc2fCt5TYA7Zo2upO64F8+tpO6bRw+rpO4Prj47ec3+l41MXhPgju+kr7voxsebLvPpsmaZcNjNMuGwm2XCYTfLhMNulgmH3SwTDrtZJhx2s0w47GaZcNjNMuGwm2XCYTfLhMNulgmH3SwTDrtZJhx2s0w47GaZKHt12eGSnpM0U9KFqZoys/TKXF22G/Aj4DBgZ+A4STunaszM0iqzZ98LmBkRz0fEcuBmYESatswstTJh3wZ4ue7nOXiuN7O2pYho7YHSMcDwiDit+PlEYO+IOLPDuFHAqOLHXYBprbebRH/g9Q+5B2iPPtqhB2iPPtqhByjfx7YRsXmjBWUuJT0XGFT388Divg+IiNHAaABJEyNiaIl1ltYOPbRLH+3QQ7v00Q49VN1HmZfxjwMfk7S9pO7AscBdadoys9TKzAizQtKZwH1AN2BMRDydrDMzS6rUjDARcQ9wz1o8pJppS9ZOO/QA7dFHO/QA7dFHO/QAFfbR8gE6M/vr4tNlzTJRSdg7O41W0oaSbimWT5C0XeL1D5L0e0nPSHpa0jkNxhwgaUnddNP/lrKHuvXMljS1WMfEBssl6b+KbfGUpCGJ179T3b9xsqSlks7tMKaSbSFpjKQFkqbV3ddP0nhJM4rvDWf6lDSyGDNDUsszIDbp4XJJzxbb+05JfZo8do3PXYI+viFpbmdTnic7LT0ikn5RO1g3CxgMdAemADt3GPNV4CfF7WOBWxL3sBUwpLjdG/hTgx4OAO5O/e9v0MtsoP8alh8O3Ett+uthwIQKe+kGvErtb7GVbwtgP2AIMK3uvsuAC4vbFwKXNnhcP+D54nvf4nbfhD0cAqxf3L60UQ9dee4S9PEN4F+68JytMU9d/apiz96V02hHAGOL27cBB0lKNtd7RMyLiEnF7TeB6bTv2X0jgJ9GzaNAH0lbVbSug4BZEfFiRfU/ICIeBhZ1uLv+uR8LfLbBQw8FxkfEoohYDIwHhqfqISLuj4gVxY+PUjtHpFJNtkVXJDstvYqwd+U02tVjio2+BNisgl4o3iLsAUxosPiTkqZIulfS31WxfiCA+yU9UZxN2NG6PO34WGBck2XrYlsADIiIecXtV4EBDcasy21yCrVXVo109tylcGbxdmJMk7c0ybbFR/oAnaSNgduBcyNiaYfFk6i9nN0N+AHwy4ra2DcihlD7dOAZkvaraD1rVJz4dBTwiwaL19W2+ICovU790P4cJOliYAVwU5MhVT93PwZ2AHYH5gFXJq7/AVWEvSun0a4eI2l9YFNgYcomJG1ALeg3RcQdHZdHxNKIeKu4fQ+wgaT+KXsoas8tvi8A7qT2sqxel047TuAwYFJEzG/Q4zrZFoX5q96mFN8XNBhT+TaRdDJwBPCF4j+dv9CF566UiJgfEe9HxErg2ib1k22LKsLeldNo7wJWHWE9Bvhdsw3eiuL9//XA9Ij4XpMxW646TiBpL2rbIvV/OL0k9V51m9qBoY4fBLoLOKk4Kj8MWFL3Mjel42jyEn5dbIs69c/9SOBXDcbcBxwiqW/x0vaQ4r4kJA0HLgCOioh3mozpynNXto/6YzOfa1I/3WnpKY40NjiCeDi1I+CzgIuL+75JbeMCbETt5eRM4DFgcOL170vt5eFTwOTi63Dgy8CXizFnAk9TO7r5KLBPBdthcFF/SrGuVduivg9RuwjILGAqMLSCPnpRC++mdfdVvi2o/ecyD3iP2nvNU6kdm3kAmAH8FuhXjB0KXFf32FOK34+ZwBcT9zCT2vvgVb8bq/4ytDVwz5qeu8R93Fg8509RC/BWHftolqdWvnwGnVkmPtIH6Mzs/znsZplw2M0y4bCbZcJhN8uEw26WCYfdLBMOu1km/g/L3aEfyQ4o9wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1049,12 +1049,12 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:56:58\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:16:33\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1062,10 +1062,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.82471 +/- 0.00104\n", - " k-effective (Track-length) = 0.82466 +/- 0.00103\n", - " k-effective (Absorption) = 0.82482 +/- 0.00075\n", - " Combined k-effective = 0.82477 +/- 0.00068\n", + " k-effective (Collision) = 0.82516 +/- 0.00104\n", + " k-effective (Track-length) = 0.82465 +/- 0.00106\n", + " k-effective (Absorption) = 0.82332 +/- 0.00074\n", + " Combined k-effective = 0.82369 +/- 0.00066\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1117,7 +1117,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] } @@ -1170,12 +1170,12 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | ed7123f4e7ce7b097c4b2bfb0ef5283eaa8afaea\n", - " Date/Time | 2019-10-04 10:57:24\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:16:54\n", + " OpenMP Threads | 2\n", "\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", @@ -1183,10 +1183,10 @@ "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.83564 +/- 0.00103\n", - " k-effective (Track-length) = 0.83572 +/- 0.00104\n", - " k-effective (Absorption) = 0.83478 +/- 0.00073\n", - " Combined k-effective = 0.83504 +/- 0.00066\n", + " k-effective (Collision) = 0.83683 +/- 0.00105\n", + " k-effective (Track-length) = 0.83685 +/- 0.00108\n", + " k-effective (Absorption) = 0.83600 +/- 0.00077\n", + " Combined k-effective = 0.83624 +/- 0.00070\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1261,8 +1261,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isotropic to CE Bias [pcm]: 1423.5\n", - "Angle to CE Bias [pcm]: 396.6\n" + "Isotropic to CE Bias [pcm]: 1317.7\n", + "Angle to CE Bias [pcm]: 62.5\n" ] } ], @@ -1296,7 +1296,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1355,7 +1355,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -1364,7 +1364,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1400,6 +1400,17 @@ "source": [ "With this ratio its clear that the errors are significantly worse in the isotropic case. These errors are conveniently located right where the most anisotropy is espected: by the control blades and by the Gd-bearing pins!" ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Close all StatePoint files as a matter of best practice\n", + "for sp_file in sp_files:\n", + " sp_file.close()" + ] } ], "metadata": { @@ -1418,7 +1429,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/mgxs-part-iii.ipynb b/examples/jupyter/mgxs-part-iii.ipynb index d75255d73b7..60f44ac619c 100644 --- a/examples/jupyter/mgxs-part-iii.ipynb +++ b/examples/jupyter/mgxs-part-iii.ipynb @@ -343,7 +343,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -564,21 +564,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=126.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=126.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=96.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=96.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=15.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=114.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=114.\n", " warn(msg, IDWarning)\n" ] } @@ -622,107 +622,111 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 07:12:55\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 16:01:58\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /opt/data/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.03784\n", - " 2/1 1.02297\n", - " 3/1 1.02244\n", - " 4/1 1.02344\n", - " 5/1 1.02057\n", - " 6/1 1.04077\n", - " 7/1 1.00775\n", - " 8/1 1.03892\n", - " 9/1 1.01606\n", - " 10/1 1.02209\n", - " 11/1 1.03259\n", - " 12/1 1.03331 1.03295 +/- 0.00036\n", - " 13/1 1.02027 1.02872 +/- 0.00423\n", - " 14/1 1.03901 1.03130 +/- 0.00395\n", - " 15/1 1.02000 1.02904 +/- 0.00380\n", - " 16/1 1.04469 1.03164 +/- 0.00405\n", - " 17/1 1.01862 1.02978 +/- 0.00390\n", - " 18/1 1.03265 1.03014 +/- 0.00340\n", - " 19/1 1.00489 1.02734 +/- 0.00410\n", - " 20/1 1.04533 1.02914 +/- 0.00409\n", - " 21/1 1.01534 1.02788 +/- 0.00390\n", - " 22/1 1.02204 1.02739 +/- 0.00360\n", - " 23/1 1.02181 1.02696 +/- 0.00334\n", - " 24/1 0.99207 1.02447 +/- 0.00397\n", - " 25/1 1.03041 1.02487 +/- 0.00372\n", - " 26/1 1.03652 1.02560 +/- 0.00355\n", - " 27/1 1.03793 1.02632 +/- 0.00341\n", - " 28/1 1.02099 1.02603 +/- 0.00323\n", - " 29/1 1.01953 1.02568 +/- 0.00308\n", - " 30/1 1.01690 1.02525 +/- 0.00295\n", - " 31/1 1.01938 1.02497 +/- 0.00282\n", - " 32/1 1.01800 1.02465 +/- 0.00271\n", - " 33/1 1.01598 1.02427 +/- 0.00262\n", - " 34/1 1.01735 1.02398 +/- 0.00252\n", - " 35/1 1.01080 1.02346 +/- 0.00247\n", - " 36/1 1.01267 1.02304 +/- 0.00241\n", - " 37/1 1.01907 1.02289 +/- 0.00233\n", - " 38/1 1.02333 1.02291 +/- 0.00224\n", - " 39/1 1.01516 1.02264 +/- 0.00218\n", - " 40/1 1.02797 1.02282 +/- 0.00211\n", - " 41/1 1.03949 1.02336 +/- 0.00211\n", - " 42/1 1.01456 1.02308 +/- 0.00207\n", - " 43/1 1.02376 1.02310 +/- 0.00200\n", - " 44/1 1.01917 1.02299 +/- 0.00195\n", - " 45/1 1.01631 1.02280 +/- 0.00190\n", - " 46/1 1.02381 1.02282 +/- 0.00185\n", - " 47/1 1.04002 1.02329 +/- 0.00185\n", - " 48/1 1.01059 1.02296 +/- 0.00184\n", - " 49/1 1.02647 1.02305 +/- 0.00179\n", - " 50/1 1.02451 1.02308 +/- 0.00175\n", + " 1/1 1.04638\n", + " 2/1 1.00498\n", + " 3/1 1.00535\n", + " 4/1 1.02695\n", + " 5/1 1.00781\n", + " 6/1 1.02035\n", + " 7/1 1.03808\n", + " 8/1 1.02532\n", + " 9/1 1.02783\n", + " 10/1 1.01371\n", + " 11/1 1.03205\n", + " 12/1 1.01947 1.02576 +/- 0.00629\n", + " 13/1 1.01843 1.02332 +/- 0.00438\n", + " 14/1 1.03269 1.02566 +/- 0.00388\n", + " 15/1 1.03879 1.02829 +/- 0.00399\n", + " 16/1 1.02251 1.02732 +/- 0.00340\n", + " 17/1 1.01274 1.02524 +/- 0.00355\n", + " 18/1 1.02454 1.02515 +/- 0.00307\n", + " 19/1 1.01993 1.02457 +/- 0.00277\n", + " 20/1 1.00665 1.02278 +/- 0.00306\n", + " 21/1 1.02200 1.02271 +/- 0.00277\n", + " 22/1 1.03748 1.02394 +/- 0.00281\n", + " 23/1 1.02803 1.02425 +/- 0.00260\n", + " 24/1 1.03052 1.02470 +/- 0.00245\n", + " 25/1 1.02027 1.02441 +/- 0.00230\n", + " 26/1 1.02597 1.02450 +/- 0.00216\n", + " 27/1 1.01776 1.02411 +/- 0.00206\n", + " 28/1 1.02652 1.02424 +/- 0.00195\n", + " 29/1 1.01063 1.02353 +/- 0.00198\n", + " 30/1 1.03300 1.02400 +/- 0.00194\n", + " 31/1 1.02020 1.02382 +/- 0.00185\n", + " 32/1 1.03720 1.02443 +/- 0.00187\n", + " 33/1 1.02797 1.02458 +/- 0.00179\n", + " 34/1 1.01994 1.02439 +/- 0.00172\n", + " 35/1 1.02626 1.02446 +/- 0.00166\n", + " 36/1 1.03183 1.02475 +/- 0.00162\n", + " 37/1 1.03410 1.02509 +/- 0.00159\n", + " 38/1 1.00919 1.02452 +/- 0.00164\n", + " 39/1 1.04388 1.02519 +/- 0.00171\n", + " 40/1 1.01254 1.02477 +/- 0.00171\n", + " 41/1 1.01584 1.02448 +/- 0.00168\n", + " 42/1 1.01002 1.02403 +/- 0.00169\n", + " 43/1 1.00277 1.02339 +/- 0.00176\n", + " 44/1 1.00672 1.02289 +/- 0.00177\n", + " 45/1 1.03974 1.02338 +/- 0.00179\n", + " 46/1 1.00460 1.02285 +/- 0.00181\n", + " 47/1 1.03954 1.02331 +/- 0.00182\n", + " 48/1 1.01414 1.02306 +/- 0.00179\n", + " 49/1 1.02016 1.02299 +/- 0.00174\n", + " 50/1 0.99791 1.02236 +/- 0.00181\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 5.7635e-01 seconds\n", - " Reading cross sections = 5.4002e-01 seconds\n", - " Total time in simulation = 7.0174e+01 seconds\n", - " Time in transport only = 6.9687e+01 seconds\n", - " Time in inactive batches = 7.1832e+00 seconds\n", - " Time in active batches = 6.2991e+01 seconds\n", - " Time synchronizing fission bank = 3.9991e-02 seconds\n", - " Sampling source sites = 3.4633e-02 seconds\n", - " SEND/RECV source sites = 5.2616e-03 seconds\n", - " Time accumulating tallies = 4.9801e-04 seconds\n", - " Total time for finalization = 1.3501e-05 seconds\n", - " Total time elapsed = 7.0791e+01 seconds\n", - " Calculation Rate (inactive) = 13921.3 particles/second\n", - " Calculation Rate (active) = 6350.11 particles/second\n", + " Total time for initialization = 2.4372e-01 seconds\n", + " Reading cross sections = 2.2745e-01 seconds\n", + " Total time in simulation = 4.7933e+01 seconds\n", + " Time in transport only = 4.7887e+01 seconds\n", + " Time in inactive batches = 3.4521e+00 seconds\n", + " Time in active batches = 4.4481e+01 seconds\n", + " Time synchronizing fission bank = 2.0174e-02 seconds\n", + " Sampling source sites = 1.7952e-02 seconds\n", + " SEND/RECV source sites = 2.1985e-03 seconds\n", + " Time accumulating tallies = 1.1888e-03 seconds\n", + " Time writing statepoints = 1.2929e-02 seconds\n", + " Total time for finalization = 3.9800e-07 seconds\n", + " Total time elapsed = 4.8193e+01 seconds\n", + " Calculation Rate (inactive) = 28967.6 particles/second\n", + " Calculation Rate (active) = 8992.65 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02434 +/- 0.00173\n", - " k-effective (Track-length) = 1.02308 +/- 0.00175\n", - " k-effective (Absorption) = 1.02494 +/- 0.00175\n", - " Combined k-effective = 1.02408 +/- 0.00144\n", + " k-effective (Collision) = 1.02393 +/- 0.00174\n", + " k-effective (Track-length) = 1.02236 +/- 0.00181\n", + " k-effective (Absorption) = 1.02412 +/- 0.00167\n", + " Combined k-effective = 1.02362 +/- 0.00128\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -771,7 +775,9 @@ "outputs": [], "source": [ "# Initialize MGXS Library with OpenMC statepoint data\n", - "mgxs_lib.load_from_statepoint(sp)" + "mgxs_lib.load_from_statepoint(sp)\n", + "# Retrieve OpenMC's k-effective value\n", + "openmc_keff = sp.k_combined.nominal_value" ] }, { @@ -854,16 +860,16 @@ " 1\n", " 1\n", " U235\n", - " 8.089079e-03\n", - " 1.461462e-05\n", + " 8.099261e-03\n", + " 1.626934e-05\n", " \n", " \n", " 4\n", " 1\n", " 1\n", " U238\n", - " 7.358661e-03\n", - " 2.302063e-05\n", + " 7.326723e-03\n", + " 2.168273e-05\n", " \n", " \n", " 5\n", @@ -878,16 +884,16 @@ " 1\n", " 2\n", " U235\n", - " 3.617174e-01\n", - " 9.467633e-04\n", + " 3.613773e-01\n", + " 1.025247e-03\n", " \n", " \n", " 1\n", " 1\n", " 2\n", " U238\n", - " 6.744743e-07\n", - " 1.750450e-09\n", + " 6.739270e-07\n", + " 1.911057e-09\n", " \n", " \n", " 2\n", @@ -903,11 +909,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 8.089079e-03 1.461462e-05\n", - "4 1 1 U238 7.358661e-03 2.302063e-05\n", + "3 1 1 U235 8.099261e-03 1.626934e-05\n", + "4 1 1 U238 7.326723e-03 2.168273e-05\n", "5 1 1 O16 0.000000e+00 0.000000e+00\n", - "0 1 2 U235 3.617174e-01 9.467633e-04\n", - "1 1 2 U238 6.744743e-07 1.750450e-09\n", + "0 1 2 U235 3.613773e-01 1.025247e-03\n", + "1 1 2 U238 6.739270e-07 1.911057e-09\n", "2 1 2 O16 0.000000e+00 0.000000e+00" ] }, @@ -943,13 +949,13 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t8.09e-03 +/- 1.81e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t3.62e-01 +/- 2.62e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t8.10e-03 +/- 2.01e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t3.61e-01 +/- 2.84e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.36e-03 +/- 3.13e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.60e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.33e-03 +/- 2.96e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.84e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", @@ -964,7 +970,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1269: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/shriwise/opt/openmc/openmc/openmc/tallies.py:1216: RuntimeWarning: invalid value encountered in true_divide\n", " data = self.std_dev[indices] / self.mean[indices]\n" ] } @@ -1076,16 +1082,16 @@ " 1\n", " 1\n", " U235\n", - " 0.074556\n", - " 0.000144\n", + " 0.074479\n", + " 0.000151\n", " \n", " \n", " 1\n", " 1\n", " 1\n", " U238\n", - " 0.005976\n", - " 0.000019\n", + " 0.005950\n", + " 0.000017\n", " \n", " \n", " 2\n", @@ -1101,8 +1107,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 1 1 U235 0.074556 0.000144\n", - "1 1 1 U238 0.005976 0.000019\n", + "0 1 1 U235 0.074479 0.000151\n", + "1 1 1 U238 0.005950 0.000017\n", "2 1 1 O16 0.000000 0.000000" ] }, @@ -1154,7 +1160,16 @@ "cell_type": "code", "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ WARNING ] Group cross sections by nuclides are not currently supported.\n", + "[ WARNING ] ... Contributions from all nuclides will be summed.\n" + ] + } + ], "source": [ "# Load the library into the OpenMOC geometry\n", "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)" @@ -1200,7 +1215,6 @@ "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", "[ NORMAL ] Initializing FSR lookup vectors\n", "[ NORMAL ] Total number of FSRs 867\n", - "[ RESULT ] Total Track Generation & Segmentation Time...........4.2139E-01 sec\n", "[ NORMAL ] Initializing MOC eigenvalue solver...\n", "[ NORMAL ] Initializing solver arrays...\n", "[ NORMAL ] Centering segments around FSR centroid...\n", @@ -1215,264 +1229,264 @@ "[ NORMAL ] CMFD acceleration: OFF\n", "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0: k_eff = 0.823216 res = 9.828E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -17678 D.R. = 0.10\n", - "[ NORMAL ] Iteration 1: k_eff = 0.779788 res = 4.642E-02 delta-k (pcm) =\n", - "[ NORMAL ] ... -4342 D.R. = 0.47\n", - "[ NORMAL ] Iteration 2: k_eff = 0.738779 res = 9.633E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -4100 D.R. = 0.21\n", - "[ NORMAL ] Iteration 3: k_eff = 0.710046 res = 8.556E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2873 D.R. = 0.89\n", - "[ NORMAL ] Iteration 4: k_eff = 0.688781 res = 5.190E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -2126 D.R. = 0.61\n", - "[ NORMAL ] Iteration 5: k_eff = 0.674128 res = 3.585E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -1465 D.R. = 0.69\n", - "[ NORMAL ] Iteration 6: k_eff = 0.664928 res = 2.516E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -919 D.R. = 0.70\n", - "[ NORMAL ] Iteration 7: k_eff = 0.660304 res = 1.866E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -462 D.R. = 0.74\n", - "[ NORMAL ] Iteration 8: k_eff = 0.659481 res = 1.471E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... -82 D.R. = 0.79\n", - "[ NORMAL ] Iteration 9: k_eff = 0.661799 res = 1.248E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 231 D.R. = 0.85\n", - "[ NORMAL ] Iteration 10: k_eff = 0.666690 res = 1.123E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 489 D.R. = 0.90\n", - "[ NORMAL ] Iteration 11: k_eff = 0.673664 res = 1.049E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 697 D.R. = 0.93\n", - "[ NORMAL ] Iteration 12: k_eff = 0.682301 res = 1.001E-03 delta-k (pcm) =\n", - "[ NORMAL ] ... 863 D.R. = 0.95\n", - "[ NORMAL ] Iteration 13: k_eff = 0.692239 res = 9.638E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 993 D.R. = 0.96\n", - "[ NORMAL ] Iteration 14: k_eff = 0.703171 res = 9.329E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1093 D.R. = 0.97\n", - "[ NORMAL ] Iteration 15: k_eff = 0.714835 res = 9.055E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1166 D.R. = 0.97\n", - "[ NORMAL ] Iteration 16: k_eff = 0.727008 res = 8.803E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1217 D.R. = 0.97\n", - "[ NORMAL ] Iteration 17: k_eff = 0.739503 res = 8.566E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1249 D.R. = 0.97\n", - "[ NORMAL ] Iteration 18: k_eff = 0.752162 res = 8.335E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1265 D.R. = 0.97\n", - "[ NORMAL ] Iteration 19: k_eff = 0.764855 res = 8.108E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1269 D.R. = 0.97\n", - "[ NORMAL ] Iteration 20: k_eff = 0.777472 res = 7.879E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1261 D.R. = 0.97\n", - "[ NORMAL ] Iteration 21: k_eff = 0.789924 res = 7.647E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1245 D.R. = 0.97\n", - "[ NORMAL ] Iteration 22: k_eff = 0.802140 res = 7.410E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1221 D.R. = 0.97\n", - "[ NORMAL ] Iteration 23: k_eff = 0.814061 res = 7.168E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1192 D.R. = 0.97\n", - "[ NORMAL ] Iteration 24: k_eff = 0.825643 res = 6.922E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1158 D.R. = 0.97\n", - "[ NORMAL ] Iteration 25: k_eff = 0.836850 res = 6.672E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1120 D.R. = 0.96\n", - "[ NORMAL ] Iteration 26: k_eff = 0.847658 res = 6.419E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1080 D.R. = 0.96\n", - "[ NORMAL ] Iteration 27: k_eff = 0.858047 res = 6.165E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 1038 D.R. = 0.96\n", - "[ NORMAL ] Iteration 28: k_eff = 0.868008 res = 5.911E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 996 D.R. = 0.96\n", - "[ NORMAL ] Iteration 29: k_eff = 0.877535 res = 5.658E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 952 D.R. = 0.96\n", - "[ NORMAL ] Iteration 30: k_eff = 0.886625 res = 5.409E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 909 D.R. = 0.96\n", - "[ NORMAL ] Iteration 31: k_eff = 0.895281 res = 5.163E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 865 D.R. = 0.95\n", - "[ NORMAL ] Iteration 32: k_eff = 0.903509 res = 4.921E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 822 D.R. = 0.95\n", - "[ NORMAL ] Iteration 33: k_eff = 0.911317 res = 4.685E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 780 D.R. = 0.95\n", - "[ NORMAL ] Iteration 34: k_eff = 0.918715 res = 4.456E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 739 D.R. = 0.95\n", - "[ NORMAL ] Iteration 35: k_eff = 0.925715 res = 4.232E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 699 D.R. = 0.95\n", - "[ NORMAL ] Iteration 36: k_eff = 0.932329 res = 4.016E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 661 D.R. = 0.95\n", - "[ NORMAL ] Iteration 37: k_eff = 0.938571 res = 3.807E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 624 D.R. = 0.95\n", - "[ NORMAL ] Iteration 38: k_eff = 0.944455 res = 3.606E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 588 D.R. = 0.95\n", - "[ NORMAL ] Iteration 39: k_eff = 0.949996 res = 3.413E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 554 D.R. = 0.95\n", - "[ NORMAL ] Iteration 40: k_eff = 0.955210 res = 3.227E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 521 D.R. = 0.95\n", - "[ NORMAL ] Iteration 41: k_eff = 0.960110 res = 3.049E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 490 D.R. = 0.94\n", - "[ NORMAL ] Iteration 42: k_eff = 0.964713 res = 2.880E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 460 D.R. = 0.94\n", - "[ NORMAL ] Iteration 43: k_eff = 0.969032 res = 2.717E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 431 D.R. = 0.94\n", - "[ NORMAL ] Iteration 44: k_eff = 0.973082 res = 2.562E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 405 D.R. = 0.94\n", - "[ NORMAL ] Iteration 45: k_eff = 0.976877 res = 2.414E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 379 D.R. = 0.94\n", - "[ NORMAL ] Iteration 46: k_eff = 0.980431 res = 2.274E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 355 D.R. = 0.94\n", - "[ NORMAL ] Iteration 47: k_eff = 0.983757 res = 2.140E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 332 D.R. = 0.94\n", - "[ NORMAL ] Iteration 48: k_eff = 0.986869 res = 2.014E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 311 D.R. = 0.94\n", - "[ NORMAL ] Iteration 49: k_eff = 0.989777 res = 1.894E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 290 D.R. = 0.94\n", - "[ NORMAL ] Iteration 50: k_eff = 0.992495 res = 1.780E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 271 D.R. = 0.94\n", - "[ NORMAL ] Iteration 51: k_eff = 0.995033 res = 1.672E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 253 D.R. = 0.94\n", - "[ NORMAL ] Iteration 52: k_eff = 0.997402 res = 1.569E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 236 D.R. = 0.94\n", - "[ NORMAL ] Iteration 53: k_eff = 0.999613 res = 1.473E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 221 D.R. = 0.94\n", - "[ NORMAL ] Iteration 54: k_eff = 1.001675 res = 1.382E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 206 D.R. = 0.94\n", - "[ NORMAL ] Iteration 55: k_eff = 1.003597 res = 1.296E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 192 D.R. = 0.94\n", - "[ NORMAL ] Iteration 56: k_eff = 1.005388 res = 1.215E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 179 D.R. = 0.94\n", - "[ NORMAL ] Iteration 57: k_eff = 1.007057 res = 1.138E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 166 D.R. = 0.94\n", - "[ NORMAL ] Iteration 58: k_eff = 1.008612 res = 1.066E-04 delta-k (pcm) =\n", - "[ NORMAL ] ... 155 D.R. = 0.94\n", - "[ NORMAL ] Iteration 59: k_eff = 1.010059 res = 9.980E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 144 D.R. = 0.94\n" + "[ NORMAL ] Iteration 0: k_eff = 0.823342 res = 9.831E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -17665 D.R. = 0.0983\n", + "[ NORMAL ] Iteration 1: k_eff = 0.780160 res = 4.646E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -4318 D.R. = 0.4725\n", + "[ NORMAL ] Iteration 2: k_eff = 0.739336 res = 9.631E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -4082 D.R. = 0.2073\n", + "[ NORMAL ] Iteration 3: k_eff = 0.710750 res = 8.566E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2858 D.R. = 0.8894\n", + "[ NORMAL ] Iteration 4: k_eff = 0.689598 res = 5.205E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2115 D.R. = 0.6076\n", + "[ NORMAL ] Iteration 5: k_eff = 0.675031 res = 3.605E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -1456 D.R. = 0.6926\n", + "[ NORMAL ] Iteration 6: k_eff = 0.665895 res = 2.538E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -913 D.R. = 0.7040\n", + "[ NORMAL ] Iteration 7: k_eff = 0.661318 res = 1.889E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -457 D.R. = 0.7443\n", + "[ NORMAL ] Iteration 8: k_eff = 0.660529 res = 1.493E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -78 D.R. = 0.7904\n", + "[ NORMAL ] Iteration 9: k_eff = 0.662872 res = 1.268E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 234 D.R. = 0.8490\n", + "[ NORMAL ] Iteration 10: k_eff = 0.667780 res = 1.140E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 490 D.R. = 0.8995\n", + "[ NORMAL ] Iteration 11: k_eff = 0.674766 res = 1.065E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 698 D.R. = 0.9337\n", + "[ NORMAL ] Iteration 12: k_eff = 0.683410 res = 1.014E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 864 D.R. = 0.9527\n", + "[ NORMAL ] Iteration 13: k_eff = 0.693354 res = 9.759E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 994 D.R. = 0.9623\n", + "[ NORMAL ] Iteration 14: k_eff = 0.704290 res = 9.438E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1093 D.R. = 0.9671\n", + "[ NORMAL ] Iteration 15: k_eff = 0.715957 res = 9.154E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1166 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 16: k_eff = 0.728134 res = 8.892E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1217 D.R. = 0.9714\n", + "[ NORMAL ] Iteration 17: k_eff = 0.740633 res = 8.644E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1249 D.R. = 0.9721\n", + "[ NORMAL ] Iteration 18: k_eff = 0.753297 res = 8.404E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1266 D.R. = 0.9722\n", + "[ NORMAL ] Iteration 19: k_eff = 0.765995 res = 8.167E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1269 D.R. = 0.9719\n", + "[ NORMAL ] Iteration 20: k_eff = 0.778619 res = 7.930E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1262 D.R. = 0.9710\n", + "[ NORMAL ] Iteration 21: k_eff = 0.791079 res = 7.691E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1246 D.R. = 0.9698\n", + "[ NORMAL ] Iteration 22: k_eff = 0.803304 res = 7.447E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1222 D.R. = 0.9683\n", + "[ NORMAL ] Iteration 23: k_eff = 0.815235 res = 7.199E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1193 D.R. = 0.9667\n", + "[ NORMAL ] Iteration 24: k_eff = 0.826828 res = 6.947E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1159 D.R. = 0.9650\n", + "[ NORMAL ] Iteration 25: k_eff = 0.838047 res = 6.693E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1121 D.R. = 0.9634\n", + "[ NORMAL ] Iteration 26: k_eff = 0.848868 res = 6.436E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1082 D.R. = 0.9617\n", + "[ NORMAL ] Iteration 27: k_eff = 0.859271 res = 6.179E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1040 D.R. = 0.9600\n", + "[ NORMAL ] Iteration 28: k_eff = 0.869247 res = 5.922E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 997 D.R. = 0.9585\n", + "[ NORMAL ] Iteration 29: k_eff = 0.878788 res = 5.667E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 954 D.R. = 0.9570\n", + "[ NORMAL ] Iteration 30: k_eff = 0.887894 res = 5.415E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 910 D.R. = 0.9556\n", + "[ NORMAL ] Iteration 31: k_eff = 0.896566 res = 5.168E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 867 D.R. = 0.9543\n", + "[ NORMAL ] Iteration 32: k_eff = 0.904810 res = 4.925E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 824 D.R. = 0.9530\n", + "[ NORMAL ] Iteration 33: k_eff = 0.912635 res = 4.688E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 782 D.R. = 0.9519\n", + "[ NORMAL ] Iteration 34: k_eff = 0.920049 res = 4.457E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 741 D.R. = 0.9508\n", + "[ NORMAL ] Iteration 35: k_eff = 0.927066 res = 4.233E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 701 D.R. = 0.9498\n", + "[ NORMAL ] Iteration 36: k_eff = 0.933696 res = 4.016E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 663 D.R. = 0.9488\n", + "[ NORMAL ] Iteration 37: k_eff = 0.939954 res = 3.807E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 625 D.R. = 0.9479\n", + "[ NORMAL ] Iteration 38: k_eff = 0.945855 res = 3.606E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 0.9471\n", + "[ NORMAL ] Iteration 39: k_eff = 0.951412 res = 3.413E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 0.9464\n", + "[ NORMAL ] Iteration 40: k_eff = 0.956641 res = 3.227E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 522 D.R. = 0.9456\n", + "[ NORMAL ] Iteration 41: k_eff = 0.961556 res = 3.049E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 0.9448\n", + "[ NORMAL ] Iteration 42: k_eff = 0.966174 res = 2.879E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 461 D.R. = 0.9443\n", + "[ NORMAL ] Iteration 43: k_eff = 0.970507 res = 2.716E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 433 D.R. = 0.9435\n", + "[ NORMAL ] Iteration 44: k_eff = 0.974571 res = 2.561E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 406 D.R. = 0.9430\n", + "[ NORMAL ] Iteration 45: k_eff = 0.978380 res = 2.414E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 380 D.R. = 0.9423\n", + "[ NORMAL ] Iteration 46: k_eff = 0.981948 res = 2.273E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 356 D.R. = 0.9417\n", + "[ NORMAL ] Iteration 47: k_eff = 0.985287 res = 2.140E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 333 D.R. = 0.9413\n", + "[ NORMAL ] Iteration 48: k_eff = 0.988410 res = 2.013E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 312 D.R. = 0.9409\n", + "[ NORMAL ] Iteration 49: k_eff = 0.991331 res = 1.893E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 292 D.R. = 0.9402\n", + "[ NORMAL ] Iteration 50: k_eff = 0.994060 res = 1.779E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 272 D.R. = 0.9399\n", + "[ NORMAL ] Iteration 51: k_eff = 0.996609 res = 1.671E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 254 D.R. = 0.9393\n", + "[ NORMAL ] Iteration 52: k_eff = 0.998988 res = 1.569E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 237 D.R. = 0.9390\n", + "[ NORMAL ] Iteration 53: k_eff = 1.001209 res = 1.473E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 222 D.R. = 0.9386\n", + "[ NORMAL ] Iteration 54: k_eff = 1.003280 res = 1.382E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 207 D.R. = 0.9380\n", + "[ NORMAL ] Iteration 55: k_eff = 1.005211 res = 1.296E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 193 D.R. = 0.9378\n", + "[ NORMAL ] Iteration 56: k_eff = 1.007012 res = 1.215E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 180 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 57: k_eff = 1.008689 res = 1.138E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 167 D.R. = 0.9369\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Iteration 60: k_eff = 1.011406 res = 9.342E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 134 D.R. = 0.94\n", - "[ NORMAL ] Iteration 61: k_eff = 1.012659 res = 8.740E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 125 D.R. = 0.94\n", - "[ NORMAL ] Iteration 62: k_eff = 1.013825 res = 8.175E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 116 D.R. = 0.94\n", - "[ NORMAL ] Iteration 63: k_eff = 1.014909 res = 7.642E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 108 D.R. = 0.93\n", - "[ NORMAL ] Iteration 64: k_eff = 1.015917 res = 7.142E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 100 D.R. = 0.93\n", - "[ NORMAL ] Iteration 65: k_eff = 1.016853 res = 6.675E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 93 D.R. = 0.93\n", - "[ NORMAL ] Iteration 66: k_eff = 1.017724 res = 6.235E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 87 D.R. = 0.93\n", - "[ NORMAL ] Iteration 67: k_eff = 1.018533 res = 5.822E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 80 D.R. = 0.93\n", - "[ NORMAL ] Iteration 68: k_eff = 1.019284 res = 5.436E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 75 D.R. = 0.93\n", - "[ NORMAL ] Iteration 69: k_eff = 1.019982 res = 5.074E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 69 D.R. = 0.93\n", - "[ NORMAL ] Iteration 70: k_eff = 1.020630 res = 4.734E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 64 D.R. = 0.93\n", - "[ NORMAL ] Iteration 71: k_eff = 1.021232 res = 4.417E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 60 D.R. = 0.93\n", - "[ NORMAL ] Iteration 72: k_eff = 1.021790 res = 4.119E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 55 D.R. = 0.93\n", - "[ NORMAL ] Iteration 73: k_eff = 1.022308 res = 3.841E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 51 D.R. = 0.93\n", - "[ NORMAL ] Iteration 74: k_eff = 1.022789 res = 3.579E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 48 D.R. = 0.93\n", - "[ NORMAL ] Iteration 75: k_eff = 1.023235 res = 3.336E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 44 D.R. = 0.93\n", - "[ NORMAL ] Iteration 76: k_eff = 1.023648 res = 3.107E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 41 D.R. = 0.93\n", - "[ NORMAL ] Iteration 77: k_eff = 1.024032 res = 2.897E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 38 D.R. = 0.93\n", - "[ NORMAL ] Iteration 78: k_eff = 1.024388 res = 2.696E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 35 D.R. = 0.93\n", - "[ NORMAL ] Iteration 79: k_eff = 1.024718 res = 2.510E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 32 D.R. = 0.93\n", - "[ NORMAL ] Iteration 80: k_eff = 1.025024 res = 2.338E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 30 D.R. = 0.93\n", - "[ NORMAL ] Iteration 81: k_eff = 1.025307 res = 2.175E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 28 D.R. = 0.93\n", - "[ NORMAL ] Iteration 82: k_eff = 1.025570 res = 2.025E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 26 D.R. = 0.93\n", - "[ NORMAL ] Iteration 83: k_eff = 1.025814 res = 1.886E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 24 D.R. = 0.93\n", - "[ NORMAL ] Iteration 84: k_eff = 1.026039 res = 1.752E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 22 D.R. = 0.93\n", - "[ NORMAL ] Iteration 85: k_eff = 1.026249 res = 1.628E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 20 D.R. = 0.93\n", - "[ NORMAL ] Iteration 86: k_eff = 1.026442 res = 1.517E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 19 D.R. = 0.93\n", - "[ NORMAL ] Iteration 87: k_eff = 1.026622 res = 1.408E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 17 D.R. = 0.93\n", - "[ NORMAL ] Iteration 88: k_eff = 1.026788 res = 1.308E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 16 D.R. = 0.93\n", - "[ NORMAL ] Iteration 89: k_eff = 1.026942 res = 1.218E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 15 D.R. = 0.93\n", - "[ NORMAL ] Iteration 90: k_eff = 1.027085 res = 1.132E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 14 D.R. = 0.93\n", - "[ NORMAL ] Iteration 91: k_eff = 1.027217 res = 1.049E-05 delta-k (pcm) =\n", - "[ NORMAL ] ... 13 D.R. = 0.93\n", - "[ NORMAL ] Iteration 92: k_eff = 1.027339 res = 9.760E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 12 D.R. = 0.93\n", - "[ NORMAL ] Iteration 93: k_eff = 1.027453 res = 9.076E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 11 D.R. = 0.93\n", - "[ NORMAL ] Iteration 94: k_eff = 1.027557 res = 8.434E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 10 D.R. = 0.93\n", - "[ NORMAL ] Iteration 95: k_eff = 1.027655 res = 7.827E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 9 D.R. = 0.93\n", - "[ NORMAL ] Iteration 96: k_eff = 1.027744 res = 7.266E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 8 D.R. = 0.93\n", - "[ NORMAL ] Iteration 97: k_eff = 1.027828 res = 6.737E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 8 D.R. = 0.93\n", - "[ NORMAL ] Iteration 98: k_eff = 1.027905 res = 6.255E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.93\n", - "[ NORMAL ] Iteration 99: k_eff = 1.027976 res = 5.803E-06 delta-k (pcm) =\n", - "[ NORMAL ] ... 7 D.R. = 0.93\n", - "[ NORMAL ] Iteration 100: k_eff = 1.028042 res = 5.383E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.93\n", - "[ NORMAL ] Iteration 101: k_eff = 1.028103 res = 5.017E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 6 D.R. = 0.93\n", - "[ NORMAL ] Iteration 102: k_eff = 1.028160 res = 4.618E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.92\n", - "[ NORMAL ] Iteration 103: k_eff = 1.028212 res = 4.306E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 5 D.R. = 0.93\n", - "[ NORMAL ] Iteration 104: k_eff = 1.028260 res = 3.999E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 105: k_eff = 1.028305 res = 3.706E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 106: k_eff = 1.028347 res = 3.429E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 4 D.R. = 0.93\n", - "[ NORMAL ] Iteration 107: k_eff = 1.028385 res = 3.213E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.94\n", - "[ NORMAL ] Iteration 108: k_eff = 1.028420 res = 2.943E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.92\n", - "[ NORMAL ] Iteration 109: k_eff = 1.028453 res = 2.740E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.93\n", - "[ NORMAL ] Iteration 110: k_eff = 1.028484 res = 2.531E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 3 D.R. = 0.92\n", - "[ NORMAL ] Iteration 111: k_eff = 1.028512 res = 2.369E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.94\n", - "[ NORMAL ] Iteration 112: k_eff = 1.028538 res = 2.186E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.92\n", - "[ NORMAL ] Iteration 113: k_eff = 1.028562 res = 2.026E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.93\n", - "[ NORMAL ] Iteration 114: k_eff = 1.028584 res = 1.858E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.92\n", - "[ NORMAL ] Iteration 115: k_eff = 1.028604 res = 1.760E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 2 D.R. = 0.95\n", - "[ NORMAL ] Iteration 116: k_eff = 1.028623 res = 1.612E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 117: k_eff = 1.028641 res = 1.496E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.93\n", - "[ NORMAL ] Iteration 118: k_eff = 1.028657 res = 1.382E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 119: k_eff = 1.028672 res = 1.293E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.94\n", - "[ NORMAL ] Iteration 120: k_eff = 1.028686 res = 1.191E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.92\n", - "[ NORMAL ] Iteration 121: k_eff = 1.028699 res = 1.112E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.93\n", - "[ NORMAL ] Iteration 122: k_eff = 1.028711 res = 1.005E-06 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.90\n", - "[ NORMAL ] Iteration 123: k_eff = 1.028722 res = 9.443E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.94\n", - "[ NORMAL ] Iteration 124: k_eff = 1.028732 res = 8.583E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 1 D.R. = 0.91\n", - "[ NORMAL ] Iteration 125: k_eff = 1.028742 res = 8.028E-07 delta-k (pcm)\n", - "[ NORMAL ] ... = 0 D.R. = 0.94\n" + "[ NORMAL ] Iteration 58: k_eff = 1.010251 res = 1.066E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 156 D.R. = 0.9369\n", + "[ NORMAL ] Iteration 59: k_eff = 1.011706 res = 9.981E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 145 D.R. = 0.9362\n", + "[ NORMAL ] Iteration 60: k_eff = 1.013060 res = 9.344E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 135 D.R. = 0.9361\n", + "[ NORMAL ] Iteration 61: k_eff = 1.014320 res = 8.743E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 125 D.R. = 0.9357\n", + "[ NORMAL ] Iteration 62: k_eff = 1.015492 res = 8.177E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 117 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 63: k_eff = 1.016582 res = 7.648E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 109 D.R. = 0.9353\n", + "[ NORMAL ] Iteration 64: k_eff = 1.017596 res = 7.147E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 101 D.R. = 0.9345\n", + "[ NORMAL ] Iteration 65: k_eff = 1.018538 res = 6.680E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 94 D.R. = 0.9346\n", + "[ NORMAL ] Iteration 66: k_eff = 1.019414 res = 6.237E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 87 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 67: k_eff = 1.020227 res = 5.828E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 81 D.R. = 0.9343\n", + "[ NORMAL ] Iteration 68: k_eff = 1.020983 res = 5.442E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 75 D.R. = 0.9338\n", + "[ NORMAL ] Iteration 69: k_eff = 1.021685 res = 5.080E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 70 D.R. = 0.9336\n", + "[ NORMAL ] Iteration 70: k_eff = 1.022337 res = 4.739E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 65 D.R. = 0.9328\n", + "[ NORMAL ] Iteration 71: k_eff = 1.022942 res = 4.422E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 72: k_eff = 1.023504 res = 4.124E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 56 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 73: k_eff = 1.024026 res = 3.843E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 52 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 74: k_eff = 1.024510 res = 3.586E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 48 D.R. = 0.9331\n", + "[ NORMAL ] Iteration 75: k_eff = 1.024959 res = 3.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 44 D.R. = 0.9317\n", + "[ NORMAL ] Iteration 76: k_eff = 1.025375 res = 3.115E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 41 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 77: k_eff = 1.025762 res = 2.898E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 38 D.R. = 0.9303\n", + "[ NORMAL ] Iteration 78: k_eff = 1.026120 res = 2.703E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 35 D.R. = 0.9327\n", + "[ NORMAL ] Iteration 79: k_eff = 1.026452 res = 2.519E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 33 D.R. = 0.9318\n", + "[ NORMAL ] Iteration 80: k_eff = 1.026760 res = 2.341E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 30 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 81: k_eff = 1.027046 res = 2.180E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 28 D.R. = 0.9312\n", + "[ NORMAL ] Iteration 82: k_eff = 1.027311 res = 2.028E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 26 D.R. = 0.9301\n", + "[ NORMAL ] Iteration 83: k_eff = 1.027556 res = 1.889E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 24 D.R. = 0.9315\n", + "[ NORMAL ] Iteration 84: k_eff = 1.027783 res = 1.757E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 22 D.R. = 0.9305\n", + "[ NORMAL ] Iteration 85: k_eff = 1.027994 res = 1.632E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 21 D.R. = 0.9284\n", + "[ NORMAL ] Iteration 86: k_eff = 1.028189 res = 1.521E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 19 D.R. = 0.9322\n", + "[ NORMAL ] Iteration 87: k_eff = 1.028370 res = 1.412E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 18 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 88: k_eff = 1.028538 res = 1.311E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 16 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 89: k_eff = 1.028693 res = 1.222E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 15 D.R. = 0.9316\n", + "[ NORMAL ] Iteration 90: k_eff = 1.028837 res = 1.134E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 14 D.R. = 0.9279\n", + "[ NORMAL ] Iteration 91: k_eff = 1.028970 res = 1.056E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 13 D.R. = 0.9314\n", + "[ NORMAL ] Iteration 92: k_eff = 1.029093 res = 9.786E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.9268\n", + "[ NORMAL ] Iteration 93: k_eff = 1.029207 res = 9.093E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 11 D.R. = 0.9292\n", + "[ NORMAL ] Iteration 94: k_eff = 1.029313 res = 8.445E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 10 D.R. = 0.9287\n", + "[ NORMAL ] Iteration 95: k_eff = 1.029411 res = 7.848E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9294\n", + "[ NORMAL ] Iteration 96: k_eff = 1.029502 res = 7.272E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.9265\n", + "[ NORMAL ] Iteration 97: k_eff = 1.029586 res = 6.769E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.9309\n", + "[ NORMAL ] Iteration 98: k_eff = 1.029663 res = 6.294E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9298\n", + "[ NORMAL ] Iteration 99: k_eff = 1.029735 res = 5.827E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.9259\n", + "[ NORMAL ] Iteration 100: k_eff = 1.029802 res = 5.413E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9290\n", + "[ NORMAL ] Iteration 101: k_eff = 1.029863 res = 5.032E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.9295\n", + "[ NORMAL ] Iteration 102: k_eff = 1.029920 res = 4.648E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9237\n", + "[ NORMAL ] Iteration 103: k_eff = 1.029973 res = 4.328E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.9313\n", + "[ NORMAL ] Iteration 104: k_eff = 1.030022 res = 4.004E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9249\n", + "[ NORMAL ] Iteration 105: k_eff = 1.030067 res = 3.734E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9326\n", + "[ NORMAL ] Iteration 106: k_eff = 1.030109 res = 3.452E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 107: k_eff = 1.030148 res = 3.195E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9253\n", + "[ NORMAL ] Iteration 108: k_eff = 1.030184 res = 2.979E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9325\n", + "[ NORMAL ] Iteration 109: k_eff = 1.030217 res = 2.750E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9230\n", + "[ NORMAL ] Iteration 110: k_eff = 1.030247 res = 2.541E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.9240\n", + "[ NORMAL ] Iteration 111: k_eff = 1.030276 res = 2.364E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9302\n", + "[ NORMAL ] Iteration 112: k_eff = 1.030302 res = 2.178E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9215\n", + "[ NORMAL ] Iteration 113: k_eff = 1.030326 res = 2.022E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9285\n", + "[ NORMAL ] Iteration 114: k_eff = 1.030349 res = 1.896E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9374\n", + "[ NORMAL ] Iteration 115: k_eff = 1.030369 res = 1.753E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.9246\n", + "[ NORMAL ] Iteration 116: k_eff = 1.030389 res = 1.619E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9236\n", + "[ NORMAL ] Iteration 117: k_eff = 1.030406 res = 1.500E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9266\n", + "[ NORMAL ] Iteration 118: k_eff = 1.030423 res = 1.406E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9375\n", + "[ NORMAL ] Iteration 119: k_eff = 1.030438 res = 1.287E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9154\n", + "[ NORMAL ] Iteration 120: k_eff = 1.030452 res = 1.193E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9269\n", + "[ NORMAL ] Iteration 121: k_eff = 1.030465 res = 1.095E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9174\n", + "[ NORMAL ] Iteration 122: k_eff = 1.030477 res = 1.033E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9437\n", + "[ NORMAL ] Iteration 123: k_eff = 1.030488 res = 9.328E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9029\n", + "[ NORMAL ] Iteration 124: k_eff = 1.030498 res = 8.716E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.9344\n", + "[ NORMAL ] Iteration 125: k_eff = 1.030508 res = 8.527E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.9783\n" ] } ], @@ -1502,16 +1516,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.024078\n", - "openmoc keff = 1.028742\n", - "bias [pcm]: 466.4\n" + "openmc keff = 1.023625\n", + "openmoc keff = 1.030508\n", + "bias [pcm]: 688.3\n" ] } ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined.nominal_value\n", "bias = (openmoc_keff - openmc_keff) * 1e5\n", "\n", "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", @@ -1554,6 +1567,9 @@ "mesh_tally = sp.get_tally(name='mesh tally')\n", "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n", "\n", + "# Close the statepoint file now that we're done getting information from it\n", + "sp.close()\n", + "\n", "# Reshape array to 2D for plotting\n", "openmc_fission_rates.shape = (17,17)\n", "\n", @@ -1615,7 +1631,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1660,7 +1676,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb index f5e5142b51c..a8b042e48fc 100644 --- a/examples/jupyter/pandas-dataframes.ipynb +++ b/examples/jupyter/pandas-dataframes.ipynb @@ -255,7 +255,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -410,6 +410,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "zsh:1: no matches found: statepoint.*\n", " %%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", @@ -435,829 +436,836 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2020 MIT and OpenMC contributors\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.12.0\n", - " Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n", - " Date/Time | 2020-08-15 07:10:20\n", - " OpenMP Threads | 4\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:27:04\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n", - " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n", - " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n", - " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n", - " Reading B10 from /home/master/data/nuclear/endfb71_hdf5/B10.h5\n", - " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n", - " Minimum neutron data temperature: 294.000000 K\n", - " Maximum neutron data temperature: 294.000000 K\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.53544\n", - " 2/1 0.62631\n", - " 3/1 0.63917\n", - " 4/1 0.67203\n", - " 5/1 0.69300\n", - " 6/1 0.64862\n", - " 7/1 0.63937 0.64399 +/- 0.00463\n", - " 8/1 0.67696 0.65498 +/- 0.01131\n", - " 9/1 0.63216 0.64928 +/- 0.00982\n", - " 10/1 0.70996 0.66141 +/- 0.01433\n", - " 11/1 0.69761 0.66745 +/- 0.01316\n", - " 12/1 0.68662 0.67019 +/- 0.01146\n", - " 13/1 0.64374 0.66688 +/- 0.01046\n", - " 14/1 0.69121 0.66958 +/- 0.00961\n", - " 15/1 0.72125 0.67475 +/- 0.01003\n", - " 16/1 0.72706 0.67950 +/- 0.01024\n", - " 17/1 0.69623 0.68090 +/- 0.00945\n", - " 18/1 0.70953 0.68310 +/- 0.00897\n", - " 19/1 0.69026 0.68361 +/- 0.00832\n", - " 20/1 0.68633 0.68379 +/- 0.00775\n", - " Triggers unsatisfied, max unc./thresh. is 75.24758750489383 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 84938 --- greater than max batches\n", + " 1/1 0.56788\n", + " 2/1 0.66344\n", + " 3/1 0.67801\n", + " 4/1 0.64662\n", + " 5/1 0.69912\n", + " 6/1 0.72720\n", + " 7/1 0.71337 0.72029 +/- 0.00691\n", + " 8/1 0.70564 0.71540 +/- 0.00630\n", + " 9/1 0.69707 0.71082 +/- 0.00639\n", + " 10/1 0.69745 0.70815 +/- 0.00563\n", + " 11/1 0.68982 0.70509 +/- 0.00552\n", + " 12/1 0.69541 0.70371 +/- 0.00486\n", + " 13/1 0.69724 0.70290 +/- 0.00429\n", + " 14/1 0.71009 0.70370 +/- 0.00387\n", + " 15/1 0.69614 0.70294 +/- 0.00354\n", + " 16/1 0.68883 0.70166 +/- 0.00345\n", + " 17/1 0.68635 0.70038 +/- 0.00340\n", + " 18/1 0.70665 0.70087 +/- 0.00316\n", + " 19/1 0.65747 0.69777 +/- 0.00426\n", + " 20/1 0.69228 0.69740 +/- 0.00399\n", + " Triggers unsatisfied, max unc./thresh. is 93.89673349356296 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 132254 --- greater than max batches\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.68310 0.68375 +/- 0.00725\n", - " Triggers unsatisfied, max unc./thresh. is 71.20148627325992 for absorption in\n", + " 21/1 0.70753 0.69803 +/- 0.00378\n", + " Triggers unsatisfied, max unc./thresh. is 88.01427279112123 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 81120 --- greater than max batches\n", - " 22/1 0.68679 0.68393 +/- 0.00681\n", - " Triggers unsatisfied, max unc./thresh. is 66.94650483064697 for absorption in\n", + " WARNING: The estimated number of batches is 123950 --- greater than max batches\n", + " 22/1 0.71245 0.69888 +/- 0.00365\n", + " Triggers unsatisfied, max unc./thresh. is 84.70931054978398 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 76197 --- greater than max batches\n", - " 23/1 0.67440 0.68340 +/- 0.00644\n", - " Triggers unsatisfied, max unc./thresh. is 63.553590826021285 for absorption in\n", + " WARNING: The estimated number of batches is 121992 --- greater than max batches\n", + " 23/1 0.65293 0.69633 +/- 0.00429\n", + " Triggers unsatisfied, max unc./thresh. is 86.59494174559383 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72709 --- greater than max batches\n", - " 24/1 0.67483 0.68295 +/- 0.00611\n", - " Triggers unsatisfied, max unc./thresh. is 60.37873858685279 for absorption in\n", + " WARNING: The estimated number of batches is 134982 --- greater than max batches\n", + " 24/1 0.66415 0.69464 +/- 0.00439\n", + " Triggers unsatisfied, max unc./thresh. is 83.04784122808066 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69272 --- greater than max batches\n", - " 25/1 0.71558 0.68458 +/- 0.00602\n", - " Triggers unsatisfied, max unc./thresh. is 60.34535216026281 for absorption in\n", + " WARNING: The estimated number of batches is 131047 --- greater than max batches\n", + " 25/1 0.71052 0.69543 +/- 0.00424\n", + " Triggers unsatisfied, max unc./thresh. is 81.86075666631334 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72837 --- greater than max batches\n", - " 26/1 0.71853 0.68620 +/- 0.00595\n", - " Triggers unsatisfied, max unc./thresh. is 59.60875760463032 for absorption in\n", + " WARNING: The estimated number of batches is 134029 --- greater than max batches\n", + " 26/1 0.64576 0.69307 +/- 0.00468\n", + " Triggers unsatisfied, max unc./thresh. is 77.87299751571092 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 74623 --- greater than max batches\n", - " 27/1 0.67455 0.68567 +/- 0.00570\n", - " Triggers unsatisfied, max unc./thresh. is 57.228951643423976 for absorption in\n", + " WARNING: The estimated number of batches is 127354 --- greater than max batches\n", + " 27/1 0.66691 0.69188 +/- 0.00462\n", + " Triggers unsatisfied, max unc./thresh. is 74.42216649772053 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72059 --- greater than max batches\n", - " 28/1 0.69435 0.68605 +/- 0.00546\n", - " Triggers unsatisfied, max unc./thresh. is 56.194065573871285 for absorption in\n", + " WARNING: The estimated number of batches is 121856 --- greater than max batches\n", + " 28/1 0.65694 0.69036 +/- 0.00467\n", + " Triggers unsatisfied, max unc./thresh. is 71.15138613523133 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 72634 --- greater than max batches\n", - " 29/1 0.67706 0.68567 +/- 0.00524\n", - " Triggers unsatisfied, max unc./thresh. is 53.86022066923874 for absorption in\n", + " WARNING: The estimated number of batches is 116443 --- greater than max batches\n", + " 29/1 0.69674 0.69062 +/- 0.00447\n", + " Triggers unsatisfied, max unc./thresh. is 69.56665853404124 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69628 --- greater than max batches\n", - " 30/1 0.69294 0.68596 +/- 0.00504\n", - " Triggers unsatisfied, max unc./thresh. is 51.73413206858763 for absorption in\n", + " WARNING: The estimated number of batches is 116154 --- greater than max batches\n", + " 30/1 0.63556 0.68842 +/- 0.00482\n", + " Triggers unsatisfied, max unc./thresh. is 66.8810661090428 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66916 --- greater than max batches\n", - " 31/1 0.69108 0.68616 +/- 0.00484\n", - " Triggers unsatisfied, max unc./thresh. is 49.71174462484801 for absorption in\n", + " WARNING: The estimated number of batches is 111832 --- greater than max batches\n", + " 31/1 0.69809 0.68879 +/- 0.00465\n", + " Triggers unsatisfied, max unc./thresh. is 64.26124103679346 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64258 --- greater than max batches\n", - " 32/1 0.68089 0.68596 +/- 0.00466\n", - " Triggers unsatisfied, max unc./thresh. is 50.80993117627794 for absorption in\n", + " WARNING: The estimated number of batches is 107373 --- greater than max batches\n", + " 32/1 0.64298 0.68710 +/- 0.00479\n", + " Triggers unsatisfied, max unc./thresh. is 62.020922539187175 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69710 --- greater than max batches\n", - " 33/1 0.67698 0.68564 +/- 0.00450\n", - " Triggers unsatisfied, max unc./thresh. is 50.46659333785448 for absorption in\n", + " WARNING: The estimated number of batches is 103864 --- greater than max batches\n", + " 33/1 0.68559 0.68704 +/- 0.00461\n", + " Triggers unsatisfied, max unc./thresh. is 60.42853939278924 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 71318 --- greater than max batches\n", - " 34/1 0.68167 0.68551 +/- 0.00435\n", - " Triggers unsatisfied, max unc./thresh. is 48.852656603250665 for absorption in\n", + " WARNING: The estimated number of batches is 102251 --- greater than max batches\n", + " 34/1 0.65886 0.68607 +/- 0.00455\n", + " Triggers unsatisfied, max unc./thresh. is 58.33270366370514 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69216 --- greater than max batches\n", - " 35/1 0.67760 0.68524 +/- 0.00421\n", - " Triggers unsatisfied, max unc./thresh. is 48.5685583427197 for absorption in\n", + " WARNING: The estimated number of batches is 98684 --- greater than max batches\n", + " 35/1 0.68743 0.68611 +/- 0.00440\n", + " Triggers unsatisfied, max unc./thresh. is 61.22701811574928 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 70773 --- greater than max batches\n", - " 36/1 0.67628 0.68495 +/- 0.00408\n", - " Triggers unsatisfied, max unc./thresh. is 47.77661998216646 for absorption in\n", + " WARNING: The estimated number of batches is 112468 --- greater than max batches\n", + " 36/1 0.69354 0.68635 +/- 0.00426\n", + " Triggers unsatisfied, max unc./thresh. is 59.98985062145117 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 70766 --- greater than max batches\n", - " 37/1 0.66736 0.68440 +/- 0.00399\n", - " Triggers unsatisfied, max unc./thresh. is 46.57810773879176 for absorption in\n", + " WARNING: The estimated number of batches is 111568 --- greater than max batches\n", + " 37/1 0.65095 0.68525 +/- 0.00427\n", + " Triggers unsatisfied, max unc./thresh. is 58.08611480075532 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69430 --- greater than max batches\n", - " 38/1 0.71026 0.68519 +/- 0.00395\n", - " Triggers unsatisfied, max unc./thresh. is 45.876107560616674 for absorption in\n", + " WARNING: The estimated number of batches is 107973 --- greater than max batches\n", + " 38/1 0.74212 0.68697 +/- 0.00449\n", + " Triggers unsatisfied, max unc./thresh. is 56.42863990001401 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69458 --- greater than max batches\n", - " 39/1 0.67674 0.68494 +/- 0.00384\n", - " Triggers unsatisfied, max unc./thresh. is 45.30341918376721 for absorption in\n", + " WARNING: The estimated number of batches is 105084 --- greater than max batches\n", + " 39/1 0.70258 0.68743 +/- 0.00438\n", + " Triggers unsatisfied, max unc./thresh. is 56.00920365845771 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 69787 --- greater than max batches\n", - " 40/1 0.69360 0.68519 +/- 0.00373\n", - " Triggers unsatisfied, max unc./thresh. is 44.018056562863386 for absorption in\n", + " WARNING: The estimated number of batches is 106665 --- greater than max batches\n", + " 40/1 0.73458 0.68878 +/- 0.00446\n", + " Triggers unsatisfied, max unc./thresh. is 54.62032757641821 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67821 --- greater than max batches\n", - " 41/1 0.70987 0.68587 +/- 0.00369\n", - " Triggers unsatisfied, max unc./thresh. is 42.781078099052785 for absorption in\n", + " WARNING: The estimated number of batches is 104424 --- greater than max batches\n", + " 41/1 0.66256 0.68805 +/- 0.00439\n", + " Triggers unsatisfied, max unc./thresh. is 53.09826256808107 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65893 --- greater than max batches\n", - " 42/1 0.68780 0.68592 +/- 0.00359\n", - " Triggers unsatisfied, max unc./thresh. is 41.60877069209228 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64063 --- greater than max batches\n", - " 43/1 0.69223 0.68609 +/- 0.00350\n", - " Triggers unsatisfied, max unc./thresh. is 42.44932759168626 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 68479 --- greater than max batches\n" + " WARNING: The estimated number of batches is 101505 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 44/1 0.69561 0.68633 +/- 0.00342\n", - " Triggers unsatisfied, max unc./thresh. is 41.34743776753899 for absorption in\n", + " 42/1 0.68262 0.68790 +/- 0.00427\n", + " Triggers unsatisfied, max unc./thresh. is 51.64336419771116 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66680 --- greater than max batches\n", - " 45/1 0.67503 0.68605 +/- 0.00334\n", - " Triggers unsatisfied, max unc./thresh. is 40.97332186124358 for absorption in\n", + " WARNING: The estimated number of batches is 98686 --- greater than max batches\n", + " 43/1 0.70218 0.68828 +/- 0.00418\n", + " Triggers unsatisfied, max unc./thresh. is 50.670570340468814 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67158 --- greater than max batches\n", - " 46/1 0.67290 0.68573 +/- 0.00328\n", - " Triggers unsatisfied, max unc./thresh. is 40.24678448931756 for absorption in\n", + " WARNING: The estimated number of batches is 97571 --- greater than max batches\n", + " 44/1 0.67643 0.68797 +/- 0.00408\n", + " Triggers unsatisfied, max unc./thresh. is 49.69199356343392 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66417 --- greater than max batches\n", - " 47/1 0.67355 0.68544 +/- 0.00321\n", - " Triggers unsatisfied, max unc./thresh. is 40.42620640829592 for absorption in\n", + " WARNING: The estimated number of batches is 96308 --- greater than max batches\n", + " 45/1 0.67103 0.68755 +/- 0.00400\n", + " Triggers unsatisfied, max unc./thresh. is 48.44632012698348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 68645 --- greater than max batches\n", - " 48/1 0.71383 0.68610 +/- 0.00320\n", - " Triggers unsatisfied, max unc./thresh. is 39.90662320308606 for absorption in\n", + " WARNING: The estimated number of batches is 93887 --- greater than max batches\n", + " 46/1 0.65599 0.68678 +/- 0.00398\n", + " Triggers unsatisfied, max unc./thresh. is 47.34469866924748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 68485 --- greater than max batches\n", - " 49/1 0.68389 0.68605 +/- 0.00313\n", - " Triggers unsatisfied, max unc./thresh. is 39.075369568753906 for absorption in\n", + " WARNING: The estimated number of batches is 91908 --- greater than max batches\n", + " 47/1 0.64796 0.68586 +/- 0.00399\n", + " Triggers unsatisfied, max unc./thresh. is 46.49104551849076 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67188 --- greater than max batches\n", - " 50/1 0.73148 0.68706 +/- 0.00322\n", - " Triggers unsatisfied, max unc./thresh. is 38.57054567218653 for absorption in\n", + " WARNING: The estimated number of batches is 90785 --- greater than max batches\n", + " 48/1 0.69638 0.68610 +/- 0.00390\n", + " Triggers unsatisfied, max unc./thresh. is 45.709058495601944 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66951 --- greater than max batches\n", - " 51/1 0.69796 0.68730 +/- 0.00316\n", - " Triggers unsatisfied, max unc./thresh. is 38.21572703507316 for absorption in\n", + " WARNING: The estimated number of batches is 89846 --- greater than max batches\n", + " 49/1 0.69484 0.68630 +/- 0.00382\n", + " Triggers unsatisfied, max unc./thresh. is 44.85300785365741 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 67186 --- greater than max batches\n", - " 52/1 0.70691 0.68771 +/- 0.00312\n", - " Triggers unsatisfied, max unc./thresh. is 37.50971908717773 for absorption in\n", + " WARNING: The estimated number of batches is 88524 --- greater than max batches\n", + " 50/1 0.69678 0.68653 +/- 0.00374\n", + " Triggers unsatisfied, max unc./thresh. is 43.84946296160474 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66134 --- greater than max batches\n", - " 53/1 0.69104 0.68778 +/- 0.00306\n", - " Triggers unsatisfied, max unc./thresh. is 36.824732312223716 for absorption in\n", + " WARNING: The estimated number of batches is 86530 --- greater than max batches\n", + " 51/1 0.72852 0.68745 +/- 0.00377\n", + " Triggers unsatisfied, max unc./thresh. is 43.787094662843266 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65096 --- greater than max batches\n", - " 54/1 0.74368 0.68892 +/- 0.00320\n", - " Triggers unsatisfied, max unc./thresh. is 36.20814737643575 for absorption in\n", + " WARNING: The estimated number of batches is 88202 --- greater than max batches\n", + " 52/1 0.69916 0.68769 +/- 0.00370\n", + " Triggers unsatisfied, max unc./thresh. is 43.14074745408116 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64246 --- greater than max batches\n", - " 55/1 0.67371 0.68862 +/- 0.00315\n", - " Triggers unsatisfied, max unc./thresh. is 35.48607231512293 for absorption in\n", + " WARNING: The estimated number of batches is 87478 --- greater than max batches\n", + " 53/1 0.70809 0.68812 +/- 0.00364\n", + " Triggers unsatisfied, max unc./thresh. is 42.78319111442477 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62969 --- greater than max batches\n", - " 56/1 0.67846 0.68842 +/- 0.00310\n", - " Triggers unsatisfied, max unc./thresh. is 35.34421893287461 for absorption in\n", + " WARNING: The estimated number of batches is 87865 --- greater than max batches\n", + " 54/1 0.67049 0.68776 +/- 0.00359\n", + " Triggers unsatisfied, max unc./thresh. is 41.90152321392576 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63715 --- greater than max batches\n", - " 57/1 0.66351 0.68794 +/- 0.00307\n", - " Triggers unsatisfied, max unc./thresh. is 34.67062652878957 for absorption in\n", + " WARNING: The estimated number of batches is 86037 --- greater than max batches\n", + " 55/1 0.67612 0.68753 +/- 0.00352\n", + " Triggers unsatisfied, max unc./thresh. is 41.247941809938425 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62512 --- greater than max batches\n", - " 58/1 0.67049 0.68761 +/- 0.00303\n", - " Triggers unsatisfied, max unc./thresh. is 34.22135922543247 for absorption in\n", + " WARNING: The estimated number of batches is 85075 --- greater than max batches\n", + " 56/1 0.68035 0.68739 +/- 0.00346\n", + " Triggers unsatisfied, max unc./thresh. is 40.46515891712033 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62074 --- greater than max batches\n", - " 59/1 0.66967 0.68728 +/- 0.00299\n", - " Triggers unsatisfied, max unc./thresh. is 33.66881484408945 for absorption in\n", + " WARNING: The estimated number of batches is 83514 --- greater than max batches\n", + " 57/1 0.70520 0.68773 +/- 0.00341\n", + " Triggers unsatisfied, max unc./thresh. is 40.05474461357334 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61219 --- greater than max batches\n", - " 60/1 0.70271 0.68756 +/- 0.00295\n", - " Triggers unsatisfied, max unc./thresh. is 33.19914799505717 for absorption in\n", + " WARNING: The estimated number of batches is 83433 --- greater than max batches\n", + " 58/1 0.70938 0.68814 +/- 0.00337\n", + " Triggers unsatisfied, max unc./thresh. is 39.36707588204034 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60626 --- greater than max batches\n", - " 61/1 0.70035 0.68779 +/- 0.00291\n", - " Triggers unsatisfied, max unc./thresh. is 32.65594936729897 for absorption in\n", + " WARNING: The estimated number of batches is 82143 --- greater than max batches\n", + " 59/1 0.71202 0.68858 +/- 0.00333\n", + " Triggers unsatisfied, max unc./thresh. is 38.66137944699882 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59725 --- greater than max batches\n", - " 62/1 0.66274 0.68735 +/- 0.00289\n", - " Triggers unsatisfied, max unc./thresh. is 32.15622046485561 for absorption in\n", + " WARNING: The estimated number of batches is 80719 --- greater than max batches\n", + " 60/1 0.66559 0.68816 +/- 0.00330\n", + " Triggers unsatisfied, max unc./thresh. is 37.98682899231639 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58945 --- greater than max batches\n", - " 63/1 0.68607 0.68733 +/- 0.00284\n", - " Triggers unsatisfied, max unc./thresh. is 31.601225649282494 for absorption in\n", + " WARNING: The estimated number of batches is 79370 --- greater than max batches\n", + " 61/1 0.65921 0.68765 +/- 0.00328\n", + " Triggers unsatisfied, max unc./thresh. is 38.029460967594694 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57926 --- greater than max batches\n", - " 64/1 0.66518 0.68695 +/- 0.00282\n", - " Triggers unsatisfied, max unc./thresh. is 31.12129365572805 for absorption in\n", + " WARNING: The estimated number of batches is 80995 --- greater than max batches\n", + " 62/1 0.66584 0.68726 +/- 0.00324\n", + " Triggers unsatisfied, max unc./thresh. is 37.381474566315354 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57149 --- greater than max batches\n", - " 65/1 0.65999 0.68650 +/- 0.00281\n", - " Triggers unsatisfied, max unc./thresh. is 30.641988019531464 for absorption in\n", + " WARNING: The estimated number of batches is 79656 --- greater than max batches\n", + " 63/1 0.69512 0.68740 +/- 0.00319\n", + " Triggers unsatisfied, max unc./thresh. is 36.76408883031811 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56341 --- greater than max batches\n", - " 66/1 0.67843 0.68637 +/- 0.00276\n", - " Triggers unsatisfied, max unc./thresh. is 30.320463443580458 for absorption in\n", + " WARNING: The estimated number of batches is 78398 --- greater than max batches\n", + " 64/1 0.72240 0.68799 +/- 0.00319\n", + " Triggers unsatisfied, max unc./thresh. is 36.18011705137993 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56085 --- greater than max batches\n", - " 67/1 0.69295 0.68648 +/- 0.00272\n", - " Triggers unsatisfied, max unc./thresh. is 30.06051080038397 for absorption in\n", + " WARNING: The estimated number of batches is 77237 --- greater than max batches\n", + " 65/1 0.68739 0.68798 +/- 0.00314\n", + " Triggers unsatisfied, max unc./thresh. is 35.60153732818344 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56031 --- greater than max batches\n", - " 68/1 0.69158 0.68656 +/- 0.00268\n", - " Triggers unsatisfied, max unc./thresh. is 29.7400907913873 for absorption in\n", + " WARNING: The estimated number of batches is 76054 --- greater than max batches\n", + " 66/1 0.63734 0.68715 +/- 0.00320\n", + " Triggers unsatisfied, max unc./thresh. is 35.03171980405627 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55727 --- greater than max batches\n", - " 69/1 0.69825 0.68674 +/- 0.00264\n", - " Triggers unsatisfied, max unc./thresh. is 29.278619659445805 for absorption in\n", + " WARNING: The estimated number of batches is 74866 --- greater than max batches\n", + " 67/1 0.67068 0.68689 +/- 0.00315\n", + " Triggers unsatisfied, max unc./thresh. is 34.51557177789461 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54869 --- greater than max batches\n", - " 70/1 0.73637 0.68750 +/- 0.00271\n", - " Triggers unsatisfied, max unc./thresh. is 28.945044018568716 for absorption in\n", + " WARNING: The estimated number of batches is 73868 --- greater than max batches\n", + " 68/1 0.67713 0.68673 +/- 0.00311\n", + " Triggers unsatisfied, max unc./thresh. is 33.96995049011678 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54464 --- greater than max batches\n", - " 71/1 0.64301 0.68683 +/- 0.00275\n", - " Triggers unsatisfied, max unc./thresh. is 28.73677928804667 for absorption in\n", + " WARNING: The estimated number of batches is 72705 --- greater than max batches\n", + " 69/1 0.71729 0.68721 +/- 0.00310\n", + " Triggers unsatisfied, max unc./thresh. is 33.74180433617202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 54508 --- greater than max batches\n", - " 72/1 0.71506 0.68725 +/- 0.00274\n", - " Triggers unsatisfied, max unc./thresh. is 29.20796537704291 for absorption in\n", + " WARNING: The estimated number of batches is 72870 --- greater than max batches\n", + " 70/1 0.69537 0.68733 +/- 0.00305\n", + " Triggers unsatisfied, max unc./thresh. is 33.2256923807649 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57164 --- greater than max batches\n", - " 73/1 0.69203 0.68732 +/- 0.00270\n", - " Triggers unsatisfied, max unc./thresh. is 29.56297016014581 for absorption in\n", + " WARNING: The estimated number of batches is 71762 --- greater than max batches\n", + " 71/1 0.67200 0.68710 +/- 0.00301\n", + " Triggers unsatisfied, max unc./thresh. is 33.22256499834962 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59435 --- greater than max batches\n", - " 74/1 0.69208 0.68739 +/- 0.00267\n", - " Triggers unsatisfied, max unc./thresh. is 29.545241442413783 for absorption in\n", + " WARNING: The estimated number of batches is 72852 --- greater than max batches\n", + " 72/1 0.71865 0.68757 +/- 0.00300\n", + " Triggers unsatisfied, max unc./thresh. is 32.735500285071055 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60237 --- greater than max batches\n", - " 75/1 0.65717 0.68696 +/- 0.00266\n", - " Triggers unsatisfied, max unc./thresh. is 29.284013224166248 for absorption in\n", + " WARNING: The estimated number of batches is 71804 --- greater than max batches\n", + " 73/1 0.69590 0.68769 +/- 0.00296\n", + " Triggers unsatisfied, max unc./thresh. is 32.26730455835508 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60034 --- greater than max batches\n", - " 76/1 0.70992 0.68728 +/- 0.00265\n", - " Triggers unsatisfied, max unc./thresh. is 29.00034584995327 for absorption in\n", + " WARNING: The estimated number of batches is 70806 --- greater than max batches\n", + " 74/1 0.68222 0.68762 +/- 0.00292\n", + " Triggers unsatisfied, max unc./thresh. is 32.10780833972545 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59718 --- greater than max batches\n", - " 77/1 0.65590 0.68685 +/- 0.00264\n", - " Triggers unsatisfied, max unc./thresh. is 28.867967845905174 for absorption in\n", + " WARNING: The estimated number of batches is 71138 --- greater than max batches\n", + " 75/1 0.70643 0.68788 +/- 0.00289\n", + " Triggers unsatisfied, max unc./thresh. is 31.818936053589276 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60007 --- greater than max batches\n", - " 78/1 0.64439 0.68626 +/- 0.00267\n", - " Triggers unsatisfied, max unc./thresh. is 29.016012595317935 for absorption in\n", + " WARNING: The estimated number of batches is 70877 --- greater than max batches\n", + " 76/1 0.67658 0.68772 +/- 0.00285\n", + " Triggers unsatisfied, max unc./thresh. is 31.799932820980022 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61466 --- greater than max batches\n", - " 79/1 0.66295 0.68595 +/- 0.00265\n", - " Triggers unsatisfied, max unc./thresh. is 28.626245464278142 for absorption in\n", + " WARNING: The estimated number of batches is 71803 --- greater than max batches\n", + " 77/1 0.67857 0.68760 +/- 0.00282\n", + " Triggers unsatisfied, max unc./thresh. is 32.26711337228593 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60646 --- greater than max batches\n", - " 80/1 0.66672 0.68569 +/- 0.00263\n", - " Triggers unsatisfied, max unc./thresh. is 28.24218910063624 for absorption in\n", + " WARNING: The estimated number of batches is 74969 --- greater than max batches\n", + " 78/1 0.69108 0.68765 +/- 0.00278\n", + " Triggers unsatisfied, max unc./thresh. is 31.82734318749477 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59827 --- greater than max batches\n", - " 81/1 0.69110 0.68576 +/- 0.00260\n", - " Triggers unsatisfied, max unc./thresh. is 27.917349908027763 for absorption in\n", + " WARNING: The estimated number of batches is 73953 --- greater than max batches\n", + " 79/1 0.68226 0.68757 +/- 0.00274\n", + " Triggers unsatisfied, max unc./thresh. is 31.40561277542678 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59238 --- greater than max batches\n", - " 82/1 0.67481 0.68562 +/- 0.00257\n", - " Triggers unsatisfied, max unc./thresh. is 28.01946018168837 for absorption in\n", + " WARNING: The estimated number of batches is 72993 --- greater than max batches\n", + " 80/1 0.67648 0.68742 +/- 0.00271\n", + " Triggers unsatisfied, max unc./thresh. is 31.182065681219466 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60457 --- greater than max batches\n", - " 83/1 0.72216 0.68609 +/- 0.00258\n", - " Triggers unsatisfied, max unc./thresh. is 27.931394620754766 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 60858 --- greater than max batches\n" + " WARNING: The estimated number of batches is 72930 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 84/1 0.68429 0.68607 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 27.713137470531738 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 60679 --- greater than max batches\n", - " 85/1 0.65458 0.68567 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 27.364539968246927 for absorption in\n", + " 81/1 0.65907 0.68705 +/- 0.00270\n", + " Triggers unsatisfied, max unc./thresh. is 30.770354748329673 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59911 --- greater than max batches\n", - " 86/1 0.69966 0.68585 +/- 0.00252\n", - " Triggers unsatisfied, max unc./thresh. is 27.178113974435043 for absorption in\n", + " WARNING: The estimated number of batches is 71963 --- greater than max batches\n", + " 82/1 0.63285 0.68635 +/- 0.00276\n", + " Triggers unsatisfied, max unc./thresh. is 30.494055493974837 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59836 --- greater than max batches\n", - " 87/1 0.64776 0.68538 +/- 0.00253\n", - " Triggers unsatisfied, max unc./thresh. is 26.941566345072534 for absorption in\n", + " WARNING: The estimated number of batches is 71607 --- greater than max batches\n", + " 83/1 0.65128 0.68590 +/- 0.00276\n", + " Triggers unsatisfied, max unc./thresh. is 30.31368198832468 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59525 --- greater than max batches\n", - " 88/1 0.62737 0.68468 +/- 0.00259\n", - " Triggers unsatisfied, max unc./thresh. is 26.73959660667411 for absorption in\n", + " WARNING: The estimated number of batches is 71681 --- greater than max batches\n", + " 84/1 0.65957 0.68556 +/- 0.00274\n", + " Triggers unsatisfied, max unc./thresh. is 29.927509740512043 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59351 --- greater than max batches\n", - " 89/1 0.69779 0.68484 +/- 0.00257\n", - " Triggers unsatisfied, max unc./thresh. is 26.490234865810894 for absorption in\n", + " WARNING: The estimated number of batches is 70762 --- greater than max batches\n", + " 85/1 0.69936 0.68574 +/- 0.00271\n", + " Triggers unsatisfied, max unc./thresh. is 29.684480624889716 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58951 --- greater than max batches\n", - " 90/1 0.67312 0.68470 +/- 0.00254\n", - " Triggers unsatisfied, max unc./thresh. is 26.24036001465229 for absorption in\n", + " WARNING: The estimated number of batches is 70499 --- greater than max batches\n", + " 86/1 0.71538 0.68610 +/- 0.00270\n", + " Triggers unsatisfied, max unc./thresh. is 30.42821373418053 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58533 --- greater than max batches\n", - " 91/1 0.69289 0.68480 +/- 0.00251\n", - " Triggers unsatisfied, max unc./thresh. is 25.936795778335345 for absorption in\n", + " WARNING: The estimated number of batches is 75001 --- greater than max batches\n", + " 87/1 0.67737 0.68600 +/- 0.00267\n", + " Triggers unsatisfied, max unc./thresh. is 30.08396896848712 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57859 --- greater than max batches\n", - " 92/1 0.69884 0.68496 +/- 0.00249\n", - " Triggers unsatisfied, max unc./thresh. is 25.695465963215582 for absorption in\n", + " WARNING: The estimated number of batches is 74219 --- greater than max batches\n", + " 88/1 0.67516 0.68587 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 29.820034394835567 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57448 --- greater than max batches\n", - " 93/1 0.71351 0.68528 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.49001212499821 for absorption in\n", + " WARNING: The estimated number of batches is 73812 --- greater than max batches\n", + " 89/1 0.71831 0.68625 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 29.52222916170289 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57183 --- greater than max batches\n", - " 94/1 0.65602 0.68495 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.350859463183905 for absorption in\n", + " WARNING: The estimated number of batches is 73217 --- greater than max batches\n", + " 90/1 0.69057 0.68630 +/- 0.00261\n", + " Triggers unsatisfied, max unc./thresh. is 29.192694352726093 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57203 --- greater than max batches\n", - " 95/1 0.72223 0.68537 +/- 0.00248\n", - " Triggers unsatisfied, max unc./thresh. is 25.157803279393637 for absorption in\n", + " WARNING: The estimated number of batches is 72444 --- greater than max batches\n", + " 91/1 0.72527 0.68676 +/- 0.00262\n", + " Triggers unsatisfied, max unc./thresh. is 28.882854654006614 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56968 --- greater than max batches\n", - " 96/1 0.67930 0.68530 +/- 0.00246\n", - " Triggers unsatisfied, max unc./thresh. is 24.92205849077747 for absorption in\n", + " WARNING: The estimated number of batches is 71748 --- greater than max batches\n", + " 92/1 0.68240 0.68671 +/- 0.00259\n", + " Triggers unsatisfied, max unc./thresh. is 28.746406095976795 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56526 --- greater than max batches\n", - " 97/1 0.66201 0.68505 +/- 0.00244\n", - " Triggers unsatisfied, max unc./thresh. is 24.653967027285237 for absorption in\n", + " WARNING: The estimated number of batches is 71898 --- greater than max batches\n", + " 93/1 0.68284 0.68666 +/- 0.00256\n", + " Triggers unsatisfied, max unc./thresh. is 28.55969476551871 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55925 --- greater than max batches\n", - " 98/1 0.71110 0.68533 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 24.566957281211884 for absorption in\n", + " WARNING: The estimated number of batches is 71783 --- greater than max batches\n", + " 94/1 0.67323 0.68651 +/- 0.00254\n", + " Triggers unsatisfied, max unc./thresh. is 28.562644465757202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56134 --- greater than max batches\n", - " 99/1 0.69409 0.68542 +/- 0.00241\n", - " Triggers unsatisfied, max unc./thresh. is 24.581943149247135 for absorption in\n", + " WARNING: The estimated number of batches is 72614 --- greater than max batches\n", + " 95/1 0.67226 0.68635 +/- 0.00251\n", + " Triggers unsatisfied, max unc./thresh. is 28.266619522119196 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56807 --- greater than max batches\n", - " 100/1 0.71197 0.68570 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 24.444112571967217 for absorption in\n", + " WARNING: The estimated number of batches is 71916 --- greater than max batches\n", + " 96/1 0.69225 0.68642 +/- 0.00249\n", + " Triggers unsatisfied, max unc./thresh. is 28.090316080385687 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56769 --- greater than max batches\n", - " 101/1 0.71713 0.68603 +/- 0.00240\n", - " Triggers unsatisfied, max unc./thresh. is 24.18957776896016 for absorption in\n", + " WARNING: The estimated number of batches is 71810 --- greater than max batches\n", + " 97/1 0.67708 0.68632 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 27.821561823906112 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56179 --- greater than max batches\n", - " 102/1 0.68143 0.68598 +/- 0.00237\n", - " Triggers unsatisfied, max unc./thresh. is 23.97797098066553 for absorption in\n", + " WARNING: The estimated number of batches is 71217 --- greater than max batches\n", + " 98/1 0.68583 0.68631 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 27.550146728325792 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55775 --- greater than max batches\n", - " 103/1 0.69936 0.68612 +/- 0.00235\n", - " Triggers unsatisfied, max unc./thresh. is 24.253000406602812 for absorption in\n", + " WARNING: The estimated number of batches is 70593 --- greater than max batches\n", + " 99/1 0.64118 0.68583 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 27.31334482933601 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57650 --- greater than max batches\n", - " 104/1 0.65247 0.68578 +/- 0.00235\n", - " Triggers unsatisfied, max unc./thresh. is 24.593482483379837 for absorption in\n", + " WARNING: The estimated number of batches is 70131 --- greater than max batches\n", + " 100/1 0.67711 0.68574 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 27.045284650239612 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59885 --- greater than max batches\n", - " 105/1 0.66517 0.68557 +/- 0.00234\n", - " Triggers unsatisfied, max unc./thresh. is 24.37904760701804 for absorption in\n", + " WARNING: The estimated number of batches is 69493 --- greater than max batches\n", + " 101/1 0.68084 0.68569 +/- 0.00241\n", + " Triggers unsatisfied, max unc./thresh. is 26.783343195485262 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59439 --- greater than max batches\n", - " 106/1 0.67814 0.68550 +/- 0.00232\n", - " Triggers unsatisfied, max unc./thresh. is 24.142311084988883 for absorption in\n", + " WARNING: The estimated number of batches is 68871 --- greater than max batches\n", + " 102/1 0.69024 0.68573 +/- 0.00238\n", + " Triggers unsatisfied, max unc./thresh. is 26.507109203671433 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58873 --- greater than max batches\n", - " 107/1 0.67788 0.68542 +/- 0.00229\n", - " Triggers unsatisfied, max unc./thresh. is 23.935477435724106 for absorption in\n", + " WARNING: The estimated number of batches is 68160 --- greater than max batches\n", + " 103/1 0.66758 0.68555 +/- 0.00236\n", + " Triggers unsatisfied, max unc./thresh. is 26.75706619598529 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58442 --- greater than max batches\n", - " 108/1 0.68016 0.68537 +/- 0.00227\n", - " Triggers unsatisfied, max unc./thresh. is 24.532504688648594 for absorption in\n", + " WARNING: The estimated number of batches is 70168 --- greater than max batches\n", + " 104/1 0.70685 0.68576 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 26.50422283567677 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61995 --- greater than max batches\n", - " 109/1 0.66963 0.68522 +/- 0.00226\n", - " Triggers unsatisfied, max unc./thresh. is 24.354532539671386 for absorption in\n", + " WARNING: The estimated number of batches is 69550 --- greater than max batches\n", + " 105/1 0.65411 0.68545 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 26.700673042186363 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61692 --- greater than max batches\n", - " 110/1 0.67556 0.68513 +/- 0.00224\n", - " Triggers unsatisfied, max unc./thresh. is 24.16165322902175 for absorption in\n", + " WARNING: The estimated number of batches is 71298 --- greater than max batches\n", + " 106/1 0.66632 0.68526 +/- 0.00233\n", + " Triggers unsatisfied, max unc./thresh. is 26.660322235538125 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61303 --- greater than max batches\n", - " 111/1 0.68273 0.68511 +/- 0.00222\n", - " Triggers unsatisfied, max unc./thresh. is 24.00069508298176 for absorption in\n", + " WARNING: The estimated number of batches is 71794 --- greater than max batches\n", + " 107/1 0.72095 0.68561 +/- 0.00234\n", + " Triggers unsatisfied, max unc./thresh. is 26.493921637614655 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61065 --- greater than max batches\n", - " 112/1 0.69505 0.68520 +/- 0.00220\n", - " Triggers unsatisfied, max unc./thresh. is 23.791656279909404 for absorption in\n", + " WARNING: The estimated number of batches is 71602 --- greater than max batches\n", + " 108/1 0.68486 0.68560 +/- 0.00231\n", + " Triggers unsatisfied, max unc./thresh. is 26.261958180540656 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60572 --- greater than max batches\n", - " 113/1 0.69385 0.68528 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 23.667941020219764 for absorption in\n", + " WARNING: The estimated number of batches is 71044 --- greater than max batches\n", + " 109/1 0.70300 0.68577 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 26.026316770700436 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60504 --- greater than max batches\n", - " 114/1 0.65352 0.68499 +/- 0.00218\n", - " Triggers unsatisfied, max unc./thresh. is 23.469658485546123 for absorption in\n", + " WARNING: The estimated number of batches is 70452 --- greater than max batches\n", + " 110/1 0.63861 0.68532 +/- 0.00232\n", + " Triggers unsatisfied, max unc./thresh. is 25.926028971288112 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 60045 --- greater than max batches\n", - " 115/1 0.68339 0.68497 +/- 0.00216\n", - " Triggers unsatisfied, max unc./thresh. is 23.259123161328624 for absorption in\n", + " WARNING: The estimated number of batches is 70582 --- greater than max batches\n", + " 111/1 0.68795 0.68534 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 25.717124436169865 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59514 --- greater than max batches\n", - " 116/1 0.65854 0.68474 +/- 0.00215\n", - " Triggers unsatisfied, max unc./thresh. is 23.06250977653337 for absorption in\n", + " WARNING: The estimated number of batches is 70111 --- greater than max batches\n", + " 112/1 0.64471 0.68496 +/- 0.00231\n", + " Triggers unsatisfied, max unc./thresh. is 25.505784119998573 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59044 --- greater than max batches\n", - " 117/1 0.66907 0.68460 +/- 0.00214\n", - " Triggers unsatisfied, max unc./thresh. is 22.874382198219536 for absorption in\n", + " WARNING: The estimated number of batches is 69614 --- greater than max batches\n", + " 113/1 0.68637 0.68498 +/- 0.00229\n", + " Triggers unsatisfied, max unc./thresh. is 25.844123747859776 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58608 --- greater than max batches\n", - " 118/1 0.68165 0.68457 +/- 0.00212\n", - " Triggers unsatisfied, max unc./thresh. is 22.709602691165983 for absorption in\n", + " WARNING: The estimated number of batches is 72141 --- greater than max batches\n", + " 114/1 0.70597 0.68517 +/- 0.00227\n", + " Triggers unsatisfied, max unc./thresh. is 26.41373710688748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58283 --- greater than max batches\n", - " 119/1 0.70967 0.68479 +/- 0.00211\n", - " Triggers unsatisfied, max unc./thresh. is 22.509869996658225 for absorption in\n", + " WARNING: The estimated number of batches is 76053 --- greater than max batches\n", + " 115/1 0.67258 0.68506 +/- 0.00225\n", + " Triggers unsatisfied, max unc./thresh. is 26.35672818667791 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57769 --- greater than max batches\n", - " 120/1 0.65543 0.68453 +/- 0.00211\n", - " Triggers unsatisfied, max unc./thresh. is 22.422104322874098 for absorption in\n", + " WARNING: The estimated number of batches is 76420 --- greater than max batches\n", + " 116/1 0.68671 0.68507 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 26.123859680914105 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57822 --- greater than max batches\n", - " 121/1 0.67305 0.68444 +/- 0.00209\n", - " Triggers unsatisfied, max unc./thresh. is 22.32834321567902 for absorption in\n", + " WARNING: The estimated number of batches is 75758 --- greater than max batches\n", + " 117/1 0.65467 0.68480 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 25.891227809553317 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57838 --- greater than max batches\n", - " 122/1 0.68206 0.68441 +/- 0.00207\n", - " Triggers unsatisfied, max unc./thresh. is 22.155196965032374 for absorption in\n", + " WARNING: The estimated number of batches is 75085 --- greater than max batches\n", + " 118/1 0.68299 0.68478 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 25.663579354019788 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57435 --- greater than max batches\n", - " 123/1 0.71125 0.68464 +/- 0.00207\n", - " Triggers unsatisfied, max unc./thresh. is 21.96683678913398 for absorption in\n", + " WARNING: The estimated number of batches is 74429 --- greater than max batches\n", + " 119/1 0.71640 0.68506 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 25.798931576936234 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56945 --- greater than max batches\n" + " WARNING: The estimated number of batches is 75882 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 124/1 0.65918 0.68443 +/- 0.00206\n", - " Triggers unsatisfied, max unc./thresh. is 21.78216358933223 for absorption in\n", + " 120/1 0.67682 0.68499 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.600502408827925 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56467 --- greater than max batches\n", - " 125/1 0.68122 0.68440 +/- 0.00205\n", - " Triggers unsatisfied, max unc./thresh. is 21.681928420505304 for absorption in\n", + " WARNING: The estimated number of batches is 75375 --- greater than max batches\n", + " 121/1 0.64612 0.68465 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 25.442400998883397 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56418 --- greater than max batches\n", - " 126/1 0.66900 0.68427 +/- 0.00203\n", - " Triggers unsatisfied, max unc./thresh. is 21.60631058168512 for absorption in\n", + " WARNING: The estimated number of batches is 75094 --- greater than max batches\n", + " 122/1 0.65743 0.68442 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.314147051671682 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56492 --- greater than max batches\n", - " 127/1 0.66742 0.68414 +/- 0.00202\n", - " Triggers unsatisfied, max unc./thresh. is 21.468291123480988 for absorption in\n", + " WARNING: The estimated number of batches is 74980 --- greater than max batches\n", + " 123/1 0.67460 0.68434 +/- 0.00217\n", + " Triggers unsatisfied, max unc./thresh. is 25.120655024301723 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56234 --- greater than max batches\n", - " 128/1 0.66971 0.68402 +/- 0.00201\n", - " Triggers unsatisfied, max unc./thresh. is 21.313238544974386 for absorption in\n", + " WARNING: The estimated number of batches is 74469 --- greater than max batches\n", + " 124/1 0.63622 0.68393 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 25.173748425745888 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55879 --- greater than max batches\n", - " 129/1 0.68183 0.68400 +/- 0.00199\n", - " Triggers unsatisfied, max unc./thresh. is 21.314008888585132 for absorption in\n", + " WARNING: The estimated number of batches is 75418 --- greater than max batches\n", + " 125/1 0.74014 0.68440 +/- 0.00222\n", + " Triggers unsatisfied, max unc./thresh. is 24.969908358640748 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56337 --- greater than max batches\n", - " 130/1 0.68403 0.68400 +/- 0.00197\n", - " Triggers unsatisfied, max unc./thresh. is 21.159444482258046 for absorption in\n", + " WARNING: The estimated number of batches is 74825 --- greater than max batches\n", + " 126/1 0.66532 0.68424 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 24.763458789173267 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 55971 --- greater than max batches\n", - " 131/1 0.69137 0.68406 +/- 0.00196\n", - " Triggers unsatisfied, max unc./thresh. is 21.24931160989673 for absorption in\n", + " WARNING: The estimated number of batches is 74206 --- greater than max batches\n", + " 127/1 0.66151 0.68406 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 24.715390259532615 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56899 --- greater than max batches\n", - " 132/1 0.67481 0.68399 +/- 0.00195\n", - " Triggers unsatisfied, max unc./thresh. is 21.164512281281944 for absorption in\n", + " WARNING: The estimated number of batches is 74529 --- greater than max batches\n", + " 128/1 0.66861 0.68393 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 24.532144885232228 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56893 --- greater than max batches\n", - " 133/1 0.70390 0.68414 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.084176856795946 for absorption in\n", + " WARNING: The estimated number of batches is 74030 --- greater than max batches\n", + " 129/1 0.69363 0.68401 +/- 0.00217\n", + " Triggers unsatisfied, max unc./thresh. is 24.350853769848076 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 56907 --- greater than max batches\n", - " 134/1 0.67961 0.68411 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 21.48684646255342 for absorption in\n", + " WARNING: The estimated number of batches is 73533 --- greater than max batches\n", + " 130/1 0.70857 0.68421 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 24.17444245442967 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59563 --- greater than max batches\n", - " 135/1 0.65362 0.68387 +/- 0.00192\n", - " Triggers unsatisfied, max unc./thresh. is 21.41235779526348 for absorption in\n", + " WARNING: The estimated number of batches is 73056 --- greater than max batches\n", + " 131/1 0.67714 0.68415 +/- 0.00215\n", + " Triggers unsatisfied, max unc./thresh. is 23.98323256055421 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59609 --- greater than max batches\n", - " 136/1 0.63946 0.68353 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.30299014546295 for absorption in\n", + " WARNING: The estimated number of batches is 72480 --- greater than max batches\n", + " 132/1 0.67410 0.68407 +/- 0.00213\n", + " Triggers unsatisfied, max unc./thresh. is 23.867449107003456 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59456 --- greater than max batches\n", - " 137/1 0.64818 0.68327 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 21.159761745415484 for absorption in\n", + " WARNING: The estimated number of batches is 72352 --- greater than max batches\n", + " 133/1 0.69079 0.68412 +/- 0.00211\n", + " Triggers unsatisfied, max unc./thresh. is 23.685770754896556 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 59107 --- greater than max batches\n", - " 138/1 0.68975 0.68331 +/- 0.00193\n", - " Triggers unsatisfied, max unc./thresh. is 21.000094566393475 for absorption in\n", + " WARNING: The estimated number of batches is 71816 --- greater than max batches\n", + " 134/1 0.67606 0.68406 +/- 0.00210\n", + " Triggers unsatisfied, max unc./thresh. is 23.73618218941846 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58659 --- greater than max batches\n", - " 139/1 0.67280 0.68324 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 20.853656171297644 for absorption in\n", + " WARNING: The estimated number of batches is 72685 --- greater than max batches\n", + " 135/1 0.69637 0.68416 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 23.69643071032316 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 58279 --- greater than max batches\n", - " 140/1 0.66857 0.68313 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 20.709591767033707 for absorption in\n", + " WARNING: The estimated number of batches is 73003 --- greater than max batches\n", + " 136/1 0.67044 0.68405 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 23.57110141899803 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57905 --- greater than max batches\n", - " 141/1 0.68175 0.68312 +/- 0.00189\n", - " Triggers unsatisfied, max unc./thresh. is 20.560813068689416 for absorption in\n", + " WARNING: The estimated number of batches is 72789 --- greater than max batches\n", + " 137/1 0.69621 0.68414 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 23.48120874987551 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57499 --- greater than max batches\n", - " 142/1 0.72210 0.68340 +/- 0.00190\n", - " Triggers unsatisfied, max unc./thresh. is 20.54814917791921 for absorption in\n", + " WARNING: The estimated number of batches is 72786 --- greater than max batches\n", + " 138/1 0.71568 0.68438 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 23.304158910242545 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57851 --- greater than max batches\n", - " 143/1 0.67361 0.68333 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 20.4177880049802 for absorption in\n", + " WARNING: The estimated number of batches is 72236 --- greater than max batches\n", + " 139/1 0.69041 0.68443 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 23.13692986052565 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 57536 --- greater than max batches\n", - " 144/1 0.65862 0.68315 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 21.229890183572195 for absorption in\n", + " WARNING: The estimated number of batches is 71738 --- greater than max batches\n", + " 140/1 0.68872 0.68446 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 22.97436361147689 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62654 --- greater than max batches\n", - " 145/1 0.69713 0.68325 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 21.34513800240435 for absorption in\n", + " WARNING: The estimated number of batches is 71261 --- greater than max batches\n", + " 141/1 0.68717 0.68448 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 22.918186999320493 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63792 --- greater than max batches\n", - " 146/1 0.72980 0.68358 +/- 0.00188\n", - " Triggers unsatisfied, max unc./thresh. is 21.60165210412777 for absorption in\n", + " WARNING: The estimated number of batches is 71439 --- greater than max batches\n", + " 142/1 0.70148 0.68460 +/- 0.00200\n", + " Triggers unsatisfied, max unc./thresh. is 22.90664792171847 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65801 --- greater than max batches\n", - " 147/1 0.70004 0.68370 +/- 0.00187\n", - " Triggers unsatisfied, max unc./thresh. is 21.596734424310384 for absorption in\n", + " WARNING: The estimated number of batches is 71891 --- greater than max batches\n", + " 143/1 0.70002 0.68471 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 22.800726503137792 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66237 --- greater than max batches\n", - " 148/1 0.68882 0.68374 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 21.447240534346236 for absorption in\n", + " WARNING: The estimated number of batches is 71748 --- greater than max batches\n", + " 144/1 0.68586 0.68472 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 22.666610393936825 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65783 --- greater than max batches\n", - " 149/1 0.70401 0.68388 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 21.424993974056104 for absorption in\n", + " WARNING: The estimated number of batches is 71420 --- greater than max batches\n", + " 145/1 0.64403 0.68443 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 22.850786290626733 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66106 --- greater than max batches\n", - " 150/1 0.72110 0.68413 +/- 0.00186\n", - " Triggers unsatisfied, max unc./thresh. is 21.27792348945665 for absorption in\n", + " WARNING: The estimated number of batches is 73108 --- greater than max batches\n", + " 146/1 0.64825 0.68417 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 22.780126501480474 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65654 --- greater than max batches\n", - " 151/1 0.65918 0.68396 +/- 0.00185\n", - " Triggers unsatisfied, max unc./thresh. is 21.378637401006184 for absorption in\n", + " WARNING: The estimated number of batches is 73175 --- greater than max batches\n", + " 147/1 0.66962 0.68407 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 22.713317279463887 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66734 --- greater than max batches\n", - " 152/1 0.67751 0.68392 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 21.25974745003047 for absorption in\n", + " WARNING: The estimated number of batches is 73263 --- greater than max batches\n", + " 148/1 0.67953 0.68404 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 22.555984073152782 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66446 --- greater than max batches\n", - " 153/1 0.69302 0.68398 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 21.16055371271148 for absorption in\n", + " WARNING: The estimated number of batches is 72760 --- greater than max batches\n", + " 149/1 0.70028 0.68415 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 22.467338789571908 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 66275 --- greater than max batches\n", - " 154/1 0.67102 0.68389 +/- 0.00182\n", - " Triggers unsatisfied, max unc./thresh. is 21.0227808264386 for absorption in\n", + " WARNING: The estimated number of batches is 72694 --- greater than max batches\n", + " 150/1 0.66858 0.68405 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 22.317894720265652 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65857 --- greater than max batches\n", - " 155/1 0.64427 0.68363 +/- 0.00183\n", - " Triggers unsatisfied, max unc./thresh. is 20.882547322553506 for absorption in\n", + " WARNING: The estimated number of batches is 72228 --- greater than max batches\n", + " 151/1 0.64729 0.68379 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 22.256511579898202 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65418 --- greater than max batches\n", - " 156/1 0.68488 0.68364 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 20.797424126850476 for absorption in\n", + " WARNING: The estimated number of batches is 72327 --- greater than max batches\n", + " 152/1 0.68669 0.68381 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 22.10461716023024 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 65318 --- greater than max batches\n", - " 157/1 0.67337 0.68357 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.67015745584828 for absorption in\n", + " WARNING: The estimated number of batches is 71832 --- greater than max batches\n", + " 153/1 0.65299 0.68361 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 21.973197597061482 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64948 --- greater than max batches\n", - " 158/1 0.66662 0.68346 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.568519956722266 for absorption in\n", + " WARNING: The estimated number of batches is 71463 --- greater than max batches\n", + " 154/1 0.68069 0.68359 +/- 0.00191\n", + " Triggers unsatisfied, max unc./thresh. is 21.8366209055588 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64734 --- greater than max batches\n", - " 159/1 0.62697 0.68309 +/- 0.00182\n", - " Triggers unsatisfied, max unc./thresh. is 20.47085213159483 for absorption in\n", + " WARNING: The estimated number of batches is 71054 --- greater than max batches\n", + " 155/1 0.69270 0.68365 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 21.693707386898733 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64540 --- greater than max batches\n", - " 160/1 0.68300 0.68309 +/- 0.00181\n", - " Triggers unsatisfied, max unc./thresh. is 20.34586209866351 for absorption in\n", + " WARNING: The estimated number of batches is 70598 --- greater than max batches\n", + " 156/1 0.67507 0.68359 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 21.593867638588357 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64168 --- greater than max batches\n", - " 161/1 0.68918 0.68313 +/- 0.00180\n", - " Triggers unsatisfied, max unc./thresh. is 20.23505377614212 for absorption in\n", + " WARNING: The estimated number of batches is 70416 --- greater than max batches\n", + " 157/1 0.68589 0.68360 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 21.661355782353464 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63881 --- greater than max batches\n", - " 162/1 0.70939 0.68330 +/- 0.00179\n", - " Triggers unsatisfied, max unc./thresh. is 20.21114215977674 for absorption in\n", + " WARNING: The estimated number of batches is 71326 --- greater than max batches\n", + " 158/1 0.69068 0.68365 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 21.5257902006611 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64138 --- greater than max batches\n", - " 163/1 0.69681 0.68338 +/- 0.00179\n", - " Triggers unsatisfied, max unc./thresh. is 20.163170350438893 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64241 --- greater than max batches\n", - " 164/1 0.66454 0.68326 +/- 0.00178\n", - " Triggers unsatisfied, max unc./thresh. is 20.109882525638955 for absorption in\n", - " tally 3\n", - " WARNING: The estimated number of batches is 64306 --- greater than max batches\n" + " WARNING: The estimated number of batches is 70900 --- greater than max batches\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 165/1 0.68804 0.68329 +/- 0.00177\n", - " Triggers unsatisfied, max unc./thresh. is 20.033653897136055 for absorption in\n", + " 159/1 0.67616 0.68360 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 21.48299566167353 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 71079 --- greater than max batches\n", + " 160/1 0.67492 0.68355 +/- 0.00184\n", + " Triggers unsatisfied, max unc./thresh. is 21.352926864543797 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 64221 --- greater than max batches\n", - " 166/1 0.66078 0.68315 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 19.916131324861123 for absorption in\n", + " WARNING: The estimated number of batches is 70677 --- greater than max batches\n", + " 161/1 0.69218 0.68360 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 21.3145021538723 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63867 --- greater than max batches\n", - " 167/1 0.65762 0.68300 +/- 0.00176\n", - " Triggers unsatisfied, max unc./thresh. is 19.85097950868946 for absorption in\n", + " WARNING: The estimated number of batches is 70878 --- greater than max batches\n", + " 162/1 0.66037 0.68345 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 21.1820405677372 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63843 --- greater than max batches\n", - " 168/1 0.69267 0.68306 +/- 0.00175\n", - " Triggers unsatisfied, max unc./thresh. is 19.729436003984436 for absorption in\n", + " WARNING: The estimated number of batches is 70448 --- greater than max batches\n", + " 163/1 0.65640 0.68328 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 21.086966040066464 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63453 --- greater than max batches\n", - " 169/1 0.67859 0.68303 +/- 0.00174\n", - " Triggers unsatisfied, max unc./thresh. is 19.61178427698242 for absorption in\n", + " WARNING: The estimated number of batches is 70262 --- greater than max batches\n", + " 164/1 0.65113 0.68308 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 20.95392716842 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63084 --- greater than max batches\n", - " 170/1 0.66545 0.68292 +/- 0.00173\n", - " Triggers unsatisfied, max unc./thresh. is 19.495197332905757 for absorption in\n", + " WARNING: The estimated number of batches is 69817 --- greater than max batches\n", + " 165/1 0.70691 0.68323 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 20.837836560668773 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62716 --- greater than max batches\n", - " 171/1 0.66716 0.68283 +/- 0.00172\n", - " Triggers unsatisfied, max unc./thresh. is 19.47044861415963 for absorption in\n", + " WARNING: The estimated number of batches is 69480 --- greater than max batches\n", + " 166/1 0.67295 0.68317 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 20.769653148158348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62936 --- greater than max batches\n", - " 172/1 0.70008 0.68293 +/- 0.00172\n", - " Triggers unsatisfied, max unc./thresh. is 19.382801191970024 for absorption in\n", + " WARNING: The estimated number of batches is 69457 --- greater than max batches\n", + " 167/1 0.66620 0.68306 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 20.676559522637568 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62746 --- greater than max batches\n", - " 173/1 0.69417 0.68300 +/- 0.00171\n", - " Triggers unsatisfied, max unc./thresh. is 19.270038663528165 for absorption in\n", + " WARNING: The estimated number of batches is 69264 --- greater than max batches\n", + " 168/1 0.66855 0.68297 +/- 0.00179\n", + " Triggers unsatisfied, max unc./thresh. is 20.72956424681471 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62390 --- greater than max batches\n", - " 174/1 0.66458 0.68289 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 19.281533726364312 for absorption in\n", + " WARNING: The estimated number of batches is 70049 --- greater than max batches\n", + " 169/1 0.69800 0.68306 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 20.880997251947857 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62836 --- greater than max batches\n", - " 175/1 0.65867 0.68275 +/- 0.00170\n", - " Triggers unsatisfied, max unc./thresh. is 19.234847030404104 for absorption in\n", + " WARNING: The estimated number of batches is 71512 --- greater than max batches\n", + " 170/1 0.69435 0.68313 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 20.817259102451693 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62902 --- greater than max batches\n", - " 176/1 0.69631 0.68283 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 19.13381099709457 for absorption in\n", + " WARNING: The estimated number of batches is 71510 --- greater than max batches\n", + " 171/1 0.65628 0.68297 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 20.7516484421445 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62609 --- greater than max batches\n", - " 177/1 0.71142 0.68299 +/- 0.00169\n", - " Triggers unsatisfied, max unc./thresh. is 19.022563643493143 for absorption in\n", + " WARNING: The estimated number of batches is 71490 --- greater than max batches\n", + " 172/1 0.68216 0.68297 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 20.633920344795456 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62245 --- greater than max batches\n", - " 178/1 0.68640 0.68301 +/- 0.00168\n", - " Triggers unsatisfied, max unc./thresh. is 19.03176453708651 for absorption in\n", + " WARNING: The estimated number of batches is 71107 --- greater than max batches\n", + " 173/1 0.67774 0.68293 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 20.517288947571853 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62667 --- greater than max batches\n", - " 179/1 0.70448 0.68313 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 19.088395456136563 for absorption in\n", + " WARNING: The estimated number of batches is 70727 --- greater than max batches\n", + " 174/1 0.71232 0.68311 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 20.39737483131609 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63405 --- greater than max batches\n", - " 180/1 0.70538 0.68326 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.98864751831452 for absorption in\n", + " WARNING: The estimated number of batches is 70318 --- greater than max batches\n", + " 175/1 0.65712 0.68295 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 20.340808883881543 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63105 --- greater than max batches\n", - " 181/1 0.65591 0.68311 +/- 0.00166\n", - " Triggers unsatisfied, max unc./thresh. is 18.911017891051518 for absorption in\n", + " WARNING: The estimated number of batches is 70343 --- greater than max batches\n", + " 176/1 0.67973 0.68294 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 20.225760579774068 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62948 --- greater than max batches\n", - " 182/1 0.72818 0.68336 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.808510226366458 for absorption in\n", + " WARNING: The estimated number of batches is 69958 --- greater than max batches\n", + " 177/1 0.70262 0.68305 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 20.188734303225726 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62621 --- greater than max batches\n", - " 183/1 0.67896 0.68334 +/- 0.00167\n", - " Triggers unsatisfied, max unc./thresh. is 18.825142861337717 for absorption in\n", + " WARNING: The estimated number of batches is 70110 --- greater than max batches\n", + " 178/1 0.66234 0.68293 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 20.36584641647614 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63086 --- greater than max batches\n", - " 184/1 0.65442 0.68317 +/- 0.00166\n", - " Triggers unsatisfied, max unc./thresh. is 18.79514029258707 for absorption in\n", + " WARNING: The estimated number of batches is 71760 --- greater than max batches\n", + " 179/1 0.67778 0.68290 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 20.266332203155603 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63239 --- greater than max batches\n", - " 185/1 0.68885 0.68321 +/- 0.00165\n", - " Triggers unsatisfied, max unc./thresh. is 18.76276176864163 for absorption in\n", + " WARNING: The estimated number of batches is 71472 --- greater than max batches\n", + " 180/1 0.63949 0.68265 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 20.153693315273593 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63373 --- greater than max batches\n", - " 186/1 0.68893 0.68324 +/- 0.00165\n", - " Triggers unsatisfied, max unc./thresh. is 18.690155368597363 for absorption in\n", + " WARNING: The estimated number of batches is 71085 --- greater than max batches\n", + " 181/1 0.66641 0.68256 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 20.06530998234231 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63233 --- greater than max batches\n", - " 187/1 0.68918 0.68327 +/- 0.00164\n", - " Triggers unsatisfied, max unc./thresh. is 18.590144288270153 for absorption in\n", + " WARNING: The estimated number of batches is 70866 --- greater than max batches\n", + " 182/1 0.70248 0.68267 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 19.994849139740012 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62904 --- greater than max batches\n", - " 188/1 0.69854 0.68335 +/- 0.00163\n", - " Triggers unsatisfied, max unc./thresh. is 18.61460656150607 for absorption in\n", + " WARNING: The estimated number of batches is 70769 --- greater than max batches\n", + " 183/1 0.68906 0.68271 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 20.092026018527292 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63416 --- greater than max batches\n", - " 189/1 0.66324 0.68324 +/- 0.00162\n", - " Triggers unsatisfied, max unc./thresh. is 18.518608099237504 for absorption in\n", + " WARNING: The estimated number of batches is 71862 --- greater than max batches\n", + " 184/1 0.64815 0.68252 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 20.010212645636404 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 63106 --- greater than max batches\n", - " 190/1 0.69450 0.68331 +/- 0.00162\n", - " Triggers unsatisfied, max unc./thresh. is 18.425351661292233 for absorption in\n", + " WARNING: The estimated number of batches is 71679 --- greater than max batches\n", + " 185/1 0.66689 0.68243 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 19.898829466229213 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62812 --- greater than max batches\n", - " 191/1 0.68953 0.68334 +/- 0.00161\n", - " Triggers unsatisfied, max unc./thresh. is 18.328779429843646 for absorption in\n", + " WARNING: The estimated number of batches is 71279 --- greater than max batches\n", + " 186/1 0.68573 0.68245 +/- 0.00168\n", + " Triggers unsatisfied, max unc./thresh. is 19.796150792368255 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62491 --- greater than max batches\n", - " 192/1 0.66621 0.68325 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.28094973389564 for absorption in\n", + " WARNING: The estimated number of batches is 70937 --- greater than max batches\n", + " 187/1 0.66673 0.68236 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.701525271465893 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62500 --- greater than max batches\n", - " 193/1 0.71102 0.68339 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.19949730061142 for absorption in\n", + " WARNING: The estimated number of batches is 70649 --- greater than max batches\n", + " 188/1 0.65564 0.68222 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.864636825432147 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62275 --- greater than max batches\n", - " 194/1 0.65341 0.68324 +/- 0.00160\n", - " Triggers unsatisfied, max unc./thresh. is 18.159054737369345 for absorption in\n", + " WARNING: The estimated number of batches is 72218 --- greater than max batches\n", + " 189/1 0.69960 0.68231 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 19.855911612557982 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62328 --- greater than max batches\n", - " 195/1 0.70061 0.68333 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.082465954324594 for absorption in\n", + " WARNING: The estimated number of batches is 72549 --- greater than max batches\n", + " 190/1 0.68178 0.68231 +/- 0.00166\n", + " Triggers unsatisfied, max unc./thresh. is 19.76297244962571 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62131 --- greater than max batches\n", - " 196/1 0.69339 0.68338 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.043133483791827 for absorption in\n", + " WARNING: The estimated number of batches is 72262 --- greater than max batches\n", + " 191/1 0.69760 0.68239 +/- 0.00165\n", + " Triggers unsatisfied, max unc./thresh. is 19.760934073441348 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62186 --- greater than max batches\n", - " 197/1 0.64411 0.68318 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 18.019303623546417 for absorption in\n", + " WARNING: The estimated number of batches is 72637 --- greater than max batches\n", + " 192/1 0.66122 0.68228 +/- 0.00164\n", + " Triggers unsatisfied, max unc./thresh. is 19.804180280770698 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62347 --- greater than max batches\n", - " 198/1 0.66626 0.68309 +/- 0.00159\n", - " Triggers unsatisfied, max unc./thresh. is 17.968092739058083 for absorption in\n", + " WARNING: The estimated number of batches is 73348 --- greater than max batches\n", + " 193/1 0.69346 0.68234 +/- 0.00164\n", + " Triggers unsatisfied, max unc./thresh. is 19.801359777905503 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62316 --- greater than max batches\n", - " 199/1 0.67839 0.68306 +/- 0.00158\n", - " Triggers unsatisfied, max unc./thresh. is 17.91968515142146 for absorption in\n", + " WARNING: The estimated number of batches is 73719 --- greater than max batches\n", + " 194/1 0.67831 0.68231 +/- 0.00163\n", + " Triggers unsatisfied, max unc./thresh. is 19.738915362738556 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 62302 --- greater than max batches\n", - " 200/1 0.66459 0.68297 +/- 0.00157\n", - " Triggers unsatisfied, max unc./thresh. is 17.82970764669685 for absorption in\n", + " WARNING: The estimated number of batches is 73645 --- greater than max batches\n", + " 195/1 0.65285 0.68216 +/- 0.00163\n", + " Triggers unsatisfied, max unc./thresh. is 19.653034572834297 for absorption in\n", " tally 3\n", - " WARNING: The estimated number of batches is 61996 --- greater than max batches\n", + " WARNING: The estimated number of batches is 73391 --- greater than max batches\n", + " 196/1 0.66672 0.68208 +/- 0.00162\n", + " Triggers unsatisfied, max unc./thresh. is 19.625879744409815 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73574 --- greater than max batches\n", + " 197/1 0.67536 0.68204 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.52848856663241 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73227 --- greater than max batches\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 198/1 0.66675 0.68196 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.51191007087699 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73483 --- greater than max batches\n", + " 199/1 0.64492 0.68177 +/- 0.00161\n", + " Triggers unsatisfied, max unc./thresh. is 19.511121460032857 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73858 --- greater than max batches\n", + " 200/1 0.69441 0.68184 +/- 0.00160\n", + " Triggers unsatisfied, max unc./thresh. is 19.41826259948151 for absorption in\n", + " tally 3\n", + " WARNING: The estimated number of batches is 73534 --- greater than max batches\n", " Creating state point statepoint.200.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.9309e-01 seconds\n", - " Reading cross sections = 2.8108e-01 seconds\n", - " Total time in simulation = 1.1321e+01 seconds\n", - " Time in transport only = 1.1242e+01 seconds\n", - " Time in inactive batches = 1.6721e-01 seconds\n", - " Time in active batches = 1.1153e+01 seconds\n", - " Time synchronizing fission bank = 2.2958e-02 seconds\n", - " Sampling source sites = 1.8701e-02 seconds\n", - " SEND/RECV source sites = 3.9403e-03 seconds\n", - " Time accumulating tallies = 9.9349e-04 seconds\n", - " Total time for finalization = 5.2200e-07 seconds\n", - " Total time elapsed = 1.1620e+01 seconds\n", - " Calculation Rate (inactive) = 74758.2 particles/second\n", - " Calculation Rate (active) = 43708.5 particles/second\n", + " Total time for initialization = 3.2763e-01 seconds\n", + " Reading cross sections = 3.1447e-01 seconds\n", + " Total time in simulation = 2.3755e+01 seconds\n", + " Time in transport only = 2.3701e+01 seconds\n", + " Time in inactive batches = 4.2962e-01 seconds\n", + " Time in active batches = 2.3325e+01 seconds\n", + " Time synchronizing fission bank = 2.0652e-02 seconds\n", + " Sampling source sites = 1.7317e-02 seconds\n", + " SEND/RECV source sites = 3.2308e-03 seconds\n", + " Time accumulating tallies = 1.7916e-03 seconds\n", + " Time writing statepoints = 8.5764e-03 seconds\n", + " Total time for finalization = 7.5200e-07 seconds\n", + " Total time elapsed = 2.4088e+01 seconds\n", + " Calculation Rate (inactive) = 29095.3 particles/second\n", + " Calculation Rate (active) = 20900.3 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.68198 +/- 0.00141\n", - " k-effective (Track-length) = 0.68297 +/- 0.00157\n", - " k-effective (Absorption) = 0.68161 +/- 0.00145\n", - " Combined k-effective = 0.68209 +/- 0.00118\n", - " Leakage Fraction = 0.34033 +/- 0.00074\n", + " k-effective (Collision) = 0.68290 +/- 0.00158\n", + " k-effective (Track-length) = 0.68184 +/- 0.00160\n", + " k-effective (Absorption) = 0.68167 +/- 0.00151\n", + " Combined k-effective = 0.68200 +/- 0.00134\n", + " Leakage Fraction = 0.33996 +/- 0.00079\n", "\n" ] } @@ -1341,13 +1349,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.04508259]]\n", + "[[[0.04969994]]\n", "\n", - " [[0.0221707 ]]\n", + " [[0.02200773]]\n", "\n", - " [[0.10763375]]\n", + " [[0.12289506]]\n", "\n", - " [[0.05107401]]]\n" + " [[0.05303359]]]\n" ] } ], @@ -1415,8 +1423,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.27e-04\n", - " 1.02e-05\n", + " 2.10e-04\n", + " 1.04e-05\n", " \n", " \n", " 1\n", @@ -1426,8 +1434,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.54e-04\n", - " 2.50e-05\n", + " 5.11e-04\n", + " 2.54e-05\n", " \n", " \n", " 2\n", @@ -1438,7 +1446,7 @@ " 2.00e+07\n", " fission\n", " 7.19e-05\n", - " 1.82e-06\n", + " 1.83e-06\n", " \n", " \n", " 3\n", @@ -1449,7 +1457,7 @@ " 2.00e+07\n", " nu-fission\n", " 1.89e-04\n", - " 4.69e-06\n", + " 4.75e-06\n", " \n", " \n", " 4\n", @@ -1459,8 +1467,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.35e-04\n", - " 9.82e-06\n", + " 2.19e-04\n", + " 9.36e-06\n", " \n", " \n", " 5\n", @@ -1470,8 +1478,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.71e-04\n", - " 2.39e-05\n", + " 5.33e-04\n", + " 2.28e-05\n", " \n", " \n", " 6\n", @@ -1481,8 +1489,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.88e-05\n", - " 1.61e-06\n", + " 6.98e-05\n", + " 1.63e-06\n", " \n", " \n", " 7\n", @@ -1492,8 +1500,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.81e-04\n", - " 4.15e-06\n", + " 1.85e-04\n", + " 4.24e-06\n", " \n", " \n", " 8\n", @@ -1503,8 +1511,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.31e-04\n", - " 1.13e-05\n", + " 2.23e-04\n", + " 1.02e-05\n", " \n", " \n", " 9\n", @@ -1514,8 +1522,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.63e-04\n", - " 2.76e-05\n", + " 5.44e-04\n", + " 2.48e-05\n", " \n", " \n", " 10\n", @@ -1525,8 +1533,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.95e-05\n", - " 1.76e-06\n", + " 6.69e-05\n", + " 1.65e-06\n", " \n", " \n", " 11\n", @@ -1536,8 +1544,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.83e-04\n", - " 4.53e-06\n", + " 1.76e-04\n", + " 4.27e-06\n", " \n", " \n", " 12\n", @@ -1547,8 +1555,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.07e-04\n", - " 9.85e-06\n", + " 2.09e-04\n", + " 9.24e-06\n", " \n", " \n", " 13\n", @@ -1558,8 +1566,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.04e-04\n", - " 2.40e-05\n", + " 5.09e-04\n", + " 2.25e-05\n", " \n", " \n", " 14\n", @@ -1569,8 +1577,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.48e-05\n", - " 1.45e-06\n", + " 6.72e-05\n", + " 1.65e-06\n", " \n", " \n", " 15\n", @@ -1580,8 +1588,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.71e-04\n", - " 3.81e-06\n", + " 1.77e-04\n", + " 4.32e-06\n", " \n", " \n", " 16\n", @@ -1591,8 +1599,8 @@ " 0.00e+00\n", " 6.25e-01\n", " fission\n", - " 2.20e-04\n", - " 1.07e-05\n", + " 2.11e-04\n", + " 8.34e-06\n", " \n", " \n", " 17\n", @@ -1602,8 +1610,8 @@ " 0.00e+00\n", " 6.25e-01\n", " nu-fission\n", - " 5.37e-04\n", - " 2.60e-05\n", + " 5.15e-04\n", + " 2.03e-05\n", " \n", " \n", " 18\n", @@ -1613,8 +1621,8 @@ " 6.25e-01\n", " 2.00e+07\n", " fission\n", - " 6.76e-05\n", - " 1.78e-06\n", + " 6.47e-05\n", + " 1.40e-06\n", " \n", " \n", " 19\n", @@ -1624,8 +1632,8 @@ " 6.25e-01\n", " 2.00e+07\n", " nu-fission\n", - " 1.78e-04\n", - " 4.63e-06\n", + " 1.70e-04\n", + " 3.66e-06\n", " \n", " \n", "\n", @@ -1634,49 +1642,49 @@ "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-01 fission 2.27e-04 \n", - "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.54e-04 \n", + "0 1 1 1 0.00e+00 6.25e-01 fission 2.10e-04 \n", + "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.11e-04 \n", "2 1 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n", "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n", - "4 2 1 1 0.00e+00 6.25e-01 fission 2.35e-04 \n", - "5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.71e-04 \n", - "6 2 1 1 6.25e-01 2.00e+07 fission 6.88e-05 \n", - "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", - "8 3 1 1 0.00e+00 6.25e-01 fission 2.31e-04 \n", - "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.63e-04 \n", - "10 3 1 1 6.25e-01 2.00e+07 fission 6.95e-05 \n", - "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.83e-04 \n", - "12 4 1 1 0.00e+00 6.25e-01 fission 2.07e-04 \n", - "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.04e-04 \n", - "14 4 1 1 6.25e-01 2.00e+07 fission 6.48e-05 \n", - "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.71e-04 \n", - "16 5 1 1 0.00e+00 6.25e-01 fission 2.20e-04 \n", - "17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.37e-04 \n", - "18 5 1 1 6.25e-01 2.00e+07 fission 6.76e-05 \n", - "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.78e-04 \n", + "4 2 1 1 0.00e+00 6.25e-01 fission 2.19e-04 \n", + "5 2 1 1 0.00e+00 6.25e-01 nu-fission 5.33e-04 \n", + "6 2 1 1 6.25e-01 2.00e+07 fission 6.98e-05 \n", + "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.85e-04 \n", + "8 3 1 1 0.00e+00 6.25e-01 fission 2.23e-04 \n", + "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.44e-04 \n", + "10 3 1 1 6.25e-01 2.00e+07 fission 6.69e-05 \n", + "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n", + "12 4 1 1 0.00e+00 6.25e-01 fission 2.09e-04 \n", + "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.09e-04 \n", + "14 4 1 1 6.25e-01 2.00e+07 fission 6.72e-05 \n", + "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.77e-04 \n", + "16 5 1 1 0.00e+00 6.25e-01 fission 2.11e-04 \n", + "17 5 1 1 0.00e+00 6.25e-01 nu-fission 5.15e-04 \n", + "18 5 1 1 6.25e-01 2.00e+07 fission 6.47e-05 \n", + "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.70e-04 \n", "\n", " std. dev. \n", " \n", - "0 1.02e-05 \n", - "1 2.50e-05 \n", - "2 1.82e-06 \n", - "3 4.69e-06 \n", - "4 9.82e-06 \n", - "5 2.39e-05 \n", - "6 1.61e-06 \n", - "7 4.15e-06 \n", - "8 1.13e-05 \n", - "9 2.76e-05 \n", - "10 1.76e-06 \n", - "11 4.53e-06 \n", - "12 9.85e-06 \n", - "13 2.40e-05 \n", - "14 1.45e-06 \n", - "15 3.81e-06 \n", - "16 1.07e-05 \n", - "17 2.60e-05 \n", - "18 1.78e-06 \n", - "19 4.63e-06 " + "0 1.04e-05 \n", + "1 2.54e-05 \n", + "2 1.83e-06 \n", + "3 4.75e-06 \n", + "4 9.36e-06 \n", + "5 2.28e-05 \n", + "6 1.63e-06 \n", + "7 4.24e-06 \n", + "8 1.02e-05 \n", + "9 2.48e-05 \n", + "10 1.65e-06 \n", + "11 4.27e-06 \n", + "12 9.24e-06 \n", + "13 2.25e-05 \n", + "14 1.65e-06 \n", + "15 4.32e-06 \n", + "16 8.34e-06 \n", + "17 2.03e-05 \n", + "18 1.40e-06 \n", + "19 3.66e-06 " ] }, "execution_count": 20, @@ -1702,7 +1710,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1727,7 +1735,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -1736,7 +1744,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1835,8 +1843,8 @@ " 1\n", " U235\n", " scatter\n", - " 3.81e-02\n", - " 4.13e-05\n", + " 3.82e-02\n", + " 4.48e-05\n", " \n", " \n", " 1\n", @@ -1844,7 +1852,7 @@ " U238\n", " scatter\n", " 2.34e+00\n", - " 2.41e-03\n", + " 2.52e-03\n", " \n", " \n", "\n", @@ -1852,8 +1860,8 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 1 U235 scatter 3.81e-02 4.13e-05\n", - "1 1 U238 scatter 2.34e+00 2.41e-03" + "0 1 U235 scatter 3.82e-02 4.48e-05\n", + "1 1 U238 scatter 2.34e+00 2.52e-03" ] }, "execution_count": 24, @@ -1885,8 +1893,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[2.41367509e-03]\n", - " [4.12533801e-05]]]\n" + "[[[2.52057084e-03]\n", + " [4.48210552e-05]]]\n" ] } ], @@ -1947,25 +1955,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.0131914 ]]\n", + "[[[0.01301475]]\n", "\n", - " [[0.01252949]]\n", + " [[0.0136507 ]]\n", "\n", - " [[0.01241481]]\n", + " [[0.01260538]]\n", "\n", - " [[0.01194961]]\n", + " [[0.01293374]]\n", "\n", - " [[0.01186091]]\n", + " [[0.01219012]]\n", "\n", - " [[0.0127257 ]]\n", + " [[0.01191931]]\n", "\n", - " [[0.01358576]]\n", + " [[0.0127884 ]]\n", "\n", - " [[0.0130368 ]]\n", + " [[0.01339325]]\n", "\n", - " [[0.014031 ]]\n", + " [[0.01309524]]\n", "\n", - " [[0.0141883 ]]]\n" + " [[0.0133674 ]]]\n" ] } ], @@ -1981,13 +1989,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Print the distribcell tally dataframe" + "Now that we're done getting data from the statepoint file, we'll close it to free the file handle for subsequent OpenMC runs." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file now that we're done gathering info from it\n", + "sp.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the distribcell tally dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -2057,8 +2082,8 @@ " 3\n", " 279\n", " absorption\n", - " 7.11e-04\n", - " 1.10e-05\n", + " 7.23e-04\n", + " 1.05e-05\n", " \n", " \n", " 559\n", @@ -2071,8 +2096,8 @@ " 3\n", " 279\n", " scatter\n", - " 8.91e-02\n", - " 6.60e-04\n", + " 9.08e-02\n", + " 6.39e-04\n", " \n", " \n", " 560\n", @@ -2086,7 +2111,7 @@ " 280\n", " absorption\n", " 6.75e-04\n", - " 1.02e-05\n", + " 1.09e-05\n", " \n", " \n", " 561\n", @@ -2099,8 +2124,8 @@ " 3\n", " 280\n", " scatter\n", - " 8.35e-02\n", - " 6.11e-04\n", + " 8.49e-02\n", + " 6.75e-04\n", " \n", " \n", " 562\n", @@ -2113,8 +2138,8 @@ " 3\n", " 281\n", " absorption\n", - " 6.10e-04\n", - " 1.02e-05\n", + " 6.22e-04\n", + " 1.06e-05\n", " \n", " \n", " 563\n", @@ -2127,8 +2152,8 @@ " 3\n", " 281\n", " scatter\n", - " 7.75e-02\n", - " 6.10e-04\n", + " 7.85e-02\n", + " 6.36e-04\n", " \n", " \n", " 564\n", @@ -2141,8 +2166,8 @@ " 3\n", " 282\n", " absorption\n", - " 5.67e-04\n", - " 9.88e-06\n", + " 5.66e-04\n", + " 1.01e-05\n", " \n", " \n", " 565\n", @@ -2155,8 +2180,8 @@ " 3\n", " 282\n", " scatter\n", - " 7.11e-02\n", - " 5.99e-04\n", + " 7.17e-02\n", + " 5.80e-04\n", " \n", " \n", " 566\n", @@ -2169,8 +2194,8 @@ " 3\n", " 283\n", " absorption\n", - " 5.06e-04\n", - " 9.35e-06\n", + " 4.94e-04\n", + " 9.18e-06\n", " \n", " \n", " 567\n", @@ -2183,8 +2208,8 @@ " 3\n", " 283\n", " scatter\n", - " 6.39e-02\n", - " 5.53e-04\n", + " 6.35e-02\n", + " 5.45e-04\n", " \n", " \n", " 568\n", @@ -2197,8 +2222,8 @@ " 3\n", " 284\n", " absorption\n", - " 4.35e-04\n", - " 8.22e-06\n", + " 4.34e-04\n", + " 8.41e-06\n", " \n", " \n", " 569\n", @@ -2211,8 +2236,8 @@ " 3\n", " 284\n", " scatter\n", - " 5.62e-02\n", - " 5.18e-04\n", + " 5.54e-02\n", + " 5.15e-04\n", " \n", " \n", " 570\n", @@ -2225,8 +2250,8 @@ " 3\n", " 285\n", " absorption\n", - " 3.73e-04\n", - " 7.90e-06\n", + " 3.58e-04\n", + " 7.60e-06\n", " \n", " \n", " 571\n", @@ -2239,8 +2264,8 @@ " 3\n", " 285\n", " scatter\n", - " 4.76e-02\n", - " 4.92e-04\n", + " 4.70e-02\n", + " 4.81e-04\n", " \n", " \n", " 572\n", @@ -2253,8 +2278,8 @@ " 3\n", " 286\n", " absorption\n", - " 2.98e-04\n", - " 7.30e-06\n", + " 2.83e-04\n", + " 7.08e-06\n", " \n", " \n", " 573\n", @@ -2267,8 +2292,8 @@ " 3\n", " 286\n", " scatter\n", - " 3.82e-02\n", - " 4.17e-04\n", + " 3.84e-02\n", + " 4.47e-04\n", " \n", " \n", " 574\n", @@ -2281,8 +2306,8 @@ " 3\n", " 287\n", " absorption\n", - " 2.05e-04\n", - " 5.96e-06\n", + " 2.19e-04\n", + " 6.36e-06\n", " \n", " \n", " 575\n", @@ -2295,8 +2320,8 @@ " 3\n", " 287\n", " scatter\n", - " 2.86e-02\n", - " 3.72e-04\n", + " 2.99e-02\n", + " 3.89e-04\n", " \n", " \n", " 576\n", @@ -2309,8 +2334,8 @@ " 3\n", " 288\n", " absorption\n", - " 1.22e-04\n", - " 4.12e-06\n", + " 1.18e-04\n", + " 4.29e-06\n", " \n", " \n", " 577\n", @@ -2323,8 +2348,8 @@ " 3\n", " 288\n", " scatter\n", - " 1.82e-02\n", - " 2.59e-04\n", + " 1.85e-02\n", + " 2.75e-04\n", " \n", " \n", "\n", @@ -2358,29 +2383,29 @@ " mean std. dev. \n", " \n", " \n", - "558 7.11e-04 1.10e-05 \n", - "559 8.91e-02 6.60e-04 \n", - "560 6.75e-04 1.02e-05 \n", - "561 8.35e-02 6.11e-04 \n", - "562 6.10e-04 1.02e-05 \n", - "563 7.75e-02 6.10e-04 \n", - "564 5.67e-04 9.88e-06 \n", - "565 7.11e-02 5.99e-04 \n", - "566 5.06e-04 9.35e-06 \n", - "567 6.39e-02 5.53e-04 \n", - "568 4.35e-04 8.22e-06 \n", - "569 5.62e-02 5.18e-04 \n", - "570 3.73e-04 7.90e-06 \n", - "571 4.76e-02 4.92e-04 \n", - "572 2.98e-04 7.30e-06 \n", - "573 3.82e-02 4.17e-04 \n", - "574 2.05e-04 5.96e-06 \n", - "575 2.86e-02 3.72e-04 \n", - "576 1.22e-04 4.12e-06 \n", - "577 1.82e-02 2.59e-04 " + "558 7.23e-04 1.05e-05 \n", + "559 9.08e-02 6.39e-04 \n", + "560 6.75e-04 1.09e-05 \n", + "561 8.49e-02 6.75e-04 \n", + "562 6.22e-04 1.06e-05 \n", + "563 7.85e-02 6.36e-04 \n", + "564 5.66e-04 1.01e-05 \n", + "565 7.17e-02 5.80e-04 \n", + "566 4.94e-04 9.18e-06 \n", + "567 6.35e-02 5.45e-04 \n", + "568 4.34e-04 8.41e-06 \n", + "569 5.54e-02 5.15e-04 \n", + "570 3.58e-04 7.60e-06 \n", + "571 4.70e-02 4.81e-04 \n", + "572 2.83e-04 7.08e-06 \n", + "573 3.84e-02 4.47e-04 \n", + "574 2.19e-04 6.36e-06 \n", + "575 2.99e-02 3.89e-04 \n", + "576 1.18e-04 4.29e-06 \n", + "577 1.85e-02 2.75e-04 " ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2395,7 +2420,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2442,37 +2467,37 @@ " \n", " mean\n", " 4.19e-04\n", - " 6.86e-06\n", + " 6.85e-06\n", " \n", " \n", " std\n", - " 2.41e-04\n", - " 2.51e-06\n", + " 2.43e-04\n", + " 2.55e-06\n", " \n", " \n", " min\n", - " 1.68e-05\n", - " 1.07e-06\n", + " 1.65e-05\n", + " 1.25e-06\n", " \n", " \n", " 25%\n", - " 2.06e-04\n", - " 5.09e-06\n", + " 2.08e-04\n", + " 4.81e-06\n", " \n", " \n", " 50%\n", - " 3.98e-04\n", - " 6.90e-06\n", + " 3.95e-04\n", + " 6.87e-06\n", " \n", " \n", " 75%\n", - " 6.17e-04\n", - " 8.44e-06\n", + " 6.19e-04\n", + " 8.77e-06\n", " \n", " \n", " max\n", - " 8.70e-04\n", - " 1.52e-05\n", + " 9.04e-04\n", + " 1.51e-05\n", " \n", " \n", "\n", @@ -2483,16 +2508,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.19e-04 6.86e-06\n", - "std 2.41e-04 2.51e-06\n", - "min 1.68e-05 1.07e-06\n", - "25% 2.06e-04 5.09e-06\n", - "50% 3.98e-04 6.90e-06\n", - "75% 6.17e-04 8.44e-06\n", - "max 8.70e-04 1.52e-05" + "mean 4.19e-04 6.85e-06\n", + "std 2.43e-04 2.55e-06\n", + "min 1.65e-05 1.25e-06\n", + "25% 2.08e-04 4.81e-06\n", + "50% 3.95e-04 6.87e-06\n", + "75% 6.19e-04 8.77e-06\n", + "max 9.04e-04 1.51e-05" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2515,14 +2540,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.47449458604689265\n" + "Mann-Whitney Test p-value: 0.4886239022425303\n" ] } ], @@ -2551,14 +2576,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 2.499381683224802e-42\n" + "Mann-Whitney Test p-value: 3.2855436374070945e-42\n" ] } ], @@ -2585,19 +2610,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - ":4: SettingWithCopyWarning: \n", + "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n" + " after removing the cwd from sys.path.\n" ] }, { @@ -2606,13 +2631,13 @@ "" ] }, - "execution_count": 32, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2635,22 +2660,22 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2688,7 +2713,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index 8d9e1096620..8684ccaae86 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -236,7 +236,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEUCswB3C9y1wFT///9ds74KAAAAAWJLR0QDEQxM8gAAAAd0SU1FB+UGGhQcBi6CkQ4AAAKlSURBVGje7ZrBscIwDETxwSWkn5TAgXCgBPqhBA6kyj/fDhCIJa2zZAwz0pmHpZWdSazd7Tw8PDw8PDw8vinCMBzW03HIsRLvhnv0HL7qD+IwjzXKzaNaxeEt9kz21RWEBV5XQbfka3pQWL4qgdLyNQkUcbwFT/FP4zjWt+D+++OY4laZQJgtnqNOwe5l9XkGmIIL/PEHUAGTeuc5P15wBbu34ucSIAXkX77h4xUtIBSXnxIAOhCLy98TANNfLj8lYBYQCuLPWmAWEBe9f90DUPdKy08JWB0U1HsoaAgQxPSnAgwBopz+VABQvoDnAnqTP0r8zealzfPcQqqAQSs/C6AKGLX0pwKs8uX0cwGaAHr6uYC9wSt46qDCB4RXBDTkMwWMhnxZQF3+s8pf1AZY5VsCWuVnAUQ+YPxB43X5koAiH035soCa/AaeBOw34m359AaQPCK/1oAAyJ8aIPBI+7QGRkD+3IBt+A6QPzeg34SH2pcauN+Kt9uXGljkse0jb6BP8AD+vwGKPLZ95A0UofbnDbAFj20/eQN+gD8h/LgRD25/8QCA2088AD/Oo8dPOoDo8ZMOoPPNeej4pwdAgUcfX9IDzHnnf5lnz88XnH/nSf4M8cIL7I+/P3yCP0G88P7W+v2z9ft36+8P9vuJ/X5r/f3Jfj83//5vff/R+v6Hvb9i78/Y+7vW94/N71/Z+2P2/pq9P2fv7+n5ATu/YOcn7PyGnR+x8yt6ftYN3PzOENCcH7LzS3Z+Ss9vO62DV5uPmgAXSz5+fs7O72n/QBQLwPwLrH+C9W/Q/hHWv8L6Z2j/ThZgvX+I9S/R/inWv8X6x2j/Guufo/17rH+Q9S/S/knWv0n7R2n/Kuufpf27tH+Y9i/vWP+0h4eHh4eHh8cW8QcxLJDBvLKoigAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAyMS0wNi0yNlQyMDoyODowNiswMDowMMjA+KAAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMjEtMDYtMjZUMjA6Mjg6MDYrMDA6MDC5nUAcAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] @@ -344,157 +344,161 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-19 06:22:24\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:28:06\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 1.04359\n", - " 2/1 1.04323\n", - " 3/1 1.04711\n", - " 4/1 1.03892\n", - " 5/1 1.02459\n", - " 6/1 1.03936\n", - " 7/1 1.03529\n", - " 8/1 1.01590\n", - " 9/1 1.03060\n", - " 10/1 1.02892\n", - " 11/1 1.03987\n", - " 12/1 1.04395 1.04191 +/- 0.00204\n", - " 13/1 1.04971 1.04451 +/- 0.00285\n", - " 14/1 1.03880 1.04308 +/- 0.00247\n", - " 15/1 1.03091 1.04065 +/- 0.00310\n", - " 16/1 1.03618 1.03990 +/- 0.00264\n", - " 17/1 1.04109 1.04007 +/- 0.00223\n", - " 18/1 1.02978 1.03879 +/- 0.00232\n", - " 19/1 1.06363 1.04155 +/- 0.00344\n", - " 20/1 1.06549 1.04394 +/- 0.00390\n", - " 21/1 1.03469 1.04310 +/- 0.00362\n", - " 22/1 1.01925 1.04111 +/- 0.00386\n", - " 23/1 1.03268 1.04046 +/- 0.00361\n", - " 24/1 1.03906 1.04036 +/- 0.00334\n", - " 25/1 1.02632 1.03943 +/- 0.00325\n", - " 26/1 1.03906 1.03940 +/- 0.00304\n", - " 27/1 1.05058 1.04006 +/- 0.00293\n", - " 28/1 1.03248 1.03964 +/- 0.00279\n", - " 29/1 1.04076 1.03970 +/- 0.00264\n", - " 30/1 1.00994 1.03821 +/- 0.00292\n", - " 31/1 1.04785 1.03867 +/- 0.00281\n", - " 32/1 1.03080 1.03831 +/- 0.00270\n", - " 33/1 1.01862 1.03746 +/- 0.00272\n", - " 34/1 1.05370 1.03813 +/- 0.00269\n", - " 35/1 1.02226 1.03750 +/- 0.00266\n", - " 36/1 1.02862 1.03716 +/- 0.00258\n", - " 37/1 1.04790 1.03755 +/- 0.00251\n", - " 38/1 1.03762 1.03756 +/- 0.00242\n", - " 39/1 1.02255 1.03704 +/- 0.00239\n", - " 40/1 1.06094 1.03784 +/- 0.00245\n", - " 41/1 1.03842 1.03786 +/- 0.00237\n", - " 42/1 1.00628 1.03687 +/- 0.00249\n", - " 43/1 1.04916 1.03724 +/- 0.00245\n", - " 44/1 1.06237 1.03798 +/- 0.00248\n", - " 45/1 1.08153 1.03922 +/- 0.00271\n", - " 46/1 1.05649 1.03970 +/- 0.00268\n", - " 47/1 1.06265 1.04032 +/- 0.00268\n", - " 48/1 1.05728 1.04077 +/- 0.00265\n", - " 49/1 1.07343 1.04161 +/- 0.00271\n", - " 50/1 1.04640 1.04173 +/- 0.00265\n", - " 51/1 1.05143 1.04196 +/- 0.00259\n", - " 52/1 1.03639 1.04183 +/- 0.00253\n", - " 53/1 1.04846 1.04199 +/- 0.00248\n", - " 54/1 1.02435 1.04158 +/- 0.00245\n", - " 55/1 1.04806 1.04173 +/- 0.00240\n", - " 56/1 1.04798 1.04186 +/- 0.00235\n", - " 57/1 1.06621 1.04238 +/- 0.00236\n", - " 58/1 1.05734 1.04269 +/- 0.00233\n", - " 59/1 1.04581 1.04276 +/- 0.00228\n", - " 60/1 1.02682 1.04244 +/- 0.00226\n", - " 61/1 1.05971 1.04278 +/- 0.00224\n", - " 62/1 1.02357 1.04241 +/- 0.00223\n", - " 63/1 1.02645 1.04211 +/- 0.00221\n", - " 64/1 1.00711 1.04146 +/- 0.00226\n", - " 65/1 1.06171 1.04183 +/- 0.00225\n", - " 66/1 1.03444 1.04170 +/- 0.00221\n", - " 67/1 1.05875 1.04199 +/- 0.00219\n", - " 68/1 1.04640 1.04207 +/- 0.00216\n", - " 69/1 1.04376 1.04210 +/- 0.00212\n", - " 70/1 1.07078 1.04258 +/- 0.00214\n", - " 71/1 1.03916 1.04252 +/- 0.00210\n", - " 72/1 1.01843 1.04213 +/- 0.00211\n", - " 73/1 1.03666 1.04205 +/- 0.00207\n", - " 74/1 1.04625 1.04211 +/- 0.00204\n", - " 75/1 1.05277 1.04228 +/- 0.00202\n", - " 76/1 1.04944 1.04238 +/- 0.00199\n", - " 77/1 1.01898 1.04203 +/- 0.00199\n", - " 78/1 1.03283 1.04190 +/- 0.00197\n", - " 79/1 1.02304 1.04163 +/- 0.00196\n", - " 80/1 1.01539 1.04125 +/- 0.00196\n", - " 81/1 1.03988 1.04123 +/- 0.00194\n", - " 82/1 1.02138 1.04096 +/- 0.00193\n", - " 83/1 1.02473 1.04073 +/- 0.00192\n", - " 84/1 1.03810 1.04070 +/- 0.00189\n", - " 85/1 1.07438 1.04115 +/- 0.00192\n", - " 86/1 1.03048 1.04101 +/- 0.00190\n", - " 87/1 1.06778 1.04135 +/- 0.00191\n", - " 88/1 1.07341 1.04177 +/- 0.00192\n", - " 89/1 1.06729 1.04209 +/- 0.00193\n", - " 90/1 1.05069 1.04220 +/- 0.00191\n", - " 91/1 1.07675 1.04262 +/- 0.00193\n", - " 92/1 1.06470 1.04289 +/- 0.00193\n", - " 93/1 1.02609 1.04269 +/- 0.00191\n", - " 94/1 1.04761 1.04275 +/- 0.00189\n", - " 95/1 1.08802 1.04328 +/- 0.00194\n", - " 96/1 1.04162 1.04326 +/- 0.00192\n", - " 97/1 1.04573 1.04329 +/- 0.00190\n", - " 98/1 1.03232 1.04317 +/- 0.00188\n", - " 99/1 1.03473 1.04307 +/- 0.00186\n", - " 100/1 1.04505 1.04309 +/- 0.00184\n", + " 1/1 1.05252\n", + " 2/1 1.03787\n", + " 3/1 1.01943\n", + " 4/1 1.03989\n", + " 5/1 1.06679\n", + " 6/1 1.03713\n", + " 7/1 1.02400\n", + " 8/1 1.04289\n", + " 9/1 1.05130\n", + " 10/1 1.00878\n", + " 11/1 1.06773\n", + " 12/1 1.03922 1.05347 +/- 0.01426\n", + " 13/1 1.05156 1.05283 +/- 0.00826\n", + " 14/1 1.06049 1.05475 +/- 0.00614\n", + " 15/1 1.01018 1.04583 +/- 0.01010\n", + " 16/1 1.04020 1.04490 +/- 0.00830\n", + " 17/1 1.05579 1.04645 +/- 0.00719\n", + " 18/1 1.01592 1.04264 +/- 0.00730\n", + " 19/1 1.06881 1.04554 +/- 0.00707\n", + " 20/1 1.02985 1.04397 +/- 0.00651\n", + " 21/1 1.01496 1.04134 +/- 0.00645\n", + " 22/1 1.05330 1.04233 +/- 0.00598\n", + " 23/1 1.05170 1.04305 +/- 0.00554\n", + " 24/1 1.02888 1.04204 +/- 0.00523\n", + " 25/1 1.04083 1.04196 +/- 0.00487\n", + " 26/1 1.01235 1.04011 +/- 0.00492\n", + " 27/1 1.02785 1.03939 +/- 0.00468\n", + " 28/1 1.04556 1.03973 +/- 0.00442\n", + " 29/1 1.05400 1.04048 +/- 0.00425\n", + " 30/1 1.06213 1.04157 +/- 0.00417\n", + " 31/1 0.99934 1.03955 +/- 0.00445\n", + " 32/1 1.04433 1.03977 +/- 0.00425\n", + " 33/1 1.05184 1.04030 +/- 0.00409\n", + " 34/1 1.03971 1.04027 +/- 0.00392\n", + " 35/1 1.05272 1.04077 +/- 0.00379\n", + " 36/1 1.06881 1.04185 +/- 0.00380\n", + " 37/1 1.03344 1.04154 +/- 0.00367\n", + " 38/1 1.04726 1.04174 +/- 0.00354\n", + " 39/1 1.01440 1.04080 +/- 0.00354\n", + " 40/1 1.03534 1.04062 +/- 0.00343\n", + " 41/1 1.04429 1.04073 +/- 0.00332\n", + " 42/1 1.02142 1.04013 +/- 0.00327\n", + " 43/1 1.03895 1.04010 +/- 0.00317\n", + " 44/1 1.05985 1.04068 +/- 0.00313\n", + " 45/1 1.04737 1.04087 +/- 0.00304\n", + " 46/1 1.04796 1.04106 +/- 0.00297\n", + " 47/1 1.06708 1.04177 +/- 0.00297\n", + " 48/1 1.06523 1.04238 +/- 0.00295\n", + " 49/1 0.99626 1.04120 +/- 0.00311\n", + " 50/1 1.04077 1.04119 +/- 0.00303\n", + " 51/1 1.06327 1.04173 +/- 0.00301\n", + " 52/1 1.06508 1.04229 +/- 0.00299\n", + " 53/1 1.03689 1.04216 +/- 0.00292\n", + " 54/1 1.02899 1.04186 +/- 0.00287\n", + " 55/1 1.03267 1.04166 +/- 0.00281\n", + " 56/1 1.05790 1.04201 +/- 0.00277\n", + " 57/1 1.04353 1.04204 +/- 0.00271\n", + " 58/1 1.04657 1.04214 +/- 0.00266\n", + " 59/1 1.02914 1.04187 +/- 0.00261\n", + " 60/1 1.04882 1.04201 +/- 0.00257\n", + " 61/1 1.01905 1.04156 +/- 0.00255\n", + " 62/1 1.03995 1.04153 +/- 0.00251\n", + " 63/1 1.05377 1.04176 +/- 0.00247\n", + " 64/1 1.02909 1.04153 +/- 0.00243\n", + " 65/1 1.06892 1.04202 +/- 0.00244\n", + " 66/1 1.04216 1.04203 +/- 0.00240\n", + " 67/1 1.03473 1.04190 +/- 0.00236\n", + " 68/1 1.04114 1.04188 +/- 0.00232\n", + " 69/1 1.04955 1.04201 +/- 0.00228\n", + " 70/1 1.05464 1.04222 +/- 0.00225\n", + " 71/1 1.02859 1.04200 +/- 0.00223\n", + " 72/1 1.05387 1.04219 +/- 0.00220\n", + " 73/1 1.05039 1.04232 +/- 0.00217\n", + " 74/1 1.04338 1.04234 +/- 0.00213\n", + " 75/1 1.05838 1.04259 +/- 0.00211\n", + " 76/1 1.03831 1.04252 +/- 0.00208\n", + " 77/1 1.03555 1.04242 +/- 0.00205\n", + " 78/1 1.05684 1.04263 +/- 0.00204\n", + " 79/1 1.04267 1.04263 +/- 0.00201\n", + " 80/1 1.05813 1.04285 +/- 0.00199\n", + " 81/1 1.03512 1.04274 +/- 0.00196\n", + " 82/1 1.07081 1.04313 +/- 0.00198\n", + " 83/1 1.04476 1.04315 +/- 0.00195\n", + " 84/1 1.05153 1.04327 +/- 0.00192\n", + " 85/1 1.03939 1.04322 +/- 0.00190\n", + " 86/1 1.04218 1.04320 +/- 0.00187\n", + " 87/1 1.03688 1.04312 +/- 0.00185\n", + " 88/1 1.03480 1.04301 +/- 0.00183\n", + " 89/1 1.05089 1.04311 +/- 0.00181\n", + " 90/1 1.06251 1.04336 +/- 0.00180\n", + " 91/1 1.04054 1.04332 +/- 0.00178\n", + " 92/1 1.05340 1.04344 +/- 0.00176\n", + " 93/1 1.05938 1.04364 +/- 0.00175\n", + " 94/1 1.02741 1.04344 +/- 0.00174\n", + " 95/1 1.08249 1.04390 +/- 0.00178\n", + " 96/1 1.02858 1.04372 +/- 0.00177\n", + " 97/1 1.03983 1.04368 +/- 0.00175\n", + " 98/1 1.04715 1.04372 +/- 0.00173\n", + " 99/1 1.07443 1.04406 +/- 0.00175\n", + " 100/1 1.04461 1.04407 +/- 0.00173\n", " Creating state point statepoint.100.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.4445e-01 seconds\n", - " Reading cross sections = 6.1129e-01 seconds\n", - " Total time in simulation = 2.0000e+02 seconds\n", - " Time in transport only = 1.9970e+02 seconds\n", - " Time in inactive batches = 2.9966e+00 seconds\n", - " Time in active batches = 1.9701e+02 seconds\n", - " Time synchronizing fission bank = 4.0040e-02 seconds\n", - " Sampling source sites = 3.1522e-02 seconds\n", - " SEND/RECV source sites = 8.3459e-03 seconds\n", - " Time accumulating tallies = 9.3582e-03 seconds\n", - " Total time for finalization = 4.6582e-02 seconds\n", - " Total time elapsed = 2.0072e+02 seconds\n", - " Calculation Rate (inactive) = 16685.4 particles/second\n", - " Calculation Rate (active) = 2284.19 particles/second\n", + " Total time for initialization = 2.4568e-01 seconds\n", + " Reading cross sections = 2.3233e-01 seconds\n", + " Total time in simulation = 1.1761e+02 seconds\n", + " Time in transport only = 1.1757e+02 seconds\n", + " Time in inactive batches = 2.0641e+00 seconds\n", + " Time in active batches = 1.1554e+02 seconds\n", + " Time synchronizing fission bank = 2.1808e-02 seconds\n", + " Sampling source sites = 1.8421e-02 seconds\n", + " SEND/RECV source sites = 3.3183e-03 seconds\n", + " Time accumulating tallies = 2.6283e-03 seconds\n", + " Time writing statepoints = 4.2804e-03 seconds\n", + " Total time for finalization = 2.2731e-02 seconds\n", + " Total time elapsed = 1.1789e+02 seconds\n", + " Calculation Rate (inactive) = 24223.6 particles/second\n", + " Calculation Rate (active) = 3894.66 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.04342 +/- 0.00159\n", - " k-effective (Track-length) = 1.04309 +/- 0.00184\n", - " k-effective (Absorption) = 1.04107 +/- 0.00140\n", - " Combined k-effective = 1.04195 +/- 0.00117\n", + " k-effective (Collision) = 1.04491 +/- 0.00149\n", + " k-effective (Track-length) = 1.04407 +/- 0.00173\n", + " k-effective (Absorption) = 1.04203 +/- 0.00169\n", + " Combined k-effective = 1.04355 +/- 0.00131\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -551,10 +555,9 @@ "\tID =\t1\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux', 'fission']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -578,19 +581,19 @@ { "data": { "text/plain": [ - "array([[[0.40767451, 0. ]],\n", + "array([[[0.41279586, 0. ]],\n", "\n", - " [[0.40933814, 0. ]],\n", + " [[0.41176924, 0. ]],\n", "\n", - " [[0.4119165 , 0. ]],\n", + " [[0.41096843, 0. ]],\n", "\n", " ...,\n", "\n", - " [[0.40854327, 0. ]],\n", + " [[0.4095409 , 0. ]],\n", "\n", - " [[0.40970805, 0. ]],\n", + " [[0.40836217, 0. ]],\n", "\n", - " [[0.40948065, 0. ]]])" + " [[0.40852022, 0. ]]])" ] }, "execution_count": 17, @@ -624,32 +627,32 @@ { "data": { "text/plain": [ - "(array([[[0.00452972, 0. ]],\n", + "(array([[[0.00458662, 0. ]],\n", " \n", - " [[0.0045482 , 0. ]],\n", + " [[0.00457521, 0. ]],\n", " \n", - " [[0.00457685, 0. ]],\n", + " [[0.00456632, 0. ]],\n", " \n", " ...,\n", " \n", - " [[0.00453937, 0. ]],\n", + " [[0.00455045, 0. ]],\n", " \n", - " [[0.00455231, 0. ]],\n", + " [[0.00453736, 0. ]],\n", " \n", - " [[0.00454978, 0. ]]]),\n", - " array([[[2.03553236e-05, 0.00000000e+00]],\n", + " [[0.00453911, 0. ]]]),\n", + " array([[[1.74741992e-05, 0.00000000e+00]],\n", " \n", - " [[1.83847389e-05, 0.00000000e+00]],\n", + " [[1.68457472e-05, 0.00000000e+00]],\n", " \n", - " [[1.68647098e-05, 0.00000000e+00]],\n", + " [[1.75888801e-05, 0.00000000e+00]],\n", " \n", " ...,\n", " \n", - " [[1.71606078e-05, 0.00000000e+00]],\n", + " [[1.79971274e-05, 0.00000000e+00]],\n", " \n", - " [[1.87645811e-05, 0.00000000e+00]],\n", + " [[1.89308740e-05, 0.00000000e+00]],\n", " \n", - " [[1.94447454e-05, 0.00000000e+00]]]))" + " [[1.75231302e-05, 0.00000000e+00]]]))" ] }, "execution_count": 18, @@ -682,10 +685,9 @@ "\tID =\t2\n", "\tName =\tflux\n", "\tFilters =\tMeshFilter\n", - "\tNuclides =\ttotal \n", + "\tNuclides =\ttotal\n", "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "\n" + "\tEstimator =\ttracklength\n" ] } ], @@ -722,7 +724,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -731,7 +733,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -763,7 +765,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -806,14 +808,14 @@ { "data": { "text/plain": [ - "array([((-0.28690552, -0.23731283, 0.51447853), ( 0.02705364, -0.14292142, 0.98936422), 1780128.70101981, 1., 0, 0),\n", - " ((-0.28690552, -0.23731283, 0.51447853), (-0.16786951, 0.86432444, -0.47409186), 1553436.10501094, 1., 0, 0),\n", - " (( 0.17162994, 0.134092 , 0.42932363), ( 0.25199134, -0.11168216, 0.96126347), 829530.02360943, 1., 0, 0),\n", + "array([((0.20665803, 0.15081559, -0.57355059), ( 0.49473673, 0.67921184, -0.54213177), 2077978.15846043, 1., 0, 0, 0),\n", + " ((0.02302023, -0.02944101, -0.45025678), ( 0.53648981, 0.51827967, 0.66600666), 206149.19886773, 1., 0, 0, 0),\n", + " ((0.19282602, 0.25572118, -0.11262284), ( 0.75853515, 0.55187444, 0.34649535), 1153689.72115824, 1., 0, 0, 0),\n", " ...,\n", - " ((-0.24444068, -0.01351615, -0.41772172), ( 0.10437178, -0.86754673, 0.486281 ), 807617.55637656, 1., 0, 0),\n", - " ((-0.2146841 , 0.14307096, 0.07419328), ( 0.89645066, -0.35557279, -0.26446968), 6036005.44157462, 1., 0, 0),\n", - " ((-0.2146841 , 0.14307096, 0.07419328), (-0.95287644, -0.25857878, 0.15863005), 4923751.04163063, 1., 0, 0)],\n", - " dtype=[('r', [('x', '" ] @@ -935,7 +937,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAD4CAYAAADB9HwiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOyddZhdxfn4P3Ou+73r7kk22bi7K5JAcJdQXIoUL27FtbS4S4CgIYSEuLvsJtls1t3lup35/bG0lJa29Fegab/38zzn2b3j95yZ97z3nZl3hJSSGDFixIhx9KH8pxsQI0aMGDF+mJiAjhEjRoyjlJiAjhEjRoyjlJiAjhEjRoyjlJiAjhEjRoyjFO1/ugF/j4SEBJmTk/OfbkaMGDH+C9i5c2eblDLx3ymjQAjp+5FpG2G5lHLuv1Pfj+GoFdA5OTns2LHjP92MGDFi/BcghKj+d8vwAZf8yLR3QcK/W9+P4agV0DFixIjxSyI4+gTi0daeGDFixPiPoACm/3Qj/oqYgI4RI0YMejVo3X+6EX9FTEDHiBEjBjETR4wYMWIctcQ06BgxYsQ4Solp0DFixIhxlBLToGPEiBHjKCW2iiNGjBgxjlKORg065osjRowYMb5F+yOvf4YQ4hUhRIsQovivwq8SQhwSQpQIIR7+Me2JESNGjP/z/MQa9GvAs8Abfy5fiGnAAmCIlDIohEj6Z4XENOgYPy/+zv90C2LE+FH8aRXHT6FBSynXAR1/FXwZ8JCUMvhtmpZ/Vk5MQMf4eVl9G0j1P92KGDH+KX+aJPwxF5AghNjxF9fFP6KKvsAkIcRWIcRaIcSof5YhZuKI8eOJ+kFGQWv98XkOfwr5c6Dfgp+vXTFi/AT8iyaONinlyH+xCi0QB4wFRgGLhRB58h+c3B3ToGP8eGQEiq/58emjYbClg7v+J2xD7BT6GD8PP6WJ4+9QByyRvWwDVP6J29KYgI7x49GYofYVqH//R6bXwbgbwfxP50J+GHfb9z9LCSXPQiT8r5WjhiHU+v/Xhhj/Z/iTBv1jrv9PPgGmAQgh+gJ6oO0fZYgJ6P+r+NwQjfxreYQGko6FcPuPz5PQH9oO/mv1/InDG4lsevC7z3sfgboVsPp58Pz1/Mvf4vUuJnrobNg5vrftP4SUoP6L9yHG/yQ/pQYthHgX2Az0E0LUCSEWAa8Aed8uvXsPOO8fmTfgJxLQQoi5QohSIcQRIcTN/yDdSUIIKYT4V203MX5q2hvgg0f+9XzDXoeunT8cF6yB6muh8Qnw7uoNi+8DHWX/X00M6feief1uaK2EsAeOvAuuIvB2wKsX/bC5o2EfoZqlVEavIth4N0rHRij8I+jifrgSIaD8rpjpJMZPqkFLKc+QUqZKKXVSygwp5ctSypCU8mwp5UAp5XAp5ap/Vs6/LaCFEBrgOWAeMAA4Qwgx4AfS2YBrgK3/bp0xfgIUBd66Eyr3/2v59PGg+iHyA6e3GbIg7iRoeADq7wXvHtDoe00M/4ioFzdeJN7vgmjC43gFNS0ZwkGIBCBuEIy4Exwp0HwYupv+tqzkAYS33kTC7tcwGE9GjN4DtuH/sHrZtQy19rp//t1j/E8j+JdWcfwi/BQa9GjgiJSyQkoZold1/6Ep+3uB3wGBn6DOGP8uOgPkDoauf7oU829JPh5avvjhONtEKNoCOc/Dkaeh7HSItP3DpXaeljvYIEvwcTNBXkUSRSERkTUeOe8aqNpB9OBjMOAS0BqRk8+Dggm92u9fIiWhxnuQ/XyYD4zDvKkJVP0//z6OaQScL6JGa/6FmxDjfw0B6LQ/7vql+CkEdDpQ+xef674N+zNCiOFAppRy6T8qSAhx8Z/WFba2xiZ1flaSsuC8+6Gx/G/joi3Q8zD0rqf/W5LnQ9Nn/6DwFHj+TtiXCZlPgeKG3cdA17JeU4JUIfpt2WoIffsL+AO7MHIJPq4kyDOEWIeeaWgLj0duvIdA62JIHt/bPL4hOrkQti0GXxe+pnXIUCvRAyfilisxJ52C5pzliFHnweunQcc/Pk9U9HkYhJZw5Cb+iUnwO2J26/85hACt9sddvxQ/+yShEEIBHgeu/2dppZQvSClHSilHJib+WyeoxwCkqhKpqPj7CQZPhb2r/zZckwThYmgaBpHK78rz+wnfeQvBE08g9PgOZPAHJup8TfD1S/D1KzD7QjAmQ0s3aE6DQBkcPgFa34KKx3vLDB7Cr3WQGO5Gw0CsfE6ETfh5BZM7B4pHEzG20pPu+LPGLISTQNrvUPc9Bw8Pp9v/HuH94+lwteOwXY/S9ij4t0HueDjtBfj411C6Erz1UPoC1P6VniAUDOpjSN8hxJ+08obvzINhOgnS/P08pc/ENuD8jyEE6DQ/7vql+CkEdD2Q+RefM74N+xM2YCCwRghRRe8i7c9iE4U/P0JRCH76Ke2DB+N7+mlk2W5oa/gugd4AOn3vio6/xn4TaFIg9N2UgTCZ0N5+D8rkaUiPk/CZcwhdeTHRLz5Fer+1H9eugoOvwHO7ITm7N8xgg/UPQMIl0OcDCLVD6W8Jt3+IMA2m2zSAIvtlAOgCozGpjxNhG/SsQ9qnoiaasHQM7S2rZi3seh/h9yMtdTDoBKLaZg7nJJNQexBt9YOgzYLgIULUI+1JcP5i2P82fJgDVR9A+uy/+boaxyKE34Ma+dYmv+NW1O5i6nmFg1yKDtf3M9QvhZoP//7N72mMTTz+l/G/qkFvB/oIIXKFEHrgdODPv3+llN1SygQpZY6UMgfYAsyXUu74CeqO8U8w//rXaIqKCH7+OeHqZrhyNHz6HESjvQlGzIWdy/82o64IEldCYBUEVv45WOh06H5zK/oPVqO/NQ/d3Q8i3T2Ef30ZofNOJ/ryjcj0Hsgu+q6s0acjg7WEKqfiDnxEIGUuUnMZlZZVSDWM4m3HiRW8X8Py3xFuW4LZexpR39tEE07HW5CFfb8PpEQ272RdShu6ZUHUoanQ/BXpR3IZ0HoJIu8L6PseOC6FxieIlp2Jv/NpqFoMcV3ItAvweW3gK/3Be6XTXU/Y8+1GHDVMdMevaZAvk8pZKPyVLTvihYavvh8WjcCaB+CZIVC69G9t5DGOaoTonZr5Mdcvxb8toKWUEeBKYDlwEFgspSwRQtwjhJj/75Yf499DCIHj5ZdxfvopwS++wtOTgfzsD+D/VmseNQ+2f/l3Mivgeg48L0Ho22Vz7VWw833E7g+hK4KwhNGecQ76F99AN70fZE5AdkQInruAtrsnIj95EgZdh+h/OprqRiINV9HVNJ+wRsFRayTU8Dzm7jYUFPCuhpw1RBoWY6rehSZ/A+qB64gMPAkMFlh+NeXNz5NQ1oN2xm6iwwcjwwWI4VejCDMc+ARW3A7bd0MgSEucFX/LU9D+COTkIMZdyqo5o+ioPB6qz4Po9+c5lIRfQbAcqTYRHHcnHnML8aKQENvxU/L9ezPyKXB8u1ip80Dv3+IP4NDnkDcdRl30Uzy+GL8kv8BWwn+Vn6QqKeWXwJd/FXbH30k79aeoM8aPR5jNANieeITQl0/T/finWO88D+3DS8DqhIC3d3ee9gdWeAodxL0MradC01wiezag3fsZjJoO2Ydh10LY3Re2L0VYnWhufh/KHkU/7WKcDx8LUy5DJUjnnBvQfLCb2jGz8AT30D5gOX0qq9D4IGrO6q3Lfgqy41F0dWbEqC8Q3hA6/XyM2jjU/P2oh+uonDGUyoT55DizMUTGouasRNPZCv1O6r0A1ChsORHX1q3UD+6LuWc+JvNAaPuEcZ6l7I7Lx9FWzKDoqxjybvzzV5UIQhThCw+jJzme5JpErPJ+KsUpQJg07kfwrQHSORDK/tD7/867UFsbocuDsmgDaH5BFSvGT8dReCih+NGz1r8wI0eOlDt2xKwg/w7d7KWOd7DRnyTmYmz/CIQOVV2I++xT0LkUTG+vQHzxPGT1h6HTv1+AtwOKP4dDX4M+THTIPorVDPpa78XU0gLrHoPu/eBKggYJ510Eh1/qnShMzsbXXYtvfB8ikRoCGHEeCRPS66keUkSPNoTW4yfO30lVNJtJVheWuPvRdG1FlF9GOOl6qgJuUjLyqLKVEC6LZ4/SxFCbwpsJWRwf8DOtsw9h34MYds6G0x8DIOIupbX6JloyEwlZ7Zh8Xvq+UYZG5iE0b+M54mLDsf1Y0+9YEoNNXJIwAI1NSyiyAYmbOu8+svXl6EzfYC7eAkljCCf3IcB+uviEVO5Gy7ebXtafgbTNQ+64HPw+hKcAccvhH34YahiUo+28jv8dhBA7/z+cF32PkQYhd2T8yPoq+Lfr+zEcZe+LGD8lZopwMp1qHiOqdpDZ9hDauOtQEhOwL1tF4Opz6J48AtsfX0az/g0YOh3ZXY9n3/PIspVYjf1RBp0IZ7wEza9T2tZNm8XKkM8fh4HHwaWfgiLhidPhon6Eah9Fb3GA04MUBwmMHYbD+RyVNQ9wOFHPuIpajMESBu0LoZ5wE3LF/VSNFTTnp1GtGkhuOhFto8SyJwDu+/jDr18hq2MnDncbdlsVmrgk4glzpthComkXbR16rHFdhLqX0upLpcW7DbpqSKqMZ8DXPSj6AKHsCrR+SSRnEuq+tRizKxm1N5GU6MsoSYI9Yg3h1TqGvtqKechwIlMhkNMXc8KnkHMZlDyNLvlxdMxATz713EQSV2FyJ0HtFmR2LoF5gzHdtQ1RmPb3H8a2a2HInWCKrU46ahHAL7hC48cQE9D/xXhXr0aGw2gSEjD064disXwvfgeHSJODGSHeJkA1pZmfEaezkUIUITSYnnmLyJMX03D/lSQKMF4iadp6BeVZjRSOfx5FM7xX89tyMvXt3SweeD13anYhT94JCfMROhfy5dvpuGAabscGUtuc0FIJ7YKoKY6GeC/GmjsJ26Icu34vwdk3Ud+xgpzoDDRPXgNKiOQ+SWzMmc3p33xIzcxzse96i7gjAYIn5HJFJImU+LG0eK+nRk5lxs4OnLUHiNN4qBqbgs8bok1rQ5zowrnrRQbZj6C1XQnHPQKid1JPX/85sv0K9HNmwKzJeN2PY2nfxur0h9ijz+e1bV9Q4X6Xj+5ZiL2mkhFP+pFVzQQeexSe+gCdywfrwrB3D4aMXDJHjaUr82Y05e1o8yYS7LcDLK8SSByJqaYeAh4w/oA71uAy+LwKTv07G3xi/Oc5Ck0cR1lzYvwr6AsLqT/1VPxbt5Ly9NM4zj4bxdorHCSSVexiRn2I7IyLMODA3p5Oa2YBJdxIOqfiYgwVVx+L6csAcquVYvVlmL2AXPqRwHAItSLLFhHoO4U/yHju2HQV0Yk1cMSBZsMZqCctxZ0m6DCtQq+mot/cA4ZKpEOPYnBTWNlNa3wnRiULabJjrPiYaFEcmuVvw+kPwJI7CHu6iRqNmMfcz4AnjkFNH0rzHB81I4ZiD92ErMwkkDMOo2kWzuHzCY6Lsj16N1G1jDRvBYWfHEBjHwAJU6F9FMy9HFQfHk0npm1PEwjuxxBsRy4ZDO4OLHo9TPw9i3Y9w8NDzsGXuYM+XzeStf993D4/HTkufFnxxL/eiLTWEo26iAZL0XZVIvVu5IqvMLtA1ZiI7tiHwTACpXMRPRoDBpuC4m4HnQ7CnWBM6X0WoUbCtnb0e9ZAczkk5/c+QCmh5vXeyVh9AiTOwKtxI5FYZUJsFcgvjQCOsumDmIA+CpFSfrdh4h+gS00le9UqfGvXonq9NF5yCYrNhuO88wiPHYQC6EreguQzIVyCMA0jiTnEq+OoF0toFJ9xSOkidd4o4nc8T3LDSOJS0xHR9UTdD6DU70bNmshjunxuXvpbtM6xyJZFiD88gJidhXvrZTTP9tHo92LqOEJcUz3tg0aRqCulNS0Fb1wCqms0BVvfpa5fFsmtlRRsWoG06xAJJrjxK/QbJpDR7Sb42TUYT3wGv+4N4hrrsAcWYWibjab8DWrTLUzr7IbS0RhsgxmQMZGGToWBnA0XLeidlBMaOPgJLH+MQMpkdoyqYUL/86hQfkNGuYIjMx7lQA+0hWD/+/iHXsylux7Ebwoh5vch4NiLO11PXpPCBxkT+Jp4xrSuxdbiJ7V0G+SFifYzEXFa6MnwofN7MTf1R1txAP0KPYrNSlhpwvDsFFgwARo/goQZyJzLaE/eiD65CL1jIBCA5q+gYzO4S6F7T69wHvQYaAxEZAs7W+9kWtxzCO0v6fUhRkyDjvGjCMt30Yszf1RaodNhmTkTANuCBYQbGuh+/XUCj9xP3P0jGV6yDXKWQUI9mMZBuBvN/pvJGv48Ppoo41bCYgOGkx7ioHMZ5nA35m6VSJeBzQPvpCkyjllfvo3lhPVgcCIA38MnUl97FrrD3VgOOAiOz6OgbDva3A4sQyfgMWbQFn+EDmc+M9d/hVKYS0bFQSpz89GlpZBR2oRwJaGWFBMXrOWW1y6nfYaVZHEy5kAUNCEMh+4hfEByOCmN1EotOuUL1JAGseYDrKMOYzQ5wOGGsm+X3GssYO1L1LqJrY6DjIi8iaL/FCdhTPoCxLA7wHcrHEgG/VTs7fuo71DJ9BQT6uvAErkUj+91Ij0VjK720RCXQktcIqnqfrYMKaKgqoasFUdAb8VsUWnJP52Pxp6HKfUVxiirsU95mo5DT5C1rw/oF8KA8VQlxhOJfIOh8h2cDQXg+gzWfQSOAtDGgyEDXMeAvQj8XdC+F9Y9ROOJYercT5Jpu+Xn6WAxfpiYgI7xz5DSQ1Deik6ejBB/tTki0AbGf3gAA7q0NBJuuQWpqmgrrifq09H2uzdIuF1Dt3Mgjp0XILrWECpbQX3CMDSuPoyii/bAC/Q7fiumuQqh087hjX5xJAgzPRtWM3rfXlhghWAbAYOXWv3t2LrjiWuoxRCtJvlQBqG1XmoeuJKQ2U5XdCeFz1cRuWQo5QPGUrDrZUSuJPuwl/bkRMqKcklbMQvddoVAvolDF+dS4S1g4cFsdN59SKWYSPsUdhZUUTypgEE+A5LzUJI2Ey29HbWilv0TplLU98nvvnjEjXTvY0fuEvpV1tHctoDU6CiS9u5HF1AQSy6iomAIecaDqFVN6HuaMGvNIIxoq7xEizZg2BVG7wjibOtBNOnJjU+jM2km06vrEIY05PBOWlQnWwsf4FCommq1mHN2H0anRLE+ejLNxx/H7il+vO5XcOjtuA5uxm3ToY07HWX0naB1wdML4eQlvW0O94CvAXz1vVfjRgxtq+hbnoatbisUTkQmTyBENXqyvlviF+Pn4yi7xTEBfZQRZT+SHiKsRMcx34889HvocwFYMn8wb4QQ2m93vIWUKIb00WgMz2E+vokObSXlBBnqL8ebPh1DzXqytR3UW1SM2sHEDf0UQ58Ooqvi2Xl2CJc4nxWk8fDB+xG1m2GJiUjqKNxWL1m7qggLwdbjr2fi28+j3X0YreN0+h1YCCPHEtZ48apZjCgOohv6Fjh1yP0vomRbcESM6KuaiBxUCQzT0TPagbIxm9qC/kQyC1ibdiHJ5RexdMb5DO9+ByWq4YhFT6JqIE9zKaGBmzCVfskQfe8hACoSBQFaGwds9SSsU9AMnsjK0ovwH9mBw2CjWyRgcZhIyttCZ9YOcsqmop/2LF/taeeMuEK82bkYV0xFmFz4DH4cNSPpVrSIPSpxWQUEfQ10jTXxzvArsLi7GLH7A7LSsumIrMGfo+FQ/AQMLbtwHCkmOiiBgT0XoWsXtEybRwaNuLjyu4eUMwIqd0LeSNA7ei9n/964tS9i7H8fuYH3qPdIPEnL6eY6UrkJA7nff9hqDyj2v9+R6mogPTNmx/5XiGnQMf4ZGoahFcehZd73I6SEyvfAmgMF5/5NvgYOIFFJV/tApJmOhuXE13yAMCVhMw/EehBspvMJ2t4j0Apx/nNQch6C4PVo6z8jkJxE60lZdHWlMvz6Q6x+aic9KekYRi+EgoG4C6wcEdW4GpcRnFmE0p3LlBVvgtMLvmbI2wjLN0Dag2hFPFqzH7+2CV3rWtBtRcx+D3YWo/p/h7A6CfYPYO+B+G11WFzT6co5hxA+Jsoc2qhiavgOAs/qsNrT0aWolB1fQ+bKSzGG9hJBIe7APjw9j7M0ayqnmftSq1lGsOqP9I+bgrK9kktnpEGinib92yR32amTZ/C1awRltT1sD9+D8m46Z0Q+Y1D0El5WfscInwFzUpDOlCQen3kxOouGgV3QafBDtY9Zu7/g6ro/osl14rEHCZZuZmBxJ+E5ZqRSgTkjGdV+FUrJfdSf0EHmE6thrAOn6Q26+JIQNTiYg2HCOfDV470C+i/xdMD+r+CKD7GUl1E3ahvW9oOkJtxIHCd/P21wD/iXgfPvmECK98LDd8EbH/9/9sL/o8QEdIx/hhBGhHQA3YDzu4hQJ+isP7jZQSXKZt5mOCeAuxFWn0WDM0BXfH+WDYljQlwVttoApi1XoRMX4lz7HOSPB6EFY1+U1Ntp7bwU05TNFN5vofqpGximfYxff9WIR91DVD1Mwo4KCpMmY3IZkeZRiMNGKMiE+jrYvQV2VkOeD748BxHSowPCOTfDuoug4Eyk/3QaErJRq6aRopTgsLWhKk5IGUObqYOP1RBXRa8kSgOBiELihla2n3Me9rfXU3TXbrrdhdQXJdBoPoUNs5PIVM34vJ18oPcxc/NMqnMtjHvyAMqMFGjeAK+OhzO/wBzSICo7Sbe/weQ2Ay7bKi4Y3Yhrd5SK+hBblUEkVh3hUFIfCmd/gaVuPg+aP6OEUawxxhPxtBDISqRM05e+e8uhoxvDsEys3X6YOhCNpZiAMUq0VoOm7FcYZl+O/vNPOXixQt7nUcSpWhzMoZKLaOFF+sZ/ij73uwMMPESowEfLrhexn3Y16eJeSCrB1hRPivE3GBjbm7BpGVgLwKCH5mPAdd8Pd6DmJjjjWDj1nJ+mQ/5fIraKI8aPQSENlUY0fymgDXG9vh9yTv6b9D20oPg9uLY+B26JnPA862176Akc4pro69gMjUiLBbHTi3b/g+ABqrcQ3t0f7QX98W97Anu2D7sIIifupMNzPyLDQcuulTjDWhwn3AkpqZicdvA8jli/FToOwMJdsOFyuGAifPgN6Apgxy5kUIM+GEW/73hklhW636R11nDsyc0ohWuQy/sTaW9Em90Kjg5ykkbwsLwZveY25otUji9YwpTOzdiC6+l7bA+1J56I293K+Pc6sThD9B/XxN6pC3jNNYE0uZM9k8YyedsYNAt8yP0Pgz0OIdzQtBfj4K9h35soWz6hftRVZBxuQ2l5AY3PgyktgcT4WhiWR2FbBPyViI4iooF9DO1YSdqkYTQlVWKQdrRqN3vNo0jrOELYE8VepBBwSxLCetrq4jBP1hCnTsTSeIAuUzk5y4O0Fw3HrKoIRUMWj+NlO/Wh60jIzsAG+IjyCc08JSuZMMTB5YbXiG8chrE7QMRjorHiZnKa0wDRu+oj4u799WTN7nVm9UN0d8KUmXDCaT9Dz/wfJ6ZBx/gxCFKQNAL9vx+hBoAI3zsVLezDtv0lpjSuxDF4JgFjDXvVe9GGBrJwdwUO3SVIox4ZfQ6KFsKQZPjiKQgLItp2TIcq0K1uovO8KTjnv0G9vJxgaTv9uy4j+9ZjkEveQPQ5AQwe6PgVeK6HmoUwzgDeP4JtLaQlwrhMsDngEweR/ACBBXpCxQ4MxRLt7iYs+7sxRntQqgWq3EnX3UnoDH5sUTeWhoMIw5PssO2mj0xlwYrVrBk+i4np51Aj3XzZtYsRYiXl5yfQLLVYagJ4Fu9gxPwB2Owb6cYF37xMwzV9sTe0oPMZ8S88D+OeDwj2lYjgKrT6Bkat+Ry9T6UnMxtD41b8Tf0gZxFkzIDIc2CKR7Fmo/90HUpzN8mtERIG3U9P3Gc4PBch1ETaU+8luf4gIiSxxDUTcMaT1tCAt8OOiO8mMC2OSGcAZXkymWV9YUCvDVgbteLQzMZ2YAllg+wc5ia2y9OYoyZwf3Ar49sqsGomgiEJVEHq3kR2jlLJGfR2rx25czvYB0PPQ2B6Eoxj/rbjSAm/uxMeehYS/z9PUv+/TExAx/gxCJGKKht7O8xfEumAjo8h4dsleGXPw/YHCA9IITJjLBt0C2HtA+R3GDkwXceASa8Taiumu345cd4QMm0oUaUKbf854IgQiB9J37UfIdJs9ERr0NbWkpb5BE19FmF54Ha44xiUhecR7mkldGge5vo8hPZXMNMIYRuEBiFfDRG+5GI0wecIj7oN/fQT8ZTfzleF46G/iYzsalwHOjDOc2N1LiLh4ZdQunowvdRJ4L4iPF2tWIL9mFs6m1v7vs6LBzawAYVhLa3037mAtknt3HnQQ9OYszFoq0j32LC7d4DJz0cVIb5JmYpFG2LtabkUVLuZWJfDUPd+NLvqUarL0Ff8FhQfUWs8WjUELYdQDALVqyM9vx/0va1XAJpWQcNW+GQ32nVu0GvAEUC75TEcvga0gW/A7Mdwqh1f/slYPZvQenVoXdU0pQ8m0t2IIbedSDSCyREhMsgG2zbCO6fB6FOJGuPYmD6QZZljSNJMZqF3AwOji0i2voLZfD18u3eFaBQ2X4Q+OIqIqQvV24lijQPXKFB9ENoPrjt/uOMs/RjGTooJ5/9fjsKt3jFnSUchUbmPiPwag3LD9yOWD4esOOj/rX9mfx3o41irrOSd6Ej6l23g6lXnolhdvDrtci74qgS/Vs/1M6fy2NY78Or0hO0REuttaOedSEPpYeTwK4kuvwxR3EHrVwbkVfOxTjdgq1xJWuReGJ4HbefiOViLqbWVqGk2+uEjwBuCkhrk1x/gm6MnlDcRZ81IxJRc5B8uI3ySjoP2gSTWFqDpcONpb6JrvBbZAq49zVSPykYZ4sDZoxJn3MOruoc5T3MqlmWXsThf4fL1qxBjr0Ht/JzQ4NPQ6kfQLV4kXvsQdC9DPXgj5S472eU+dOFGeqYMoL7GjbJXwdWagMvbjV7fD+IOIJMsyOnxRGsuRNt/Dv6lQxFtPZgGZEEgu3db+MEaMNXC9FsJR/ugvn4pmjNCsH0UsrEcXZ8zwP0GQYcHiR9D3o0Ixw6k8gn4w0QrFJpDKbhaWomenIL5yEg0UqE79A17fX1ZMe5+Bteu4hh9MpZoNTQ9RzT7ehqzNOhJI5GLepfRdbfDikKwPM7BfpUkeJJIHHxp7/PuehI6e6DNCCOuA+Uv9CufDy46rXdi8Jf0KH+U8JM4S4oXcsecH1nfu//YWZIQ4hXgOKBFSjnw27C7gF8Bf/Jze+u3nkD/Lv/3nuTRgpTgKQbboL+JEqQiafjbPEmDQC2HqA80ZjBlIGWE1MhH3B99BFtaNuKYF5GH38WWkYVvUg9mxwxGN68nqC/C2L0Nc0qAUGon7UvXYe57HJHmWradMIjINMEYWYVvz/uk10yi+SqVmvar0HRY0MQPIJo5npL1X5N7chfm9Jl41TrUtneIG6jlwPEXMahjDGL3dXD4MtQOF92LFfpVlaO7UY8mLZPklizkXWuI9jchkptIfLwB/5xc2ofrIVdwXugRMr9Zygt948m16hFnbIb9t0PtSoSnknAaxK2JwHFucB6L4hlPctc2dOu0iIm3Yl+2i/CQbZjG+GmyDUPXcYS4uAeQH16PqNqH96qhaKY+jWJdi7Ghhp4+Q9Bro2hadkN7DZTqYaYFit9D1zOSaHsDYmsK/ikRDDlLYOurkPIYelsWDfIBkrd+iDa0H1GfjtraQ/DTIGlKC4FHQBdtwDNsOEZlGsHqVjLKtNyzZD6qI4ySPh28O5HZv0WTchEZJNHNCpq5GzsTMfkKEI54mHUOaS2rqWx9nsSu43tPYa98EDZ4kWdsYYXyGctxY8fEcUxlxNPPwRU3/J8Uzj8ZP+0k4WvAs8AbfxX+hJTy0R9bSOxp/qcQAg7fCPl3gHPc96OIR6X9b/NkXglHLkFGmomwAVX9BkmALDQo2jnoDDciHHo61Rpy3J+i9w/FX3MJs/wmXh17JpfvXMf+QVNQNQNxfrmJNt/HNIpEzLUG7KmjUX57N8rSG9C//hG2x3Jpmekk88t2uPt+ooVuLDcNoX3XI5SGnsLc0UBCPDSMScYQXc0R0wqS03UkrH0B2RXBtUch8tQEgunbMIUeRHx2LVy3EOWJN5A+0P9K0pocoCM4DsvaUpJD1axOLGRoa5TulGRYfDIkbAId6NzldGksGKoVAksP0Zp+BOvuKjSFATynjCLc8Q2BEWNp9nTzTdwFjN+5hWB+Ik4WIE+ZjvisCt8HW3AmJKFUbYHUKLZVbUSsYTStQQj2gcETYfMnkJUN0xYS7fcKDHoYGb0PLXmwswJyUhFrHiOQV04wtwltTRDe9aI0tGH2gpiWh/HzCqTHgPs3j+PWfEpqaQZJPSa8w8ZjWFdMRFlLOKilc9Ik0kWvKcLBLGxMwMMSWsJ3Y8gPoXWfg6UkijfOBzIEjw2kc9xw3j/nTHY4Kwlh5RBZXEcWw6rc0FALE6b8Ah33f5if0AYtpVwnhMj5d8uJCehfCIlKF5txU4yPSjJZhNGah7LvDMTYnaCP/3NaIZTvnWcnpUR9vZBAbgX+gU6iyn3EMx+99hkQFpDf5vmWqhwbWdsr0DjG0JqmIbm8hf3WTHQBwQDtQsyGi+EElYI3TsaTWkF74gwOak0c5ksyj70GtdiG5eAnpB7TRcmpwxnid+I2adgSXE3d+NMY/to3DI/uIjziEpTPXiJivxhDqQexai3U7kSTZqTn1lvZG6lkjL8SNm5Dpgp6Et7HkqCjZWA+cX9sJ2OQh7jcT7ATR/vQRykzbOeix19ja9xkSK1EOifR012DrrMD81sSUdOFYf1OMoQkMNlJ0K7SkFpN6yg7WQffJavLS4ptLU73Dt4pP4/b3LcSHbMR85Awzhfj0A6bQNh6Dm73w7ja9yG2gOwMIbwd0JJHNBRG6QrCx/ei9YAsXoNp3xE4+XMOTR9AeO3rDFy+iyyNjq4FmZhdjTBATyhgQRsJoNEpiFoHosGKaWsCmvhSupvLMAyfida9kfZEGx9kn8PpqSeQLiZ9r38omLHLs7A9+wT+S/NoMa5CGRuhoErDRy03MWGolQTjZhauUrggfRaaIZfhNjrQ40U8eC3c9fA/74TuLggHe/13xzaw/C3/moBOEEL8pQ32BSnlCz8i35VCiHOBHcD1UsrOf5Q4JqB/IQQK5t3VeGqeJThA0lG7GQpzsAettEXuIKLvPZRUQYsOFyYE5j/lbduDmtaJaU8GfmcuFww5hqtwEcfbZH++kpRjF39vQrFds5NB4QLEoedJdCgEcxUKy+ro6TZiKVkHQxeBkERG7cTW1oLd/Aw5iRPYwK0cxkL5oiJGP7UUc+bT4HTyubwcX8DBpJ5WZin90eWPYNfvdQycfxvY1uApSMe4oQHsOhiQgcidx6ojZeTX7EIGKpFfPET3bQ78/kxUYytJAyegNe8hsrwMfRrIjU18NGA90xuLqT09jvRt+1B7nETMB/AnDCCSMwWHvxQRAjSHwd2KqUYl0AjuKYm4QjqUwovwbX6G9cp8zjIqXFD/KkFDPLaSJrwvxmM93QCZZyJ7LFifDdA+Zzjxw7bCoChy5FOI+EvZtPsunsm/i/t0v6HPa3qCuV4MxRrkg+eSFwyjC3kJZ1gIjhiPTK6kZ04RHs1TyAOnkppSR3COE8OUb5B3X0ggvwbnK24ImQhrPkO7L0Li+MGkpMxga0IRx377rDrZQYAm7AzAvHkLwgkvJ5+Dvq2UcSs+oX1sPnM9BzGN9yGXZZMYrkWYSuCDGWhdSWwZ1J8ZuQeh4S2IuxQMju86QjgETVXQUA71R6B8Hyx9CTL7wTXPwsiZP3/H/2/jx0vEtv8Pm/fzwL2A/PbvY8CFP01zYvzbGIadSaaMJ+OhSxFxDuTHexAaL46B70DFUHDEo6ZkET77YtCc0Zup8nMoexdd61zCmiN0yk4uCm7jiCGF8ep8OivXkqJ8pz13RnZi9XnQdleCrxptkwbPqHHM0K+jMrU//QfnolM0RFofIGpNQTpuQ3n9V0TOegR7TipD+BVlrq+oHVaE/u3nabs4g7HuIA26Xaj2gbRZF5M+dTP9MhvZd+kiho8bgvmGe/EsPAvrma/B3QX4A5tpbZrOnEApsjBM56/jqLeMxOoOkWpvQ0SCMPJuugfeh+vmXaw9cSxDN++i35YdBNKTCRq9EA6iYxqptQL2VkHVAehThCCOcNiOatZiWlqFLV/F2qpFCT7FH2afTmvEi2heQadhIPnRCmRtBkpeE0JJhi+70G58ndAdF+PQv4LbfCOGA89hKLkaOaEfk4JV5Ou3cEv9bAbEr+Xa555AzrXDeTdSXOhigHcKbf5yNiUdxt60jThjLSm/OoPttwzhpMerUTp98M1UvDN7MJZ6qbx8Li6LF9fDHjTOZoiHk1wn8rh7C7k2GwNw4WAo9dzFIfUBJi+rIXz6E5zmvQrdLh2OEh9ibwdc0A57T0R8+RXyhHhk53I6Bh/HiuQAszevA0cNbLkXtj8C0Qxwp0DYBVo9pORAegH0HQGTFkLROJhxJhiM/4ERcJTzM6/ikFI2/7kqIV4E/qlz8JiA/jkIeeDgYmjaCTIKI68Ba2qvdjN8DuLlyt7lVEqASGQt2j1LIDMZSltQqo5geO5OKBgG3W+ByULn7Ic44vuIYFsbOeYUTnhyH9GLRnGxaOQi4YXqlVC3DjpLqe3TQp4uHorCsL0P5I/EOfIdsg9MZaM9mT5yH2rTnQTDX2DMWg7Cye5pnxFpupNA9tlECONvO4j7hNMZd83j5NXehHTW0RVexsGkFvLpwcdyrLkLKCpKIfjFJ7R+PJ9QwkH6kA/n3obpg99ToGlHyXLiH+BG7jWQE3cs9vpNRBKPQeg00PYyou8EGt9+mprq15jpCaPWl2Acp6Vy1PF4nW0MCt6N4Q9PQG4RTDkb6u6Gyz5DeeV2Fk9zktVsJ2t1K2pXK4Hrh2MNBxnt3w6dgg8yz2eEsYVofS2mGUB7B2S9heaePEzup1H3m/Glv0/5kFkMeGkjBsOVyIowiUc+4NXPwnxx3DEsvH0Jv8/4mCTN41RpLma/fS92+xHsMoNMXTG5r47Hc6GbE5u/gHQBTTa8Ri2BuHbsFVnk7fXhnbcHxXEqDNgOlQ2I/RO5cr+f+556jl+H+hJvTmZA+Dd4t3oIhJtodT5AqrkN3ehtoD8NmVqBqJqJUKYQ1O9Ev8UIPXW0VG0n+bJUtNo6pHUoQmrAZIGik6F7JwTrQKeH9EGQNgvCHjClwDEX9h6WK/W9fqhjfMfPvA5aCJEqpWz89uOJQPE/yxMT0D8Heiukj4OWvdBdB4c+AE8TBLu/n86oQzUtQ5pvRjiXw1XLem2D3m5qqu6hzdYHT6QCe8MD9M97FuveNyH+9zCiFp7cgX70WyQ7KtnYtZ6x/c9AuhJoFlczyH8slB2APjbwVgIQb7kZs/dhaprq6CccaDJ/j4Zez3gjmMUu3SdkfbmLr2c8TsC3mfS2Zg6fM5y+b1+PMj7MgK1TSI7vpKMgGbO3DnXTcUQm92P7OZeQ9KgH+4M2VBFAGXQfPuNk3j7oZ1DuFtxGM3LYVeR98WvCdVE6hllIagwTTC/A3j6eSIqZ1hwtB9mP2ncoCZWH6ee4B++hL/G9OQH1/GcwNXqgroSOY99hl7maXP0BRu2C12afzW8jy9CknY32rmcZOrSdlJMbeezqmyiq+ILo3l2IxBCiTQND+kPPBgiWw5YGRIMG1wcpOJrXUPL7aQz/4AAMaUTRqBRfM5C5yw7gG7eL+0MT+K1hFUbVzEDlABY1g8T6z3l3ywJ0XcuJDhHYVw7FGH+EwOESus/SYdqkoBhGI8Q7GFeZCfT9EFMgDKPPg6/ewFDr5srlN/LUkOP47aaV6DoasB5p4t0TfsVU227amYetdS6WPA8kXowozYTS7WzLv471FYIrjztIWdIRjn/4G0RqGvL8q8F2LMLTBJvuhMPvw6wXIXcO1H8N26+D9t1ANwyMg4QTwX7rLzwo/gv4CVdxCCHeBabSa6uuA+4EpgohhtJr4qgCLvln5cQE9M9FQn+Y9RRI9W81FTVIRIQR/t1Eut5G07IajSkRWj9FJs1nn2UZhwY0keQxMXmrA03yTPjqahieA11aSB4Dg+p4fM3JMDSNTe5Giv2rmaG8hsk5AmE6G7xnwZg3YN0ZUHEPovkI43WH+VRMQEk5hkK+24kmRl7EiC/2gUeS+vulfH2tk/ciZ3GOfy09/lYcJd2IWcnEF16PZc95hLQv4nm0Es2RbXibptN3wkj0TVtBex3YLmS7ZRYjXB+gt+eQzkh0pnlEhtejeethXEEXXPIWbtebJB4uJ0nXyKC4cfTffQTT8BupK3iLuFemYfRr0dxyO/vsL5MdjNKQMpk6p49BMh57vhbrhgrGWCupzeqmIOFrlF9VUtk0iazf1nDWZbU82OdckoJ1zN69ElGmJTDgArqanyBFasE9BFEdxVBnpPPVs+irfR41tQFljQcmK2yZOoZdySdwfnICp2+czIHp11MfqYDIGdh9S0ldVcSxDzzP26++izn9ZUxTKknblY/b0gLGKNY/tEHKOrxZFkw+I+GBHmRbDsLYB45/EkqvJnnFThZGrfwhfzZXrryP2vQ4TtU8juyyowkrCHsnYZ2detsQMhecgvaCEUyatI2J/Q+yeNipTHl5G2plFM3c+1HsC3ofpC0DRt8CGZOgbT9qYn8iedPR5Z5K25HLCHnXIrxhkjJ+hUoZIQ5gYiIa4onBT72K44wfCH75Xy0nJqB/Yv7mNJQf+hnZ/Ba1+qWkmK5AE7cIJVQDmS8jd51BRIxhQOICBovTwNeCGN2N3H8BMtoIH+xEOqNENyZQpeaRnL4a28pKTkptwNz0IqUL88mdPBZsXghUQPXNSFs70bLfoVVzULo7ea7PlZzleZ8862j02rze9ugtEPbByAuIvnUGBYdO57f9z+PhjL6cNacMdXcVzrJHCDnW4ptkQKMdhoXLMdY68ferQ9Ym0BV3EU6lH7jfIbXnfgpcnThs5xLuegPVX0mrczspqVloTn8Lf9MLmOzToc/ZcPBcJul+hamjAOHuR5ztZaKJtxJuWI549nbSxlopmZaLNfIpg927MARyiBiDCEzM3+OmSu9ne3QIGXYr748+gxOKyjF+uIw73lrFRwv782Hf+Zyy5zOMa36LzmFG1YAMjUdzzzMIrQZXyyJYdRg1OR6ZWciRwFiO13zEksQRREUcNcOGY9aYGB/2cXuXhlXKfWzYdRW6kxYRThtKAqcTVJ6hbnIHcS/ZcX5ah67HgGrrxFIWJVBgw/RiNiKvATKehIN2MERBb2CosokyyyIeOvNuhhf3EFf0FtoeF9pQF0INoK/tQ/rhVwkqd0E0grK5nsPXXoqiG8+SkwpgdCNJlYuZE5qOTZ/a+yxdBb0XUM+T2ORYWgN3U5Xbhk6zgHHyKjqUO/HyKfE8HBPOf8lRuNU7tpPwJ0aV5YQit6JRpqJRTkAR3w6cP91nIUBGqWieSJc5zJBOOzLhOLSW61ArjhC4+Tdop81AHtiP+tVnaEZbQG9CpGcTGt+KyTwcBQ2PhmZx9dbT2DJtJkPe20R4XD4lU4yM31qCzmIB3DB1N15DJ97OF0g6pCW6exufDD2RNk0jcwdVYrOcSRzfvujfPwPfSXdTG36DPoeHo8Qb8FS9QV14J6n+VnxT03C2DYC4UwhbI9g5G0mE9SzAQhuFvIKFXgc+Z61ReT15HlqLhYjnKwKKFdmRhq1kCrgctOU+Q9w301H2boEhE2BKBzhPB78KQy9GIukJfY32g/uhp4XO9DB1x1lI6uwge7mWw4Mc9FnVgtY2FNWwhXuOv5V2jROHt5v7ah6jLWMgrt3ruNt4PTUDChhVvI3T967Epm2mcuyNpI3WYNuXB91VyNrthKb1oE26E0UZR1PDe3Qk348vOgiLPpWMw9XYffWEWg8iTKOoNBTS1dPBMJeZmkgLKYoBNbgZXbcH4yY/3v5FWD4GefavEHE2OlMa8fVLIH3/2xC3E1Jb4PVByFYnDDhIR7mFGy+6k1RTA1cEXyblgBnR4wFLAWqqhYCzGql/GM2dT9KTUE7Pr8Gg0VNf+gzB157BtqCDLlcuocJJpJBPPiOxEUc7G6iQlxMNpjDonSjK+e8SUdbj5VOsLAAEVhb+wqPj5+Mn2UmYIeSOK35kfbf+452EPxVH2fvivx9F5KNVziUYORmdphud5qbvNOrSp6HwGhAazM5f0Sk+Iuo5jEwuRwsoeQUY3/2Q0M3XES35BO0ZJrj+JfRqKjSsIxR4DrXtIF7XNC7ffy76hiAGfwCNI0Rblo7WJBehQC66+HyaTatI1MfRGXyE5AMtMH4xmqFBTrzlZHbd8xwG9400We7Fxkx0JCL1Fho77yW/cyBKy3NE6tYgR/Qn3qJldc0ihi8txz1vES5tJn71C2ispiz+adqNLiyc8WfhDHBq97Vol29H1RqR1lyMXWVoOzoh7TARUwYKFpRRJ0HBJJhxNpg1UHIW1EdgyEUIoeDQz4Gz5oC3gZKSCxm5qoyQro2oIsgv1aCJ0xJOqKXWPoiQZRQF/k84vfMDCHUR17CPxgnncPPLT6FsilIWl8fjM87G78wmr6OSkyt2YHv3TogfTseVd2LT7kOjmYhE4kyz8kF4NrN1B8h3x6Ez78PtOodah4Ms3XH0TTobFD0oOnZUP82MqiV4s11kmc4kOu81tO8eIZI0Bt+MqUTsZqKE6OFtwv0nkVZaS+T9sTQOMmKoDpK4PYx3ZAKndLzH6+nnY2iZgxj8GyL++wkLL11mP1bNq9S1PkfChZ3U9hlHJjNxyelECzIxZtyFoeQILRkdGHPORGfMYQvv0yrLcEXbSJcW+mgfQs25lJ7GuZjTf0MyryBiw/7vc5T54ohN4/4MaDXHYtLtQYg4gpGzUGVDr+ZcvRgOPQVAiu5sbF3lyIyH0R7+BALVACgaDTwyBl6ZQPRIDZ5nLkGas1GVEAeEjoiw4z3yOdEEPY39U7BUeQkqduSYmxkeORvLmTvwWAOEzEakZzdqxzZ0o18DtQOMKkpyX0Zedg4pFZlk8BRuVhCmmyOjAliiA9Dsuhl36i6CA+Iw1Jdha29kaMZWesrqOdL1Et7XJmB77Gm48xIKPNdTzwT0DO394mqUttJPcJmiqKNDEG6ExkrUgf3hzg1Er7wOz0A/1oMJ8PxZsPZ56KgFnQuyfwPGldC0+Lsb2dVI5KNbaEkSBOxOTJ06hFDQGRWEoRm/oRGHs4Q7957NorK1BL3QoXHQrHNxyFeFsdVP1Kglx9vKr997Bp2rmcOKi8d7RnLr8bfxh9Gj+CT6Jo8Eo6xSd1LPuzSqN7A7OoH88ARk9HXotqLp8dNkCKALWqDsHdjzMOqGK1nw1u/xFDaTecCObFxOo91MJN/CkdunUGUvoZG9dFCOlol0aRsIx1vQVVexXk6kMWjFPcuGfpKHsV9t4uH7fkdPfBwy8DRdFoU6Wxu7NBEO8hn+it20brAifKeS9NJ6NNdN4KaSlRi9Teg7BZZoD5bd56E5dCKF9R8yv24VeREVU7QIt3iM6PDpJD2+G+u+dkTbV7/oWPiv4k8mjh9z/ULEXqU/E4rSD4V+aJTZhCLXoFVOQJMwFlH5NmQeCytOICnZR3PBfSRnD0c5cgUi67cE7CVEqcKc8xFND56Gf/06tBeOpvHamRzKLmTkwN+zZ82dGAe1k1O1goLP9hC1uUiuLMWaMBcZaEO+upHUohCdym248m8DWQFNV0H6Opi1ED56EfYcxJ55IfLlX9PS7y7qT41nxLLVhGwp1MiBpMSfic95A0Ymk6MdQOf891mcdArB+QGGP7YH65xENGtv5ESzA5s5Qo/nLeS+d9CEu+nr6A/TvRyaUETcm1pcpnmw+AmCeW2454Uway6Afa/B/VtAZ0DKTiKmV9CY+yBKr4FN+xBNpeBIw99ZR2PmUGR1MhXjJpG5u4uegRdyyN7DOnUN17Q9R7lrAM1OHdqojoa2gZxSVYxVaySSGodMd2CpHY2tfT03qsncOdDJnC1vUNShY5PTybOGsyGoY1L963S4dmFFEIg40DSux5eoRak+jEkjyckegMGUCInJYEpilbKJgXxCyjczUXxHkLY2tE0Wai/KJc96KXqSes1ajTXw1C1ElSDeq1s4uGgMH/WZyugB39BlTSAtbEY/uxPT/igG05V0az7Fz+/J4I8kNe8F725CK5s4WOSg4LMnOLzoUlaVzuS4qpU4U2ehvvEVPreCbW8bu56dytDuvijbnyFt4BEMOhO6sIdIkoHItUPRvvYgHNMXtC6kPQehpP+nh8nRRcxh//8wnh6w/u0ZcYrIwaB9n4j6PMGiJgyhvoiWjyDrAI7WE6kzN5KqjCTabwKRnecR6PDTahkDea9SZUnH5s4g+lIT0VXvMueMAOrLCxmb1MPm1D4kuqahX3o/K67+NbO6ixAVH+PvWIsuTYfmgQ4801TidZOgdgQYj4GKbdC+A47JhfpS+PJkRKKd7ql9GMxl2I6dAGqEvpuO5ZOBmxnSMYEC24MgbZSZyshf1o3/UISt9/dn2JY9JO42k7x9J5Wj+7Li3Ol0zDuT5GILC3ZtpUyTT+IRSUKNFnHCSTBGoho+xtylov3qAyLXHY/sXABmI3S2ImUpqjcZTZkWMXc2JMaBdwO2mkpy6zTUJ/ZQGZdF43gvhzQvUG3KYc6ybQTNEfLMm8jUPkjIdYj4jN0c1A8jr3ErTemTsNtNeCf3xWb3EO+8gMuiXdw/vIuJe/Zx0dCJZKkltLrrYGsWhSfdyoquB0i1JkP8PKRtH+qok9C2ukm3/Ros2VB3kEDxPRQMWYGpOISx5n0YlEs43YErux77rrFUpJ5K5lIXliYjpGT2TvDlD0ET3oi7qJXHt92O6XAP7QsctFibCBryCTvC6JXHQK3D3OXC034+Ol0mSsSI7WCYSc5CKBxFTaWbL+L6cEefCnZrU8nsaMF66W34Pr+JAY+tZP9lbobrxqNXKyFUQSiiJxoqxJD5ISL7IWh+HQKXQn4OMvGPCCWtt6OqUVCOst/3vzRH4SThUdac/1K6tkLxW1C1Hea/AvYB34sWQkGnuQKNModQv8vRb3wMoSxCzHkeU/AOIns/RbS/StgTxlrTQvewPYRbXSS7bCScex/epc8Qjt+D52YDptsuYdvnNzCwpxy97UyUmxdQaf09xVYd+ekjKZZhRjfV4n8+HlOdHXHPZBimB2sN5G2A/JHgDEPpu3Dsk5AwjWzaMHy7Jjqs1BGMb2RB5SC2uPR06CoY1VhK+pL9DN/yMcsffpbBr91FTVEO6688BxmcS06PlxlBB3Eb99JYcYQDBSaG1dXjyBXI+AQiaU8g1m5AiXbhaI9HDi5AWfsmYlU1YsSZMCUNmXY/QvkcrCtASYT7voC6Q6iX3kdc3RPUJaQQ3KHAZwlsOXMI59R9jvLbUrov0JE4SWWnfyMjEk5BI2zYUtYRSq7noPUR7F++RfewQwwU7XTUdjNow1nccaCMz+afwCHdBkZo7sSTsgNf/98TXbEee6EenWEpirYOk+5zVNc2+PRzDKsnwYBCSLbhG1IH/QSG1YPg3POg8X5orkNjnAGj3iNfrKTqumUkRUdg1yxEVO1BfH43hrCk6OEKjGEvxl0RUve56bmviCqbh4xvRmMKz6dm4/1ovU3IUVGC0zyE9zWjTDLimDSdrrIlPGi+iQfiRzBYO4MIfuTQZYjKVdSd1EPyCC2FoW20nTiWpJ2TEZEkop1xROvtBN+fSGTAEKxrXChJFbC3GGaeAX3WQrAd6pdB3tn/gcFzFBET0P+j2IdD5g5o+T18eiwMHgXmAnCMBvtoMKYhkfhFFx7HXFytmwhlr0PzYQGJajuduRZ0/SNELTOJaIeS0gWGDU8QCfhRZ+6mu9CL7H8RBt8hDvx+P5pAKwbtPrQeATYXTuLw4KO6+V4GflOOaAnTMS2V5NJEmN8CX2bDtXfAsFFQ9yZU3AI9CeDsXQv9J+EcZBe+8B04zTWILc8x2ZfD4dkdfBVdRZY+jiMP3YAtUo6ruYvW409kSsV+4g+9hzRnUplRQGmgFZvDymTFjGIfD55WZNYRlEMHEREL5u01ENVDz14ItMC0TPxjczHYP0ApPQzJ50Pba/DocWAvgKJCCL7F0K5DoC1CFNxJ06LXmWzfjLTtg3cc6FMSUP1N5KVsokt6iIq++JRBOORt5PbZTDTDgyMuC0fTXtrWXsi1uvu4u2saww+Z8Ge00O5wkmCeSbTfIXrS7qT/bitfpJwOjhvRNu1AHjoE2g6YkQf9L4IR59Otvwl79A30rQZI+oBou4Hu6RNJiH8NIi8BmyjgftzeqZQFnyB772AMF72Nqs7CkeiA3SuIPPAyka9vwO3xkLdVwTR9LqL6GhKGDKPuqVri9hhIezcIUiUgBSLrPb6a4AD3fgavehgGX4vGmo5MPYao7V5SQ4V4s8aQsMNFRbUP1dlEqjWVQE4pgexqNHIE+n3ZhHbtRp8SQXQr8OBuyL4KBvZA/g+fFP9/iqPQYX9MQP8UKDrIvgJK7bD6XVj4CogW6N4G5Q/S7amhI3MQzpK3MXd0Ig35GJu7iboGIwa7aU2yU1S3HY1/IaLiI5B2OOQjMtaMTvooT0tgZnAwhtQwOzKSyS/7iJaGHqyFr2FQhjG++n0OZMeTX1mN2dtGOH02qihDV2iGxD8gjysgcsd8fEvrMV54Ioais+HAIYKyli9bdDi0OiyBD6gLltCknk4KI5nMy1iDqfR96BziZySw/Ky5jFIzKVj9Ier8KxnY1oBIOZFo/hhqK9+n0VHH0AYP1n7HgqsfmLQQqUZxPwyD10LTJRAtRe3jJNoTQE2yIJq7Ce9eim5IJX5bMur+B9HatPTcoEHf48Sydyfatho8dgf2Ha3I94fgWtgP0eNCiRuHVt0Bjm10Bi5kqy/KAFOEeOahx0BZxzIedd3OTMONqC2JmHQh3jz595h8HsQ6+O1ps+l+z0G4p4NP5/6ONGc1bruZHHc9Sr1CdN/ZaFJHIgZfDlOfhkNnwIBr2EMpu9XRnFd/COFfhyypJ5iTiiX+BYRwodX9hnDoCmTZ61jEfhyJKZQtCFCEDRlwQrCWaIaLauf7ZA2cjtGmQWfaiMZ7Id27/NjvS8Z4zOM0Lv2E7FffgH4J1GcMx1w8gI9No3jwxZvx+kNoCi+g81xI6gqiXRJFN7ccU1kdStjJu1tuZZ8xkXOG7OKMwcdhqOumM7MW/dABMHQs4s1fE4kEUM714ak4jH3lHsQHdjghG447Hay2Xvv5/zWPdzEN+n+c2edAWiE8cSPc8gxs/gpeeIs9c0fiUDaTW348cvsGuPJxaH+caOUKxLyLsTMEr60Ae/MrMPED2HMbaKB89Cm4LMfjijyM0lPCft0+cmohXx5Hbc4HVNT9kdSSTLSzqqnJm8uwJgfefkX40nU4KwOQuxHad9MlfRx5REN2zfk4XC7wH6Y7LcSyhvepCQTJ1VWTJMo4rvUwiuZjNElRxBA/YlMd9I0nvl4yfFMNRlcNHfESxbAPV/dGZEspgYObMSZo6BPVoyS5cUc+IawUIQ39UCxpKEpflPYz8Ye3wjkp2GqDKLpGVJsVz7GnY33nHTSuEOruPWgmJuEuHE1Qvx81shU7zRwwjaCgvBHiihCFAmN9EJrXw6wMKC6AAgNx+gUYu79GMYdJ4rhe16vd9/Jy6BPU2uXoQvmE8n08qksiuvZJOo+NZzQbuXZhKzpZitN3JjXWKRj166jLyYNoB2H9aTBqIOBGIzSgsdIT3EyF9wlOr12L0lyEmjUJ9dB61LuaoWMWwTPORnf2aWi36FHD9yHnjSaibyKJEwjRjGz2EqWawJDhOPebiKr1xG3YizvXAkUa9IO1aD7qYIDpReSeJlpPzMSW70bXfy4v2mYyuL2G6L398H05nJak5WQlXE/AGSXa9wbsra1o8nKhKoF7Rz/O1K/eZl3/w0wKfUTm14cQkxPpcKwmo2weInMa2sABZIITQ30JLX3gUNPxDIv4sP72fIQ1CRGNwjV3QXLaf3pU/XII4CjzIRUT0P8KP6RV+Lxgtnz3eeAo5JYV9Gx5B/vCyxBzzsZQvwR/7hHY6EZOPYmOyhVo0o9gQ6I+uAeHpYz6MSF0nhp0YgFiTzPyhpX4bMtpcC9lSEcnzd5PWD1kFCdt9KLLmUdG6rE0lDxKU1IFpjcGorlOxV1gQFcWoCOunJy6LCJtadQWGCFcS4q3BoPmNSJfm9BubcIRCHH6VcuJJsyiZUstqeV+SJkDgVLYfAB0I6FzB6R1wZS9lNV+wHFfP8Pbt5xJQC85p7ILdXd/dGZITi1ga9oZhNKXMZwS9FgQohA1qEdWNKPaygmmmVFtOsS4abiW7MRQ78f0TS09cx5C03U9FpmM0hOHSfM6Ah2snAu5hQS+9mKY0h+Of/a7Z9B0EZSXgl+BtddDsIZJfTag1MyFwnJanG1UZw3A2vIo4XPSCKlBMsuhODgDOdWHarNxnvoGbsN0UnS3obvvIeT1t1AWPY4Z4S5SkuMI165HymUYxJ2ghpEihHX/JOZYEgj0+waTvwYl5xsodRO53o3uuVrk2vuoXbqYxIQklGPOIKx7A6+aSx/3BHqMOzG9tQ3fBbl0d5SiDysYqnzIIwrh/lEMPj1iXSYhz3Hohi5AbLidpII2ItEoXdVv81XuKBYvfRS5bge1Aw6gV4J0PnojDXcnMsLfD6IDYOAq2O1F31PEkpPWUjrxKw4a3aTta8esnYUnsYqo4US0LUeQFV8SWRolfNbjWPfeQ9GsEzlsuJmm9Yk4yo4wrrUY7eZV8MhrMHLiLzXC/rPETBz/5XRWQ81mGPoX2+w/fBkmzYXcvkSJsJ8V7FnUCJ5S8nwejJZk2vpEkOEgFfFVlJ9jpYsyEhnAkECIwKguPENyUMPldHVFSWzaiTdHQ4/tRBw98XSbUjCasqmOczCyw05q8miU9DmYvA0U7NrLwZmnkTBmFSNubSRzeDk9hRak4qKqv0py02qyFrvBbUbWRdFkAtONqGNCKFVhUANoWt4kNau21yzR9yR4924IFsHCx+GbX4FaApvvJdUkEcc9yGn3Xk3jzLn0hNNJTF0MgxaDYzxjgaU+I6tNZ3IsTsK+W4hGPkJJ1qBzhzHHe9AxCUwvQ/s4qNoP807BjqA7PRmHwwIdbkRrCcRnQWIn/qp89oxNZOSgPt/d7+BSsKTQPmQWNkc3eq+PHrENr66AtIK7aLe00MZOVEWDOexCYzuflIffRAydgHFbA7a0CJG33QQuOROt9h7Mvibo2Er+7b8l7g9DwbeLa7Y8RHT0zYTVVSh1bxDpXkrYVkplXiH5hofwG3W0tC7HkNIPx0ersacbECN1vFZ5CfsGTua2R79Eqka0wTSaPVYszSfjWr0fb389srMBZaAB3f4gYZMHTbEkfqkXMXwmFI6n+6NvsFRVwm23Q/0TuMOZzM69iWtrH8XqXkNLWIPtgji09w6iYdpmzH49urPX9N6bjlch+Qo4fIiUmSHid+1hw6hs9vTNZfhaFW1uJtKRDo4RqB1PonlvBz2uZmRCOor9WgxZQWb87nM+OfEk3k8wMeyc0+g/cMTfnF38P8v/qolDCDEXeIre989LUsqH/ir+OuAiIELvgYkXSimrf4q6f1Fc2fDC1F4PdZNv+HbbtoTrz4T3NqHR6xnKPHLFCFoj22mN7iND0wbaFOo0LtwL+uDERGFVG9a4GVis1dj3HCFJ7UTRJiLM86H0E8yT5mPTPMTHrlsZEvwcje23JHU8j61zPV2mE4lHAUsGFJxJYVce+4f6GNLzCkgDik6S/9sGdHmVkAyiFUKGfmimSSioR1phrf1STMG9jNzlQjvzNgg/Ct3Loelq6FRh8ElgSAB7AmiHoe75knyTCcbdiG7KGDLl5+B2gfME0KZCsBO6Sjj2/fN5d8FUNujLmOzZCIdmEz3UQOjKVML6VUQavsJYuQbh3d7bExKikNgPR1k1arKC4p8D218G9xIiBbfw0ewmRlWsB+fZ0Lwb/AfAcwu+2oEEHJ8TV5WLqqmjbnyIfrWjICOZ+JpXiLcOB+cdSPVlGjzrUAON6JV+mEytKLmr0YXPQSqTMagR2Dgdcr0YPl+DqfwwEZMFTVhDkLvRlWfSlNyFzDqZLWEHww/cSbQwhYboVhL3f4RaLfCP99M9ooCd2plsjTuWW845hCy1ItrfwESQvuYKlGI9nmMlFQmFyJqBjPi8hkjcNqRxKEqcExEshuoD8M0aZLlK8+uPk3z4HKj2sCHJQnLYzdBV29i+ZgCZT3jJOVBP6KR83Ekq/UN/cbzd/p2gmwR918KOJ9CmZpAo8unIrIPnXiF09mm4gy8SFz4OrXMY6hVWrLXtRGyTaLbvJq0UDH0MjF+RjaLWk669GsRR9pv/5+YoE9D/9k5CIYQGeA6YBwwAzhBCDPirZLuBkVLKwcCHwI84n+coRAgYfAoc/Byi4d6wlIxe384dLQBEokuwSwsFrmMZ6bwWn6wg0X83inQzxHI3o2r1ZPpTcNWsQV9ajMYYRdO9CDF4J9RkQeFT6HSF+DtWkV5/mMQDdvy20ZQnpyG0VuLdJjjwNWx6FVJPRig76dtUgvRDVESwbguhHSyRKVYCfdJh9B1oR9QgDDtRGxvpae9k0PtPk1tYQsP87XQZL0XVHUCmduE3SPyzgNolsO5qUEpg9JOEU4vw9MTBiouQgU2onVqa7C5Yuxo+GQWLC+HjWUQNFZzW8zqHzIPYLe6G0HQ0xV0YAg9g/rQvUVVBfnYKzHkROek65N63kHUvogYVhLsKGl5Adr9F2GrF1/Q7Bq7cRFFFCfKT6bBzFITORm5vQGPcRKroj5qbTvGsCPHx16FxLQONDuJPgLJfQeXNiKQTSW5yUj83SjiwFn3bQVAMCHTo5Qyoewt0QTj1PrjQii7qJGRqI2JvIRysptvcQ5x+EWFxGtKQTbb5GMwHVjN4y4MkT8vEMUqDabCgszKLQ3vn8+TQi3DV7iSsXYG+IBNvxiL8eRbCpwTQtYfItadQMWAYreOdmLpPhhyV4F3JBO7tQ9TXgiSC7sLhVDffjj9jIerpGwhnTmf58ilYp5jJWTySJE8f9IfG0pyzk9y2Tnzr3oZoAA4+Cy1LoHMVTL4WFj6F8Gop+vITCpQkoiPCxFetx7nrTtg7CjyvIcIbsde8hautDb85iNljpaVsGnXaYlwci6Ikf9/x1/86fzJx/JjrF+KneF+MBo5IKSsAhBDvAQuAA39KIKVc/RfptwD/NQsuJZImviGVb48HmvMArHsEyr+BfvNg9kJkyQ6EuxtSMlBlMYocgBCF6DBSYHyV8uCpdMo2Ak2nYqhchjC/CfHpoDkOhj4A36yCka1QtRmm/RqEICIrGXigFFNrHSUDljPa+BT6hMeJ1H6DKNuNsvpr1D4JCOlGZ5LIcALoO9EVuRDDT0asa0Rmr8G751W6nk9BF2ghbijogxFE9igcwdsJGQVVwefosu7FqhoJ+sLYkqwELxiH6YvlGGxuOLyBI+d+SnnnR6R/eDV7J48kXBclv/ogHAlCdjpM+iPqkqV0zv6GOGUoi7pKeCb/NCzeZvomOlDa2gk1ZNAzrgvT+BuoSHmK8BDIPRCHrr6TYPxg9KFDRM0ZKC0V7E0fxGBh4b7Ka3lvyLmomumI9E8QicdTebKeVNMNKKX3EfItJa4riim+FWQQgqXgnAxDNkCoHrXqZjSBTgLpBloNOtK83zquivQgto+Drj1gGw2Vv4djXyGUEMTruRHV1EGr4QSkx4OrZTkRz83M9wHBYrCFiTo1KPpk6KljS9Ui9oce5fozLkXmf4wMnoIrOodg7XrcBa9jpQvN7Xqq77qT/mEvx8lyPnKdzBmh9wmF52KpykcbKALrpYQLI3TfdyHpXz3A1lYLRem3Mq9+E3K0H/p04JGNmALdBLUqIhSPxXUrgfKbYP0SaGmBSBYUTIPkTBCNMH40Yu920r5ahhoFozcf9+DJmD02dBottH4CogbFIolb66V2kI9AvI/k2iD64s2Q+BAMvgL0tt77poZ6/ZD8r3IUmjh+Cl8c6UDtX3yu+zbs77EIWPYT1PuLIBA08iW1LAEgookQmnQpcvNzEPKBEFQcl0/VFzdQyQ6kSEVS/70ynLqJoJ1DoLsMX3YOql6Fi2aCyQ/7bgWNHt66AObd+edJyKjs4Ej/fmC1MKj9Xsxd0xG6j5DUwPHXIq9bjKJRUfrMQJPkQmMJoNXYUEIRRMmnyIxlBP6YQ/vvOzEYNSSen4gydRaaKVehayxGfD0f447XKOpsJTlyJYHOXNrt6Wh6ZhHQrKdrGngGnYs0VhBpW8ns9ffRNciE8Lhpj0/FujYADid0j0T6DtI5cTmOvPdQLOPQNGzlstbVfDLAQVeBGXa9gba7lBrTMI6MWAPJ/WgN2OiMayQUrUbvbad57BRCqY3s7zeYgmYnoeJ9zEv6HEUUITs+wmsS1DtqsVmzMBx5GqSVSFiSKE/B3lAMoTRQTb033DYC4ufj6/80W/LsxHcmEohXkHmXQagZgkegqwRss0GbB9N24EvrQ424m7AtgkY1YJOz0UY66PEvIz1QjFZbhbRoICxpTZjAgdypHHh8KodXWvnVsT3ISBBWHYux4XaMHeNQbNUkRkcTarFTdt5spP4Njmj206S3U+DbwZd99JS7llNhe49gmg9e3UV0/Ajs75aS8EEXk57/HNNnUcJxp+Hu8wQuMRiX/0qiWKk/MQNbVTdK1SMoQkcweDzoh8IJ20EmgPNycPwKnJeACEOGE0IaDB8fxGS+jc70dFiZBq0jwKFAYgZZT1egN3diackhbXc8HQVd1Gd8THfjzajefdDxOZTMga0JcPhcCLX8QiPwF+RPW71/zPUL8Ys6SxJCnA2MBB75O/EXCyF2CCF2tLa2/pJN+4fYKaSGDwnjRqBQon2VL86LsLNtEVFC5PVdhLmsga3yHYLCiirr/pxXqp0YIqsJSQVnyn1EdCPoir8d2R1FLrdATgokb6Br6jq8yilEeuZD16nEVZ7PkIO70ITiEaEslDoXWnkRGvOlaJiKsu1ZopkTiG5rgaETQaMgJx8DE4eBPR5Rkk/c8BGkHRfAeVobYnAOygALhmMewzz9eAgbUErepVXOwLTET9zacvIOV7A3LkqwwYK9JBurIRHRdyBDqvaiHf4INeOPJykUpqdbQ9cAFxgKkGWb6I4+jfXQCHTagRB/CsTNx9D9MZdrxxKMVNDTvIKmixIZGPFTEjmVZcrdjOmcg6sVSGtFE6zFKyqpHDaV/MkzcATc7JHZJHkOoMhk2GUlkvYaamgolpq1dGVX449uRtftRshmZMt28ISIbr4ODp4JXWshUINVycYh+rJqcCK28jak6ABtPBS2gleAxgEjX0NqTYRopO/KVoxqCuayiSQ1bCWlvRVd/LHoB5fjHvwWEY8GWXUC4VVxvHpWLkbzERZMWYn8Zgbs2Iza2oa64Xq8pt9gSv0jFsOrGFz5eAcmkKp9jrAyhb7cw2TvhVjaOtGK35Ie/w76zY+BtxxjbgL2Z59Cb3NRO2kE1s37sGdeTZJyHv3ES2TtXYc2qYjMbhfls6ZRN/NGGPICb+zvx4czN9BlTPh+x7X0hZyrIKUDOVoLyTb0z9xAwLcbdfvbsHgbKMlIx/FEf5dEdzgZg2405sGnklQeJq1KwRwowdt6IZ3RPxJIGoU37xxkn1dBn/SLjsFfhP9RZ0n1wF9uQ8r4Nux7CCFmArcBU6SUwR8q6Ntjy1+AXn/QP0HbfhKyOROVMAGasNGHYVxPnCgi3P46u123YzTlkjZ5Oid81cT+2bvI9FWR7OmDcNZBz7Wo8W2E1QIIr8UR2IOMunA/lYjlxS5CoTZMdR7sZWF2jTYQn6vHF6fDIgpJqF6KxR1A6NNQbYeQS3+HMmtj79FZI69Gees2gq4O3I4y4oxRNN7FcGg2JIxGOt6EgjY0fRXClSoydzGi8ylw74V8B9o8QUS9mvaGcpbOOJP5FQEcFpW0VzdhbqykUUkk9cP3iIYTiSgKgbnt9GQfwdWeSTjTQt28TJJ39Mcr6rAsaUY36tvjvPRJ0PddODAFfeWZdORrsbZ20u4+loy6e1hY4MDbej/uaCd2gxWt9IG04WzV4E4sokv7NgdPKiDQqqJYxhOpnkHAGKHV8Cl9vnkFkXk5Ju1YqL+LqB6i3SuINvfge9+JEt+B9RITVD0Jgd3gGEth1h1Y9y/gYGo/hmrBvmMAOHPAMQYKrgAhEAicoRGQfTMaz15EOITIfAYtXdTwNLneZeg7LyVa1J8vA6/x69/38GX+fHJlG75ODz15g7B6zXhHdyFUH5an41GuHAnpZsz+OBSCpIipxDECgQLJAxm73sRjceVM71jDJP16tJ8PBNcguMiKONiEOctK8NQ7MEQOICq+AXcNdO1BFEwkwT+TbkVLlXk5ycNfZuRTH3GxR3Bpt5tqKbC4D4OtL5IIIn4UdD2DolURiTo49h1M+05B+g8S7rTitqURkmdhiPpIbAzRnLAGd9xEqmfMxtGmkl8GieOeRNUFaeNpOnkFKz5SeRgNjn8wav4L+R81cWwH+gghcoUQeuB04LO/TCCEGAb8EZgvpfyv+22kw0oOZ1PJ238Oy2YuBXnPMvLDI+R6R9MyqJh92Z3Et2zAVvMF612b8UfyEdWzORAYTbmaRXVTHHRoES2jsIcSOHjMKLwbwdsvm5UXPU/tkPNIaDuenKbjiCupp70ghYOnpFGZVY671E2oZQKR5t/2nqtXuxiluw2TXpLQbCSaZyMSEvQkrSIq90BCPOxuAHUQWqdKqPtxZMNrcHAYRF8DxqA17iHe66R16V6i8XrqfSfTPTuZ6jPGYEw/jtYeL83r9lD9xm5aHlyMc0k7tneP0JgdR4UzE8+mxbSMH07kiEpF+Ag1VNNKK26xheZUC9r25dgt/QhYEilX64i6cqB9HZYWLbbqMIcVJ/tS70S6JrNn1ECyD+wmIZTLoC3VOBt86BL3sSX+BqpO34p950rqc0aiap34rduJ9JuNZmoL6q4H8Lyeh/mWp7C+sAuGvAXOZCABOopRSi4hU46lqC7IDtsaym3ZyLJGWLcMlr8N4W8ne48chPe3kLBuBEIxQjSADiciKoiqn9FtSIH2cn7zuxBTLCsp7NoOyUEsk17GXHyAaM9eok4thkYTorUL7psIi+9AI9sweCsI0Yoe25/7j2Pyg8wWA9kXF0GM+QPEj4PuGsgdB+lGSM+jTumGqrdg9/XIineBdlK2voQQRrI5jRzOpoV1ZJ98Mq8s/5B7bQqhlm84VP46AE28AXU7kZyNqriIJlSh7lxE/L5WRALoC4YRjvNgtF9Hq66Q1PorUVKvoM5uIFE3lrjUE9DkzkK+dRqatnYS+Q35bCWR36Di+UXG3i/KUahB/9sCWkoZAa4ElgMHgcVSyhIhxD1CiPnfJnsEsAIfCCH2CCE++zvFHbXocWAlh072fBdoiYec4ViWXEhh7uuM+oMJS7lKT4YVo7aTNcqzVJt85LUU4NYaSDOmgxgIh4zQ6GaAdgS2g5l0pKZS7KzD5+zPdcMHMjc/kc2Dc0l3OugvniDusxQ6cwdy5IIwO4va6ai9Bta/D5526DagmOehzxiAlqswd4ygPT1ER7wRtc9JqI1tCLMf5GKweiDZALp80Hr4utrE5SXHcfXwu4nzhwn120XloGTKp2ThvOm3ZJ2cQfaTYxnc48X53kMM2BLF8cSX9Ndr8MXr0eqimGtmEU3qQ/brR2jp2UsJ++kKVnNf8yVUOwegad3DrkFjCLaHWC+zaPMYkdsOokscQr/cO1ifOJ9Sg5YMMY1OWxOycyeGzgaGt+1ixo276Xf6PvQpZ9KQnElxoZk1/WooNgRo7XyHmpP7Um3aTvSP6YSGjaJariPcvhHcO6D/eOiXTzi6kzbTMrrGNJMZKqY1fyDlcSnIpj4gV8GbmXChAx45C7JTEZnFYFcBkNJNbvlaDgUkGvOvKe6cwfu/OYmXT/oNAAIzRM5D6erEk5qP+EBB6zYiHlkPs/Sg1hGuPECGcSdd6obv9SeBizE9jcxiDyHnTBh+O9jiIWksJCSQ0Lmb+JoHoOkTpCJRbW1ERDeqYziES9EFG0j3TaU9sBrTMSPQfvwRl9os2PtcTaWpgOt69lKvvoKqe49IpkJkhBc1MwXhmIfSsRcRH6XjvDq0HYJ2pZ3srzoRBXOIp4BWDjGIk+nHPOIzT0Y5+SXklzfh378YXTQeAwXo/uE0038x/4OrOJBSfgl8+Vdhd/zF/zN/inp+ScJE+YRDpGNnCMlY0JPFqZRwP06GIBDQsBzkfpC5oGoRQxwktyUQMOjRR7rxmwazK6+YuV9/zLEBN7p1fhjQCfoCog1RZOdr+E4w4fxDBXnX5dHf8ylTXBriet7HrDsezA8Q+sNrmPvOwx7dRXfclXgCSzC9ugQ1IFGMaZA2D9neAsk1iK5ctIa+JOmvJVx2BZ3DqwjlhDF1TMCECXQfgOsONrXfzc46lSPN1bw95xiiZguard3k2STOjCvZLzaiC6sw/Su0ux+Gt+aQVWyH/iOJvno68ZedRVhziLrjB1HwwkPwyAa4Mp+RX+2CU+9C6iVPFYJQZyD25TPU34Dfnkly1S70LSFE+uX4rC50HReT/sof8eibeOJePWHfdSy6+hVmWbcSFql8deco9JVdZLXtJv/Abtw5N6BZV0LK9auQkwwEX5uIzz4cWfMlZf6naTPtpCExQobjeGx1q3HnDUA78iwcFZ/i3FONRqRSEP8Ke+15HDivltxPNejUfuhmloKzEin+CDsNsFMgI4vBaEQ7wEuCNQ1n4nqGjmtH804+wt+ETIfu+YNxPLYFZVgEZ3wZou/z8MX1EL8aJn4BX5yEzjQZK18SFB3fda6ORnjrd8i0Yvr1uxxl390E3UvQ9tGjObwEIqkoU14k2DgXaUiArmo0eWPpVKoImLpJqXsJ0fA7xAEtfUe9TmnS88SNHUNww1IMoxcx2/chI/RXEAqV0WQ3k2S4D93hdsTGOES/Wghb8Y/R4Xd4MZdEMNpKMKle0GrJYChjuZwe6kmgb297bUnUnn0fmm8exrzmebzzriFYMIx4Cnvj3QdA9YHjZz8B6uflf9TE8d+PlNBQ2rvh4lt0aBhLBs+xjYfYQBQVLWZcDKONTXDkZaj7HCa+DfN+B0uvpjW/k4NC0qpR2aHRUNm4l0GLVxHYKznmD58S0IbBPgQG9Kd53CSa589DOyVCz8gE5r/xIfndL5GovI15jxF8+YSXvomIU4lOzgJHPY7getLD32Cs7kToDchjDRBfAu2rwd8OYTcMvhG2X49OycdhfRD74Wye0p3OE/7xNBqm0x4cy0lvwMclMH/2JkK6OpY5phKJ1xOtTSdeOwoX2fj1Au5eCE+8BOsOwXU3wtATiWjqSZBraGA4kUFBpMsPh/fCyBnw/t1QuQ8hBIoQCI0N1X4+tprtLHnXxdW/u4/PSi7kzN+N5pnHeuhuMeCqeok+rhJ+f9GNJHzoxjjbSDDLjE6JMN6fzYBR95CsH8eRcX1pi+wi6cVDOIfPI+WuQyTaWhnasxSXtpDs9mUUKcVkCzsY2xFpJ5O+p43U6L2YO1R0OVeifCoQPf0ZvNRAxutNVM324zl1P7XHjadn+HD8o5IInTYedVEc4iorysXxqHZIkU20ZVsQbi2iTxOoJqQzFcNL+yBDoAqBmmiEgw8jCg1ED/2ByKvHw/Gfo/NkcjA8hmjzixBo7+1cL16LXPI8+mdXo3nuCdQ8heCls1DmVEO4CNmyCVl1L2qrgZA+gLQlEo2cgkx8g1b7Ffi3psLdWtjrwtj4JYn+XPS/9qNJvJlI1RQwCKKGhTgbj6HcdyEndyVRIgoR/gCsfZTI8U/QOulYkr6WNPVNJP2tLoh3QdPtsPdU8g4vw9W8HyK+P4+HYmUHO8cX0GCvRfP+xRi9vb8y6N4BW6eAKfcXHLA/Ez/hKg4hxCtCiBYhRPEPxF0vhJBCiIQfyvuXHGXvi/8AnQdh172w8mNQEmDidLA7AIXMsIdbEjPwaPX8LqmMM+pbyfYH6FAfAuspMOoZgiKCIT6fQ3ltrB+ZgbUzyPFVTczesxWKitkzYhThSAeRDCumYD25y/aiYT9pjiKkr5iG1Hy6Tj2G9NPugXQ9HdkWEnpqiR60Em3IwXjPA2znVcZv7Q/D70cq5yFcx0C/KIy+FTTHw/70Xveheif4GqFlI+hHorOPRzfsK67/6hKG9n2U903nsWDbRn4zto0LJyVgaryf1qQkbOEItXnnUq8rImX7fIqHzuFQy91kFA5n/KbtRBbdRqDldazaiQSzgiRv6UYZ1IVlaxQ134PyxPmIAiekAm//Cm5YCUYbRAJ0dNVTHRrHiZO/INucwIIBpSww7oLjToPDWqaM+AqqBTJuKNdWOWnPDdIY6IN9aDzxB94jsaWFaPtHFA8bhsvfQvYLszGYHoHDmzA1r6djvIOwVhLRDsTUNZN412/RkQpmILMfbBoH+kRY+g74euDDDsT8cdj2DqOPYxFq1Ze4jZ20pNiJr1HQrz9C69zJGExRDNFEjkT3MsSbir31AEp5F1LehEg/iNKq4k/bhKm1CkVvQjYPQk09gOaAB8XWhc9vQTwzDsWURro6G13B28hlkxHD3oJuLzI7GRHqRMYV4xmvx6b5vHcCsStC6Pg4tN4Pib5zIj2nbkKbNo49SVVMNl1N3PJDuJtUusfPx5F7DPSdSWrpLYT9O9HqpiNMs+CPr+E9z0iKfyuT9GkUaXZwm3k69/quxZbspGXILlLCN6OZMJ+c/W+gbFsDc34NSadCTiFsfgXNu/fBJX3BkYNvxQNE+tUhTFZ85z5PgmcApm4fmCVUPwPmfND/D5wO/tNq0K8BzwJvfK8KITKB2UDNjykkJqBd/WHc4+AaDc/cjazVIOblQscHEPRT1ONCDp5LkXst7+aaMXY0sjBkxJdbQsOOy2g26RlTvIaEhHTO2G7AaNtJJLWB6CQvmvZ0BrvW0jZmNIH8P+IpvoDNIhl9an/SM+7GVjuHmjQY2wiMz0FZ20xkzrE0TNtF/LNbMM4dTshdyyHbN+Q7FczhUqzr58Bps6GnCWhHmnWgsSNsV4A2GSIhSDgNjnwAWiMIge24N3nrg5lUk8ySYefxTN4mnB1dBEweguZ4pouXUdybSI/3oOs5g9yDVWwc1A/d20upXXQxcbZ2ZEASSmhA5o/Gus5DcIJK2ewcktZ3olvXjWjWwlkTwbSoVzgDaAwknPI28Tsepy3lPk7Id4P1Aag4H0ZdCnmFcGQudDgQQ7LpoxpIbS3DvKsLUT4YtaeL6J53cPe1o9cEyQ17UY2l+D4ZSTChFG3BYOwlkvrUXCqThtFv1QaiY9vRxX27ISVuMoS7YGMXbNfCgAhcMA7RbwXRN09Fe9VCxMYnyapVCSvpVPfJoP2cmQxWTkTg5eOuvRT6d9KTX4G2Nh/RYCRsfw//pkrsXgf+Cy8iuP1R9KkTEEVjkN27ka0WpD2EVutHVESRAQ8pu4J4VB2B1Gb078xi9ylDGfSuB/2ABAJFCmblCRQSwNMB+PClO9HvOAHdH17BcJoTe95i0v8fe/8dXUeV5e/DT1XdnK90lXO0LMmS5ZwTDtjGNmBsosmpyU0TmpxpMjSxoYEmY7LBBiecc5RlWbJk5Zx1pZtj1fuHe+bbM7/pMDNMN/NOP2udtVRV5546qqr9ufue2mcf/xsccl7CxO8PYLrwKapLe1C9uhrjvKsQRn+CpuVtqLgRYnqILH8AqeNGlE6J/qOvE2EtD7jCWCd10XBWDhasaIVMqHkKQ812GClB2yloOABf3QdFs1F+tZWhyrcJH/oEa1UrSXHnkzzuVlKEEWDmdAm7IDwM4zf9va32f4afUKAVRdkpCELmf3DoReAu4Nu/pZ1/CjSAIRGl7DJCT/cgrHsX2npQ8jWoOmpRQi8gtK5D77NzTcs+nAnTeG3ig0wWs1k3fhO/6LCjSgVb4RQOHnwVY06QGLUFW68fc30IpUfDUNoAaZVnkK7yIPTHEIit44dloxmb0IEYSCHy1SuoE88kWruR8o4Oxvh60QaqcQ5pqDGXoMbKjiKJk+o/EDfncs5sV5Nx4DhScAdojOArgS4fFEyG+s+g9BcQjAIK7PglIaWKMukYk0PZlETu5MGjd/C07T4iCfNJDlwBex4DsxYl0EhzXB2mITu2Qwaurnqct8ZNQz/6S3RkEOYwImcTqTyOagBaHHam2XxEFgqov6tHSFgBR9ejjD8PxANACkL599C5B00+yPpfI9W9B9pmOPosZC9CDqgIWWKJxOogpROiGgS1n9DYciI1Ev6gA0ufh5LmYtpHnUQ7eBhpmhbr8UcRq3ZA4RyyFS0xylkM97xF14+XkrxyJ1pMIGlBnQhFMXDpQyBsArUKZBkhNx+2fgS6IujZinr+PeRmpJPk3spAaDFEB3jhncf4OrMBu+os8C9EiXmN400+GibOY2WkipRQD25ZQqzaROTDnWhXWJCTBhEkBdEjELGqEUU1kbQegsWxSH0SpPZSsncHiksk0qYgmCTUHY/BxDugpp7oyHRC1gqE6iNICeDtjEMT7SffdBPdX33I4EoXwuQSCsllULsa/+HncYz7FWRcDfbp8PVCXL1zsYydgmi/C/2PL1H7Yw+B9gYGzGbU+X50Q6OQx0mIi/8A9fvB5oWuL6C6B3nylfgqPsFf+w7RsXMJXliKpj2TcNkSdBHVv83o2PgbyLkH1P/fpd7+V/KfE2iHIAiH/2T7rT+GCf/55gVhGdChKErF3zqFXlCUn0248b9h3LhxyuHDh/96xZ8ARVGIsB5ZOYosN6J7PwrFyxCyMmDPPTD7Rdh1NmhCoPiIOqby+9Lr6QwcZcHhHbSOLqBI2Yf/iJZ6VwHLc44j+ftQn1DjTutFLo4gGvUYm0TEzR44sxSXo45TMRnkbW6kcUIGo57pof/iC/HVf05qTT9hnUQway6ec56gS11F8qka+hOrKDT/Hq1gpm/bWOJyT6HYFkHzfITyb2DhE7BlFUx7A8q3Q3cNhNcwePVVqPd/gin9BaLD16H0wpvZj6KNFnFVxX1Ea6vghANV1hANZ6cwPCoFY48XtkbISM7DkxfCknErw+qH0LmTsXRMpbzyc06suJrz/J+h/fFHorIVSUlFsHRDshYyfwlt8fDtBYSW2wknqpHrU7E01kLXCDC6YdUpnF/nIk0oxaWRSXC2ELRXo9sWYTjDijdXIsb+EJ661zCf6GVAayc5ZiTECUTCR/F1jyEm/24o3wBb3kVJTSUcG4935UPYjWUQGgT3KXBMQQl3IaybDI/LcMFi5KQ2lI9/ILzYjvacOQgRCSzjQTuaIW0K3wpreHR5Fid+9RmqCjXqHfsJnOPkrdiLmJlipdQ8DbQxBD5cSk+MnsSBZFTLRyBWfYQSpyEaTCfS7EWuHcIQPw2Pqo76nFykqQL5x3YytDcRrdmFbTiIYJUgJh460mFCAkMT3ajvPoYqbhCV9VykmRJUtqPsa+TrJ+eSFThKrmzF/JYNf88hhl/9iiRlPHz/NlTuoWblCdKGvBgLtoI+AeWdsfjramFFOuF2F0Obfbh7J6D09GKUO7CNjMHi6yJUakDQh1Ayc9EnJyHo82lRPsc+bCBsV2MXWxE1Z4N1JejyoekZKP6LmvR3QxCEI4qi/LfeUo4bLSiHtv5tdcVY/ur5/uhBr1MUpVgQBAOwDZivKMqwIAjNnM5P1P8Xz/O3def/vxEEAbWwCK14P3rVuwhX/AGhYiO0dkLGAhhug0UnYEELnNGIZC1j1Y7LuNS5nWRdN8v7thF7agR19UZKHl+HujYZqTcRuk/RFU7AHZmHMZgLu/pQegJgaKLHkcWITS3ISirm2Pl0zTTjzTuLuIYoanuUyIoMDAmdpL+8kIIDuwmp3Ix2daAVzIQIscWeRVSOBUF9OuPYsd1wfAsMnISvH4HYHFhwLYR8GFssaN1ZsO0uooMWpECAG1W1+EJwU+EbhJe9QmDVAK5jAn5LlLTyI9gaejg5Nxf/GT1oeneg2n0/ll1tmMurwAiJAQs6twOVag5EFFSdfURTB1DslyFsSkHQ3gT1nxE9247brEfqkhkWgyjHfSgNlcjDbnpWzqO/LoK+YQ2Gm/YRea6RsFEkMlLEoBlFnOBBf/hT4j80olKWIxVeybDKilS+D/eAAaeqCl6fDj27ITcfYbgRTeX32N+4DL5cdXpcVF8C771BcONMlKgI518ISasRLMdRzBMZ+sWr1KZkMpRxG9h/BZocnO0PoH5rB+fY9qPtPYqnuxqlW6SpZDY7501lqOgGSF8CCVPRosXe34dYNh+BdaA1ocSNRDV5NfqsKXRfdS6dN95D2xmllHlaKDhZgdNtRBjnpUPJYeM5i+g56wpCORMgcICIrpWIaz/+qj56p8SijEyB6Cxo1kFuIjM6GikJVGAKbYdrLkavSaPL9yP+Z8+FaATl1+8iRtV0ZM7nlPc2fEduwmdwYxi3BMO77VhTYsj49R8oTq+kOPk48SXp+MLx1HRAb5mbnvIghrhqhNBG8LyHbAQxMwPJPIygUcHudSiWyVD3EOQ+/I813J8YRYSQ7m8r/wVygCyg4o/inAocFQQh8S996J9DHP8RoghXvw5vXQcFoyGwHVJnweA2GNqDJ/cOquI7mOj9PcGhWKKTN+NkPzFFVeRMPIzi2oY0PA7FYmHP2FVcvqMDYc+3KLkKXG5iUCwgdc1hhDwDqqY4tG4PkQwVmjevRTvhAoT0Xswpt9Cf/VvcJY9h+/ohYtStIEYg8WKCSaUoscnIQR9iZAAaqmHQA/WVUHQTjL0dzGkQCUDmdMIxrahiS/F6T+EpiCXu5QTEnm+5Vb+B3bpxXJBxI3dHFpCRfQDrTlBnyjSmxEG8F8vRKIGAF9fsWtTeOWh2bodjr2COT2XOd9cglAVQpAhRm4pA6tOYA17I245yZCnyqArcYQf6mlgkaT++YQPh/ZmoxncQaNbQfmQnypR8hDg9urvj0CULhPUhpLznUYezUPZPIGLYg2r5O2jHXEm8t47DluuZbB2gK6OETHcd8n4Q02bAiaOw6gP49BfgV2DfJ/DeJtBkwJV3IE+IEmwPoms9gTxLj6CeiPJULbHv19F34X4apa2M7ooihOKxNh5i/7bzWHXnt0R8MsE7EwiEfdRn6FARwcZhwrQh9A0ixtpQVfUQjn6I7IlHo3EihPxEfROQ8r4gK3EJ+103kaM5QHjOBQQjBzAcqsQzLY6Rra0czHocUacl9dR7uJdfS4/YygRXHT6tjS9nnsssTwhL4wforSG2nzudzLCPiDkGTyRMVvBhXAtGkvXgB2yZV4C7uAdH+EFizXpGv/0egRl5nMqow1PkoNQ7C/M5H0DLaPBUwXWfIKSNwzT0CaYD+0i0+fCXOtAqPqLsJpKeirYc5FgRobwDQ2YnQSWZyDgnGt9LaMyjQPf/VluJEELF/+5ESooAEelv9Vnl/1zbilIJ/Ov8+H960H8rSgSCjRBx/tv9ogRnL4Gt98CBN6H6OvDWQPxyysPfUEAExf4FqpZR9IbvwFL+BKOcRxmIyaHx4SDRgRDdWeM485s9CK3NEAUhrCB86sfw3hG0B8MoPyiE25pwPPk2ab+tIWCKcjyti772PezsepWmzl5qdY/w8WVZtCTEIJ8YAm8q5vZhph2vRBoKwVcaBEfe6fzMc6+DqY+D4Y/PgaACj5rwpg0M+L+jd4kBY3MyEgrkXw+ZKUwL/sibOx/hoZH3cHT09VinBQiM05D6lhHdagtiXSUan4g+eD2iMY7wyAhM7UTOy8EUHEA5NIzYK6Og4Ox/nqOOOvyznkbO6iPSnkw0fjKq/AqUEQYyNHFELjgXTySJvqCe2BeuxpQoogQD6NNMkLgPrXgXKv0iWL8WIRBC5Rag5xr4NBn1eyWIg34GOxPpCUUZ7NESulhE+foROLYJ9LmgS4e2YmhIhFFxMLcY1tyEoLSgJPciF+6mx6DjkK2R+nsChFybKZavJbMlGVcPRAJ6vpm1korwDMrGfYWKcXSZn6HHq8atsROHGwkR9jxHyH4QJbUbQ7KMqtKDklcM4aUIbQ0IvSYU4yDi8C7GdG6kPdFBY1RFWH0cXVeIGDlEz6WJXKSdwW7FzX5HCmX265nxUQOaPS7iJ/ey3K2hx5TCMXU8xrqTZB84SvyhOgoq9BT7i0j5YYhxm77GmuJlxvEGFj7xHjMPfE9atAnxlAtD5XFyPq8n56SRflMfYXUQbAvB8Qo4rgGdAC0PovggEqdF0s+HqaOI5IxGszsMBc+gqNQIiUH8WjOa4VakmGVoTh2BzNv+1VRkZA7978l/9mdRBIGoSvU3lb+GIAifAvuAEYIgtAuCcNV/pU//tz1o10bouBFCDWC/AvSloC0A3QiIKBDqgEvvg7dfhfC5kLaMEGFqlFOM1RTidF+PLUdFwtYILZ44Pi6bzfkdaVS/UknSH95ny72Pc7Y8EZ5fBNdMBXcz+JxI+DhQVMSwEoN5WMfoT7pon5PE0NRCNNEO9JaxdMdMo9KsYpXwPYXKVCqmhhE1k2Hba9DiRVOUQX+ZhThrKZxqhsP9oJhB/SfLb8lRiITRIuEuiBBx6mmVu8kXQL3pOzDUQraOhIwA3598miq9Bs8RFdI5It6rF6J5UYuQuBrFmIxG/g7B4Tsdx237BZFgI+8tvJUbv30Vr8mCKawhraEOR38tFWO2oh/yEZ/VSdy+CpQ4Hf6YMJp9JxgebiN6ZSzDaZeiX/M+7fNKSQimYot24xFeRsPM05OAVt4FW1+AoncIhx9F7Z1EyN3DsMHK9okJ5Da6SfWVIRiiROY2o9pmQbjvl5Arw8pFUKmHmvUo+k4CZ0zGG96HYlYYGD8Gq3oAo+oMNpSMIt9ZRapxPiV5lyAEBwlFd5D63bfcNe5jgmt6aLAlU6MozCFCRD3EbF8dPUopxe5m1PU1YH8a38570d1sR96+kUhSAqoGPeKJMHTeCiEZ9V6ZxHg/9tSXCY1Voa4OIZybjaVSxln0FJNWe1m3KJ0VHz+L6RcvMvyb5WhXZZF9tJWcr7cyPFzP7ptnMxAXQ+6GfSR1d8OBE0RlFf4iNdqECKq4KMzwI6jaibhikDP1iFEB8+Eg5kQ3lN4F6MCwFAJ60CfD4LnQ7UQZ2kUwkIcUeBpRvBRlaCmR9CrU5R8TnSoha12Eyu2gH0bX2ALZt4P0/37nV7OXNmqZ/Pe23/8BotJPM01QUZQL/8rxzL+lnf/bAm1ZAOaTMPTF6dSTwXoI1kD/Ngi3ATJoVHDJRDh0JxhVHBgRz5z+D9AM70UtJhA9oYchD+mxQyzZU0vNuBOE1FpCl8lEKo8gfv8C7uLRCFIeRnMKTrsGOWknI+qb2VmYRO6XNTRdkotBp8GSOQtry5tU53iZte4J5qgXoTo3G5/4PCbt2Xhz1JikDJTqJgyRfnojCcQPfgR+H5QZQD0AshsEE4oAglpLMD6WmoXxZB7bQYKzmKOlHQR2dKG2HIPFBaC3Iroq0er7GNM1DuXFbl6Z/yRXBO4nRQQlBJrhDuTBHoRpEfi2DqViK4H7RrNUfRVSztn09tei0VoRYqpBNYhdDuFKEqgfHklPrpZi7VWYHn0Goa8b07N2hIMDRKyv4zbaaLboKFHOIrrmcqIrctGq7jl9b2rWgz6AEDeFHuExjrKROO8kSurfo9oRQ3Z+L2FfJ0KXFlGQkE19sKIIoa6RyHc345lfjO+SMhSDDZ0vDeOmLVCiIq5oPYr7copcl1KQaILER3C9u44OnYBdkTDIEgkNPsK5FgYDMgWNNZScnEzArGLpl0eQKjzUXbEDHK3wViy4n0XRTkERW5CLFiEdWYNsVBBdAsJwKmjPgu73SRzfRyBWi3q1gOADmnZh2qwmFG1ifMjDhI2z6V02gTSjH6UhjDTyQwR/IkjvYe3eTV7Jc3yh2Ur0mmLGHNjHqKgB98jtWGrSELR1KMl9CBoRRX4OC224L1iN9S0n3P4BvPMkhM+DuQ+DIQXC7RDzBGguRck/E+HUfkxbmpCtf0Aavg2lzUv4rO34ih5A9oXQDCejk48juGRkoxMpbsG/mtDpfOlNaDH86z4/YTZQzzABMrExk8zTX7o/cxQEoj+zRQn/bws0nH7JZr/o9N8qBxgn/dvjShg8x1H67yQYW0UF+xhPHJJzAKJGgo099L30AAmvvEbu/iPsKljGBd37CefomDu8FtEBNWc6OKAtwRcVkWgi1V7MXGk3M8UdoNHSF2+mL6aQQ5FG4kMTyJYrGJylJT9mFYhWhPAhYsQMBtMmY4ydQCTxTqTNQeLX9cGKm6EgHb67B/Tr4J1fcPzMSXTGFuAzOJhCL3JoLDFHB8FSRcmOHpQI7E6+Cm3WbYyT8xFqzwfDRuh3IXgVtC1uRFsEaVYEj8GBMe8BxIEw8ranoKufcIka38ReEtc+QeNZl9OjbSV6rJXEThW6lIUk6c9Cq3kSi/4xunu/ZX/wHVIvLESXcQ4Jxmeh2oerdArmOa1c1LOanv59xHgFLP4bcJnd7B/6nnnHHqNukoMT0deI05QyRXYSNV3OntE/YlcsaIW1SJphRHsKyq6LiU7aQ8j1Ne7JMpriBISJicR7Z6LZuA/OfBAqvsc/0oPy0XkIqi2gLkdSDUJ/CDsOTAn57E8dzfZakT3VJq65tYRC/+/QtFSC+VyGfrcddWkEy4ZebMfMEEiFvnqU0Zeh+/oPiM+BkNsExrlEI/sQHAUIQ+UgfYCU40WpB3dmHoEz7WgHT9IzI5aSrW1odxfjW3gAjXE3Oa+fwlf0EZImgPjcDJhyC8x+EPwrGWj9hJW5K5n77UvUjZ/LV6khSjqOYy37BUrP43Q2R0hyy4iqO9B7VPQUJ2GYdRGq/bcg3Loa3noU+s8Fow4WF6PIMoo4RCjBhTjSgewdRLP+PqLpbiIlEhHhN6g7qgkmxBII1KITRaRtaiKXRhD7X0GIvwMEEeH0+jTM4WJkFCrpYSctnKCX6aQznYz/FeIMpwU68k+B/l+GoCZiNuIb0UZNrJHRLECtbyIceB9xZzvK0ll0SO8RXD6b+CdWc+1v30GVlku5kE9+XCU1BfHo+2tYGbZhyjyPdvtJjPwKKW0Xmn2gtrjoOZSIKe4kU63zsWcWMswhhtvLYOd5KAxiNSZg7H6LnqLjREZ5EXLeozPXQU/7Izg08xiwGhitSkK7cZDjk87hYKqPRE8/Z1e14FW8FB2rRk5LpqNUxmtykH+gnSn5y9hPmPfF71kenIZp+y4EaTvkiWQ0HqZtQhroRaQlImnOo8gBGcHvxf94HH7DAmKEL4naBHwDu7AnTuW90pXcF+tGfegF3NmVJGq/wM0Q6du1FISmsePsapyGQ0wbupw43ad4hqZgSu+hzpJJ3sv1uMfmYRczCQU7OGE8jGvMKHJV1SyWZxHkVQziAzTQRhzZJArjUWMCTCgEEaIDqNKnIo39A4bhHtjxCor0MPy4Esz5UPcdjJ2NeOob5O5jSFMTwZoC2Y/jr/0afdkHqL3PMN39AE89/ygdPXoce1/HOKIZMn5N2DYZUd5BRLQjZLWzo+h2sruaYN4aZOd3dAmxxN/gRVU2gjpbPcm1cZjkXJRT5eBywwkZLFri3m9FDvQj5PVx1Dwe0zQfEaGLVM8kXMZaJEVFOJKPesogkUUzEDujiBoDiiaPHZYZ3PndG4hZ0yhMTCTT/Qjl1hhMvU+i6yrka9MIbj76MkqMg1CaF8EWwR/9DGO3H2nfnZDdDd4uCMvIB3sIzZsCmnTUxzOQTY2EJ2qhpwP1EQFVdw7a0HGUATWD+RbiUpOxRBPgIg2avTuR47YhDfXBiGfpo4UOWuimgyZOMIoELmc0EWTs6P+hpvufRUEg9PfMxv838E+B/gsoBPHyOODB1H0+jaowy0+uRqo7gjgk4RkjMCyexOPPY79Jw4yz09GUBkj/pJ68TRpUNogs0nPQMpWMYAPpukdIIBlN+GV0x3IQ+4/iN9iIy5lMd2I9dustdMnPMbrej+qeH/hy3R3MCH6PtfcE8lEfsmUzAyeM7LqkFZXBRTAukRhVhBQlEZ0uijhiHmWjzqeBXzFDXEgozYdS+xl6fzvDKVnQNkBC0rmI6ndRKtcgpizmgopy+gZ+pGHSDEYeO4DW7Sa7oonjhoWUxH3P7wNXcEezE/3a9xGvjaCLeHBaXAhiKhqxn7r4JIpa9rMiOZ4P0xI4R7oE+2d3opSmoanaiyzZGFi1mBncz7BvP8O9r2POK0NqWYu+oJfYp0zIx6MEb3JRW7OKqlgL6phkJmzYTWqRiDvtZYw8hkIG3XxPOrPJ5GzoPISsUwjGHETvrIfIMILfBbZUGGqHSC9y2VGIHULY/D5C2yykkTOQ5/YhGfbCKQvBna/TV6oiXVEgeCY0nSTqj/LRTV9TamqC1h6IdtKech/G7GSGHRNJyHAR29ZLaO4TaL5Wo+x5AotdhVyaRFA1hbBmM3q5AXx1kKmFlhRYGkAQp+IvSEWqrkLvlpm/t4e9IxNJizmXes8WgnoLw1MjpPWOxOARiZpO4pv0LfqjuziRPp4LqzYjqAOQ0wvRGYT1VqZ2ibR5H2VTtp6Zfe/gn5iIP9aKkiigaRxC1QKSWAYj7kLRCQgVXyFvWk3gtTCieJCo3Eu4LIhqwEAoIKN/1we/+BpVzSVQ7kUYMYoYT5A4eRTwI6gU8GtQug5BcAMnbHn8PkEmkzBLSeZ8iv+xBvvf5J9DHP+biIbwSj6qsTOBxyDzFBdsvAiGOmGXl8gVOqRNaoZyddRMy2GsrgXzxBkklO+h60wHyRv7MbXqsA94mPjJOvKKPUTHXIrs+QLdljJUR5y4p1+EafQwBuFRntf/lieEeIbkeIaz8giXmUj5to+Ytf2oVdmIA1ZMKbkIE2BFIBPZMIUvpSpGhoBvXwNTJmTPRkEhoAzT6H4Tqz2dLJ9Ef7GO6PAgqanvwOC9kFvGUHQA7fFv0WUtJ2HG/VQfvJ/2yQoLxuwhfFAhXXuI1KIWpu7cS3N1H6l3SeiHEhlUp2MUHyLO20aUe8lRLac1tZL0wxsZzBtFc3I2wSyR1C/uQBY0VF1dhJ83MJN7Oj/GyCKkzvfIOdRAT20CMUI/dZMKSOmXiHPMZlTHRlx6GyklubjTyzHxGyRyOcFXFLCYeAoREFFi8+HNFNRJ6SAaQD0HpXE1yggRontQXDeCRo/cXkZXghrdrIsw+PvRdd0F1gh4Y4gmHSSmOQq7bWAZQXT2vVz+yHYKJj5Mt/MNElQtKCdfpE83kqHzJuOzJJGXGMeUfZ8RFd6HE/MZPmBELfpRb2/Dc9YQ+byPJF4HiS0oMbdA0lHEAR0MfQbufARfKsz+nPBvb6KsqAWn+S2yvdcjLV9AN1twat6gJ20p9nAYY1cbnlGVmHp7SOi3gLYbvv4G2d6MqWiAqPUSrEIFGnMjKbphIkPJhAUnZn8BKrUXJVxF8NLrkT58Fs+FqciX5SGdOQGxqQV/filWzWuo+gXCoUWgNhGoNGCquRs0Xig7Hzq/IKfOiEYfB8EIyBKeceMIH6khZhRI/mqKkMhlNDnE/GPt9Sfi5ybQ/wyz+3McfQKT24VHiecom8GeD4f9sFYN54URTGp0lpEkH/Byhngz6d+4SK35Cl1PD5qUKOVXLCO6JIuUgwGSFihIJh2R2LfROS9BteEgBA2Yc9pAb+J7zb0UqxYiKAoF3kRC0Wqs12czuuEw2j4z0uTr4fFbEVI/PT0dVdAi9m5DEY3ww0sQnwvJk2HYCShM6jZiGawgsWodveOTECuSSXA8gNDxGwRvDegUei1eUuzboO9CNG23sqBnIwsyR+HTLSf7x0bMHjfhDTpmt+5n7a0L+FS+FHtUQ5rtRvaoaqhwn4JwD8Wcx0z1A3ROnM/ZO+pQ7ekmxnsu7QumQpaWMWvqmfi6h9LGRRTJNyOVr+fbwoV8NfdCYvcFEf0aWh+7Dcf4E8R06Uir9ZNo78FVUoHpRx9SRSNB3DhpIYHi0wmFAEUbxndmKlG9i95ML660LURjNuMXk/AnT6Vz0ESlNJHK6E5qJ1vo7nsRTdPrhLKNECmGWDMaSUXv6AXI52wC1QzcH93I/F3vs3/rY3R5dChyM845PxCTfhHFnVXk7XoZeeAQxsY2apJvI/jQK7gykpDm2gjPKsQymIKm+w6izlMcf9fK4PAz+IwHCGoSUCQDatcgEd8JOrc/w6lcgW0rJlExajTbz9Wjp5gcfklqUi3JhoX0WHUczhlJQ3spvlgd4fwAwiEP0WVB5ORKorWD9FlexBv7AZI4F21XKtpjbuIacjFU+1D1iqiQ6Ul4lcjQcaSTDVi5HWvCATRlRwgaVqISRhHVPE7AaiGYfga6hSCbTxFImg+GnZCei7nLDW1ZYCqCsJ+oTSYcCeFPKia/cz2xJDKSaf9IS/3J+Jcx6L+l/L34pwf95zCloqyZhH3GeexNVxjdkYPoqoVZ58LM+5AaLmPIXoWm10ryqoVYWlwImbmEX/sVtvAtmGzDOMsWERs4jmPLeryLLXiFZJS644ilZaiL2kAY4qjja+y73iJj8304M/bhzVThNhsIdX9D7+JsJsY5keSHCXdFGcofRWwwAWlgNwGpl3DUQ8jkRj39KoT6V2DYiYCIY6CeBqMKTUoWMRuOYV58HExWSDoDKi/FKzei+DKJz98BO2+CSCm0dqKedw6WwEkUfoex3IfPmoxwQ4TrhLd43Xsjr9o+5iZdKYuReTXhcbJrAphdbXRZepgu3kZN/Enyfvcq/efbSMp4mNYxB0mrjqEt2sYP8ofI1QGyG9KYElVT1NCNNDwEJSKm4UqoeR48/QilRYzTrIUuFVL8XFj7ItUlPRQKy/7N7ZG979GaoKU+Lx3TYBirW40Ym4FO1Y9l3gri995FStCMqFqOLzgbw9CvUWqbiMT5GDTHoZkyHlXtMezxjQxYvcTlP0ur7CO2LY/dCU4muzqQtR4GPJ+S09yFYCrC/2M7EcmJSh2Dq3AZR8LVjCyQ0SwsQUx6AKH6QRjqQTJpEY/7ODnfTMbBqRhMH3NSGk2uUId50E/QV8vB2yYT44xyrmMOTZEAe7zPMvnkEMNZYQJiFY5BHwXrKmnOiqF+SRY9RQqFqQqOgQ6iBSqi6SlYh1uR2p1Mq3qYbfMvRj25gPmH3kRoAAE/4dwEwoYBIteNw7x3KpzcBvY8VIn5ROV6GLwOt7mAgHoLUsSFf7wPo1lEo22BnMMQ7oGkr6DFCccaYNZURGsphsJONHtHEio5Rmr3CeITb//LtvSn+Tt+xpwe4vh5SeLPqzc/J1LnI9S8w+jmRrzWsQx1HyOmZNbppPW2ItrM8bjOmEN83AY0nWHk6ZcgdQUQr7wezUxgyVG0GTqUjw8g5C5GdO4mZo+Et7+SwHkygnsZgjab8sGPOb/xS3TV9fSMcGDePoho0xBWNExM2oU04S7QxqCxzMZe8zwB7xcIcYtR7zrIrPHptM3QoD+xlOG5ViSpjmBoG1i6CSVq6Ajlka4zwvtJoJIgZzKMPIMTQS9hT5CgZEQ7bx38YTah+HxkQyqn8r9hxJg02vIWYLHEkGBqwxbQcqX9fd6sM/KJazfnqwa4WskmqtaxQ7iPBM6hiwq0WRH0uW6SjnnYHruZasWGKdNKifUszg89i7+ml7g9beg3NxPMUiNbILwQRm34BnlQjWBJR/DWEfHGI6oDyJn9hMkg1LqP2IxV/3prZCXEiaEvECIRsgMp5B/JRlP3GWj74OpnTmfx6/sMRo6D6HoMJzeilHxE04QBAp7bkIUBXJo0rEcSic/wYDp6HeSMIVfqQGUJMdUpU9q7A7HXjUX4HlH9OMHPfoP2qotA/xyqxjxmRhzUHVuF4e4vUG29EeG7S8HcAdJ4BKme7MsK8Q030/zVUXKHAkybeYJeQUPAokIpCLCgfC+hohGcVD6HwT7cqmT2je4jRj9A2q5SLFsbiIp2OmOLmHvyGFHdeDzx/YTjI0g9YTTWLlxqHXtyZjDgyEbtdrGyYw3S2Ceg401wHkHrVpPg1BPJWw6OFaCR4d1RiBkzic47AcoU+nSdGEN6DI3r0cSakcNOAvpYTOo4kAxgDkFBA8SHiegvgKYXMY1/GeGF6zkxfxmj6j5GSoB/E6ihKLBhDWzfCEYT3Pc0/ETxxf+TnH5J+POaDflPgf5zmDNg8SaU9UlMrU8gOO5u2PQxHbj4vPcTfuHeS1rRi3RGNuEoH4F4qZlw+UYieQGE0RcjR3eju+44iD5Cy+cSVrVi/KEZ7dkJdAd9RGO3I7iPcE6VD2NXI4ImSEyzhM9lxF7fjzBChZj0SzAFwXo7itKDlF2KobyfyLY1SK1dmFM6UELxmDv9RCNGmh1lBBadwmEvIG3HXsj+kMapJuxbxhIz6mXIS4ZgPe6UdvL6qtE0XgZqO0rhfsKGcjq7T5CX8DnaKydxQu5jjG0dWvwYdV/gV67nfmkTT/QvQKc6wbKhjxjWGIjzV6JSGeBQmOH+Wg6NmkCfHEtc4gjOD3eQZFwOohnqp6Js+AOC2o9shf7Z+Uh0YTMNQ2kAt3E0JtssCB2nv+M4CYNuaKpGU32U8YdVcFYpTLoWBAFR0BCrjwFhMWnBi6BgCFq3Q0oRfH0hzHgILKmQfQFU/QjWXIS2+8jOewfZ9Bxyz36E366m5rCCd9m9xA1VQvwT6JERml5l5onbUBx6GrMmYf/SgLD/CWreVVHQ+w1R63gEjY7goVUcH3kluZp0hMF+SJsBOSsgcTo034RxxmrE7pcZeeAuAp+EOTkrC7t9mIEJMcQ2TkLfGEQXWYF6dCGoLsehkdiviUFzPAlLwh2wrJWhkmJ0rveInpyOKnkvVtMaWqSb0auPEQqpEAUrouFclkS6MLftpnvkaOR4L8mCGU2/gJzrwnRsFRFbAyQfQhHsNJ17J17PLoaUTBJiH6dRWEsKQ3Q6mrDqdUgaPeYaI4b6axCtD0P8M+D+Lei2Ew4/hbHcgmD9LZELVlH4/u/ouKiI3NYXEDLu/H+2U3MCXn/mtNf82Y//K8QZQIGfXZjdP8egAYUoUf7tVG9FiSBLWoJT7QhBPTqMyHVhLktYiCyfQtbFEtalE6/5DVJ+AxH1OGr3ZxApjaM/uxVt/u2E77IhzlmA+OEHmN8/hlimhdYuclc3k7XViaq7j0hmB4G5MlGXikinQnNxLGKenkEpgZaEAGFVMrgeByEOxXYIYdZqhJGXsPm+RXwxbg509qMf7iVlqIqYQoXpPEtZewLtU64iabdChu5uLNMCKJGlp9ND6ssoMC3EpC1gd95UulJvojdaQEf2Miz1cehqqwkkj2S7XcvheCuSEgPocAtFBIsSeaLiG3b2T+OKkscZNClkN5wiZ30fPsN4JNcEzPPuYUlGKcXvbCKSNwVsWXD4RWg4iuCphSl3IY68AFvJHxgudKAOimDU0zLcgrPvU5S+78gNNoA9FiUulcpbHkI8/3to6vzXn8kKIbQxDoZlGSrXgRKEzGkw49cw73lYewUYolB9PRS9ixKIQ6k5ALvyEFuOo0q+Den2j5GlIaRvXoZZf1xj0NcJKivypBchqwDjsV5izrgH1pSTFJAJSF3o1vdA/XZ6dkYptGUyXLEKim8G0QJZ54DeAdnPgxxCX/ESJm0GMcs0FLzUgLklQLzPhStaTyTRg/q+G2HFPFDdQHzgO2bvPk6vPUzky6ugt5UYdwyp5kV8XlzFh7b5vCC/xs7odDztdizGQSw0UxJ+Cm3Dq0juk7ilAYxKHhp/G8poHfIIAcW5HtWPr+E68kuO6XZzIm2YtpFjsWNlY/9vKPLmk6N6AJOcTWugkHJVMT8WGqm17KXC9Bvq+ZZ22ughlxO20chNVSg/DuCN2YtSpBD2yQjOneCrB68HnrgbPvwdvPUlvLcWpF5omA+NZ4Hzk9Pe9c+W00Mcf0v5e/FPDxoQkBjkZWSG8JNMHVsJI1Aq92MzdRFRHUXVeIDqwglUmnNYauuFI3507YUoZe10TziTez9QSE1dwWWlmxC8LYSFBYTtH6Id0YlkbUQpmU2o1om6sYWhc+5kwHQMnbkcMaBwqGwsMYNDHEkZw5If1hFJDdKfmsyhei1n6p5B05hGbHQNQqQNWZ6HSA+za8YSMkfRpS6mt9iNsbOFkXGPnk78HuzBZlhEwLqa8JEH6Zxhx5p4FobBfejb0kj0iIjNfsqKd9B66BH6k/MZafw1cZPy4O1r0V3zewKaA1hkL3rhyT9eJQkTFxHIfZrlnvUcO5iP2KtFLATlVDP5W76H57+G/oeh5FqG67chrfkARush8QyofxWMC2HUQth9BGNTPf58DYL7AgZjmumTjIw6oeCZWIPKvIioN46Q9BV56jOgZCps/QD62iAujTC7MPomEOjfAztb4ZvbYfH9uHx3E0nSo542hKntJdwZk4mof43oWE/EKmHbEkAM3YtQ+ysEfRG2cy3oe3vh24tB5YOUUcgFcxgSXqAvZCfV30l0/92o8n5DQnclfTHxiLk1RIxGdNsPo55/FybtXaf7Nu+C05dJkU9nF9wx8fQY7n4d4mAQ2VqKNHCK2JgczB4Lh+deSfC5ZJK378B+/33EXZqCOe1CND3tENvJkOkNuqJv8mH9paSbtaRH1jOuu4nESheWiIrKlByyVI2YQp1IJgeqlgkUvb8TYc49ENONnKkllBuitT+LthuWILlrGckssoRsNvAJw2aB+b5VaNb+GiUlQrauiNzyWqRIJrVSHZ5LxjNiaDd+7UWEQ+V4Iy76Y/Q0nVlC8pEmutNSGFLGMnYoCL4e2L0UPs6Gm26BESFwPw+udvDaAQEc14PlrH+Qhf9t/DPM7mdMLHfQxmI0DDGRX1ArPIM3okcSzZwa7Sb+u8uQmh1cWmtjkakdoWwkka6DCAMOXuj6kjWaEOVnPMSA0oIx4CKkCaKo+vFac9GGg/g8TUTnLgf/mWgkDQORbVj8JvTSClLFK+lJv458lUDMTb8mvPt5cpxHSJfLGdTGoIpvxO/yoZEjKBYJtTgOEQ/a/n30p2ZhVY/DKGbCvtth3M0QdeMQsglrbFhMK9AdbUKbdSNK05vIoTSiwSYEQwXq9rPIcrjJDmvpr+7hlN9I3MjJ6OUDTHIeY7qtHYksvPSjQo3yzbdIUgNjgnGkOzaz3raAS4MfIP1wHHmFHbH7bnB9A6FWfIsMZF13HPa9Ck/+SG3+KAbffRazZZCU6CCmUDdEBeqFkbRpW0jUFCOUzMSwoRxx3Pnomzeh9EYR8lrBPhUmlcKnv4ZbPiakrMX4civKtA64bxu0HoTajVhe+B5y50GaF4pexdLVjGJZiOwcxpOXQ/iiEjQeH2LHJ+DeS5xFj9ClJ9KzgwGTjo6SWCwDb2CMFQmpC9EVJKPq2A5Db6EkXIut9kPC0Vhql6UQ195JMNeI6ota0Cqw8WMwGMCRDq4IHNZDt4jiHUSxgZhiRtMOwaRT6NQ2ptZtREk7h723LeNk234WbnwdscBM/7TlRKUeTtgU1gTP53brXJLN8ciVG6gOvcWRKWEm7ipnf+fFyHGbSYr2YbW3wSgVgq4Y5VAv/aKWhsQRBKISGZ5hRtepaCtrJ5apfM87gICJWD6xWYkpKuOc2g9Qoo1EA8MI41dRYLgCZWMF3rMKsfZeRVRMQZBHkCV8gDCri2B4Pu2ymZTkNI5F72LckdmQ3gvnuiD2Y4jMgbjbQJN+2rgUGYT/HT/W/ynQP1NEjKTwKSHqcPf9jrG/04O3leFLFHJimxi0WNn74Hhm69bg1/TT3a7gLiumZ10avU2bOdL3KPJeN9IINem9PQjGDxg2u+iLLSdWNxLTD230h0dyIOF9zqj+gaL4C2hI2sBAoItW2wdYE0dRrtUhanehmzMZRW4mqnYgKjE0uyKo9S6yBr3EOxsJ6fMQnJ9D+zD2ye+ijUTB0gcnN8PwIZAqsEd1KMMDiO7V6HxdULcNIeJEtJaiMo5Drm1As6cPYaIEKX5SQ0eIHjHhTu6ibvAU5+/bgnaFlxB7qKUC58A6dH1OOtMcGMbKGPv9XN/+OfKAD8+bNkxx8057UoIG9PeR8MG9dFx2P3XiMA2qo/gCDVStzGPpUD15DbvwTj6AXphCb5IftXIRaf0BCB1GVJ8Jxz9li2EIcheT3f8SnWkmiswqrPY94DuG0FxDRJ0Ao2aBzgD5s8DTDOf8Bj6/HvoFULtg5gMIJ+cjWc/Bqr4bAL/2CGr7YlRHf0/Y+hlRpYf2c54i/cheyr78EaVQgsb5JJ1/OeieQAmvwuMcJqLfhdYg4EGNNlFBGKsmqX8/TKmHYDqkvAfPTSS04nrU+8oRLn4Odl2MYCplW14GfRl6JilBUu7tg8d2glqL0P0VU49vOZ2IuGwh3k3biY44yerIZJwqI0/EnI1WHQeihDhpJeknNbgjr3L43CUkRadTMvQNA+EggnsMAVUVVWdMwzmQSHxNO3ktfcR8Wk/DJVcirPk96vRcDsa+hhypZ1THEUTDANOrd6DOvhMh6VHYvQFl8VaUjbdC0lSE9mRMA/cQ8laCuBud43MEJIJ6Gw0TZ6CPlKPfsI9tg28yrioCrQ5Ymg/aUSBl/j9xhv814vxz9KD/d1y5vxMqkjCEJmA5ZMeZV4XrTCvOjBTMLbNIPzzI9+HFjApWYNkToqN4JM2RYnYmjOVJ/a0kZtYiTvUSp+4inG1Gbt+JNJCNgoQ35V7IFPD1PEmqrYBQ4kpE3iP2xxDmnpNMUR4gR8kls7oBrzJMsWsDmZEerOFhYsInyDTXUhCIkFTRCSoNuzMbCSKj0ihoZSOYRkHMHBh9NwwbwGJC6t2Jyt0Ecg9MfR4sRTD2OViyC6XsfAamWIloSyDrGziYg7xdQ692KjXznyI0aEan01IZmEqNXEmjvB6/0YR0yTYc99cwaPfTbE+nuqiEQa2daE8WXQ4L+41JfGhfxQu93/OHy6/niKadhLELWUUZV0Xy+N03v2HZwQ5ISyBQoBDVDGFFJm7PdqzrnoOuVuSevfzY7+MMZ5SpsoMYxU5/5E0OGI7hC7j4sPNt3o6M4bFfXsQpq0I37tM3b7AJ8ufAoodAnQdDLnhnFVFnEHq/JkA53dyAjx0I/gjseR+z4RcMHzmTIsvFWPomwYlRKJIX5lxC+OhKjrpGsnacmeDhA2htcegyb0cV9JKqKkOOMaHll9CfCem/gr4bodiL+OFTuG2dUPsqTLiIbaMKmaxTKBV2s041myPzx6McPgoaB6RfB6PeQxGMeNRVNJ/fwW5pBnFVQ/zCV4L2+K+gaRdUfQ1172J5+WomH2ljUv0azuh7FclUStQ0A6XjJC5Rj81wgnx7HbmqAey1HQRKUwgkuHAtHEvepx2EWyoxdg6gDRtRaTzYRq0ge187tPYi3PIFdKURWSBC1ngYKYDRRvhEOYZAB8KxxyHgoTO0kfL4PrLXNeCscHBusBJuWQ93nYQxm6D/C6g5F/x1/0hT/i+hIBBE+zeVvxf/9KD/PRod+kWvEY0swtN4GXHNXhT7ALWFsxnhGwBZIGFEL76hu7h/Rwaf+YrRpAbwmTQkVvXjDFkYyFaTtr8Bo3sQ1cwiBjJ3oQ2ryLDVY/3uIyznHSIQbEJyl6OvFwkfGkuMYxUlgSSqS1MQDZdilF9EG+lC1n7DoVeewTR9CNfcYsJyJ2kRJ0PpY9EPH0aUohB0gtYOOctgzSJI7IXh+yBuMsz7AxgzYKAG9t4GKTNR9l2IkqzQfN5SekOH6b76EZRDa0iwl5KhHUHyyJcJht6kdPOzBIVjCHPHUqa9Bfq86HOmklVTS0jXxUujn6VEb+CEYwyxwXYKKwdZ0JdJ3MLrEF64Ai57AhyFKIpMZO+PhMZ14tG+S9RkIiq4CCky+tZKxL52hKt+pF2I5xZlN5cF30Vp2MRwcoCExF9xZk8l/nQHFDVwyfvvEL5jE1+LZgS0HKOFWRSgG2oGWwYUngkTLoa1T9E9cjoq3etEUkOonXcQ55uI1PIDHL8PSkeCZETTVEfoxivQXn0D/ed30U4WIypWEDCkU1y3kzGb3Sjd1XDKgDA6DoOsZrjwKGZn8unVa3LvAPtSsJ0NlSnIScOYtlQi//YHjrpeJj5mLxEZNH478wyfcmLeGN52rWHB8XcI+GTS+xpQK/X0Zt3OV/oiZgSr0amr2NTezIi0eDShd0j/ZDWNY7NJyY5wauZyItZMBHQUuB5EEsYT7Z6GergHd/yF7Gq5gTPLz0BKHiRloJWRR/qJhquRYoewDVXTbYvFJ2kQTHFYd++HjBth4rmw9SVUXwzhfeIMtHltqN33E914If7RMzF6b4KYb+DxLLqW5TLWI9A5bgSq1DgS0n4Fh5+BxR+czj9esgdc+8C1G3S5dAs+hgmSjRX1z8w7/ff8HD3ofwr0v0eOMtB2L7b9BwnOFNB5AgzoRvBZ02TO3vY+ulujdG9P5jpLhLfjzsb6oxP1uQLKyCjOAjNGbw72yBLU9kNQtghb9jkc1z+ASiqkNyeTXGeYaNcrqCxjEeRK1L1dKI5S+uN9xH+6kZ2LFkNoDCrxBVSGsfRvfYC1NfOYPLWJfNVStsqbmRGsxChcTtASQlq7EsGqgjO2np7ybDRCxzAs/hZ2/w4MaSAItE+6EZehi7SKs+hMzaY5YMAU6iTreBeTU69D7OmB8bfhJcCjfMxwicD5lmLkaC/Tvz+EY2EBxCfAOUtRBV9BZS1ELelozV3KjXUvQmsHBuc0pLQcuGc0rPwVgYRafPwWavaj6upA0yhiLZmE3xhmME4h56swDZN7Ccy9kG+VPN6vLeflhl+TqA4QjJUwD0sox9agyhvG5nmH6HdvE50Qh9a7mwtDC6mWZNKH96MT+6D3OOy8GzLVUP97hqcVIkW+Q+uNYj8iI5k7INQE7hjYGIANGpjcheHKK/FrBTS+dzEGqhlV34Rs86EuSULNl4AZDk6B1mpIXoVm3XEMjS0Ywnqw+sH3EVw3CoJH8Jhj8Ey/FOHcCejqtqArWIdN1Ye+ZgzZrrPw+o4RGpWNEI4h+vbz2OP8qKydBG1azLvf5VZFgYiM4bCfprQkItNtZGkbkGPsZO6ViE6ej0e4marj6zjT9CEdhlSebH6F+Rtfov6cZzgv/CCzNQKOpAFsCSpExxICR2pRuxNQRl1CsH0Xgs3OKF8MHV1hjCN/AfFLQR6CpC2IIQ04u2lShUiPLCcQa+WrKgMX2O9B2yugnlPASHsZ4aYd+OMFAoOT8MZtwzl+FUM7VjE06xmGpChD1licVhM+DhEgwgG6KcbBdZSQg+0fa99/gX8K9D+acAi2fgO7f4Dpi6GvE/q7TpdIBHwdMFyHUGynOQPM/jSEPXZiPqim8tp7uHripwRatdzI07wlv0j2V3sIX29gwBtLh8VCun4GAXYgNHyFzqOCkVfQKLUSpJTmnFbSExcgTVhCqHYa2koVrpxCvIZJ+O2r8BbuQZ6UhDSYif/Nl+joCcINMuk5Mcx7QiZp9zGs+0djnVDAkPIVkfAaFLEOfa0Z140Tsbqfg1NhaD4Fai1orKDSszfaSrvYSC/HmSvV4k86h0zvByRtUBPJUqH11BF+dQKDV9/LfjbTS4ix0Sxyh5x0a70kaaI4AkOIvnowJEJ2FdTPghQbU5Rieg89hvn+RgbuG4tqbCX6tw9BYTo0vIjGcAXa6CKEx96DkrNh2za4eSO98/QYvSFMloV0xPbw5tA0xvfs4YutdyKNFYh0DBFJHItmSw/RgSqkc0IoXxYjWfKQVDlQvxrl5LN4ZuXS7JhN8Q/vw8Apwj025HCQgaJYeg0BiitiEU6OINK3B0lfiy+pD8MHBlj4C7j5t+DrQP/uIvo3g/X3L6DbfiOirBDOuBU1OXi5GREH2pLHkbqvgLxC5FnJDMydjHH9DygtFyJU/ojw3CpI3oNh1LkEdu7ki1/3Mkc5jibgxdA0BZXxKnjjRozHWrGMzia47DxMU2JonTOHzUOtHEuYx1nWKejxYVGM5PercGx+li/LQpg8E0gX7Sjv76LKdhWRhpvpcM5mr3k9S82X8e60RDy/a0Z3Xjq+tbWYnGsRi8sQmotROn5AQUCMaPEf/4Bo3kiylDKEpttxpCxCUzLxdP5w39OQ2AlLf4na+QnV0XTuEq7DOuwhJqWXXQm/YKp2K6GS0UT13XRnpKLIIYo0PjYYw/gN20lKT0RV8wSZRY9gJwE7WvSocBJkFQGyf8bC/Kf83OKg/+8IdDQK3390Wpxb62DmUiiZDDYL1DwEYR8066FqAvaRF1C76HNcUSdbLyjCfDKLjKEWuhboePSjp3mh4Jfkx55C3p6E9qs8dMlRVENODi50M5HZ6HrqYcb9uCQ/B9jPElawT/UlGZFrGKi6HFO7n4HpZlwpNjqP3kBH3Sw8x7To+g7R5krmDs015JccRbctQETO5cz0r8g80IAi3ca4cg1q2Y7avxN38QB9GWGs761B8fahdBqIZpSiohEh6ARJR0lEx3tqE1MajmEMJRCrT8BtsNF61lRyftdEeO40tli95Gz8gKlRD9GJDhSVyMkkAVGlIVGfiBibBN2PQnAqGHrhhAGmWwj2tKBFJnT2OFxxFzH0yStEL87EpxIJBlfSInlYVvUN2vdqwJ4B/T2waT7G3m6c6hJO7eznyewHuEzzEdcd3oVQ1EEwmk1QFrFUa6D0Ar6forBY9RvkvnQE70HIkMGqQkhKR7IrBOWtMC8JeauBwOJyELLpFEoobTOh6utHtiShGgwyEC0gorZhWHEK1B6o+hYOP4ZizCTsO0X42MeEvV60vXZUTS2oA8ch30F00hyChs+Qkg2oti8jZAuRdnAkkbWFRL98DSG/GNHVgeBMQWxtJKb7JOe5hhDFIYLOEPrvNrBrpsDIM3IwlwZIirYS3vs71q68lcH4FajitnKFEEMsUU7527H5tAiGAmxdW7n6tlq+uW8F8Z/uQVvopUT3McKYTcxUOQBQTmmQd15ApH0DgfcLsdR3IcREYdNhiFNA34/KFEenKp6Ybg86g4LTUUskPhYl8wABTxoaeTHlgXFExFV8P/ZmqrqmMU21nTc8m9BXf03PsktJabER6vbT0XeKnUvOYQLnkKt+EyXjFHPlNiK+TgK5CpIXdL4KLIZNiJgAiEFHjKKFcAA0/7XVVv9e/BynegvKzzRwfNy4ccrhw4f/504QCkHIDT/Mh4EG6JsIZz+Oa9RYtvAHkoR6MiIbiTmQyaO9k5g/VEHjNBHjsArH2HEk1b1PtqGKQFRLpFOFdUCge/GlVAsq0jsTyUi+hjV8zUIWUhN8iLSNm7A5nYQn6pDMffjrJ8H0a9GLBZiidvDfD+tbCSb7qCxNJiGQi6WvnJqccYxv+hwl+RY4+BQnpm3AxDdkbnue3o5kBi8zkvKQF3mFE21iHi0WGyP2V6EquhM2/x4KLiagOUWnv5rvS5/GqdayoukG3s1agc2lsPIXH9B1Thax1hRUqelkbfs9QY2f/cvHUNJcQbhMjeZUDnGtMQiWRhjxLDzyJtyewQcZ88jp7aDkjXsxR0qIJvchS3EEghGGF/Txu9SVhBQ911btI7f4LQK1W1B9fiMem5o5EzcTZ47jAfuj6G17KP3+BKKipW3E+aT1DCAGHARXvMPxTydToq9FMwDI+QgT01C611GljKJqzpXI7hpiq5oY1XuQ5pGF7MuaS7ZhCiZZi2Ggj67+w6RVH6agU425vhJB44bOQZhggKGLCbvCeLTvY50qIcedwyltNUZpLOmuQYT6dXBSD1IsysAAxPmJBiXCF+ajVXkRpLNQOmWEPgml/h2U+hjkhAhhnYR0FFTLuhHbVXQJozl5wyo6HLtIbxtgco0HyTGVU84TbEgoJJS9hOsNYXYFDrBg3Yu4Jifitxdj3LkDo9OPKAVRWgVERwLijGsQsx8EQA57qK5Zju7dY6RPm48mdSecNJ/+37Z1wHQdQ6oRfJy/lAs+fpP9c9JIia8jp7eRaKIVQ/gifj94M7/sSGe6rpqXHF8yqn4TLksqZtcK+iNbqJl9guKTbuw7FPovv4sDhi9pUW6lTz5MmWxijHoare7byBvuxWK5DmXnasLzrBg1v0VQYoEgwnfvwqyrwZb0P2bOgiAcURRl3H+njexxduXJw7P+proXCmv+2+f7W/i/KdDOAbhxOZjE0/kaLv4lzrgEnlBcfIiXVLooRMetkVvoDpXx/dEsXq19mAZbMcljZ2CySChDO4l6uwg4vLgIYdkQIdwsECm4ga+nNRHOSGMBl6F1PoS4eRsmi4v2yRNJ1kYJ1LlIKtpHl/A1ejKxeV4Aw0vQMY0AdtoTPNiifoztbjRxo5B8B4karUhyP9FAJvtUVzDtxodouyET7WgBo+lq1F4fQngfJywDFB90o0mfDTu/BoMfxRxAidEh6Eo5FjcBl/M4DcEsFnX9QOK3vSjlIpFiLVXnLiXFv4eAVsYVSCJrXzXdty4h2/AgnPoMrMeh8Bu4fQXRm828nV3G1a9/hL+lCq1sRRxfhHehFuMtXchjDaim7IOwgsJMwk4dWxMSaM0p4uROI3220VzS8Abz92+leYaGzOoW5AwBMfkGJL0RKncTSUshoqxDao8QGTEd1dFahvLm0m2r4avE+SQ1hBjhlpi090vENCfNeVkYJz+DJi6XiEZFuHsv+6N7sblc5McuJ+2LF/DSgTa9D21iAtingf4KAj9MQxhciLarknCrhKqvFmYLCKPTIM2C+6QDvRxPZOArhHqR4bhEdi4oQ9caYGFpCCnrN3D8Jch7Dj5/DD7+GvnuAYRQKvSqiKrSGZqdTzj7U0JyHHJvGEPcaCQpCVXvAFJjN5EECz32VkY0VBFJmIJqQAuhbXiSDGi3aOjOTEKTrMHhq8Kfk0RUdS3V3tEUPngh+uQktAuGT7+gO/w2FJwFAxfAt3aUcalEktWEqKIj04bkUxOzrQV38gJ0Y9/jmBdigveSYHuSNMmJvGUB4WAE7aE2OM9EKGEBQ7pa4tZn031hGdpIiBjvNlymM9ku9XOEHEQlEa/7BHc178KR9gpK9TT841PRhHqRjv8a4Z1fwO+9IP7PBY39FAKdNS5GefjwvL+p7uXC538Xgf55+fN/D5wD8NSdkJgKZyyFxSsBsAPPCTauUTQcp5wVwlkESKSgdR2TUkYS6fST17eD6NFKFHkUQvIypLoPMS74LabA5zAjG6XvPpSvH2HaujFse8WBvudzlOpdHJs3juK6NlIsE7gnXMrKwhk4xCgVHGJyqBo0SxCwE9LejVv3MIei09HJARZKWqTDVYQyi9D4b4KmKiTPPoqG3oZLVCSljqfRZiEibMRstqAeOELuoITKD3QfBIMaJT4FuVhEIJPwQCcp+z6jUNaQqvGzNWEm6QsVSgtzMT/7HAUJ++marqY1P5XkwSm4YpuI3XuUY0tUWJRD2BJWEhD6iY7WEQ7uZxAb8sFyOtPTscW5sHY3E1HH4HrrSsSohKD+JYa665A022hNHEG6tY7YpqOsaEkjJultmJAPRfeTWbkaZa6XgDkO7b4fGKx0EOzowtF0lGOXFJGS0MFQdxehXAeNRQZM+svQa0VSTKcoc09F7F6N2DtI7o9uxO/mwexVsPJZSFhC+hdXQu8gWH8HKz5E7ZhBte8GsppPYHVuQuiNoh79PIJuNcRPQt01Gjmah7hpKfK2DlqyHdx501WU9rUji9lk9zUx5tsI0w5KWGorCcwdj7HrHtCNAkkDPi/otSiJCQiNIwllnWBoXiduoQslGkNQNYFkbRzmNlALN6GsfwdhxgOE2z7hm3Q7/iIz2YOTsMSo4d1dyOdJeOercUSaiIZUKFYV+rZuxJMPMiJgJdolI51VT0AajebEQUQS4Mg7yBkCwQs7iLrrUPnj0H88QN0vlpOtPcgXCx9jxQsvYJ8E860BcHeAFvA4CdcGUOcngF4D7cvRVG4gTqhCyRqDzd+OPuJl2PI4rcILjMfGbIo4IeSzwyDyXko/1zpvwZzXhqbdh5wgI7ZsRihd9D8qzj8VP8chjp9Xb/4e2GPh6Xf/7OF8QUs+i3HzHoq/G4O+gLaEbkypI3ALbSh2mUDaSX5Mn0pJThDpyO3oi2/DmLEYR9EThAslxC/cXHj5BvxLUtlx3XxKmEhmfCFfCHu4TZPCR/jp5jVsSgghWge6x4gE9jBkeJXY1/s488ofwSOiqxxG0WrRWL9CqX4C0bcXfBr0g2qQz0a1R0X2MT8N83uQmmuwKmGiuWEiqWFEfwdSYgbK6IuQo3cheauRs95Epbkfp9XDcSmdZa0V1Gen8H56Ost3OrDQhwYTo45U0Vaq0Jk2kYy8Gnpcu7m+4D4usBoppAFxyTl4xAhlzjo6Lh1B5cgLaNT08qtHP0W3b5D+hB8JjCwDoQEpYQQxLZDpaqD3SBJ5u1vQqDpgix7MPoIlW9Emu1GcViI+N9FzBwhPUkjZGoaxasZlV+NNdmAKd6D4BGypfRh272L2YQ3aoxUEc9fgcgwSmrCA5F0/QmoKXPwMSBp6P7iQuNRYBK0MIy4A7Sn0g98w2rCK9tSDBHt2EHv8O1SzroTeDaBpgvSjiIZnUHxl9LQMEenXcsO+z8nPSyTlu/eIGGJhUx+eawx4U03EVmZD/BBI5fij7ehi4ghMGYFAC86CCMGiJIKiGsU/lpD2R/Iil6KzzUUoXwZf3YJyVxF8/muEfQ1Mb1KhXbICa+0JmPMV0cS1GKvdKFmjcGUfQHPSz6nCQpLFBnTZbnoy8klylBNINtBl7cfSdxcWWSSa24+osaNxxqBrzUFoN0I4SNhfw6HYHCZF87ELCnQ3QGwfqEYDIO9+DLV8CvGTKKhToPIr6GpGmKogzF+HVniQOnOQMNuw00mA7cRE5jDZ9xqTw3XwQxtMz0PRLkPwN6A6tgC5ezWRW1/8meWI+/P8VFEcgiC8C5wF9CqKUvzHfY8BywAZ6AUuVxSl8y+18/P/Wvs7IyAR5ABO7kYIRBFTr8GhvYuB7GIMiWdjyniJQN5MirRbEM0Gcksb6TGLHHS+ws7cibTEZHJ1x20cw8v7F49mKBJDYfRiKjPjMWAhBz8PkoYbLacUE4Oai5CFAQb1b2AK3Ur7uCx8chk2txMlVUbILUIIbqbTMUBF6QSaxl5H/ewZeJbfg3D5J6iXvkmu/zd48jR0WeLR7FShMkURvYOEbYPIHCAilSBGrQh1B9AFTRjaY1F7JLQjtrOn+GauGHE1wTF5dM2IJzE6gCXBRfrvKxhRXocs6xlX/ypT6/fxisdIYV0Lc01ncdgyDo9hEhkps1ggWxhrNeOf6cd3SoXQcQBb67skRK4lZ38m9kMeVDhofMfIviMJRO9+H75uI/DED7y97FfIKjXCmA+xL6hHTPiWzqQiasIpBGQNUp4X64lE7M1LMXjDWJUCTi7MpW22yFB2LNETbUTkJOLHXgsFVmgVAT2EWjgyOZ/+lAxIjIOKk6C+CTn2HTxiOfr273BsKke29NMhfoQ39RKwrQfrOpCrCRUMElCL7L8inwlVvaQekBFiElBbQJUQRXJFGRprQPxgK6G9ldQ4QlRGbqFf2UzPJQFU9YPEZo0nOVBMjCdAmnMPCDKBwDUw8CDK505YNRIlPoRv5RFaPl9JQtZ9pD5aD69tgK3fEZpxJ8JAEHVdlGPWB/GljiPxswH2NY5jIJCCxldAz/OpGI7oSQ2MxZBzHtGyRRgrFQz+TlQJsxCKepFHaFGS8/GLViLRICXVe6F4Eux/H8IHQD0RRZYJdwSIZN4AqTq45zaY0ApX5sH4CAFpPOXaY1gUgdjoZkyhTOLdOWh9u0B/Gfw+DkY+BXYfsjoOMeUDxIMnkBxnERH2EeT3yPT/o837L/IvYXZ/S/kbeA8489/te1ZRlBJFUUYD64AH/1oj/xTof4eCgl/ZQHzrWIzGy5CDdxE7YGdQfZi28Rakuu9JrziP0hcTSOzR4zbrGKtP4SxXBbNCzeT3HUdo8FKx+hJcOjsXHfoalzzAIU4wl5HIdHKStfjlMooPH2an0kMP52DiIpynnsedbCGlaRdCGBTrWAgvhtTnSS0+SVH8Q5jtewimh2lwXUpN5EkGtNWI3qPENWQiSYW0TEkl3KlBNuogOBq58xTqChmOLkGs2IfO2Y2lIxXJUspBaZAS8jDqR5JpnUxsyWjEbgVVl4Itz0/CB62kP9mM/ZsKbvj6bW7e/gofaI/gf2wpca1uFj65H/fhQ7iSLUzkLLRzPsBRFSR1rxu7M4Um+Xtqy7RERzoIr02j9KYl5D/9HBWPv0Ogo4PeCEwqX0u3Oobo24uguwpLZzJlq4+TEHYRiLMTDOnZee5y/DkFaG0TsUcrGRe+mYSs27Bnd7Nh1hR2DhcRvGE5/kAaykhg490owTAO/R5+p7+QoCoZ95gx+DdPoa/7FrQfHyLmUBbSiPNQmy1Eh9eyx/IW/ULt6WnKulVsMF5GQ2Iq5x/+ErHUTcj1JUp3D4QTkMdk4146l+H0dLzTNXgn5GDtaCKtN0iMSyRtMIRkK0VtuIxj+jwMxg0Y9xWiV2xYVScQnqhFuPMtoro2Bj0bEEwmslT3Y5l5MZH73IRv1cCRb9A8+SbiV4OwcQ/jhnIJam0YM4sZ19KN2n820W2H8WfokbPU6K0qzNU9mE7aEeKnwuBNEP82xD6JoN+H81SIoeRCpvrbYfcaGHshVGyASDmoygisXUukfCOqo4cg1Ah9lXDBRxAehtQJSKEC0pQh1P4nievbhi06B6NpPViegz+shrIZUCKjSFkgmBAGLKA2IHRWo/9aRZRTuJmKTN8/2ML/PD+lQCuKshMY/Hf7XH+yaeR0htO/yP+9IY6/RsSJrT8FxFlE5ZdBPY5vqWZ8q5/Ogt1kDpYgVF4DNX7s6XEExpuJ6pfg1VvxHiqgdr+N2XfWEzWYuXbXR9RlnsER9VouYhUqehliKy7iWFnVQoNFyxRhNTKFeJteoNGUwMT9zcghO+JoG9LxcZB4Cvq24ok30mp4knj9bRypkrjAfwta2ugzbKfD/T0RJYrF0Ypj3yRa8oJo8JLiXIt8RIUU54CUGaht06Hzl+BrwqDN5yAnuYXlAAQnxyP6mhEsSTD/IwSvFZxzkfqcBM+RyNac4taqOq6yVrLt1odZ5hYxzgKmb8ci6E8/SVYF5t6CsD+M2nQDo/sEog2bcMVFaHraT+pH2zBVrCNlpszBXxRjvzSRPNp5s+daNmYtJe2HLs6cuZB516/D4L4J4cdD0FZIQVIVfb1HOTDiZhao+9F5VqLRr2Kw5Haqw3o8eTaWHOhCvb4Ol1vB0PcmQ8e2kXJJM5fFnsSlCqAbGoF2qISErw/BtHugbzec8SZC9Xmk9O1FSUyjJ3grlv7pSIP7ycZDiiCg7hSQErwMlllQHVawfXEK0WfE9+saErpyaMntIT/xQWwVn9EZqkZsMaFk9qNMfZc90jfYycUoOyAaBjSIj94M8zPxd9xKy4hOkmv6UY2bg0eoxayMQu0aQUjejTxiG1z3Lsq586DDg+nKC4meb2Fo0RkoRg/i8FoSXw7hbfMQKQyweupyMm0pTFl2NnKxgirnEJQ9AaaLCB07zFcXOVneGY87PQGKx8Kar2GoA06aYKIef/sWrJOHEb4/BBPjQHkPunaiCIm4xp5LkHJs4ZlopIsgNg6nkIjedxjdwUcg0wfTL0DxP0JENYB05DJo+wDsqZCUh7DtDTRLNqBo+vHzEEZe/8fZ91/gX6Z6/404BEH40yiGtxRFeeuvfUgQhCeAS4FhYPZfq/9PD/pPiBCie+gNaLgOKtaD183buvMplbJIjXuR3GoV/gI92CSU2WFCOQN0imkMYabrpTChrBbeUy3DscrAqiPvknXMxWHjCDz0o0FAJJkTNDG6y06yokNJjiXuVCvmoV4M79dSdqge9YQ3wDeIsLMPT3ocHn02SsNvCfp3o5XTsQvn0CqupMxVi6qhmdTBV0lLc5CjS8PglPHM0RC/fQgcMGi3oDKHUOr7UXZcBQOtRNYGoeh56gb3MyZkRFRAiQzhS1mH+btewAjWWZBcBncPIFz5IIppCgFDKoJN4cXqx3lTczeeyCcoWctApT998aJOaJgOxgD098Kbv4Lay5FCXdi9KeQpXzEwfwqyykXCm7VMnaxCtdbI0B4zNy6YxXPuZ7ij0I0oh7hrVzqX7LmDF8z38Gr2So51xZFOCpOMI3lRexOdopaw/wPsk27iup713FK5lcqphYQvn451TD7C0tkI5w2giBrCTQKatVGM+zYjzrwTxpwPx5+DMh24m0CwI9mXk6HcTlG7GnXHYUJKPNk9PcSYFxMxmmiaGEXOnYteLyALCkKoH05Gsbnzyd49RK3wDPWlPkxFv4TBWghG8AgBuijHQgr0VEBCKdLJAZi1AHTvEgg2kr5R5lRZOkPydoRtjyI8vgjhptfRPOAi2teD+MVlCOfJkAnCVA/itAL8uu0EpAHCtjEEU2PhtkmoQlGWtVeQqHbQdW8ygaYAitOFXPUwctTFzgkDjKrrxtAwjDfSxvAZZ6GkF0NXH1S7iVTdxZFZx5EkGSFFBZP6QduJvOcg4fgKtMONxHnOQhMwgWoahILo18+mpflmXNbD7J+0ghb1AXxKBR11rQRfv5bAyQ+R518Py5+AW9ehavRj5A/ouAuF0D/OyP8C/0kPul9RlHF/Uv6qOAMoinKfoihpwMfATX+t/k/iQQuCcCbwW0AC3lYU5al/d1wLfACMBQaA8xVFaf4pzv3fQo6iEKVXbKGOPQTxMm74CHLMzYgVbzJsjGN20niyTTK47iBGO4Ri/RHOvwHhq2/RHXWSG2NhsCUG7+tNRGLTybs7hGAZzYGCWryFEplrtzL2ZD8Nd3cRNhQjBfuQGt+ia9LDjPE+hHrQSaMqmZHaHdCeDwm7USQbynAJznGFGD69DXnB/Vh3PkTM6I8QEoykW71Ee6P0f3GSxGt0mHsPI/U48cwpQyuux1h4Deaqdxg0a4jkqRC3hIg2GtEOrCayGzxnhYlklWFqfBd87yP0foZZZUe0O0EY/6+XJ0gvQt4MNK1b6MqYTko5WCMyj/AGzxe/xWOup0jtsEDK+eDrQ/F3oKTICNNB+X6AiMmKEFIj+E9h2jCbERYvkQvTGFpiwLK3kfxbHNQfH0PF3c+S9/JnbN73GstPfMiKlCjR8Yns76jlgao3uMNdwmXSGl4UYrlMfSlug5HvTRM4M9KO9ew/oGxeidGZgCdyEO+5yUR0/egTLsX0/ttEIy4MJangPAVVZ4M7HUpDKJ7DCGYvVO0Ebx2kroeOFgRFRJ8hoszZw5CpkWCcnbSjRWimvYBQ+S7EqWBODPZtQxj6DyDoe+nsyiZaGMa9dw+lXQritwMMLmhkFg8RTxHsuBmlfAfKNQHov5dIuoRn9Fn0DFcTb1iO1b+e/hlvYy7rRVGWImxoQvWjg6jRiRgnIqTICDtllIIwwqhRxIZD6MN9RPUN1M5IRzoWi6z+moyKCoRhkeA1s3AOV2N/6UUqbtlBny6HxPwOQu//luSgC+upIsKZk1BPsaG0H8GzrJoW+QKix3YhnVEKHAd1CWLDboSJT6ELO6DnfvBXgBRPqzqWjycvpWhHHZGzzsaldWBxv4M8KBE+INBy5kQKOrUQl3/6Qcqb8q/PlETm39XE/7P8Had6fwz8ADz0lyr9twVaEAQJeA2YB7QDhwRB+E5RlOo/qXYV4FQUJVcQhAuAp4Hz/7vn/q8SJcIBVqMV9DjKX2C47CIKBBsiIWTFRDjvWgYs67E05zGi+m4wbgJdJ8S/gWC4EiXogcjvgSgkZhJtisVyMovd8YksVragsIlwtIC9bjNzP/sc/4Ux5FzzIXuencbUmr1ExDR0+65BSHER9aTQpG9DHSwi984NhIx6nF9sIr52FzGVWbSUOkg6tRrJOh2h9UNIWMQlKWq+OnKMkEtG7huB2DfA4LgSEHXo5bMJlxSiH9iMqJ1KGxkE7szBcLyV5O19dFiTORy7h1W13XiCzchdvfgyYjBW2sAUBVMeAF5qaOd3ZHSuxZXoRK0qw7fyXPTvXkl+qYYx0Uw+ijuDleHfYnK9DkoUYoNYpVikiX4Eg8xgxnjMrRXoDDbC489DrX0EjWBGExnEM6YPv+cow2O/wDZtBs1XX8uYO4w0ZncxYmscknKUMbF1fL5rJsPxdjRpWrTaH8hEA+oLGQUg5QDgypqIV1qHb5IN2a8n75te+pI/pXFcMakdqRg9myDtdWhai7LoZXy+RYQ1hdjcLZCyHPRa6HkSYvKR1d1EdVF6o+ejdZqwa0DVv4do6GHELjPytSLR7MkYAluJDlbBRoH87zupj59IqKCKIYcGS4eb+uBXqKK78A57SPnmIyLZJqKJdtryk/EmeUn6+hvSNJMhR6YpP5XdqgOsCvbTc/tSVNMqcUz7AiouQG7dh9Q3hGBRY9h4hPJp06nSyTiOW7CXJpD33I+sWXwWC/YdgMzDSAvrMEpmjEfHsv3+HKpM2Vyx41vUoSjRolQG/B5806ZgSJ8OoTyUU19ibA8yVV3B/uxJTOzrRmVZCCkvo8y5nYHUr4kfjkP0R2EA5LRL2ehxIXSHWVTZjWr5VxTJTuTIg/R1n4Oj7WXWXZ+JI3Arjn+Ugf8X+ZdVvf+nEAQhT1GUf0nztwyo+Wuf+Sk86AlAvaIojX/sxOo/nvxPBXoZ8PAf//4SeFUQBEH5O82SiTLAEO+gpRgvzbTyPRKDGIREdPH9WDrXoU6+DU25jPCRj9Y7Lie9KYg6NhPSzgD1BSCmEVTF4xRa6Rj6mMor5hErm5l8ZAcdN1+Opn8L88JBzLUFaJQ2jo4p5RzPLgyOCMNrBd59+wpKTx2iqyOWlOU3I1c+hNThRKVMYX7dVh688DluMWhxoOHw0jlMbOghtvpj1BNTsZWbEC74Ato/Ak8PRnc/58YM8OmCB7km/m5imlPRpL/JgO4lrFyJpFWhJKdh6ohHZZpIrS0N11iFwTg14as1JBgqeEtfQq9YysSCAAuOfk403kzIMRpDN/TzI/18R57rTAS8WLu0CMe+IhD/CYo2hahtJVe5PuY25WlqzOcxobcGW+1WVBN2gfsBCOYTydmHYj6MSlAjGs9CUm4EwXz6hqhiMBGDwZqFk0YSc89CengjA8UteN/OoiMcJLVxCFdrHnET3MTaB4hGPQg9T0LSW6eXvvrjStFtfId6lAHbBjsp39bhHNtLy/xx7EiNZ67vUhydLwO9cOQmFK2N3pMXYwm1Yxs4DMObIT0BAi0QPw9EPUHHCiKtn5N4IB4pZjTv9ReSo2xh2iMfgd2AODSEZ7CZgEWLQxoimqMlbm0jGatteMUqtKl6pKKJ5H90glPXx9CiDhF9PAk5F0KCBv0oCzZnhMBoEX9BAo1iDcelEBmd30DtRhKGxtGR1kVr78MIXi2nzvwlM3Y8wprX7mL60+9Run4f1XOLGMwxYhqCkEVFpz0FdU8EpV+P/1ABmogXQfayadT9ZEab8Zus+P09GM0+7D0+dIUt4PIQzXwUX3IPpu/6KUjT8WpqKWMNB2jUN2LtPhN5ZByD9my6EmMpPXgesucDdgU7OL/6e4yiFok42HkpcimgmUnCmAdxV2/HFW/AoB7/5w3yZ8pPGQctCMKnwCxOj1W3c9pTXiQIwghOh9m1ANf/tXZ+it6kAG1/st0OTPxzdRRFiQiCMAzEwr+NuxEE4VrgWoD09HR+CgIcZ4CnCVGDCR9mFlDGSiRiERBg+Eno34WSOIvoK4vpiA5g1D+HWv8M2KewzxTCSy/QhBYjtpZyHIN6cnWnyCWek+NNhJ/dwpzbXoPgo5DUgrLfSqn+KK15NQxfbyHl+36yhmtpsmXyxXnnsfz9bUyeugRp4Dvktk0oSoilLzzHDS+kcENNH3P/8AG4nEQ8Atk1AoQP4XxzIt+fOwnZvZvZm9u45mg7142/h+FROmxeNfqtF5AlWPA5bkRrvwocWjQBNUPqCSS17CDgcaOq8rF9QjGx6liMjmwKxQPEhLuoKpuPang/NYYYJnQ30csHZMlLkXrfgOwvEDu/AMsn6F3DYAbz+u/g3C95TgxxuVuDLuErRpiWkVJ/Poo1H7cjgmoTOOKyUdsbwNcEkvn/c29ENMiEMH/9La5gDUKKCfuSi+mQt7MzaQ/jt99MwpffwHkPI8X1QOBjWLMe4qaAUkJz/nl8kFDGA5F0GNeFMtiHNm4Btp2bmV5kpGvUCCrG2ZlboyOsTyccM0BI3Yc7koNbHUUum0tM/3cMGyw4LaMIWD0E4roYSH2YaZFszK2NZHQcYZs8h+nBLyBjGoJ3ALHeg8kYQhVQkIwCkXSFSI0GY7wRxhj43eIbeU9O4OuXL8Ts8DA4LR1VsxW9IUzaCRV0GFDi5lHv7ycQ7aIsrMFRMgcypiLM/5CUNffSkDFIzzgLeZX1ICmcYXqNyNOHiImmMO3LHLymSpxVEQJnqrjG9w6q9DAqvR1VtQ+hMULDtKuZ1ruXLK+b2jlWxv7WinDDL3CveQ6pdgmaC5/AGzkfU1sC5OxA6ZRIM9o5PAoKj7TSaBhBu0NCX9dPMMODIHezZ/RY5jXEYZn1GB71WjpL36PH5sQcbafUdRekWVD39VHSMBXNSNN/bJByCJQwSMafxL5/an6qIQ5FUS78D3a/859t52cVxfHHgfa34PRU75+iTR0lpPDx6faREf79e9HCe+BIG5v9W8ljCP1zq0m0jwDhBTh1E+NtR1Dpsv+kk20oO95DEhUa7FpGexqoCBbgF0/iiRzGZ3OjzDWhr/ZhaI5gjJ2IftR6Jh9toGryaIr0azBccA3u7x5D544SsaoxDQUYs6SFSZ3DrLaoyBrKwzLiEOU33M68246y4+H7mbXxEZbUCWxL8HByYjrlZWayDCpkkolM+RTN8ZfBGofRvQvX4BPYtvci2GU8pt8TlAQystTUaxykHAkwb+FEgrUf0190CDF6OdoNEYZLBeJjVTw14xKaSOb97qcg4REQdaeT4Q94EfUlCLaZEHgbtszHUriCh70R3k+5ngTL2ySaKgn76wklLcMcb0JoOAiaXBghgLwNIqNBNeLf3nMU/GfPx3fwZVKm9yC/00ck1Ux71XW4s4KEb3oBde0xhJwzYOAjyJwOw4PQ+jR/SAhxhf870MRB4moY/yNR33GOTvYy89112I+9zLhcJ2GbgcNv9+FbMIepSdswhloJJhQh04AneRzoU7EmXUBQU04B56JCoF91N5rsX5OQsp2Dq6dAwmhY7oCW6agH3kd9REK5sIJIVwti/FLES/cR9Wp5y3YOXU1VPN/3EvE6J1HFTsL77fSsTMZvsuNSH6f1jHmcEnT42wNoki24VCbGDf6WFq2D9YF7MUzUE9vTij4pGV9+EHFnAtp+L0beQKjLhmE1OpUacamB4ItO+qN60keG0cUPQ4IBIdVGlv4dbAEzjY7R5HVH0d76ABjmEXPxuYSuHE9w6ofoAgkMSRkIhhxUjlgW2JbQpmrAn32Msf7ZJPdXEhy5iBr1d1RMspEiDnI8pZvOyh0YWzpJFhxowhKjCyfD4HPQ8gFBc5CEzDhU/xINoSgQ6YVAE4SaIVAPHY+DNh1SHoLYi/51MeB/NAoCoZ/ZlJqfQqA7gLQ/2U79477/qE67IAgqwMrpl4V/VwREFBTCfIuK6YjEgiDQM+JaqL2LEw/ey1nx+eA8CIFekMyoPDXwJwLdljGDoZVbUORu8jsqcSpaIpKKYfkxzIPnEdP4AkqMF/q8NLSNIlNqRgBihCKmH+qkR9LQoNpMblIswRO9GGx+SBcQRJkrDtxBd8HdWB6cTuiHLmxX7ODwQDPaj3yE2p2Yh/ezTKkHjRVZPwPVxRnsdU7GHruXXIOH7PSnGBbOJRitZWjE4ySfqie2s4WQqKEuVMSJkelM3H+MQ3EdGBJyMET7STTeQ+Pczai616MmxK26uawP/cirlgt50DQVXTQI3VsgZjxCSwgqPwVzNsRXgvNNSlqm8XT9FtC5kHNllITf4OBscF4H805C3ROguRi8i0GwgP0ACPo/uStR2pR7MKddj3hvLOIfniJ17EyEMZ30qUVU/z/23jo8jjPN176rqpnUklrMjJYsW2ZmTJzEjkMOThgnzJlkAhPmxEk8DjM4psTMzJYMYmaWmrHq+8OzO7P7nbM7e3bOTGZP7uuqq7tLr6rqqn5/j1499UCkDcbPAUECcsG7DSXjSQasjdiNRlJqkhFy74SKNfgzF9I38DpFoQfojzlCpM6B4tMgBn3k3J2D/ekymtcZyV95HKNKAc9P0PMydl0ChMpp50dyuAiN10tsewwdaTdicV2Itr0ZkoeBKgBGAzrFhqJRIHA5KqkJpR16GyJ5vPAJFh7YyPVbVuKZrseZbEDV7MU1UoVl4yH0mZHohB40uoNMjJSIooLuITNGcxwuScvxuLvI1eUTfeRp0k/tR3dAhXPcxdiTiwirCCco/xGlOxuG2hHjDLSoUyme5sK02kXv20HEJ+LwT70G60AYoebHkIQQYVuaiahwwpU+SO1H7vg93CTSp4olZDOQcHAnYt7jCInng6gjm2R6AwupjV5HR9IwBoRdDKKgE+3kcQ2JqpEYC80o1b+nJ7WdZ7MWsPb1V871Y5wAWs8ghv63CNi3oqhNaAgDdTRo00CbCuGLQNBC5CXnjPQviP/bPuj/E/4WBvoIkCUIQhrnDPGlwOX/bsxa4GrgALAE2P738j//ewQEBKIYIgcdd6PjESrMZsYHozHFjAEEECXQpYLfDFVPg6cPELG7TnMk6zRx6hAjOmpwx8UQ/5VIrVsi0leCqvMQQo0WZeEqKm2vUJuhYXRdOaI4CmHkKgRRRUzPQVT9m2iNPoZUlkRodS2W28x0RMRSk5nNVPurmBqNSCYT8Z//QNWXN2Bdf5AOv0SE6hS6aBnGOlC3NjK161m6NSLxLX1UqhpZ778Og9JFvGMI1Dqeyn8IMcuHTrQSpoSR1e7FGWyi+KOVCMNDnMxdzofmk0xgBWN7uwnzdRCd305W0285nvY191PH40EL0ZZUSLsGsgXY+QcIHwl1rVDiALUFqb2FYAxIvaNRa7eAGAWaJPD5ULQ2HJ5vEAcuw5QQDd5PQH/O9aYgo+EEwmAcryYkcvtiidQLXoFtQSK//Iauu5cgJF755y/PPB2ldQSEbefz6K9YtmoNwryVYI2C1uc5XHcR+XEu7mk3cpVtJMMjj6BVOVF5zMRUVhI3M4re+Ic4cu19lLz/Hlr/UZy2YlwRyWiFkUj8xCCVROvGoOo+hRRZRWBwByppGYGZt6MWvgDXGYRGFeWJ2Xg7Mhn9XQd75k2iNxDJH7a+gTWjGWW8BleJDcnmwbBqAF1JEYwfjtK3Ad+AiKWrFctGGVmdymtLbuZZzQQk59MU6EfQoJSRpQiozHMgNRWTbT6uvlUEBn5AbShF4SRyk0JXhJ6C2ho0Qy6Ekjww9NG1YhAxsBz3sDBOFE+jWxfLnKGzDFl6Cdt4AcSaGUydjm74Z8QbRyOceRjFdxSPdQNqcyHqHWtg+GJsvVWI6kza2mvoSE4hXPJhCpnpVG3FQQ2SSkFZvJEycSzpSiN1s/xIXQNI4ROJluqwaxU6k1qJ8z9FeMTl51yJf4lh2N9F6/9V/kfW4viTT/l2YBPnwuw+VBTljCAIvweOKoqylnO+l88EQajlXHbNpf/d8/53UDMBLTcR4jhBDpLHXry5Mair7kBT8gOCtxnil0DUIjizDKX6DfCUYZAEphkuICzxj6B/ijDXIcSlo/B+PkTg9Y+xzxhFpHoGQvXH7Bh1ATPbP0LK/QSl/RmC/bMRw+9B1OcR8ekbGB8VqFfH4zgrYFulJ3lpB9lKAz3mGD4Y+zyq5ApGfn4DxRlz2PfDJKyHzhCz/ktUZplgigol0UhIbCKmx43GmEGR0sawNh/BYBWnI5KQq7X8wfsoJKTji32TduNk2syDrJ0cRHfkfVak3IfGfoI4Qx37lUlMFg9had+E1NOGr0BiWOhmHhFPUFl+O57E80iJmQZthyB/PmSOgh+/hugSfKZeehfcSPyGbVC/n0Dgt6gD30BCNcHep1DcKxGZg7FBB0osWMNhcAWEHPRxmj6zmw/77sIc0Ul16Aip1jdhiYg0oojhz9wDY/vhuruRVW6EN2+AS67D1xniTFY0d8YXwvFNKNOvoGbESGjYTa/SwuS8j+iPFUFx43aq8B/U8ZPnfnbF34MFP0NjZjJj/tVMfz0d1VgjicIjCKiIpJjoPz0+cRYWEn70JN2JjRgmZFLV9zmF1jao3Qc9XWQmLWCX4uGxZ28n7OQg91VvRtQ7oMWIoPcR3tGDChnJGwI7+Hs6qPUmkNqjI2ZHA8roMTQtP0sgNwQHp3H04kn4G7Yze8CFkH0L1HwDU14GwJg8Hd/Aaeqnl9J0Vo+5tx2z20F0Sy+HF4wkqE1nw5UTKGw5QN7azbRVW9AU++n3jGG1Tcelrc14UyQaYh+k4PnnEeKaCD7+Gn2WA0TGTUNXew/s+RbOHw6d1SDWcdo8nj22aHJPOrG7bEyt7Uc1uAlz8U30F65BH76YCmEs80N6wmreJuh3E2pqp/eia2iOWIUdM9b2NUTop4E+/h+o9v8a/yM7qiiK8jPnYvr+ct8Tf/HeC1z8tzjX3wo9TwMe3NyJhWl0hB1E6z2K27uCMHcngm0SIfk9BNVxBL0EYzagUhIIl/3gLUMOnkUQ9BAahte4D51gxNlUha/9DKrgMHomTyNZKQB1FETMQbBvQwk7gfzVHYhNdqQzWeS48ml9fzinvt5NcXMEmsQGYn0O7v76a1r8/ay6dixbFR9aR5DIwjT0EXcwYu3rKHIQpfkEgSgZXeTDEDEB2m4B7URUcdcyXDMTggegzQETD6AVzaQNbMPa+R4rVLcwFFHA5eoSfNE/UeedzTBtDp1RP7NVnsFvvJ+j+FWILWFEV+cQJY/md6NGM54+FijKudTfqmeRZ36DT1yGLzyV8Maj2AfGYJTLUOk/R+kcIBCWgWrobQSNAbW7DbKXwImvYPIjoIkGycyJVi/qrjqmRLWzpLeN9yNGMFVxoXG3o956K1xbCE2H4ao05JtTERqOIX2wkTU3XseFfh/KiBko703g+NQKGsVOQik5ZLGfed6NeKqMqC0SLY3xCHoJS0knt4d9Qmz2Ql7dmc3xe98jV1iE/rEJRN7YhiEl5V/nhkIAT+gA0b2zcUf5GR61iuNd0RR2l4PDDkkKFTmX815UATHegxiKPbw68jcUVvYQO9RH55jbSei4CoNzJmnpJlriZvON4Qy3HDmO0dUD509GiKjg0MGDzKqqYrN2DjEt0Yyr8CHseQderwf5SwCCDNHF60SqJdRdjSRwljS9A/e4AO7fCMR1eUCTwQT7T4x7/yCnbiok91QbwUuqGObuJe7BWCh5FSWmFE+wjJ9f/gOTGySMx75CO3UmQ12v4PY9QuLt+6DnUugYwJOUSY0ulSvooankt2g9J+iZuBGDYzbBsvfQfucirN/K6ctSeaZFQKrQgrEHWpupmmvDJZpIbUkgPmI0yFvA3gXBIgiffq7r+y+UX2LLq/9nMwkFRASMGFgB1BDLCHQ5H2Oo3klr3E66dM+hiFmISWsQtUWIqkIw56GYovFVX4wS6oXwt6BnJT5TAsotTUTUhGhZaOZMpMSinrcQrK0EgncT0N2BONSDqudaxJ2pcG0Oqg9qEOs3kmgVCM0zU9/gRAHEgQGE9C2kjYvhlvLt3DDQz5LISdhEB6fNPQwlmlAqVXQkTsSTHo+iWYfSdT1+68W09jVTEdxM09DtNMavRG50QNBDW8NveVVq563cJ3DXjWD0mHuJPfQg+cElXKk7n1H6KvpTIqhNK2Jr0xWEVDFomlIRK3ugfSNPlR+mWally9mXcZ9YQSCyFlfCSwRDXQSoweWqo/GZ1YR8FkjrpX94KUqcCSFnD0Ls9+dacBkqwToF6jxgnQbmUgqyJzLCXs9lhg2oB44yu/UgPw88B7WvQf8pUCfD4hdg+XGUulh8YxRCvrFsTriEcW1L8JmbCYYaMe38ATNXMtV/HpH7nERdNUSyYxDTKZliTyPDHFmcP/oJwvv9tB2+lScHbuTBBY8zLHM5HR9+x7Frr0WR5X+dG87232M8E0SY+xiCNJ7kQDNS5XGUhhooHoFXbaQl9hjva1p459tDLN7ciUpsRBXmIGXcK+R/dx+yR+aQsYBV6eEENj/MvR+/iVmdB5d9RmjihYTUBna7GvBmHqVg3u8oUE9n8Lnn8EQvQhFV0HMCZVsm7vphaLo+QAo1Y+09iC6gpmK+jfLcHKoCxbRYImgwH0EOkwmfN8SENw5hLNKhnWKgd89ZOneF8OpB0aQwyrCUwtRFvDjFQ122E+vxUxgrjWikempVFxMwFEDMXk7ZchlPG8k8hQsDCfrJ6BQD/aYdtE3IpPFWHZ2PvUxYdBRS/+sgNhPsD1I+t5tg/SamPHOW7A92gPAKytDHsPNN2PMyyPb/tRh/IfyLD/qv2f5e/LIcLv8ABET0PIWPz/FHfI+hKkiyuAq3up4uviCybiMq6zzURx9DTlrAoOkRNAdjUWUvBlEDnkuQ1fV4z3yHwROGdWiIY1OGMVpVS1C1AzGkR9DMh4AL5ZE7qHv0PfRDr6G65makb19E2bCdcTntdNi0KCEQtoJqRh84v8BjzEXuOUVuTy05A8cg0IASKyA7RGLL1jFIGH1GD72xGvTejyFCRtUcgkgv3uQ0Ao5u3hx6GTmxgAvVvSSyjaOlOzmSGKJwUxWarXOxjxlNIHwaGvVMLoqw8Ll+NFNO/RHBGwTzDELpx+DENVz3mRHC3XRbrKjW1BF+XgR+6xwM/rX07VYRszQbOdODd8hEuFCHIuXBwDzQP0hL2Hj0YaVEZr6K8M1y2F0Gd/wO1J2IEfHQ1Q0575OtyuEQW+kMRRCbEgbD7wHFhRJmg2sfxO+s5eCb4Uzevx+LrxLZuwxvZBKpdX5yJ41hYGAGkidE6AIJVZwWRbHhS9ej8U3AcOhqcvs8dE+bS9+YXYT/LGHY8Bqz3n6D9gE7ffv3w0QBpWY7bv0aonM+gg+LyOvvx5qVxQF7CQNXaAnvaUfny+b8xgiGMtS0XRGGqnYji/fa6ciZQP/QMtIGmrDt66dAfhmpp/ncqmzGOEKanVB1AK9SgVcZYHyTxKyznRgbV4C/Gs1DWkLd36Pc+RGDhSaGNGG4BQPBkMhAdJCEqiEM9gAGKUBgkorhOxs4nlZMY3I4YfVuXhr3WxYby4n/6jBSvoT4vJH+ul76LHb8PIuMD7xObj+5h4jGKkJ+EXXaXEzGIQJiiC5jB7ZBIxp/C5HkoiKKbmpIDJ0kSfaQotpAu7AKFxoOIDPJXQVzPmKwaBdV0mqSKgfRdQygatIiPLUDxbAeBl5B6e1AuOA7ECP/0XL/DzkXxfFX1+L4u/D/poEO9YOgBvHPsblaliGShmvE7zDUv4Ah9w8Y3FaUgY8ZiqhA030Qf44Vv6mLsEv2Ifg+AK0Ap6rJ76jEnf8DxoYhLDvDMZRUQVcDuthPEJv2oOTfi1xRSu3cDA4IT5NR0YKnzUlL4RxGdpwk4tQgMfmp+JVGFH2AJuu5mhWJ5mGc9bUhqALorAmYBlQ0+SA8JkCsqwGVLBNxyk80HYRSUhEHXNDax5mFxdQ22TAbw7nJbsQYNQcFDx6lk271YXJCJwhle1AG8hkqP017vpf4vS7iT33OTRESosZJv0vAVhiB8+cR6GdUoA3LR4qYSk/XT9TUFzKh+giWEQ3ItQGcPwTIfvgkoqzG26yhzWZFoI1Y0/2oTI8QG+ahjs1UxI+l6IIhLL2bEO47inS+CVXUDLAfg6HnIfIjFjGOr/zLuXHE+Qj1J+g/+hxOdy9JHW5cC0W+mT+bJ7evpmlMEbY1tRjbHYRmTsbuuA1L3UGCkgZVRgJKKEggz4RauQqhei8M+iE1FdXQRiJNM4lc8iDyPCeemt8QphzCsmcmLTn9uPs/wzD8JgQlFWokBqKiEf1+vtZfQlz18xQV/xaTcx9S1z4iki8lQv8q8rqfGVw0AWtUN3ZPJ6GhdvRtHkLJECiJxNvmRxeRSSglD0Ju5Jo9tKRGElUVidHRil3pw+L1INglVPFR+NV6hGQFs00isUKLypCPIvaDox6dfghfvoWkYAu7Fo8gQrEwt3Uv3hwDUgL44530FFgJXz6ALsqD8fqFGDW3/Hnu6yBo3QMNd+G81opXl4OqaTP9DBHtO06ZLZO8oSqC1ttRPFWo5e/4WrLxoOZVTIIOB9H45FYOhI7we/caaox7cUf1U9I5gLSpjFCpDh6fS0h7D0JfC0JEK8wcD3on54q3/TJC6v5X/BJdHP9vGmjRBD3zQQwH872gHQuce3goalfgi7wHPYAhHSFyNlbT4wQDi+k7vp2h0ZmY9pciRZrRiV5kRws+azL9YU4irp1OTVUN1lA/vSkqktqjEVxdCB++j3/QS0aJg7SNmwjVyBich0GMpksP9uHno63eharKj8NqQLuxgeYLM8mKu4lHg6k83XItk4fqkZQM4gUnRnc1bdY41FEi2txlqMuOod5rhoYfcE2NJOPscQrCZCRrJkLZC9C8GiVuHgdSj2NSBdCeicQge1nTXcjy+MXozgr44+MJWbqxevwI/iF60myENTjxF6Rx3J3F3LgN3Gn/mpaiAsLPm4V10EHAWE7XGwqmB804LQH0Bi/6Vh9SbRyxx02IQy/B6P2oXenkSkYy1W00RCdTY+og/zfl2FYNIR4rh3dehdbnQL8VXaidrCP7OKprZ1QwlapZy7BHmknqVng1vJEqXSoDPzbgyUgjoUGD44rf0pPwFRENhxB8gCUDPHWERBOq5D2Igwp0fERoyjXYs+PwBg4Td6IJhDsIFM3EVbwZnT+BUNdOlD4dSl8Xpi3l0DsTTP1YkDiVNZcdh+ZQUlNPafeD0OWHuFhouQrS9yLO/AMRX76E6dFPEdbcjhITjjeyASnvYlryBkjsKEez7TuIdEDWHfj8Ks4E8pivRLC2IIVsp4KlLB5lxgPgfQ17/GlcMeHEKHqkkTYUA8idp+nUxxOzpQNXCvQEY4j2aDE5tQzkFaB1V9HsjWNIcwkjDn5B1cM+4l/rInnL9/jz70KjjTk392tOo3rnVZQn70XP06h81eibQVX0NuLgHORIESmoQl99K3KnzBXeEN9nX889aTFcRSMDVBAtxBImSJRH60lUzSWr1otz3ctoEzNxzIhFbxNR3JUYzspQshRil4Hmr2sl9Y/mVwP9S0DQQMTH0DsPXCtAXfCvq2mJNPRRPwIQDPSiUoWhlH3ECSmdEZElmMOz6FnwMJ2e+Yzx3Inc+DEvGCcTP9DLi5aH+Gj+HYzvPE6peRC79igRb2zFX/8TK9dczsXWL4lkEtqrXwV9DJTfwpGRi0j0t1PXE0v84Rryyg8yqE0gs0NENDzJN95kTlvyaCs8RfyJWai1H+PzKxi0PtyyjaHqToLOOoyBboRZUWAyY+r0ITaEIHoQr0NGqx5GTdQeQqiJ6+0k89NauDPARP1q5jevRxXqQLBK0CQjNpiomBHNoMnGh4uvYM4XO1lYEM3kxASM3lo6HWkkS4ewO9rxmy3oPlcISCo6BTWyWkD2aMmvrieomJDKYxAXdyEMWsFnRBVmJ0vwEqrppyk/G+XWQWLtHej7b0KwC9C1DE1DF1P603jvmosZ5s+hTl9Fr1KLNj5EJSO4KvQR5sl2wnVH6T9PQO//GWNfAEuXH/EJhdByLzj8SBnPIXQNwtZHwBbDYHw3nSwnQ70eYVQatHyEv/EGIhslAhltuGKz8MXJuI3xGI+UQ/cpUJlovPBpEowJPBjxIfEJ41Erk6B7F/hroKoV7I9D/ALIGI5m749QXUtQ8uApNiC172dwjIdY2tAOwInEyayuyOJ+ywDTanZxMMXA9nF5TKnbj9IXRyhxOXKwjEElGqdJje4VFzH1PkKqdrwzHBhcYaiCemwtLsLNzaiEWGitQOnMpX9SAqXyQbLkCNT7y8j4yI27VMYsT4eXZqPcvx/74RcJ+6YMnv8UwXc7Gncb1EQRiupH7+yjzR1JfncN6iE1/hgVqvQ+vIqJFFMZ0d4CxofKEKR4dql9jBDWUNipx6CyEPr8EdT5QVTmXnT2DnzRPixVIYSaa1FWbgbbHig5AsseAp3hHyb7/4z/qXHQ/5yoEiF6PwRroe8ysDwB2tH/Zshxx3VYlBbSdp6l4fYbaBEa8OEhmblE65uo0X+IuEAhvL2XUT1NeIcdRWs6SE7Lj6h8WsI6jsDRIX58fQlzaquw5F+APG0xknCuypdb8HBGbKLCZeK6gSHCfPtxLEhn+B/PAgmgr8Gc+TvWGG9jgns8rpQaTHUjcfbtxus24NcUYT71HWqVSPeFSfTb1FgCfpINszF8v5kDlyZj6jETJu7GcFhkbMMgajP0j0wmPNiDSR6i64Jw1AMp2L7woPrYDnHxOJ4cjltdSmbASaJ5HwtNHl5bdYLnPlhC1vwz5IY1Eqwbz83XvoG9IppDDSOYNfcE0eHl6IIKIgEcWaWYv9uB//D16OeEwFgESikIaqSc+aR/vhh50SfYwx9Aq1pHvS2PlMZqVC1zEafN4aKaHzljbUUj5ZGuqBnvM3K5YTOjpAP4Uq4l/LsyGudJJIixmPvPInr2o9wPqBrOBXvWfQRN22DEHSjt7+LQVpHiuAfdsQeg+RRK0IIp5hIEdT1BYSuamImEzAexdJ9CUOJgwQrYtJmUgmtQNX7IbXk7aVQ6EeLGQngr+Lsh+nzQ5EDHVxBthdn3w6z7kAcPEpDXoj52koITx9EVDKGYJdQl8VxZtp0fgxeQllZLa1EiJZygMk1Lv95Gj/lKSjumktKzjfaoaPRjT6Ckqwl2H6ffbMTYrwfzEII/Fqm7A3xloE5H6G/Fus2ALWjHbH8dOVPCk6JBt89DcLSfYJgV8UYbnYtjsVz9EYLJAoF86PseTUQeSm8d7a330BNhw5r5E86OB7G+vIPQw5+xX/c5E6tPIyeFs0oYxaWtGxmnHMYQCiEMBVC2H8VpCOCu1aFeFIM22o/lxyQC7pk0jsmgdW48GUohiRkX/mIyBv93/I+Mg/6nRjSDpgQiv4GhB8C3BcwP/SlrDUqaUxHe/JmBEVbsUQ565WgWeOdRqJv+55RxIyyLVyjo3Yuw4TuWXNqFPRbyVD8g7p3BoceGYUuPIjP2ZRSLDY+8FJU4C0HQIwvgwcO9X55Er/mawBYR4/AWggVOJMtZ5PdBrHmCgWfzCPywHK0mhYDrR/QTFby+MPzukxh2GVE7nLTPKkWQ6kgO3ABRLjbdJpN+oILUXfWok83IF2TSPOMCWoTTJKkuJ3Lvyxjt3Xg9Y3BEeggqNageGI948dcU2R/FoDUyqtvCH0vGkBq2hvHXXcSBS7TU7IxCV9jDqPr9qFICGIvuZljrZyQfOYNqby1Cmo2gzYAv9RTKK6mInUcQXpLRjK5FLA2HvmkE+1ajHK1A3joCT5YVeUQYjnSFwZAObcFJvBqZOrWH7L4GrH0X0V8wHafWgEV5DFHx40pcjXTWSeZFf8Sj3I/BbkTuiIS0PuQINSFjAKmhCopuQj77PSinSfrMgyTWQc5lcME7YIn983c4dD7SkA1J1qPtvBhGLAJtEhjfQX0qBrpCGLQ68q11YJgMqXY4mwMpj8HpB2DUR3+eU4JAKNwCpKPpO4nbdDlC9wdITUGshbuJiuggNihwJjgM76CdtLN2hsJnMeX01+iMvYievWAZQYoi0ZFp5/ioe9lgupvLhGjiA8Oh+gVoWIHgyIAxM2Hz95AdjxQeTlqfDgZNEDSgSvTR8mEMpoqTWD7QoGoPkvVJDyw6Blvugqh6GBYPRQ/jiCyhLOpnsvpbMfT9AVVfFnLKBlyrnqb1olIMkb2MEYxsjMxiKOpafG1XYAo+gfL5XPq61TSNiMV3TSaFhi1otxYhbvFSM+osW0eKjBHmksiCv6us/0/5n5rq/c+PaITwd8D9I/QtBevroEpCKtuEOCxE5/j5FLGMLm8dtQNb+C42iklSHJOxoUFgqgHEsROp73ga2a4ne0sfgVEfUzd6BGctGq7pzoM11yB4B9CMTMKXfC06w+uoUTNeHI95wmiQv0KotCAWeRCsWgJlRpQr+jD0KTy+6xHWvPEQsapRjGpvp1XbR8LqFlRRiSi/TSfw2lpQWihWPURQE8Ne3e8oqUsjesCMYNOglDbTnDQPyaVjkmMmXRboC/gxT1hEhPl9XO1vU3nLh8T1xBDX8i0GdTtoM7HGx5FdJjFgHMZo/1Ja+l5CtUBPRWAEkf5a8rc3oVx4mFjzjQSFB9GaDVAThqD0EdGnpz5PItnaRugCJ54UD+rvZVA2Up9xKYZFs4nevRL3/fcRW/k79NrJlMeqGFV/GnO6QszOQwh+BWdKE0FcHGUL4cJE9Lio7RbJHTqG+NWFhHV6UPIlOAHCYRAuDtDrtxHbo4DnJjaOnElQWwpyJtjSETQ6DBxGI0vUyScZPqAhw+3G+Oo7aB+NR/ElIkRmwrYnwBBCCYxByS5CSHgSQR0O3s9BGQGq7aCNAkkH7hYw/LnagUQYA5ShNvQj9u9E3e2lelgJ2S0X4HO8z8uzr6So/zQ3NH6OKyjS762jwxJH+pGdyDETEWf+gGRU6ONivkNHSvAsucp0JLX+XOSQEg8TX4Gh16ExCXKmQNWrKNlX4U2NRrtlI2JmAqlnu2jMKsb9fjzR30Wj+/4EAe1H6DQCwk8SnIqBtU+xc3w6WaMgK+JH/M63CUnvoZkoYw7UIihLCZ4xIYx5mOtcn+E4MoMIfy/Kd8vwhLQ0P5tG4uBZ9KYT0JDMgDieQ/e5SVEXcbkwn+h/UwXil82vLo5fOoYLQTMaBu9iSD8DdbIBjzad+OKniSQFX1sL603VPNZ7hB0x5/N7pRKroKEPHw+4TxGVcJioF/VoZg1w1rWWlXmLubOpG0HeDBeugPPzUHlGEriqFvn4fWhqdzDzrUOQOwJKo5BSIpCtA3RpkjF7OgmMegHjkW3kObaRu+cT2osddOvtOI06AlPPI6QbQky4Csfb35DorMbhOMFJYSuThyT0wx8llDAJ6ZN2hJRlpIZG4tjwMuUXRKE4+8kf8S2+4LU0ul8g/Yda2i+30ZVYQ+yefoSoHoiMArmHWXV9rCgdza1bPiWzTiTltpdosj9PVWQmpWUnsRd9hPj5R6hnXguqw5BVDKHX0cX1keftI9Ao4RiZiC5gJzTOiaAuIC1pFM3yfpBHkrz5HWSDHlXfT6S6jAhqGaF7N4ocBX2DCGk6fA1bkeMymKi7moAwjZDuPW5LXcqje54l/vpGiAkiJIL6sJmK2lTkcIHYHjv4ZjPvk6Ng60cIfQ8WDXLiRNzREXRZUumTzuILRtBvDEM9cSGJfT8ietKgZy2KLxvfRAOBuI1o1VegkcJBcYHvO7D8CBOuOTdn0m+D+neg8M89KjQkkuX5AxwtgSwVbqMBhl1AcN2XBHNFilpa0bX50OX+iK76EBFrXsOr6UWwQ2tSO4e7jhGfkg7SGGaGzpK9rQ7dLCNK/2Go2YwQfQG+hFEcPGtlim09ypCJ0KjpyIFe1KcPo4SsqFUJBIIuEhvKEMwNCBMKcI3PQ/t+HUpDiKHH43EXg6DYKNvrYdw7TrDfjzojGf/4AmTNYTYElrJgt4CvLREpuwmd5zoMA120HUnAddH5ZEWvIFtsREnpxe95kwPyakI5jcRk5ZPGbCz8Ra0Nvx0q34CIEogsBX3s31Xafy2/ujh+6agScES+T+XQhRQYm7EuOI4kncsyUw5/QFSphg7hWeY4DzHHvojd2dGIvffTqe0ipm02ltE7CGXdw/G44YwTehlKVBFq3o7YcB3CTc/CsR1od0/Dm/g2Bt0AQmkJSpgapdGOP9JJSKNgzXkO3be/hQ19cMG3EHM5wiYNidm1uByNqCKsBKLSUQY34hReQcFKj24mLYGdZLkk9IoN2XsTgtEEI8ZDXTOhzm7akjsJBfUU1k1AW78JbW4ATfkmAvpDpB5biM/WTrVVT07NHoTyUpgcgT4QIl1xcPZ0kPyzPvS9zYzZZuagKxu30UA3EWy6fgp9UVFc0Dyd4kEZf0siGoMH1CHUL3kwPHc9+tYfUUQPyunjhIxlZJQMQ1F14DTp0SfeT5f4Caqf9AjHDqLcrkUYciK4AsitNfRpwrmwZR/+8XNRqYqItNi55Pp1hB5VccpbxNvf3Mql9g1MnNzFO6Ov4uaOekL97+FL3YgqLhvNkA6Ml0NXGWKXD1PFOkyKlww/KG16+hdbUTr9MCEbZcGXKEIb8vpZyPG5aJU70TRuhJhcELaA/j4Q/iK/y1IIyVedex/wgloHbcfhjQshy0+wv5sjV17O+OavUI9agt+zBqHbz9yfy+HkciicimDUok+IQB72Io7GFcgNK4nvb+bj9Bnkms6SpT6Mz5eCEOpF8ocQk+5GIwsUCWdxhcWjmXgS6aeRqBzbgRTwxtFmmYcnJYSh7X2M2VcjDHyN5OwndIsOeacT89ZW5NRwnGGDjJjSweDUZFQUoG8OoS1vpWtdMiOzNFw3dgl1ObfzXt1vmHJmE3XubH6+fSKJ8WEYew2oo5yUu+YT7FhNft8Z3BNjyakOw9T79LnSov/aF1WE9p8hYSFowkEX84vzSf8aZvdPQIgQx4WdRAuXorI1IZn/lALcW402fDgjOj5nU+E0kkJNiHsvZWJfBLU6HSnuR9CoP0COtiPqNzEnNJxE8VZkVQC/ZjWC6wje+i4sJ1sQTx9Euj6SgK0Hf69I79hWzMdSsRbdi9j9GJhKYOxicA+CyQxFX8BAKexyU3feZdi6NmLtWo2+t5mAbONYwmwi/HYC+vE0qcqIUm1Hp/oE0fcdFLbSfwjaclaTucaH9vBxpLZ90OcFnxldzwChFi1e/2F64i3ED9VDewC5+y1EVTEMBZnaU8vK2dPIc7bD8Sc54/+GlZ/1cO2EN0mu72fKuFMMHK1F1vnxthxDH5uEeHAkXLcCrr0LvceFkjIFe0QllsIa/NJdiJu+xD1vCpaSzzmgbCDbeT+xSyoJJYSQPWZUzt0oXgWlrp2ErjbEvgikraVU58URPrMHoSKFuDlNxDjU/N72DOvSn2RFZQK5BXsRc6pxjpmKr6YKW8P50PYRmCpB2Q9yPmQ/B2u/QvF18+PdS9DbTjI91IkUW4xLuQ5JdqMNxKAT70JQ/CjmFqieBLoIqD6JUCpC4oQ/TxpL/rnX/W+BW4bDH0F6MxSkUxM1FZ2vDiGiGmX3FtwVQ2gn9nH27sspVF0H39wG1gDccBBR0pOvf5fk3nIcag13NXxKWVQ6P+TfzHzNGIz1SyE3h6BtB4Q+wpjbiifZj6ZfjTjcBp6b8cRdz7qdbxPaX03+Z6fxXZ9CXPKlyDvP4I8+c65x66QkNB0h9GFjsQZn8ankY756KQHfVgT5QbomjKVxoZd0JYu3u29m5ZH5GAZ7+aT0N0wy7+DCyrWcNg/DbjNwRhhD0arT5LedQFkso+l7HDF+OmTEnHP//AuKDP5B0Eb8nZT8X+dXA/1PgITEFBaD/X0Im3NuZ9AFR55EiOtE05pHhuBCdO9AUUFFegapMbeiYR9UNICsQtw+gcQJY2H3a4jl29CltCJbvAwOH8Tc48arUeOJW4I26z00rV2krNTA86cJSZ5zIne1wZJHYcdr587vqIWwbPBsInfTXlwZqUi2y+mXv6BRH4VH8JBueJK94mpSnJlomYbXcQua3X4a06IQsgzkrutHI9nBlgct+6l4YTWZHV+hOrqTI8XzGNFRQ8TxJvpy9XROsuFQpZBR348otKDtDjCj0k3vMBcxab8jRqymtTUHc9BOX5qNnIpaNFU+5GgnKjUIw3461+n5x6fglk8h4Kde/wbx9acRYg5i13zJQKGF0M9p9O67kNpLZxHvTCdYuwmptQ3ZbEJOtSDiRDU8CskQCWErEKvfI7H2GXY1D0eTKCF/r4clPuJnLGfppke55LcSfZ1Ojr12Bc/KM5k+/UMywo8xI60DKXoMbAs/l26+ZTvc8QbdBTHsCx3jsboVyPlekN0o8nB03lJQrQZfFbTUQkMrJJrhqAfOdMOs4v//xFEU5EPrEZ1HQBWEcc+BeTNibRgjVh+Dp0I4YyazcYyPKHEIbUU5cscriDeugsAgSH8qcJ+QgKk5gCHrbdZyH7N+2kFwcj+Hh07gTr+cUcEzWNmBKDcjxFyERkhjDU3Mbd6Pr2QpoUMvY3W7uSr7Qyx3DzIhooPlLR+g6xTQuzJgygrkI08RNFWiV24D5+MQ+QiCvw1N2xsQ9jxVQgVyyz609m+xBqp4QjyNgIFabTotbcmMkU/RaklGHxrLwheOUZWuQtMWoLcnlSjTOpAKQEr5t/dHEH/Rxvlf+NUH/c+CdTzoU6Hja+jbCNIhKDyKxvcWeS4v66OjyZh7nARnD4ZQL5ieRXHIyNpjSN99Bfs3Q3Ix8qxF2DOXYm29Bjl7LMqYJ9EY4hAfGo98kYQc0wXnr0PBRyevkBBWBHV/BF0IlO/g2AkI2MFdjaLXIzer6T5/Acnaq7GrNpLvaiXxTDbOGXHYlBRyqkA4sQP36HGcndJAXEcAVc40lPJ9IPeBx0tw/GLSNl1He7GW3qU2VK46aqICZJiuAucW1Go7GX0nCNlUOGPNqCNdZOdW4VbC6A7ez2S9h0uHPw/DIKy5EV9yJKJKRjoYIuS3IeXbIbvk3L/7zScgfRSpQ4VI2iAhYyYVgQr2qj5g24VJzOv+mDs/WY++eyuOcCumCZkQpkEO1iAKZpIrD5Ew4bew8kE8WfsQM4YQSGTkU/voeOpmBpMqSFLdglQUwdb1iRSZ4rg8ew+XW9R83T9AhukYz/U8zT1FF2E8uBfOT4WVr+KR4Pmhz3mq9RRisga8NgyOe3CLtShVKYjuIOzYB6kzYdJMEPcjq9SQIcGPHyIsu/PcPJFlaK+G7x+B9NModTJEFSO0vQ9jf0/SpkcIjhuLusfHuiSZhT8fINwxwOnZ6RwtfIIPX4rgOev9RPSWERilQyV4EVricXx3CanR0agiZ2DsuICZ7a/j9+zk+xHnkUc9w1T3IkjzkAQf05WVfJ8+jxzhQ2IkJ7MG+jj5QQn1Fz/Fi7ZLuLylj7RRmdwQ7Sb3m0mI4x9E6D5J0HccOjzM6X0V3MdAlYPccSO+hGF0hCeSn/AOYY6PIPMeeGgmy4wiPqr4Lv0hNnakknBiJxELbmPYyatRvGAMTwDrK9D7ALhKIeFyMP/zPCSUEX9xqd7/zxZL+s/oNUcg92+DM8uguxNyPqVVbcQZ3YumYR1xHi2WoMCg4RKosiOI0ciRZkSnBHMfhcXvUHPXM7w9sQenvxU01xDRUUd/RBWS40u0C2PQ/2EIjXc8ONbg6FrGYPBb8FeC83OUQCWEZ4O6G4xRoLQRTLsK7bgBEtZtRHR8h1pykhC/BqFrM9XNr5K9Zi3I8TRfv4C2gh7yhNex5awnENiJHNcGHS4C7nK6o/fQMikSIVxDjDITW/R3nMm7g6+tnRyIyqE3EEXryKVI5kx0PR7EgA2VEiDsTDiN8aNpSJzNM2mv4MZIf4oFnAJyRAAKZiI+Xw+HzlVi44InYM3TEHQz1P05H8fdyg1yF/s8l7JUGs2DnGVx5AUY8kfjs46if8CAXTWE2FtJIEqCsBjkyCi6DxhoumgioeE9SNnvMOnbH5GWKsQl7SFPlUj3YCxn83PZH1uE1BiD0qGF9teZnZKBJmI4j7Zsw/j647CgBG66hxAKhx5fxo1nnseUcRmYU8E2Aq9nFW7X14R0XbDkbZh6HxRcDfIKMDyEYBoOtd8iBx8j6LgDOXQQZf1rKA+OgKT94CskYAzgz46DRAOs+xDDuOsQHTLHbSrSTh4m4uxRBPcgMZnHaIlfz6ArxKgN73Py6tH0lbYijHwXbv2efquR/LoQWt1SaOlG6MpH2+pkadnXhO1qp37/PkI1R1A5E4kQVzJBHE+PkENCxtsI6dOJHT+P8SNLSdF7eLnhIW5JaqS6Zj/bYi9kZZ8ZBh1IR69G7N9Nvn0zKKmQ9xkeXRyVCbmMCruFGGkSKB4w5sNdP8DJE2gcA6TEfclj5mrixulYO7wZx3A9glaNwT4Sjr8FZ7Ww5/fwaRocfByC3n+gkv9rhJD+qu3vxa8r6P8Ndno5bjjILMt0hDodTBuPlXZWJtsYrQrD3KElwfYAdvNRBrz7sNqrCEWCus4PIyfge+I3rPtmGVGacAy6Jmg7iIYKMD8MvU0QuxjmFCN41kHSu/ikn9DwM0rKdQjul+nNvZhwZwBV/35oPAwx0QSSf0u9J0jm0Be0D+2hMT4blaOBApuaHt8Rho3/A2eiVmHznSIp9CLCoadg9vfodg6hiXoQeAnF3EGYI5LYyiDK2C+QTr8AkV+TmvkAh02pJA1cS7snAn/AjSnUhinORsDWiTiYima/npHlDTTNhaE71Bi7NURtH0QsupuBkmqsSasJtJ1AvWU5Q9Mm4E1MoGv+PP7YsgF1yiMsEg1c2fkmkuU8euijEQdZUjHMK0ae+xhn5XJyGu4g7OAg6uI8lICMrMtla2kc5zuSMO+YCMc/YdCfgn5kC/rDXsSoIMO8LfTHVHBgxA0cz65ioLuHuH6RqHffxlDmp+WFhcTPWo76y5sA+KLzKBOCkKHNQxEtqIN6ZN8aZHc4cY19SIFHUeLyELpckLIPr3gtlf6dFCUvRLx6EXx4P8p7m5GzKuDgCeQL9GDoQhrIgLxFqIa2oagvQzC6wbEVbY2T1t5FzD/8EiENCAER2y1djHiuhd5rdyF8ZWZzZQT3jrsMdn0EhkgqDZNQJZ4hafwlAITkLpxd4NauJfzAEPrgXrQnNmKvjkSrN5GssyDGRrPPdj9jKitwPv4wu7R+8re8xXuZv+GpxhdJHwyD05s5FNtGpzaWTb4lXBS7lrLYh5j68ysoB6JpyUhntn84ueqiP4tBFCF7AqRnIRuaGbWnA+38VAT7Hgi52J86ipKCHDJyHjsX/QPQ+jE4fws2Haj+5Iv+U6PfXyq/+qD/iYgK2vjZ1ENaZCKqDJGA8ABaYpmrmkzv8f2sTh5NdszVWBwbGSy5l8Fji2EshHsHCEXHsO6GfBadTcJWMpfO+GeIiNiNsC4Ha2sDQZ8OVZYMN58P7y2HI+NQRv+WBPFxBL0BLLEMBfbiDxtF/B4XwqSvoGsxQVGNxVOJJ2Mh6m/aCb84wNDpV/HVtzDM34uq8yIyF16I1roI4eDrMOo5+Oq3aLs7EXUB6POiiXSjOR2EpYkc6Xya0vodCK5OaPiQ0aZiiG7F2jFIoK2BxkVxJJd1oK/QIB7twH5FPIOaAULpLlytWtyuVHS2OoLOD6mPzSTIQ0Q4/CTbwtCuehj7nW8TkZzJH357G6Z3zkLIB96zyLFPsIuPWehZBBvfh5gkdLYjTE4o4yfdEjK67kdsikBJ8NMYcymxNOJvuocBVgAAlcxJREFUXAMbvkXpNVL38MMkSpvQ+/YRLAugMhrpcmeQm13NrIYTeHsVrEck5KCE90Ib9nEB2nifUYqbbfQQ1tpBqm4AKlIQSrMRlLfplR4nvqMEZ8K3WFROsFejlJtAvgKt1MOplPuo4UVmcCURy55BuK8W5+SJWNJ9iP590ATBRT7U+kkE67cgmL9GiMkG5xKU0x+i6TqJIkt03JKEkPUsuo8fRv3tZpLu7+Xe35t498EF7DEXMDVpDRx8m7k9jdRk5BBatQRJ8iEFmrCEhpC8KmrNmRTo3CjZzWzOuBBd2I3M90WT2rkD/anvKFu2AEkpJ2Xjx8zaV8ne/HGIPbs4ZSygPX80venFlJzaQ0SkzPdRz3DRvkdwmu0ItjhCJpGCE09CxNNgTAX/ERiYcq7ofulxxCYHujgJuj5GVvVR0rCeYoeKw+NnUz9wN1McChoF/BoHsnk0GvsqRHcnqGMg0HmuL2HkNRB2Hoi/LHeCwq8+6F80Lqro5Cs0ig2P41smKSa0ra3Exq9GE7Ceq+GhMpFp2Yyp4hB3jpjBk4Z7iPWtxmueQH3fVsJkhR9jjzI8fS4Z1TKUhGMnHZeuH+OUtQS1rQhtv0PVpoPq+8/5mrc34R2+iaghMzhPgWsfMScHUTWuwmOORrX9EtQ9LZh3pDGkTUEjNGPzewkehVbLSDx6I7ahHgS9Gu3KN2HBE5AwA1x+OPgFvukx6L64Dy5V4LCI54Ix6OPuo1u9jb1Js5jYnoHQfxg8a1DcoGt0omkXiLYO4o62ok98FLJX4wvp0LkHCSsXCahnEr7Ph5R+PopuB3GhcILO47gyC/HHGbF0VhLW3IgxsBnnZUtRNn6DMKYfIn5DdfdGinYeQnd6KxzajFI8AfmyQ+iFKbgtyciA6G1HdijsiBKYJHfRYzhDjCcScty8rZ/JJUd7SCo/iKC1Q9FcRM9hMlhCc5wdTd04pJJDhC87jQYraeXH2VuagHvffrLSVpNaLSP07YHs6YRw0CW8QVzwSqT+lzGnpyIYH4HKqSBkoaw9ASE751+Zy/vxvfzE80zU/IayBxNI9jcyInU/wg5ADeKGEErm69yc9Qo3VaxENxRNUFNJcWMd+qEY6rMzETtH4y34HvcN43HZFbIPHeXYiPN48949XP3MTGKWjiEvZi9CdDEDcUlUOI5SqI5Hmfg5J06+xabxkchSiO6tfYSr4ihw7aRal8aA9nYiU67BkrKEYwPPMHNvK7kH2wlEJDFOaMKZaCAvrJxGYQ5RhQ20a2IIjzxJR/yVvH3Rg5iYTnjt81xW9wOEzYXmkxA4cy7227EPoiJRwkaDw4lv/DY0q69mYPYABiEMyZjE8IY4+tuPc3JBIrGCBS1qfBzDoJyHNXg1qqAB7FtAdkKwHwId53oU/qL426V6C4LwIbAQ6FYUpfBP+14CzgP8QB1wraIog//RcX410JzrLN3HJtr5GDlkJ6O8g8SE5xDK9+CoPAbBCRDqBN1EyLkHaaCe4s7DPM27PK0WudnTTFrBE6Tt2kyr1UCRN5FMaT2MXwhAFOfTyTcYo+9DL/sZUj+FlrUIMTNAbkXx9aIMliP4+sGUB4c9GBuH8BfmoLfYCETl4xqQOaLEsUOeyaO1L6G64FVsjtWcSB1Hw7Q5XNYUheK/DLw5CA27YdFW+PAqGHU5krIJPAHYq4abZOzu0xxtLyY+tY0G/TECgffQ9IyEE50Io0BpNxKIVYiqHcJjMDEQ9SwBXQBNpR+d3o9Z7cIn/IQ/TEG7NxOhv5+w/GV4IwQSOiuQFAjFF+NVymiPk+hK34O1cg9ZHiNG2+3kMIAwLB4uHg6tdShxdYjKlQiqJYwTeikbvojh1pm08hbnH3kUkyEO9doWlJQ8BPVpUk61U51xM8LYG6HqJwabP+O9Wbfy+tanIaTQmneQhj/qEFN/Iqy0FLnjcaYf2IqQbiV92Z3IlnCExRJyRj6dPECsawSq7qUgJCA23gU9T4OpEMKmIIhnYc8xwr66n9K7n6BHtHJMXo1Z56X42S9QMvQoecmIulbob6GuNp4rCieSMOwlopc3oar0gSUKvQf64xOYlHIJSoOXusQHUQdspPw8AeORrzg0fQErp21k2btj+eNdjxNbdj+hkXZW6O/miYcvJ+yDLeRrs3lK8yHPD+shdn4ONYceZWPsFMKFFr7wv0Vhp5/4+joW6wY4NHYSeV98gWZaMREhLe7IYtTityyoOYbkVLM9cQw55gMEghu4vmI7XdqfOKEOICdpwBcH+n4w7IWQDyViFL2lzxI4eBVxgX76d99EeLIFVSN4ckRUKh2qXgtRH/ZjW7iO/RxAh50SXkUjJCCpVaAG9Pn/SJn/p/yNXRwfA28Dn/7Fvi3Aw39qE/gC8DDw4H90kF8NNOcaydqYi425cPJScPeBMQeCW9EtW4/a54eTd0J3H3hfBvtZCDmI2lbEa7p4Xsq7mDThCVx5F3LZ9vfR71sB5gYIuSBqNDqS8NOJHHIjusoQtEXImoNI4ReCPQO/0I7eexwG85D7m6CjBzlGQzAZEGTUOctRv3sJtVH5xJaoGMiLQRp6Akvmc8RygiNSHkrafDhbiqCpgOJ3YM8KSB0LFR9iaG1GHpmM2OiCY4No8hQe33Scdy+aS3Kkn77uPcQd/BKCaogwI5zvRBNMpl6MxZjYhKzPxtwfw3brZdT4D1AwogW3bhAxSyYKC5nfyVi2PwGLvPgiF6PJnIu6bz0x3WeJiHiGmJAXqfZ9BJ3lXAdzSwT88UW491K4/iGEC69B+JNvMgcbfxg/kcITP7OqaBHhmk4u/3AdYrMP4TwZEm7mioRwWPcjNFjpnjKJDsNGrlF+wj1Xh7onhoQDh7BOj0fon4i3IQFJJTCQ7Ca6voPQ76Phgz6UY356Fv+WcNdvUfeVw6c6aPXCs2PBtwuaa6BpJWTEwe0b4MQapuyv5tPCGhad/ApRbafjCiuB+GSiYx/BePgbsLrJnPIRmT3VBD4xwoV3E9zzFqo9tRjdiVRMyMZfsZwhyzE0Gh9aYzH9t0pYe2IwdfWhltp4Z9Q6bnxiDJ876og9Wc7FceVUZk1gnNnEnkveYbomhjxrFgAZnliUqp9JiG5A/i6L4Nxb8U65kDLhO2QHrH7yXhZtOoQ47BpMpVfAhjJUk2rA8igzNq0jVNNPgv0zKE5ja9YULj7wLKRFoGTMRxioRG60onT68Olq0e27HfOQi1C9kZgvj+F4LwtNUxuh0UcwdT8Oo1+ANS4gnuksppEqVrGKaBKYxdJ/nLj/CygI+P5GtTgURdktCELqv9u3+S8+HuRcA+3/kF8N9F/i7wPbDEi+7lzBpCmPE9BvwsEOLHPWogpYQW2Era+D5TCMeRGp9xAPVK3h9aJ4VF4P+qAbDPHQ3QX5t8FgGXRvx2rxMDhwNxHJz6GPehL70PWEVz8I7Toq66ycFGYgxoYY0enj8Pxr0Vjy+cGaxBQimF+3k6ykYhoKr2F2tIfYs80Eur/HPm4P5sjdFNFESJiO6pQGylxwZjG0qyEmDoaXo0gS7eMzsCVcgH7wU0I7S/kqdzGh/ufIjLqAHWN3E9tUgzDlB/C/Cs0mxN4DmIdUHM/JYtZjZ1E9+SUXCW4+ikxkle80i9WfM2H5GQYfHEbnFTLVHcPwdwcIRmsJzZAxOYoZUb8b65GLiAhqwNoG34+AiL2ERg5HuutZaKmHrasQEtNg9NRz30Gwk1ydnnVjiilWRjHq1Leofx7gzM0TyXF7kORVFFS/Cee9i39vPwc6VjC8qgFxUESeFE1wcC+BUSaEsD5kWUNHTYDYafcj+9YRskpIghm/0cHAaxcQ8Wwn6tDXMLYQPKPgyQ8gOg3yroH2cnA0oESkIiSX0J8ySGfto0xw66kujUXnTySr/W6Ma8thzpfg7gS3C9ZfjNJThtpaCO+8S3CBDsEcJONEO9UTRtNVbEQXeR/hbd+i299D67TDDMo+RsvPIYycihF4LPUsN3zyFSsC19F9aRb9lUl0nf8EG8qbeDJVhJNboWIdbn8zluYu4jb0I3qO0tL7OvnGXDIsR1EZpyI21SJYM+DAT7DqOVhoB3k8BK0wJh4xYz1y51WUbUsib6APoypEQOdA8OwG3R0oMfvwB77G0NsNzl4IaFCiPIQuB9PRRpoj4ok/sxUK3wFJggef/VcpJZNFN0Wc5SjlHKCIcX93Of9X+TtXs7sO+OY/G/RrmN1foomElBv+tZodeit6FhPgBIPcSVDtw0M5jF4KcghMicgRuazLiGaG4QZu9UeDMR5OfAh5V0LTp3D2d1D5DJG+IvrMjSh7X0Glz8SRmEHQDQzUkE4FV77yLkt2ruX7GdcxSnKT49xHkmTG0v8tgVMvIE+7kU5PNCMdXlj/KeoBAbV/DE57KkJ/Ar2bZqG0tKL0K+B2QmoIZocTalRjT0wmEGbAJ7yOYhGxXfsKsupRVDufRrPyDmJOlNM6AkieANYc6BlAubiKiPhMZvz+NMFhLuShNELqUs7jfOLULdw28CbN+gtJVa1i5LFTlNp+Q8nRs8zb+R6JfRUYHDF0xF6Jf9g7kLYI+rWQWAEV16CcvAhX7WyUe86DZ56BfRvh+bvPGbjWZUQeieTIej0xH2yhvFJg7fXz8Y4bpMXSh2/4Vwjj6gkpMrXFJ5i97ye6zsZwpjcV844S9Kb1GHsWY/pcg6X1Eqz+DMTQCLS6XLwZBtyjI/GmgkfThGpMEUy9BH4qA58KolPPfe8VFdCoRUk0UhPdT9vgLOp5DCUzlfStjYQdjaJemgYFU2DRVHCGoHM3JM4DUzju8+ah3PgT2HKQWuLxjoxkaME8Rn+xFT0ZRKlvR0g9H6nMQ/L7NvTH/HTvuwbHig+wv/ceReYHuXTm5zyuu46gWEWJ70sOr72J574twfL9VSCqYMlKNl92J75hWbg/WYHzhosZuKiYllt+R3BmGkrPcgSpHapPQvlecPbDBhHlx3hkxzECgUP460bBwEFyh3/N2Ij3aLaFERhQQ9MBqJqH2P4MKmcXBBZBzH1gyUSOBCknBNEqVIOjCFRuhIB0Ljoj4s8trURERjODa3iQNHJR/jXl+5fNfyHMziYIwtG/2G78a88hCMKjQBD44j8b+6uB/l/wL5NJQcFPIwLn4WSADm4gxABY4iGqABp28LPjTdIHeiiWM1AJ0TDkQwnvR277CeLnwbgfYFY5SsoVeExO2q1bIOjHHS7QMnEmJM1ESTYjRkfQnlHA9Xsfp+DEdxRVHOCV9fdzzVdfk3dgC72+pQwOlGFe9xBc8i2kP4Zhw4OULD9E8YaDiB4/ws1H4aUXUUqtkG8Bv5XQGQOOKU70fhlB8RMKWw77HiNBauEh61H8Ui8Fx42c0Reg9DSAFAEqP4IcxDtzD2rdENrtRgKvJHKstYjTpku5WMwiV+zkkSULUHrmoSQPYXL+Af3MIIp/EQW7+kl0baZWqmNLVAO1ccX4xm8BvQgNXqTYZ+lM6mFAs4Zg/1cMXC/B3KXw8mxCPT60La9y6ukSCu96ly8OjKdQ00S4ZGT13Dm4dSnIrhqUM3cQ21SG/upTpC16jI6c86HrDIIYD3FXwpl2qDpJ+NRvCUgyQ/TRlhZiqLENYUgiTv0e9hve5vjVV6GMSoYjZ+DtlyAQgI3vgNtDT14ihq4QrUI+w+riKWgLoZpaSG57OtKZM7g7DkLwW4i8AFw2yLkKRZBxxR8h1LseRmkRysE3dQ4q8SjrrlyKJ2YrQWoQW/oQetuQ5r9KuOEjTPtVSG0rUKcfRZXpAa2OwMwUwo/AYKGWcdqfOLRwCcz9AxRdjF+tpkPThiVQw9bWs6iUE+T6dOwb3I7c1UYgMhpPiQfHEyUMvTCGpues2Bd7cC/ZQUC7HdmkIxCZQKhvNELscPBlsDbuIprjRkPoYtzKdewvnIs8Yy+c9x40O+C7XoSDJuraS5F3RhAxrIjDkUmwPBeatkJPGdQehs/uhnUvQuNJAMyEI/yCW139C//ig/4rDXSvoiilf7F98NecQxCEazj38PAKRVH+079av7o4/h0+qunlNVTEEaQDDRmYWYCNh3HwDCLd5waOvxe+u4Sj02eR5DgEW86H3kG47BBKdxdsuhhsh8D4A+jzUQEW/Qy8kVsRzq4iQ5pKc/4ZfMXT8Vf9Hv+yB9gm9zIm34MqKkCgT0fYkUY8KXpCiWac7Y0Ira0oX26FgR6EkmtQJWVAsQ1T2R7KEyYQNVQF2p/Bp8CRMLCcQZmWi6llkIgfq2m8Ih7Lgcdh5AOQMJm7hb1833EXl18zjcTB92g89hxpgVqISIHGg0jucJpGhxHVcBbtD/1EWlUUxV6CFJvEh/qPqN+bRE/yE9iU6eDxErIqhEpOozq9BMuet5ibsZUewUtVQilnxDa0i29h8qp38B96EPPEZdiVfZjq12JwjoWht6C0ht4WCV1WOMufvZWHm/q54MhP/GH5Q7RtyGBS3nFW3/oGU5tPkxSUaTDdRoQhCuOo2Tg5DSkL4KdlMP53YIqEZvDbf4/WUkpcV4heKQxdEIQwPets72GXvYwUbfTM7kd75x/QH/ahvnw2wqQJyEUa2hOPk7itiSJ1OFpDB/TugqI2hEvUFK7M5mftD8yLMBB98iNodoLGTDBvFAQOEmx9B9WCzRD2M8ZXbkCXG0tIZ8cdvpjOqKsJV8YQePQVNNYx6GL/iDcxEVf6CSyDVXx54CaOWJJ4y+YiODCMLRkuioJfMD6tlV2mc5l5W1iLgW5Qq3n1lUu5YKIbYf/3TIn/mqFRCgZtOHpTPsa2HxCCTqqNM6k0Z5FWUUd2dAOKZSHas37khiZqbJBzookJh33k1pUjqyqQAy2UKiG0wnkgqUBwgKoXMWIKsfOexvfpBWjCtpM2woni7kH44UpQjQTRAnVH4OKnIbnof6uzXyIKAiH5/16YnSAIc4EHgCmKorj/mt/51UD/BU520MdbyPiJ4Eb0lPybn1t4giEeRUCPXrMQZUQpDzS+wsM5f+R210bipl2O3t2JZPUg20wolU0I3WNgfB1ookk47cEu9DMQuQLNltNYytM4sKgPizsac9Rplv1+JcotqYROBLB91cFgbiI/Xnsxi99dQahfj6HThbJoMvLVvUjer6A5hLJ3C4qsoit9OJV1t5ITXoF4KAxF44WuHNRXLMQsuCDlCJEHtuLNiEUfOwqAotETeeprWByEPOtv2DzDR2pDEpzejKviDXprAzTckEV45yV0Jq0gdasfqamPQFgI9DBRp0Hpbic4WkQOLCFU+wHeUA/O0dsId89EtWY38f11xFkPI+hGQGwyQ6NH0D3Uhm3N81giFHxaP/2GfJLVVQgmmQMZt2MMHaKmSOLpps9IdauZmXojRy7No/rnlXxxq5F93t1MmWvE/OkrjNx2GTqzFQ8B0JjOZa3VfQv3/gQ/P4V6aACvZgVKYgEJz2bi7S6jo1ZLvfMWJh7dRuH0+/BnHcEX5mRoVi2BMUWYX9pBf7TMoKqU7Olvot90DaFZd+COWIhUN5H+9JtounQOI77eRUd2DpqGDkx6Iyr/EGr9eWiaX0JbvA6CfjjyOILDh9YZheD1ckYWSGIxcvInGCoMONtrsRZ0oEptwzgY4P3up2lXp/BmzQsIzT1I/Xn4p6p45/g7ZLao8HtVSG01WLWdTEjSENLL7O0o5suTyVwxzED48I/oPn01lh4dqnQN2NPwquvo7FLhvVzkdE8h2b5mBGE+vtOreeieN7n8swcRe30Qr+AttNJXWIxn+B1Eu7aD+Q20ISfKuhx6h19DRcFoUpsPEHdTDp4NZajn3M3hmDzGJM+DvKUQ8J2ru6FS/yMk/N9CkQV83r9NbLYgCF8BUznnCmkFfse5qA0tsOVPD8UPKopy8394nL9ilf0PobS0VDl69Ojf/bwKQ/j4mhDlCGjRcjsi8QjBAKjMKEoAu/t69M441B0noHwzXX3J3DLlaazHa9Anx9AfGUnBicMsWL4C3Swd1iyBgYoUbJHtKNHddJ4pIvAHsDQPkdnYgGSPgoJxhL5qQ9rXjyz3opSokVr6Cc7XIB0PsTtuFp/ELuOVyCcwByOQ5yYjDGxCdRpC73vY8+giosLPkFEnoz/uRekZpHHmNMILJcJ+WIugGk3vJXcSOPQWcQYLytgVoI9nfXUXBu0PTBE91PoPoVdV0mudRMEHn8OwOzg98yjt0khSj7spOvomWMYjFxrozioltuYiAsqdKKmHkOtS0Z1poHNeEdHKSsSTK/F1b0OTeSfCpqcgxQNpExlsDmD07aJ/kRnVGRnJKYMKDAkSGiGZYLCCvuh4BkMauvRRlDS4EBL6OGOMYUzZCOj+gkZNPsuPX8GeV1IZO9nBbx6IZ/3EOi4UxpO1/QBC5++gOBPWdcPdu0AbS7DpB+Q1D9E3VIi1RY/u+RcQtrwNl7wAgD0APX7IMELAvZvKwI1kC8+gtSyBik+Qgw76h+Vgd/6RyPYjHE0pJaHjJIZNAWKaennvrruYK5bBUBN9iWl0ajPQn9WSUHkWIbuP8Be7eeWFG4iKK8YtDJHDWkrch4n9oJv3rrqVKwa7+Uo3BlE1gYd4EgQb8tAufDojhwWBJPcc4j3j0H3wAL6+DoRgiJqxGegmunjqg5e5+boCxouPwLgvOKzcjf7kIdJOl/P6nNt5y3ALNruTVGsj3bKFbO8gGTV2DmUVEGZVCJOrmeT5kW26GUyReshRifjIo8u9hsRT9STL3ai1ESjDXiFGPRwVWqh+Blcogh7th/SJ8aSmrCJS+Met9wRBOKYoSul/5xhiSYmi27X9rxrrCYv4b5/vr+HXFfS/QyAMNVOQqUKmg4DjRVSNPyOEfChaAwoCev8Qco8P5bQfOc2ANnYspQnxrMnN5E5fJRstUVQMX8iZS2ejDbYy3HuGCxJWo7JOx2z9Gc0VEgFPDP2pw+mucxMrdiF0VyOFlUDqGZQGJ+JpAWYsQmW3IKsOsMm6CH1KDutjRmLW2/im+UkUenggdAO5N55k1MYNNN2UgmazA/n+g/gfnYv2RC36zjY4GsI9rxo5ciOeOTK91XUwOAn8iYyNtOMO76LPX4jOkUi9OpK80CDqbhP9o7dQq0rE6bMwc9gdYCxCqV9LoKMFa+BrlG/3wqRDCBY1freL/vFzMUbMRxRKYWYpvQOTiHvjdwguF709yayILWVq4k6ispPQhrmoLcnF4riHnN3XozYMB9tc5JZ3UPQeEtUu9LHPs2/4SjK8KnIOKeCqhIRxhCe2cG/GR9yVLLPpMzPOT9PQJyXwY0oF4ywVTD4rgvsgZN0OLWch6iyqbzZiP+8L1BVvEpgHZwa9HFFP4OhJcAXBrIKFMecMdI30OlGOIKroc5EHSu5lhHYuJdKxAJv5G4KxOxhddQXmnhAUzwNPI6Goiwj7dgOxiYuQUx8g1H4M6eRyPJddT5/4OLpLLmHkhqNccOXNmNQmOgYaGQwcJHCFxKgeFS+aLyVdn8Yd4Ykgf4rcMBp/1CCtWgFZtlLrO0zaRz+jeJz0xkUTmT6GYHQbYQUZ3FRczpG+OYyLUvDJ95OjvMfR3BEU4ub81uPkmR6kxzSRRdU/s91sYr9pPCq1jDciFa2scL69mqC7iBpDBhEMIMpnSRiqY9zgCeJaHGh7jYSu/RxFrTpnnAE0URhDp9H5+vkm404UYYCHiPpHyfZvgwKh4K+ZhL94JHIx8CpKoA/B8T2Yo0CbDHE3AKC4K2D/OEKRAnJmNKHeQZaWrWSR14xDFc3nSj+h9pMoCSa2jqumPmw0j098DKtPj0GYTslAGCOll4i13suZ0fVo69yE91UhDBuCvgLkBx5BFKJg7S44dgTsHUw2bma1LZUrhz1NhbyD6ZHvMtmyiG7vJA41Wxg3awdxp9rxyAp3ndpPg+ENHul5jYLUCcS5VyBHlaBy70UIJBI5NAnh8I8gteJftIuvxCOYdUnM7fuKtA47AZrxzUxAqWtnsmTF3/szhoTLwFQK1OJOLcdwwAFzPChRCrJXi8qrwVbfg9z/FSTkocRNRw6PoPf+JVjWHoG+TvJ7+6jPzifbc5rwmmdJzL0BQddKIEuPe/cJjI4WNAveRS++TMDqJtETYMBkp1qII7N5G6SPgpzncRo/I0LoQV+7A6MhmuHRWznbMYtJngoaItPYvnAkWd1q4k99glTzOoHL3+MDUwmfHjPz9apyqnLGUTPjBwpEWFZ4Pua/UEEny3GqOhBtAQLKb841+BUklInxmGhAUpyovNWEJBvoTkPnFki5gkuHJL5b9Ap3nDmOuP23iFX78VxwFWoxFwENkbMfYcaVeZgal8GlnUQbytAaJjKgCGyx6TnmNJEXtRXN0FjkFVOhz0zQUoDe3IptwiA9MQZa75tHQ8wVSIRI2PoQRZNX0hW6h1zHSt45eQXe608CNkThfAzuTPq17VhzXqaZ5cw58h11RXeTVPMpUqqfsxGxXO1rwSsdI0sVQZNlgFmB/VzWVoPelEey6WIE7ROw8A3QRtIv3IeFq1CReO5Gefac00rOaa4RfVzHGRbThJ4QeiKIJOvvKdm/CYoiEAz8aqD/aRDUkRB/0/9/f8gClxxG6vsQQbsZW+5qbM6T4G2BsAVQ/QlS7TGodBGdHYOqYh+qcf38xt2EXTubCs8pqhOSqFOeJTWsAX+UhGwVEYVIhN69qLc2wqKX4dZr4Or3kUWJuNOtvOR5HkpfIy/tJiyD99PGVXTlXswfjz/O5/lnMB3spDdqFLedeo33r/0MQ1k+ph//yFBqJObqXegaw5Cm99Gb1oChaR76DV/x3byDJCg7GTZoxBw8gjFGTbfHT4NBJr+8A6+lF8+haPD/BsIlhMhwDJ1+5OQY5L5TqOwg6D04jLFo3Qa8URp0MVMQEAmXb0A4uRFfio1Q8QCTPKsImmSETgG79hE0LSvRyadQWcG1KB2/5gU02+4mbOQdOP0fIHTdQHplNoNjJA4tmseoVjM+WcLRewhbRyM1C65EF9nI8TYrTWIK0XY/Cf4eduaM4ECUyJg4N1N/vwv3jp+ZmpzEaM9+wkc4mLtrJXN0IwkVhgi4KglWO5AGVciRWXTmHSe9fhGa4zsQx1di4hlQ6XFrWqiNehcPzcSq0gmGD0dlr8do7EDwfUz84W+ZFZuPyxeFcftnsPR1XPExKJwhjJtBrcY6tgC+34q8IIw2OQl7zAVUN6UzPe1Vrrcux+hSE9zZD45oupJjcf3mNvSaCgJsIKgU0ud3ciCwivt3bYBhTxAyyCihPHZcHSLwRR1iRybquNdxCefhMdei21iIse12ClLj8GZNIHrj03gG1ejTNUxvqWRnbwppY0RacbKYZczVZ2HJ0p+b5MFecEeCzkYDq3CzmkPEMk5JILLzVVBZQIkDQYeWHq7kOEfZiY0xTOOuv6NK/5YIyKFflkn8ZV3NPwvmhHOv4Q/AYA0cXgrJV0DjrTCqAn/wO1yXleBX+tGENaOLEpg3dJJuUzsiCo5wkdRgLWp1CJVDweDQ4kh7jd7ud0mbuwup9xOouxiejYNCG8Kws1jC8zD/6IW7r8BzxRTcS1QkuGaSVrYVdWE9ta4UotVRpEftR1Mbx7sbJyJdsh7l43fxXxHPQHIUJ1KvIszfQNZgP8KkzbQWxjLN8wIqewh1RwD9liCSbyQHF1iJsHWjOCQUj4Q1vwfFeC+CUQ/2VbhyRtI1qEbbrSN14DiKRUKK6gFDMRrXAeS6CeAcQh9woLg9GNa6sfT5cdwQhjUiCaFRh9xehXe0HyE+iL93MvpVQRqzfkeYdYiO9vXEbQyhmRjA8GgFo0tCuG8Ip0kbQfShq3BOySf4g4+UGatISE7jwPcd5I3rY8TPfYQPHqFwdgfrbpyK1u9DStUR1nWYsI5GSLsUHv4Q7nQgrHkW1eqvcIYHOVMURD8kEHNqF0mtISJ7OiEhnVCdAaXpQhiyYSg7SeH0PBxFA8hCPY0Z8XgjE4kd1BJW40EsspDkt9Bs7yEvZSREDCdSmkgtd5HGs8hCD847RPTSSMToD+j1v0nJms3kJ6TR4W9AKhrCeSaM5jFmjFNC2MyvkCjmMIAB05ldZNrGcSS8iWWHDyFGDAP7ckIhATmuncrE8WSkpVFencGohGrsXi1RNZ0Y206ifGxH/8oY6joCDGhHcJXjC5759jQvXvgimn0JLOnaQN7spzljqCUMkd6Ai0GViwGpA48tDa/3IVyKHYs6n9FSJhHtT+Eyx+MfPE676gD9goowkpnLhdxNDhoqycNBIrr/UEK/SBTgVxfH/yR0oAuHkS/CiSvB4oaefdDRgzYqE99QG3IgDvtQJLrXWgg+GMOeuCSGnRwkJjUVh86P29hMX2Y64WvuJck/C7t8PeEjn4SuIARXg8ZNU1ox6baZKHetp7s/Cetz68j8LhxeWI7gXs2C6JM0+aJ4Nvgkz85bQ2T0Wfz7mwm+cQ8myYvWcAFSyX0Uun6Dvq0c86lWZH0mESMG0VX24nWP4GzRFYyeU4UykEZvbhmjanfjLo7EHpdH2OApDJ6HUawpkFKMKDRSJ17K1qjRZLhGkVVTR9FP5Rh1HQQKk5EL7kSVPBNFG4kQDOGf8BOu+j8Q1nUF4icvga0GaeYnGDdcB5FGtEveR7lPxrZhAoPWMJrHD2Nw1jCCrrP0lllJbaoiureHsznJRO7eQ8ypPnRRTmRJi2q8A91nIUq2rSZcsqMt9NF1/kxm9CRy3CFwaoZCUV0ClHXAh4+AOQPmL4WS80E0ErbmMOk7Gzl7h5WO8zOIbxwksi4MRj2JYJIIDDxHxX4/RdHRiOHhhJ2sptPYgVrvIe/tRoSmID0vzmIgKxy7ZGe3UIIyWEL+/m8RUicTw1V08QVRjMavOkBo9CSI1mE3peHKUdHn3UZwDajrrBjHuZA3mDHJXtSXLycU2o+laxGyy4amYAkTyp9CW7sRLjoMxl60zs1Y2lPIjHBjiXiR7QeSKW1YStNFSYz/oQk5SSS0UMfu8EnkOStJCG/j2OUPMOH0U9zdF+CucX24gnGsEZ6kVYoh099IetVBorPvJ6DVkFS3kojBMg7k3EqpVsDa8R1nbXE0a/ei10JeQwr5PPSvMc6ZGDhAGtsp4ypm/kOV+X+ELID3l2USf1lX80+HDvCCJgJGrYOaOBi8GU30SDTx79IkX8ZZVRypeOm+uYv4b5tZfN8DCPXzIGU0Ful1/OuLkPMHUY+4HZrewvqFAk3LobwCFs5AQcCdl0igopymdDsRcXPRPuqG9Q0EOl6kraiQWGUL0Y2dDFmzGbn9XT7VXInvyluRvl5DQXocFb4W0g7cis5dSyjbQu8MK15tAol71Qhl/ehGVjEYaYchB8SPQQ4dpkcTiV+bSKG/gQOxJeSp3IT5H0AxzmIvL/B2eBYXuDZzfdIBvCmtqKc6OBAy0tpVTPGaH8lsXI2oMxIwlBO8shWrTUFI3ANjb4Oa3fDpXRBWDL5j8H4xwuj7idA5UccvY+FuB0rdRvwJURzPDeGsGMUYdT3J/hQ6klvxuDSIJTHQOQul5xEKCn0MHfYgXjmBoWgXhuhllC3fhL/NxcHbbsFhf58JCY3w0JXn0strzxC65SpCnQMoFh/Sb8zkvNNHy01jUadfRVl6C0kEiKAQxdjL9+f9HveeHYz7aSfc+CkD4vNkrVyL4NagJKuJXu8iWugiGLJjuryN+oijZA8eRzWYh8U0i15VMyEmoGMhSvp8fPWXkjLMhqbzSrbEDme+8CzefJnEykiE6gGEMQGUg1uR4zUIre8jTtkIioyupwZcS2HbDhhlBN8uzImfM0o+zED6l5xYN5mGWW5sSXM5+JaZyU9vxhen4cH1r9HviWDb0gmMa3+Ngaw4pOjhGN6s5+iVCjf1OFFtOoDHEM2J2a9yVF3NaJceKeZ6XsmqYZycxpDyLGdj84kXo5jEleg16Ui2o/AXCSjD0fEBar5gIjIy4j9jHlzwH30B/5Z/wjv4y0EQxHNFyOFcUfPoJ0DSQPcWfDWP0Kvvh/52SjxJ5GuqMWucKPUhmPsBCAKi34w44KfLL6DEzUaYuprAA2rkmD4YkwMHtqDsXUP48QY6jcdJ3d6LZd3HBJ0JdN6WQEfWab5dn4VYG8A0NI0FWfV02SO52P45PmGQtIIuTKPdTKr9Dm/HEZxmA6L1RQz2UiKPVDGY5AW7jEox4/NUQ/s6HJ0f4A9p+TL5YiK99ShDLRR4NVRLYYR6liJvHc06OZYc6uk1XUpt3BaMwWg0mpsY649gfN54ym+ZxDsvXM/XD83i5A1Bhn5KQKgXoW4Dii8KxZgBmZkwfT54gHA/yr4/EFTlYu7rR3RuQDQ0ElI18dX2y1EPmCD3HsgeRlBREa+JwPGKHu+aZrAPR1xgwe8X+P54D5Xm+Xz2djm3um7hqbhnMVgklKgkyIkHbT9cdhPyZ8sJDnqRPV7Ub19J502TCN50P8M3dZB12EpB8H6GOE0Zj2EX87ilZQn1IwIoOh3K6qcx1lahaTfA8/shTgXtrXCqCVXyMHKSj1FvfJGO6BRcO57HL1cQwxw6+AyFNpxRq5F7gpgPjaaq4UtGv/I9EXu6SDndQmjfHEK+bEJHsznuj0I4VYtgG0Tw+ZAP70eZ8iHcsQJ/0IGycf+5OOt9j5JU8QPtGhGVWkVfQiKxspuCbY1UpmTi0JhAFKmbmYEz0oY7cRFhvSloTtzFbP1XdK7M5Cf9KLYVz6Q8LZ/Co8tZFryAWOMM9pgGmecIMnXXLsKIZbr4LXk8hInhSIIFbNP/jR4WYOQxIuhD+uc0zucKQv9129+JX1fQ/w0UuQvwoMg9CGIUhN8Gna8SshVSHnsCh2Ji3JAZ7djHoW4L0rKVyA3dtOalU+0wkXVqJgk1ErH2cE7Nf4ysjI8wpe9G6BoPY16H2iF83y7GVHkAS61EUBqi7RIDknYXtm0+NjUv48qoH2g+kULm0ge4pu9VEsNOs8ERQ1b1l0RF92LOdiFog+S7G/DXNqC2n4+QHoEQcx6G1CdRRjyGMPIeUFYSEmJp9KaQX32IQIoa2fYUAgexRL6DxKs4rOVYwyqYPtBIrFLGGZ+JphgDObIRdEsRzJOIV/xM5W7Clctp09/Ml6ob0S+q5Lz2PSRZfeD7HXKHiCZlHIL/CFy0EmX/9bjT9ei68mHqdSAICMU384ViJC72SqYUDNC+9zGi93+IOCsc51GZqPvfQ0pNgI2XobG3kK5zsGmPgf2ROUwubeGZuIcImXsYvaOeOHE+stxCU1IRkU/egq7vDJoFOqAYeezrpPpr0dTehGxvRfnqdlRls0gTVQSj42gp8NMbHcUkxyeQvwxP90FivzmDMOoC+PJZFJca5e5PEfa8AT37EKs2Eh3n4pZJD7D2nUtpqn8DT24PGiIRmYO2z4xq/TqM8h+ouK+UpHseRlsuE9KeQhx+Paz/LUqdQE5tBV+W3sQlrSvoCl5GzLYugnem0fbS9bRdEs6ULVPhm+MQ1obmqg8J6j8la7hA+8kCbMk/kXjsBILBhL+4lJ72ego3VBEdM5Zqo5N4zQkEr8zyd+5myrJmIkMyqdnPkUQi9Byk4/i1bMobxsJTa/GttGPXhVBNtxKkATXZ/6EmbiAML/L/ffH93+BfDPQviF8N9H8DJXQIJfgj8Pq/7gtophEyryIy7ElqBteTMvweOLEH8t5G0GfjioviePAjsur3EOVPQ1Ry0Fc2M3JtM5VP3UmYaQzR2leQ6m4AwQNTpmM5uonAMAt9OfFE7hlEYw6hTPmE2cKLGGyLUXoq6L/8MhD7GKH5mbicdNKOmPl23psc6evh8ZyHsXmfRhu4lpDFB2IPgrkaTr2GmHse7HkCXaaH/rhh7E50MbseJn92ggRfFbjtcFsnBZE3sz/CS5voZImnjWNJ5xHVn0xz08/g6QX9GbBNIkgHQvAgwaFhxNqTuTd+J4I5AzHrSYRmBWHzjQRtDuSoHxDD1AQ8fagMftT6SKQ8H3AUCsdS74jhp3IrqxZ4QAhnSBOG1aPgjzOTPH4R3pRMtvz8BeXdU5ib2MFIWzv5SjmXFP+Rg2MnEytNJ6+hm4p5D+B640XirPNQPv2aD68bxfjwYkq3tyFWH0F6biTCeA+to4oQJs4hsflCSBkBwQCqgVbS+hqJKS9jdV4h8zreQdsooA7IsOsrKByOEB1LMOEU6hlXwP5D0PwIF//UTokhiv4WK4ktnQyRQkNuOlLzHkw/VhLKmsiQ9RSeOIlB+SCpGj1i0nJkzXeocvSQJWI6mM6Y3W18cfEjKB11XJP0NU3f6tiRcpoFykMwOx/0XbD8boj9EONlyYSNPciBT4Yxrd2Oyl1BMEvkqZQFvLXrdwwVqIn07qRSH8/W5Hvp8g1w4agy5kf9SLRYgBisR5HiOBIlccJUzFXfPkX9kwKiSk/qi8UM4UJF5l+lC90/4+oZzhnowD/6Iv4t/y0DLQhCBOdK5qUCjcBSRVEG/t2Y4cBywAKEgGcVRflPy+z90rHjYLdKYZKYh080EA246aTV0E6GT6A1uIvSvn4IewiKVxKUrBxlM/2hZuaeOURI7kZX5gOGoEALuYvJ+ekgPfqz7L5vGqODz2BoXoou1AyCgKZGIaz4cQZG/0yC8Ro4+ByUPAzRIxA0eiJGrEYZ+JRQ1X5O1eiIGhXGoiUrKRl8jht3fMkTUVspvbAdcfkCgrE1BPNlNGcrIMEBydMIT49E6f0INRkkby1G6+qAtEqIDcLRcWiz5rA2bRYTI7oRTu8nvDOZQZee7PWHISEHCteC/RjChHzU6moUfybq6rmIab/7800zfQPTHsWh6oOzb6Hb7UefsQ9nggGjKx8GdJA6D5+5iLv3hnhp/GX4hQhUyghiqsvwXKRHn/AgnWVb6Tg0n2GaGiYPn4R10IvnvHQGp+ezKiyHSzR3Y/UoEN3IKE0pvk3n0ZVzlq7xySysPoUu0kXPcBPRBbkIGckozcew7dmDOOtVSMk5d60SYEtFsaUiBhKI+7IFXbMadV83ilZAuOJhOLULJp9HyPcyak8uCGFgGYOYFyC9rQePxYO6KwebNZ/wDw7Sp6uk7XoDlYaHmPL9jaT09xDr3gWpyxEN+cjNy1E6tiCM+QC2vkaWxw09GXydr+Vg5Tjs6gymdfYR1z8P4l6F4ksh7glY/XuKySXbqBCZp8b8fgAlV+CUehhP/PwavvtuQnv8Dfw7q8mYEYkUPZqpa15FnhxHV4GVru4Gem27OMYPNEtGrmqNoeabIhLubkfdo8Ex/CpkOtnLKqJIIo0itOj//qL7v40C+P7RF/Fv+e+uoB8CtimK8rwgCA/96fO/7xDgBq5SFKVGEIR44JggCJv+s1Yvv3QsmCkUSvj2/2vvPMOkqLIG/N7qnCfnnBNxyDknSSoiihgQc17XHNYcVldXXROraw4giKIgkiTnzMwwMMMMk3Psnunc9f0YVsGIn4ro9vs8/UzXrdt1z5m6derWqXvP0Q7DyHrG0pMqz3NkOS24jnSSGriQcF8CdGZTGtrIXj4m15XDgH0vQPLzIOfAjlchug1yH4OSHQh9BPq6Gnrcv5RjYw6QaMuhMt2HSuEiUu+j1XEjmjoT3uJl+ELCkPfdiXB5we0BpwfRXIWwqRniPojyCy/yQT1Bl17BR33e4t6Dc1iyw8EDo/+K+h9z8BZsxhujRsp4HSnxAhzyMlwVFYzcE4J2/Xro3QS9nbDDiE+pZ/2GAFLrSqDXThwrfUQPWk5lwlNsuXse3YPO6nqT31aJctFc9HYHVbmRRDR7vk5if8C5hbh9z1FrD+HA9Dn0MT1KfNtNtBqjMB5Q4wkqwR2mRtNWyJ353flLz06S26rwGK1Q9inG2GbyUnpi0YwiWr0QQ/0RVIGtuLa9z7axwyjq3wufcHHRukoM7utBGwyKLXQe0FA6LY5jkzLobxjI3ToDT2/7OyZPIaj1dJquxR7ZQVCNGbHxLYiaDK5D0PQcbDiISO2N5oCCbGcFnSYLgW1GpNQaCHsDhsYj0kJR0BtZvxFhaobQvhAxF+XqSzBlT4TAYti6B0VCDMGRHXRoGolwWdE726E9FaR6aH8DNPehONiKLzEIKeVcxEPT4YP7SP5wMbqnzmHBpdO45ukPSTn/FQhug93PQeuHuENkNs0dy7HUQJxSMpdN+wfeET48cYEMiNtP60ALDR1LMX2mwzi9E7McgEoxAoruhYQNxAc+isezj+3q6dja3yB2bR3Vby+k5dWrSOpcgmbJYcqjY3ETyTG200wtLhxkMxjFn+0B/E/o4phGV0AQgLeAdXzLQMuyfOSE79VCiHogFGj9hW3/7iTI0Uz3TEC0fUar616ClWlILgkSJULXNmDvfQ+t3replUuZ6p6OcndPCBwFkgFWvQzWeki7AfrfDhXnQUcmxjHhkHA3uoUPosjfT7zVx5HsRKT8JuivY1f3RHqGJBCh1ILPBc42aM3DG9oNT0UjHelq9KXn4LAuwpDjwx2nhLIlPD14KCt3rWPG/nN4ZfYjRC2/C5+mJ464RZS4QjjgMZNEG6l8AGYlNETDu62050DzqBdYGZTJ30QCR1hIzV93o2gbQ0r7J4RXemnRZhCkT6HVsgFVdx9ytRa5zYampAKPq4NP1JuJ3XMXHZ86SbjlDjI+exP6TcLarMOdqUAx5F1cRbdg74TnfSqCtD6Gex4Hl0CpGop3Sw/2jVNjNURQJD9LTGUeyrQmtLKDz0ZOoSIiltEPHyMl9Sx0ky+EgAAAGtbmcuiokUH98slob0DRUsbjdaWoyqy0K/Q4AxNQ7Z1PIEMR3mPQ8CZUvAPGnqDsCdYOqE5GmJ0cuOUZut/3KJLJDTl6SBgPuvGgGY4o+AhHaCvamL5QW4KwT4C+LnzSCjzm/qhTI2Hisyh2X0PM7iVEFb4BChWIOtBUgz4XZDUieAikjsCn+BSF6WK45Gl8ezYx5PM30GuaqDHHkfjKSNS9JmOjFX3qragqj9GaFUUoMdgaS+h8XmDc6qUqQcPn2ZOJ6vQyZuVifNP1dAgDxVIgOdZqDLIEaQMBDb7SzXTrvImh7eU4DsTz2kdL6fvxP6hv1ZPoFeSIYYCGHoxDOsOSqv6q/AkNdLgsyzXHv9cC4T9WWQjRD1DTlTDxj4UsgysPlNHQuRI6V4FsRdb4OGpspod6LXWefTjaZ1MSfSfRx+ajXPw4oWf1Ql/8CY6W29GqJNxSMNpV5yPMLqj0wYVXHE9Fb+w6G4FJcOQcDl7+KWUF/2boglXE1jvxBClZljyXwfJewhdVwMWvgikcOpqQ/5NNy1AjhnY75hesKHvupW14AO2HvbRLc3C7E3nFm0tVr8mowguYUziAh7pPpadqNYuKziXXexfj7U2UOaMJLIpGF92Js88EHHnvUzExkPc0Zq4W0ezmbuKZio/91FqGENH4KoFCj2r1CPLH3E6IdiDK1p04orVE7UyBAyvwHVvH5IrlKNd3oLziFcgeAaFJyEuHUWMIQTIkoSy9Ho9GYmtaPEs3h7AsdTYtjVWUJBpwee4nPrKJTl0SOWX1eDprUQY5cHRqsSb2IEjWkqG4lIJrdxC5RsJ32224W1vYlRSErlc4vQ8VYeudjmhtpi5IIEWHElDXhHKEF71Wxq1oQSjdoLscxKOw/yLQhkOPf3el9zyeiiuDTg6Oncjodg2MvRA0pq5+cU8vpOwSlFFeZL0L0XQARCRyWRSe0aMo6lWI/tg6IuW9aHNfRtq6Bkn3JT5fJ16RQrHIwSteou9hgWgtQdLPx+29GKdCh1NTwL6UZnotW4s3RodaMZ33TO9w6dqrcf1lDtolzyIrTcS41fRWXMKOzmtpKknCaC/iWMZELmyoJ/hYLd6jY6nKbcRl0hCxcwUtITEYxt4MUW6aq/5NbXAcTenD2GWs5XxtMw8cqgZvCCg6oO8cUHU9B/2pjTP8MQ20EGI1EPE9u+45cUOWZVkI8YOh8YQQkcA7wCWyLH/va97jWQmuBIiLi/sp0U4fnkqovwYcW8F4DujH4wm5BYeijVKeJ5Yb0XauJ8G+FNk2Ele1ikVD5zIzegmq/3xFwEQXzojuOJ1m5OLVVPZKpSJqEAO2P4Tj4lF0DuqHM6qa+m5BRCc8iallFUVyHpN7aHgh/XomrF1Bxqe76RawhbBRI3BclIPuvZth+s1Q9BLOLCf6HQVoD3sQHg1uSYUzMICtSamMLd+KvqOBv6fF0+SuJLDyDbxTrkMxfwUixM4c+2Iag8/C1Pwc+qoONIdCYNYdaDZcy9GYW/Dtf4MRXz2I6pbHsRqOYcAEJeupCJxIoXsUoyrupcYWRtIH/8aneQt1sxJ1aDoe3TqU6cGoq7fhqluMd8gAlP2Gg7sVpDrcsYEkFzfgPBxDcaabLUVTee/YHP4ZfyEV5nACEq8hp2wxmh1v4/U2Iip0hMa+Rq0hjvIdE/k87jIi2/dyX+3bXCxrkUVPlg3LQzf8HKy+g9xefBudajU7x0Ww1dWXmrgEQhPVxFXlkxR9CFedGkO7Bcfw+6jWbCNKHozYnQvaCFCmQsFTkH37110gGh1vDenHwLZg9P81zm4HtFQj2eLxBOSBbz80+PBpqxGiElXpZqLc0eT1j6TZfSVJ6jeo6z6DcOWLuAmgLTyVWFFIiKcOW/SjOKOqkemPUujQOO6gUjmZgOD+vHs0FOPwQ2Su8PJY5zQy52TT59lelJ9/ObqPVrF97QDWNn7OxbWHcAVpqE0ey+iaOnyd5+J5eTmq/7xHYOOjSI0baQ8fjly+AqZs4pB0mMOBh9hjnMZQxS5GcgvC9xDujY+ivHIF4uD7oDX+Hlfc78Mf0UDLsvyDS4KEEHVCiEhZlmuOG+D6H6hnBpYB98iyvO1H2poPzIeucKM/JdtpQZbBVQjmS8B0ERjOAsmIm+20cR2JrmPoOndiFaG0Bw1E9mwjQFvCuMDBtER1IIaoMWx0Io89hM5wDlJWBsaEu4l1tUH0A2jOG4tqVSHedUcouTGL9xTzae9xKTfWPIspbiW3NZzFwcQI8gal0/edNXTs24e9twGNyER8fhGiew+05EDVAXCGQl8ZVbQbua0b7lAFG5ZdzoTUT2nbMZK6WBOmDCPu2stQp7hxRyciSU50igo6My7laMwOIr4sgYYiZFMqsdkxOI6kYt1hZePEK4geORbn9WW0BYSyVOxCFaCjtzuRqswAZKUbizMSQ+JrENwLQR2Nh65BWfIiymQnHqkV9eokhFsNIZmoLKmIpET0+dvJiB7A+bUXE69qJyQ8mbrQALqVLUXYm6DXXzla9SXmVh3S4UVEqRop1ioYpfwCyd7EuYESU4PBLiuQ661EVTzM1KbF2LUKtnT2YcRLm5EumoNrexmZySuoGuSio8iAweADQxumL55HHZOELWAYhugbkMxDu1wch56Fg49Ct3v47PBmxixcSs29k1mjt3EWdlzk49v2KEqjDXl3B1KshByqxpczBoV5BmLz/WDyIadOJHHjUjpDtTQqzyFcm4wl/kVcRVcjqatwKuKxe3PQelPRb8lD6nEvVL5Nadga2s0FDCxqwTDexnaXln8ymop2L1ft1NMz93leePuvWI3J9LBvJ7CvILjditpVBYHn4tsYjPvTBajnv4tQKLBYroJt8zGWtdDu6uSAazvl2kb6aObRbn2VPg3hBFY+CMWraRk8ggrl48QpWwgJu/oPkAvlV+SPZqB/gqXAJcATx/9++u0KQgg1sAR4W5blRb+wvdOPEKD/7j1KQSRmx7kYG/6G0zIehzkZHdUoihQ4zRlYhQpn+DCCkjfj0XXg1UUgRfaAuFu7DtBaBXIMipl3o6j7C5JDR/ZembDlewkcEEhjdjwWxSocx6pIoR5tWhTSaJCz1TQFSHgaj2AhA03GPyHvIwg5BLF10J6ArDqMwXqUKdYMalRf4dzUSP7df+Owugpj3QaCbRJKtwEpZzUKTVJX1ITypeAuQIy/GQ5sBFM4jvrXCJIkWt0m5IuUiCmxbCp/g+AKNYHD29BIWoIsqQRqC1Eb+2HetRP6dCU5UBFOiGoOtsCVlGeNpb6kiaGltYjcW1HGDkR0HIWiF9ivy8BT0sok02dclfguqr0tZPS10hR4CSG6nsjGmdTFbSc17F2o2M3RjRfg0ZsIb5MwtxzjjvVXgt1Ga0sBd9kOI0Ij2Xv+HbQotcyY+Rckeydh02eD3kDLO3sIW5bHoanJ9HrjEFJ6APhAU/UGEhOojdhDhHEePhwcS88ibP8ijEeeZmd6f8LP3UmUcBPuqqVJHYa6yYdx8xqEPgApYhSs3IR870Dw3YrvzQuRDlWBeSru8y6iKXEA7e3raQmvoVURSmjBPvo5ApE9IFUpCIhfjOiYDoH1UHc17QHncszUk4F8RlXM+6g+eJp9/WZimlTLZaoV9DMk0Dt3OS2TRxD3ZifhWRuxO7S4hEDZaMEauR31NRtQXHchIjCwq79pw2H0JsTzIygacwGdrkLOKm6lvO6fDJdqCAy9ClYfglEPEtaxgaDmcyj33k6Z5W3imE4o/f4Qaat+ET7A8XsLcTK/1EA/ASwUQlwOlEFXfnUhRB/galmW5x0vGwYEH8/HBXCpLMv7fmHbvytqnxm1LQ8C/41Wfz5ajMhOO+5/mZCfUGDmEOoyO53eDixBTlrDorHFjcGADMiI954CyQ3mdFAo2HTDhZSqjnCeOxJblUy7zUbL4keo7hVJdsgoirJ6EF9yE4ag6eyKbSQ83U1Uay1pm8ciWmsQkSGg9UFoEq4GO7qdHSgceVj2GvEqBNGlH9I7eBI1MTOR972IK1CFV6VA/1+Fyj8BPTBkHFR2QMMOAuX9SDoNPR4Mwa0GdedGkqKqUKnOxluwiyNbCqmNLyCizYHZcxhvkBtlw3pYsADmzcOz4U7U4yeQ2O7BknIUtyUAglQoTWOpF/D5wDnk5C+hu7mBx733oexw4sgxUFcQiPbAAuTEgbR0jyJQFQc+2NX4CsbkbJJcZchfbMOTpmbJjJtR6iKY5FIgNdVBZR69t+6n7dg2REcn8tkgFt6C7ewpeOx1xL/aij48hJKLB5L2nw0QooQgGdXBfCIK86iZdjnhvpnsMC8kPUNPkrqOAbxEYuZuJjS0o9vlILjfWdAoQ6/piBIr8oFFyP0icG4swbllEg0pRlbdOpeOoVOxmBai6dARHzqTHlYVvtorsUVYKDLPwdj+FR7aoPIQBI/Em+yjWhVLpXSExJ0lVAUPJWLn/ci3fkBPvqQiUqZ91VnMmiZhOxaAQpTg07eiqNTjrU6iKdxGtK8NaVEtij4RqDJDvumwLflQ9CbVPRKIa1tPaKEbOW4Kn2c1M7MxBM8zd6NsN0FYDBzbgbLo3ySt/ZL4kLspj6mmnL8RywTCGPjnNdR/RBfHjyHLchMw+nvKdwHzjn9/F3j3l7RzRiKMyCHv46MYQTtuFuB2L0fcnYU+4UpUjMeeoKTRMRq3tR1N/Au0sota3kTf2EJoyKcofQLRupPOK55hl34xs6v3U2tazv4+ySQ1SxxMiSS9vYr6mEJUJV/hMEo43YvIqg8nxNWO2aEHhQVM5cgdNchFRgjahMKpQBp8K2x9m8DUclxWJfaOXD7r2YNpmnNRNb6Ic8gLlEqLkTrtxKtnoXd2ILQg6wSi+TDOyS/ie2kYqvYWNMFhHFlvoO8THSjKitC2/ZOhZUr6hek5tK0VZbuW7sP3o+iXBRtnUG+Ow3LbGjrugJX1MWxVZtCgms3ZAU0MNY2l7ugbuAqfIi41nvp+oSw198fjcjGxaQmNZQlE7EtAs3s5ZcMLaPKqSJcHUn1JPDFDuhMRkwo9Z1M1Zz7GY3ms8+3kRk8QlsoYSO0PMTm0DziXF+x7mNRxCz2TtuGKSqdWNR/t8H5I+2MJb9UT6LIij/DiEyOhZA9K3VCkKB0R64txBs9lVDIoAn1Ih40kHLBgc6fTu7o78q61UHgUgtuQhwxBXvUQwi3jm1iB+LIWg9OHrHBziWcB9aa5rPc1cU79uZhqN4OjCffATynTzaWePTjtUcR1lFLTNAd15Coa1c/ibF1HzrLDaNIvR71wK/K8jxBh2Uw8uIOV8XBYKfFlHoxUdqLTrsNnMOIcchvGdY+wM2MspoNNFF41kx63zED5r2txefajKd4Bex8AoSQw9Gp0X27AlrmGA2F7kJzTaG74GJVFTcC8OyFlCni+AllA4ngUMcNIBOI4iwq+YDf3E81YjMRhJP7PZaz/bAb6fxmvKKGDq5GpQcUkVEzE4HkBkRUGKJGROei9gT4d9ezLGo/VdyfhW6MxGLwEeCpB78KhCUIRnEwzSi7tSKM8eS0timCiXG6OBGYzgm6E7v8Y1u7BFuPCEwaaVh9hwY3URgfi0kdj+HQ3IlxFQ9xsDCFbEeWl6MrsoPkKAtthtwq1xU3C0jWEJ1iotD1DuKREp80he10rjog4msRduD35aIUG+4pL0SsstMurCHJL0OwiJGkXw6ZLyPkhdFgUqAOvRCNvR6MtI3bYECzyhUh7r4PNrVg3SLybOIhLx7/F0vJ5WLNHMHXx2wz952dU3ngxGy5OYGegmhu9KYzauwq5j4y3xYKrWUlraQDtJh+lA7U4LphIWUgowW1tBL7wKFETOtH2MkD5B9B2lMjOeuq04ViNvfD6jsGzE/DcM5MNlnNYodRyhUgitcWGT5tG5aACvJoAIlIfBOVgsOlROYLBI+iMzEONCbHpY1wRoXhTBXKbh+B9HbTrUjjc/Tp2RBzmqg47eNsRF46DT9fCrlLEyoM0J8Zi1htQHJuAYuuziIunE1C1HFkRjoN2hlf0Qr/iDmhPhLP+hqpRQ1zEs9gUd9Kh9uD1eVArq3nC+xaji3yMOKBjfuR0xq1dR/Kcx1FHZ4MsEyKFMrqmg/OnOFn02oPoey/CE6JElp0oa5pR6JJRSHGE1TagYwIHVOvomBtCuvV64jM2QsYV0HkMZdW/2XFzIM5OF1mHDpC8PpemBAlvfCyo27s6d/fHoGwBjLjo6/6uQE0C04hjEhV8yRZuJJLhdONmxJ9ldoffQP85kPHgpQANVyJQouLsrk4a8E2dA/Iakt17UST8i0hNCAkfzcXqsfLRuPOZFvEYqTlmVFuuAEKJxE1e52r0+RVkKnS82HsG0SIDY9BAXIVPgU2NNUWPrVVBzNFmKpxhGEa4cCmO4AlVoO67jwZtEVg6MElt0OcaKH4X9NfDbcPhtnFog9rQaq+nw2pgZ+BX9F48HfOmo2jHJBAtNyN3dKA1ZdOe0hu9vieWnU+j6F6Hy6ZD2ecBHBsfRatVYrYa8MkvI0suhPAQWdCO07QX4fHhbFRhaqzksn5vYU3M4NKMJ7vm+86dimN0PvvshYR+9S8eqalFc85byNtugdYylEltOFtD6Xj2CLEDK+g+9AgKVTiV9TKiyYM2yoNGZ4b6j6AuCka9iaS0YNl4Dk82tmELHkFTSB9ebEglJbiAh1peR6IKudZGZ2g6HdJO9IxDIYXCkLth/z3IOQV0lgfgzbFhj7DSHhpJp5yIaU81cpAT2aXGaC0jYdtDVMaNpCHteqLq90H5PeDthAwJPqrDnhnLi7dMI6S9iRmDIwjdtQ3fpMug6ABVDW8xqiET6aoCsHfC0pdh62MobrNjN07niFlBgLaG4OpmRq5ez02THuP9pDCuuTSNw316siUhmhHWZqjJB0cn4a425IrJjBybQ6czDp/UQu0l4WRsLEQk3g8hlcil8zFtXMHgyX/hkP5ZijtCKLHfQ3Z5CY2mQPSK3fSwOXAqmtDFtaMMehfPVpCC3GCe29V5FVpIvPjrKYYnIqEiipGoMWOjjFo2Ecnw03HZnR7OMAP9B100//siUKJmOhpmAf3x0v71PhmZesrQuB/HqL2NTksIYQvvwjVSR8JZhVxnu4tP7AvJU3etiO+gjh3Ou4nKKyXd3kFrRyhTbGamUoh4ZzRK+uPITELTqkEdq8AVL0gtLCFqaSNShw+HXo3sfRurXIXS0A9Nbh6Uvwp2LQQPh8oj0D8Ljz2EDQtvIObAh/RVnodVZCAbTGD3gB5ErxHoE7pTG9GMbHShKYmgNON8XOf+hY5kIw25U9nfbRxbxv6FecNeomnIOXj1JkToBAjLxTowCV3fKOSsIFRDncQWHgVJiRsfNdh4X7uVpKav6K7ZgdqwH/mxeaDIQmjsyOqHcbaCOVRF3esCW9nZyIf0yGaZsKgmAqdrkUenIxsGI7fLNORfj1s40GfeQeLBvxO0YS7Vw7zcsvUZZjfnoA58EiHH4DNBbUYnqiZBgu8uBBL06ENDbSCS0YtiXByWmn6Yd6QQ1taf+PYg9IF1BB+qxVyrxxo8ArVQctYXO4na3ggL90NeD0hUQEg8DI8jel8tF322k86wBPYMGAC7PIijB2lqP0yIlIbU9x6oWQM6CfeYBvbdN5g9kTkcUSup6EhAc9SK3JTDyJwJnC8Osqd8DapxY8kedh6Fko0l7QeQP3wKProD3553cBmqCOp0Yg7pCftdhOaNx5M7jxb3EzjYhZwxBHnnMlxzZhLQUsXAN4zEthpZkxbDkagIdInvUmMahqaxjQO6XmBuQrroBTxmI9Q1nNDJf9h1ocJAJMNIZc6fyzj7o9n9eehkPzU8TQc7MDMOCTUAJYDVW8tAPFg95VgWPIZj5IWYogYh3NAifUzi0R08nBXMdeHNyI6HyV5TS2C8AeJXEKJxohLj4DUZbdUkxKyZiIr5BO3bRGAlVMzORjukCLk+iJBFVlqDTfje+ZJ4VznaqS9Bwi1QFwOR2bBmPOjGUaczc/sz8xm19d8MZhe6zPGoN/yTvXdlErGzFlOTGf2I12hSLaTKu4WokqVoNXkEF5TjGl2Hj1b0AVdw6JWtLL47gsvFGtr0IyFwD1KUHrMvHNmWj8ddiru7ExTn0qkvw1VzK59FTeZzJB4M70P06y+i29FM59XxeC+vRBewG9cePYpFd6EwKsl7tCfRQeOpf2Yz1auasWd0IzDbCxIY5FsgrQDh86CwbWRLy3QS6rXM73kNA0q2MzlrLuLwdDi2CamkAqn4GO5REjF5oag7IpC4jJLw4fzDEEruiAu4VLeRzqhMdPVeSM2H6LOxGrw0i0rCM+YhiouQ7n8ZpUqH+sYy5KqbEJNvBtub0KIG7SAYFgNJm0k8Es3g+jL6LVsBAeGIV3dw5P176K28HPnoa2x0fsbHEXkEhKoYIdTUeqfhLWjhxq/eRTdhLvZhbRjaZ/AgRm6KWcTRjv2kOGWurrSyPGo2zYotWCqbUegk1P2K4cAzODybERov0s0fIT3xHHJ7KJbKGNxFBXSWDgaXDZcrlhU3jcEoRdGbEXhxskx8ijeinWyySK5tp0GKo9SSRKQ+gNC4sN/xijoD8NEV/vZXQAjxH2AyUC/Lcs7xsvOAB4BMoN/xd3U/it9A/z/R04N4XqCTfWhJRU1XGqz18gsMca/ExOdoPvoLjHoFTXRvAGSVTGNQJiWOXejbSnEY7KxtSWDzgFzOb6wgyZSN17uAgOeHcdRRRN3oMmKUnxCdrEUckPDhI/bdPFpGZmHRujg0KRJts5FqTwexn9nxvjEbz/QMVMPehMX3QJ0CW+5eVmZPYW1EOLcaduAstVFWMR1lmhZNiBapewQ6cR3SJ9dR2cNMSbdIymNyOCc8logPjuCTRuOtKaPa/RqmfdW8cP5C9FEJdE4pYO9EM1pfPvHeFryht6Oqfg914G4aFBW0D2nBp81lB3rmEE0CkUhXvM6mSf9CEVvMwI5NKOsVqFSXgOlDVEfNNI9JIbhoBbrYStSXZZH6RTOK0jHY01bj1jyLRvckIjkIly0A1ixgf5iKm166lZDYTOSdw/ENuQTFulq46T90bNmMSK1AL+/FEWzhmbg3aWmq5r7NtxGxTg8Dp+OT66FTBz2XQseb+Fo/IvbzbFrNt9OaFUbcHSYUBwNwtepQOMuRqp4D8wUI63rEkIfg85kwfTGyMQopfwzCEQABJpx5UNtWxQeO1zgcFkhiWwxXr38dx/DLiFT8g3937iBXtY/qPulUZIGl0I45+BMC6M8dHaE8eN69vKIfgcJ3gEmHrqJyak+s5xYStS0ItfBCfDyiqQl1dxfu+lYc/8xGF5aI5S9T6Gi3seCBKfha95OkAZunN+3qaGqwIhAcoju9XFto0AWhSEgjU0QSc2gebXof9qb/IGKmIWFGwoQCEx2sQkMOKhJ+pyvtNCLTFc7t1+FN4F/A2yeU5QHnAK+e6kH8BvoXoCQA89ehSMBKO/08u4gtGY5y48Uw+Z8Q3euEX/johpHukSPw3nwtov9hshOLMNa7sUTMgbd6orFVgSKS5Etep9XyCNvlFtI0F5OjWI4jTokpz0rQxkOQ3JOI6FQCP15M58ApHL3GSI8NO/HtKcBzZAjYoXWwCY/eQ0rLfj5YPZc4exlbw/pQEJrBhS2fYjlciSzGsSukge3TepBcVEzc/lrO3vgxWAYg11fg3v8M7ZU5rOo7BYVuBf3Su0PVTrTBt5H99mM0j5tGRchBlOI5wlubcZmH0RgmMG21EjZiIv8i6Bv19UkMC7sC56LJdMQHczh6NCm2rzAYYlDnHqHP6x+jC1XSNDWEDZkxGFIDGbR2E5EHc/AGuGjRLkYtbeSRnIuY19ZGlreQ0MAmXNoOiNGhLNoNnmhorqWzVyYBiz2899ynrLEe4oaGd+gVUo88cQbezkqkfRUoInT4PK1IKhME3EDA/mhQ3c+enrlYohREBg9Gefg11Cm9cPmqEfYaaH8fzCYQyxFJbvB+hK0tHGNUFCgqkZsP03leOMfsgQyOOsx4lZkGr4vXzffypOIiPEeXke4qYOzRfBKUIZQd7iD32jdpW3E9bTQRvuppppijeb1fBleGTwZ1BEE1l+NVNSM5a/B8GYMyNg3UJoS+CnGRE723FZEagC2kGamtkMy8t2lO6c/YolqUtudBFwsZfwVdGB5ioOVldogWejUF4cu6gBDzBFq330pH2SsofHn4onPxKez4sNLJOmScBHItAcxDHH9S/NPyK7kvZFneIIRI+FbZIQDxI+6jb+M30L8iOl8dSR43yk+WQMoYCDk59bzT+hjqw5sRcgKKrFrYLxMVXo1LUtPe/CHGQgkpPwwW7UZqXUpP7fNkbh9AZQ89RbNjUCsdNA4NIGRbE5okNyFL8mkYGIc9rpmoQ8UIWSBlnoeTzTTHxJM/4iIMRUHUrnyZofqdFEwfw5bgdIYv3YS2WsvKCwdzRBlPn8bPuLb6GIrKasQxA7jc2LUHEFOuRn1sERu8fbh43v3sHTUad3w3NHV78WxYQP2UcJxhpVicEFKiR6q3ozCrUNabqUhrpalxFsa6INReBZRUI6/diS9HgWb8g+jib6T/oufpaNBQdt4ooldeS+RAJ6snzyVEa8OuMtCkCaNzYjSDd6zAHm5A37KEsJownlmxGkVLG47es2mIXUfg4G0oay8F6xzwvY1v2cMcStHx+iN3MW3Vo7wenYEIDYTGhXyRFE7o3AYKXVkcFCO5X3qLrxcz9zgH0oLJaHiEqqo2vCUP4brYg+qJapQj1Rz1RhHobUZfp0O9ejeqTBfUP0SLOpAAezKMDkF+twRDTR1XtH6MURqKIvopor3tFNV+yN0rV/LEgivgymvo9uZ26JdOQ24UirnRhBTeRrBWA+4qsoZZ2NxWQ9H8GaQOUqJNnE1b6/N0ZLdS1pRLwoFjqHqVITWCZoCM41gd7qg1GMsOIstuYur0DPYeYa9Qk2yPJ6g8H3bEgjEJZf9/gtmBSZGCM/ZiTL5uICk5bBxGunUP6uJF2NpdmLI/BiHh4igqErv89392/LM4/rzIsozsvhdl42y47E6I7H5yhfZ8pOK3EfYoPAF1yAHd8Bp3Yd8bQ+D4WArIoX3qYXKnmjGWPoNwH0QdNgO1vSeZ25uxdXSyNyCRA/1SmRS/B4u9hLZhWhxRWnAdRtbIyFhozN9GU2Qq5XMvYqR0CZclHeOCTwo4ck8SxbpuTFqynjqfhbfisxlav40JtZ8iVyqRCvUw5m5EaiGUVaBPT8LT+BaioYL+51zMB7nzGf/CfOpCZJpuToKUXMLfWo5GoUdKGY0662/I265ndVp/BpgvoIfXhV1RS3HIvwk5rMF46CgdffsQNOYGFKHjQEhw3s0YCneS9OJlOJMVKIUXh1SBU6Okj6+MaD7D5yokvGoJyvyj1AeF0zZ1HHUaG9Hvl+DRaTGVVFFSfhFpUgudQWt5Py2aXcmJxFs9vPze39B3lyG6Dz7LXzgcZsan9pHLWzjaFxJmW0Jeageujvfpqz8XndCAdhCxukicIRfQHLmB0ENbUY8+Ai8Fk9ytjNZkC8rYdhSln8AeF+i9tA7OJDXudYQiCvkjC94cB+Yn6hFhb4BqKSJyDDPj06jTl/DYrW8RaFwFH1dCUyUh+29lQ8/zGdayC7Z9iTfFjaw0cYfzWe48/zxeOnQvCutCLC5Ba4KBVNMGvD4V3kQ3FEg4QwNQtHkw1etQWnKxhrpI8RnxmPbRfVcb1WEdHBoyktTKvxEWlgUN88F7jPSmYA713kwPaRzkbUFqrUFtk3HlLmMz+8C2gCTTWNJI/j0up9+Hn2egQ4QQJ/qQ5x8PVfGr8j9wW/ztkWUXHtdlSNIIROzU7xhnr3wEj0nG0TMI+6AwnOk9aEyuwnPODbgjPJD+CRn6NqozerE91IRccDfYjoc1GfIBqMzo22MZmr+Lc/OWsSdmJqbVDvT74tAQgKnYjrnehj0wierqQCq6pzNKuoQFdDJY3oh6ahiHB03k3Hc+Ao2SupGJXL19CdmLDkFJGKL/5ZA8HDHqAbB0hwYFG9Mf5s2wUfgUFo546pnSJ4fy//RBMyqPqA0RxB+ZhCX1NpSLNyFKOgAQthYajKMowY6k0KMgimbfVJYmGln+l/44+4zF9+mncE9vXG/2pLNgDnJwDbXTjGjqBuI0jyVMHUJyaQTJ7TqWNG0nbulMNK56iuP70zT2RsK0PUlz3ItGm4AjoZWqkWGEFq2iIr+Yec3deaDHdUwxfsLtS/6JfvJjYMvA7g6hQPUcFapupPMXnO42Bql7MVruQVZ7OwGOMg7Vn8sR53bw1oEigkRxEbWaeNzZtyKHWZASeiBVWHCOugWNsTvC1AaDX4fsm7EHdUerMMCedQibQHIZ8Fw2B+zxMOV6uOJxmHQj19c8QkVMN1oCL0P2FeLu/A/eWImdyXa8a/YhmmSUbeloO9IJCp7LeW4r/+72Jk7za3glNXKrRH1hEKL9YqQWBaoyNeZ9YShcWg4MvYZqrUx4yBTkMQuQY3qjmXKExC8b6dPWRE1iMwuTPDRnDwfjNNTlq0ne9A6trt14UruhtnbipQxNwyZGhd3OSpOJ59mM/UxLMfJb8t+l3qfygUZZlvuc8PnVjTP4R9C/CrJvCz7ve0jKmSeXy16c3r/hdr2ErO2LV2ThkYNwNL6HJyEKvfYiPDs+5IDaR/fE15l1bA42bwn1A14lvKUQnI2gCYHuDyJVjoXgCRg8qzhn91I6+9yMlP8mqxQPMW7DvWjldho21dJ612yi+93Eg+ygghhuN25g+4ABzHzxU/KvvgRt8Zec8/IHCGUQvrMuQkSGIUXdDbsuR3a24C6rpzmtmv2qJ8hQNlGXOhVjXTiWtJ64OEyEPB33sJtQHtmC6DcTb99pqP9VTPN5e9mdMZrEde+Qb3JTqmvCoVaQW2QlrtrCwoZoSvrWkDOkmR42kEY0Y7Lpaax7Hmv3p4jV7sF+7BAB1XkE2maiLF3KeWXX4GtqoiLlIg5ERTJjyX5aR72CekU9yk4F4f/QIvcNwqx14G5WMnffIp6QHiZMrkVRLWNrfJGazHasxr+TUqRmXYyBsXWF2JorWdR7BKNoQJU8iG6H9iB6fo4s+8C1CxRRKFATTl86HB6Mtix8NwxHuvQIkduXQGgRvoTzofNd7Npq5DYThBmh9wjkjf1Qlu9HFL+Fb+o8pCNroeB5GnP70zl5Fj3NbWxraOMNOR9Pi47ErGuw6zdinf0cQe/fBJtLCXHUQrdujM2+lrXuvRQ1biXMGUmI2Y1ONKPf+yKeg6Bw+PB0K8R1OIH0T+5BI8cj5HK8aQHImjxkrRUx+WE0C5+ix6xJZDq3YFWt54vAGfSPH0BzZCoNLdeTG76WwyN6EtucTU9HCGoUPMlEdlPFXqoZRPzvdGX9DpxhLg7/CPpXwOfbikqzDIVi0jeFjkrkwslotjyDIT8I7aHDaErKcDe9jVLrRuc+hnPvGHQtNaQ8G4HzrSDs6npMjkrCi+6EiPO7jDOAJQOQwFiAUj8W4SzGMPIJ9GmXMHVnMwsvuZC2piB2PZbEzr7j2EY9HpKZxS72WoMZs3QT60bHEn7kI7pHX0XpFbkcuXk63l6jkZueotl9I41jS2hyXYk9upRN6u4E5pmJLLNRe6SB/vkbsTeuI2bHfpQffYEqPxqP/X3k8BRUhmdpvekVZtntOHzrye/fRHG2zPCqT5i1fzWpfWYRd2Evrrr5ajQtPcjdG4H3Iqi19WRTuYcZhU/x8Psqtu7+nA8DQgksLUJx6D6IUBGoiOBA9lS+nH4e6T2ysM19BF3cS3QO7o639zwIGoBoDkJR4ibMWcrIMbdjigxHW6/D43JRkOaiJVSLEQ+t1nQyS4sRJV9g6v4w54nzWS6ZWC3FYzeHQOOqriztnmpQRAEQx3gq7B8hou6lKG4tvjseQiwrAV0qLQNvYJ++hWKXHbsmGN/65/C9PBXJ58UVqEe+8l9YAz5BDtyHTevF11pErOlR6oSdJ0MWsLJxAoXeHtylM2IhHvOh1XDrfhj/JJV9cqB0M6wezh2eGJYEZGNpsKOw9UMfKUAByoAs3ElXUjw4jY2XnI2v0YDP0IgcfxHYykAEgIiBqG5QVwwHQS2pCLbvZZQYiqF1H4VSE23haTioJVczDBE9C8LGAqBCwQDi/reM8684D1oI8QGwFUgXQlQKIS4XQpwthKgEBgLLhBBf/tRx/CPoXwGF8lqEsJxcqI1BSl0AIWtAacGufx3XwSPo9XdiDLuF9mNXoK36lM64mVgPryMi3YwcvQJHVDH2oyOQ9s5CEzEXOXsmyDIqQybCsQWqbNizJkPLq8iDzkX35nUY1bn8+6arsIWP4EpyaUbFPrmU9rpP0CkVHLytO5lnf4FlRhq1V91CkFaiSRhw+KIxdbYSUGpAUj0G+dvB4+SsDZ+gMxzDbT3I9szBsHM9il2FmIfH4zt4FMXBCrzTLTib46mzDOAc5wP0Sd1L1NoDjN+p5v7LbyfQ1R/GTQR5FXLzP3Bpw7ih3kHz3EgiGnoTorNQN9HNc2IJSpeX5OV7Sa7Lw5MMUoaNhiYzSlUdBwfn0mzfRaNuAz6+IkwyY2o/iitxKLqhS1AsHoEvMQ7JHE9Hw/1IzTVYq30UnN+X5Lx6jCXteHPPZ01ULOMLl4EiFQ7Ox5d5BytWT+KBrNtZEJbMiEOPkTh4JHhrQN0N6Fo1F3nQSnV/G3ZvMwUjlpH5iAxfHMXZbRbRS53UjQokuLQQX/4e6qd2J6A1B+Un+6jumE99VADmbhYiltsIqzAhf3A2qb1GEJi/jn+VfMDF5+YR2qClxXCMOrmZ6IAYmHgzyHVg3gFMImDX3QT3HYDd1h2t4Qr48isY4wD5MEeTo5ElLVnGa3Gr8/FcsAcdk1HIKpTS7K7ZAkn94dK3YfVTMORBUE9GU/EmvpCJDA59kBrsOOmgj2IATpxgDj3dl8+Zw6+YNFaW5Qt+YNeSn3Mcv4H+FfiOcf4vSjOEnk3njstxSDvRR12JKbIr3KjGEYA7NAFD8DS0e5bAkBV4RDttYgvtkdGoTA7iP/4bHZXv0TnoMox6N25Ld/DU4QiNQnaW4pj/MgdiMjhszGZc5QYkYxsGQzo34+Js10aiCaI5JJo09wAsUVuwRlRiPZKEo8cQKkQA3eQLQf8EoklAbg9Y8zTUH0F38b+w/ed69CleVPEDWa5NpcnWyVbH9cyeHYjxyGcE7PiIva1RXDf4WS7Vb+fc0iJyjKGoQ/rjjcikY8obGNCDz06ty4Hr87V4RijQV1ciKUdSkHw7AbTRU86CxamgjaQ+1kWUrRjvEUFYhxZp3Arcee/jsrXT/VgCYt4KDNITqFta6ciywkfzaO85EEPcHaiLP8enW0iAt5UCRybr0ubgMK2laMj5eBtrGZL/Kpv6PUWwMZvwhiLe+egNnve8SvjuCkKzi9nf404KOv7JGG8bGsW4rvNXW0RMs4GdzlcJ8DTRqdpHR4aX1mQjAa82oW73Uq6MJrO9ENHbja6sBU3D5ygafBi2FxIUF0ZwRATGEc/CmicRGi8XhIxArvs7AdZ2hti38rg8micOLGZNz2QkuZLZIhqFtxO8LWAKwzr0CnTtrxNQvAb2tMEeDUwRyCEQXKoguMOOFNOGK/smPEv24Du3BKW4BiFOSOo6YDZEZcHyO+Ci98FxEMn+OIFSIIFyVtcLW8D0W14kfwR+3XnQvwp+A/0b4+usoz5tBZqGQIxhN35drlZnUzcEIht9KNL6gDkHFRDuvZDw8g3IsZcikv+K0abHuPgTiOkD6Q/Bigk4Pv+SYkcU60ZNwRwqc2/0GrSGS3Arx/I2LxJJPL008TRJ2zD7vFS5nsZ2Zxj6vzVh7JtIcs/7yd3+LhReji9DBW1bKEGP2ukmqvYQf69o4JLmVuoj4tiRCqL/WIJKtjI2sA4lWnwZCQTty+UfPS5jSMlOrml5iw2jpuBxuLH2GkUdtTzAYqIJZdpqO8Zb/0XtC+PpNDVgNY9FGdiDDvZio4VEoSe8x7WgiyXAdQNydTcU7QcR7cW49l5PL1UnUUoTjuRgwlY5UQSsRGpuxbhcC72nYwhagrLxJTz7dmDusEF6IllldQSIUkzieob7hlBWNIHl6ZMY6VBgd+TTtPElUj1qvho1jCyNncgoN+Plm6gsvJAFiTEMVNiJw4lmxwJ8PWfiNC2mxeMkqrWOmusnIT7dR/mkYMJkmZ7VAdQ4IomhjYCA5q7c9i5QNLloGmfGTAudAddgHJgBKzfC9jmQ7YPhZm5IuYmzHcEUdvRAp9OjK97Dfcn1XNu0G7l9L16Vh3JDLbHOfXiEGpW1HQSQeRDx1QzC0nfg8YXiOjgNMfog0vOfoS6fhEj4nozbcb1gxP3w6YMw+ApIehT29oH4RyDy8tNzMZzp+KfZ/e/RVvswOk0woSkb6cpd0IWUeBmyYx5UL4RZn3/zg85CMGQiTCMgfAR8thoiq2FdMbyygubx6dxz9wxcEtytCCDA9xg69Ru4pTj28iHBVDNKPkYbOfQ8VIA36SCWTWXIgRoaEqPQHauhc9E5aG0KmoxKtgbMpJfjc/7TDjconHiTsrh15V10nhtDQ3Avrl20CBH0Jsv7zuALn4xTFDNBsYjGNPir2Mm0sDqs1r6MeOt93NGRpGQlUazxEWFXM+rSe6ChmNYvbkJn20K8rYgWYyIxXEAeR9jOOr6kgMnNOzF2S6VZHUG85iHk6osgSk1z2RGWjz8fbcxcXB0fM2nvURRlFXBkA6JfNzCvwyky8Wz7FE+aC80yDxgrsfcEbeH7mAK6QcM8jsWOYqYrh+BdbyBrVFzechFPD/iMIMVwfA0VNEcVg5CICbuGCwuuZEO3IXwurWG8ooi0lCnkiA4OY0GnHkJ93Jf0DAevs4Sl+6cyxbWO4PR0Vk6/lBH1H6I2lkNpB85oNamlhZhqkvHEOHDplUiXLYAFs/D0skIQCEU7cfVK0j9cyZeXj2S8zsGgyq18pk9galQtakM+NTXZ+AyT8cT6UD29FhKAJ3uByQzhMYgsEyJoJMqal1APNMKy8+CSEjBGfbczxuXCguuhqQyueAsir4HKpyBkOqiCf9Pr4A+BzK+21PvXQsjymZFZ6tv06dNH3rXrJ5eqn/G0cysypZhZgED1zQ5Zxr4nG3XmQhT6nG/Ka97puljcPeCZR6B0KYwaDulqfOI9vogfhd6qIT2oELW+Gcl3PgHqiUjqBFDHgMKC3PxXyhUlVNuaUOgt9Fqej7IuHJp34m6GPZN6kDt2BZQ9DMdW4bZUoa3sh1xXBE2V1PeOIyguBnW39YjXJoBrDXXxyejcSloVgtaEGD7vdjYQg9JaQIMpmeC6WsIO72CEMZNaaQAx119HeHABrX+bSnDSUzS1bqbd8hgaVzwlQcMJUvfjM6K4S05HWj8ca24M5ab9pC4LwtUtn4OWB1nZEsKdyx5GnRbOhpERRCifJr28HF4bDzevQS5fh/uLhyHeg1ICKSAUX14T7kgf6s5ARGgCuMHd0oJKMkCjgfkRl6Jv3MNFngUw6lGoXU3jWYEEKZ5HctlhQyy2gBS+Sr+Vo/JBpuidJFb2prPlbvTWWhpTQnGXKQh5spmR6esYkbSFh8Tt2Meb2RA+hl7OLYR/Wk9DTAieKAmVXUF4ZQX2JCUeYzgiMBXNnl0IVxByVBDOqFAMzUqOGerwBkwkVr6Ieuds9mqG0vfLZazqOxO3uZ3pny8neFsAFByG9/fD57cjR27AE52MIv1mpJI1YOwGLSpwNEK/+76/QzYchTcugms/A2MIeG3gqgdd0m93EZwGhBC7ZVnu84uOEdhHZuQp2pwlv7y9U8E/gv6NkSlHSe7JxhmQj72IIyyFFv2HRPFIV6G7FapehrhbIDIannoZXPdCxfUQ/wp8GsWYkuch1oPUAHt7TiWzKY+awAIiakOQrIV43CVIkUriiwMwhU9lt6aEfb1T6Gu/AJfuKmrGXEGc7ii+9RPwrtuL+oEEtiV3p7uiCr2zO6r9doJveQN19WfgrIFuLVAeiHZVC+ap/8Q8ejZxgIoCFrGQMT4ttrbdjNhZRaO6FJbux1TwHnlXZhLQaxiKhl0ob+xLeEM7xnF6vMFNDB96MRWJwVjEx+xpPkJ3bRsNxrkY6lwoO1tpC1Sx+Wgn9/n+TtugsdjXLyLBXsrm7GdodjWTlBaDc931hFSVsm9Wf+LchwhZ2k4nHqrOGoAhr42kQAkqakBtQZUzDTLnUfvyG6ySJ7JwRibUjIJ+50FtX3RHHseRsQq9Vw9BARibDzJl7Vt4g9Np6/8ge9z3k00HiEhCrRJ5mcGET6wnrbKEUE8dFbPC8JWrGPTRJhypJtrPGYO2ahfGCiuiLJl93fuSGLAf6jvQFqtR5u5HFD4BtqX4OsGT9BpKKYQSVqAsu4RIr4cpX37AjqTeOK11BAglkl0BmiDoPgyCE2DuYjwlExHVrUjKc8HcCoevgYEloAz/4Q4Zmgy3rIOOpq5thRF0/0OJYX8Mv4vjfw8lOei57eRC22FE6w7cPUfjZMcJlc1g29OVJeW/qKPBOAS2pIP7LBQtwVgTvRwKTyRn7160jgbcRrDG9kWRPoy21haitmmhXUPAri8JPyuVw93MxIt+hJGN+R4rLbv+hju5E8begkKpRmdfhaqkCv3H5dDLDlvmQa0OWsrA3Adr7kwKpS/pf81F8IIWxp9LZruPG7ccobreTVtsJ9YnViNrfJhbBPlP3sxTo6eT5X2T6IwPYVgsbFmCI/ZjWrZW4fpkEXEOHUOi2kgclEdzhZq6/pX0WlfPuqBoEg8G8teIHvh4lUARj31qJmFr8jiv9gtUwenI1RXI0Qk4QkeRdNiC8IXgGh2LsnYxcVVB1HbY8TXuQer5KAy8A5rz4PAb3G3twWNH5yLOeQ0eOhtiu4MqD115C7aS6yF6GpicsDQWir5CMm5G7F9NW3I8rd0fITI1DFSDiK+7hPJBvZh7eBlBAw7gPBJMRd8g2vUyWFtQ1A9C6zJijitDPLeTcFsI7904l4EHCvEOuo8+6x8EgxFM96Fe+yQd/W4jKmMlrrxW4j4vAo2V9kFT6aObzUFTIf1XvYvFnApeO+Ttgi3LYegU3AEudHsKoOAtyLkKfC5oWg4x1/14p1RpIOB7XCD/6/gN9P8eem4/OcCMNR8O3Q09XidE6E+KJY2QIGwWBHXNRcXjgj1LYdsa8NiRwr9EStPj0SViU0uI+OuQvlqKPmYjVstkAtqi8axahBh0NySfj7RrNt3XHkVvTaE9fS1hIdmopgyDN02oZ1pQDUxCqCdjri+m3dmE5b4+sHQzlLXDEAFHOyFsCo1JEexPOETvbRNRrVwAlmZo96KtryVzx1dkPgEdbaCcpUJ7/ov07XEfU6QJKBXJqP67VHj4+XjZRWIjrJh9ASZZRVLNrXhLxhBRuRfN66vYF6BkQN0+dNGDkT2PYAvtwHx0PZ7e6Rjmfoj04Vg4tAyhBtFqQz/rMXTBqeDz4dmRQPOwCwg/0h31nqvJN3UnK30mCsBuzmFD5JMk9TpEasMmWPdXaGuEJ7pBUDzShdPQzN+M7+YXkVRjkafsAgHyJy46lqvoL9rRjHochxSHZ/dn6LqVEJLRTGhUIepaBxrbVOKbrkS4rkbZVM1W91eEbi2mY1okygejCPc2MrTZg7Obnk+kQlqq1QzfcQBXWiHG0CAMH3yFZ95bJKddD4q/QUQ3NPYoao7eiCF5OFGqLKTSzRCphHIf5PRFxoPPFIowKUGu7orfHHsjuOpOW9/+0/ErTrP7tfAb6N8YwQlv1GUZtk+CoKGgDkZCEMaNJ/9AdznUH4V1r0F9MfSeBld8AA2roH0DGPqzO0FDhIjDRD9In4VUuh7H/uugKBclFkTmtRSyhcTkm9A0TCPBNI8D9o3w6nbsU87H8Z/rsQ2+D8v2BKREFZaWFtpDhoAvAIaZoP8q2DIE0kqg+CUi2/oyLr4KxaW78XQ68GxZh2QIAV04n147llHGDfgyNRiWwNEDCwnLaGWKegVh3HqSahVeBQ5XPb0qy7mnU8co12T6iE/xTrsW1doHydXHo2qqgpodeDLAYXBTGW0n2nOIttKBWHwSUo87YewdYK0EV1tXEJ+iL3EZcwl7txlaFlAw4lE0Rz6ET6+AOcv44LCGB3fAxtEhsEANQx6FomawKKF4NyxvwjqmFypxEHPbVwilEtmmwdv3ISLPHYaiW1/IuxBVeDLc/Bg4W5E+S6bd5cbUqALnIbQH3kGOqsKdI+MpcaEtacf4ogpx9V9weJ5EXbsNX1oAc+zLWXrlMxjyLiFiXxWKmx9FY3kQ6csb8ExMQZkTAyMXoH3tWdoz5mHupqS2WxORz6UjBdoQPVSw4jJknQedaISOTlDnQ0c9GMJAFQqOVtAG/NZd+8+Jf5rd/zCtO8GQAtnPfp2xQsFxd4bPC4vuhZXPwbC5MOoaiMn+5rdxM4AZAGRRShyJX+8SicMJSMyDzEMo3hmDjWYqyCcj/ApQ90Fp+wx9t9m0z+6Ffd9/MDaUYJgQiffwTKTo7ZjKXFT2S4b11XSmJKJqvhXVoE2wfRpE7UNTmU94+xik3Etpc2+muWIlSosKdbyeUV9txhgk4e7VgTpJEFlfhHynFuU/1Kh13/KFKhTc2eN63qioRNo2BBGeT8rnZUhzFyMpFIjsq8H2NnJALbI3jdDSYoKkXbS5AvDu7cWK3t3JyL6WJAK+MUA+H+x4GUP6Zcjb5sJ5D9Atuge3x2by7OqH4eM5rDQtZFICREWHQHM9BKdCn0tAb4bkN3G/8TEds+II8XroNPVGrygGnw2V5Z/g2gGbdeAtB+t65OJCvJYy5AQbVl8kuko71vHPIZddhadpInJyAeWhCaQ3xeFTC8Sm13h02s1cX/gOcUcHUWfazXVmDweefJnnHFVMeP5FVOpUcib5aDHOJyPQjVAl4b7uOQ6LHWSTQMib91BxUxxufSdJvjUoJAtOnkLlGQaFq6GxGDY/Ap31gIDydZB7HfS5CTT/87ObT53/xuI4g/Ab6NOJ0gz9vwTpe/7thzeCJQIufx0yhkPgt3yE7fVwYCkkDSQuMAa8jeB1n/yp3Y89NZZ91tfoL58FZqDvC8i175FQ8BlHstqRBo0gYqMFdUYjvuKPENPexxT9FlbfDpj1KuqXz0Lq7IQhuSCPgDwrIrkNnWszbOgkcMx8tOc8hOL+G2gYWUHp8LOJyVuBeWUbXlMUmn5aPNkzSdDc8x0Vjd5ONPZGytq20ukZyfhr59PeKeM4P4XorKnIIgNvYQsip4JOo4xKk4OuJBlPqwutspVRdY18mbKP3c5tnLUf9EPPgQPvQ9bZ4BXw7GN4jDl86GwnoUlw8MLldHt3HBeEb2fqsP4Inx2vpqUrxWlCD9j9ARwowT3bg7GuAneqhKQ+gPtIIqpiJXJGOiK1F7h3gb0OShvwdDfhUu1HXzmFiG0rcaq0aL+aydbUoVij9fgaZlAb4mRLTxPezkLqhv8Fu8bNx5GTGFCwi4HtHrgwngEGFQMa29g9ti8LbRLmV2qJP78Aj9qNSqFAgcRq9qJst5JWryFIP4tmPqZSup84nsbLAbTKWyG7HzgrQBvX9U+2VsGel0EXAo35ED3gV+7Ef2LOQBeHf5rdH4HyPfDZ/XB4DWRNAHNEVzLWb3+q9nIoqQVr8iD6KWdCbE/wefFtHogntTubgmtQqNLoy33oCUL2ehC73oKiDawcbmLcyjaoKsTn6ERYixCzNSBuQzb7EBHJUPUaHKqHxHkQUwMPbKX24RvZGLqeoXkNBO5aTW2PMALbDBhbaxCWXghV4NdqdCj2UUdPwms3cEXncual7YUvF5K1YweakHCMAU0oZzfjdpso7x9AsKhCbFJhaTif3WfHkd4+EtOW+dSGJeB59RNqNPF8OOV2qrV6nslNJUhxKWtlF6vVc5hj68d6UzA3CRM+RyW+yudR1LyO7GlDCowDEQkHjkCBg5ZL+2MPysJyYBmK4nI0X/hg2jnUnl9NqHwfSnUMtFyNfHgLjswg1M6pKBwBsH8tsvsgaGSwgrCkYK1qoTQxlvRKUDYV8tCcJ7m53kBg8RZoLofkBBj0OOhOnnfc1taA8u+z8PU5gvfsNQSQxmO+97jpxc0Yzr0NorqemGTc+OjEwS0Y+M/p64NnOL/KNDt9H5mMU7Q5e/3T7Pz8l7jecN3n4HaAyw6GwO+t1vr5VRwYEcm5ygfgvy8mD70D0QNRBg8jpWUdhUFlaBVmaK9GHF5BfdTrhKV8CIZFcMkN4Oxk3rTV3DLnPbwVDdjGbKRGFUh/Ry2BCSaE2I1ceR9SxAC8T0zDa7udTnkUOxO0TCxIJyJxMJ36Szgk7yCo4DXMKfdgMA0GoI0b2VZgYXvSWBQbg+jXZxbvZkcSe9cNdFQ1ooizYmh1YisIR9FdhVr9b0h5FKIH0J3z2Gv+O70mvEBEdR5y7DJCvlzFG2ddwm7nEB45uIR690ykFC8qhZk1zfGsUbQwyFnCfl5jTshEWvRlKD/eSuD5C6H5UtjbBheG4VaXYAp+GkPyFfje6gFhMiK3nVD7pdRr7yGseR6KyhbwSWjz7AjVRyDlIGsOIVcYkXsHI+kmQd18DEYFsSXHKJr2MI7OtaSElREY9TT0nAv7FkDNOqjZAklTTjp3FksoXP88vq1zyG/+O/qgZ5ix5CCGvdtg3je5AgUqvKxDQb9fv5/5OeNmcfij2f2RUGl/0DgDLB/swKt0YKOwq8DdCUUfQ8wgQEFc6PPE2BzYC6bCa5Ng3wJsUSrkIAMqbTAur43yrXkYWxrpVuWj25JCQlfXUqWK43ODhmOqabjTLsQ+OBirpYVOyybU5n5MftKBwm7GbgSl+UICFelka+YR2WsbetOgr+Xz4cOWnIivuZHI8ndxXBvLsMtv5UCPdMreuB5tYgTiaCjKow4UopH2T1/Bp5dxGP+KwrOHTC5nNY9hi4pH9LgQTYiDhz78C7fnvMoTqltZoLqNfkHVvKD6lGEhApe7g2vLa1m/rz/r9m/GFtUfEZwLVbfCsbORO0JpiniQ4sAU8loexVM7FWleFL5zwFY5gFp7GfJhK8ria/F4qxHGaQitHrlXEXLEEORiNb7zbkVouyGyp8DU/UjR47Eo2zDUvM1nyRcwWW795gR1OwdqSqBm0/efQFsjUuilZOYdo7DzMVJXb4RpV4POcFK1Tm7CR+n/sxP5+UHOwKzefgP9J8HeWQFCQQgl6IjsKtz9DPS+GYQPUIIQpLpm0Vm5EzTAoFsQVhW+NbMxr19F6+JbeO8dG7MvDYaMgSg6E0nfGMbNC4K4Rrqd6KYXsLMSST+WUNNXeL1b0RoGEDzwYvotOsiGvqF4fUHIpc9+LZdAnPB9GqP2vk3MsVoybIfRL2tA1UtJ2PCxjDiUxp6pc/EBuilNSCjQ+5rxzbDjfsOB3XYeTa67aPFUsMV3M75uwxDj5hByyd+47I1nUB9poV1zIcGKVAJ8y4nQ/JWeviXcpHyJlwI/Yny/y3BS35X7cctAmP86IsGAw2qjSETgdVZTH26mRu1hZ1xvPs0p4EOzh5bwPpTmPkx5DwteYw3ox0J+T3yfvQWDXkIOOohI+QfsuAfeHILvwCZQJ6MrraCbqwGjNOybk6RQQVgKFH4AFV999yS2VYMpGmWfN0ndsYH6iZkw7crvVBMEoOGaX6Xf+DmBnxew/7Tgd3H8SXCufYizi/dRed35aFSh0FELTfkw4F5wvfP1i0lV2MU4e2zAW7cHxdaFSFPDcPdzUmHuSbWsZMdHudx5XRDcOhJsVkgaAEteQlx4OwrVU5jWXIqp/3U4TZsIaZVRdzwK/ZdiJIVQexG7tbcQH1QFVAMCpRyGSsTTyRbMzlFEFpTwqfJxJhlWs3zPE4wQn5Py3gqET0W/fotoi3gdg2THWJuNPjUD5dUL8SqnIVVvIzR8HbnCTLDLjYMr0Q3TwJLFKLp3IsUEs9PSzCBrJBztSZj9X0wNzKWETNrqtlLVcRHW8AAe6tmHc7blkZCYhjHORPS7LzBukofghlrUwoXskTG22HDmBDK4sRObWU+1q5W+tWnYY8dhKNoINfUIeyaU34eiMhsC/4UzKxWftpY3cs9moGUe25pepLv2dWzef3DiM4/dXIO2pBLhtH73JK56AiKyoOe56Lo9h6PoehrlvYSIXidV0/E3FP8LWbZPN/6FKn5+KwK8wZBzAQmqq7sKtj0CA+4/vtfDiac6IOJWvrjwFTIOVmPQJeDRHiVF7k1hYwW9eqkRbifszYfQcLj5eRgwCYr24oxPxGNMwHTs3ygTG8D7IMJQAs1/ResZQN/CnqzKLsZQYcbS71lkbxueysdxJg7BTSXtmq/w5aZRsTOZ4shicoN7oHXX4D67GvUbK5A+exSTN4zGWDWe8KFo6neiGrwcpdIHe27A4XEQmqbB1GSiwaGjIcmEeVAllmVOXKMG012ZjGb9RPbHxKASiYS2uLGojqL2urGp1FR4gxh06GWidHtQ93GDz46co0YrUlB3vwushxCHN2KKjWVY1Urktv1IOjdeg4SszURueBkHrWhVMrYbArFrLOilMJDteKUG5DAJc4CDFZINj9STuCNO6qL/jsUwEOn4OwFNg0xbt0CMrrrvXnwxPSHreNKH4L6YO26m9uBNlHa/jUS+8VmrmP4b9CA/wBlnoP0ujj8LcX1g2PWoMEFTQVdZcObxnV7+a6BlZPawl0Clj+KefRAl+7Cv2UHIxbPZ/YKHC2dIoNGB1wyRx+fQDphAZ2Y2O/QNvDX4YRqCNCiIROHMggVvwK4QGHIrYvSTDKk8xuacXHyrJyK29kHlM2NkOAksJdr3HMoayDDsZXTMMg62bUDfbMEaZWf7X+5AbijB17mLQEd/6u21uNs3QNMxCO4HozfToFGh3VFIR1gDmshN+EQ+lupspBSBdu8azNaNeAZdh6RWktXcTEZDPT0SniFsYj7xLidnr/qAsdvXEuTrQLndQ4czEGuHhvzMXBqSZnXFHpEiIOtjWHEBFcGDYZ8TsbcdKd+AqXYoOnkisjYSrbWE59quxHZIi2nNAswHwghcn0GMdAcbaScwrDuGTRWkVcSA9UDX/1GWkYQWQ85HNER9hvxta5A1CbpP+3pTipuFShFMa+l9uPhmxH2i28jPr8h/p9mdyuc04TfQfxZ6ngvq46sWtz0MA06MZuZBdM3+ZQdLCSOebGYSLtRU95iByBiGcS4UfJ6D/u/XU3P2NNxNLdjC++A8fBjZ50OPngBCqBGVtFh2sNrUj+K4GLyT3gZU8NkE2JeFzu0iw3ABewadBc42yF8CZUvx4eZo0z/ZlqDj1uDniQtUcNQyhKaIuVQpIN2Yhbj0P6gGb0Jdm0M35WU0Z8VR59xDB60gKSmPH4AvNwpL89WY90oEHKgBZydy/8fhwk/x7q2msnkJ6WFJiOyBEN6MnD8FeVkftJoaxJwCSL0fcUSHotCNcWkHki2KoCYfjfaD0LkT1DVQf5S8ITJt4eWQ4MFuSkVUV8Lqz5FNM7AGqKnTj+GsoHKeSB9OU2J32h1VtKZFE8xhJqMjUdHA7lgJnzQOqfQh8Hm65iibY1CFjsJsuZkmnjz5HPae2fUi+DgCQWjWYlLrI7AfuLrrGH5+O/4bsP9UPqcJv4H+s3B8ZSKrr4HAdDCcuIqvy8Wxn9Vo0JHBYCwMxsBR6kQ9zsShrG+azoThm4ma/wKR50xCkZmJ3GMCbe++S/WFF1J1wQUE/e115t35JtEl4fSVptIiN7PDvIbatGJ8GcX4yqxsWp1F+cc7sDm2U5dzC51SMY6tczjsuha1S4U1ehrDJTOdUf1pEIUsZA1pvIiP13FKXyES+iNG34Kq+GX0ARHsCC1kr2Nhl4qabqhbQ5Hj+7FPnoUoN7O+TsdX+4/ia/wbXks+aVsK0Lib8IWOw9rjMjriJbxBZRhc5bAlAxoWQ0I8vHYIXj6ANcVDaN5aMnfeBo4K2HeAjjX3Ud4nAUuzBt/APFpmTkBc/Dlc8BHe1ufRyC1oxhaQcdZ60sRgSlI+x9wQjCr9FoLRczZ1xHKIwt6JOFdcjd0bB+VPQ30ehHaFljWIUUgYsPLZd8/hSadVwhh3C+ZDH0LVp79R5/ED+Gdx+PmNcbTAgVe7VpEdR5a9uOVmdrMUO+30pCudk0AigOHEEclRuZBFn/Xm7BvzYeUF8PkrSMmpmCZPJuzhh4n+8EOi3nsP94wpxOYeoPm+BrxP30ufgnsZ2NFGmNKHS5dAa48QUqZ3EJj/Ds5/O9gnb8OepcN+tpJ4TwuqyGAmGGejaG0jwNJCT4oYhhclSrzU4mJrl9BCAb3/g2RtYeK/thPd8ApNvlkIjqGKegNH039QRhRgiW7gfcdsWoamU9tQTYVuOEIOhkUbkXYvQl/fisLdisvsxKeQ8YZKODMacJrKce29GtmoRx58Hsb6BqjfA/pxoA9j6wUTGBRwFYo2Jd6mQwT/cym8cQMeXSq+9GpUAXcRett5BI7VcBkRLLXuB20EBmV3ohhJJOeTzU2Mks7G0KRGZ+8OnYehYhmEZH59bgK5kQ5W4OTwj5/XyPGIMdugbu2v3mX8nICProD9p/I5TfgN9J+J2p0w5hXodf0JhRIe14vo3UtJ4+RlvyFMQcl+DhwTlNf1QGUyQcJFUL4Pgk/uhUKS0CUdxTb6LGL/EUTQWdsRAWlgmYIUsxif+R84RTqR7XX0GNCK1m4k5I0Wijdn45WCOaIJ57DwgpBp89agVvq4lCZM/BsFoQTxCTK2ExoUtFty+fvI+yg6EomjsQGLfIRO7yPssh7GqfLRZAinxZfA2UcexpQyi92p01gw8g7c5nA8uzai/Hwx2vUOXLoeyLmFOLGgttXjzDBTMTGNSu9FdCgWIMKVOG063PZAys++C5MUQgBBeKODsWlKaT97AHJQPK1XnUXrDAUdS7VY33kPYTaj93m5ZfntbI/ucdL/S4GBrMCJcOVysDVA+CwoehG03/xfBRKhPEYTj58c1fD7CO4LvZ7tCrjl57fjDHNx+Gdx/JmIGgQJ404qcgk7rcocEpWXo+Pk+B5e7NjJZ9/CafS4+CAW81DYDhABBw+CvfGb0Xj926TYrqJKeTEoYiDu76DPxOrewrHOS1EgEV92DJrq8flkegxtZlvRaPYsjcHY3oxlRhGJITPYoXiJPiKECEwYiQWyEShREI2Ru0+SL1iXw7xhSbxUcTEvNITSW9dCi6GOowYdl7V/ycaaKTze83GkkKGYNKHMjOhGtVNQGhBPXVQimcY6QhL6Ywm0APmocitg/8sYa5fj2i9objuGu084rSIAX66Pxig9e4OOMFU+CyFUOGJ9KA6uQWTFIQKbCXnqAXxxE2i+/npsywpwHKsneGwpgVXb2TPwWrLwYDnhkhIIiO4BQQmg0YIqHtrWQvDwr+sosBDMHTRwL6E8iIIfXoiEQv3D+/z8Opxh9z+/gf4zof5uZgw1OiJVtyMUQ7+zT0Ug8dyPu2kPo25uxFPVG+WjMyA0Cu5/6xvj3LYGOp+hWTuUZvP5xFS+TYveTbnvXnSOQ6Ta+6DN3wQVodiTx1E60EOo+xJCkj7jPNcKvA4N4Ss30pxWSH+bEXVHA/W2DoR6Hkb1zK/lkTCfJF8LWvbq3sKcFsbDngv4qr0fj9V7sNh3MEzTB0PlYgKSamkXRkz7H0HoY4ls66At3MZ7g64jePfHGGo/QyUFozD9pWsuuDoVST2KkLY6NLmP4jaqCap4Crc3iZ05RnIdG6hVx6BWXEqQaiaqvFfQHD4A41dCcAYSEPL664TcegHyoruRZRti0IOMijuH+VRzG3HfPS86C8g+sAwEy3eDF2nIREEQFUwinvUnxw/384dECPEfYDJQL8tyzvGyIGABXZkljwEzZVlu+bHj/CIXhxAiSAixSghRdPzvD97+hRBmIUSlEOJfv6RNPz8PgUAoxiGE/nv3BzKCW+6tor9mBvm+lchN9XD5rZB2Qp5EfQRo9lMZeC5a27vsSwmkhYNkS3eSblyEtiESd0MVzYPGcWxoKC7l5azTVZGYfC+pKQnEDpH48vwJOFL7oK5sRTithG9y4XS6kPj+dEsyMgH0ZAR9uW5XMt0/fpab1t3MJOMz1HrC2Vy+hy/b5xA6aDntPW+iqN9ofD4vHXtreG3AnYwNuoziQS9Q1K+ZMhHIgf134zp0AZ5lV8D+1yG7BzbNdozuZBj3CFtG23FjI1u5hAxmYCCcwN11GAr30NwzBYIzThawuRTRWImUvxH630MmBpzIHMWO7/uGYVU7ICABgiZ8r74WLkZBMB2sOpXT6ufM503g2yf7TmCNLMupwJrj2z/KL/VB/5wGHwY2/ML2/Pw/EN8zO+BEss13osFMubyH6gfmwbQLT9rf5l5Ii34UBerdWOUauiv/TjKzUWGEbY/jO3A/+87OoSytjUN0w4uWGVxMsEePLO+ksduNnKV+l+SgxRDSDWYX0jjhcoymcT8gUdeNxUw2EkfR9Lkez8y3WT/1YvpK29iePxpTnoLP8wbiWnIbMR8vRlsvkZ/sYf4VNzM7YDYDiGS3wUQ3s0xSXBrOtA851/4B76um0ho7FpTBuHxHUVe9S6X+FfaZo0hobkXszkGU/QvWXInS5ULV7wVk1fc8aHY0dLkv2lq6MuEAswjjZorZxfesEtz0KBz7ih96hlaTRAxLkAj40XPl54+BLMsbgOZvFU8D3jr+/S346RVHv9TFMQ0YcUKD64A7vl1JCJELhAMrgN88RJ+fn4eKUFRAr+jrOZp8mOgT9lWwjArVClS6oUSXtdISdBUKNF0vq6p3QMEDVA/oT606glYimcY0zFhA9sLeyxCtKuLzyqHf8YA/g54DUwJ2jqE9IenA9yFhQD7+yryeJpYqt3DroVpEYA82Oq/i7zcmoO71LrLPR1DLLN5Rn8/I1mYia45CbAgKBC5fIWopjX5G+GsClNeN5WlTJjcYw1Got0HiQ9TwPjl46W0Y1JW49+AmyLoRUucgyz5oW/9d4RqK4Jq3YMvCr4tCUGFCwW6s9PuWu4aABOh+ydfG/PsQqNAz+Ef/J35+S/47jeOUCBFCnBibdL4sy/N/4jfhsizXHP9eS5dN/FF+qYH+yQaFEBLwD+AiYMyPHUwIcSVwJUBc3Pf48vz8piTohmIi/uttH24CyCJKtQGFx0G1fTbCfPyprXgZrLgYX4+RFGVfhwEF3cnAhLnLeOffCnGXQrULNCckwQ3MwIcLCdXPWhFXSyO9vCVEhF4LOVdxllYw5niICrvnAV4LnMiF0hSEaR0llJBEf7oRQJVnBYnK3gAMDwLGTgKlmjZWoT4esjOeIYQSi2jaATu3Q8+rILnrKaJDbKIj4AgdbMNw4iwYtwPMYTDum6BFASj5N+m8z/fkBUydDEljT1lfP78HPytif+MviQcty7IshPjJV5I/aaCFEKuBiO/ZdVLKjB9p8FpguSzLlT/1qH38DjQfugL2/5Rsfn59gk94ySWhwvTfUW710wRH/RUN+i4DvO0JiA9DZD/BSE4O5kPJs2BMg/CzoKcGVCeHy7SRj4GsU5JHoEHGTpB7DePEdhTh/wIhmNwfZFnG5nmeN5RRnCWdRSIhwAxKWUApC+nDWTR5t4Pmb98cUKk+LsM2wuiKWxL2X51dVri2HLTf3FB09MSHEx09vznGiVPdJMVJ8hpQcPl/owmeSPL4U9LXz+/Jbx4tqU4IESnLco0QIhKo/6kf/KSBlmX5B0e9QohTaXAgMFQIcS1gBNRCCJssyz/pIPdzhuCxgm0vmrjj9+SOOsgeADoDIvgE4+xqgeLHAAmyji9jjhsNXtdJh7OyhwCGnFLTCuLxUEyg/DYabygojscUka0UuG5gvnogV4gppPJNUPtEzuco7+GVlyB8FZRJASfFfpPx4aERFaEnN5b43a6uwEwYtyDxzRJsKnaDKawrj+S3DDSA5I+V8QflN895tRS4BHji+N+fXBr6S18S/rdBfqhBWZZny7IcJ8tyAvBX4G2/cf4DIXth/2CwfDN3F18LlP0DjN+a2VD2KpS+CLEXf1MmBCg1J1XrpAg9KafUvIJEOliEVdmMUvNPxHEfrsfzCh8rY0gkkPBv+3uBZGaDaz3p7nUUe1eetK+el3FSgvfEhTE/QhAnvzRl3fNwZM0p/dbPH4lfL1qSEOIDYCuQfnz22uV0GeaxQogiuty9T/zUcX6pD/oJYOHxxsuAmceF6wNcLcvyvF94fD+/N45SsB8FU99vypo2QMb9EDPrmzKfC6z5MHw/GFN/8HBdEdykr4M3/RQKkvDyCWrRH5XoWiYty25agRnSZWSK5B/8bZg0gnblZziVh04q99GJgVwUPzDF79uIb18mIcmQNup7R89+/sjI/FrruGVZvuAHdo3+Ocf5RQZaluWm72tQluVdwHeMsyzLb9I1P9DPHwX70a7wm+YTXpCFjART2nfr9nr7ewP+nEgDy9GTjIx8Si8JlSQiYyNMvPO1URdCRajqtm87KL6DTspFqVtMKCW4aENNl29ZTTQWzvrJtn+QtFGQ6M+W/efjzIvY719J6OfHCRgFkurksu8zztKprX6r5QO0xJ3yDA5BIEpCvrPK8JSQ0lELQb9vTV0L4jwEqh/40SmQ8t1VmX7+DPzmPuifjT9Ykp8f59vG+ReiIoh4bjrl+h7ycLMfJyt/uvK3+KFZQ7/IOPv5E3PmxRv1j6D9nFZiuRbNt4I2/RhK0pCxIRHy05X9+PlFnHkjaL+B9nNaMZL9s+oLNOi5BsUpzvrw4+f/j98H7cfPz8bAFQi+P9iTHz+/Hj9rqfdpwW+g/Zzx+I2zn9OD38Xhx48fP2cwfheHHz9+/JyB+EfQfvz48XOG4jfQfvz48XOG4p/F4cePHz9nKP5ZHH78+PFzhuJ3cfjx48fPGYrfxeHHjx8/Zyj+EbQfP378nKH4R9B+/Pjxc4Zy5r0kFPKJCTDPIIQQDXRlaTkdhACNp6mt08mfUa8/o07g1+uXEi/L8k/lcPhRhBAr4JTDJjbKsjzhl7R3KpyxBvp0IoTY9UtSqJ+p/Bn1+jPqBH69/Hw//oD9fvz48XOG4jfQfvz48XOG4jfQXcz/vQX4jfgz6vVn1An8evn5Hvw+aD9+/Pg5Q/GPoP348ePnDMVvoP348ePnDOV/0kALIYKEEKuEEEXH/wb+SF2zEKJSCPGv0ynj/4dT0UsI0VMIsVUIkS+EOCCEOP/3kPWnEEJMEEIcFkIUCyHu/J79GiHEguP7twshEn4HMX82p6DXX4QQBcfPzRohRPzvIefP4ad0OqHeuUIIWQjhn3Z3ivxPGmjgTmCNLMupwJrj2z/Ew8CG0yLVL+dU9OoELpZlORuYAPxTCBFw+kT8aYQQCuBFYCKQBVwghMj6VrXLgRZZllOAZ4EnT6+UP59T1Gsv0EeW5e7AIuDvp1fKn8cp6oQQwgTcBGw/vRL+sflfNdDTgLeOf38LmP59lYQQuUA4sPL0iPWL+Um9ZFk+Isty0fHv1UA98ItWYP0G9AOKZVkukWXZBXxIl24ncqKui4DRQghxGmX8//CTesmy/JUsy53HN7cBMadZxp/LqZwr6BroPAk4Tqdwf3T+Vw10uCzLNce/19JlhE9CCCEB/wD+ejoF+4X8pF4nIoToB6iBo7+1YD+TaKDihO3K42XfW0eWZQ/QBgSfFun+/5yKXidyOfDFbyrRL+cndRJC9AZiZVledjoF+zPwpw2WJIRYDUR8z657TtyQZVkWQnzfXMNrgeWyLFeeSQOzX0Gv/x4nEngHuESWZd+vK6WfX4oQ4iKgDzD895bll3B8oPMMcOnvLMofkj+tgZZlecwP7RNC1AkhImVZrjluqOq/p9pAYKgQ4lrACKiFEDZZln/MX/2b8yvohRDCDCwD7pFledtvJOovoQqIPWE75njZ99WpFEIoAQvQdHrE+39zKnohhBhD1w13uCzLztMk2/+Xn9LJBOQA644PdCKApUKIqbIs7zptUv5B+V91cSwFLjn+/RLg029XkGV5tizLcbIsJ9Dl5nj79zbOp8BP6iWEUANL6NJn0WmU7eewE0gVQiQel3cWXbqdyIm6zgDWymf+qquf1EsI0Qt4FZgqy/L33mDPMH5UJ1mW22RZDpFlOeH4tbSNLt38xvkU+F810E8AY4UQRcCY49sIIfoIIV77XSX7ZZyKXjOBYcClQoh9xz89fxdpf4DjPuXrgS+BQ8BCWZbzhRAPCSGmHq/2OhAshCgG/sKPz8Q5IzhFvZ6i64nto+Pn5ts3pjOKU9TJz/8T/1JvP378+DlD+V8dQfvx48fPGY/fQPvx48fPGYrfQPvx48fPGYrfQPvx48fPGYrfQPvx48fPGYrfQPvx48fPGYrfQPvx48fPGcr/ASJMkYJYPV1xAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -954,6 +956,16 @@ "plt.xlim((-0.5,0.5))\n", "plt.ylim((-0.5,0.5))" ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file as a matter of best practice\n", + "sp.close()" + ] } ], "metadata": { @@ -972,7 +984,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/examples/jupyter/tally-arithmetic.ipynb b/examples/jupyter/tally-arithmetic.ipynb index 0e8b53ac71c..5f04c688466 100644 --- a/examples/jupyter/tally-arithmetic.ipynb +++ b/examples/jupyter/tally-arithmetic.ipynb @@ -243,7 +243,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -421,11 +421,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -478,78 +478,82 @@ " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2019 MIT and OpenMC contributors\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.11.0-dev\n", - " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", - " Date/Time | 2019-07-18 22:51:02\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-26 15:46:22\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", - " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", - " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", - " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", - " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", - " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", - " Maximum neutron transport energy: 20000000.000000 eV for U235\n", + " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", + " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", + " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", + " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", + " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for U235\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", " Bat./Gen. k Average k\n", " ========= ======== ====================\n", - " 1/1 0.96168\n", - " 2/1 0.96651\n", - " 3/1 1.00678\n", - " 4/1 0.98773\n", - " 5/1 1.01883\n", - " 6/1 1.02959\n", - " 7/1 0.99859 1.01409 +/- 0.01550\n", - " 8/1 1.03441 1.02086 +/- 0.01123\n", - " 9/1 1.06097 1.03089 +/- 0.01279\n", - " 10/1 1.06132 1.03698 +/- 0.01163\n", - " 11/1 1.04687 1.03863 +/- 0.00964\n", - " 12/1 1.02982 1.03737 +/- 0.00824\n", - " 13/1 1.03520 1.03710 +/- 0.00714\n", - " 14/1 0.99508 1.03243 +/- 0.00784\n", - " 15/1 1.03987 1.03317 +/- 0.00705\n", - " 16/1 1.02743 1.03265 +/- 0.00640\n", - " 17/1 1.02975 1.03241 +/- 0.00585\n", - " 18/1 0.99671 1.02966 +/- 0.00604\n", - " 19/1 1.02040 1.02900 +/- 0.00563\n", - " 20/1 1.02024 1.02842 +/- 0.00527\n", + " 1/1 0.99327\n", + " 2/1 1.05535\n", + " 3/1 1.02165\n", + " 4/1 1.02898\n", + " 5/1 1.02521\n", + " 6/1 1.04782\n", + " 7/1 1.07014 1.05898 +/- 0.01116\n", + " 8/1 1.07724 1.06507 +/- 0.00886\n", + " 9/1 1.06268 1.06447 +/- 0.00630\n", + " 10/1 0.99628 1.05083 +/- 0.01448\n", + " 11/1 1.07257 1.05446 +/- 0.01237\n", + " 12/1 1.01603 1.04897 +/- 0.01181\n", + " 13/1 1.01047 1.04415 +/- 0.01130\n", + " 14/1 1.03639 1.04329 +/- 0.01000\n", + " 15/1 1.00699 1.03966 +/- 0.00966\n", + " 16/1 0.99098 1.03524 +/- 0.00979\n", + " 17/1 1.03990 1.03562 +/- 0.00895\n", + " 18/1 1.02076 1.03448 +/- 0.00831\n", + " 19/1 0.99685 1.03179 +/- 0.00815\n", + " 20/1 1.04833 1.03290 +/- 0.00767\n", " Creating state point statepoint.20.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.4427e-01 seconds\n", - " Reading cross sections = 3.1628e-01 seconds\n", - " Total time in simulation = 3.7319e+00 seconds\n", - " Time in transport only = 3.6302e+00 seconds\n", - " Time in inactive batches = 4.9601e-01 seconds\n", - " Time in active batches = 3.2359e+00 seconds\n", - " Time synchronizing fission bank = 2.8100e-03 seconds\n", - " Sampling source sites = 2.4682e-03 seconds\n", - " SEND/RECV source sites = 3.2484e-04 seconds\n", - " Time accumulating tallies = 4.4538e-05 seconds\n", - " Total time for finalization = 9.3656e-04 seconds\n", - " Total time elapsed = 4.0859e+00 seconds\n", - " Calculation Rate (inactive) = 25201.2 particles/second\n", - " Calculation Rate (active) = 11588.7 particles/second\n", + " Total time for initialization = 2.5195e-01 seconds\n", + " Reading cross sections = 2.3830e-01 seconds\n", + " Total time in simulation = 3.6172e+00 seconds\n", + " Time in transport only = 3.6041e+00 seconds\n", + " Time in inactive batches = 4.5282e-01 seconds\n", + " Time in active batches = 3.1644e+00 seconds\n", + " Time synchronizing fission bank = 2.4821e-03 seconds\n", + " Sampling source sites = 2.0826e-03 seconds\n", + " SEND/RECV source sites = 3.8781e-04 seconds\n", + " Time accumulating tallies = 2.8497e-04 seconds\n", + " Time writing statepoints = 8.5742e-03 seconds\n", + " Total time for finalization = 3.1545e-04 seconds\n", + " Total time elapsed = 3.8763e+00 seconds\n", + " Calculation Rate (inactive) = 27604.9 particles/second\n", + " Calculation Rate (active) = 11850.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02889 +/- 0.00492\n", - " k-effective (Track-length) = 1.02842 +/- 0.00527\n", - " k-effective (Absorption) = 1.02637 +/- 0.00349\n", - " Combined k-effective = 1.02700 +/- 0.00291\n", - " Leakage Fraction = 0.01717 +/- 0.00107\n", + " k-effective (Collision) = 1.03497 +/- 0.00681\n", + " k-effective (Track-length) = 1.03290 +/- 0.00767\n", + " k-effective (Absorption) = 1.02663 +/- 0.00590\n", + " Combined k-effective = 1.03085 +/- 0.00535\n", + " Leakage Fraction = 0.01728 +/- 0.00077\n", "\n" ] } @@ -631,8 +635,8 @@ " 0\n", " total\n", " (nu-fission / (absorption + current))\n", - " 1.023002\n", - " 0.006647\n", + " 1.025847\n", + " 0.009998\n", " \n", " \n", "\n", @@ -640,7 +644,7 @@ ], "text/plain": [ " nuclide score mean std. dev.\n", - "0 total (nu-fission / (absorption + current)) 1.02e+00 6.65e-03" + "0 total (nu-fission / (absorption + current)) 1.03e+00 1.00e-02" ] }, "execution_count": 21, @@ -712,8 +716,8 @@ " 0.625\n", " total\n", " ((absorption + current) / (absorption + current))\n", - " 0.694368\n", - " 0.004606\n", + " 0.695924\n", + " 0.007275\n", " \n", " \n", "\n", @@ -724,7 +728,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 6.94e-01 4.61e-03 " + "0 ((absorption + current) / (absorption + current)) 6.96e-01 7.27e-03 " ] }, "execution_count": 22, @@ -790,8 +794,8 @@ " 0.625\n", " total\n", " (nu-fission / nu-fission)\n", - " 1.203099\n", - " 0.009615\n", + " 1.203449\n", + " 0.01381\n", " \n", " \n", "\n", @@ -802,7 +806,7 @@ "0 0.00e+00 6.25e-01 total (nu-fission / nu-fission) \n", "\n", " mean std. dev. \n", - "0 1.20e+00 9.61e-03 " + "0 1.20e+00 1.38e-02 " ] }, "execution_count": 23, @@ -869,8 +873,8 @@ " 1\n", " total\n", " (absorption / absorption)\n", - " 0.749423\n", - " 0.006089\n", + " 0.74975\n", + " 0.009032\n", " \n", " \n", "\n", @@ -881,7 +885,7 @@ "0 0.00e+00 6.25e-01 1 total (absorption / absorption) \n", "\n", " mean std. dev. \n", - "0 7.49e-01 6.09e-03 " + "0 7.50e-01 9.03e-03 " ] }, "execution_count": 24, @@ -946,8 +950,8 @@ " 1\n", " total\n", " (nu-fission / absorption)\n", - " 1.663727\n", - " 0.014403\n", + " 1.663575\n", + " 0.020557\n", " \n", " \n", "\n", @@ -958,7 +962,7 @@ "0 0.00e+00 6.25e-01 1 total (nu-fission / absorption) \n", "\n", " mean std. dev. \n", - "0 1.66e+00 1.44e-02 " + "0 1.66e+00 2.06e-02 " ] }, "execution_count": 25, @@ -1020,8 +1024,8 @@ " 0.625\n", " total\n", " ((absorption + current) / (absorption + current))\n", - " 0.984668\n", - " 0.005509\n", + " 0.984639\n", + " 0.00883\n", " \n", " \n", "\n", @@ -1032,7 +1036,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption + current) / (absorption + current)) 9.85e-01 5.51e-03 " + "0 ((absorption + current) / (absorption + current)) 9.85e-01 8.83e-03 " ] }, "execution_count": 26, @@ -1093,8 +1097,8 @@ " 0.625\n", " total\n", " (absorption / (absorption + current))\n", - " 0.997439\n", - " 0.007548\n", + " 0.997372\n", + " 0.011707\n", " \n", " \n", "\n", @@ -1105,7 +1109,7 @@ "0 0.00e+00 6.25e-01 total \n", "\n", " score mean std. dev. \n", - "0 (absorption / (absorption + current)) 9.97e-01 7.55e-03 " + "0 (absorption / (absorption + current)) 9.97e-01 1.17e-02 " ] }, "execution_count": 27, @@ -1168,8 +1172,8 @@ " 1\n", " total\n", " (((((((absorption + current) / (absorption + c...\n", - " 1.023002\n", - " 0.018791\n", + " 1.025847\n", + " 0.028224\n", " \n", " \n", "\n", @@ -1180,7 +1184,7 @@ "0 0.00e+00 6.25e-01 1 total \n", "\n", " score mean std. dev. \n", - "0 (((((((absorption + current) / (absorption + c... 1.02e+00 1.88e-02 " + "0 (((((((absorption + current) / (absorption + c... 1.03e+00 2.82e-02 " ] }, "execution_count": 28, @@ -1260,8 +1264,8 @@ " 6.250000e-01\n", " (U238 / total)\n", " (nu-fission / flux)\n", - " 6.659486e-07\n", - " 5.627975e-09\n", + " 6.670761e-07\n", + " 7.788171e-09\n", " \n", " \n", " 1\n", @@ -1270,8 +1274,8 @@ " 6.250000e-01\n", " (U238 / total)\n", " (scatter / flux)\n", - " 2.099901e-01\n", - " 1.748379e-03\n", + " 2.099931e-01\n", + " 2.336727e-03\n", " \n", " \n", " 2\n", @@ -1280,8 +1284,8 @@ " 6.250000e-01\n", " (U235 / total)\n", " (nu-fission / flux)\n", - " 3.566329e-01\n", - " 3.030782e-03\n", + " 3.571972e-01\n", + " 4.203420e-03\n", " \n", " \n", " 3\n", @@ -1290,8 +1294,8 @@ " 6.250000e-01\n", " (U235 / total)\n", " (scatter / flux)\n", - " 5.555466e-03\n", - " 4.635318e-05\n", + " 5.555637e-03\n", + " 6.200519e-05\n", " \n", " \n", " 4\n", @@ -1300,8 +1304,8 @@ " 2.000000e+07\n", " (U238 / total)\n", " (nu-fission / flux)\n", - " 7.251304e-03\n", - " 5.161998e-05\n", + " 7.269233e-03\n", + " 6.814579e-05\n", " \n", " \n", " 5\n", @@ -1310,8 +1314,8 @@ " 2.000000e+07\n", " (U238 / total)\n", " (scatter / flux)\n", - " 2.272661e-01\n", - " 9.576939e-04\n", + " 2.276519e-01\n", + " 7.674381e-04\n", " \n", " \n", " 6\n", @@ -1320,8 +1324,8 @@ " 2.000000e+07\n", " (U235 / total)\n", " (nu-fission / flux)\n", - " 7.920169e-03\n", - " 5.751231e-05\n", + " 8.069479e-03\n", + " 5.324215e-05\n", " \n", " \n", " 7\n", @@ -1330,8 +1334,8 @@ " 2.000000e+07\n", " (U235 / total)\n", " (scatter / flux)\n", - " 3.358280e-03\n", - " 1.341281e-05\n", + " 3.363165e-03\n", + " 1.147434e-05\n", " \n", " \n", "\n", @@ -1349,14 +1353,14 @@ "7 1 6.25e-01 2.00e+07 (U235 / total) \n", "\n", " score mean std. dev. \n", - "0 (nu-fission / flux) 6.66e-07 5.63e-09 \n", - "1 (scatter / flux) 2.10e-01 1.75e-03 \n", - "2 (nu-fission / flux) 3.57e-01 3.03e-03 \n", - "3 (scatter / flux) 5.56e-03 4.64e-05 \n", - "4 (nu-fission / flux) 7.25e-03 5.16e-05 \n", - "5 (scatter / flux) 2.27e-01 9.58e-04 \n", - "6 (nu-fission / flux) 7.92e-03 5.75e-05 \n", - "7 (scatter / flux) 3.36e-03 1.34e-05 " + "0 (nu-fission / flux) 6.67e-07 7.79e-09 \n", + "1 (scatter / flux) 2.10e-01 2.34e-03 \n", + "2 (nu-fission / flux) 3.57e-01 4.20e-03 \n", + "3 (scatter / flux) 5.56e-03 6.20e-05 \n", + "4 (nu-fission / flux) 7.27e-03 6.81e-05 \n", + "5 (scatter / flux) 2.28e-01 7.67e-04 \n", + "6 (nu-fission / flux) 8.07e-03 5.32e-05 \n", + "7 (scatter / flux) 3.36e-03 1.15e-05 " ] }, "execution_count": 30, @@ -1385,11 +1389,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[6.65948580e-07]\n", - " [3.56632881e-01]]\n", + "[[[6.67076123e-07]\n", + " [3.57197248e-01]]\n", "\n", - " [[7.25130446e-03]\n", - " [7.92016892e-03]]]\n" + " [[7.26923324e-03]\n", + " [8.06947893e-03]]]\n" ] } ], @@ -1415,9 +1419,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.00555547]]\n", + "[[[0.00555564]]\n", "\n", - " [[0.00335828]]]\n" + " [[0.00336316]]]\n" ] } ], @@ -1437,8 +1441,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[[0.22726611]\n", - " [0.00335828]]]\n" + "[[[0.22765194]\n", + " [0.00336316]]]\n" ] } ], @@ -1501,7 +1505,7 @@ " U238\n", " nu-fission\n", " 0.000002\n", - " 9.679304e-09\n", + " 1.382881e-08\n", " \n", " \n", " 1\n", @@ -1510,8 +1514,8 @@ " 6.250000e-01\n", " U235\n", " nu-fission\n", - " 0.854805\n", - " 5.239673e-03\n", + " 0.858278\n", + " 7.512185e-03\n", " \n", " \n", " 2\n", @@ -1520,8 +1524,8 @@ " 2.000000e+07\n", " U238\n", " nu-fission\n", - " 0.082978\n", - " 5.346135e-04\n", + " 0.082753\n", + " 7.469156e-04\n", " \n", " \n", " 3\n", @@ -1530,8 +1534,8 @@ " 2.000000e+07\n", " U235\n", " nu-fission\n", - " 0.090632\n", - " 5.981942e-04\n", + " 0.091863\n", + " 5.596607e-04\n", " \n", " \n", "\n", @@ -1540,15 +1544,15 @@ "text/plain": [ " cell energy low [eV] energy high [eV] nuclide score mean \\\n", "0 1 0.00e+00 6.25e-01 U238 nu-fission 1.60e-06 \n", - "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.55e-01 \n", - "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.30e-02 \n", - "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.06e-02 \n", + "1 1 0.00e+00 6.25e-01 U235 nu-fission 8.58e-01 \n", + "2 1 6.25e-01 2.00e+07 U238 nu-fission 8.28e-02 \n", + "3 1 6.25e-01 2.00e+07 U235 nu-fission 9.19e-02 \n", "\n", " std. dev. \n", - "0 9.68e-09 \n", - "1 5.24e-03 \n", - "2 5.35e-04 \n", - "3 5.98e-04 " + "0 1.38e-08 \n", + "1 7.51e-03 \n", + "2 7.47e-04 \n", + "3 5.60e-04 " ] }, "execution_count": 34, @@ -1605,8 +1609,8 @@ " 1.080060e-01\n", " H1\n", " scatter\n", - " 4.541188\n", - " 0.025230\n", + " 4.542677\n", + " 0.037555\n", " \n", " \n", " 1\n", @@ -1615,8 +1619,8 @@ " 1.166529e+00\n", " H1\n", " scatter\n", - " 2.001332\n", - " 0.006754\n", + " 2.019002\n", + " 0.011992\n", " \n", " \n", " 2\n", @@ -1625,8 +1629,8 @@ " 1.259921e+01\n", " H1\n", " scatter\n", - " 1.639292\n", - " 0.011374\n", + " 1.622688\n", + " 0.011889\n", " \n", " \n", " 3\n", @@ -1635,8 +1639,8 @@ " 1.360790e+02\n", " H1\n", " scatter\n", - " 1.821633\n", - " 0.009590\n", + " 1.834317\n", + " 0.012322\n", " \n", " \n", " 4\n", @@ -1645,8 +1649,8 @@ " 1.469734e+03\n", " H1\n", " scatter\n", - " 2.032395\n", - " 0.009953\n", + " 2.040064\n", + " 0.012967\n", " \n", " \n", " 5\n", @@ -1655,8 +1659,8 @@ " 1.587401e+04\n", " H1\n", " scatter\n", - " 2.120745\n", - " 0.011090\n", + " 2.107758\n", + " 0.012781\n", " \n", " \n", " 6\n", @@ -1665,8 +1669,8 @@ " 1.714488e+05\n", " H1\n", " scatter\n", - " 2.181709\n", - " 0.013602\n", + " 2.175480\n", + " 0.014165\n", " \n", " \n", " 7\n", @@ -1675,8 +1679,8 @@ " 1.851749e+06\n", " H1\n", " scatter\n", - " 2.013644\n", - " 0.009219\n", + " 1.983382\n", + " 0.012931\n", " \n", " \n", " 8\n", @@ -1685,8 +1689,8 @@ " 2.000000e+07\n", " H1\n", " scatter\n", - " 0.372640\n", - " 0.002903\n", + " 0.372359\n", + " 0.003689\n", " \n", " \n", "\n", @@ -1695,25 +1699,25 @@ "text/plain": [ " cell energy low [eV] energy high [eV] nuclide score mean \\\n", "0 3 1.00e-02 1.08e-01 H1 scatter 4.54e+00 \n", - "1 3 1.08e-01 1.17e+00 H1 scatter 2.00e+00 \n", - "2 3 1.17e+00 1.26e+01 H1 scatter 1.64e+00 \n", - "3 3 1.26e+01 1.36e+02 H1 scatter 1.82e+00 \n", - "4 3 1.36e+02 1.47e+03 H1 scatter 2.03e+00 \n", - "5 3 1.47e+03 1.59e+04 H1 scatter 2.12e+00 \n", + "1 3 1.08e-01 1.17e+00 H1 scatter 2.02e+00 \n", + "2 3 1.17e+00 1.26e+01 H1 scatter 1.62e+00 \n", + "3 3 1.26e+01 1.36e+02 H1 scatter 1.83e+00 \n", + "4 3 1.36e+02 1.47e+03 H1 scatter 2.04e+00 \n", + "5 3 1.47e+03 1.59e+04 H1 scatter 2.11e+00 \n", "6 3 1.59e+04 1.71e+05 H1 scatter 2.18e+00 \n", - "7 3 1.71e+05 1.85e+06 H1 scatter 2.01e+00 \n", - "8 3 1.85e+06 2.00e+07 H1 scatter 3.73e-01 \n", + "7 3 1.71e+05 1.85e+06 H1 scatter 1.98e+00 \n", + "8 3 1.85e+06 2.00e+07 H1 scatter 3.72e-01 \n", "\n", " std. dev. \n", - "0 2.52e-02 \n", - "1 6.75e-03 \n", - "2 1.14e-02 \n", - "3 9.59e-03 \n", - "4 9.95e-03 \n", - "5 1.11e-02 \n", - "6 1.36e-02 \n", - "7 9.22e-03 \n", - "8 2.90e-03 " + "0 3.76e-02 \n", + "1 1.20e-02 \n", + "2 1.19e-02 \n", + "3 1.23e-02 \n", + "4 1.30e-02 \n", + "5 1.28e-02 \n", + "6 1.42e-02 \n", + "7 1.29e-02 \n", + "8 3.69e-03 " ] }, "execution_count": 35, @@ -1728,6 +1732,16 @@ " filters=[openmc.CellFilter], filter_bins=[(moderator_cell.id,)])\n", "slice_test.get_pandas_dataframe()" ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the statepoint file as a matter of best practice\n", + "sp.close()" + ] } ], "metadata": { @@ -1746,7 +1760,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, From 8d36b9b81336c45b2bd9d9de84278b71f5f13bd1 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Sat, 26 Jun 2021 18:46:14 -0500 Subject: [PATCH 0114/2572] Add a note about unable to write errors for summary/statepoint files on OpenMC re-runs from Python API. --- docs/source/usersguide/troubleshoot.rst | 17 +++++++++++++++++ examples/jupyter/chain_simple.xml | 1 + 2 files changed, 18 insertions(+) create mode 120000 examples/jupyter/chain_simple.xml diff --git a/docs/source/usersguide/troubleshoot.rst b/docs/source/usersguide/troubleshoot.rst index 7fb5723d932..259b21aa45e 100644 --- a/docs/source/usersguide/troubleshoot.rst +++ b/docs/source/usersguide/troubleshoot.rst @@ -45,6 +45,23 @@ with the :envvar:`OPENMC_CROSS_SECTIONS` environment variable. It is recommended to add a line in your ``.profile`` or ``.bash_profile`` setting the :envvar:`OPENMC_CROSS_SECTIONS` environment variable. +RuntimeError: Failed to open HDF5 file with mode 'w': summary.h5 +**************************************************************** + +This often occurs when working with the Python API and executing multiple OpenMC +runs in a script. If an :class:`openmc.StatePoint` is open in the Python interpreter, +the file handle of the statpoint file as well as the linked `summary.h5` file will +be unavailable for writing, causing this error to appear. To avoid the situation, +it is recommended that data be extracted from statepoint files in a context manager: + +.. code-block:: python + + with openmc.StatePoint('statepoint.10.h5') as sp: + k_eff = sp.k_combined + +or that the :meth:`StatePoint.close` method is called before executing a subsequent +OpenMC run. + Geometry Debugging ****************** diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml new file mode 120000 index 00000000000..96f6fa8818e --- /dev/null +++ b/examples/jupyter/chain_simple.xml @@ -0,0 +1 @@ +../../tests/chain_simple.xml \ No newline at end of file From ae7ff201606ab7243061cc0ba605c4baa19d6493 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 08:21:51 -0500 Subject: [PATCH 0115/2572] Apply suggestions from code review Co-authored-by: Paul Romano --- docs/source/usersguide/troubleshoot.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/usersguide/troubleshoot.rst b/docs/source/usersguide/troubleshoot.rst index 259b21aa45e..e5a68d68123 100644 --- a/docs/source/usersguide/troubleshoot.rst +++ b/docs/source/usersguide/troubleshoot.rst @@ -50,8 +50,8 @@ RuntimeError: Failed to open HDF5 file with mode 'w': summary.h5 This often occurs when working with the Python API and executing multiple OpenMC runs in a script. If an :class:`openmc.StatePoint` is open in the Python interpreter, -the file handle of the statpoint file as well as the linked `summary.h5` file will -be unavailable for writing, causing this error to appear. To avoid the situation, +the file handle of the statepoint file as well as the linked `summary.h5` file will +be unavailable for writing, causing this error to appear. To avoid this situation, it is recommended that data be extracted from statepoint files in a context manager: .. code-block:: python From ff5b73d4d90c39aed69d19278bbef985b330cb0d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 08:57:14 -0500 Subject: [PATCH 0116/2572] Addressing suggestions from @paulromano. --- examples/jupyter/candu.ipynb | 691 +++++++++++++++++++++++++- examples/jupyter/mdgxs-part-i.ipynb | 83 ++-- examples/jupyter/mg-mode-part-i.ipynb | 49 +- examples/jupyter/mgxs-part-i.ipynb | 392 ++++++++------- 4 files changed, 953 insertions(+), 262 deletions(-) diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index 30cdd78bc62..3e5bc72834b 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -121,7 +121,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -130,7 +130,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -176,7 +176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -185,7 +185,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -251,7 +251,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -319,7 +319,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -397,11 +397,684 @@ "cell_type": "code", "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
level 1level 2level 3distribcellnuclidescoremeanstd. dev.
univcellunivcellunivcell
idididididid
03441100250totalflux0.2113850.010406
13441200251totalflux0.1820150.006170
23441201252totalflux0.1849110.005997
33441202253totalflux0.1931770.009182
43441203254totalflux0.1934220.007610
53441204255totalflux0.1888080.005277
63441205256totalflux0.1914240.003950
73441300257totalflux0.1549320.007590
83441301258totalflux0.1536880.006581
93441302259totalflux0.1614710.004471
1034413032510totalflux0.1512020.008047
1134413042511totalflux0.1539050.005394
1234413052512totalflux0.1532890.010077
1334413062513totalflux0.1715590.007727
1434413072514totalflux0.1638020.006032
1534413082515totalflux0.1537710.005325
1634413092516totalflux0.1590700.005367
1734413102517totalflux0.1500780.005973
1834413112518totalflux0.1500650.007176
1934414002519totalflux0.1054390.004338
2034414012520totalflux0.1085020.004957
2134414022521totalflux0.1030880.007733
2234414032522totalflux0.1022370.003835
2334414042523totalflux0.1071330.006022
2434414052524totalflux0.1041770.004377
2534414062525totalflux0.1102810.005753
2634414072526totalflux0.1031710.003678
2734414082527totalflux0.1035340.005595
2834414092528totalflux0.1072400.003574
2934414102529totalflux0.1081620.006636
3034414112530totalflux0.1185500.005535
3134414122531totalflux0.1144350.006232
3234414132532totalflux0.1092270.003769
3334414142533totalflux0.1131480.006165
3434414152534totalflux0.1120060.005454
3534414162535totalflux0.0999730.004764
3634414172536totalflux0.1049520.005613
\n", + "
" + ], + "text/plain": [ + " level 1 level 2 level 3 distribcell nuclide score mean \\\n", + " univ cell univ cell univ cell \n", + " id id id id id id \n", + "0 3 44 1 100 2 5 0 total flux 2.11e-01 \n", + "1 3 44 1 200 2 5 1 total flux 1.82e-01 \n", + "2 3 44 1 201 2 5 2 total flux 1.85e-01 \n", + "3 3 44 1 202 2 5 3 total flux 1.93e-01 \n", + "4 3 44 1 203 2 5 4 total flux 1.93e-01 \n", + "5 3 44 1 204 2 5 5 total flux 1.89e-01 \n", + "6 3 44 1 205 2 5 6 total flux 1.91e-01 \n", + "7 3 44 1 300 2 5 7 total flux 1.55e-01 \n", + "8 3 44 1 301 2 5 8 total flux 1.54e-01 \n", + "9 3 44 1 302 2 5 9 total flux 1.61e-01 \n", + "10 3 44 1 303 2 5 10 total flux 1.51e-01 \n", + "11 3 44 1 304 2 5 11 total flux 1.54e-01 \n", + "12 3 44 1 305 2 5 12 total flux 1.53e-01 \n", + "13 3 44 1 306 2 5 13 total flux 1.72e-01 \n", + "14 3 44 1 307 2 5 14 total flux 1.64e-01 \n", + "15 3 44 1 308 2 5 15 total flux 1.54e-01 \n", + "16 3 44 1 309 2 5 16 total flux 1.59e-01 \n", + "17 3 44 1 310 2 5 17 total flux 1.50e-01 \n", + "18 3 44 1 311 2 5 18 total flux 1.50e-01 \n", + "19 3 44 1 400 2 5 19 total flux 1.05e-01 \n", + "20 3 44 1 401 2 5 20 total flux 1.09e-01 \n", + "21 3 44 1 402 2 5 21 total flux 1.03e-01 \n", + "22 3 44 1 403 2 5 22 total flux 1.02e-01 \n", + "23 3 44 1 404 2 5 23 total flux 1.07e-01 \n", + "24 3 44 1 405 2 5 24 total flux 1.04e-01 \n", + "25 3 44 1 406 2 5 25 total flux 1.10e-01 \n", + "26 3 44 1 407 2 5 26 total flux 1.03e-01 \n", + "27 3 44 1 408 2 5 27 total flux 1.04e-01 \n", + "28 3 44 1 409 2 5 28 total flux 1.07e-01 \n", + "29 3 44 1 410 2 5 29 total flux 1.08e-01 \n", + "30 3 44 1 411 2 5 30 total flux 1.19e-01 \n", + "31 3 44 1 412 2 5 31 total flux 1.14e-01 \n", + "32 3 44 1 413 2 5 32 total flux 1.09e-01 \n", + "33 3 44 1 414 2 5 33 total flux 1.13e-01 \n", + "34 3 44 1 415 2 5 34 total flux 1.12e-01 \n", + "35 3 44 1 416 2 5 35 total flux 1.00e-01 \n", + "36 3 44 1 417 2 5 36 total flux 1.05e-01 \n", + "\n", + " std. dev. \n", + " \n", + " \n", + "0 1.04e-02 \n", + "1 6.17e-03 \n", + "2 6.00e-03 \n", + "3 9.18e-03 \n", + "4 7.61e-03 \n", + "5 5.28e-03 \n", + "6 3.95e-03 \n", + "7 7.59e-03 \n", + "8 6.58e-03 \n", + "9 4.47e-03 \n", + "10 8.05e-03 \n", + "11 5.39e-03 \n", + "12 1.01e-02 \n", + "13 7.73e-03 \n", + "14 6.03e-03 \n", + "15 5.33e-03 \n", + "16 5.37e-03 \n", + "17 5.97e-03 \n", + "18 7.18e-03 \n", + "19 4.34e-03 \n", + "20 4.96e-03 \n", + "21 7.73e-03 \n", + "22 3.84e-03 \n", + "23 6.02e-03 \n", + "24 4.38e-03 \n", + "25 5.75e-03 \n", + "26 3.68e-03 \n", + "27 5.59e-03 \n", + "28 3.57e-03 \n", + "29 6.64e-03 \n", + "30 5.53e-03 \n", + "31 6.23e-03 \n", + "32 3.77e-03 \n", + "33 6.16e-03 \n", + "34 5.45e-03 \n", + "35 4.76e-03 \n", + "36 5.61e-03 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n", " output_tally = sp.get_tally()\n", - " output_tally.get_pandas_dataframe()" + " df = output_tally.get_pandas_dataframe()\n", + " \n", + "df" ] }, { diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index 972dee54abb..c37547a337f 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -490,19 +490,19 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 12:02:59\n", + " Date/Time | 2021-06-28 08:47:21\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading Pu239 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Pu239.h5\n", - " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading Pu239 from /opt/data/xs/nndc_hdf5/Pu239.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", " Minimum neutron data temperature: 294.0 K\n", " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", @@ -569,21 +569,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.7487e-01 seconds\n", - " Reading cross sections = 2.6530e-01 seconds\n", - " Total time in simulation = 2.1335e+01 seconds\n", - " Time in transport only = 2.1311e+01 seconds\n", - " Time in inactive batches = 9.3940e-01 seconds\n", - " Time in active batches = 2.0396e+01 seconds\n", - " Time synchronizing fission bank = 9.9228e-03 seconds\n", - " Sampling source sites = 8.2374e-03 seconds\n", - " SEND/RECV source sites = 1.6638e-03 seconds\n", - " Time accumulating tallies = 8.4502e-04 seconds\n", - " Time writing statepoints = 7.4764e-03 seconds\n", - " Total time for finalization = 6.2220e-03 seconds\n", - " Total time elapsed = 2.1623e+01 seconds\n", - " Calculation Rate (inactive) = 53225.7 particles/second\n", - " Calculation Rate (active) = 9805.91 particles/second\n", + " Total time for initialization = 2.6337e-01 seconds\n", + " Reading cross sections = 2.5312e-01 seconds\n", + " Total time in simulation = 2.6057e+01 seconds\n", + " Time in transport only = 2.6028e+01 seconds\n", + " Time in inactive batches = 9.8414e-01 seconds\n", + " Time in active batches = 2.5072e+01 seconds\n", + " Time synchronizing fission bank = 1.1907e-02 seconds\n", + " Sampling source sites = 9.8847e-03 seconds\n", + " SEND/RECV source sites = 1.9926e-03 seconds\n", + " Time accumulating tallies = 1.1168e-03 seconds\n", + " Time writing statepoints = 7.9423e-03 seconds\n", + " Total time for finalization = 6.1998e-03 seconds\n", + " Total time elapsed = 2.6333e+01 seconds\n", + " Calculation Rate (inactive) = 50805.8 particles/second\n", + " Calculation Rate (active) = 7976.89 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -1090,7 +1090,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 21, @@ -1137,31 +1137,6 @@ "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[CellFilter\n", - " \tBins =\t[1]\n", - " \tID =\t3,\n", - " DelayedGroupFilter\n", - " \tBins =\t[1 2 3 4 5 6]\n", - " \tID =\t13]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dr_tally.filters" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1171,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1346,7 +1321,7 @@ "11 (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11 3.37e-13 " ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1370,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1387,7 +1362,7 @@ "(0.0, 7.0)" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, @@ -1441,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1450,7 +1425,7 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb index dfcb88aeca6..97a73d1e0dd 100644 --- a/examples/jupyter/mg-mode-part-i.ipynb +++ b/examples/jupyter/mg-mode-part-i.ipynb @@ -197,19 +197,10 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n", - " FutureWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Create the surface used for each pin\n", - "pin_surf = openmc.ZCylinder(x0=0, y0=0, R=0.54, name='pin_surf')\n", + "pin_surf = openmc.ZCylinder(x0=0, y0=0, r=0.54, name='pin_surf')\n", "\n", "# Create the cells which will be used to represent each pin type.\n", "cells = {}\n", @@ -393,7 +384,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -402,7 +393,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -563,7 +554,7 @@ " License | https://docs.openmc.org/en/latest/license.html\n", " Version | 0.13.0-dev\n", " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", - " Date/Time | 2021-06-26 14:36:11\n", + " Date/Time | 2021-06-28 08:49:29\n", " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", @@ -589,21 +580,21 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.1712e-02 seconds\n", - " Reading cross sections = 9.5683e-03 seconds\n", - " Total time in simulation = 1.6152e+01 seconds\n", - " Time in transport only = 1.6108e+01 seconds\n", - " Time in inactive batches = 3.8534e+00 seconds\n", - " Time in active batches = 1.2299e+01 seconds\n", - " Time synchronizing fission bank = 2.8962e-02 seconds\n", - " Sampling source sites = 2.3951e-02 seconds\n", - " SEND/RECV source sites = 4.9397e-03 seconds\n", - " Time accumulating tallies = 4.9472e-04 seconds\n", - " Time writing statepoints = 2.4687e-03 seconds\n", - " Total time for finalization = 1.4768e-03 seconds\n", - " Total time elapsed = 1.6182e+01 seconds\n", - " Calculation Rate (inactive) = 64877.2 particles/second\n", - " Calculation Rate (active) = 40654.8 particles/second\n", + " Total time for initialization = 2.1582e-02 seconds\n", + " Reading cross sections = 1.0391e-02 seconds\n", + " Total time in simulation = 1.7437e+01 seconds\n", + " Time in transport only = 1.7387e+01 seconds\n", + " Time in inactive batches = 4.2441e+00 seconds\n", + " Time in active batches = 1.3193e+01 seconds\n", + " Time synchronizing fission bank = 3.2421e-02 seconds\n", + " Sampling source sites = 2.6798e-02 seconds\n", + " SEND/RECV source sites = 5.5395e-03 seconds\n", + " Time accumulating tallies = 5.3440e-04 seconds\n", + " Time writing statepoints = 2.9755e-03 seconds\n", + " Total time for finalization = 1.7629e-03 seconds\n", + " Total time elapsed = 1.7465e+01 seconds\n", + " Calculation Rate (inactive) = 58905.6 particles/second\n", + " Calculation Rate (active) = 37899.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", diff --git a/examples/jupyter/mgxs-part-i.ipynb b/examples/jupyter/mgxs-part-i.ipynb index 3d5711adc16..10e02e96cc4 100644 --- a/examples/jupyter/mgxs-part-i.ipynb +++ b/examples/jupyter/mgxs-part-i.ipynb @@ -33,7 +33,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAI8CAYAAAAz5idmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njwAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -154,9 +154,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Material and register the Nuclides\n", @@ -179,9 +177,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection and export to XML\n", @@ -199,9 +195,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate boundary Planes\n", @@ -244,9 +238,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create root universe\n", @@ -283,9 +275,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -384,17 +374,20 @@ { "data": { "text/plain": [ - "OrderedDict([('flux', Tally\n", + "OrderedDict([('flux',\n", + " Tally\n", " \tID =\t1\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", - " \tNuclides =\ttotal \n", + " \tNuclides =\ttotal\n", " \tScores =\t['flux']\n", - " \tEstimator =\ttracklength), ('absorption', Tally\n", + " \tEstimator =\ttracklength),\n", + " ('absorption',\n", + " Tally\n", " \tID =\t2\n", " \tName =\t\n", " \tFilters =\tCellFilter, EnergyFilter\n", - " \tNuclides =\ttotal \n", + " \tNuclides =\ttotal\n", " \tScores =\t['absorption']\n", " \tEstimator =\ttracklength)])" ] @@ -424,9 +417,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=3.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=4.\n", + "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n" ] } @@ -464,146 +457,138 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n", - " Date/Time | 2017-12-04 20:56:46\n", - " OpenMP Threads | 4\n", + " Copyright | 2011-2021 MIT and OpenMC contributors\n", + " License | https://docs.openmc.org/en/latest/license.html\n", + " Version | 0.13.0-dev\n", + " Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n", + " Date/Time | 2021-06-28 08:44:52\n", + " OpenMP Threads | 2\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for H1\n", + " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n", + " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n", + " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n", + " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n", + " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n", + " Minimum neutron data temperature: 294.0 K\n", + " Maximum neutron data temperature: 294.0 K\n", " Reading tallies XML file...\n", + " Preparing distributed cell instances...\n", " Writing summary.h5 file...\n", + " Maximum neutron transport energy: 20000000.0 eV for H1\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.11184 \n", - " 2/1 1.15820 \n", - " 3/1 1.18468 \n", - " 4/1 1.17492 \n", - " 5/1 1.19645 \n", - " 6/1 1.18436 \n", - " 7/1 1.14070 \n", - " 8/1 1.15150 \n", - " 9/1 1.19202 \n", - " 10/1 1.17677 \n", - " 11/1 1.20272 \n", - " 12/1 1.21366 1.20819 +/- 0.00547\n", - " 13/1 1.15906 1.19181 +/- 0.01668\n", - " 14/1 1.14687 1.18058 +/- 0.01629\n", - " 15/1 1.14570 1.17360 +/- 0.01442\n", - " 16/1 1.13480 1.16713 +/- 0.01343\n", - " 17/1 1.17680 1.16852 +/- 0.01144\n", - " 18/1 1.16866 1.16853 +/- 0.00990\n", - " 19/1 1.19253 1.17120 +/- 0.00913\n", - " 20/1 1.18124 1.17220 +/- 0.00823\n", - " 21/1 1.19206 1.17401 +/- 0.00766\n", - " 22/1 1.17681 1.17424 +/- 0.00700\n", - " 23/1 1.17634 1.17440 +/- 0.00644\n", - " 24/1 1.13659 1.17170 +/- 0.00654\n", - " 25/1 1.17144 1.17169 +/- 0.00609\n", - " 26/1 1.20649 1.17386 +/- 0.00610\n", - " 27/1 1.11238 1.17024 +/- 0.00678\n", - " 28/1 1.18911 1.17129 +/- 0.00647\n", - " 29/1 1.14681 1.17000 +/- 0.00626\n", - " 30/1 1.12152 1.16758 +/- 0.00641\n", - " 31/1 1.12729 1.16566 +/- 0.00639\n", - " 32/1 1.15399 1.16513 +/- 0.00612\n", - " 33/1 1.13547 1.16384 +/- 0.00599\n", - " 34/1 1.17723 1.16440 +/- 0.00576\n", - " 35/1 1.09296 1.16154 +/- 0.00622\n", - " 36/1 1.19621 1.16287 +/- 0.00612\n", - " 37/1 1.12560 1.16149 +/- 0.00605\n", - " 38/1 1.17872 1.16211 +/- 0.00586\n", - " 39/1 1.17721 1.16263 +/- 0.00568\n", - " 40/1 1.13724 1.16178 +/- 0.00555\n", - " 41/1 1.18526 1.16254 +/- 0.00542\n", - " 42/1 1.13779 1.16177 +/- 0.00531\n", - " 43/1 1.15066 1.16143 +/- 0.00516\n", - " 44/1 1.12174 1.16026 +/- 0.00514\n", - " 45/1 1.17478 1.16068 +/- 0.00501\n", - " 46/1 1.14146 1.16014 +/- 0.00489\n", - " 47/1 1.20464 1.16135 +/- 0.00491\n", - " 48/1 1.15119 1.16108 +/- 0.00479\n", - " 49/1 1.17938 1.16155 +/- 0.00468\n", - " 50/1 1.15798 1.16146 +/- 0.00457\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.18505\n", + " 2/1 1.17297\n", + " 3/1 1.16184\n", + " 4/1 1.14929\n", + " 5/1 1.09928\n", + " 6/1 1.18675\n", + " 7/1 1.19772\n", + " 8/1 1.17470\n", + " 9/1 1.17208\n", + " 10/1 1.09993\n", + " 11/1 1.14342\n", + " 12/1 1.10127 1.12234 +/- 0.02107\n", + " 13/1 1.19914 1.14794 +/- 0.02834\n", + " 14/1 1.18411 1.15698 +/- 0.02199\n", + " 15/1 1.14556 1.15470 +/- 0.01718\n", + " 16/1 1.20337 1.16281 +/- 0.01621\n", + " 17/1 1.13853 1.15934 +/- 0.01413\n", + " 18/1 1.18208 1.16218 +/- 0.01256\n", + " 19/1 1.11842 1.15732 +/- 0.01210\n", + " 20/1 1.15248 1.15684 +/- 0.01083\n", + " 21/1 1.14903 1.15613 +/- 0.00982\n", + " 22/1 1.23456 1.16266 +/- 0.01110\n", + " 23/1 1.18876 1.16467 +/- 0.01040\n", + " 24/1 1.13591 1.16262 +/- 0.00985\n", + " 25/1 1.19559 1.16481 +/- 0.00943\n", + " 26/1 1.16947 1.16511 +/- 0.00882\n", + " 27/1 1.13198 1.16316 +/- 0.00851\n", + " 28/1 1.15329 1.16261 +/- 0.00805\n", + " 29/1 1.16538 1.16275 +/- 0.00761\n", + " 30/1 1.18229 1.16373 +/- 0.00729\n", + " 31/1 1.15060 1.16311 +/- 0.00696\n", + " 32/1 1.15460 1.16272 +/- 0.00665\n", + " 33/1 1.13875 1.16168 +/- 0.00644\n", + " 34/1 1.13479 1.16056 +/- 0.00626\n", + " 35/1 1.21125 1.16258 +/- 0.00634\n", + " 36/1 1.15914 1.16245 +/- 0.00609\n", + " 37/1 1.10457 1.16031 +/- 0.00624\n", + " 38/1 1.17215 1.16073 +/- 0.00603\n", + " 39/1 1.18462 1.16155 +/- 0.00588\n", + " 40/1 1.15361 1.16129 +/- 0.00568\n", + " 41/1 1.14983 1.16092 +/- 0.00551\n", + " 42/1 1.14087 1.16029 +/- 0.00537\n", + " 43/1 1.18725 1.16111 +/- 0.00527\n", + " 44/1 1.19094 1.16199 +/- 0.00519\n", + " 45/1 1.17371 1.16232 +/- 0.00505\n", + " 46/1 1.18552 1.16297 +/- 0.00495\n", + " 47/1 1.14194 1.16240 +/- 0.00485\n", + " 48/1 1.12045 1.16130 +/- 0.00484\n", + " 49/1 1.18476 1.16190 +/- 0.00476\n", + " 50/1 1.17063 1.16212 +/- 0.00464\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.0504E-01 seconds\n", - " Reading cross sections = 3.6457E-01 seconds\n", - " Total time in simulation = 6.3478E+00 seconds\n", - " Time in transport only = 6.0079E+00 seconds\n", - " Time in inactive batches = 8.1713E-01 seconds\n", - " Time in active batches = 5.5307E+00 seconds\n", - " Time synchronizing fission bank = 5.4640E-03 seconds\n", - " Sampling source sites = 4.0981E-03 seconds\n", - " SEND/RECV source sites = 1.2606E-03 seconds\n", - " Time accumulating tallies = 1.2030E-04 seconds\n", - " Total time for finalization = 9.6554E-04 seconds\n", - " Total time elapsed = 6.7713E+00 seconds\n", - " Calculation Rate (inactive) = 30594.8 neutrons/second\n", - " Calculation Rate (active) = 18080.8 neutrons/second\n", + " Total time for initialization = 2.9038e-01 seconds\n", + " Reading cross sections = 2.7861e-01 seconds\n", + " Total time in simulation = 3.9987e+00 seconds\n", + " Time in transport only = 3.9829e+00 seconds\n", + " Time in inactive batches = 4.9461e-01 seconds\n", + " Time in active batches = 3.5041e+00 seconds\n", + " Time synchronizing fission bank = 5.2042e-03 seconds\n", + " Sampling source sites = 4.4431e-03 seconds\n", + " SEND/RECV source sites = 7.3804e-04 seconds\n", + " Time accumulating tallies = 3.6977e-04 seconds\n", + " Time writing statepoints = 6.6301e-03 seconds\n", + " Total time for finalization = 1.9945e-04 seconds\n", + " Total time elapsed = 4.2948e+00 seconds\n", + " Calculation Rate (inactive) = 50544.8 particles/second\n", + " Calculation Rate (active) = 28537.8 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.15984 +/- 0.00411\n", - " k-effective (Track-length) = 1.16146 +/- 0.00457\n", - " k-effective (Absorption) = 1.16177 +/- 0.00380\n", - " Combined k-effective = 1.16105 +/- 0.00364\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.16239 +/- 0.00461\n", + " k-effective (Track-length) = 1.16212 +/- 0.00464\n", + " k-effective (Absorption) = 1.15435 +/- 0.00325\n", + " Combined k-effective = 1.15666 +/- 0.00304\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -658,7 +643,9 @@ "# Load the tallies from the statepoint into each MGXS object\n", "total.load_from_statepoint(sp)\n", "absorption.load_from_statepoint(sp)\n", - "scattering.load_from_statepoint(sp)" + "scattering.load_from_statepoint(sp)\n", + "# Close the statepoint file now that we're done getting info\n", + "sp.close()" ] }, { @@ -697,7 +684,7 @@ "\tDomain ID =\t1\n", "\tCross Sections [cm^-1]:\n", " Group 1 [0.625 - 20000000.0eV]:\t6.81e-01 +/- 2.69e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 5.93e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t1.40e+00 +/- 6.00e-01%\n", "\n", "\n", "\n" @@ -724,6 +711,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -741,16 +741,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
11total0.6677870.0018020.6680830.001798
012total1.2920130.0076421.2920600.007737
\n", @@ -758,8 +758,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "1 1 1 total 0.667787 0.001802\n", - "0 1 2 total 1.292013 0.007642" + "1 1 1 total 0.668083 0.001798\n", + "0 1 2 total 1.292060 0.007737" ] }, "execution_count": 18, @@ -829,6 +829,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -850,8 +863,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -860,8 +873,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
6.250000e-01total(((total / flux) - (absorption / flux)) - (sca...-1.110223e-150.0112924.440892e-160.011435
12.000000e+07total(((total / flux) - (absorption / flux)) - (sca...7.771561e-160.002570-2.553513e-150.002567
\n", @@ -873,8 +886,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... -1.11e-15 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... 7.77e-16 2.57e-03 " + "0 (((total / flux) - (absorption / flux)) - (sca... 4.44e-16 1.14e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -2.55e-15 2.57e-03 " ] }, "execution_count": 21, @@ -906,6 +919,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -927,8 +953,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -937,8 +963,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
6.250000e-01total((absorption / flux) / (total / flux))0.0761150.0006490.0761030.000658
12.000000e+07total((absorption / flux) / (total / flux))0.0192630.0000950.0194410.000093
\n", @@ -950,8 +976,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.49e-04 \n", - "1 ((absorption / flux) / (total / flux)) 1.93e-02 9.46e-05 " + "0 ((absorption / flux) / (total / flux)) 7.61e-02 6.58e-04 \n", + "1 ((absorption / flux) / (total / flux)) 1.94e-02 9.26e-05 " ] }, "execution_count": 22, @@ -976,6 +1002,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -997,8 +1036,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1007,8 +1046,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
6.250000e-01total((scatter / flux) / (total / flux))0.9238850.0077360.9238970.007833
12.000000e+07total((scatter / flux) / (total / flux))0.9807370.0037370.9805590.003729
\n", @@ -1020,8 +1059,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.74e-03 \n", - "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.74e-03 " + "0 ((scatter / flux) / (total / flux)) 9.24e-01 7.83e-03 \n", + "1 ((scatter / flux) / (total / flux)) 9.81e-01 3.73e-03 " ] }, "execution_count": 23, @@ -1053,6 +1092,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1075,7 +1127,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1085,7 +1137,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
total(((absorption / flux) / (total / flux)) + ((sc...1.00.0077630.007861
1total(((absorption / flux) / (total / flux)) + ((sc...1.00.0037390.003731
\n", @@ -1097,8 +1149,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.76e-03 \n", - "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.74e-03 " + "0 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 7.86e-03 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00 3.73e-03 " ] }, "execution_count": 24, @@ -1131,7 +1183,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.3" } }, "nbformat": 4, From e6ce90dd782b4b11162f0ee0b073f8e561883729 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 14:38:16 -0500 Subject: [PATCH 0117/2572] Removing unecessary DAGMC include from cross_sections.cpp --- src/cross_sections.cpp | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/cross_sections.cpp b/src/cross_sections.cpp index 942b088d905..8124bd79479 100644 --- a/src/cross_sections.cpp +++ b/src/cross_sections.cpp @@ -3,9 +3,6 @@ #include "openmc/capi.h" #include "openmc/constants.h" #include "openmc/container_util.h" -#ifdef DAGMC -#include "openmc/dagmc.h" -#endif #include "openmc/error.h" #include "openmc/geometry_aux.h" #include "openmc/file_utils.h" From 019316e18d5453c047e6047646e9af4177ea0aab Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 14:38:51 -0500 Subject: [PATCH 0118/2572] Updating DAGMC history reset conditional for clarity. --- src/particle.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/particle.cpp b/src/particle.cpp index 6acd989faa5..ef5c3199ad9 100644 --- a/src/particle.cpp +++ b/src/particle.cpp @@ -383,7 +383,7 @@ Particle::cross_surface() // if we're crossing a CSG surface, make sure the DAG history is reset #ifdef DAGMC - if (surf->geom_type_ != GeometryType::DAG) history().reset(); + if (surf->geom_type_ == GeometryType::CSG) history().reset(); #endif // Handle any applicable boundary conditions. From 648054fbc7829b6342729cd9e1b67310d53cca2e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 14:39:35 -0500 Subject: [PATCH 0119/2572] Adding a fucntion for printing a warning if a root DAGMC universe does not contain a graveyard volume. --- include/openmc/cell.h | 7 ++++++- include/openmc/dagmc.h | 20 ++++++++++++++++++-- src/cell.cpp | 4 +--- src/dagmc.cpp | 34 +++++++++++++++++++++++++++------- src/geometry_aux.cpp | 4 +++- 5 files changed, 55 insertions(+), 14 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 8bcc1ea0675..1728392953d 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -74,8 +74,13 @@ class Universe BoundingBox bounding_box() const; - GeometryType type_ = GeometryType::CSG; + const GeometryType& geom_type() const { return geom_type_; } + GeometryType& geom_type() { return geom_type_; } + unique_ptr partitioner_; + +private: + GeometryType geom_type_ = GeometryType::CSG; }; //============================================================================== diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index 591ea647486..f2cc1ee2a3b 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -5,6 +5,20 @@ namespace openmc { extern "C" const bool DAGMC_ENABLED; } +// always include the XML interface header +#include "openmc/xml_interface.h" + +//============================================================================== +// Functions that are always defined +//============================================================================== + +namespace openmc { + +void read_dagmc_universes(pugi::xml_node node); +void check_dagmc_root_univ(); + +} + #ifdef DAGMC #include "DagMC.hpp" @@ -13,7 +27,6 @@ extern "C" const bool DAGMC_ENABLED; #include "openmc/particle.h" #include "openmc/position.h" #include "openmc/surface.h" -#include "openmc/xml_interface.h" class UWUW; @@ -114,18 +127,21 @@ class DAGUniverse : public Universe { int32_t cell_idx_offset_; //!< An offset to the start of the cells in this universe in OpenMC's cell vector int32_t surf_idx_offset_; //!< An offset to the start of the surfaces in this universe in OpenMC's surface vector + // Accessors + bool has_graveyard() const { return has_graveyard_; } + private: std::string filename_; //!< Name of the DAGMC file used to create this universe std::shared_ptr uwuw_; //!< Pointer to the UWUW instance for this universe bool adjust_geometry_ids_; //!< Indicates whether or not to automatically generate new cell and surface IDs for the universe bool adjust_material_ids_; //!< Indicates whether or not to automatically generate new material IDs for the universe + bool has_graveyard_; //!< Indicates if the DAGMC geometry has a "graveyard" volume }; //============================================================================== // Non-member functions //============================================================================== -void read_dagmc_universes(pugi::xml_node node); int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed); } // namespace openmc diff --git a/src/cell.cpp b/src/cell.cpp index c58fb176790..4de98aaddb9 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -194,7 +194,7 @@ Universe::to_hdf5(hid_t universes_group) const auto group = create_group(universes_group, fmt::format("universe {}", id_)); // Write the geometry representation type. - switch(type_) { + switch(geom_type_) { case GeometryType::CSG : write_string(group, "geom_type", "csg", false); break; @@ -995,9 +995,7 @@ void read_cells(pugi::xml_node node) } } - #ifdef DAGMC read_dagmc_universes(node); - #endif // Populate the Universe vector and map. for (int i = 0; i < model::cells.size(); i++) { diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 775c130f75c..bde33157f50 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -85,7 +85,7 @@ DAGUniverse::DAGUniverse(const std::string& filename, void DAGUniverse::initialize() { - type_ = GeometryType::DAG; + geom_type() = GeometryType::DAG; // determine the next cell id int32_t next_cell_id = 0; @@ -229,10 +229,7 @@ DAGUniverse::initialize() { model::overlap_check_count.resize(model::cells.size(), 0); } - if (!graveyard) { - warning("No graveyard volume found in the DagMC model. " - "This may result in lost particles and rapid simulation failure."); - } + has_graveyard_ = graveyard; // --- Surfaces --- @@ -631,6 +628,19 @@ void read_dagmc_universes(pugi::xml_node node) { } } +void check_dagmc_root_univ() { + const auto& ru = model::universes[model::root_universe]; + if (ru->geom_type() == GeometryType::DAG) { + // if the root universe contains DAGMC geometry, warn the user + // if it does not contain a graveyard volume + auto dag_univ = dynamic_cast(ru.get()); + if (dag_univ && !dag_univ->has_graveyard()) { + warning("No graveyard volume found in the DagMC model. " + "This may result in lost particles and rapid simulation failure."); + } + } +} + int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed) { moab::EntityHandle surf = @@ -645,5 +655,15 @@ int32_t next_cell(DAGUniverse* dag_univ, DAGCell* cur_cell, DAGSurface* surf_xed } -} -#endif +} // namespace openmc + +#else + +namespace openmc { + +void read_dagmc_universes(pugi::xml_node node) {}; +void check_dagmc_root_univ() {}; + +} // namespace openmc + +#endif // DAGMC diff --git a/src/geometry_aux.cpp b/src/geometry_aux.cpp index 80741b3e780..af6ebac9f82 100644 --- a/src/geometry_aux.cpp +++ b/src/geometry_aux.cpp @@ -80,9 +80,11 @@ void read_geometry_xml() fatal_error("No boundary conditions were applied to any surfaces!"); } - // Allocate universes, universe cell arrays, and assign base universe model::root_universe = find_root_universe(); + + // if the root universe is DAGMC geometry, make sure the model is well-formed + check_dagmc_root_univ(); } //============================================================================== From 263addffc1ea2c63f5666934bd9c9d7b26f98c0e Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Mon, 28 Jun 2021 15:31:22 -0500 Subject: [PATCH 0120/2572] Updating test and test results for the DAGMC geometry in a lattice. --- .../dagmc/universes/inputs_true.dat | 14 +++++++-- .../dagmc/universes/results_true.dat | 2 +- .../regression_tests/dagmc/universes/test.py | 29 ++++++++++++------- 3 files changed, 31 insertions(+), 14 deletions(-) diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat index fdc8b07bc96..e235c06a8a3 100644 --- a/tests/regression_tests/dagmc/universes/inputs_true.dat +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -1,13 +1,21 @@ - + + + 24.0 24.0 + 2 2 + -24.0 -24.0 + +9 9 +9 9 + - - + + diff --git a/tests/regression_tests/dagmc/universes/results_true.dat b/tests/regression_tests/dagmc/universes/results_true.dat index c9e1bc8fa5d..0dc1789d458 100644 --- a/tests/regression_tests/dagmc/universes/results_true.dat +++ b/tests/regression_tests/dagmc/universes/results_true.dat @@ -1,2 +1,2 @@ k-combined: -7.687742E-01 3.415061E-02 +9.436168E-01 2.905559E-02 diff --git a/tests/regression_tests/dagmc/universes/test.py b/tests/regression_tests/dagmc/universes/test.py index 1458462c48b..01963986c56 100644 --- a/tests/regression_tests/dagmc/universes/test.py +++ b/tests/regression_tests/dagmc/universes/test.py @@ -1,3 +1,5 @@ +import numpy as np + import openmc import openmc.lib @@ -44,16 +46,23 @@ def _build_inputs(self): # create the DAGMC universe pincell_univ = openmc.DAGMCUniverse(filename='dagmc.h5m', auto_geom_ids=True) - left = openmc.XPlane(x0=-24.0, name='left', boundary_type='reflective') - right = openmc.XPlane(x0=24.0, name='right', boundary_type='reflective') - front = openmc.YPlane(y0=-24.0, name='front', boundary_type='reflective') - back = openmc.YPlane(y0=24.0, name='back', boundary_type='reflective') - bottom = openmc.ZPlane(z0=-20.0, name='bottom', boundary_type='vacuum') - top = openmc.ZPlane(z0=20.0, name='top', boundary_type='vacuum') - - box = +left & -right & +front & -back & +bottom & -top - - bounding_cell = openmc.Cell(fill=pincell_univ, region=box) + # create a 2 x 2 lattice using the DAGMC pincell + pitch = np.asarray((24.0, 24.0)) + lattice = openmc.RectLattice() + lattice.pitch = pitch + lattice.universes = [[pincell_univ] * 2] * 2 + lattice.lower_left = -pitch + + left = openmc.XPlane(x0=-pitch[0], name='left', boundary_type='reflective') + right = openmc.XPlane(x0=pitch[0], name='right', boundary_type='reflective') + front = openmc.YPlane(y0=-pitch[1], name='front', boundary_type='reflective') + back = openmc.YPlane(y0=pitch[1], name='back', boundary_type='reflective') + # clip the DAGMC geometry at +/- 10 cm w/ CSG planes + bottom = openmc.ZPlane(z0=-10.0, name='bottom', boundary_type='reflective') + top = openmc.ZPlane(z0=10.0, name='top', boundary_type='reflective') + + bounding_region = +left & -right & +front & -back & +bottom & -top + bounding_cell = openmc.Cell(fill=lattice, region=bounding_region) model.geometry = openmc.Geometry([bounding_cell]) From f2e0af378fd2c87c71c5384020f37c4036f0cbd4 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 11:16:29 -0500 Subject: [PATCH 0121/2572] Addressing case where is not present in surface HDF5 groups. --- openmc/surface.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/openmc/surface.py b/openmc/surface.py index 70fd0083189..b2f3c6710c2 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -454,8 +454,8 @@ def from_hdf5(group): """ # If this is a DAGMC surface, do nothing for now - geom_type = group['geom_type'][()].decode() - if geom_type == 'dagmc': + geom_type = group.get('geom_type') + if geom_type and geom_type[()].decode() == 'dagmc': return surface_id = int(group.name.split('/')[-1].lstrip('surface ')) From 9c849f405862b8c48a8c9338c2c1c6741fce0c4d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 17:49:14 -0500 Subject: [PATCH 0122/2572] Updating IO format documentation. --- docs/source/io_formats/geometry.rst | 41 +++++++++++++++++++++++++++++ docs/source/io_formats/settings.rst | 8 ------ docs/source/io_formats/summary.rst | 10 +++++-- 3 files changed, 49 insertions(+), 10 deletions(-) diff --git a/docs/source/io_formats/geometry.rst b/docs/source/io_formats/geometry.rst index af229bcfff3..c94657f9a8b 100644 --- a/docs/source/io_formats/geometry.rst +++ b/docs/source/io_formats/geometry.rst @@ -370,3 +370,44 @@ Here is an example of a properly defined 2d hexagonal lattice: 202 + + +.. _dagmc_element: + +---------------------------- +```` Element +---------------------------- + +Each ```` element can have the following attributes or sub-elements: + + :id: + A unique integer used to identify the universe. + + *Default*: "" + + :name: + An optional string name to identify the surface in summary output + files. This string is limited to 52 characters for formatting purposes. + + *Default*: "" + + :auto_geom_ids: + Boolean value indicating whether the existing geometry IDs will be used or appended + to the existing ID space of natively defined OpenMC geometry entities. + + *Default*: false + + :auto_mat_ids: + Boolean value indicating whether the existing material IDs will be used or appended + to the existing ID space of natively defined OpenMC materials. + + *Default*: false + + :filename: + A required string indicating the file to be loaded representing the DAGMC universe. + + *Default*: None + + + + diff --git a/docs/source/io_formats/settings.rst b/docs/source/io_formats/settings.rst index 53376b04c77..6ea8cfc56e1 100644 --- a/docs/source/io_formats/settings.rst +++ b/docs/source/io_formats/settings.rst @@ -88,14 +88,6 @@ you care. This element has the following attributes/sub-elements: *Default*: 0.0 --------------------------------- -```` Element --------------------------------- - -When the DAGMC mode is enabled, the OpenMC geometry will be read from the file -``dagmc.h5m``. If a :ref:`geometry.xml ` file is present with -``dagmc`` set to ``true``, it will be ignored. - ---------------------------- ```` ---------------------------- diff --git a/docs/source/io_formats/summary.rst b/docs/source/io_formats/summary.rst index cf0eca4aae0..96c3f8ab472 100644 --- a/docs/source/io_formats/summary.rst +++ b/docs/source/io_formats/summary.rst @@ -24,8 +24,6 @@ The current version of the summary file format is 6.0. - **n_universes** (*int*) -- Number of unique universes in the problem. - **n_lattices** (*int*) -- Number of lattices in the problem. - - **dagmc** (*int*) -- Indicates that a DAGMC geometry was used - if present. **/geometry/cells/cell /** @@ -49,6 +47,8 @@ The current version of the summary file format is 6.0. - **lattice** (*int*) -- Unique ID of the lattice which fills the cell. Only present if fill_type is set to 'lattice'. - **region** (*char[]*) -- Region specification for the cell. + - **geom_type** (*char[]*) -- Type of geometry used to create the cell. + Either 'csg' or 'dagmc'. **/geometry/surfaces/surface /** @@ -62,12 +62,18 @@ The current version of the summary file format is 6.0. - **boundary_condition** (*char[]*) -- Boundary condition applied to the surface. Can be 'transmission', 'vacuum', 'reflective', or 'periodic'. + - **geom_type** (*char[]*) -- Type of geometry used to create the cell. + Either 'csg' or 'dagmc'. + **/geometry/universes/universe /** :Datasets: - **cells** (*int[]*) -- Array of unique IDs of cells that appear in the universe. + - **geom_type** (*char[]*) -- Type of geometry used to create the cell. + Either 'csg' or 'dagmc'. + **/geometry/lattices/lattice /** From e2826fa57b2754f844b574bd56628a2d373bbdda Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 17:54:57 -0500 Subject: [PATCH 0123/2572] Adding a from_xml method to the DAGUniverse class. Ensuring DAGMC universes are read into geometries from XML files. --- openmc/geometry.py | 5 +++++ openmc/universe.py | 17 +++++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/openmc/geometry.py b/openmc/geometry.py index 36cff94e30d..13113338a59 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -198,6 +198,11 @@ def get_universe(univ_id): if c.fill_type in ('universe', 'lattice'): child_of[c.fill].append(c) + # Add any DAGMC universes + for elem in root.findall('dagmc_universe'): + dag_univ = openmc.DAGMCUniverse.from_xml(elem) + universes[dag_univ.id] = dag_univ + # Determine which universe is the root by finding one which is not a # child of any other object for u in universes.values(): diff --git a/openmc/universe.py b/openmc/universe.py index 402609e2cb1..6f27148ea94 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -10,6 +10,7 @@ import openmc import openmc.checkvalue as cv +from ._xml import get_text from .mixin import IDManagerMixin from .plots import _SVG_COLORS @@ -729,3 +730,19 @@ def create_xml_subelement(self, xml_element, memo=None): dagmc_element.set('auto_mat_ids', 'true') dagmc_element.set('filename', self.filename) xml_element.append(dagmc_element) + + @classmethod + def from_xml(cls, elem): + id = int(get_text(elem, 'id')) + fname = get_text(elem, 'filename') + + out = cls(fname, universe_id=id) + + name = get_text(elem, 'name') + if name is not None: + out.name = name + + out.auto_geom_ids = bool(elem.get('auto_geom_ids')) + out.auto_mat_ids = bool(elem.get('auto_mat_ids')) + + return out From ef70e121420aa00a0bc70292eb34050c783c1c18 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 21:04:23 -0500 Subject: [PATCH 0124/2572] Enhancing to/from hdf5 methods so that DAGMC universes can be reproduced and exported to XML from a summary file. --- include/openmc/cell.h | 2 +- include/openmc/dagmc.h | 2 ++ openmc/geometry.py | 10 +++++----- openmc/summary.py | 6 +++++- openmc/universe.py | 39 +++++++++++++++++++++++++++++++++++++++ src/cell.cpp | 9 +-------- src/dagmc.cpp | 17 +++++++++++++++++ 7 files changed, 70 insertions(+), 15 deletions(-) diff --git a/include/openmc/cell.h b/include/openmc/cell.h index 1728392953d..9e977bc9374 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -68,7 +68,7 @@ class Universe //! \brief Write universe information to an HDF5 group. //! \param group_id An HDF5 group id. - void to_hdf5(hid_t group_id) const; + virtual void to_hdf5(hid_t group_id) const; virtual bool find_cell(Particle &p) const; diff --git a/include/openmc/dagmc.h b/include/openmc/dagmc.h index f2cc1ee2a3b..6a71e617025 100644 --- a/include/openmc/dagmc.h +++ b/include/openmc/dagmc.h @@ -122,6 +122,8 @@ class DAGUniverse : public Universe { bool find_cell(Particle &p) const override; + void to_hdf5(hid_t universes_group) const override; + // Data Members std::shared_ptr dagmc_instance_; //!< DAGMC Instance for this universe int32_t cell_idx_offset_; //!< An offset to the start of the cells in this universe in OpenMC's cell vector diff --git a/openmc/geometry.py b/openmc/geometry.py index 13113338a59..1ae0015b2aa 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -159,6 +159,11 @@ def get_universe(univ_id): for s1, s2 in periodic.items(): surfaces[s1].periodic_surface = surfaces[s2] + # Add any DAGMC universes + for elem in root.findall('dagmc_universe'): + dag_univ = openmc.DAGMCUniverse.from_xml(elem) + universes[dag_univ.id] = dag_univ + # Dictionary that maps each universe to a list of cells/lattices that # contain it (needed to determine which universe is the root) child_of = defaultdict(list) @@ -198,11 +203,6 @@ def get_universe(univ_id): if c.fill_type in ('universe', 'lattice'): child_of[c.fill].append(c) - # Add any DAGMC universes - for elem in root.findall('dagmc_universe'): - dag_univ = openmc.DAGMCUniverse.from_xml(elem) - universes[dag_univ.id] = dag_univ - # Determine which universe is the root by finding one which is not a # child of any other object for u in universes.values(): diff --git a/openmc/summary.py b/openmc/summary.py index 0af45b77014..6e641206189 100644 --- a/openmc/summary.py +++ b/openmc/summary.py @@ -178,7 +178,11 @@ def _read_cells(self): def _read_universes(self): for group in self._f['geometry/universes'].values(): - universe = openmc.Universe.from_hdf5(group, self._fast_cells) + geom_type = group.get('geom_type') + if geom_type and geom_type[()].decode() == 'dagmc': + universe = openmc.DAGMCUniverse.from_hdf5(group) + else: + universe = openmc.Universe.from_hdf5(group, self._fast_cells) self._fast_universes[universe.id] = universe def _read_lattices(self): diff --git a/openmc/universe.py b/openmc/universe.py index 6f27148ea94..bf7481cabde 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -731,8 +731,47 @@ def create_xml_subelement(self, xml_element, memo=None): dagmc_element.set('filename', self.filename) xml_element.append(dagmc_element) + @classmethod + def from_hdf5(cls, group): + """Create DAGMC universe from HDF5 group + + Parameters + ---------- + group : h5py.Group + Group in HDF5 file + + Returns + ------- + openmc.DAGMCUniverse + DAGMCUniverse instance + + """ + id = int(group.name.split('/')[-1].lstrip('universe ')) + fname = group['filename'][()].decode() + name = group['name'][()].decode() if 'name' in group else None + + out = cls(fname, universe_id=id, name=name) + + out.auto_geom_ids = bool(group.attrs['auto_geom_ids']) + out.auto_mat_ids = bool(group.attrs['auto_mat_ids']) + + return out + @classmethod def from_xml(cls, elem): + """Generate DAGMC universe from XML element + + Parameters + ---------- + elem : xml.etree.ElementTree.Element + `` element + + Returns + ------- + openmc.DAGMCUniverse + DAGMCUniverse instance + + """ id = int(get_text(elem, 'id')) fname = get_text(elem, 'filename') diff --git a/src/cell.cpp b/src/cell.cpp index 4de98aaddb9..4ae5177fa0e 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -194,14 +194,7 @@ Universe::to_hdf5(hid_t universes_group) const auto group = create_group(universes_group, fmt::format("universe {}", id_)); // Write the geometry representation type. - switch(geom_type_) { - case GeometryType::CSG : - write_string(group, "geom_type", "csg", false); - break; - case GeometryType::DAG : - write_string(group, "geom_type", "dagmc", false); - break; - } + write_string(group, "geom_type", "csg", false); // Write the contained cells. if (cells_.size() > 0) { diff --git a/src/dagmc.cpp b/src/dagmc.cpp index bde33157f50..630f665fae6 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -354,6 +354,22 @@ DAGUniverse::find_cell(Particle &p) const { return found; } +void +DAGUniverse::to_hdf5(hid_t universes_group) const { + // Create a group for this universe. + auto group = create_group(universes_group, fmt::format("universe {}", id_)); + + // Write the geometry representation type. + write_string(group, "geom_type", "dagmc", false); + + // Write other properties of the DAGMC Universe + write_string(group, "filename", filename_, false); + write_attribute(group, "auto_geom_ids", static_cast(adjust_geometry_ids_)); + write_attribute(group, "auto_mat_ids", static_cast(adjust_material_ids_)); + + close_group(group); +} + bool DAGUniverse::uses_uwuw() const { @@ -546,6 +562,7 @@ DAGCell::contains(Position r, Direction u, int32_t on_surface) const void DAGCell::to_hdf5_inner(hid_t group_id) const { write_string(group_id, "geom_type", "dagmc", false); + } BoundingBox From 25cf7a3e9712f80b05e918311fbd5e65cfd6d627 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 21:05:03 -0500 Subject: [PATCH 0125/2572] Ensuring that surface names are read from the HDF5 format. --- openmc/surface.py | 1 + 1 file changed, 1 insertion(+) diff --git a/openmc/surface.py b/openmc/surface.py index b2f3c6710c2..ac0cb1b0b25 100644 --- a/openmc/surface.py +++ b/openmc/surface.py @@ -432,6 +432,7 @@ def from_xml_element(elem): kwargs = {} kwargs['surface_id'] = int(elem.get('id')) kwargs['boundary_type'] = elem.get('boundary', 'transmission') + kwargs['name'] = elem.get('name') coeffs = [float(x) for x in elem.get('coeffs').split()] kwargs.update(dict(zip(cls._coeff_keys, coeffs))) From 4f5654a152e6868d52fca7677b1e798d95f7cd1b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Tue, 29 Jun 2021 21:15:33 -0500 Subject: [PATCH 0126/2572] Adding documentation for new entries in the summary.h5 file. --- docs/source/io_formats/summary.rst | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/docs/source/io_formats/summary.rst b/docs/source/io_formats/summary.rst index 96c3f8ab472..9b92b301872 100644 --- a/docs/source/io_formats/summary.rst +++ b/docs/source/io_formats/summary.rst @@ -73,6 +73,15 @@ The current version of the summary file format is 6.0. the universe. - **geom_type** (*char[]*) -- Type of geometry used to create the cell. Either 'csg' or 'dagmc'. + - **filename** (*char[]*) -- Name of the DAGMC file representing this universe. + Only present for DAGMC Universes. +:Attributes: + - **auto_geom_ids** (*int*) -- ``1`` if geometry IDs of the DAGMC + model will be appended to the ID space of the natively defined + CSG geometry, ``0`` if the existing DAGMC IDs will be used. + - **auto_mat_ids** (*int*) -- ``1`` if UWUW material IDs of the DAGMC + model will be appended to the ID space of the natively defined + OpenMC materials, ``0`` if the existing UWUW IDs will be used. **/geometry/lattices/lattice /** @@ -125,8 +134,8 @@ The current version of the summary file format is 6.0. :Attributes: - **volume** (*double[]*) -- Volume of this material [cm^3]. Only present if ``volume`` supplied - **temperature** (*double[]*) -- Temperature of this material [K]. - Only present in ``temperature`` supplied - - **depletable** (*int[]*) -- ``1`` if the material can be depleted, + Only present if ``temperature`` is supplied + - **depletable** (*int*) -- ``1`` if the material can be depleted, ``0`` otherwise. Always present **/nuclides/** From 8500ed57ffa91706046cf46efbcf75d06c960518 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 30 Jun 2021 08:44:47 -0500 Subject: [PATCH 0127/2572] Apply @paulromano's suggestions from code review Co-authored-by: Paul Romano --- docs/source/io_formats/geometry.rst | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/docs/source/io_formats/geometry.rst b/docs/source/io_formats/geometry.rst index c94657f9a8b..6494d148778 100644 --- a/docs/source/io_formats/geometry.rst +++ b/docs/source/io_formats/geometry.rst @@ -383,13 +383,13 @@ Each ```` element can have the following attributes or sub-eleme :id: A unique integer used to identify the universe. - *Default*: "" + *Default*: None :name: An optional string name to identify the surface in summary output files. This string is limited to 52 characters for formatting purposes. - *Default*: "" + *Default*: None :auto_geom_ids: Boolean value indicating whether the existing geometry IDs will be used or appended @@ -407,7 +407,3 @@ Each ```` element can have the following attributes or sub-eleme A required string indicating the file to be loaded representing the DAGMC universe. *Default*: None - - - - From 00446222405c9adae9418197923d6177f20502fc Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 30 Jun 2021 08:39:03 -0500 Subject: [PATCH 0128/2572] Removing indications that material/cell/surface/universe/tally names are limited to 52 characters. --- docs/source/io_formats/geometry.rst | 9 ++++----- docs/source/io_formats/materials.rst | 2 +- docs/source/io_formats/tallies.rst | 2 +- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/docs/source/io_formats/geometry.rst b/docs/source/io_formats/geometry.rst index 6494d148778..1d04bb8fbf4 100644 --- a/docs/source/io_formats/geometry.rst +++ b/docs/source/io_formats/geometry.rst @@ -19,7 +19,7 @@ Each ```` element can have the following attributes or sub-elements: :name: An optional string name to identify the surface in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: "" @@ -122,7 +122,6 @@ Each ```` element can have the following attributes or sub-elements: :name: An optional string name to identify the cell in summary output files. - This string is limmited to 52 characters for formatting purposes. *Default*: "" @@ -235,7 +234,7 @@ the following attributes or sub-elements: :name: An optional string name to identify the lattice in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: "" @@ -301,7 +300,7 @@ the following attributes or sub-elements: :name: An optional string name to identify the hex_lattice in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: "" @@ -387,7 +386,7 @@ Each ```` element can have the following attributes or sub-eleme :name: An optional string name to identify the surface in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: None diff --git a/docs/source/io_formats/materials.rst b/docs/source/io_formats/materials.rst index ac2cfe271e1..92b0165a43c 100644 --- a/docs/source/io_formats/materials.rst +++ b/docs/source/io_formats/materials.rst @@ -31,7 +31,7 @@ Each ``material`` element can have the following attributes or sub-elements: :name: An optional string name to identify the material in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: "" diff --git a/docs/source/io_formats/tallies.rst b/docs/source/io_formats/tallies.rst index 8c1ad91b58b..ad722736903 100644 --- a/docs/source/io_formats/tallies.rst +++ b/docs/source/io_formats/tallies.rst @@ -31,7 +31,7 @@ The ```` element accepts the following sub-elements: :name: An optional string name to identify the tally in summary output - files. This string is limited to 52 characters for formatting purposes. + files. *Default*: "" From 4fd6f0e8dfc158c5a81016d7eaae4968b26c56e9 Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 30 Jun 2021 08:43:29 -0500 Subject: [PATCH 0129/2572] Removing export of the DAGUniverse name attribute to match behavior with standard Universes. --- openmc/universe.py | 4 ++-- tests/regression_tests/dagmc/legacy/inputs_true.dat | 2 +- tests/regression_tests/dagmc/refl/inputs_true.dat | 2 +- tests/regression_tests/dagmc/universes/inputs_true.dat | 2 +- tests/regression_tests/dagmc/uwuw/inputs_true.dat | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/openmc/universe.py b/openmc/universe.py index bf7481cabde..5cfb985793a 100644 --- a/openmc/universe.py +++ b/openmc/universe.py @@ -723,7 +723,7 @@ def create_xml_subelement(self, xml_element, memo=None): # Set xml element values dagmc_element = ET.Element('dagmc_universe') dagmc_element.set('id', str(self.id)) - dagmc_element.set('name', self.name) + if self.auto_geom_ids: dagmc_element.set('auto_geom_ids', 'true') if self.auto_mat_ids: @@ -758,7 +758,7 @@ def from_hdf5(cls, group): return out @classmethod - def from_xml(cls, elem): + def from_xml_element(cls, elem): """Generate DAGMC universe from XML element Parameters diff --git a/tests/regression_tests/dagmc/legacy/inputs_true.dat b/tests/regression_tests/dagmc/legacy/inputs_true.dat index 45c10a7aaad..2f56410466b 100644 --- a/tests/regression_tests/dagmc/legacy/inputs_true.dat +++ b/tests/regression_tests/dagmc/legacy/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/refl/inputs_true.dat b/tests/regression_tests/dagmc/refl/inputs_true.dat index 13bc9ecf52a..938916ece17 100644 --- a/tests/regression_tests/dagmc/refl/inputs_true.dat +++ b/tests/regression_tests/dagmc/refl/inputs_true.dat @@ -1,6 +1,6 @@ - + diff --git a/tests/regression_tests/dagmc/universes/inputs_true.dat b/tests/regression_tests/dagmc/universes/inputs_true.dat index e235c06a8a3..4443f9a35d2 100644 --- a/tests/regression_tests/dagmc/universes/inputs_true.dat +++ b/tests/regression_tests/dagmc/universes/inputs_true.dat @@ -1,7 +1,7 @@ - + 24.0 24.0 2 2 diff --git a/tests/regression_tests/dagmc/uwuw/inputs_true.dat b/tests/regression_tests/dagmc/uwuw/inputs_true.dat index 54b7a9cb83f..7d533c5db2b 100644 --- a/tests/regression_tests/dagmc/uwuw/inputs_true.dat +++ b/tests/regression_tests/dagmc/uwuw/inputs_true.dat @@ -1,6 +1,6 @@ - + From 0aafa81907ac79b9bdb4a86aede0b63fee3a9a9b Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 30 Jun 2021 08:44:27 -0500 Subject: [PATCH 0130/2572] Adding Python pickle extension to the git ignore. Nearly added another one of these by accident. --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 6838e756892..c8d4d7140a0 100644 --- a/.gitignore +++ b/.gitignore @@ -116,3 +116,6 @@ CMakeSettings.json # Visual Studio Code configuration files .vscode/ + +# Python pickle files +*.pkl \ No newline at end of file From a407c12aae112c15e0e218256357f107f64b0d8d Mon Sep 17 00:00:00 2001 From: Patrick Shriwise Date: Wed, 30 Jun 2021 12:45:08 -0500 Subject: [PATCH 0131/2572] Update openmc/geometry.py Co-authored-by: Paul Romano --- openmc/geometry.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/geometry.py b/openmc/geometry.py index 1ae0015b2aa..8f7bf05f85f 100644 --- a/openmc/geometry.py +++ b/openmc/geometry.py @@ -161,7 +161,7 @@ def get_universe(univ_id): # Add any DAGMC universes for elem in root.findall('dagmc_universe'): - dag_univ = openmc.DAGMCUniverse.from_xml(elem) + dag_univ = openmc.DAGMCUniverse.from_xml_element(elem) universes[dag_univ.id] = dag_univ # Dictionary that maps each universe to a list of cells/lattices that From 484858dfc144beac5439007db20e87e645396a44 Mon Sep 17 00:00:00 2001 From: John Tramm Date: Thu, 1 Jul 2021 16:24:17 +0000 Subject: [PATCH 0132/2572] Added fixes for fission bank overflow memory issue. --- src/bank.cpp | 4 ++++ src/physics.cpp | 28 ++++++++++++++++++---------- 2 files changed, 22 insertions(+), 10 deletions(-) diff --git a/src/bank.cpp b/src/bank.cpp index d5d3647564b..f587197aaf6 100644 --- a/src/bank.cpp +++ b/src/bank.cpp @@ -96,6 +96,10 @@ void sort_fission_bank() const auto& site = simulation::fission_bank[i]; int64_t offset = site.parent_id - 1 - simulation::work_index[mpi::rank]; int64_t idx = simulation::progeny_per_particle[offset] + site.progeny_id; + if (idx >= simulation::fission_bank.size()) { + fatal_error("Mismatch detected between sum of all particle progeny and " + "shared fission bank size."); + } sorted_bank[idx] = site; } diff --git a/src/physics.cpp b/src/physics.cpp index 21214969464..ea578d7c613 100644 --- a/src/physics.cpp +++ b/src/physics.cpp @@ -167,8 +167,7 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) int nu = static_cast(nu_t); if (prn(p.current_seed()) <= (nu_t - nu)) ++nu; - // Begin banking the source neutrons - // First, if our bank is full then don't continue + // If no neutrons were produced then don't continue if (nu == 0) return; // Initialize the counter of delayed neutrons encountered for each delayed @@ -179,13 +178,16 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) p.nu_bank().clear(); p.fission() = true; - int skipped = 0; // Determine whether to place fission sites into the shared fission bank // or the secondary particle bank. bool use_fission_bank = (settings::run_mode == RunMode::EIGENVALUE); - for (int i = 0; i < nu; ++i) { + // Counter for the number of fission sites successfully stored to a fission bank + // or secondary bank + int n_sites_stored; + + for (n_sites_stored = 0; n_sites_stored < nu; n_sites_stored++) { // Initialize fission site object with particle data SourceSite site; site.r = p.r(); @@ -203,8 +205,14 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) int64_t idx = simulation::fission_bank.thread_safe_append(site); if (idx == -1) { warning("The shared fission bank is full. Additional fission sites created " - "in this generation will not be banked."); - skipped++; + "in this generation will not be banked. Results may be non-deteministic."); + + // Decrement number of particle progeny as storage was unsuccessful. This + // step is needed so that the sum of all progeny is equal to the size + // of the shared fission bank. + p.n_progeny()--; + + // Break out of loop as no more sites can be added to fission bank break; } } else { @@ -229,14 +237,14 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) // If shared fission bank was full, and no fissions could be added, // set the particle fission flag to false. - if (nu == skipped) { + if (n_sites_stored == 0) { p.fission() = false; return; } - // If shared fission bank was full, but some fissions could be added, - // reduce nu accordingly - nu -= skipped; + // Set nu to the number of fission sites successfully stored. If the fission + // bank was not found to be full then these values are already equivalent. + nu = n_sites_stored; // Store the total weight banked for analog fission tallies p.n_bank() = nu; From 51b452ec4868579165c0cad08e7a92bfac92fafe Mon Sep 17 00:00:00 2001 From: John Tramm Date: Thu, 1 Jul 2021 17:31:40 +0000 Subject: [PATCH 0133/2572] applied fix to multigroup mode. --- src/physics.cpp | 4 ++-- src/physics_mg.cpp | 28 ++++++++++++++++++---------- 2 files changed, 20 insertions(+), 12 deletions(-) diff --git a/src/physics.cpp b/src/physics.cpp index ea578d7c613..c744d0f4c9c 100644 --- a/src/physics.cpp +++ b/src/physics.cpp @@ -183,8 +183,8 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) // or the secondary particle bank. bool use_fission_bank = (settings::run_mode == RunMode::EIGENVALUE); - // Counter for the number of fission sites successfully stored to a fission bank - // or secondary bank + // Counter for the number of fission sites successfully stored to the shared + // fission bank or the secondary particle bank int n_sites_stored; for (n_sites_stored = 0; n_sites_stored < nu; n_sites_stored++) { diff --git a/src/physics_mg.cpp b/src/physics_mg.cpp index 06ed845deaf..7eb594e9b36 100644 --- a/src/physics_mg.cpp +++ b/src/physics_mg.cpp @@ -107,8 +107,7 @@ create_fission_sites(Particle& p) nu++; } - // Begin banking the source neutrons - // First, if our bank is full then don't continue + // If no neutrons were produced then don't continue if (nu == 0) return; // Initialize the counter of delayed neutrons encountered for each delayed @@ -119,13 +118,16 @@ create_fission_sites(Particle& p) p.nu_bank().clear(); p.fission() = true; - int skipped = 0; // Determine whether to place fission sites into the shared fission bank // or the secondary particle bank. bool use_fission_bank = (settings::run_mode == RunMode::EIGENVALUE); - for (int i = 0; i < nu; ++i) { + // Counter for the number of fission sites successfully stored to the shared + // fission bank or the secondary particle bank + int n_sites_stored; + + for (n_sites_stored = 0; n_sites_stored < nu; n_sites_stored++) { // Initialize fission site object with particle data SourceSite site; site.r = p.r(); @@ -162,8 +164,14 @@ create_fission_sites(Particle& p) int64_t idx = simulation::fission_bank.thread_safe_append(site); if (idx == -1) { warning("The shared fission bank is full. Additional fission sites created " - "in this generation will not be banked."); - skipped++; + "in this generation will not be banked. Results may be non-deteministic."); + + // Decrement number of particle progeny as storage was unsuccessful. This + // step is needed so that the sum of all progeny is equal to the size + // of the shared fission bank. + p.n_progeny()--; + + // Break out of loop as no more sites can be added to fission bank break; } } else { @@ -188,14 +196,14 @@ create_fission_sites(Particle& p) // If shared fission bank was full, and no fissions could be added, // set the particle fission flag to false. - if (nu == skipped) { + if (n_sites_stored == 0) { p.fission() = false; return; } - // If shared fission bank was full, but some fissions could be added, - // reduce nu accordingly - nu -= skipped; + // Set nu to the number of fission sites successfully stored. If the fission + // bank was not found to be full then these values are already equivalent. + nu = n_sites_stored; // Store the total weight banked for analog fission tallies p.n_bank() = nu; From 7cc508ea48df6e8cf22674c1c65a06147014e41e Mon Sep 17 00:00:00 2001 From: John Tramm Date: Thu, 1 Jul 2021 18:33:02 +0000 Subject: [PATCH 0134/2572] spelling fix in warning message. --- src/physics.cpp | 2 +- src/physics_mg.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/physics.cpp b/src/physics.cpp index c744d0f4c9c..f0228ac4ec6 100644 --- a/src/physics.cpp +++ b/src/physics.cpp @@ -205,7 +205,7 @@ create_fission_sites(Particle& p, int i_nuclide, const Reaction& rx) int64_t idx = simulation::fission_bank.thread_safe_append(site); if (idx == -1) { warning("The shared fission bank is full. Additional fission sites created " - "in this generation will not be banked. Results may be non-deteministic."); + "in this generation will not be banked. Results may be non-deterministic."); // Decrement number of particle progeny as storage was unsuccessful. This // step is needed so that the sum of all progeny is equal to the size diff --git a/src/physics_mg.cpp b/src/physics_mg.cpp index 7eb594e9b36..1d47bac0d1f 100644 --- a/src/physics_mg.cpp +++ b/src/physics_mg.cpp @@ -164,7 +164,7 @@ create_fission_sites(Particle& p) int64_t idx = simulation::fission_bank.thread_safe_append(site); if (idx == -1) { warning("The shared fission bank is full. Additional fission sites created " - "in this generation will not be banked. Results may be non-deteministic."); + "in this generation will not be banked. Results may be non-deterministic."); // Decrement number of particle progeny as storage was unsuccessful. This // step is needed so that the sum of all progeny is equal to the size From 876bd925a32c75a553a8b65c81b7debf21b59407 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 12 Jul 2021 06:58:04 -0500 Subject: [PATCH 0135/2572] Add check on num cells/materials for Model.import_properties --- openmc/model/model.py | 17 +++++++++++++++-- src/summary.cpp | 1 - 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/openmc/model/model.py b/openmc/model/model.py index 84650767afc..ca33a96acdc 100644 --- a/openmc/model/model.py +++ b/openmc/model/model.py @@ -241,16 +241,29 @@ def import_properties(self, filename): materials = self.geometry.get_all_materials() with h5py.File(filename, 'r') as fh: - # Update temperatures for cells filled with materials cells_group = fh['geometry/cells'] + + # Make sure number of cells matches + n_cells = fh['geometry'].attrs['n_cells'] + if n_cells != len(cells): + raise ValueError("Number of cells in properties file doesn't " + "match current model.") + + # Update temperatures for cells filled with materials for name, group in cells_group.items(): cell_id = int(name.split()[1]) cell = cells[cell_id] if cell.fill_type in ('material', 'distribmat'): cell.temperature = group['temperature'][()] - # Update material densities + # Make sure number of materials matches mats_group = fh['materials'] + n_cells = mats_group.attrs['n_materials'] + if n_cells != len(materials): + raise ValueError("Number of materials in properties file doesn't " + "match current model.") + + # Update material densities for name, group in mats_group.items(): mat_id = int(name.split()[1]) atom_density = group.attrs['atom_density'] diff --git a/src/summary.cpp b/src/summary.cpp index 64d9a63629a..4e6e876ad05 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -163,7 +163,6 @@ extern "C" int openmc_properties_export(const char* filename) write_attribute(file, "date_and_time", time_stamp()); write_attribute(file, "path", settings::path_input); - // Write cell properties auto geom_group = create_group(file, "geometry"); write_attribute(geom_group, "n_cells", model::cells.size()); From 4484fd69f5b51d5c3d90801d6ae129cc839b2de7 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 12 Jul 2021 07:26:26 -0500 Subject: [PATCH 0136/2572] Add checks during Cell::import_properties_hdf5 --- src/cell.cpp | 15 +++++++++++---- src/summary.cpp | 9 +++++++-- 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/src/cell.cpp b/src/cell.cpp index 37550e9f502..9686a1d1b2e 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -304,11 +304,18 @@ void Cell::import_properties_hdf5(hid_t group) vector temps; read_dataset(cell_group, "temperature", temps); + // Ensure number of temperatures makes sense + auto n_temps = temps.size(); + if (n_temps > 1 && n_temps != n_instances_) { + throw std::runtime_error(fmt::format( + "Number of temperatures for cell {} doesn't match number of instances", id_)); + } + // Modify temperatures for the cell - sqrtkT_.resize(0); - sqrtkT_.reserve(temps.size()); - for (auto T : temps) { - sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * T)); + sqrtkT_.clear(); + sqrtkT_.resize(temps.size()); + for (gsl::index i = 0; i < temps.size(); ++i) { + this->set_temperature(temps[i], i); } close_group(cell_group); diff --git a/src/summary.cpp b/src/summary.cpp index 4e6e876ad05..0ef0056476a 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -221,8 +221,13 @@ extern "C" int openmc_properties_import(const char* filename) // Read cell properties auto cells_group = open_group(geom_group, "cells"); - for (const auto& c : model::cells) { - c->import_properties_hdf5(cells_group); + try { + for (const auto& c : model::cells) { + c->import_properties_hdf5(cells_group); + } + } catch (const std::exception& e) { + set_errmsg(e.what()); + return OPENMC_E_UNASSIGNED; } close_group(cells_group); close_group(geom_group); From 024b1080fd778dd2a324a6522738e6d9b3888183 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Mon, 12 Jul 2021 07:28:21 -0500 Subject: [PATCH 0137/2572] Update id_map docstring --- openmc/lib/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/lib/plot.py b/openmc/lib/plot.py index b029d675708..da42ea49e0d 100644 --- a/openmc/lib/plot.py +++ b/openmc/lib/plot.py @@ -228,7 +228,7 @@ def id_map(plot): Returns ------- id_map : numpy.ndarray - A NumPy array with shape (vertical pixels, horizontal pixels, 2) of + A NumPy array with shape (vertical pixels, horizontal pixels, 3) of OpenMC property ids with dtype int32 """ From 731f2975bb8f840e0302def583ff582da7a7f29f Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Tue, 13 Jul 2021 06:57:33 -0500 Subject: [PATCH 0138/2572] Replace lib.Material.density property with get_density method --- openmc/lib/material.py | 37 +++++++++++++++++++++++++----------- tests/unit_tests/test_lib.py | 15 ++++++++++----- 2 files changed, 36 insertions(+), 16 deletions(-) diff --git a/openmc/lib/material.py b/openmc/lib/material.py index fa27c4678b7..8af251c14a3 100644 --- a/openmc/lib/material.py +++ b/openmc/lib/material.py @@ -87,8 +87,6 @@ class Material(_FortranObjectWithID): ID of the material nuclides : list of str List of nuclides in the material - density : float - Density of the material in [g/cm^3] densities : numpy.ndarray Array of densities in atom/b-cm name : str @@ -176,15 +174,6 @@ def nuclides(self): return self._get_densities()[0] return nuclides - @property - def density(self): - density = c_double() - try: - _dll.openmc_material_get_density(self._index, density) - except OpenMCError: - return None - return density.value - @property def densities(self): return self._get_densities()[1] @@ -226,6 +215,32 @@ def add_nuclide(self, name, density): """ _dll.openmc_material_add_nuclide(self._index, name.encode(), density) + def get_density(self, units='atom/b-cm'): + """Get density of a material. + + Parameters + ---------- + units : {'atom/b-cm', 'g/cm3'} + Units for density + + Returns + ------- + float + Density in requested units + + """ + if units == 'atom/b-cm': + return self.densities.sum() + elif units == 'g/cm3': + density = c_double() + try: + _dll.openmc_material_get_density(self._index, density) + except OpenMCError: + return None + return density.value + else: + raise ValueError("Units must be 'atom/b-cm' or 'g/cm3'") + def set_density(self, density, units='atom/b-cm'): """Set density of a material. diff --git a/tests/unit_tests/test_lib.py b/tests/unit_tests/test_lib.py index 3b6b02c393b..9a4fc8b9fd2 100644 --- a/tests/unit_tests/test_lib.py +++ b/tests/unit_tests/test_lib.py @@ -1,4 +1,5 @@ from collections.abc import Mapping +from ctypes import ArgumentError import os import numpy as np @@ -186,7 +187,7 @@ def test_material(lib_init): assert sum(m.densities) == pytest.approx(rho) m.set_density(0.1, 'g/cm3') - assert m.density == pytest.approx(0.1) + assert m.get_density('g/cm3') == pytest.approx(0.1) assert m.name == "Hot borated water" m.name = "Not hot borated water" assert m.name == "Not hot borated water" @@ -194,16 +195,20 @@ def test_material(lib_init): def test_properties_density(lib_init): m = openmc.lib.materials[1] - orig_density = m.density + orig_density = m.get_density('atom/b-cm') + orig_density_gpcc = m.get_density('g/cm3') # Export properties and change density openmc.lib.export_properties('properties.h5') - m.set_density(orig_density*2, 'g/cm3') - assert m.density == pytest.approx(orig_density*2) + m.set_density(orig_density_gpcc*2, 'g/cm3') + assert m.get_density() == pytest.approx(orig_density*2) # Import properties and check that density was restored openmc.lib.import_properties('properties.h5') - assert m.density == pytest.approx(orig_density) + assert m.get_density() == pytest.approx(orig_density) + + with pytest.raises(ValueError): + m.get_density('🥏') def test_material_add_nuclide(lib_init): From ab80feb65ac4d82a50aa054a7c52281218570e53 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Wed, 14 Jul 2021 15:12:46 -0500 Subject: [PATCH 0139/2572] Get rid of try/except block for lib.Material.get_density --- openmc/lib/material.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/openmc/lib/material.py b/openmc/lib/material.py index 8af251c14a3..6770721ab65 100644 --- a/openmc/lib/material.py +++ b/openmc/lib/material.py @@ -233,10 +233,7 @@ def get_density(self, units='atom/b-cm'): return self.densities.sum() elif units == 'g/cm3': density = c_double() - try: - _dll.openmc_material_get_density(self._index, density) - except OpenMCError: - return None + _dll.openmc_material_get_density(self._index, density) return density.value else: raise ValueError("Units must be 'atom/b-cm' or 'g/cm3'") From f0e0806fd9463c84fa009268aed29313fcd96fc7 Mon Sep 17 00:00:00 2001 From: Paul Romano Date: Fri, 18 Jun 2021 15:40:30 +0700 Subject: [PATCH 0140/2572] Remove Jupyter notebooks from documentation --- .gitignore | 15 - MANIFEST.in | 2 +- docs/requirements-rtd.txt | 1 - docs/source/conf.py | 1 - docs/source/examples/cad-based-geometry.ipynb | 1 - docs/source/examples/candu.ipynb | 1 - docs/source/examples/capi.ipynb | 1 - docs/source/examples/expansion-filters.ipynb | 1 - docs/source/examples/hexagonal-lattice.ipynb | 1 - docs/source/examples/index.rst | 73 - docs/source/examples/mdgxs-part-i.ipynb | 1 - docs/source/examples/mdgxs-part-ii.ipynb | 1 - docs/source/examples/mg-mode-part-i.ipynb | 1 - docs/source/examples/mg-mode-part-ii.ipynb | 1 - docs/source/examples/mg-mode-part-iii.ipynb | 1 - docs/source/examples/mgxs-part-i.ipynb | 1 - docs/source/examples/mgxs-part-ii.ipynb | 1 - docs/source/examples/mgxs-part-iii.ipynb | 1 - .../nuclear-data-resonance-covariance.ipynb | 1 - docs/source/examples/nuclear-data.ipynb | 1 - docs/source/examples/pandas-dataframes.ipynb | 1 - docs/source/examples/pincell.ipynb | 1 - docs/source/examples/pincell_depletion.ipynb | 1 - docs/source/examples/post-processing.ipynb | 1 - docs/source/examples/search.ipynb | 1 - docs/source/examples/tally-arithmetic.ipynb | 1 - docs/source/examples/triso.ipynb | 1 - .../examples/unstructured-mesh-part-i.ipynb | 1 - .../examples/unstructured-mesh-part-ii.ipynb | 1 - docs/source/index.rst | 2 +- examples/jupyter/c5g7.h5 | Bin 59512 -> 0 bytes examples/jupyter/cad-based-geometry.ipynb | 780 ----- examples/jupyter/candu.ipynb | 1110 ------- examples/jupyter/capi.ipynb | 475 --- examples/jupyter/chain_simple.xml | 1 - examples/jupyter/expansion-filters.ipynb | 1548 ---------- examples/jupyter/hexagonal-lattice.ipynb | 411 --- examples/jupyter/images/cylinder_mesh.png | Bin 9928 -> 0 bytes examples/jupyter/images/flux3d.png | Bin 100719 -> 0 bytes examples/jupyter/images/manifold-cad.png | Bin 99580 -> 0 bytes examples/jupyter/images/manifold_flux.png | Bin 61388 -> 0 bytes examples/jupyter/images/manifold_pnt_cld.png | Bin 120957 -> 0 bytes examples/jupyter/images/mdgxs.png | Bin 23788 -> 0 bytes examples/jupyter/images/mgxs.png | Bin 54562 -> 0 bytes examples/jupyter/images/pin_mesh.png | Bin 132261 -> 0 bytes examples/jupyter/images/teapot.jpg | Bin 103729 -> 0 bytes examples/jupyter/images/umesh_flux.png | Bin 198669 -> 0 bytes examples/jupyter/images/umesh_heating.png | Bin 132018 -> 0 bytes examples/jupyter/images/umesh_w_assembly.png | Bin 112875 -> 0 bytes examples/jupyter/mdgxs-part-i.ipynb | 1491 --------- examples/jupyter/mdgxs-part-ii.ipynb | 1186 ------- examples/jupyter/mg-mode-part-i.ipynb | 696 ----- examples/jupyter/mg-mode-part-ii.ipynb | 1570 ---------- examples/jupyter/mg-mode-part-iii.ipynb | 1437 --------- examples/jupyter/mgxs-part-i.ipynb | 1191 ------- examples/jupyter/mgxs-part-ii.ipynb | 2745 ----------------- examples/jupyter/mgxs-part-iii.ipynb | 1684 ---------- .../nuclear-data-resonance-covariance.ipynb | 963 ------ examples/jupyter/nuclear-data.ipynb | 1504 --------- examples/jupyter/pandas-dataframes.ipynb | 2721 ---------------- examples/jupyter/pincell.ipynb | 1583 ---------- examples/jupyter/pincell_depletion.ipynb | 997 ------ examples/jupyter/post-processing.ipynb | 992 ------ examples/jupyter/search.ipynb | 234 -- examples/jupyter/tally-arithmetic.ipynb | 1768 ----------- examples/jupyter/triso.ipynb | 366 --- .../jupyter/unstructured-mesh-part-i.ipynb | 737 ----- .../jupyter/unstructured-mesh-part-ii.ipynb | 647 ---- setup.py | 3 +- 69 files changed, 3 insertions(+), 28955 deletions(-) delete mode 120000 docs/source/examples/cad-based-geometry.ipynb delete mode 120000 docs/source/examples/candu.ipynb delete mode 120000 docs/source/examples/capi.ipynb delete mode 120000 docs/source/examples/expansion-filters.ipynb delete mode 120000 docs/source/examples/hexagonal-lattice.ipynb delete mode 100644 docs/source/examples/index.rst delete mode 120000 docs/source/examples/mdgxs-part-i.ipynb delete mode 120000 docs/source/examples/mdgxs-part-ii.ipynb delete mode 120000 docs/source/examples/mg-mode-part-i.ipynb delete mode 120000 docs/source/examples/mg-mode-part-ii.ipynb delete mode 120000 docs/source/examples/mg-mode-part-iii.ipynb delete mode 120000 docs/source/examples/mgxs-part-i.ipynb delete mode 120000 docs/source/examples/mgxs-part-ii.ipynb delete mode 120000 docs/source/examples/mgxs-part-iii.ipynb delete mode 120000 docs/source/examples/nuclear-data-resonance-covariance.ipynb delete mode 120000 docs/source/examples/nuclear-data.ipynb delete mode 120000 docs/source/examples/pandas-dataframes.ipynb delete mode 120000 docs/source/examples/pincell.ipynb delete mode 120000 docs/source/examples/pincell_depletion.ipynb delete mode 120000 docs/source/examples/post-processing.ipynb delete mode 120000 docs/source/examples/search.ipynb delete mode 120000 docs/source/examples/tally-arithmetic.ipynb delete mode 120000 docs/source/examples/triso.ipynb delete mode 120000 docs/source/examples/unstructured-mesh-part-i.ipynb delete mode 120000 docs/source/examples/unstructured-mesh-part-ii.ipynb delete mode 100644 examples/jupyter/c5g7.h5 delete mode 100644 examples/jupyter/cad-based-geometry.ipynb delete mode 100644 examples/jupyter/candu.ipynb delete mode 100644 examples/jupyter/capi.ipynb delete mode 120000 examples/jupyter/chain_simple.xml delete mode 100644 examples/jupyter/expansion-filters.ipynb delete mode 100644 examples/jupyter/hexagonal-lattice.ipynb delete mode 100644 examples/jupyter/images/cylinder_mesh.png delete mode 100644 examples/jupyter/images/flux3d.png delete mode 100644 examples/jupyter/images/manifold-cad.png delete mode 100644 examples/jupyter/images/manifold_flux.png delete mode 100644 examples/jupyter/images/manifold_pnt_cld.png delete mode 100644 examples/jupyter/images/mdgxs.png delete mode 100644 examples/jupyter/images/mgxs.png delete mode 100644 examples/jupyter/images/pin_mesh.png delete mode 100644 examples/jupyter/images/teapot.jpg delete mode 100644 examples/jupyter/images/umesh_flux.png delete mode 100644 examples/jupyter/images/umesh_heating.png delete mode 100644 examples/jupyter/images/umesh_w_assembly.png delete mode 100644 examples/jupyter/mdgxs-part-i.ipynb delete mode 100644 examples/jupyter/mdgxs-part-ii.ipynb delete mode 100644 examples/jupyter/mg-mode-part-i.ipynb delete mode 100644 examples/jupyter/mg-mode-part-ii.ipynb delete mode 100644 examples/jupyter/mg-mode-part-iii.ipynb delete mode 100644 examples/jupyter/mgxs-part-i.ipynb delete mode 100644 examples/jupyter/mgxs-part-ii.ipynb delete mode 100644 examples/jupyter/mgxs-part-iii.ipynb delete mode 100644 examples/jupyter/nuclear-data-resonance-covariance.ipynb delete mode 100644 examples/jupyter/nuclear-data.ipynb delete mode 100644 examples/jupyter/pandas-dataframes.ipynb delete mode 100644 examples/jupyter/pincell.ipynb delete mode 100644 examples/jupyter/pincell_depletion.ipynb delete mode 100644 examples/jupyter/post-processing.ipynb delete mode 100644 examples/jupyter/search.ipynb delete mode 100644 examples/jupyter/tally-arithmetic.ipynb delete mode 100644 examples/jupyter/triso.ipynb delete mode 100644 examples/jupyter/unstructured-mesh-part-i.ipynb delete mode 100644 examples/jupyter/unstructured-mesh-part-ii.ipynb diff --git a/.gitignore b/.gitignore index c8d4d7140a0..f328d7b9a6a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,6 @@ # Compiled objects and modules *.a *.o -*.mod *.log *.out @@ -35,9 +34,6 @@ build # build from src/utils/setup.py src/utils/build -# xml-fortran reader -src/xml-fortran/xmlreader - # Test results error file results_error.dat inputs_error.dat @@ -79,17 +75,6 @@ scripts/G4EMLOW*/ # IPython notebook checkpoints .ipynb_checkpoints -# Jupyter notebooks -examples/jupyter/*.xml -examples/jupyter/*.png -examples/jupyter/*.xls -examples/jupyter/*.ace -examples/jupyter/*.endf -examples/jupyter/mgxs -examples/jupyter/tracks -examples/jupyter/fission-rates -examples/jupyter/plots - # Cython files *.c *.html diff --git a/MANIFEST.in b/MANIFEST.in index 795bfcf2367..e660a1161d3 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -21,8 +21,8 @@ recursive-include docs *.tex recursive-include docs *.txt recursive-include docs Makefile recursive-include examples *.h5 -recursive-include examples *.ipynb recursive-include examples *.png +recursive-include examples *.cpp recursive-include examples *.py recursive-include examples *.xml recursive-include include *.h diff --git a/docs/requirements-rtd.txt b/docs/requirements-rtd.txt index fd76dfffb9d..bf75537a88a 100644 --- a/docs/requirements-rtd.txt +++ b/docs/requirements-rtd.txt @@ -2,7 +2,6 @@ sphinx-numfig jupyter sphinxcontrib-katex sphinxcontrib-svg2pdfconverter -nbsphinx numpy scipy h5py diff --git a/docs/source/conf.py b/docs/source/conf.py index ed790a2e27f..5847af13ba8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -44,7 +44,6 @@ 'sphinx.ext.viewcode', 'sphinxcontrib.katex', 'sphinx_numfig', - 'nbsphinx' ] if not on_rtd: extensions.append('sphinxcontrib.rsvgconverter') diff --git a/docs/source/examples/cad-based-geometry.ipynb b/docs/source/examples/cad-based-geometry.ipynb deleted file mode 120000 index ee3727e6725..00000000000 --- a/docs/source/examples/cad-based-geometry.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/cad-based-geometry.ipynb \ No newline at end of file diff --git a/docs/source/examples/candu.ipynb b/docs/source/examples/candu.ipynb deleted file mode 120000 index 480d99fb164..00000000000 --- a/docs/source/examples/candu.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/candu.ipynb \ No newline at end of file diff --git a/docs/source/examples/capi.ipynb b/docs/source/examples/capi.ipynb deleted file mode 120000 index f69a37093c2..00000000000 --- a/docs/source/examples/capi.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/capi.ipynb \ No newline at end of file diff --git a/docs/source/examples/expansion-filters.ipynb b/docs/source/examples/expansion-filters.ipynb deleted file mode 120000 index e7573413595..00000000000 --- a/docs/source/examples/expansion-filters.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/expansion-filters.ipynb \ No newline at end of file diff --git a/docs/source/examples/hexagonal-lattice.ipynb b/docs/source/examples/hexagonal-lattice.ipynb deleted file mode 120000 index e2b63d24373..00000000000 --- a/docs/source/examples/hexagonal-lattice.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/hexagonal-lattice.ipynb \ No newline at end of file diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst deleted file mode 100644 index 38bc51e2686..00000000000 --- a/docs/source/examples/index.rst +++ /dev/null @@ -1,73 +0,0 @@ -.. _examples: - -======== -Examples -======== - -The following series of `Jupyter `_ Notebooks provide -examples for how to use various features of OpenMC by leveraging the -:ref:`pythonapi`. - -------------- -General Usage -------------- - -.. toctree:: - :maxdepth: 1 - - pincell - post-processing - pandas-dataframes - tally-arithmetic - capi - expansion-filters - search - nuclear-data - nuclear-data-resonance-covariance - pincell_depletion - --------- -Geometry --------- - -.. toctree:: - :maxdepth: 1 - - hexagonal-lattice - triso - candu - cad-based-geometry - ------------------------------------ -Multigroup Cross Section Generation ------------------------------------ - -.. toctree:: - :maxdepth: 1 - - mgxs-part-i - mgxs-part-ii - mgxs-part-iii - mdgxs-part-i - mdgxs-part-ii - ---------------- -Multigroup Mode ---------------- - -.. toctree:: - :maxdepth: 1 - - mg-mode-part-i - mg-mode-part-ii - mg-mode-part-iii - ------------------ -Unstructured Mesh ------------------ - -.. toctree:: - :maxdepth: 1 - - unstructured-mesh-part-i - unstructured-mesh-part-ii diff --git a/docs/source/examples/mdgxs-part-i.ipynb b/docs/source/examples/mdgxs-part-i.ipynb deleted file mode 120000 index 01eb1172d46..00000000000 --- a/docs/source/examples/mdgxs-part-i.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mdgxs-part-i.ipynb \ No newline at end of file diff --git a/docs/source/examples/mdgxs-part-ii.ipynb b/docs/source/examples/mdgxs-part-ii.ipynb deleted file mode 120000 index 2d9d339074b..00000000000 --- a/docs/source/examples/mdgxs-part-ii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mdgxs-part-ii.ipynb \ No newline at end of file diff --git a/docs/source/examples/mg-mode-part-i.ipynb b/docs/source/examples/mg-mode-part-i.ipynb deleted file mode 120000 index d1577f70007..00000000000 --- a/docs/source/examples/mg-mode-part-i.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mg-mode-part-i.ipynb \ No newline at end of file diff --git a/docs/source/examples/mg-mode-part-ii.ipynb b/docs/source/examples/mg-mode-part-ii.ipynb deleted file mode 120000 index a093063761a..00000000000 --- a/docs/source/examples/mg-mode-part-ii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mg-mode-part-ii.ipynb \ No newline at end of file diff --git a/docs/source/examples/mg-mode-part-iii.ipynb b/docs/source/examples/mg-mode-part-iii.ipynb deleted file mode 120000 index 6fa0d180e44..00000000000 --- a/docs/source/examples/mg-mode-part-iii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mg-mode-part-iii.ipynb \ No newline at end of file diff --git a/docs/source/examples/mgxs-part-i.ipynb b/docs/source/examples/mgxs-part-i.ipynb deleted file mode 120000 index a04b1da8952..00000000000 --- a/docs/source/examples/mgxs-part-i.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mgxs-part-i.ipynb \ No newline at end of file diff --git a/docs/source/examples/mgxs-part-ii.ipynb b/docs/source/examples/mgxs-part-ii.ipynb deleted file mode 120000 index dd8af3c4382..00000000000 --- a/docs/source/examples/mgxs-part-ii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mgxs-part-ii.ipynb \ No newline at end of file diff --git a/docs/source/examples/mgxs-part-iii.ipynb b/docs/source/examples/mgxs-part-iii.ipynb deleted file mode 120000 index d6acc76bafd..00000000000 --- a/docs/source/examples/mgxs-part-iii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/mgxs-part-iii.ipynb \ No newline at end of file diff --git a/docs/source/examples/nuclear-data-resonance-covariance.ipynb b/docs/source/examples/nuclear-data-resonance-covariance.ipynb deleted file mode 120000 index 0ab0dd62b59..00000000000 --- a/docs/source/examples/nuclear-data-resonance-covariance.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/nuclear-data-resonance-covariance.ipynb \ No newline at end of file diff --git a/docs/source/examples/nuclear-data.ipynb b/docs/source/examples/nuclear-data.ipynb deleted file mode 120000 index 59721abdc9d..00000000000 --- a/docs/source/examples/nuclear-data.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/nuclear-data.ipynb \ No newline at end of file diff --git a/docs/source/examples/pandas-dataframes.ipynb b/docs/source/examples/pandas-dataframes.ipynb deleted file mode 120000 index f41f570beb8..00000000000 --- a/docs/source/examples/pandas-dataframes.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/pandas-dataframes.ipynb \ No newline at end of file diff --git a/docs/source/examples/pincell.ipynb b/docs/source/examples/pincell.ipynb deleted file mode 120000 index edbbb8de24b..00000000000 --- a/docs/source/examples/pincell.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/pincell.ipynb \ No newline at end of file diff --git a/docs/source/examples/pincell_depletion.ipynb b/docs/source/examples/pincell_depletion.ipynb deleted file mode 120000 index 1f25930fcbe..00000000000 --- a/docs/source/examples/pincell_depletion.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/pincell_depletion.ipynb \ No newline at end of file diff --git a/docs/source/examples/post-processing.ipynb b/docs/source/examples/post-processing.ipynb deleted file mode 120000 index 1f3f4cd7130..00000000000 --- a/docs/source/examples/post-processing.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/post-processing.ipynb \ No newline at end of file diff --git a/docs/source/examples/search.ipynb b/docs/source/examples/search.ipynb deleted file mode 120000 index fbfb67bc606..00000000000 --- a/docs/source/examples/search.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/search.ipynb \ No newline at end of file diff --git a/docs/source/examples/tally-arithmetic.ipynb b/docs/source/examples/tally-arithmetic.ipynb deleted file mode 120000 index 7ffe194626d..00000000000 --- a/docs/source/examples/tally-arithmetic.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/tally-arithmetic.ipynb \ No newline at end of file diff --git a/docs/source/examples/triso.ipynb b/docs/source/examples/triso.ipynb deleted file mode 120000 index 0e51d8add9a..00000000000 --- a/docs/source/examples/triso.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/triso.ipynb \ No newline at end of file diff --git a/docs/source/examples/unstructured-mesh-part-i.ipynb b/docs/source/examples/unstructured-mesh-part-i.ipynb deleted file mode 120000 index b1358788bab..00000000000 --- a/docs/source/examples/unstructured-mesh-part-i.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/unstructured-mesh-part-i.ipynb \ No newline at end of file diff --git a/docs/source/examples/unstructured-mesh-part-ii.ipynb b/docs/source/examples/unstructured-mesh-part-ii.ipynb deleted file mode 120000 index 9074811b705..00000000000 --- a/docs/source/examples/unstructured-mesh-part-ii.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../../examples/jupyter/unstructured-mesh-part-ii.ipynb \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 3b2f5b05334..a01055374b0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -36,7 +36,7 @@ Forum `_. :maxdepth: 1 quickinstall - examples/index + Examples releasenotes/index methods/index usersguide/index diff --git a/examples/jupyter/c5g7.h5 b/examples/jupyter/c5g7.h5 deleted file mode 100644 index b3dea952b161b9984697b55a429f812168f98598..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 59512 zcmeHQc|a4#_g}yxA`0SBl&ZlKDGDlTRLF~fLhFTiq0}0VC{(V7D0oM075#dk)}x;B zLbPh*4Sqc$9#BtE`HB}JRz$4@&oBL1^|#5)h`TX~@)ZcM`$y)Uee>Bj@4cBf*`4d{ z*{6blwm^i&ii~;5A#{b zVN@P>RDV513MyBfL?QVl{jmAkf)dbkMa{8wDGQ26Ng^cDu@gs+l}3(_il+0ec=UAA zbRyFHlKFwzm~KPbTWZ<|OTq#sN`l{YpsT&6Gy2DZio^G}q8iY%OLJp>v`jiaNH$(7 zq3ZGB-Thr^Q!a1ud^0gYlpxv&jTH4HI^3#k`^ z36Nu849)*U3)CT@8+XqIRRjSt|NZcIi9SiI!31F35cTY%s7~Eo`l>@R??mcE39i8d z26~!MH7R@CRveBZq+h|0DD5{Jg#ui!FrHa2Z9PioZ$P0HuBU9mtQXe~r4zqGAsMHr zE3=*rtu57;zzca5@p=>5Yo1*n=oS>M3wq zm5eKVWbjUovhHI+`CvI_LA9Y}#hB3O=+MZBfWR<`re_=4j!@rQEe{b5JgQHxeuLG+ zAL2U{p+FZ9`bhY`eC5&oN+nTJNwg$F79gX$r>OdL#aeV9q<(00q)Zwa6&j?ut6IJi z`CON7&7-1&0%S6YbaZq`K$My?b^E&SwfFZLb0!utYja-QF3G%aYN4Or$hC zK!)0@p9Q&pm}IOZB3LRZZdXI27b4aD(e(`JgJKJduKa;ZmwvQDmLKJe<*Ok%Onrem zCw%mQTX9*H7y-5%CUVRN^A8z1TmBMxLdriiL~_{l-Ri^}3q~@3qHsDM%TvOi`RYkd zU!)$J-%)AR`8A#NI6O-(gTcz9)JWB*>eOLq=xW%V zu*d$-jY+U{_tB1caMZde7KZ^L~c*7nG*)tHt0{u&Q;>hViiTKZ{*^wAc!% zZ@hB;a!vs2E{+=-W&aKj-IiTmcPR&AGtKhs2CLxPw3bPW_iTYdyW0e8KXX@j`TXlp z*WKNOZNs4P-4j>fK~zxa@^Ba=3xk_VJKwws^~SWCuY4+jnP2|weEH=~ zu-nwX`Pp+5;90el0lUBFgY(SO{WgA=0fM71&n=;T0)OAf-!=KEx6o~6vcwUFf^<&* z;5^d|__cM@d*ZH(A=s()&GGUXFvYXq+*Xq^fbcEeZlLfoN27bySYUI5s*Zsn<|4^w zhol}^r?ErAFLu@b=$n{x8eYsgy>HmnnxPFKNMNB7+gUADkfk4LQ^ z^tvOI#vNPND4~nc_DBMjqV!=Tl- z-iC=m_?`EhInL=&@cRk(ouLasT4(C0Nu;$dEmcCqHkSAb{ox3z=dYKi$J zzsYgC-vU>HXTyR!z1Bp+-B-E3%MPrB&7Q|(f%ZvYetGiXHj!JwtbNj?ZF?WXsW*Ej zKUEe&jUdmB>3dW#v1aV06v-B7HE~;4^J{m6tDZ#W@2cHZn0fxx?U&UuVMWMw%aq3> zp_AkC2g@I{5Z1V|deF0>w;)7dzQ3<37$i0^vqE;>0{3g*tlJPV0WA7UmOlTYE!=3Z z=wIL0y9DhPxbes5%z(YmgiCh~@eoe$(4H zKuXB41C|pXLqoGxt3-?c0;;!dS9$m>Xc+j1t*Tx$jBr`I)?(pCXwmINouYqK;O{QX zzqaEHY%)1q-MYa&=x}PN$`FK63jaXEHQuea#ZC27V2_LC&Gpyg zePvi;D7B?uPpzQccR3Z4P?C^-?@Su6T zcz;I8;laQozk=ZLVZSz8z8SKs_-&BDBXY2e#2F8 zPAX|AZGwy)9Ue-%%hX|j+9DzPmFQ>E&QK_elm&#*l>!2zBc)Nu2d8d>{e4tWNT{ZZ zi1F=7mL^T;2@a42s58j@#hTL3|6@o$HNdwdVae@4z7^_9!5D;b+JR;iA5$4i7>7Z1 zal!^iL)sSSeJDockX7psl~%qyLwu#ZzOd5pI@|B7+yA1%{3Kw2r_E1tMtON9N*|A+ z3WbLu{Vl)(m&}{;dT!i(%Lf6r{w8uZ4WAPtRDJIAPw&p{u+AlM=XMa{rG0M4kX(rG zgy`Vp0+|6-D`d+Btn{cHCngT1eI6&PGrAwK&nXv_=HqjVgg>)b=N)zJ$15jUH%XDyfma=61$-xGr6e3z`zmu zKO#W%ixT(agTq2V5ncW`r57qUBMo_s0paoHVpsB1Q(EqqmkE@qcmtIE07^{=W&a1BoAwge6}u`(XK_Akb?< zW0OC~4lMS^@jMyiY9(xNPLaD-5hH`hA%%#MU1<}aZufkuKd$g2^_ko#FfM&O?CYa} z8q)8^frlaeorc$|WZoFXL*$4kGX&WBo5)!|yzfB>DJ_2-`~Ux(KMo;Y-u^iL07M5T z7sw2#S|M955Pw`0Yk#w}{c#}!5$Tgu!RTyG)?HjS@r6IdhFc=M)$wd_g29D7G5dorK*#0=V zKk8VQKTd>SY-GqErwT^>*A-{&qZJHAp>2)-&i zJhC`EXl5?<$64VmWC4T4we7R=l{;m}AKn!Nj}QMpjxFB|*;OL?oqg(y$U&6ieya)s z#IC|>@!k7A?Xi9&IB@(G|&(`koe(9Sn~C<9Cx80&}%|tlgDg-+{)1mankB47{!o!gM_8W_y z6qz^W_1w7omJb4K{Y~U-ejt;c((=b8iqU<@d$u`$9Kxf#{c%e4S^>_kXHJ7ElVZyS z;*X=Sz92x9wm+`udvqtV|HdDO(jxpl0iw5xuz!x!)3qN<=&tOcwjG3!BKVYieG!lC zAA&qWQrjMkGb)JL{y0U5QIS_+h|%SbW6w)Nc4wOUrNJ!viMzItlAX zf&=G|D=U9oI#xUi0=*{GUN4ugeyTfX%Z%okDwtAueM_A=TVVLmzpVPWk|x9{?lb--RHL; zf1qvV1JML{l#`^merfI<+mxI!w8b*&D8v5?g-oKjH&ahL#|LtwwuEmK< z@a9VFgEk#6LCdiL1AG3H0rIJ`hiArSLP6fPy2*`mLGxLU<4++m>-aE_<042N-#RS$ zc72$;KrBx`>;m1w)L#}E46)t2_M4VD4tn4Gui36$5A>9J-d6e=N4Hlkmca2Lost>q;y?gX~(PxBhbB?&KxdFyIA3={5pem!i* z$qO(&d&uULtnJY8P3mBU*ct3z4KRyvd@kJ5cEYsPmcI)x4LN#lrPmsvc}R<}*h#5S zy<_@*C&fXKei1NF8af56_O1xux^oa%4-fKa^w-_Ek_w-nnZ6m3JO2ds5=>o*3dQ_BxTI+5D#Ye2nuYnDkF} z0=&wAXCv0tjCqgWx;0Vx^WWpQ`%9O5Jj$*QYZ^7Le_!bWwU%5;z52H={B&X5pVlaV=Q!f}w&a`Q zlW<=x79y_0xVl#7GVbQWt8c^UPcYHni@KNI;>O>3uytZ;yB^lhVRLFh`2TgTF6vuU zQ2X!Q9#CM}dC!}dF5ox*I!k1-I^%DrV)(n4^P_n!9J zY7=}BbN)r@sRRfz)1%_DFte8-#n`@A4IblKo*vWvp8)g87hSP&*$&Tpwmp}))I zyIMeh(M|;|4~Peh5BG$EA4Tnw9BP5^(4D2X-EIqqZEX`0G;g;s{)W@~9X9^~(<@i@ z>_2)APWE#D*(qc(te(DIHGP`|1goDow3{vhuPhV#6LSaQtN!2gI=JFG%o(?T&JiCa z^jKy=4ZRr$nN8RF)IDSk8;+&7>~rxrysgz?*cEXO%<9-;P{@eiU|shvcM{tK!Vc+% zr@u+^pzG4e1IZolz~;fjg=e3sAZYqEw~1M1BC>A()bGxfbUreaE|<`W0Q)?S{VbhT zH3c@f*k-2wdSo4UkXHN3Lh(MgeB&;}JCx4OGAwueN<{9C#R7 zzvtuqYBFyagW?jE9s&0Hoygf(?Drr5l-7A1!H?(;e)xXec^rgCd7sA-EkJZ|a)Hc% zsui;30y&Qp&+>1U_IaGRg$Q?I$18Cgn>wR+XoyZ3m&5OzBc#w*3=lR8X_e<2cVZD)K4}F}lvI60eehY2{ufwo*k7BBf<6xmlLVaerW;zzC%W0?e&af*E&N3m2t z0rt39wM>6KJ}y@NtiPT!E>2&rzn%ye=d3^xqQ`hEQQ98!4Us3_i*bW83d)YJaw-I0 zl^q^7lh7j;FrKuwJyO2&tnBdc;qah2q4+#bTm`}7!{>3>^39N4C0>K30g;1@Nx>y5 zJp#n8^1~w~`q~@oM*;wM9!LLqoTeBk5|-Q!RAKp(PoUR?#-^Xx{Gc--8fRTK>4S_2>@y8&LSU{c(hT3raw#&sLgHmb9(Tqok53sU%twAqzm? zoUczW@M~$xQH#*%NSQP;DipsSkt%LqSJR%@GtnUdQ4+d|x_#aE+WUKr^7m>x%736V zSR$q0A!R{1Gwx5?kBOAhpYTBK)pL;SGnBVKEyL`TcP=QxF-3*3Gecs4e{>vJXS_yYp}Q@5#r-A|;9t+(U3WZV$>Q zdO3^rJaO|%7nB`e`BVtL(xoTZd}2sXSnXr-Q>9}ZZa{hwVCxAY-xIN%A&rSX_*~z^ zG&G;dK0OIbZcoSI`J7LNq6v+CzdqyugIuiy*+=r*uT{jzAbNt@%gC;@2}68E&H<6o zjIz?$HdZuH2rH2Wo9c8*t$R&PO*JaG*#8_g6@h2S?vSHu<@{UvcqCRZJkWC`^T&#Q z{^~3L5PDE@Y;YOUo76E>|K0pTN$$6bAi&m}gr9PfnE-t5eSOmV_{dJ0Xn16-OJ_Pp zhuzc}bSO|q#)L*kj}8h62oID<)%jz`hXzYV%f<()H`D1WCP1bsAnOaUCinCI)Wh>j zaS-*b!uW802!uy@`yu>}BRV)a_W60gBJ{|XV`QHq@hFq}()L3X;y?5gd89}|_g9@n zA>q$7R=tqJs638EFO&Mpo6@%L5K;uklCPVJDuo}yTUB|VeQA!&kp1g@8r_}D%hIHU z>)ZGcVCxNMOkxg}*M#DV>tD$E$aQTj8EUzNZ2v-hs(v!~xLA>fB1BK5;dK5Pluzch z;1q69$pdXYidJ-`;J2EFpcF6=HMadoUH%0&pBT~;QRmPw2|qCg%_S;50&G2Dk4MZq z%N%;e^)L9~`JB%fkg?5Yq9^#6%Crj%apprs3jCBTC~tf|nMa>hwxK3Sq|u>~5ft0+MC=W` z%|Vfn*l9-b&^aPF1ULjZ1ULjZ1ULj32zYr6?5EMy*pWi?w)($O>MML~EaOW>S(YfD z`{fYe5a1Bt5a1Bt5a1Bt5a1Bt5a1Bt5a1Bt5U4l^6u*b0mgYSsV?x6uvWc2km}mx} aevb)yPYN1S@qU0<#~_I(PDk-4X8%77DLTdg diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb deleted file mode 100644 index 99be260c7f8..00000000000 --- a/examples/jupyter/cad-based-geometry.ipynb +++ /dev/null @@ -1,780 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using CAD-Based Geometries\n", - "In this notebook we'll be exploring how to use CAD-based geometries in OpenMC via the [DagMC](https://svalinn.github.io/DAGMC/index.html) toolkit. The models we'll be using in this notebook have already been created using [Trelis](https://coreform.com/products/trelisnew/) and faceted into a surface mesh represented as `.h5m` files in the [Mesh Oriented DatABase](https://sigma.mcs.anl.gov/moab-library/) format. We'll be retrieving these files using the function below.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import urllib.request\n", - "\n", - "fuel_pin_url = 'https://tinyurl.com/y3ugwz6w' # 1.2 MB\n", - "teapot_url = 'https://tinyurl.com/y4mcmc3u' # 29 MB\n", - "\n", - "def download(url):\n", - " \"\"\"\n", - " Helper function for retrieving dagmc models\n", - " \"\"\"\n", - " u = urllib.request.urlopen(url)\n", - " \n", - " if u.status != 200:\n", - " raise RuntimeError(\"Failed to download file.\")\n", - " \n", - " # save file as dagmc.h5m\n", - " with open(\"dagmc.h5m\", 'wb') as f:\n", - " f.write(u.read())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is intended to demonstrate how DagMC problems are run in OpenMC. For more information on how DagMC models are created, please refer to the [DagMC User's Guide](https://svalinn.github.io/DAGMC/usersguide/index.html).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from IPython.display import Image\n", - "import openmc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To start, we'll be using a simple U235 fuel pin surrounded by a water moderator, so let's create those materials." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - " # materials\n", - "u235 = openmc.Material(name=\"fuel\")\n", - "u235.add_nuclide('U235', 1.0, 'ao')\n", - "u235.set_density('g/cc', 11)\n", - "u235.id = 40\n", - "\n", - "water = openmc.Material(name=\"water\")\n", - "water.add_nuclide('H1', 2.0, 'ao')\n", - "water.add_nuclide('O16', 1.0, 'ao')\n", - "water.set_density('g/cc', 1.0)\n", - "water.add_s_alpha_beta('c_H_in_H2O')\n", - "water.id = 41\n", - "\n", - "mats = openmc.Materials([u235, water])\n", - "mats.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's get our DAGMC geometry. We'll be using prefabricated models in this notebook. For information on how to create your own DAGMC models, you can refer to the instructions [here](https://svalinn.github.io/DAGMC/usersguide/trelis_workflow.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's download the DAGMC model. These models come in the form of triangle surface meshes stored using the the Mesh Oriented datABase ([MOAB](https://sigma.mcs.anl.gov/moab-library/)) in an HDF5 file with the extension `.h5m`. An example of a coarse triangle mesh looks like:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": { - "image/png": { - "width": 350 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"./images/cylinder_mesh.png\", width=350)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we'll need to grab some pre-made DagMC models." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "download(fuel_pin_url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a geometry where DAGMC represents the entire model, we'll make a DAGMC universe and use it as the root universe of the model." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dagmc_univ = openmc.DAGMCUniverse(filename=\"dagmc.h5m\")\n", - "geometry = openmc.Geometry(root=dagmc_univ)\n", - "geometry.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.batches = 10\n", - "settings.inactive = 2\n", - "settings.particles = 5000\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unlike conventional geometries in OpenMC, we really have no way of knowing what our model looks like at this point. Thankfully DagMC geometries can be plotted just like any other OpenMC geometry to give us an idea of what we're now working with.\n", - "\n", - "Note that material assignments have already been applied to this model. Materials can be assigned either using ids or names of materials in the `materials.xml` file. It is recommended that material names are used for assignment for readability." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p = openmc.Plot()\n", - "p.width = (25.0, 25.0)\n", - "p.pixels = (400, 400)\n", - "p.color_by = 'material'\n", - "p.colors = {u235: 'yellow', water: 'blue'}\n", - "openmc.plot_inline(p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've had a chance to examine the model a bit, we can finish applying our settings and add a source." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "settings.source = openmc.Source(space=openmc.stats.Box([-4., -4., -4.],\n", - " [ 4., 4., 4.]))\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tallies work in the same way when using DAGMC geometries too. We'll add a tally on the fuel cell here." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "tally = openmc.Tally()\n", - "tally.scores = ['total']\n", - "tally.filters = [openmc.CellFilter(1)]\n", - "tallies = openmc.Tallies([tally])\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** Applying tally filters in DagMC models requires prior knowledge of the model. Here, we know that the fuel cell's volume ID in the CAD sofware is 1. To identify cells without use of CAD software, load them into the [OpenMC plotter](https://github.com/openmc-dev/plotter) where cell, material, and volume IDs can be identified for native both OpenMC and DagMC geometries." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we're ready to run the simulation just like any other OpenMC run." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 0aafa81907ac79b9bdb4a86aede0b63fee3a9a9b\n", - " Date/Time | 2021-06-30 09:01:58\n", - " OpenMP Threads | 2\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Set overlap thickness = 0\n", - "Set numerical precision = 0.001\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.0001\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for U235\n", - " Initializing source particles...\n", - "\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - "\n", - " Bat./Gen. k Average k\n", - " ========= ======== ====================\n", - " 1/1 1.16613\n", - " 2/1 1.05158\n", - " 3/1 0.99824\n", - " 4/1 0.98119 0.98971 +/- 0.00853\n", - " 5/1 0.99111 0.99018 +/- 0.00494\n", - " 6/1 0.98946 0.99000 +/- 0.00350\n", - " 7/1 0.96858 0.98571 +/- 0.00507\n", - " 8/1 0.96709 0.98261 +/- 0.00518\n", - " 9/1 0.97415 0.98140 +/- 0.00454\n", - " 10/1 0.93691 0.97584 +/- 0.00681\n", - " Creating state point statepoint.10.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.0815e-01 seconds\n", - " Reading cross sections = 9.2095e-02 seconds\n", - " Total time in simulation = 1.5220e+00 seconds\n", - " Time in transport only = 1.5148e+00 seconds\n", - " Time in inactive batches = 2.9113e-01 seconds\n", - " Time in active batches = 1.2309e+00 seconds\n", - " Time synchronizing fission bank = 3.1554e-03 seconds\n", - " Sampling source sites = 2.7998e-03 seconds\n", - " SEND/RECV source sites = 3.4967e-04 seconds\n", - " Time accumulating tallies = 3.0953e-05 seconds\n", - " Time writing statepoints = 2.7201e-03 seconds\n", - " Total time for finalization = 2.3371e-04 seconds\n", - " Total time elapsed = 1.7509e+00 seconds\n", - " Calculation Rate (inactive) = 34348.7 particles/second\n", - " Calculation Rate (active) = 32497.7 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 0.97672 +/- 0.00532\n", - " k-effective (Track-length) = 0.97584 +/- 0.00681\n", - " k-effective (Absorption) = 0.96793 +/- 0.00613\n", - " Combined k-effective = 0.97097 +/- 0.00582\n", - " Leakage Fraction = 0.57298 +/- 0.00284\n", - "\n" - ] - } - ], - "source": [ - "openmc.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## More Complicated Geometry" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neat! But this pincell is something we could've done with CSG. Let's take a look at something more complex. We'll download a pre-built model of the [Utah teapot](https://en.wikipedia.org/wiki/Utah_teapot) and use it here." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "download(teapot_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": { - "image/jpeg": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"./images/teapot.jpg\", width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our teapot is made out of iron, so we'll want to create that material and make sure it is in our `materials.xml` file." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "iron = openmc.Material(name=\"iron\")\n", - "iron.add_nuclide(\"Fe54\", 0.0564555822608)\n", - "iron.add_nuclide(\"Fe56\", 0.919015287728)\n", - "iron.add_nuclide(\"Fe57\", 0.0216036861685)\n", - "iron.add_nuclide(\"Fe58\", 0.00292544384231)\n", - "iron.set_density(\"g/cm3\", 7.874)\n", - "mats = openmc.Materials([iron, water])\n", - "mats.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To make sure we've updated the file correctly, let's make a plot of the teapot." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p = openmc.Plot()\n", - "p.basis = 'xz'\n", - "p.origin = (0.0, 0.0, 0.0)\n", - "p.width = (30.0, 20.0)\n", - "p.pixels = (450, 300)\n", - "p.color_by = 'material'\n", - "p.colors = {iron: 'gray', water: 'blue'}\n", - "openmc.plot_inline(p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we start to see some of the advantages CAD geometries provide. This particular file was pulled from the [GrabCAD](https://grabcad.com/library) and pushed through the DAGMC workflow without modification (other than the addition of material assignments). It would take a considerable amount of time to create a model like this using CSG!" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p.width = (18.0, 6.0)\n", - "p.basis = 'xz'\n", - "p.origin = (10.0, 0.0, 5.0)\n", - "p.pixels = (600, 200)\n", - "p.color_by = 'material'\n", - "openmc.plot_inline(p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's brew some tea! ... using a very hot neutron source. We'll use some well-placed point sources distributed throughout the model." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.batches = 10\n", - "settings.particles = 5000\n", - "settings.run_mode = \"fixed source\"\n", - "\n", - "src_locations = ((-4.0, 0.0, -2.0),\n", - " ( 4.0, 0.0, -2.0),\n", - " ( 4.0, 0.0, -6.0),\n", - " (-4.0, 0.0, -6.0),\n", - " (10.0, 0.0, -4.0),\n", - " (-8.0, 0.0, -4.0))\n", - "\n", - "# we'll use the same energy for each source\n", - "src_e = openmc.stats.Discrete(x=[12.0,], p=[1.0,])\n", - "\n", - "# create source for each location\n", - "sources = []\n", - "for loc in src_locations:\n", - " src_pnt = openmc.stats.Point(xyz=loc)\n", - " src = openmc.Source(space=src_pnt, energy=src_e)\n", - " sources.append(src)\n", - "\n", - "src_str = 1.0 / len(sources)\n", - "for source in sources:\n", - " source.strength = src_str\n", - "\n", - "settings.source = sources\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "...and setup a couple of mesh tallies. One for the kettle, and one for the water inside." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "mesh = openmc.RegularMesh()\n", - "mesh.dimension = (120, 1, 40)\n", - "mesh.lower_left = (-20.0, 0.0, -10.0)\n", - "mesh.upper_right = (20.0, 1.0, 4.0)\n", - "\n", - "mesh_filter = openmc.MeshFilter(mesh)\n", - "\n", - "pot_filter = openmc.CellFilter([1])\n", - "pot_tally = openmc.Tally()\n", - "pot_tally.filters = [mesh_filter, pot_filter]\n", - "pot_tally.scores = ['flux']\n", - "\n", - "water_filter = openmc.CellFilter([5])\n", - "water_tally = openmc.Tally()\n", - "water_tally.filters = [mesh_filter, water_filter]\n", - "water_tally.scores = ['flux']\n", - "\n", - "\n", - "tallies = openmc.Tallies([pot_tally, water_tally])\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2021 MIT and OpenMC contributors\n", - " License | https://docs.openmc.org/en/latest/license.html\n", - " Version | 0.13.0-dev\n", - " Git SHA1 | 0aafa81907ac79b9bdb4a86aede0b63fee3a9a9b\n", - " Date/Time | 2021-06-30 09:02:13\n", - " OpenMP Threads | 2\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading materials XML file...\n", - " Reading geometry XML file...\n", - "Set overlap thickness = 0\n", - "Set numerical precision = 0.001\n", - "Loading file dagmc.h5m\n", - "Initializing the GeomQueryTool...\n", - "Using faceting tolerance: 0.001\n", - "Building acceleration data structures...\n", - "Implicit Complement assumed to be Vacuum\n", - " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n", - " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n", - " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n", - " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n", - " Minimum neutron data temperature: 294.0 K\n", - " Maximum neutron data temperature: 294.0 K\n", - " Reading tallies XML file...\n", - " Preparing distributed cell instances...\n", - " Writing summary.h5 file...\n", - " Maximum neutron transport energy: 20000000.0 eV for Fe58\n", - "\n", - " ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n", - "\n", - " Simulating batch 1\n", - " Simulating batch 2\n", - " Simulating batch 3\n", - " Simulating batch 4\n", - " Simulating batch 5\n", - " Simulating batch 6\n", - " Simulating batch 7\n", - " Simulating batch 8\n", - " Simulating batch 9\n", - " Simulating batch 10\n", - " Creating state point statepoint.10.h5...\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 4.8117e+00 seconds\n", - " Reading cross sections = 4.6543e-01 seconds\n", - " Total time in simulation = 1.4498e+01 seconds\n", - " Time in transport only = 1.4493e+01 seconds\n", - " Time in active batches = 1.4498e+01 seconds\n", - " Time accumulating tallies = 3.1628e-04 seconds\n", - " Time writing statepoints = 4.8840e-03 seconds\n", - " Total time for finalization = 1.5489e-02 seconds\n", - " Total time elapsed = 1.9328e+01 seconds\n", - " Calculation Rate (active) = 3448.64 particles/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " Leakage Fraction = 0.63090 +/- 0.00246\n", - "\n" - ] - } - ], - "source": [ - "openmc.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the performance is significantly lower than our pincell model due to the increased complexity of the model, but it allows us to examine tally results like these:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "sp = openmc.StatePoint(\"statepoint.10.h5\")\n", - "\n", - "water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n", - "water_flux = water_tally.mean\n", - "water_flux.shape = (40, 120)\n", - "water_flux = water_flux[::-1, :]\n", - "\n", - "pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n", - "pot_flux = pot_tally.mean\n", - "pot_flux.shape = (40, 120)\n", - "pot_flux = pot_flux[::-1, :]\n", - "\n", - "del sp" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "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", - "fig = plt.figure(figsize=(18, 16))\n", - "\n", - "sub_plot1 = plt.subplot(121, title=\"Kettle Flux\")\n", - "sub_plot1.imshow(pot_flux)\n", - "\n", - "sub_plot2 = plt.subplot(122, title=\"Water Flux\")\n", - "sub_plot2.imshow(water_flux)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb deleted file mode 100644 index 3e5bc72834b..00000000000 --- a/examples/jupyter/candu.ipynb +++ /dev/null @@ -1,1110 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Modeling a CANDU Bundle\n", - "In this example, we will create a typical CANDU bundle with rings of fuel pins. At present, OpenMC does not have a specialized lattice for this type of fuel arrangement, so we must resort to manual creation of the array of fuel pins." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from math import pi, sin, cos\n", - "import numpy as np\n", - "import openmc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's begin by creating the materials that will be used in our model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fuel = openmc.Material(name='fuel')\n", - "fuel.add_element('U', 1.0)\n", - "fuel.add_element('O', 2.0)\n", - "fuel.set_density('g/cm3', 10.0)\n", - "\n", - "clad = openmc.Material(name='zircaloy')\n", - "clad.add_element('Zr', 1.0)\n", - "clad.set_density('g/cm3', 6.0)\n", - "\n", - "heavy_water = openmc.Material(name='heavy water')\n", - "heavy_water.add_nuclide('H2', 2.0)\n", - "heavy_water.add_nuclide('O16', 1.0)\n", - "heavy_water.add_s_alpha_beta('c_D_in_D2O')\n", - "heavy_water.set_density('g/cm3', 1.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our materials created, we'll now define key dimensions in our model. These dimensions are taken from the example in section 11.1.3 of the [Serpent manual](http://montecarlo.vtt.fi/download/Serpent_manual.pdf)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Outer radius of fuel and clad\n", - "r_fuel = 0.6122\n", - "r_clad = 0.6540\n", - "\n", - "# Pressure tube and calendria radii\n", - "pressure_tube_ir = 5.16890\n", - "pressure_tube_or = 5.60320\n", - "calendria_ir = 6.44780\n", - "calendria_or = 6.58750\n", - "\n", - "# Radius to center of each ring of fuel pins\n", - "ring_radii = np.array([0.0, 1.4885, 2.8755, 4.3305])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To begin creating the bundle, we'll first create annular regions completely filled with heavy water and add in the fuel pins later. The radii that we've specified above correspond to the center of each ring. We actually need to create cylindrical surfaces at radii that are half-way between the centers." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# These are the surfaces that will divide each of the rings\n", - "radial_surf = [openmc.ZCylinder(r=r) for r in\n", - " (ring_radii[:-1] + ring_radii[1:])/2]\n", - "\n", - "water_cells = []\n", - "for i in range(ring_radii.size):\n", - " # Create annular region\n", - " if i == 0:\n", - " water_region = -radial_surf[i]\n", - " elif i == ring_radii.size - 1:\n", - " water_region = +radial_surf[i-1]\n", - " else:\n", - " water_region = +radial_surf[i-1] & -radial_surf[i]\n", - " \n", - " water_cells.append(openmc.Cell(fill=heavy_water, region=water_region))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what our geometry looks like so far. In order to plot the geometry, we create a universe that contains the annular water cells and then use the `Universe.plot()` method. While we're at it, we'll set some keyword arguments that can be reused for later plots." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAD4CAYAAAAn+OBPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOWklEQVR4nO3dbYgd133H8d+vshSjRIopqmuwRLVt5Rg1D8hsnAbTtIlNUBI/QMkLGxLi5IVoaIwLBmNLlJAXMaUpSQwJLYvtQImoCY7z4JAokckD9IXVrC07ri0lNm4ay9jYelGy1DTS4n9f3Lt4vbr7oDtnzpyZ8/2AQPdhz/nP7PzmnLkzO9cRIQD1+L2uCwCQF6EHKkPogcoQeqAyhB6ozAVddLpl2wVx4Y4tXXQNVOH/Tp/RmYVFT3qtk9BfuGOL3vO5y7voGqjCsc+eXPU1pvdAZQg9UBlCD1SG0AOVIfRAZZKE3vZFth+wfdL2CdvvTdEugPRSnbK7W9KRiPio7S2StiZqF0BijUNv+62S3ifpZkmKiDOSzjRtF0A7UkzvZyS9Iulrto/bvsf2m1e+yfYB2/O2588uLCboFsA0UoT+AklXSPrniNgn6X8l3bHyTRExFxGzETG7eVsnFwICUJrQn5J0KiKOjR8/oNFOAECBGoc+Il6S9Lztt42fulrS003bBdCOVPPsWyQdHn9y/5ykTyZqF0BiSUIfEY9Lmk3RFoB2cUUeUBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZQg9UBlCD1SG0AOVIfRAZZKF3vam8bfWfi9VmwDSSznS3yrpRML2ALQgSeht75T0EUn3pGgPQHtSjfRflnS7pNdWe4PtA7bnbc+fXVhM1C2A89U49LavlfRyRDy61vsiYi4iZiNidvO2VF+WC+B8pRjpr5J0ve1fS7pf0gdsfz1BuwBa0Dj0EXFnROyMiN2SbpT044j4WOPKALSC8/RAZZIeXEfETyX9NGWbANLiEzWcY99d123ofccPPtRyJWgDoa/URoM9bRvsEMpF6CuRIuRN+mMnUA5CP1C5Q74edgLlIPQDUlrQ17K8VnYAeRH6AehT2CdZqp/w58F5eqAyjPQ91vcRfiVG/DwIfQ8NLewrEf52EfqeGHrQJ+HDvnZwTN8DNQZ+JdZBOoz0BWNDfyOm/Wkw0heKwK+OddMMoS8QG/X6WEfTY3pfEDbk88N0fzqM9IUg8NNj3Z0fQl8ANtrmWIcbx/S+Q2yoaTHd3xhGeqAyhL4jjPLtYd2ujdB3gI2yfazj1RH6zNgY82FdT0bogcoQ+owYefJjnZ+r8Sk727sk/aukP5QUkuYi4u6m7Q5NHza+I8+cnurn9u/ZkbiStPbddR2n8ZZJcZ5+UdJtEfGY7W2SHrV9NCKeTtD2IJQa+GlDvl47Je4ECP7rGoc+Il6U9OL4/wu2T0i6VBKhL1CqoG+0jxJ3ALVLekxve7ekfZKOTXjtgO152/NnFxZTdlu0Ukb5I8+czhL4UvqdpJTfRdccEWkast8i6WeSPh8RD6713u0zW+M9n7s8Sb8l63ojKyVsK3U9+tcwzT/22ZP67X+96kmvJRnpbW+W9E1Jh9cLPPIoNfBS2bXVoHHobVvSvZJORMQXm5cEoE0pRvqrJH1c0gdsPz7+9+EE7fZaV1P7ko6h19JlnV0fdnUtxaf3/y5p4rED8upD2Fc68szpzo/xa8MVeS3oYiTpY+CXdFF7zaM9oR+APgd+yRCWoS8IfUL77rou+wgypLDkXpZaR3tC32NDCvySIS5TaQh9Iozw6eRcti5mZ10j9D005MAvqWEZu0LogcoQ+gRyTQ/7cuFNKjmXt6YpPqHviZrCvlLNy94GQg9UhtADlSH0DeU4FmR6m2cd1HJcT+iByhD6wjHKv451kQahByrDV1U3MORjwAcvv2nN1//65L9lqiSvGm6VTegLlns6u17QV3tvzh0AN91ojuk9JJ1f4FP+LPIj9EgSWoLfH4S+cinDSvD7gdAXKsfxfBshzRF8Tt01Q+iByhD6KQ35dF3thv67JfSVanMazrF92VJ9l91+27+0/aztO1K0CaAdKb7LbpOkr0r6kKS9km6yvbdpuwDakWKkv1LSsxHxXESckXS/pBsStAugBSlCf6mk55c9PjV+7g1sH7A9b3v+7MJigm4BTCPbB3kRMRcRsxExu3kbl/wDXUkR+hck7Vr2eOf4OQAFShH6n0vaY3vG9hZJN0r6boJ2AbSgcegjYlHSZyT9UNIJSd+IiKeatot2tfnnsEP9W/uhSHJMHxHfj4jLIuJPIuLzKdos3dBvtFCzof9uuSIPqAyhL1SOu8O0MQ3PMbXnzjnNEPrKpQwpx/L9QOiRJKwEvj8IPSQ1Cy2B7xcujSvY/j07st4lZnl4S70FNsfzzRH6Bo4ffGiwN1yodfQe+uk6iek9UB1CXzims69jXaRB6IHKEPqGchwDMsLlWQc1HM9LhB6oDqEHKkPoe6LmKX7Ny94GQp9ArmPB/Xt2VBWAnMtby/G8ROiB6hD6HqphtK9hGbtC6BPJPT0ccihyLtvxgw9VNbWXCH2vDTH4Q1ym0hD6hLoYNYYUktzLUtsIv4TQD8AQgj+EZegLQt+CLkaQPoemi9prHeUlQj8ofQx+H2vuO0Lfkq5Gkr5cwNNlnTWP8lLD0Nv+gu2Ttn9h+1u2L0pUF4CWNB3pj0p6e0S8U9KvJN3ZvCSkUPJoX3JtNWh0j7yI+NGyh49I+mizcoal63voLQ9XzhtsTlJK0Guf2ktpj+k/JekHq71o+4DtedvzZxcWE3ZbtlI2sq6OoUv6jKGU30XX1h3pbT8s6ZIJLx2KiO+M33NI0qKkw6u1ExFzkuYkafvM1piqWjSWY/QvJeSYbN3QR8Q1a71u+2ZJ10q6OiII8wRdT/NXszKc0+4E+hByRvnXNTqmt71f0u2S/jIiXk1T0jCVGvzl+hDeaRD4N2p6TP8VSdskHbX9uO1/SVDTYLHx5cc6P1fTT+//NFUhAPLgirzMGHnyYV1PRug7wMbYPtbx6gh9R9go28O6XRuhByrDV1V3aGlEKv1UXl8wwm8MI30B2FibYx1uHKEvBBvt9Fh354fpfUGY7p8fwj4dRvoCsTGvj3U0PUJfKDbq1bFummF6XzCm+29E2NNgpO8BNnbWQUqM9D2xfKOvZeQn6O0g9D009Gk/YW8Xoe+xoYWfsOdB6Aeg7+En7HnxQR5QGUb6AenTh32M7t0h9AO1MlRd7wQIeTkIfSVy7wQIebkIfaXWCuVGdwgEu58IPc5BmIeNT++ByhB6oDJJQm/7Ntthe5jfiwQMSOPQ294l6YOSftO8HABtSzHSf0mjL7HkG2uBHmgUets3SHohIp7YwHsP2J63PX92YbFJtwAaWPeUne2HJV0y4aVDkg5qNLVfV0TMSZqTpO0zW5kVAB1ZN/QRcc2k522/Q9KMpCdsS9JOSY/ZvjIiXkpaJYBkpr44JyKelHTx0mPbv5Y0GxGnE9QFoCWcpwcqk+wy3IjYnaotAO1hpAcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqgMoQcqQ+iByhB6oDKEHqhMiu+nv8X2SdtP2f7HFEUBaE+jb7ix/X5JN0h6V0T8zvbF6/0MgG41Hek/LekfIuJ3khQRLzcvCUCbmob+Mkl/YfuY7Z/Zfvdqb7R9wPa87fmzC4sNuwUwrXWn97YflnTJhJcOjX/+9yX9uaR3S/qG7T+OiFj55oiYkzQnSdtntp7zOoA81g19RFyz2mu2Py3pwXHI/8P2a5J2SHolXYkAUmo6vf+2pPdLku3LJG2RdLphmwBa1PT76e+TdJ/t/5R0RtInJk3tAZSjUegj4oykjyWqBUAGXJEHVIbQA5Uh9EBlCD1QGXfxYbvtVyT99wbeukPdnwKkBmroYw1/FBF/MOmFTkK/UbbnI2KWGqiBGtLVwPQeqAyhBypTeujnui5A1LCEGkZ6X0PRx/QA0it9pAeQGKEHKlN86Eu58abt22yH7R0d9P2F8Tr4he1v2b4oY9/7bf/S9rO278jV77L+d9n+ie2nx9vArblrWFbLJtvHbX+vo/4vsv3AeFs4Yfu907RTdOhX3HjzzyT9U0d17JL0QUm/6aJ/SUclvT0i3inpV5LuzNGp7U2SvirpQ5L2SrrJ9t4cfS+zKOm2iNir0R2a/raDGpbcKulER31L0t2SjkTE5ZLeNW0tRYde5dx480uSbpfUyaeeEfGjiFi6seAjknZm6vpKSc9GxHPjP6O+X6OdcDYR8WJEPDb+/4JGG/qlOWuQJNs7JX1E0j25+x73/1ZJ75N0rzT6s/aI+J9p2io99Bu+8WZbbN8g6YWIeCJ336v4lKQfZOrrUknPL3t8Sh0Ebont3ZL2STrWQfdf1mjH/1oHfUvSjEa3ofva+BDjHttvnqahpnfOaSzVjTdbrOGgRlP7Vq1VQ0R8Z/yeQxpNdw+3XU9pbL9F0jcl/V1E/DZz39dKejkiHrX9Vzn7XuYCSVdIuiUijtm+W9Idkv5+moY6VcKNN1erwfY7NNrDPmFbGk2rH7N9ZUS8lKOGZbXcLOlaSVdnvCXZC5J2LXu8c/xcVrY3axT4wxHxYO7+JV0l6XrbH5Z0oaTttr8eETnvGnVK0qmIWJrlPKBR6M9b6dP7b6vDG29GxJMRcXFE7I6I3Rqt+CtSB349tvdrNLW8PiJezdj1zyXtsT1je4ukGyV9N2P/8mhve6+kExHxxZx9L4mIOyNi53gbuFHSjzMHXuNt7nnbbxs/dbWkp6dpq/ORfh3ceHPkK5LeJOnoeMbxSET8TdudRsSi7c9I+qGkTZLui4in2u53haskfVzSk7YfHz93MCK+n7mOEtwi6fB4B/ycpE9O0wiX4QKVKX16DyAxQg9UhtADlSH0QGUIPVAZQg9UhtADlfl/iaKLK2iLQ/wAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_args = {'width': (2*calendria_or, 2*calendria_or)}\n", - "bundle_universe = openmc.Universe(cells=water_cells)\n", - "bundle_universe.plot(**plot_args)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to create a universe that contains a fuel pin. Note that we don't actually need to put water outside of the cladding in this universe because it will be truncated by a higher universe." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "surf_fuel = openmc.ZCylinder(r=r_fuel)\n", - "\n", - "fuel_cell = openmc.Cell(fill=fuel, region=-surf_fuel)\n", - "clad_cell = openmc.Cell(fill=clad, region=+surf_fuel)\n", - "\n", - "pin_universe = openmc.Universe(cells=(fuel_cell, clad_cell))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pin_universe.plot(**plot_args)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The code below works through each ring to create a cell containing the fuel pin universe. As each fuel pin is created, we modify the region of the water cell to include everything outside the fuel pin." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "num_pins = [1, 6, 12, 18]\n", - "angles = [0, 0, 15, 0]\n", - "\n", - "for i, (r, n, a) in enumerate(zip(ring_radii, num_pins, angles)):\n", - " for j in range(n):\n", - " # Determine location of center of pin\n", - " theta = (a + j/n*360.) * pi/180.\n", - " x = r*cos(theta)\n", - " y = r*sin(theta)\n", - " \n", - " pin_boundary = openmc.ZCylinder(x0=x, y0=y, r=r_clad)\n", - " water_cells[i].region &= +pin_boundary\n", - " \n", - " # Create each fuel pin -- note that we explicitly assign an ID so \n", - " # that we can identify the pin later when looking at tallies\n", - " pin = openmc.Cell(fill=pin_universe, region=-pin_boundary)\n", - " pin.translation = (x, y, 0)\n", - " pin.id = (i + 1)*100 + j\n", - " bundle_universe.add_cell(pin)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "bundle_universe.plot(**plot_args)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking pretty good! Finally, we create cells for the pressure tube and calendria and then put our bundle in the middle of the pressure tube." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pt_inner = openmc.ZCylinder(r=pressure_tube_ir)\n", - "pt_outer = openmc.ZCylinder(r=pressure_tube_or)\n", - "calendria_inner = openmc.ZCylinder(r=calendria_ir)\n", - "calendria_outer = openmc.ZCylinder(r=calendria_or, boundary_type='vacuum')\n", - "\n", - "bundle = openmc.Cell(fill=bundle_universe, region=-pt_inner)\n", - "pressure_tube = openmc.Cell(fill=clad, region=+pt_inner & -pt_outer)\n", - "v1 = openmc.Cell(region=+pt_outer & -calendria_inner)\n", - "calendria = openmc.Cell(fill=clad, region=+calendria_inner & -calendria_outer)\n", - "\n", - "root_universe = openmc.Universe(cells=[bundle, pressure_tube, v1, calendria])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the final product. We'll export our geometry and materials and then use `plot_inline()` to get a nice-looking plot." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "geometry = openmc.Geometry(root_universe)\n", - "geometry.export_to_xml()\n", - "\n", - "materials = openmc.Materials(geometry.get_all_materials().values())\n", - "materials.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot = openmc.Plot.from_geometry(geometry)\n", - "plot.color_by = 'material'\n", - "plot.colors = {\n", - " fuel: 'black',\n", - " clad: 'silver',\n", - " heavy_water: 'blue'\n", - "}\n", - "plot.to_ipython_image()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interpreting Results\n", - "\n", - "One of the difficulties of a geometry like this is identifying tally results when there was no lattice involved. To address this, we specifically gave an ID to each fuel pin of the form 100\\*ring + azimuthal position. Consequently, we can use a distribcell tally and then look at our `DataFrame` which will show these cell IDs." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "settings.particles = 1000\n", - "settings.batches = 20\n", - "settings.inactive = 10\n", - "settings.source = openmc.Source(space=openmc.stats.Point())\n", - "settings.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "fuel_tally = openmc.Tally()\n", - "fuel_tally.filters = [openmc.DistribcellFilter(fuel_cell)]\n", - "fuel_tally.scores = ['flux']\n", - "\n", - "tallies = openmc.Tallies([fuel_tally])\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "openmc.run(output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The return code of `0` indicates that OpenMC ran successfully. Now let's load the statepoint into a `openmc.StatePoint` object and use the `Tally.get_pandas_dataframe(...)` method to see our results." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
level 1level 2level 3distribcellnuclidescoremeanstd. dev.
univcellunivcellunivcell
idididididid
03441100250totalflux0.2113850.010406
13441200251totalflux0.1820150.006170
23441201252totalflux0.1849110.005997
33441202253totalflux0.1931770.009182
43441203254totalflux0.1934220.007610
53441204255totalflux0.1888080.005277
63441205256totalflux0.1914240.003950
73441300257totalflux0.1549320.007590
83441301258totalflux0.1536880.006581
93441302259totalflux0.1614710.004471
1034413032510totalflux0.1512020.008047
1134413042511totalflux0.1539050.005394
1234413052512totalflux0.1532890.010077
1334413062513totalflux0.1715590.007727
1434413072514totalflux0.1638020.006032
1534413082515totalflux0.1537710.005325
1634413092516totalflux0.1590700.005367
1734413102517totalflux0.1500780.005973
1834413112518totalflux0.1500650.007176
1934414002519totalflux0.1054390.004338
2034414012520totalflux0.1085020.004957
2134414022521totalflux0.1030880.007733
2234414032522totalflux0.1022370.003835
2334414042523totalflux0.1071330.006022
2434414052524totalflux0.1041770.004377
2534414062525totalflux0.1102810.005753
2634414072526totalflux0.1031710.003678
2734414082527totalflux0.1035340.005595
2834414092528totalflux0.1072400.003574
2934414102529totalflux0.1081620.006636
3034414112530totalflux0.1185500.005535
3134414122531totalflux0.1144350.006232
3234414132532totalflux0.1092270.003769
3334414142533totalflux0.1131480.006165
3434414152534totalflux0.1120060.005454
3534414162535totalflux0.0999730.004764
3634414172536totalflux0.1049520.005613
\n", - "
" - ], - "text/plain": [ - " level 1 level 2 level 3 distribcell nuclide score mean \\\n", - " univ cell univ cell univ cell \n", - " id id id id id id \n", - "0 3 44 1 100 2 5 0 total flux 2.11e-01 \n", - "1 3 44 1 200 2 5 1 total flux 1.82e-01 \n", - "2 3 44 1 201 2 5 2 total flux 1.85e-01 \n", - "3 3 44 1 202 2 5 3 total flux 1.93e-01 \n", - "4 3 44 1 203 2 5 4 total flux 1.93e-01 \n", - "5 3 44 1 204 2 5 5 total flux 1.89e-01 \n", - "6 3 44 1 205 2 5 6 total flux 1.91e-01 \n", - "7 3 44 1 300 2 5 7 total flux 1.55e-01 \n", - "8 3 44 1 301 2 5 8 total flux 1.54e-01 \n", - "9 3 44 1 302 2 5 9 total flux 1.61e-01 \n", - "10 3 44 1 303 2 5 10 total flux 1.51e-01 \n", - "11 3 44 1 304 2 5 11 total flux 1.54e-01 \n", - "12 3 44 1 305 2 5 12 total flux 1.53e-01 \n", - "13 3 44 1 306 2 5 13 total flux 1.72e-01 \n", - "14 3 44 1 307 2 5 14 total flux 1.64e-01 \n", - "15 3 44 1 308 2 5 15 total flux 1.54e-01 \n", - "16 3 44 1 309 2 5 16 total flux 1.59e-01 \n", - "17 3 44 1 310 2 5 17 total flux 1.50e-01 \n", - "18 3 44 1 311 2 5 18 total flux 1.50e-01 \n", - "19 3 44 1 400 2 5 19 total flux 1.05e-01 \n", - "20 3 44 1 401 2 5 20 total flux 1.09e-01 \n", - "21 3 44 1 402 2 5 21 total flux 1.03e-01 \n", - "22 3 44 1 403 2 5 22 total flux 1.02e-01 \n", - "23 3 44 1 404 2 5 23 total flux 1.07e-01 \n", - "24 3 44 1 405 2 5 24 total flux 1.04e-01 \n", - "25 3 44 1 406 2 5 25 total flux 1.10e-01 \n", - "26 3 44 1 407 2 5 26 total flux 1.03e-01 \n", - "27 3 44 1 408 2 5 27 total flux 1.04e-01 \n", - "28 3 44 1 409 2 5 28 total flux 1.07e-01 \n", - "29 3 44 1 410 2 5 29 total flux 1.08e-01 \n", - "30 3 44 1 411 2 5 30 total flux 1.19e-01 \n", - "31 3 44 1 412 2 5 31 total flux 1.14e-01 \n", - "32 3 44 1 413 2 5 32 total flux 1.09e-01 \n", - "33 3 44 1 414 2 5 33 total flux 1.13e-01 \n", - "34 3 44 1 415 2 5 34 total flux 1.12e-01 \n", - "35 3 44 1 416 2 5 35 total flux 1.00e-01 \n", - "36 3 44 1 417 2 5 36 total flux 1.05e-01 \n", - "\n", - " std. dev. \n", - " \n", - " \n", - "0 1.04e-02 \n", - "1 6.17e-03 \n", - "2 6.00e-03 \n", - "3 9.18e-03 \n", - "4 7.61e-03 \n", - "5 5.28e-03 \n", - "6 3.95e-03 \n", - "7 7.59e-03 \n", - "8 6.58e-03 \n", - "9 4.47e-03 \n", - "10 8.05e-03 \n", - "11 5.39e-03 \n", - "12 1.01e-02 \n", - "13 7.73e-03 \n", - "14 6.03e-03 \n", - "15 5.33e-03 \n", - "16 5.37e-03 \n", - "17 5.97e-03 \n", - "18 7.18e-03 \n", - "19 4.34e-03 \n", - "20 4.96e-03 \n", - "21 7.73e-03 \n", - "22 3.84e-03 \n", - "23 6.02e-03 \n", - "24 4.38e-03 \n", - "25 5.75e-03 \n", - "26 3.68e-03 \n", - "27 5.59e-03 \n", - "28 3.57e-03 \n", - "29 6.64e-03 \n", - "30 5.53e-03 \n", - "31 6.23e-03 \n", - "32 3.77e-03 \n", - "33 6.16e-03 \n", - "34 5.45e-03 \n", - "35 4.76e-03 \n", - "36 5.61e-03 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n", - " output_tally = sp.get_tally()\n", - " df = output_tally.get_pandas_dataframe()\n", - " \n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that in the 'level 2' column, the 'cell id' tells us how each row corresponds to a ring and azimuthal position." - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/jupyter/capi.ipynb b/examples/jupyter/capi.ipynb deleted file mode 100644 index e696d6b01ec..00000000000 --- a/examples/jupyter/capi.ipynb +++ /dev/null @@ -1,475 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using the C/C++ API\n", - "This notebook shows how to use the OpenMC C/C++ API through the openmc.lib module. This module is particularly useful for multiphysics coupling because it allows you to update the density of materials and the temperatures of cells in memory, without stopping the simulation.\n", - "\n", - "Warning: these bindings are still somewhat experimental and may be subject to change in future versions of OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import openmc\n", - "import openmc.lib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate Input Files\n", - "\n", - "Let's start by creating a fuel rod geometry. We will make 10 zones in the z-direction which will allow us to make changes to each zone. Changes in temperature have to be made on the cell, so will make 10 cells in the axial direction. Changes in density have to be made on the material, so we will make 10 water materials. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Materials: we will make a fuel, helium, zircaloy, and 10 water materials. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "material_list = []" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "uo2 = openmc.Material(material_id=1, name='UO2 fuel at 2.4% wt enrichment')\n", - "uo2.set_density('g/cm3', 10.29769)\n", - "uo2.add_element('U', 1., enrichment=2.4)\n", - "uo2.add_element('O', 2.)\n", - "material_list.append(uo2)\n", - "\n", - "helium = openmc.Material(material_id=2, name='Helium for gap')\n", - "helium.set_density('g/cm3', 0.001598)\n", - "helium.add_element('He', 2.4044e-4)\n", - "material_list.append(helium)\n", - "\n", - "zircaloy = openmc.Material(material_id=3, name='Zircaloy 4')\n", - "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_element('Sn', 0.014, 'wo')\n", - "zircaloy.add_element('Fe', 0.00165, 'wo')\n", - "zircaloy.add_element('Cr', 0.001, 'wo')\n", - "zircaloy.add_element('Zr', 0.98335, 'wo')\n", - "material_list.append(zircaloy)\n", - "\n", - "for i in range(4, 14):\n", - " water = openmc.Material(material_id=i)\n", - " water.set_density('g/cm3', 0.7)\n", - " water.add_element('H', 2.0)\n", - " water.add_element('O', 1.0)\n", - " water.add_s_alpha_beta('c_H_in_H2O')\n", - " material_list.append(water)\n", - " \n", - "materials_file = openmc.Materials(material_list)\n", - "materials_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cells: we will make a fuel cylinder, a gap cylinder, a cladding cylinder, and a water exterior. Each one will be broken into 10 cells which are the 10 axial zones. The z_list is the list of axial positions that delimit those 10 zones. To keep track of all the cells, we will create lists: fuel_list, gap_list, clad_list, and water_list. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "pitch = 1.25984\n", - "fuel_or = openmc.ZCylinder(r=0.39218)\n", - "clad_ir = openmc.ZCylinder(r=0.40005)\n", - "clad_or = openmc.ZCylinder(r=0.4572)\n", - "left = openmc.XPlane(x0=-pitch/2)\n", - "right = openmc.XPlane(x0=pitch/2)\n", - "back = openmc.YPlane(y0=-pitch/2)\n", - "front = openmc.YPlane(y0=pitch/2)\n", - "z = [0., 30., 60., 90., 120., 150., 180., 210., 240., 270., 300.]\n", - "z_list = [openmc.ZPlane(z0=z_i) for z_i in z]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "left.boundary_type = 'reflective'\n", - "right.boundary_type = 'reflective'\n", - "front.boundary_type = 'reflective'\n", - "back.boundary_type = 'reflective'\n", - "z_list[0].boundary_type = 'vacuum'\n", - "z_list[-1].boundary_type = 'vacuum'" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "fuel_list = []\n", - "gap_list = []\n", - "clad_list = []\n", - "water_list = []\n", - "for i in range(1, 11):\n", - " fuel_list.append(openmc.Cell(cell_id=i))\n", - " gap_list.append(openmc.Cell(cell_id=i+10))\n", - " clad_list.append(openmc.Cell(cell_id=i+20))\n", - " water_list.append(openmc.Cell(cell_id=i+30))\n", - " \n", - "for j, fuels in enumerate(fuel_list):\n", - " fuels.region = -fuel_or & +z_list[j] & -z_list[j+1]\n", - " fuels.fill = uo2\n", - " fuels.temperature = 800.\n", - "\n", - "for j, gaps in enumerate(gap_list):\n", - " gaps.region = +fuel_or & -clad_ir & +z_list[j] & -z_list[j+1]\n", - " gaps.fill = helium\n", - " gaps.temperature = 700.\n", - "\n", - "for j, clads in enumerate(clad_list):\n", - " clads.region = +clad_ir & -clad_or & +z_list[j] & -z_list[j+1]\n", - " clads.fill = zircaloy\n", - " clads.temperature = 600.\n", - "\n", - "for j, waters in enumerate(water_list):\n", - " waters.region = +clad_or & +left & -right & +back & -front & +z_list[j] & -z_list[j+1]\n", - " waters.fill = material_list[j+3]\n", - " waters.temperature = 500." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "root = openmc.Universe(name='root universe')\n", - "root.add_cells(fuel_list)\n", - "root.add_cells(gap_list)\n", - "root.add_cells(clad_list)\n", - "root.add_cells(water_list)\n", - "geometry_file = openmc.Geometry(root)\n", - "geometry_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are coupling this externally to a heat transfer solver, you will want to know the heat deposited by each fuel cell. So let's create a cell filter for the recoverable fission heat. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "cell_filter = openmc.CellFilter(fuel_list)\n", - "t = openmc.Tally(tally_id=1)\n", - "t.filters.append(cell_filter)\n", - "t.scores = ['fission-q-recoverable']\n", - "tallies = openmc.Tallies([t])\n", - "tallies.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot our geometry to make sure it looks like we expect. Since we made new water materials in each axial cell, and we have centered the plot at 150, we should see one color for the water material in the bottom half and a different color for the water material in the top half. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "root.plot(basis='yz', width=[2, 10], color_by='material', origin=[0., 0., 150.], pixels=[400, 400])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Settings: everything will be standard except for the temperature settings. Since we will be working with specified temperatures, you will need temperature dependent data. I typically use the endf data found here: https://openmc.org/official-data-libraries/\n", - "Make sure your cross sections environment variable is pointing to temperature-dependent data before using the following settings." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "lower_left = [-0.62992, -pitch/2, 0]\n", - "upper_right = [+0.62992, +pitch/2, +300]\n", - "uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True)\n", - "\n", - "settings_file = openmc.Settings()\n", - "settings_file.batches = 100\n", - "settings_file.inactive = 10\n", - "settings_file.particles = 10000\n", - "settings_file.temperature = {'multipole': True, 'method': 'interpolation', 'range': [290, 2500]}\n", - "settings_file.source = openmc.source.Source(space=uniform_dist)\n", - "settings_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run a regular simulation, just use openmc.run(). \n", - "However, we want to run a simulation that we can stop in the middle and update the material and cell properties. So we will use openmc.lib." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "openmc.lib.init()\n", - "openmc.lib.simulation_init()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are 10 inactive batches, so we need to run next_batch() at least 10 times before the tally is activated. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "for _ in range(14):\n", - " openmc.lib.next_batch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the tally. There are 10 entries, one for each cell in the fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 4178272.4202991 ]\n", - " [ 9595363.82759911]\n", - " [12307462.30060902]\n", - " [11772927.66594472]\n", - " [11892601.29001472]\n", - " [12203397.88895767]\n", - " [12851791.20965905]\n", - " [11760027.45873386]\n", - " [ 9293110.94735569]\n", - " [ 4511597.61592287]]\n" - ] - } - ], - "source": [ - "t = openmc.lib.tallies[1]\n", - "print(t.mean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's make some changes to the temperatures. For this, we need to identify each cell by its id. We can use get_temperature() to compare the temperatures of the cells before and after the change. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fuel temperature is: \n", - "800.0\n", - "gap temperature is: \n", - "700.0\n", - "clad temperature is: \n", - "600.0\n", - "water temperature is: \n", - "500.00000000000006\n" - ] - } - ], - "source": [ - "print(\"fuel temperature is: \")\n", - "print(openmc.lib.cells[5].get_temperature())\n", - "print(\"gap temperature is: \")\n", - "print(openmc.lib.cells[15].get_temperature())\n", - "print(\"clad temperature is: \")\n", - "print(openmc.lib.cells[25].get_temperature())\n", - "print(\"water temperature is: \")\n", - "print(openmc.lib.cells[35].get_temperature())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1, 11):\n", - " temp = 900.0\n", - " openmc.lib.cells[i].set_temperature(temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fuel temperature is: \n", - "899.9999999999999\n" - ] - } - ], - "source": [ - "print(\"fuel temperature is: \")\n", - "print(openmc.lib.cells[5].get_temperature())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's make a similar change for the water density. Again, we need to identify each material by its id." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(4, 14):\n", - " density = 0.65\n", - " openmc.lib.materials[i].set_density(density, units='g/cm3')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The new batches we run will use the new material and cell properties." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "for _ in range(14):\n", - " openmc.lib.next_batch()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you're ready to end the simulation, use the following:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "openmc.lib.simulation_finalize()\n", - "openmc.lib.finalize()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml deleted file mode 120000 index 96f6fa8818e..00000000000 --- a/examples/jupyter/chain_simple.xml +++ /dev/null @@ -1 +0,0 @@ -../../tests/chain_simple.xml \ No newline at end of file diff --git a/examples/jupyter/expansion-filters.ipynb b/examples/jupyter/expansion-filters.ipynb deleted file mode 100644 index 3b0413a0582..00000000000 --- a/examples/jupyter/expansion-filters.ipynb +++ /dev/null @@ -1,1548 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Functional Expansions\n", - "OpenMC's general tally system accommodates a wide range of tally *filters*. While most filters are meant to identify regions of phase space that contribute to a tally, there are a special set of functional expansion filters that will multiply the tally by a set of orthogonal functions, e.g. Legendre polynomials, so that continuous functions of space or angle can be reconstructed from the tallied moments.\n", - "\n", - "In this example, we will determine the spatial dependence of the flux along the $z$ axis by making a Legendre polynomial expansion. Let us represent the flux along the z axis, $\\phi(z)$, by the function\n", - "\n", - "$$ \\phi(z') = \\sum\\limits_{n=0}^N a_n P_n(z') $$\n", - "\n", - "where $z'$ is the position normalized to the range [-1, 1]. Since $P_n(z')$ are known functions, our only task is to determine the expansion coefficients, $a_n$. By the orthogonality properties of the Legendre polynomials, one can deduce that the coefficients, $a_n$, are given by\n", - "\n", - "$$ a_n = \\frac{2n + 1}{2} \\int_{-1}^1 dz' P_n(z') \\phi(z').$$\n", - "\n", - "Thus, the problem reduces to finding the integral of the flux times each Legendre polynomial -- a problem which can be solved by using a Monte Carlo tally. By using a Legendre polynomial filter, we obtain stochastic estimates of these integrals for each polynomial order." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import openmc\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To begin, let us first create a simple model. The model will be a slab of fuel material with reflective boundaries conditions in the x- and y-directions and vacuum boundaries in the z-direction. However, to make the distribution slightly more interesting, we'll put some B4C in the middle of the slab." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Define fuel and B4C materials\n", - "fuel = openmc.Material()\n", - "fuel.add_element('U', 1.0, enrichment=4.5)\n", - "fuel.add_nuclide('O16', 2.0)\n", - "fuel.set_density('g/cm3', 10.0)\n", - "\n", - "b4c = openmc.Material()\n", - "b4c.add_element('B', 4.0)\n", - "b4c.add_element('C', 1.0)\n", - "b4c.set_density('g/cm3', 2.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Define surfaces used to construct regions\n", - "zmin, zmax = -10., 10.\n", - "box = openmc.model.rectangular_prism(10., 10., boundary_type='reflective')\n", - "bottom = openmc.ZPlane(z0=zmin, boundary_type='vacuum')\n", - "boron_lower = openmc.ZPlane(z0=-0.5)\n", - "boron_upper = openmc.ZPlane(z0=0.5)\n", - "top = openmc.ZPlane(z0=zmax, boundary_type='vacuum')\n", - "\n", - "# Create three cells and add them to geometry\n", - "fuel1 = openmc.Cell(fill=fuel, region=box & +bottom & -boron_lower)\n", - "absorber = openmc.Cell(fill=b4c, region=box & +boron_lower & -boron_upper)\n", - "fuel2 = openmc.Cell(fill=fuel, region=box & +boron_upper & -top)\n", - "geom = openmc.Geometry([fuel1, absorber, fuel2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the starting source, we'll use a uniform distribution over the entire box geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "spatial_dist = openmc.stats.Box(*geom.bounding_box)\n", - "settings.source = openmc.Source(space=spatial_dist)\n", - "settings.batches = 210\n", - "settings.inactive = 10\n", - "settings.particles = 1000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Defining the tally is relatively straightforward. One simply needs to list 'flux' as a score and then add an expansion filter. For this case, we will want to use the `SpatialLegendreFilter` class which multiplies tally scores by Legendre polynomials evaluated on normalized spatial positions along an axis." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a flux tally\n", - "flux_tally = openmc.Tally()\n", - "flux_tally.scores = ['flux']\n", - "\n", - "# Create a Legendre polynomial expansion filter and add to tally\n", - "order = 8\n", - "expand_filter = openmc.SpatialLegendreFilter(order, 'z', zmin, zmax)\n", - "flux_tally.filters.append(expand_filter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last thing we need to do is create a `Tallies` collection and export the entire model, which we'll do using the `Model` convenience class." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "tallies = openmc.Tallies([flux_tally])\n", - "model = openmc.model.Model(geometry=geom, settings=settings, tallies=tallies)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Running a simulation is now as simple as calling the `run()` method of `Model`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "sp_file = model.run(output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the run is finished, we need to load the results from the statepoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "with openmc.StatePoint(sp_file) as sp:\n", - " df = sp.tallies[flux_tally.id].get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've used the `get_pandas_dataframe()` method that returns tally data as a Pandas dataframe. Let's see what the raw data looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
spatiallegendrenuclidescoremeanstd. dev.
0P0totalflux36.5236010.081540
1P1totalflux-0.0028300.041466
2P2totalflux-4.4119230.027161
3P3totalflux0.0043160.020245
4P4totalflux-0.2772810.014558
5P5totalflux0.0106040.011350
6P6totalflux0.1092120.010280
7P7totalflux-0.0027050.009100
8P8totalflux-0.0884690.007889
\n", - "
" - ], - "text/plain": [ - " spatiallegendre nuclide score mean std. dev.\n", - "0 P0 total flux 3.65e+01 8.15e-02\n", - "1 P1 total flux -2.83e-03 4.15e-02\n", - "2 P2 total flux -4.41e+00 2.72e-02\n", - "3 P3 total flux 4.32e-03 2.02e-02\n", - "4 P4 total flux -2.77e-01 1.46e-02\n", - "5 P5 total flux 1.06e-02 1.13e-02\n", - "6 P6 total flux 1.09e-01 1.03e-02\n", - "7 P7 total flux -2.71e-03 9.10e-03\n", - "8 P8 total flux -8.85e-02 7.89e-03" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the expansion coefficients are given as\n", - "\n", - "$$ a_n = \\frac{2n + 1}{2} \\int_{-1}^1 dz' P_n(z') \\phi(z')$$\n", - "\n", - "we just need to multiply the Legendre moments by $(2n + 1)/2$." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "n = np.arange(order + 1)\n", - "a_n = (2*n + 1)/2 * df['mean']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To plot the flux distribution, we can use the `numpy.polynomial.Legendre` class which represents a truncated Legendre polynomial series. Since we really want to plot $\\phi(z)$ and not $\\phi(z')$ we first need to perform a change of variables. Since\n", - "\n", - "$$ \\lvert \\phi(z) dz \\rvert = \\lvert \\phi(z') dz' \\rvert $$\n", - "\n", - "and, for this case, $z = 10z'$, it follows that\n", - "\n", - "$$ \\phi(z) = \\frac{\\phi(z')}{10} = \\sum_{n=0}^N \\frac{a_n}{10} P_n(z'). $$" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "phi = np.polynomial.Legendre(a_n/10, domain=(zmin, zmax))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot it and see how our flux looks!" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux [n/src]')" - ] - }, - "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": [ - "z = np.linspace(zmin, zmax, 1000)\n", - "plt.plot(z, phi(z))\n", - "plt.xlabel('Z position [cm]')\n", - "plt.ylabel('Flux [n/src]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you might expect, we get a rough cosine shape but with a flux depression in the middle due to the boron slab that we introduced. To get a more accurate distribution, we'd likely need to use a higher order expansion.\n", - "\n", - "One more thing we can do is confirm that integrating the distribution gives us the same value as the first moment (since $P_0(z') = 1$). This can easily be done by numerically integrating using the trapezoidal rule:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36.523562389125146" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.trapz(phi(z), z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to being able to tally Legendre moments, there are also functional expansion filters available for spherical harmonics (`SphericalHarmonicsFilter`) and Zernike polynomials over a unit disk (`ZernikeFilter`). A separate `LegendreFilter` class can also be used for determining Legendre scattering moments (i.e., an expansion of the scattering cosine, $\\mu$)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Zernike polynomials\n", - "\n", - "Now let's look at an example of functional expansion tallies using Zernike polynomials as the basis functions.\n", - "\n", - "In this example, we will determine the spatial dependence of the flux along the radial direction $r'$ and $/$ or azimuthal angle $\\theta$ by making a Zernike polynomial expansion. Let us represent the flux along the radial and azimuthal direction, $\\phi(r', \\theta)$, by the function\n", - "\n", - "$$ \\phi(r', \\theta) = \\sum\\limits_{n=0}^N \\sum\\limits_{m=-n}^n a_n^m Z_n^m(r', \n", - "\\theta) $$\n", - "\n", - "where $r'$ is the position normalized to the range [0, r] (r is the radius of cylindrical geometry), and the azimuthal lies within the range [0, $ 2\\pi$]. \n", - "\n", - "Since $Z_n^m(r', \\theta)$ are known functions, we need to determine the expansion coefficients, $a_n^m$. By the orthogonality properties of the Zernike polynomials, one can deduce that the coefficients, $a_n^m$, are given by\n", - "\n", - "$$ a_n^m = k_n^m \\int_{0}^r dr' \\int_{0}^{2\\pi} d\\theta Z_n^m(r',\\theta) \\phi(r', \\theta).$$\n", - "$$ k_n^m = \\frac{2n + 2}{\\pi}, m \\ne 0. $$\n", - "$$ k_n^m = \\frac{n+1}{\\pi}, m = 0.$$\n", - "\n", - "Similarly, the problem reduces to finding the integral of the flux times each Zernike polynomial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To begin with, let us first create a simple model. The model will be a pin-cell fuel material with vacuum boundary condition in both radial direction and axial direction." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Define fuel \n", - "fuel = openmc.Material()\n", - "fuel.add_element('U', 1.0, enrichment=5.0)\n", - "fuel.add_nuclide('O16', 2.0)\n", - "fuel.set_density('g/cm3', 10.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Define surfaces used to construct regions\n", - "zmin, zmax, radius = -1., 1., 0.5 \n", - "pin = openmc.ZCylinder(x0=0.0, y0=0.0, r=radius, boundary_type='vacuum')\n", - "bottom = openmc.ZPlane(z0=zmin, boundary_type='vacuum')\n", - "top = openmc.ZPlane(z0=zmax, boundary_type='vacuum')\n", - "\n", - "# Create three cells and add them to geometry\n", - "fuel = openmc.Cell(fill=fuel, region= -pin & +bottom & -top)\n", - "geom = openmc.Geometry([fuel])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the starting source, we'll use a uniform distribution over the entire box geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "settings = openmc.Settings()\n", - "spatial_dist = openmc.stats.Box(*geom.bounding_box)\n", - "settings.source = openmc.Source(space=spatial_dist)\n", - "settings.batches = 100\n", - "settings.inactive = 20\n", - "settings.particles = 100000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Defining the tally is relatively straightforward. One simply needs to list 'flux' as a score and then add an expansion filter. For this case, we will want to use the `SpatialLegendreFilter`, `ZernikeFilter`, `ZernikeRadialFilter` classes which multiplies tally scores by Legendre, azimuthal Zernike and radial-only Zernike polynomials evaluated on normalized spatial positions along radial and axial directions." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a flux tally\n", - "flux_tally_legendre = openmc.Tally()\n", - "flux_tally_legendre.scores = ['flux']\n", - "\n", - "# Create a Legendre polynomial expansion filter and add to tally\n", - "order = 10\n", - "cell_filter = openmc.CellFilter(fuel)\n", - "legendre_filter = openmc.SpatialLegendreFilter(order, 'z', zmin, zmax)\n", - "flux_tally_legendre.filters = [cell_filter, legendre_filter]\n", - "\n", - "# Create a Zernike azimuthal polynomial expansion filter and add to tally \n", - "flux_tally_zernike = openmc.Tally()\n", - "flux_tally_zernike.scores = ['flux']\n", - "zernike_filter = openmc.ZernikeFilter(order=order, x=0.0, y=0.0, r=radius)\n", - "flux_tally_zernike.filters = [cell_filter, zernike_filter]\n", - "\n", - "# Create a Zernike radial polynomial expansion filter and add to tally \n", - "flux_tally_zernike1d = openmc.Tally()\n", - "flux_tally_zernike1d.scores = ['flux']\n", - "zernike1d_filter = openmc.ZernikeRadialFilter(order=order, x=0.0, y=0.0, r=radius)\n", - "flux_tally_zernike1d.filters = [cell_filter, zernike1d_filter]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The last thing we need to do is create a `Tallies` collection and export the entire model, which we'll do using the `Model` convenience class." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "tallies = openmc.Tallies([flux_tally_legendre, flux_tally_zernike, flux_tally_zernike1d])\n", - "model = openmc.model.Model(geometry=geom, settings=settings, tallies=tallies)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Running a simulation is now as simple as calling the `run()` method of `Model`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "sp_file = model.run(output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that the run is finished, we need to load the results from the statepoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "with openmc.StatePoint(sp_file) as sp:\n", - " df1 = sp.tallies[flux_tally_legendre.id].get_pandas_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've used the `get_pandas_dataframe()` method that returns tally data as a Pandas dataframe. Let's see what the raw data looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cellspatiallegendrenuclidescoremeanstd. dev.
04P0totalflux0.5434250.000599
14P1totalflux0.0006350.000523
24P2totalflux-0.0559890.000342
34P3totalflux-0.0000530.000284
44P4totalflux-0.0007490.000230
54P5totalflux0.0001110.000149
64P6totalflux-0.0006920.000177
74P7totalflux-0.0000640.000152
84P8totalflux-0.0001390.000142
94P9totalflux0.0002010.000119
104P10totalflux-0.0000510.000112
\n", - "
" - ], - "text/plain": [ - " cell spatiallegendre nuclide score mean std. dev.\n", - "0 4 P0 total flux 5.43e-01 5.99e-04\n", - "1 4 P1 total flux 6.35e-04 5.23e-04\n", - "2 4 P2 total flux -5.60e-02 3.42e-04\n", - "3 4 P3 total flux -5.27e-05 2.84e-04\n", - "4 4 P4 total flux -7.49e-04 2.30e-04\n", - "5 4 P5 total flux 1.11e-04 1.49e-04\n", - "6 4 P6 total flux -6.92e-04 1.77e-04\n", - "7 4 P7 total flux -6.41e-05 1.52e-04\n", - "8 4 P8 total flux -1.39e-04 1.42e-04\n", - "9 4 P9 total flux 2.01e-04 1.19e-04\n", - "10 4 P10 total flux -5.05e-05 1.12e-04" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the scaling factors for expansion coefficients will be provided by the Python API, thus, we do not need to multiply the moments by scaling factors." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "a_n = df1['mean']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading the coefficients is realized via calling the OpenMC Python API as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "phi = openmc.legendre_from_expcoef(a_n, domain=(zmin, zmax))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot it and see how our flux looks!" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux [n/src]')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "z = np.linspace(zmin, zmax, 1000)\n", - "plt.plot(z, phi(z))\n", - "plt.xlabel('Z position [cm]')\n", - "plt.ylabel('Flux [n/src]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A rough cosine shape is obtained. \n", - "One can also numerically integrate the function using the trapezoidal rule." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.543424143829605" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.trapz(phi(z), z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following cases show how to reconstruct the flux distribution Zernike polynomials tallied results." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "with openmc.StatePoint(sp_file) as sp:\n", - " df2 = sp.tallies[flux_tally_zernike.id].get_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cellzernikenuclidescoremeanstd. dev.
04Z0,0totalflux0.5434250.000599
14Z1,-1totalflux-0.0002360.000398
24Z1,1totalflux0.0001260.000325
34Z2,-2totalflux-0.0001040.000206
44Z2,0totalflux-0.0642910.000404
.....................
614Z10,2totalflux-0.0001300.000099
624Z10,4totalflux-0.0000570.000092
634Z10,6totalflux-0.0000480.000109
644Z10,8totalflux-0.0000560.000092
654Z10,10totalflux-0.0000460.000098
\n", - "

66 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " cell zernike nuclide score mean std. dev.\n", - "0 4 Z0,0 total flux 5.43e-01 5.99e-04\n", - "1 4 Z1,-1 total flux -2.36e-04 3.98e-04\n", - "2 4 Z1,1 total flux 1.26e-04 3.25e-04\n", - "3 4 Z2,-2 total flux -1.04e-04 2.06e-04\n", - "4 4 Z2,0 total flux -6.43e-02 4.04e-04\n", - ".. ... ... ... ... ... ...\n", - "61 4 Z10,2 total flux -1.30e-04 9.86e-05\n", - "62 4 Z10,4 total flux -5.70e-05 9.21e-05\n", - "63 4 Z10,6 total flux -4.79e-05 1.09e-04\n", - "64 4 Z10,8 total flux -5.59e-05 9.23e-05\n", - "65 4 Z10,10 total flux -4.58e-05 9.76e-05\n", - "\n", - "[66 rows x 6 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the flux in radial direction with specific azimuthal angle ($\\theta = 0.0$)." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "z_n = df2['mean'] \n", - "zz = openmc.Zernike(z_n, radius)\n", - "rr = np.linspace(0, radius, 100)\n", - "plt.plot(rr, zz(rr, 0.0)) \n", - "plt.xlabel('Radial position [cm]')\n", - "plt.ylabel('Flux')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A polar figure with all azimuthal can be plotted like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "z_n = df2['mean']\n", - "zz = openmc.Zernike(z_n, radius=radius) \n", - "#\n", - "# Using linspace so that the endpoint of 360 is included...\n", - "azimuths = np.radians(np.linspace(0, 360, 50))\n", - "zeniths = np.linspace(0, radius, 100)\n", - "r, theta = np.meshgrid(zeniths, azimuths)\n", - "values = zz(zeniths, azimuths)\n", - "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\n", - "ax.contourf(theta, r, values, cmap='jet')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes, we just need the radial-only Zernike polynomial tallied flux distribution. \n", - "Let us extract the tallied coefficients first." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "with openmc.StatePoint(sp_file) as sp:\n", - " df3 = sp.tallies[flux_tally_zernike1d.id].get_pandas_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
cellzernikeradialnuclidescoremeanstd. dev.
04Z0,0totalflux0.5434250.000599
14Z2,0totalflux-0.0642910.000404
24Z4,0totalflux-0.0006010.000223
34Z6,0totalflux-0.0004540.000227
44Z8,0totalflux-0.0000110.000166
54Z10,0totalflux-0.0001020.000161
\n", - "
" - ], - "text/plain": [ - " cell zernikeradial nuclide score mean std. dev.\n", - "0 4 Z0,0 total flux 5.43e-01 5.99e-04\n", - "1 4 Z2,0 total flux -6.43e-02 4.04e-04\n", - "2 4 Z4,0 total flux -6.01e-04 2.23e-04\n", - "3 4 Z6,0 total flux -4.54e-04 2.27e-04\n", - "4 4 Z8,0 total flux -1.15e-05 1.66e-04\n", - "5 4 Z10,0 total flux -1.02e-04 1.61e-04" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A plot along with r-axis is also done." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Flux')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "z_n = df3['mean'] \n", - "zz = openmc.ZernikeRadial(z_n, radius=radius)\n", - "rr = np.linspace(0, radius, 50)\n", - "plt.plot(rr, zz(rr)) \n", - "plt.xlabel('Radial position [cm]')\n", - "plt.ylabel('Flux')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, we can also re-construct the polar figure based on radial-only Zernike polinomial coefficients. " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "z_n = df3['mean'] \n", - "zz = openmc.ZernikeRadial(z_n, radius=radius)\n", - "azimuths = np.radians(np.linspace(0, 360, 50))\n", - "zeniths = np.linspace(0, radius, 100)\n", - "r, theta = np.meshgrid(zeniths, azimuths)\n", - "values = [[i for i in zz(zeniths)] for j in range(len(azimuths))]\n", - "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'), figsize=(6,6))\n", - "ax.contourf(theta, r, values, cmap='jet')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on Legendre polynomial coefficients and the azimuthal or radial-only Zernike coefficient, it's possible to reconstruct the flux both on radial and axial directions. " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Reconstruct 3-D flux based on radial only Zernike and Legendre polynomials\n", - "z_n = df3['mean'] \n", - "zz = openmc.ZernikeRadial(z_n, radius=radius)\n", - "azimuths = np.radians(np.linspace(0, 360, 100)) # azimuthal mesh \n", - "zeniths = np.linspace(0, radius, 100) # radial mesh \n", - "zmin, zmax = -1.0, 1.0 \n", - "z = np.linspace(zmin, zmax, 100) # axial mesh \n", - "# \n", - "# flux = np.matmul(np.matrix(phi(z)).transpose(), np.matrix(zz(zeniths))) \n", - "# flux = np.array(flux) # change np.matrix to np.array\n", - "# np.matrix is not recommended for use anymore\n", - "flux = np.array([phi(z)]).T @ np.array([zz(zeniths)])\n", - "#\n", - "plt.figure(figsize=(5,10))\n", - "plt.title('Flux distribution')\n", - "plt.xlabel('Radial Position [cm]')\n", - "plt.ylabel('Axial Height [cm]')\n", - "plt.pcolor(zeniths, z, flux, cmap='jet')\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One can also reconstruct the 3D flux distribution based on Legendre and Zernike polynomial tallied coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# Define needed function first \n", - "def cart2pol(x, y):\n", - " rho = np.sqrt(x**2 + y**2)\n", - " phi = np.arctan2(y, x)\n", - " return(rho, phi)\n", - "\n", - "# Reconstruct 3-D flux based on azimuthal Zernike and Legendre polynomials\n", - "z_n = df2['mean']\n", - "zz = openmc.Zernike(z_n, radius=radius) \n", - "#\n", - "xstep = 2.0*radius/20\n", - "hstep = (zmax - zmin)/20\n", - "x = np.linspace(-radius, radius, 50)\n", - "x = np.array(x)\n", - "[X,Y] = np.meshgrid(x,x)\n", - "h = np.linspace(zmin, zmax, 50)\n", - "h = np.array(h)\n", - "[r, theta] = cart2pol(X,Y)\n", - "flux3d = np.zeros((len(x), len(x), len(h)))\n", - "flux3d.fill(np.nan)\n", - "#\n", - "for i in range(len(x)):\n", - " for j in range(len(x)):\n", - " if r[i][j]<=radius:\n", - " for k in range(len(h)):\n", - " flux3d[i][j][k] = phi(h[k]) * zz(r[i][j], theta[i][j])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us print out with VTK format." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# You'll need to install pyevtk as a prerequisite\n", - "from pyevtk.hl import gridToVTK\n", - "import numpy as np\n", - "#\n", - "# Dimensions\n", - "nx, ny, nz = len(x), len(x), len(h)\n", - "lx, ly, lz = 2.0*radius, 2.0*radius, (zmax-zmin)\n", - "dx, dy, dz = lx/nx, ly/ny, lz/nz\n", - "#\n", - "ncells = nx * ny * nz\n", - "npoints = (nx + 1) * (ny + 1) * (nz + 1)\n", - "#\n", - "# Coordinates\n", - "x = np.arange(0, lx + 0.1*dx, dx, dtype='float64')\n", - "y = np.arange(0, ly + 0.1*dy, dy, dtype='float64')\n", - "z = np.arange(0, lz + 0.1*dz, dz, dtype='float64')\n", - "# Print out \n", - "path = gridToVTK(\"./rectilinear\", x, y, z, cellData = {\"flux3d\" : flux3d})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use VisIt or ParaView to plot it as you want. Then, the plot can be loaded and shown as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f1 = plt.imread('./images/flux3d.png')\n", - "plt.imshow(f1, cmap='jet')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb deleted file mode 100644 index 07c3ab8344c..00000000000 --- a/examples/jupyter/hexagonal-lattice.ipynb +++ /dev/null @@ -1,411 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Modeling Hexagonal Lattices\n", - "In this example, we will create a hexagonal lattice and show how the orientation can be changed via the cell rotation property. Let's first just set up some materials and universes that we will use to fill the lattice." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import openmc" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fuel = openmc.Material(name='fuel')\n", - "fuel.add_nuclide('U235', 1.0)\n", - "fuel.set_density('g/cm3', 10.0)\n", - "\n", - "fuel2 = openmc.Material(name='fuel2')\n", - "fuel2.add_nuclide('U238', 1.0)\n", - "fuel2.set_density('g/cm3', 10.0)\n", - "\n", - "water = openmc.Material(name='water')\n", - "water.add_nuclide('H1', 2.0)\n", - "water.add_nuclide('O16', 1.0)\n", - "water.set_density('g/cm3', 1.0)\n", - "\n", - "materials = openmc.Materials((fuel, fuel2, water))\n", - "materials.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our three materials, we will set up two universes that represent pin-cells: one with a small pin and one with a big pin. Since we will be using these universes in a lattice, it's always a good idea to have an \"outer\" universe as well that is applied outside the defined lattice." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "r_pin = openmc.ZCylinder(r=0.25)\n", - "fuel_cell = openmc.Cell(fill=fuel, region=-r_pin)\n", - "water_cell = openmc.Cell(fill=water, region=+r_pin)\n", - "pin_universe = openmc.Universe(cells=(fuel_cell, water_cell))\n", - "\n", - "r_big_pin = openmc.ZCylinder(r=0.5)\n", - "fuel2_cell = openmc.Cell(fill=fuel2, region=-r_big_pin)\n", - "water2_cell = openmc.Cell(fill=water, region=+r_big_pin)\n", - "big_pin_universe = openmc.Universe(cells=(fuel2_cell, water2_cell))\n", - "\n", - "all_water_cell = openmc.Cell(fill=water)\n", - "outer_universe = openmc.Universe(cells=(all_water_cell,))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create a hexagonal lattice using the `HexLattice` class:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "lattice = openmc.HexLattice()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to set the `center` of the lattice, the `pitch`, an `outer` universe (which is applied to all lattice elements outside of those that are defined), and a list of `universes`. Let's start with the easy ones first. Note that for a 2D lattice, we only need to specify a single number for the pitch." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "lattice.center = (0., 0.)\n", - "lattice.pitch = (1.25,)\n", - "lattice.outer = outer_universe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to set the `universes` property on our lattice. It needs to be set to a list of lists of Universes, where each list of Universes corresponds to a ring of the lattice. The rings are ordered from outermost to innermost, and within each ring the indexing starts at the \"top\". To help visualize the proper indices, we can use the `show_indices()` helper method." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (0, 0)\n", - " (0,17) (0, 1)\n", - " (0,16) (1, 0) (0, 2)\n", - "(0,15) (1,11) (1, 1) (0, 3)\n", - " (1,10) (2, 0) (1, 2)\n", - "(0,14) (2, 5) (2, 1) (0, 4)\n", - " (1, 9) (3, 0) (1, 3)\n", - "(0,13) (2, 4) (2, 2) (0, 5)\n", - " (1, 8) (2, 3) (1, 4)\n", - "(0,12) (1, 7) (1, 5) (0, 6)\n", - " (0,11) (1, 6) (0, 7)\n", - " (0,10) (0, 8)\n", - " (0, 9)\n" - ] - } - ], - "source": [ - "print(lattice.show_indices(num_rings=4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n", - "\n", - "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", - "\n", - "We build these rings through 'list concatenation' as follows: " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "outer_ring = [big_pin_universe] + [pin_universe]*17 # Adds up to 18\n", - "\n", - "ring_1 = [big_pin_universe] + [pin_universe]*11 # Adds up to 12\n", - "\n", - "ring_2 = [big_pin_universe] + [pin_universe]*5 # Adds up to 6\n", - "\n", - "inner_ring = [big_pin_universe]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now assign the rings (and the universes they contain) to our lattice. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HexLattice\n", - "\tID =\t4\n", - "\tName =\t\n", - "\tOrientation =\ty\n", - "\t# Rings =\t4\n", - "\t# Axial =\tNone\n", - "\tCenter =\t(0.0, 0.0)\n", - "\tPitch =\t(1.25,)\n", - "\tOuter =\t3\n", - "\tUniverses \n", - " 2\n", - " 1 1\n", - " 1 2 1\n", - "1 1 1 1\n", - " 1 2 1\n", - "1 1 1 1\n", - " 1 2 1\n", - "1 1 1 1\n", - " 1 1 1\n", - "1 1 1 1\n", - " 1 1 1\n", - " 1 1\n", - " 1\n" - ] - } - ], - "source": [ - "lattice.universes = [outer_ring, \n", - " ring_1, \n", - " ring_2,\n", - " inner_ring]\n", - "print(lattice)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's put our lattice inside a circular cell that will serve as the top-level cell for our geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n", - "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", - "geometry = openmc.Geometry([main_cell])\n", - "geometry.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's create a plot to see what our geometry looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot = openmc.Plot.from_geometry(geometry)\n", - "plot.color_by = 'material'\n", - "plot.colors = colors = {\n", - " water: 'blue',\n", - " fuel: 'olive',\n", - " fuel2: 'yellow'\n", - "}\n", - "plot.to_ipython_image()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, if we wanted to simulate the model, we would need to create an instance of `openmc.Settings`, export it to XML, and run." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lattice orientation\n", - "\n", - "Now let's say we want our hexagonal lattice orientated such that two sides of the lattice are parallel to the x-axis. This can be achieved by two means: either we can rotate the cell that contains the lattice, or we can can change the `HexLattice.orientation` attribute. By default, the `orientation` is set to \"y\", indicating that two sides of the lattice are parallel to the y-axis, but we can also change it to \"x\" to make them parallel to the x-axis." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Change the orientation of the lattice and re-export the geometry\n", - "lattice.orientation = 'x'\n", - "geometry.export_to_xml()\n", - "\n", - "# Run OpenMC in plotting mode\n", - "plot.to_ipython_image()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When we change the orientation to 'x', you can see that the first universe in each ring starts to the right along the x-axis. As before, the universes are defined in a clockwise fashion around each ring. To see the proper indices for a hexagonal lattice in this orientation, we can again call `show_indices` but pass an extra orientation argument:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " (0,12) (0,13) (0,14) (0,15)\n", - "\n", - " (0,11) (1, 8) (1, 9) (1,10) (0,16)\n", - "\n", - " (0,10) (1, 7) (2, 4) (2, 5) (1,11) (0,17)\n", - "\n", - "(0, 9) (1, 6) (2, 3) (3, 0) (2, 0) (1, 0) (0, 0)\n", - "\n", - " (0, 8) (1, 5) (2, 2) (2, 1) (1, 1) (0, 1)\n", - "\n", - " (0, 7) (1, 4) (1, 3) (1, 2) (0, 2)\n", - "\n", - " (0, 6) (0, 5) (0, 4) (0, 3)\n" - ] - } - ], - "source": [ - "print(lattice.show_indices(4, orientation='x'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hexagonal prisms\n", - "\n", - "OpenMC also contains a convenience function that can create a hexagonal prism representing the interior region of six surfaces defining a hexagon. This can be useful as a bounding surface of a hexagonal lattice. For example, if we wanted the outer boundary of our geometry to be hexagonal, we could change the `region` of the main cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "main_cell.region = openmc.model.hexagonal_prism(\n", - " edge_length=4*lattice.pitch[0],\n", - " orientation='x',\n", - " boundary_type='vacuum'\n", - ")\n", - "geometry.export_to_xml()\n", - "\n", - "# Run OpenMC in plotting mode\n", - "plot.color_by = 'cell'\n", - "plot.to_ipython_image()" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/jupyter/images/cylinder_mesh.png b/examples/jupyter/images/cylinder_mesh.png deleted file mode 100644 index cd9638060d199d877c0065e7a6448b81921c0aca..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9928 zcmeHt<6(C~3^G)NtYq;yIs-Hp@_0paK)q#HfrKpK&55u_UqIF2s4LmB~a#PjUT?#}M)m))J&*?MniphZFYh!g+-P(0UGHv$0gApdZY82_K9BioPQ zAH(xE(ozM~PcZNO69}AD^i=?W#uTzYcA$SUyMwloJ^&ER0|10a0{}PwRN=b-z#CBj z;3pISkj()A7<@i;zK{n1@BxN;CK~??{{H>@kNzM0Uj+UaM!*?%;PB6F#@8B<*T&vX zuLJD-UI7g4T)kdDG0-q!hdq5F@#L9e(pMM&zzKe?u3{2sbC|yvxx`1V-t_nHl$>YO zxW)&82+!cbp!xA@8MzAs;ev9wVxC2FC%-j2}H0A2O0!VEG}LikX>*9 z8H8^IE<~d4D(^evMx$TNnRwu%5>IWn8pV8opVGML@e=TBFp|X^V~L_s+lu6p8Q~!~ z!jDk!#MRGJO-14h{5NVE8ehW46P$H3i!m50lc>Suy*$Io6|pftC>f{JR1X`1ihY9s zzm*BjX~n~%30cQlR1Hw|kpU6%CU5MeyWQ|O%EZ33LVTorkh8=4OVIRIft>p3!C#l9IC$bzDyM6qDKS4+x02N!AjZ+3T?-2)7dT%aYnbnZxPjL%Cxs zpAVDkK|>%-?%Fwi!Txms0HFx|I?;!xQ7Wvo_Xo;X(#T1mihuqH4 zBygkL3iT2C5OIn+$Kj!G5~jsWyny>^2j;xQ2)#N?LN%K-gHiAGXHc}z4k$rl#;QL} ze$aoIV$rh5PqXWj80(k=ztlll$veGrf+5ZaLe!~i^jnQ_F~7cG+`Lgkr8%Bmn?lh; z8$p|)NBPUX-@}5**jcRzw(TqU`%^M+9;Xnk0`|LVCn<*Z@D8;E>l>_Q-*N6ACP!^0 zI+P~8nAKu8b%8vJz7|AClV(dLmJ`f~l&S;Kq8rK^s|%0t#~fnvEXT!WxTr>Aj$lQ2 z0nOti2ltJ1qGl(0Q5%H79FtcMbM|ja9p3yGH5VSlQoF=9+SJ_=X|G@nWgw1xH9nlk zsZi}__Q(w-_TEQq2;E2H+vVhnHZ>GztBVFD%Nc5)%7gO86>Y#f2MO=931HH-si z+>4r)NMeirZ^`cWn5vLEIk>GSJSAyXh{mQocR{g{Sgz70L=?rw_=>hbj`%2VD0GYU$kAiHF+W@H)i zmH3f4w?w*CbMu12G+oB5!KX}y4=M8{Hl{r`m`J4+Tzw;@>Zw*vV_=bk=P^xe_Bnvc zMlBzpiwwPw=_}S)*p^XGOyzyr?l-S8SvJ6HZ~^XP)6j8@>caHZnR70FnWXjD>hym= zUI>5jiTIi0S9Of!o8iyx@AuBG?DjfdH1ND{CL}k+kF^>AYkw}M%&7dhw%5)xtDD+4 z5)RNM*Fec(uM4oSTM`eLW?3+|QKpaq85<+oqSmZhXE7xJnG`a`3E*W2{8eDB-uD&J4sLG z{A07ubi+BOpzXVG&s%X1HwxB?Mp(8rqsJ6@69y%#YL}9q0f%rxN>gy~x@qTptyAiA ziy8uO0!3*$4@?3}l6{vHcAC3N*c?5t;Lz{of}S+7AevDW)IM~$uoSRgFrpH+|MnAF zXDlQ?Tfb>Z+Nt3e1cNXjXoJfKH(XdO>Sz!G&15GA0J*ydqBm=j&VTgGS=R2}x;oeD zqP|4uF`QLO6&(<0x`e=$l|Q-V*^2zRi;h%2xSrrrp`t$tZb*JsFC8;fce?tRn zW-MlZ?`U@7YO<@iMG+29>cZ=6mPsYoOR@&#j=TB=Vuu8ZQ$=c)WuOtUf{D{Vv!hFw z3G0b@1JS2ltY)W+rIL3a*Z_wTyDXc-ScaGf!(&X=q(Sk6 zK4ZIeR_qWOzYo{#qSObUmxe_kBH4@yGTW>Nu{8Tmuf(xwyvs$9cLiQh$3~$BJDlfN zLME_qF%8kgr|E6a!eG&+Ygg!h;#C3$E5tSTgA~3EfiJwuKfXIBL!&2g&s$LQXzcoAlqy;akd_1 zy({9oksr*&RAi>zxLP#(X`qH;Rv{T5+H!<$akX{{Y0VkJRg2%P%gHZtOXHbVr@6pC)jBs(U`?Ax zMWb9AfkeNsdh?@9&!`WE!n*^afRATlqFC4$-zxQL9Sc)-b?m)hzPk?kv*cMxFSoQ8 zM;lcgGoVvZ=M!cBuF=n`koZs#$}-hE3&311n6B9c!n^5;SY=p+?g(lr+fI`1`X{p zPDd|axEbmkT0#3$4Rql)kOyUb>oo5y{c#+}(1Sa;|b_~{r=GM0Hb zG33L`iklM^Nm>HyMw-X$YwLp>`UK4pxeadbb4g9T$s#Y*tkeXgco|b8BYfZawl$Oi zasIf&hjR}h=V^(;Jxmz46fz;5-zsE&6p>)=HP+n$O#IwCzBy5>6Ne!%@iwcAJ38-L zoP*9!Ld(QK*LmsQ!keK_&gk=JqQk$~^zjac200A<+_Lm1NJ6$q4Lkv0gcBU~gbqx^ z^F-$+{_3NEc|APS#KV`N=9k87|RH>r)bU68m}A!yS{Lw zn`xN|Yc43dNNmv{Rb7I6*@N{4%^FUKFn#uq@a2@VpCJNNE(Nmj$WjhdP9H)Be~Ck1 zRF2%L=aH^EKK~L74@$C!zm+i&bwtU*G2-zWL&>HW^&7n8D`nWzbIq{pwSv@Cef2qT zI8o`b_He0v_?!AUJj)9A7at|2% zz9x$eueOS&fwX~ShxhYh=iq_fzzCin_+9=zv!*9-z$^hWbA!n0s7gMSlzf1Lx)+*v zAZgt={A9Q~ylde#gW4(tOy7ALWG&$GKC=R^Qj2Sh#d+(o%<4uGbnIAFvOohv$kjyv z`CCKeiStdLw)zFYq6_GZQ`=|!L5{$QuJu7XK6*%oJBMa+)5T{G0->{XMlm0l;DG52 zZSBgny8#}V2h`vGc~h=^8k6vj)j5S(2waS19R&#mKo#q!a8?86Oi4xrLbDBvE@f2{GxFs_c$yA=Ql=Pw35n=FIECP#^ z=}~54<-nGs)6pBL`Jh>m3|UA(y#?_TZO~l!8#U%sTtIl;-_^30q{gwGFRkOWX z0I~@J7H0|<&bZT8$d~{%bA+^p+SvpDOZEoUDzTOg8-NlLqJF=i0C#?0Hz*IlcT3Tb zxr;o!$rwZTS;`~%IrG9MdQ=+^S5S^T=J;8ehTpm5z(Pk3B=mtMfPJpI8M=Nx$af)a z7}U5*!IjXK(zGt?PV!ON_79aUF^~}KMAdq9I_K?JFj_=>hzmYf;1Yjl3{?p=g#YHl zaGb0s=2WO`?Zi25 z826GKj0An%U*fq%O;IW!d6%-6K9Mf8Q=S^5IjYmYl=jA&tO)Yuh4`LuU{E*l`;=H( zFWt`Xdqmh$AUc0LDPwlomnD<*8?SD~VPB|M1ttBk*;!lRP4B*JHrW%vzPqC@GS{&> zs)B4P%Om7JKIGjhC)lbC`wG*UUf!yXPiE6?<-CB~Giu8qZJ*xniAD8lt-inOLbGlX zKFFQBV=t6*oFiO({_Eg)bBQz7ft`4NqORY>o`KL?sZ$924Z7C`dVvd-!5%G=7T9|e z?3*OY#K(NDU-R+{=!}7Dd8su&QI8)oDHk^1pZcZO(($vte|5mUTTuGK$>f1Ev7LOf zK~;bKHuepjpyx9Ff!@zaLDQ?tylxX8UdSnd(Q|cOXc2VJgbGjaVTVp*FR2Hq)3YeN z{|wSTh-NYkmNpR4Eq)~5j0|k}nctXzDbbJEsrYrCtvJ#S-LryrfLCa1hK%WM12B)^ zeOxf#gUJWpAh=38#YS=OT$%h*npDKq+(v#Bs2s@VF*(wtYb}-Aru7wU*p=N4jj2nC zsFV6@iLROKpPpl`aa5?Q9YNAq+iYgja{^%$iTLk-y+9vBct95tYX~iJ3`LFbYYOjwkRCC=$#a* zcr+LQ33?M|FQ)o5S#e1f=y74*4Zngi>AuahTXTgU$j(-PLC2E7~ydzQyizwZGkZ9A3SMyzHQ-9+qg)WL8Gbw0YIkZ@X zPtfhx_OjjnRt3$6v9=yr-gEiz4{-+N6A4Pe!aoqQ__q?Y`rPs$UtxP0JVws#k-XoG zqQbvB5Q|EZ)Uv#vB$qcFlNCv#`}-iQmff!$7c8@;#~Gg`*)KQyUsT;g+dPLbQ;`6% zjEKZ!-3zw?uSWg>08Pq5L>EmBrr^?@Yts@l+X3(u2_T5in~Ta~twK~{eUa9SF-Hcg z=9qicI`LFXx_&p-s6_A>gIFVswjeNeZYRKr`gk|svhsemqJuSSWYSv5FJ8rD<^XxDUU=xR*?L&Nl5Hrz>#S8_}GdB_CP%ZF9x2J zBCke`$(FKSofzi6{GmIs5oa$|QNpMv`(uhwo7l2qk0Odhd(1X_PZ~R%wTW-K{Mi{D zS<f-C3iBk_$c5t90FBYEQ<`i} zrgp02u^fhHo&yh7wE?}H6JC`GV)o!QxI+A#l*jRk6|>A2^*v<(?Qca!AJOttdjxYl zYDaOaiOxEz?gss;BWX8R@byf3O7Y_8vbw!RF0JUa+w0H<|qseJXEMR(H|L2;3gy!APisz6pvBNU@0IgA8;@@g{-gx`l_GvyWEIgK#TR)Yq#tB?9S+rEzYWXevJZWy7a4 zQsLcvyidB&{pM7kA4_q6OFSXl%=Qz^(hh>wbEeh%t= z=yAfb@o4p!aKEDZ!s4qQb|~AsVUvf}M5y62^!tYF0o^NqIXmf&ld*(rsI|R%nWuoy z9Q`Mrul(SgU_x47Uo*tNl<<0wWzwwGaA5VRwr!9{Jj61Si^snsO_R76<X&)ot2So@vZESEx$_((tY?qdYUWjb};nq{_ZlDV5n6 zQE{DxHdXY~ie|EIj&VE8WMkR=pr@=Bf5x{mgFBWt*7R!`lcTQ*5qRp>`Als{EffR0 z9^;TB&o%;i9l8#TihF98KuI(ky-HwQKIy}^c+`edXPF(!Dd!f*=t~u3WsDY@9K;Fo#?T>NwRs#0o=cE4akL#-`@K@ z4w!OQ15il%fSXp>2l_lEXDQ8}&cJ`A)L z5Pc-F^H&q8dZi0#6r7f2}o>0zcfPV%PNp#n(gabRNEY*U$Mf7wrQnKLPtff2^Mm`gt2CgnN_jCh?I)TD|C{@Q6}= zAtbCEtjceG;*m1*zL-Yi6Mc&VghuG2W1ap$8__7J;%&_XGr%HQv@R;JnMz&|vr+Mx z+M|m)u=z3~D|9_CvUq*wFTA?do*!A`40~y)7{fdMvy~77n^Jme!&?qSQ_6()UAWb54@@#05&LC%Js1IGphNFZv&BvaPp^x6B(z7cR8+io$&Nnf< z-I<>yeSc2Cc@ey1=pn#V(2)P%Su~{w(U=A5=4(?Z8}@1J?DqYw7M-PgnJ(pc`Qm@? z1}5JBIBQjtZ&@ze@Go{+qP&>$+@esRl7`k+^|);H)8~Tz`>8uv0SUPOYJwDixGGtR z2B9sWV}{+YO#+WJnPg||ul@AjLnjH=CVDj+UkQ=Y9wRe_B8sXlOls#-lm6%RpMvA-redAViHYAgGU4mF?fd+j|G<_#q_UGg zTGBxe`6r*IC1~(w_V6nwK1wRDEZjcTX&k~k7R!C zsIBie3Ayp7)D9^XV>K|Old(%1R}mdNSe&p~g*N^FDjRt2LE(ruuVk)0xiQgIErgfoV0TKWks{F+-X2 z|DR;&sVb^z&F_xybM0lX_;R+oOOwZ{P7)cDK$YPo6Jyq2^Z8REQLsuG<};*?yjnhvQKwo z*hG`&6K$?_fOegRb0I_swjsqnwhz%Je(Jn4lj6nshNE9pp*Rn8L>&x3q`Ry>A;mHN zL>|;iyA1P2&es#c0w7B*uh12=uS#$n=mE3DeqL9zu;TL4hCX`zH zCqTNTER|HGrAl?h-o(W9!LXW9_Bga++`Og{LC;e}uwS5uU(3Mx{p8ynv|aFDA9#SqdDTDXNhunhJKSn zt`GMZqgf^{_^EukmHa|l0eY6i9zoUo^1XCIU9^K_8g&{q25d;qQM=8IY@f=yOsxoR z70^Ndxf2{EK!pW8ma0+XFbrQ3*>)I^uqw&+Cj8NN<{ zxy(p#vNrEvOc$-V$Q+y_q2@B-PW>EsN{^;~zu`-pwiPvHKE)Un0fjAKU(*mqq1MF- zc!N$^1G5Pq&XJ;Df;sD`I$ zRs0qMWMNo1mOP|iERAr=Hu8A+EDP{@B$Apez8odwqDjbvP{p{e&*<*kq*;YW0d>3C z{r=m{Dtz~my?FH*DFTmTI$iz!FRO#nW;pcb+6cO(Wj!m*LT;YPWR07He2C&fXZEPcJ{9*SXU82&Y02RB~6RX$){# zm!|R}`VMJa?@a-gN_TeLw*ZKc$d+{iDNG)$N01=%#W_W@)X?VU`+=m_8|0%VS?#Kb zM}!I2sE61uuA(C)_UZmYIji4x}kt#@HUPU+o#%tn_ zIr5L`CTdz@o8vFhzdJ4W<;@gNfs&!#XsN)l@5@v9@<2Z>>i|dOg@oZ>I)VOOmZpQ+ zP?b1HNEq6vEK6=}wCH5T2y+9~U{kSUiU+AOtZH|w+un;p3J;C}dF$jv8-D9k;ynLh zeogjkfdwU$*Pu&=R|nJ=pzAK5naa1{H?1_3 z$?WO#MC{O(?F^RQwPCNn%BBH zK5vj<2k~GEi?P&Dt{K;%iO$`nP(A*0^#F_^;*(_6Meu^@vpkHgyxz&gaHF;6wGioG zN;Qcg;t91v|IZ8w%&c7~8FtjYtU-hJ0I@y;!y|-L(brmQ?BZ`2X0x??A)NfZ`uckP zN&-6O>7|_*$(Y}e{*nRa?rY&^anYhGq^NcWoWigXS!%KV_e4rl#fRDN+QqdW1t z@99Dz+m0)GDdi8ukZz1*RWk>q4*RbzSFMyCP$Fyc*zV=BD8k%+QgEW`SJkU6V;WlO=9`Ie`nQc{)(fuQy*StH?ol4itLi^@Z< zZ9d7jDBMvii=hcoX1mBA{>G$g;t*Av>H|d++RKPxN>|7mEzQ|U@<)e zLj$zNv(}8hH>?LQQTp(Hg5DL2{(7%F!|{=TKJ?CokB<1ndAQml2Zxh^DVa@1#1xjB zfiC6HUXH~S=Ue@S2ZPPt935oj{WJ1!8h^^arN!30|CnHZ<5qhwRln@>uz4U*YOoJ7r>*)qQVUvh4ppN|-zHu7?3%7_&H@>x=*?r`5tq z&f(7+$oKX^u3Clt;D-lU5N~|Jnrwh2 zG1E)LubZs)-b7v3Ku};r!SD_*d!_$F={1N2Yf1xf$m~J)Quqv<8p`SY;{0czCq23= zf3@O#Y9aiss#d_o!*>S5>0nCIMs?{kIgj1yBmn#Xu-2HDPHp93L;i9yyv z$Mhj58G+Om+b4eM0>xO$15ZdHp5>VhLsXqDH7@R)A4z0E=&?Bymgep?aKnsE%qB7O zE<97u3P^5z%6%xt=pK)$Xeb9d)w_*MP-dgVdB5^vj#iY|*NMY%IpUJU0bh&%!y?pm zWzbeLC`Y1uo)YR|6D!Wx=8oImB=UN_$nsf2Yvp&vkBj#~L&e13{FCY#UNhvh5@T`3 ze{C;dm>XSyp%b||sYe?9`K3(m&}2gMLJAmT@f++#Hx_rMi#GkOaM>Fi`-Sc^<4zvL z!Fpg;g_$T6zpFyKSGTdH%~ttY;WJg-v0)^-tqjOfCK3A?Dzfm7;eY;9Kyd1agzQl8 zZDdazT4U^Ud??07eYND3?Z7`v4xjI+273|Ut&-2YP0a(_NB=&|0D$Kj2I}>y(8&J> DM8J6C diff --git a/examples/jupyter/images/flux3d.png b/examples/jupyter/images/flux3d.png deleted file mode 100644 index 4f706f9a2305ccf6e6083a242d796e7bc78dd201..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 100719 zcmeFZ*I!d>)GZvhZKEP80tN(Bn)DKiG|`Qe(2MjY0)Y^U2n6XiP`dOaG-*LfKp=pW zfQW>afV6~;iiQ%Z^nO-+zxP~!f55p4KQGp^o>|5mbIiGnGBVI&KF4_u1OhQbwI7&( zK&N^^pp(x3{sa8W=bp7wAkYmE^uhgy&+Hc`t-Se1!)e<>5OlC$-L2AwwDJ~fcc@dF zJn{HN+?rsU{G+z#6@D`TKhm)l-(N($@DOKr9^sIejWe$+i7ESO(w;@qqy3C&`#1Y7 z>t&%^GD(yCExeb8yPT!0Y#Tb?wEP}AItV{<0L}z~?)fh?pJ)65x*hp}iShg4nlUTm z*GM`~kn#Ke@dJIv@4fc77{>4O=*w~^7=N(wxij7#B%(R=_fZdH|K9`tpAA5u|7U~$ zKQ^F*Z~(b^@_ zk7X#&%GN$Wc&IORm0IH}hidF=e~I^RGf0AO>M29zaa9G}+*;8e)v@&+c3tI3%LhNF zMZFlb}iK(W+qmK3?`#as&bWuy^}k)RIo^qh zzeoz5JXBp;L60M0bZcA@d_B$Ui)^J-nw0vXTjvFD2LS=nyTiJw(aRfmeq%npG6aD6 za|Hu+pMCxEFfgdpj$fK|bWpkCbR_DUx5*XQv+<=^&k;zP z@ZLaT!J-W8!VCuV+<}{6*E_Zj^9wrZ95sWi0VI=wxMV5yin4Je3a^`9DFfRvLe3A^ zUvw7gO4}@I?4KppcQg~delnvE0#i2fWKSyBG@Q)?fmS~Q9)CW4+chwOY~U73?;F|q z-3d5`_XupW;OjJbF{0}uSN*hXTdz<*;xVVN;{Z7)_tgf+XwayC?>Yo0H*2&aiV&0svv`~x~@U|4K;YNlVFEF1u+Ioo5txieQQ{+p7+rT^(i)3bVk|j!}c1LyRzXh;?K&Afy z1-b0&k^NGe;n?)~4AZl%m(!zp_AhHH>>C988a}3(7DSHXm*;f`BNpU_3)5tiX#=tt zkzmS_rJvro48n^gb3e8Ir&gKkImG`a1E~6AFn)AW){_JfSMS% zdT2sCZ~L?-1g2Wm$dMM)#;7L0-X#yR0#E#Ip$@!$3cjU?MGYSAy+*S;B#-YMX*EJ9 z2C0DJ0?H zKcd-(tYTk8`2O9Le#ramTrmh#_Ad}1MzMq7#>!*keO|Jsvg(*|pF#GPGzuA6{nKtR z$S#;9QyF&KwbgLPW4vKnVr6vtkmSUI8^5Wn*Dx)$Bw;%P%j>e3opNzbk)GD6+6Fdy$m{y++zek{UW!fi69uAS@*y1cI8XSFbz-I0WW47)W@WoUgI2uv zxNki2i-)Dc0EN`egw~YYwxsP;UsZhyG44<hw^-T!K(#PeCNHH}f^P zYC5G}O*({vld)B8{#*`FmhMiyTmM1eUr3Ys5Q-|du}-W6oJmGPptZ%2~_ zwBx_Z{qNsOLVT0MxgYE(Q{DC}(u{RcS>YKq=A+=3bTBY zkdLAo#m$>3lz6|;vbBZ2!t2@u>tEiMVnF(yN%9LL&NFcM1Q3+jF^2@;$(Yu~-y|pl zxohiTR}4|xk^xt+UD-%6JROZJS;Srya_W;$B{jpydxHk38Irf(mIG1h$NHFvQ_vf6 zf(r?+HK#*2v1llUry3YqGwMVeP`o~~*0t1a{V1xr;V7c&IJP}r_lh<$VwJwtQ@xS{ z;LOldlixW1@n$jS@R_QPJ5B;d>t!}1(g;WJu2DYfB>Yk6_2}A8_VEiFe^%e`9tWF0e^XQZN4NzN5H<$Ga3fwvul@wCyzsgBF_YTn0kezbj z+r|FlZ{>axry2;Xoou2xQ}KSj`Nd{PkG%ox_CIyKR>|xp4xBoKpBZ7%Fk@&CcuGkX zy2x_|^r!|1pw)r`e!VtT)YrG-J4|1RlCG4ccUcpQ51cw$@btFVVBPG>hw}#By=CL& zmZ*n10_(TtlI;Aw;SOW*VNbdrbcCFtahz4Rn`-Hsb@j@Z0)a%e7&mwnUa|Y52%9Q3 zXyRi1<$`zL750Nq>2=HHsd+a=aFw;fWZZZzKH{EqrHdLfy;d4(m>qrh&u@#k{Qg0k zLNV!btK_|b23aHYN!@cU{qaM~@U*of`av0?Iq~rB|JP2bJk~mkP&@iIH4T~fgdq^fdJa^P?XJKtmyYzsNMtDVfH-;v!T zBApURc~kEOhP1>FC6ArXhhNQj0Rpik0JNyZZl1~1ywld|i{A>1Y2oNvvTJLC-Iks% zYAXhY_V2NjuoLsM8T2db(sySzRqRGhogW)`V6ENuXwt;Sr!m--f z+4MEkzRp*dKM7O@22~Do0qO&9nEPq1$CRws=x2?=IfOJOG@Kb<$cpX%n3NmXFZOA= zS#T|gPddavlhclld>a116+;~G?|m_p5GEqx+Ku9d-|+Xn-ZGC!jEnI)qXZt`d;$WQ z$1`jj@Af4nmEU!~!%48cqn`CYR&Ne{6m74#;1&~PO#_*9!TZyXLD+P-GG$Jawe*2y zx|%2Za0+(7XzU%OFv1jHRvSAS6Q;>u@0xzqBT=86M;C|AMmz=rOqXGhS7XzNke%ZC zmh=(kj5}`>W7w%>x$ICVgt3+GnIn~03rQ$5vH_e3!{suONoI6)M`o(L!MythZaE4lV zzU_@N06jn|*MP7sGYVq%74)>^5M;ltuL^lK`SmqeG*BX0%1i|cll_JjAmKSCt?ABo z0!hxq(hCI~13`)JEe*7+llLecz}x!Oi&u%mZ`Hg~NK{-3e1cy)TB4ST0*cHX9>o?j(j^Cey9*U{!77m^)=_&gVstX8l&Pp%GT6%M`PC(S>|r6aza*WuUTKal>Z@rKNjtd@!0;xV!M@{3Y~l&cX<>aQK(`rfM5?0Fhr=Cp z-*Cyr=l-+elfTe1$)orrH20;&AQ*MeVj?~qsR|UF%AQ3RwZJPdVobE49<+`xu3RpT z`(_mxCTCZB7P(egQ1? z`MJfFVF0Vb{EU*>h2``#Oo0aTtpn?_54w{n7V6kPH;B~UyPS$5coIufx9GWD#WxLD5yC& zWN9CJ4vbmWMGCue87ch0LQ@^Jt68xx-zoaqr=&@>h0|Pn)qle=WY!0EY=WcsK_$L!`2V-ZMX1H8Bhex-vVYl zZCUEVcHQ}M)k$a64=+=T9^}h!>{uONt;hL`V!{!eTYA{6m;7>N&M1_KFS2cHqLTE@ zFVE+rfjnufFt9^Tp@8g`IFiAy`vGbPAKV)fw<3}{^HhsP#ZBxnrnC(hq4`eB;*$n| z{+|H2?Pu@silZMza)=ra6pvG7p9EN-JT0r($))+Li`ozMaz*OMCUpN&ilwPDvB|~S zM@BpZE6_LM0)(PPi$U#W*&E(xnX2a&Zdh!|((VP#?3>SL&2DgNPtFNd8pU`Vk?)OT zN|8Z>jE0^E1Z0;9KuXmP9!^g~?2^tGI~A%b|HE!2t{k;BfYtMwq6!K1_mjW2>5{-a zG>45y|CX}gSgBhLWPDWd0M3zkh34#%kCKLEl}&1AYDM{wF+(hx(s2UQEqEF1eA1E_ z0Z&0F|C#u)ySHywTEq@?d;rnD_p)WLXYa@TYbA>kR*fG~wbqpQBs*w{!mR)yyQdw? z&udn4PJ53Itt~FV$1JhCbfGk9Rs>2h1MWKDS2kbc1>u6unr(d3;nY?(zmbqCP0+86 zxbO<1TV>-s-h93((xhY0mVNB1G{L zgty*Cf7as>4IE{<^mck7cw)|Ni?UVFJe15n;PR(buxI)f;%oesxUf>S7DqFZlOmcXC|M9)54JSv-~sFq%UA+LOfhbhsT*}Oz_TmQC($y!~Rp0H?&)VkZ984T7~{@_~B`CNN1cHHTr$B{YP|D8ln zv5y+*EG`~`2TOT?gFdwrq44QF4ni zSnkV97RK?k5quA9V9`8Tl*}XYW`yn>muD05hpPCdwJL{AvfIpJ?8)7LQJq+XK|%I< zv0*j)?q{BCT+1}WqJIf-cEuyXbqC#ZB56~=mGTSxgYTMF-#iT!^sMo?wn{V^k3LsP zdh{|ni)EbRso^~vl^DtW>oIr}SrFy_sOyhLPb~SK*B9HYT-mo_j|st{P2APXe5e_P zeTw&`@}CuWv2`#UYFBLjQEx2zAY$~wDbO z-&xgXYZyK)wyP77+h${rIBEBJPRrrbl2%2v^+YfG_8-1IG?1F_6GfAQX! zUFa7Nox%6INIPq>N!|>y*uL;yYZnkc9;cWfDco{(`TwJrsNBgDd_Ft3a*SN*=}+8H z(_E>INf2B|KSWnq%QuB`Wi9JXEVBysNx2H1=^vl-a}m28c?>grs_>1tXS_0P-!<3ufq#966{%Zc@qBkW&*9o_o=0P(Z49FFRJo~`m~pBs23YzIr1HpReLW7JuTo}}L~a{c1Eb4&Tc z*EEn^SvZcsj32dH4$7NP^%AnSakjTeT=cJs;uPqRL%h=P^fjjA4h~B;Ick19wwJW* zSlnB9CoKo>5pk*wF&+%Q5xw~WH_To{WvE4E?8dC0eJfim_E$|^!zE*VqGr9*8!HT3m}OJyc|^Tt87%qM~F|9}8zh9frPs*(zV z!tEV$B==7Du?XKSx8f3WKh(g5bGUF}$eM*}_n<)oaYJbhCR&{PG!4S23~}rNex!_B zPkt(fd=K=T4X8xVe#T#%9p$*N z0!|D$EgT{T&((CTrDVt}Rr~yT>)8;NNWQo9$Qu`GDCW-2y%G!7X;|4&ZLn%@l>vb! z062fveMAdCb~&C|;n&Ex)5crnlvRmT_CTHXzSZ7sAj^mKCla@^#`JV#2`O7Hl~<{6 zy|$q-ie8Oy)JF-K9#pDSz0VE8zjr^UI*<9qEqi!|j?Gyl@;hbvkhiuz^*0}Xua%R? zl&@wk6fG8De%ZHDI4c)jsc_O$H@`Ez+imQ+enwVJs+ZnBAQc3VBZd^o>!_AY2g3{b^z+C+z;PO-)ws&Nmn!9^> z$WqBwev;A3n%2nFXmhS&uH`WpmYQJaMGcIn^*2tCb(7zQ`^o>w^lxmIKgNB9$zj^ zz*c`x{3CYa93-M1QVF4Wy~io1M?7vcq4UKV;$}I9{o1fycRv;+#gBfr_HLl$ly72v zr(f+Q7xe8@-i%Wg?z#ssa;ffvzeyN}k>6lKFd>ko!96LiN)o;XMMROszBQ$B&S;(Ljk9M z94qji{$7{Dl{nGu*zZGrfj2FQv{DHH5`?yj#3?YZ6eE`PGa^pUU{-8+uQ*Kg-){Uk z?_FF&lsh|~p5*nDwdJ%+EJD5zxd9nL=F!aYZz#KdIfhDfaRVx#8lM6XhIOq+jlTLl zcs_XE*2E|hq6m>TqgnprO!1Y+m0m%hutQ+YMz0NC>en;YzM&75s*4o+;|!Su2o zFja+nlfk3e7)II1(|x|E+2>^x(v;L-{$0;zC13l+fPugdgNL&v+P1PRFkX`@25t4b zx5|+6CM6~3uF$&I6ARuOBXvt$?9-eE271Ju%6qr>q?a`GC#PUxPXIyeiS4@C?6(Qu z!ry0ZK;%!>6xLR^x|>#I6ch4hFmI^z6)L1k7zFwaH25lBIcnnZ7mx4n5&U!Pa!76t zdZI?w^w7uX?&j`tN8-!8L+h;*T$L@lV=gs`UitH_OMYZu!(P2RDe}12*;x?Q-{m1= zJz)z-A6&|!4_U3BC8;XeS>Ve%^oP<6mtZ&L`<=2+c}J7V$bZ{^#+}PF6$V@O>BO~z zr$N{LcNf5zuM;gOPA=$qzdv~JmRIzo9PS^Nv7nqt{z+?Q{13AzIlaag6kG6q$8gha zJ|w3?y_R%OCB*UBBWQDqrJihv1i*zavC6R4_$hZCe5dhJLn<6ds#M)HX}|<%PFPus zzv|qjK4JNCFs|Qz$~+VU$oeOM3abV8gBkolIn`Ttjuu<{yB7!)_!?rK{ON#sl^g zm2I~CvnJK)yj0n<-jU#srXMvZJy+!w1mNXkyZGv6;wNC55L9}Yb^%oS#P5B}abr7q zGe`TJj{Md}ft~b)^`E!j8f}TZe`kmEfSOeMga>p=IE~4XMqGYWx5}7wagN!N%u00! z?r5V}j4T%b{{Mh5w3M(}L!<1k(`cUg8Ei7MC6d$Xr6f)m&n^&@gMHw1@d}Fd(3$hY zvb=2njxbdzob)dk?>xR$H29aqodX~|fi!)@J4kTgyuVk;3RAmnY_B6;%sauWo4OUm z+jx!>_Ygy|S}(FY8LJ`IIEJ<~VUDQu-O|9ho#9f3L;7yRvV&;@ksDw39+h?4mXuEA zC{SECk8DlU1!B;O=-2bA)`J=RgyzA%(To>C4ZY>Q00p5o3~JP|hnebCmfqPp^yT6V`HTjlGb`pi82X8pu{j7#}WcI}k z!3FGrF54(5w`RGj=rIjUm1TlF^s_JuV=*FzcrJxE+A<8N`L&_U>w|}+J>*UhchOB2OsCrerN^y~`%WD_X?(B~9 zbf=#M$m1YPj(CU6w@!D*CqHXU9LGwv)(i4agr+3!6 zf2%-SoMWpGIc$U8!9}F<(>YH6Wqkhtcs%pOPwIHT1tH0PEb__8dGeWy-L>;te!1%3P*@1GrxhZAzPjx0^^kzn&PWW!Nx0o>%_ z<376Cs>euRM*UP#c8%P|vPfue8O*Gt#F(Ul9f-uE17+O`qXkJ%k8W$DMiFo_?0xL{ zsveYgO*-KzMedtRCHY>(%g2pB#GQ!vvgzTEi~~6t3HI4fDfvk7ZQ`*=kl!oSH2CqU zEaNuP2ZJ%z42la=<@$*plsepwQwR4mH}Avhsb0$q0h~VG!ycMH4Zx+^sHlt=M|1bP zfrDJVp0V5kFI4^dMDu!L*gLP!R|~$~uocB%tY$#N`|Q+IQ>ee+v-17r(d73{@ITH! zUkF%GJb?*sq3n8I>w48{(wY;BQwry|w005bcD_GFax&UylZ zn09#8#0_G!EJJ&S_8|w1C8rSlkx;^qP5n6_?HVXe=N8yE;>il8_{0&?{R-9q3jlhyMrLqKxJ}p20}^Ka&@0juCG0 zD&YG5sCn&f>4kHkdyc@FE&kyf9gSLNf7p&WoIWw9mV$h#Jb7m9<6X3I8y<0|@>#%# zH&-cazfX-9udm?818q8G!ab`uE7L%qY1}zI2VljUK(?^$*UH zW#j4@uC~>}gtEu&h-^Si!<)8`|B=?{F%#RbjF`bppJnoG_B$=_WV_C?(K_(~$vHT6 zVMFIlQc$;h`jUTng_iEYY5UuMR{N*>V%f?4d85VRjk8Rk=a+z#KTnD|{;RGvq2SRm}MY?ksn+?x{CTX8@U^=d#vh@HrVK2!+>X z*^lfoP45_iy>Ebx!UxBt>E~Es2BCK8op}`lJt!_EWl^7Oc&Epib6Tj`{tu(?IIGkk zTI;QKasSO^gFwy<&TwiftdVfMcAUa~Kj@&(q_(#^ItwX2b!UCzbBeyRp+GkExgi85={&V8i1{8PMCb)@|e6k zt}=shye}h$E{DpQNpjxR{~{LD00Z(*;0oZ+L7Xq0+Sgv(X=zzF(g6v&r&8_(6;(T57zG(fqTkdTn2O2ieZ( zD#*$gTJ$TYPs}Cf3N2|)*TXjtD{~h)VNv{OK|V)zC4hD81Dvp_#4v%MKyih(Cpq8T zvQmRv_kRE%2HnZ2Se-lzQc-6BP}}nIo+NL4RwIY`+Vb=n#r3{C>Q(DO^oPz*FbB>v zTyJ^a>!ip7#R~xF6)v@IaJy~96*-d1gbZzeP8Md9zlfjDBKk>6F+ueE;iO;Wd*GW8 ziV4?#_R7J(PwuXiQ5Efgg#%`QS#|u#sLSa8fR@&}mZo%^pK{(K-JrO72skHb^M=-A zof0TFJUBvbEErPk!y%*F(*kxwznh=DaCOfPfCZl$(QATkDRKTI?4F6_&b(K>g#|rJ z|0JZX{;*2MZuezL1ko95FC~B;y?SSHIa0{q`86p}=cH#Rb6U?lwCx6()`j+B@yrD} zhnhtpc@0auaF4lA`l|wSKpIIp8X0*dmMjJNN|XyPVyP^_24q)%{Y1+k!Sy7)+2xoZ zsiR66HyixT$$+D*FH8s9^Mal?F*v~+gSz4QS}ld;-jNH(hRt7KKDa%**%CrfQQn^0 z%jS)|onqAKeJ6E6M_NEBte1N4e1Lrjox-4@S-zgebo0Yi1Au||&TN3~pwLeUia!HkTYU2K=xO7VEPv$d&x0_L( zXWVu4=jC^jc(r3)9cjv`!-1Z`*ekv)^bB2J)~pXQ%r+(= z5M#b56QZ93qG|CHt(2?|-3&P&>@k(+LgMWyE01f+&13oLV$hz+cA%jSw1m_>S&`ks zCmk6Y0k>wG-F1c92Y}?O_6+@f=hcUz8V0d7Vq%2iFYSmiLaReXi@DLmgSK&&gvt#}&bJaS7v}0kq1h8cyX{1|>3OH@_m*Sj@yaE4 zcC|c2OjNvd#Nm%8S+k~8aJbd1(;yX5MkaSHmqTGNFruwHv({t=v#GXbXPWrtdV3ih zC+E3MM34)&qG{KuBm+b#M3<{&e&~aoM8xI}sW-11-yaeV9e#=cmW+ILa zFTQb8{Wc5I6(vgl!_~Pl3&r(@u6@cEHLa>qEvpIf1j+)1(1j=4~q9EznqJ@0)36?G&_DTP7!Ql$Qt&)@a5hcAK1Obpx)Q{|MK^dWz=|COfH z;OxZ3G5p`J*}WhTTOC>p#Q^Z6jkZ`bYTzX!*0=DYe1?NP~tP0AnL#m zbuZKBzShhlkM+tY?910^(8?2dOzP85^;+}&*&@1kAW0IVV|#=n;k6?HUdqP~x$5o1 z*G*8F3}GkQ=!J*9qTDDof_P2g(wyZ0bz(3h$=h}?thPe5#vhnzrvXb7t2<4hEj)1; zY!mvLjIKw1&9A7pc5v6x{Yl3nT8om)Fu)C4vn?l0oFhS#=Tg9R>(81I@oPp z--*ZB;nDcFKma=htn*EQq@bIKhMfZD!@3MHHsmFrVM_j++J6Dwq;9crI>y-|3(`2y zP(wtv1FZZ4xL$hpvnvl67-2*f16quLi);TC@hX_DukvlJY&Q7rmdBYSx$^p(dreMI1Zze%a{)6wFtBS@4&LC&wtc8ut=ltQ4rCKU`O-tKmB6c^(5E z3bQ7+I$n1*$G)p)bWICqeUrR;n~a+5e@~gZ<#<|fp7QSGK)60HyN-C!;uR(3b8k=vwr| zA(T|{KR&^<7S6bk*s|+yLl?B$-f-@VnGkJdLNM%JdUTQRI@Kd!S!ZI;&Ur+)T z7oilWsT!jz_NF;D+wEukV|ztWSKT-S%@76t^NYB66DpqdC~kUpDmuNccQ-3+iznMk z+XM0~KE7J*qNHEaq&gQX7j$?B|F%huXKeJ84K(MZ2aFEB-O-q{j*Z?k1 zu}blqPPWxfagWP`f^i$=iMHFKehpX)BK(%`FnyPu7-W_qH3&EZ=$;@(TKS9Qdr>o( z3P02{AG2Ejq^&&htjw#U{8y>*A?-))jnl`wT8s`=hmj6nfeFWhsBt$4;I&1aahCpD zhynr9e}7^TK?!Aksh#*0eERzH4f#W7k268JtkB8? zlOGK(oa=Sv#$5od0X``DewpqE4CVIb1Xs#1{PW?-M;V>mv#hu}6N%njab_N`tG)=7 zb$^LDN=o;arp4GN*pV>_IAaA7yE(6-?1_|>kS?#+14H^g(ax8~))!ZsPH&#}&|+b- z{&{>71bWQyLtWM+vEy|~MqM%z?q7V+T9hy)Ud$?&ew`{Slz(-S?HAkl1w3T0YGCCl_OgOGexv+ix3?(4xE2qnGfsDs z@zmv+E6TVkH6)2~lF+MFVIYv$9s|HGBQ;_7pOA8|h^?ETL*0PgB=z5#wL5)~2ENYK2kNt3hzyQ$a-eX zf*-K7x6)X4&bx-X#%$k!!)bKLO}iVKl=Y&+EsSml+g3YwZg`%ls%eQAU^|rQREw+U zL%0|Z*KQ6^boQo|kBvQ+j@$Zn1?bIndk&X4Kw)jj0BYFo_XEG{>@qg`!ne8NNiGy% zze$|5h^2djK?%?``(o|Ctc# zPR#zo&;&-h2rI63u0UCjTMSj(9d3we*Y{)cEM#s2G=L}oL4=>J*9r_5?oNJ!<<)!h z{Gl*i6N;ur1JX3IAZ!&S+KH|W&?t#11O$C@i0G8veZ^Egl*Ysx((p62!{0$TdSgj} zc-B?SlwZ62$zkglYv0OGIOu>8MmN2<;ymb{0T8N&U+LRRhP)=d*@m`EvbzfIhXIMf zAvI-9B&JN3KPzUv}( z$e;d}c#r-9GNR64yU z0o+0&GfJQ_-#e4PzEJgM@dTVOuCAPDuhH{(8RV)lH8*&J6ADoMN+sQR1m+SwG_@Gh z6N_lZT;G!r7Cl&8DYAV+AOGj~zK+xkW+~<$-r>A2aaDm$-=MkVdrWHs6mz!rr=ZW( zz&D|*5BwT7dq2u4w0uBHJyhlyE$CyroQ?iM!<591{8ALJb^ZL#Sh-RB#J(bLb-i4dc zm|wt_Pp=R86$udyyjHc1bKT}~?gt>1; z#B~9a(@8!<&4E(4tPbY5q5*6gb&7{$k~g_ixqR0k_;;^zR2MMxZkkuPPqgV=kLrb# zTe*POf8N?Fjo?7LkeIVvx4*&m4b)t{vo_yql3R8Lfts&&q`o%{0)1uF8DVdGg>5w} zwHp~Crgjazs^yARL`miQVBhw`*Go$-`wx8q1s^_x=@n&z7&COz0JDd7^$8D{%9Mzh z*JuLYg3f{~LB-S7SHY4cnEf!W9ow5G?13TQD^xC@nZa-~_(dA9>A70a6iSVL0CX|< zyOhkFY#_uM@TnCV$UslIvvr&1{U|fb-ngoA09xTLB)F%ir?h^EaS7gs>~aNL7prKT zxc;DfgdBPX$^*<1=t>z^cMIPF@@`>cYOl!jwF{tUy$pCtnauj0BbO{XA<=FB#8Au^ zW2|S|E|LzJn+fw?M0DRC>w+>Ur{Fac7-TWp27|f4zum=~C22T-JI%|0#eicUf z2@|TJC(U38rH#oNw8PHynaJMW=oD#Ig_Ag;%u$SomcuzK$ zJi>R~vdZTtA2gtfFHbK2%p_Zx*l_O6P7w2vjWhGPn?d=8Kp?K2L*#--2TDJGqJ=%l z|62ZDB`qO+^s!Z+YM-DfFk58K>dRl6gs~ZoZSJBg>T)IS&Tk$PM$g9m1A50m`Qp(@ ztI}b-rE~d;QNyzAO#!HyZmyGk(Gsj2cX2be*%K94T9bFqLmTLmV8E20-(~;JNNfSwX@1 zca`*u-wN;q$|xsjk$vKQ$S5rSCi7G0p+fZHtSaTFCc%X$=RmJsNmBmZ=IUQ$jGyahOzgvT=<$2Yf>n;-C@`E%D?Z&<)@_nAKBD9=K2y|At2pCAg55yH)5b zk)0WTn}st1@7ZQaE)ABf^+gDPqjXKUp+AgvhrKiG^${G#Vx+#pR#>;vqzC0sM#NQ} zu66+izDS=Uy_Wyo1?be9P|9!sIyNJY{QolIty9B*|3jdU3Go7?a(~6X?v`iiRAS3G zUpvXS$y}FHyKx{0X6uu|Yd8}StF@MMB$>w=R&&D?l>mB{gr;D+V!-lU2eE77;ZXWP_5YVQA-RyR` zT+}s`VbpkRk|{6ZP6XNI^_raW`yc>dI8wx&Xq7dccl`u({{~B3Op3Gvot42(QyGIh z3~=Bw?}Se43ub0vm2dn$w!Tj2u_A!AWg zwSA-AXDwgO*R_9d-gY3(ASC9+t*7w_FaE5^TX2gGEpy!^QSSdc_%s|G_qGl&9z>n~ z-J#iCykS~YI?S_mA^c+>$4#B!nlF^T;L6vE=8fEwY!C5n^9xy1+U*?&K$)UlKv!1* ziU1hmXkH{A5O(dvnQgo3X>fsuZ7IP|eF@j=hq;*_K6*&|urp?WPCsMtZBzha7%g zNb6acK6a3*pt98d-B?%od%8j66r8FxL*g_1LJH&?cE@Cu94##WlYC*4ie$Mj>~DZ| zyaKDa@^@E4I6a-CG{nm`iz7Z!<9FE(tc+9sN)Kx5RYG(T*@L59W{ddqpp1S0_k$Au zEF4|TP}kEq;B4Lnzg_=JQ-MmI=#mqlKHyv9&&f57gsmQ-S%88dyUPnA*gH6vOJMCO zTt&WB+K)P`O5-#)6(KYi9q_s}!Cp2VkX98vZLR#NS2TK5GG07p0_I2(^Y3>a$&l4;LfIZX& z4^vmLjVob5$z#9Em*g``=-u-#_i_h)_BG$@5CA^Y4_>bdq@F=np7eK|l%K&!dtshd zCs!NKh4J@|BwDX!2Fk~zSN3Lpp;(1{g@jFE3(l9?Z|S_LD2a8uO!FgW&2y~(YE;q4 z-uvMw>lzex-)KVQ;?xC*b+4(y`Rph4xC%i>ue1_p*zuO*ho0tgUz#4HEOfpp5qw^> zxEVt$BBWGV|49@oT6kb{R?9$9)bNF|RlG5MIX;i9jwIJM}X0NnsS`}r=s zk1R^PBnQA-$Jw(b1@coSAmXrRe2YC_PJ+>AB-j`m>Qp9oEy0j4#M%QwiHKHUx;} zSngsxBKwpfw3hw1VfFQe0hNgDMG1>I0ndLxO$?~~iIk__<Fd550mBs(kB>IWjXrOS79-gvS(hyvn4wckOHuV6I)Y~3j{C6PM>tGB!D z4@OT~Ltd}_DZI#nW7SXdlb@@6%?^4O2RJ1vc)MzI?Q@B4ITX9V%@|%LZVx_KKu$W~|H~$#JqXr^|WrJx;|IW$simI`F#W;SN{`!!O_6P(-_0 zS;%_&=3oGBTA_dXlAo-}%3bT6a*lJOK%IB8{*3Zw9B zOF7pSnHK0(Hm>Um-P!JZ$mXAgXI{B|eiG~!x5&p%pdmJa?Vob96lu??4#N~*h!IgJ zcaG_Hxn+~svww7A3E<8URlS(6dm}Gy!!uqeK_iyeTmKJDR~gXc`*sINiAaNhf=Ee8 zj*?I$q(!==MvRV;5)uM}(%sTAVsuN#=l7Ee@!4 z45h9fH!avON)BlK`z`nI^hV)_nUY7D`IfPGJ7;vv(~EZy%iQ-9;kJChvG`5m@6b4T zU!j4}^pKC^4h87N96xg=V`}mCvt6t?l;+340t-6sp#Y@C(1-E#Z*TbzUT%!;Pl9~& zP$g-5ZsE~)5$@dWyYs|WZSj#yg%c#P)+*ggOdGHgL(@NnJ&xVv7er<2wU9lIn3p<` zmQeR=pa4ERh~w_osqh!Pt>f@Om%FUp3>st@;Awea;?kdjb#ZB@{8bcSF3;N?#Tn6B zP3&FQLx714H%$4rb#b6mLBrFYaA{kJ0@c*KRZ0S2pry$Bdpa+?+yT&_CXAE@VIVC7 z9U)o84g>AWy+x8$_DrepbFG+46Zymbm$*JBHa9HLcjnYAvB*@$#q=~dc0IBu@pWpC zKW%fcPb@m<6Uv574hz~=d|K`V4lhS!cG@hkk09%s_C(Bs{y8f;UFT+hGbXES1a_(s z7}>ziMdAkKJ_jHY>wi0V^c<6$G?_J)jzlDuQ@&K`f@JVI{jp*&warUy)0R}8f)oo> zGwr0Fel&O&rSuhLk}}QoBX33ggCH&(h6?4AXlBDixu>vqw3Gc})Fzz&$|5S9?Db4-=XCDQ3K*|OV*T$^}g2--VcMoI2}cdc+@VRJpy?b{pkjiFrE(A zTaIy<)Qdxsm&cbSuF@VzRFR(XIb=#9HFg)p0=uc#160tD> zJlK}VN5HWZJ6XY+NM|dTQS$H#l~c#H%vVA6`K;!lG&-O+On}P1Wo%xXTdkVeXVKFL zLyw?lH(7B~o%IUl$#r)$bY3|&j-v()V+dC=%c^j#3fIFA!vCuJ zQf~Iqy7WMq=cJgQ6By=_Qtet90k~pIEN8Cc0hms#@2OhZtI-Td4$i8kLDht>P1SXg z4PniynBq)x!WT)7FG5*9LG}q@jiZ^OO#tnsJ>mCO`R6k{qLgT83Ie5}X4zY-129#v zrBV1+VtOm}WUi{Go)iD1b+Pnu_pwT4##HR=FUu6E6bmSu&iGZ| ze$ilRGQ*>*JasyCmp}H@B`HA(`PXQ>Ay?j7{k>oDaQ#1NdoV!26r8?|;jFLrGpN{E<1S z0FW%JqvJI`jLeMoYPk4^{i)pyuXiVb1ntcyIKVa}bQog!@MJyziB!nPkZjBMrI=f; zB&<()(IMP^R^ldZo>ru5%gd|*r%Hs~w6mWBU{&enbC*JCNvkl2>lMl0_F$6 zk!s?0bLpSfSrWJ~;-_JWH|HD8v5A(*pI&rho_=hvjpD@I*A;|SZ_?u*CIa)A&-!P0 z%BgbMz!>Qvl?9pz=aOx;n0Iy1zflhhG9<~OAmEH5YW^a${+7y&eq~R>-^f`VS>-Xq z>*g)fj5o@@E)WOzCvd5&s-#pXwLVo5kP76eTG^_e?HVoJHp~~=``uq|0H7T&${4)F zu5D?%n>Np3e9-a?%URJUkXx~VoxtCRP)hnQF!H4DWYoqfi~|oh-zP(6h6SxheB za$E-en)V_O(}-j9?K+dJhky#e=ftL5H*Cfkdjh{cFj$sG0G|(9Y?nbdlHWJ`r-n)i zOL?=V#a;iKy_G*6v#~%62Tl|(&$u^5dizQ{64(^9<=*_f4XVtH*7o{$_^sOHY@HHC z3tg68?h-t~?5NmsD3F89*5>x}^)144S2l}xaw;+_EsmJR%lrFD=9)&~d&vK2(iAgsR zy~AZH02fq-8a@LKu+09kSA2**l*9*|nA(1(!22w)=-S30?Xj#bU;|xy8vbkl=~QiQ z)Y034kH&fkH{yOauXXR^+bre`y_eprmas+V44l(Q2^+ ziuO}3S8X!TKLwP><8>;eX{`8%t^eL=)MQFyWm%HQKrO^N#(|XMAd!D$VyM_=S90{! z>dW}Tc2Xx#@z1qyt9hg9$7vb|latbNbi#MoU7l1s9YXrNcmB-vYF6bl0Z6T=Yu3E> z`h?7?-=_eEJmFbeO+iK`@BhBzGN;x@0R~8HizBx0Qei_|Zpkx|ji*zUW8=BwiaV{& zRCe){Q}dI(u;J2+3HOo?v_XkkinY_gXx^iehldt&szpQsLpxK)4zs2t+s4~36PWhe z!b31Oi5*Y-$j8&d=AsW8dei>484FXnehIi}_zlV6Y$$)o3A#T=I67BiQP&qhtI7y{ zUBxaNKPVKyq-86YJlGk_gX`3A{vqQvJdwW@;Pg(c+;I1u%6^TgPmGGiITedW@|9s#m_Te4(59t8rDXqhF#rmpRILtznQ2P&Ip{=bw1$9ck3ZbzOY_ z%}#gSO2GjKd;lg!qd-tH?3LkPnSL`pp9aQsM#(<%`<-Py)POEcpa$%sYNnw#9c1E&}0oZ7FqN(YpRbxEvK->9;L~ny3_ur{nLLS(tN4 zSG}=JEhY`r>%Qt<1no9>UmKC% zus`7WKL4mm)pY0sAY+a6?|gRzTwAy(HkKiO*)?sy(x{gYg9iO#n(VLlf)-`tnU^Rb>R&$5!29NX+0`7O=Zt3ysQLfKp0L?* zx;~dQ?e9#xIW-?``-qwNdJ7>(^`x^#i< zCc-w5Ci@+5*`0*&rRdz_(b?CFJ@({$eUO*s`TTnp{IOjk=2@g*hq%7nDupcy?}7xG zS7TmD+k84Z>{r_1+dH{M$=74a20sU9^V|QHqJhp(jJDKHVDyKGtn*5C*NZyi;aWcB zX1pWEZZLIHJYdae6Hb#&pv>tEJRx0sHq~f9tK9_5I=x96ss<%l`gCPP0jmtJ;_Xn& zcI-)P4&H^5cyh!xa8x|kHo0EZT}8^1AGBPsOc4RxXNr2@iYvNF2}U;&RhZ=NZM>uw zc)+6~-6%39UHtXbqhv05^TJbCDdodLq;&5TIbm8%yaK#23!1c3+^yM0V<`YhhF~}#F^-DqE^o+OrNvp`g z-gfn>KR=;xYauDPSf5zoKar&-_W<6L{>MxbJVqwdP8uR45;*RXAHcYDKa%_hylDNN zN|^+uw29aaW@g?_G-1wfb$!*>!)<35>Y9gd&(|cji$mDCd$X*a*1476x3h2_x+u&a zt%!Cc%-SL+B2UIwE7kv&@tVH^Be!~P9(yWb?R*+N$WP3*A(p_2EYpl8Jj~zbYggh; zL6UfWO{-L7!Pqp-Ahjr^|ITKHuU_#m4yLHwRIE61p}AzpQ@jY?v7UzoRRYNX8v zrl18S+MN2*lx;K0i-9Ydir^dvZ%O--PRhZo5b#10Mvc1hN9F8^Z=SYd=S3|QC;F}p zU%b#hgqmFWs(i7@wE7~9>`sVvTtgR52>o%`UI`oM_or$R2mtP(q~Y_HDIlW7_Pa;5 zpu&x^WS}@=hK!33#(hshBR!d$3}{Okt24%eN-5R z6D4?LhC`w{rpf~4E6584{*a@n3=4i#zd{^2$`nAZPJVbDR#JNm_R zkt`0KWSbhWVbj2dod)fS2krDlUyvTLvMFCrcL5$OcU+LQLYL4`apm^N*Zz85wSE)i zg`sR1P@9Eh^LI=biAUa^me2l8=BKnE`3c)dT9&X#2A1ssyaf(y6j9mIiN%y!9axS| z)XJh{KdFr?$8<2Sf6Hh*|u`=`EBoVp#n%GXJ@cb3>ZvgD|9zssdY^wMshlyZ}B zVpxf%*f#HBcr#-q*%kj(m80sgEOX7=VBS|kiJqUW7gj(t7>aY&914Y#(mUGy;iU-Q- zbKAHn^SO14N4PpoGdB2RKsWwW%0j43AS`5}3t0XwOTy98N&RBIkzXUNqj~>J75*3r z2UD!V>)1&L(g@QrZANfq*7|oGf-tbIOXz}nTV;9F{>Ivkj)rEbD+z`gz<7DBv z1P&_1VYM1a5c&0xG1@P{s1Q#DYmPmGF@O*qm7$XMjS~-inTF9T#bX0c^Y@d=W+0Yj zGkeJ7)C-LFxw|hh5&DR1V+|bjCAX&vRxkH^HvE*L*;mqbJdIN73U$fJIy|T#IkZ;m z5y-Vhh06@eTvO9$+o+^Dcss!mKnQZn9N-~p(=G^_ye_(W+QY!xu$&0%Vg4yJmZz^; zlF1RuJ@xLvBUT+nM$<5Dvb;e-O%LJJx|gdktn8B~wG47(spZwD7_ z)gn{5`#onVt#Gt(DMPDuIrv++iV5aoM$uqRGi<%k0X+obeFzr4tV zFzSBwRO~ja zh@Sn;Gc8+~eIod61eY$rYlG|ljehgBDIu1{LpU<qWvNL@obcy?3^jYNAFu&h4r|OV#ek9MhC!2^3$1WK-Cl#Xx zzK}2&+8+f|bQtSHCW7r>^Y z!t1>fP;jzhnfI-2hnGn})~2*&ZnM`PHS?+VljdsEsBa!`_C;UDeDxdLPukE!_jh5Z z;C6US;@I3lRtX*K>i^LP&e+J^0;-Gg=kyiw+A94&63{6%W{fIs8_)RuBveV3z!5R{ zic7a%Za)uwwJZF&F&|T*ba;SsL(4EXV23-mPJgVCL1t-MZ#6_!Y^#;)ub+2LgOC?2 ztC-p-fy_Rrb(KPrypO!fj9le*{RgXDZns1X8KbJi9I!SLzyd|Qe(b8}Lkrzc!fThC zGD;SGbc2r|?uQLDkCJP7{tFA8O>)J2BstQlSZT7?O*@zsgd#_V$9WVH9a zWzVAug6X(OkJ_A!a9Qf;s`t5{dZ=`O)^&_ZjP>uD)YUCNq&y&15q{h~ogWnMsAg$` zRxw(I9+%tBMq7IFxD2H^1jR8y1R(0sl9mk++9$JWR%QW4mut3DY9>gk*!GQ~!PXOl zpc-Ad0Mq*K=wZOB_r6g?CMOt*J2gSyp*qXgLZBwP?%DA3mZpo#{^K#i=S>DlwU`Xi0VZD}KrL&bLf!iP_Zwg)t;645HaVPB zy-j3&%2lg5G~YR4J7N|2Y%QvAKPkR-_#cvr3^A+dRnMJD`o1}gduq@$@(ekVxNf3g zK4z<HFs zAH2M{bhrmDwGjGaBGM+*0%lR$KeV}%A;iD``o*pvQPrO0x4CE_snoyP%@*CW+x=)=n)E95h#0K&r)RB}c(X|gd5x~L$>WJ2Yt~`EGS9* zD?h0|LT^&;B`rzY-8&th1(&Mi;Y=mU4u;z*7J@G>S z{6N!s>X;kp)p^#{O4K$Pp@3@x><qZQpOa8wXK=&JS(V@FGbSOK-D6fsnT)ki8TV$q7q^ZXv@4lZq zhP|7_AwP7j=Fw{~HPsEPaWni2&h4LRVL>f`^yPkss-me%q}8wKmQ@y60*+46sag2_ z2KF4yE7XXDlzUvBe<;;IDtu%2We=lISu^B9^f5`O9pUS~uV_mXyspQhQAlAyd<52u zu5pI3C!u;FazO=EP^)M^IB@tj7Ot57A65U=~0?uub=@u)n2!Bhsc3W8wNQ>z$f zl1u-h(;O5Ey*^gdsYsbd#Tp~!7~`f|IYtXNw^r-5ctp#CeC2y9KTdEkwsDD8==t!! z>MAtSh>~R^B0k`Tlmb0L#5g}GfY8{;uo~JI%e*n~RxEPxju)vc4KCKE)y>pL7)li? zT(HZeXh$PiG0S1(mr5}`8=SYLHd28V>H--Sk22&TjmFVC&uL9GM3c$u`vs(#PJC2K z__%)@hKSz>0>>?9H;_ZYwa_W{w>RHB_&bZ;HbSstfux_F1B>D#0$*t|Ai7!%GvMfv-1=zfq+(S8K3sU>6~Fsuf% zEYx~dVOv%w-OgvJoV(iYm})Sjn+pA;q0f`LpX4OkF& zm-R|I!u0)@SvU*1$)fSQ*PBm=D&>I!MEejQ@c`NYNy5)mf6Xt?^Y#Y&@C{g&Th_wt z*=TKEW`$Ll_Cfh8Me)y%##*Q~K1lIY!4z$HB3hl6m{fs?aO8^9FBgXg@h#YdREA1 zEjx??$*u(NlM&Y#MXZXh1h!^NF65GwiL|4-h=~HpEGo8?P_gy#Jv>O`#U6LXb_BUK zR)3)!U1HbBt`Sf!+g&p7%^Nmds&TUes17Nq77~>74hvjNl9*x5%ds`k$tM?U6L3H_ z%|RsN#HK=McaQdFqgX!SbD*xBQrZXvfSZtoIh%3^?E$0{!h1z%F$WeP>aav4UQypX zlIbzwH@~ce8hmPLHi~;b2WE56kgoGu1eyH2c0NgcS+uZ0oNY82&*ASD z-EKkI#uaF4wV$NJyM^nm(lXA3p)bB>`W#rLFjTS3j(EtX9AIx?b`!1JN)E~%1HB#L zfR46*@Un6CXGI;SLPv_Rf zLUL@RtCIDrgNxJ701DS~+uHX^<=As~Do0$nOKiO?zM(W$TH*@#QB%0i>0{iNIsjrNwRax46OFc)VW z@`cFym{mg2nw@`a7GR}+m3{54pdY2}GQ(83KX*(gK}>gn@BNXp(S6g47RZyHDJ&EU zc>2_~?pE%VzI#;HTPjV&z$|xegFY`NxjF+PIO_>w9jV@LO9oln_!_hu;!p#VliSc!mYsm>Ed;rJqX310{w4g_#B2CM+}2EPPnM zQR(b1O|U&kK6x}2kH+-?X_kPauWQ0&R$@CJa zz->!K47Z0Y_y}nWZ3Av-!_eT9G56Mz-N^je62XEKP&{3fG)n+meNNx}^ojOp)3)D_ z|H#qd^nVvuB85@WMPR-yxqhWcLO;PyDbWxzviWe1O!1jI)S_l|MWa49Obt5q)T1K- z9LFNv>ejSaZ5qy>Kbdk#+NbduHWs|i78N4CEX#>uw|3C;4X%Z8%RqS)Vh?SRh5XEO z;4$vegTh^Ir-kIBom|~eNCLBt7!6spmD%e0&?;E1krB#`e*%dU@ZqpE< zP}M;eN#OqXio=c&KqsNe(pNop57iiQdaCoIKv~6G6I{th>G1?|vOGm;Qg4;I&*@zh z3{`Pm&wj(BN%aWqpuQB>h+(|JZ(*Lgc>E{dUXT2bI^1U9Gf5;mVYA zsAbLpJ8Va>xT+GWqnW10L40C26=S~W{L~^-OH8x;7cp{7BNj|Dz{MsWNG0HI<6`#X zJMEj>$`@V5n^A!Q!R@1V$op+pL$!sD_@5e1b@Hk?*-@~-PpaYEQ$IeinV)X?$9X*# zIi5%%ThH=}AQbT?Em{>+)|F6sz5en_D^jmvGy9UVib}_Zba=Q(W0E%Se`)M?pT?Zn z1MCxFem!JQ(a!TrMGh>d_DpnnfiDA_sP|12nrcLlxI;cahhukgZ`y>;RZ54KU^wL$ zlbAQ;LK4&$hP{16ws3w;!?m;9pkO49Y(R%94`y*D5R%;(s5ST64lrW=c#8mx&3qH@ z#q^(OQjB(LdDT9CV1-%}*!i;c6rXEWF)m$VRrzMhJ4Pq{p9eU!#a8x=uj-$kslH0w zz(_%QBEF#7W%v1WAZ{)`Qer+>mb&dTG{>af`h7-k5T1vE9N zF|&ONmJQjUfd#1cLUZ3<{Gwsr4Y}udVuJx=PpQ@P-g%ZTB#1upd{V!^Q?Lt)&ZJ9J zS8xm|(N}#Bu?byQg}|X+zeps0j|+q!n$N}{dqa1*s~YkFly6v17*rSf|LEu|xcLwu z(Nja^36BC+4Qizg1jBpZsE|T~EdPx1Q46AIW{^f%400BTdto5CSgW89DV*JPCtw3B zqNJxx8wEf8h8l3jLbur8rI^KiE@ra}7bRX~9p(q9O5}a9vw8~F>r4)Lkpd{9_@6Kw zt_*Gm{}>6j1u-R#8JrlEYFslSh@q};L4BzM*u!k)@VW|C`gShO&s<#>u5T*oD#7_| zi5vS#_xmb?j@sA4g9;>;Tt>Pp>Wj%}= zymqFI$Xek`tIaa1&zfO8Oo4Z|1~!^!QLFM!%@(peI{N&lVqe$g>fv|$T<-!!tWlh! zyY43!S7sw;k{DRZ=dm3g2}&6$^mhliu#FqH1b99evbb4zWDZpsu_gq1^gewys?aHsQd$!o+{j>p{lhuYyfsv_ zq(UXad$6$4wC&YRw`!P5Qz4&FCE*O5MOQRcScj6%299pTSqW48fm;^YPpT+XV$~t0 zoczP+lD?zjR0xk5!~J{ngc`z>^uubo(0FnWBq7S3ImOj0?I8ewd=~z-oB$9t@rIa9 zF2)Dbs$uitxk52G5Bs7T@V?EFTk(7tY-}c8z51OJkWQ!(${k1`JXEmCzvbAA+;3s0 zG03T~?F|!A>i&ePVHl_yR)Ca6`ZI2HWV%u@Tjj3f&do?qzl%va*_(JB;m6zET4Xc&1rc;QDQI&N zs*T{%=2bAKF5qRy`rBZQ()@3A=mg-*3TOb#!i*O*~1sz`}VdHa+zQI8uVb*F?xX{me7m=br5 zk~$MADqX>n0=dfXAVQVE%3SPZqJrhwV=6#I{q^FiMw_rHc5)cTwg1V(%uu9)c)zo< zDPd={+U}|5Ph%Lsz?w#^%u>PCgBTsU=+H=o@T~M^tB~Srqkf9`^DHGSxyj`?R|c&G zF2?)pDy2ao!tqR!pnVM-(%?YWlpKHxnCg_G+iO&bX&A92RX>Y`U%5{}t zeUM8Tm;|n5B#li!wWf&sS-3vi{ub2wn`W?37NUN^^H-U%{kX!@r~P_e7*rKg^QP>-jT!)zHwGKJ9CFcR$#lFL$jqvvhfGnfk_n?pJJnkgLJXw&VTxXE)IV!x+ov zs&5o!+2Hfa;5nBCgX2k?2F%KqRA+b;)(Y{MX0OAZ@3-uWi|CJWY=B z_+Yo<&0Nfm-E*O$R+PefQGXvFpAEF37MMDGq@ZXPrd`WS=RcAc$2 z6JGN9W8)f~33B(k>e3+X>I_2}iF`r_j_(QQl7)W;;K~z`~?`^--wqy!I6yTh)ew zEB`G*kKCS2s4>5FTOw@b4%29!j*}WR}>< zTXS+PfI#CH4rD4~5i3!c)9x0m8DQL}T_|cK!lv*a6ZwZQg;5;Yrx}v^jXqj*QO{}bW ztbk|oockKGWY=#jq;i6rpZu7YT3s3Qv(}XmRQh{oOc=!u#_P^0@{dDYLw@aToHbbO zMx|bp_LOfUu=X`-CvfM&4V%M3)=hPv54(;2Doz;E10ABp3RM9sh*O_i3$*vnFUnE` z;QI36qdmv`0!(c-Y~6!?(x()}d$l6A&b6Gq9w+i$K5%b`ad z`FrU?@aFHp{@Bu4jndSO7#^SraNdJu4Ks0z6!H!gmNt+qAp_bdL%6T?hU^yHwk&*G zhettG=4&{I4Or?K--Uu-WyS{>rViSrmFjNNKY$Y%KX;@om8;X0GvyO@-e+{-!q5qQ zMc|j$R~26+Y&Yc>e3MKaej2UubL<@Z`uVFn(Oae06?d1icPG&$V-K}3M`wBEl|ue=>osjbqwHU3=wGc|Fm;C=_)8W>olErl&w^lPCsQWwITeV?--xC!thW(4o2I1>p&UtjdU zKT9AM5Vr{!B*9>m#P`DqZHy7-dc^zA?%%p>GmcBJ0p}7)o!Rkgnq?UF?R&e@>SKI`{JNgu@YBS`x?F<0?8*nO_ z)vd7T;CsDKypOMckRy^=kXv#cKZ|_IL9b|z(}{2V9Md=>Sv>1n-bODvF5k*m9nwwx=)>dPFcv@+fW9gFrLh?ct{& zjj;DmdEIB5FTJ-BPdOcO6Xm55a^EL#;}E0FWLwO>xW3%pno^E5 zz7ogc^trp;E@)aoJy7XGRI4oZi#O`eO2W%1x1Jh@%MOWSe%(=vOLEp%OlvndfhmrJ z7GQbZBEIwi|BYhaSsY6x;IPW4;3L@p*(V`a#42O~1|zwEEC7~#2QjCg_aLr*y~5jw z@egaMOZxb=)Dd-~e^v~_E9pvf5jlKG->k`*k{Jl1C_Q^+@K^M+>&@!Y5^b~gXAd4A zT0QTHEcBDO)%vZ!6`z$=wmGe)%0Pv!0yBV~B^FXhJgWWV@SUs3wd4BphTaW3Tc%CI zg-UH4Fp+H4%EHVT>J6Tn7rk%Jq>D)Y*vXW@DNUM5(p!GjfO0PwR9`ZZZUtne`h24W zg^gVBHmll!-yL0V++Os52Fdt{TwUGm4h^_h$piw6)BfQmHXSHzZ<%pkzr`_CmHQ@3 z2znnL%k0y8CyKvJ%2_EtXg~hTnQ^G{MT%=yus%LsHPtAC&#Xvlt9M;=LAv=Rui;We zTHRsD5U!{U0rSk)E(K_@zWljfpe?i8t%%#f(%xkj|68(K(T4T0`CE?{=+=r^5Q`n6iB=kxP;DsiZ zMwQUGu02Y{r$ULh z%(#egPPEh~-!-##KqVfceCyjXVhffyd+@h&%kCe(@A8;BPfBO&2AqEZ=)66pnDY`u8g1hb%3a#2a!I3FtYEXiqZrL#Rus6kBofdQ?WmmktEXU-_&?Rm9&77 zhy>5KSuh*DlEA=^s)I^4u(s%(3 zt?~WmCeH#!cgzqnD$wF`cOmevup`a_2wWulEU&kXh- z$Ip3G?#xWNd44&YZJc4zF|OJ?b2}1U3>zV%J@h3Krgu(T)00kpXZ^UGg}{PuG<=Ji zdZ*o;l*-W8LGuXOk~xoPku1&R zkd#aQp}&ujh9S0bvw+6>D)aY)glTEQ1(~qAFQzk*D|vCh)faqu`uO!GGDWI}rhQHrSL3+;de|7UcSb!R!!?R9`({xQ^*AXwieA44~ z_NF6Y=%4JOhIDGU(zx5=&Dq`l-}=AIrME4HX%0*PJ-JRovBREOH_B-Z;IpB|l0>Um zRmgS8SIexEjZ_mwBl9z1FJeVl&sg+cUk>|*I=$p;P9 zGSTi^CiRsO{RfIbq#qNpq|vS29TPcP6lPNG-r70gu0>B}h03b?2AgtUB-50GWx6cE zlj%|-g11fkNg7sHrm_vWf_do$bq9P3CHhViy>E`Z8gjdY?1ODd8j^qW`#q>dv-PFm zPZz`V)l({XdSnd4T2^GEFnO^Q!SY3pqqnhhlY0>DT1Zu~%9*^i#%-!Qnm4DEFBQ3( ztRkG0R&(I`9H0wDz9`ub`jaGJUs_O`A>r4PrbK$F=~fXz&n4kO?e-sTH~Jb1FWtUI z$M?7Vj!yv0aAr~30SIG$O!7sK^>9`8)I&>}3|UCOx+rzM2YnTZ$JQ6%v;HRVzG7>h z4|yRA_|EkgPe@uhLB5)0&mH^t9p~IDmhPgSIE4+WoCbCOILB(WF<|leN_#aeZ2-P< zdm%n0r;M+($4{wx4^@)-9PNEDFBM%Ji;x;Nz?PTb}c!g_miLo zbu1gINhxCyg@A+QV}??=4JGvE!mobB0$4{koc?5?o4mnUeC>Wwv$Ncc*0y^5J=lm1 z=yL{vNf8@pd5gU!W~y+K)fq?^=P!>d3J}Hvy;_=fPghBN1%z@MuP6gbZ#A#|VMS=w zAm=oYG8IBaq%MRzQU8w}O{#%Esx=3pR&sZp!6H?fSFu+TzrGel=xzOywIwXl3Y9U z1W)&yBSq>iuJ6d{M{w27%-@6DmtB&ZDdNj6O9_F_A*nP$AJll8YL)DmRWt4<02gHU z3K{t5g+-HQA?L&aA}ff03NDnG6RkbW^6W)ScReBST~pJ^Vc`UHzm>$lO@7}>6aAmp95Tpeaj{e9MH?7kcVjbR<7gPxSz1n6V+i!c52if!-cO?A z(rBK4V2k(TMCRrqRjPW>76~D1Iq47r7X$!aGP8BS5=I}uUHy9cPNA)mjheexMIZqa1<;H3`Sw`$ z;+y7mANk4{^h|;2x1QY`G8w2;8234J;uMx! zkd&pOdQDCDID$z#G`mE^(%9fnVw#08)vG9B(eHz>fsmqv35)@TEvSoxDWKgBQK}dC zo(Ldk)g0wx2S8W2Nj2~3b;E?P;eO<0Myh>6_PEZz9J)KReJMvkeUjXp*hBkzp?d<# zxG=i-H)D@#)5l+6P9pEyMh?#`hIKo40XJqb z+I^s=f1ZI0Vdwyc8@g3O*|3+r8fkn})>`syQV?mGd_LBm@4)H_DEx&fUL3t{;7-L3 z24+qY`sToy0H0nY#fJv%a(zVNvSl!{9GM*<1_tQDTRt-eZy5`x!az|`hGdzE4NgAh zUzxs+F^pMBZW2lVif2nCU<8V5{ZlB7c6m(7)iNag2)n6k4sS2SK(SXUH^#AQvt`vs zGR^xeu@>W(+m|DGkiS8LfR^0`80h^EQId}7&3qNK{ym{|uz$b2BG28Isg?n|scXto zVB)eU3n3bc`Tp7u5d^b-Zvd#NK!e}sX7a6Fy(K0o5t}^upaGpy(16Lu{*5Ffw1ZF- zgvY;#w|zJB1{Y+3#=`X=!ZMSkyTVrE#rF^d{~%2ah{pu4c3{zvaLoVk?dGrZvBo(` z12AV9^3;rZt7SDkVoUTfT4NiFZ@R-5uUp*bivFZV!RsCk@+g6iGx!er0`=T`6fN-- z^ym>UBI1K%nWefZ4rpBE=^vPD>ziPc92m{#lk<>}$n$s^2p^;ON?bFJS(;@T^9+h9 zAXN&)7-qhn6jG6z&r zxtYHvP#X_H(rD=26g)-{zumnNygr2eu>s}YMY}gbHrW$bD*a4^h-9gZd3ZUxKXWe@ zamd=<$m?3XeIX9Zm&V^82Rr7GCm9K2-w7saT2@zPRYBqXxQt`<^=!lS0!}vH+#B@f z**S45i6?R1^O%sjAd+IxYX@(IBhm_XHZX^=U}2Opx~+oLV%H)Kk{cXxuL_X67ZwF! z6DCMrkzM0Q%df8)@Sx)JHkg<|5%&49wY~qo*)00`I?7#gQE{&T8wfs5de7?`GCv1i z`=Oa+g%W0+H5}dobEX&0!(rU1h%X8iuz~T{%`PyLK8#RDl`YCpt#~61o2hXwW(+B9 zp~7`uLONO;*4qRFv^W1cYH4>6&+XVnwVWM;-ca;CmIo!i4CPlkz$;>5tO(URaoo5C zb8dpXg@$?EGgC_s0AO=Y-m1OGj8TLTHuQ<+q1CfUk_3qNdmos3YCKM z6y0x+J_}<%WK&XofHwNTD43znS>QEb?D*?&EWB%_%iOfXv^@eZklozjhOFv)=>`A> z?P#RjuEGbv?V8=_sT!^DQjR4Wsz2!a967H{E&-jrp#1$rj-_j;Yo;KbRX-!j;k48% zT5>{ur1yeUaB$)Md^p7ckM13#@TJAE2M{;l#lA>H-UY(Te~eeevuJPPLB#jmT1q3H zXk~QNA%`A{d)j~2iC4ME_5#74itjuAQI>5M?5`B~2q_FdeIz4ufvzo&Z-XmEx71An+V0;OKW2^Fho>ifNgiWo?~z-& z)?vwiE+>6<_vJ3@*%3J>5$H9J&e-u!p3gY%3BARpMT7?Q-vhYo*F6FIJo2{sP;%@W zNQJ7Xjm64h4KW13L`CF~y&B+H^cN~ydF4|QG;Gw#ySoCo$6xKSTTbaou_%TPYLo(p z0m#XQ6jZ`k;AC`JlgWbbny4jDs-!T(;O&XYlpG46!9Bvp-*#9LPB3hnhw_4 z>3(qpAVhY+X$Z&4DiE(a1a(u{xbG{im1{E7&2n*WjP< zLTd?@G4{znpoXB$Kj^ajwsJ8In;2IM2NN!ubBFGllx*0e&A$ZlJW82gF!Z=O=F(nm z_k4WW|6dRs6hs1!{g2V{1&qU?V>mM~#U2_Z2lYk%~DsDvPh1i9`PzMj<-7WkS9lH4DvpD!uM*(UkKpWNwQB?@go8aPUp!#E zj8v*l`ahn&GA_#Z`Fd%P?vjvRy1SHaNl8T-mJ~z@1w^_VmXcavVUb#-rIA=_=@gI< z0cioj?{ka4|MQAB@Zq|zm^m|Z=1dd5z@z(YG31=ig~h!J&8ef;npx7=V#BHuM;R5l({LD|xH_LID)jPqbrUUq4pMWyXoRDtF!I#40d(^IWHO^*S*H9H&Un|)_QW2Qh zq^sTG*W_rRX`|{}AeeU|Lu8w0M3TKkx(P=&W1;TLU^bSmEd*}`BpLefU0C{d&GQVg z&4P2P@pg;>y>^YgzT^4CaJ<9QM5B_+Uj1bRQTfP^8B z$DB2oNx*F`z1(_mDFa}kM+CW+8}Wa4pR<-5Ptal8LV(C$xP8V}`QY&ysY??P=eb~3 z&fjP66BpTDx0lDZzE9uELt?fh{bp6;F{5aI>lpj3;*iNH{*IA^!Kv5F_MXZdk6{_* zH3z|K%BRIjy1z3&SnK1qF09}AEYUlk^>%^u#eZeg5{=tW^|XQm(9(ilaenra0Z+XY z7djErjf17vUkjK0^%%8Yj=N{!HWHATnxH85^2;3na+aH#kNceD*YcErk0yFZBU6r7 zdh8G?S!%5Cecm|Rm^JB_yU925IM>RI^We@ObFm)qmF$fxiC1~ivm;CU`YgL;4Uc6r zO-+V^V}61Du-r8G$KS_)k3iW<53u3xL*CZ}{f%a?{{Ww8F{tePnO8c)0rPrpQ6GwV~iq* zT!n^u-N|EOs|RS(!U84BsMKXlRO^e(9*~Ne&J^g8+r&Qlp|?dreLPatpKB!7@6}VF z=uY!R+u2!a4pHXe=IfYrMBepZ+@cmDb|>BqWr4E9h#UJm0?Lz)N9oo26t{Cy{NXMkk;q0!$O_WGR@>+pS-*=i%E8e46Mt}aC^)>u&4Uwh8+ zKgxS)O#i-B_9FH98C4OykAac@XNe73S9Ej#+0{OL5nOMrx8~pihIpbpN1u1{nz0I5 z=z<>PZ9jY@YFaj}ZH?DeLypis;OEV!ru*CjcTnu*2K`6<-v=i4o!g51Jj>K8;?-N6 z&jcTThoQtIDBc&6NIolg+Cl>G=|w>VB#f1r@ugF9e|On6f-$m%$l-lJ+(+!#t@%H8(m?<{#~8gtnq>7G%y zvJZ02h04cPH4^?b(i2fAcfRBZ#i;dIHo8~ZzHYo5Eq`K0Z(f=p+KC++w{!ioVZJ!v zy|2S>{Z$6v&_r)R4GgWfUz|h-gA68@?nRdDs{eORAr|({;lmG)CI}Ef(%vPvNV}iU z-z1waYw&-_>H+^ze46)0&}+`!?rkJHBGTWquVcxl5~iiq<`j0IX46-;R#su-o1o8Q z-T3W&!rlRNdkHIU|C!99#*BVS3DlxQSc~4iXF|WqNc+EZw9EOFg@Xu;Ash}|hD;{g zLG#>}s{5>-Rp2ee!Ol3`nY8{Fw_t5ZQy#6~Advt_ z_lUS96~6geC;eF-qc2d2t7iF&5#_PRGPsk8oiH|h+gdXQv$uySpABz*W)N|5Mgf5h ztZJb?m|wshNYqh1d0?k?{3i=M(R47X+L?gnPn4KUrx2bNI34tRr>vbhn=FIvPaKgU z0-N`~?wd^@uv&#{JF)CyMqR57CgDItJ5E@LAS2RIQp~+5}O?ARmT5qDq=V zcwabt!_<4vn?QG zr%;*hghEKX8P(dRb6H3?`W9C=g#`z1%{R4UTlnnD2c;Kw;j@eMafNy;TRR^VL7&I2 zEB_Z6GVRGTrEe(3w}T#2S}iT14^_!4bWQWn$tk&V4T-z+u3>kNAFHTFzAhCZPr$~} z+&(3b2D;8_&G7}j`zvor^~)X|!1TQN?`sWy&IG5ayyh*R0jglO;#oRN$rJx zEW{;(tR{$4n<;itVy04x%^dw6Jt*AIxyN1(3m2^rXID8jal=4Fr~N`najM=xorph> z*P7ZkT4saOjS%qyZWN}pFL-efR>voAqLtKSo~W7;w?*-RUPw=jUvU6cuiMbMpBaJ6 zexf>_1K(ZlEtOy-4Ji{my5oG}-6g$A2g#w%+3c#(;t#v6)2M)w^!JT@qq|J<`!6Zv zyqArTNabROcaXqyp3iY(p1M0205j-*3o}~vUb(B{BQE=X-WmJ8Bz_z{wgW@Y%~8(q zE*?Tjrspk58k7zkEz!TI?AqdIeG0ro#)gkb0xd|sXW&t2~`;>9wVI;1Iabx zP7F$>XX*?INMZz!t=Y6lp3?1rJkf_cZ`nKYvE$%X&h;r{^mlPz^`+sb1TV1N zs7XtajIRa}-jpZLmdMg4Fp;qe5#D0Ao|5rYVZ-wV&_(@PZb-k9tJICxr2&3uxl5y$ zl;SpTZ_-iGl*0$Q$^N%VcW>{(-P`N+vAuaBrOF?f^=(l?jArLaf{O3!xO}sR4-V-I z0Ls}puha%JjxrzQzcg{1IDVj4e$}vw+FAQMpvmAqCm-2SWJaxjiZuQfu}Au(eo~dK zPYKrjSP-ULnzMHXJ`F5WH4AC za2m_(f3aGCv@E{db7`$qwJDePaWa;Aqo6Mw#`Zq`-%FrsK#}Qhf5*Fa84D}Y5rjUw z|0jDh%b5DbQg)`722qz5dh7A&8|LSl=b(`a=1mH^@3utl^hFgsff=t#9CL}Lg|Qyb zF!#Sp;~rSq1LZ2~ENl|658cNakLP{rpat_D6SO7DRg)Am%a!6o`qI7uWzAm=(MiTt zhXLJr{;TRpaqPRCPuhaOi)XjP=^O*?gE-4y&l7zKHvw@yg*Gnb&}1Xq&rQf zBfoQOLtH?^-0Nb;T+r%QM&SNquvWSK7y8*SNhUk*&7o9Uj8`1Msn9&t#kli@DtB35 zye($~TvZd7^J`kK#GJF69*D4EIy}=D0WkC3M@$L{=NDPOEpNcSX zDwCu%`|Wfj#ShDM>+~c3tvrg_WXQdzBmROC3?kDZ*7>rS0Z8 z!i|-q?VA2!TaY~IkeRHaU;(>z0sR2tmTU35%zAk)H?G1eBKEF1nL^QtIr#_hoN^Z0O{a4pei;Y-GL@ zrH~i@t<`CRRu*@I0Y$YT$7kodd|tt`g*Nvwl{D+1{Q!(0AWX3s4GYFid9!|@&+w}c%M!xxPa-y z>SI&w<6XL5C!w|xLsJ{w0gD#IZ1O_yfkBh|sYWoNE$xWQF@?cT4l(<%fgM{fp0+ew#mp7aZ_#PoK zql-ffEUOPDgg-#uP?lvkyq?nacm#WqUcUk)TG zoY@c}-n$PZr;{$9wyo!m3V-tE`{i+ODBV3tXj`gbaz2=f>93+oAfFrkL+HgPsXII> zeD?|oz0)Pfg*CXs)S?*{uNEA|K0^NhywRPxx+Vw=Feal9>FQ?Hi`_(V*CR7L(%(bXErP!Ndlr4xlYg4?hr!$1|!g zn0~RIj-P7EfZ$lh%QO&suZ+4qF=fhN;L&k|XgiVa@{9?QX~C8gr?3M9f~Yd;whefG zSnXJ#8d@ZMBfkYmk0^EjtRHNSBM23d-5%3*_^lI2M?&?}jMJAi>r`v%XVf2v*kAjX zn?0V#|%M&NbeN zR(31p@o15mUBf@sPgB9KoCYk28_sDT=8edH)mY7_sk_8pAkLr@3wTRQ)Kp(SyWdE~ zsMm)vrHMr5C=mVk{wLm*22alD<{#;t(X{qgO5Lag$G2!Gy}Qp{Ujf;_z4s&Yf9IXb zb=o*Xbf}8Zq}p-L(x2f^8D(o=*3>{9ncV+@fz3ng=7LXQ5?G)cVi20wh6Ol^C;9Juf0HT*i+V+ zGfWeJ0io?$gK~drLk*Zu?DM#=YPn5O45_-_QR7`4+s`2FF10~8$th?{&D|4 z)?oSzbWa9%QL0C*KkCxxBb~{TME}HM;qeLe>T@-KYp|6Km_QrjTnDTCC;gnr;uvou zYx=6lLa0KbhkY1e7q6MhjD}aZw`(oHIa9N!J&U;K#pa@%#7nc3^O1j-=;N|{(SH)s zD~*w?x-J*wSX~pmw>U~1j}bp{n^`_=CUq2lH)3QZI~AI`;I9Q!m7@+-+461a z;Y`yVirS?Qm2@&LWBx_kZxLZ+`Q{|)k||wZAM`g`Oq5yqy_7Z{BJKs*0JJjZ#Qr&z zJEB1_eIgp>GDiid8QGwhtH2sxfR~n+YM~0KWBdn3`RfS)@+Nbmo9LPVhdk-wdR2>* zN*N72m#o84swb`u39?=4$78y{xl4}T%}qZNaW}gSv+Bxx zkn^IZ+S0Vjt^D=Vr&OePM%r!>4%&;^7_Z~9d^(9t&pd`@O-1}PG-1Pq$NByQTw-TY zr$hTnM+aUSoyrrcOD3CIy4J8KW$zoi6u?7MqwcmH|IN$GtNK9@grYx1kcoam4( zEdun4|8Ki3e4{v#gN6k7gC$^K(=)|Jtu#&Y85-r8FWUZ2hYn9%@k;!hVMGF3#7H{h zm5CBgfL_7)YyxwUTzo0^;FrMMhFZ{UkjMl8GW*YH>QUS4)3HEC+naD`iUjV zmO)MNKj&vvok$wvMUZTbCZ=DiGpMu0?Dd9B>~$;C)g1wunp5KllOgx#iI_n*U;s5o zq1>Wgir^l7yyQOb0ir`M_3ti=IFT20%|!HSHKO!iIAX$c5o2k<1_9Z^w|7Ac?Ix?o zHwSi*Q8a@k;HLriAq#+?A3%u~a3t!fS5)3!Sg4}e3AjyY@sZicUQ?*L;(+Q?aMbV) zZ`&{uQ!g}fHKXoC_}GSqSey-eaZl&?*67=^MjA}L3CpBkTz8&96r}@j3l!ZfQ|UJY zf5a?oxEu;y4N1CR;I$XflOi-lBfJwQ`yDdRyo{g3TZq$Zu72{Bm+R9w1@RPZk&lh> zZzc`zDlPo0cirWWgw*k#*4{VmfqG|tpJym-Ygh=;ImsQQQr?YsxWn}N7Ew!w5o z^NpQt4gj&l_tN5rA1vj z&^%~Sm`}FuV=yz8z>+@|nj1k26>%cU+l9XEKvqAde$bLE>l-pu>46Cp!OIfi$ohAF zqF!eo%PCPUZ;tbh+2wuTCnUTx?7P@oaJgdq?elY}>en3%{YCf}hIVq`xo+BY-?yYT zRFRWwn>IlMiaBoCj?xf7V9nY;qOxHHB=Q{lr(pZK%LO49+D@rMZIs?HT&nrB)dKX+t(kTTzYU9*|GJX%g>Mtq#cThm{# z)_90>685Ebk}eVc^Pe|=rX-M1G`OLM_`fGj4e8zlQ>G(T0or$9Y!#oh*%wSYI|v`G z{*JJMm=rz!uF_q_q2<*b{NM~o0TMUH(-Z&m*VKfIixT7Benv>US=Fi{3EI5u4WLN6 zqW+7fO?#EjK~@SySPtcU%2!y0pUbz9Bg3;hv`mU&3%}t%a5_v2aI1=6Q3sWX&hq0( z@Yy@YS1KO3wYZy1d~%%`D~=qMpId5ma1()PO0Rvj5;M6Gd#aK{^d1ORplM64A(^_% zVl~XTzNrlZD4M&Ns^j1N81iX7iwT%;gT3N2U@$MOO=le&|BX^<=&5J;>Embpat)@X zW1&ot8=P5BZ_BN`Lx8z7bq|~eGdg?^5;p2g3Dlh&8})A{lHVehI%%1EVOed_-r{I7 z09OR_*`rvj2gav9Pg?@bX0oWCE@PK1fNBpAD4WgvS&+Q{Ww|UT9yWvm+%G$8UHh8% zAWh_=1M7nt1tQ$Q3ipTkMBgkNtVr&b`{v(rbti|@K(3)2s4UE}F~KKK^e8pjV5Y2c z0W8AOq_P9(3RxRQtECYiwJohVNG03Qla=AuG>XT<)g4Qd zR@1IjgkgZtQU~9`2_9OUzL8dE=tyYGIFIXeh53ZuFt#ud!!KUhNRn>&#@Jol(0C4i zm+oW7ivTUdEGGwRwo1MnVCgEN7#wK<%PgE}0RfSK6p>ay+xGbD^Lt8J_diguH!|3J ziax$-&F7J#-wh7Qy<7j&f9rQrLf(|W4pYY>oP0sUN{}2IxERQ1pnq&f4CL#pKlrZ- zcpN|x5Y;Zv_cfZdzd~_@n`l}vQ8($9rcHaf|>K8 zt+aFVt78T2*p3}Ji@L1c{hnJ2g+M^XdslT5itjwZzR2Llm?G19_qax$;8u>Ecoto= zFjNJbtf2>e)TMWIUihkKmc%Q-itQgpmreg--t-TXV~d^o#Fu+Mv{x^l&2VjZVP@0! z1~rA%h^(1aIVOZf9z$CQ{1SdwW=xNTWg4u3*_f>wsy zfR=3_6OT-upBj@+EZ+6Zjq}F>q0Q{4JpEx+e*l4$N3nNCIcv~&OY5DmYQ6ODWtp*@ ziDySEUdQ&6h~Q{4ArgiJLSF$*@W4dDhZw(D6mAoPi-t>t@+awm;CrKDUg^=k_aYdv ze9hg2)sTo*1H;+w*j$5qo*4`_(2g5!u@9yIj9Z=EpAvVzi4GP;Ng?4E^Oy`>kq0xE zrWY|c9hd4YX2B)%Y&l-_nR}er!dZfQNwP@BMpzFCljut^Pzpc6| z$H$7ed^0SVu04z01Z*~g;38IF7ynW=I9YpvpTE*(m28-}tRH&-Q;*u!qn_poVA^mk z%X*z0#o;vFxXQFHPIi^WHH~=~9=*00jiHpc-7U+*D-G_ZF}x+Ov=_dtZBh)4iZG0O z&9;2*TYmXnC*ELwElIH`y?1vrpjyxBg?Hy?y}$L7-{F6&($5OQtARQnlr_5yLE{9V zxxiqcFzwXlbhYtN4*1b@Qo7WoPngSwzIIuDTuWNol~9-5kkhhDmc65K-WaOD3gQY_ zF-q`BCMbucEG=YPpD$H{KWKtcv18oKy-u_X3QxXc*pze_|LCB4WtySukkv@+o9S3< z5nrhV`x>>iH`0cuc|L^m2t>?bHbk{mf6_GMz`Um0=VDQQU)Ek1P?Gp2pE#t<{JBV^ z*vj2~TlCPbPxr(AZOYs6R@V%mx*GWJ{%ABo^d~*-q0uptVIEw>p%MuB0D#{v{gP?R zjxm>bA7q2{Kci>eJ_E{(FYG)Bn{i=HB9DG|$t!);nC)(AI6}A2JP^j)4lqd52J61S z&bB<}Z_`%eExVlcU8=R9w}*2gp+>L;+Xuvk^i_e?t)ErpwT(xn3rSZT9p;QYs{yw0 zz>P?u$Gx|JT&7W@)xl6ad0AS(A`4u^>3yGU(qNh+fhR)Dee+s&HqcGL+CE4}D5jG= zv7ub=%{A4Vjs;%EWI(vaLG8{*Rw;YT5yepfGIk7n24-G!*pDl18kYL>@p1)94amR9 zE@@ts5|4(7oViG17hp8B$H7sV2zSxsNE1?vQ=Y+t#`^Dav-jP(8Bf^9LSZZ}B$OEjq8k%anKE+VqIcql3^{ zW>6Q<nTkDMyixR&(i2sI$|F?MWJjn>nScXL$C^37Jk%cvhrG{mnSq`AuER~eX+4ke6 zQ;O}ArEev?`rzfKX(Pf$#!3luRx)lFqI!HpgvM~Y%#B53I zaec)@>FX+S7%Z5(9%1rN2uf7U?0L49&Ha2SU|}_zNu5(?{htrJ=W6q>fmG9T%kzS_ zh3K99KnwB|9n;Ul2Dfk5*1V>e7jtBxzw9g-OEA z=pX&8!K{ef6#Dh@m3y(uG-z5RuxFP3x2pfMu|g2jz^vax=` zT!xoKQQN*|wKcpV3&en;#s)31ASgW+vgjoLi)Q=h>hh=HcdYpDnuSZD*bm8`L`Xbc zvEAC!x&OysNkd}1;{|%Z$dQCWq7f#-?(6aBqM#AHB`R#fg?jWo9)ZQD47yM6JcK)k z_(p^5janBAj37VR5<{ZN@ej>cl>tlsMKm&GMnb2B~V@;4S+2 z!49~IZ)QKu+kaMK19}q5pHMcW46OSTvyt9A*66Ap*^gUQu-zh55(J(aP#|^e3fprD zA%Ky36jE+_T!4gbBRff|vB69rxj77GPMI7SQt_fF zvqD`<{#5!8FmoS#v}|+dv-ne4j3YW8DtqR#&JVh5Xf04kbpDc5PEj_Unu(>W5E)ed zK`I!Yp=+^j;)PB2`uRLdtsnjAyL5idf+QFuhlr}!#k>Bz;lWo-i4h@w7g4f@WPONm zlQdTR4LI3Hc`e-WSmwo0Q(pN*2pmtNfF+<4tx2!*T%@5rSYKiyr#JmIi{Nnfto{HW?L^VJPA>qFA(M!SfXv4!Ra@-vGVrfm>K$xot zs=ZhK??~|f9mzYuEA4t?{~kmlN0K1=$d$4{bM)$`wl0OPms>QZ;Gg=%*^9f&SE{Su z6e@T{GWJzPEI^B{;Teh+2D;;mMtmD7dlINj%c>x2EVyfo=^LXwI!x0|?(+S3Q^;pG zZQpyK3!6A50)aeJ<{HZZ`Ch6}aGrvb?k&a^#K*Zc&t?Pb@_5br;-0~ zpM(%1Tc+q6^ZSFQDx8Ryi^%Zsl&!L{R-pmcf9LYRJp$piHiM^y(J=%43>hqR0-uuyN-=U zw#rtCz1{Y{$8HkvjvkcT0@kxw52hP8B&D;x*Y(n#ko%Xq;eX;sH*SH=CiH1}={@r0 zj-rpRb$rFFKW}I9Htl>Zu|(yUI&$8#_^1XT;diW_1pgj*^vkDdOA->J8Qg!G(hGF& z$vM%7T6QcEl%o}rR<*H|?KLVdG!Lkr`l2PJcRX1!6H@}mg+9vToP`x3#4ZGFOU?6a z&901j%Jz!iK|%`)e#2eMFVh#YTS!cZf1C2)&_x0~Q_$K=tjI72)aVt=bo;uMl0W4;N6JIt=E3y&^^rWUIf zdek{b4i!J~k=Rm`g^GlnJ|x3_XpQ;rcnDu^X*n!N~`IqYOa`-cVIAr8{Y(B@x@;>ez8fO03SrwrMPVVM1H(nI=Z|AQxwfDige$ z`}Vk9_w`YgN+9N*EKmjKN6cU9@^Z-molR50Lw;oX40HSzqks~1fBC$;7(_HVp8^&r zj?PvpYvuw>8m9_eiwxTb+5B}l?WWlYu|7K5_UEi&?vgmGKS!Yw!;WgN;pnPpk|Ah5 zhjiUve@evymabH2qG;B_cr@jx%kidmV;sxYrTT#d2E2=Pr5p~|;W8%5m3Z>3z;4@69x{wy%G)Mx zk29tHf!$<1Vfigf)(T(Hq20D)%4QiJFm>2}bx3*PYzL+#LGX@Mkm;YrQfoiqNZ2hB zXgN_;@f)3G7}Kq?g!Iw)#m>ZlK(ITPr{)uNIGisyYySJb4)XX z;8ITGBeG5^Wxo8M>Ffa{B6!O9<(27nw$=WJC9g9tS)J$iK?%9$=+47s#-fnn4nv3B z=jr2UVfruC0egh{M12k)$n=M<%QI3-a#7A}5Y$@7zc(*P|L=n$tOGVm%=%s4TcSE= z*kbWIx03H(EwG0X->L1etl@}f0MyWVX7mN zn_Yh2u+)sbJk?OeNe5A_U}@#XsEoBsk7K~PH()Thz!Il6ZRl6=owX$NqHkXTL?c{N zu+j&;X-?t_r6EDJe0%}?orvuW^Hz(ID5`h*UU~boD`}>mL@mE{#()u@hX+uuG|5@G zc~xZtGA+&Uq8|0lkhJIf_r9+f~PNrX+Un&ta`x^~Vyv$>! zY1<@oj<*(MQ+la#tOW{kVXu^OvmTUK&#Bi2a~e+!%7^ApgVy2I)a26m%t{ zqF1N4hbGNMDT}GglboE>hc^R|mqV{nxS<0@<;Zr-Be@xcMv1b0&U$V|vcefj^NZT% zm@N{cU(tT74dIIJKlpyTD24?=D+Qk_8@O8fyJAXrkM8(9QLw2`#8r7K8?PTVS*}Pf zQ}g@RGu*-cN}y%&`jMElM!qkbpwyB~)TaH2ey)h!ynBQHYw4*Mt@jqu=f1z=-~1mY z$RY{4WeoYSO9w#CjqlqSQj&$d>STPB^t|#QR=c1_5=^Qfr)SshB2lxhCRGS9`AV>& zVy=$|L=~;4C?RM9HLc`)(>~t32yXL8Iww$S#*-pVI8VR*mf2RDvRs_2;_S!DY0NhK z25RIc(Iw~~q^@STTI@gIwg`T2oO>Ov3a6ctg)i7sYQPMJdmC>#3k}BMh}T$e#UHOp*EYWwFG6REEKYy&3A@7NK|gTO$qvgF_#fK~AiA%AihI z9u5BZ%Eo$=%6x@$>#o;JTJkg!W#EEiGSL03&)-shyz(-({)_NLi{O8aqp2#uDz-nY z(4~__vj`pD}d35n(n;qS6Ksr`z-XAI*@^frhEu#`^e5Cpl#1Vy?MD z_h_K(D!A!Qw5#mgfOgK*7^68_oG3>2aAxtbwaK~GdP0p}a-g{hBpcv5)<#`ml86ss zds9N#7+XHLD;nlMmJh|bpRHY4Dn1wL+#m%}6_@KTp zGW}Ty*IB!v-WX6$iN!k(1_=$QY~SQY`LKlU@L-HRv`c#NW2j4$H&!p{SE(_STZ{7) zTzzjzUc}O^Us4mLriL`W0q+$%sUPFcUQA}fcpo45P%mvKXv*4eIdd9;%i>n2jiF5Q z!wtn@l3K0Q3zy$yptV)Uk-!K^7UNI% z6dC|A(jFZ>F{PL`r>S#u#Hrb6Ds^h5=*icM-!jGy7$$Oc*2k#T zQn>4jV;pYSb2mbL-4{SFN4q0(`*(?!KeDtiOrpl(@r>UTts#j#6c~yCgh^RMm3P;$ z6r;TRev*dbM^lUAVM@??%&Q{}mKtj-=$oMqB!m!(D<8d+@Hi-iEl(3_%hGrHA)atb z*1oVyp(^$dfoi`&b48v?RipqC13}@V%c6(Wu5{a>X$84SS_x+u&FBN1SLc1MOsTr+ zemO5L%lTf+P{9k`Le+XwV~OL!ym#7*!t!qZ z7%0O~*}=Mvo~vqmRp*uG;|55u?`(b_NvhN8zw&i1tkU_{rJftW&QjeTC&I zhAz8j$qY9Z!89IEyHUIFgTs)U(^o$q-3T5V^xEy5WNmTg<(M!vhjwk!n4&ED2-76oFJV`! z+RMHYLB&fdqNHjQ>>Yh|lG*nEX9UnBCW?qX(i2-^G@s*uCk2qcBt2&zWqMu{V-E`2 zb}8?; zk(FY)=e4CV$26_nL&jCyrjkcJ9h(&)!{PHkdP*9OzH$V*MIo*5jRc)r z3;hX6OD1O@fM)ZYeZeCy(?2#V%SpYfS}}TkFN*zh3R`o#>QxHkbGrln;||*H6b-kC zL7r(3|1kv&;YT(KRhGxMtS{|KM6rQ!6Q4zVR`9h+4<^MDnu7*FXP6`y%AdQ-8zfXi{oC6w}9^YpWtp zMV5mKMFlkl3}Y+dgab6gl%uhbRfH~4YJOdD@@wMUH|)hNNljlIhF^j;p_Vh0D4-+k zbN4AmHDCv=v~qm#;&kRCrug{2Kld4#FRP4@$-Tkn_uMs=NrdSuotU3(>}UyXuky&s zZiYVxiBG*5vuh9R2)3{8y<~!Rc#0?b zTiClix^;UAJG-<{${|X}mi`yxZaumK>T0fCu3g7ugAy|aKEfnX3l#1oZ08=WA-?0E zTXvrQ(0_BFlVe0jxg{-ANTaU$_eR4Yr(+x4#PNwhe1 zvm`~xbLv`}qPUhGH8NY0Xq@UIbS)za*Hq+w3_MdkXfuI)V8TsMcib)63#wdNv3$=F zDSO6vgl3QEvx&ju@!T0IWWFEu@!;2wu+;`VAQa6b{TDS5+V68QcEh0eb&-HHvR{|X zeLQ)Fh(GL7J*YR5VyJCrP@ZkzdpO-P#kdRcEB$=>1A#xxvQl-h1g{l?bfeaablsuN z*W0*e5@qS!%*6rGPJ|jg+!AHM_s6H<{tEp0cH2~9Ztcc8I?VPLBFiJ)jqkEGE$^-8 zC2DlrQjWm=Num-zheYOw@A_qHTNaR%Y&KlK0qgj|sek$^ynn#*`cNC*@PxlqYvqMB zev(2_Ty^RcFA7&hh4%JOPRM>8M@{7bEn!JP1RVU>=9mo*-pq#vLz!Bx-~kL{_)DWt z^gy1Rm|uU>%uu~DL|j3m6HSe0W%|Vwu|QGB7vDd~NMrrA2K4`6xy|-h#ZmcaOe#W^ zWrEtX0B)<2h>?WfcVi z{*$x+&DXozv-J>LsY2e@Mo!IGPc^u42Coa+u;^F#$VQcl_!EhY{W8LqlX{L{{?n)f zYi1|s)onkvx3EX#M1+YLrnD0u&5Ssp!^WpbG!q~EWFGeV(0|4xB<4YZC-qUc%#sVT zpiSlw%>`tZ1Th`&qA_tuJMZ=c4I3MjgiWBP(y0H7MuO5z+@G%o^FKP76T{IDaC@9E zY#Zz5LN>r}J&-3ag`O;#{F+P+QA|?W5PpLCGOy(#{zGw> zf8LAG1)o)F2e5>^qZhpQm0(#6!aueDTE{Q`iIQFy;Og1Kf3H!j}3UoVs1 zwqLXd0ba+xO#Y%zU+2GRrJ!F?RS?32|MnHnRbP3^%F4&chn($3ie*XpP5(nrH?~Tq zfjEy3nj#tTK*hHzuRLz3bib*MepfW2ZFnmotoVx~BSrylqsq%n5%m)4$itq)8Do1J zDF7g3`MvKs-CrBezL~RBBx;N$*4-3`|E&}7Oe^aEucE9nTwx#*bbkPY3o52Yig17B za9k*&jkhUX6H^*xCN3V?qk!kkzmjPov)?wT0lFc7r%`a-dnQo5Bbl+ z$dqtyuWM3;b^q<(r#~!p1WqG0I=)_hWUi_4Ge_JD4SGAxqKX&p{a@ejUFRs$HE-CD z!<)uc`X9swfhE141O1x)v)m823Rsx()PHlAV0m4pL{+WOR6Qx_=2?~__>Fa_0m5bZ zvwxJ5+ie^)c6Rpq1u2Mieo-ndvwDL>=~I6={=>%>TEy*4MK$LWxKYk*t`==oRa^p_ z&0JmgfnaZsxUt1YFY{2E_;F?FiqKmS$m*cp5*}Wki}gC9cmXXPj$}Sc!fa6#ohR{XiN| zIPl2uXVDMO@-qEH3^FF-Vzk&{6w(=X;Ve5dyz84QoSF&h=|vx1#{YhJo^uVQRH&i% zA)oVuiq#llqGBLGANcFO8w6JACUo{;JxF{eoir4=y$}o^enK~QW{B1WlsJ4hpfHDf z;xno-&D&%z&*}6e48rLl)@T+pWR#Pso=<8u-48hUDd?O;jZyUO2|D_^5(@(_vQNM+ zro4xQ{66HfqZV-moOg?Oop@&adIWs#)`ivei!_C6udl!BXj}j2kQ6)Q(s?q0sneH7 zN>{xKGP(Z!>uupiNxF2MzHzW{@LpHYuV;ZFmpkhT`I<5*2>+5^H2T+Dj%681c+&%$KGqVh&n>8Zy!qXoChZ3npc z@&k?^&X&7>8jm4;v}w%1CP1*H249ixXP`G=iE)Tc+EN>3C+yHEfFuOEeZuc;)ZUi) zA~mWtl%o471zfdbh>qbR3TQ5U5(lhqqNS1L!M_6$2&iIGr3NXw@f7i3YFPBUmSr5_yzG48lmxN zslI^iR$%`-=}$V}MFEQ$9Sg7c^f(SR9h+zGmoqC%0xKY&`d&HUe~uL%OhUcq(8i8hjo*~ZV({1;9~9|3_OE7#`K+hhRVkbO!-?!VeA7Nb;7)YqQo zT4D&Ezc0Gab850BAr*A}e8utnqeX+iiLUycLj zcA+U|_=ouYzIX_E@x?pH2T59qmRn&dr|910!ZwPP^npc2mxtvsTT(-;jmzGJhMP`H zt*9gT-NcL4RsKjMLOg#w%7(jPE>|R;9E$FnqMY`Gdkq)dXtPW@Mr9m}o4I!4eQC}} z8ebG1&3rjkF({O+GISj<5;y#2B%BDjFrxm7KjwHo{OJ27a#n_$?Y&s#MnP^~>UR(t z6j_m#p*v*=tU%{qZb#Bq%PM>{wA7aKtp2ggzCvR$gWL4*JAGQwnw$>{PD9OHL`(6T zP@r0AdE+g!%9Lt4pH~|g5aWZgKQNy0tVRK$u6G)F^UdQmeyEpYeek_4K2E-h?r*>A zfYvs*>a*Z4L&_HUpJv#iMq(iF^n9Z9;G#(NfPi@CYBbg0_3N)!;`Gr#7)J5WG!naX zx#AnjMV35I#&`8y_!xDrYED=iTZa z08Gwy26V(x?%RWM4FIqtQLkm$U}GHk8;8 zgGGE~l$`!v7b}#6;ZQu8Betl@p<9z{f~T3S7PXVaAWV2KbNwXt`iW<{f|@nj$Z)^W zI7FffsOF+8rM&CQW`|JD5V8CeV~Zk#O!JCfXJb0jh#DO2UpA+>!%4|}g<2!hy+15b z>lhhe%bo*QIczxOFEw~&$V`nQ7xZ(gi3W-zb}(tZF1gA4%3n)q&L&@0F}Zu^97#+! zL5l`9I5U_>28;u1GH*TQVG{9UFv2<{+H$?c3fWodQqC3Pp? z*LdrytYiWkhN=#yGowdWvC5e7XH^ll@K*H5A*L=kmxFQU{T`QI#Si_t*%>a2CQn?=imV0gyA4A4RkTDCu_&?LNB2;+eA_QKy>j*lsd z(PoS=Mq>`8T=<;VGugK>VQDEtkLJ)q3Kh4Du%A=8OTbl{asIgn1mu7X8qG;~lf@BW z7nyj{NThRf(nzA|0c&&cYxU0xR}|3Q+r5Wdzf9l0ykKj;e6RinBCrcY5q2XgdbO++ zG#pq!>mO!4^aCZ}b+QWYNLdpESX{Wlw6ru)M#E9j%%`6*upZP7EB{{8?X`MAVtak^ zZ5=jxx{v1uxaRI4`J@39?)8cA=0iZS3?UFjUQld; z%0pi8%LS&`V^qXAG(&9M0(x#z1u86E%~>i%4s+_G(&P*Hci!(n-4~5-H?zdPvb)Og>srSGsXaj zhZHi=$jay(V3Wsb>vsbTHW5<=`?(Y6P7fdYBofM+Xoy@m52}hu59_+vOTlZ1S3~No ztke}+gvh27uJVMlOa~31VpsECgolslnI(y3rpZeEBK?fxVrFuK-yOX+5WZq=k|@IG z!n|;ylj3KJlrJIpz>waC!QtJopd4|@*RW%7a)~*untK!*JYw)mO2EU{(+`;@NP45K ziR_b!XA^V4w2PYFTOMv5;xKwput)Eb5H5xvZjHy6^d7&j0tc|d<(npdZ$T=k8l2%H zlu8=uMx_HxTxQW{94xFfu#VZ}bhK$d2A#8jFC{2?_%=-pxD)RM%Bo<_VX z?Ns)3tNhB~@c~?wyf0X=3EpW=8e`(=a#=)Kw~E)ItDxzoVs%`F#2)k{jbJ4Nl+9(^ z*Qn5-EDuzp7{7hq{iXF3L5QwdYFVr zzItUHM7OE^Ze5S~>L3ogV9BImDz=%bF)5BeS_kBH__3a3_Vi1 z8Ql4j<0yt*!6Kf4`h;kyIN1e#q9WO}Y@(c_8;cDxGpz;QHdmZ$1R8Kl-)2d$?1mx% zb@pLqN8byZvD+eiU6zwwt_41S3fy1tY1@}eXX9j235m`)2YPVxwjFM+ zvec=O`DF(!`mfEBH<32M0I5Dn(7Hgf@XK*@`bKP&tN+#bXI+jUN<1g+P4K5Fp42ew z9k;I$-R7U!f7+!uj((E#iy_+H1e^E%D%QyK#D$6&WB$=BIjl{X!`G58`+6`V>H!0S z043#Ee3jyo(#7n%N7br4^+r1`+cXmoA%R+^IA(KQ zAh{c&Ip9$yy~Fk=Ub~&2RbO+It3X(y&wb9h2h^{&4a6rUKCVHxf%D8p2KCn()RTi1 z8OY}}u+jdQ@p}2;sR=J6+JQOX4#rASwuw`o)#KcFvqP{Qc*3&ccpCxL2lC7s0Np5fLVnx9G1l- zS&u&W`lfLa8pFeP?NtfUHp`X``RpSVdxhoI&pt=_yfw>s>{%wxt|pZfEu?hn!RAP5 z#*7rewF(49>Ix4=pkV@5YK5fvAIcE<_c(pa`0nn#YCl;%rv3*U2q2n1puW$%?F4CD(pjvfK?&qKNX1pbtD(czPRnk>^tA|5HR z>ZYk9+B^nJ!}EUt=&`A1jfA3xKe-Da@ zec0=dl=QV0yNl(q=^ADR!^QVsy^3=r<{qdERUdyy{n%E!tX#@aUOa|DY>cih-7kUc z7o{zN`eiIfa*jFUtiJ##TjkTSqx|Bg;M}`n&rEl!0Q@`s4^0zqX2ptu zn()$jAU~<#ec;Fcp39ZcHXN(PMJ8p?FmU#Z{v`6_)ud9_-Lkjq`v7;AF|v#Y9k*(hu9 z^_(OCpc@RrBpCEN`m0&aZdFA8Vv}(Bv2r`DB&lWg&OdYr-ED z*e#7;ynMmD0@h> zSmnMcWLp)bj9aI-8A1sO3MUbx4C0_c*Oh$_%@z5t~LqugF-hb#i`h{ z{1dM96N(Nxfw^l^Y_D>g`v&Ohtc&Qce91#gy%PKqr!`_-baYk3ZzOVTBfdc*9o}mW z?2V{uWWD9DNCTso9_HW%%SR4dI?9XECp1B{#Jz!#^lPOwQwt66h^&-b1T7~R`-E&) z1ID?*8m|wuJild~lS$l-&RXIOGmSE?L7J2Z)PP*Krr(A|KHvMUpJW3c8f`~JNazKT zvf@~mMIeSL3)CsKX>d^UVrrVY4lYQ1iLq62NyR|KtZSuW zLl_BWr(}Aj?XaP)B?1cNRF%C!>~CUyT4@+@n^nhg#bw=G8S3>o-gh$XCj_kngF~f< zq|E8PQQU@X7n$g!V4;*d(FY+#{x5+B=bx_8TGLk|tei|QQZG+i8;*esw+3wV5;qJi zz(s=~@N*!gQ&dP7;nH-NbFij@wqRig!O-Kvw026s;h#D2uII#b`eLMa_-JtvnZyGb1O`*xop%){5LC6Jt2cpQw3UoL<`HGMtv!2fx*e|DhR)u zWhT3}7IpGwLVh+oJk$B%jk&i%=D)R0!F2(^i-~}Bi_0SD{DO3IT52<|#HCtBzW1a@ zX`GSmrtqs(oGEzAeV_c@2(FZqEbwjUK>f{G$3x1V+u>EO>O*J1H5`-aS6Gv-R$C~Q z-0nV0B*bpNO8SF@!>a0wR*HEqXli;vlspp$PCb1@Hj&m1qu4pBGHNa;>tZhi0|n1_ z^9KTorj1_5)=Uh$wP4EFQY$LttPhn))O5^nVb_; zf*p!doN06O36sOEcMOJ(W@ga8I*u;MKz!K0ddLj2Xa#skpCw^l>HTy1?dIQvBD|3A zAj_^atlkk!D7dzGtt=Vtk&rOAjIGyQ$@A)=oZn&2s){D%R_)tg7nmX~9U0Zy znd>^ES#AI+t^IS5u+#73Ky7NQ#Vy*xg=5SNZ|4LvHGvZB&;=@2PJ6pIcyp2N0?FTDBYk)GLJS+@{Htj7(5WymEeK%kK$@TjC@# zgKuD);_w~p^Gl?f+F<;@1mE|2mfLu|7KXKmTNY30F&I5jpTHnKBC~#_Goge0R!(P? zcV`m3)dLE)1vtmiehQv4LIbe{^O+JX^v(DhL)|$}8x_$=YxpAF2*LzRU#T$E~U zS#;6HeDYZOD94?z1{YL+uPW8Qt7c9qZjs~_ozrh;*p7suUm=Q|EIeY7%=HVR-@^x$(3z3aNn;yqIE)R1xR%fIv zB1gSA$|WxEdfqR(27rH8%PPW1_Te{=9~ng2h4)R8YDXxSaKTpMw}-u*f+V&JYG)!> z&Os(X0)Jy~W$GIOWV^s7DC`GSN+6O{5S?Kx1o>-x3*s6c@pQxtaUiT6%?EKR&zbwQ z9j_SY8@W-tD6l5o!eNwGz^p-rEfWGz@Xbnxp}K81ucB`2HV+D#XQ`Z9&AlVc9O6Eh z>vDM2mF*-X_1N4h?68kB^{I^lt_Y(&023?o$g@zSX*at159KGxoiQz?Nnf={u})u9 zZBsdaeUJpgiX7U%H)#~?ewaORO4vPGl2o8g!plR_er2hd+a5Z@WaFvT~=46q>^uGfWguUz3|o)+13F_1_sY! z>qFcSJftm*%We~ycrD#&uOQYgjMU+is?j((kJg)P3GdNT_iz8KeKh}-**jp9(;T%4 z88su~!?;g5L9YRs$k*m*TQgbyE^`F;u@APhh z1xxnoC{Lim;v4>n!fU}D8l0qs>50OT$$6k;!X74|Ia;z24sEAZ5NO6d$QDizM zfzoM z-5E=(#z;p&SQ9!cv%#!aq~rJWSOe04%eZ;aB6?Xcur4IYRUw*$Bjb~B#U)=b@kb_i zf@ke7U+$qkz9W>n1xZD0E{*3J%3$=q2zNAQfn>xlU){86uhPn-a}f2D?KmO6sv6!K zZKu@ZrptFQ#ElDf`!2%{YyzQNw&?)G-p^4(O6Se{G7@j1uAAT%e+-s6W(&d|?EgAf z%yiqbjD`^TKHDqrft1RQBUw?@hvOAc^vRbV@*!XN3jhk6MJk`)t0k-Ddsq6?Gmche?Ab_UASj{6=WB#I=3`aF!!w;oKmhf?*e2M@l+$l;y zTewlaoVQ^}Vl6_N#-~B4l#)xLuQ6Sq?)X*yy=?G4P`TIl&Z^JZ{P@UErnYiSVO!`V zg-+yxMgu%XlHv_J3qb(JlUe)pI+0s6*;fomt&b^$qsv{&!#Sz)oC0ECT3&073O>Rs zrSXhGVf>64N;c|?_hxci@?qQhAy!uLmJjfG1ZQ2-{j&To{4ERozZy~T3$IEXY>)j< zk1b)GlRwAQt~{xPvm_?!OpB3F2PLQuhSrkpeRERvBecf&s42<3Bt{|hlZSOVI0Sj0 z=O-mWM-^fY1B&)haE_y<(Ge52 z=>{sV*6PeRVx#Vy$-Cbzc>-l0$Tl<5Nze@J$WPOg8~l zqzNF%_V?L!RYB`__l5NB(h4Y^cd%m_is=G2T>1l+OLh%aE#;QFOcjKj<{0*VghEb6 z!@*BGBh;~lMJ2^bzT4)4yUB8-tc|T|xiq29GbY-EFtkwYCq+H&t3_buX5cc4(fswp zyTKgV?Et%3?Nsab;rtg)!4cL(yuyUDaz;f8bxj#}UM>8p$pXz2o+W)C(WboPKS{K) z*5;#yql8>KPOUWlL&i(CUNKFOKb(ymE^y)NTJiADMT1SDT%A?>lz%GJsX0iR*FSCaW24e| z94e0{%b^8v4XQq)W5lu)lUzrGxd4O|_k-$LAE1OWG*Z0F4I(lWWD`y@dfpUH(HzHzGrlm95pM)z`aU6h%T)V@#4n(SxSt0v+%%a_)5?p31(f% z-zZ!#@T$Z!xG8id%9^FJ;K|NibxKb{Nf&!w#xm=st{2qLs!&tjlcCDl(RAj{6gTX& z2$B!%Ln)nwTHE^UZC&I?o7e-mxZ~4=Fu6m|T-Z%|w-K@YvY2^(j!92I6Afv6f%cIQ zcrhKB@$RCdycB~a6NU<3s2LTzc`DMr&}UTCGcZJ%RifvYn67WgAU4L3YhvAVA|Nss zSWJVXn{q>EY~u9fqj5GQ@DK3;8&_V*D+7SX+kRCO-vg2-pZ&T)yB*Ekut9V=C>>y= z@Jrh3@X8kN>okO+gvmox-xEpjgjJ9ixJW6 zIdl7P{NG^?Fqy>zu-8$&#SyuBn?65{uA*1~t09Av;hMI5eqM?CVhL#Bm>pbP(8emq zZhBWiFEM6ro0c}fF{vi^?=V>x&krxmUIo!1ZacS9t^6>Fc70pg)o4V^xoQ7^Q9~cB zxoFm05JXzAySLu)c#6&|Mk6Z~uVtyX0I+N{M%F1K8JT1z&z(~+siLa2WZq)f0eoli za`i+jKrce>WCA#mYl)~g!RpIqkI#kk_~Vz2Ghu zp@NDDgW%)vX7-ym)LQwgBK+C+3}SQo{>C991>UN8)1Zp?JMoUAh-jyuDVl0L=~=@R zWTjkE)g{;6GpeiHzN!6QiZT+RNisz#yW%Mj2D1oQ>T(pKx|9{~bR_(G)tljyPIT)s z2EZ=_HTHVtTs4q@VQ!p2aDmdC$p*Ai~aDUNccvnz&Z(|p9 zmwKxqV!OlESzT$*u3+(|OjUPM&k7ofBg!$~ z%hKFvvWPh4L&8XWx25vVA`^`t`&WaF<8VI00mj|v_zafKC=hKtVwC460BXo6-`MlM z)OGxh<=|VEjxH)}rqI`wY|g33#p?oWeMW850U64z?Rlf+;rO|!G|5xI<~bFmp_@KC zeBr&@oI=h!7#rS`@H!OKp`{9~cXCBlk092HW?cs0RUsN}+@MZ8KPfxT;{pi4+N$Dp zMlu{*QTjo+_%y}aeoFmhBi_M8Am-V@ zQ=gj;nx?5W6>TCX`Q#|?%m%>%(l?G!vw6ODR;|8dJDMh!W<<^hii*K@rL6)c04TqH z$F^^wg3iL%@LCuH(9#KOYF=DpIvJz*Q(1SZbqeIw6q_{7^UMimd_%t}3Ab3-1za zQmO|v(NF=_k|{0|nP63GPI4TDhSaOi&2demrUgT;S^1`H zr^#>Pp(7fRRlkQCi)s`(|89D-%T`$Sf6l{+w=83XN-x}-Au-Go8~Oerp2YoNiDW;6|A3rXXt(E7Cph0_aqtl~pqWlEcXi``WxUx7R@}6`oc_I-JO?LW>RCgyrp|Q zY$~(kK{}3J>YG}dmQO-mc!RT$aYMsr;Gmp|qDs?U79o^({L)Qb@ZYgCOR3l~sGg|- z5uG-f@%aY%6+0`;8w5}qcK9X+gn0tNJLuT#63G$09GJ4z11jjSLYPWW*USr5kG9rs zux9BeZP3E@A6Y~O2qbU&nx-AZX{Fp40DZXUoQjty4lyvg{<*CqviTsvf((yoc~cZJ z>Z%*KvYgbIwaB2>9d{UuL{+P*Tbpl+N0DryfK>&kUp2;puE!f3P#fIxO3}x^FJ7|% z6#zc|Kh*WsS^-xI#<%#k%r&2p-2sYam@Yh|>nVWdostEiFv!jS>zmo5`HKw57;X;; zd$FzuM4JJXh&@|pH;dCdsMI33AF~O*irlRFrk;l1JQ8LdGn{|Hu_lc8CzdvOBcmrVBUUpeu=n1(80{8QxM*?D2d;$!Op%WX=>Gt_9LNyP2IykS^%d0Bb-8NHxnY1J`BTmQ=KZksWtHGD8iJa| zmLv$U6D=sRQGj(M$R59r$_`}!KJ z!bR5IEy#loDR5GTEd9y{3@z-rT?(NNIdA)7(z^aAl|x2DT(rIO@DDu&Sh4nFOYQ=P zZV5>;H>L(WLbeyga3e;mmOzn}D)BjvZdxL9aA>ql+3Jxy^AH22mqggpd<)iiYUge{ znI7F)qKx%DnO%Jv8)Y7zbC`8`9u#^)Fu0gzO;68kc62@r5W1U;_5Nc&`UA;P?>gH4 zCgk4WF7V`6_im-oWeVBD9NF_e!nQ@{qx|!4m&!*zsOMD!pACcOU-qU3I}ab#UStF` z(~0^|1$?3Ee|}2bHDGI|Tc(vp!LuWbj1}6xlOeXbrGp0 zSL>-HN(5|p2o-RdnwN`M_EuO|%SUH)XBLH(YOAZG3Li@ z>c#xmw7t9LG43n8;K_|Ia;9qx5{DDJt5(47El>vM4DW6br#Zyn!tfz!vXLxcOv)yv~mDxv#( z^oKLQO97!Lvggz5UETG-e|a>3GnIq7M;S5PUGhz!Gws@|M<(c!02DF`s8IdgDTEOq zrG7&b>+%&!mK*$qB$lg~sHsMH0bPhWO@v8sdJF{2bLko+H%{=5e>I@tyr)cug$}iV{O2O{Zpq?rbzNH+Iv&jmyBZV&bErNX}8?|fc){@r?tNu~%os5s2Z+!s9BTjyEc?19DJIW z%-VCsCz5W-pU$}Nc;1oke6aUDZ+w1S^gXfg-NQ9F!a|(-SnH{;_~F@ zzXu+yxxkb9$D2yQmjIG(0!@ zt}2ZFpLJF4RRNOM(|S6TSv9S3vDl-Nw^!mgWx`1y>I^i5(;Eql>fcJy8CH=XqJ}lp z+;G2?HA<33FzhN;?qc<1@yRX$a+2K0;NaSkO;f{yPp?MXR{!dGJK6n>sJZ%uNw)jm z==0-q*~ zt%T|3-i@iNUEli~yni^#f?eMu7Yha|78vKM-t-Mdnwd@CgW?@yvbL^@q0MZRnX;k2 zDVvG2bgm+*D?HSqgr+KkS)aB-t1{RPt|}i|t#&ki5u`*@VcAsoRxMUfAI2YJZeQ^z zAh${@7fOdIE!a^$rxHI-?vnT&Z2~?0zMH%z&v20@nt$yPw-{m4R^Hbd;In2V>@K;D z3we*O`d!tYtjsUFkvtk?(?tXtJ}iE}ZB9GAX_yuKE$KJJi|~8L@2>pR>)GI$tn-RL z_Hp?#I(h~8s5y;KBdncw)}80K<30_pq9EvAs_2T>0y zW~=TiKRv8Nt@*<`Xbv>z#o)mF$xWlKc)bXfWA53UqjjZQXU{UR`_rmHt1?o$d`DYB zX|9gW%niB<2$KK=2Q0$JoJOsQgtMQK0cp&hdvYW`*j&0hf>2$x}7M(}E zkAvJof71G&elMTGRyF_g3#mIpFfEYm45CIvt&Wt^Aq_%%tHv{3j<8q(5>P z5cv5umGL5`4vMDPWXg=)B=y2zpkx{6>TOuom#G`7d7tiQTy?J#5I!&1I1j(aTo$*n zd0h|%`xWY!qr3}ac#;13`aoOa$D2Y@4OYe(D>WrjA5XdqR>ptP zfC?CBszCCCf`e3SAi!f=PtSL1yHI>$S+DnBCEysVwi$y=Y@GTpSX2 z3OIYpxnSioBTKwh0Lj31<`dVs$yAy zG5M0BX&+ne0=q5A4B!(9^j04|cFDdajIdM$ujhT5pC9JE(@F zoJ+c33O2*3!~+QjlE5IG>K&_93a;&vwi#B+Jce_2o2?m1dK?VR zW)S&D#RDPowLs@kwt|fOKa=2w{i=5xEDXXJWSr{X&K~B>=nh$oM{<`$@cDJl`XQ3k zW{P^dVoCNqHdP$4ifOdx3OYprk7D+z#fdX33(;?!j#X9U%OBO=5FU~7?q}DQEv^%{ zE5kIm1*I=SIsBZp?j$OKAW2&{I+zekoorJCH%f&sgjv;HAtPV<$LnsLL8ZdO4~Zjf zW(FFCSVho5zD~<{x+^PrISA3Ekx(OUxaPWtKtrurXgv@YD|VbAoW~{}9i4PO3>z$2 zjY-dYO(17EKMtaX9PEtKpz$-pO6J7{TPWqEGNW0bq?Ja*;6 z;=J$WDn2fuKOa;+uF#TsYYN<)X1Sls+k1~LEsdNxlJFTA7*xz!bK6k~1=|ZGJbAwm zD|WParTYf-@Zl`xn|Xnz+u?bYQ*wv_0zSs5_Abyphn&F@TJVUty&xT1(_YYYe=dD4 z;CwLgqKUR1T3Ps05K?q829s(QVBxAenF=)}#~us`vL0yKv1$q-#^UL z3Z4~@KPU>F`LKFFIpg6kRz9^e^{qY}3t?sZOwW4Xjz1rvLu+bjJ!z4ah-wj(|MG-ML?Ha4oREW6)iZqX~SrDD%WnVt%tcN{GM#H|GkVhvUw)rlhb=(Gqo23C+lwkv*Kbk-)&obVV@k-}it zNNRJ`T~x!)YusV6r3xDA2g%10SzDn;7HgmMxcAxLAYWvtBbl*=dOCT|h=+7>zDtR^ z#SH2ifJ72msc{*2Mt>I}Iij<9X#@^Cf2e$3T z@GB&}NMiLH+%UdRGPtbK@SPr>TI}5B_no``Ld@OhyC>=Uq_cVhCVO}~{nQ9nT$y_) zcWb*BCG*ALdg@*DJw~ncx@_z?1I^B8G&Hyg{l+JI{DSZ_=efoFFZ(H&SI3{7dPC_u z5<~)|%chIg-C2 zGS_*UkC}dJdd+`(_*SDtaotm?Q&2$2N2?MCV9o<|tRy>5pP%@jCr>+_Gl~-1P9MoU zSMP=3)-q4;65Kit6-QvH9-k&E9}f7P(7vLC=B++uUT=|I#H@PRKSARg{c6B{A$wg# zdO!hNqb(>pFo+wk(s_d%cnF|E=eLAwN3BTJdc%4`?;jH_U_McQ3GAWv;`gud%~O{w zf8)r#!2jom)lnK0)XFhTQ5DoXZD*g~&8~3xP^&_6cBOYkNT*02Uwa~mPWAeoX=UR1 z8$OGn>$u|gB4`l<<5#nYHU%;uSdniQ*u7X&Hbafhl*{QkORIQ^Vs;;qhUAvcSOz(U zKUZigC3$_VMo|&Bg%up0vxZ4GvKm)?S~YA}nP}UIiA5nEi_vH@3kIToRCmn0Gxtwl z%zs{*(7b-oq^7NkDvy@(YPfq}V$jN8o>85I8r_O-+S~fU5z)rxF#NkYvT##zAxmCT z(wPa<&kIo9K0Z5d4q2d+#i(^Y^}VWCsqmF4d~H`;oE&Ri zQ;>h9Lol0RPiZQwy)S#G<3|4Ut*WZH<3@dusZ1EWC?g{%6@ED?6=P}z>+Ag%4nD+m z=hL^#G(ITV)nb}z_(yWU2NlL2Ez(Fe&4m6}ha(;HY&ZNa-m@ImsaR9+$<9>6);<)VAm{pfgN?7 zX?@NlNR9d8Koz_)*G(*SP-~yVHb3uLuWxDc`t9A&UGt=y;^{BFH{{7NHTS8^q*;{Y zL|PpUN8Z`CfHu|Dot}!vj!lHQjZl>Ck6RO7l zdI9Eq?I2tZR;|=%lO3w%0P$$PT$Avo1FUwn#7^{?pA;%7f*EKAEP4F{)~xT>r!c^F zR_nRXv73BHN6J zGRRcy9iNa3s|p_Ip+6gwy7grLCY1X|JFqE_?z61&=sC{Qm%j$nE8S9nVGDl6C_D$j zo1KblD3*sm*Wzh?;STxt1$CTVOea^6h?xr%r;xtX99eoA0?J}klX>fFw08hM^fWis zWd7c}XnAMpyL8z{qrH$EWT@dt$DYsE-@Xwvz1?osB`cF*q%Q?G@D`H+p1A{Yq>Bt* zxjVzM#5R$%mQu@Ra6bd}?Wc%il$Ww|Nr{u@A$B0(pQ2zP4NQSv97@-{x5MDSnvjDw8VY3zUlF zie$8C<@{?A9QYTr#|qbZR5r-GC*ITBs6<;YRONGzhAq!y6#keZew^p>$51#J>MPU! z+_D+_;vo+Za2a@`&2i$M<~T~zv|SA_$&v(>O5w3`S-nQuI0eGmjj5k{0LLwR+Gc2X zhjw|>qK6-P#v(OGWEo`vNpfdmQrY}_-qg;!pGe&s_;gy1^5hAG;&%jRU(fH?V^mP< zz5zya$9|zO>S4sITf9Py~NV?QULI6{YMDzvHK5|HdXG*e@*g@2X{x};FQ-|jN(_R9F3Nx2*KR|?Yn?;M6c3?|#sF}5_G z#>?%msmeoIC3>A2Q2o^?p%AeV=A?V%q-5*S8U^(SKeg;-l;h4w*8P+R?IlN9JAJbS zngeo%Q<4Kz$yLj?w}$n`NZ;@R;<>K0Hq2ff26U^BJo{tZ3OpG1s7D5ik9nzLUXCK= zR-kSsx1i~I0wa$iXY_-2JJNC8Z@}O^xuBfxjCnzy#gHTy!{g030L(?Nvo$Z{h4#l@ zgFAV9fL;`u{Pu(dX*g9C2!QQ^00V7+Q#NgLx+L25D#RaF6*}3MzoLth*2PQQQS}Fw zhjkjMO@ZBcz$o)bLFnB`gF{s`(K{IpP%wLa%EV%y1EPJHgq|mkeSO-epSXWqt8zzb zx4U$ae;g++dq8#Z>)HHC#W(oqNqJhws|#iGOsA{!WoQVi!S!ZEiJ(9yv{iO)F2*|k z;0YJDVd=ZXxKnNLgbk%2H`!)rJPKLEOb&rn>qjU%Cw3=a-iGnN zr16Twjx*F%hi6%XpL*Ii50`~c=U>R~H-#R*T@F-wY_EF#IMiRZ8520)+x2}sK3!U3 zAn@`9{Tt4Nk8C~7@T~^~LYj1Y@6l+RJ8xc#OgF^$3W|p(NP>}V9D@L59aW`(myMea z&h@^yA5f>Xs6}290Pk0v)sU`kc9=EOFb>w~t9h#rDEssL3aI zXH^TKi+T1#nXOMHKB_o4NT|sNHaXKVnds-2-2Q*rPSZD6ZM%Ypt(_N__RrC7kF||$ z<+F?TXN}&Khj_;w9seXm?`ntgxCv3l+vj<&i+BBFcVpLHb)_Y$@g?~sCl!5MhIl5h zumOW`@AkiAOKsSo1cAs?mY5ROXsOV#*=?XhsN|%YQcT&k5MRsA#T#D9YF1Q!ry<0` zrXhUCWf^@LB(&nJ-@dCi=A}Aq>*n$(7@I9|c`!XeucpLD_Q+N5If?mX z_6b(S3eDCL)jt7~;k_Ppv~w|B9I1Fqs_hJpJi}|F;)=+bcU17D0pIO{x082gN)R{; zn!#dh#w9YTTFsCa4uxh2G72dZvkAo!XJUxen#iJR2MGI&_!%ezx%Jc|snTX5q;{eq z>y#Un)wGED15lqBC9Lv}wa260hKYWeX1*>5&kF7wSiiZfnr%Nrf4=ow)>>S+{U!8x zNbI*8YyXgQYOo8sg;ue}YgS!No$!s1Gw@~`d>{$$EdG|V$v>X=6b@&S2|Gxj7U2)(2_DI%vQb>ly;p^Lq@a-t|e!bkMCfb6C7#g3F0Uv|%5t z^X)M#ud`XC;!t?f82PNl>=+Y^Y&r#l_LJZv8uJHT8mip~R>1<}oXNOnnTCZ-YW)PV zpZkCAe6D-W>I((7!z-Vn4Q|ebR;BA-RJEQ01rXt<+-b(TypQ?ZsE)BD)gwU^$sWsZ z5Vyf7(k(Xw*!80}G{{|p^3XYr%!Q^7QzgI(AlodBvg!Q9>R}LVL4=>|f$A3BDrqdQ ztPp8dWod{1jNHPW(Z`pH@KI_%VHZJwHI^VTLOiFv7ZeS zaJGrTx~S?3U6sIBtv&vWhy=X)zP;%*4{@|8^1o~0FhPQYFzHrLB{*|s{m!az4b8>x zF^6+NNsh)s+u%HDPflsdba94AwvVMhE<{{4Sd5VV=zZ259yeY!k*y*0X@(GsvX^(h zp-eZ`hRun(fmq3?m`SD*z?{prh%-NM<`=bjlreqbgT?GI`*+hX&Tkh_ZlY(~F3nh< z%W2;~f$`hdBo?n^G5`8SD0{*)lpp(Cj2RvneF4v!J^P%O4N)poE9$D=Ei|%P5eKjQ zHW*T6(Tub=1e)#6<5!h53y@c%pS6{tU#_ook~d&4$h?GKs;ar@(Yqv0(Tr_Z`SSyM zjZc%s1O??w7t{F1`u&7pWGqY<6In>O-3{W=;%o@PJX3bXE#Z8NT2+pv_#)93st({B z@P=fiNDZAQ4dx55mSAf6cwJWt{ywvK?3$#w(yh@|#ia$94;Eep(_gzGlqdlx?_hp) zK}juWS4I7xrl;=}(gL>fnsTzhpVOQym5bK&1ha#`V~pjn`zbTc;>?y>V<^3{DTpWs zvyd>*QD1YzDap+C-fDkZh8?2LsN5gonRU=ls~BWbKyoZ6-f@T!Pd$utX6=1HS3i&$ z^-*>OH)FAfV9wGQ^w2VjobHNklsY*_X51e&1{pmm1W9n;%!!DXQ!>L0A!a@Pl&BH1 zy>uEzMo6&_eEY`Kcwqt?MyPGRZI_qTz4^whb=qTVS|frnX457^)0$H>TTkH8Qql7~ z;m!R_V|zm7lokKH98H>>0u7t9I18?EN9xNdo%L3m;kI(*N}tY}GhLkK3#LPbUHHTqlWzU+Nd!g%u9AU;bu)xM z&xL-^cFuPHBqp?&?MPQc(9w!Lk5z7pQYt%EuDh`3x_^IRa)S9>SL=+H;!Qas)a0we zm52)xVDRUGW+cb8Ua80I^0n_p*8R#&OZuDZIY~8jSw+u{`pUb@?B{M|PRqs(&p{oT+b%t$=r_jQVH4mYOtZSbU*BjZmW__|3X5wdpEX}!o z?x~z8Zygg`{3E0IplV-tYiO!;Y0L&)MPAO$uH6qZiEetei6Y8ZDE<<#SqS}US}N6n z$MPAgeW3MC99hHqZ%1{F!6Z|dPzbWrQ`U3VH|3Je3}xycOTW8Jkac0+jU>wa_GkNO z9rR1fqPc!iQve**De1`VZY?^K+V=&ri;s z7s-vQx0ha1wkd-Da&?Rhmy0I>FSQBD?bBK+@vGyhY6jB+mQahdcLyLX(D30WIPik= z#G4Q+LF5ltmhFP+>Ve-znUK%qVI)bBG3SyP4nTi&KPN3|kN+T;voh9eDq54#QemLg zkm0e%=#|&QgbaNj2H}5!6!D8R(?oJgShT~?v3x;wK6I&mi|)XIb+I<_XNTl{A=WAZ z;iG%G50rZsr}u-x&kA+o^(ib>uZDa4OBjHQM+l1q04~4xDuPgP`(d(9=jY*a1LH=? zyouG7hyBj>+sTE@P(+z;)QEvRP9(#Hd3QtXed~uV<_-tC@~=Um$@4VMjIQ(*@InP~ zw7)dbRtnT@IdqtyKM<#8McU0}M~VdJBW9}@6Af4r#pObplTj6OYOMIv!kpxj3DZX@ z;(8H3ajkjuMX56bc+FSGJLJ8sLVm+`jWoYx|U95iy&V*yx# zCNL8mi2bDzRihty!yI5c&FUV4v^ z)MtK1>=}Gp1rmg4mhGQFlljPBNrA^{D?a@F{k;&5t|P?*BRji6B8LfXzfzIBHGmW_ zK{r6wWunQyI~d~omQZbcvsTiJiz=hVMn`?BLu^1l98&m|bCBb7tDC8%uAx76Rc3aw zO$_T-Y*eeSeN7IlTBg*YntWIF7-8*6%A5;8gFj;;wW>2^B+6eX=R zdh^$5KqAh-yZ-1%B1(G$f7L-QpK3^)suYH|1v6<=G#DsNBP-&qd^ugC(D$~7x&E4> zs>z40fbXkw?^|FlLg80KQ5Bl?%#o7_>eXGhhINlVi5iRx7u)aBNn!8}_*C(PjtlDU zY!+mDlD2=srBse#+SlZ+NR5x+I)Bdu(5=TS901mn6lisTB8&P*ktvy)NL#X7*N^Tk zDD=V26%Qu`A&W8mou^`EpZjKkR%b`D>{7!SG}`AS@z${<5B?kK!ET-g1K) zkeFGnuiqF8gr%^5{2~r-96HO2)&$w)KmbO@l&c>b2-_IJq`J5rKE%j?0gg?2!iXwO z&P-$*Y!$*hoYM|B4B{ZntCtB7WQnM^^(Zt5Dn_AHDvv!@?N1iFA?J_Y&VAloR1-v= zD4?S#z1pSPEtIwLgTl<L;m`7hF z%oHuH=ee58iB!6o-^J>Ed_`Wcig)?;LO)i{wvc>*#;=9?aRnp3yVhycbuA5(tVfPb z@3Yk+K7*7DM$Z=jHUdZGIL&r_$tmnP@zLIqBnv^< zhFyj|&L@c!M39?7^MKVpQ;Rb%{^~cpg&|V8q-cT-%B}>{ z+^g6f>9JKP!}?1K%gG_(O3M=siTz>OB|6ohK1K0gIRlULjZT4jL`{S8I7i6CeW^R; z@^oz}G^TRH$*RTXm_rXZh1&v)$lHx%ZJLSvEWB?j=oex5xodXNXTuy2%dd;selll5 zy`>a7K;Kc4zX-6=X1{l+F}FpSaQ$8iSKAVe4vZ0CRy6H-iGV-cBLf0;XZ60SiWvt! z`u3#xd7U>jm^*D?(&tGi>^sfCLdlZG(vZs}TfI(NllCg?qKZP1vgej=l4>Pzk!i%v zm%Yq26UuCe?~o-L@*#H)Pn&}1^IQrO79Xii<5&~W#<>&xxbh++y5aEOsOqAx` zCJzg8(DyPcRi%E{Xh=2?M3`Q?DGbsstE0w!2+k|900C`*LbQ41??x;9A=>3+%6VJI zwIC%G_Jd0Jy}^Wuj&?i(ZRqb^7jCnwg)Ry~ybIbJiR%C7&tpc5m6D{%(uHeo7@I=M z2N@?5@Gw+EY`ZAXw(20m3I9g^WX*-pnXs+wS7(DI_e5I){0lzEqNgs|C3Y-HQh7vX zVt(N9aJcWJuc~^Ejp(yY#d;mQlIu14XJatew$N04to~(8%MY5w7j4%GI~{ zf!A&GDDxPirbt=7qvcplhPN}vG+b{pD~%!VA(LZW-H60)_X8h%JD0mbs029XkadD} zi0?O928OE7Tg4~4RFoN^6`OkNO0Lv#>#!c%maDK&KnX7$edpf_v^o*v@k!%jqnLKr zi4Mk2q-*!#6EYcR&OWjh2@?~w8>{oS=Bn{hYJlF+gMkw6Sas9|TjEPP&gG?IQE=M( zZr*0}mnH*f2B=wdccMZq`ZQN6EXd&7-3KOJx=>Xq)%qzjE{>qdq*tAK zgDoPa*WqAM=HBB${;4Gq*z>T8{Vr*59GbHvVhz2PIpHPHAt$;Kfr*o+=rR;M1HG26 zm^1tqNa%wFe>Zl+zl}WyWc4p;pr8{M=UoKMp@?kxd3^;-_(~Wiga&EHthrWMoH#Ue z_7uBH=kb)5nY2WQ%lZSL5YstTc@x5%{D*Ky&HCi67fuzOc_mJf+ywG!YZBQoArcG- zk8kBCF=t2wpWcG4e;Uf%UHD6>wUQa~n*@v0hWI#TEXO)fKwEsdldPyJ@V1KJ|5{Bd zWsSzq>PrMiJFOL#bCnK?xc%twJ_ik%a$}Tv;zErxqpBtu~R5vK{_+ zRJ6Q^ZW(K~71C63WK*0v>>NG7Lp$U(z2G$K7(<_Oedi}*U{HrJMvfW4Z)JSLg_-^~ zNh&uhbBa7ABPY^w7tAK2GS(9;hHXYuoIHV%7KO(Ls$T{{mw?R^ri`H%TFvv!v2%|> zN?e|!_G*~y1~5C=ChT;vD|8k=T`X-nJEugP(-{&gEVdHq3|j~J;2&v=h04quCr^FM zd!I@v@XWh55_Op{p`AVJUp+GWe-$JD2=2V1+qDb*N~zTf#VYl9fd=6OkZ2BD#i{P- zQtjdbhYd$F(^caFT)A{gw0#r1FRJ1?l?O*N7FAu0=##&D;X!oykhz=7@j*{5S~Quq zww8Gg+p-$W(?^Ym^}GuuZCla}=}^6JDi@#vb4p4Gw3aE`ylPb)rHiPN&3l>7lu5Hu z-AM&E;B;wgP(OoQP9XeFy{S9kJ+1sL>Ckw4l80I0dm;hdjxU`$M}}}vUbkhg5iM7) zKLW+ik#3#>P$#J!@v+PsBMuE$R66(_xx{-0$GPm*<5^SeE_+%MX@~P_br2pNWZ;ZA z50>P1$3bfqnHk5=PIscJLd;U`$Hp$M>`lyjho5@}Wg zW*t7^t7-!eNf=5xWz96T;AnhQNVfqC=0@7Cpj;Dzhs@#|=q}06sW$f>7vseeF#v`l zA%I%B_iheVXpb>79+q6}TB1z(c!N6UIXRMg>#Ijlc@8|F=56%B=GCymp}c{U*5Kyn ze%5_{A*Rym4X>^Rc4n#Nx3?4upx2HJh3juw`Q~*yKnGT?;Exh?o8LH@c9e$PeV_|p zx~f;t_yi70bCC3;q)4%EvbLrfmo8vc*E7orT(E4~Ln=$A5+BsSV9Fh;d|*03tD5Xj zfz@g}I7XR8eCexpwYHpHmZ545L;Eq)Gn2?yr`JgPX|r)$5uZ#dwN~cu6EOVu3H0d` ztj^#kREPU$v&9*1xLT$bcYjgXm~V;x!b4+TR_EX=Im)!1q-j!EyiNrM6qI7H^H!T@ z2@>(wsoFj~3^FWV3z`|_?THjiYW!k|HAjaw5K{VajB%A}_x8&L>V8EzC!3Z42@~9t zl(z_l;r<5-^>T##4NZUJ*Ii(rYsikz7Mw)y zI~YxWPn?ydL--=koNeAX+QBbPLx7{O+v`+%J4$q6pH|2{C2F;!Fl#$S7zoru7<~l8 z#pVCApWs2fh6@HbdPz3YUj<}6JEg4$6dBA}Di~GqUznl!noA8^kQXCT<;lK&;8mYb zD~CQ5JP)K>K1I}s9v3}Wzq(OAXG)T!h5}ST!$D17O}Q+*F=n3f7prz>um_) z*m2>~CW5Vg$~m}DsH+LCLruYBAKme9t`sHEKs&=X{NK$D4=QI&-(oF#Ggw*lrX(R!Th|keZOqQW~+k)_G{VI&@KAf`DRoUU{J2wNd=u?DQ z#wVzunzgSC5(q(_egAzP5xv?-QoT7S{VY})v3};(45)IL!02sGEiiY6%dVEVSL>Uh zLv5ilTW>}pTc+h?^?D*s26nm<_01!GZN~9OACWE5f~W@8zWGdustZGdj?&Z=<1(&# zx|@I;NeNVx;G5Vj3DCeZNapa4-><=+6nS#gJfg^ z2mT>{ukV5HmsYE#+gW=ks<#w7W?M+tW6s`31Wr9TjE#NuUSzjRDXuyo)f-w7ZGvFI z%N><>syIf?4*3;WYP~!2^^yQhQ_iC=lzli|2%Y3vrS?va4=-fu3!3?`BTsB1xEwFJ zaa6pbm0bRhKu;G|L)1v|7vhE!SfnwxB*dGtHfnWo9%(?M(E1(@MJ&S6DG zj&u2o_nF-u|H}fH@W0vi3ka)?&=RK(OsP*)X)rX(F4xuvU6!S@@|P zYh~BKAMe!iJwhG!(jP+cf^M|WQ%pdhK17)%3X8F*fS%5}8QoA@U^9Y+cHccJ+T+?X zD4U;wc#jP_?65`^-+IT=THQv<&akY`9!}M+DtAAC@9J0sy|s#z2PGb5E|;to?9ghZ zlEH&g!M=>%OxJ>*NH>H}4cy1NF40i&ZDx~Df#I46I635e_n%Y)pI5?3t(6Ul%-8&6 zbaD#Upd4>t!;@Sq{6#;hC~Wys?A#UJzD;ar_py@wI5^F2KY(mTb)iA|EA(O4A+k7E zRA`?;30umS#rTT6n#$x;vPxPxQAMEpeaMudKJ)SmFZNrZ^SL~I^Co)yIkuILd<-p5 zoxyWSq+BK&ojUS++9U*#B>JN3iczVps7^!fO182UFfTBU{cnvv7_+!04C))@=AP%* za>bh}R4;Ru*~^SM{C<#Y>Q=4qDt)m+^;M-yym`QH_-wJN$VyXguZxx-d2XRibB}Z{ zb0a^E@jc$Jj{#nVp-9osu|3S>l^D|K3aq$$p>fW0$}NMkX|dmfe72yip?i-RuRAAP z{8w+V-2tXXJmRl_@juy)<82RpF+GYyx0S}adD^O=m9n}ZnA=sdycsLaEKtFtqqY@S zw-U1RSIdr@!ZgvXIs+LJ>_;w1I)y*>3W3Tge^TtCG8#fjO+4$&66CqsGFJCRz>O(| z$wDS$RG{fw(O|@BBln<}eaXZLuPfExOl?HB$47n+;vtyU6+pT}lm53aOv6e!;+MEP zqR!7tkN$+f$&?W3^1!k1JSK)_$+Lr%n`CjJNRsT;Vr(maU%sJsS1(GV3RZO?uGoXSn$> zRh7r@hPXk-nme8aB3)T_nkzWidsN-0Y0T&7;S5*SNa)A&Ad;^Ss`ceI6Pv;fD=^=^ zthtVKqe)S)|7E~G_pc*V#MBWxKPrBlGS^MrqU`}+hXI@Cwy_=sL{V`PBT9|$BWKHi zWM9cn2g|Jy+>&fZ|I3?gjO+f(CjTkq$l_SC z1Zw32P4dx7b-Un+9jXlcD@D*g%YlmODDEh^@*y(?`!9ESPeUj!R`K6J>y^7Lu#(b zSj+~WW)~%zRWK#aU60+g5*Y`T|7OgtP+hr{o0BYdDyd%g$7B9 zmdH}u87D(>N{%T>IU?!3c9CW3C`SF&9N~r~02 z9A!f4en`uS8nkT_k|McMHOg4BA)L}8^*Sn(8;J?PTi%8rqWqD2?^kzFa8M|KSaYR9 zD<9~=t)f3f+J2~Hksxn&SlAQl%iG?mKKKhH@Fp8M4I4*prN*Q!P}H+qeaPdDI4%W` z8>GDGs_`^7l^;W+u4Ff?$R^+T)J8&;5)}^8bF3p2m5G(oJ-UoA7LFQm=WRK4f5Jp= zjT)9T{nV~e>kn{oWGi{w(WfIvLmMPtvd0-n5i^zs{bp$~grIiH_H^%LdFs5EZzR5R zzxCTR4Z6>|tejpRS^W21{p&Vvlcv3vD$0aA>YF_uCicUHZ#(=ue^i|^k2r7su5B}K zYN|y%^E;Ckeg^9;2J0j0Pb?<_6g6-5?p)-Tq(h&1owZ{~R*1wt&U0C2gjs%mNNN#k zJg>Rxg7&xUsrCrnnf`SzXhW;1OIDQ#O09G_w?tiyWeuCcf@5vV*0vW`5Cc>cCv%yXqS)o_-rZ6+>Js$azd#`_(zdL_S3fL4Zqm zpYirlLzvF^oTAAn5?-EgXM{|lp5k72@aL31;G>GcDI_e~F-k;EAMWGic#^qSY)!)W{g>6ymaxAqn!v3x_t}pn` zP8fKwdzm$zMwKJA6U3xiqDn`r6yV*$nJOHSs<)lEePReTnZt)T@2wZ&pUg5CQMRzHaYW zSRdtgu4Ba>ErIxJkY$@A#g4hAh!8%PAWt7{?HJI)@M}8tFWU7bt7;L#nN#Y^qNP%o zXUk8diq#to=CcFn+B7Y^-mG(h4nHQKlIK=m{Tvlt6K@OsR@hr!(jJv_6`+h}*RNj7 zH&b%-b_UhCo}%9V=UrHZLd719F}sOjEjaJ6-*Z1k)3s4w5PCTjT2%WO5=Ep!`_OH7 z8dLA*l&a{2%h$ACmjkWmy_20W@w&A`k4Hw8TYxhl5!U>3C467Uu(z|ne_JN(^GNgZ z3n{mZr1d&qT;)|%5SvEeHcq-P;JpknNY3iamXJ8p&TpR0_A2e2T04Meu*G8~NtFUi zdZp>A`6d?JI$Ox$2ova_Wot^P%c1*0qGPh?9IS_uOQA@Kbiu%jzF)fcfVV%B;7S}4 z>^7$ooc8xJp!#4tsTu1yzm_qA(ZcB<9ieFD5H9(V;Au0g_9TFArbS2vn%JjlZ8-N6 zDxjkC#Ql&jYs6HfR;$=I;m?U1Y3jur4;{+$yZG#XXO?I7&T9{wrgZ*DZ|k-;&-=*f z%O95KeYiZYKhsawO5aug4!IASaaElx*c+5Dl{~+wzawklzYO6Cz$T#uo3j%iYtO6N zN5ODw6ahtTs!Rp_PN69Akc$F(ZJerc()92G@;T@3l1K0hUIrsOK4@h%;Zq_pnYSf% zh^yL1sTo*)6dX-97f|0%P{M$UY5%;%?+woud~r7lR*Q98v)u0c=_Yv4p*-aD?&pKh z%+oZ#H=qx`P9HaEfE6a%pUKv>1-~P=F3If^Bq^+OK?}O zvs16|-Ey7ZkICMb1L5n5ljSANqR&^xf6riw9#&wfDoW@HEHh@Gc2gDFD`i$CAZe*L z!bP#KLOmc*j^Skjt~7nX)FC2vgiN4<9k05Ev}J*6N9n=+$uRH{G_X!H-f!BHI;GE zb}Ln2mC3kZU+f<>GYG64U|cpApor#H*7=OVw}hbiXUWpRhrs2;AW{{(i_70iqQg=T zEMTFLe?C)VMt4a^{(a5zY}WQWsfm0Tea7m!6*+}5sQp40+9E(G$KcHN_Bg#o#{~}S zwfqs27xE503+Ksd6Fh)n!R@tg~TlSN1{5BVQRqeoXw#N4|kH=zRm5 zW<8G*L~3q&JWF!tFP(cO>`&!QagghC=D&UA#E6Bn?$GODYGx*sHJSac8==n`0DVg5 z=>lvm_J9LOtIKB(wJvRn>tIP=1 zHngt`W_Hh$Y9`@sD1-bomX{8mQhf4!A~~NN--bQf+&EX<*p5T}B%)8!F01jL5k8LJ zg^dU#hG^FsQV{$LE!dB?$VF6Hnr$nkqUfMt6LDz9bL!`9wRrb*A{mEf)Q8I;r4|e> z%MMF=Gsfj+MMRS()diB+mW$7e7)3w4e&R_-lv+J5QLfbONfPFi4Y|_+b1cH4?S@bN zFs;uzA2*#gUAKUhKmX(HWBHivk*v~}TaDi5E2H+GaSyl50an=$n=iL|y|CX`8pfQu zx3zjMCeOY+*1gM|KET1bJQW6QJuQ8Dxl?+1$f|qZ8xg)$sbVN8^WDOH9-!g1yx-nB3HO#aW` ztc0&5r+Y7UhZ`o>ZV#Ngeu$Gi$21>QZ2caTD|z~}EPVf1=ik`=9F*rbWX<_Z;{Omx z(tWWo{pZwGOH0mb%CYD7pS)LI7YO{%`>*%vNV#N4yh}J#D)XB{?E3M0pa_=?;`2;JP}WxW?%s=Z0h28#(afs%|vXZ-ZKJi$g`w2Eq4&mT>)*_@Zr^)ha!8S9=Dx z=FV?DR7o6Z=9X_%e)BShmUQC!o_XkOVDx^-zWSA)tXpW$4ise_>E64Y1HV6=C#`ik zcHF#g`}RNu;_=^Ydi|^kfdr=??`WL<03R-g_TqeDFW{?5V5KBp)^>e1*TtEZhat?L z@FU`t345Gjr(D;EKeIttizG!s$T!ipYLMsZNWQP6c37NwNYTqM&+;{BoadeA!iO0^ z_q438+)}kX6$6u~tfk)+&zfbXU>MZWQ87&;+3k79o6qrzE0YV>qa%m4xSZGjjJe7U zhxn@%1#+2r^ddA%qx}`Y$)jT34W#oUWNCb%e8N>8>qz5?EEWEIpA**TpGm0VUpKLl zFwSE9Ue<~`hHpzS)VIGfG#Yw9h%ZRW5+W{%<8VyN_tXBk)I=i6+f=o~lK)4Vot)~L zb37f?HU%q~6aR3;5RF@oy!&me6Vc^xVLS07Fv){&mQ& z18+pN&mU(RJ%32t;kIT9shuZv^HfD#gX@K`iT*!uqFlUNz{@rV(#lqv#NwZ}Vo?j$! zk{X6z&-mw!f+8VDOe9{8QxUomenR>(i@fti3IF@Fm{&?%uO+_QYPn^0glK3mUVr3=4r=km(QGsyA{bOGi$*A9mvs0~ zrX_2V(*t5K$~*D72jQoM<6Ih&oHZ=4!wfp*HbN;&X?CsHDWqoI zqMPstX|%(CFij1k%jA{~MYl6bo#9b|)XP5W{63Oy25E$*1XOxA&@82&1_I?oo@JPQ z#d~x%e@1@H(@X-WQWU8Fm_a55x^iB*?1XA!c3EHa=aV`4;n&qQkC1mEYnbHA$Sy`1 zr%`Anxw=s1Mgr>PJpyxsC)kjK$5durbY?BzDND$n;~*a&?q7Gb{2=;51mX;B*Cafo zjOR8Vmith0N~_Z~LAF?Iir^PgTf-4gz+tY$_I3hxnw9*WopA(#djfgJMda0KFkT{9 z(G1beJr!@9!^&+9&~=^h{wqKub=Z?nZ?deUw55xW^Pkkir*O4aAX;fP(9Lq|oaI>} zb)R=>Ft7Y*d@Bqka-QUdPy{W-{LQaCNud_d%yWW^lLti5CEZUU2Z&1fK-RQ?!NndX zQnp?9JLs)2l4N_IA!d@SOlK-`Mnh$Nx;H#_RRA+VwKpk;6qh7Wbt~|XBsm#m&DeUO zQ6+^6)!dGn^wz32yF#%0h;ZBq(ZKJ}8vjQu^PsQ#x?WN7#6Hz+4<&%{_%Z(udAtBS zTq6dqQO82AX}vah!-IDoXo(2K;e1SvwuSt(Jw2S%Gzaz3#q-zu+%Q}geDmW}73tJQ zHqm~Pd8fBDTeNb!%_9C*(?w=Ct1$s5iqd&mR^PyfBIXM~+VhX(6*@&n$dPqI$x-j8 z$sb`*_n5OccPFqQs}}6b-U`1D?MMs&2nrOTzmj=KP7+N&g;Mo9y06S9Nn_oWNRb5K zgc!~YNp?ct?Si5OX3O^h;i+?(p>MdrkmANpa6&X#01v0`4T$Jb+VGuga%G*yw3WN9 zmdCDDQ^U!uH)xp_!)tNzjE_7Lbc<{GSm-$j9W_%)W{L84Q%nb>U*#uQGGY!x&b90D z8n6&ju%3|X7WJ7EZ7~K-{$DPfz(|#8HhREUZm(RjUaiM>6tKcnv2Lm|=41t9=G5_0 zhjS8F@&qaFn|U|^2jHQ2FYyIu@XVqnL1@3;nqi;m_$Q*kNje1!w#j~AAEztsBk?ts z;n8_@SMpD}Yo{D#Cst&x=_k_djZH~%KYjawFzo7oFrrBNLPHK1f|Pv!$6O-EA>89c zyMvAXb`9dufAyIvc~(U!V^1l!Ye1qXOi8j_5ox%^DjaAf#YWTN;-wA)SPERnfWwd7 zdx%bV{Lo0o!R^;!X@bp~YU}{8z=+yr5!H}SE#B-0WWLIU6U<`H zL!=f~kRNN$=%X+JycGmM`wIV|6oMj#h7krrUEJ7f%(Lc3F>w!<_Ts9+8trku+FVlte8Sah1jeH`>@~jh?w|wMFsqrm z4_J4Rjzln&JKUp|w>UMLCPTAqX;`IYR`K1Mxg#eg7yiMNVL-M4@F@KM2^r|oa^R;p z0*98eu>S7Pt?^|jb0 z^ItHnwAviZ@fret66RXi;#5VKFMmPOyy;VnS1Kl}&mlF+XlN-lYF+4KR> zBL03h4M<3~4}m7Ug)1=LD5`dT-=>X{+3lkPm{Bqq!g|z+Q&vj}+|3~-()TifM4_~p zyIx&J8975P{?#NyU8KI5M}-M}5n%S8xaG{}#SR4umH}opFd!f&yC{G#wQ6Z5s>MwY z^Sf{fK4#SPXH6yAm%J4oAfomnW%MELn=~QEIvc1CnN;zro-a!Y=}1)|pra%v&8(;aH}8Lo8f=h2V0N2O`5~BN z+f-4H(>NvKxJ+Fn<#I2{$<1^0)em>$%hRkG|QWTm0SXQlb2BI}fpD8!v zR~}I`YlSD(>z@fc$#22+_Q=KWssR$$d5{uaJs$&Fi^VYWYVpECL3rCSF|A}hhHjdG z@edLrU}E)4LsuW=FcH@Bw-_GSMS6o+z>WC&A2(wEyM!d3S3h&FR|j>!q+_WE7c%(l zA&M)q73{rL>D5}Tz;LzojFzKPe)hf=5#-Z#7`k}%X<}CI{fB6k6zUFSY_QCbVT-a@kuPbK zjWS}>el5FDqI=yUydgG--!^$pUEN0e0Ka~zue3tnl@5L)6Ef>tmv-y<;E*Q>UR1Y= z4gL*oKsH&#se^~&HaW1MeQ-V`|>t-_;4!)->7Ye0Dgj2~4r|p2micg+T@3W@R za`K6jb`uMxMAic?@5Ss#IS_W@|2c7kFFx|Gvz$T7Dn8luyc%wu_HI^dWHUmakKC3P z;6xe&MDr6$T_&rQ=PJUauEN0N(c36(iK7?wWi|$gNiOcmSPSa*kt=(dkGe zr1r~e-*cT}-g>vf3Ip`caGQjKoWoPN9Xd%5Y6>P5N7NtLeXs9=FNNx*c1%IM>i0p$YLI?wSW>O6faZ1$j2OEZ{t&L$LWd5> z)hZ)jM9~#a;OLg^A%8+A0xg2@VcoczhU@qtlo@W(J5rfa^BDclKX$)HdFH;5m)G6@ z3T=`Y*5AvS^lT}m&XiNAUg8`1=|!3JX{kTxoC6FEl3)5fUqUNNME@Vm%k*6w$$Py{ zh>TzE0X>cZlml0+@li}LvAc~FFx)wA>Hw0tu3nO`O5J1`%P*B`XFJBiV%b%36w}42E|X}&tfANL z3yAPQ)FChH*JkKJHM4hcch%UYx9F=e=W^D-Nv60k*OGCx)*PYn4WP@q(9Y%m z6F{e511K904G9LPN@V$T_BgjrN6xVrV?B6l+#tUt`iNI>wQ*+)CWkX_Jb*mb!B}-z z#uGC}r#3_8@g$Ml#r>34GR6K|M0@WWHw>BNZAd684$Yutc{vEZp4z24xessNo=W_J z@)(dCHVO&k52Kpkz1-Z-rfbA-1s;jH-cCRWlmo#JRiaZfmm_fTRL@sEEf9Pd*_{NNCI_G7uctFX%2+1I zOgDO!AnQ$EF=-ux2C>jhU@VRg@uKKg+Y|htlAK3rT!EN*gKbt@4r^}~*Rwm!t2*5I zLB8-L&2Bgn;W(X#fDZj-Th%I`ZU)xl#>ZX1dc44laKGR36*|s|zG+K&orn|z-ov<0 zj)%tY{auae#gg1t3h=x~c}Sn=_E$Um91U>F+<*|NmF5uDhD8t}S^LI~sJIf3dZuRl zIK86JeTK0A=ZLEG($7{N2?3(DA3(UOryLK^NJ+FB*8SWUAS?hxM`r4t#N;o-8nl9%(t?auOsyF3 zuFpIKaKQ(aN@H_(MxjL~n=Aq+o3}TSPx6bZqk~nw#gPY&(=Ah?P+AGe@W)d@sJY=% zyh~|ZSQ_kP>dRjBv-}3GsSt$k#GWQr)LFktHUn^@xp~?jLNiwh^+K4zRXh6SZ#fJE z6~>4mhNCCMbPXP$?Ac#MudzH?o5PYWkemA&Hy-%GR!~QHLc)Zy;Y(uDgA<|k#XCxq zV3#2Davh}7lPbXdT$Gtn!K&)+*BUe8G(-|Pbf&y*++xHT4wW0u84S8+iAye%5fj>) z3z@pZK#De=fh4C=0}w95Yo{1{Zh<(a3~J_Tb2;}koLl6RM2$6D&4It`i2{>`Z`r@~ z2eHjxQa0j`GMj;v-a&pnk?w8X-P@m#!S!g|ftrw;{9$809p=RuA7sniSf=Y!isQz~ zipu0GNAc9-VOY6LR0sIS3l_2T2-wX4btRMCOd8%@QO+`96!dm&{Z^(Fx@l6S7@{W zkY!gmU~4nq$HiFAqu2MZj5o@!VhrN-{Tt2ST}%IcS2QUtoKpLI7>~fKf)qq$lDx-Y zgup(`0>JP(UH{HfQ3nY#szWx zBgJT{)RACG@MFzqG@af0h5=QyK5B zm;e1%G@e%Z5!u=`4vk`yA!L^oQk&WJXpI71#z_@v!onOY6niuLbil#$Bt!tSALIGh zVX4wP#cs^?2vo#ORFKZKW1O?sa|vyr31*8_xqqvqeUYS0b-irH?UK0-49-^D`W z@=0K>Y?*AJ4AJ4;4r^ncSxLN-#FQi*T?d*9Jz058P(B5o7^XE18*>o}d?@v42mh8g zaJ5^`5bWX&H!IVl!sqtT#`J1vZkkizzUP5rP zg{dMq8Ly2kPEAD@%9hO%HydWEvZ~`t(s-4t88B+o{ND=uhv_}9dXCPWMn^bP*KZ7+ zS)8qjm(-ym*;|GPQAp~mif&twq_^Y!hG65@8X^tcXQuuUe_KrdERLrNa>d)-i-G`C zv+7>V%a!baCBAA&_e_}u{~*$b>mIh}(1x`in~ve4DrIN?8u1=n2q+`Ez4QrsFyz2N zpLsc#uSf!|LZQ$CmtU#ocQcmgpm4y+JGsFB`>s;sWc9UcWcr^#p;lwLThd|ALH5Kj zd=Vy>XyqbqsFFfQx4Vi$bm}f1mQ)F;=K0oR;8z05+abj|5_L*;QI|%%eO8b+mppZ5 zvucayoU94x)E*p_LI?TwmV0kvB?hoYf_%wJiGvZDE>)rA|g{l4U2FZUm8e_Wm%QuA*8AUshy+U`47zHOWuQB|+9Lvj}4rC*KUwg^GmA1jE56u3N)hY_Laj|^k z>{KWi@NZ=IhuWv;pp?b)OV|OHT_5Ft0?6@eRSH+AI+@pFM7MSVqrl&SJkb#aMHa3} zl(K3ttnR6fhzB)5(D>C@R;n??`HfEB;?OVE6TD^jU{$#f!O{w_>75++(D6DW^`&}s z{WbSdw6{28HAO~OuI8CUJ4e0=+ z97=^CAHc>6f$_!@lNYSqN$0A*si&cUuTWz~wBS{FoI-!JJg?C%=6+a?&aUnFWdPQt z-y8VR`AmA-r`Qc>m|Y6Ha0+#2$VyHvDZ`2DV@!SdO{~6By^Eu&e_MT%)DWt*^ca zSfSJYzfj*D`PAxF)o%XYXaXiRrwLzm^e#I-f)4~w7UuKxwRHtQSzZm0Jbn}2xX^H4 zhw97Pd@6goeQ)?=w&!v|LGtLYl(!1pL2oww+(3A|?sMilElvK`58X)a74e|`U`j!V zJs1P;)G2}^1Kmk3DFsJC+0UQc4l%FC3O_zk{}$1&G-O@5IKg7YyHUw^gRe^27Z*L!%1-!Y}&0uyVi3@cs%g`hNESI?OkMtvf!H;2QinXV|=$%2LXE=l|2Jw&zH=`;K@Ob>nu3#qP70 zN(Vr;G!mBOKJS_~oJVbWzOP$``>?#R)$=pj=(&6AetKE(!DOrBEaqq%_o4*2&yX|@ zo<^~BD(C2{UO>`ik@nl~&@b%k3zmwFJ#}lo@=j5*P;@yc4dv7VnaNfHC~XP&baUzS zCh8n=MRZr(O>qT$SDFUXHD>wic{Rtbc2{BUTAt3Cpi8{HgkNUm=6$89Y=_hAW(q=n zg|RB;ls2|w{*lEllVKPk&84GJrx58@y%JU=i5V#0ROvDo&Ax<~Mz$>8To=`WHI`Wn z2Y?I0-YPTa+8RElSJinf4ESA?^0@H}fm&*E9;dVY*U!3-=kvO|TU!R&{=cauVcJTt z*KmdZ@4()BtSk9pj%bMVyt&-yA=s z>fEA>4wBPdF}V1gy10QvssrOj?52H261KLkIq?PC;dd)NvEO?S+@ z8R7yylpzeA>4InY8s|}t-y7ehiy@j5(`!NJ<6QR-@4j!Jbe9|8@CUEtRu~@Jk6cY~ z1X;;Z^MT@xKGX`g`2MMYeBAlZS2qroI=J{oyrw5?CC%Y`{sGknOK4(HqOq)Ji8CA9F4+BX5jxNEK=)UPJ_B8N3 z4nY9*k;+M0*)*6&*cX<|@4$+Tj^cyVaFfoTCa&Lbd|X79&{?WeQVi=!CQ2d9td0=V zDIK*3MY?4t_cGrExCh+?&Jk*kDOkuIDeQ5WH;%EJ6XCR~Y!f`8Z79{Zm|(JRsx4U{W4DOnZF6 z|2rFKi~JR1GC_wldLGMRd0ga)WI*kYh&S(4NQ#f+qq0Pb7#{HgL|GXn)EiM|#?9K{L5}smrl%PP zuuau!*FKb&>-;X2(Yijdrwl8O6Xd3)w|=c8n9M*i1nG)Tk`54*pZ(+drE^0W9Ew;v z-s+X8cM5kPd!CmbuK3Yn6!O0{kBT&4FN&ndh|uBl-sqLx!)?a&0k+gNZyZNXlxrWz zm_0+zUk*T&JOv-SI`p-lj&NV@0OFEwI*H^_r-Te!)@@fLL9+L@_8v9!xaY~^Y`2Vx0pC%v?&Z&5i7N& zTyuor?;(p5>qpKak*Xe;eQaLc2+bHD=6d(g$_?5<6ZZ74y?>A>ur4eO?F}N2A7{6Nx z&5y=5HruZ-OODaYlC-d2w8PoZ9+dx|m#3@|p@##*hi^kef&pSC7r4*U2SWR7S}!-@ z&&)e2xW58DLe-Fj*ZMXAJ+X974A!2y(FB#yxgyRUta)>y8cebYCCgAUTbQXoI2cb# zEb2OSPlW-OB2W7!E@?$+=`2S7UBdW$_ziFiyUWF?^RZWC@lzQ^l5AFg%J}&EI{!(Q z<+k6?yFdMFHqJ%ntXqw)9@_qljr$#MoO!x7Ez)5l{q}u)UVhqNU;7D29MLzbz_bc9 zX}}TTcP8op#k0AwqXB)KM+?{(nW9PIkle|BHxhol$sKumM}e(2JS?A54#Y}4DwoPQ z>DYzx;By`~shQ0fA0i-H73}8MnmgJrYj#|?DsD;EJQbrz(B8iso;9lOcH$mwdx@TQ z`K0u=HYQm^xZ|Um--h-mn{IyMxc4IGo9S;KYZ=X65;L0PXfjAPJ5$A8s|HLpuDrt+nqx!Dy|)%y-2htrH0wj1#M?oF zo5NvcSLb`9w_M3Uqgb6I(AJ{yxpK~Y@Ndxp-#-?aRN-nh9Owbhp%liO`5l~O z395X6pjaW0_^=~rM>ugp*(w2APiZ+hIi?OOAl#7{DabYmRjz`JICnFH-b6cT5&bJI zmizap-db3*_aE$B&HLg!t}z6-$8DiRAj^%Fr5%G*pfYawLRed(Oaw!d;%r=0Fdx(8 zwFMp|TyQ{rjbG>GP}u%;v71;4K$A8Dd}rZM!qwPb&##OBuYp9P6oG_rUBJo7-lGOXxuA>YGQo>XY2h!* z+O&*Jmh67qs?V=)yY=Xs9s@+?3<#@YI88gaLadL1XhmXs4ie-~Hw>L{ns7QU$)iWDs8a+zNtZ zCRf?R{V`_!l_@#ie53JQMX=|9j-8>5C=!uh{08@NS;tvyUDuI;|IMuHfvuK7 z$HiRU{bzt(LRlKT!7uCits{KCpw;tGGZLq&r>*$CpLvtNT^LV&3`7Kwyvu zkg6{HwuE|hl&CRTyq`Y{Ucu#NPMY`lymb8ja%N`qXN`yAtxtpD*myvf=JQ47#u>I& z&mF4K{f@A2=*HB6Ys%u>nB37T95?R8XmPdK|_%7|Qdz zjou@HO!xR1p0?v$LRnn0?zyw42qfK~yE`1NL=q$Ugw*Jl1?|28rx$B!NIofqNo4_q z;`f~j>VW_MaYPBXf;W%3+7-#YFAp^SFE>g!%SPw(b$GGEp2mF6zCT|XblZ|_dFr#f zgF1(^Q=x6=<)_~-7n^HKQ$K2bcKz?Cg@3R2K3W;|o~)OK8x5vi*1Im>ym&h9Z4z$Y zi~a2W^ZflfV{+b#+kZzo58``XkG##5gCCxsiT5!mP9D(*PUga8)n-s`WDZ@L9WzYg zCvRd#P3_s^H{h6nIZ(d4ZEQcjyOaHC0I*CYdX9fE0d(R1m~pF*J5`0|0%d;7}KcZ2X{=K8~N9**Q=q(GbS!vH#A zZPzXOvcY3S&c?7&4}&csu&2-qR4ovBAUXrtqvjO_NzoLU&|Q_ujr;ldh4f5ZxdzoK z=8ZqAtf&reQ^QdVnP`Jt%frOq2e6-Di003=*S3>pZB|(JDfSPDC5jTRi*hu-23J!M zXOGPh)i{h^<7C7prjN)+>?FJIVck`G1=s@|EqSEv|4&_C0TpG`G`xy{h=7V9A)wL% z(p?4!OGnLBr8 z?#$eK=T0|rpGcuL5hE-KCH+YVgT|u_Qox9N3EaFYb!WpV3fEz*<6{pk&0b%G_x@G9 zTzaC-j!JUX*33?VQBucg=T-GaHjAQ!$P_x@RGJ&5IIYr(p9~yQV87T@OcFPMp=>ct zJ^6VdSl3;g8}lb4;bD>RnlycM*4*xHOde}m6QJ~ldS)y@yk7!dp@iEkiSlr5d8B&G z6!y#pBt+6}1JZWZ9WG$h@Ox6BZt9@hAm$0hZOpLrSY>J{lJgMN*?(?ZA*^;stZbLq zE_k<<%#Cd3fd-enE^9}r1CD^no@$lOR!!0+q-g04N<;rrnWV+R(WhPzpD+3Y!uLwy}VgLgw- zbUnl2C}U$+Roin#jP3++vuMFV>W zcWY!?e#*j@$FDA8glVZ2vd!s=qCa7=uMs43RC)_W)6`TeqqnZm`wMM3wpU#Z^w*cI z&lLje%dd~EC=Knop9aKolJ$Oys-p@@sq{V!r;*`pH|g}bRu{Cj z^u$0z;2qZL!wZhA)bMuWuOES!@IBHSjQH$+H#l;QxR8k;ogJ*2*`b8I+t-10uBy%1 zuM;y#nugrQl)^gBGXW|iR^ec2u9d`xuJ<}ba-<+j{Td$%RmXVur8^ealFQRyug^t`J4e!O}hzR_c~=fgncAfU6@$Kw|0ZY%QbD~w*G<2%)740ergTq&~_I*S9g z`M5fTZjPe1jHcdZ-z%;c^A(QNeI<4Kv!E=Q6Q@{&ZsiZf?}7FNTL)yHN5>6g6etJn zM8f2EN+FH=RkQApJOgiFmK6CPlYJ2RmK9XRQ(B`jCV0 z-=T7#wm*vb9arJK*VWBUbJU)5NCbMBZT=H=a>F-GKJ7;1M^~LQ2uS?!jbfVmOTd(5 z$hHg_KEiHJj7*C+CS*I0122Qf{JGhLiJchUC%o>BO2+^jaoJmul% zLpj`zucB17A-z)0qTcKN%07NCz5sc>jGeoFWFc+6rsdgBJ7{DWpfJk8wZrS+i>=$1 z{QZdU4XGXjvh)1?QH{)22Cl~=Yn}~tm(~{8z;(6vo0@5q9%_$EH!Dn}ILJpfk-aH< z!C|;+RJtac-i}%RAuWLm%J6^1yika3e_{7BrhW?7g0 znn!wr2$r7`7^=C#DD8YdY`-uRc&)9&u+o%w<2Of)FLh&crqU8}*|W{RxRr;4D|GR?rpk(_dXx zWRe1wuOsD%&-vs)Q0qMZ!zJYK(GU<1Q6F9V9{42Cd&q(skNtG}az+1ay+Uk9uopc& z>(g?5=4bZcQ`-e9Kr@fW8#6CN0c<7;*4 zyY%LXRMsu0POQ&2&CrMBNhxMHs~l$jv83ACkj_#p9w(vFG%IVZ)-RuVDL>7zeqwd0 zwY9J92)8!JoN5Kf&MJn`GAdm7P$g2Ytz%NZCDEQQcs9)D3=KuT0*iIf3KE*{enA`O zzhtm!&lW0Y8{y<_9w83Bf=N|;7eFHA?sMIckQT8}c-Y+Bykj+`?z3H75}Ty2F1_gmXm)m39NQPSQI!87iIgM;)Xvz0};t@ZV_wIer?ODUWH zW1kTahy6nm#V9%g*~D03h`bE{rL_hwE=dkn4jon2ZSVK$1I(WjqRgLrRHgscAG;fE z=^tl+eq<71Kh>A=BXWRG5{^#I|CYeq5z7Xq{=6cWQ!i4lN#Jwy5#gc5Hw_1wI`{`6 ztTM1Q!=u(T`qiD=K^TraA{1f|W4z!n*~OBuM1ok912y49NA|IeY$QJ0JdV!EKiw3c zJqN-|b(g~h-WeV%ZA6$2D%ernqcN*~AMJx93+9Z$#EB%alZMzKT}{!2yt93a!AVd4 z%XP-0d@NA=64K$&Pl!G~nk`LQtYi7gpha_Xs8E?$XiGxqcU0ahtot=aqL^eiBhU9W zUbF#)w`jgdJ6p-;o*BR$_ROtz63x%t99 z!i+Kdv)@c#TWj+w9yb~?6?@v8g6L<32qgeZVpK0qG8Of2_~NZ#U}%Vi#d^inUJ4fP z5u6&fPh{J0fXU-Ga6Il*#9Zo$msSU7nr{cM)Y~7v%J#q)lGPU(^bi6)`9&v%rjzn$ zU_A9jDFB-~@{7vN?+XN=&En~>kNfHwygR+pQ==cc*HOHO%Pl1HEDq8Emv7cwSmx0N z1bQ)@^Nepk)zb`n-gnx?i|fhrB`b7hS3z8_iUOXHLggz&rxigt=}i^DGev*?u^%KzuKW9(yc=~D&8^Hml25WDl|WjUR+#e$mzlSZD>>v_?P zP8VN>;u&W^U(9 z(#Ap_&mmtG;|W?#1f1HezOPHgaqHQ5x=X%w|NJol?@91iHYg(mcY?RU=cxdc0tXj2 zS(i9Z4JC2r6yB2h1_4JqdV~pseBHW{3u2m+%LWTvF5MoNVIMx6^x=@jn_DxKC5lB@ zh3~#f`rEAOYv9DHXl#EJ!b1 zbvZM5-<`FmZ<9kf?0`2aNUDzbNQ+yn0PTXV(e4M-tG4tg?FyhLxAQ|^g=*87;dUP+#4el^zX zep-C1a)#bI{`1khov)>EV&DvCUA%dz6FjEFgc=n2@af##egUQy{)hGJt9hz2F<$nq z8Js3+`3U3tudqINUYvTEbwxi!a!6@Nk~2e~6HK;F?*^DKfz>U`Q(`^g0aN!7JCsv_5#*RBvG2c^#wwlA>53RJV@%i(T*+bYCpI{?7kVH688%~ z2b3o94baoj(lrOgJq<$&*>Jx!$(+)x_wnX=ZVT_9IIWYf4}8%|r)dAe|64+-i(1^D zKQ}peJ1;PD_37ZcSFi8K6hZEpWA!F@T2N+Dp~oFih2>U*KWA5vUL*YI-e1vgTZ; zLFxGPwD`9Z-FKglh8918h{Ugp-y6JF%Ix(1WfuAJ67o&5a<(&f3Qvd=B-%3Ro?a6) zRtAoP%k#eIoMA!+;-sz?-9(ZtI$(NrX>u3wW8NWyl~vx1o*2~HlZ%k_JLii}P-y!; z;I5(XGT(ri6aTI&ITWg~t$*UzNdzcc?hy%)uV4VTD3kSDY8MA4CTkppH=M>st`a}K zamc2zrOM1&bJNf>;F;)|N={wc~5}ZAIqRLdLKuXssb*Eqe|i zy&k-(8Ba?{RFkFn0=(3uCI>u#BzKbqLtqLYy<>bjDpV$IOO%>So(PgsDv9DSBH);l z;_;Vko=RmS|A_Z)p1G-)=FGLkb#!Yt*PKiCUWGU-vG_^K<}7ux(dPLssEJn(t=mn+ zvMJI6;!Z1pGHg1B1)ZpT?H_L&JJcsFY1c@>M*mY79BODWQ}*ZQNviVRVZeNFe`h zg)N_N!R9TwtgfR)dY>LjP~(S4J~qNvkq_yIuI^ac=26%%Q8E^{G#d&~3|C}J zNlq!kp}bV>?hg`aq01lS`ZqU9;s_Eq+TS~Hio$aq>tM%R*4Iv1fg8?-iHeIK*l0Fa zxEMd)($eGpsZEh&=!%&pxc)~j%vN8r7@Q55MJ|9i^2y8xm&&!n?#jlu_JB>cHkJYt z%sB=ihcs9HT@Od4yhnnE)zU5D@Dc4$9aIGc(P?SRDK_bXJSojw7vy78?lWUe;yGU> zni%ri2t57_Y~TJM1RJC3kd>8{UX^iBYH8`JI6o@!ce293(iZb-QomT*3;vN`3yh+) z-u7A@zl-5p!=5)khIGk5P^=zHux{-YtVV$ayYx7X?h2cSPMXMd_R&zA$kI2(QlgK6 zw6Uee(U#hr)dZ4vrj0zKhxNyc3_rCd1#Z7eUocVfF}L8sQ=y$SQvrVkg*VLqBGMQ7 zEP53GsNwR;_F@X!8|BoiS8Z=Sq~e( ze#3ea3}f!;nUPvsyGdDKFx)rx;IY^Z!-S9truNg{i4QobB`bIy-WSN**OXH?a&Us{ z=abh8j4!Z{-qk7EjGQT4b$eK-94yeP609%DJRDClf}_9*UX3d^3gC7g-*B!-EHfSXU=_|ueBEVyc+0*olR=S-E2ICyZ!iflB$SA-CaGVaesiQ34O!*gC_97 zJs0;wQsK6!HNr9KjlIP?tlBRk%dx}SVA_z|0Bc&E(NS~*;Qiy{N89N{*{)u?M`+=o zxAGRe#N2D|(o-=B3wD7nH_J74#`Wubjg}FSSM$yz3YTpi>)@f3_2$3;*GyF=dkh#8FvdAhsCK` z=^Jq-y&pDvnb)#BmVL`{xm~<*sXR_Y0Pr^eXM<6_`*$~Ew(H;m<9arJ_)jM5TPV6CD&8mJ~v1S<4zS7zfm9 zXQ~DGRgPCvSqM~mN49udaFK(OJ@Q$D+hnS;Ids+VN#*nRFk8pIPji=bM#3hHQg^9g zc=R1(x;E!Ol_{=3Wb6+_HHei8vk`^e>6?4%`OH~$bq7SfSN)Y&0NuVT;`rM4ynm>w z&pB)CUc$XD$P^S@BBk)n>Di`8Oyj!ou7BKI^J|X_1fLs~1A^{K7JH?e1S9*Cl&$v; z1rZ;Uqk3M%_fiS2+)^@r?N@Z%(CVoyz~NdVQE*@YCBQX@<;L*XN4w+qO~7?^#p7== z%3qSU<3+RrCVJ!QpRF0{sYG|(m@~a!ru5~)8q(a!OG(VWHua3H%x6Tk;?SQ*+_+W) zT_R5EU!)y;w+FG{r`9`$)`TSyvW(v4FlWAY7+6tuRcG|K8Y{W&kAG3)dPU>mM_W?3 zDnxh7$;U4z3aLdgWL0WrWy#1=YIR##S%mqpff86>Q(~W8NG07m9;7CmkeeiPN{r+S z$ctd)Ow}s5BQs%opCSwpi^vsYvQ8Lo0$ZIKU;S&3QL8D+=%--`=3X9t zw~|)<=xfrj)q4dKn7d+yZzDjhY~LXdiu_!7LID%3q;;zA%X05%f}o82@NOacTsZvk z+UnZc#@bqlGczcG=h{BgNsMyWw782xl+d?K80nB6jdQcM{@8&QRX_LOdd#@~hBmE8 zvP#UNheuG4O(;&d>^oOf0-m8r$T;qM<8o{YDzU_VGR7rjVxo|fbX66_V^!M^6 z?DSZz+z;rq-mFs=$UwP(R#m3gzp`>wgc-(C8PV36hcqrAKcqkbkP z!}3yt;&Xm?YC=((mN9E#sR>3iooGv(a1l+Ssr$qkS7iV^SN5jRE~aE z`l52DbfH|zIu5z47?I9`D9}zVF7&m?j1x+o>J!Z`dv)*;N=m zobRiydZHiQ#uq$LhHp^_(kx^-=Ec{8GMybxg^>3)<^_3{UaRM>^@pAQ%dy9W9avaW zp*mxyxxc@o{g<2df~YH&v(uN<<`3Jd~{J&4wO*~U|0en-I}NqKUMwd?;krd5{_v39H}(e+5P+HM_fw! zwDgGp{|91D>jIQH5f0C!ZK@!4uOQt(_c;E#t)psNJlg#N*ZK@fk@sFVAKNm=#T^aD zM7h-;Mqc@3w5%ITN%ZI%_A|RimNr{me6p|F!k66|_>Q6!C+yjM;ksVDL>Le?a|BW_ zGkL^Yk^wn7JoG1UaXyjjkujXlu5?2m-W)ZF@Zsl&TBxW@$`)9;?e%VWi5cW>D1;WF znHWEq##m~TnBM$j-6k7Uz3*zp(znHEv6S4@YM+-Y$kZj9$mjC_Lq)KY+rrdtl~Gg( z9dh|O$Y0y7Ji`s#d^z8XttLI437ns^%EarydyE-;gA8Ai7o~^7QIG?VX72gj!cmCO zpk-jYn;Ixe=xo0hm(HZMAzA6fsXnoEdS%`QGk~b@7^z{zE2FN6DDahUebI z=)@FBj_%-LJMo)C1|fCE51M*rF_-WcvcVN!+ver8%0+4~2IhgS=n4)l+n~k{<4twm zsNaAMG%`GGGGu2nS$~e95jmb~nA*#T8>#qJhkfEkF-o%};L5s$+r?D`pTZ>U9qG@X&reX`nMH^nqq%n0J_;r8Q!(NBZ;iqQKidgZkbh6J-Uli8lKmi&z z(elOTWpXqe*wqTZdyy-0UP_ML;Po3Z_o`-EjOXF3IJ%P7f;tKaE;6^@@+D@eOf4zx zHZLWFt7`J_GG;hg4+kXG`?+=UQYlQXCEIzOzVgqoZ4`%;1ss!UN%mC@%(N@TO_mq( zpl%~@0=T`G1WTSlWgM2E)%)_vH$cB22LUxK`S=}CN?x@E$Lqs!reFvP5(^8;davoU-uidL{q|#I2;q#e#N-;NqI`6Y!V}N6H5Ox( zUmA}hdM2bI@fcD%E8C-~?5KaX{ozCNWI0Iz%*05=akt6>dv0dH-Bck4n z>Kht>Bm2+wf^yA?My;Nx#m3FNgM)Fb8W-!mK9cc zeC`eQzG^6w2Wh%db;ZC`{3kF(T2_9zIl;-A#;TKK?o`ai+WCUD)5dx&Z@d6e!hn5@ z6axJIN8Ps8+Y`0+w!gBHn7N_7nS5DMa#5rpaYAJ#ipxCK5N5MAC$r?Y4UARl`pFPz4Mt+88Vkrq*u0yOT2S;xmcfQJkClOtqWy6VN1xv79l6DgG( z3>fwxkngQu-LVp=O8a~WSDA20_5t+n_!a=%rM(fbdF%pJ&kZr&bwkhkoLrV@Byd_n z`e!b2GnOE;ZkO3fN%gSPZN+E!0@qlCPV^}1J+_ny92osAs^NI^nFWKv09HoZpn-~t zf&I+>zGJLeAI!e^5!YapxKk?{{I;;!}(8yu!vaCH+y7Gv=v>g!_=Y5nd8+xu$P;KgY zM16<%PcMM$KB&dFdU6xY&qohPVA zbtZ*V?T@2FSi#G`-OHrquo0FcyWDGIyK{jdf)&|Zrni_K9PAfEQLNe(J}S8tH-Sd) z6(mCSSd)J@#MsdNX_liY%;Hh`NQS#d@5BMdG`T<#9?g;_dx>@FjO*&N5*aW#DcsON3go_@Ie(dgisU4dXx_}$0%uaOMpe(b`fKNb-1vFnB7rK|41lGrTExmd1!GO0)d zz{J<_k?VS7FwNn|?AaNMn=!{|8a}YKa=>Ctu~&WX$aaTqR)~uzP{-GVCwh)xpbJUkO2A|ZcW z{308qun5$wa9sp}TeJVZHdfKphW@+;OQ1}jSzL3EFR<{y)lYi+y22OJ z+<(si`|3XcDZ_H{0x8S>AmK}AqV^ZrB0s+WLd@KXzi(pecvwTP)M{<*EWZ|5OQoEd zy4cULMfTKgulE}GtYy&G?Tw7kT%O_$3NFydh-$n9fo`XRC0?oi?R@K0qU+R`xcIu| zQTCx>epC|gf^EH*yWix|FC>%}iaAEaC426bv9Q;0FhVUBOBnJ0(ND$;%X%u$wv!aa zqQ|jfSury>ILMe)l~z}tzn8Kzdp5vAiPV{BW+1xzn9~MQ;4~0;UUEHBpwLcW{h`KX zhu9pB)1#;t#}hx0tPy!rd>KavMkUdqA$aWbr5hlc0oc5iP-F29tN+zt&r{D>P* z2bf&fSb6-YZw33814ITG2TUQeOIndwbL@S%=jVAfKcdL0&%ws-IgT*94i9gP*jiX` zd^yYtCb@E$W4e`gS%Z? z)@^?wv97<9Q4c-V8PP{p8@tp}zGr;$j~oMI`4$R1|ABGQCII8EzQn(V{f~@0_A*}1 z>f5!N0MU`>{1XC#(*NdWt8elQe0|sK41$OLWp-(J8Hqc9io4@(ZaLdbyK*dOV6((zaN0LI6<)^s4U%wai-zr6RS_}>WpB^gJd zAD{VO5JN*O>w!W)(rc*ymN7pyRO-{e;^wD~N;Uf1=>PtTKR}$?f9-rz=l-uIzX46Y z%lU)Zf03-Q5ohARng&5 z84$%oDkdB3mY^&>!Yq~!%T3kk&?Haih4d|%g#R2Zfy`y4)=E14qhP4fkkkJYVUT*3 zG3{}{d$3G;)ds_0=9*fIB@b-s)+VmnPp8rRd{RB3+daZI?OiDSgN;Y)>+z6YTiFV$y~lAs_ClE_IXw22zMsjJSjcs(R1nAx*8gqVgLkwdS!4p zM=b6=A*&8gfm2gL!L)~>qGEC?5K@?^ZVjl1tpHwf;kXspJ>=_O!g4G_NFPy)j4?6u z4vX>dSP5mEmabV_2ZY{xKUU^x;Nvrs3MRk2#DW)RK=ck;8gx~K_!&KAfr5VRy`idO-h<6Y|7tG>d-=1j#`KAlY;*#_+~PjZ;4vEW3%GBb8jL>hxoda z`43t*G?wJM+%G!BZ}skZ)amc^2dETtH8^dh^@^NiM@rjPts%2VXRik!6iP{_ z(s+WIjb$4i78WP;p}JNm{+~3#&CU8cxfRmS2!;PB(B^j^DsD}A&i2+jo-_lB^uP^W zVLj`8MMPb5%Dr@U=B|FgsOU1|J<2vLd@OSA6_aZ2K7O#AbljO^3uI<)%sa3y*fqxNZw`^N0iyv(CkuhJ&M#@;4Efi-xes z@856MOar?yx5E=IFy(q01?VX?4X+V5P0vF6!B`@d&H1k2wKel5*|CzVBg zCxTZJ?sF9%U?SfiV^Ax4I{!Han#}iOE>2N|Smb@hWPrb1>|002^lQv{%LssBSlr!h zw;5etF8A}JHK@nz$m`r0w<^EFOw0NO#OXTCT)1;LGdv_&cBQ6XgIS_UJ0$<`tnaN) zE$wbYLwsIm)w0RnDcL_33OTu46^ew#U3-%#z<{QPl#ugA=bKlZTr{%@yKG;MH(Y7# zJ~LHu)OlmFLH{W6YAU1v8z(~!anX2Ef3y=JdO1^`*c2r`qv3h9E?Zp8RZ}B{#3CW7 z;^rD4qP7a&C+#p}7xk9R6};9uzWtUvtE|tv8!iU$DJgXV{Qbq<->v~p>hWjs{ORH{ zZRI6w7Tap0;T$#FM-S!BGhnsWE8E@?;%5OvLqj|bO-)b1!me9&XO`Acx&ntX<0JVtNpC@_({_JFN8v5vn@<)T@gYmx( z;59~|wqKAEZwx#bpQX2_3zeT^i(W_uDi?ORW$%B8Nv_*X5U#(Ri;06RBZuQ8^v_$^ zy}JTGjE}42Yk7m$ehLbXt5_28iCwjg&bYhkEP8>-$=5XWFo>3n-ee`!zM-jOU|GiJ zq`ONjnSEIYaOM73D_=|>yZo6aq`CeI=KrWf+CR$p|C&Jl-wNjjMyFjN&jdg0U*iv! MRFKGjZTR8;0MB&=qW}N^ diff --git a/examples/jupyter/images/manifold-cad.png b/examples/jupyter/images/manifold-cad.png deleted file mode 100644 index 81d8aabecac7aca9facd15ab8ff0c15b3f49cf80..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 99580 zcmYIvbwE?^`}WzW(OuFdT_PeqN5?8uhjfWFO1E@P1nH3OMx?vP_M(12 zzjyy^JLl}|JkMR%bv^eJuA!!gk8=+P004YtC3!6XKtlrn&?p4+?nvblCG74E-9=XU zIppr;19=s8_nq2RLC;m&!OGRc%-IsKws)|zXKmb&)<6KX+d;1 z3X5G2Mt=ukW#R$J!Z=hq?#0r{Tf6?iaQh{K)-u;F-DF#_UA{keQMNfYvQYb}xZK-h z>7c}=_@io;lq4ts%18u32M0i@T*5+{(Efb-d%*1Vh-nnQ8^hmoe~xqz!6g3gbO#ki zIOzW_jSB1t`tQ0RL7Zc}e|Nn5l?53q=Kntv@k7G{_P_rVKvbH_m~1o&Sq+p{u?MLI zPX70+D8bXSK|+oRQjISrq9!Jvc=ZmgX85oNK6$>|XbL)J9KM^0zYoMACpbgMbPZIt zF#6w;Y%zgzuzY=Qb-1vDh5|bOy9grdTXPK+oZ)}ZT+J}c-b_mgVa3_T`tRw3%!0n9 zPw>C5)W02Ks$H0xQV-0ws{BqUEkV7l_P=37xea7VS736tRc0`c3ztY!DYc@9GWc~8 zU0AsO|2dJ1dJftBXvEehcxu8eu4B|7{;9{0c(iROYw7Nun6cLX8@U-I4ktjax}?*k z^ka8d$LOvXYaEg&mhm;^|4mg0q=@J-XEP4dRr%N4mPRUHW+(=BpgZ({`mbs3Yy}=j z7omiw0Hu@~VlkJY>oHcRrbvVzty5>5+(H~N_DiAvrukK>MvbI4;6sx)#|?c=xf{mv zfr`62?%!z;Ff!#KNyQ8kOcf+KBg*sPMngkkEzf`W%5%gP#mfX;4B+~2zJrM0WBB3< zfrqOtSj)#otZxf{m2`)%E#dyiB{+M~+xV0edAtM&w8`y}LRL%_={;FJ|7Utvypv6S z4%R(rVw{In+(qKon;t1Nz4fA$D0;PIgzINePuWI67b-|4t&5U_;2Vka!ZJv6J@DL0Vjkv?N${TNUFnr zV4zZA# zcf$@U*{7!q=vh?|;z+?9^q-qG*UoheUOj5bxe4#Su%@qnnj2ELh)_*q2@c>NH0G0N zc()Pl4VnjMb5}lDxV7{6)vD%5T#2#X|5FNLs@)^PC0&q6xF_O?)dySrXUz_dZ-}cc z0#QEZVL7k2xfKvJs(jigUBtJjmvLz2r@v`H@@HGeSc%lbW^RDD!Z!G|U~xx#$N8IEJ( z)``6%=`TNk9c>x{4_CrI*La704cZNGE&cJ07kHU@x(6VbD~C~mJLT!`^FD|*?bA@X z{WpmMawbd6@;pNje>{rMo&G)-m^LrT=HyM7j)sWn;i{*#UT^e1s2xsF`!-8bN34BK zqBBA_&J<)34eb=tEtW~umqEr}V?Murq4ViEvCu8h23l@)Y%h9OIYMYEeW?Ill;$IH z2RlkfV0_~~^YBj${9m4sLs!0LiN3@g9X7AzAnAZ5*x6ds3+@=xHle|puVI1-72%kQ zmodtxt(_)kc+mV_)V8(8+s2d6rjEnMB{?z}R~WBjxSDOHPde}cdYEdv7$-z3fo=&+ zxipS$xO>?X>l#FhQ%?kA;XPQQ2lnxPU+DIV69NB=Q`t~K|3z8BW_~59NWtZ?j52x@W7r`>RAm1pGlu zD!{m?cBTWTD}5cG-}wtb7fnxBndWT;R2r7Y=%K+yf>!?ESGzB0KEy}nPz3;-7JU9Q z`N!CJKhcEYwd-!Tc9o#>!kXJ(Z8x6sS%MxPPw<;z(yzMZPFtrslAqO=lM^%Vr7nUW zD@zEKtCBculnh=UcNL{MBo;-v_qZCyn$-H^!6!$01*J{8ImF4BWEUXpD1|A+(l;vO zW|rt@u~dJvSPuC5+FnD+|LMN=CNB z;H%yKb)MI~+51NX#AZ%bH6e528q$3vJIsP`O-270pb{P4x$E_M5_{ld+T!Psf=7-^&Cc!RZ!GsNI_>~{pUyiTi4T6IRhcUcvSGTNRX#tF#8I0QwcFZD<4{>uH6zxTbkR>DR1vboYq*{+ zqPE>94om#{4Tp3z7yx`u(VBDX59oo&#J1-~{L5Skp)O8)wNuM3S>j(1Wl^Aip3spB zySJYZk8;E5X@B6tZ+zONTut(8jSSOEDRz!qLtrk;D3sWrjn`JhBiUp+d8(Mo$=JKM zjYV}nQh9Wa%(>pyax*nAzweL?4CVLqmTbW>W#HU$a**0p?S8J4I|}naZy_3p(NZ4 z);t=yp@5|BRCgsmU6I+V{9oW}C9XNOf>h*8z7 zZStORN!oo%9IPkGew_aNnA|#9?;~N~GO6^6R`|$YS<`;_mxGz*HQ7k1KDk zd`PnPQt`d>I`i%PlaaZ4+Lb`Y^_JHyDQJ(`)}Q>KeLeUW6^7t9p{8yvd^=9HGqxg= z)h-geExuK>vBbt)-pKm<$U^B)C{M(O-iJ$*HOE@w#qZTQkot*KrMQ26$k52oh~PWQ zDJZ&ge|#JGyl10ZtRmfa<(Bf`C@&e@2u0GiD0QFL{PSm~S{x9q#3O=4Q4|GYF`(aR zP*`_u+9C$G`LiJSe?Fj;g98&4QJ*kb|6B?p!u3s>LCReib8IjAYzkI(_>;vg6W$7j z`I}M2Rz!{72V8!cunnyUB1Jo;o0D1R4#H#i9M=c;tx>#kmy*md>bhHbsZX6Hdr8G> zyq0+cIdBZ{STSDRL#onqcQ7FDM5;tGU$gzmQ09+QrV5|9{ND2h%+T98mfZGb7Yn4^ zWz7GA)b}6uQIZ%_iw`k)BH+{J)X1^aWg;N{b)-PnE+Tn@9oyM&nSKg#6L}zsAq#ok z+5RqFeRH>6XK_u^F)=tbZ7RH7ZSsYdu@0oL(?FDRW5T7j?o)KVJZw@&=40rgN{vcjv9R3!Y_kUgEtyK6($Y$5+L%-|}ep7ZYDw z3=orteLQz?^u`Kd_IY#p{$K6J#PB`2Vfz-7oICT0Q4(O@mtGh)VMEN)_!!k7shy0Q zR$UKgP&NttMuo?qUNq|3sY0Gt-g981veO|@0RnHZiIIM^4pw#3yflxF&r7z)hL zuXzQvikFc=P#C1A@uBz56OJRdnrR=W*zZ21HO};+6{Jk&*?iAHK&PgVt?=zZpQ0ZT z5g%DzDx!H*_C$4_U^%EIojM^~_-_dOhWr|A1!;~G)gT9dq1#f!FF=Lj2^=$j0-^kLY4T=3jvt4{V&`m8TW% z8j>;^bOWy+D9u>tG7lq$FNjl6Xb|UW%mbZ0N^n>Q_UmL=rthX0JyM0g|E8W zhRDGk#yV3>=sd)XP3w#oZLS+qrLNJZH)E>(VmLkbxQiJ~Ss-lk0SI-qu^geZJ)A&t zr6mc3d%Al>r3k=ig%4AvhTpVrA4Rq-1}q`YH6;#z#{d%L6P)arhBgRkWeTla2#Zk^2?<}b2 zlq#&L{Glf$4zb9d%(zos(gd#@a_^jRC^D>7$mqFStluwA{%a7{QwYc*NiDXu)4zAT zGqC1&R=Q}MT;<#u%Pu(c4+1+$uYOWs-;CI_yX_ob=p|x`mi5vtM3xHa#A~&)O^_*_y9gK z!Ty(DWlV=^PqdF23TjXbMmsDKkGDiDkaon&i${t_z$^^ zMNx^{`%(bQIl|I{yK+14=h{>NDCYqYuo#mY*V;lrOWKD^zd-N6n$OV@i~3X1|UeogEKUZau4CW?#I1y#9^- zk%Xh5-qDvzL`2c^!$e}-hzTEUyvyG%g#}E*PZ(fnK)Ks`ZAtQ%0-aQ~)wEFtU-X5& z*6SawKwE&LVD=YsPi&vFxUt1)^uH<#l|`yh0in})@Z&e;hz9(K?|x)5%d80cV+I~D z@E%^0FYGXBL~=rOoK-J3Jj~FJo^^XFy+)~Sc#nc0e<5_HV#HgP>Q3lE(RWmMkt1Z~ zYAfn&HhTq~PL+33l;bU1Wd)bFS((z%gd1HF52W83XO6}I?uquFa%|IfcsrQ2K?2bIykz8rv}K^ zr6WF4Ewu)S?{;rtm>z~8s8*!wAf}G{0Q$H*GAGFc7i5p7VU19YAA}zsCZw1(^QwJg zQ8(VN-T8EN;f0p{DgB~wF?}~QWw%+W?!H=soe_`*hEmF7~ zo2F|qgPq#L{LgMSSNGLqURg{I#N^cGSkUf*t$09LP-;G7!-NC?LmJmc7GgDKc+Ux&m2%%1OvA~siq=095_=^^t<&$GiP+>tCe<;{_3z3U zQ@cw}MeGUNZkqP1e)vH;JfsZ~J;}1YF#RO101fxU@op0okRaj%X@oMuUY1E5)t@{Q z_Y}=^UfgDKmz4kMQvuVQBqJTLiR2W=!55xoLUDb-5Wx4|y_5rpjlFL9e2&+!RvS~u zs#Y$2Jp1@f+ITlHUNMQ|1QU7a{w87~{4184QLPr2lqauC?{@?_(R`^vm1DGt20~-~ z)y8Y7Zyz|JCVKn@v6IoK7Dq`lZ3)Bqn)nz|=fa1VH7-Y6>h$64SvzCB@Ae1U%~j@I zzTKu9WByzSbmHIz4CS*pdkIJ?&gA&Dl@LkirxbWv)=n)N0kI}^RP>|6U%5+46_BKN zWg>odYsnFf5I>^!d6hl<$VfnOFu2vD5yIew`tW=2#v{>SdN7WiN{NWT`x`YDS&omMh_w2nL8fVz2YoB?o|4o8um%!MARJFtZw3LYpFVkly# zGd3ytdnLUq=2*{~IZI=sfT4XE_%XaMMX*21i z=!-|0-@wgX-3^~S`p1~?K)LNb3broxnGs>aL<;zwnqwaqHft`&E{tE#G`d~&Viq~@ z@82m09`XK3r7F&E1_QAYP(Wb1PG7q_@jQ`W_H^~!^PS!q^;*JW-*$*VSo?wDbmCqz zF<`dFa#I)+I{PKfJ}Tv5{*-1#THXhpkzWj!kbHcd+z2fJ+zrk|>StsjeqUn!eaWkN zMBfxnW=`8~;+lq>_OwTzN?S@Ro;Xxf^-U*pRhU15`O+?(RAK!E03D2hk$?D=YOl0y zcC)Rdoz7D21iJ;Xy^$ZudUBS{wO_Ll0ifr+v?a<6jX*DeXzUbrjwvF?&COgdkl&(K+a$CJ-a%5|r#7lgDlLENU_COdqCJAcmxN9CIo4~$s}DT^oFgn4`n$a; z?xS$RgK$cdX6>u=O##L5m5r$sgB{HL!PsH92A%A5(ySsSj00KW{Q}J2q*XUs!F|?rRnx*6As9=yLZ|0; z!KVr~_lrxJdP*wHvHh;c`kh9V*;BrXHbMV#Ofi^kd1e+i46~_ua?l;L?Hq!F$YdLO zFP+-H!@P__EfD(R%lW0um>ys?(=ySSvFrepa=*Jdp7l!A^N>VhtYPRH|PpC?-wPKC`Ok2N(Y^tJ8p6 z<%tT@*AE6%0z$ZATa=$kCDkidNpV={n$2d{`(r~OGY*hRxnR@H_uqVQ(e7-{19p61 zxV9j5#n20W>-bMU%E26H+ZGd`pay3LJl=lcjaY$ouo)-){!N#MJknkq z!M%TbRgZs6xG~RJYm9E%7Ye#I?}+uPp9hcC18l+;&nwDVoj7M`t13w62= z0xEHFT&BYi@uTl;H$OO4!YwM@eUDDuMy1Yj+GRFWbBLC^)$QW(y{(s70}`b7!v{*V zutEhPg|)`1`np~Pc&l0LWKVdI)UaDREwgeO$8QJJBPvO9H|zeTE@pqDLsnrRQ<9Q6 z@{TK6xyaU$zebCG29O1#l(dF>8(D9OjKNmn7-JU8)ffOb!!6T&|8rUZ9ueAZ5`bv# zwWGQsWGro5Zx5m(+M}^q)ksD(&psTZTYDX)&3D9)m&bKa8a=UUu8%>Q;Yvgs+$()D zu4w=v!Mg7wUqidwfRq<{r9xuF*3cayweWRe~zdZ-&Lt!3# z10S#GPtR}LviTpLn27wg&0`VNi9Xc=7$Df~V>Sx6k>K4C1|9K$KMa5@@O+Eyu9W_quz8UdTc|$}eqe`|;!5 zD+6b;qLkCENs=7}3wOuZa+1_EYy11pr++DdQFttyb1D9}bBaef?O8!n{wLWw#=N?x z)by1%Z%@zZu$Jwc%k%g*Vu1$II;E!`WcPOd#W=#Xj$5&4KcBU;Xjp?nJUee5fA_|Y<1=nosM8*u zQ0>BqYc0dR1aIxb-eG>4H@(WI(R~9vtLjfLqZJJvApYX8^-!1vYY%ZurmNP?@)rIoNA@=yrfcy^F{xW9T zCCxktJm*S`!uwYes#K3|%K1>|Zo%wT0xnUb0 zLJm}w60So4zYKox08-+3edCVjjU7d@^L33RplEYV!VxKqSCg+&pFVe`uqGz|#dSae zLNw)D!<({>*LKJ~;)~?}>jePGL9O+#Uw8U zc|S970B(dL+i5W+l%<0^$^$0qE$Kkdy}=&S`fU=DZCCdzk=Kn1l&x1*sj3*E`!z&&(z4>F zJ6SR5wa1fu(Zx<4+1131_dH@l$5I}jTeS}3ka0X6n1o-g?nKupFGDxWa{_>(Akt4i zDol9fbyUAB>@vzv^LNaSU7JtH5(|(x!c2|)PRtA#vQ`H{Dz8gO2k)4|d%H*C#4(aW z&8hF2tcLFDNhGucZ(g;SOPIPc{%zst zxMKe&q^U5C8|Uq+NlU-h>U~)&#vU(lsBUH z>-+kj)`dJ0T_aIS&BXY_=Yaeje7WaTPXg{-#+i#_6?XtRW&*J&X2BbWSSyN;Kh_&x z3qkQ7_q}8lV#&6hG>$A2ktDc#FFC8-eUv4J?UrwOR`yWl3EkFY1C{5?ELzW8f2$8O z4kk-GdY`zGg@igAw z?y;e#7s#8Z4AC-!;#!wJFphde#|$;bB0`Zc=>_MvkI*brdZ(n(y(RQzsD>R` zZ(fA?y*NF^latBzqjZh8_hpOqwI(e?hhN?U$+4o>8er~0!q^&it5uG!hO@MC7UKRY zsAlraS7S~(vsG9Op%{^bBn}`o6Z9VfMm&cJ4Q^M;M6XPkCeH`jxhNWd5o^=}7^uY7 zO~!1%E*Zmx4})x-9z@6kcrvkyeAIW0gNuvAR z)@L{7myd5uxUZ&E+Kx-SkKzcQ{U~W~GdWPytjxkpVGWE zI$K0T2eQLpne@xGQ=`11x)qbE7Z-VC5wh>ZgiDr`@x*0feZ3#%Z2OU(_#;8QC<2R8hAgoJB7pSQ4*+$ksZicWQnczw=Kuw+obmWuA*DuP z0{12v673&f9R-Q+`kCc{4dvYTOkKMT5SP=LEh`EUsovL0SxYL-0v z($RoW!0k;PyEM$BU0hNvjd_qY{KlkYW0*#*=Gx6%4;#F8&=Y65A%Mx6258_42|rUJ zCdVOIbdmD9A;5CD_W#xU=_xGZV0U_p#UO=|DRK1=9Gq+Z>k6VZaJ`EaMu?RkUtWv_=ZTFb36Tsox%Z zy+y_d9gd&i@)mF6V>y!ywp2Ol6GwYV~bgWXp?W__R zgi8?YTC(b3f(Ua~G9(@ucu*pU9ICQ(Ukse{f>8@;f#6-xT%wxThbD_4to&{Rd*!hw zTvr|exM0&Tg@CIC`IWiUv(4~*CTRTh5{;raT#fw3pgxR><}aoS;pJ;bJazRb3*~sj zXfCZS5A}*#yFWTY4gWZ}**Wc{2xFq4`hLea=%N%-#kLFqxMayt+=?{d8c3y-S$b)o z_GS22vHY-`y@Sd2DUe745`85kE^&sCeOvd_RpJ(bk4bn+Y>gFnvEJC*9%sFgl5xu~ zgRIRzUSFCdz^g!B`}tfRcy#QvdupIsc$_ENh#YazYAXnsh?w!dx*J`Te2A9(fom^L z^mrrjJP5`;m?ZKs2vAg4zZ&7h-`nRnRRywsAjNtru2OlC2nPvsoC@@EZhT6p_&O&{irw|wd}(?$CDQUk%K(G zHnhoc3=?qt5{whD%?v=f(A$p5O&8pVqqt;Ox~1teWMLLcav_Na1?+u4Zj&uf9D44f zGNoxu%*`0sWaT@*u+353{sog}dW;J=U&9~2$(>1saXeMH8(wuV+&RZyO!S2Ux`0_X z>$qlOn>VZqLMU;~0#lf_oU1?sbR-bB&N(uCTIrh@Kxb>=X7$qi2NiTUC!Tyixjf7( zOTFC@P3kOMuJXJqz4T_vVM&2O(A_D8GiehX;M^;O9ps7mw{V=rtq$+NFH`#st3|8!TH^*DMSB)xvb&ivuy z?>FV?KQdY6LG9aZ=qN!3ritA=Qor>yQ4M0527k6&GE|LgYQfH@i0R&>LqC| zq>s_!_9=ijP2IQvoU$V?&X0+f?6`v1ua=G)LCS2I=FvG= zM#-z0PGgocww`vK-sePikN|^}tlifWwEVgsX}_-#>SF^9&MVTM&AXuq=ah*k2h)zA z)!Y{t9ck7d@R7x zES)XS$gs{*Fh6TFy3pJFVtHYHIuQ3mzW)9P|BA&-mACq~1o?y1W7mSH$y)#*{ozn} zRs=x~PsfO$5Mm>9xKRuYhM^iTrXvE5C0*W5_Nu-HwhRP;3ln2)mYjU{_*5yg(%S~8 zne3g-G#tCsE6B~DcWX>cMp&^VN00YIt}so333-5uiv6M(BG3Z*^x*R%mU0~%N%X7o zmo7|Hkped9{3`+7L!_Ud$!?v1VS;Z_TmI)ChQF(yZ?Bs~3wx9i_KcQ&EoO$^tkhwj z%~Cpa+-H!647a*QS}D>6V?D@OXZ=a=fZ<85-B04Dxn`#_x236+tUH;<7$Zt}MnbMkB^#x;5^RjdJ(OPL&+nn_i zvy(~ZKAK5d^f`mc@>fZU4rVr^`9rGMdXoYJQ0^TwcLWw*!(_!Sq*?f=>c>KTx@C>;t`rdD8>h^7g zsX0Dls9yic(E<%eqZzu`kbn-?iKD8k4uW(3@coYe+1k{m7F^dvh#( z1t&x;%igxWou{Hy+m^I+mc|@Y(@iN*QdpIa$3UY5F(4QZhs3wqmu}VgwV%-9ee{4) zE0Ui#gGsD4*Bawly{_in|e=zTpEFmo}Sy3TD&u>M7C8u=ZQ zXxY%*E^#6raqCE;Q*Z@#wtaZ-NjX~ov2&)MXunvEEOwo@kCEgVv97iL@U&O|y@EFN zfoHaz3s2Rku7?9flh--b!9 zwd(%z9&{+>op?xY&bUDGTpv=31BxE!s6tvHymj0wGW@lQ)u;HSdQ=uC+?zk{Fb+P8 zWmUe;eC9Xs<~MFMb^dGMl29dYB{G^%t{lxOuRq>4ZavT6o|(pQQn?Z>%uzouLx_#; zX(g_5%|xqtAa6Q0N58hSzk{#VND)`-UZH67-l5U_5jzb%^5q!mrC zZh#-NWpENMcYPD!dk%WZyL>(u`a#cyxN=O5^CyF}F*|a*U58uJzV?NeVa@&0rT43J zrj!1pM}kGGjO*cM`y7c$j>xU?LFJMe8r%8QKV>23A>TnpG{>9V=0@Xtm}J7lium%H zV_TpR*#MvblKSW>KD*Ar(7nMtlEFYJ9vvFU@n*ZTEs0S^B~jfZ+k$^|!yx9V3^PUm z7cl-1Hr(Xp_xNd??V@pbwNBg$v@+?GF@8^Jm~A#wOrq2pvgw!y<+uhU*O|^>+o|=h&4z3foztM%W4d3T+m__dBMIDbe8hqZMz(h zAH43%(M+^gKewUhwX}7JcE&Dkm1Vx|_=z#W%sGiG zccY}1-2~Ta-?ww0EERGC_r@M3%;P5uwl#--HHjfI+=v( zr4n=__NypC`A?m;V8H7m7tr!5%Tl8I{>`L81f7B3kE(kFE3Zz3N%z6WyQLILnI$Cl zRt2G?yK!>G3mOwUb`iSlwYUXSzGW;5;91A>-y zc6?ieYvH2E^fwN9486A0$AaO5Zp2-xReVq{5Y;m#_!QbYG0L<4+50EQ7@X=$T%`&2 z49df!WYr8}=?)btv5K}A%oW+O9n9yQ=Hk6BL^E|2ue7iO`KoWMP&*eGwrM=7#vhk(Wg@O~Rj@7Z5}DE%LD3`%NE7g^rixY5Ntvo#S~1p}DvB z+1M`f-Y`d)7qT`H$MiGZP+RijEpSrc45cKBUT0oVm?X~`-^a|gmtML+9{wgTj&~I) z;;UZa7vfT?t(U#YpJg+SP-J&R=nbAHI>&8LkDTC2mvro&u7}r7fnua^I-N`zJkKKX zcVgv6*Ti=6yK|UF?z||Ry-#7x5+k5u{&cI$Ol|3!BGv~t1Djl{ErEySbWzYsfCB&Z z6oORk>%sF(-ZMR{zS)<7xcn_ZL6+Z>XkV(uKjzO_tWj!`pjX157XD^(3@WU{_yBKVOPle^&6{;q^`dGx?K&H0!Gd4XV(L| zjwlwCRS-4^F|>=$Jh5N8qEpu6r+4%E$3hA1wUcK61vht-&PiIzz3R$|! zjuSX!EuHz_VoYWU$VfXE2DxpG4wcjVKIm4IZs7i+CPvswV`yZAez{HS`N|KKKs0Dqh4@Aw)n2E|CX|`CX>_ zlT+!_e^Dfbt z;Sb{;%FfpV#;K?hixCkEJS^%9Am z6Q!G7`g+>OC4=msUE1+t;i{R^ZPXwZ8x&ApeyEZCV5*x|=%F}^C?f`FIdeutS~~-| zS>W|75sVEh{=-r_sJ^pNUNA@8d;REszBt&@;Qgc3{kN*ns7%T7s%>7q`8*<@FcDN= zMo^SLCbljmpz?s;W_^L7BQM@Z{dbJ=b`rHGC&8%;Cx!e6KAp=LTx}a|cT}la9DGIm zd)ahJu%q|uS5&zC7C+63pO73lL<%i**0;; zI&^4$IB4l@Tl(+xnrq}WEc?j^M|#w=b}ijl?=!<$;WZb1F0k35m+gXb_`Hhh-rTI6;xw!L`2;D^_O3~d zDV7ypsxSNwt9X}Vo&D&FNv^zD0Y{csS24|9;M|>z`A<-o8~XhGoJMKdVu(g0e7A%m zM=R`fn0fN`_}*1$AFf?)R%SPmIF98itU|B{cMY2vnQK=M2q=J;nJ?dD41_40rgexu z7e|af>nBAU>Hn2uQ8S;H@fPde1(~Iltmi+cIQU%}PoPMGz%zM#CVYwj?tT&x&j=vYDY%M9@fT1~07Z0-%8x(nDJMWps z?XwY?!&MN}j4f#TCHDj;*Ul$=Oy0#VS!8G7?qCv>u`%DSen~CfmhWy^!W!5_h=z zs5s&95ya2TIZaB=>*STJ=2YNpmCA2_=~~8NimU*W=DO*j`|0Cj48ZhTA&1E& z%>^Aur1rBjf;o-%v|5zG8t~YzTa0S`-*_dHDp&-*HTXP-9Q;j&?eN__7Q5X}oZT@h z)lDnQj&W{Qq{V*X$n)^$32Za7L>KS!umXkiwu$p_x90s=W`u+P+!px=ybC5;Vscl_ z{E(md@zaAlKQC|G?ho5H2#!{D#P!}S=|njizFX;v7jR_U6o_ zsf8G=NN?{pa{;VZ^^%}%X_3ZF)pYpf3VTz#5ilZ9N@)T=pv zsSXJ-<|livTz8^UoeNHy-6g{&tlKMl9ukyNS&{7VylAkFT^XT}mKiYOo_dj6`uY0X z6gJ)?Td^Q2QUGDQhm@$F7AZFH$kZ>m-^&($D&n` zMo*1YUPbKLTaW-Huq6igQxUNvKhn9RYp4LJL5A2&fz|in)YofFs5b@xqUQQd6PM!i z2c4&=2Z|q^!%}T26TtED=?eru57wXY$2sx}#|EX?eYfqpRtue3y3D&-O{27r&lUFg zwA~o08BOO70G&=E`(B47ce+yn4RmadG!I7HiDXGSRtwNrrQ+&5jk}FY(*~Mh zRke9UWp^tXPXNE;1l$^;Y^vxVk7>ORTXoWcRSK-FKcP1p17Bi~dME%Zs@O1;E(Y}e zgK;;|VsLAV0ux>XRB&{Zfcw_PM1|?wK>{v>fU@Y~^G#11i)0B)xvT_Q0Q!8E!LJ#K z9#%u;?Nnbq29rIzan+9Zd+Ciyejz8_Jm#9+KL*ogdj*o!{YEqK83roTgoZnRFw8M1 z&%c=7Kk!4jdXY4_!;_LzP^|}zt!N&L*q?lyyJ$|o`BsF_);};cPIw;{t@#8mn*77x z>`t{7h|4G2XT4+k&<=vx;!fN^r~;%hMM zf+T{mhY1u<%X|NzoJzuZXwIeUpT22cOMn|{w9hsNA zCY}~m%@1s!!0d(?F(WEQbOwlWxH33i)Y8Sj{KCgCE}+rc`rz^2!?$%uc&wVF(5FKQ z;MwbOeo9jct89+~#nGT@`Su=d$3;*31NaMFQ14Np%L0^|AAY^#2lyjSxG{dPKGH+6 zbnWm%-h**SnTbM(`7V3A%q3y>aK7I4fdwmZJKYFa&4|jx+%u=l4(qA|V3Ics%)lpp z0#fnh%kXx0I(eihwQf(l_8sA4s+e$z+hjys`_pwLphJ%cRvbaKL%7AJS1Vfk z)q=(JE`xRcVn9RNPfc+1WrovQ`1gU;{H_gTC`*ZX= z0G(?#8M0D|AJiKMfwDu9Jr=AUoY;)l3RA`%6@2%H%H598Y{pbBlNL8f{DjrdhuF|Z zhricb!E8d!qfEg=^OWD?4Qr~`XkUG$;P!Nvl%uukYV28X_=;Gd5`H(WMi?p`*T@ZI*CVnM49$s?Ge(49}!1UY=PMlkRdGx2SD&x8!c zY*y|0Y#E)X#kh`3208e1>CI`5I$jTWW3JD(VSSdwPfasX^6g*J-g%^P{WEJ^j4t*G{X zwAfZs#t0^>u{37%(goNtI+S%cnH~8&b6rrb3;S9oGgQ~y_d4>k0*#kwStd^^ zKe~K!9Of$v(g+;a$ScJ>3%lg6;*&jY!LsD=H#ML5F`va57Gd0K8I2goAI}Y9!BDp* zpSgwo>9J@5S~s$~h1CpU9! zl)uEp1Dr{s?MX>XdWd}qbF(S=ET)mZaV?b{XtiBs#EQB7Q^F=d(nE@&U&n{|-RaMV zjdaxqecqR58JBzpn7fVajyq-gB5#lq55QSo&p|R!Atv}c5|zW>W4dCkXceem0jEI~ zG#uRMvd4z0;*?4snAkrxNRE?6T%)%Qoh$7TpYUCU+I0yF=z^$)>mTNH}!d!~+eE0`3(h!RI(Zs(99| zVJxMRQW3%B&E1n2S~{xRWjH~Z9n%ZYgb8 zpZ-m=+w|y8i}rP)`wA@f4-x@DkD??WMOB-#qqCShOAPhr#b-f_$}Wq_TAJPdkq%d##UM9~j{N%n zq8(dD7>P8K`$sZTykYm(dhM0o{`+4iWvwlL_LlePt-~0uiJzs!Z-bGK;G+hO_907m z#_t_3I%DXYc34-AdvG0}K$1tD;{)`#4;I*g{gzUz&1{5BF4xK9ym)N%cqW655(o9s zJ;%UJQCFmXI%?aF!CfBu#BrgM1lPHQM}myCBBIPUDlWA0F{riE%NDWg&}sE_mZvNnVTnuw!SrNyX-=z%+hHqVN5C zHcp%+-KTcBix|twVSdKCvcCz4WRWf=gdJnV{`eHVnY?gZ}gEVy- z#@Xl}4mER~K)Gfa8d(u30gomU0Qd-!P+ZV3RO;*n|9!MI^H9m$bi0ZMT(@Hck zeu`fjp{0!E>Z$(Z4o2?GNJzy7O|=|8b`)Ix$y!Myo2_MESUX&Q-fWy3a9_Hx`+Ts} zQE@VaD;&9ZSb0t)?;<+t_P9giD~;?SBjgtW(0@$v3eNT!bw--%bDTjq*h?h4`$9I6 zWv^DnJN>$w?#}yKBHD-!_It607g6wJNI8*S8ds6dGrV?xu(Rc{F;McMrIApcXX zy`Orjg}RE&vFNy(;|GlYbovg7B@^N9YRPL5IxRTY$5eTUhirA=|JWEhckCvSnVM(0 z&ynd*{__0$_lwVHx*|mAWZIZ6%s_xp_JKw*|5F{`=!?rvUGp0zQl_X@xFWt9^!QEC zA5!p9&7!c}YxK66U-pa$ty@Z=zasAI)ML2zou*}-pMJ~F>d|FN%ReFxZxELHF*wj_ zg_iFq_Fi?3$_H{zlHd(syg4BOP|`2aI&NVeQScvmDP`S;PzCc$5sgHMl3{AVp1%w^ z(ub_(x#DGssXPj;PKYP_0^jZT236$Eefh7zz+q6PPss!O+OVOQ-a7KSv-qh;ONM?Q&w1P;UU>v-^cTDtZ@t z_L*aKgcv_d#w?3m&+;bH&mrM~->*`IzD`tK#p$qFvfIGQuT)L6=U)~~NYvF{)%eU< z#?f<93=GHoNpDV;tbsL?@BA843p!%pQu>wJGk%iC;_7G`%1|_g_bLW@U|_X&gGv&{ zl8yJpy2I}ocpW>-{JyQAmS%?bGSV^L?}iS<3o{& zW`u-^Q7d|@nx3soz52yxbzZ;6eABsw&6*%7U8FzbG5r=mCEloGAn4;-waXTmRzz%N zkUrQh1KW(!$vJ48oLM;t&06=m2_Lcld0o*cKy)Q^k>(qDPC-Dhb2!1($W)(v=pjQt zVb#e)jBdAi$@3?8Nqc^V-Vf}0E6J@IT$1Z_Q7kIAyt7~SzP-LV2p4C)gK9PqsOJ>3 ze}Nl)b!QUPDcL{yPIcg%Cf-`a$F)xT2((6to0i2y-PY{&HVk$D=_haauc4HGAO%E* z4CH8Tvcg@L2nx;>kkcYF8zzpg!0(T^PNM0sn#~^X4A&P$`!fVHh-1?H$^QQLA%R}0 z(}kq>&268+pd;L`xDikgt=12&H-~Si>Lr-Kc|0~j9V0wsARu`$fE5YRn$UqtB)Pi9 zN+Xp<#|#hoqm-0FgAgtuq=^Mo%1mmlR+GZ=ODNQ9>-wedO9sm{Jq0EO35SX9Dv#*A z!Hep#4e|3v%^hgzRmQq-r%yS=xpeQXqKMY>drzPt!ffE;^DYe*F2;Ahz2nJkagJj@ zj8}2!YLG}Mi+B8{5U1}sf<={&o+ki}lxmuEjin&QfAc#Iy*%n9?eR**2WP$qV}<^g zgi?AOz}gToN}K4P2YK}U+j&XM-HW{8-*R$kQa&5eq(q}xnDp2w`s4dHX#Zb#+6?|h;%t5hS|QRO~_3M zc&c&9R$_;ci%6hE9|EPbFnTQ6Eb9PKxJ-1TQ1j&0?Xfm#WDGga@ioT8*#u7CX);At z7XBcZ3DXzj(j2Pu~TnLYqs1bha>6WI==a-$@vyq?CO=~<3;Kh;a*FYvc zNNRHWEVDgt!JbF0!rue#O)Ct#Rqh`wd(`TkzM#!u5~4nH21~R4JH7~r*S}481Bn}$ zaVSEGu8vVkPf?~H18Xa)5GN3N_V{O;*f(+=uZ(wu#Ql1&I=akHT^JDcn-c7oOow%% z_aEQDoU6XNbS@5(`cKtGcrg3JD~)&a90nkt`={9UQo_ro?{{Dnji z&<`T}N4~M7dtc+%AC-8>ErJA@5VrksCho8DoNrK z=5!o^ahiIhgJTl-EAOATCmU;nazyPiiO~bH79MBHNcp^97z0ByohpcaQ`U3>EIuI1?y~P~=j| z_u@`g<$Y(s}zy=slkCKSQS`y&Nh_vDlOb!x5wuA}kdKaf2~b zA62G+;=%N0PtYLp096RpT(>E5bwzv&|HmD7^QGXvz+W@R`#-OiksLr1L`gPw%oTr# z34A8QFW=Vk65*2l>~HOs$+w&BL1N9Ag6tT))$+R|eYJmjjIvkdMnfqZzYbvZ7 zzsa2+=Y(w|GK74GN7?Ws@ zOvIu}!m-t_kp_4QQNmsQ<)cp><%PZoo9%kqUTqbR-&opu#en{~O=vy$joxt~4jwD|>{-PrDfL#l zF^c0NNno>2Yh_{=wkkvWICd_hL(D|mjBUgl_1O@xv7Y#BVaj!otaR=6%Uc z9J~E+vgi9%CGY*4Vn4z>9%kTYK287W@VGBNx;b=jMlkk1SW+xq{b#(=^H42-7T*o1 z!Z(PA^p^EaE2!dIt4^AfzlHrZ!aIR}n6N_|T=4)=_K&n{lBD++;u))w_j^CU8IkUM z&g(TyfY@Q>dhMembu)SrG`hfNdGdQN8~_~j-9-#B9P=ir?Rg6Z!aHiczd0R z#eIyT%SaC*>>TFsofksUQ4&P*YL6qaj0Emo+?8+NTlUUA8$Sxo&G-ZX$=NTt*gAHt z1zSkgtc4%A=1={p`DFE(=)WHHqg+nZ%l2T7ZlsFD1BLnS! z^G6S4g>N|!OB9|eZN1zi=Hc%fm#YkqhWL{mv;3!3R?fyj$Fn}{VAY7lIbRp#gS}|q zlJr$sZaLK2PLaXF{>Mez>20WA;@=~mqvEG|b(^exE1Sm66)8+t!Aw{Rd&{I=SWt5U zBgjJ2zSGym{FxES`Bq7@C-;{R_aU+u={aHG^P{-K!*l12`wQ8pEceJvOg;C%t2>k@ zTQ=|LIM}~hMP?Nlw?IH7GG)422kf=NcuoE%U50z%Y9H53W(Ne&Z;}+Xn`z2H2hSLt z@4i?`P`9vRbC0fZ@0X|O_zL>R=ftbo9?hF-3G~qR1WOz+d4rB9L!N>e$dx5QbJ)jZ z{>l3*F`Qd5VhnAd4v-ftU|hwO`G6kRboXfR>Xs0+)jv%wJ|5&LYCVMcJcJW5`l(og z)|>messf#V3`LSf8jT>ibUu$~VFldR-Fg|mpqC!m{GCOAFJ}L`uF;d6{6G{Z0bOY9 zZGW#Bnw)2e%-#UGZ9|+s33})<%PpN=-KFyA%FHNUYmz;jem~Ud7GU4$=Q^);Nt%r` zihuoxg63&#>WEZ0{b+P*tBEr``8@k=2eXWRlj*qB z_N@ORA&yV`crR|7cDEW{FR>tE1l5?CKYOV#M`$wH^cdE?_as_iWFRunevGFxf-X4p z#)EG-=pHS3Rw=aCOeuZm=`(O3xitz3n@#4r+#q73)h++BFW<6$T*GI=55|}n^spMS){fh4bF9Pw^ zI?cHzi_A7eE`qSpWK=nzFe^5M#zvsGHU1_pgfYQsc_&QC8r|p~JBiDnjz+5gMt)gJ z=<2{cZLU4O=mCK7%Z{Ztd02*I4_Zbkjdb`k_8jyRtx~F+M z9+&P=~+bWIRDL@>d#rm+vOB2Hg(TbLAcjqJJ$%>mo&7+|~c|h)J-1u$J4*}X=1Fj&GZ?~f2 zAcF3y%-1Ky92IqzajDLvf9S^pxDYIVHTwwfxjk}nFJ&rFJ2mbrWLz0)DU7Tv-?-*M z-IK!)HuuWk zIsuNU&I|7R2?=h}D{K$B4E=at?$K#UG6iOFZQ}+uVww@^Pr#p9 z(tEP+jk*M$4tnQ!%*szqHNAX(MFabv#c?+U`nS`MIx7C$`y3wl^w~6vRM=7k!4T+! z!al^;uWrJwn3B5qTzwX59B@6jmkv$!7_8f8pkr^9=VJ$S7yw8@fZL7c-=P$e)+@bn zuJ~=M$U1D4_kUd#gBmXJ{-4%r-?wC4>N4D<4L;L>>-!Dd?8bAw{cE|P8f$~Hs)!Id70%Au1P)3$z94sQ zajF6Q`QN^PfzfQq!g2xAFKXDsoRM@g-qLLc(rw za{MOSb8en&&=E|ZdN!DrtM3m^d!nc7PKNa6t$)8Mmi&pR+ zh7$hY#wEWF-0$9+`kpacnECa4 zUyI1BkS|FL3~(GY;TFSjdomf!PjpOhH6KI~UCu4Fg|@x0G!MKEd={DFxI7G%ki3L} z7-+{hBYQo=I5(dC2vB{p&Y?AK@VUL~YHIVbo&7+v&RA8Up%?34LIgxcuHXG!Hi*hW zk~&6N%L$#|rQk8`?P-1f7v00m(c)$hKbUFvI&kZla>jO{W{;=cu*B^t23@Rn1Y*9&>^ zdF~9fqc+>0&+quG9Ea5C5l?d-#QAT3dz4jp;GgY#(RioOU@io7s$I0-aRmAtmi`TW zfBScJP78X4TDJdB_IP1F@!rHZA{OJqJp5q__Oo zT5jRJ;tJpZpEbFoLmqmOYmYQT155G`SvUOn_zv=_#x%Zp!69!i(C;S*aAs$oC{1_ zv=8}5$SwEC2Su;UGfHc5hejvAIViJfdBZ)-{Ko+MPLwM2z}6y^QW^&z{fNfzK><9= zh=koPxVzw2O)f+TSr;6Jz5jOHJA@!VVjCk^<03jj0=`r{di|sBew7`X^sQ$!#~tX+ z`N&0JjxAVZDe%mG-IHYZROZ?8usSqUXoVQVCi`A&A~evSj?VW%(qIp>eCw;*+rFm^ z5HxtlL>u6Ho~sJ6V5Y+pxM(-n7-@ZHn2h}fqHhMMuc31b;@!k8M1Jg-GCx{VMV!eg zyX9H!Qak(4i|0(wnZ{5MTHIk*5J>0Fk`$>*uUxZFCNQJ$8|erD^m3y^vVh*xM;DFA z%4j>G!=BU)v-@%YD!ty-LIY14P&7p6#Y904N+!sd=eX>0^bjyUG29j7M=W(pCzPp- zT_h^fc?wF}UC45p!7@0d8S}y45b3YO*jB%-^>tCapxt=;56}h8zWxEw{O>w zz*f#bM>I8c+TuB>4~ssHa_C4>fOc$7@J4gljYdD{DB!_I7 zz_`R3nvg?vFzWPYPx4+s`}4~>aEJUhV=AoHuzNVc7fqx98Bp}jttEEELjc- z@oci<{frEOGC))aMz59DJ1eGFg4GidS6x$m47?LcM0kc&k!QJVK@4vk^WuHKZnAH9 zA(fOe_LG#X|COSMoD8RxxQwWhqQ|}c4TcizsOn^I-{I_`;`^?cY^XlnUi9gz(uFMQ z=2dh`ZX8XhU=tr z*?|fAoXqL=nIzKizvlg6|FN#4`e^Ij!?cVFG0^jAKRn0>7PcUpyU$!8S(#+TG)_Rk zczDdbA&jk>&P=RVc}DUFC0`!BtFxpP@zCCwyTpW8VWLZo(F^eFe5=d(S#nw858=lO zID-M}!yBxgS0ph)hiHjQcSJYcTjc=g7McqA%!Y*u5k_`WbD25oK4)s+QV$K3J~YT{ zq#y8WDytrBZi9%arOB_3FDW^#9O_{Jq|2^RQxCVgJ(z)68^;jqAS63va>=c~u_yjL z%yYE7C{0&)4yK}iz84|(=L~%gM{3guDXUR+nbYN)nu5Ne{$8sDSowcK5x1f(kV^yEw%Y*1OQGQ7wODyM$6wP)}cN_iR>&2B)D$- zyk{`_bB?s4QpFZtYes>wB2omnJ1$9vlf^UWMnkEk>GwA)=-~KNwGU{?^R~h>X`p|U zq2p9j&z0uiz+wG;z53hZ|9U@m8qr6Td6T6p!5d4Vz(+Q#M-|6RWL{Nl&==7VD?c{I zuD7{(;YxfSLM~AuZJJD{Z(vF6IQx8qQ1aY_B{e0-z5qU!lM*lyY!$YkIy}0KLs3#jis67hg`K@IK z-bu`v?yK~52du(gfsv3iLA!zNccLAN6U@e-Z%IA@5lY# zD8NG=4wDqomUnB6Nc7D^(Y@X2+DC7S&x!9z(W+;3wB%6(2E7;v&$=Ef9*EEnVLOyb z@`7ZBEaN=-Bi0>kgmKH@D1p3{WDs*ha|mYWKEqLnIH=QyZ+jj7kq<)YO*22RF+fdt z-$8rv8%Lx#ZA`D1aKC=|MuA4;6*#@HmCXTkY*<%v26b$)znA!g^OnaJ0!G<90G?0{hmR;@o{|XpIVM?YbuH2QE8>$#cbg}CE0&CPC=lL@+WzhMV* zAL8=mvqTR02cFLfyK}$5BSM9Omq4s$jC0c2A^?(ZEj(Wr>V#KHKjWgJn&U2efwvwh z)I6PCU}zmtTz;8Gc4od7PLaHZKx8<~y``e`2JOcsQ?e!jIakNoo;&BQa_q0@XPk;1 z(Sfg@ZrzpqDZWbeW1wYrcy_n@{?-h}DPbFK**TOBr>A-kdpP8I}Wzjp8&fP9^t14Y}e`3@qU&)$`7 z4fwT$COp z25@xJYD0*W)6$phD|9T>=8SgTrvt{i1%qN*P&L_eGR8W{^39UR$$3>kzU&1y9omnF z_Zodt7+=w;zMj8Bshl?MRjN't6=(G-PJAYoLW zxNv({Y-Ab%Oso1^Dmp(;B8L1<&i5)@WN+X}*f9S7WeW;^E6$YSzpe8rC3c;M0g4oS zUvmC1dvgdpi{iYi^0w5ILH!63rMp5wKK2Cq&d6A@2m(S5 zN{;1|=9DrF0prj^W)!dp5{RTBJx9Lk;ayXQ6G#8OsM6N(7#TXWeX5auaJaksW%qFR z%Td|o+gF7cnI3C9TLE#}rMwq&n}eSBpL_s$lMm0=x}F?L=v58}Qm~iJ6?}wX8u=wk zWhE=CPl-=uz%7_ylMxt#C$+wuXY1uM2(Fh*=3j`%7-yIRouCWFYmHeEXX>J^kXch~|z%)vd*$Qm6AA9BTZd zNwn+%M&d9|y~Q|CYZkKIL!7oV+lTzQ=mSg&3HpwBjT@hYNugOJ9cY03Bm7OC>gd?* z1=VQB#t=&1jf-ZSeUk+;SwzheX}Dz{PdHJ-mZr|=*=UN88hKnuy71JBUqqxxH8s|2 z@TWrISm4hog1QEsS>-nFpwQ^{uV=SN1!^-S1KC7upnZB#Z5^$JQM#Zi}>)B zT86&Mv0Yh);UK6I41u;X2RiHj{I+Y5=o3w2^@fT;t&DFMWp`Xn>Bh{A8fT8c?Y^Nk z)g0-43_J3>5_I@JOiEEA+$4!vELabI!y2>SY|nDXzj3)IbkL&a&Z6^}QWa~gUtyIZ zG^7*Q%KB|rI@1BRsSnsXVD&T%%xV-RFPkpm+*1S6Epkf)e@MObPGYO22gs}EVNIu9 z!CI>{E#_8S(uqk4)pCeH{zN$&tN|HZxErKMJJT%GajFb(p{&Wv7? zm%*vByjJ$4fM~qz_iMGotjE7o)R{nMzKKbir60_GXDX-in+pBrlEA;?unSGj9gF-_ z=AhU-5rr0hQI@@JA8CF2`9s-!V?C6SYj^^hdk+;tIw=Pg8pO}niLf?8*`|p^N_ib< zXS<^kA$oRjR@gJu*#58bIA78ziA*Hckfr)fw=z$Z0l+rfRN?O6CL#mxm!BIx12c2G z#P-KLKbse{J1)zhgCUjfrW^Gvp`KPCtR!8fjdPJ?m&2Y2U!zU}!cuA6=u181$XfQ_dG;{Gh8*U zr*Gk1q1TA4;;!A0v1>(>&FV%m6rI+o@6W*TiQsINW~GfT+v_A{#x8{y?X7S3HhzU4 z2(FuQ{b6HUpbP$53LVNA-MYH_>|$RMv|0jNj66I*W*B<)Bw5X7gjgc(=r8w z5w@ilzIYMApzLKkHE`S?rP7sHBOGQo6jc-2+8c<#a$V{@3YKsxgg%45afQU`Z`FbA zt!v|=lRH&l($=gD(^Jcg*fuLEUah8*7;gMlZ=A7JD8puD#-o>TOt?gpvWVinfa*Ob+?> zNPMys(?S_1&KRskFXdJnWzF|2Svr z_39+9Am6?02G}&1U|;w4q!Ccij~A@vJ|0EP!{)Yv8__8yKR)UKa7w1&)gQGc7pE^# z*~FhML(9VDax!H1yBFzFTNQk2> zVObA;JS&Cui^eEM5IUo3*6fpdt+$hM28I^4X(0tXuG__uKGbQKvHo#VICRuV;0Ch6 z0+D;h=RBjPtGf9C)|6{p)J2bKlTaMYL+BP-^{OvTNo&?f4GP%Zr?eZqUcmh9zv$n@ zO!s>(&6S`l33h3Im^hAZ5M#|vK`y1d*?v3fw_86IF@U$86|rZ<1giLn5W1ynW@&~& z09366p7xcpqXaW|fsgU-&x&=`84{&bs2E|dDo2zNGN#Q+G7~n>D&@(~LZhqqg_q?M zliUuYbW+LILuBbvYqo8NCOP?>Va`Z0g1XPIBwE+(N8+=V3B1G^c}hA-lIiq+2gRcqsMV!WVZ4$aJ04jG21X3J`0{1#8`F9 z==EFC_X4`txDZlw4zxe_7}m;%v?T&SJ+z7ezKrtdaAo0XKbvoz*TVHf% zndvEcYNtn1twB0>pD*8KZMruxV%TUWvBmWE)0;TRQW5l+mh=Z)uHQQ_K%yXc-%XsH z-Xrk=@=>~*M#76O$CLT*BwzYtWG0MJw!34Ifo>Hs#M$zKLKHSxARklB?CAu3YYdJy zj4!|bSpBYjH+|JgKUT{L zEcy_A+c`~u1uQ(VYXYuqdzZ(cNs$Jc%Hd^h;EAL>&49G&7Q598I!5<=-9gXgkUZfj zCGdX2nQ92I+hAXU4G@Pdm0Z#pdXqNK#qRyy4Ze4ZwAjyWj%T!LXpTQrvXG#(c##9} z$f_MuXFgdt9Zk?|m{*Jy{w*o6rHY6iagJjrrbD&8v&w&I12g1U*^b_N+kxwI$efvM z?0e^b)S>$8O`^%Nvs#gYbfQ#O2$+iDB@;Yx1yRkz$2~wD7Q-yvu2+xOlBsMk^vSRsVNJ=j|OG%&fS{IfVKWtmmv4 zHb8Q+bkDBfYFCGOI zj?kh{T4d_@`%sUF)-4OTIuVA8htH9>ktS#LqHfQV-CRTJa4I#3!jFURfpsFtc@xQu zC24Tdzp1N5l?2x0H|3O+SW_>hpV)*O&XSyrL;&9r5J@>p%1e_Tl$|3_`OwDliL4{a zsK#BYUAsymv_#zdssA=UfJBd;1V}6ae#fy(SHX;k3zYoduEa0-DBTQP=N-)dM0c2Fw!yZdP+qRGeBX$sFjX*rROISVzO zmG{D?lIEu&_Hn)Etz&31O_pEAsdRqnmGY?|@QWzzKg_7C!={HE`7Q{?D0uz>B!8Qqty z@)1w4kiA(x1G7F>s(FI)7#A)8fejE$~bLD@x`<_r3$1Qp*S78CG7nE z{rEp^yU&w9$ng31pL+)^)ztoYl}F^80xtmFdoE#Bi-zPPo8!S3n!i>O0f!-1xug8? zMDfYE_DY_%SXgV?PfB0E2z?@$)J*EXl z=mIg<`uS%jdF2q`B88QNQVlgqA-8_)Kk}Y5^;lhe5eD5{t5f+dY@g^fzT*=?U9*=$ zWoa|(*@e;Z9$^8p!KLDUveM&x3tZ`oW)4Pq}1 zS@9BEj(CkoBs$eMW_lO-vxWO<>NL1i94- zk`CWlke9_&Zlz(M+sve2)KvC?s#8o5ei|&;Q0k``dH#ZAl255 zx77Ayx%U%C%c*7KpxdodhW8dx(mBkjX7eLpNIasc>(+CJ zxht^lm0GNv{>7{0NdYMw<>SDB_mZ5-S5=D|-ADH~qz|HvYh&{pE!m$9jb1tKaC>h) zb)NL`@}9K668Ar{!74YKI2n`#JqD236~&?j`1UjYWqF={zs)kUf61{OU3gOGynq&& zaa>)ge6hoh8e+4EG;UlJpvKFY^R&C&;VoPBojH@O-w1fPrBtB#z)itkB}-FEbv(ph zU(B2JiD1k_n|`QzdC5w?9xUn@z*jVE^eItK{6a&)nK-Y*rHtO0)QrfHa7x}LF|bHb zRl?Ve@i6r%^`^Elvofy1v()F1`8gmO5Sar^i?@N%q=qN$!MeHqy{kh9&~6LU1vCRj zw9R2kF1hg>2!YHLyCT5C9Qf5~QVtKGL8A!3KXS#-mBCA)f+^QkpcbD3v62q!nv*HY z+a0c=B`5sJdG&{Z5Hn|YuxyK7d0@t7{={<=AM`xa7PQAFpj-_j3+;T8I7kyKDYC6X?pQ6mb{ zBtYi&Nmq&p1pGT#a<|cX>#S!z9I`|YfusKE<-G$fi!YU~9ot?V+6nD+W-&lCbAn}) znHuYrAM9PXiVNQ|OO3Eue01`8APC8dC|++)@ef_}5YrYC{^DmUvU0km5Qz%7z43Rc z5=b;ozT^hAz1t`d9Z7n>63t9?-}rMyNAW_AWkU4Wi2 zzNsTpVnVzU^}&jvIr|7Dg3|VLqM=ZK-lcJl{u=ZV?rY)GXjnucKjS0^ve5{ zD+B)Zuwecao-AZaTYzCVVyJGt+%Pu=q;x!Z(--6#Xa0D&C-HHWR42ZWtlgl3wf!vo z-PyM?M~N7k#z&`)oqrTkMK)MrLmeu9@*&I+wb*zCM5b6X@niT%e2Af1tP-_r4Q!ltZP|Q8)uC!_BRr0UlKd-ms>ab1JE^)dVEji7%KU2uJ(Q`pjGBC4DkdV z0*{{~&&G=)oeO#Kj$4in-(u&0_U^#rWZSXWYN(1$T7>3XJ=pdjUYcT^^Yg@D5$OT9 z@K{nd16>iKrnl@(4&yZMv+rA*6yE9*@Q%`{WmAUsR#7Rh zIN%7rB~3KRxB5i7{_|!;4k7ZFm8nR9o!PYNO$+e?~7*~2Mo3)PnYPA_2$J2 zQ25b5>YGS?Z=28En13R@j2NXhH3Iy`V=2TI3LYAktpAoOR`u4YXb*L^fd8v+#JCkf zF7ja1z(GJN_KL--9G=uLo8VRSa^;#J-i%?OH*uh0LID}09}E8t z$cr1ndaNrK20fZQ=klc!jH; zz~!iOm}i*LzwxJmxLEMQK`-li>CmReURCK@;$SRzq@anhhJ7GEzTqbDQ940u&^zvn zaj;#GVIcZiQUVw6U)0Bgg}KypP2}@@A_pe*FhmbR@(HtL!L^qZVw)wJ)8iGvUwNv3 zx?^O0s@D{_#-BeDEtNAlPyZ(3Ht<3Thtami13pPFPE2l&45n78TZ4aUwtY__Ez zyGj+kQ1^r?&=bk!Ce_K^f@S7_bwSlTj2KoFV!@BV+yMY0eQNs zlRyzBRsBZy0pvD^RqHO8p00r)saDBw(UCTg1-7R|NH88r@wi~|-I`snQ48BhBukvr zWQk5hO~Ik2L*h#BIK@rwA}h?If1UvrlYOBC z23+08Dm$khW@;WykFWb+OF7@eb(qT^vE&uimt~)EvLszNd zlg@Q*FjHm0`$PeXR4a)fzCl(E4B1EVY2+j+PlEmBBBY)YtyDqZl%zp4BLiJ~Q62bm z_CZphP9Gj$Ow252R|f5D^zh1xN6QeuQ)Z@RhN2&iY1-5r`rs=q)`c`4qK#Z<4W4l$`qndK&z`fpGbwm^K zV;S^iJAvL_4tA0*cX*8J_{uzbXt17LX=A!J2H(qtvHZJ0o*mMC37*o_rgQ9)S-lk(bDWfqmvkt7dM0|#UTyZ_} zKe|Fo)HneW;Y%%ym+cs`!cQ^O*tr5c4Y*>MB9OnZm@V8)=k@5bfYh{*)cQi1&Q{;v zT>WTqOOqHVN#Koh=`b@JV>1lIb-eLkMoaat$GG(wxCx0*OtZTy#-0n{b#VqgEB*mS zt=htv*@5Hf&Wa}%(yY44*k#jP-?t=0wc+5zPtl|mR*x|5+!$e&)nbIod_>!?>l|9& zGNY$@5oXg%n$D0dXFN~GC9)mNQC{Ot`#G9@dq@DUloykE1z1F9hxz2Pm zT_++l_sb}_hu*)u_i5b4(YZsoIeg#$l`67tdAsRocYN|Vpj&d}+?=*VRbsf5{ z_n0EE?xp`Z>EZ33`YhcI1~5?Apu6zE$Wm;LB`o1-B3hA{*gHmNevuFHSxT_xy;m7b`w6kwCd3RWHqa%m<+ z=q3ta!%lNUJ~2ZD@}0nSg(IXE$Sjb{8npMlG_<0j4!;-}4{-$x7OJ(Fn{Buc%tlHL zIZ1~Ur#%&X%S0c_kkbs`Z|V|x{v!E<45!rm&mpGZF!`x6kwE z+PLbbnH1;8Gp0ahNYB^n1I3^>g(?mvQMP?BYA45V1!r3#TybX-0b!grS^FtsL#0^X z2>GE{_nQTawyW#A8a3#ID5t%)dMoNMjYDQHk7k!;C(2f(g?!`P4@a_n?b`8!W)ZSh z2T2&qv+3)x;>r)Y!{_hLlk#Rvf7h58)TZMV9)xd@E`TNO%iJ!hRxWb%V`92?E~}Lv z4)_kn{0*X~>Q{% zc8&hSH7!V`Az?U-nzqu-6N)kSoQPaq3UwyHrWW&AuT%sf-69jKW*)w}iNqK^{dks` z82qXC3gHuvlO1N4r^>;%raSzMxy-x_@goz;s3kpmQ5T+@eHjie9fU06OvXIMO90Q{ z*|XWP&n4wz%OFsxYVr|xkjnH4!a`r^cpp)&5WE!*PUD)V0gU3P1Trtb`tc~-PH$$RoxUkdYodME@c z-&sJz0CaEw<|MK_@t~11YTAnD`$)A>_$C9!5zP``}chQ zuXqcGbDgu#THn3)_dnEWK`#C3koxV2j__dge{V@Es|OF?EHrC;v~eyGRxBcy18qd8 zL|C}x`EIJWE!ULK6bcODvZbhk{=T55yg$~`7QfngsrYCY^Y}iw=sJQ6%kB%{<z>^);3p&IMiC+CgiXK&+YpEQH0JG&B4r~busS$+;Q8Mc?bSh{t)EtJj#{kL z@B?Gpjf@yM_S&UPs4&mpC7P-W@@Y{bA2aynq8~t#x;UeA@ZWXLl&hiMb+%OeGVN^u zOAsPtf%;blhX7N>Op1OnP^F;&RXvG8Q~J}(58*|^F~bonlC~T1C!Yz}-H#Hf^fH}^ zUd(7C)E*llo8o#Y=D<(;N0Sy4%zC`9ieiQR2N}lihE{~uiaQ|~qB?}HYZ9=8?`CcGP0Fsi<*gg^gMvgoG% zAMfq}&+|8p5x+@){~MiRItXeK2kNny*GNcD66alKzOz(E#Xhs|mBQMNcy(~pfa3Qb zt|_2q)^xlj)J`{pL-9ruXuR-TdQ`S6fJ?dG9+ipL*=;HqXa^{VBA#aCNQkB(T(OGm z%W{+j>xkdzl2MGr!*;48mUM}SZfL3OM2r4q8PZW{GFG4R%&`!1R(nml7M)l@lq@xI zT%I~&6j0D?fq<8X0O6s1Xu@Frlv0CzYwyXP{44icd-%djr*|6G3&}G)B^0m=`AAArbNGA5D2bA%r4&3QY- zsGsmR{XgGVpPB+FQJncaK^bX+PPU3L62g5agdKug8BKbjoCD!=I7Qz1J_HTN^j4q0 zndYqMmJRm?*>>AEK#ciCC7m1#L6u!Mt;mqNyk>;o9IDtJU03`$=_ybe}B)wH=a9j!WHjA$ool?36-0=(}h$hpV?5e z6W)Y8Wh0!vKmMaj{=v{r&s4+Gj|2TeE|(lkrEZtmD4#V(Z-w`192{^ayTu4MBS%td zO$r(RnI7E`?Oc2wl*tP`hdYSSz31Vp+X|?Jlv|f-(Y&sAx!wIxJ-*Fj4Lz@kk>VB3 zr5R`yWe69%ioia_J2zXtCW?Ra`_>Uu47y_8bU5|~Q zT{6h+TP^<_Xx3sNM}BHfY+Mg51+^s6FX52(ro;s_sTwcU4(2x6C|}_H5f3C{gCBvO z%gA3S`0eXeEkv2n&=7;e~DDE4Z zc-nqh#KGjlrdwG!&`=!6Wt#BQiS!F$l#trxL=E)L9ntakL`lM&aeu1LGVI^n(qn;D zrG*Ju>LM2LvIPxw;wRL@gOYS9{pKCxEZRStmg;qW4U_QJxV-sAe2smbf@#ZvK4m!; zk>@UbXB{EJkm^m6&_#D9#~kzXTJ{H^7zV!QI5?1E5fg@@yp@ug_B1W?q9Yc6kPpqc zO$pozwJoHW4}xsqT_VyG{F?NPT&#oc8$nOV2T+qvg?z>fA+~YKusAi;Kf;$PNItg7 zXNm@tHt}rTXr&}S^!8-ni^t(gP>I1x<-a2AAUZpT%dUsIgyc0_z0S4+S*{497?hR( zYHV5^-feGyN2PhdS~%vmQ&_OE4_IBN`Na@H#x&8R;NI302TkaO6r29NA6zh@cjKQ5 zF&_e&v>^`oi`Ab~i>|Z*Fso|lYWuWDh2@aq`((oVOG+TQ{Wl_bwU+Cp^QM{29naFS zPZNQ;I7GNVt8ac;Gwb6eSl1HK!MUhi780u}tw$gPA+!5qTx?%X57 z_-sab0;g~WeEZr6f*jU zf8?*GDazF^U<=;lV9J~W<yc?E$Y)v#MKMU6+~P5wQim@9x~KX`?al7!_Ahrpv&1 zOm0KlhlmYlm_z{GgMXeYQQ`phIGMBp))ajP1`f`|sEiP0+Pk3N#FXLRvs5|YU4ZyE zb2;}fdt2m%w>pOFvHcMWJ_VI1d`#By3K3m5zNuo5#py;3BYWu?{+$u74ogQl-$oSk z$XD#`0c*YcB0WRPDz}!>qt#QmsImmmqe`5L>?1)?l zJToAse1!w>MyI{7@OjrZ} zY6ZY@-%4JMwDyS^2q5QFqSGDB*F1TDI9Xpj0>U>A?torXL0ZBH{=zRvhkLd64~&*V zq0^4k?DAS`HSO!HE#5Beq4XQ9GRO{bAs3ed9g z)oD=Ta(w|q^rwZ8;h+iH?={4VNn@Y6!x808Uo;@$v#ZHFk7xa{qwaX`M=lFWrw88O z&59dzR=(2_wTCx|3y`X*?ZhQZQI_kN#jHGi&!8RV&|A{!w`^>?#%!i<> z%ZEknabrR+v@8j)j7swzK#0X_MNqL=8Qsq?n58l-Etm>N&c;;4fPKdsp^&~g3uZ#U zGwS>q`P8lLzXBgd;e01yW+whVZZ@I2)TCb$=f_BvjUTCjzEXvw{RN}W?{7rG77oAf zc&#&j?4e=vTBgpBGJQ+Q4q=j;h%;>~Q-AqS432iUv~Qq~19!HC2}-R?`Ax`yaCzh`f*M+Hvq>Vn`13>e zkhw3#=pT^C6SBV6D`7d zI0^x&IAh_4&3x^-FwL-BSL9C-qVJFUQQXz#JzoNJm&vl`!tZOU=)5ZXEsNk`x#49A zJ+`sGrqr>(FEkSD9)1HvAdSz zs(76`W>NiQ7lbV4r=$(V-_tUdg@w-UJ1vq}ByM{%B}53DQsvBSW?3BxDgi3Vs|S8^ zkpD2#n64|!(T`ixmv0k#qn5pf!@bAHi2lYpO-Fjo_#0XtYeIl}Bq1oHv`KkY=11^y(|y z0LrsxRA&SiC>RcpOHx5Wu1oM%4L?{JNNGbf8INJ*L^>zrN&RGC{a+tJ0Twd&R8PDr z+-Y{vYGF$Qdlt=;O`mZO#4n6?bI})6ujbJ}`A{00Y{rO!_x?Vvo=!p^Rc&AO6`;7d zHFEyak8HHIOzq@c+EHFjGQ67lP&@M6K@Rd)@stnHIp9Fc95)^9WTpUQkW<7K*+I(W z-w+Rs%)44q1);4U2rr=Lu@R3nn@{p1ye2YPdfw7xs!z%^Qhqv@m&$4y@v|arBU+kT zK7XdNauG-gOKGS3{Z$n4HwgV9-L}&m0oL z?zi14AjH~3<&KwprURb1kkY{YFI*#PRslzBxe|EvB4U-yUWrGP;jP}E&MeC81Kk}^ zRR&87sk4b$#Ut`3(pO^zR;S&OS)LCY$=|rw6|3e-p^Xx)VqAW2xuvtYnGqL^aeZ|8 z^lN!CNl$kFEmc$L`~=ZiwU3ToQp5AOFaDiDk22FsK9p$S&lnk~6zUE2%`r%6e0AnK zIZ;P_*XD&X)N*QrK#_EW=u$D*4{Z=WLk=}8T3l=z7CAYJPcM01{?g{#;5aTVLo#y@ zokXZiNHPAoxlqtih&s+nwax;Oub`2P#v8f^!o~mU&}<@NFieb<_s{{s&1Z`*X!&aE zzqRSikUrKbJbHV{&)f&H5?NR7MN>j}m5>miSl7{BQ9T^|CGSQLN}wXMBmrf4-_oOF zoiaF?J}vklbuT8K3to>Hx8JR^)SPGJk@^i5hW1+0}ekyqo(}5oE?Z5Yn+G zVZaMN(O)pT&-mZzd7;hzf!uEStdRilqWF3Ku`{&61;GWm zk%15jD2`E<44`Znzp=y6IRpAZp1T|G)UNro3Y8~>PX9Q_S(Mqv`e;Yx<#`n#zArwc z0DJcp(SWD&0Y5eLdeMr*yj-BLX!>ZGoM=-rzHyFaZS2H9jr61a|BiB8GA9?a#O=qU2cm@| zk@_jvMKqF4{R7o}LNuxr5|Vhr1y9RpZM6-r{d>YUJeT#$cUOK1pp-`>O8)H8gznXij#hu({ezImRq8?h+QX0q}VQ6V=V3rOv=)wnl})B=3Em) z{Ja%?O%?8Imhk1#W6Su3DGA~vGz0as$;v8*elf(mG`bboW#?4KJ|o+p1?{|4y2 ztk8Nb*=@EoQ=GcI;+Yf2a<2Rzi%%=qsV~Qcf7k#@@~64^Y`2j9GOBjTwg2FI>(e#M zt+O@PZ2D=GG}#tsh(D_bv#<6qV=J#iofOIxl`j#}>+>I4`Zv$WdOxPJ^c|$qZS6e; zqS^s8aBq28?}qNy;*w& zdbs{ur{L_5v6Pexo7^^qv;3{A$vK?~kL)1ro#&N3&qY<9@N}fZ%Hr{l(PYevis#?c z4G1iWQBvwWn6Ra-bMK5W@;L!ttIxc+WfVll}{~)%_-!`IXK(E{|o-^reYX+=dx0nRE==fDDZiv$0n%v8?(As8zz%r73 zMb)LhJdj#Z{KX-*-dhCk1a7bWqaZRIu(X@o3!!`7B)|5Ef)5irZFc86?cMwPH+C#y ziS|IB(5e~h<~ocz2qlorbqgT-2rq<&MM9f5ZPR7er@Q)Y>Oq~=K3gF_SF@y!%Vb9E zCQsh#VAVq&-!q~D3)9=7@)e~0o&r_S(l7GR^2t$U*dddo+A|#M6=vC5@)rZ^jrSut zvZ#bR9MCtObsS}u-<9gfK$mhj1!^lg@2-y4y_9fe3_hYUU*LC=Ic9-R<1c>8zb8Cl znbv;k!GE}FOX4yzLTzGpk-5B-IQBV^TchoyEs;n7M%qS!dJRpAPjp(8NNQsQeZeU& zb^vroXh$>RF)FMC&UbEbQfjJ1+*D$5B_6DwMTngVL>u-#@)i@^=u?5M?T}Lef!|t6 zY_Vs>-g0(@+h`pURJThO{*8-6S~%SmsY$?W;yN1xjq;1e-W);uhshaGEH05x2*~E4V+V)8Y+ddlAxh z<#J}WxSlR>CFVN?%-bfdBZRnbGxF^^dk(;*P|U&E)lidGoes;ByYDW``q^x4>?rCP z(VaoMdE`?zAUn09?zfIq?C9U<^VbXOUV}lxB1fs4*V|>S=4HkOzppt{J!!`a@XpI! z=v-4=b1(=+)6SAsZwrnv9fGA36e5EOk?|6>d5$w>b zw1KrZc-_WM4w2bjWjn^whS&JL0r$g&I9@F_%5eGA)3Q%y%Vt!&$}q~N$d?Dhxk>U~ zopL$l1m~XX(hlruTL@R{K_+-p-WWE@O%q3#cG=933wtWd!nPG00}TzCk1xeCMV}*o z$DH!j%G!S(|Ej}9nG9@-8VFetwMvFQp`QVBpl?;a{-AYHSLX{g3BHCwwqI> ze-GYkfM1?Vaf(WZ1h>u`rpxJ??re+fVRaa+Jx;-o9YKl}QYA>np+t8Lg z%-n$IFj&_0!8Zq({t@t4c#`@i8Mcd=NZjT=)?Ut=1N|h({rmW{2hcr{)w&X=Zk~)w zsw*<*Nv$7|RKc;vmwJ|5RJ^;+-;qqYsaLaNQ!{s=H>R(mGuc$nei6-7L^7lN*nYL2 z_>xk87bL=ZIC0OdUgpGTRWn62O?mBZ=K`JN?-RWLo^e3Hx89JuDS%~lE*h>VLLYM=-iH*0@q4IG#VHvWh!K50 z+$aV)gcV))GR323NGiID2jKGm{yCyG84C%$!l3xnZ#2{gUzEE1gW>w0e+a!rc819) zl(Nw$EVv6zUB-aMbIluOho%I=u=YF;;>?Z`RkZ>lko1&g%Flm8^xlgZyFM?zi(r4_ zdeo1_B+KVZY;xyz?WPSNM6m;61sJ;xL(`Z5imWy6jh$&q?4*Ny&ld$M1W-oZ(rrtd-P)&@F*^T zN2&&?0qV;Zhb}d5R)=_fHR!|xUF{a18StO5ZIiBx-pDmoYQF3=gwSjKQJ_0ZxI2?4}3VWAY5 zvhEaugrIy}zggXVY;hBkrWFDLm6mX6!{&d4;}&zEW=+L`D@S(;+y9RRfY(lMxp1N( zhMDxdHq|KmK;?(_^jS3t{ElgA$q?g8gFDd2%2+hhg)9gi)8kRs0BKo{?ft~LYJ42d zZxo-vaL%Ct!In^g6pCI>0HM3zoDJ=cV>Gy;lwr|?;h`TKUK>oCx#d)N@QLy1zr&8k z9==Xbqypp(Rb*sLl4yM43L99Dl9M;$h)71jtt)_sKbl8M(GkF4O#*xd z_kTjh*HU@)6k1_NlasZWs{}bE{S(F67+&#kBo@>pq77luc8 z&84NEGpVU9yMLMjVTyf^W?QuNMY@15`OEEzRP#Chq8Z$gFU$%o+`${(c&%6>_M z&ad}AlxNbMONNvD2NsB+cd4YZ?|0rp_GK$&u}I4|-Xr&arv`0%+(~+vlY_D12X4Ll zUsp;0ql~?8yHgi)Zh76&IPv+tq>bVhNm#3ltpS~NX}iig;0m6fQ{Hm& zCE11}^@G&anqFiY;t3ICN zNdUAi#iip?b?0BqhGBdbr}#R1fO%g^@1_9pK82G0Q=@yMMqad%VtDOI2Eigw7ChOP zfBid%+x1OO@W$tlIG2xNF1_vmKrP_>(Bb0;7Va&b_LGI@>s2DZjGr}rxp}!x-dW`I z;}wcXz4^W{!2 zwC1DD$XTJ47A^%FF%Bp#mR-=ILgOz`rP3G@X4VyIgyv@0NI%@wDu3Jfl%pZ-0)loD z$m9H~fRvDpm9)#-AYHw_aa<|tME$mk zKuVVljn}Ap+v|N<6!FVaD&QZGQWB?GO7&kMk{jGhBp9+K>!>Jdf-k@`Cl~P%=617R zGDRl0Dfio)i@<`VZg`=F)CKKJFdWqH&5&fgat&#`PU!F8Zqn+x9 z-=8y__iL82ExgHjq@D%Kj$EB+#EYXa?4qaFR52>l9^((^t+3ZXyksAcSg)$s*EQyM z6S-c%R2a(`{K@<7lvwkY^FAz&OI`CW!a9;2cB!ePUll$Dr8W-Qmg~k-!nnAsa^q-7Y1;N&$`TiPGk< zt{AD8y;bnghq-?by>bQ5=#Qu>RH2Knc3VB&M|%=1d`8Z^{zR(gVk8L)@&8eRo(x={ zsprbAK1n)23KeOrQEeRbGH6||_ z9KC6G;f%QHkvtd7CFOK#yZjM6+2$)CMA(J3opnu;iHzij-W74WCnt^IcbJHNVw5sQ zelz>U$}kl?;TV;0eI0xlbsIodt^-cU$*zC!!2l>Oo_X{>(;DHW1`h=`A*tS;$scWo z2)b)5Zt2I;>(;;-HeBy^9dF{Ay=~(D1pU4Y{X2B8c`;11_p7ex`%GKQEB62S^zG_G zojZ&TX_rN%PnD(d*s=e@C-6DBuqH1a+3HW;N>ASJ-r5k8!h7=P6->B~)1(;mP3l?q z;hp^$5a_{@rwai!1?U?E(6T5Cbr}%iCtxeYCHbhq6^RNn(bOhejakIraSX@yim>@n z%j*%@xpxtweZFI6V_m~uoGvHO%3pgtqoSE`ijLg96!tJkTLtVKFaqsIPJg5l6nE;aG`=PRxG;S+ZsNy=J}NH z$dn^SQHTuyxsX;Vkppl<`>9yLLjPn2Yy|t-71T0s!COGhhS1qGo8W}Dt8|2H`hT0N zqX6zOEIyLr^ey3i4dhXIR4x`=zzNep({cBJHF#H~hXmEbCGbU_7AwQ$*cb2etMx8H z=H*A;)u2c)YaB2F^3F%DrqoI0z_GpYr<^V&&8XUe1M=^o+^@79DsNYtP6}nOZXblm zwV?g1Skl=D^$V{UH25a65g$!{%FRG{1l|I_o zxFT445GVnJ324(lhk~8?&MSp!D_9M_yT)GcFGL9xyY!*u^_eT38iQZ1P}KkK3~O?; zJIw6Nx;8t~i|&6jTs=r}L?_(B5lr~)gnuNtD53Th&108qI$)c9{Wlz`00N~^ z_8WXZBt>>Wgas%fD!FiwJk^XJ;T1KQk9dy`xTcoT5?v@F3Y&d#$QtYM$8Ti3mtJg9 zB%L$>MRTO~a(J;#)?<|t!tnJozO!IDg0~J_6LHs0s3Zn`G_S0JgPt1f??wIGlcMF} zni?9yyXAaTqg;%|WPcDO)w~8~Nzq4taA;2^>kZV}x>F*7GdyAod^VuO z=K{WW23@BS3|umN@gg`JXu;5xx5Hcb*JHG1CY2~aLrL9DYzBIggm2EKs&-}j@$dNM2%R+0_L_Hy$UB@%+pfkfEd&1Kl+46<1|^&)9?6FO#)9J_IBXBXD$W>4=T%I+e&nNK;-Gkb$4jnm$;z#~x!FMF(K=dr-arx|9g1(W)A47j}RZk$p~gz ziPD5z9zW!BQDq;Az$T(!R?SQmIaHzPDfGn%zGwht@dci;GDI<8)+k+5dlfD(WaoEQM@`UFGYZq==J zhFDd~wFL)=|C;ZQ=LSDlD=Moj08Xpz3X9-_Pv2_x9I@~;94E#2>CHl%B1w};;Yw~Q z+G;%|6DBpP!wG84 zX}{U_0j#E{iy-!TM8at7XKtdIVjS}w9-`yN6tP(QAEFfgmY}O?6$(gU&^27Bi;Er& zmU1|+S^)H7(`3AFaI;-EZVtfU*n608)Bec-=1ZqlAi<_32$bE`jXW~B|>{qkkBor z9gq7sCs6Y)(y@H3NGBZrcU^oaZZ{v_&V;>$kY`O1lKI9SI`lLkya3DJ`Xh(IbJ2nh zd<|AwTodsd*CV7r!azme9vsagBvSl%1vvn#DH+9-^4NmOZqXOF#nPN=6fKw~e9Kbd zV&)66DIIAx*8c}XuRTsjmRykNdy#KkZ1rhrDSKE>vYR@piE58JG@Qo_Rk|S?$vMbL z?#&+o+v*RN3aulV?!N`P(Kk)@@&ElxRvkp+tH9&uDFVXI#ilhu#PcazN8$$eteZ)T z$y#VX)N1yf*zYA?`(#=VJ{PQE;j0le$3|s_P!*G1V5V7~b)I-tTGgAX+36Cm{GRoD zLVBWuHRh_3%8ByDAR0a`UmcD1kXN@iiEK)i_G=o6)slTJ%?zFuAvaV(|8qcv`>Ttw ziPs?V%rk@2{w%nZ5(D){f~Ndw?93Ph+QhuRqF=Up ze#OU0^P8~T$6T4tCvyay5lp2CWpXo^_OC3(3iaTJNH)fxluT$|9g~7=hA|b1C_;0p zLPa?rbrijEP9s6EXknyJ-X}6cW@0gKaZ(%ffeA-^-t;MD-BNyd&|;Lput%)|W|5Xn zm`<0`TlTN@%M{XJ=}+YiSmNzTIA;FSrD+HkwC8)5g70ty5ds6?$655_!k6E3N1+#% zP1G%~nOu-=*loG`lt@<<#jHraqdVRbmQQS`jH5tUc(@iEA>~Pd-O9lT8=#rX3h<_* zvqRj=Rw(fh?%}GG{GgjSQc0CZgJQy)OW7Z)x2~niXj#L}ov7>L!`R(XC|MhELcdE# zDwwrNbq?9$hSF2g7%gt<C20+xB+ z8-tP`f`uXdj2v7 zo$~iNKX;GhH*mx_DEwDPHexxI%F56yk9t~7DhESUgC&rL)B)D)1F9m+rUHivPZ-$w zzjki*9C_$=QbUtR9Kd+*ijx$;&jEYxovI5D@+_Pasf1stCMteE7xZ;4$r*-nM(;#4 zWME<&K9BZzf|QHqf4mx-BMLraqJC2VXTGSz%YdWA0IdGTzPZ8gFM_YtBrBhviBr^uy3 z#zHlAk!d^ob@SY(BLdJ+uR08Nx~)u&?xO^LZ!4kx)$de6c8AeFBMKF&;>R!NZ-lz7 z-N%xZ#E4u(^*@>ujLHsrKSOieP#=FX^VW`>zka(eMbMY%DOK#T)P14{C~v+hLLD2= zk9Uvw(lqZX3E-F}3warMF)~DVsz7*0O+TU^Lg3)S$wb35e$qnU)-QkhY8Nw0nUi{P z*>+Q9j2`Oh@O{khBEsR(KchGHHTOZgaJJaMTFe=Bg2an3^ zN?7V}AMqAc^NthPhHM@Xv@)@4Qq&Mgb|-cvPuN7AM!RvW>g?*QZip1w;Djw3kY^ zZv+bHh}Mz~MA8LoofD>+>+?5mZ7TmV2wP&aElAV9`0m@ps-v zgFb)zPF3smGA{=Gki?ng)H{jkWt0{F^p~L1)9K>}7rktU)rRnh_H~Dm`RSVY&BV{_ zc4RBAR0A@2E%*q-MOLksqZ`-w0}vZ+20 zizeBP2+@r`+Ql1CEH1U>#8GUCA)~=!_IQ{k62zy@q0^|B+)A?UvfvMxV>4`^3X&;t;<+@(>S$I|!q={v<6y&_1O&tAYnqn?=(~*z4mg7~ZGdpR(@Mn`#tJs!lt%=$ zGE~KPk(n}i5gWq8Hus_cD%zs=q}nPy3;b1X;NshrXhVd|>q+z+DvtuimPr)}$|G05DH0AOXA1)( zfv&%apM*+J-muP0-oG<5n>G0&DHP);u$db1K46`C_Q?k=hfyZMAXk2}i*Wk514jx< z)=zM+pBHFTAnFhA)GVf}MC0Tg_Wkp-W1?SaLjQ&Tk!Qx;K zw9|iF@#&LBMaG#a@2L(xh*D*`WC~IT$i^pAYU#X55rfgVt4} zAcwA48gbmyrtO=M7#GfZZt9>&1u1N#c~t7`?>)e_P4-t`$|ckCnMJ?!{#OCN`O34j z$Flm{aZ1x8;SfehW|EK5pF0m&EQu+2<%*M-x_*a5^&I@Jncq$8|G_cR`&Tw}aFPgw z>lx~Z`5%?_@R2JAG=7Sx;_CzNx6QYoeTvhYV@PN3xwzs7)>^#m;_B8zx#2|zB<1WC zP=ZZTI$&Iamm`niH_aTN@iU3+-z^#JZ;Q~iTTrAP&jZfS~r za6WmZYe1kt?G+^8fycmG_NR>#a%$5d;yHnecZI==&~i@k-@A2^C?Q1M^nK)?S%@>N zoh6$+)}KAtr6$Epek8aLQjNyMt^3AZ2v-r0%_TQ;I|g7aY%tr1th43qvs6ubK{e;R zgEQwrcso}Pl%h|kiNzpEq&Y9atRDSpa|$j3iu1!D=@CYp&u^K4xs{{JK|vCtHoxqd zYaz@bdmeR#U+eZdmZUw~K1!Kj(LOdf(B=6Lr`FDS)(#%(` z#Gwbo6;GQ2B8T~VH{8{pbQWsME-@EQ?#2ZofX}ji6w>s|q{M$M815NM2FUqc z6M){7q9R3-N!jo6-J3}_h0@gUl{dWvDnIA6H#Y+Fg54&OQmQ0eR%{;?-!?g_su-{- zi^SAdNh8JJ($^`v9LmiYTSq-vchTag{RxoQMul~ud)I+UHYuku3@t>x7BQB^jT0u2 zyajzBVs}_0h?F@ESHaEV_o7rPo>e=5|EbCbwi?qmpH8g!(PyTOaaduRL2!QmCF%b8 z5J?NxE%SjXZWZd|yy)!)YY-28Ty@J10^hJ^35!X~%i#b!yC@pF;$45-{yB%f7iLDH z|NO6gN_AS3gw1zhg9K8ET5#Ch9B3wHObgmRJCQ9a_{(!w(0mWwPVTcVlZr9n3)XH7?Z*h%!3mO{SV}^1?~AD5czS?qJk!@qzMV4bVm_R z{6RazNRmuK61&-Ds?{_t`XzyMKbwtv-FR(cHv@6p;2Z?+xb8t4bn8(x>T#1}*TuFJ z)x$HDwgY7kggLn8swhM%q;|W9VvgqkvNy{G_B*@7jbG&AyxDrfrPWHgdFqoA<;DM+ zK~xs|R$=Q?5P1Tutk({qVV*N z20lLdsfg*5J6>IQINB$u%+-0H)_g=mV@x(4+N0IAGdy5w-a3;fKu25y?BD zvVOltcf@R}@hyJ2IdAlmO9RnGxgzzRAzv+H=Y1bv%^N)YXM45~%VTmwfBE;?cQ36& z%c}j^OxoKM&~;HQpV8|uCUm}$5wgB^~q^eIRWLH?_MD(K%OVTSIN!*A&IRs zm$VcU(IEZd$0#WhF-=+=NVMG9DtvqT?s_PeP|!3C!7BP*Gsrc!oJ4SkC9kXrM%S9%|R1^ckt_(vUYE@u`og1y2x)Ih{^el>&R z#@b)1ztcp;Up&=iM>szLP0+U9>kA#c-0^lTI+U1#MQfUW*`i)kr0Lg=e$Bs*{?$P_ z1)Pp=SLwiKzaF!)qt3Pu40!7c;w9>O>|Lyb+`k8P@U{QfXhfh1gwfE&zD_sqojIaG zj&{dPkO4MmwxYVa6}x^NnhU|mX0wp*VRBp{D?JA3zhM7qJm4O)dkknQ_cEDSR?84f zOxkldKFmXsbkFk&9%;9b0aF9A)kZ@ zYZF%Qzw4)5nLNVnZoC9-Y9$bh}d>lbj zD{vj8C1PIRVIv)DKX#KlP?2HSKdh$cvGil`8oG#$@IBcs_ zc2vel`sJ_Lf?j)kRf+hV_YMvMTp39$(I;eE)QtrVYP z5~%DNk3L#i7#Y7t_PL4%-YN>+0EXS{ImCeEI^|FlcdnY6)wi?+FSsei^xo{DHZwgXPR#q;H5pmXTS;~=l^wkI57s({uRjrP^%X!+fE zzD<(cOc*t(l)oq4x}sZeQ~F+CG|A|{G^;YO`OxC(6D)PJEl;j~n;~-mr?6+f*DDdf z=FM5T6Y5dv%V$>`{R%=EPDV4*E*HSUZT@^b%3@5(h{66Nj^nBnY{1iCh)ZWvJI?;b zq(1%$xP)tO(LVcc*2~O_)zGB0RGTlfF-`+&7XAz-=_06jaIM#??#%`T+m$Tu`cagA zu4q@JQBezkUtk19^;+?FEo@&ZYD;;YXIb0MT-mQSs@*A^I24C~o$TuKF@sM^MSvdI ztj??8&WKd?(Z10eAHUz&ANH6d>Si%n@>uF5W4Zd;XPmf3qlEOimOp8Z<@6O5%Ccqr zUMt46`S@^^&_l#f=C(Svr>t+f=2g_tqATz!N(Q5bJCsG@7)$28dafdxPbi;)iyZad zN0MNhrdM7IpobW@)OQ+w75QmvULYV)yiG^@>%8ElkUhl>Kyfuo9oUmvZ2$hHt74ul?IZ*{(QY@Tw zxnYD78?EoeNnH=}!p+#>N=glosUlNoFu>|f#-M)8;H#*`D#38f-b>=|KhSt3eEy02 zq5gY>h;h-i-_D2B=xp1%WT@1F#)8LgRf{98>I&m*Yb+N{s#_87C@k*qQv7El=`fz0 z(hV4%y1tbcC;a;$rj#}IcbXOOuo_ohnfJMe>g;%30u&6z9;SjD!U1XT`u?7UP6-zya^RL8SU~7{@g(mhi=-Is?~e~b>P1k?Pv+R{;qwFEt(VtfMhwtMSsjN;R#`QSRdh0G{Fzzgqzee z78p52AuF-}mQGT|Q*OA~=j2Ol1Nu~d*`n0%!`{DM8-smdHVY@@%$u^M?!p0 z3H+Nm7=L3kfS=){fJY>b4^)vCCljr`PQKAQ>B|;a?JoQ7P?L zP;$gLeimnlB^q_x$CqkB@OVDRJHO8N&5M;hJa$H$`$9JHC__x2x=dV&o(5Q&d+$fd zdM1yUwEm9?drt*go)1%GLZan}IMYbPOBj1F>AM&Y)V|D_K*L+J*+bOT96tCcDWXB~ z>)(Bs`Os~)|KjAnZ-^DlKCK|2C|tKcyxxPZO9qaB9a+!agEWKx;?L~i-Jg#!trjFf z-r;+>!M0eEI&Su_+$Y?AoK7gl6|d>)Sl+I@+!g4|314{s$S!OVw=;YHK6E?pKOg`Q z_WyqQUbx3SVQYF0F%^SzjRr!#+Gfd+x}hI}koG_@>2~d&mD7^{ZAdI9t{~Z#vvfFhFPSN zSgzR5Nx#ha*NROEIsVL~zJ9Aq`+$9KI8bJ^wNBjzOO5_7uCLBwgax3R_rPXX-m>{pMGpSaR40LIAs6 z@!@oNl-Y>7@Oq@4?SCS&-NK@KljMCCWG=TNY!R_K&HD|aF*Y_l*(o!4Oi<`wYhzwH$h#Lx^ez!Z?+MJ8BfW5Sf4^&uZbcHt zJ>Qvq@Th)CAqA`jc4nU}doc@LOC$Zpvz{0HG=5X@6Q(CQNqLy6kF;HA*L8<5GbCw% z+#P^f0rA@V@6p~>T8?<@bZbm>aAI?OIeDRd89g9V2FyQNw2%1w4HdxCL9`IyBA`T` zDjU9EAlxrfU8#R-d6-yU_uIQbv)?*?62~g}vjE0#9;wPwtVqAc5loB_7U6L!IO&;% z{k{paosC>wW<3n(I>Kfs&btBsrX7Rma^WT&jkZIve~GQa;h_7bOiCUQlnSZzLGI9H z&Ygm5u602*8{D^sz-{3*aV&vR^94AhER?>XY4SA!C6Nf=zs98k>cNggJ*)@+dW3?> z$`39-DvXN|#A>~AgzV1M#*96?T&wTXFbzVJpfBv>fnPEwU)(1lmVwuWNvmCEsS0o4+hT&~9hu27kex(LYIw`+6Z{>gBemT?~6%)K489?=qCTU~8o{cVeopm|OL z018&hJjtX`iPa)2R^c39MMft@yBhe^&w78RMR(1GwgVF(lw2*$7Cr1W5406pd>5_4 zoIp(vISpTiE~_AYfrv&Nm|vGPcyKA;whIOQrGpZ*Zu~faCxF(ZINpdnKP={H5KZ%( zzY#{#e8rLUf9JG|@eXLv-6iQ?S@*wmPBdwFw&wHZ`k^gE7j(Y_F%`|}`BssEVLb_! z$tbjp{-2cqV8q_XzJJQVH71EnZ}EYW&=?W^km^z85gx_Mmw&XbYp9}`^v`obltF1c zaI&9KF%{zu){*?hG4IhJ9H-aTIY@>CIj*$7GqKmVSEgZYTP6nj;xZT#h~Yee_^$R5pj?j_Yv} z9xL{=rh?hD-OsTbWpa`zE>4Rf0S|jMF5W(T`wU0#(WGw~YmI{QL(+oIt=j8s_*^eX zX5K&l*sIpZ22fK6FUliAIvycuf{NF7b*HT~_7X=u^alNx<-j1a&r1c07D9)Hs?!rh51IR;DhNWJ3p#|cn@cLDB3Flk zzLI^HW`Vqs&BoUvdiO&ojl78L8%Fj!R^~=3Ekt>_=1F76;!1gIGi@8ORvQ2XBk6P2 zK7gO7aCu`g`h9W5SRs7TJH%W3jTe6>Ek1`u6)}Ex>-F!Q8Mfg@&HPW~brwTIOW7xs z4)PHNA*%!tI88t@LQ;koVK0^=hjQo+sHS>fOW_S*m0Z1;FA$;28o*T&rayS($(sdr z2G7{|zqFuSe4!6X>F(=QGFUy0{aW3??2QknJd?xA0Q8=3%o~__==vr=uwDVSI+!Oi z2~!y3D^zoujw%`$hi&eD>-(NXt!12J{>md0OymD1ml@P#CQN$^t-ZWy1EdA`clk}& z)}bBukRUI0q{@A-KDN92K@}ok_0N&S;+D%n(}dy2VGp>GJ`u6yz| zWFnAXBIwC$K~ajoP5X^qk!^4yZUG;^;vI9opZi>PNK7{j{4V@94yuNL5b)BYVu`!< z+hV75+%sR8=v+`$T1qy%ow|-ig{#tV|C_yuct=K$zLq?T%Sn#AnWPJoLZ=|S8Lgz> z2=tYV(07TcO}eOR5%z)B#SuYF7Lx-1Bn{N#HHX{;7YxRmNeWEss=u|j{84=tZ6+T5 zjK39COY#=+M^_)Nw%BwYyyi8!%))29;=sI=sP3j^v`0ApYod~PKWo{Y!+$y~C@^rBc}Q<5x=LLOPVhfCuMbN{^V46#$tbwat1<*$h>==Yd3`z#`PZ)4zEL|)ZcNkrKT1-!%CJG|v%Qd;sI$4^Oi`|m+)1$% zzYC!O7%wfD0o2VjRKxCwYJsQ)3&SBjOi4$bXtNSX?fN~MS)YTq81Q<@2VEq3?pgk% zs|gZeX<9)z`b1u$8@dMUGn1oVf{&rTyA~mvzyAHl*I6jA?fXnD?V|kb$w$hB-m@r^ zSk-Dti5Z~;%LLI|G9R3oI!zjxZn46SP=0PX^R95g(2_n$j5B?_>ILWZsj$epXMVNq zpMQAjP|0+0zO_ItnXEYhXPxX34K>Qnef8k(O6ngj8ojmsZc?(nmBy^IHQ)FvC2`Sy`WJDOewvjm4fq^h8oj(M zM?Tt~3jG0qx}N6oJw^2TaIB;u3TN!l)6T8B9)?WDxG>6Jg}k=)BRkK<7HJ}UUZoTK zADR(=46tw|AJ**$9d!3^H95hepK5i!I9ukZIDcp|)(o(7I4GhWoO7lI{Gq6#k-+}p zLy>{nhf|CxVmxJ?TRoY!A8s$jjCxUbH~#$VJ@w7V{CE#|wUy)YX{X7&3N`S4ru_WV zFB48?!D6k{G`;NnGS}kWJ4S8oV}+cYlzS8B+Qi`Rzr-f(fmzNAEfK*HGpJ5RJQMHt z29fKNEqr1yF3(!Y#*3^Ok}!RMnK?FW?PHMMtOPosl_=o;mM=W_GilSFX`N}o000o^ z2*6s3lS4Jn4>|vt7#{U_JDCMW5v=;ENA=n<%nQLAWMX@MMyd;3jis$?^TI4JXz@4` z9dhes-co2FIBU1c{#i9Azi23J+6BD<=h<4(iK~=5;Ab6ajNk_2Em+_8S2wS=0awnd zG}jQ4xwaqe8gSHn%tn#;SotU21X|O@d=ZIG-ao%5=lzdsBYBJQ(3-g1PE z!P7(Ub8ig?Z(=K&j>V*_E8%AvLy%_}9`9uNQuvbKXRL?Yn}FjsvbN(J@4#opOqAD% zfbd);xCFEL@k>WMUKR*Y`2|uTEd~h2J&aM%$`(SpYvF;&z?LzrwYx$3Sbn}_wXfP-0zNqL$0#r0=Hpz1Jh@^KbLEtt3Ar=gSEtFRwXT+Pk`m1{Id4jKYo+Q#&y zaJiK`LwC6aH3*}HGYe?_m}*KgdKD}xT(BhSFrV}Go~-1DKfvQN&s01=!iU);`or(V z<%l0Y&eDYPaWg;pc&{$Ll5=sG1GF8Bb&UTiRSnha+Bo|4IK{J|pACH4MjEId%XQtP z8{tr{N#(Yb#rm;U5KEcX0xhRxue;z7Y4s!?T!`RDMXPC+u`4s%~(E~9YL&0hI3 z_TA<5Z53O!YEIYl{n$zm^tI4CeQ2`DAQ3U5mLC8%AEd2I4(Py)0CB%u?)qB7F zB>+p^WAdQ$o4qN*evuuo8^P2LxaXOpb!IsoslTQxOFJ6aulAE^n^0i#EaPSJE4=r(ATA&LY3&_blqd#2Kfzsyg zWH$3I$+MFXbCTlfkiP?|D5@pl$rPh#GOBI|*tMi3|89Lkm)a0N9pAma=nL}wlRdtTTiAkjgG!Uo;(7m$Ov_aVxE62gzy%q(t<1NH)1i}Nc2Gzfl@7${Es zk)yn`XzXlJ=#JmLRpeP}>F7vfd~5yDNKneB&S zv^B{05+5|R%Uu&N`<8S0Li`4C9v}7fccqfulFU5iZzHtvp!v4**M)4W=9k>ts?IKPq~YK za~$A=8t=s#M2fZUdyL4}Ru!Oy=!hG{ooDFt_bJUxEC5bPjWIf>!BpNv#cNpE_6SF) zd@Nj*C*J%{(R++9Iqu=g3|tfZ^65;VAp7>m6@PgNtpKkoKcA~EP?)-G(f z*1ArK|CqKEPNqB*ihJx6bfT#XD|aG|yj&bygn#7Q@1|Ex>KJEom%VlPDtjwmsPuaa z2n6Sk^rHh$DZTSY%qUSa)wu`66Wj4g8V_EK#nZ|J0+VBb*M``u!#9DSa8AfswI2u@=niv*Vn&Rm$cW0q_UubX(kUYI|Y`@uoTA@uK^3i>26d zBP>{sXQ}uLcU-0RoiZuU^iKL;qa2zUH4oKta+H!T=>k8#=EnYmGc@CI$@!q?V;|@H zBbQAD^R9bp*R{}MpOm+W!Ni=E;kCiBU%s|vsJ>+VD|;<`d!IV-%zEGI1~$8q3tmF> zlP20O$=n65YqK1<#*#|71U&3N2OtIwzDwtxGYwEFUSAbB`=?mz|Mga}M7?=Zlr}Vl z$Bj;FfKPB)Bsaphh~a|RM!vj6>NS~Pi_mJ&DShG2fyDt^)(E_YlbP$U&~y=PxxBhe zbE$V7_Q~5%>#qFAW_EHIJ&&d6CY(Vc40O&)$u*p41{Qd!`vRFcdyii?wi6X*h1FSJ zP+YoOy#4g=KxicW9^3rOj8KE5nC=f`@4o0166l65v_V~!2!XCQ%y4UXH>@*SH8lFrjS=?P zi&ovu(Im=Hf}H`Nf5%Ntfv<573jSgm;7q@^W|~hc*8u_INg|lx{y0Kke1y5brX4Vq zT&g;W4g#WmBXBlmOr%DqBZU+z3>UQ(xrd?=|8g-@SUy>s(H7 zgDgZz>d9@bmlpMDQ+i5W)rH)5Kt6D63m#z`xwQ%Np6uQYsJenBz zJr4K-%I`!myfuX|CJolNeQaxzjL~sg<2~>nBk?E4&9agL&WOn#SZDbN+<+v|{%f0h zXng5ZJgorCGK&c|$ORVKw2_?y8Wp2z`DsTA*xv|XhJQ2}9^SBkxs5HZFTU+;--?s- z_b!@@*CQ(S>ABi4Qb5748FfWnF;J0RDVjl^#_%!AM6ZmvDN*(ES1s>WeEX$C)P32* z^KGi76n4u3h6a3MC{Ym;H?R%vP1JF*Z_|}>`Ym`Z$6~PO$BQ!)mxw>kxsnV3fY0ht zXMsO^JEgf=Flw=I2YSKnp{;F=bH^C@-Y^1dw672KcO7Uc^X0!4ARB!lnwT;qbf89& zE&!?=yPdq(4Z#Y&oMb<2xms^I>2@emjYhM^BT<%BE9nkex`r)G#10 z@b%&Rw@lF&k6EGQX#!H0nG3GrJ1z1qX*zI{?xOS$=(`F-$ziY~ni(5nI)=6O?)R~b zy@<6zT(d;#W+^Ob|5GuYd(_%~7xMG#a=mK4WIVFIOn1N?{Ge38@eq*0ra%o!bdsrY z6&rj{?&^#?XE-F=+gM;7-G(oW!uXQO4%jmi_-!ijn zLHzVK_?P{!!qgZSwA*Fwx1VQeCSR6g?R}IhfxRRDpeFIu()Nk{xZkV)Q);R+-dTx3dg2Lhv6gR{H#3Ti!$im{PeNB( z7FjZb%ar-j*t1Yv&jw`H<<6f~@B2S4w7sgY*QA$@fHFi3Et@rAE%Smz8P*^_+!qMDl0wN&NSB@OjLP0#%Q$xl;Pc9w&2@qKlt$Pi zaWz3BJ3|MC(VF|oG0q)gPIO(PvrfP4%wJUPm9vTRC%=HsDBqTcV5q5y#P)dd60|~F zaj?Uwo1`Gp!8iUTU4@tL+cLXe{+wL3ZI$iYWJcR05Va}nrjT(1Y{;>T>J_{hEy!PK ziSFj->;-WqSrs1i?=yJzKHUuRIX3((8Q0cSUT#GSAJhVq|DOxsWzb;rZ~xb5UTQwp zSW@0uQ5WLFMZxC7U2mHAj6)SES*qkesuI=7O`yq37#s`uj{Fq_9vZYw!Ke%$U1Pyr zjYj@GB58l6YbE7Loqay2C0gJ_r|Tp797qF-*yx_}wOPB`4UE)5oQT6r(M`fJc)?jC zJu^ahVP7c_i$KSh>!{edY@A)kN}L9snB48|(q>lii`CYzV_$jSd^Bvz^&zzw*?vsY zE0auJ6KVjXJ!2U*GsYBlq}eVB`*$fpEY@G(hw2g}&9USwpdJb*!xcb?iH4HYf%Li{ z0jLXP@Ae~BCYnR}v7L=B3AZN=;dg0j(G4i9OZXwarF^LYarE-j_G98wS_{VGZnwkP zd1kw)KI+ZU{0I8M7-2<1o9Yscpv{6WGI8Ng6d~JX2Lys77BW_aqmiOlGskm4f@;+@ zY0_q?p=8C4hMl~IYKLE=Vk!TvCRV!+T$l3ig{fY*eDnaO()>rje+wZwT^tfH&Q90e z8I6Rwu%v9@v8cxCH8X%o6#7CjdpW#meSrDh8}KB^>XalROCT9ECsYBjG=J0m3&nl) z*RdQi7Ao&7B(CZO->D^9rKT-5+@Jkl&MyvreH7y_-C0`hq#)zvVfM+{$O4aIO3A)VQaV>JJA z*P2@Ojj&=rW&i(CSBvHu8|hv%ufaPzOobxPGmyz%opq0L?5KyiQFHZ1|()8PDD@R3lxug zNm!IQ5-ld~?itP#c?n)J8|3j=ZXXjDPBNp2G*yJ}GG-xkuVZJ0WTT{#b6Cm~QU2`R4X_=0?NB*VBh`wocWh zp?W0xJy)50f9HE-C=9S>c3Eg;XUUKD7? zHv;g`fvVW4DF5*ba8kzs&FG0aC3ea4JtSJ5f92La>S9`T4wRgXZ3Lw4@Er85lgr#B z4xV|w#mVx2AiFq<|+7uOcJbv7)f@J~v zrIK|i9?L1$yQ4EPbXOOPPIN!5f>R@HqiHm3m{|=|Qt*NmAp0D9qIcKxF1t3k3xp#@ zCbFg;f?2yN1yA5Xd|iK7zH$a#4q*9I3UiHS#INAEb(Y*54&#HBaTsqD4l**nUhK+Y zeTex(+?LR^MwUr%%99*coEU6_A12)CW-?!xDN1*6oeRSmLDPKZWo8z-)CFpS2)+T( zuJPAA@VL7=0+K)%prJGXgat@J#6L;=tesZyQ9Be3JwXspcFYX;nQC46peNBfmys^6 zV@4DY8Y@*tp4+EecVZ99G!78S;53Yf-f?jow@RH%k%_bv*h)mcm&=7fu~@rt*nKW&C5Rt?M<9mbYWzn4R)-FdzK5T9%X z(_dTltaPTN7c8FKO#wdD=SqhzxBiqf!WglFXG(CMy3{ko^|J%M8-YES1f1xOvTZl= zJSI%$8~@v{$gX^h|MVNSo()4BW|A(jsT!~`DzWh?4UcS7cXVqqf)EV|ok7xKK^Y;e ziBI><)8NFFup_fz-s6xhju&|yMQ2^mh7Cwmpjle{>Hu9F_T+Rb^K_hC0{Pl>f&#$G zT%GDw?MXp!dtH-R=eqv8xu377Q8C^=0@P+5B+$W^M8({38nF{k;k<3h(x7suWAjSsL^Bv6Bu&f$qkjg~O2y@M1WV(py*9US%_e zlsl!Mq+~a7O!Eg(Z@JX-sWZ2TRw*Y^)Lbl)ehm&BA3hlF&D|;*bkRh`e_ngya9iHL zUjLi>yPOcuZ@T)ecl9f(e* zCBJo4ZkG{RsRlwS{o8?6+E>)c)(h1?Z58F;DtlG$K4+~jwqV`OKko!1jhHZn7@9&n zf%Pcw@(xSb6@*rKU(0}>_LAU2zsbeh_|u8`MFpN&e?LUl3`{&fnnJg&5^>JcJ2wV4 zU2%_x2Yw2jx}+IvhGd5Jp;1GS+&Nlddi_>{oqV4a|5d)p(aX|ithhJG4ciCfUA-?; zB)~(fQE-3lv7-X+QI4*%H{GJQ^dz3U9m}@f= z6LlG=TLcRe9x_B}aeXLw&jv~&d`q)m@1->!RxW6=LD2W?uvUN<3axH41iM3;mI(@b zthO)QdUvrEe;+lTK>Q2HA2uaOFt1(aWg1LpVn zu?{XlxVkkN9d|OfE#KlO1GxI+D*IG)`OJ8#hM;m(6KS)M*gl)wb`^aM??1PUYkcXN zDfIz2YRRF<(^{iC?6;4!*(6py3Pd=@zNReGPeO*NZI*MB z&)$OO#;eH_aCA-S|45Dw1?05Szo8*`P@K_!AL>jF{9^v^pwbJz z?+D^s2+Guc#l8Athib+fV@?COA~{4BivgY)=((YJ{CbY}qp1SKmj#!nelYLyypk2M zJ3A5y>0kMbHqm2YZ;y={ev6K;g$4ig2_?rkGAmN%A7_Sf#6oZnHAN*#d`Tg8{QH&? z;5SeFk#2Q#5$hI6n+cNl?f^GG`mKhe!bpemkz@!EN&phRrQ!Aqn)GIPLE4O@ixx=$_g<>3LIrn9l?hHO|H?P>uy;)@*!h zxtp7Iz3D6z56o~O1wKr4$pJb@e^ z%Bp#N=V5{9fnxRZ!rP*g|D+(jDJ0IPYeeNvcYkItZp(MR$HHg{W3t}kQq1s^a=Z>y`t8n3Aky zz{E>?!F~_OSP|EK&7h#tVIG@&c(7O3el5UX0MSXdA_MwR2Vi7mF7Cx?3wvG2~J^cav(q>FOBU13i;_5rW{W&z)5B-fljBeg=i?hp6uOPj>D^k@kN6=O2t>(bD|=YK?Y=#aUgVY0k(lF#9D8 z+W>06U{8aZk7exn6V$$Wsw^A~>z6vT;OFI%8+ zK0AF~9(LjVemv|4OuxU?S9e38_J}K#c54~4E+yJ!54sQ@1?b%_p&9b@8XZqrKM*k@ z0diSotDS&3H2db{p%Yyy)|`^Z;v&|f91ZU>@p}i@krm~X^wmMv1~CFRq~#bY8?~n~ zalf>aL+sx^uPTe2y^(DNok&toEc$ByW!U=Lj2i)u1^}OUQJ5K6%_ScM1GJ(Q)N;q# zTo%fd;S3Okt)Zuj)+U1!ivRA?kO3j1<3zMDufij90@}hb2pQvC=mMWJIa@7qLfs4m zbUts6nj}hIc^Zt&Mr5|XbfiTc(4`3^RCc{T-2@I9TB;u@hebAOS4hy`OwFRLe^Oim z#8RLVbGNvgaT;u)l4{P5o3O?KbvL0_m?8`Wm6R|>8GWdQFGTPo#w5m1hO*v;P>m@j z+|nZBh>;(M+y{=tNoWfFp7bD5#Og3p#ZMj9Tla{Mc-Kbx)RR&*Imofy# z0V|ixuBGcI0vE*{sQy=}=}F=_kQ&=Vy`Rk#amir`Giw zrmj0qXH%p{p)A3{%_Flm|3>I@J7A=F?Z$F9Z$h8dQo?zQCp#Q#FMA>h^j#WtuCbaI zF?@H6vgsjQurA`=>#0$}5*Dy!QcO(z!%UHLa$O%g!vEB-Mk+KAnUBn?nnWgDXp8gl z!YV4UUcz;X4oQAC7-$!;fGY+oM66}tR29L|L#<0{hzMzJaYT>4|4atJ148P1fKF2w zQX={MH{Qu}z%G|1Jaa-sOQbAy8I{+3skeZj4oOq>8=VUY@~p7Ngy^#~FrWkGFtcL9 zT89OFIK~xOxGn?R(gSYBWZc-_BC`~RFK;k2OWtUyCcN`>_g1!ucq3aJTvu=_#C7%q zE+-oQ8m8|~w?BthFjFqCm~Jq$2Y6~Il$!o0e_Bv=vP2?jT4+V=O0OX4Q{mC~vDQ)a zuyYRlLGjDQp#i$ZM5pIJKDA3gxJ-|o^9r@K|0UgHh#2B2p*`bKD2oZd@+ zp$}_M-{i!u&lxe+mEnnn`8@W6Elq&-d=$=j+KP^VM1fC~+V|Z{BxThd=?Dug^@ut! zsYwtovgf}Fen70Wtc?V@O>O*Hnkn|i$XJT}ZY|S_jzX};MFe2;(4$v}AcfQ!W;l_8 z_;Bv6LgjMuO7@no8y;{0NoP*_-WX2<5QO<-FMcR9LE(hp7j;t5WIE%PMj&eU^PN06 z@3o~&`%_&|d>r8fYBDC2i?ql#JU6Kcb)2c_Omk1N2hm0$Az=lS+P)k{IN~AC`VEtl zU{WcceDI%X;PT1SHqQlqP)#t;LF=U@Y8l%}$SV25`(DzBMwB7;wKBpgfC7j?DNz8% zcq|*JCOiGEXhkfknhjx^1k%sC>~h5&DKPu4aI?udu1aVm6|V(&yqmSG+nkF60fKK3 zvz6Czp)gDS0~{@*DS$(4?JgNMGmf^u?k}AoeKJ~FlSoc>?cN-8EOuM!iX);<&sn3h z@fR+hjK~uDlt(pGbI`~;+y2>_4rk1g5kcE4*oF}r4W~n`6P;>A9dR>Iw^3HIp&sGj zw-eXcRC(@iW{bEu>62vTT1h*n_y`qD1{BYT!S7Kq;R^zOpByFm*QCwND*mxf!{yOT zqdB_VH~jgE@tRTXn(Gev0!ZvQLhnbxTF&Hj!v@g57<@`-7Xufcfip&(H}Sp?!!9rX z@&}3*w>}9{cj3EGt*XhH4upD1UEfY58s2;#Juc16PWx(7n#t@o9fJeo*9FQ#)|Ml( z0&wY84;I_ixh0@*34)K64tN$4EaZE!Dd#)KvKVCOU9O~03xqxv2VDQ+uh91{%S;#A zyU9%dn16e9T8viH8-#0`tLfm$QRkuHm|5Ei5;3AnCjIFL1|!QO&*7W=J$Lu!BCU5-WI{+l9^^vX~HUvRN@_&2U{e^h)E zLeb$u)y#}E?N=;yU;mWVp{R1U=A-k0k&(U^A4u49C8;4Tn_6bMGfqmJcBXKQ^44rF z#-IM8p2lTUt8@4-)x^BCz}$;k0#w|KCavnsFe0tNnEH~DD+B%-xgUx%PA|BtFetr8 zhwH7U9SV5GDI}vD1D%26@9TrEg!#hIE7ja|kQ|g`joJFHbh7D05d7k~KY%Byg(3!kHd;y7+s%@g$|##rt|X@j z!UJ;7>I_mO%fUl>Ez@bz@_&_11l`7V0g%i|E@*~?=3X|1nAV2JD^HT>CXN>#!6BaX zK(Y4ks|u(J=^=g6Kd9val~vThLKaz(amSBjj&-*MF=rq^?JcHRxJSl`E+eQH^t|N8 z({ah)HkBG`H`GT*0PJj%$=jGV0+9`o^4y<-QYA0ZcWp6aLya(IMz!!jyY;M8(Fddz zuBc?7JQ!l!7jSWty4r`t5D2?95vpu0AES<8e=@0~3nT(x1V5Z!)vH5frIW4l#@`C`-WQ?77b@`+BqtK`@cdy*Opg)CR0D-hg|-)@6&_Wh-!5*3Q_ z+!GZYJ~J9eyar=Cp6(+0F8QzT)53mfK)+g;;W74(-15f(vorrKex^TcokIfQ)vCI{ ztv}3%v97`yF9?9oT$Nxj)>pq3>-SRsmA~HXyucair|o^S{3{{^G)$;ylt%Q3ea4Y~ z=m&;D#U<3uB4qd4ZoFJ9{)9&3v#e*E|2N&(ur~o``GV|HB=6m?1SxjEEfK!X%;3c2 z09;1^vQDM#wP#!*h%Dxgn?O5InTN84(k}9@$j;OdPJBlRq2&{I?Aca83EfI>tK|f*{jbp(3^S zezDkBGU$oTn!Lc?XgwKNW=#D_wJerB(udXE&_V#V8hOvBNga(CH9P;?C(n9xxQluj zMUNd|UQ>rlli$avA$c@Y_xHCdPl(!-CYRV}H8n?~a>6R4?^)*oXh^OL(W4~d8ltNK zlB2*5(`l!Je8}AHG3Fyb1)vu)WW(U59d#n7FXT=qH=76Bv4QWxtD2VvYc$u&Ccir_ zQiE^gdwp@=pa{B&MU=EaXmncC2bxZ zz-^=#3IttdQZqpL_E|})o<~swa)T-C6Hu}9&r&lm96v0Hz{sgim?qHirSy6dZ8AvD zq{}s{jLd7ja56J0v&op>Cw*G_T=*%nO*{9PV-J1zlM<ot(BVd_G<{3F{_9IA&*V|5GOGuuzdrxGrwS=ld zdmFuK&aM66mmxiFw&NDvN$U|&q#UhwSU>8!4;C~+t^Bl~H_^l+PUnWdX!l1RVfjm5 zDN}UB&4qrqG_#c+$qB%>vZ#GyPC|eDZDc6|ue&4QWgWSTAP_2miI1+t1P%N^o_K4@p@61fBTs>JE$ z=uWlcbtITh27HqtOys?EbV8C#P-kfsS-eN(15KH&uv+dr=2` zdH(^+ppcp3!saG0 z7?;9{DrOns@%YfWaL_}HT{fz;Gd0U3knueZ!)GGkPzoqQ2#TOihLC|oDyd(-`RLEw zRBpcgQk@4w1LGl1Yh`28hCC-q_T@TS+(d7Cw?dY%%M0Tc_YmQ>BnF7tk2^XluSs{; zB=z3SEq#zC0a+5yI4hB75E%=em^NVH7^;OiKaT;GrJA(H(( z7QV%!h1TKbI8e6qkQt@vC|G7Y!pN5>R29nck z^+hIt+1!=^mXh@Sb@9mP?6_db4j*>c!4>dw;0gU7=jmX5&@MSi;>BmXy(O1$AyM3& zbpRE75qx(VQSUWCk(G&LAqXP(nA<^blsEo9g|JbzY-&~?TX}w%_4AAoy8oU$l2AtW z7C9n-e_@8bzfL)kvS*W=d|@s{?1h(Vzo~h6FxuYF_tY;@fyn z4R??^L0strt_ui5Gdp*${(I~zJ-{21qx@qq&+@&h9>?|koj0jPY%AHzda97H?0}7}F5J zDVCJ@FlX7@9M9--GsT)$R6!u2{Z^gY*5qK_(~Wn8a@O>BcY zRB}_YuD*BQ*ZOf%)`4@EoGL%{jQ`>4!9D3>$Go#kWu%ujUAq&Pil%h!T|k#PVVF_n z@py$CfkceuURTz>b?Og@CvU-_)6vIZe4j4)Tu&+9NCT@Z%4(`!ZLvBSRh8d*u)t5F@A7Dx94ZC z!p6GPJ%Z5@a)QZzZ2G}BDbGZ2s?~&StP+wz0nJ8GQYDGv)a^E=^CX@IO8g<9euk9M zV3x*dME34ynqnnVmRl^$2~hA@z7zYqK*M1fX3pt}NxNLGqV9UDBF@mKy!8Lb+05K> zf4TyJ88p|Q$ms!_%wUek@=Q~Ia*h8A(@=X%JcQM=V<5QHs29%OTK#@9rv(k0&m+l| z5Oi~({*es^c{-2%${%7y;Ek{MS5-KGI{0p;Sw5?7jxem_4B4d;19D|4%nd5PrBaHo zA;Y7%I*uM&>nzX0NT(C;yr6L$y@l?6cbgc&$MJv`PIiD7%DYBYQzOEE)Cv5^>09xp zg;il4$hB~lf+WKrfG&=*!o1RmGBlA1ZxaPJlLMYBke(NS(+J2oQOs`nxJ%BI@;>Bb zW#NA3{gm{5JtRe$l@purO+Uu>tyy)IFKYDs{maa^C*BuplIp1(N-5WGx#Ax5=7#5N z6I8z$;d!M9Hj2g)C{EYu$1^+q)D*x_&(XQ{l`hsA@A#W8P8A++4oC?f`o*ay%K?uyz{@5tsx20fW6Yp75>RL8ltypy!}>9 z<>aVKT+!F2gQTwgKVqOu9w`W(F=tagaL4@oAt2^qGj z=|$da^Y4b|)1lqEMu(rida{kpi#KAOUL-l?=E^Ija7*^HB1Kue#I!txoG2f-kQoc8 zNXV!&@R4uI1PbeL#yM2Ql;uJ?EH=~gjnj!2nX~7;r~|mTA~q45S8}Ku4E`#8P?OMKPhj;KB#Nj^cuPn zSNDjH`h+7P{07QaboJIV$dzHc`@JQSW&v>Q=K_ z>ln&a%hWWs|KBm_w?|5Sz)E-_WC2SyR+K=@yaoiC<&>m$S7WV(9|#cOA;(>3{p|<@ zPu7);&PpG@h2hHc!gdv-tX?)W>ow^B1QU4@Uiu?{O)A)WBhL9%cGy~0o;AD-#eL_! z00%K;cgUBWtZD-yTkP5j`v|@CT(6;l?J;&hV`vrD`|dgpXAvA0S}(MWss&6WK7{)p zt7DX96_srB;NX8~`pT%N|EBLIWxcS%zR>C!nvk{zs5dO-(&$T>0B@CN&D?dLlsTZvy8!u zR3ne4nENoT(M7^q4zk1jzsc%?XMcObuhwZ%Ab#)N8Q!32JXZGSCV(+}av;Q2T{WQVyPH+AWD&8^ZE=zXkG3Sny9r z&Gy%r`Hhl@Nw#aoU;DgZIgXJRHS}OrkmkS;hE*BSVwecg&HStP_1%6NV1NJ|;jMv8 z!%J$y?V2L}`k3wH6b8$~5yIKGvNpb5&=WJsK44buf+y2jIrWy|x6nWdl{l zSxAwAfWJfsQkJ*wXUNOlcXl_ie41bmqbom(Nit^29unXN%C{l%ZZnnQOt=<SAU?@8 zj(e_AF-K8V&ORSM4rTIJ(pqxw6!Nlt1!J?v@kUC8ah`EPLNG&_b6#u7T`Y=H(d8ba z5N_(KNa>L`IAbWzSHsUhf%FkqDnMRU$>Luu+~3}ngvPNgW;ER|>kdZ``3x+QsdQ7|Djc2~J_$Loxmb_m?}z8w>l;}5uyAWJM) zv%A+-^*YiPgPU&*Zo)nLZM(366S*%`ddyO4H?xA(Mq;`J6jm>-OpvN#{=7j~=dJLz zve(ehrQ6`Hj{ia!sFFP;v}rN*k8*t>w*I5T2&_d%6>nuxnHcnv0xb9178}T53=116 ze~ZTLBKeaDZIA5S2C%+ROrukyxrS|NVd?%Cx{~oYsSik@^U3)h0PY*!m^~X$ zN}CSv{LaEeVHGt>ng1>_)k~3FGW3Ga270jRv462(8fEqAj(5gGE){*56}?t;OE}

^&{g})>rBk`AOYlX90vaI_Kq86=UM^c3wuHTm0Zik@selgd6(=u!U-izFFA zU>+8fE>M0M@9U8&x5XJI=f*cAq_Hp z-hi|J*4Z1HK_m-K&9ddjO4D}ohFL5a2|hn1LAwW@A|) zSHe7gPDH} z+zDijSpFCKU)1WNtQO zGQnAn;KA6AY|i@kHCaCnpmF(DyUp^JP~g`I)MCX}RvDCGF*LY=LC^(lB5SlNIH9^B z{I6g5?^XOw<&d1JA&D`u>?2GmR#b`}L^OK7ypwI5zy=gzL(?wlz7T~%^nfh;ErI~Evx46n_L@r=$ko2jhY2XCS{zS^aS%|`Oi7m!?ECx*1*ME-+@k|f z^gXs@zU%IhSsu!jO@Gk>RP*9Nm$Y-7IGa;4jcI2%Iz5hDR zoYlE(A8HWhz4C*2B6+0gU+o-Y=#Vv#h6!j}HH$L)L zof*!~rW+fgH`cL-1XXJYP|tebv+`-V;4VG%p_B zwsdkBVbJ?HiH<90yV)C{NRCg>eI^F=LT+8lJB}mTQ<|h)l?6?Y*-&$n?`|*^uMp*^ zJs;d@wT20ex;M5K4Zzfd#l*NR$>z}4mWS-zJ0#}sp(V25;vvj@H%&9Li@~(I)%W=b zm5pxVBh^ViNZCE>umFgGoFB;+f_E%RD|n72eCt)2mVETsKJ*REmyj_!@nynP7hd2e0>uKFA_xG-gr1*T%XuDzj;jLADEe$ zb}s*IQD!!+0#b2zmUFE2cDkQV_RYcqG>M=4#@@O9be2a=%w7M%=C7VQ7AtHN=wRh?VFS_vu!MFnl_*5 zB_<+-oC5B*kpx}N-g1*qn?G{{=@5P1{IX!c$wpMQJ@QBSZ_fH+d>dpzs7GUR1{D&q z7`>?3vwxyE5+|3eY|J%YRIY{L)4JDiy?z*F9)$ug@n*NF79%QRY`eO{-GFKQ9t^{L zYI^9!9~6+rIC0+=^W{7zPRAe=+_AsBSI+oqJ2IwBqjMc!NddY|2U{ZR5$+UqM4N_9 zr?_inwSkUu*0#MeKV;HXs^Gf22D17}s+38^@xvYCWzfZ;+m_y^_`~$SQ`9nTdX3q$ znnPESrT@C83pRQjOgp&P*+W>bSa^h=K(Lgx;*!;|CaU8ue*w`glL!Kz=@KJeG#Cp} zvwNf)7IpuZH~W)7GmFNbt}sUZ`G3YN6cX{7c)FpgtRt!qW?1eA-d}5{*Q;0>lR+Wo`V= zqjqmyKFE^TYm9K6ULk1RbmMBApA0Y^a{VNL zmoS~@laa7i7El_Wbc@hdAqeqeFf|$Md5m&n2BO}|qe%8(H^NP8l+iq{jva)#)06>4 zb_D{YoC7($7w>AE9&P!On7J2!MYY3G(n8Nb<{d0l)>he&YW$v$&|JO>0)SsV3<;t) z*P;t)5ODFStG<<&v0;Vq!JoM&{Tyq-_PsFt^p>@R#P;j=|irSfYFY+$#hHUj;{%(k0k)}#N+|c>loJs)z)pLJO z;LSxf*#hO*?*tG`z#$`yKXpi0Cw+JYc9#@EE*|dF;3bxii>z_h==F``6eLD&#_dRx zXcPA&ml;c}ntN&!5o{;mHbr4htuQL*FBF9Q@Xv?+qz&i(lZAu31*>+_K-(GBr#v2f zPS=U3A{~{Lq>6aVG;I0ijH1Q=^xmhrok5fXbbhSlJRHB@9F1JOKvgoiHEL5|&pn>v ziY8FuwVJ)vD*87NWz{GKI|-FOjFLraQD_Vr~Y zP8@0PeOLX&0-^ajA z*2M54Ux-^I2vp;|)$y1M@F7T}dbU~!C1*@beK4}<91H)ZLiDQFi8aMCRj6f#IoG8i z`}ao-sz9ZD6W-RD0EvdnJN@=X#|S%n=ep%DYHLC z?C2QZy1Bl8i`4>oQfXBIa@PP$7>xdq3ttJ%`E-E55@Z@QelCU>6A#4CdDyxJyIY;W@F?JJoiLF`bT}Xl~}KAzTZ0DrtJmkR)7YMFI+b z@}*?PMo~^Nh%F4wP=f(eX%578YcK?GZ;Bn_Em}a$4$DS=!kZ)y=hAOviD1zYQldyt zJ_<*<)z6kukh&*Qu`=8YF1nFS!SqwZ*lsChBC+U=zr;)ZXL~e$wiJnZ-IElaxMpo# zi!bt>Ilqp5S@s6XNs=cv$}NwEi;aq_qqKgu^X62 za^KUht#9V$cmTq0$nwfR8)HGQ|1V8)jYOE)zj#uM{aL*o#s~+#Yrux*`S0PjAMtUu zoH#fF03Wg5->$91c)@^I>-FlE_pvcxz}!Q`GFOtiz3_d-DEvuR!}tT*3+Bs<<{B*e zb_{az1?Og2@A)GR0P;FG;Ms_N*aSfVB9%oR?_!#?@Dtt~*M&WX`>KaQgB5>v+l@EM zx(CWryg1w?fXFeM$+WM))!aWeex~-^s8wFK3Wr>vKg>6`U9?I)F8`J1w3iXi_l#Yp z3QF3If8Qc~8uw~{4lvRMFUy!WIzoyi>Jxkn4a~`Nb4Y4D6Mdg&7F)LbOp5vwh3!>r z!pW$?zo&Y2h)HBOe>_rmwj=H!Z<>sIzGYv+h<~|iskK(S$ib5d`IRRh<%!Nw+01@t z^3qb{e;+A^&LEE0{D0enq8rfCu3CrDQEDyaj9`$X@NN-VwSQKE6pel2y9%Vn95$c^ z(=kP2iM1$V@7G3-nP0L{Z56iwg{{`uH6I-Pm`McuycN_|WQ&NP5vW7VD1wM)r3CSM zJXZM`4DdEew(7P&X*ZV?oQvSQ>F_Adm0HUeIcaeag;u2$z8g}T1fz-1uCpgDf^W<& z^__!70IUAjybQbT{NpOFWaSbLiaca(7VFw6BiDcJct=ujIeT$EOTF0;}o{LgG@ z3O9_g?g}6lIeq(feN_c^P|5Df-_=Uiq??3}))faf2ul-)rv3x|7#oh<$6V#Z1`-2~ z*ZdeEpa^AyU>F4L^(+HE)b)@|Fer?mue9OI^r}G}9r^f+c=c%;;SXnR+fGYw2p+w7 z)g1vphTuHC|Ak7-_<*@Pw;wghz1+0OYT9kb*D;KaFo;PvL^U@I0|TcxZDc?5-2Sk(kRp34&IachXqA~x|kXK@+2^6*vRc^JxE4xw zZ!xs~>puWUE(2hY)6N8h&M@la8d-J>n@X+QeS#ogx?7Yh=|l4(Yt!q<8Jif@6Co(d zH4%2rX|{sQ2T1sn0-v=A1@@Bteaju43pd4dsh;ORysgd@gtKfJII zT5{ogQo+HIl*C3XiT`zFz7Vigg)STmE!Qk;4SEU}1#fAKfqCSK}dqw+|D#k6N>dJv%PjJ#?SZ zCL7OO8YNd(>Da?i7z`ueg@T-}_PdgB8K9S=;oOD$I9k}35&-kNaL=Stq7uCo!2ehdh7as%Hz9I5}M zh$a%!OYmMDEN#$mvO&yb&@T`Uz&S7`Op9m2W<+%}O}>vV}?YGFgUOy}w^pYd5gFHDM zBFu4Pe|Nrpqci*EeXu0rHp`3%(*UJWg{?Bw{1h(D>DFy$o4KqBoFKWaZhM1`f|yej z8`rzJuX|!*ox8}yX=5$cR&9_g+}(lEMB(AmSucq~srz+-y@Mfril;>eMBE+^K7bTvAL#AG-3ZYH*DGnC1be#|_Sin~7x<7;?ML&pM zIL=ypA<5iL4fPD>1Iya)^T-Z8n^OVn4o_*P@}MlXZQlMwX{L4ybF)E@!;hje&X2`J zaZj2L6R0{lYn27E54OZ7gH1lD+B=2RuB?g!5G(7A^Eb4DJND$L-E`fB4eZ71vFleK z>`cP*c+eFf02SD%jM5(JAYKk6+C3%8*oZ>LPVTpgAOwJnoiDKB_gky-*;{$4RFPPx zQS0_!i*>@by6w9`r}DF1#Df^1@)Tv9@F^8S9QJ6UkC@^38@&7rZO z-&ra*T-JYV_x3J}Tu9mBO{O zVxory8}^t_1U|W(v0bFalIYhd5U2$CfY@I_9L^?U{qdcbJ(+7lZTK!d)u)bES82zq z)2^RNLHHpVP_by-77|^NJ5*w5}FD_Q8hK#Q4a&=d2NQ zMN4(_`#6~1Il@9&W^gO2LHytfy1Cil5`g!k&egQ~k`F#!IndobD>|$3Z~6DiuBliv zr7C%4KZ-&Z#c$@ts3CF24M32IchGg)4icYCBmr)uKG>0jrUPX0xH7^4GRv&>M48mV z&v`mWQNt{TJ~UB0f^WS;llmr3@g)oL>T+(icO7!|85?NoMKZ6(0#Iy(uE=Dwzzjj~ zYEfqFqz&*(^jP%4Yp=S9 zt7B5`ulOmMU6)<2-cHO@A3p_08M``Bp2*S_Oh({6kUf@pF$xl)RSENOPTrT-yj6+; z43(U|$xfI)+xplwM&E`*?&qR03zM| zS=TS9THi;*Sina&Q1PAuxVwX1EiUvP5+V}Y4X`0tf8O*OX_lazw8>x${>> zmksm|J%-A64U`wFn3uYs&TY7F&bh z=G9Vs4Y8Kzu6T|tb(NX$*A$n>LXPT%*Tk3Fs$ru3o-v{#Km@8Tmv- zoCLTHc*{|+_71ofPp^-eaP%USB?+Tzw#IvSf*zzvNh_FD_FIWgWeMeoiZfE>gR5cU z~x5v_&t}k2+`9K`)+IU`t{hStzkP z-ILnU3eqdqzn_KY&_tY`>C)SDL2eC~mAEwNOU~jH1eUSZWY%nJ?r`W@^r=n@rTJ#sUbQ=!bc&W2iKe-qg{@ zgWO7dKb!b+De{FstpvPk?5-&Wk6Bp<#<~cuppOJTFWnx88DI@+W_L!L&bC#joBb+_ zYF#}`#gaS4dsmwOn`E1Ky0;#K5@KzU6!vt2jm+(!tD)EcP)bxgVs^-*X5GB=%0tp+ zpA$l0K%XI&03fapWjpl&wT>qMpoG@!A&Ac&s@fai&{*>Ne7SizApDK8w*QFw7ENL^ zMDTrqKn|i(akabhVBY@u!zlBBDM>D$?6j5|VZ+2U5MX|rr6HA|>GrW$nEcjt&RGww z1xv!d=w3o<0n&;m)Bc0_hADp}fj!BCCKMa`hQBl3H&sgKPO4Gu*NyD$=j7%Dgpoar zM{3`Ppyl!GW6HvGb2l2l&+?Ufm*B`kX^|P-XM2VGzCGO5@$* z3{`utu$9mm?-F(Ko!91`7;e5+MSfJ}rU*@RaKfhfRtdfH#Ah^jCJ{(U2 zFn3bplg9l$MY-+7QL@5i8I!Y$jDj$OmY)g%*Jxe)c)%UlTy=|V~K ziDjRz{aYIddL@8gME$2R!0X8-;VcJRef<nhYrVro_EFhUu z`fRJ_)i03wL6{d5{{8@mHVmZw0qVx8V6YP64|mnD^<&dKreY zem5;L^D{g~*^eR%g-C)%VG$o@DFl3R4dt0>3 z##lo3O29CC?hwLQak}p67lhsfR73vtYItaWjur40`5eQ&c+do z+NdJSRzy4_$=n0qFryv!w@MjfA;6^!ywGt$?3oF$2Y$fczXuwXF%# z$nQ8O(ipUsf~b2z2FBP|&$LhSVlMZ#mM09g2v5GGsceEV(C;mAMhdm`RIR0xQW3G` zsypds-#|XDnT4xwH%%f3i*``PbbYtnSN#;7gL?8;>)?{|qui1u;<^ zm=Kh&MI$a@m_tlnui|?Xm(E;#FpQE%4apD(fnZa9gk-6J>JZC>0^4nI<{pv}H7y_9uwQJSE**DZl$d{ztJ?3$8r`_}qCS`Zi*%0p5IK z)>O=;;z2a{-cFz{gz7-3u1sgV`M_w=f9jjUx+eEQgWmD z&_3$!&E4WO6@i66o#YM0kTFX}d&G;@LnFi7l_c%iT6X9~Xc(dGG%I4P6G}+~dnvII zfpW9UA58J{V{J9i=O)5MQJ2jslD9-5AtbKdTC+gjeH!4ZJ5NI2nQBJ${@aVGk?f2h zF(^L?2@Qw>`bjZ*)!&MYBkwUObTI6tTqz6^>YBz(320_wwcTn8(GhNjCB0t7Y940q z+ZLYSCD%on`++{d3lgPA4sPgShV`-*zaf(hZw;BHuJ+aZ&A8G_6 zn&qOLoe2=>FFfLY+DEb08GP?C^UCq7qjrr5AXBpg-@N0IlD1{ynS{;pM3c}EU5rfE z^C4qAo)z{lpLTy%$h|cmr7gTNG+Uz)k@&2+@Yb-!;J1&4bRbVpGH1Hk;r999!MU{_ zVRDHp@Oy>n*H>A%8x|GWvd7I0o`<~C|FMqRoC@#W^6x~q^k=iE6rb6408SdhGOO>Y zBIa({wz0$ST<$_mPez`a_#0a5Ww_c2Cf}`Yoef#nT<~vDqgP<}&62K&9+uY42G8CQ z%8vKZC2rIb%d0Q^SRUz^dvB_rk7{OdQO&H6Ad1|n_tOWs;V1E3FC?i>4MM6zIPF0F zf&eXz#ey0U)WHMnjo1FfcAMK*im%;#ht){UJS~yk?N9oZzk0_Kb!+07Db4d&M1txg z9VRi&M(P~p#Ka#8?g9n7I>@8!Hw1z6+d_qkMMB2UVecC0{C8dkoR*=xJz{K&kdfQm zo~fcddj~8x;>AKDyf#cNU}0grJpb`b1&X`1v6{_l_g!S^fP8m$gw|j@K<97`jV4l$ zGcp8&!3Ph7i=H=2?i!oddHkH28u7uy^tWbwV( z+Yf9}ILH!^_^0rF0EVRxB|!@<3O&N{rsw-8_3X;PV(XI@Wv6+cpH+dgnzDjHafw%{ znm1a_uC)s}>j_DzVLGyGhxLaw`MZ%4Af5AF=$BqKc6*~EkKKlxLk zGhBFA=tsw}|EZZt%fr-Y>km}buT2?aw)OV)V>*ulHm$7xtjJzHHmV>GpIK<)JS;GL zqu;wq!8w(YXaV=>MEOcb+G%|g#SZzPT=4DEVIuVt%=tzQ8+{ zX60sCC;d<>E_5C|~$P;|54txak$ftXIAcqTuq2Ms6!=aLHYwAh6%JPH7(we>e+ zTX=Sy1jBpauXlvOE$IBdKNGZN#W0zmx~%S6593q%ZJP6^>sxyLflKd<&|8oG%>O9h zw2chr^Bk^3b^9{s>1oH>9i#(c z!Qbjm(5>T*D;W7Vb!ChG`fUNtqPABy&q2bs`pU0|pTQJxuGOjp?qd~PyYcK86fX_?^YiTp0YPSU zhoYYjct-UrfV_NNl1zRXDlL54_k$P!M4x!T)Dv}gc-HAkZvZ(BybwU4u}Kp3PkxlN zo3zN=5XhH5CsMjbQk#DE9czZb;6iuBe|@g$2bqdkjF;xalfSX;9o`8f}jxQ#0g z#t@MEl7Wxm^hzw2!qp4E-oe#t-q?*;>%aGUvsKa#^^(yE`tEqL#Gk<-J|fS;vA($8 z(3(iN5c4(FNTfSRjeZivWzX?p`tQ&4@_)JtgE`}stdXbkPUsA3Rq2 zQ)pyKVgzI{O3s3Z9EPtMpJ)%OTC-tNC|=lL-W~DZF!~+Tz9XO~q9vg%|F%6c6jITRS)3YTQh} zy4Dk!`C!7{HoyLRt2Ee2B`q2~S1nsms_u^*Ha9lLF>Lm$*7n!*Ba$e_!Lu9lLqgh* zW)ZGrBn-h1mYEiRAHLQGm5QHRV9A{XWZmwF{s?pt5utwcd_Ns8Cg?KsO)pQ(71jHx zWK=ykLC}tNTe^Ls#HW^eh7<~FKY!9?qq%>h5~iKwhs~Ax;j7;x?+^OUzR}(2LM7hqKEzN-Wp@0{?P*mexGvj-fYgy0AlZEBO6PCY>)B4 z0~D*Z05W-HFmTB&3|xhA(xWCMg!Pq!1#W%K8v1-5)+XL7-48aVRhrd#q)FoeJjmPW z8dsKS(1TfeVRRdvlUNQ1Azxpd51vuuc!Mr3>T{#TaSMEGy@0b-W@6cib71k0sM(c+YV1Etn@xexP^T-EX zFG*wqr2-zVe+mqL8-v5IhjG$ATL%Vxar#R>|MzZM|Z2 z7ok4&{6r{>;}mCR_o+(iQEE{Qb${%phZBUlyc9gJ!o6QljMHjCv6!$RZX~D0KL4>_ z7P{0U00q3nr`+iC!z!p@w6q-f+bDdsRQ|*3Jp_u8*75^S`bGqgB3&#EynqN|Jgk4ZMygl(&xEW& z9e!6mrc`ezFV&y)-^jk2pZOXydH!~S$^#BiScl;d8cuZnx*+CQV|9IpZ3(=#sBYs> z`TdG{DeAm%|0v75)8YBEjOyGENt;>PzAe1EoDaIH@o&CqXh|oEJ$!*j6U){>!1Cy0 zWlQ^XJ-A6~@q8o8ceBeZg!~48#bTF%{a*xh{#XDDf2Tw*11yV82EN(hx!k~^gN%0q zw9!dN`SySsU$mFu7(;KkVk3Qz8cHGip(n&I27fw9HjBkZ294mFdK>VQI$x%ko(!-b zcV~P)*8QHmsLs0kzD!otRnHRY$-@`^F#jW#(S84Q1 z$Wrga{_=6~fU??6zDw}@Cw_`Yv!e|f=0zE60E||0Q#VZT&e#_l9s*xhT~)g8xncxa zxvh$pkfO!7RaIgfk0rIKA4NAKkluY#B?l~o>rUBYve&8(THJByd&BjcS*M5U=8o9)Flj5kr zO}3zkKgY4shP;75NedlB;A{aQM~nOJi&PUB8b&4;7J#+!cyj_(!ve5*ud1Oj;<|5Rh?UTTWHhag#m!c%}y+aU&ONr3k}Mdx}5-Fr@_l@)uOF9 zxr2!xUT1BR*!tXNxNx!%y4mWd6mwcu9*VzC^wLEo+eKQRE7nO6nqT94V#g z%OJM2xf17_>x0=Hs7-y&ais!*0wJZyMej*S_Oz_H~9s6|hbXf8+a3PIs@ z-j6crlr;0!yESF|@5K8*3jYYz0=q*$QVkyNUBU(bbS9~oy!aOoA{1AfL9g8th1>i9 zbkrU!Ic|YOqB765OHr0nOeStL%NM92vBnZdvxpal{+Wq@2FDk}+0S$?^Y-%=C}c?7 zz~`UxTAN|i(Gy*88<}`8FBnKibKURS@SPM&PUYHP1;%&xWpV>+@6l;l{??_PJFlXY zWT|u^3&+_kJ{gIVk6iTQ!71>%k*8?KSkF#kLZ)L(D6Bt+K``wy4QQjCe?Y(mJk+-O zR=JZ2Xs!pn=K?Oft;zvP`L-j7@{B-G6vK&W?{7Q0_Ab^k`?DCM+H(T-&&4LGbdC9@@^WYE(=D?q4iN2NsZ#rFWH9Yl4MaH z-I%Y@wW%nP{av9_6Te-;7T=EXhm#e298#GnCAN9`?>9-}p8tlqS+|MdTUqoM&wocJ z;OyZZEpOm^UK?MU;*IAd{C_UMs~7FA8ZFe$nvF(wy~6<^8hB5V`}ao+pEoTTM>!P0 z&?UIwDm>gID~Z#K%~xpsZ%T;uD`^%wWyc^#HwBFck_e)r0o>v?mlDNjEx*qTR_EfY z6PhdfG6HM|Zj2t~{A6fe5HuuzKlAzcBG)YU8}>%c{jcw%W>uUZ3LC|I{4hU^gF5ns zMf&~E6LAjFS_xFbe8`K`%7dS zY?|x&rDvCW`Ui;v^yVLDH29ohD#PRA1ONv+Hs-wu2qghI4m1cJ)XiE%~Ez2_x;@ETAXpEg6BOe}{ zmx|;_>WysCW(1psmqXN~I~R=pSUKU>OW4&}2MsgJ&G-K>VIQgG06#;6CL1i|_iqq1 zLo#`?aF%Gh6)B#6#6J=e3fq}QznLxuPCAJ6e+)n)60;e{-=>4`zZ)LCwdnm(oBkO? z4zLuigKrYpQ}XZD^+d+}I?$l`$~a=vLRc#Y2aU`fg5dbppr0>~_<(oAh$F-VJUZ{H z!a7D^JCUPrO=3ao3XrWG0Iav3do3E4tWVuMZxnL}*=MJUNd7ZO8oC99 zch{9$QLd-+FSI7{pG@(3_st9UTmLFcvA%Zr0)dN(-O1P^xEul*_26MU^@uP>Re zf&L9zh^y+nBR0xtgDbi!jXuSlcM19A=U}iNAhkB7s_t}hI>3;aUwlf8XZ0$p^94zn zAfTwn$@tM$J}O&4fF5zP+f&P^H7VBXFckl^)_1*d_4qblv)tvr>Io-|h!Do(_@4fa zDFp4;cLL5+kMGUsGW>E4(B5(f64)l4z_yP)_Q1~;-dB)*>lT8Qz|+JFE}Wia%l$sH z@EIwK{lZ#hz|cYn` zY>v#!)n(`{7B9703DwxcLS68?$;X`C(oT~RAF$^h8G^DIkB}-%K+sfG zdu4VpDC3{=m$i6-^lzqO&%ni&$#04O9kfZD6u@qCvq-SaEPU2-!11AtjFi4+g*L!xuY3|2c1#kiS%t%#<{4b4n)aXgN}pJD01f@)Sl!LtD~K3Z zE9IZ}R?lV3{2nxBcc~b|oenPQDGj{)#9V71VOcL!6o2=OI;LRbay3TR+(P*F^-bCuF{+HU7TmWf0&%#NM=MBG&Hzt)6Q*WOPs7Z zHKaWCrTO80SPs$KuTx(!XCz`EUm>#}EwNO%l4tv!F?hpIhJ9obtq9*#nasVlMJP)K8N&FHNwvBdko@feU(xr{rpsuYX6?hb0!zC9Ae z)YkwETC)wjFZc2Sz}pMHnKOB}xWgSzt6O)aaQVOhcJSiL_is^?PF2MQU`VY^(k=8sN znMdCRomU6ZvQvHS6$U8KW2%HO&erdQ8}gr$t5|A*ncas*##Q(UnO{c8c~K^_(EhG$ zWF`Jlf!^XX2H@O zt~4SV0jYDFy3PSwH#M`Uy?#IAV1)f;!i{*1oLFp=LAop$M8cKq?@ESM6jc} zbGMV(t!BkoM*oJ^@mO+Ve!gfOz(It8h&k7XkCL}xn1&N!=dW%>4)TMAA6fPbzCQ9) z@Dl5~n>BTOtd9!z;5BQ#l=HbbrAe0gY9asAQ)?rab&)@JaODw$zNXOFB zjY=;mAR!&njdX*+0!s-9(uy<+DlOe5sf2WQcQ^Yket+>k&-LtI=eqVj=RWtGGjq<& zd}hX)=s42hE2#{sC@3@xl0#!6Lea`^>rHv%HL{Xa>fG}=7y}pnMDy!%MgKb9K`4#4h9Dp%^zI=*nF-d``dOR4f)&a)GAUyi@U{U7yEf)-C`!eYEA zdV1$l9&a}&@b;6i>YhjfetI7}1Rin(OD)AGeRMV$fQCQ9zYOwJO9@jdlgY-caQ@1qu~? z5xHq^8X)x$6-=hE+RZKQi+C^BT_mA8z(gnE;dVmS5oxXiU7;V!**OmoT*Iw2@A{p! zf*}5yUY66vV!I6#O!EEK1%D}AqR9Wg+RkEbCUv}zrKes%34Hd5Czwt!V#b7ge|$)1 zS_}iVX9Bu{z8_9GHEi6PK`=^!y!@w|9U$y%G_s)SrJpzW9|bzQ+t`A3on%8AIM*As+GRqfw&fPr+`Ls~Pn>V~RQbGXd-~B{aPn7|I z{;oet8BjSqBOL2gS8GVnBbDBNbSSKB91(rR6G+E!|4ih1LF2-s*NtbmH}}0#v;x<= zL<^S8h-c9SGCR=C*v%`nIe!0h6+mkg>*tuZwm;cpfKT=Se8+_*UWl1ADwOiKutj6~ zLWA#6+A#z0gOpU8GUTpEcG-zHyDE;Zmzcn3nI2Ox5&7olvL2qPjNN~*Qeb6eM zo>OQCPt|ug#IYm5j>zde0Oya-a)pLK-dEAq~G5rmSos$c2Yzsg^CJExXyj`_`|Ud8BquYR28G|@^{V1^w)3^&kSQrTK#ZP zND2ahd$K>(SSs>yAWe^W-$wP9!}(5!b9L{kVWXr#H;~YKRs_^XxzE2fMUAmE4GjwH zizFudzIu%V)-Xj5H}kUM-Dpimw(zKJ~;8|o7i2a8D3atXKFrF~? zWikjF&zshr<}5R#JEhLU;rn- z#rhzSTw|!RtA!ZTfK%ZarbeLjC!bxsfTPj?gEFNi32}t zT7n@5=lY&A-gBhw)zDHmA<4G}hHIU&o z)BC8j&sWK{gIbIcjwS(YsJoJ9&e*wXL?(ZbMA`DX^CDWMPSv{1Gc~SE{z?nlyHN>FJ5!jy{E~Ap zp^SA9N|bB)df_un@eN5kqg}rmGbMI3JLt9953&|)z~M&0fx;P^{|~;)B?y{p34B*! zG~H_!RJC6#MAg5^I6TR-J5R|WI3=9;4_~YjD z+CB|dPw}B{N1)2a&u5q}Hn?xYxoJD0yM(^~E58O|TLI4W-EuWB!_gueM4(np_ELpovEGXg=QVC)!%V zp-AW=P=2w?wZ+ZCY86x~>3T7Q8(&8kV)9D-73aw(y7*!ac=&6SdnRRh>89;$!aFaZNF%s?esAS7>KWqV9H*71#Fy}W z;EbBf8GaV~r#%fkH?eU#Dk2>wl4FeT-RXJ5?Yy?Ogc+3V+Iw1>Y7nic6tDN&eItxr zIk2@ZzaI^@FUx@r4Yu@R;yPqM>eAm>BhQELB>@t$ws+S-9+UHvt>S+!YggWPu&mr; zC<*{!ZX~BOd*7!IECKL zhpR;zsZ}mGviu2n#-Mvg2~=~t&FW&$_*~SHd^vsLuw*(Y)SvFtMyh9p`kr(=^x4wQ zQ_F?Ym2IkfkU*kV+VgO`f$Tp~QG!feOC9?LMmSz`M@dBxpS^Lck6s31hS_gATb$;c zeD6uB_7EvZWkG)2tSg{tg8Ok39UuCJgwbpC$f`r>?)fv5Qrv4PvNgF|H@*>*8!icErzbE6JJl`p!# z(epa?xiCs5ez-ELGo|R(ZqtVF-}7Yn}rtY^38 z^tIvK#SqW$n~s)Nf>LqH3#4C;I`1xqSk! zof7R3Ywmfh0+cldX3>A9R;#plMc5wXDUbYeWMb9qz0XWeP%my^7Q=7%ddhdbm=8k3 zopupsEbEJQbV0WrZX$if3Px*1U{i4H^?(MuW|<2<71#H6Hsf(4Y^RS#hHYa)?S&D% zdd@PD=4!#poac2IUKF+@c-NB-@wklk(|fUckX5%wkZ1raL8a)#nww>kGBlg01hl3G zJ#T4MGFy~YfM81C_27P}-L3W?AX{i|AXqDL_&WE%E~gjM`(qJS_wTYCW-d!Bba!G( z2=xo3_Ms@mLgQNO!5=>ifN8@7b@)%4nC{)FjJV61=~61yXBf4|N{c4g@7&4Yv!dm;zPVb}0UyBQNc+y96D^br*(ndsCA|HJG~G>QBJn3Lo;4W^2VwG6shlSn+OP zqDoj5Z*+Y69Xf7g;_Z9aD{MD$SKYComT8a?4*s&HEq}I_3K}T@y=-cBp`7St_8aTJ z0KwTB?P?oA8HfAc=@&0Wid#@#Dri2{wGJL)rc!W}G-dXZg1T`~1OVW0x;N-;X6J&( z-07|fMei}`x;pbR`J&6I!AYoFQB|5O_5tAuRGRiF-8ac>!8m<{6SyX_(<5cHzu6pc znC63@im_2D75iGgE~k~1a+M?w&GsZt1FIM-Ne{hKW;AnoL}?E}S-W%jcn8)W<6W~- z<9cz^GM8WMOtsiA?b>Xd^)_yJ6UnXpN0;@I!M9Gf1%^NP_h-;3D1A z7z927x*AOm5|7i=%((+m!h>YiHS6XkE#QI{$ya1_Lu2%o@Af`pC<>b4hgmu4JNR1C zV4KwCicm(TqUfSOqhiW>JwSan!?0?n9RY^s6JHD4(38)WJ5U2k^v^Kx1Tp!{;*gzv zRW=MULj~(i?~Ij39mMOY{Rs{H0kFGzbH?3?@S}86w7sfCk-OJ+_KF3Y363yyJs!9( zalos?@-d8H_2Hjx%gyy$q<@fqsZ1Sw2&MHVhMulX{%{>2w&p&$7hjmR8TcHp@!Ycy z8)Zx~o2}i*HM60$F9bTA);z({0uALhL(t1|bND}l0qmW@CYbF>Ux(coNYqKNAdaax z7*7xMYK7|}KFJxQ#}Q-Zgr(`sghIKL-uV8adqpcPsBe>hcYAX3qEqsQ!ewWU%rtdE zE$-TARFk#leCT9oRwRMV{O*l?&7)r={cEQ^NB$+wM_m=4{zk`(tQz{G zKhGm;hQH0DAX>y{N917&!DAY-L$t~wmsiP~zdnWmTZxDR;T$EtHKbVWKA*tU4X1j) z8yJ3X!?yW0DtL45lAf@d&Mvz1Ivv*C=U5q&tl|Y#PN#&ETD3>L-BjPX(sr7LnOR4=t&bq&8& zKbdN;4)DHF`KG%_YPbh9r5ML8XzyAjz1+3=a|6VxENOV_tM}Cg4*828z=5gqJBV?% zPHX=wcoyY;DL-(^!leIt6}s?if%A_d6MQ#TsP4=2KX8{fBdq0oj|k1Wr;UT#yB;&% zR3YG`P@x0ADC}XOx(@@nyo&zWhL6K&zkY3|x*(f>*8_3e8IXxL!2E-9$YMxO7+Ht4 zt@d{343l@FPG!*&Put9hb3sCK_d-VNjJ|>PY+VjV_21!6(C~V$BDm=8#o#&!IkyTAzCbr{i z`n}<844T~`D^ymlLz0mppA9ndX+73QJCk+sE9Rdt;xAo|b9722}DJ>^xI zp4mk?>+~CHYrZK=I(CxYVA3$4heTFIw>Eo|+8UAOt`J9BV(c!pJqMe2W{&pCsdu-VHckos3-QQfusI zfkUc;Kp(FM{MeeHUj7A@m`$!2kyaddx83$aoLk2AdP%23Hj8A9SO$%d_oYp`^dodT zG_}iQx}$f8`4}hb7;61|-PF;*9X|E*9P9fz@lt>s4;q3jr%G2E4~=cD3Bzes;t`Ib zI3^b?xRT0pv)$hmBbbQZrHadIbb;!YOIBtQ1Mah!YITvZ5RtxrNi-n2AVN00XM0n( zv3`QdqJ}vxUICeDJf^2cxxox^f+J`R!|86bul42zm`;1uy!J@})5M@h2aEeNH_sl< zfpGyyjJNL*iHJl{Xbew@$ifvPn-F|%-iH#fsJmm`%i%i7GmcR*eE4YTg_G_Ck3P@Y ztIqy0w>&pPa)|??@?hG4{r32e-216-*afz%93^oKbQ(Mog(f|}I(&3e$}?`DAv#9S z-IARvsXp3L_;b+^MySL2WV)uwMZt%uxH~rUq@wiu*Fo+g*s206q4t(i|mfXnDdTJVZBwQ{I4lRq%{QJjRp!gMUxIY(;n+mF| zc!ntLOrsmjs!8TtoOt2>{IVn#`8_}ad9~k&Cbc0i{@>o5IgN9X=L zusps<5yv0~fax5gZdCCW8&$63ylu~$;!Phj|8ojZ)nu7>{5!t8eEEz|#jvnf*!1I? zGLkq@oIIoxcq{^KY8d%hSIQxlm>?^Z;v^33oNY_63B=g9=Ds1j^NLz<)KoaXa2TSQ zmg0l|R#kdn$XS=X_QY`1y(_?fK9qM6$kf&1Q7p~eV(h<`xNP{#W+2vKuv211GD@f> zh_2$u_kDWdy+rUaDs z0MX_FJbdxP9l5dYl=pDOM8%x=9mkTOyomk%3>^}-)(!khU#-`hVUJ4_o*N60V93>{ z>f4+5Q@uS${)2`jid^cx-s^F~$ zjkvXYgV9forvL*#-eI75;bw?bU47ClJ@2j>i|#dpeJ@p`Sng)@X^Ty&0$CFT@b{Fb z56+H?x<}_9<4?CJm3sOWySHaA+x2SffY-n35y&!c`MBRkVF$J69>XF(LwbCPjm#^~ z$VZO>k+<2kctUbAA8VzI)ViTEVf}c^jQ}2qRwK7uC+Z-0cjt|y)|tO9-n_9_VCXCV zZ!CcD3?bR~pL;h8gRDfL*5aDS{nR{{HM$@arnvqHfTvp`HN8Hh9w_P<0PArXsxg;> z&OUWFJn1jCM|`=V&2(14Bdrelvjjj@-fgIsMX%hpt2dh67*s2jS?68=x@{QWw9e4= zUdTp)+Aars0b$FPdC4$()!6~!lp)_utq_0+t@y{ol~>b>PIrjNZKrV(^w;LT%jdY_lRC21~7AzyPE1wPO8Jr5#UC;z@hkxfr7^ttD@Q~ z2LL>yIz6OezolsHG^4oMlL5@4#vP7>LO^_c>fhz__Wop#&Rb>`B}AxFvbZGlrV<-G zHSi#?`kX;`%zSKar*~d2#!KwKl|b)X^P-221c z{AlfFfhTTF{zUOJ8$tI4Vp?q7C@V!ekc;+4mVHNgxl;WrUk#^o zK{MD6u44*6(Np^^jkKV(+v}V7*o0@Puc?dGGWcomg=`0|JnUaVab~O4c@8u-*+;>O z^4G*NEh?OreWlGyBPb(?^K$)|&1HoLH`1fd-cW=VZHop~^v3Gx-RXJUf@;u!L_^Js z4ynui`>Vm*bAH3DOdw2AsU-3Al^NojYBFO?103uWXBDkPx$(4C7fay%-KDn)JTm))Th6uC-2> z7J=*p4Mvz;zht<~1(E=zF8RK>`1GO{`Qg{o6P2G$sUg`VZ8t*}{fBLY=Mu7ZOkYVn z-*>hkn`RGnanb~zfIkQf0KBHLYZE5$V2Tl_FY96LB$aW8I1W|~5A@VGJV~9n-lGe& z+x~`W+`Q&ATrGKjr5ko_=+vzFXJz9$k$bIr+%AW7AR=}&L=9Kz{c^zKR7077);n4t zD~Ekm=9ZUFA8&TqM0$t%%DeQI@UJQ1%_>^j6xs4tFU^s*MZVRQo_X>usx9mK2 zh<9UNQ}s4&(E08X5n2NBZo#K%TX+wlD>>hf4Q3osF8<<~r3D+S6Xhy(hqJwkODp)GKBSnLL$gK*)Rbp(Z2eZ@d;Qln7!O(c9pnkc)>QN zA3^G<^bkkZN<-so z;Jl^I{TCcYkuUOI_0ku$#c9+kd2cP5MRsWYK$Sopp%&AnT>S7|%5cVQf9?JkZIhhd17keJcq$>+~c z8@`sbK2wV+-3LR(myhdyeT-#NPCMRHp z!-ka7=>PyhZ!7OUU-!X%jMoAIz{7B3$uIj&VEXJ}CK3=L-ihMGe6fe|J^DQE;!>?q z-jn4vRKE9n6A`h3#%A_6Tdi8U1BY?AB0+7{Q`>5HYi;LoP{I`bq6V9Ls#5m|!Vnbj z7GpohWUCR`Y~^s&@DTwBqcQP*qqUKVwIRI&6+Ba4l2-blAhi?nMO{&_+VkyiZ!&6M z^d?Zgw^meGZ=zbH7&a*OlaJQq*4#EI{};StjnHG| zgI>LdB?7+M%lb!`C#?0O&EuQ$3FB7nMtRiPWcnM$Pp+3l)@J4}Fo?#1ia$+AmFSgM zJA#(C<&umx1=$KVi3)47w$?O*d=+OK9mO#kH!(^%9DBiGpQlQ4Ylvi?u+gX;+P%w2 z1(b7fA7h|^0l`x&6XI^kBC9fBb(N2`@B@Xyt+0^S?yj@1`G{Iyb)hAGFq>~&9!b$; zp%Z<%${491ef!23kkpQGLeMs~`MOZO{x(W&)PKEowbwd7pyh7)_%umrV(ln}r&OZL zWwDX6n~n}$dY>O9_&#|XGotL(ye#4%1?oWAI&fJ}t&@#b>3tSRc&%FlRmqXL6pn{)VYnNxvsWYF^Vsq97 zgn_jvY(PicbKkmG8CnynF1DK^O6BvGGRSwrcU^6i>Ndcm)p8=Jrb8c*i~ER z>WkCFDD=wFd`8lbF+D%>*6vl8A;Vj-Lqu9AYR`0jW}bhUi%2AbDZb}p#oPu10f#TL z0tF|yMd;1VgZtvilR1*8G7QE4koMFOe-D#-jg;~RFT1ehEHR-x%BN1FZ%Mrp|X{qt{BbS#wxo<o@U_t zN!33DzehrAueNufeO7HLf4-O zO}Bx?vfVEF_jBKIXqu`dUhU!^POIgusas46M8X&G)zzoPZ=Xb>!6Kot&Ie%%*{=_z zDDqyS>_n$q^0uh$Q4{0m@I?R$`0O|98Gp1SPfm|>6DeaP8>#+<)Huar@fd6StW;d4 zg_EtY-GA7N&~He|P-LHJA~!lckWWi7%CQclMfbZOz>4-a;@Q;kw?ym3&lIoMaXB%{ zUUacA~) z9(n^*&YZNjAgcnQsyyU(Y+EuGOY}BA;-GgNr8iPd@2hj$YEAitn*&gA=|k&$`M>3%nzR~@7aFQLx#bL6ND6`P3b*cgNbQ+bekck>OkQQ4O;y@@Q==E{ZXFYc zA7Dl%f1WqUA;m`vvQ$^7dd^g6c==@?G2;%2sVeuP&~YJ>Sw5#2`f`!iPY2E!+S8N*S;4Q(R|k_qkjFS{lCOpv!WKH?`Tw+S!?Qql-&{%Q7UtT?e!-23TQ8`*0>Rzoaio_xY^9hy6u>;mZ#>yGIH~HGRNM zr||6E`Q|dLk%&vlMDPVn5#r6WhXD`Q$$2jiQWA00Qzs_&LOud4Zc|BE(ZR==m4S^I z(qOiAy!i58%fF(_Y=?7Dg4JjH+xfYpQ0UP84X8gguIVI7ldQAIknKIN&oURWWI0hIJ>^FD^v_iJG zS!8P;lI&LXZvo&|4$XMQ={|=O$|^T=IKiMiZggp>9`a6#T8D9Jlmpx;2+3Z4_*Dex zJzsZ(9in5VB6y7rWqi5t4hTr|T`PaO)#IDiu$sY!z-G?7F_;Oq5cySjN^ohXV6|QT zIisiX32E*~vNptNw{CR##M4i~&QSc(ldeD>4YaCXGWv2D7L7H#aGEx(Y{#$RZkUp9 zC*z(Wh%veG3dx1@i{U=~>GOnu+NBg{N@u_)pT!SytqnjigSp~YDbp7(BE#TsiTJ}G z=(k_HqTbSccYbv4C5~QlUp<3HD8}m+lC9c|7K<`&bjc&v@l??7uWPz_Z^A}+_sXUo zATqZs8~83!ZLUw%3$4U3+`d95&jgorS;Rk?*s#k>18_lEWn)lx;L7pZllhO9&ZWh!|n{Qu0lV@wA(f#EZpuT=TIH$huz~ z5+4YOLvT0BgC(ZPZ`(xU!@(3uY`8p6(9M5HQe$c0I%&d zaTtBcigsBM!q7wVv=u)(K_mM){!?e%8?rQP#|# z31}jf0Xj9v3|}L?AcMi+jCm|4y;&H?_DTmVON*AxhO>%AQ@6BK zub>p_X=&&Bj1+7x{Up&FD8;0sSI&$dXtBs~kn`l^5h{2ahb+MtRJ{;oV;bT3{wScy zeNII%U1rE&<*VobUziD&i{l?2)(B$Y0f-mT$V$IKnr#D*Hw#9CLttI7f!0^2>n}=) zgBQe_F{7*RpJGz3prSh#007RGtsfF9U>B%#ly~bp47vb@Sc_CRAki|WlhO2Cdwh|D zV5>Vfd#**B>c-o^JTl0%;5&`4uX%Df3|v8(gJb!8ICu86N!%UB_$6 za@o;@rX6qJ*M%E}bQL`F+gyj}EL^|-xg^Tl~cU^@#DCK>qd)O3vI{uNyeF99|{v7bDu z!vKFk z6xAvdN6F!YB7XaWnKU@qb<*ax;Gceu#qdUvGWTdSw@pxJXSl!Xr?L%Ph2{MT<}}~< z4>8;|<};U@)s>$HxM(%ocmevl1V=L#vU!(wwGi3bc#b49SR|tLhjFv_=mg5#i&mU| z{-ylQS!zdDRvY<#s@Eauv(MnmN9IKEgtg~6Fchi4(2mwg2&n0lptbshVX--49R!#! znDXCf*&POe2?VQzj;qeG+lkGZ_Pmis+7i^~o$Kx*Yvauy+}bsPB}nN|>P zGAC(25=#z6pp>0v6nP-9@ZjNefp`M-3j>H4>2&?gegE5XzfhFhfgrYv-fLz{&?Imp zK)~!6O=aVihG~05jO`&ziUo2L5g)ZB|4YGfMl)U$V;*;yhxxsWF&J!3;7D0Cr55AIaz|UiZ*}xeUs`!^ZH>=+(o)7s z3sQzo`bN}{c{Lp5n6yS#JTIsG@OmWr^=q(2QBY}g90x|Les$A#$rlZ@@Csh&I{w4T zS(2k}ZuH8fO`s>n8LW}mp%k};zDP2lf*k>Qk5d8hy+%tJpuZDxSj<>pu< zD);LXd6`RfMID`ac_*HzTHVoSdMsWA))}+Au=EzwnK!sWmm@TRm2}1c@{;=fP}emp zFyq<&Sq3E?a|8)sBbyDdx{$A3sK%ND`%Ojvz-_#IejSKDawlm3peFL15HLP_eg{jw z^Fng&2iVdkQA){BxYDDtTKb}J^vYk!xd=ojMnH9+5tL)1EW+pg?O{e&xLL^UzbW^( zrvcUoNs2u}8jQg5Q}H8J4x*S-7I>-Gi9Q4u?oM>v(h_s?o}fBMOAf^O(FAz`M;Ffe9ZD3G(ynGFkwlJ|PcH=7Xl z4=iT=Jj_4t+Lt0vPAwfg%g$3yJi7v*f>LwX5((l)8P#>x`wV06Q^)$T(H=xgyqT#- zbTGRxf?n|>V~P!$4{MB>2!OM0Nz!0Ha1wDamF5?=a2qI;#NW}W^n`7!wZED?PEsW0Pg|=XmB~~%oUe_CmD;r47q&j`mE^X0-QsXPBf`}Tp25b+g5|W4Xv|D4gWz{NV|K{_=WzTbh zV1xBR1}SC<)c*odh`!-Osc@Ajv$ATTx){Y#S4+MMSZi!B zD%70~?csYW%*e;gvpM8nLGtC@UdxB-Q7Gp~W(;k`zyODiC-TsLG{OHh*+b#O4DS~9 zVC8()fs_E?-z0Qdj5Ie)>;O4|hp1HJERK$x?b!?RNE#H_V#@D_3q9cSldqxIU4?q; zzfS`3Zi|EZOH9JCm08n1+$drMLF=z=EkTE7B>fxHB*QdEvFI*u3sL7+oj*O(8F zG4AYdyhI*7rmHjC*OH-nrN3kdFN_4C317OSfd_4xpoyW<_#*1sV6anZvAfblze^n7}N zL3HTsrn`Po9Wkis5gPR!orHwte2Gb5lRi0B0r|r!bkSS%3H+yTJxE>^BKY9~oJ4^v z{+fy2Cnp7BiO5cOVZp|ba`tuBJ7qv!-cUydqm4=wP_1AIh6+uK;>A3-iO-@aR3XU^ z$^I?6IIpJsUZ0E-*%5J*9@T=60eE?|DyceBy(Cc!!ze03N6r<28D`*5B~bbB=8)sO3(S7<~tz0^+6Gi|wSt zT(qE}=2?53W`lP-hpJFj*guS1T44i_wMN3d!K7|E&og$YMYMGHh3?PRxSY zm=O`kRarU$5W`xh0HwFaoGwHhW2x6QhC%^@?P@X~!@7_?Ztr~a0RJefXZ70;6Hd?s z+5u2wEy@Az|1@V}NxsZ4D4EDk+(28+G{*yow1Xq=q@cXi)TED}03P=2U&5G=-{jnj zYRxdt4QotpLg-V`-xB;z49fzYcr5XUiYthE0v8F$)!O_zM^X~z-Lx9(m%~ZTtadXW zKkx-Kvz&%y)mFqvQI*0#Sa}F7jW^r%T*%f0w7ix{&zZ%<`C(7!SHE;<r^)1pqu6BjLg^6x zoxNAs2qr?Eu#CJ0p#+AFzK!7r|uK3J_+@{#r|Dq>^e;H-M zu%+PEy3hw%5mIwF{7l@EQ~2Qx|Icte@-G+G@Dz1Q45FGQ_AqT*V`&Q?5QHBF|hwy$S%A99!z+GOOBuoJt>Bi z>nYtwH~sOg(_E@)0d>-AnSTo-&lQj%piYlk?G0a0@iDmmqw#1{+-Md%#hpo&pLV(A zKgwH<$_&D-#%XA_fWM?D>@uYWMCo^QD3G3opNvU1C5$5fYXx4I4oJ0c z3;ihHp!NSm`ccfNn**M3>{USaC%ONk9tTr572q<@rj+b%Hl^`JZXM zmf}+n7AU#6ozKiU^tZiREcd8Omo<+-EAwv-`p+mHAqGFK|AfEI+$*$*tJ&A!Gy~l*_7~g-`MltJuTkZeR10k?$5-YZ-gMzI7N92T=|4+;8KUyQI%P9)<@i6hf1?vBg7VrOdMgb1~ec(08yZ>$_ aAqlUul3wRu%S8iFzZY_H=x3krTjn@{2To56+a%Pip_2Xe2l|)Yrybd9Pl9yn&RH$?&w&PZCuX| z{7fGPdz-Bp{{QeO(Vk0$-{6FeeR-hb(KvKv>cHkrTXF-TX+=uVeyE0J_xg|jx9h1- z2lN7zc(SFs$uTnFW-}o%%#uFm4~|T-dj_vU-7@gqS7lnxOxl8Xh2Xf`TnxJZyPJN8 z-FLUm^V{)oYY7EN$($|)h{Juk_o(Sl#&$)>9khf@9Sv@zc(Uqf+{ro0`+TAF2iA_Y zG4_Vga$yx}UurK3`{`Zbz^NJ2yo)NJoCbdWd(dVdaTqL$oPrU+ojki1umcn4i18%^ zQ%y58@x~Jh+rp$1{uZVYRDj~fQ)PADYuI|&vKL%oUctrB$IDA5SxuZv4a0qzi*kFT z3$%lsC`)$EhN@FXO!;2?xY}zK@NaEfES7hCFX#C0!Slz?1+aGzZ7x8Tl8N|-K!S_CF2!36CI^5gsSh~3I?t;W5-+^$^|Cn8R|8GT%*qPa?Jo6os zsk8QQxD&Od(!;9*(KEnvM3cb4r38DXHlkL=l+FcWn6S8cT#HYGSM?|zg(5`%xR|MP zg;WNfy#(mZ8I%hXo;>yZ^z~9kvLj&eu#ca$<~1y;I&rLVVr%v))E6O=j~RSt_koo& z1+bnPENDHZlxW!Ln!!CL`$LFeB+?t=S>ASekdcB?GEt+%b8p?Bv`J)^^!t4l>{D%A z2KGB&dLwijNG2S9!9>>!lu8Aj{gryP2*}tG`Yr^ES)lb>{gQDrjC-PCbK_nF;Ta<6 zjqOJnJ?fmUjC&v!@xwdn5$h7eC7E`YFBkMW9M(J>({O?Ri=9@HIBaq04Xc5^x0Th> zddT1P>0O_Kkt$wiD=Vvz5DclsdAm^d0ojPKbM16ERC;pLi&oLG);KetkB_ekuf4rJ zZ13u$#+#3L0x~j)MRaIYRn@xmSz~y1yFURVQY7+02uBu@`)ul9HO(UX7_g>ZtVZ4N z5{3DJqm&WA_bPTJKO`poSCS;0#BmPIdyVZ*THhDSbP|C6{;N=o1ZEFoFh+4=Avx!D zi0pvfzT3FTn}nz5Z$8$*#RKI#l`l@U#t+l_Rt151=&P0Pq>GCSouW~vT9=t|?>TR? zRAD0{Bkr`m_@_2LKBpbgG&>yMme0@6Pj^3&n?2@-K`K+Dwq(eQHZUo9 zfqR-?8>GWeAb@2#oMfArg_RZi*S9TjNRfWkqcL!=F!;$KgG*14`N@o8SO58F5%F*L-%v`&|WJ?8Ve0<`4zQ3eV zV8al(!TrmZ8&b&>TR5$&AKN~`P5k@+j-sNX ze;zOamGH)54U7SSX6wdI{|^EryPML^Id(c=^xcSIm!;2@2zQfX0Sti z1>3%>bec_z{4!y3zZx9Mxu3X6j9y^<*0Gi03CoK8@Fy+7L1`s`78UrSt)&5r3UCC8 zS{IsD7ROWMiNfPJb-Ld)47-%{$7&op`cY^hT#z?zh6^2`q!}{=)y0hw=wL??K+qQpEL!5?SlbpLbP& z1!OY#X-g3ekh&Px7g85@FVO9@NEBAtpLKWnO&T!C^RT%=Ecs90f;f25k)E{)`=&@E zrvM|~r#}P{xP(0YWO_t;hB%bLrmcjPf8YIHym;Ky{D z%LsddpQWL?UjCh?nBm2j?U)WeW@Ull63#lmy$-lW{4S*Obic5T0h9Rtm@ESXo71F} zKTHtA%I@@E+g8-p4Q)iNlSYI{5$j@s)>kM{2Ki*8>#PA7&}eyIL3^iOhG@f)9-+Pk z`m=?)dj#Cc#sl_qA>TsTcl=5l=+JIEEfw_-j{(N51)nU8C}=x!xM&c77ksF*#O`_m zr)HkQ9^+|-a;@j|{Bwv2iX5y%8>TNYc%|Iz5pIwj7o_ijcoIfrJa7U~cEIN^e;Pu# z>0NzBde3Zc8p;kt#3gE3^0g{?glqyI{w$DLpubUOLXXOPYMkvhB-u$94=pge76BNG zkVIjGN~G8>qaxO5)bQ1US)7fk)N*cO0iU?fa3qmjuf)RTv+Kuhn}BIotvWM$((n7e6!6i{x)7eZ=cv!!9ZGkFH{+ z^*YUg8*@qe!`HgTG5NwRV{YjJafFjNlK@S2P1zh@a=34Q0TcZR>iUcuB5u~)VJDiD z!2EHOGgp2LK!$V-PO3Tp%lPP#BQqz`X(+A2YIFpSQq8v&${(VPiuyUEfbz@dz}l!E zLIK;J^uh<04+a>TE@?Ddp=eLUWe-EQhxdX$b&A$QqpQ8yp9zT{1K_7gg(mYP+e}@;lF00z3$B_cm;ae`mCj6^Tc3wB%AqqbNB#F^dz&#Mr zAIXUSwcb5=2j_ZxMewTLmx5-lp8=lY8JLd`6uIP@${7y#%6L!zwr9b=SRSelB$#|< zL#5Ovbm@R*35?o*HWj31&O-X#%3vzr<2ni{5PM0knp5E}Ud$(vD?vrw;WuKfR|vIj zF5vp&K^Gu<3H`oz6IvR2PR7mB=-gdZ0NH;U+q>5m{Bj|dnodi5I2izF6(N*(EdBs) zLAwhP;Q7~wJm&ZR!FnT2e`A9`;%*9rvZ`s`0(ngr-F!-Qg&AKgPne|Ghyr!=73+SP zLr^6#iHGeZY{0z|-yV~qi6`@=l!x|FEN}(Lv{{>0P4i2vDs&%cq5wL>W+AVem!To8 zzVA41UMM50sWJR_F!ks6N8WYK>C=^`O{WOxP{V(=SGhG~q+ZR1K+aCuaBls4B^vy+ zAF|H@gyYr?oF#uE6aF_4KE%|wZH^HsA}A?BteQ|ZQ!U&Wo!SUmJ0AbOx7PziVQ9}w zp0^pHc3lI{bV2yK57KX&`sO7QzG|9Jd#?;d*Sdb1zfa2t%o32jCuE>_kw?w@FxI}9 zypie&&56b1^Q!rz)I)k-t=$2dH{~YfGmV&f&Z&Xrf^9?WHPl}H1vWHx_;2zt@LzyX zDCRzi8p5S=svK&Vb;>?s{_+jLdslK{w<+btw|Sp!?+ccu&R08{1#=&ZIv_{PU1#lR zPxDPcgt%*F^EXl2wD6TGHeP#5!*KqtrGHevieRxaTE*e)|4F!TqqvI0@uU_ zJye{fkiSm{I|7sq3NTkqsHA)hqa({ASqk9OmZRzdF?xJT9)cODm>)8a=2AP|4US^o zk$II=o`HRJwbEBE9#3l|j!V(&RK*%R4#NM+|El@MC6S8s4?AK*KHnZ9d%ltbbrd-c z&!l^|cC(MOcua`Sf@+VR7HkVoKSV}*B=C<*Ci)}u7x?cEl!R`89^2G`(89u)O0qT3 zi)BKi%BDr3p8X40oCY64BOID{vbYG?jJr{;_ z%{`LwUzt?dq2|YvbAT98!!t67HL~6oBC5z?Qr8O-ENsLA91*N@LlV6SXR6N?@BS80 zM&j{a501%%2Le~^C5FhVaNiiiiooZ=0fNZ{OSp0^>WHcri2LW0kRpUN`$7Ihi$|Y= zRi6R~nd=NZ*?swCG-Y{JPGKBUDB5It=E;*fz8lcjen)eSkfiRH4k%Xc4O&6GOA3Df z+QGEEo?M>a$dUDZKNnsUC+P8ID&Mm^E_1`IOuYlh4G`dv@4k~8Zt`&2-M|{_0bOjU zXHHM!OB*r70+KR+K*QHa#+ybZDfm;m-%im*h^NLZ@F~tBjku`Ik zqb=}snfLn3-SInHAebnbSo7s7zR8aMA0*QtIY~TDcuQTo>E9!W?LlRAcrwZnwoTsUf{4uBdB@X?Z>>)hY_Wq2FTx}N*{s2hwXHLn*{rvqNg^#`%Bn|x+b0#g zD{;{t>Ns#8o-E0p^~5Dr*SJD)z1$I2NuU zHL9yJa&<%P?$womMa1Bv1dkJ(83I0yc#`%LD@Xz9Dw0( zL0$#m999_pNBE_HRu!fI;gJW{-ItTstnZfz2;8dG_d^P4f;qyOYBAgU7!#eYlYWge zMCk*Ga%+9~@IE5B=jXa6eTda>o-o)rsUBDux9W;ge8xl{rPZ5bRk&5=MSH~|$P6)1 z4Rgh=pUsdHN~OoCCM%ga3B&cGS~}LiZQx+l;E}0}{?{jb&uTz4FTPD}#fZZ3gEGh$ z1-M{kq&$hMbC#1(gNY+hAy3d@-yfq;{LCR@#B2>?qC%RCp>C+7HQ7vKn>bOBVsQP7!2itaf7h2Airn9TZkFIqa7|Om$28l5=(?r-(KGAB?h&$ znV;WJzc$iexZ<~<+w<71khWiWY1ySniGKUpH~Af@UF}ftwM1f&X)EHEHPH#H?8N+GXyAJVym`^U zg+OU5bW?j>uappF2$x0k*_)lkuj}Rvjhe=EZ-rv?DJgY99w!#>;%w=DVB-cd@F;6r zX%)3QL~IG7P6*;b%yg^Sfq#?C8AS5bwyXXBj2{sIaXR?I*ULixgIaC|Xy2@>m+5T0 zZ?89VpO8KqKuXFvHZ0;EZ8hfQX5O9Q4i)naOb#J~UgB41%CXLir0WUzW1=UeS5KX2_3 zS2E#TUN@t|ZK}Tlq-1=q)>+iduY1ofU3sc1?shk?wjz>eal(j9p*1z(pVK~ z&YV!btm`cF4cHPW!fZ1`;rDW76ZP50_7znj0_I!y)7fJOqVve%hWf8X(eBNkRLhvH z4B_`{*m#*Lz3a%}(1?qJFKaH=f&6t)_oqAnQcS2X1?btAd9!(;!~@rB`IJnCb>2c@ z$J(}w!9f)0gv~#*ev*Me4(GV(Dw~|CELsxIr`8#zfhI9wH#URjk-w7Kn5J*Tu)kuW z#4VdP*aRW9yObs1KVq!szm|6vmF2W4aV<*Nd%C}%;Zf{U>tdE-HZytWeWuM!i{^K% zo zF@yw}i|5JU7a6=0i#>%EmxqF{3%XLj&bdnhW2umwu>%hS#MF4SB$7Q=9iUPCFopXN z-*}LT&}J{p2hb_Bqy2y5l2w7UTpJO?sv_tsZGN-SD!R5S1~ zAda!N!YjsB`q32KrB7RJ%SyN2c@mrfRjSMQv5nw0JQv*&{gZBMoBU*(B4mcj-=rUX zf$anfPJLBm10<_{KMqJEdEQ@L+x{T!;Q1H+=kAssv7g--1fi6c9s7t4kkQTH0%lI? zy4Q9nlZ>@lM8zI$I^1BO&AK> zX65ct!T$4t7*rbE9kgxk4y7!V9%q^wSh*tGd<4%9bM4ZpK)1V z>YpT`w1*+H7rAfl6T?d%ef6VE0^v#-^c)V;yI4(2x#Sub7i(Rklc;+KdUZ1R8S6I> z{3D9*t{!&grz;??i~rjp5LeIf zmRHVwquy#8_W`ZA1{cpxd@OjKhuCxpdHv*MR7K)eikL|4W*{%TKgK#noS6@J+&D{p z1XR=D4?!yby>iIbiH{YpeNR)0reuJNU40PBQat_^<=z2ZqdS)V;?#ZzuFEEJNoHQe z8c6kZ0PSIfW%mZpK6mF@EQ!gac3+Uv#e18GF@n~H@7CE?M-a&iL*Dg?KAFRwqr!_G z+`buLD;}Uwxesmd*+Ts3C?Mqf-n#I78vFe~wj|P037XT1AVgr(65Ful&r|V$oT4Z1 zAynI-d%gr=qPMjjML)<~FG0_Crfi;%;~?|EsTXN;2<*+#(b3rg0{bG(ET6k&u*5BR z+XQ!M;o_HXN6I>t{eA$|HUSTaZCu=~Woa5lbZ3HpCP2ebwVliR6Z6G}8S!#M-laXR zWvO3A8SB3IHomj=Pwj_AXjAYQw> z*mH{@a$G^1%JfU#%tu^{){z?d z{a>peC92#B>{I1kna#1vTF&dME9JdTy#f7EIeH~tcCEFbQG2^eh zzQsC}_s+P_(bo(ZNcaFvfzafd+GzmDq7tB~9BOIT|F;Pf{r>c{=X&UiL62xojwKT> z9U9m4KPgA?65jq0l{PzCJfQPjFeJpgc`4k&hLHZ{ef6}}TCtHA--dh|u2WBU1h-rX zH${;xhqDAOWeTmPFL-oZ5$8q~9`_CCX;Uy+QG}0cUl$!DNdfZo^VBiDl5o5Wf}ZtE z<&)OQ3|M}r&1ZjP4~b4PaAoeH_BDjTW1O|^NE<--mn|y2;l}%;3=pltS3psuxT+dvHFIF z$5K)sPrJ7nrwMbqF1}%KyoW_6gVuiptljyE@`+5+#9cg0AHK~!;}$bv5$!Xf^vjBS zbpXwmw#TIeaM^|qakIS#a_o0h?f_^Oo6AvsfjB zMnCY1{G*>ZE_jiC1TbC*c89K%|Hpa8tgdU`DK7FT-oAn13#O(4q-N%$-fr3{py zU@9R)nsY}Gz~VY!uK;o5rv5d2DvYX;9Y8W7&lbiO<`V*e$n|fAH)|0P3lziu(9|Z_ zu8}jSaeA+WrjjUhsr3IYh?*?-=359U?ocLXl&+IbsvB&vXLCiJ^rOtNyC=A6qOND*?_lpOC{Cty)47~5wyLzy1+Aztqjg#!Xu+DVUm%4( zrZdB!8i|L<<}VY*8nv70O}%z6Zn;1}Iq-f+lo?@dYWT=WIw3rO1y;tm=#99+4{>oU zUO(-et?K~;fm)@`f-Pe7$fEl|!b3$T8^O29RF?m%1)v7k)|sg2nS+c>ojwO3%RN^% z4#yj_E#E&0)gK2>9hSE5ACJo9>BH?m9k&%+5mIGI5_6DvFbvNn8G`unRHFQ-a5#$T zrBH%c)>HsZR6|0X94s<_%Qs|W$>JhjVOd00@_Uvt@$a?QWa!(~zI*eN>T7uEv-ZX) z)>lx<@Bkp{MEUz@5I1FSNcu7B^3s+=l}GJZyyMx^uJ@-Whex_YD(QB8pqv8d?yUXu zWvft?LE=IZKwn>Y4A62DkHGo9L~$N1Z_Bpdn4Ry-y#2|l>FS4>b}Tvb9$S1|qAmqb zVh9(ndrV}0CGy<`_8!0suf$!gc8W&HDqH7k4x|U_Na0Obdcb1j+`yqrV|-G!lr5^d zVH6&~gA7C-L}N(VGDGi5!O`!y0r-@3?9&({`Vu7Dw|m@&SwzpYu#g|vM>>ja?B_!E%96nWMm-9nK1-HXiZS^u%)W#2!?9($*@`0$69SKPP2kN%-aSofm4iHzZ5NRqdPA?9t-00S#`yr}fXi`^xA3fCh zvwVsgxv7H=w%MeEBaVYXjw`by(Y+n9>6ta zPyOE7LA>NWnJX9*^yI>fKS6TzD(lqJI1Fhbj1KhR_1I;~BOorF5tj!h>w^oKT|6&i zusd&;Kn6@#tTLCxfYw+XNFB!%nxA7!*>Kp1F!j3#EW*ckufWE4+O-5*Zs2Rm*aqy34(kqh$4?&~0+8E2O zo)w-m#^gnfOu3lQ7j&lm@Syi0ciN6VlqK{T`^D}sRpopw`ZyQjP{%O?go>#Oa0ygW zMm-W>f}H?>Q@n<TF}|S|B(co19;SxP{Rq z3C5L{+aU%cgOq0kZydJ&fElSGxuGAJ|qS8LBt{lP}KQ_9v>)n!>7zjy*{)zMf zbB@!Mo^#RH+1|6kl0DkD5I9Ny%|F$Y&jav;*}2xvHGdJCDaSP;jSlB;YN^E94;dcu zSr*3>@$4k$mjE8(pYGkF+T<~1MOr%nK@(!Z_{b!o)a{Rlez|j7PjFGe>sC(00$=&X zFC+N8#x#)*=J;N#^eAE8sc)&}mlHrFYRSuLcp9<+;-t<)R1GmdNM9cMHtJ;d7qrHC z855ron&blT6ULzEC6Q6ActZDR&_w|q;rFDF+&9j(K9a<5WvV%lwEEGsw)dXAylJF| z(L`VVV+A-B(h1*AV98K;N|c)58g{GmV1CI4?6L%~gWcEL>vziv%MacyW?@>#`F^jb|P1_-WO_Nm8JHR7Ngon(SGG$L4}N8xIdkthe2i=oVP)#~Z%aXHJi^ zbBkPh?W?!JTKe~Jbe34oc}Na7iC68EJ9%M#`RaRs$8_fUCwoD5V?jYls>#cH1=xiO znO_APl{?QUBeOc!MYZc>n0h<8lKwnp~Rp2p)7j=yMlQ*O~P>7cbSk)HeAVB+o85F_4chSj%amXM1$rj z@917L3ci?L6)2Oe*$(B?6-=GNg2Q#aC~euZ&X@G8tG%4J~jf~ePhuXHGVuHAB^$>$Jdb_yTHlT zNc61ug%a}7q(-W8vjiQIK!LWisuX;J6iCk${7AHqD)sizwnd9krOYo>NxaCkiq8_d=NN}bV0616mST8 z2bJUd<(?N$Xq=8HBuxq#B2#jOYv_9B6Tr1cVcMI&B!S=9;T>ItKcI8Haat&lvyI;a z3+^1Ie97%oJniYF1H~2cZ>wrXCtIAI92KdjWk{8gH3Ue8PY(Evmx+gKZC9EH`Cg>9 zstg_{V)b2T1R(>eI;)Rin*aeaCAI~EA9dZS^kU(N6{PhreeCI3CaRTx4V1iul@6Dr z=Eea5e4`Knw&MU-ymN-%56T;GzxavM& zXexj3rQ_xE%zO>5;v*1VF&M2DSFnErAT!hFONe6Aw#|kJR$ku;TMsbiwo>FmOTqVs z8_CyWUh(3S%1ab0&qKUs$hauo7->CX5;<$cpFym3572z>Ro+0LT(Ra=K<0+;zC z&cD6`C;3*!j3(Nz)oztNyzHZxy&^)k$h2_Xuz*c3JtI#6wmiaaEi9MMb#K3;GG$CV zw&RI)haLkiuHGeSJ|CHZD$-n0Ux;vgsMsb zOx`@%R(3hNesEj&eq7eYZrBv~>}}O*3UHxa(GJSUp%NsrbRTQ65VTy^!-niin6OTB zIpCXcpOT1wfMH-3gXfcTPX$bz0O?C9R(5b}NU}a$`DBn`1!QzC^HW47nALU_ZcX>Q z{||_pggQio_#P;7{iFgxgUpDIwO$X0`OU?*u{T1J2Dlxiz&IL%poT`EYAvc$MyMUx zXI%Xt;b3VAk&g+i4(K43%)G6O^;f0;(x&tvAiD%1I66Xq_XJ zUN#?a9&j37D{l^J(XN{9ox|Fc?JSMDfjA;Yh@Hy6*|@Np*-^$5oLGstXBTU$$dYk`%0`fR_ZVul9bJ zaR;KRohy&m$$B9i_tj5N149;el3v=J9mqpi91}gB&o#+Tf=YVe?!^XEYSK^W7TFdt!pZxMDSnU42bO zf3!}&>TN82_4L>8Z{a)NJ!YPl`x;FDeOH>vDpz{ezNvHQGz^K#2*vS%{HV?|@s~cJ zsO+b@bmr|9k@({M#DxJC)~S>1=Lf8;LO`91#L`Pjc-JOU;aWl1n$;$^FgNW6aR>aI$v+9az@b#%M%^u8WNv!>_?9|R(U4~ z!}7)a(W70=@|`D~7fn6iYl#ZJdj!Eyv2BNm%!!hjj3~WIxyq1G;p4rU$SYiwCGMj1W0Gl5H>yT`}QWsDP3iY8DzE51mq zP54E|CH)(G_f7sttl}msDYiZ$<3rU=_fHQ_YSEk_pAXTQd0~`MADOE`4Df%DO;cD_ zk!oKkLn|xCgRm#eX^LIpu&8&>nVf!UeK*%pAM+Z6@>hvQn;NPAn)b1&<<9b%NC^O5 zufm5_BXcDZu6nQ`rU7(e8C3VqUBu00g(b4(kw#U&&!BvP?G0?|x-||bhEiD*w4UHq z1&_!VEP^iZ7rfA%-5&B`f-%{25}eF7VFQ$`M`E`crfk03mbLK}$cfaq zRc3;<|1^3oUd>nY7!^&(5Lw?W^EZKG%R(Q}W75#)yaD0tXdVzE<#}`B2E45g0tWqt z_YRKiA08XZ{u(xEul#Bkc+V{n6g36Ng#~bi`An7_i&EPk;$UbZ9c+j3fxVci39Z6P zgBFij57PtnH{Rpr!ptmyMlr5%ajRXXy7_#W_Vi|Et|9E1+>K`gxuvIQ#wCAuKzI7_ zIs^RBnak{Zd5@e=YU&vYs?Zze2f*f?h}S@BV-VrCf`{a~o=xWc1n^W6%S!t7PuP=B zll~8`Q&S{ji#E;hqVfg(4m3N6<4C8qH$~iFyo>>dr+(h8Y^3v2UZtFzc*$ER_F9nI z<=Mn}Oj$32dzOLLb#6ES>hY>5!}R_MK(`d#2*P#tug#UV@E8UR#0DV1Mop8?-Tofo zX@Di*TG)COKIJ_~f@`4EYEq0W7slR9MddP`E!Y?*$-=2lWE`0Ak}LwJgr4$gKPD7- zKiF-8$t1loqRx3%mTlT2iA*QS;XvMzIx%!4;%M7pVi5$G@czN~BwZ)G@)5k(dl#~g zsy?}zy*(eElxg3`qBB`V@7jNLkh24ZM-dXE+dNudPkbhrs=a`6dPBZPxOfNvewO!y zT)bH(1mb8{SDK^&Gedj4wiPBRI@7YVAKILNyuM!T@>Mp;03jf+8=qCY<&B zWUkSCxkjt?*Z}8COLRn{FwPrsMiyk7wnTQ9Bs$^XJX}B(7J!WDPk9CXZIZHyms|w6 z)5mI*IIpS!{lu@B1Rm2-rpoUGe*bhO(;^YbhxFYE%=SC_d2wfzW&cmpuhh&$BA#W_{uxinOFkX?+q8SEa|cLR?~)9E zsAuNJ+y~nDd`uq80;j!ECYT1y&V6R{N#TRrt9iueO~0nFx^2k5erFiT;tz|E*TgR6 z=j&V~X-^A=wj&h=g#7s4QnPM__vB5f^-{R&%ZX3Oi?F(bW!;!ozW!d;c9p1#d6!N@ zbR9x%`6RcQaju*Y?gV)_KOTrrl24Gs5YYA}p;NwC2TEC8Gz;mV{x~7Aof&4d3t=cRESC|yBzzhiHp1-jAA#p0j@2dI2-LJ4avk+T9 z>jlXPT6As0gIBo#2<$Isf;mU#N53K07*dKB?8uD&-M9H$IUYH}zY6gjgybqF2tq&0 zRB<*ht51N^@6SkH4}L+^t_oN_`1G1)J+s!^qbKDv{M8v1F5lvmkSR^m(HULnv7gyZ?|k;W)??% z_|$m=Z(@8a5&CxOt4LoRIR)|^cWW6>ao#AQx)TmR;^xpWuld3~ZyS=c`v9jRQnD*# zaAX1C_gx-O8EkSzQSd|O6HAjF>Kk4K2Bjtx{n7f9`=sn4GMzjs5P3||UBiKHErCM5 z#{Xo9$;5SKxZ2*CKk(^kuaH$N!fmIYaQ;-zrxMF9G|?g)%<)bnM<|edGZ1)w1AP++ zye1 zF6Y{1vL@E>8Q6;EjGQzGFQiht80aawJYRG9xKmNDRz}klMaG4uaz(yyV9wo5F96S9 zu|nO;1}CMH2nA?d%gLKzn;@TGCx17plVpdsjW-@mMrqEG+a2Bl^aaS`>waX@RfGIh zD^|$;LNm2`Tz)1kshG0?oz2>AeP#5M4{qsi)fEFow6qavk7md?T#<#C?RX7s1Ay}q zWTKnUTYN391J$CQ9{5&P&KPs}nUOLDc(l3qEt|*rv9{F2Nu`+|_YhfB5dipnH-9{q zzJO79`As5*&@XKAPf(+8jp>iiK?|!ltQnwJ+pep;P{7o;tUgj7PTJMF)WjTe6t+9K z+BGbRgZU`KC6RdXc)Je2w`*k^{=_^`x}T+!g^?}zK?z|8>HwP z`M_7eFlXV&)8y=;a}zs^^%FM0^*LE845{WX#XpvdeN5LABFj70q<-zB?*+D|MzRkO z+&TZN?Ne!=zd;x}iIhk=bZI0^*O)p;^A&a=c~@y(SX%O1AMf|iOIA$zw63B*-1Uq^ zQjdRsZ2l%#lwHIMa>vXXLWX0jSXfVae8IWol{Pc%+&~T<5Ge?mCBSJy99fW7&Ll|E zwLf9WuArY(QgG$Eq1QN>W*+0l<3oGg4w&Kz-N5qYt*3j=o&7jr686;M=3pPOZ+!vJ zrczP`TXI8RjmIJ`^H9~MDSVP{{|QPZ#cphYT@CEV(?1I#Pn;6(?Y$SJiptgQR3!_L zefrwG;y<>Wyy^$?Mxi$=HDG2*s_4a(hI!n`Sb_sCTVUOvH!5acx)BE&KhJbpt4&qg zUeT#b8XUh-PN(VCAPU<2zO40bltCPAS1fH)5L9t5!ODr~Y@K?Dj9k03w2;L4$aP;T zapoZ~FizJcwpfz;Y-=)q`fss006zhtp=~&+dKDkV9kV4CG?6d)b^{C3qjz3lny^*f zLpz%<5!|7}IDG%$s|W>mQ*p0{Aeo|W+V;)9lc-Pn9mjg+nS^F0o76>ILxTe;i?N|A zuIz5|&wxc+KJzE2R6_VDux~T{|C4J>Jn1~&3JFiDkSl&Hr)(km*1YCt|R!xG(3D5~P zEC4!GB5}9Hi4-Sj{M}u;CLa$Y%6mq+B2TRE>XiXn{H9Qa(-Z&py9lY?et+TKM^(GH z>7;<}9L3rWNw&_Wsvs0_%9L}(upF5jPUU_&Q%T*efDjKm1X&e{5(nk+SsIJvd>al- zFbfRN;hxdmx!)e7*29PyETzUlHT%yf8>ErV zBwuXt%{1xFA9N0nzQpFfYkt|g_SYoU-E?v^H4yl@qX$9~)O^~Vi>SJTTf&ImMQK`| z7lUDrko37?oVYrV{4FPF$H7q#*F%{Kh}jtv08HPPbPuUr9jRd`zy8hNQ=A`x*ZT<3 zVpp)=z!4NPVDCd_9C)gv3C?;yiS5EHu!1LjYv0gHybCSFys=U*MjD+X`c}RG!ef+D zfG(EpN~S87S%B-U8BxdiF*BjX{vXft5Tp96G3p%SvbyGGNf{aJt{dVijP#-3@z66I za~Hh7_#L-L%cD_Dq`|y`pO5dOba236w$zv8Ao-S){;8mrD3RAck<{ziGEJwx^=h99 zx*IPmB39vS0}f`=x>(+8))Y)?5v#>Zb-LNAcig49&W?GgrK?c3-sjx1mlVqMgU8Zc zHu#c|0eD=6R6r*4JGC1^E+?R_Is~GB@QM2Lesu166ot9whrfeXgI^6R*;PE%@pE3Q z-)>!p2zU|z5o1R+rfr*r{_GBofMsEGEt(uW1Ub*=!7-c5osvlF4KtZ^I?-CGkY*h1 zes{XkYUT;GsHIQ-=hQ=tPTO$!O?TGsbo}y)4W40Ffc>A!0j(jd0ULp&xBnfT$-lgT zJQBhYUXM=p7K5XDU#gROBWd6+ncu^E4LJWg(f;g72aXheYDfDxF?B-^a(Lo|F zF^pOhT$;Eqql=A8CtNhWNH)WDn^EGRgmk=DE&3Y%)9L>VuTxah+)QTa@Bdt1|J1F@ zNKgOdfyIRiCEhom-OLXkOpJ{))6)<>sLeicLcK?C^4)Rs_R48G{Eacxh{EDmcolZ0D5l~zS%2+0h zERHHdHV_)^0>8132A82D4ttnCZiHfAfcFo@Vt! z4sr4E1O5F*sUl)xUE^7g-T(ZFI@g=?TZPwj=Mc|1uhe<*pqK&$e?=o~fX z*D=~g#}}upi>*x{w0tIv)gnVdaa#T@nN&Qb6wL5LbAXrS9s32dlrGc@RkduXy$TU0N$+og z;opNse+C`LCW0Nj%>d@-1Z#izUH}Pu{NoadD5uI4#APz;1Tg=+Pr4p8Xiu z@>5pBXJ{h0ONX{90{G_U+?lj^jc@=oPIYR1&PohLFA=Z(`ToCJ0K1%|Nu>lDoQya< zUs`O;@bsCcB6xllQW(AahOtTyE(#IFXL^T6DUZQTfzej4Y{zFT(Gphs`S!m;T_{oy ztE>{iIN_@5N%SF0$X~tT4}Au1%kBP-y^I7=n7z!^6!7!sPe707CTsWlTGGTv8n-tp z9FE)F^CVOeZa6F<*!hQ5`zG<;({9xr*9HSU36QTD6T)}Dzzt!lgyx(;pTn-4#8xNdckzda)SrC{2*C_u82@Tof)}z z<#hgWK>cLlZ9m!PF))tol&~v8T9Jglec156@#FWTuFq+|ZY38Y$Q}{D&Btg~SbFfX zU!$a;$EI~49H;#jb&EgvVB}+;IVFEwoIdEqTZ3mc83#!(Er=O$XfZ8L^j;eQeVxT? zOz9K&VhM{230(mC?s~mBn_#oyn~aL0WoX=0DmSox6$o9QFKzU;~Jii+kMDB5Uj1?q4&QO|7~wFX%}?bvZ##)=k)K!nUPRPlL1Ypbk`@lH|kvm^cdDs z%$sGgc?_2BXa*#C^5;SIco}Y&y)a4sT?B=AiN&yd7T-0FBX+=TeSp0^<{GCoz}kG- zHl~1L31u}eaShI2dVSB$!FH_3bBE_0N&1@(_g5LzE`>-^e@Vh!)FbaTAgmYxy@+kh zL1nWv=S|Bi6u-Ktz%$z=h2S&2gMAMgq~oT@#6q&*H0qIl93jP*%EN3q?gt}TK>9|a zAb%j3w2yBMB>DWHWwLjN=karObx?E^BXJQU<&+eCntCEj+5CR))#q58GIR#BFUXf! zE8t!GVVM6{Txf=nzW$^4iw^qPbjksM_Kifj8&MnnBQA6bkne(vq|iOcNIn?Xh$@&L zU5D?r+H|pACsQt0bXW+#xQ@$z8ncC}2a&k|>8^9tdy13)ho`rWimH9ThtC9^(nupI zARwJHB1lTNA}A@XbjXZ|qymykOE*YKGlZgofJk>Jozep{^E=PyyWW?zn6+5&2lqL5 z>}&6R?VC{ZzyliCsbqspt2MqRRR{wYpcZ^5PuK!{gbU!7$wWCcQs)bH7+B=9`pxC<_2 zg|&og-2D85{&WAPe5`F*fB@0`?-c~li^snc%iYVQFV_sA9Y5Ztkzz6x+H`ya5j zPZg!qT$0BLDLr+$U@h?AfYZj^v+8&(#}pUvaPcJkA#c&h;g!vAjY4%CYd)5%^wNrs z+rIm5zZ(!+e7bvA1gY*uZL&*WkXL^2PT&k?wf5K<``Iq4#z2n+1inKb!u($^lTqkX z8-FbdZO56KrAX5(2{g$2K<^5` zd_MxHbN4J9Ixc4jUy^{Qqu?nQ{prQP`_WCPeD;))uD*Qe?}WWmZbir*Wao*vmwn?b zM~KX<4UGfi7X&DO&YjJl{RPc!6`tM6y;S>6>3sRha^}DRrq_x?Mg%jgTu;Fb=$*$O zEL`>VcSbOj66ZTJMmIP-?1q#T=t_y+; zyB+beVs*GOve4IWZe61-8~b$ zmJKFMXnzr3(P+GE$a@pwgfv?s(ViFQJpn*jFNPo9cR+|Qt8}KY3pk#Acm9$)2mWnA zN%$%8=vFuj;>h0T>fGKotnVbeAnuivEZ~W+%4P7nu0Vmb`=C>zWF_&g7IMgk{Bin# zcaW@Mw=zR=2v1WInIA$0yiun^%K(LC1P9Q+-mz^^D8icNq5RYtrD|5cUF4|o9!qji zNO9TqYT7A}f{|SB*j7=H#T`~S`d`WPq%aj$RZSj!R~xu?6LOvLHn1HJdSp4}z5h$G zAn`t%4*-(|Y{M={}YI_IO>Zy>iL13)oeX3 zl|JnzXYMT$tP}D8nBF4fyWHs3@Nu*-dN(yILIt`S1~o!Pio!4_I5xz2$dxDBqpoqU5V z7Ar-mvfkWL=~UqNV`TRwEBXzVi=5zm_x~ndr`;A8kbX%zj@ogcoPZ&>a^=&rEZy}> z22Eqi2*I=8`NKm+uX5!Y%mrL+~Qj*viD$y@CMQ%|Vy=)cuVWlH@Q6LvJY?4tKHehn|ril>Nj6ilIW z_7duDNWPMBB5NkmDn|(%7u6O=Z+ah(Na%CFwS10jeR@|`4|l_-VDV*in4R78CFfje zHl@`&3N~-w*tDKVExw|Zn{(y1AH3D8`^PzBS1a_`U{Ul|8tEBW!%lTHp5<^&$>h6{ z1NR54T}U$)j_Ve(%!kNnjHQg2_u+uZftU)-(kGKci$@I*N+)8~koEM=i*Nj1S3e)4 zl!kakPHq5SW6R#s7UY-R_50{f3`({?mbwi36e2^=@4!`?6|%$da-cD+;O`ra-iwEv zU_J7CyzYQokZoR9O`ACx|NWp#M0!&H_08^)F^3TJ7-L_cGCK_c+}cvZdA7o$M-fH1 z@d+7oU7xxI1qH3F7-bnzG*gRV+V*+(uX594=fnm`{;iZ)yn9j_faw+7W_^z09we*m zA$I@p=DmT%RMhsj&C0ni$k9P`X6N|io*4fj`tL1$s>82GdBA(oGq=DePUst9C!hM3 zieIL2+sCa)vx)bhrjwwy%(7NT?cV2R#q_|)xG!ZL1OOSx+a5sAiw*z#jxU%KvRyL{ zy1>VAH7Le4I{cDkNgKGkFX*7o#%&HUIw$CXI_!VjS0}hiF$>^w-J6daH0BDoM3}O1 zsAf7AEok4v+Phc%X~A;QtAGDq%c;nE0y2-FYt7$xsQ6>qw;ekD=jGFmQrcYdk91S< zn|cY;1zTZ-S_I0R&JZY4vniXkS6i6&=4+-ng_mX=?N)lO zPw{=)iZ>zh!NdAouFKTlzt1f66(M)cM|<6Ye0_8jLE)(^hK{@lw8=}x1WuYR+&+oB zRW*P3q0_44?_EW-j~%SAuMTpTny}oU*pB?PSWx$YY_vy8o!kMKU{=23f zKSZx=Lw+TNRR+1%^P6MZG!b7A>@mj%S1*LE6s3!H18=0x6>1ZZFO$+9e>+jF5HlG# z09be43)gwf=u=()jG!&POs=IOkNgB-^%nGYFH&h=8AfNmT>cH6bzo&#JaX>Qx`;X6 zjo_)j^;93Dn`w8wXAr}G#JEUP-gxZ^=Axa2~Yqe3M@(o{o4 zlM-`Ab1O!!NT2&F*r%yWqy@6UTfm&_(U)NokogJBR{?t|*V&a$fXg#{mn$&Ua$@ye zhLQ=-%z)tOK8)J9%L@m^5n9#D>0x6MiBC;azq;Dqro_mVm>=n-M zSbKc_I53Xd-{PjhjDd8&NbK|PdEsgHji(xQ(=tWs_fuTR?onO%G-I7>h4GSXiA?y# zckfB18Oyg!1@i_7@x_~XmeAZfGx?WQYGOb1EG<#_ik9b24C~%co8`k!AYbGG zD~!HmiFR5_;#t#4)s;oRl*KU5_#7<@l}HQH^8M*#V~TO)gX`3HIB&1}0H6*dTQpW7 zt4?$9hvT0uNw1&D0{VRUy8xWcu1HV&M=b<=81!Iv3m?drutOX+6quClVr_LIv6{ul zgjCxu>D^JMURL%49^OQVJdIsP7w9Pgi=dOQmfa4^gw492Jp_u9>mkK)Af(m93CdNw zlC_~fWRP3Mtxx6M2g5#IjzS4-g5;sAP+(VmW_d-d2y8h_lBslcNL%V(3$ZSlU^Oht z(1U_5Q^6aIi3~VnVkHRh$`_UR2%e-z%D0VbqBeS=YSJW`Ma2?1UB-JkX<`cWGe_O0 z;A6eb!(A5=VtcYj4h078dXm!ixC4>iP+nLzlJ2P*S{L<>#{&RrW&m1UqG*U!%JfE?kghM-u`+EdNA|Ih(?jo=w%`#3l z_~W_EBTF1Y$q4gd8c@#$Y6#aF!-!LBbya)G&O7X`tVV!nL6S&Em--J~2k_9u*|r8oBSJ%ntrm$Mr6h=#8-wlQeRFn$VO?}|F#-ll2QXA(SH1&umG0g7LcMc0tLMoIsXF^ zLpSFbg#g2MS9(Q){3?j4+rQF-D`a6(t4X_0`vu8?p*dotH&ipty5J^WnphDsbgFJ5 z>dY&8Z2OwtGMfr8yfw}IGeM5+t{@!DmvP$a4FtZ70L^dHHXye?=yD{Uyea_f8cCEt zn3X`EnDh0*HmWHwWle9Mdxl8={B{Mnkws6Vzt+jM?=2GEeW>Ka&?Xn(&co&|Zw7+H ze+9a~U!Fp53`v5`^R|J z%7y;Gx+tiw|0?HoDrwS`wU!&7ci4TOnJvjhz$?BSL0SAvYj> zQu$o+zLmx4M?S8ESEbLMw@750o%C&?A~iGM^2rB?Yp?z<8Rr#FBpC zK)wCK=)T|Cygpujemy&+Da12icrdg3!jySrAV{TS0Uw5%4f-4NvK$|DZku|VEwN#? z-#!78I6*z-)jiE}Q3lZFEK`@P03{Lqzu4JHAqiQ^<)?51(l-JhG!AElrBnd>Ev$^(&;64{?);&2IhpGdl$N z+uebslwayI`Q#Y!pEi3T$-q>hY-paw(=$N4?MMP$D+HmnkzD@G>=Yt?@SJ-9Ayx#< zcfJ2^3nCwo#d5XatdIMQI4%rytRh)rNd|^;8;A~05OfQG^cObt1GqR&Wiog1>Uy;AO&#NQF3I4YO&OS5MBjnAuMB;PILRCw&J;jHJf(P^vO zm*d>L+fjKrDJQfU4-}f1Z&6#CWvwyi3qXrB= znlN}&mjdhFfrRZ#l>mJ>YP5R;fvGJ7O;k!nxWwk()59_3MM|GH9tA3M#_*Ak*6D>W z&8?mP;dx+quvdUPdT=Sz{n~JfcIc%0LxBp5Q;yvuY>mbVVDVKLtxJ8E#wUC>xav_` zceh~*o;Ukqs{5Y)5@V$GRs?(?5Nw4&YZ%xRq86gl0w>}tD{#G5 zTRt90Wf_n@AHT04IY>@!C+JbkVMZMJmf6(96~C-J}i!W@}&b&QiDK|O{nRclsa-WlN2;Ux43NW!N7>HL=c z1B_h(o2!uJFEF?Mx3X@v+CEt}#T?#E6_nR{BI*j$8?`N*AaazqsFZP(=r}6F&43jmWuV#$Y?1?#vCgHO++zxsXUgc! z2?)ao^BL3V zO?=zY4=KMdTCvW7$3#IS=zD*Wb1;{2g8PRRNZ>gN^>!Qv2)&XJa&F-pr<9=KjwF}` z-b5Y)X%`5Y8+X=4S@CBs&sa#;%a*7xV&Tp7wZp`L)q?ACf`ZhIFF&NU*2=J&!%hvd z7oYw_{rgYPKnq#CK4v3nO!ajf{}F_;vM_yJJ4RXIGjjx>GAv zy|3`A?lv2@_gaLNBX#SS9JiamyzJ>sdYr9aCQqEqhOwhpTS-pWO`Ce8SYkQe+0B=e zo6=3yKA&}@CmEnjuSSzkgv%*rLv>jJTS5W<32`3J0x(Cee09(ZcC91YkI&{v!!84y z%#f>m_WEWzMJn zI=Fy#m>#9l79pbEO4*JQOVD80fH}V|cs;~03v+waHewom2Qe)}`NTCMgVs<9ZR6Y3 z*CV@R*-j9YCIu)JwW~;K<)=SBut$I6U_08^H!A!Q|Bb1~lLB2qWvGlM8;#|s@m}+P zfslLpB^33yezCpMX4RO2kEaYJNtUMu{ny#$_cEJs0J<{UwNC&l@|O#u%Gox!241C3 zyfef+6EZbDJv}$4?6);$(S;p1Dbe5E*?HLVyhwBa45AE?cz*$EgT99=Uz~Gaoch&- zaQwoZa(HLFXjCR)t--61B=S+#c36qzjti^3B|lu zcF#bmduM5H1Ku!d>%d=zru^G8Y72JK{nT!~xj8J~Fab4WXqb$*Sb^l#Lnxxd7@c5C zOK|o1n%f(EfU5_ZOp-CrAJ`vw!J(~nJqQ3EfA-@5gHB2+rQQwMa$0$Mn3D3TS>_}r zqK$!adP5bYVh(<&WFHFVIkX5bW&`sLG57>Rg_79kU?!*b(eIE~-F7}c$=P7gaQte> zUW>DjPyM_nVLBJbT6BE8`CHH+r`>zG=Yy1UQ&ST)N4Z=S2^F{#aa?h855HZxTH}ie zr|)4Inb)RX>RDsK1dA_!LYZMfzh14&!Pk5|>m6i#WAe!sTo2S+p4j3((`1*3hwIJT6!%3`c^(?`ktZrxpg1gk1W0ic+9f)|GxvN=NN#!aBta z&XhMb$q7E{_fJ^?!eBo#0-U>@KXU`uZE{z^t?ECokLwRx* zn(vz)_ygD`VT2u!`*}GupN)QfauLbshy>m=rQQ5i?Lk=@9roIjeJlj676uyEkg>*^ z9-N>wMPsLCO7I7Hb!6=59z>trHMyM>YXdM>yDo@_(4m>3Wu>W6-}kD&!ybZ?c7IRd z^kI!!{pw^@xE%1Ax)c7*$Nn)-2dYT(#lTA z(gwKUvH`AR1d-Il_AfBeN_>|F%NEyb2UZ$%&`soMu>Yp`iYM%0VOPWine;f0uAFKl zk|5S<`&t2#OjR32XxU5OsPRiX18sRyQE}t~N+P>k$SKU(8VQu zv(db4m|XD5GB5Q7l!~AX*ZqiXNoew@2o;=2V}gG(R$Ma#G{U?aM6yPF{Ma*;1pgNB z^%iHI>{VANE}=hsm3g4|@V^OFff|0RQ4Xa8T`tvrrMB!O{lsF1mc*YVPKJl*c`Z!6 zd{^&&@9ZI63(ru1q`{Y{fVeVL)iJZxZN}Plm2B^>iA@?82X}+iD-X%r^6@4`9`yccv%!C7JpCI>D zfQ&-u>1WbX4v$V3PE0)imF+C0T?xynQHthJmIE`t;C~jSlid9`1$X-SX_i`y9Hv*e z-lPjq#Yg7AsJ9^pTZ=yS3MfBH?!4oXV2~A~Ah_8R9%3uBIPH8w$ko(O41|gz>{Lts z3@Sf&7w#w3Azv~J;9O`trEz%%mILwNzwg`09}jCI@h(eb@wew5V$XQZ_pll;!0V&5f(3z`1ug)pZkRKs{4_yuOU0J_9mmO@IeFX40!#a#o13Tm2^)_`gR(OF91%Mbqpc zo5P<9p$)CJ5gqDrMbOBjj!(CtqMvcz;9)MULj-~^5Vu$kk$G>5kH+5xRRz#J8!#{a z$w6b}k$L$IG>|1N!@I3zUVw1T(yD}!YL@O(&b3jmHF%xJG4Jz^c_y{KsCU}BW z7?=mB3tb{UfrBNPb@6XA&nM@`AUI0suh&sMH6UDsEa1(n@p8dpPe~8+7#ow2Ge#C> zgg5AwFWJKADn0R${f?j7*j#t!Qni=v(!czV)6U~XZIddpL?&p@c;0#>C#-$^b{SfU`t!R!qNlW%l-PM< zP7IM+kEqB>#&VAIPs28+5IK0Pkn(eN+rs`g5MiCmFmhKqBqf1agLP{wI#6wCREl~0 zfd=Sq(+0Tk2-0&(q{;u@h&@t;B*G$x9ctG`@I1b0>^>43PiYNUzL2Sh%&W)GB?cWH z4n4}?Tb_`QGZNMuLK>M7?g-xIhq#V+>4?YZA3ta)1jyGvzGTWzWJk=2WR_aKn~CMN zEb1cVdgJg68gGQ6pChilhK$<`BhzGC0(UBk66nMPQP9j&h|A)$u`e*o(n2WZ_glbr ztLa^>f$)u>QR7UrM zoBw@q4>IrlZx8yb;)UAo=yyhp_Lsj>B@w$o_ZFVyc}&0|)7PB=Eo5n;O!OUnuI(El zkN0>nU@-kNwUNr@=dw$Ep-YrS(xl&Sa{`fIMRc4l!4FRZA@ki1b5`Qw`{~TRrp#kn zXS&jL5XMd0q8I%}z`zs$7C9B?X4Nqyz|szc{Y%F{orHMoH}fH*p*R#ZnN)$O6S4{~ z#}B#v(aNe3(oP=zj{7r187W`M)^6h0^I}lJY}y~$`U*rdvenXU zir1m3y>v@e zM(-7GX8305vpZTHBob#qvkGX?*H4>hOiJ!8g~x}ieDvH97~oKT4u%we7j zSo?zDd8dLd#Iy2oFFse73sVx|F@;n;#QF5JEY&JOs*cx1_aPF@l@(8DiHOLyW=pqS z*zQSJ&k_5A*ie}i@`rk6>{_EF#s=%dq)S9u!2#9o^0}{!Mu*U0?)jOPYR{SY82!d) z?AjdWe}|>`b#y(kwwrA?zYld-vnm=S2AUNOpm6H?1o{2*LkccJ@AcdzD>_@~7z2o$ zfdu7$(Wk*Vtbu>tX<9B83LQ7*bE!F@KfS@8eL@WFkj(zQMjnrGZ*86BiW_Uw8B zoyF%f%>M{omZ>)WdZiJVPCbf{#&;Cr%gk0X>7f4q!_vnqv`8JTr@OCw3vamOxh%|Q zM*W<7t$n6D<$-)0;^yq@Tcm2F)V<{n_?REg)Xgd@6hk8;hwS8o(dy>26^DS%*qkKU zQnW4|hDew!Z&t}KDj2rKE1lL@FRWOC zIK(wp4j>^a#&ZSzd3U+jmhd0Ba~4R={a^YoDub~@o&lBPRYQ%L9@+iD5aHnF+$!^P zoP6GdO(tV!=y7JMyy$_FA=~9-j|aM7kJ)Mvb{v60AxY7%J&^sCGa$4p0MBr4wmc9f zHOg`aOkI7<9n8^xR}4J=yqP8-S_Z`JF~2Yz-3Q(dyfj~hFtDqj3z`G0nvgt+q^K^r z*y)9@0)lzvDA3aF&@L5oH)(S%qFn0>LVyOOBFw}88^G_VDW9t4tz4Twtbj=)O;kGZ zp)%TE&~BIWvFn$vCK9yG_w(Zt1XY4&e;X&3tQ!Bj67UXf7Jr6SrpZg$(S3RPOK4tX zK%QEwvB3@%Eb#ieaHo&uI}=BGn%eir%nCDJJV3^zMFl6?16En&TzUbrca~#a(Od9y z$`KGpMHf(czr*u_^*%fu&uf%%YZD^z8M0cTRJ$D}rC)}=d7Z}26YxF*Zbw#y^FP}+ zCfKX}0~${d>{YY&`si7}M5H!5KTq0w zNKi5`hkap7z)r?(abdt9H5cohpctRRMa5CCBCE&71h?!LJdD>W&*-kxYUB3T{ti8~ zjkVgJoeb$SO!Qf=6$5jTHwC_>n-aErPZbH1%!(PrYe{_i;?(!Ia_MR5SjOI~3sSO= zcWIe2?7;Ilh;MCm`C8In&#@@UtW7?hu+H}fL)yh~e0GAr!-eh_jJXRc>gVR}1-Ae4 z5(gusL>Wh-6I(-JzVF+dV4p3<-$^`}V~K|U=AR)KL;zU!rAsg6PVk+p z{o*b)lmEWTb=7&sINy8ENRhj>cRAs3jbhu1=gEB63rDeB+>SF>;1a}x*zR%jT|3vE zZW6B0gKm-+oR}MjAJ3HGOCzBff+mDl=3^F-1a)nIbI>W=@78g^(esdio41Nn=+r!I zbItG|2}BJW*FjwixoO9G(p8Nsu}C3V8rupZ3z5a-Us4F}qlGA^5TAPT9V8iRYgZ

cK>DJ`SLmUnKkzrA4cXTJV_BP; zN@jmoR@8{4l%x4hxR@`sfeo}35^-XiF!e#~H+h0uiX7r!X9IYo-s4Th`&iPT3wNe9bam0@XK~&% zfb+?|vGAXum?Q8N@s$zYjP`1)uk$u$N4&a20sJI4E%H#x&`v(&qX-9pYtSpf_#f{S z=#t;cyAs{e{x|{GaQYxp3^fyaROH>|+3CXp|YjUQPAr z!>|Nw=@P9Xuy8VBaGXSnlp{-^nPn{ks441IXTNu5@jsg)%D?LY>{kByqHLl0o4F*Y zZ_EJ+_$vL%sJ`xSW1-6#q`)v??4A#_aBVuMp?uD~`PdP|K1-K?)lEJcwhldVrUzdv zF~r|mZF_za*WL$QpE{m=+Pddr0+AU-j9!gxd7x)QWJE~Hhh(nbTSMsQ*X>JBHSEl) zZaoO8XO?miEHYL^Xi4vDJ(3TlcggX;UhyvHpJ8%R{Jm;N>~Gg@9_GaF_BEs9i zfyGKLhV5s3R>klQe(lwoENA}8ro*$DZhOKU?3@!w&=QZ?QynF#RdtEk>RPi3;8y5zYo7PI5fk6A{a-m$(6R!hON2hez5hUz)jSihGp-zK<;;uNS< zkj^7e!#_k+q*gO9z9$ zV(ykjO(0Z9*T==S;cv=F&+@D5oSY06GDp%HXY!P|WG#Aknaj43FFp!5TNf<0?>*nIs#Oe+uEcuSA_- zba(k0%cxvHSW|3@lmg-X9PvZ9&0&`)IKW@(qjEAO60ZKmmWT42b*|@kzZ=(+SdbDk zRR+*Er-thg7T-#mT-}k!^IcQ4aU|c?7MYpPVbCB>lxd9SYm|jvT1VxM zr8XWvkW}K>Zp?4x!UtDIO%3H0m80Sn1;+J9V?fc&B;~|tR)~_xkif1+>I2oLt2ddq zm6!Nwm|pSJQCMj@T})evZ>O)OEcr)upTinlaXu}uj=*Ro6~EI?Esy7he2p&--p8U${YzNUo?5NctHC_43BvP;%_+_nRmud4$L6;}%iH!J z(C0#$7Y+`(ipX6advTW&_}5?75d)G&OT7fncW1acgT)txh@n`e$u?lZ`kGQS^}5Ds zcCQDJEdf6uvAqyMT)s$nmaYFgoNqZ!<7GK^|1ZNYyg1HU>7Mq}I}qA!nuBF?_)_A7 z7hNgvktDu5kn7>zG>L4SNJn^L7!jbrX!i?87teSHob7!{RA6D}6W%^Z_S3)r+3@%) zg}1p+20U8p=5dGL_@CHpgB zc=!WO47Ca2_}lfCb|Q2TkVCcf0`sQ%+vY?n5dz*x>%!Pq2Ew6xuNEnXAyEd9l~<}o znd*qVtq{h}d+nogKp;y#c6exJ(kB z7aV2kF|)W+n?0~6i-~EW^8SSrxs!^wjxJ;W#y<7+;o0!}F$aZ)F$QRhv)$Y}fiwal z`8S2Bl<;g5j(N_Bk-X}1`zEg~i_Sid1buLUF^;$|ksrvolC;AB~ln*y8`C z?{{AA3Fd|-5dtJwfT`KpkgLP~A`!1!-@v=C6n3c^cOTM)b!5ABce zmWIhT57N9;MqtK=>P`=>E@ulRur`7Vlohq-K{ z_+W{woG<{qfW#bNIuY*_O~N{}0x=!okvyyv8ewgr}{J1 zJU_m(2pzUM`BCpYoTKQsHSL^BgcNr4p(f=SMi7+$Cmy7X7HB5qC#z)1e zgaw#4cH+-^ed?Ela4I=}jmA;>Pq|I^Bi>VBxu+}$&(M`~-(z>5F@5H3Rfx|=`>yOY z^gZ;ZNf}W^-$9+enu)thPRl143ocX7_ijm_8iybJ^LPOO=A~j z25-?qX=>3ywTM>VHSad}k!Hy)J~h2o$glUu`KQxh+bqBwD^Zz>_kUA{R=B>IpmUzP zvojuuzWJ38ee0@59R8e|05Y0se3-^wNiy_WBeHm^OI?mHvzUo%&56Su`8tRW6GdQN zOL3a+IOOi-W!oNj=qMgCy_$ds`|sj7f|J#RYvaVkMCWml zxQDy@L-6+tA^TtMM-Kyo&C`>*CTQ26U_jN`_HKLOMAxzh z&0u`q;<%NYy?$L~DkGo%_xz4+C{6|l+q)#(=KYhscv)1EtF6uMkJTPASZU|e!@&&w zuO*Fjk-mlff<{O~d)^jzQ$ZHR1jQ%=9|+41;;Ut!w)ivqb!CY{ko`O zza=ihs1lI%&<$qE)zFMhSVfl+POX)uvY^qjioYJ=M*>3m3ySE;QeAA z?N`6{^yg|_NTOG?^q?EhDoC+7!YT_|uJa=G)RB|^RX6wj-{ut>!j6*rzg!wRJN1P* z-rjR_b9?Z=FUXRNFcje7ZFlP8M9WoUpy=6h_SX`~g?zS+{klx01?bm;b zJu|xp8npe0f=M@{xh8u%5YnuocC9b4vDbuo6-_BhadVDl}f2%c;$$)zJXdM1xK63BlnqC6Kfn#Jz=V&$lYS_|MF zIE5~uE{7K{Oi-ZlMSm_M?w_YjQrpCRZk#oVIvc*^S#9w)OXs~YCMdXH9A~n~hX8HZ z^(ha`?X6r>zjZ#FPgGg~U$tuveNB}F*p^B!HNZ>e_%_9j_jWc_FlxUMZq3Bt$2RJ^ zAFOSR;PO#0A(olUPQtYVjp5x zpd8i9qjc0qZ*Xv!?cS}MynS}G*)kEhx!+RUezxn?apZmW$6AS$ROHgit6vW{OdP-@ zy`uqGYtz0d?mk>bT*`?HMqG7po&V4z{LJ=x@pJVV3t@gggfcogd^DeX*U*^I8-K5n zvOq#Y&oSoC?G2*-C)vQ>3F6}&e6=#oQYl^mOeyk)4rrl-wsPy+sq%W-5IKc_qMsw( z{cCJ6rbLjn0U0QeR7@j(&kxz^A-rMBgpnGIAB-ALzAl1ee~+{c9o3 z(aJlQ9P!kgCG^*OQ`N6zJTs=4@K31tlU1AvBKTPaL2&?@z_9?UZEQT*C`j;hcdrdZ zpx?fITUAxH?EP&hn(#uH4Jd)d_OBjhEo|J0#%c0EkQWJVbf=2XTGG=(o?@y)!nW5R z9sTV(n@8ahctlO$(PzA43RB+S{m{djCY|f|?ywMbcz69H5jgR>MZuZ^M^g3}jE2)O zOklb6m$~Nw2I3FAM&=<7yCEz$>fhp?mg6-9LZs zsjq4)K5D&V!dt?^JW!6h_h>XLy@lfKug6xcr6~QD`u;3ni2+}-eYfx02R$1DZ9U*J z=Gq&74}v$tKNFAh+`IiI>Ga6gzm5B%(ntS-*n=;E&urM`qmW6ve?q&!bgR#*(vP5{igprzbEG z9>fZ{!{Tdw4Y`FBxQ-m^ciTKfQP<_Mpe`2p}o(yRg;B*m4jhOUK_pdtX~C? z#}^TK!sP_~4Rl5EzTqW@v_u=!yWb`StnmRf1ZOZgvy4wW6S72{C#~F_ zV*ETvR|fcK7i-Cw=tSpoV&ATkq2ouObOQ8P(KD;{pnIXe#(9je6y!htHmPMqQ|>ggS2AuIoR?!DKYnJ9=H92NY@=mu$Si*wD!kJ~8^jetLL-awpbS!xfe|hN2~&;lsR+dr*Z|S8BV!lsV@6uarcK;}7SA(&Ph!s5#wf zY1`=!DpP)Mcg#`;NLmmeJZ!I+YVZjU9=9?6YT(xXHil}>giQ_n+x%!AQOfR^?f*td zPl$+?M2ax7MVnzl8ujtfxUlb%vi_w?&vDK z=492UW)hcbyiMTKNCNBO@=H_FKdMRJdS1s}%J%L-^5w&2a_wdb1FJ22qP~CrvD<-; zv&ystT+i^%Uw(8F@3lTjGA#tPJx@>ej?)Hz_9MLjDZn1;et%WMg`WrQ@Fs`z=g!J`FTWP0MgXiW zacIbV#%ddU{=i%?85>*`9T}0WH^B1Byb!bwm<$UU3h)41=AHYNaYp)CZ!o4$d*>HE z93-C_JVw^1z1#U>>YQ)IQF=eBQfx?oH6(vMV*V!Q$up@6=%Yb1j3;xJ+T z>nNYgvckbR9rv&+m|FXkuIoMdMkLP~^XxON-_Lbin4*N9($Ru<+^RqSF;wdac1AYX z15y^(fY`_#Vb`1>;C6buqjl{aq`?^%*E7&9n(2m}K; ztd_=DsaS1lJKbyDY5J7$ptw~j%UIhe^%030DSI#lwD5krf2=E?3S%8Z74V^%UK|de z=s-b*o&NREuxKLOQs8QM25{{qFr5rX`}>P~y=|0}q!|%nTsnM++KM;N$)p0k-69U+ z;`<6SV3hjTJToJLTEAKhrHQS89=A+8IXO<{LUQ>I*CDK z8~#8c@jFZ_d}a?acF^^DH(Q%EYBhV)dvo3=F36uZFY!lZQ`60G<6~GnXf5~)0gW0@ z%`Y@R-!lQ+aE`W+cZbqm3bNvCbxbmUk#C<6Bq9lH4bbgoPtX@DQcEUtH7wBx)veVB zLe3Yf@oq(k)h#|b87e7Nyr!TiryUdCOAu(v-Ei~p(E?u(O$&=|x&M0+mfU2uK*7aXu=4YybEfLn z@smSSv?<}p1QkMG>gIYDSS_i`v8Fu9@XSk`)Cu{Vh!^x;)mvMhAT%il2ewf7qinAd z(b&Zg`;_Bv)v9N(J~%LCk6A$M&Yj;tBWE5nF%T{!N`75dn0Jy7vL)bmYYEb@h51DL z&6KE5xMHEKjLNVG1C zuIz9Ba8Eww2Cq8))=5|ou2Y}-;Uo5`^eT!g%<>;G?|u}Hp8q?<=I{;a74*m&6CS5PaWPI4K$<3f@4Lpvw^pY&Q{eXp@Y$x$B@Z-XN_wJ`F&W1>?+6;Qw+=|@~* z^<97F!LGYZ?kCHQF@*DZK>rkCodXfJ41RalVI~QIo(yrC^2#K%|E;G2 z(J!MY$x{TwPz}cxAS4V8_igC%+YT0dr}p+Cj>LyU;fuqf+GVA#ic-BM zSzz-Q+h*%d0#3GUmlUH2tb5vk@D*Z|b!KP9>M|tVssroWycF3@X@;Af1>PSMrU+iky`2ZjPa^~K1&OUpuz1G^Y)S#2%P{wPO%*`l}f(rqa(W=^CFxSmUmoiXJ zY^n!4wbLQ)s0IX6`dSUt*x{9Q;eA8g2KZn&$W2KVpJ0&w8z{0hhO`xy|AK%A9QIxm ziApd98A)oJr90``C6!+_zaHGnN+>~oawUekqYPEs{{^8)-LV0NfCAWj;xT+%`szlM z1T^gz>eg4WURS%%aVzms$AU#oAzE|SbJvBS@g$@SY0Ztcjcn8$_e)J1D!j@Q7&066 z``OU9K-G1>!)_uKN{ImHp|22?QzVN)XUi2$8AxjU z${@)!yx>Cr%XhY{@3dIbr^FHk=aj1_z`Jyk!cC-FoC)^3(UXJbJO=0;%zYXm6U!nf z=E%qg0TxGosV_X)1SmDE{BQoJ$42sI1apUG!{A1{Q2K?>;a|}jDS$P`dEyL+DP#f_(ak}G=q$#dLHb_lAV#F%I~)&q6ta_r#S7|P@4pMK0na80OKUd<$6k7U+T=Z-pgPN)95eD5psECp4=l` zVA1F6!QuU8#*ZDye9_)SqxP$LZ#cuVW+{#d*jU+s3MJO(XK zKyw{ijFa~VN(RuQ;MpE#HEW(0+xxI=9rjdv}7VC zzY1-;14BRqJLqE!Ijg_2>mCCyYc6i_lJK-D#%Zi<-1R4Ntu1LI{!!j&VYpPIhiyE2 zFU4r-zWC}kUv56r#>bZ}9DYViUYHO+-%Ucr65eM@5DWb?P2lR? zYgrN-Wu~C&Y~T*O}fITou+k2-><#w3PWve23hvSS@0`xR$YCEe5@PrLk^;)#(00Drekr&(^qZJs=q3zt zY?nqNl;*B1MGf9D*b}+Tuu0-q6PK6=|Nb|-EKmZnLIVt}Bau)eimR8l;Md(&Kl<_x zNKDHr4RGo6J^FVc2c5xtwg0w&rDmOaz@G|&pr3feNbJMu+^U$qEVA{%NTPsZu>esW zc`0FSCZ>9}oaK8RdZu{4n=Zff77}(gkkv^R(2SdB1`fUZTm=AyU?SrS_(V{?j{CT-?pWUaUDm%`0o%O=g>3Y&t& z22#oz!+hn@5K^>vdjq^&S%QA9qE!#~;mQQ)_nQR$D)?NPoEB!KX7m zL|^(J2miZF=laL}ZtdTJg(Tjk0=e0~*KF8{HER}cHy8zaDfW!YxweD9=fV1WJQ|Fo zUus+#eo->Hj-X!*@cuR@{$6Oc7;f~ui-4}82a z`*yB#e%i??7@s|$<7EWKbze^|;IB}IJT|j&`fmkqv+FkV?LGGtqvlFg`JZ-#de3b) zlmg2Wv1YyUt=7RM_MHi`cL?4s93g`VZxjLyas^Pgiq?oFULUo=A<98U&g<9#EVLNC z#1Oo0h#$0&XyE`pI1YNtv$y`f-CFd%tgNuhB2U-KU)>d>pZ5aa3ieUS%y*{G~cib zQXWlF|J0R990k$N+k|~4CkTJhlcP%>ul3BTU|)Efg3d4ntt$}c=}H;svQ7wp14G5q zt^|IE(fssuE{B`ng}L{t>vWZ?Pc(0!f4$pKm_Q{2p)`_*;SMmmIf?8P`L(pPq3eo) zE(J8N_$Ktz?p)cxeCHT@m3LO~OM&M;quOgIah9OW;x&UoN2`p;^7bY4Vh3@lRioC#NNP0sk;aX;+7fLdnTSxq3EWbRB2estCE813Z9)El;P z-)C?(=zmx*Bq|e%%FW%;(ut$$qZCWxNBzWc_kyIUZ}`OlodWjhTkN`V0ZeNX;e#$Q z6fWeZxG3u=6Vwc`^K`rbr9lCK?W3|@6>G}RNH384>~lY(5p&<8v%sx5PmRNCPKvu+ zBT5h@(o2}O;}aJUNgGa`$~KY2u>`B;!s_Cs$EeygyKAzshZzZ z3wZp)6n5AA(r>;_bU;=(Z^$6BrBjG9GMnZ&u5w`PjLpvwlW}uhvYWIug zgB!QgFeEv|JY5={p+_38jBV_G?2L%m11$F5Yq+QLlwhg_*& zU5YHDmm$ak5vi{cYB7FB_~=4H&QHR-%BjUVDX{~UeNOKN+^Ni~BIF3b^A;S2Bq4ek zzF%N3KU1yAMv97xEx>5%;RQ@;DGB-fs6h6DDi`}L;4=d!XG!$w1x!5fC}8>gwAQHtb9RF{=)loL0@UP| zTeTb&pm$bt&BJIj0+HlA@3e@Y>Lh=v9=-RF3$m_pVTyI-E>Er-oj~EORw3fu8Cn}W z3A-Z;gBo1Vtr>9CaVw6Z5$!)y)?i$BLQHKebg2Dj`nx{K&+MBV0HTKA2TQs+Nbt79 zg__@i%_(0gbgpxC^pDxJzE#fEk;Blq#Mvs|KRp`4aGkKa=4*|yF z&%R~vN0>mM5aK09(PnA>D51&K7YrK6yzWwmGlx=8SMpnnZmg&3nrxr9rv@H?tKxzy~ss)pGBb7 zx*i8C^V3eqDt>+uN(-p?$EM5n{4V<3THZtv1OdfzrXYXBkQ~bG3t@NYp@ z=O=!~h2>I9P*$`3D3(g~5*oPh-O(#V(v)=EIaTnL6#ecr#O;TUvk*?;UE`Aw`NjrC zjSO06fiFc=LZ&y1pkZ8yaNZ~k;p@#5Y%+_ZAmwhIM|ZV7tXlIQN}Dr691dZwvA9}6 z*pvh4%t&zBm?iDl18WRGOBOSb1#t}i9r@enBxy2)^jQ*udznL)#n7ce4f?$}oAv8^ zv_!qASPsQ17ZPJIej)Rt?fRbR%r}vzH+O>Y`n~iHCC2~+@ltSM^8$$cgHX3cqYw^q zVn(5l6D{~0xdFnfe_fGlF=aM|$-A03}ZmQk&8K?y;RY=@ZNE$+*?msI7lh z19Bm@J@H9Mf>+5;oYTV!q=xUxgCS9sOpd5Q`6)lY|0hiH`E`^Qvhom}!4L<$SJF@~ zOdy2bz5cC_7y;|wIdr_Y3HwuLFOu-%EcfeQ>l=sE#Ycfff1dlK6FrQjSFw0gze$k#thq=3wl-6i*pr@Q^dHkl=Ry(hIx| z?`;8lF$!efqilHeb7lF@>Q|G>TU?6Riy!uhyNa&Q!gXoa= z4RexQD)-84A<``7!mCSLC6gE{f?3Vv=z)xKEt;579aYzqFC}^3f|GtPU%VK5=f}-| z2&lAB=p~YTW(N?{l4YagKxwrYwIvps{@t*|WuNW7qU1wg;<`^qa*Q@9`S2=;>u^N`cYx$1R8rg z(c6$r^62Y+V9k&B;75}`+V;SUS##sd zL)Pd_W=U1}>|F1^04hX?e!OBd6tZuuBb4yzqk9A8Z5cG9nY&pfr9u=pQP6+IagOJQQHk^@%*$R(Z)2OUt$sI!Yz)y zv$}1}fw%^PLq{7i_A`~Y33Rtq>=2oQ8T^kmQQN=8nC;I+EfZPj_Qs6wkp^9>Cc}C4 zr8HfxJ2Mdio!;hw4OGr=p{VwafDSuR$G?&(2z7Y5AZrB9D%27iSf@;%N2{E<2DC51 zbSjY1tEj~zkJBl!az-BL2?L-U$X$LmfGPE4q*P2U1l9CkWdY*O6&KB}YWIzQngmCi zw3khSKbZBPX-z~Q?LIq$E@OI|v72aIH0GA5n`>V}vklb17r zk75BQNpq@?go8BcJV#&1AqPcq2>d51mjC^`Wk0bDliBY>aW1pwhmf!mPFIniN^5iaB;E43*T)FdOph+`k3#)Eg2Qdrf0+Kx9-C2# zw+!8dV)?8>hmxjH?6_6?FoxP3jf9OM^&sFuaZcgIyox-tEk1;^;)uXFpXBTO=>lSP zGC=p8Ie7UZCGimtGtieUvwgJz40txv~0Z{+t>xl=<{BQ*+WYc+S=Li{}qHcS!8zh((}$*`e2bC3KuG zSd^{=UlD;&bK|bEro)1oQMBTKU_Hh7XO|?@4}RFKQ^1|9Fy}~qK9h=`n@wB`VBs(T zcsjL3N`}PcLM~dH+-b+}O^Y4mrTn?_)z`iAkR+ZlTY4exzhlu|#;vuMS zrLFk+gWRzFF`$FLWAv016&s5!)z6!n@ZSg8W@NnA*n7!Sn~(`p1{Q)b!yielH1Lx2 zXYX&EfHi`W!lM-n4P%UFtSV3hFrNEh4rZpky!k+__hm;C5M+G@g&5&WusDaf6lb?d zU0D-Bu6F+zx!mBwRjaZ8P}Li z;ys}>WU50{Xw9w*?mp;s45FOt!;XB!EzE4CKsA;b$~9U`g;YNt5>vllIJm$N(3tM>EWXNOZfI~Xu5U}gS2_jfzt zg<(tq!%*=$dBi^bml5!mk|sqK8u7^!HoU+s4pGXt;gB`qy~jq6rigD>Nb-p11cU5PC7~b2s8B*4hT_?{f>M^04e*b^iDVISVl&nzi0ZS-3Fgr_ z-Nq1{6;Cr^94B{gyqCi^XR3I0<52QeTd!upI7H9CF$m#AS$9=W>v~_%DO+ z=#I_?&W2hq%pyVjR>N~LNYA}vH${ToAxTx`!p!t^S7r`_mwhT#=Ofm55@V!djGLc>A7~7L5LGlGDBFAK|Z+ z(Glg@ti%)$7Cn>xxq;*}S?*tIqIe&Ghl2R~eHPSj>&hw`>`2?RebayUQdXY1-6Sb{ z2Vf&#MwUjlYn7CdodU&2bP0hEVhO;>SKZpbH03lXew;vMaki0%T0V8HwjB1Z43S#z ze?LB_lGD#mPoB<%J^X2JEw2#~g2D@{`46D^*r1He9I^=?!;BCYRv_bG2E!OR%B*%m z4H<-fxss`ITqMz;Z1KHdPcC7ci6;s-J2{E`#myQy9?)Ji5y4Xg+TV60k9}bG)#KTJ z|DOwRtr6^=B?Ww}M==0^ysYbJi&~cD|Gheaz z1R5>wmtQ(q=bL>wTB_^JSAKLgGEU__c2}kPWW?;JtbJ zGxIzQHS*-6MgL)L7SO{fKN42X4`4bgVf5*!WRkgu{&#QmhoHJlcYVT~PONuSrTV^- zWpxpIw~p&Z!!eXqwB=C8FMSwZ3{`+zb|NH}O$QJDw)v<_KHhFxH?GKW zZ~a0>z?_U7LjL}H1bO6WbVbQRuw;EML^!>21yTUHLVXIU{a=9u8B~Z|a6WxVViF{k zzOVM_)C!WgGqe#eS^SOzK0+nQU#((y;OpBG&_0vIzOg}Plht%zeB!mcB=sjcKiEDG zND`lpG^}DXzQZ5)O4a|%?c=rua)rct9)==`wMyAZdWKkIs=42X7rG3^Y<3U_Fw#=qfgskSD?uhhv=t{$W1i7`qTp4z*M> zs^u)Pb;;pDnB7N zrcV0L6B`*bgiwBScA_|eWODj4L%iBkq0*;v(7Q?{x~D!kEqKf=eRYZ^Tu3#rZqK$QNJ5H*0ii$IJ)Of8k#9H!<7soy{c`4h%sEnkA-wmCBuT51&UM-*?dkfL4-7yeA=$8M(GB+ zV;X8z0@b7VfA7s4_L)BjL>}5ae7tmpLt0c5$S0_;)BVf575{0!p|s{}B3(}hxGL~* zr44XBLz}`h=fNL1tLJx&)w_aFZ-+6IDAcli}TROGOK6KtEZCcB*BXqa6 zo?@HPFFd{%3`fX@X0|r_B|Sa=NScmnBMX-dGuR3NuAa*f?=6?1t0Wy1>eFve(M-+Q z1v{gnj)T%3mppw?o=zW5JGk+x{+nCZtH0%gE4}<* z=Rr7OYBBo=iQ5`RpQYGUe?5KfNdpMCf^v&-s#s|-FJs`B|UO11ENvBJy7hwNiy_; zdP7-Fl{Kb%=YF?p3n~4G$D*m5;x%>7U%w2OtoVi?Lf%ffy*XHtK+ree97)~*kJ0CM zw}bhm0EA%l3m%UpB;X2Q0o+?Ub1`rDv|Aeh{?#$-zA?E zi$phg@kuv$Xq!P@u7?w`yNPKpDTm!C^w@#HSJn4`@o`biffoiew*^U0M!9j9Zo+?_ zN%B1iSzcyPnGrNUsm#u0xS>e;`I8^u^1x4FnzI0?%zmPY>UM=iTb{J!!Hc01IinG0 zg+8+ht1$ECmI+(uWA13?1|Q6xdWJb(BhjZ$%YofGe{?=F$$a-F_?#`89+>)jQ0_0d zwxm3f1glu&-u!H}?Ve@S&Gwl1tgQ;P5!^96a&|ikf zPd=V2d?W&Emi%SmKQuPrnpuS)cy@UO^+H*Fc!1>zzZp#VVMqX` zdJuwka(dJ`TRE^;7v!U)^jtZUxGNez79F}_jDGAiCcDLpl5bqs-TJ%PGbD3e!}NwN zi{qRs)pRL+p)4zX#g9kn(tIf|aX+UXe8l^;2i4G4x4<{}|Ou2#_`hf>AV490GXE<=-e^ zKrQTzqBQZLAR~nb861?yI3R6WG_`jA)s<*PZsLFDI&S~Wq}qw;l;<2k@)Y@od0K#Q z77adXT>t3xB4_=kxb?o@kE>@(g7Dwp&j0UXx4erFTHkbY!e5E2~`8b_f$9g++T2HSGmKFGoIIFtzd;2+clJlJxr#9fI zm-4w?V)gTmE^tX6ysU|5(fOs{k~PIioUANm{4#Dn?3!;#L)m3!UBjK^G?H%w@F2|` zMiauk4nBRJy8jaS#66fWY?m&}rR?uRViFLc>M?V~L$RmhZ&hmv$HrV@yLJl4!%;|) z?UzUh2>kcQjQPw?2Zvm*INZ{q6pBy+9%@oBMmhxeN>Qo_4??29d@r=k{f#RKqchBO zu?fs&7!yl!*0~#+nSgsS_ZkW2i&h*yXTh%Dn*Z0R15t;P;4r~nhAM=+PNyjK;-Vo*Qd-l#&OgK$*b+cm2mk6Vm19v%dRufPG*+ za#=wXh-Y&6zPcuD^~2$_qd=YCB5Tv?EI7extL+rObyOi4%9^))fA)}joTg9ZOO(Cn zO(1{q>u^M`*N*uGbpADwe}}<;MQsD~hnUY*yT5s)&fEgP5_yob4?6eoawv8|3 zi&dE5;GYI(F#Dw!tP|oup?FL&E$B9{3Ha^zQscQFJE`tLU0M}M$dDDHRb0oCbIVee zKRH9!!1kU3WNrWi&{Pl#uD&l*n2?OTZPMB}L6{sOruq51d4T&Bj*i$Uxh30LY&$>5 zd8{;mli>}?l?%2n*WU%w0J$ylf=H$iLs2wzM{r>Dl!ZRa! zt&X1-^yG1|yaftV?anM+OB;6a1fc0?i8(B6Onc@a3Ug&ne2>#QY?-f}%u#C{{$e)d z73xKI`D&6g4TA2LCheENdnR=B<{xp-)9+QltWW)%4GSl1iif{IuAcN0OR@?Zgg`=P zt*KWIHf*x-?-Qj%APVZBLVf2ff}Jy_2O5(cL<#jnOUWQnykh4ixIG>8_y;q zW64JPpm>cXvHcUMGFY4&i=?dh=-~My>~WX#hUZO6O3iDNM0^ghXDhs1?rmXD2tT4L z?oz$F#&_cjOm{lxVCFWhO%caFF-4>0(X$?2Q+1uIj?B5zQiQN8kI z1X4Kdg3f#Su8TlbySUa9jSpfW`) z_ChLR$eNEa#!LBoC)e!y-b~31+_jS($uQSWXKSI_Sps8+G_mI9KMxPPAMc#jvD+`v z)@iH|W?vk`uQJr+h!vfBEn8*q{@DRl-o^NVZ;!y&iSkLipa|Q7x%SZTj%pLXCBM~!k zqJB(%!0K-&w0I4M+i7Yah?Kh|zc%?HjZi={wlc+wEquxM!vkoO$UHzB?F$Zm>^{vn8mIbyDJ;4`dgKl@jGV>MH9IRRhnFz;U0 zVHjfIxJ~I9!hxjS;WAoI!!5(+z`?F*&CzZ!d%(nL;_LT86NJFNZ$Z*i4Fvngv3$^s zk8rXWwIhw$ALCu;j@v9^-Qw#b9(+$^KS7T#Fx`1)M*!<#b)o^JS$ea`^=YI7T&Tbz z{f%lt&k?8*pAXjYd(v?q$GMRo3}Wv1^bG?xwE{d5{KM;)6JEDGGHn?nr>1o&;9VF1`&+hmzJ)!g)CM(k!;S+x)|en?vN33Gj$AdM z`+n*p(FdtIMZ$muGI0N7(hHp1wZ6$Km^hs~VxBG=O|5xxRC!=;Jmf-PQ{1w%W-Z~=9>)!CyM;OH{|Dt>s zr7u2t#4X7`8?xlhpRG-nNH*Az~RLvQV~)nhM}}J5%DXIiM2)xBHYg z_DVBGWKzj`Ncb;ql5THyic6K^p`}kqkti>t=7sYEL?X8xW6ZKGi}0{ViSw6=%cIEf zuWOm%oWb>jkJdmFItl-7BXa~}e=n!C%FsF*hBF<21NI>t8FaTSoM96`h{>;0{c-g?? ztZVqtlkW31NnYNr2TEG42(9YEIs_uVv1-iTz2h2##m-Zo>`&i~87wT<^LBO|{C;bR zS}6YzmvHh zi@>t5y+Db>0?>o?^DG#r4W`KXSg06m5hS67yMssTgQlk%r3PYT&Fiz4nl+(0S|ed; zT@R+(?Oix>;w=>?V(u1Y1`>yod&w5O$y_{3iI%-aI-iF5nXyY6D` zS3>lQtzgJ}Ap|b6H^$rBWCdsa*~>l3=C<692@afZ8yX65bOBS>&;o(yGdM)}HQa#W>C_nSd7nomP z(Z*W@`Sv#3i^NhZPyAgaI|MrBD0iYu|00zu4VnXN2 zXxv?*&&UabUQE=exBluUBHzfaO@lcXP~-DKK6Fah6&>J_Q3S;{aSUM!L9NwvDCz>@Po-sy{uB>OJ663%=-lfbQ!Dde!{ zT((%Zp8x85va6VI$Qu+9&xl-ZQT=8|_TdAeCw6;S_zvl??y8lj#?L_wUBS?e{(!A* z;YT}9V}%$L8zzixamcN8M+U_oQ?if4Kz&tS0MKFj7Zq@Jf%D zTF?lG4waodqm8Tb_?yYrIB0u+jcbID7Eakc=q7zxF^Ejb(Y_9{T3BvJbQ-0qJJxde zy$3mMx9g%JjH1Q9^~gFf1Z_B{9nk^F2qs|g-5RDY(hsb&!JMooo*b1~txsV}V8uP) z@9H4JZ|c|m37E3!JE^is)TutD**V3Gh1__7WCESIzsc82V}#$8Kx2mA@evmq1DPaE!+Aqn{y~@T2F33C$`o|$;qb7E}g;#B?Qv-PiGMPV}Eo~Cr zyLpS(wV{XGO?=jDl|cK)AC_=n^;%8}zxs`H+fnO@a5n<+;Ey;_ZZ=DZOFV-2;IHo} zfgb(Hx`vL+!kRh83+{tJsQ#^hL#G1)yA|ZVQ&tQMTQ(1SySAGSnUh0(o6|x=-WC=3 zjvh9z6r5DT(QRdND5bBW)9YK18}dZV9A05hR;^Q0a<|_7?Af{AjV)6&=w?aFRnS_6Tkv%Js8^1is$lmDRG zI1Mv)pn5=e8NIc|y>;~w$=KWx@&|QiG{H}+#!ZZ9NFQuk_7rKEwPn$?E{vEu0&^Zr z#26t@$L2GBnUX>`5S$>8CO7FyjD_4-Jh-Pqb{pqo7TFcYkvny(IJ$hk*|OIqut?;P zKd1VlBoWgfc=KOO-IwHz+O{1RM_oAHm%x`~vcw3)2z{zF>3ECdkIJ3a^XrC3B<^xr zyUO9<_wvF2`~4@z)wCN16q!qik4k)4^0UGSD}|?M5+g?90b47DD0HLpfa_r6t?k|c&Ha=^za1Q9JP(H zR-8W|u}kf$R7MB(1s|c`=8%_nQU8S1qx`(!k5V?~`8tNJ#M9yrvfj#wi__9w@F&h+ zZ$xu(WMcMZM#6z_vo1J_f!uF^Nj6gKn~L{f=bTtp_IBJwZ8P+Ii(KZ8_{)%|nm#xr ze=Wu5G(xQ_^adwMm%NA{+2Od0WbjsX>qyZg0@~W$DTN^hdTW*DP3LFx@cP7cC_|Bf zVxO{Uzuk9ZQ2+Y32SQkZunVmCmA_jb!cfz&tu(LawM5}L#pX)6?t`@aaR@D85*E+_iGh4FQY@3w4mm8tf5d{X;3PE6!y8)YrJEPi$Cs0z9f)gU zSTibf`0-Xg{Pj;N!6mbn_-VYkx)x#}@`5tHIGrH*B0N;plg$9RGb_yXKJ}tIfBNq* z;E9{f1Ittb&NkqUdH|5j0!6WPg?yx7`RS-Ym^{{N{l=zOcN0->k-YGAM9jYFmdZh zrDmKy#xq}Aa5r#UBuHDcGCBd;^z2_SL!G2AmHfcGb-M_oibz)WO6xG=TW#%UjNcd@_bMo>wVD+DBggT;{@{R8uycaVo>`wvM@(N?E zD|_)Odp>q4CGS)5SdS>LK3zU&Lpz5=K~xmTU&SX6LUg zS*aGYe{>Hg`|Q#$I;2jro38_{x?qSe2T*O)>$(Jzc}D@8`e5QV>x4I(eN_Cz_(X=~ zKMoHjczLIFsGz;VY!6dMnIu2dPpzemxV>d*J%9XrffUcoj~-V^#qQR{G*R^C{)>m@biO>Pn{ zQ7I+fPv$@J}e#76Vn7(ij7xS8XZVNi|@!8M@ zaHs)(@5x|hub3#|qqm)(e+#ado|LVXt3{hkGkv`mi->h$OWA@Jxg_9@!H>UR2PuYD z#fcz0F*)3YQK_Q=*^AYb<+oBuGiZHysZd4C55aHu=wmK+F>v|uT@3%&i7oEyRm9Q* zy{%I)eTW@*9|{uRAVMVeTipTtf_i5=g(b$CZ7vWttPARS3ulSoM%O`MG=bZbL_`B= zSM1NIE`bMI=%UHUh=E({(0lHK@LhYpCO2GQ#H=cG4U%6ba=juTw52l=CPeN3KNrB{ z$-WsQTdAo)86NNC~XnRf5BTK@EY(RQ=&wI73U)biTv@ z3ge**R#*de>#a48RdRpT_WI@?w+F_>QxE>M;4Cspdt!3h|0|(}cb+@!`H}ZWM{)qTIZ8&j(IL0O2tr@{^CoF=ofL3& zei{>2K?7}XCAW$i*%zQ?PEGF9?Gm0Of+$_b-|CsO44)d8+CimtOf@K2TE=%;v1r2nj96Wx7UUqO5N+BP$T z`s37f&nNcp@D=AL6ikWSEcPSZ!1@uN|DxiN>4&K4#8#<57 z*4=uxSq43)Vd(Ar{`?gql*G(y9fVx@l{preEB=6&*Jz5yKnV{WZoqz{bQNvuE>EX= zX1^0T3A5g!7C8aO9Mlw#k@uL5n~>9J$5U2V{rKAWsRrMbT%_BQ5~@c>0>2uzUhI#G zAxDLQb6WT#I}>+2Ys|U7DIc_JSmalT;{T<=441FvV|qBR$(Fyt5P5_6)5$4)92#NXv^}7~sp8f#r=!o;cyGzf zXK4U@A-Yccbk?dxp3e5z$lt*``qm)zw6bW29hFcIF51Kit~SzD*<73J*3fTW!-mZV z!3#XML?|?)%*6Vt{KkLrO#}X@+{a3GE{Y8UU5zJiAJ8RU zAp`71pqO+6b68M)eeLD143rI<0W(gR?B+dAVey-RYGHg4h*ov{d2?u!l9r|IxPcC6 zR?41r@&fsM_wF}VNKzZx#*WPZ7Yp|jsi0fW61rDg(8rQCutH0gLQ7h#T;PH=FYkWV zdD!Tu{BW7nitvGbo--%>>)-*CNoj8rF-B2%rC@-cw|v~wpgy``pluCZieykDlU2j7 z?VibE>E3(MZ9M?JR2unPn(!td)xYD+b{B?Hl^@&x$;bn+ua0p_*E?%Kk;}R)ors*S%?PSk-THAXjixTC|wAlWm zKz<3qyx!h51skDh0Pr_!pYXPQ%bT2J96}SiI6tVT=|)>E$@&)&Hpw4#F7EVXBs2d=c+dB+D^c&ha)1K|Bjq= zI5Y6(3`#?%181Ro*AWaHx+q_s0l(SZaeUB)$*z8)a$P=KW>`0-kRk1Fcz8rSI^UGB zEo`UvL#zKN%+lux!o-rXC+G2-GQmAnX@U~U(Dz4NWVq26eP{+}Ur0+9W&#Skmc!t<$qrAStb^`rgJ?)l?WSKn zI%pJAkoNJBK+J%K&a=7v)0pDi*b zhET;RUbX#8?$w{<=Eb%|fIZFtuveB?p43A1VItn$!{ofbGZ96*GlOW<;+1+FzxH?S zJhv`H4_+lgO*(?47GqU^K*d1GOn5fDZownpeRDe|pdPF=XDN&7ROiOO z3QxD^2&-m@`L&HS9SUFkD-*U6bdVt!n?Y-#X`ILIlN%uOgnrz#SREnCETDy0d3XOP zeZvW_#@F}!w)kcwL2q3N+xZ=h)dNPs!4E@dTjc$79stN^C;i*)?|YPY(-CVoBW*X&KRM9 z|A7cd!!8r1dglIBV0f=0thRJ>KC)vNcrfACjWdfLoBlfn)yMFrxw2Nr-3dF?z(hcagHuBe&90eDhy|RS2@WT^K4z<1cAiq%&c=sDw zAre!r6V>S}N$pO}RR`mlNl&~9-RqCGk-;y6-P!DOkn1GyD#8uRsF9y~&J&V>inSEp zJ+kPlF~%$n5?LIIHP;2Jv=EkRLXPczyJymkk zhE*`QAa+WU61OyN6;;^PZT7d<2P0m&5$)tPLlpuAm4Vj6!*R)w^ZNU`$ib>OZd0~^<7A^RD(mfv- zJcg`!+aDGSNV`hZt;{1YxO>iCt&Kf1T4&RYm*O>ycgaBDyU9L2ig;L-B+Kq*^*O|4 z=6Vad zOYBdgnLjN0ic|SPyY~5uWASgtS|;%}3>cg?4ymL7m{(YFxBC@(TeG*wFXxvK=;Qrk^m2t(G<8tZ& z7i`e=ipP@&CXT9>9n4c@gA~Y+M9`3DKja7Hrhr%Xc(x*4!5uxcy1QoFts4))TOiuB z8)j(YJlRwO+~OC!s@qI!@TGObD^>fye5;$kk*y||r}`qQTvEPKa_XSN1l-1nQZB-UpP=(vfmq)}`1ysN zK03g=Z;BMGi$zZ{SXE|9Qy$(-|BZxwQKEABDS6gjvDl)EAMD=S3U+~VMXpp#WOULu z#?AhcJq-!hkO{z*G-TumH+pcS4xMB>pJ32--~GhK=&_p9Y?a~jrYfmiBdL)&kgS~dO#wa(}3^CEwHwg`oo%o>rrOhz+-8Efv!}@eUX$C z!*TsJxVP%PkgrJ_sLAx_3qMuU7!{wuXJWDLc35bzVu_Rvr(TJ8x}R4*Yi z(++#381*ecb0?MSiVwe=#jSlZ-gXJm`S#Z=?Zm4da^u;vunyDtTK(;Dj0ziN3^xo> zR&HC==Q6?iiMyyx(WTPNSUo>a&pBK`s!>LDL zV@0&6dZQ8i`Ib$=oXM~57n`VRMsH=!z&FR6Y)nO_t;5m>IzBTs`h4PLAc%<`YzyH% z!3?2S{@;&R4^h@;tEXSvFZV=U2^t7XsA#>LT zFw&q9av`-U3iK&t->_g+X!bt69X9vjNlG~C{rYC*LL6W1N#QIkRw0V-l3hXAJh^B8 z+8knniDUJ7qeR-`9|3)uCC7iyy%TEBJ@Zwo;@ZXFEc>@~RTdE|nwR=5Fi*=v`0uFV z&^Di_FVte@Xk-Ei{spZe6)A(ZZL1gh?@?YF8D{HB5A~c7OO1}iex5QpDKO6=PFpu1 zlKX$pfcL?m*0CNGAaI|S008z?2OEC>Zf&O!Os$m1Cr}BvTq6AzW|#k1w<=v zL`y?kbd`bV<2f&280(x{fRisW+DMHI%hni1YNRASGeZ zm(>z`9xZ4qCOkP~eVYp9ci?BylrKMt3D2n7@b#NAvYHI?JE_)Rbo}eh7w&dyp^IY4 z&cO{+N#S6MFdi;wd-$aazEVdWPh9PVfRi-l#s!7w*i=0q><#-O zykSWp6~n;~zvj1d;Q1mSC{b7&_NMG7$FLKB6Dixv0(IZX4)Rn9cNHD=oWt0}*9G`) zxSL?~_mRiO1{9R=RFsncs;A182`Sx7T@$`q$n3ciA znnzCuAKml63qjx=FObC-5S85{2tDJfpxx6EO$rgVzfd0e>SS-=@ALN9C+D?0XLHu7 z3RU%V&6K;-S5}xULp&~NlcsN$HyU(G5)UbQbVY;Olo!+F<;`V7>`#075xZH()~5*( zJ$CLBZ!0C!8~)jj`MM_wyJUDwdKaXU)yGZ9OQOnqorUjm^Piz1%j6ie5sXKd>SR-o zCDHB}`)JhD7>?eF*{E=ySNjrvYvJFECs@XKJm8En)EwU_=D25?4V?k6NELYKNpO<$ z*q4wubM3IB)C~Q7^Q|T0OCG<@UMqiMe1QD)O}_DIF|&;A!18BrgBLC_he8=cV}tlt z5pTqeI{P@YPpEFmZ;fG|7Oz;#kuIhB$=yoCEU9LJW(QZ z9ptN?Q01=C9C*XpwY+nuGFxkZXF{{js(1O5sar)I+l*jF+y|;E2&-LFVG4KRObzJa zgMMMmde);yZ5}!qrqMQDP$_5Qx|YAq>%Ff+PzTIjVf}|KE6D=wzhC0LPB_hGn*U%s zyUV26JjW?#!}&9*hOqIhxsF)TQ)>f0a1fB@qR(IL=Wxu`a9~b~*5(nxJnis_8uPU~ zXtbyT9J3S1kHrH(aC%1}J|rFg@w;2iB%^kh2Q>5pUjI5L2x?9T<-=$3Bj5K$^HqK+ zAFE2auAgrGW)(kqf>PC^%^+sHKR$3FDN^cGZ5NR<@}gevf#ELs%kP)xg@>BQtDiJH z-nX~dz|RC26^~uebH%V0Mk_=*CQHp0I@po=rquJpxo>(Vd#@7%TjIw@&n^xxRX>QV zGLV(^T{ltgRJ1U^FBB)`#S7{^@@ExKjDZ-if0SxHJf=G!G*nTL@iZ{P{2zhkbJ(bp zp;e0LWrvz$A*Fd#KHtXcq`7I12zk0vi8L3UaSj1|!9hsH-l5IJN`933&izqvP1ZD* z<8qxR{L9fNz+?!mqeS1_dwd}5l7F4;wayV+WDn`ZYh}{jQ)^_To=k#u8K&-6K^Jio z)tn7SoLlgTs_;Gy>I5&uQ#kywJsRwi+ebH*?6s?^VK=#@Ae5M%!Bv3Z>Ww_#RcOx# zoURNQznrB7^+lqFN9U6f8cb?C860Gw2%5{^1HVT(;}?7_m-#Jo(C97RE0ZVx@!l6N zm$BMum}-Hm7?i9OkVOkdX#AZcmY3yJ@h(WGdhCg;#o>5M@r5B=3^X@^0!QUne>8yr_O9agj|Q!F)R!Wuz-3s%sD%LTis!ETH>_ zrjhnLb9dd?0kZbDzT0=WnH9rL80ju+flXjV$aomMX?ek%@){wvBsk#V{wl1a-G3tV z4riQXa`lC^nPt$pu?us=_HZq%b0I+#seM?nDWRk6iP1EfZ!R0JcPI)Sg;w<>dryVB zpf77e2VGxZXLvCskgoNTQBPRO*W?WJ_pG+Tht_}5>fV7(8ic6N+ab3|;wn1}!f98Y z8nq*^a_lFgF`OAIGTZvl{n4YwnA!P9%4@_vQ#*~%-pO1_!5e}#yD94+H6sg{`ZGuJ z9e4!7B1`PmRRFr}kkt|BTqa(j6Mgx8?)j%ry1&*B>s9a6^`sp9t9HPJi!ir(s zFl>qNumj!b4uxAQxUcQC0?+tccNK)kC=_TTZM*kJTai*difoR`q4iE^2d}|LTZh_0 zX^%JWAH$8BWwr=DpUXcdKc^YScvRpNpfN?Rgc={Jo{C+*6f5Ey(w7OX995|_)D%TX8|P}W9=tLX@ztDaZ}mD7uzuyr$9XYA~P8)5Z2Ho=+u)&oq=ydwWT zgREhn5gBN=ZbFNi)ZoVa5*crfMJv1ORspHCL%3=E=rB9 zEY;1juvrvoR)_07X71z!HeSDmp>#bbw+)7E%bjj;J z1E-qc#bGCZefqtRMRg0RT5UI0r{81f4YYp#Fs9A7a*fx6-aN(3hU7l}HrtC@ivLvJ z!MV#RrE+jR;)r}f2#-vAzd90tnCc+gBYp`IoU(`Lc;(ItXy%$C`a=%M;6$n=_C)Q% zXXmhgqII#i_M45DuZtw_N%uRz+T~OZYX)vN4Me8>#AE)N_$-NPTvypzG%vfRPA;B8 z=NpGyt}Km2j{lwF18XXaCWwSC2Zzmw1h&DOrJ=sv%60YK(ELJQo+jJ5d>MeWe=CG5 zrFG2WH}&&002T{vcguuoh+1c1&Iu#s%GjG<_l_lQ{>64rm>W^5^7)n7L)!*4OAi5g zb#d66-VuwMa8;9fFHgvAzG0#?qZ3X{eQnRPBLQZuz+Pc*{zlty7(T(YgViymq@uoH zR8Sv=7bd5!sxj8DX=#@yQ4cE&%-@x|V*=&X?v!n3O@xtb2b|N}!{f;NpC~NB&m#9- zXbuvU7o|FWGl)fs38#l-)^|(Z=A{VX-@`IJw1_=N_J>T0a6bFgFxX>d5aeaD3!-F~ z9m3OwYbW+|Uct3pVVv*Br46Wge~q{pVy$h0Rs`b|;I`y}oG^J)aM1jqc4-*9&WCA2 z8^ApXZ4Zz-E)%_?WtW8a$qaXrDas6#bAr8JXe|F)eyNQ_+D2O0OCy_WevektHiH>~ zipvMg7UqOpv3cdj#4xEeEX%h9`}APKr}498)TS$qSF-VraqAnP7iQbX-QKjdTw+aB zF~~-AeRWI90eHzc@7E7TjU)Co*?)2UJ8}I-BCAuS0XV!*qoc+ofzelLy*mRrfb6uOix4a5D zV~E{-vrB>J8qtyGi(?J*6@GSavN*Trn~`K}mO6!(pQ8sS#5HtJPaiuGh`&U-q^V#j zchmpiZFk2+5%!WTxiZIh==X>f%PmP1nPoZUU3V@El)d-w;)O~hSZXeVGsI2|&(Th` zK0eRn1zt%JU4tGsH<=2g8#W7fBfkyxkLl_J=h7lZrT}#LII~dB+Ir0r7jSow;bNG*cc| z^YigFl@jMdo>#|^*M)L^mG_6rH%?+O41JDfVULrQC+a$1Nt93y9;q6RDvZwH=!q>A zt7MvyTp-9r>kS0OBP+Q|aWp5)@}pKI-bnyo0V(PBbGA}S+g`4)$x=?6W#l|bCDFV$ zN@=5c8+BjifBkUW+3<1SBHrWX1E(lSi)wwFB=Tixv=3=UOui&4^889y1erx`(w&?m zvxru%gN&~LDCRP53PDW+j<}(#vCB4)-(FTvqC2PW9Xphw`!Mc!5i;hyPn9$IA~%4d z)eS20E(Nx>J^jmGLC9m8Q?pN$MABp|T6z2Yc4IueTpMa`#0*!+_WC$E8R52^-hDFQ zG}dXYjkx*^raeq1GaJ?}*NI9NU(5wjHo(Y1$4)t{fL%^|tDaES;EQ1rk0Tde+Q+QN zef(Ra_S5&FvUKvU4m?3o=y$Nc?i#yaqUAwwwBAzg*zPp14kxRU9Zw}WJk)+#3qgmT ze444eJD+EUg@BB}YxYM2f44$-_0b}m&>4l^8me~aNDiv*XAO})68NLYxq3NH8S~AP z$1jO8XJGPk_gy;v*R`e+r9$Q`X7sx=yMMeP6a`;i9^YYgk-s##R{F92wTbXqO)e`` zpHC}cs2iv9_zbf$29lo$vU}3vEC2wej{rTKErDqrS*|N_SXp9(GB0h?W%%nZc|xGY zY$+FIjqbl{X*ZE+>}k<~j{^8XM>40S!}v{YFQ z3pqCG%S7L$7tWjyq(v082dq$1(01mGkSRh7IHAq951xhFBX+ zc@UV$L9MUTrdqh^vKBfqafENQ8ydk>ng0f{YlXsg$mQq|K+bB6OfPZfEwk`M`yHiM zDS+Bj8}zy+ahog^Hf*V^RkUzS7HMngmOOi)E8oDya6hN_W?=4B5$3Myc^R@PGl;B` zOWc&d6+bO~gZVac0Y6=k&v)@QGL3aCtt&EigAGBY!ayif09HrWs@HJn&U9)8^vgbc z&IC>d3-&Q1)xvl!@-r5KL@@f;)0TPaE7}iy>B{yzU!=RBtfinPWvrGo`f|`&oaFFI z6Y&uK?C37|Bkt|=%eH8#fMI~KY$e4G2dt6TAN7Xj(dD;-2|!%|C=;`mWG!U;{Rce= zR3TZkX5I3X#1K=AB+}>1^Cb(9N6I##j^9`@i3(_AN?3tL1Un|uqdrWy6n30?x1JPn zI>Grx>=8cf*25*$K?!d$h70NoxqB{+GE5+7EmfVxI&BX0S_`jFNt4&7CDnY=cba9Kb^VlSGg&&>%Vg8T?1ob_*h=Ae~0*v68O0mA*{$zDOd*67_ z7q~3JbEXrPwsc3j$Uf$z-c06-Ru6w&QvR2VC-hEC?*Ibw6)NL`yxy!va9(NAYTiBH z3|;HCabmwf)y(|{zkWD2px=NG5ciWPtTx;DVc65_y;#6jzu!Z4Z=T5Ii(#2qva)`MT(H$L*Ck@fD(nj_RjubyuMB%)KmFNH^ncv*euqM_!RfC%3AfLxwYTH?PUa-@5O{mdS9 zaA@0(s^b)JrjyeLfnOhbYnQ+-3dY`$mfA^8u8k=8xXr234RxIcG{ugIN-W^-3dyy- zwvU8L^El+fx&JJ9@aWG)6m!1g5(uj7y}0-^oMq=rf^BpHQNGuuMmxxHn`_aA@Ns8$ zo^!c#+h#cbmNXLC%Z4nQ7*DR0jPEx_uYCpEbBG5?CYB^z{?Po9eT9P+qZ>Qz)><7e zEddzTn?_c$80BE~PgIk(Oab4)8zNt}N${zihqK;b7T#gPqTnf|0e)$1f!t3Ya^TI| z#2F0LMYvt-`kUvQp!#s@J%mWfJIp4EIaN$m z*qW13|0Dxm#`qj@+A|bEl!{97kQY4dBlD&Kt6lI3x{k z-2ZRBwv}aGJ*fb?eI&3eElxEI_~9)ERCg0DFp`yzVerk7k*wg=>4uM?9I!9S7EV7g z4~<3PFHI-|pa?L}JC?1XOs^LB`G(+1tGR~jvf+=>!j9z(^3Gy}K_}eCnSAs?m(W4~ zgY4M~GA>ReBh(zWRV}6Uk~ynH*5m=hk-C6WS@=A7roDaDd4Rn!K1#?3JI}!WxQ1k> zlO6&f4Eu{0BAiU#WDilAZraL@T0C?*6d@Mw{2FeJ9z%z}JN@mAd?g~yn90b2o7o`Q z0A-#u^BbpK%vl?A1%@S?5qAe_Ev%^l>13e}Of?W9P=kSDP0O`Mg?$3U7kT|eDvsMo z78XfPwrxMW(otsOxD|k9K#*ySGauMt?6V``=2=*JWWCYer#7Gq++13oZpIC{#A!?4 z`tvaqSR)s&;*}=Hn~&JpX{(C6Ac&yYupJ@<9Nr%cM;9R+0I(OT*ako`(!ZM#QcgR4 z8z=`b8_mGI#7+4C=i0PKQ3uk{OH&3Ed!fMn8v8WoX)O@!WqKHdN);kow~pt8VCWA? zu%pEe{%|hHH`WaB6oc>eQC;3Fzk7jIJ=iu3#);R~KyV)uA2foCDu-JNG2rP)1qkX< z?wxTCnk3tc|8i&NfrlP*>N+c;ZOOc+noSQ9`!|)R-VU*9Gg->IJgBSzSN$ICvG;Lg zFf4ZdPwog}{}lDtVh}VhQPo-kn=dP;4eo`+dB79h0C)CiDlr~z-usS$S@t)ov$mY8 ziV~Q96Ok-C`ixfqcy})Vr{*ekT<7Q6LZ$bi0lgK;fV->D>~91x}?a=_#>}+cYwTHI0dI+TP3Q*`(8$ZGIjh})_ zRcU=2XO(wAkhmt-{GV!$B0jB+6J@j-TsF=I$1gv=B-+4iE2n@Zo*?a4*xZNqZu?UIy-CPi*q7gLR zkG2#;Rl6zLx&0D65amAbH!Aa6lkq@Jr(<@)pc#2ua0j7(s*MRqaTe{P!%-$7<2#A# z_rW*E03W}iNeGGvd+jKwFfikMSA&vzuB74ivy#c!O!|yb4BMhQUUKW7>*6G`sCD}AV<(kK~0M&XQU;`)% zc8pX4oA>TG;|7% ziU28SpY|X)FiT$2lq(Dv~?NhUy9;1}sF9?o@QT)HP zE^s``ziX=zKH<;CfYQJbp`PW9j0ZxO{zounN|qOZv;Bsfh|~P=)Y)GKQ0E#Bgs9ZY zi(XlPc>1p=hAOPfgnWh16dw*;Nf`^4uh_IK;63^hjx&?H)VYwj-BJbN8pSAo5@8CgQXYBm9T=Xa1FWB9-b(fYE z^&U`KkYYi7bu)q>5IeE;?^5(tN`r*6m$P53j)Zf_@(pSx(x01WpLU>*;S8&e>a zn|lS$t9EzH*Go4Y}gH$58s1T5NVRrSBEU>y;7z0Sf z8+1(ppv)V*vk)TKem zjO z_;)Z6F41KGR@^>qz{TW%4_n026<*V92as1lmJSjy+)!4slid;SgF=wYc3`2PqaRmU zyKvl_{NW$wD+IsDb2@}SZqcp3MR#Xgb_ky4bAtdsQEddq93cq&ckaWzLAi8Z7)3bo zUmLn**pJoGZ>3#evCRK9G?J+Q7$&G|iosj0Hmc}zCnhpv!4)M~g9OJumva55MD@q| z>^w27&MTGy+YkSKrlW1TvLf?eok{6geg)=vQ6%`{VvQ(90wNs)YIHz}Ag@zk3!#@iSQKzY=*`w2$V~t&C0M1i z!2SR>N}U1U1D0q9xEVN)2O#JGKS%EaEdopP_y523|FY>Zj5^PV5p9@C1Nj&P>FXL} Ji_f^+`+xbnQHB5j diff --git a/examples/jupyter/images/manifold_pnt_cld.png b/examples/jupyter/images/manifold_pnt_cld.png deleted file mode 100644 index 94f0a7a04715101fc0f21341b793f107f3be0ae3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 120957 zcmeFY^;=Va_&>feLL?1JN<~Gbq&pM^5h>|LLb^LQL_nlOq#LAT)M&;KWOT!5Mt3)3 z+h?!$`}6r9zCV0-J+Eu$I_K=#^W6Ek@B48g)KwL!Za=sU005|z-oO0>01y)nfk(H9 z2wwpa`a=M~Lx9rT*IGU%yYt>2S{HTbs{`_c-}k;h328_BvsO`2qq>NJZy?`j-`tJ4 zP;HxyU-%flH8FN@cG|yfGSphI=)dC$P1%Pvq0;tuqKK2zT36|U z;F}NH`JY}enKBRK%9<-mRWl@heaN*tz|OWUs!4k7GNy>d#2;O%uih;6Ty?~sgmZ4t zs134brpro+gMHj^hyQy4lRtk(lqbHASeY5(WYoF$G}?tjjGcYS*o@ZZH- zkGk$C0siOU8>8a?f8+mG#M8jUGF`$tT@vk)_@9!meY6O`$?{+^Sd-G-pz60mQ%rpeBI-f>9m>=^rhhVV&==;1e76fDwv z3$o_YS6CjAK3p4;rq_W5(r_zR#W&8L_AnE%%G&)}u}_&j0pl#2}z>6G*d3@)Mhx z;Wv1Y1SI^Pvoo`zX&{tfe{R1D$K;yx6Fl0WL?%X6=o6xWrk{r4z7 z3I<*~z>}RchxFLO&jj-bYYi52_5HJqfZ4V4_@o+a!VhhKfHDT`8a$MX!pr}()Ed#Md8*XCjs81oxS=+ujcu%&%YD?`?OxIr#*f8 zw7k4LSneSUH3I{~#rb)OVB6&~h056V_4R%m9v8vkW%%Ow%UbOvdMiM!pEV0w?GxJ<*Sh2g6wCrP@r=0e^50 zdzYTXr7Ju(GoLgNx?*{l9Rkx4ww2WyJP@NGp?vGe{gNi>8E{C;1o)L+Q-KG$}91#BL1SXdlAPWm_6e3pYTYFbst7(c9Y#c`FXy9xq1prw9hmIyLj0^{J| zji7uz3VIZznij>9y>JXNb_e44bs_ReDu^q!IJIzp5z2Ici)loMg~1}TYVMNtmIPe( zcYwQU8si~8GmZG>iYF;}A4ByJkEP6z4fZAd>G=$sBaAlz`KeI`g%SL*NJzzyBztq%F-@0M`7j zM$pV&d)SYI3D*c*=;id(N!14DfvO&3T1FOk1)qHckG)c(VPUacn`Msl(^2h--+ z2nqnJ??~T~#Z5y$qGR}eF+~z&l>JOCQ|dDNw8>_3A*r2STLR*97R8a{fKm|t#=4%)ykKCJ^V%8~3U>|CSOmr4;9Gq9cJ5Szu z`jtcn)dMU&pqcbCB75TuO#YLUK?=p$N~Oz4rFjkJH&dDQa>_pgkmHJ|EmKBM`M%NS z{Yp!lotPkLn0JCTsN33ZZDfIEKL7m|m+9B>lNjxnlZ)q1z|;L-WsEG6G#nW62JR5u zrtg$@*7iY|r5tTm~10d})bi%U0wjSh81d96GJX-KozT0?0eVzNYed zAp!K6nfNh|Bj_aV1%4X|bv-xSvDu#A$n;m8-`v_FCk?sx?4ymNV}_XPGDobGo8aEV zXXFUa&t)01KfdAK+$a?vgWt6-i47i~`Q>8pnY|CymtiqZeN0v7vQzoZ!i47=kT5+&{q`UtvSc%Q+Otu0MTh}a^_cjbab9fVi+0>^KpGr-_7m&+69 z2b|viMdW0+;0jq2I*ob|jW2L<3wVTWX&W7jc?bhPa(av!B@&WRB68`_v_*)S4(fboUz#LH z0UKx8jzc`NUt8Q(qbCb)!_{YU;`6Xc=X4Jl$J~uXey&tQYNXlF=YMI3?|dM=E;)zZ zg$ULEMnc$QjK&O|hKatXyWPbvXmSE5H8E`|{bfQAuY(K)y!~2U9(S}ZupcfQYY04T zsObgpo;NnfkcQA;?ji?B;7YWT;%HdvrzIdS>(2nW@5~9cfanR~zy}egI(8I4qX4(~ zbIEkB+xE|gO}ErM6?1;D2G*}C7<@faycc;9u4XG93rd$xP)v6_cs#wq7J+VPM+`wZuUR}lRyeLDff zVNGyGxQd)4Yjdz2K54@bBDiP?-FH;;f1iMdDYhbxz@LX2M6c8*XLI~ZV96O#q<3NX z(hJOH9%ruuA^XrN0lGdA@@M9MyN*g(dp%JNUTYxh_BNCgU9bHYXYNdS$u`uQ02I#i z-UDY(F*i?b5aE`uhNn$pb4FZ8TO9Sf^CjgZ!$LIO?b{Ozcs;SU;v=1?4;i4tG$mlk zlqWC6bL^DT6FSsm&*EVYLl~1crH_!~yKE(87WCIep+%*AIv0;LRWRQ$-x^)%eV;dN zziq+ETSd^DmQKK2VcX}e5t#cc5J)%pns z1rK#Y4?I9MveAFo(oH@CHmE7<I^^h^ z)RNwDeBO6jk)%%m`3GbK+jAh3&esvMP9P6xluYR+f8-=4K=exI7hvvCq49kYt!SxX z5;5c;<_V@H;f)$)@@Awlc>^ROR&jj(;(YxNPg(JKFUgm+(84ctAfBAMlOWLm3b}L9 za49<&yP2{k3d+^J312QAV4j5-rq*Ft4=}RB=L5yxiA-7FXFaC8&*K51iZuJ~ zBD>V9vDdBRxNS-;c3Crlv(_aF$T%Jp@-23~sw9!JHS%O3TXyGsOydu@*m9{p&$4}v z@D==1f^o+sJDln&ZYPOvpRZ`&5vG2%3@Lhv2YVILyz(FCC)8#Q!Ak%8RETjFKm_ie zkC%6->>YSG_xx~r^UG_T&qK_)RdAEM=&9 zP|fHLRp%*7Qi9bCB4*$bdf`JW!E#w#fEPNXGIKj=sjR@6BVZQ$durAvLe36-LF0o# zzu}^1`LN(I9+|M@Gk^bg#Fxj2qAp^s&P7)#+;joFr&2ikCwOuD)?qz9gG}8%LAZJ5bLw-cHge0uIf1G+^dovX2dJBgwY5ARJr$0V*?JW$6~+P-gO8DLm#KEG z#p=Ez*l?*FyI55pRt;xQe5>mef3t`*-B9W?Lac?v)y{zbqGA7GL!dsXN7)k~K_;7g zU4_kDHwaELHvm}cf^zrJmb^);O17`|fTw9~cA@4v`|t4Hl_oc5qs~x$csvl>q8%tV z&Zf;@9N=hb|DH@{VWZTsTihkb`j0iG`eg%^N9KF{T(2HR?MyWi*pDDG*r0A)*v!HQ z-uHn{toVC(vsUBYYVF}8CFc-OnaFQtmpkJ2p`-ydd{iy8WKF@z!D zo=k18ea7-TfP)oE*0a~1mDGVQ*&`YT#(ZYT&SD!DJQ?AY_RgW$lxAzSNB{-YSvNc> z39zH}xiH2~(n%a*ai`A3ZRfHN89q>*vO|B|V8CcqNuRahTNzT`x;qF?N$3_Gp%irX zaMwB*ef*jbPxZ{1)R}J6iq>Co`9r)f)`9UgxsYPp$P#}R7VQ$SyROq#DjUKrIfWr} zDfQzMh$Ka~z+SxMi2=PS!E=Qx+Mc=&9QgAAO*&H2h566y2YNGr0V9FbHWV3LN2 zj~?s2#qi7EWF50x5?he4@we~%r!I5H#BPT6jt%;Nc&U5c;_fQ)z>Bgv2+gd~_BpaS zdR9kF&b!k_-29A9jRtOrp+xHrU-@x+=SX_zU}mu$3EPF-g2Bq+zZNZb;x?PzrGndF z!N}6lA-p2ql=#w2@y1C%lssMbRY}%Shbacqn1z)5%IleiDDRM#zvYS*nEwTcihcqR zeHDTEH?WtZEiE_of&N#am16mCQD1q%JUuyI6|=AY>nL1}U6pEnaU;p`_@1F#F2Xz5 zU9hG=t-pzCGb(_prBxD%`HmOO4>JsCp6pw&=MW)E>Q{w)W`bi!2Wtb-t91-?eZV>y6R;O3qs{@d`^e?6;|g%1wH5{Vbj~9(N8s?thIF~a~b*}GyG*k#W$-$uzbe( z!-{Ds=ZpZS>`v)E0u{boLqv}{B~q+k`vW%2tAX`o>Ftwl$`k{|m6yHAyz$n5z5u+< z0Lc2{;9G@Qy<=j!wTpBYNEvcD?W=ep+1pXs+;iwQmi<6A2+w{X;so{B%!+LJ62dT5 z#lI~^PsLPGi8gZU+FO1xIT+pJTx=6Tu_=}+WAoDhr@IcO^C}Sl*K*q7d!O6mf0STL zT_OIm6jFX7GT^+89PWn)jwzH6JEJ?hYv*bh!~WCzG`U_Bd&~-V>Rrk|QKYow=l{x# zl9*w?qLxxKWSebQ*529s3H>URZYV*y3853y#Z{kNQX6k2g`EUGJ3M=IP%AMW-M#@( zMv(@0%r0ZG?z*IWr$-a*KK8@vFnoK^ubDQ11_Zm$N+Hw*@0U*%0M{jh&lLh`P*2#6 zG2a-bu75P4L@&1>x3S8J$#SzJr>_EM-144&cePucZxEQo-RmHVoHRuA^i5aAbMT!B zmj9&+xa@epLCC?x1(-tcPIDvHGE06r>9Hg*GOcGt+ZZvCef2MAxBg<4r)*zjxjjIj zpso2YSUAzhnX}JhHfG|%jaRNSy^Dk5!m@BHq~f14ie%p>r*#YFa|%5NEsV~Q%$-FS z7_?dQQ5aK|-1Tg!!N>eSPh|UAFKN9OA)0W0zEI2G%4hQ&7A@Y11a{S3TYo*!pt=Q5 z!O_eGbOQEy$ceyMNi}o6$_=kX(i>hhy9)g0`EUMmoVZo-B)tG1t7?$(#6Er?h)S&0 zcwiu!$UckFjVbqY(G0$~i}BDE>7uK?6KtY9JM?N?Ob!{MFSjHK{BzFN8Leah`5(-f z+z+G`jh0M2BTCFOrb^9+>lO3q7|fSjK!75&p4`GrW(69@Osw1bCLIwoODmEmNo8B0 zbU73uSJRxY{T8Uwjzn%A?DlAj(wASkjXV&l{j}()-sHNH5UztP+=I7DTAx?1-i;>{ z(MM3s_Q^Ql>8Iw#fgkl*uw{J#4x@iMZHrH+KFw<~5zEY^`7%Dr8TU>3Gl3%VL>CfT!FTB5NyA%vEgHKB^G2JknWLyg45j26Lv`#*qu zaA1ME|8N#*RG-{JEYhN@Z@Yi+auBk@Qjp=EFon`CGd$vvvkM5`3il(wRPkJ#HWB@4fB}1ZUmIkFV3+!2 z%n;tw&bE$_V2Ut>jUC9&JaEag<-9FQs7h03`qQizfRN)316Eqthngpwkh73n8i8-) z@whi_E0H5!J%y9F1m|;m?Pv*g4R%s$hvS-@LaIV4)ci*HElR`YEaeE{JDw2J2t{ny zvov5NbJF(tR?Oy=6SK>X4Em5-Z93J5@A9qtODA^p$UHyiFDr@oIl-o~x=y+WcGYS1 z7eInI%8d&3y*zSJ6ag)=%+I0ZHk-=!Z^G;SN0!@*lrP|8e&>^Omx}-T%eD}+wWT8v z_8X_{+=b-&5@*cxiJbZEgQ&S~L&^BaAnKKsKzJ10(dSgK)}HB8023h=&uM=~cSvXE zs4Cy{>3C`K4K7EvFUCJJEIaVWEzr*EJu1EWY}f7>KzG)oXxvoeV!Ud;cuO)VTKjy3 zE&HGoq7TH`R4;%XYFq;h_R*XLgCJGIP0gr($`hjlR+yh_yMY76ddmF~ zzfo_sCcs~1iU*vOq`bd6>nLuLp_@72hW147*aE-W&vdEmogUyQxHARvcsxX`_5JvL zl83=P2C~>b;uIQS=YpzCD*pAg^(XCC@|H#V>z2OqFCg8?X~e>Sfx#a5x%S!m;QI4d z!jiYs7nXoh2L_0WITmJRH%79lqf^e0}{f zoZqlgVh)=P4KH(@z(W6CD}i%BV~fg8+HucHrLsbpaNLXHzr!s_mi-=c04#*p>un!> zyHrY_(snV|eH}bIGE9Jn3>0+w;*gxi#EG|%((^UKDq?+k_)!KfXi9a58ydk2jVKwr zn`EGy)m9;cM~DcTBOiN}l?+k^4Ez7f_cv~05cAsFtx~q8adL0;I5>uB$h)f?F8o+B zFd8eRzmA$Q7@wSZV;wwu;{NRF805?pTtCWLIB3k2-t6}6hnulQ`yuC(BHMPvPLjuB zp1~r>ZxPhd$_Z{j=GhxjQtH_j<~lW)oS$0|df2U;Vg1CtJ8#xJb8bR=plK^V$O2wA zkQp^u*c{D0Tz_k>N?cS#qdOjcVw6%SQRZan$$;o9N72rvxb$z5zd~ z5N*P(;6-J6ZXu$1s_qtXgI+q_FFD!htoZtL0{F0OW@-d-qH6d&m(CxsUa_gYQ6C=k z92N;7y0FZabbIRhqJD|%mK_zB11$L_S6I4u_FJIub7d9QqB0UGIrEl&ud+_wHa6+L zmCtycmAIE+XCXH`-N9`Mg_Sz1&!kRwX#FLbu#4JEq(7u4BlbWAw|8g$_-XaOJ#BN2 z+{aSot;Rl+EmJ)>{PPY!G`C3R!B)5lD#ZllVS-lr@t;A;lM-}09f;t8%zrz75j#*{ z$Z^x99#Z}u2ol5JV-0bI^BHVM0=Lh!DEM3J$1Kh8vA;HsOSENT9oU9%jo+4;O zl>&PMf=`ROl{-1d`x?6CSO5;TR=cCli!MA$Nu^FFghMZv6a(ZI3`KqSObwGS>@TIP z$mhZnFNo^Z`bgmceAt`WlA2&=FY5Qgn)2XcY~)V#I+njLF53FPI@^*--4&!U0#=-A zu=jP|rq;Jw*xHcafJ|p9LDsw^jY!SCq!5mJjx*?3n4;qZ!@fl20AO72wT7A`iDs(; zbtSlTVM@Ky&6un50tUqMvXyV|{ZKDRsNI@JbVchXqMl*4CcdG=buiw}J-~gFI{=yK zEMa@wTbJuU$u6~V9%%Vr8SiZCgsi5lALfm}0NNEepJyvobodiojVVXcd=CD&0Dcu2 zxHU$J`(p7}{?*-k*}=%ku*E5Q{B&R#pE=E~Jj1UA9{_is`8xZbGd1P$pkEw*467j% z@y!e@p!8Y#cY<)8l1v(Eo5|_0iyA(&C*Vt59E=}5UN;i7{1N&J;(Q)$ zw~6+^dv#w$wnD7N{2jW>%qBnFyYxDUO~2!su@fXl^ZXFaGDMq=-_BcoJwfDwd0;rN zb0s&Z9Aq|M>hwKme2HkV7TEuxu^T;XIuqRK+5VIqF%@;br)QL9NeyYZQ!L;W$LU6h z2iT?;jw8y1MUZ%!$<9kdP^FdT#^?jcS8V2PVf5V;YPl=BCVa9(?lFW%J(V|Ovx8&Xz`HpYiGeewg*dqDzS6d`(sbcpo z!y=U@uc4$W=rL?hP_pbk^Pbi`|J636q}p1QuIcafsBO@;SnaMFa>ku(kT}S?`Dk`=zE^H>pWW~m=%b<2E1M+Kje%i#`E=Q5nVRi&WC8ts zFJ;_2D2aY^50T7JpT~GSE~Hrn(CA)rUbCR>@UC=N5Fc(x-b=RV81KmLF@{Ig9pWd0 z(Q!GMgo&e)do)TXlU29{~s6NB4=;EM>q%&DOmp51(tbQL=3Q@`mdz~aLjQ>EbW<~ zJ$7ik8P?vF$^A2+b`ZR9%&+R5SULshhI~Wo|OPp}GUg}LCjUmfE1DPwt!n2BSYBM+o+1_v+2 zxh55Zv1!I;8f2AaygoL{H$qAK+A#2J)%ZiD?&H#9e6QcEm&^49nNL9#amN#S;JKrK zu83aZz0#uCr(U0D;TlwJ=}+Ua1KSe~#AP<$0YmsD|Fhvudwg#rD6a%txD^z*nlCkU z)&#i-S|eh+Tuz+{4`Qp=^$+Irj=9MLUKRQ?zLaEntBoaNpFRmOs24ez`&C?e42ksr zRYUaixUGTO242!|>v4*eo_x(GAPKP2yshS-dlOusxqp7NAzlkk$kxT@G8N?o;i zk~Q?Yn0uUxtUFaD`^5&fNCd5A-CjniM&MyGtBKtt9L7G&dtA5ee&x>7+ zgLzLzziK_UzTQW70sGwCxBo4>4&9K;Al!20)+CT_xn@E0d)$W1q#vTHqYWk0MpCb( z1A-6~++|1d0;g&jq_6zAw#_m1or}Q}qjK0WP}m>)9`JhUj`o^NnLX(y_4#oVt&2;f zv%WE{UX@JA0-cb@*;RpU>RKn{*Y|^z2PSiwMwT^EkBwxqh5dlZ=#-;fL49_Q^o(>8 z^;BUppQWpsiD=Twnin|R>3iDod~?oD!oiC&{uGBd6dVp6ure7U76&%pHFcQU;X=Vk zotTU|$mQ_sMdo9Q=jUBAGyG;G%&=AbGFUYGlisn3x-odvjn8EbUG!#;eQCReCHpiA zUmmEBaDF*fGtfyMl9l$d{Cx3DeSiKR{b5HYkOxy-vCn>ZpKf!5cYw@~hNh8NJrx~S zG5q!G;CC4=XRSVM@+0@A#5)l16(7#(f96~j4Gj&=&60e4P{KiJX=zbW5e#;=JyA*+ zd~I%Tk9tbqzwPPaai5OP)6r2jM+g=j4G}|}qeRJfN7u7!8=3k~rsIYx zw<;q!Oo#CVIA8G`(x)s$d^FRB?B3@$_l2T8 z4fk3dVHumPjgGJ+T+gH~*-c7k;Pe{NGo56)zTi7javB`+EP$#t_0V)Cm5mz)OO4yI<6sK`drW{f>$h zYS@U&-gaTzPA@~ud~k3D?S+C~_u;+io0_w;IrZTK0u+$0e1_n$@<-^wzYfNN*FTh9 zelh6oeCyi%t~f+$uG*RJZ*dOcDH@Gpye{l9MFS`}{k@!i#M&0kfX$Dx-tM*4@iR}& z2xb2+ttKI3{)lwl7Fh+RneLhB(+o33(D_S2tQ3KTZ9q%>7u1ilk?*XpghjIr%5S?p zlMLoXt2_H>hk(Kihd$H-fOjNGzwOD5_3fDzm4h9Tc6s%ky(;6lgm}YwME@D3{29^H z-_Y2Z7@hSDoRyG_x_WIVi3f`cPT}rd3~}tJ|19taxv>k&!zy`1c>*sQ6F5Q`-r61Q z9CFAE6`xwJtLb8NKMY-Ra>s7ihVSWH2XvkGA~wbHX1071e*)!vtQ$>*9X#)8kWRVm!0(g`!K!o z-k%#Eb9(z<0JhH~3QPd~`9j1*l>mC;MZ~s7+ej-bJNsy8?T2{W&-~CbIFT|}*Zej) zZ^d~VT#!9k1B5-2=mh1hUpnZnLw8fqXMj>WD%ruza-EJ(5}1yhK*M;`_o%4Mn_c!B zoPHDY)aaaejg*h>=SMCy0Ll7qrQ4K<^%FG;Y0ssm$XV&$H;r{=SPJ&tv15tKG`VGMY?r#?Qr*}dm+LJ;)A=9_h>BZ@SU8!khk7YMtf&^QeE(pE@9F(U|L{SkH}%B^knuT4 ze6#cg_K7|DSz5y!nTH3p@0*==cre8at8?V#Y1C3!&fUQSDzTV6dH`WOz35C<%XDB( zjVw&xiu)SZKyEBgF?{)i9C5}~^*5GQ^zp>U+@gf%tzm*a(jn?lKcU(F#s`;npXrd^ zYcxTA9iwI^OO~~*Ezcgn2qJB^%NV(NQ1aTwY7bJx4{ZfCI9QoddnBo)RDY31pBP^~ z5)f~U@m=}r1y(9%ua78SC-*rs>Wk@0e}Cpj=d$8;1vs-Arv{d8dvxn?faI0Ru`~zu z?jGxv{x#a#&VC+eHT8>{tH5z5qzs2MKJh$->gdFM{^^bgEiAs~%Y;pY@&#a|eg4D3 z+!*w#U4LxTWbFOJ$@vO@hzNLb&sVT#Ae5reKx(+!@`v>vscAp8cP_)Vg`vq_;PIA{ zj+2NH>A47}ta?^p?)}Z$*-`lT{xbkFLh;IOeyDRelx2GOS94r zqdxSkuU~n8Yg6oQQ=3AXMqVIlWltj2#2$|}ou<$USA=is5yJM!C^z~zSPu7V_v>*w zOTFi3(`((V-*hBwK3!A}f&2?x+E;%_XBLw7o-$LSHx(!@ZDt{hZhbdK=$JQ*^M2X3 z6*9>*gvJB(nr{wG8sp3|T$V7Bx|oWH*%%`;v80u|Sr)orTu>tZ1uz=YnjJda=oZLc z_M`v4<<8Yq^U-i3qM76RiKy-wwV9nzTVIs76?ZW;%X~@#y9Y#t%nfpVY}xmg;6K;b z=A3TgXT94Gt5gxL4ra!IGCUcGuYIyB%+6P z8u7Ph8sYU7(aI&SStEs5Gh3MoWQ0pMQ=&AGQVt6K_-$|~iyf;o!|OYqiXNjaX+wLU-k+Er`c zv^FS`nfShF963&<;x#L(Ed&R-%sFRe64jDUNTDxcOO z>I;hg?2WwF9UibZjX`UDMm91UpC%Ts#yrX@D{_X1`@_1(H6$HuD$3LR>?W;&G!@%C z$ChKJD6+?1fNt+3g~zX5*A{z(ZXGvg;-$>JaT`SS4DB#OjfmruJl3=~5#ZSR zb$W%erRzRnl?%n$9g)>itAM{cSt0k_AKR@vdFi71F!O5K-sSSV%#FazyHwBuok?%`&s>-}HfGv99f*%t$V#VSp6E)xNLaCk`V8YhEWX?mt3+~X9wgx6p2Q*rLBMDfTE)b6a^<-bU zAz)kJ3Hma&#)+WQeUlX;ndv(u9}=yAj^U&?xsudmU&@aG^NJW2r2b{?Ibv)kCP|=0 zeOoIiN)BW_>9T7fLCHCQ6w-$kYRaV@ zXsV(^luQ1eYOQUXj;S!ZyB8+$b8JP;*r&}dLlPocT3S=YN%=MsXt!MrG?;;eA!lv= zr3(4GyOWtsYeW{!eh-%-3?5WeblHt247a%oWWNrHPz1yEtP}Nz8FoH$XOnMY#~dxZ z2u?O;y{@m?-(Hno^nqMT!E0)}Pb8Z20cWODGr+-i`>R8C!2;RoTSJPucmNr4*ob%D zUa~5su{Nxtgs;4SH7#}4iyK*0Gw?>!dp0H^)onBNbFJwudV1(AfRKn}<31IrMftYc z!mg4;JSU~&$Xdq7C17)nZJpe5p_rpEobSZZo6F;~M`jp>L@3Q#)3ComFpN2%_2;Z; z_H(#}g^(vQ#2RG<CKHUCvHP_OCfQ(SC zq|Oe)x;b2#oR>WcK%paee?x;E!7TfQ&1G=e0l%}w@x;2$KyE^l`kPm))M^8=6uil} zo%3yE9H*#t>P3S!1WFkJqr6N*KX=;WUjk{D{mgoAA*S9FxwO77yY>4ejN-XPwyYTc zPm_m7eNFBJ#C(q*QCnq%eeGCfa@)g}mA8egJ`GV|{Mk&&^P2A2^%r4bV_S#x;i>ng zwICarU-OlJ7^a#UH*mcRhSi+FoCqt3;m{~F;L2$ypGjHFUuc0bF9ScdH#O%5R;#sI zli*pDMzTASRS*2_yd{@uZ=O5MR=dn|uj$JsV~hoJYRcYAN_`w$kMpMmJtj)(p)qYF%L|(-`_Fflmg!`Jm z^)@TS>qro40h$~)1#N~bWJ&7>>**%y(|R$J`b6zwHw@b^(8R2ITFiD7p+BVl!I-P- zopzhsg0t_ClrS63SLVGCfTprvHw|ItFX#(R#a<`igQW+0y3eLxQf>B}otkZgdYWm6 z+^hQP)KOpEPIN95U5+`QUQ7h?Nr6q~Ggecs!WjlZnu#fheEAgAITNAStQiHsXGK@n z+y9q&;b^vCKqXeg!v@Tn+w8 z#Ts0Vy|RGU3$3$jWGo z9S$4O430_&m;r?B_=26tk_TqP$&*iTk$k;Iv@smJUUrLf~h?Ns@Gaiow=^ ztDeGVk|gmLEdjn*Qa}xKBj526dP#Vx^QE*^P$9HU5Ug9HeL-A2xa*O?I|2zwTS@tZ zkGAQ>$I2z(6C9yoIwDW@{}lpHX-L#zuR&gp{j`+0;leNEX@tRhaBtV9;+_TOJptM$ z0E!$T6FLCfzlptS0vj~WH=)?6k}A5H14H{dVwFnXeKnb*n7_lUYH`o6Xb99z1**0_ zl2rUFv;Bh>2^yC#%IlO*3Z-anh39}xE47Tnn_88R2}^N~=;QaDq$oht$kc-4zq4m? zY~3bi%Mwem+lZ@`X&UHmHLLtsEulnUMT&>KR7jOuGoYHP=V5vvBJ{g1JP5Ge2TF^v z1QKFsBI+$CMk?zxtERF^a?$wq?%Z9X!3)?axY6fP-O|#D#y;_`KF#=m8-PkBbVf!H zywcTII>`hUH!nP6eU`=cT1sOW;l6{Mkj-zThw_`v2%X^mgfwVc|D7bZ?t7hjU$Z+T+O`tO- zW~Iz23VL6N62|q)#o6^0|2=fw2iD6ki!OW8*jXaIQ%*n#)(-Yr5WD1`+iTYJ9{v zc+*=U;OHznzby{8=(8mn>~ zgdd~@soeyaT}z$JrR*zd0v)$`UZ#nkrXK7k#5MjCd{gD~`@FX=NrN2V(eOq_5V{|6 zI$>cVu~|BgFiSY&H<-AVN?APN;S}LXQ+j=14T^=@xiC&q|JEoRss9p^QiM%{#u7D) zH0g+bk-?u0Fu|$luK7&ZnYX_!S(RbAn%0fTZ+;U9-wu(lqkQsYn@uJsW%lf7khkY& zQVXY!+`_W)ITH7L{FSAa`HrZkD}{1vXWGnZ(mF0i9gnmmU7mWX86a=c|8aQ;|U0eXJ^+i`vll#|-D>(cW(OcCxN zBg_+&ab*qkzYdfGu?J49WF;CCTc(9RDrFGcaBgfh6G_dqr{pAnh^b?&6oa{IJ?7WMuxG{{ zhqxp8z5RD$bs`p-?&ncq^ferbtlC#yYs)Rw3$*Cdy1z74hWmCtxngjE;*?o>31%)H zpY5YkxhZ47u5ZE&75z`g#$afTQK5IIA$UBt#XBB%_yDuB550#ZT-6WqIk9v3cYO<6 z-1kie`=NKgi=fnNdwl69iGZ-qCMoRPi8EIjPK$Tuiy! zd|ss?^y*Fhpt=fi(>*5bE#u;V$5$B*p7*A3zeu=e<+?{(!cI2*TAx}nz0e;m<~b^q zGJ$SA$R-(QH!i2}0o#W;m4;GDxU~4LW=Z5-m@2bV-D+EK$4k)cNQJNy;o9e9&?hw^ z>`ZBzU|T{{g?UTr5pMhsiA@xNh*}lx76zdO+fnFv6LESo%)}%(K0tb{4Kj}ZS~9Em z=lJ)?`*Zh0sF17C9_VL7X<~11Uqc|3vR_)gdb14Q-Y2i=)^xi?cs@;0mYPS`ycj=S zGtGHBJbjjI;-BY$$F(R@JE!FVS!G6YL5iK9Q-2HrX1v9~Z=Y&rnTz>-tf8E@nt4!1 zH^>`zg zNfGMrg%e(FFmU-CjB%Fd#hh^1;mNJ&p1I)Uc5x4=e83nNtGI1W;cIYg zvq?moO=(tb@Xvf;c$&gQC)DD%j}3*pd3?>W(plu#rYUD_XQ}76JUIe6(P*Fjdu345 zkLc9fH%pUoh5U2XFPY8MVpqgIs`Xrbih`Z<8A7mP>>0^F8v+kj%8SYFdH*9o&sPev zxN@CakHTDqz1Z~HO1>p4dEFUilTdbR1y4k;n0#W*((-!<=Wx&}C!4wHdJ8+Isq}Og zsGEZ4tEpUt`j}^L(U_A34-BwUotU@1Or{d6qpJ#O`qGr=wK9{gUdDX5JYjxkjFl!( z>(F}n2Q8GPl&H-@QEQl;ok~tu+Xyi}pZYXsP4 zqiO&qAF;}$--H~Nc5FW|ZbDgDsvIWTiC;;_(F;TMqZ5-G3M~gdr5zJF-ir*@Tm4i~ z`ec2ixji>7@+1*C)4UY=C%h#=AhcE1Zz%>bYRl$~ znPT`#Uv1DM*JbXQt{(?pF7vV<4()Go$OKDPZcN1G5kqr=5@wnTTOkI$BD4KK_!jw4 zzc5OArRn~6mKhse+0dDfjzuP5v|h~#4Sp$|BG8n`YP#yp(6~P?N;x>Pu49yHW(lV> zicH(<<%AJdAoM1A)ofy{D>I4POx2VAaAFXU5=L@YW)WwRC{1T^ymWNeZ2xavXLMX< z+%%Gtljwr`{Xx-v(0>6H+kIi=n(;FO^3U_J#)woMm$$3-;&KW6@(z+zMEJk>sWRYh z){OJfW*{(U0!WTmuyzNjW18Fl#1b{(+ zG0Z4BG3<$LTB@Pl&KQ#Fl@TMa@S5ho>ym*kN2*4;(vTIi#^|!|bS}1REas}m5dc;E zm!^^Ir2!Qg{=39R&g>)fTF6~mGrwvT7u7whg$`&-z)}xq;eEdXfStta&f7lG={}Kh z`;jw`V3Zozd+!WdiWZI0DIdX$gUW^=8XZOFgZ-fEElhBdNt4S^$)@zXvEU@rrrJkV zZ(d^K18v*hw*S_qQqA#K7~W3#^^fERQM?BVx?xp`8&CYpP^YNXDyxBG->vtYMw}R4 zL$sqsKn$eV4tBWOM^g0_0L6|u;)v(jE1wQndr8*tM53jksp@2-eWu4q*1xkM(wKoj zqHOT&x69v~os2QnxD*z%lSrJlnjSV!;YhY^9=ho{WSOse`2Icj(eDq~ZB9RDEH=wk z_>{+nT6IE`u&>+bk#Y8(3qZEoq=oZg6mVw^23f&KAKAm>WN0lr0=F_Kuz)1ZQ8o)lF5G@obb3Pbb=g~&Fzyq^loHj;2$i@BxY|L|Pdi@2c{XZ^%pW5n= zYuCHnoI1v`mc2PS@AiJ8y7wYIw^KihYru$XBfOgo494wOTdt3_39Mc6_H^=M$#3t< ze7e($u)5<^w+u}k_TM}1K&R2bI%k$@)v_lBfxnGajueutQH(7A^xx|!3COGlUa3Lsb_rc-sR8HerQ>I}#sD0PDZthDUz^nDP z`fL{drVj&nLl3bY?k4SN8+Dl%)R&vurZ5aN;jr$D1fpR{<1`g*RY*)dvjrl2w%k`u z4e;Z3wdxTM$L%v5uICs2*cARz+~YNN<4~dSdJDLLYnYKG0MTAuNs#c+wzpKNGx*U- z_S=zascfv-#p#4NqH&(7IwNP!G>N38?VSS6vW%Ec|L`5~vd;{82#3F?<+cTA|JnOJ zu#ixomTl#G!rPc(oMV8_GjzFhC?T!#>di;^E09lTR3%;-873X<4CiBXT+IAU-bEX0 zSV)36aOwM*W}jUrB)qKbbf>C|HQ(U}(hP#b6o-ev_tX8-e$0-ankN5Ib@D5x(9_Lg zZ=3a?vq>gK2LwJCUY8;wpxSERn=8Iq-0rzjjH7trx$O!4%DH$Mvr%1s=P7eG+W>TVLIEGUPUOcE9{!586oH8#_|!e709H zi$xlj?i<%TdqWK-j0?BLSV(@cq__g^(}@ojW#>Nr+W+(a&~%kiQ9ZzZcad(Sq!Ezr zmRdkcM7mP}>F!=6r9(lwYiW?KmG15i>F#FV{@*+AocX+aKg^w(U(DP%X|oT#6g~mZ zeGR6AKwx>^Zn|gE5YhQDr+d^t|J7Z;)muY@Jonct3uQ<%rICOY`7)@S^-VMB2R-r) zHim62kdf8NDP0Zp<_8r8@mLi9w_Z~lQ;m-=ut5}(ssirT+`s9jXFZ=)+X@{Q$@B#8 z6eb>06o)T67W2#X?1()9$K(%_hePB5t+G+(Us`IHQCZM)piai64X3U0_4O?)FdL7g zDsRH`U>_Znlbs~&;Q4ZYn&tnM!jaiumg<+?_t4JKXi>dNQ9TDWIAa!7*aA>%p?%|h zX<#Kmj8}UMmwCmff9qcTsIS&q)v&>x{r812r&wgSB+daj&1(|V(l39`7M6No?6_tG zQHHlTNFP@po!)#1mR}^X&4>8jXVd3+v!XQF6NixicI@@5Yu2wj7YWtKjht-os>8KLvIVC{G<8NR%A0a7=QoW%G#^Y>H`$m zDjR?~W@G1&5$J`X!x2u})_^9*fOYK6yTEO1fo!3D@9Lh8a&C@OVwRrZ_kR}-$^O;o zsB(GfcHzPV{VxT5b5HMr_>XKhLUxq_$`Zmx6%z1Ab=ahV@@NFVfXQJf)_~4D;LMs% zdSl<>9cWC#gTgag7HQS0X>l}1+6oR{0O5v5R-#f|`eMbwm2dwVw&&ua#sbrEi3hEm z>b*kF8~l%>)U}(+PX!yNL7C?{WNPGLRdVEuNKj z(&Br!^2N$Vg-RQFD9&o86lVNYjWvt=-UZ(F%s9-^Y1#EZ7!Q=3d*;)A|3E8FgWmAHNA66(ian#uPza|Mz7`85^YA-K94Qr zK}n?^FZ`SiToBRr^_$0nqf&9IO3=0`Xglv$4Y#wY?a`m?F*;oHtaayqcLjUMniHL} zjxy2rv$@_`fu9FcbD4>y_qfHd`q5F)=?>~}l(XnZUP=!lG-T9?%1f%dELcnJNVTk~ zb#yNIex@A$C^(qyE>dIV|VJUZbw{+JB{4Cm3=ms04`WvK}Y`P=cdjlj{5>2Lgq#7^AO#+Nc=dbboN*mUe*H|s# zHDkDEewEk75{$a}C%2`K&!)s)eQabZ&X;{M3#JU_vA>??IbLVA8M5`f`2QAbXj%I> z;FDat4If)$j<3%;`X&9SSoU_Hpe0;3-8=XI0O z5MLJ?TopfF@S(Q4JN(pQ)}fNgLkZs-ByHSqu{ora@?Z$NyPyQ8xZ2#jHS)Op>*U7Y z)fBjRDzWn^Z114{!xjKpSc#~`)S?{PmDQ@@xnF}CM5VSUv2E0P!U9tk9&=lLWfLc z?O-mx9w@Vxp|X6Z-Q05O*YZ!o*7Tfn>al7nd_4p+k6JT67-bCHV)UB6MLWo4(7c78 zxh{O`&HP<~h1kj2`m0KPf``_;%l%-^eRR3dXnM^K@=vZD6(cJDlCW1|XX(>ZJ!Hh% zO*E2rwJMo^E_QrSZw*Vl?pvlh~rPeo}9Ao6^8%3@Y!13y& z$CP)Yd*BQgE86zhcoSo>qx*7Q!ly&r&^dQu!bu3WeJO?(q$vLwDJSd-5^Nu-)GxO-tyj9>daNP&vEsq^;s* z2qK9fsZh1-g7VkJCCo@%S4s7weegLa*mvt4?UX|N^XI<7{1&4{Ee=S~BP7X`*GBu{ z=RiZ`skqfD%fh5x?!MIV$Xa4#A(FY9i(PK%YZ?k*)Y2|+F@15%938kCN4#b9&P)AVESjIy7i7LSfwkF^d^LtoTfk*M=bM=`JwUzL!5 zX7e3tLbpXjOT57vR0u1FT&e+z(@@vQIl+*KZj_cH=xDtfP6bF8GyEBY%ixpXZGhGV zeV-t;S~Zl6NI92OeT+zJrs~*uw@HG-9k;fpq>@-NspRb;0HxfF>J(%aTkMedw+(Sm z5}{L3&*91b=);Jmh4id~5u|Diwed*|7A#k{AEK~+JAzSFmdRJo7n-H>1xZ|>o6;zk1R%;$UvML4V^R*k$t$73$FB5$>Lj@xW+%u-`}N$bl-+S*pb{T z47TQL{yytxTVbiEm%2D_jg(vqMek=D!F(ikZx zD^O35DK`wu2GUp`Rk1xmOBnVdD4 zN@Pkm`){E!yjE#kqk!M>|Z{(#mBh@ ztTa18gxun|3+qa(x(eJbn<2lhPuSC>l*l>ad7e9#mzzdye|0x|8R|sn3AiE+9UkYC zL&;F^SSfWILJ{?7p^&&L55&fS+)6Z>e4PZvs{%%Oe4SB}~I>IaS& zGd7n@xYI9FRb$tnuJqm%ZI>Eg2=3#K#Be6uWcfb4$;diP&ezbV(vELF>s4sPs|qZu zm!|E&imPaUauj}$;r0Y8_~6+Jv7e9B)gi=#|YRx2kkN%#dq?iy^ zev{Dz@W>!H)(4&n@a0bC;^HHOY}Ia+$1BbbiH0LHI5D%6fRJC@yx!2s@_Sr-f>hry zN3c?~1+@V_ONuY6iSUcA?NgN5y{Ft5gX&jVq|Mrfp9`0W*z7)rQpR0ZVnBPql0>Ab zUzH*nk8~;v3ZoQARF=a55&hvKy^so#{Rqskp1jBp)pepJdro3y*}(~7H8LfIIt6}f zR_BORi^sxsjn{a$Lg#;{V}@<5qj`$X(2VfOKk%AP6Pg?&C5L=*-_);l=d1eitb1>5 zqn+H#WM>rMztUQnpzYcx(3V2d~B(=4_A!<;yKY%_7A?{(=ixKcV_^J&c? zT0*4+3Df3!x*I}zc4S>dUB|`Wd35q?C&k#7Djlv_e(gJFlj#yb&Tm8ei&`i+zemz! zhK#YwD@a$SDj!Hw{%rgvcA8l=qfKcurLT`7@(LGyQm4HaWV>{B`G>Iqs$GfzPVlcL zEG>7Bnj|&2E9Z{u%cVQVV-|!GB6J|%W#TuEFm%Hs2Zs#$G)>%A!+-7ijxEwA~k@*pE42xxgj4qyp6p? zGAJ6o+dh9oukeQ(^C7az2GR2d5jUJFs0GJPvENMx=lop!D=$@83t1yz{$vfyJci zPnGr)yKDnGg!En+|E@z@r*J)PS1@5Avg1}q{% z77eYVTCd-XAn^}y?lcw1HgBW;U8jN6PU^k<_gA&Kv<6zdJv=K~VRT}w&Go?!p)?Vo z=Wyzkx7Mfv#>9C`=)5T!+i$WR1T8>6=9hr9-EU}S9xg{Z1W$bFg~o5!>KZ)mK&NNO zt1)vvfnBu`khJ#31jB>f&cA*kFgx5G4a!f(8_4aesrL=OE(K4K@m>n>IaKlWRgJHx zzZ=W&gc7Gt_N=SfZtQMW_*Rq|fe*B4dlr>gt$vj9iZA!{u=Rd@NCo~qU|pH~9zh~J zZ8B9a6^Jgk^MW)u{vek*P1UKXOtu%GHab+VS+GX#Bg0x+*s`Q&g5D@fFK(($tjOKY z*-=M<>K&5kJwMH`r#LCj@Y6wbcu2E&^zAL}PN8KymEDt9Yq+neq5ydYi2z~td4}{a z40QSe4ky6Ic=+xR=C(1j=}t7IXF~Jf*X)uu{ozKE^+-jCTEd_r={rVDa!FliDNB;& zWWKt+Vmt@I-w#DR&8>e}(dZ+GfQrhfJw(@&Lzzpj2GVJ4NY$p)kh1|Op%H~-CBWd$VL-2=FWHX=iqm;f-`QEGiqY10I<*tm^ftEa$bIUa=ad@&q z`whjCQ8;|Jh~!XF39yMmcBtLWTQH%H@fP1SXH<4x2*G( z>(D}_zJR-68M`v)4MVg1h#z_QB0s|jd9xNj;j0`!Q#=qW83iK5m>(1WycpN?K`On= z%+UqWy(w=Rzq~Zp+Yb9qIApkh_+O(s+_n9YjNZ@rQlCyS5v}--p(Y?WW%h-^y^zH_ ztAZ0bbIt4pC(B3#!_)t{>1f0!ZMatKcGAjAy8d>dMqCyuYSM>NZzs>8=T~qeIT!I} zC><7mtj#blu|GoFj^s2He-ayO;5DS&^8~Mua+*6iJU_tta1>2D;309m){sqj*0E}Q z4OfLLlS#zy?K)-L>9T|Wnv9>`YHMitUe9O~5fNF8rZcm%w>fTfcK>)SMpbLtog*E_ z!ot%2bTRaJ+F`8fKN?UTK=G8GrA-y!`;QU0dp@)|PPG=Km<#RBzr*PSJG7K~E@#W}C={fR#l1@^i`b9K03yc$8Nr2m zQ|Y8tRvZCG-vg;8klZyt_w!_N|2%Tik4TXH?jFav!)i;mm?pqgq!1u=Ken3*OTfh_J{?xjN!~I%zWP@Hx@( zZrd360^W*DW@L8<^|>1o6;Tt`T<>PYnrW4}_<`doVYy^9FZ+i{SD*3E&{D8qIXCH>TXg~azU)65#u$HVx_TGgTgN)w8s9%y zdKDAOw_DZRfLO0=oUUC=igiu2Wjlz$R{s(KLF*QE054Ihjx|n)VyK>q%DQ_?7%ugR zqmcO1nKW&8qtjMIWaP6yJQ)48w~r4dCaN~K|Hz%NM5U-d{=Xr>#a)5pSSOXq+Gwtq zVkhLg^V(a9-08F`+NiYM+q*5o#ZPl#Q)_g-HCeF(ma)gY=mmv3<~WkpaBaxl4q#nE zbU#xja6&Lc?cEx(FQw3R&&dlfa8jsxOjpy;8qHAng6d4df0FX)6oudp#JCG8U7CZ@s67gW1dJ(irx&Dx~PXl2_=Nfx4z~#K8R2zdY@KTt&GV!-cQ2- zmo96G2X5n=F*^9CAt3k~H|gE;nmXL1lW!I1oWR z1#E)PMiq*K*FnjqiN4$>4v`qcEH^+8&E7zgCDfvI5i;y`#F_(R78!h^fHy<+eIBuM zfbNjC{+qJ$-Rwr&f377T$}v0>jHiRwn3UVEwsvAZ2j+{7+4h$s}Rtght_ex zXV>Es4qcP_gRGimOi!u5%pp!pclp7)3BxzK>Suz>#d$5{AX?1d~1-rnKirFUD7O22&rwS<3be!kh=*@k;d zTWjmn`rn}K?d_G7m9Efd1p%8uR6xLDzcOfaYYx)j-VAd?q}KS0H9@h#CR?pn{#iY= zrm?)7b?GuuEKN&Fa;2KVK+_QAPo4Xpy42*cFD~?gHAoE-#4r~@*oGuW#lj-|>~RxT zpO{QQZEkR8990H*S=;aWb*#0Xr^qPIOzt9gVCkYiH;k-p{YOn-8|6$>He5*#d7+9L zKFGao^zHLew4tNT=YIK4wSgG&4R-dw56#@e6hZr~WA`Ek5|6KbNCEWisXB9kn~~2S zP`_jiD&3-&)~%Vzz0qPIqWnTU{Y_c(Qb+N%>?acRQtRXoYq|5mT>pZBU`;kW1agzE zaj$6?X{ckez0dlVJ5@-{7|5b?r4du+NL~9arhma+gBnS z*@6_PmMDQ9JSX7WHME=UVU>Z5kixVC{!?elRxxbX_~b=~%&${83NA6k)`jWb?`mCp z40A$xjjYtP`R0L2b@dK=EtP7XWhiHZ@2*33GBs@sxI6e z!E>49f~8NB@+i`1FK_7(WLLl)>jn+7Cgx!~)BLhuTuXZy=3%RCW>l`FT6%2raIUve zibYG{a0_a=@s=lgrHxN-!+27ve5FrJirU_f^` zJ`7Qx{)HmW8e^04AIQ0u8na&jVOdE33mEZR;now!u@4Rb)5a#%Ne5INJ1F)6)9``A z6tiO3UbF|Kz%J`>qaXp>!ct{sP%7JQDDKtf1@Q4mE{G_$SHefkLS=4Godhy_Qvp4g zv}`sz1q32-Yl~Sl^QUffA2wf_^Hm^7&*# z3|$zbJ(~jC6y&)^WeA+^LIdI5){(}oGc!y6@ZG%>`^*-d@1OKBAP$vG^NGsD~2 zO&H>@NqN|@n3CRvV{+I#zv=B{!@OH7u7~i|icI~&RQNkjeH#U~G6hISwv;kOX zA2=Z=1^5wX+sZGE!LEJ3fjie1Tfck#LxM>JDZIlMN$89y}mr`+CX|^`Uh|xV@ z0XhM>{+FCQ7`e7!b%PA8U<)LWsV04kXkUYKL4uzZwkBKNQU5vzD}F48$D(oqN#Lcl z7J+q*g#e$$EBNfs3H#kj`I7}};7==$xzBk`<(QWCWNL+Vx9&^8sr3(&;ZREmPtTvj zU>PR;9t6){2QVF^gm~|*s5T6nE@E${Nw`K)pYITpjC0|GQ-t*AGr*=>_o1PxfR#3g z^Q|d_0zX?p?4@uG5K)1tgjA-GqYtF8mNfVP)U10?o=@0r|4#I_QwDZX(W~S>M0tHQS^{GP zHPn=(%>B1k+-1<*I!)`*V^vLNo}J!kGmUWKzLjurcycPIjKdCDoW+$Y@u_JKK}adu zFcJ1S1y_50b5tDr(UfjMrkbF@1jOx=U8?_R%TrI9+TEBiBnq;#ew)BUT3w!>i3Qc- zI#~2KhG{o&moHicxI)W2)U|&Ih1%#4H^>pFalt6tKe59fV}csPD>uO0z@}P-3W3_j zfRkuH8vxtqeSM6TB&*FZToV_P&0cwT$BlNG*2j;ukj!zyH$b%YXv%u%0ngswow8JN zp>X%A-Q^z*%P{ERR}+_r!qSMs*bc9|^D!7}9bDm;@YvGLHsPhL4P#SpRMO4K;^GA& zc6L6ITz>aSfT3w`qU{02K{|!H~?VeU?s|H=R3II*Qy^Lg$TClZ27P(ZvG1 z;)&C1!I`kH$SV{Eyfr6bSJJh-3mrfNsjLE;$xo~8~icR*;O3=Q0`RgOaT zzkO7f7Gv?znXh|$_pNf5Hfxu5E#nFlv;!1hJoI8K{cyJxz;)s&ls&@!>Z&0iR1{1j zNt0uJ>Y6zD!kTphRC?s`$-fCejxx}|N)OnM#pgYu7RG6WKMk-tkdvbHbHko@kPHM7 z8RjcbGvV;ri%KHG7jVWH>q~-QAti@(r|D!Ch92&}44!3{?YuemVgH5uPV}$*qj;kE zDxU+9A~j0iD%BOr{ZeRT3%PJewC}e4#>eFM@r@RvodiOU+afONVI}_s zg61ypYk$HWYWOx~u#vw8HTzyXI76jWjfNRnZ3C zP~i;7vX22dytDx`C7&f2V(+MYg6Cc=wh#qGegJMEb2mbD!ufH{PMs0S&8+6F%YgxG zARDoib11ioy>JS@yU8L<@|~~`^+HUR*?V(Xt|}Yw-P}il|H{ZeUclV{B}*$ZQ>-zD zGfGu3)Y8zk#8O~}84s7^$H~%u+;qT(Gv?Xg>?_CVfTJ@iPEV4`Kb%HC@M@e-{&??j zsTlf^k1sA(7mNg4eqaAt-Eig8D_IgCp)rTW`=O?8+xhw`{kr{@{a*!u+I`-^hf83T z_sv;_P@6?*k6Wzx1UV9l9qLb68y5>hLt(*H#s#hktN;uKAC&gN!Y)C!N_XKeQ->QO z#B!m+1_#5a0Q}hLSc|P*-fKbXzU`ymI!5kyjXaMp`*BrFGj4nWhStR#A!SBaYW1-w zzx4fiJ9qxwI3>m2?t6?XQj=u0vx5;ws-YpkXl~n1DYjtE8TM!EQ1v+H_AhZ!m@W67 zEfoZ}Tf?c-g^Wk@O#>TBK#itttx|Y;6f4b~?T2TrLQ`-<`fE|E#k7rja&F{Bs8)B<+$*gqAmk$DoD)NW&OOM-{9vQSGVeURS1)oSXdE1c} zV?LD|X}t!x!5NkVhe0JBfS1m{sRh;q>A;;=sO4Sl)6OJJ1rE;Rtev<2B zDx0qu632L=$BGdmXE2r{_Mt$GrwA(d-jYtxEZ1dg!3AjrgbE3`3w2DX2^Pq8%~TN7y-jFT`bXV?pjrIJ1|p@l-0QzGHxWuZTIqoQIGA z1Nk>yw2+IE%_)yx!is?NRo1~{Ts=2rO_{kK#>d93FMu!MoZ_!EA`6Efu-yg0e0yAN zkE&%jfRTuAMARqs5S~Z>^J`QHk4ucu6UXmShKEj4lmeM3UgK(~Cu#6E+@BF?|8l=p zch?e~CuVWm*E>JNA$_Hj1+G-rc0F;EdNdzi_tKq>MKMqa@YD$3M*s44No+ z13;s9JJGanu=-Phk=DuR(N4%5G*DF5IYI!2H9W$?;f;cNH#S2aEY}<8)N+toulE`) zu-doIZK=0A!5ewLNkLRo0?V`g{3F&U0V2QV7YzMRS5q&6yJVc4hUIMfc{mggBE>gr zZ^Y`pmb)E3u-;RfvF5mnRZqV#yf~?Q?s~VO8he}93Q*}3z{l$ZU*jT%S5$b~`RhwS zaxPa+G!>kSK2a?hkGzpc{be+?v1D3Nd6>_ZXPsWmu9%5=3!M871Pa@I& zBVg2oM^+zyv!H;5f4c|VmiUfT1YVfqb#JKwPZ2a;0q*o*;!|qqs-OgxP^>X=1IO8; zZN0sQ$=YYR?Y!b7iiL&kQ%O=ymnIz((oRf`6>7^RH8eIOtB1u{qK9{n?||jyl$yT^ zz&1gL0Lco4cCAfR$p)w7Wc;%Nc+NJvlTMzVi3e6L^aIZn0}{1IDZ*c zm!o;zH=2Q+g5h-McdYn{;c;V|+qqz-aJI~*2@54xa=8brY^B;6F7`*%oQM0>I(qO2 z1)0D)PhGc4vp<*G?`~|X+ZQ>g^v*Ms+=v(CfUl48eydyU)nj96kmsE45C*$9@C7D}@< z^$Z}!mNuyc4IZHaKVt4*MLI})RT<92?7l|x?#}EMx}e2NabsNU=179VBjG&qoCk$4eGLM@ttp z(o-cJO-XY}2LqAnJd(RzI5Dz&mXpOTir9e%Kpi6wcUol49;*e%b;V%6V3M37^;)cP z5&3Azv*4y(`pf18!TOy*;`S*=Gf-zd@p^at?-#(MGwBocUo`r7YQ#a^dn{Pp<^$aE z-#Kew6vhQV!0YK8|09TSi8s%THCagE1IE^D;@vLiB?c|bO%*q4@9V&QS6YRg?{WYCc~{B>nJ79YeEvi!djTZWM$?y0A4^gtF^G>#=j%A zu}#hT%Z-!F-?MM(G@ffc{USj}R<%f%r8ET(tW&4RzlEvRBVXuD(mGN*wKKpgYAr%n z#3m`+L%EAYgicS{znw{VPwX5Hij_ky?F+J%UqRGVH99~}N41JkC}@uiHWGC2Zyj7f z*w=J{nL;6b!%h9@E!cZ88OUX(tP$dO8aQv&zl|YvQyU@OEXsl|?LM225(smYpWl_u z*!1QzTH8I@_geS)W3)w4RiCfaK$j3!PU*p)Wn-6L8Id{O07&f)e=Vp3I`JPm0noR6 z>`s?rkn38BemiN&H~g>1tI+T=#H>RKDPMvB3@b++oUfSHS}T7>0lLEn_WU4+kCiC` zawh}vSHY@;;l0CZ_`ZLc#??)XI+JE?n&ou}(}p(HiAH9Y9{=%RkDYzqB4Cb;PY1Kv zpx5*Fmd*(U2ICtxEjD>clioG=IeqgebB+!x3oZH_NrLu%CgTvf>W}0X$P?U=9Y#I^ zP$Z*Ss{Dn4g0fkfnf_Z+iWrD62e|!x@^%}0=kmPJ$LfEZkixfAPkTSjyOrgiv>T8- zB`ZLVkpD+NlAnE9S~stw^tgk>lP3CBE7a9Hsv(P8#U|7|_fsTv4-?tb{8mvu!T4;j z35+>CGM48ul>t`m9uG&9JXi848M_=(#vT2z0ynX4=x#gdVeMt@d0MFF`);gXlS-q* z)wS2ehAsfF-=@%3MQU)$qAZO7Rq+_$$mJ!~7; zD>{9-d1EspvCCE4;l%;`A@Z?LDn-Z{@>iu{5|hsk-J}^}xyX%NDAp*dF*}2P^$ROf zxmj0jYkQ`GtJu3g5NUpbxHOBp%McWv;u0!J<772ef7Utpw@B`uQRyg3W-5btZGQ6= zRVg7B=WP4^-qHL8#_pXz?n0xadop#JQMHx5)0G&-4ZjUbwp6+()7+XUi4Lh4c9}7! z;SO&g!VA3g(sqaRVe4a{w6D7cvuluW5SF%<;+z!jgpl!bpye=%4xj3J=ixg%Xa95N z5USplh};Oy+caQT{O~Iz8rrYV6=I>|2&5?X;I!Kq{~;zh6m)*hJEdwmz6%t6S#880 z=zkjUjF*V{7U-EQx3oy=_tlC5?U6?yVX_LtwS||(M`7Fyy`0=T@K-6~HEmAdZ1zEL zOJzofhjMhTbhJaM$pXa)7JUDhR_3t5VY+nAOL_n@c3@D45*UXbp&>;BVzUR#SE~+} ztM1M+IW+u>dz6fXC@??&4W53vzQ5iHt2Pbtyg83J5`!W9oy8Swmg5K(9mVGA-XsGV zjZrg8iLn9guDgO_Yxx4|TlSkG2PU=Pz;l|Dde0 z8BmjRp4QVBLO=8JS!=6HNk}{Sz1@|B*L{$A6JPUD86e{$hGH1KjFYPKCRxCZvin)tewfZ8x0MqS9^8t1^xms zwB5Z|Ka!L(8aOh6cbgB`M#cI6{w;L>TYy}TKavY_1!#(@t6^A#r5H~kZO7mx^3OE8 zf26JQe=bQ4RWsRISOU;?r}~Vs;!#Ve^DKT}1^+1~6q{b0uZ6BAQV7o6ACtgB>C|VJ zf7&FnT&?5Y&~r>W3o}(ognZ^^Hf${^w^NAtaL z%aSscX8WnSxL%_udzjVt+lu9SQ}P%O!0MR#Zbi{zXU6){y3U{SD&YN~_=$Nd<@}m$ z=%<9|qUud2%&O}GdXqV<^`Y_{)Pp0ufFfp;W&N%2eGKM<05h942kVTE5;rp8Y5|-P z-)#p69*Id8+B7KI`q>WcZahQu@9#DHUht@2;_LlMFAPJNv+o0)kn>)SjoabwHamMM z8HOIyo9iTTIgP{ec!P7KFeDTmS7T_YP>(bA- za*2Ri9ix7~TQ=J;+aRNEFL*jcri&tBcd4xod3aFGy+kdW)%n7*QmF?ppGD%B|FbWu(m?~f z@-_hFRke_K_Y_1rpOzj=5p=JyMFMtboRv1>e{h?eCP{k39Ug3@q&1x$?5hnqMcmN$ zE_SX6kaxHpJRkn#G5m`5QeF19?oS27Y)Zuej}y`Wb%)xFXW0s&F~2o!@!5*z30H*qZyTYGWoFOWsb|Q5 z`_1GH?SbqM8VZwwOJt zi@W}#BL4dGENPW*21+{CqHNO^tb$dGRFTcdM#;fN7t16-{VIOOz2v+DApu5*KlO^eouZ~@o<5g$Wc;f;cYr(Fem2A--Kyt)mHCjy zZ6PiE^I@SQ1MTU=I0m;F-m4h`(@v|b&6}VG&QUTxnQs<->UkOZfbghlf)id2dEx(T z5CXUP`M_a_3-L9g!>gw=b!xsch*6}A;9XKW&!oT>_#N7uJIQDY5urMyWX-rWb_e0I zp#uhT0v2mN2qhdL*9VB?txe3H+NWPQHu9R3l&~Pn|5oDdaZ!)9GtJ_0CtR;5WQekSFtqx(b7A&;KqnjX zpB>-D6Wu#Ynsx7TATuAhaCLc`#!=An-G~DqG?_R2DI>?-u6igdZ{mi5Zpw|7G{ZjO zyVO!RLHPd04YnVv)>7c6!K969C7}r7r;D&L>pR0QTs46zKV8MwrV-X3rK5w zYA>k4Tr6|V5npGi;(6Pxv!AjE5n}jAcX-lQAqW0L@XUM27aU}rdaafAm%i7agw(Op zM3-6T7prJP4M4hg!D;8QcuIz1-_7mJVqkEN0h+qm9Q?+?0zmEhL=)RSMW~+5D2{{qi<<8FKct(Ra4n|*F0-rzWLv`8&X%U~S#H-( z`&D)kH}e#2{a-#xuFQ-9lG0yT?w>b2!U`DRx-J}kP9~o1AWs!~|EJw7IUIz{S&3$~ z5pa95k}%%!@^EMsHl|XTeWaB`aBmBy3ZS6JlFB*0JfJtWV_| zi2a^)zu<*b*ZymZHNN!wH0Kxje9M`0bcY<1_o455jd=p{_2?Eu}>dHwgA7x77j`6CfrOh`RGtf!wFyciv$mmr?T4T2~=w!$V^ zL{zeIkT`mkAC9)geY}*fhD(n{|M1w3uMrvg=q_|pZmo{xgkBrV>1JsG>1M^yV z17_q}wui*AhmS=t9mD=Ie~#wR_ghi`pFe!Kj|QY89cae_8im49O}l*P7_O5YbupI6 zo=XpaWl&R1F@#~n#;)zfq-wmOX0;*`F#;(l(8ahuL}lyz`6+Y%=|RQYVsXc2ZrM5Aaoak!Z@8dQ`5Kvqs{&RA z^vpIRb>td%@|4SVx8sIz87f$^IVdFqXST3cs3O;aTmaux;2i?aOINQM^E6cFs zqyZdc$$|Ju+%WS|q;gJVg*qgVnLJ`e3R0*OYDEVWdh@wAkY|?(?0;(IJ5G#BTsgqp zhx_hIEP9~hrIZ;q-MIy+Z(Q$`!SN6VwtS<&o!^OKqTb&m@6dG-?X?T-&ayknl6uoH zj|F@8I0zIzEblY~4eQ_oVr-8S&HutkR*DLVV(!Koh{WNQ60L%hm=sI)-_ZYs7Q`+E z20SL4!lnYma6Dg(l?SCA1aT7Dyfzw&0!3^vAhCMR80082LM6mH*Sf#jug)rMI`()}=ZS#?W5Q}we4!chLafV+n8@>N`1P)DrSJsfyC_h}@ZEbbUVVoKh z(%2nw(-41D&V(B502dojSx-gK^RYLEw7me4)``##o*m-IY->$!M{f78!7cSiFmNB3LMIXGrwH8rpHhJhzJqW#W18^+}mwI>VOQzSzs zCbAUfAu{3eC>cg!;ype6XjGMqh2*+%d;Mx~@Eu6vm2xASXL*-BGxw7E6+=r;b!_1F z3@^g6U+#Lqg0RI+7L{lDeW%_dmFT2SE*O3m!*!D8$;X;lp7+H0op4Bz-Y`k8hFci{ zxT5~UMK9305fAEKY|2PKiHb@6<6d3kuMJl=IsUF=p{BQl3DMS{Cb||T$I2NJG-_!k ztTKL2M9I%=|3NgXLl@1DmjWy~;pnsxbv{5v)OaijjtN%$Z-Yf})m+sM^wY(-<-OF; zXm_`CY#fioNFr>UHv}K9AWBNYb3UW1C}9(C|64S$g|Z&+c<1+eRwJ8y$uK%t*H|k+ zI>y48d63d~bn<(3rsCK;*^tdqq&hM+W3*{d9|EBYYxk0tWlWOkQY3ExE!=~ z|;8GVXy#wUgAgpz( zku&$6Vh=6gv<};6b77(bo^j9PUalLi=Hl6{hj1 zG6QW2tZ#4;!h>*o&bq@tQ{O=geG7gQKhYk7rs6;W?q8e^Jp@6&8UV}HTuh3A86J#* zBFiBkf;L;mTGu|UeW37k|Bkd5ZQ;xRSl`n84L$o`4J->9vzy zE7>ex2gL?k8|nVlx4J=B{fLKmLqT|%Z8TSK=yy3dRz7EyPqh?P#xOPW{WJH;fZ8?Z{DcxWE_=2d_r%0^I9jVw`$9M;@HlA>4Y zO4WMK&Hc?^4kzR1Yv-p11AwgLu#8#N}OD%@jsJIPyYES zpQR5V@z|{sx0Tg_924?6u?8@ZHzxkKqs`NK`~A|O+qW{(&ky?kSx&7_GrWR;-CRa_ zWB~lZLC!&UzqNFljZ9VJxS`|O`~H3TG`E&N=0bVk%k|;s>=T7p+AHv;+7zb$`VWiy z-Zd|ex~-tum%{cG*5zy@W%#w{`hCFj#*Su+<*^{0ETH4@_!dTe_vMd2^wlSZ$+5ig`wgelwxkK{?|>;}seMRc`YQ6(zvw`m zHDCTSFrswY$RLJYUdhad-SbE=MhdYlVr%rt8gzD;<238Mf9Hta+9KTkXtg|hJn6&xy4dK1b{hw@gBXGtgX(!5 zcCRqut%nBHVsdB{yFY@+?<7)s`M7uDKz!5~pgLAXe6%#b*1A19j%QqT#lX2%N(dbM zPN2Ay8kx@=8L6ega;Ovt>Jc9!IR!nzkJ_|-2`!qhA1g;!)JlIb;JrcmOiJ!Ufnq_9 z1d6Axa|Wb;6ZHuo*-bA!HAs(ifcvj)3;QgYewSpjaCVK^;<}lSqC-gxaXv{uFle1o zW$*O7q4IIBEIm1ZTDz&tdC#_FUN8H5B%pL0(RqV^P5lp1XZ;p+7p?sXQc}7>q#Nl* zk#6Zmx*KU`Kmn0b5J6hHLAqz8k?w|(kPhjbdFMIjI@dct%^$G7d$0Am?{#lEU}3JT zQP)@W3o@gmmbFPN6%m5tI@^DF{_R6kEdJfouOEu?j$W@c&fU)!nC6{{QAQNYjbj>8 zhD4BNn<9=pp6lonBGQJY$A+eTKH>CF+=uZAci01ebJauVieEw(1LkLRtRQ8`?2&!t zJF0`!hPDj$GEWdknFhH3kx-sBWSV^i-h#=*bbquqsnKK3wCd`G$XmfKS;p-#!N&bRnDgW-aP zhxzWp1Eq_`vAWsJR1ZxF`+}!f=4()HTmtLKV({{($M*Nr1t;?^5ig~q45D>aGA~*l z-$^h#?0D5TR5Jt88&$I}-x5*PR>=IevNU!Fw9%StD>jtiNn5b(&A#}|zI`!Cbm&HB zZZ@>^yOa~gOvqvPm`kXq49)hv?pUL%7Di4TQ5-_J9N0sG&36cA?-f}%Y9TeKR!Y^` z@f$y;to-{tgWO_D{y9M5{`rSA&+$a=SAU6KelYURJULt(6C z&d?z<+FERy<~mZj?A_?S(@=iPXm$H`zdwb*JL4lodertFk4|$ZWjY_*E)4oyRyP~e zF-?4#s$!W<@PJnj|(cFRX-}A7I6xjit0gQ>E5*M6H~wAa(cS-6EFI z>ChI(F0#gck*A>3y|yxx4Qlr69Z7jzow@xSKVZ9WPhQ>8ScHdP&3%uCR?QHo?OsRw zSOzunX+1V3)Ck6^!#@XV!~dKuzh1FB_rBE2Ty3?CLnx~*2~%NCt+p{}rdM7pXvg@- zmE`C9U&8x5neYRKd|9)0Fy3=r*b>eqWk^4zf&G%}!9NRh2 z(a>K+?`e8tLpe<*zYF<>msuVgqdaSwLP~wKIb}+NGnIdTS6pJ2m%!v!yu|A0l?bbX zs~TBdV2i=p{Y#IR-#;AK^e@JhF|FQ~id$^e+fg~7#KVyj%hb2_JAjG!pgX8}4B^C` zwAp;!j?<0lJE^-r*4R;&*>*IH*lo1g2iz>y(kAn>PW-Wf32RW|O--nlJ&AM9hAlxc zGG?!9*i&kXPI$xU0E*w!VIc(xtzN)6+>cL2Q99K{SZIRlczSxzMTBBJt$Dezo=3b) zP>WH%4gSyoi2zz9mD06c2j`UG%$N3=#mhTp``(Z3exBpRVFkID8xXbf+ZGfBL+{cjMua@DlUt+sxKR+3d%$&euZUQ#c6 zmF@{D4$^{obD$!3TJkOP-U)x)4p=?ENqSA|j5F8uH*1N2t`#%=T;;)*bP1*ZK&c;q zvQr@gz=B!cKuK<}yZOPPUMze6B8T+f47|$jnkPeJf@3;vE-ij8Ey`Gi+=L4N~kE40}USAevXm)gYMKu+H}Gc6QvqI@gGh3z=;4&~x}%*p@8l;#DtGSE-8yW_=1 z&Y&Po{N|?8MSfLYyL%nH{Atd=Pjz&FlUm`EE2xA5(WXO5A1lx$R;*pq8e-!cT3ug0 zsL){_A?LUi<$-quJG0zc4mb0r9o2lx+5E%cEq=YGO*cF>x|t`{K#04-$)%&b^+Gdn zJbJi}C(XznOppMuH!%5<_N2!;Ny_5q{=)QBggm@J@4GZ`;EO0UId7+=z&VSl$53@Y z)i6n0>3LMmbSBq@se!Qyg*y=iPUwA(-+-;{an7TET9XIb|Gl5jsts9-pP+_nGEdJ@vkpCg5(CY?KD}d&r(edDTqb&RpDcI z74UNd3HVrlxOEu$L529t$41dbQ}qE$dx=*Xx4h*E$qQDY{|ph+5x!Q(dUWSs;;`VL z_f*91UamCm`iRY&1FvR7jEu@7NHxRrY7N;9ott2H1q*eD9b#xa&yuBg4tFS7 zMzLjS1&Hau539Q-h{T3cR!l)6?ny3vXa{ZZ5QwR`Uy0jG`X>Bb-LJOohy zh#y9W{H+=7&i?4xad*ek? zhJcF7{i~*5Q>?@|gAm!<*$1bc0VXe{W`5aGDBv)gECvrD!eRglp|u%3s~1!=9sB zD}r7y-AmVL#{tI^@6s>JMt_zR)JoDC<;;ViG`B%aMD`3Tu%sG)^gZw4!_N{1SJ%l3hB;e%=lL>-Nuw}${Scrahas<2x-Yi%AexuBpBtTDyB7LX=Q*285kUe7 zy!S6#{oc$RPD|ewS;vv;0jZ~5$>c6h7{dsrFG*^2VXA6*0?!RF3tJ_Bn+>|6OYGb~ z*{ky$B@!AuTN;5L&X?meujpZfy%UqntzC);kks${p#RPao^m|*V{()9kP)3-i6KonF4Es?tXCCGns#+^sof0k*qlAp zi!%WNZq{7Af!pJz&~NkojA4R(vA*M1UOtn4H$CdjV18IW2k^@b3ab%C`-@aa?_HgOlW+oU4(hW2-0P7oi^VV=Y0Hp(}NcBZ*_jEisk;7g7W>DcynJZ>&=IqJU}+pOQ^ z(fPBtZC+mxDw^OCqwz1zWV=rEz_YklNh#&S{(D|`Y;)ul2^nPYKeW@>nw;8h@2F;# z(1K^m{N5c@%2bI`dJ}uBvzinWDsSLyXdCwiC0(al1|QQ9_Z-g+CWICok^EBnL-mmw zE0Ju^mKINGLo%a(hRolE(BePNGW{ll zyHpQD($ZTa8MqCzNfd^hBK>--!1tzDKG^KQuWUjsEjK6zL)evC)($=4Rrh+G1gUsCe-k&NZ8CS5fnLKn)Dr`EtAvUr zA^zVEGZ;W}6U78w-_8{-9UTmZz$=o4p$gj+Tc@i%4qhf=Z2anDG9tly7nwtS&EaNm z&R8ba7}m*j>lQ(Iv*4p6@T+?w0uXmyF}cY!-!gxAu>PONXGd0unJokB-YScny|0%_ zh>AaPrsQkT3%De0p-lt8D$%0#lKNN}XZ)Du*ES~I&y?ZFpK{G<{NO;B6nf7m0$*${ zu*~-|tOuRP=ShVefUa?@pM&EW8Zp+M3DRqj3nC0z%VdB_>nFnvwqxd?_!hp$+gjia zIzaw+vWE2pWOn=7>e+CfVQze})j=hRQ=;FXBUKGoLgN$j)2lIhs#eM_l_82FAUvc3 zhH>Xu*tzDsU|ft{UGdmn?~rD=ks`w(n70_YsGf{L=W^-EF`Z~{kDg-vx}8%3;(qS9 z1^G9Py$zsIB=D`0@83}Kb<}%+JsV*AWnD;#TGhI|8~oHK`r7B~I*l~~A2F80{ZvOa zqnuZW<{-G4oE`jpdKqJ?pU&2K2&~ksnsR6Tr&sKz9^V`1qUB|@c*?U^Zb+JsZh+cb zkan7?XA>9u{iImtraAXnCEBL%~b)WKIo>UXcx=ItYbU+r_?mJ+@ zj1}HFU0!MJR+DK6WfT`gI&>V}Uvt$sDV70mN_h$Gtt{<8$f<4Or)?!|^hL`fLYkp+ zf30%%Fv2rFTJ`oTs@%i)klR)#AmrwH$-xJr%~&=QZVjf2y5- zXqwSL$d}kU|D2@GD)jew1!Gctx@u%H>)1~afTeH>30%c_fX~*A{ta59hS-2gO9xXd zQg%~(;+x~kLZW`M%vf-h%Kdr?ixiH7|Hd6>`t09@PJgif$)d{XLC!+p+&!~pT*7Q) zK%qQ1`O=Q&uc1EIRgP~>Ma1%R$sF2cvVJC;$X*X$BS2^X4`C)})d&XKBZC6dT$bok zj_(g(L8GthBJNU(r+zQkMirI(#eL?e^=BRN-JjOWgl3q9c6VA6#Gc_V@2*RtGw)n) zXmUtw`l4K_vg69{kteUZi)f*a5K(TGn!0nK4(*J z3WATVQZl)m^&}p>1#9S5y`4G$YKd}trlpez&~Lb)K==Ed&wlOhJ>h*hzWlHM24nwa zMUInO$XmkN5M^C{BjRDmYOZHH3=srgj#$vSt<-bG24l@X&HH>_E#?V==m<8i=ciitsoG5cRh}F!$)Q{Xga_l~Y4PdPPIgVX>7!;mX8#R9 z9Z!uh<%$8JN_|>vrr;86Kc__f_)pAFA-ECy$kBa&M60%VMDnFG)J`px-d6tpnUPV9 zSHu7zrJ1e(?#QyHi?ahe|DgV>K)CH=v>E@_+%K%wbRddT47`LE?OP zSa1EL6LFdd$W74H$nJK1aQ}LmDfvP+!@;nk0XiyWbDi*M&q$#<)=h!N`u1Nu=ArsA zv4V@jMEONQ&M&elV!>PB5ama`1!2yDME@_7^4gS=P_edn_?N^3o=1@|;q&jz^W`?z zLt0V&1mhz>%M%@3Q%_mnHVT^Wl)ib8V+-{-}q=2?%$Jgj}fyUhL6xx~m9&N!og z41=73&88#GhQDr0tCM5~F5utTVog(V0Mvyq?G`<+bizYrJPE5L^gXhdS?74Z*BXE{}x%Uf04LG`m2VS?7HE;QcC zjW(V^vP7lJW%w;Ot8Edp;|kBC_ccMB;td^2^6V z$?mF2G95H7$5>fgpOh#T*)iC>F(T*;j>qLA}@4glqi zY&PYQC#@JUAo|tTFh_XR=@)M64#NMe}XdO z^qB0ec6#3*ye~0y01x;?zHw!fUYiqauu)ymVCIF?`ALW?EhnZ!r^Sgeq2hM^CFUP0 z$xHo-GHg{{PTJdIp18pWVp7c7*d65dvL9I#_!97`MjjIxefz`YGe}RhSe)s*{xx5- z%hfO97CJTZ!FUZAwQ6w(ku2^s2=mHH{;h2R?WRm~&=eBeA{^(_ zUF!C$o2+kdUl&6_GzK&3zStkmT%25KK;_4_Tr_LL)gR}lXx`riycjO8JJkj?R%FB1 zNI%UYbt)JeY@U1NTinu$esxyzW_0=|t6S+u#OOKgQ7zz64ame^(5VIzHoY#JgvhY0 zn_}u*zvX**99}CySR%bx3_MU8^Y51Z=L zoI;FwC$UeS1%!LWf4Xvx7hxe)KtWL46UWboj{BB-v>C-m%yt?*XY{|RCeJ-njWPSS z9ixW-{i!b`>cQ&P9P4zm<;;hHe;zFJ?nwXr*9j!1hxvUjVQy33+x_`Dqyne>`~o|c zoZdarpE%K?w-!1O;N(fj(F>+xBJl{<&69S}>8RNHNRG_1&Ce{U01xEDZ5p3NGK+Iy zVBnwbD2Mu~wyRkqD_--K)0I|Vv^f;V{ek@1|BMQ9a&kX$KEvgnoY1QRKRi{b(R#xM zi!?04c3>a9eBXv*_o0YAqF=S`YgSTCf-ATV@Gth)69_?Tw9+Gzr_AM%8w3Z;OGL4^ zEn)fO;ur_rdT`2fil`>>P;^Hq2xgvFe&Lvw`iCe`almW23)h4J`*ru$clZsnKLz46 zfw~L%@b*Scit%)va@M_rnVkryD7k|5$WF;c{>qF_mdSo9JMzcFkaz#hn+h1aJsSdy zq9?NaY1J*0QKpTTtrlNG5Y^q$I?ghDCh4kQt%C2zH9CY&TmHL%i~A+8co4H=t>|G< zahnqZJ0eO)-vjHs)baExRPJb#D1xPchC}}vNO0l+BuT#e-MmqHCzhJb36(2? z)%@jT^YMvzJBb*ur9zU(NF1#pkiQ6b#YFYvwu1U4DzVSR=aHm#dMW|`L zQC}iMf99Cz4$hhB8&4K8mPIo|Hzi+puVeB+=vDcNix+4-l{SLBj|)*S2ES8-()GNu zo_C#pyEFb(d%20@Oh!Q!6(tg!C&kPt#P@ni?nZ2`6bilsBK{=bkVZ*ear8d^d_iAp zr;t2>JmD1P8&&~Zt_B6|>#q8*J-E1sH@@o%5)?SzyRYnV=1SUsg&h9e;L|@6uP^H+ zvwC$kK$lm!ifeq3;F^giEi(c zq#!K{(k`pkplN%nlV2V&jU1hY3rwcrCCm=(e&d^f0-4+-^DZ%8Emdd35L$RX3BWvM zJxHl{mpOIV(lm>(0kh;byzzn$5mV4V?)^1&To}uLn9)+~?j!}5tDt(j4vDwDcdYt7 zc0dAgpoC4l}Uo~QQS@o~nmpeIiym`DqkX!~f%LVNJoc@(33KqfiV9UOY zK|?+)y-OF>%*s)Y8i$cpJUUmS7zzxiDO$TpGA$ygTKk{`{J_=!aangH&{dbDwq%&l zjrXU(GZ*~r{E!Bbrk+33VSu=1z~Nfhaal5eqNl@~vbJW(!ct0)ppK5%g4!h&OJ(j; z>i3uOC8EXg@!*s_fYIT52lqVN+$RhAMWeOqJx`xpSATmNc>rJ(%ulvKQ32DeK!hUw z&e4SaM0jmhZ!%xis_9FH6&xY+sdR=yWqoefUaf}x<JQyJwFi;GLkZgcxnH58YsX!A4}WfUa)r${>?~7inP@k?o6B((ooNM z7lmTfPNIgQpO#1WX%F;*fh{8{7jK|&b>_SCZBJXGoA!-jbQLoUcp<+P+WLzv>3dmx zhz6S*%@7%Ym}!M|0V_Bias_}J7-|O<-*FE^&ZFuU-6-PhZEU(X|FN*M%Uw(;r14vg z4G#-tjxH`P26u+{$5ZD*khdFsaS2KV>nNd|d&Zd8z|#u!{_|Hj|C8{=if_VD8P%ji zsit)+bDlY+0=|dHJO02a!mJRpr%iIQG>Ol7ILB~`A+m&CN`I(f7Gl8o5Pf)daG#d# zRBtu%6O@LN^D(jTVs6edZ^8u#GbB8(mzwg4U}VPn-Gr%(S0R@M_=**xHe9E$gHb2L zhRAZ^8Au1B1=a}>I{vLRwS2`3r$5#)?LKM{ zcO2L%0%I(m_BEB$7-@u<68rtSE-#V4p2o|>@NfgngwalnM#xi=yG`6E?tn%OK67#a zI^xdY+5k8LQR)XEO zz?9V&A#JuIdsZ)@?DqW%5@?t*z4d;U4qAQc^$u9XwY?)uH@ zie%<~TvIE$%am2*+g0#(Me^IUcm7uD@svxbP1ZMUmU!qc+{*I{Be$-MA#>+EL-_u@ z+15_;Xh?YGE+8?I1Iu!ue1&`og=eq`*SWyiY9U^dJC*-AMvGw3>yNnrxmIk~bG7!=LvTu(jL@`2%WM$y=?ISyTTzpCD;@ z`jEu{h1@qo;ud`d=s$`3U#|w>g^PrsmmTeYfr{=Qy6%s`$1qfQc=rE^B&2K5VOlR| zeaUAelyc6LoPt{GAB2QN!kjef3JDxo{t>{#RQKQ#E4v!-0!*WIxvZs05gXnoSpUo^ zJwoo^j!zi?l9yF$ajD3XV)zk!e5ytv(yxqVZDD?%vbl^2oeeR;UXIeoKsU?FE#-Z^ z0nN-xRlJdpqE?GLN=1_H?F|^)rz~lVEi|78I-d(^?gf~404Ui!VBo;e6k@`9Xm|nb zp!Yt|6VCbuz52gY%+}XZSG+((ccNdzKM9IiBG(N)wJ6#Tl;kIJD!%4dzSxVR=9;Pa zB>m$hP?K>SRQ?r@^qL|N>etEGB0xP0KAl#LTF5@!dwOUX7!H`utBsBst}Qz?se{f% zcWrzkODz<2d6OmEmsRksftLV0r>0&f+F7u7$3o7*kwVz~n>tzo@On{&S@5YKhnx0& zpdD8!{M!I_tF%fmL#_X|IAND?lvSTb%16vM`@l5MH(Pr29zQxnN!_6FJR9Y*$Bd8{ zeJXD&W5`igsPgA zSt9b^fTa!!I&MHmA?!IdgMmL@_$p6Ne3mQUGhuSVxZ{Mq!8}}8)-!i)6Avw5_O98S z`jS_VbK~on?g&{t!VvE3{ElT+{C3Hl$=WL0)4qt1I2BIbPO-9{xa;{duntcX-NAF* z#5L_ht7RBAoKc{fjFzR^ZDifa=^I-2+$_kJs}5UDMB>f-~U!80aQ4sKffr zD_(PA;384{cmKd(y~ftPHtA4?x_Zeg?Uh_P%g{KSQ>5k=wNICpsMrKlrgA3>z$*JL)$9(x&J$;)gWj{xBWN<$W46uBzB<|eCwGc(O#l@=DE zFd-0aj%X~3Xe^?0*8^fT1`QTHdUvAvd#`rd1#%^Nv7h95+ zJ%Tq0e6dH*2NNcWuA6e`v0_$A-HWdAFiR4W!Ml0qhtdDdab&G-_Kd8k$aUaFwhRc{%%ZX6&icx?196@|gzT2~L0|zj;_fz{x(=ahI{lgWkkjo|a z^A*1m=D-EEat6{W(BoOHtY1AuYpnW*aodO^@tPr6joc9;)HCk$}Y z!s=Re#8U*s9?dr*!JZWS!{6GA<9OlxYM{#2rpYw90= zC$&ru%Oo=7{N`R1hzj_hirl>Ux9K`psalJ|2$%S5uc(2kk+CZsl09i^(;-kc*h3$) znnbA~YbmeHKId<`H-^3CDN9a-TQQ!SSU55_0_ZyLH=_&C1SM55j*IYt6glp9YpI`J zZk{(BWde?^q;Nx*a(lxDWyS7TR<5w;7MVC_b9>!kUtp;79p#k2($s8b6%O`(f(~EN zugbrpzM~$Dr3^$R1TY&FEO3Qq$z3OFHj_a8!p3fgez+AR;Zz6KnKeB5V5J@`e=#o! z-OP3&R*XD5j_n;+7d^@+J4$cAc5>`3E2bvlIqiE(|Fj2+c|Sfi4(5~U#(xq0DAw_m zeK5aBkjHVZ(q{sqrGbfM9-hgAjUFo+T^E)$-YO;sO_{V?k{j(+@mjy(9jobh{ynHbqU{L?Gh{lpXXTfyhxn^FTzvae<~YTx?X*4%%QaV8X+eai`PDI_`_ zwoE9TzRDd|Zw`DQOktaSE;UYnp49_(D}QqBALSee{>@NB4SUBWx`yr7t8NqDP$ z$xiR~q>=|*s1asr!dn$mknv;Q5JczxNez_!WYV&uK(sk(0y?Af$)zt z93E5K<(!*>MU2gLqY{0bjs7hE4emc=1%O^-K;#l?ytmC%28Fu>liz~Y%JpS_q%3>n zuP+9bNZKf4M0=q}5P8-ixpzeT+(Q7ZPRLOhX^X@I`WZinHX5)%u;}E%AC$i7kg6c$ zXkAp*b%ZEv%C&g2NciL*kCdlmw86Rb^&`tSX16trrg2lx=yR2CxS{CN3QhO01TH4| zBcWcJYaZX%cO>yX1#Cp%$pKbw{L{n2UPnCy?z*8zsZ2V~wHvOj^edNx zb6I2S`EtNoZIIDex-$k9h;4cr+Cx8Y^J+)GNseCFa0P;UvxeR~z6MiRWclxH)amCH zuzoTZ73QF`xBub`_tz?UQQhd%0JhF?Ms_^Gl6gyVuGK7lgsAiHvOh>qEyS`jK|1Jz z_&T2f57dB(4Z8Kefgh+KEQC$L5g7@8Bn#UYE!!COc<#d;)0VXz;Ezu4zg}>7b|4t-OnsP1DdNv$-Yu{yCm66nDK+D}g%Y73*0%>9O za*V)tGdlpjW1OwxCc5bPLP3si;Q1Xly%BkRA8fewVRIG}*|#4a$cr`-4E4N^55DJd z$yN$Fs%hjpxh>|Z%G>+0Fe?n`m{imC|6suh3PK0z{BXe#U!dCAF?B+r#Wc~}0D~wl z;ZyDpR7a>^HoT{*j+Ob0u@!{68F%0FA1OTrLD~LegZLB5YX0*0{~b+G(~K)F+eyVm z{Y7ZxWRr*VO77Ao}`-qZ))27b_q{*BA572u>!9>EBhk=|7Jox-)=#miE zU;b)uhAo#wqJ|9Y@);JY*LEQ2svE34ui`yX%QxZOi??zc&%_wqQN8k++QJd!)A=n~ z1c7&OQOAiHShK2E=Ydgx>Ez0&qXMHF&Mh1_;9<#o6Du&Gox}pp-2i-2JA*c#5Z1ex zTmiwy=gg*KRI3uL8je_Z4+WbHv-m00>GxmR6+D~pFZkjAT3_OhW_gaS%sZHQVj5q> z?~EKs3NkFv*XHg{0q~51?{~H53LRaiY}jab_50hWB<7sUTTWgP3g>2F>r<3y9Z91x z2_T!-Tl$*uP;8_EAinl2e7_*_#pcFUC}eBFTKmde-fok)62m2j2E4znhPh7^D9=d3 zltqE3V`7UVg7rbbVeVN7Q3HonZ2oV%Jl8un3PDOYX_MxfMcio@l=r{OAMhm~)Qsig z*^)-?VXK{<<(Tp#SdRWYb$*NlkM9^7cJdDUhmA0e;w)P6efcb#Ms-G)pQfXoWIz*`v6n_*Ygj4 z9$roe8s(AI-D^bA1a>Z3=~UVLHW!#g(#9I3(je^gcw*%y#od2;dpbNh6?tc^)MelH z#|c84c-X0R<7_^%{u!$>C;M`LSgwEdt;@xZzjwK<+j)I))Of~K=T&{>>yo9K)LZrL zhjX!CQen%%Anz|60Ho^YJS6Ue4-=++U}~Y^hoGNfs)4&-{q@vxJuRz!>2}5Uu`5|6 zs>OEr&Hle(K^aUtT;!V|HwDH;Hg!DTfA>1vFgHgx zN+^FKD?4f~?ZK>&s28T{=JbBqQ=5>=()e>^c(fSe?6*fkA-G?fB+qF0#S4Kyss8(4 zW>k|tn(M1)_{k_sKKkaK@u+JjO8?2*c~fVoI{fsz3Ot^@+f%Vm2@Dm)uDuKB6Y>&Y33rn*5@FDfq<=4O4_DQscU7)A?AM5nJ0e#_?^ zn>Q7Fiw3^QASCqUb%|j|TJz4mqNr1tLKfg=>NkE~73YY>oN<_v=97t4H+FHbaSN>X z+fto|wP%GugGR;K96d$u*22v3TABD|FVxsm-Ekch1#6@TjHr!M7mp zrqjg<#nWq(nbw{$_rATr#Z_vD%e`QOIruIK6Xzzo)1nm1uuqVC^v%Tm8@SkpdNYO{ zjPS5a*IHBJT`sR`N=LcIyBq0xdXhlx1Hg#szs9UUj})4PS%f)#mM7C+>yMOO^%4(V z?$ubvR*D`>&o3sTxo~Um0XADGe*?9bUMx0yOar@dS=c}f zy8p(-x_W&016XS^L{izjoE&`F&#>c{nw)<6B5Z*i>9oE_wWUw9G|IIl8qarS8bofN@4V&vR|dff;987(O5!C5i)bqCRWnD(Qb&)?FGs!Bw{)3`iChWL zC6F7Z16h&ugXt4)P5FQn@WPuc{2Bix4;}UJjs=v5BiU%D-E?>uN3FZTj0bN>%$yO% z?4{pR8R4Hz(&p)lLNESl9l*5d8;iQPw_GI%MD@#|?+X}7G)_kwf*|5=mgGaun`%YT8(0ak%6EDuWL?vr2( z|M|3qzMQf<>Kb<#V9Q9^fF>jW9r3M3AfaTHI62?BW7^MRy-C6Am3J)Jj(d$-ZvuHC z5geynKW+D3wQwHEeXtzRtS?U4i-Q_GcdTx<1qUmy@$MdTpm&$A-SmE_|qf|#>~szER}^E zn&CuwVOGZRhlf;a=2>;E;))fbLlbjLW^I;4YGwjuLO>Rgmz#A*+B8o9vgRG}fuAq? zU+!QoQUh4@wLQ?UC@?LeC%KPtpKOIoc6#>HPyJk`#N0;3WbeA*JRa@e7)t1m&cM74P(Dq+p(!G2dRUj>x6>oBKq3!tquM`@fV{pF@42D zRbw;1vYPBa{`gC6Nvk~_PBT2tybXcp4+E8Cj1uB_9OLM?2Jy&hKh(!8E)3vkMPaDw^*=}<5y0onx7}K-|04YC1!XU zl4smme^ZdV$+!4@D6C4q#);b=*X+(NIDlvnDPbzhB{c1~g+8)^(ReO04%Fz_rf4rP_ zU=Ck6&gP;E8-`r3e=xyF|d<@ z18Os2F$8!}#DWmH7`XenaDIv(PKpQb(}vnGb7>Q%)|S@R__O3dt>xeU^q8j{DTlHu zI?E3ro-pgY$pQxcBl)tH%(%7 zcq{%y$B;Z1UjZ=`Q_zq^k%&)CA*6D9j6fwas+hvDvb45sX)-OzXa zv^{cx|BbVCpt`<@OKEvXX{-ptmr$M_i7|a_tcZb5P$3ewC7BG=)|XeXlfO*#%i-2&-Q#v=@wT8AlTgR~5OaA@vThruF{3egg9fB5Mv?3PF$F1*M<=?iK z)+c*^9EEIdZIb%|@r9tm`;TrQ*%g*38c1Aug0t*%XjRfzb3tzT-q_>H_KVO_jmSmR zE2P{E)h&x|Dtu;Qd{+{Ph}t)oe^CUg>ALIe*#Y`-0S>;gmD4L{YxzBUR@8m{e5xSDlyElxMy7l zzt;)1Ie<;VeTAY%-(6yH{~xy#(9?tEe>up|;4?@Jeg5U16!=Iu>Ug4N(hS&*|IY}{ z1o6XY3BJxxN-Q)LOShZ18+aLmR?!399v83pwzF7W4f4`IO4s2{+yX-X<=ZStMnkvO z@49G3mXL)&hj3p$xWp{jm4e!l*Ax!b3uKA>BPtufXR`C4kjtPN9;|2lI`ja#H%X8J zPw(^dx)43uIi=seeH$=P{;Edh>R~_q>4JVT8hscc8Pm4g2aWo*EhfYx=tktP`oh15 zJNLh`9zn=0ai$sAfvZNrXw)g0iaWvwd($SZw~LjL`PFUUIsN>2o77|(dDJg^D^F8_ zXc3DVP_%iLWWGdE!@#E(hg^tOtMkgB^qhZq6g~Hz>Gmdb8=4l<;Pm?uc;9@%80@D8 zk+EA&*_Rl!wbA=k#FCAW09yyb*2rbPuGy^p2fnrX=%KD>E<0j`A5*^zs*KJ7W#B&h z-slpU0>!!s@>(xha=s-M0ho?#nM^qmX1*=;^3N4 zT@dc0P^8A|Y!B+8DH4CW86Hs`E$)zPLC~2G5MNZzN!bl+EP0(|UdANh+!MLShpM=ZkB_4y&^ui7)iS{syO&pLOc9yT<&)Z7 zANi*vy^_$-eeK6#iof}_Zw|QRc+y~xah65()zcf-$zMHq1>?S$wPr}LX8y8GlMAP0 zFc?#if4&!5r#!a@991DI6H+(2Pp5N|dKNx0^6JJaZw!Our+F=^Hvk}~`OpFvpw+1m zMk$vCPooZC<5}e2DI5G63)WTP6Djq967cKlYod&4#(%)5^MsKdof2$cQLp}RP76kZ z&_Y@CPFkZPm*A75d~_N^Xe%*7{a+pd2aGcBcCfKDG(&dWG;wU30iInoRKfF77&wsk z)0WKm=j8UODyQjxj47~z99dTrFj!>SMv?s+-hLQ)QJgi9m^Lm8e8Fdm8!N*&LS`z-!?{& z$+Sow6oB5no3r9A*IlFjxq_klT3R2svRuc)D~qTR6w`nrn4OAt;hec|FL>Eu5f(|D zDQsiWjq5kF$XK_}=WGAneom^YE8t^+m)w1~G@_B7($K(Vtd;LtvLV1-6zQKUq;#M3 zI=t~JaxncK+e3Q=SL1a?zFNYvCz$UeG@K1 z$is97^<8Bl1Sy12y#TDRpxtH=daW$+rvV{$Z<(%Q%YeNJEh12P8)9P+Oqh(){v$j* zo-@*sTG-(PtnCu&!)ML-GS{xr0hnA!V=B9SSEUU)6`!Q;^8vQ803`Avp&!O#cowuK zH2vHga@*S*1pI;cH^DRIH?R=&31!)4s+)0N^rM-GWjt^N96C{9A;B#gvNf)(aqy{! z&cpr76a5qOY~wElm?Qhbh#5g59eo#I+fp|HKCjhCl7yLSJ#%sH;Z&?{|6E=b>e3_3 zexiy{UwQ>kIPCNA#e(@ayMg+eSw5^!tKUyXh5B?gjL6#~vIL1HPNQ3&k+so$Xu5WL z%v3W{cogK-(PIJ@_|W}rZtZruPPhX<2{6}V_ITYfyBNpz-WALLQtkI=#59O>E#h*! zT!A;@x=k2Pf3Wbz3AEqVbB3Brps^kYqCaUxAb{t~2wZsqCLvuV+Tq>^UI^D6M{+;^8|hxEKh#UX z6*Y^MCly@ku5wR#`CeWl?$rNZ_j%mA!wxOwJ+z}oLc|B1GRa;3>_-0a(&#hu&j80j z8TJpv=6YlB|MLP|LdsuZcg!A|v`IMcIUF51Lr+#K#74P4HSa}?!BPi=SW!DsX%rwS z%h>6cf@lG-xetf#J4qbb6cY2p6=OY**LyFy9D?2T1Ph_KO5m#sAdAa;7%nc}#OOvb zN|*Fz{Jk}*!l*{pd*iivz8rZzSIe5^yz(ogHsS+xe{zJa#Z)l~=`W&ja0jgMhBok+4L z%QQ=isUOzzgdwkvN+KE!To6vLhn}4-CE{)L53%L?S2z!nGug;f{qw5)b*~?FT=vAG zTnnpz!`|nLkr8{%`5n;J)ek;-ce9v1Ka0eaJ*oA!jg{x`@*BP~@|YQgJa6gbAh@aK z7FF9Vf^=S{LSR3tx2GsItUVCtTb_);$kAx!ce{M)@g+bJ-Gs+pc zH%J&(E2{kzi9vGHu5cwDCzc`A9u2GzSLHp>a;Sv!_F@`nxi+S?DGXs)a9L*7qHzIU+`%sBVGPuCDqlMiFj12YP!rvbXdQ zzG&Y1zQqtTv(t{T73xc9y%#xAAf!2>b_S|}RC9M8NsUdy?A~qtp-u_sbbVVK;d=on z)AIIN4E@`H|K^GZ;besPKKhi$@pWlyaO4uCk48>_hW~9T&Btr0O4JJ+H1`w zGSgewgd&{wP!0g|zt?3?0!uV+#pm=@(qz(-#D?MLXi^~6!^Voq-xbJR8+K15AAe<~ z?@D!b>EPthOUGP_LXEG5L=70^9IV3(id|=TmlyiUGAT?+aNPA0yuY|#uElE-u&nO| zSlkEZK5BQ-layDq*{L35+6sJjQ&D+^Lyv#l#8aU@?O1h5-{D%WR7XH~>zRHJ&k35( zm>h8u{(m%mWl$9E8|?<&-6<_8-6I>NO$K764FR_DIkq>?cV+U z@4YiSJ3F&q-+AJk^PJ-;a~mjgOPo;3VcC{04u#x2K)X0y*;rfdjO-H@jG!XM3a~IC z{VjJBti9m!`JgGt+7U;vDem8;-{Dl=FSS=flA95chcvM51?*Jx7u(A1Pq%ItQkQGR z2gqDbgx8y2aJQNfko4i(G0j6FSQ`DZk(bQF}`$)o>9_c!uy_eqH+AT> zj+>w0<8Kv!(cYm1u)Cye=&XQ&DZw2Y1vI_?@$+d&qRx3N8k`+DorqD@z)^dk51-?IW8a&TBR zrfNBc?ua~6>ggn#*E!Zig;Z4{isoy7h=PCoMl1%_2({5ybmZMYxSXUCS2CM@bAC3A zyG%crS}Geb^j0+V{s8704Dr#*%`Hp-BS38FY3PGk)0%1Mg1&e{y8XvTkJkbTsrP^` zYwW+ks$;-M7CHV1Xpb2yaT*It`8~ixd6=~qv>0aK2XHe4(N&#;(K}sa6=|VX2hp~j z2vz62nQZ{5$8~S3v;}#9{6$hrSK$qQ_zQup(IbASYt5_0*XdJBb2;v88R2d{!@xK2 zUh)-)N**bJmQ2@*wX(sY_Lg2~3g_>VM|t^2w;;(Bj`9zqr%TD>2%ol5K0;ci!Z$-j z@k=uXtIB8W>fdE{&_Ng{h;2uoNNstXXLZa5Zp~hO)OYCdH6M@+27mqEd7Y{1u76qH z2EENQS0r($b_~Pmofjg}eXf^_Qc2j&y~12r(9|!<@ZuJy8Dk+kHdzv+x5FloJQt0x z=|(1R%E4Z39v&Jq2M7M0`84UqxACUtVt=XmE(zA8KY=!RCY|x`w;&IO86tk-K*p#P z8w#N=yFt;YK`HaRdo2;RORm!jJYMheMZP2*lW^XlOJb>S_4V%am3n`v7~&{3wery! zQ~>5Fl9byFl|stgg)nQJAkK+6c86M1=#0Qn!3Nsz{%p+!F4?$gOGiZ^Cbp>8eXF+Q zg(Jdf3DGZ#E}^LGi4NccVDq{9$Dzfx%~aVwWsI(^6raq_5qVR6$v;v>WWU@%m^2?KO+(!w z%_*Fu4tud0xZ3^4`vZQvQUo%kZK&E~UM5~fz$HP(Gd>-xR(aR*Q*ZwG6tJ|Gp?UrV zXIhH(qZb!6@hOlA3z_T(G6aDp05T$KpPE`BhI}HHS+!bl13nl%d|Wsi0l!yR1jbT3udt z$v7K7`0je)ciBdA3l_?AwqTvy`XWt8-`G=8O95A*Jf zTw2u_;c&a8Scb#RGlANB8rN~HZLQ@f?d~f_@!tRElS}M3c>1vYw{9z{3=|fxP$FN} zC+2m9zo4znF!slU314Umr*HY|yyrd+L^SV3fp{)p`;I#HUXQp1ICX-&@= zCW6KTyW2AROMVM@Cp7bChq_?Eahn`7O zZDd2MuU>cmPa7BGZ>Yj*p7YCU*yQQfKYrZzM})?1u~+1JE52U$U)PLeJ-&v08KNJo zt#wyPYbqUe(#w@~SY_ZlBteQWAhF!RpB4;{m^@QgjTM(v<>O{(lo=T8dzMyN!b*y` zz!a%EZxV_xt(-48US!E!7@DyR|I*ra5c34+HI9Neg}EL0MHY$4Oog|XdpY2%dj$Q9wB z3$FQ|THuzi@=ARaG0F`}HwH&vr@KmM3 z16J9qm*k<$I-1!J-#jV~Hi#qK6~9uSi`TpZo9XE>>or;*PAPWtZ7gt1HN_3@_vniXrn37M+IliQ){%{igZIR}8zf5c zo%1h4jD0`v=0J}Y4Wa3IjP3O|yzcHfU_-s1w(sBj8bmwf*?0rG#C3l3r6O(FHhj){ zi77RHs|oX5CTyXg^?&nds8g+R1iaq}=yVj59XhXP(*co5M_3)e4D};kRLHtjxw)?A zws;%a3>{<&=YIVSV*9GKg>}@w$L)}=8=kJ~UM}&ujoJ2nf}UfYZ+2uOYZ&Ycr%|fJ zT1|E3E9m3cwQt)``A395A0lOxMi$7137X#QGbw6X5H~;*&0cmzxi#NB~WhaCP6r4h%RH*L z(CNZW_}TSHzAV2pVfkZPqDx&|&C{^amS2eQ`S3}+@>q9Px{<6~lo=<*$($tM3QK}PE1cb7ffLuv4U>rZrj zJ&k6OoYd30=s$SeD92}wJVCJq`^$n%xkCJSF3WD5b)E)u4y?zv<-%K9`f3uTc8kg(UZf%l&)EMVBy4pqSF zij?rSqXsA1131~1lW#A+WDy~$Gkp37F{htvLCIwf0gV1x)>}t-y1}lw_?3bd7Yl9( z!>X|FtaI7)MqS>@{EIYU;}V5WyLRyM6J;(kA?7(SDA;5`qb4t5oHIuwDy@8omqt42 zEi}X8S?M)^Mb0lgpe-C4Jxu#elInc?p$eK&CP;=|?W7yJ?vXOGae~1UfK@gFr!S96 zYrNH4Ma+3<+2^p+)ix1hVi4UupQRYxjj~eLi%en(h26*~tg*Qpc%z-~<2Bv?{xhYc z_Up?|5*qory>$&cLEW>=+!h+1$hS-;VbT_juF%$ui4@mJ^bcH9C6Z@Nr&)9=RGYYO z+06i^RKHMR_;Hj=h}+>?8g#PHmQOPvS<^Repwc8F(lk_AWGM-Doxs@ks%qpeef+NZ z9MVSx;!iyu{bvg94r$~@@p`jkibB)fq}B~k)ze!@uI#Dgbymt zK=~u->t+OVJNE)9sqxinP45r6L=WfoqLG*F=Z*gu*u|;bAyG0&nG1fj0WbN|x?O4W z4-$RKlx?dCK<2kew#cx3^eLYlbr?v!3J2oDGri3JQCWp-dQQ}NY#~J^LG^orb(5o> z^~tUQxT+F3^l2YZu$RJu=VKJwU~JMoIvr)t-1(>;H<@ z_pU`vE!Dfe{hr8Stc(U-QT#+Z^e~1>SHZs_sXpJ5zBS*#9fRySM0*U9YnTvFn)-)A z=dm9t*QhYgi3TUla`;icDTYhXwP;fKbc~$j82Q0EQ8eNi3p>$@;xhMN>jZptfo*d@ zV9FG~`)-FEdUsL>3D}OvNQfb|oaOvRPST0|Xq-mPtj(*FS#piA!N2>ifN%BKjsvbA zhoF_$JcXWNKa2VjZV=t|XzCpJD993rK>GOz?mqQlES%W@A5>nL0iFum@2p_6{#V~x z5$4kjuTB3=u`u>xQoOkZl)fS*wG(^KNP<1xBVnkOuA{Zb5BD&9xM#Xxr5TvkTXquV z$B2V2^D#Iw5~XRXBoB02*uq6q)s`h9UVReiz$85o56o!iqdLTQl6Ln#KG!49dhkts zR<=qaRuh$9x6mRB3`f=|*mvp6=IpQ1ma%h!b3%Uc9US~@oX-ECwKqk~Cw=wuD=F=f z1Qrw+&G0{Oi3nAmY!yGFSz|yW->6N;$6G2S&Hu&4GgJ%J@eAFV(ad5}LdA9o7I=8O zf>kv>%p@L0y<6;Pe%4Cu`&d;w;m)45!PO*jIui{m9YnQ-tq&H3SiJYx)7$4~gC-ky zW!`9N6<~U`#m4;5AcPaoeY-Ztp3-2(W0zAUTY)&{Dcr?8t^A%8?r9rD%0iEGyHs&` zdY*k9sv3I9wyyCgk$WGIRYAeSLn>am5~t8!D|Xk%g_0CGCLGzSgeeMY<3A+S)ehIG z?7x_^z%O#j1@d?IrM(GChw!UU z3}T^zSVhk=x8v4Zx4d6Nc#zB_ydxDo<(21}gYF|aM&GNH)BMtBnaigZm?|&xCPEQ; z#IAKB6Ft?&(tP^b%0!s@ZdQxheb`|0D})J`C6@=lgNq!nA_wM%Hpjk{|8zKHl?}3y z9erKMthR_E`ON+6&QSroL~#uifk$8^Y^|2U_*ZV7O*Cayd~8|>XCM)WL{S)EyG%4E z$v(pV={$?LBv4 z-YSf7D<2!weF}={Nst4+5nwaVX}9H-;+nMhQ&zE^Vor#Z!Xl@Ug_F&05in1TnJL8e9%3z#>}ZwxZa`fGI^3j8$ei=(#s|JZm;XCpym7jqq5}lLK|tB*t{E!D*g1*XRk>F#0%eUy4K)D z8Tu4$Hlz3R;YuLJ;+^ep13RvO8>8Y?P*-ay@-Wg}mXV*ua;9!#P{_FkHu=V+-xub! zg1G%LZdK^J*dBVj%A3A}e?N}WOLplqHpWR`uk;YPgRHt4 zaC#%V-&Tl>|Kp3f?oJ+hhEkvrWHB2d-B>NzUQMG%_SUCTeel8)koAhN$AgsMCN%Wo z`tFO)JoXx;K_Qrut}jW-_mkDugXJVU(6aU9Fcjtis}v2|_JIANwCKv_ov*jZ5uC=Z zTAsCnFQ5gbJ7+X8Fx~05-Os0_-ccqL8|w+kCo@}-h3|z)E;*7>pWVK7ChBTSnO-HD z-CE38J||p!>)c=Yo^Ibo(ZGjW!hDPh}wk;mB2d1Q*Yel5Rs0p5Xj|zH;M1_|j7v{)O=siv1IpTFgr#5P1sY_wU?$e_LQMEd({TTRx zTdigh)GGH~;-sVXzR1XMvVbH9Jb!&Zf6XV3A?l8Tv5~09N2JD3k?f%R8xn5}T4tnP zSW=SHyi^R*FKg}T3hQ@*{?Yp`CM}We4cJ{UrCx%eljt7-Cv0>~6mwYavIeg|C%Mc%Z&g{2&gD{vk%!&s@c z$fv)`C&KUM5Z?+qfE4F+z&NZ1Q45)c=-#Os4wS;Uoi!JS3CRGe=?BAhZDkx z#(?Xk-!rpxoQ8X%Hf}lPH}r#Gq1L+LDelQ!T&{|y=$8lXCR+w+@fLdGT#a%+s071q z5cEQLTL~wdQmLLVqZgM$PpEBj=w=5|SR4_z3&^ZfM~uCtZdK)z!0C5qmZ4Wl;ur^y{X|dk5dHsws`TL3Rs=%#uNNKOpxP;^ zT|bpBL#Q|ye?wq*cf7yy2(?|j0S6%y7)G{Tq3?ct=2dB=;0*lb$-J6<(n{thukgB} z59)@~MLqcGD=hU1j4W1K7W>l5c6cxsENJDQjY%^(7~foEH1g2fH7KW!i$hDJ57uj5 znGso)F-upp1oX$T%u=|yT_8~A0jIX-uE)KdlG|t$sjub-(A+H3HE!XJmZSst zoG?-P2eLkfu4bR%CV&{F&VPRSlWs~oGVN`t4+CDAsGDC;gzPSGWCtUmQ4)Yk{m6|g zcZzQs#!C+1(I^dUh%RNIrfL!>?87|eL~11`HPPE|jJq^j2A~cARXKl$2*SrdQ=-EB z&Ly|5&@7I!%Z+l*8g}S32+{`k^t_F*M51OJ2VXRv$$zT0BS48woR~Q2T_aNN#d1_FyE!x`^T(p-89QORHjaGNw!4`F>=%4F*ck!1pN3jd_(pZ~ zk&BDP#SH&x0jR5~#m2|;7}jqM;LCL1tT^}G@8ulyeQ3L{5fKv;-!JY zLhnbNe~$T%69y}$M{>%L2;J#Y*4>IOpb96s80D+D0=$=_9~piD2(B=0c*n>93d4N; zO4GHr<&C6?%xO?`(2{8~GZ_0AJ2YwwaDn%4X4WuS!ibSy2d7fM;{40SDYeRQRqZ`G zz2|AV^N;!XXD4qxIXY(4rdz3^y8o{Q&^5cn-{mYl`aou&Oq!tB3QkBvEBRXhI;o2p z{Z8_F)w~UCjg=|u8}9pHZ)TNdlhD!nl0I{K7~ohAI5@h(TF(D+!@C2`-3k7A>R2^F|DE{zK|E2QM6zJyH;>5^(KB)Qo)zp=z}@^ z=0Bn`q#6sG8=QiKKOp0qFsf_q3`~v>D~)M0g&1iumgsV^>#J}F-pFl@Qs<#{n*Ago zD^&UZmpbJF~6|`76dt9@e@`%;?U5FIhhLXb_bh7sq*q0izQps8<&n|BKh!e)<4g~&Ik}W zB{9i7oEHQ6G+pin{!&h=VHQ14nd<>^SM$z`QWxO7|dpv^w z3aOBl(vO%xZ7{nQGe2Td%op)xRu*S9LP*?H331{U_9$0YOn^;GN`zE{hZ>k*OEn_k z#{lg&m5+g`E9eOz=a+k(y>J{D{com~zIMmMpIWH~4z5RhOzKDJvEck44JoS#ZD$bL~2JiZJWDx@y5+%^t>##pbh{rK1OmNjQx}ZQG_mI`#FhpOEbm1QK}kmqw;G)v;aR+9xYOhq4%{U8k#6iw`GUz6OG2e0lv}f zgQy?B`<{N0TWg)e&tlUTd0u9Pc&FICUn*4S zBHfX@IT;a7XlkT_h)WrS0Q?a0yaSs$^?F2qL)ptcQt7y3Enx#l$tI zBP-G$((ki;f{81^-9a{eCA)-S%+f`8qT#&g!V|879viC*m?} z{iO=TAn3dw)n29rgmAdXYk7HP^RNEXrWF`Xo^*5DmnT>yej~R4u53f1h(W z{cC-}kBinPU8we?4UDdhrj=@*pre%C1HC*_>pkVn#6O7G6Tc*db^+=~d@RzXs{Ofs zp_$(m{ey>E(^X)^bRNVyli^g{ z1Q*Zfp*D{LFeUB6<_+oSh1KL2SM~$nT?*T!Tlw#lEPa1Z)K}^kVC11MkaPOUwUELV zH@3y%tLy2!*vm3pkbGrsbfR$^3xt7F-_O>l2E1Yv<6>eyX1kP8B;MJUc{b2%f9c!S zW0Yfs?(NWYCxcV%C@avvQQzFGFTGSoT!E4w;`EA-MD3Kfud5%_y-5r5ahK3qD8_QEXkKQBs7kUD;r@i|EU|-<&tm8X`d3z%}y=ViIq9chbI5U8q z&U?xhRKuI)`2#7j5ai+A2t4aS#U`VOXs`@Dci@+n&dhV5@%9gb)m&NX@VX zt@qeabQZDq{|YNoO^%%}LJJ=G*RdsnoWWmW5&U{)Trc3*4`)^v5$sCt)_X}w)ca~6 zi=#>yHf@uu5U}-1jzwJJp{f!KVB6w^nk#*CF z(sJF_mIu^N7i)@8F1^QI*&OBR=H6~qB=yK!TK6NLo=2X6AigOek)1C? zVM-0frs%CLIV45(AWIEj7Z%L?4(!l~v|YdV8&$HJYL7&BM)5}u^X*bVS@#qO-Vxg- zpT*aqN>j^lEm;A1vzii8Zx1b1zC*so=U4V;vqM5m$Q!F_6)2;K6_X`JM1A%61#?hX ztV5SWSh%VF@FTc)7JY*D-nAxWrgx)P7-E!%-EJpDe+D{ed#4s|tEsOWDc^~5^@t*r z%6Ygh>pM31AXsJ{{On?>pxW4PtnG%1lPm>x^VOQSHP6Su1@U}@c1$L+(NJNs8+1(+ zm%};^R{D48aW5{re@h?#CCCGx-)<0`-?l4g)bwE;;M1vkOAsS>PsOo1&1WRV?Z7j- z-#;Mqa;qT!Cpp&?2{&#( zIHSsAUC1v~uWqMwrzz<^3crib*tW!GK{hZ{!s&6DpUpQA$|A??+M{IV;OlvVMy>); zkDZ%U^+C47{RZP2WXmKbCZ?pMEC{|al9T%$>wC|njC?*x3={C#I}xUXfj!XlK>6hI zjk8ccaKh8}>1iT;+<{s?C(u?g8A#bwXV4%{@>8B&))FLk+VuW+YHAxn)c^)M;HSig z?n`mW)GKgRI$EaXn(NV8m=(pms!G&x6oXGnF*oo0$y>DP#k6{}`nKV6e~QAgxwy$p zL5?ItW572!<{VE0&^1qY{x`S<;gO!=5R7y-`{!`$9q=U_^tXu|Y{m3m3WD`Mcwqjf zt)0igP{$F~$0y6BBE+BI{j%GyiaY%+Aw9A(7u44cjoRopK0jfu{$j5qIF&D8x&Cua zx3-9LtCVB$IW-vFw7zb{;JDD@(%s#$7$8E}X^})~L>K*?D8O&v&;rerlS~wPx^PkU zMjQ68(o$wtO+Bobid5A8l6J!$7+@?VQpZoa6HJ?q_;OTTKQdHkk@Su7M93eevj)iwJ>By9OBrKV zs8^cCr-m6W2ed5M8<+qM1&GYn^!9pb(XxkCfRD^y6n(YAMVm8c&CG-qTG^77#mJet z++u-7OeI}lEJ5#Q=lRKzv5}e}XRW(ZE*s~kMLZ<1oe33EaP)>?MM8KedI-4_Z}rq} z9V+>Imq=13Z@LK3U!B%$D>|EIEu8!D-JrJzzU7oBnLk>GX~IeuKg8hCxL|JR*cIUX zq)eYZytv5OTmwCw`qEFiPa=V_!N3rE*|*4DyYdEN<4>ov7le5Nk(=S#`W^2z zeAY+yp-qAUXT?Q$fDiGBm5t5Q)8=91?yi0Dh?||Af`WpboxS~bBTyq>ZA0%-g%OGN zQzW(mzFveCb+d!b&Ng_6W@>)jF<1uuGdMN-Kw+gK!rZk=pFbUooP^@h9uZUG`w<(} z6M0tsatXgZO#Tf%hjS-gO()_TDYmG5hsr^Ja#(b37kZs39ETO{0wOm2*?0ae4rJL_ zSvmg|eV^cjs2bI^^z~Xuh7|7>a;&Ye$myg|D+M>z0%)uNRXc!EF;C#|A_APM|2(dt z^aj5@QW-;S4nB;sO+pA)O!GnGp-7upo6YtP@Hmrae;9r#TZp9jO^j82MyQ|Gq9zgA0)%E0V&f65XW)xWOS+%-5L^e%1N zCMAzmC#kVYv)znS!@;ukQTo7fsYvAe99HG>WR`vfqIby%h30qG-R{iP6(ngTKR{78 zq&a1RP+_i$4c2agV~&xdtf&-tnL)+^^8I&SO|08G@5Gx8Ebb4BUOLJ}7&+6rp!+qB z581l+pOl;DtT*_lauA$VrIOtn2-6Ug=LbB!-TD(ZToPG}JfDrEWGH^dX1>Pcielza z{|a|&(NH}oVejS-`^GQaL$up+5!Y{*U;@oo-_wv>vX=H}z$X~|p1#HXm1nC!NnBU`Q?GB&MFI1xk`Mh#={yFZJgf=1EYCJugPMXgQLxbhs+;Wy%F_pj7$60@| zs?ceHOKrWC!AJ=u!Py$MyNaDbu-qG1c@vx$8e^hYy=`KMxdcI0lZPVk%q*m^& zHhr_-P@qh2Yi0CF%1jecj1_5fPQ%2<*}89XIf6o%fw-w%f`@q66{mBLweA)~)^+OX zxo&?_yZ+5K!{iDf3ATKdfB3VY=B>V0A0L85Zx7^sjZCmg&s^(j;xsg-wfTwM8M-@j z(nwi5HkTqrN{-KXrL72TA7UaoeM@f0bMAviD;?m#jz{AoTWO zE+Il_YYAxeV`mfxmE>vm=5B*)S(@Q>^}9t^nLUn zt$%6vETDS1&^@Q!+t0qG;$jwmUEMg~R^q=a2IGtUcKGkdfnccBKx*`$J0J;;`DVc| z%x}<`jrlX$MEGF*J+$lZ53j!kU9E?D2@ay93|GgqF`;0GNxEe*%r%6r&sFovwV36N zKX?lMfb?>^XWP}j+yHFt>@bVzPk;nk$O$fMr$32Fxi~BTA_89UAMu?4ym(dUSbE_W zYWs#lJX$4;(;Rpq^kB{ljU304Y1p@wwZ%R;OEuS<}-F} z6Ec@tc&HrZkd6w?7(d4Zy~jTK-ge!l_rb22=FNL9vX#EVI~4(_YGq!}37w@nvy5)| zvudj|^rwsOP&hd^28xiy_xiSNLV4|8yMtz4Uoxl@SaO?Cu5R$m?%4G?s6l4YJ_?>) zwsSL2b)H~0O6cNC_W6tp3sSFu`qZ=sM0xzywcP+#QmJ{~Z^5K2%<*BHbKJAGIJh7h zo4cm0Z)C+0o#(PN@x=PY`VwUZ>q%De*z;d@tTQ^8%_X`cN03Yu`%6`PLjz$3j;^3R zKyICL%{h#cKQnGT|9s>yAOei0xp6QZd%oyIFf#k?6`!2X$j#d$O)FEKkB%kt-@dYk z?S2{6|@cZ_~CvM#u-@KbcHOfbCBrqr2Ea_)12#53NM?%n%!rEk{Vj^Kv2D?4b z=dQ`MqNIH1ViddKIhQ%;YLaWK75&#~;TZHiK5S`e>03cac1DJX+lHhEJ1?)Biwl>4 zfR5=GgoqiS^rDa25nGc=hMk1;K$^io7kMoGQPbC6C7GEr+^dhOA&){>sQU5D{ST3R zo5Q1KH_2Yh*vKdp& zPssIla8$eFJj3?sqz>wJv>pzVAo4CXK;mnKO^-frG6`GDn->??RZ$B9QgfI^6w z0_L5U7KoaM39RwVJp6KB4aO$_*u|b9~>Rosoc-P9LF#dvOv0!0Php9}M;B z&kM;;-<*RQixT&weoanl`n%fu*MyZM=}3i9(p`xyj)6Bp!Yl$r@PSaM>+-z-lnv6F zxyr(z@^P16hR;$93OyCu$Cl8O8rvza_63`9m%Qk@JotfhpDIAlWpW$DVRUvP(e!C$ zOSK}s&O%Q0cQ~I0%X%poPoq>l3l6slIXRt^7Y7;Yj|)-xMZB_0xfLeB5#VRo;>w8i ztxzsplzv1rRL{FpgW8up%S!41Ga-D<=&fz$;#~eUb>r8<{5AZ_H;3Dk1ey!)Ymr1- z_e?0o3s#98TEuGW9rTyd5vT(&_;1d*iGJ%(Mjy)Sr0JezlNDPI*T&cD(i$=z*P3@+ z^{$>+2G6R5^jbs8ZvUsusr*PNT0a>0eV-bikjyLSktOBM-`O5= zHTfsD77@j?0tSp1$Uwe-LPdsF4dRNd0gy(PU^z`)P!8lcVr9y^xOkbsOi+D1%;i{&3^L5PKS9miOOIukz`opBqri2peu!i%}A`z za#WI!fxmM2ZOiy@$a=)#E^W}6oVieTFjyhg+KQ&{$GzG|eiwM_8Nr4o8+uM&*4LU( zd*LUZq(JX-e_bmk`$?a9sUC6iL+jXj)%h^MN&cNGET-wMr(Y1vd_-3r+*k}nfcu-z zIpz|KXt`-3Al)=gzh2e20k?08Fi+_-_GQQYN z{k>+61Eu5nqcuzZfzJnR*(8zoj9RrF#TuQM~*U=fD7r8~io@{k#kE9RVrG=o@l?3|c9- ztm9K40Up8m8{akFpJX`evLd!WZPS4o(=SY1@F0HU7512`36{ChE&{Qcy5m0p!XlP; z#G(ZkaKszVDuCTqroMX`e-N51AA-{v9(D#pBKqZe20Qo zd-76*Zu8MbE9=mYpNB&i20E-6pswxo?}Mf0n{C+Zem2uSle`a>S;$WJeK-Aic_uBr zr_qX{s)|H*mKcXQ-^kN!H{~C!E%b>KASFrFC-*8pgH!1SM42uri?Z#e?nMuDyET*x z%W8Kqs=+>RROK#VMY=(*bML}dtkXolLey(;j$OJWwcl&yeWI6rk9ct(8I|a2Yssfz zuEQ3E777X+%9Pq|Uc5BdC`6hYH$>yD-}MlJZQj9Dwrt-`egd8);mjYCPR{ZJyURwN ze?L@(8i>%;nCHB}_gdAbM|L`jSK7D1y}xL7Az$P43q?MBaSQr(HmhNQzE>{{WU9pu z5-pU}tm&Rj>ep=EPTQ5W3e#RHJa$|-T-XO9Jl`ENn$FSblJv0`@El^oDh^?Rc_CZT z{VrSM`Piun7wv^Nh|6f4t$|Z)7hYuT+rgxsem0I~yR&cFPeNN{!ma%)4CKLN^T9D6 zzdaj>%VHy@_e*`S(9;Ic9I0wxzS%`rL_!Z2|J?-rRfh_~zb-8ZnD(ymp#~B@IQoJa zC!AlAq<%T5{LE_CNBWIS`N~^C$CflvHd&X6MwTB?&C~D|%4~BmgbqogGsSx2yMdgg zkW`(?;v2h#0^r~8e~_s~Wa2T-Q9ZSWM+IW0LU4;8$&wV$M(^I+*dA?4F}INo8J?n z#sGJJM9Qk4xb((3f12yH;R+Zt_gDPfS}f1>j^68C0Q8qrhNoBf#q|5Kp1J|_IT2G} zxYtEFYr~Qd6N&IA;XymggZHw&M}{78>Qa|!Zj)&<{FWNuP<|dm#06_`T$i1fH>A1k zs>`Rm;&8w@w?VgV=}2gy-WdG`8EEIx)gZv=>S&`XLwGrrDAnG>!WHotE$%Q7-=ygM z7Cw#QE{zY1CeGaVN)yQxm;_}jr&e?se=QEscrv*i`0lnR2J~q@fDyN-4WFO>lV6jpZbpxQO%y^z+4{ zSK64+X-M^8`2G3A#M^V%Vd#IeOs%07LJFyJ^Ht|z_u^&uDp(Vc%SC$iD>+BY%Wpjw z2`oBP2ptKmio_0^AY=|NkSIfMM&&{m!_|jA$Luboi5%RR?fk>4-PlUPaCFN%%gsH< zGt+Xo@`Doe>*c@DI!e~G8$@tDQdY9CFh=kqRf#GCb@SOqJPr}a5CHgp+g(P4?4Sr5 z>oxfY`uRkhq;2q$X?bg`Wdx&%Ak(pSqntJ~2RCuJ^b@5fw1e+SzA-G#Wht*n9$LWhfiSWa7q z1`_~Hn_JT2^G*_9omJ}o@O=ILhj^ePU? zl35|9>4+kKzf9`^{2U~E^QL|K73=ONv_QVYQD3h@-Qm?{U%@%PIg%llH|N<@U(5B^ zXUJH!9_w62K&JVyGIK<>F{Q0~pL_V9&aXDGp)gE^iRAhcJ(E5f%k=Bf%kRX+X6?-z zO16BTrs2LVZ(-H-fxlRR<&=P32Q)C3yPfpR0SK+RNjB7OBgNUQ?t`@d3WA$2K@=I# z%4_P=#BvpuiW0*K}OAYe`Y~V95)55uK{@uYAu|5Ol1Gv8hSn7mJ6PIJkCO{nIQ} z3W>Dq(}V0r16fgo>7_rWaxX%lQ1YCVYR-|KFV(HU7qPgN1}C4L}EcYFtcEKzy}Knk0Eppb9;^}#SS1`hmCn%T!D#>4U z3IIcE&Rr`?Z!2l7d((^<2CW>FDH_K~azq7r@&Hdd)-gK`c5d?6UOGi+u?`(jB)qPt zfCoGG=V3SL&ysbt z0URt70esZXj?vAxY|Rb+zZPKGYo~&Z99Y*K@VffE8qTz0}VHByESpWBm{@sB_ZI;>S3Kw|5&80_S?ar#ASy;PJY> znW3ilMp#H9%}{(>u>JaWe|><*ycIdWt#y1Q64;odI>OJ)R*auIl5>Y4u_7PqhGwE^DN zBnQjN^F0nw7-@{^%Y&nQl6`VLQ7G+t@5#FPD?%Lhy3rXw=5l}9;aydcU)9^X2Z)HI@KA$KS`(owLCSEee-#zqLNH=D9UHiNHb2A3PO(yo_+s>T4f?yN^ z9FKJ~XlfdmAls41>8NOYr_#_sR-5j@YUYZP~4=e5|@$fjQTw)l~G9 zFaKefEkvmW!S16*sn0S!Ps906+XuBhn1gJ$1wmWG5rF@}@M}=Rswu*DeB73+dj2Ep z08~U(+s-83m2LxA*gb<8i=tf344)^mFPTh!b|JEX4uI(+cE|H>Op#j=wu zf@tIx3rXszoXZsvv-l>oJDT&b^W5{dl{J2^1n6iPje$%bve8UO9`ej5OYTYLMhHR^^FQs9B zT7R}5Qn3Od(KrTDzv8TbFAxwO$VQXcxCc<0 zRu+X$d}dC$yXJ}w22|!dHki_!i93T_Q7V8Q8vI5 zv%YL?k`izf=mvCmczUkkXggUWTUsY3^DiXTOWw)TWb~RiVI53(VSQlG3#VpS|5xa7 z1?JBkn(s@MeHbK?O|xa9A!DOPNOG;izg#4Uq$R+=a1~Jt4-WQAT)Ayb)&I`A`Ww&k zr4=2}jM@fZT(>ai2KS!(Eu3Ra2e3C)ui!3C#gN<>?$|zSBh3?AVAOOa^!LA&xzEOH zA-XIFicEqC)*UL@FaxfIINP^9yYl&o?2=7;H_c|oA4>cLu3GNLoXqT6YA5$d{(N5% z;;;ArW^Pn~>eUjPHs8To_)SP|Gmr6n|Dw{xQpX`$=~wB(wznOgrA_#vzL0$!iZT?} zHB6+M{}!dQ?H$PZuvEzV*hT2a(42q9J3nTm{KT(@7bjID`%~Nd2GTj1@<*SRw~E*N znhTZ*bl2g}NZCwhn_s>uK3^rSzF-HEk)G00T23FWCOSdS40yBkXEDCEI)U_^Id{ju zhS43jm#=Mc&gUp$^8XTsO-{B~RZlpna0*=&qBo6`pYXd^2?cQ!V(+Aub{Mq*7{_j8 zxza<&ZXDjh(xY+9wcm^{O<+()U+T=8ag=OW)G3g|L59Q#1=p4~>Yd+(VX4G}s zq;Hqj_=n~@1zTY#8np|}$fR7+TWt=}8!!C`plPwj5UJY1N8tp5=M^+^VcccGz&B5ffnG|DgNn z@+$-`>b2Tab|#~k9);!@W`5l@@Z4nYkfZGRj}uCLI*s-Kg}qN$PjyS>K8%`@pZ&m+ zbZ5>3t+JMciS>N=W&eL@dh4hr-2Z)e8{J5E2}pP6P#S3vB&17PQfi}1=@KNRyBmfe z-Hjk2NO$MhZ|~3Ze9zh8?Ch>xwaajS+l~wU`)ffStTHD*A9~ez-H`np=(b z{N&5Ihnr%O>CUO=?COBLBF5qpd_1_LS+W9PZuu;i-H@cAr@%R6p@R#o)J4Dadq*zp z5!lYvr>H$GozPZ+*Rs6}kexNvb7-JI?zb|ImcQj^Fyolt_(8enlbaX66Qh?8e-kyk z&W(}X;6Sq?Tz&0;1Ms12@$r|Ws5MHE z4Zye3ua{Gp9NJoM4#%P7{>KuRZuamk>Kay?nIfMId;$0G9m)3#w@3<$G+||8fzfsl zyBKXW0&*KsRYjJe6~Ts45Qc>;Hg7kFHe`IVHbP70@NE=NP~;77)Z#OQGdznMJQ%lh z%dGN9{syvMdf%c)7Y{ai)Vx$;K;nrP@0*=|AJ)L=x{xR876EHw-1F>HhlBs9UZ;R} z({+>E@F&7$7?-XaiYg|{Td^oEDuqO5#VUXskR2m8E#PoVH7GSazOkurN<7g6M+Yc^?B8<`opNm{3DP$a0XHvDgcOdGuy595hy;7V19!x8nKwE+@f0aE-9 z;v>o8ms~Ct%3v`8KfKwk_I_E zFW)PJH1Hd=<-@0z8bQE48S9E`JC)cE`~0;HmV|-#uApXT*}5h_rrN5VrvvO`B7*^k zA3q2fUn2s&maXQADH6LkY<34K(@asqU-Wm?F6TL9-U&1g+%HFdL}<%j(NDI}!q#64 z(xe(Iq1Z{odlKjK7Ztg>B9! zbcFoXbZE^TkI>gjB7_SeWK*x74Yx>_*2&z~o@3ExD#M`?9v_4nN$O0WESV{_<`Yn7 zjvgE7WxlNbQ;#wYJDK>duzY*EANha*6cB9O_dyY;FAo>LX)`?H;>|ncB6Ni>#+J)T zTvqwoPqq&C+bI4$<{u%$6E3||9r;AcZQYB__Hn2N_rw+Jr&n8GIo;l0OU>iIhKW}X z0xLPij-I-dF%3-dhZWf7%J1Bye-WUd-w2^5fflllW$g5;yC80}5?d2peFNRI^&vIF zR6%+EBLcH2HpMp*^mp&l8``Br3|fE4)YhA{S)X+;GrU>nNDt~9y>$e7Z=^52?x9IX zrIE5)7n^P@N^wc1Uur&1uOhXwGzQl?KVtQ0XU{7^3~@-_eN%82Wr4mXZP1HM~g z<~Hs{YaQ~KUBT%OR-$up2~!ae&Pgd@9N&}h+?pmn;2;_2#+r;16JsO7F1IV(A^?Fj znr(i|Z+Th~7l&}UZSC%}n>##P{WmBQeQ5CW|BZ*KZ#8Io?S7^c1K8O$q_|qiTkJ#m zwiLVmg)rB0D2R=5Re)r&)B%t^c#RZEB~H9vwUgh7<+9>s*qi;}ieeN%@Mg&Zg5l;* z=gFfi!?NK(2!f|wEk^ct^%_l`9kxV0 z1iw73XyKW&>do3w7-f9@5uAnWq@eursv9^&OFc$!_u{%qUAnqIJCn$J)kj+bf(F)o zxhe}>&U_d(v64^s3pS&)cRoTH;TTBKpVF&Q`%-|}THVIbUO%S~5SkynQj>Fhv;I(T z_`t|B_zLBG8DjAneQJ6SEynbH44H_0yLhy_xiem07;OO#G9r`FSF^Dyi|LcLTy z(uHxr!q1MY5t9{m3YmQuxNQ|b+4)bJgU9u^-`0)ugFv3mBA~Web)?}fs(0rnRT{_> zzkBZxy*AT(*7JRTXaU~N^bUKE5AqG=H7`(xj*gQPV99#6siyQpsk}K3O)5X9*>pG& zuB$l#>fV#!-mzSCR=W!kb~>c&;&Q#Jml{+j4&JS?!Uq#^mUH*~%nTb;FN4cokGKGX7Wz_TM6KuRh`rC`ZtmIMSoB!zIk5d z?wDz;0A-c?3GTNd?OvRj$IVvZu~xo6-;`#!_5y_kEPbloP7ud-jSpJyRDnMH+|B4t zN$2XfPEC`c=_n~yMH6LkQefZ?H1t2^?zwjZoXpDHW7^-#Q@qj{oh+)rtO3aWffL_%j9fG9)gRnSFGH!Bxi22C zFe97X33oI|kmATEVDOBqZiT$NofCt$tz{vKR%%J_5kX=0Ks zBXk#MP7yaP5Zv3_Pvbxf_V-5L`n_(kz7iQqpx1UTe)gDVgElwbL{h)>(k0*TT3h}l(iQ9| z%lhb_7FZPHcMl|4*-u!|fJO!c4CSqbL{ zpFa&A+`Lj3&Ov-rX|c&vyiW~|h#%-U5m#ekoOez0iVZIa|8aB0rhRONiy+#D#a&4C ztS+Gli5BX>@(*4RBmqkU`$qVN!Ph@NAlJ}e;#%)3o$-djL0txg;vBQrnI^hv(=aCqh03?5*qY;pwrygt62ilMQHQ8mH0> z@Ts0X6|)z#en`0{wKdlabn_@9;LG?b@}~LKA6*lhL4pJap{k0F`UOevsktmt!Tjyy zmLekaM(2m&XDegQ0l;c7&gOzsGO3{<(G&mFwR;72)B@Nb#7?o|Gq?xzf|qb=A8zw@ zK)t3j;41MG)>8NJzf9EuyD`*)Q2#EA{jrU2Z>_le1MXAjmLVye&t{7T)Hic%a>4;? zkPC2Cj&gvYwavLA4g=~lxtWAfT~VH;njG94E~(pbjLdiS{os3UXYT$D&Ct|YLW`Kl z3i=olL4WHD<&5-$?nbK2-@uk^FxG15%Z5`uD)8)o zi#Qci)xhm|so~?KCp5X%}RzvJhjWQ(J<3k=jjISO5Z;}T5^Yic`DKW zaHrE&0C2NQ8f#$e!-YlL!AWC%aa=|pV6yG4+uSc8g;6%N#4v!-#Hw?r8?ODcn~+v{ zI@wm|xpB9A$0MV=zQVN7Jj1szVYBJi1H5aq=?@r%ls+E^U-HkWUxH#@ksLKr9NC6S z%gNG+swG*QwN8rozsM`izbn-vNiBcz!szu)xc_)Wn1+0?yC@*-ayU5Fb(yP9AkQ~u z^a}Dlw7lgwv<~2TXayxM3n-;OOA^0gjVJ?XG;yKq+0XR3=fe$Tv0o%>3XRl!AOlRL zWQq!WaMqBS94hMTLnbpP0)AZ@HFUZ+MGUk4L$GbnqkR=N&0BN;;0b3}jQ;EmR(zKd z9=u3}r;sX=*ER8~072{jdZK)8HbnZb;rC@neP(O^R&}T4fB2{hkyF>H#UIM(ec`@{ zr@9=IhNi6N0DaA9o8dfr#!&0bWPZB4j7#d4xBFMQ=*%jJ2v0N~0A&O&Lm~R>%uj0` zo`I>;#0xM!Mtw8!-$Li-UpqV1Cb5*Jp6P4Y03XVy)y3Ama=#@Moa=)QMJb@t9lB$0 zDMpe6F5C3jbEYofF(DtY@<3<{XPW|}k6(k#T`XA7-VAQ6#^G+LwWLSgAg+?xHN;Z> zHXC`Zrj71i*5fzDrXRw1?U2h*JR;R9uF2=Qo3;}P>zn>VYlPbO zvSm0^!YY~|jmy)#J4V?d>-c0c%_$iPL!q7In+dd;V;sOlr1AP!`O5;(l0U6isba&< z{=mvh*;+BbG5E)!^U@^C4hH<)u3fBv2C*I7*M5q^kVML>xo2?L#k>U?M*_*hcB$(8jV5$o^rdr-i^okQe=HG2bYSX>N zCHlHDhZ@rX+Z<|eP!p92WGH>OnRmENAE8V%+YqI0V$t{VvgU|**R4?d+9KT%NXfkM}d$V2;9U2z;`AWYIm)8n5o_jmy9I6Lk@4`42137 zal!(US%_yvat_lk(7G3m`eR5rli~_RQD>f9Vhkt}todF zYTN;E*#7|)+yfe|DO=dbP5Y>{U-Pj0gpi#9mVb#)=on>g9lY3;#yxWG2Bq)-2)Rj6 zVlbArZc@^uL1Co10rwOP>K!-pQSvk8g&8P}SiP+C%*JBZUxh5H z7374!1vkaz#aPbaiVUAt9bMp5ptuFq}nuz;&}@Bo7|eB zf|Bi6!=FP07AcrKP%;lBIqCP%4%Q~Pc_w*yO1O#QQrfW#CUWgvr`DCOV+gytgfY~a zfPjzqrZ0Qd*b%OVF9kQKhg1`cY9dWB@E`%v>@tG!5%st96}*X=3Si@Y=O znp1VpBOZIp<l>{!IS4R54r03EH+ zvI>C4m?I}}GsDu%+PoT$a%^XTiB1 zUOTZh_y2#cx2Q1*z{~lWJFO>Ex)mjL%w^0&XoR5<-a+Pc^39Tk&j4-yPJKwB&kuZwP@^e=H~F#&xF7{ zZ;7-YSDH&;Ll4EClhS^U5Pi8MtovcQ5<8G;R8GUPotesE1)xi2Nd}u~#Qs+^P+9F1 z=(4Z(GAvTkr)N0PmAKt{>A}OG;ajLQW?quju$QmImvWuBq2d4 zUZkn62xX}%Z?eDVOUAsZLg4PBF6%5dSlIaXsd~fG z9@1HY@WG8U20qWeUa_$m1PmgIH+bu7GNXD75-lPNp3X=!bm#+Wb&z@GBuKtuUiOrx z(@M_-O{CIYMUu+W;uelSZ+^lmu-)=%uMePQPJz)Q9+(uW7%Wws3k2g5#aX5~Qyt7t z-4|~4{6u%~3U4kJ_=qbAaOC)HHpuKB6(x*-lX;mIS(p|vN|3yr!xKG4CR;|1_Nz_F zX5MC|j*MF!6+gYop}79MtHLigR--(LFyF;5Ms(%s;Xyvc913_!cdil|>1IF0I7J+N z6Aw@baeJQAZ3tHDHVo|LebiyDJNM3WtoQzbwj^4Jk%&|T>58yCtzyk0Fx=N0Q95+S zr0;mAsjpmNZPMDALJ+DMtR94Kfi$?E6)b_Ld|C5zanV~01*{SE9k3s~-JZ7Yk@mgS zLQ#$vs?M7`e>0~VO9o_dWNLRsu$)6~Woovbg9hQy=dELxV-B9-MS2wy>|Jae`4X$A zU8oQO$e_~VB+h)2pW9q?uj*jAl9RXDa2J4lHICN|oKVr@DR;}gG#|au%yAI5R-oNn zQxPr8wEXrd_WWN9p~n%xqQjGuizD1^s;`SxRgh$+V^YHC+?*h00m{ zp$glOn-Y)=Dj&2RLYK(I$lgX{_^tp4_bF`!$Mly;h6xGpVHfyfc|~5gv>!iIVyY-2 z4jCoR9NDHd? zCc1p2mbw3N_;o6)ki^GOM^0<)4>U1)wm3f^##OG);WS$Md0pD+34&k5p`w56(ww)P z>g4)EHa{90e|5c~QuD+;(;^f9nm{wYc)S;|86Du>Hfu*?75nx@&aq81zk|e5oPkDi z3={i`ha&Ykp(gX8RuSP=SB+U}Q!L$_;pg)_u6lZEdg!uJ9 z_78}7Y<_D#sP;g8p zCW6kF5%qZ98&fWt$}wRh*)^#IivO4yTU_MDpk4_n3t+9@IR?%f(x>E-^|nLnVA z59l3$TS-cmh9EaFdT5;|UHS!UOXJ>87h}hkAq*NvO8g&A4ldn7f2sbrk! z$4YD`&oWs5xddel?e)hVDR+E^X#3|!f-gN8?maS?Tm5qXrv>oIdx~57rZ%dCvRqaJ zP?pB=7CSZ@!*X%xH2T0#W?!k9_)5MZBFO3&PUwT^g;+-^|mPm`NT(_2t^0K1BFVCIi)hlsyCby`rx6RrE`( zs=$Ezx>af>JTN6D5Pg_J2j1<5$cbR6^~l9`;&_MdjILxOGL_|k`YF`O+ZF{mm8Ye( zVe`oD95-}A(BMs|i8Okp-(JUHDk6&bu z8@G)o%JR)r#v(Dh;D4AM>pzdGBf{aCrIA)I#=y94_eZe^b_?>mzzxVb2SbgH6hBw$ z=(C@dmKpCiw2PS6(34r{%|r$Ga05kS*RX{6TCxkRM94P6dpm3k_$N{@ILSR1Se1U_ zetP*ala-ZHJiW}#XO!_wNT73)6YP6w${pL6S*zzC>s6CMaXEh z^byS{OI{~G5?^~O;U~QsLFFgg&+m)PXi=F}(sp>X;~_*{*I>ue%J0liKf*`ErtWU{ zU+=p*9C36pTBxg8kw?ckr06W#=e7NfseM-3MjYl>TE5j8J3uP$9l&~lo>ZE5G-@xLs3k*f z5BOGa%jU)=;Na^Vjw|D8_}dI)>fP4M=daMUJjB4*e!TN`sxz#z%vzBpJ#YYYSZfS? z{MZy8=lJAij0ekqC11>LVfEMT!lc>4IPPI>8U`?H4J2syOkTOf|*#*7LZV|Kb0~zT_fd#?fchta5G-hOcqE64+xwIAy8Dq`itsra0MWR z2lUG2UH{-`;T+&6K8Cl@sQFoNO+8$i1a%)7rqNttClfqI2$y0Wn=u2>7i2M8ef~oMB+MYrc5Zed-lDA(ZjVP zIB5VFH=`|21dU7kc~Sq?9q88fNyxQE$UV-o$xFx!$Wrv#LU?Jp(tlHJlnhu}w>UGi zw9P?(%0a(A9)+?_o>u6@UCzF@L81x`1>@)62yBZx_UzjK*!hJh26Q~q+~tJ#@YI) zdITE;1k9A5HjV@>hWL zIUFXIyHm&#Vp6)yV1Z6_MRxkuTE3}ggQ{W(?>QG`>lwB=O5XLIsc&72@OfLOiF1_s)z+BX} zz4_AwvA@-5%Rj)^mB?}Nnvylq5oF9R=*d~06e4yk%YuNfmpy)snUWp3o_oe;p?xU1 zw?cAGGleS2Z&!fQAup_8%QwHccKR!-*cgX^S6Dw$vacR6@2~RBL8~Y9Sg>9TaEIp{ zJjl9-46tmM+vG?8Ns6gA-OzsfZq&U8zKY9kkYR1vOpnYck$11P+C)Fs5v!qDWES_90Snhz& zc@o`ScYPQ-KX+RJ~t5f8CokI-jaJiBb%vhI z=F3eca_NvSfCuUmm#EKw6!?3z%HM?r|NMS*qo`SU4!KztAZCd0Xgl<;*vV~@JKjyc zehedK`ouU!-XX9OMsDASWV6=QG!z$E>Ik%q#9!E#e+`VHHZW5VK^M5DfBC}+t0MTz zTFx_#KP0ZJYYitbP8A;8=;7KbHX+UQRzTXDa_DkT^~Nv#y`Gn3ITuxCM|19wbwoaC zJG9??^18f7o1M{ZeLYXz)W!SV3i2d?4VD8cN*40k%`90UfUj9Ww3Y7P8S87Ys|RV_><;O2RdP=u;{Px>VK|B z?)T=4wl}5Yk4gU+*+(+e7L1_MRL<*;AV+5%d`||w-FlE4Un+ov5Wm|2k$<3Wp5_r7P&Cvp<=SJn&Kb!2Zh-B80=$r`L%5KO64`esHcLY7^Pdw zD+!-apeY^L8&_!Hw!b$EJ1#OXy>}uSGUr|+v&f`c83&1b^0O349O%FfPx>Jl;Hp+d z@;bHlE0O`pkt%E!Syj&j)#;nD$VcKAyiWmHK4#hAZrSb`COUG%$l0AOvt;;HYg6<$SnSN z>BSD0e3~jpJ$xi@KH=Nv>yZc>ESsMl1iwYNX0Oe$+B!cr79EE8%-Ne8n4S(a{lky& zIo3s9Q+pnO!h)D3-H&C*+|_USQO8Xe&GZr()fDw{ZV%YyDYWJix>h>lL%DQYH3wOK|u|M;~8nQygUncCA_Od9)tdKBjrH4HZJ>Bp~L zcUoz9X)(@Zz;W}E_q&ujca_*<&ZkJLyulB-}?3x4O%%3eK5*;>gk(qV(I_Uhyq}7JT`yz-A~iZs21R6Z5OguI(@7` zwzayBrB?0o_~I*cW8zNO6212ENh;9h`_1Y1E0(EGCntB;6Q4^gu=}){b!_um_Jx}& z`7D^feL8&uD5+Nc;fmVma|2X2#Th-3&`*Pu?^ige?7p~%df#;1g#!C$mxY|x-Fv*& z6(&qbKoBWXwQ9iiDZvVX9z{?{4EfYUJ5*;5TqM`duN3e0Bk9wjl_gv631z3b{UnKf8aog9+ zXu5YKqYQnx)#ARamMm^z^+d+n*w~i&Pp-un+QUV?HcCNaoUD?Qu8F>|G?b@&2*7OZ z&RBXE5;N&koBD41K2^@#^^GzYu?W1&U0OPPuNq(XC(-0Eu#W&EufTO$7K9kfKwRo)C$nZtZ*YJs9b ze+6{p2>wYPG=$o{O3YE-{I;g~`z70L%3d`ctYA{q993^SdeFshKhRDVbDGk7Y$&}yy5awia zSdFkIo+}FN0%&RicsdvS`!bz27U5+b21Rqvj=Dj<>r=-Vc5lvve&urtF{H%812Q;V;mkRZ8(+_3*P%(;cf zc_0$rbcR@HWNksoa%NE8+V&MMdU0EfSUSg(#@Uxs6x zJX>xh(j4M!S_!^dPg8#T8xhimkXd~%QSHfE@Ug8;0TIp3O-&#)IFI3IFxeQuPn!0T z_FQ&`oDd6;veQXW0gzgqxy#DB!B8ts9(x&X(^IDl{;VzX*WAiqc52EkM;SDAcaGYl z&yR&!5!Cbk!_>@D5&QwIp38+aN_e@ZOnAChrbR4`L0@VKW3Hcchbri94E-7ctFx3* zDa{1w>8l5`A56!GZj~7DJTnT}xFmo=t%X!rw|Lh@?Cw>dT#X zf2)s6v?#)SJKlquNkB9Nrk~f<15#u6f>Fi{ZP4Rjet5gmXIsx$21;f|;Cxt1 z)e@stxO&MdDk+ z*!s8b&O@tn8q|5VIeCtuCi1>Nqjpo>HqW`_xph(&u@zu95MZaM!`xLWg>qMWzP$5& zb9|?nw$7lgav7bb{QSlA^CC+eny+&U;QBNbP1`LQa?uk*Y6* zckjc<@jc^ANp}<8Chrh{kqhYL|FS&p?btr>1lRszj`^iZ(Yx%kuI$52g0U+O1+wnNIWYk^GLPQl>acuDex}mZ}Ljy8)^DcNyJM21xw;08%AmSq7KW8rvsyQ8knDBB7`&9~{~yP)M7GZdR`tSo{>d3(fm zwL6kvs14MJoY7l<|6-rNe4-V{z8dz0?&|PwtM|E5)$PZ<9m(-}bxDszb`RkFXy4N{ z5WQ>0S*_7^9i?z3{i9hIUac=nIXd5iBKYCXt2YLH27URi8v1ftiPq^GCoDG*WLhG;63ROdxAiMqM6n+d^|L3P!=n9H($ zmYfC#_C3LVD)tEDiUE|BkHl0uRc;PFj@ONgzvMlUaQ0?Vfw#UmDS>LqEWMp`4igzU zM36=u>_gS}rU?>wJ~~)^`NVr7^|)wx_@~nlzqxj9nZ6w~Hk4?qmno0tCUq)|9`kbi z5heCg0~vT<6?tTdP0{CwI8SkE22>#nu#dM`UC(ziayfF45-gUF*i;f^8?KXZzKcUI zSL$Ya|Iyjx;N!_$>DpO;*Uwysbl+BJ?s3cD>i-c1_W#iar2Bia-GKBEOx;@)RN-TroK?QVq*V#(Isp@>VkGErXjcN-^ zMgVHTqn*wpBoWs>aawWw%`&vz`0ATvkpl9^^mn}q5!`YHSmvD6H@$0BK`CBkS3!69 z>|kZ}QKh@niH7Q{dOGwX=_wy4vpd76dw~c~w2^+z_1^z&w)2y3$f)p7VRd@6`Q^D; zt=vBq%-#ETjrbI$hG<%d@~8o!o_#7C{v``l=VMAHE8vwMKr24T3B+#PXj`8#@zm4U zQP^KGzT1r<eY@4mG?A^fPj66^>>g;g<4lMUF zKRm-+E+JpdW*hr$()w-46t?Oo@7x>A3p8^^Y=|Z1schx1SiMPa{Ny)7(P6bBTu&I zI4~^w?^{IA+*ld-Z`v$4f}e5yEFyXEeWAQO0{Ip=SNR{+kdLGqu+_i9$qC1wab!Tt z9Ym(mA#Qp9g29It{==UbYOb%BxS5dRf%{8Wa8f03#yhbRK* zxV#-M%nIz-Y2neHr#e&NkmE6eul}xFWvX(5`?ID60w)t5 zkQ`^N!EM3b%qOKcA6LA7=CwPhbFoCt=w$u*z#1e9aMO#{Mh`M4SOm*oGOgQJsINJT zY2_x&Vjr9Rv8kv4EGB#yV!Ep4-yc@s9W2i(rl z9nT~F-3A0U0hf}Ot^|vO*^Hi%hZg6VR~9{6Er&chTydCx{}lS(w(&aRRZgKKp_)B6 z{nbAf{3jya9@Q4>4>#e#B{?~0CfB*nYdK70~@E=9++&PInOkS|m0CE*4aB^m>D zW)|rY=pb^Ga3A%r)piPQE{Hg43r z{K4fKm(PAgQ+^lAsf_=}Y;yd^Y_2P1olFk%j35~N_?m7Apx*MhXvU?1tP8y8M(g#z zB%h6Cr&i3Gv%A!0g(jk)Kgd0UXU`LI_2Lk0C!@C#^!QW$KuZbB(S$TP=uNb;m}eL4 z%0l+f@>5#Vl?ar`P6pnN`N|eOUF(Vj3pD=!@Mvh(R!cgs>RcZ6=jMfS-3td%=&rXQLxnG^s7wT-$hpzHFWcXee~)z zq12&DXz-I%sI~k#M`D`H+7VaYQmT_&bopb)hn5%@**Cvi*HDj+l<~_WkUj&NRQ|6J zHcPIap~?!rn7`djnd_U%Cpn(#&BC56wCrUQ03*k0)7@olYMO)ht}DkFbTz7C6f~^Q zWU;wRLTJ7YyOi#HNT&)?05FV43?=tDG#f2NQ?^Gy{oIvn^YLmVF|i+?sFEcpi8_yiy23S&jc; z?Ef@$GRy0POa*jqn8&hANI!={+bPOS=VMPC7dh|jGQNtl0U3zpmY|1rxdjrRQ+1vF z?}oMihb+3M2TWM)@yIk!n7@gw?H+R@==xpHJ`Ik2j!WJ)`1R86^Czn4J)56Z2DEC^mvu-%DWd`oW#Q60D9LX6%(zq!0X>M~B@A#5_F7#sz*ti>!I9E6;mb@L1h8Ca{19qzBUBs(Gd{fD@L&A- zuPWMJyk-xFq(7ORDAYoKenG-?c3saJCEyz+v>*bxRQeewUDknq>^c>J{zq}Rvqi?s z7thJd7G=fgA=)0H@(6%M8+d;wBG(~9`E~i}SXsNC;+E6sIliYu{cnmwUHm(W4wQia zH43pcyDpaX!36Q2yq%tv%rmx1Vb36KBEn;`hu%xsNgrG2)92XR9k2I%PN~OQJL1PZ zc(WK8?Z<)SP0<~lkR|3jL5NuX${ZF7tlPsF#=BPPh>q`S zM~&80&j@kCc1w!%$*;3V^;$VC(A}O%0F(1NN^%}*!5$g%7*T+Db-=> zNU~3&W~5fYLVxm0BQ9H_Y1x|$o>JQ=@`}Mdn{BHsDqc~~UlO#bgGMh{^WMUUOEWCc z4i0r%0>3a3g+BP1+RfL*OdLO&gX_9Ha`4hx{qCqxk69@Kgg ze*X;hH3vapheOKwVaupwxdAsX?;wnu#FyoP(I@yg)JH4Avgm2c&dPLt?wCXR2H zVmo;CoM|N&gvuooHUo*{@=70JUdmat41<*b0)vcTB(Mbs3*Q{Xc^bzb(;{;uvv?nP zy5A+AH7|C4dOzEEoi)~zlU#C`^OTk{$_937Z?RZjf4-m$ zTpOB>quX7uKDS(tb97yl|7I%nv2;<^wVxty*SsB}nJLSJ{HJU7BBgFKNO-%hG@T@) z{ag`vOFOmJ5r+F>nd3p9jf@ov{cz7=ALuh=Ue@Fd?KY%fyuog4Q79xrnziW{?TW9L zs-{P|xzicFsO1N%lz_L1hHN$IXuPq;B)3;gHB6MpyDyNnRVgoh%)~aXf`N5y}GX{1vkdD(df((-3R~Sf^`#tg@zTNV& z%*ap&(05(~3=i4si@wVdTz69}eb&>9YF6G=VRq{?>8WJLyCx5g2`2}r)A16Rpqa4= z8e5|kQ0{5Xy*&NKD71_E9gO>bS^$>@Yh75t%Ya=bmrh#klz zk+5%a^LAY`SV%$VYQ`=BLB`J;_~J|obuc*RLlX^!taRx^Jx^|JsGZ)C7uLIU6==je zoy6nnL*fsJFWudykJpp!txUS}DV5#8sQ#%pqkynJjG?OtD39m-XS)tQ5>v-_vxM^W z`QK}8{H2txH&kE=pu{WqM;B_(62Fh9~^ecQ=zBadQ zFB%ak*WD^#>P#hn5RRERV#J-Oz55Mw`ig!V`U+^c(P(nBzZwi3%09@Z#as`ITS`U{dxrUZvs_v;o6%MP~5_90z$zR)`t}qjXrZs}7Ul zyrBw|(L?IGtFJrnOy2qr3CCj?HAgR2Jw7WfJflsv*amp2KqdxH=urMV*+^_^Udlp| zI<{AgwD6Mq-Nc<$40*{Q>@Zrk1Fd*H9Ogj@miS+2^l65cNKto>^w{Do+^C-Scb!P+ zFRJx?`6dYbfbl&u?!-#mPp3`0VD3fL^x3EOLfRnWgoPB~%(w@?5#a4R=)~5NQuNF6 zY0UC3PCZe~7L%_CZ{HT|%XyAC^@MVl6wZ~~GZMF(a6h60`X!v4-$-$PHI*l#O9X_m zjssOqKY8?#P6VLz7}m9R%P;diQ)orDA$QgKG%+LCG5qgO-J^XhdV3+bYL@yYPGm)_>Q2h`Anjp_-d z;8DvKk#{%rrM~UQvmY&MR;(bD*10TomD}cX&&|--4h<|PlH{EcG_HM4zg!zxpyr;6 zXJ0`pLP}Y+)1_0BUN7~IKhq72c&%ut&4_|=IPZH5X%yNy$D<)I0pW{&otyPAZNfdO z(r!z;p)c|_ehm!#i~e$|&KU9I5VStA1Nw;;W=IOQml7 zMB(wR7L{0je){eEb2ClK*ZS?*xW>#fBvV#o@sSicEFo7i;LLT^X#s|kW3MVU1PcFg z1@iBl0{HwBrKdvEvDY~c`QtXvdmD5kGd!~0Xo(Fp@SzNW)&<)ecam+;iMMp2BW-uD z7~S9zJ@8i3gXV|q4$%oGPQUxE0IBC!SkX&XB1gNRkN3~EkpY-)73an%?n{@9L~snL z2SqtC!m7QmL^|*cTq>%VL9UgdA+)n=tLU)a^C~`JQUv(kNIFSy_#HDPimiSjUZ@Ij zw@I}>u=@)sYV6(Gdl~FN2t)DuAOQxzi26F+L|Yt)so8-CP(PGGVSC>OJwJNUBXaMZ zJ40Y?(gr9sfTXyXa*TY&BaZ`F8-A6A?04Oh#r~wzfk))HK*w-Zo(4EC64-8eLgmnt{$Rbj zi%3j&%M-bArm@So`c-U*R=e>blJahjcC2BK@$3!HEbb%8M!$guU)VJ@E@2!!4l?Hw zCy6SQ)!kT|^(^o)j{=R*8RKd`HQCbO@1KfAK1UXQXLX*l!|u7>r(o0P-f^rly~7)l zZ#^qbIR~!>^kgk$56=OShf>gj@Mf4B9>E9IgQpfCY1%2_GUBCGwO2e+QAGGZMbSP8 zDg=uzi0lJD8b81Q3cpW@ijUP%v{J+d6;nX3wCfw=<%VczrDqyI*5gNP*59m2U(zm6{e0kk7ZDH_pE%2lo|X>gW1?5;>cR zl|8P~M!t-$ZYUtbxu5{WxB2m+XjNq*VZzRkuNVJxJpgwv#17-%uBsK<|G?_PA?(57 zeL}U*Y%cuMUifD@`Qzfh%e1BSyQEK!ISRm=Bix+Z45;}1Gr<;FO=&X#!%-(26N*Jy zu<&7hPZ?<-{S6bmR%(EUH?*Yw`|~d8WpNE218bpI}h#1)SC?ccD+R_%0pNuJoC zgYw#Pz@zQ|@${8pZ8g!>3Bldn3ba`9;+Eot;_j3dcXtvhxE3hI-L1Gw(PG7lI}~@< zBwza8dq1A@gMXQGX3yGduURv@d5)y&HCP`O0P7-O!Xh4s=~>&B7!0qx}D9*g(Z&vKs?W-#S3AUg!IJ#*AI)yP}}ae8ik zV0+GTcN%m9cOjU#3cb%z9Jv%~&#Zs_7ykS!4FeSbm)QGg{#^`8yb`+7`IGG>x41^q zr;7>M=O#`M7UBHxcQPSGK@0m{Qqb@C=(oU_rJfJ=DCBqF{GbC|JBTE`69zWZ%^ZRe zMD$%s%R6V-H{YIy;1uknSmhN%JtHM`@t5Qbv&Z8+oG2RfJdMMqS59M4v12}78o!wE zZ;LJaS9{)Vcz=`(d_;8n$)`}Drd|j5G!c;#+l7bx8}yPs-`N=W=g1n09*QILjxLg! zaTUT3A|jLXEs_Mchnb%Ltd35{KAWThvjwkEqPhNUpVttDp>!V;fQD1Y>3+O}yp!K% z+iDE+Z&0fTt2lL=^KsYYK6~{flzyP07%Ol!Z^Q5ifRY1RnJUc`fjKMU3&H0B-Oc{b z37y8halMvm@noA|+p>(Vw~}TWzk`pVze?7U^zq79Ttz95HyhiI%Sf2*E$+%M4u$Kh z4G96WoiyV)0o>W3*^>*Xb@JQx4&8HI{9hmHyjP$_?1K8YNtMG*p%sNc+NbbIKQBY< zkBUysHooc_en@+=nrxT4bKE|&;q>51ysBFD<*ZY7R-34<8_}^aH{C(DHLozrxiXF@ z#@;XZ3rAC=(Mr$qnUnMO_-X)%5C4w0Oz>R21$y%XC~>=K+I?7^LCOW}IVZVmUpUIq zAOo=lFFtd|GyZPwY4h_wi{UjxqSx(0*6DEe*nL&+Q|Wi}ZS>W(_}tpVt8ahub5juU zxdZ#>y7cQEoy;{})dNI>)rdZn7zm4kH%P|5^wB$+sD z+GC&PM5S5d#HE2KBY~kP8yON6khi+&$9rwV?@K3}ORmig`G`xZ?M(0I)t5!t-9A0^ zB2ACBO_Ja)$9dPHJa^J2>Ct_4;<<9`h6`=Z^)F}N-Qd_hL1W2fhLup*Y?zm%Wecd( zrpq1FB=IYC9I*5q(vbOR&EmQ(kmJ*5n>%>zmRNz3Q^A?ii{K~Xtp{rylG}M`CTiE% zvsk!DlF+8LT>R8eb@+|jIfav?PliG>H#l7$Gh>}t@(X5jh45?b!)c!(daBNMMK z$xe@<1;!rwiy@LUV{5nlx{Y;`#f{Gc2!k%wEc&UJBpn>nXYGw=?POrt&F=i;rCepT zrv?lQ)Q8gK#L|TqyMgNV?R?94XKg9)>o^{S)dF>KvMRFq_UU6UgBG5cbnD+tJ`R7M zEUiZciW&fzm&2j?-5#=so^f?iV-LY+8?#n2->o=lfO>}rw(nzdJ%kf**M7ceqt}8H z*5MQS+w?qpmB3-ET5AUnBYNcm73}W82!g);n1TAT#o2%H7qyXu^x{)zhlo%1Is0%g z3A8BajTg`Jy0&M*l#E56?#XSzTWuWc=_CN^+ahbi%H^({ii(P=Dm9a&kek)hKv_G- z^elH_B7_#JMwej5_eb@?`KBT1uMaqhz^W_|CdOCnhcEp$$`M_?pgnw6{Nl2CM~l*@ z5~9XoyH|Na{Y109-ZOUErfKxG6#}3HZ$|Nh?rp$}&+@%6AR%hDOOkQDFpA67& zIM(eV{{?4WC$a2zkgh0;FT$q4IVhktN@u^!WK+AlHAVM>&>`tn;$xF<{=-ya@&WP@ zPo28#u1oH%F3}GOr$=y*>!Uo}*y~j&KO`t^+zY;k&dp|#wLsR=;NFo_Ih_WbMrc6= zhriphw`S(H$exh+l(veGj3|(}aNo=m4iB8Xc{$)A&;_!VmlEv5b9TWn#@C^uFAlv= z{{5{>29u~^3}b)g4bq}wdJGkC<^loEoF_Hqirj^m@Tk`eLshM?a|f{cD%zfFch`4 z+76GIq0rZhouJIknbparxP$^B@*%Qb^7ZsYHF7R31l%aYTm%OSI>4aO4)oT_Ev@nJ znoS3s=yrLuI4*t4IEAa6?>mQ-LPF_RL$|i# zE7we_8CIV=BIQu+SbzY`&xIi}lVQzd3>NRX` z~&~ImlY=bn(vRB4B*syu0tiouc41)ISgN5e~CW#~M-8HQR zbqOuVOScVNgobn_5%*!$9N7ZKt!shzgT-dy6ga_wL*ni=w>>x6%nJk=H6Vf;H~k&> zb72a8K=Xz{Gq1C0tw-^~>lWL|yrg8$f`=3n1|f`R+Fb0~)`G`c^VzM1zP~UAiY-aI zNBg{<9jahcVg`40RaN1OMc6#VaVFXTj`p-&u%cb0iD2kd=P5C$2Z!{WJUna7Zu}az zStM-fE(b%&ocbtMnb%PM_f-Zu*4&_QR3LTvOuve%Ipc&ojz3c+^ngv{I{af z8400ZP?-Gy8GV4W4nbJOvxYLsP4~R8qSke{BqJI#|NJQ-$1he8z^r28yaP36`Q^9yaAegJzb39dNJM|4=*otA@GyYWt{4_cFQJ+%*Sn9yFr3wr z#=jO47JGyHYnv^_&S+*T&a4KV%4kB>>JQQEEeN?=^{BDKD`dJA=qM&W{D_1>AWsMC z&^}^CFkvMSXS6&##e&Pw3ZwBw9OGT>u?vQMB2&q2d2eU&N{-CSMnf7icoL|&y`1j* z`eoL^rtf=;9cAGOqQ;=vq};%f&D*Z7TgP#q?xK3*ID7^ai3`i$1S{vA$6v}NyQ{p; zb!9>u`BUF+XV`+6Hb>N#4 zi?A3n+=vz$98%sj?glFMA4-z#Bz7~dIyJVI^!exYth z;LTvGCtvVqY*qat&=pZ)BqOk-x>^{w_il%07?gNb-f$4le{i78rOR-{L{52!O+e&< z>D0_$>A9Q&g_!YtpOyT!ReWHkWo&$CP7Zv!l!ebWC@isqTegG2*A>yTl^nY4)7}nV zhkM7onUJcCffE*&S5WewI~q~?U%>{aV{W9_9S#wV;dxXFe@gu~GXbH%d0l-S0+64Y z8+vXYM#y3e#R$WlT8m3)^G)*ZmIhrmgYLli3p_MOn@$bq^j}?Sa|p}DY@Oe&D3sB} z3s;?g`Uei8z*aN1=b`1o8M4u6fbQHhq5EXr{7*(r+SlPmq?ceY0Z7t_f=^u?ZN^Ha zY^$u=x!Ob!o0sqlII_8IPr&&2#XFJ+}4I!rhS7CzKrLprxgBrfzNZE6=~Xksg4k8`|BXw6p1v<8I@iIh4X( zIASaXFZ^cBGt?okYkTp_`p2mmQE;!=B_1+qQ=q?2PdvQPCd>r9R`qjvAKG=io85`m zOerbbBlm+1DuU}4=tLV1F0K3ua%$$8sl8RPVMS8aXc zt3Wrasw6n_ilY5)N@V@Ak)h~Y3&G5bpi9=f_WLR}xg}Y*AwI9W%HHx$j8)`)Hb$Sk zEq>eboXwWoKoF02Z?PChidGM1#F+*@>Ykj0Kg^Zu2d4|8Ql|Le)JRzn0m224WYoXR z+yzqq@^P}(kxyv8$BIGY(aXG|v+1@^{|zUS8+sijwLtzWIh;kjj~A^>aaz_=_S6;2 zAVb|mQ;4+iH{H@8J_g1WW~Dz5!biOPvoM_Dkr4+62b#_ng0nSux}wOBF*L$G+|Vp< z@KVr(-e1t!-*-MlCw#1aUQJ(xi5ZFW4z*m&==p+hOVBmt>*OHnSN2Lcf0x4Y(5evaTGaSC30Mb^Ks@7d4wj?1gz`GEXd^PB&_hFtEFDTt*eXXseNWW`2WqY|tEY5ZX6>MCB-CFuj)t=2~m>Gq{R#`1oT$&V+%= zo1Y2DLk@6W1^6_dv=$-)dkhT(6X3Nac}`aE+*RyYtkods7+b`ef}4ul+f7cL$r&Oi zEw(kx%*^l`8XDl+vcTsy%?JLLTLUpx{j$T6VT9i?CgZyG+Tr~QH?}6 zjH?F@59M-q8X*?Dh9)6JWZBVK_9t{5I)@hj^a4@Ia;UB5kD1$cc*H&Q?)t%p3j#f9bDm1$nY_&dIEaUh9tGoQ& zr;YGBRge69_fwZbaE$C(>Q0vp*R!5%N0uhfdHUw!gwY4O;t7soskpy*!S622p}S`7 zB;WP@^hAPVjS~XyEa!yN#mpa8x&RDKS)!}{H~9~MiL>HW&8SP(I84GrLWA}JyugLv zZJ9X3%K9KDS8prw2|7N6eMOX9uy1kKYZ4gpmaiEOu1O*#rk0j?U^m`ycO4wX5QT+G zq<^{!Fc$W}xuIV)&I!cLl)5T#@uv96;y|}nflo{;lRRh*Jo@$Y(5HkfcHf%3%8FJ0 zb-{yDvi8Q$ucg5N8F~*32)RADUkB7}^M@Oy)xnn6n0|vPPtR+ax(e8E-)Hn~TS{L1 zQ{5L5GHU4K%DOdCmJdH>x9a=TX^$hP-}#sxNn(9YW?PR&5ji$A-S~`vAD6ZoY90ZE z@P3r5$&YBDYSp-tGJ1C9>`atg_!>>iwb!&#Ury_~i0JESDt)|a(jaXRG7C}*Qe7_T z%zeRau4Tp+i-xL7)25YXB42Bs1Pf2ghS%tH?%|azP0Sjs>{mx-i_BEIaF&je1Hr?8 zt9>3yjLvnp=XGt4TRB~v#*VU?6+YeTzaCwlZsKSoaSzy(4q?v($bM{ej;j>MiT3Sj zvy$CQWn!H;(Oqg;Y=73=K1Dy%+k@+A9CLT1u(DW=3DWS*&kc}I&KIW!u2u_X3(P&` z7LAduwyS}@>1UdoS=HHxWIZ-5#n>vqM3LY$B_3@z8ig#!eXWv~sMxXt9A1U?PwcN#Vh`1$-c?cU#( zhLJYoj+T1@-`6@rwyiFm+Tp{ummH?ARWm#J4?CY~0>(`N!HCCzekyq%mTPg3qrBUv zn;XNS=kM4v@IK(1hX@!~Nh;?(2wp1cvpjy;ls1_b4Ltv`5fx^-$i3b9;S8yp&?J1p zp|~kTo-W5l)(N;7OsCFyuNdg@DMM2qc~ZTpY{gzRu{4ei!)!he$vD&DYPVmeK_nf&|?w5_4`I1WW9 zu?M-dySz056}Mu#>PY@JWhx>$Us@`PVg0Q>&+?VU(>7twvH+Qej;^SOOxrfxAk0+@ z*TO1g+T(^?FF~CtVhW*O>@913J*^QmL!1P|1(0iolwg*vxi*xx(zF66v0tWP90-4Z z7HL4b{WfOCbh9^}oUP2WNhh7^A=)}eQ}Rf|bh|vvXeYh0YC>G_e4@Ny80X1O7F>^t z=_yNMB|WYa6Ie7X%gFwMJ=#CSM%in9EVRmI_%ftCR%5GG$};z}?Dw5(nCLvpZa}9W z|BFL&^AzgeL=FJLFo4<&e*4Y9&~A1Mv(=KvI6^@XU={>(;nZ8|9)(2#7~%C4C+KED z+r}3DsU&yYzRo_Xa-I)G0=az|e;$JysF+PBf{8g0g}abMX}?F||MLRW`VEIagA~$y zH{C#oy&WIoa_H?c&GPU$G3^$nV+DeO7N}Xzq&X$K+lTLNcdM2QJmMZNn8g;Gi^t5n z)s4rpYhZ#MK2oU|8Ox6F`q7dz6e3fs0eIFa&k!uH^s;>CPJIif3xRibzyMcxal%Q3 z61YGb7he}_SPqqQ-(p6_RV*^DB41cISTQ;Qv>sH@WzLt)a{SbR8WKJn8Wo>Iif$8i zP{6ljIr};LCttNNb-p&O-F+^KHGIQ!&Z3Ip@OB8M*jwn)GuvJ5iPTG!c{;hd?HwFu zo^3-l5FZy8jYK{Ku~~oqsRM|vUC88J6>;mCf8@@2X^Le%D;>+q1ApP z!cTT|&c*J#%n<>{)_Azdx=lJ!SmqkqL7hbpfyga`5w!5n&wdJ1M0kU{P;;*Z79R}` z^(-tVn>)LPO!8>vn>c7}7YO+s9=xE!##)@~#>c=MUfOX<0xa$&p4 zj-e-axf65O^4nOIpQqKKJQ6j-z~yg20i!~eFrkv-thSTZh z%x{4Z1}Tx<4lY*z*2A}QKGHhz5BjdpOQbXikBme}MOs?MP2f%GRgvsK{UTBTn&PH7 z1UrLF7I0!BMS2%pci(%jVQ0pYxEQxL+*jKkcPIu(eN4fX`T%f!|4ah>=!0^MQ;w~~ zD2wujspuA>AWJ37nOZD(>=L#yBotkeXE$$}YfC{W*kks5NkdgW{*;<-rq@9)1ExLV3Z9k~N@)`I zCnL=L*v)$tPwu+VPN?x>jNmacM2@ryiDU)#lNZkUB`Y?LKN)LiC1z12wE28hI`%pPI3J)ivhZu-S%jC_TdjN*bWDV`l=ESC4JV8 zN8&b}B5TWXkCeB^Yw*j&Eplz|KWwi~GqL!d7Y0BDR@NdDx_LTzPu%xj@p6pdikbz! z%gA+zv=?95Zim=DwZWF7{&6CAt!UhU>tvUx>|sKElcSh03=01)kX(RUZ+0h11K7SI z&CaZlvmlr1{GjMmh_#-#H?lah`}4b?G$3sW7JwinZ99Ai`h)$=^Vn+mhKowpL7n%n zTV$&3Wt=>3*)oh@WNEA{|hzE{s!khX1Yru@?93 zAm=+_My%2qM*d3oY)q+-zK&SFzkvwjwcMisV8w*)x^noYvd5mNaq6j55xidS*JE@V zS%%xC+}mT8Gm~0o6(;I}W(K(oaIg~jQh9FJ!Y(dQk04)E=zJ)i?#3A})Z1aPEJGB# zJeyCx2zWK>B_8SiB3_5(@?yk%t%>Qd4S$Y;DS{!Rs525B{Lg;EFn; zPhINyg*dsS*R0*9FAcopqOGexJDSy7FFqMX>{TLuqb(<%XUVx=BGb+5kMVGR-1rX# zzTSYCouD_|r{n`Qz0eDTGV@|EE(Y{`R@u(ZPWhv`1r>sI^Y#(wbP8cExxnz9X5pm5thjs`Y`VmX$A+P^xnHmDX^afvBsJJLAan9iiOYS1;-^h zhvlBj;Vl4P7(b5e8Cz&xiBLB*es!zXuQCr&Sqv&{!kyH~;)&2lU&<4kG?Qp)U0tF% zo`^0f!Pg?4DfQRTxr}~JJ+R}t;1M30=Z_h-LXNfRiXzq;X{4RflGi;9Q+Z&Lyj5p_ z0fL?_0xI&Mq1TzA?^ujr1}%_ho5D`v+1wGfKWh={xqd+tT?6%RXQGwLCppXNUPPv-Vo-0Y;sy}xTb*S&BUs76fHxJV-hzB0ltv@aIsEM9~er?{kc>9 zJLXLjMOMdU`U!*fP5Z^g-1(t?7cg>8#I!7}*;4Z=2_`cxEVE4^X@!9%vgDy$fFS$x zwb(?}B`j0~FXyjau_{rS*o8#|MbB$h!}dk0DL|*=Nb8UMpesv(`p!Rxt_u&G&4}6- z9Kuttz&D{v>Q{hp4`{G$x{=70>)}6E(iV94vA&DM{8ohO(p~=z?>~$ZgbHNlU_C)ph zq`DfTuW~@;oTM_eGH8j(3}>tU>{a_(mxi#%J>S#GZL-xiz%*swZZK2U`&mMLZOHfi zNTQSHQ&D+e1yO(0UTZSbhSyWHx_p|2>2p^H4c$PT_r@Bl%Hbuhu0W3uG;UMopoIXM+j$}Cxh@W2dd)}$GeO7_b^sxlA6~K`QP*xpx>^~!vNO~;_}Zlj%<~Ha_KIAc%jELJ1WCj z!=OC>d9eB@tXR8tV1*3plO1f|I1%ktm8yRtMZS$*MI#ku-r`^aZFJYez-`^gF?iBG ztZd%Q?-186Ji=msIlbBp>q{$>eXMMg33ORVRsd2=mLus1@R3fXo0WxXZ|9FRwz@Q)>BxartvE?_Y6-bZ5pg;31npfj4r zy9x@qI8i{`9L8c1`3tGtquE*U6oL+q`-xNGCezhta)^mSL=y^{O;g(IJIO(p;Q^Y6 z571u#i+Dy4gfN=CygXP%MdhJ#bJH@Xr9~8`r$9{B#VAQ^>*$CYD3$#SMxovUetTt1 zs@{Q0aFu`Ugkx@JYs==wnmnKK_ZTBh%sK+K%O$i#_XmmgQ}`64gOMvrVO&^9=;wS1jVQkpnK*JX`BmqD zSk;>8-~lXOoSHT!zDGZ=WgV`fYbUWie|bA#PFZXk66%LT*)8TD>h!-p!G6u7#}g%V zsb_;2weB_t!5X#0pq*bIHlFJ0zlJbU_i;7q*I6ca*ftoJPuFs5)xIdb_Pgg0%)_87 z3$QPx6ImtnCr<3LPFd>zyE1O{5v(}x9pD`*7jRC^I?*olwj!fc zAaAtXHT+%obM<}#HvhaxO5O|+EZ@9w&|gu(^%Dt?j{QpB&!=UcR^pzF1?)?6g+kXx zf5immw_fnuyGgkQ2ZVlP3rT*|zqJwpi+2iVfBpminRfEVswHvQ-8fPGD}B}qI!u&V zh2<0OKgUqPRaa??*>!fT27(>AW{E%r`LXvtmP9C6?1-r;E!Z-{8zqE8o7v|e3o(~( z-@X|Uys^D;!l1sz^AswR-#=1o9=ua&Cw&L~bE*Y|VAW^cjH5hy9&xvFrw^i>&~B}a zL2CTkD^E>Ed;#C7ktdj|~hv=p=s9WWH##IdW-~JC00M z9rK7ez%~EXNb&iaKJ0*8iZx*wMRAu5?6w-n|R!g-uY6xvEFIqACTj~&T`&(y;?J1 z!>?#}3SNgo!$B8$l;Sp%tx(hu5`DRM>Xh@QbVkUHo%t3iiNaA3ZE2LEc(x!~aGS(# z(})NDjQ3cT>v-waQKiu7s&*1i;L2CMqQvft;+5{5Carvwcfc!oZ3$xEO-O0?mt%IC zEp9Nm-ibNWy$u*0Ks+QJi~RU`|NY5TA_V?)DP0N+3KnLAKFBpg^3qW{e239sN07*T zfY;S2H4RNvaa!2QV(f0rxRgG9AmcWceejoNTU%Q|(wo;gox$G>93=>|&p#w&JyK9I zL{%-{nI^z*@gy-{HKrb3i$9aXcg!<*$t8kn-~l*DUxd{u^>3(M-$q{%M?Me2IRRjz zZPLZ3bb@qDPx(e$x-3Q4L>~~9U1x7K+YKON7Xl7*qN{dYKmN{cfd61}_O!CN)Z4PS zvT@htGj8A&q&(iv02o^gmDnGFX|uQcn$yyj=&nr2P29>XVD4i;9!HJd*4W{-=(fzvly^uU#xWR5-g6q|JPT>(j{cz|UJ~=nYj-Zn4r-)T|j73fm<)3EttUBkG^Qx3@vZ zT5+k0Gx@ff0Nui~suELR?-zaHSG^NUGSb_kiWTh27fyk7R`UY#DQyx5c}KZrmf9Lf zH{GnisR&=5!km?RDDdI@)&b!^K3uy2&AmyDN`6X=r{_SrfBEHb_K~}j{&@#K{Vy3U zU7YMMg4G=ZYuVs_&COki1>x#xdE5ztrLVgLM+OXY=dJHvz$x@Z$@N6Nos_=bNjo35 zyXc~8XZ9_3^*5!DeGKNj<=Ku8TA9q`CqSOFH09;;J_Qg}us<|7fMJ19KP=!9TgboZSa8eS^ zAtMvGL{e?YnI~_+Ws=`CN2qdR>~5}u5fRw(oea!iUVVDbHgRY6^^PzK?{wgBMI~DU z8cGKX8pQ2A4vu5<=7Fq z9Q_%)_b(BA>OCBLAB>&RA^13J6Up5*X+rqSQaGuE9z8>dAOZ7tJy13hyNJ0& z0xcd_nO_+aA?IF)R3CqSdWF&&Ve9PNhypeLh+G)Hn2bN%5mMRe(w7W<%7~eF>a0?^ zH8qnWH&Pn>F4W<6og7%K0MIKC8ueOT%HJcdq1;jjwM{0*KG*$dPH=B5N{(lU!thWx zF$TCouPZppug@hgNTwwqqZ!%d1h6%t4&2UV-_%-!?||r`11?j+v>2LR7p*`|R!HHp1ck z-ohrQ%PR^t>ENB_?c8u8Eq^h$?+0sy7&w!>_`>#2hppBR=1d7nEMFnK`~XTFrEAFa zr2d2SbBP~GL9NTDU=CxCn?l zRElMHBE{HIF|DgH0$|K3E1m$8=?Y7rOviT#Pqs(@BpB<`5Pcy^JUpzc?)QHh| zN*Y>b=~Gg5qLgmm#2$K3v)i(|lFP#}G%ZOVu!<-- z)&%r!m#+A_Ss@Q&Dg2vVriA5qB)vOhoIxpuXAidkPhZ~YZ&@>1z*kh z8}FmOE{3GO{ImGnGOO+0!mAV|7UH3p=zoURDNNgm_!FWeU&eT;vk{}F-xzYCoxXZ0 z!S`I{JBq3Nc!aV)9aYRQsA+Z+L#-YY!^82?0(OA?3jynVHCf2ycvobK5?u{n{3Dg` zW&=@GiK(1crm!uv#XDV8-bD)uT{%I5ePO=T(cyEvV){*;p25lZ^GVGk@2x&8tId2= zqlnOkTd5yh-<_N^ZPeZbDZg?Y003ZapBM9b6bv+g{#JKypdSa2TL{7EdiPK*# zh0KAkh3muVbt1m-aq~dQ1r?*;un;5HA?RQE{3Qu4%nWKmRaL)CBqS)vAV$(Q*o84P zcwg1)PN47WLw{YX2GU4aY1E8vDi}^0#`~fm&tV8Y4Q2wBs>w;l@WT0 zr5zITftfMq6#}4vfa4-ExUG6nxB4RPB$;#1h3DlxP6)Q#X_z9=0Vjz&sf%9Yv3*UxE_rx~_+?P*E)TSLAd= z{6&{T2YkXPg+oM<4bUhk(uJIA$@MD?5^670*f==s?d)1+t3>op)}~-oVeq1SbAJJ= zs|sW_F@B)Dz`pneS#w<1A*dktsR-z$s-j|Q^>Hc%MD2Fjm!SLAG*jLAge!4)kS+Iv zJJQ&mX|24f?~7^c7&8MK91fv=aFQkrmA0I9ft|eC%Ct~Qz56#u+wcIu(v|>MHpA8) zpOxNGMelb1wjh`ICsUD*ypQJcXqdMp_`*4Rp{ib#dqOks_R>HYt=0`Ui@L>l;_QRe z`fAlCWY=#e!b&>IP zaFi091c*RNl)cP*pNLQCbJ%&Cs_{UINdP1RG+@)V20G65KUF`Rh+46z<9h7^jNVsn z{$PhPbG0gcr>Y>*^)&TrjtB+z?i1f+T7Rm}%`e$YoBnZFR+cXxG4B>Hj>5b*v*O93 zikJA`WGN^hCkvY$1~Da`K{VW07i9-REEm)*Bf1*0%&7mwJ*c->l|mfkmoDCJqB|g| zTZEvrsv^7JQQ$)ssTMIJZ;bI>E7;N4rkyq$>5OM81@-uU)!K&}(s^okNmpNes|I{$ zi$ziPkmASJSQ9OGOaRIz*_Bg_A|W>2+{?`^dmvpVld_Thg}To{N1aS5es#K@2V z$}3lNgH`4iKw|>|pw?eFfCg0c<}&Pk$fn}foP5@8gda5WRm=GXA3y=nZ`hWs$O=+@ zROn}5QikFWG8liYOL16$KXKgcq66wb7oKEIJ%MaPPd9kh4!NIY~!bnQE#+LU?N4LKyLkzArf=Z^VI6fud zBct-);(pKcFUh_HK728_mhQgT9)XzQPEfGtWq$}lIM@Y6^&xGJ{fWV^Ist6KItIEs z2_~BemAw(t5}v+gbIpwDAH8quQ-1HGAKZ>AFB^8PJw+nZ%Bv;FtuO4-ANfWk_(^VG ze&B!`NFs_iV)7X{qWbp7+&oI#bj8nVocxH&=olxIbCN%bkAgx_N2{Qet*%dRS4k$S zP)=W={fqC2zdNu0CoB*NS8HSz?7?mc?)(#RTsugvJc^%l|49OA%w)TbbAa#7S_Tt? z+|qCT`U}indyGrmJeMr^phkc^8=cm=b7$>yzo(pWKSS}YYqvpYU?LA07)-n+khMc| z!gHQ*%Iu(J*Kmdt{uhDUU7*k@o#<7Lp&GW!P#;{E5#Q7dSKM|sB>?ut{> zYU<*V$|p*$sR>ZdS*jzX<9VMSPkrpgdwDeK!u{fZV|SK8j@{RHGlI3?3h~8&6)>xu zhohmu!&vi^WB;D#&qFCDnTp99oNFH5SbCtvYn2b0eCKr^i{8H1YQSdOMAiE@DZbpwTA0~xV-pu8uWgcqoEc-b84R{;>lJ0Q$?uG z$g$buSPArFMMYmF5FJAmrsJ)H45ZymXR(U|sNl@MBp-6vvm`K~~%> z5WJDBFwEP{-|R&zBG@%o5Ea{ymUq?|UG}BpfH1(TqcQGGikF!b?bh$1kk_b(gxwp+ zezJeM4jCOv+&)NyMYIlHi=fPO?4mx^e8Pr(Vm?TGj!kJ!%a_t;%gbdy`y@AIf6LsF zCptf0w^IKK4VTWecVhslOGQFo<3IgNltW`t$juXKaxS9}rl-qP>f1$(b zu+DAx7%6&qU!#&8Mw?Q}6A@fG!s+M7-~)Kn$|-Q{=iqyvm?SNRwfDQ-qnW}df>Abs z+T4H`MTMK}ty`;8H0m~0MNK!8i3vVVD`VIyEfH>kc9S#%8%dUo?6>I~5PqB(>nf*X zhII9^d%(eni5ZHMoUDi>8^;EqgN~`8`fX|`tGXwwsB?HiSjeY{TlqiSx7ZQr zmlO49RN@3Y0K3Hf-^A1Q%ij&&uieMg{mC?#gu|}XyG70^tS&x4SMf)P*xY3QMQkHo zzjs>xc{ZoY3G0ateQOu02k+T`U|&c-?~x+6By#DsL%Lmzy`Dgt3@PaZD_%P~39 zgR#1k30wuGlIG@HuryvsfqxzYQq;A4F-~ZW)HT2JlIOIM<*egpm^pegy+(1`;hI54 ztI>xoZt=c{i04Qm#nh6T0PI$kl{Q4)m_a9Ap$R}Z;+h;zhc&BNEbGwqwkrL^0Jn*yX2^u)(nS4KksOdC2?jF?HS2FuQ zFF>PJK2XLKu-a3gSmN!7-^!ET`)NhA&U%18|v24VJ6r-D^%?M-QgFjy2YQWEG2KVV^`#x z!>Uw1P)r1naoGn1Ha1#CInA=vX($uogDcd*CRg?O@~-}_rsmUi|5iVXF#o^&yR7jX ztdhb_Mq%9Kd<@wgw^*pPg6%$X_49DA`t4@+MPFFN0(Ho2MMdiO5*VQ5$5=T?CF|Fp zGwNHkR{c9Wp&;IZ4^sr-X~iaBzqgzIvs_8W9n3%Gp`C7!EEi?_xQcNY{RAZrm)wao z9TDyy1|_wj;(8S0C>*bxVEw7U)ra+`+QD9#u{k>WZA40SlK4r8&bY?XRTz6aC)_QE zES+xsKz)EB8#MfR5RgF+)tDRLN?juO7urlM`$+57`o;h|(5Uu9!~{%_@~rM(W9V;O z##IeCU#Fk+eB+yK`D|w()X2=-H}MnC_EFmOcX4_LClW`L9BIFIi@*!{1)XcMKYl0D zli6vUE1E!wjaQTIm)`#{3d1?L-MO==f3W%dQhGX6fTXf5*t1(&7%=@MfWVuAaH&<`)>I=xNajAi|t1WB$@~Ui*G?Mjp~05!&pOsL3Ef;7q`;{dI0cC^yVE==sQZP zhG6q3h|h@IA6fzNz@a>QF0DzU4i!S%i|GHI$$Z!5b%>5x8r`iDgDt& zoN*O3V-HQ|Vvp+A1SRj;?bg53S~mswM3+{_=+pr_5d_*hAF-GjtpeMH*w5c~OXmx^ z^&{Ug9FTJ&X|u5GjP&nnbRg883)4)*1pzIz|2*#pWKxWN&C;OAITbJ9Hp8wN0)#sE zqyKx!LhmYR0Oj4SBI7^RWlh>0g7_Z^X536#SG_Md<1L(xG{U7HdOhTf(h zgY7|P_RWF0TK-u=fb#ZlTW_r2rme z*a8Xp1xxvT45YsKkSYtnk5702eJNa(EHtAg&Ilha$_m41kj>6b_g}f&sn4Z?KJ--r zCJ8GmEt{)5PYH#d@8@PxMDuj&6^)|oIm_dKYH#)bE!t1S|FR;n3e{WT8Z-nsJa{R^AH{*F_c01qnpByeTmPV`13XOtS9D7=Ep?+5 z^{^5KDg2VCZvdioZc4^rRj$TXpfhRwoUia?EsC*y_*yORhzY^Y*E8IT|?^DEMxi#OMM|&kt zZsxEBRdOLy$_?&Xz^;%GLbDJC!!{B^-zBMvmLu**v<7#Z8H870Xx!DJ#3Hm{-65Jv zZYq^It5)tr!K9Pf3#v*BBM5ca4M$x64#rZ(HV#w6X;CQc|DSA&ALr)f@RXN^wN6Wo z$cM|YwxC*4=Dhgs^YGU!a}s%7*466MrE;}q{Y=;DmL1n}Z3f<+*A`z|O)HFpy20mt z+Q^e;KJl}UoXFl`U5JpkD8G^C8*6~71)e*)IUApzy@|@Y4GCSn|Av%e572Vpt_*OQ z-!H*If2G8JCrCy&G5|?uJTdq^FBGPi67C`w>5{gC(AGTxUdI9eZ;j4hczpz@|Dha} z$v-}PM(_)S!S z0)App{GSg$0i?{M4yi4f^O`p1YY zBLF@q~_MV)g)oP{r0?` zQ)HP}MQ4;E2BGI>lN&O|?T&b%#={pdfy^*`*o>1elxhm$)9(V|cz`!Ru%%A?_eY*g z_pHx`7VIq$556(glJR9}fY;;YNRc^u^lxXew&WTg;C$voXAo3uNkXAyDG@Je40mH# zGdQ67{8)e3)Mv|e%e6Rz;9sRA$8Ufn7#3Ul`Uuc7&GV?lkm%=?_s(lecS9Co z%p(+XfbE;5Nv6J!g%^fS4Ub{7C-u+%(<|2h`>^Kn4?Je=zK=J@bBk7%`|za8kiORz z5lZ`r+x+cc;ba8aI_`?`lA|x-K(7nb{3epdxpUC-|?GP`qXsl zzu8k)XFXlndLBs9@FQ=`FW<7)NP;JP9V?p8R9gVA=&4dC;pYG7h=v%9zb3a!??jqe zYPSt6`%8k4?fN7H7fl+@z6*U|&Ik5LHtrMHV@QJzx@a-(?&ujwH7K}&2aO8Q+UG*= zPxa@It^C%yNe?v}#GSV5M!y#a2J>h<247@Y{Kt~eiT;rfT%>u8X|C}44d+pfSkQF& zrw1`${1fb7OcV3(C75NHnTST_MYc4KJ$gi*Q0R(@&L7?MPse2awtTeX&Hu6)rY;0= zPq}{;WE_>28(WT?lwXbcAN*-1d>N&KJ~8%FmmpclJ4zIiz z`tJ3^?)6t|Gy8PW$-SeqOD^Y>|Hsr@Mn(C3@5A@Z&~4Dtp_Cw!QllUtsf2V(H%P+_ zh@_OLNDqP_A}QU7lz%E2ck(V7GW;Yzj5-vH2UeaeX zFAg;We|rt0d_I`ihY`Nxcdcs?1z*ukdn~BVGOd@JhA8@9naWv7$T{T8>1oomzbhjf zthPx^Q;Q2h)0rmF!ShzJ`0OV;S>D9(=NlC9`$+1Ok8%wRYKfKPyBO(>Z>qqz zW^T<;OWAxNmwJ0j@;^UBfwy4%6MfQJ--9a8P9tLA(_@S>?r^|7+#)v%;N%Tv-7B`B z!%Sjx-5Phyws*3yC#0mT9Ao5niunNn61rAqxO?s6*!qE?0*PtsWtigR_=g)HrDRSQ zZg812hcOdA;~&R|54rd}p+<|&BBFV;&V$WHTv{R(?*FDzp3Pc-IHZI{11;bI#!pYV z@&UMR(xA{{7tJcTp2wINLL%7eOQDZ1Jp;6qJCLA+RHX?@m zL&{?cKZ*#4ab*|5b39eXH#GnO%QP7Sg@3kA=XuJLica}hk+EAI4!eeyFW2^oKM`x8!*j;6JBJ)pq1kzQv zSKbZCy8@-gL4S`|VK0URbH2PJj5zy#0`;}rYHl6dgc+O&UKu8W`zrNP)ybJG#JtC4 zHJ+TuEf`;jm3)nR#>bi%>l_-z&G1HD=(m{Um*IyK7nqS$pQ0V7xK(p(sN}~s;{1cY zZ$~aF(?vAqWL4ELF#;>67Vc>9#n+1eYJA{cJSIyN@=<8}u>%QHa zG4;WA%N32yxvMHiH{UJ3zV6_>e*i=g;;&lHS(Lf6k=j;g8H{xa;&*a+;yl_k56zvi z*7CGeG9m>}XDKTfU*lf!M+FILdF=+BHERZJXBp!(yAJ394zZUHW&)-g1~ZBxxWyZf zSTB~J0-bt*4?enLzDYubINc@Pg$IiAz>&OulX#fyG=DDdIxuT7vbZ$xyxw zXfK>mP;R)J|CetZGMw>~h>y!=ftfl`p@0x!gT7Ty=U}FBk@ynKnx}Gz&>2@&f8fba z)t28E1BXhRDYxE@BCVcqq3?DqjTnWE%z#|S_cuCt0sPiV2@;UUNv+_(2*YKe)(_}9@>jiryd_a@>g;+?O z9*fo+pM_bq-R2jLhb&PBB+6Vs(I=9YE`3c*kD!B#_*pwH&!%*IYq2Oic6}}XL!;;7 z6L|V&g~ld`wf6c>fQgZNE7%?mQOKu)*paBul&pykKv9GzJ|4$V1-6Rrky+!Ht)J;a zS+Irr9OUr?uk-RVr0amG0hx&731D5b^t8D7QMT5dOlloqSA_LB(9Mo~c_#_;YY`E> z=ekC)5b>9OJNfn=hjvK#>*lADU;GLgMkycSql2we5$^GuVb>_P?POUB!jxAC14)$; zUOy|Ro!%v{<2qi@TGUEM#T11+zY8f#@+^7|EiwLZdut-H+e2kZYFzf6D?3RkOXPAz z$0HA4pD(nLw}M*l?tgAByQ_wnOddXtL!mcDLzG?qNHi=6^AGa&5il18GoUNBx%q#X z@We3@cIDoA+>_12)ldCz_YHX_ZhfM2y&~U&9W^hF$mK3PZ#I3S!<~wIbv9>(B(^Rd zG@qeye|3xM9^ibx&~gl&+Qf_A_@?e37j4M9QBk*^JLvlrw2$GMeOk#QRpE!ikjRkW zftP&Vr5)Js6PgXyQLkm*w(upt&@sPVv!6Wo>&JSi@R%p=?JnayG-}eoPDhm` zK6?9)d*Pr0IBGVrKp&H3%{Ukn`zqkR*<7IOXD3sVX8-qO3zKcg;|_l~380FPETu)Q zH$qU$2c1UpxmPCKuKkJx=nyBQxOGQ@N1wp36sp_~XfhVCg;^Yzka9F%Qywb#(mr2K z_V^Q}$*8eQxc@)~Qu)wvxKcuf#yL139+z2Fjp>qC z49BAx_yi5*C%T!X?EoOB7H|o+VJ|ik=?9Al`E{*gN>8$oRSv}biLbig2b0tAp4Rgf zALh58mkWunyz9}P6AFsVCok+*I>zZwhJZYPrOkkCYk)vwb(Udafoep4U?0rp<$SKA3L{2Dp4ex>RYo-@QH^j5`rkXzwYZB;J<{s^O$r9Qb zB8+aa>_wk$+c_>Yu2d)eH%hfnP~=`%<8Z#jiKYRuP$4U88=!jlycWAii*pp;J#A5P z=;FuMS-!+NMXOzJ*-8Sh_>`h=(I!t=yDmdn!OLQv8d7t_rxD?}TCX2%jt;i5Fkd8N z74p6B?WMR__&ns8#kS|UnxGjY+xbNeLkk#sS8^60$aJyNO42(Oe@SW}dvhAc^%|>s z-&+gTiwKL%(h!UjU%A-Ru05mk9ft=0b^3ame%P~L@C(Ct6}AlILAgKbtwwW3*|)Rg zVI*|aj0R6fNJ!&aRB(|C357VE`*EMhGvajSTQ1+FzTlXtIi`#y@1)NhWEAcRpfGjYQ_WU95vJtl3{_bML?U;k)8e)WkN*$?!hYl-WmJKS%v2b$X50G}^RiTu z?^qN*j8%vJ8o?>=%hzsb4;^5z9edxAEAB?O6Bj)nm8{}an0EN{Z>F~K#AQJy!sU>( zVN`unKg5W&_65MS1;*YdSFzV^t_6~EuQENj{={sb%7~WgF$d(!Hfj%vt7HT;s-5=P zWYoB$I+gs1d|deH=(am`ABvF^wV@S*6$*=A4@&LBTu)j_-DVnsFa*9?XmEqr+lK_5}=Zuj5x_8pv)t&rERS-xVBK`6WP~e8y-K}2R8Qo|^)?f<7 z+~2+-e`9r9R2`N&vko(S;zIN6U)~yr;{*KTs3k$>!6b|gO*B*;za*=@b$XI_ITX-zBiyaAS;tcXB0S!~m9cvZ{>!bY`P%>{ zMmeW;Dz@h?l~>JhQm*`fE2YrsOKN+LCkO-$>{!rkFdIH=Y`=7xjwf0Hy z9ID$*GqD!7aeZ5y?Wm(?SHd67DfcIDB`hK%6Eu;a;f1`H;;|e`?OLuw6SIaCa-$xn zl4Ln&eZ_-vy8oqwo4OFKH>esxmDB^+n0ui>5b0>% zb5M1yI*rM^EE#ffBIcK6nUgl9q>{?)$0XmLJI6zt`m;^c)|?hJS%|iXD>s|z5!~AO zR!SxkUu}A@ z1oA1fO+j2NJXq;xuM&(l#shj9?z|dOe<9Naw2Vl-W9EJ@B!$>qVJSv8a12*D;pv$` zP$ZEVCL*aR8FN3r!z(=9cZUm6N}lx!uT&pE+_TZL73U08XBK+%FjiXFX@C^c*IC|o zS^(}@AF}yeZe7#`PIh=7U81W8u=U#(Dan%6%97Xqu3(zBbEbDV<8JO!{-@;y zzmE}MFB||TRYO=&^3PN zbjBp^zZvz6e9}?uW|GC<_ zB0dnUg!&3WJe_)Pet?Y?(UYUB#mLDt&!=^1m8>69dMEXn)75w(oq|=qtoeOW|5D~M zl$DNW?0OgPOTpmR+XOTIbQk_=p#~w#$;h$(gKtTSL2^`Ey-|2L38|We@*@#{8@A60 zwK(*GM$4(+PYzWIe#Fv|HN_>7#OUv2Lg!8!G4MfKQ^%Oqs*J7;5^?7mO05d^|4j7X z3U-7oFZVn{>QZT4UUb{I`L&Xo)6v-n-WW11;p1RUzUa?$uOWE>5$1t&Mu=7$rD<`H zwf(uV);EkNY~XzE)f2di`NCIoEs}{Wa;J}mVQ3m>!Hf*hbDL)RLK79(%2s;$wu|pg z3J9=hTA!Cd^&GYA&bZ)z6Wh}#f!dQB`PyVj6ByY2w27vGrg=ppSVff9kheJcd`sud z-?#n60I5rIm2dG%q05Au8Rz*{bJYLxSjuB?Z9C-^p~ApewX4}fP810gAtJ>P*fJA` zmkLYdO4D8$d^yOIYPw93tPSFzvUNV7U5x57lw#Dt?$9Ik9+y-Y-%3zr=7&2SA<5jI{xx%)5BwbwjYg zhT8^i+q1ebG6ox)>E`Xb61}o45lKs|nNL)Io*>ROv7K^Lxe)~^ioA(t*HfR&6?Htf z{S`b;h>#g(5OQLqPH?RVT&5@*b@-|<+mhMcnlW$(UE|J0B_-SW_n5d>3jgO_ff!~U zwZa~A)|<_a%Px_VmMRt+n}|l9OLF%i_)Jquv3Rlf9PM(gz|js7Sxb!K`|rT?GvK|aG!rVA)NRe#)M zwX2}4j`UjrDPtq=kM-3J{?l>J5b5` zn!%uap1IJRSK4QoMS`#SXWU~SaYe<#g6q>hKa0#VTvs~%CZ%Y_?@Dh>?1ejaGD=Ib zN90G>=b;`>Ro~y9UMG!%E3kvPPBFk7J(@m))>l40S0u^HRZ^a4@ zpRgDZ0^d}SdBdZI6zWgc5m!4So%V$UxnS0AN?vLfcsQVx(*(PkhU>1=f<|6Q?&MQ9 z=2@9X&0@jUymN>nUgUPU2ktHI(#*EC4R`8G8jg}FFeZul{a2kCcmv-oN-YT zvzxO4+d075Twnjq7a}qljlbk4i3~)QL%gkn^wj zaJy6ErArW{)%XzFZC)^pKtGj4s1a1F<~|d~lr&aJ^pd+9!-UY%rl1?D;)MRQEC^^> zzQQ-vi1}8rWn)!Cq(pe#BBcn|8BswO!naP->l)=PL!Oi#yykTp?*;I&yEMflybc3i zgKakWE%TONMgt#9xJ$VEsie)6>Mh@&-C{%u`#2|dM_!!l`cL%p<5@6W+`j-8=iOFF zjVDL(M`xmgPtuP>gm&K5_`)K~KJUH)6#C!mGcu?&JP*`|+YLx=S|r}{P(bm2#s3$j z`-OP{E$Mqpcy13Lw#?vztISf6hq9C%`%qadB}+f#@dQ^_-&L!ZPiTQ!#m1*savg1C z+Rj0WNrY8+9lGayO2mYD^f$7t7FBeHuQdl;gMbR7EI z;@B+g=i+7dg!=+>Gs^esY$VC7pkV>x5&rc@sfUrxXxfi3l zIchkK$9ggB-Z@$FF%)QVD;J=Biud?7N@^NzSERhTy&0h7JwJZ42*hwkGx1(`;2wX>Q zGH{TXnB7dP-Z|evM}R)OltxM=ulv-z;Cd*1Yrq9>v*CuyPAhdF%YLe52)-?{cU}YL ztw|yj)zYB-sZg~F)?E$@{e&=^Lc--XLd9)<+-}v9wPwL%@2gf(lWYT4W75L& z>(~_J?~y|AM`}Yi0;E42VO1bPMS0;??^YkW0H6djhP-V&ea9c4i8MTWLhA^hjFKEF zXZ>$_53i;N?V4&1B7;9T7bYmJzxZ_|Xg);2bf|fp8iix34}?$95m0RQZBOZvZ;qSP zq<3iL!6SY>APDHS$xOA+jZo$kB9k80`SVtdl`PC6`IKu9P|q)?aJoQ9gAb-T zxZ}hM*Z`AJZcQJ2+uWS2o>fb?NSz1ALA<+9f_GVKR>bd#;8Bl6HTko(s2qq@tPuDr z@KPI}se*2l$T9SawBNGLnA*RiAvPT$*?^^Zq5Qdht6uF(>pE46n*VHkakbf9ixscv zR=hTq2IiL2?E7Dx6_>GB9K)Vd4isp=f$IHId6M)0^#VY(=wUZ?V0WVNz)OA0e0FB8 z@5m+9c$K_?JlSuTtwt~r!IG1$!?<^Xtx4ri55){}4)`_dtUYx^9EO9@dti9d`GP_E{Yt0~8R{Q=rU79RRwtSd80r2_ z4+|_G^>sCJ7@{a?@LtROmWT)LbKGkO4uYCle;0EinIm)ff7 zkh?^!PgJxa(y3#Llrtk(NF4fN$kI)DrR%Y4^2!nL!Kt(cF=_ZG>1^eVr-_S=kHly7 zkAStT9C4RtQSnUHxNW)u;zg#!MGO#hblt_ygYDU|~^GT+Q%w{5xm@V~Cd*qAkp~vyA{D&{BEB*8_YX zEW;`IjRebe>-)Zipj=WNKObyt>Zz;2{aR>O{ay8^3)#Bj?pU~~XIY+cS;_DSz%IEt zb@*hZCoAOmcY6&DDI0Wp_MS!Pk(Je|8A0eqZ&SwDI)3cAp!vwHPx&hm9v%&`IX_jM#2}=I6#WSk!KGk7a`1q)^$v61kt@DzJ*hFCQAcYHsI4ej^T9 z$jI(D7j2;$r>s60S7Im)P9lTl{Zb0>J`vyiDu78XzW*ntOv4A3htLQ746;qVj z;O1GG;pLFL-GjS=1rLE~9)B0dq^$3gU0V-6RSSBqr8`bCY$$CNPy$y4URl;^`*!j2 zU62iwnl)jTvOnHch*xW~?}*sypka;lLYH~qXa=%YeoUe3CRc)9;8RJSKjO$o?w(6o z0;nxgGdt&|H-N+_e#D)Kl`*Q`upF^SE)}Dh#A&Ti@Lh^Tlsj zui$08;sv#&zkdPXdrOJ1))%?J=2J-RT@;g)cUOLY+y=s}d8)+J)7IYe$&x0-FF7HNPwb1jlN}LLvbFt6Bvz15%`C1YXk#1lhh(;c z7je-QkeaG*6-~~UZZrLUL74I}%I}u9R|%@uTGtg$7+Yfrs4zN&p_5Z^FEd{ML%*|o z8%N%qddR7gTav;u6~{JqTYX(V_XS{V3Lm8mbt_Nzbh zGZyYn{8T9)S0NBUp6*Gb1bQf`mEYv#Lk!4?N*V?${yJ>zjsO4T641`_Xz$OCUBtc~ z5gQEob_S2IPAz_$fH$KOZg!3ZX9k|f^B`;4`T|reQNg*1S;+=o z#_BMj8<<6y@)@7wr@9nhyi>&-O?8z7;#q62(3YndzhEVgTw+bQL^SU?nf(rP=zC5W zIVt`PXgO*XM)HXxkNu2wgOok%A&K1;OlkIifo~>*-iP=H{=-nDT6p@z-eNIuK8*|Rj4T(7 z|36>qSfm8*pc0 zE%KgpEfNTsfZdP2=;aA4uc=SK>H;{&txDZk)dw(D!JA81Sd(CGXM8QeJN<>m&_jgV zvLG0omZ?e`-mA)O=@ZPyuJ3ky8sP@mZD~`Vw{eWb3#a29j+%{_A;wR)$*6|KdfIWR zqZ}rPdkJGg$CF7V;f3fF!}XZy?!8n#`jSqHjYmaDyt=1wY%DCy?OwhXk$Pn!4*H)35~ns7K2wGhbw`;YsGa)1awJzhRLnV@|`2%-V2{!Ez{n=yXn&6@Xc(HO?GfTh>{ z285j7iBTGbgb!pjz_=BOSlMmP`cPb}d5`fVZDi%hS{dg5c&#v5c?K|LW4`{&Z$nXfZMoW zeAllZi=<**jmSxb)^rnbq%}rFlLfg}N09Kuh@lagx8XrE@>7>B-2Wa~pGin( zrRTXnA*IaSU(C}x2vlbRfS-#ZPJ@tp=;2*9d9rb5X&A<)1i_8} z8UkcGd}p%z^4wZDb*NyyPKeAv1W+db6^Hs`Yde+pAw}jsrtz4C%|8GDCwY0_+iB&P z4~JjlQ{=K_dC_&)@OXC`FNzR;h>qbR|MXZ;QL~TI`Fd=p+^^t}#m1K()X@Rz3}JdE zA@9q@i6g=38G2DT00ab;1==C#zmDv%{JRgbN?aJRsHhA0kP#(Hk@FmA$lOh3%rzZs zV75pC91UOU79LjKN*%mxr9$s(8YL?HLwxy6AmZ!b`Vb@7hFmZ=H+EazcwoMjcL7Hj zHs4=Mt*9zlOkzOI%Oj(=F95YSEMEyPceoyZikC70`ZaUDz72U;RLo5E>;?ermBxR4 zQVY}ZOkIS0T4gqkzj@~2YG6SR%3C_4(e*wRXMEGRptzdg5r0l?tP9Q&2p?A%!wyNG z$*ZL;dL}^N*9HW6yOyz_PW_e%SI(pSsYoAhH^VP&TjFNcl$~s<1TmF7?9b_DNzY8K z+sgCAu=!j)@oRP$?%qR2q8^Pr`#X3OzXcp7-d#H>?r8(5Lm@osoG<)WD$S$4cb~v2 z zVOX~MmwU8Jlh(}@zXIP*2C8pgef z2B-;Y*^w&+c-l*B8(q6gGR+vliS+@7V05L2z*b%>#bcH_>k zpW6MNWdy^BrCdJ@8q*Ie`yS(aa~T&LO4<({_}!i@Dy|kg(**JS>*Gd;Ug5|4`BEF7 z|Da9qcV7uqdUMF8nV;F1kd98|{Xm{N{!er0J_IRt@xpzPot66M6Oxek_Jrrpv6wjg z7iP@==gZ6=HYE<>$z(p>9Jk`S*c$Z)@7&c5JRP-&_Ec=EB7yJJjqG;7_9l7uCJW+R zZ$A?qulRyUT%|EOW*-o|j#>C23Zn4s86}@j>mWPv+xU7^a3ZtfC_UZJ`KsL3pO}Dx zW7nl4rTeM-a@Z5GHatBu(r-Hv# zBbF{`{l@auGdj$x^$c=!M}UcUQ4=@6r(N|G9NxY?^xXf=!RVg<&n+{z`b~}-eT+kW zIYhDlK{u})L*z|OMy|Bb+}Y-UlkLHsv0i5I0NJ{vA(lIng>>aH z=$X)a_aFSa89d8$zWo!CPA<|AsBxeR>kW7+y*BuFOWFQidxMAoPo@&??bHj>m&EIau{;y+S z$lNXGRT|NvdSY|ypN)j-j9$J&{O>uRyt$(u8#R# z+}@}4+B)9kTzYR zKlbAdqT);Zu0RiAe_pEefG5l=G01h z1lnC{o+}LLWUo|SuE)tEJ$raTplzIPPVh*~usI;p7`UP~K%EK+>Qw!B-ey^eMY8(~ zvxApb`4-Ow&}H@2^8(28?WSx$Am9Eu4Takxu7xD3W9ZW@u9a$4S9K%h>XSIly%!}kmDIds3u?6sDWpG!? zhWjn=TP)5uDtceX4f z+QS^WoQH2|IOqF0Fjf_`2)OgHA^J# z1iDqcwelnDMhqmcfz$cX#tw7=E6>Xp%6HdFhy8KWGjzrf&(KSLu9B}$?uq33$<#D3 zba~zrVP$d9sHS7i{d!&AT2^L$;yks=p{&cg_fZHRuKEwC*>UKGc)mo=N|F`*Tl^YO zM~=27y0}xiyp4N&iH)0XBxhkDXBpf}p|$gMw$6X@yy+=nB;xDYbJ&W1K+z!KibG&6 zfs*tlM-o?3QH^Wi*xCI;?1qC|{TidWxe#W&KXuYE zni)8%v|*CxO|3Oa9nBfmR2*u5b=Ql@WK{Y+NDxS*OQfex_O-COBzqnd=9L0-UJe?! zf7r>SKNtC)k4ub&>pD>U(_%Cwzk-j#DPHJPhECv1LU3DSNV4;h3Oo?fG*GHG)NiIQQB|(siu`>=L|2i1Z>{EPCp6GcSL*Kf~l6WHc z%2<|Z>7sYq(W|xljP?^HMR?v*cH^xDje;WgNw^3B=9CM{;u^BZdU<%~J2+aZ{O(h; zjfvy0elikHwOC7u0zVn#O^GDCmq&IF)-4XN;Y5e>H0~R{dbU2oaX)orcH~xF5L7?l zKn594PU6)%C=WmQK%$HHZ*PX8LATaqJI;{H=?TNl*d z*Pc71P48)YTZ;p4a*(Uzri%B0kr%$eNBtCWkRPUnceVuNVdB4IN~Z6K#h3|KZ=BAm z+YJxL8oSCJTV9^tYH2aiFb zTbLTo=rK}mVuz5Ss)lZx@Ilj|_vJ1-de^38?V)EqrV#PEePL-lu9t=$7>i=*aSZ4- zhv$Z4N{r*O(l^Z92-H*{U~b&xoA-6SpbG(>5PQ*9bW#m4H+YGu(#xWck z;aq$6v{V(j?1=;WZ`S)CK=PLPfWbx0SvHLN>bCtuKSx-v=Xha6=dQuRyAg zf5-=!w*S~buWTsr`qQls2=FQ_gt0ox3Q;$o!H+IIra)HLEf!9MzdYH~>7?w@J4(SG znP{n(v)Fw(BUxJu=)@+TmeF2AO>&UYzS9xb$h!N}1gA7un)|(NB$P!^Dx48#kvBZV zA}3kxek8hd+bO_&`QS;=Vp$ny%VkQ?l{hA}bF=NIM-I_zWsCA#Og9)o zm|^{yeIFB({h78R)eyADbw)JzpO1OtWE8g*JJNxXTan++x)NO>4jAvR4Md|X(1|+n zu)-!hZOWRnj0ogfk0YbF>#)UjVo#}Oo>l_rL0L=Mr2hs?cr{?=R|7wQ=2@2b&Mn&J z__&fl;FMux%JNIK#WX9ce|<&4cZ_z@LIlTN+gTvu;Wk2KDU*esU~J&IAX`2=|Fz^| zn0wi9gTUX*bvI{0iK`ZrpNcL+g4`9`VpNjX0SPFTVG^-=-8IQ`u$dCCUL(fEgaTVv zRYK~iw8Tj+Ql0ECs2J|_%WH3_w(8b%e56p94JdSv$R~yDhLu28nuz*6m#abTVE2>6 z%WUU4lW~0O?BPG6uA;N=*AW|fFCqV&y+8pUAZfRxj)-Zk4i zxpxj#qZZ0n{)|LVq!B3~5T^Yf8%hMzjzs%}C}SqhAAe$LSx*R|>Y+_33mInRBqPf% zF;TykN2On$^@p|Kq4aRyPUyJg5aU6Mzkm4c#zar~i@Bjs~wElj`u)MV5 zyC6UC>!=Uv7GhHHMiq%WOjTs5HOAFp0noPwD~|02()2p_<*-yxBh|9 zdsI1dJwoo!JNhI!xkPGxzpa1ImG3tdPlE57Bc~|sQ>r#Gu+a}hiCgj z^-Y^*cUFnL@$CKECZuu+V)BDA8}-O3Z`9QMBn*CuQU}k*u}=p@kr8k{6Vi|LgCiYB z{C1Bv;D;BNiUZzOq__J#;hqoV9bFXSn#>E3iKGO%H(aktN{dz;b5oTC3Fw@tf7!MK zyE(!~?+U*T&R{Nrz9kkhIT(iIMp!FRnfv3Gyu)b*_R9t?UF4eTY#fWT9V|U-Xzc9k zT~mk?A%Yum&fp@wruuCNBAFHn!(nw8klajC}=3emZ6e0M`lbyadq##VxAJE#x zbc`Zk6U~Sjk$`zZ)?kiVy89ioou9^qTF5hbVP3m&nX!gxdyr8g?X_zq z0a6(qUr)lU9}`4YyrHtb)7FeVG5Eh61o;2VJuw>LqS$y0Uy$!bROr-Eh69I6BKNawgP*nrbYi7dl*b?)z?mU|p(W*c6w ztk?sJAJm?xa_yZ`(f?No8G<0`(Vmh8B}_4=tgNiDvGKHFzVRk{?{RQhBd(E?3io5k z-QnSk(>VUed|mZjPw`;uzjo&UcsZkGKL$rlq;XCY>D_3N0alJ7$v@3QV@81@8)0}y z)rUVO7F8H!UBQAmzUd$p4=uXzl_CO4xxfkz!e&OZ5arHhSv-6pFs|*ShWt@ z?sY4RBMjvK+f{g3OjG;lqkT> zzu&%m`7z|Ro?NWxkb+q;hg{kifS>)QlDm_>zHBFN$P|~=}kTnhwMq&vEo?%o+LRr@j8MC(I zZNy%S!@@q8L7Avliydxoe+-hMEPl)Un^~GTjB)j&S$8hoU+P8^q}UMmBmPlj-gR{; zKIDi@(yRHYoa~tonkIb~p2YT6VC-_;cwhaB9fJ4c#$ZQj+f)40HeGmwGo0b(6z|n+ z?wS7cJ=Zp7f;ZuT4qiCKv3$V!EO?YAWh;&)<=5g|i44)tfY~aIZ&SD|ctwDGl=gFc zRC5!1Z$=et=uwX#Wa8&4(AwT$&?my{z=SI^PM31ZBs+=8oIG!MIZ=U+tii>spX5h; z6k+sG4Pm{UC~cF6ofWD+g@ zTkov@)*FN&-yR@kciO)%JjQOmD<3Yzrlgoi4@o1Rp8#y9=@;07sHqBCFtsq z?GbMR%Ogn0U%_^F%c?9!V;{mu<`kNa@tT8;35sz|RQ?+lYef+W zqmmTX$B%<&Xbh)RUNE6b0`RFaW*vKG+ZQ>~2r82GfLsEI^I)VN`+OQ;hp}Oi3-W2N zZJSu(-uf(htA3iUSpBIZ*9Ammy3QFp>AAj%Qa$OIVE(aNg*=<>_ML1AAdli5RoI~U zC(v}TT|vc6x_IL=vknxB=SySb^tm$@m1q)~_1h3$n*Zsm? z8iX2o!Ef9iLbhEa{cnFU-Y|u+p;*&aBbyC&M9AJslL+=KX0XF z;>eBpCzsKgT`j)v5B*Qg?W^q(`0u^}K$(6oGk^z$jNiP86V4JAjH=!0H0jQX29Bo? zIR^kbbDZCGs}_NBSIHN1XL#I*ElU)QK(1=L-=g_#!I>!Y3S`+IIY!>fU`$Pvy7Ygv zyL@2iOg?uSkE5Vdd_Z*RpV3rrUbXM|@&SWOnA*Qc(=7V{EM|$HmTnBiy%bhdnF|eVv0KMMA22sD;oOhuUPGga13!`neq7#* z+7|zG;T$08{4+R{_M*15=uW>mNL;Qi{8p=rlbeFOAGFXl zB4QSJEDCR$_hWjJ-pWJU8{y^L<{3&kPvJ!O7K(ap-~=%Ibp#62`>01&YfqC^>=5$! z=Uc`YN-mEhTQsIXLQSOd-Wv91WM?tvt;5@|5ja+SMUf^K0dtfGbs0Ta*9xhBi zV#~*^Mz~gMaZCzHNEhnnxDDY|a#47B0rY%N?!gXqxJ}%-H=QQd=b`1uXKqXDH zTms@#xH~@U%}kQWxa%eQ3c;C#VV)kQbsuok?D@2AsVHyMQjCOgY6-L3^n;{y%Zmtg zZo?BY(bq3n{+O@(%rAZ%m=dA3vu~^)$bu+!C>v--2-{5dk07_vOPp|Q!M|c{5ef>2 zHxW0>iG)6k;FK(DFT9&)Jjx5n&j@NbvwU>) zN69VBc%{Pr8|#g$v1(>oHsek{<4AQvNTsoGR;}`_d~-{Eb|wsTrfor zZ%JciDfj~NR%~}R2%edojOI1n@(|U(Z_JqcB9&arzV6*1ZWZ;KsiB^^PVvKdjwQQArF@GnU zQmj@fw(buQxAL4?2Jg|18lJHkGr&I0Xx$;xAc?E&dL{k?Jk~g=RFGPH7qyk9GH>Gd zs%qpyJzHhO`SW+Z4p@=%33UDe>&VFW$^i(`el5-<`-!;^r`+|qmpTwF%bTZ-;bF$G zuOD##K&Ua;wfcI2vULV3k}BL{^>nZ~*#yqjhO`smy@9h*!O^1n&&}x49_YJm2_U-# zAp?!e6ASVo{$o5%Q}-9#(nwLbCSw(Tt5??!XI*>x&M9hosXsfC)n=QtPbbd}gx@ z-lrH!;#?6*U-9nHn_s26H)dD(Q`&FS2-iaiN$%aAyub=D^WcMB7X{{spC_@qps_6x)>fUV_E2dHPQKf^@n=f zU=TQ>#J>$mw0o{{|4f8=tK5~|VKRAqM&gGQ&fj%QuNgK*y?d0m4Gpl8t#6f*x(I>o zIRJrammS>Adn(3+vRetb0lq2AirS|D;e={0VY0%1Jn6mQr&nbfXPG!oKO)`oK}~u6r zY8#Qo=`uXP!@fVVIjiJYXVUe>HX>;@VRUEb|5bP8e@z`*{M?%a0%{Qv2`(&J5nNbW z7b=*5PsAb!f+EFA@EOcqyg%WcbMN`#&dixPXTE3d=gw#D%zO=1v(05co3U2)m}my0YUGFI_{SKz z%&J147xqR=_waj%g}AfOzm%v^Hw4`*G*!v4j@-NX7e(Cy4?_ zT53$Fu1C~<>l-auCK}_zW$Ler)y}uVyl~sm%3q93(GMrgj{a3Qv-$OjSF=}gPVb&% zWBj=_><{ZZY>Ylm_c%_RjobTix(7*>EzeNP=e%&Gb4NMl_PDq}$d);`xcg+_(d9=g zTVg?EYxJ*HaJWw8zZh6PaXaOaQhDaXT+y_Oi{3nawB0KyLsxtx`7+kw;1*8zL5bSA z#efR5l`Y9z7R*1ODs+jG-Rrp9*AdQu^1kflp(;|s`ZVt}t?S{5vJlMET%{fx*i(G4 zi%uEJ^&*`nskQE%6+c{QaJg*xEKZzFE*Z5`Y`a@Ysy?2I7=m14{MflX4Zt8K*+$GO>he-(Dc)b@9}xJT4$s-Z|4NTo_ff}! zUS0-$WMm}Sb3Kz;YlSb6NSp+&(;UVgH3R8iMccHved8FBt_4zJwLJC@fN{Dv@uA|J zgnn-!pWj;Ix7ab%D){bT$HnQoQxr`l(R#oURT&cd<^zz(J#l6lAdNw%@m^tGLwpBq zy6oxU($mwkzsNWJ!UcOK^BAkYD6b=E(WuQdr)q)q{F<-Pep<+JiveEwThZZ|8GwI7 zJl0%U`K?P%A6^!U9xgXUsou2^EXOztaImHok8<4r2ulo7P6f_1FX1`r=;*X%&y zrb70XiyBvJsT^Kma~Oi$%rq_VNIyiff{0GBBL+{Ay`xo1!2nrA!j6pq@6Mc8&7q`i zb*!=G^lK6pxK50iiI1O{YM7I#Qn1r#N>^azEIOB9mm2nuONSTocjy zfc|CXbY}Q0EU+-O0AR^p91=a*PubV7VhOhfb9pKh3atRF7vmt+K$ahyC~SW0Hyf4& z(C8+a8Ff^-5v^Pzv2v3P7!&X30JJX~ob(|v&edGNM(pACLahqpqKFB(C2bAM#UedD zy{)ZnDKHoqbk>xx@I-}zeAClr3~JtJxGq<${|rbf4jVF?a!q(c^*{v|BtF86rk}-3EnV@e%2&(jm*0)?16$p2K6zjss$jIVR z6ez?cT>z%bDJ+O}n;Gu+4U6RtLl&s1{M=i#9b>V+&#}(0Qvvw@xn+=iL=6j1A%JFu zT%0wV0^@w-18lXww7)+*WFe)3JA6hqAI6*jY*QMOVsDec$B|Xiw7Zi@43KIbrZYI^ z5!@-NtE=1XP7B{teDB`dNplE89nJAcbH*Q|0me=b2-B*kgxgLSia<3S5bxHm>cg@8 zIIHTeTj<1-kOrRMGjt1F-jy15zIk))e)+w7!R6sE{>LkV2ILj%FT_97#G!J`1fe%BGm31oDOH3p0_`&kuvZ8A-hK$M^> zo|)fb-YJvG6pCmseOQ82y1E^oHW}5+0M2*`!AMAbNOReITwmAL{($`Viv!&0D7*ms56c6^EPIBl` z@pi?%8IF=Kh9neou5QCQqE*E`b)HKYBlCh}ApOG!!?T?Z_h+SIFSip}vAAnIfX7dP zCptnD2CQxL^6dMsy|k;eIAu)fkH~nB(m&yBDYWd+931Q;08VWj5>)>eGwQs7;qHx zPCim+)t)9E)CtYPihS3w!XH8q1j&7bl6&Yzb%C|HlyJ_B!35Ir>x zPw~mjc0+(WMFU~dhAGw5#mNXl^T!yyF z`qdY(cpQ$q18{UoY((+2t2p6^K;3lyPerH#)2K#z$6V1W&wV(csNaMH@ozek9=aHk x%hA9D4o*}gvwuMX^if$5q456y&wN@<8EgIi*`-B(H>NE6)@j?On;SW?e*-f>KXL#7 diff --git a/examples/jupyter/images/mdgxs.png b/examples/jupyter/images/mdgxs.png deleted file mode 100644 index b93d0f0423065531db8547394919d68976f370a0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 23788 zcmbTecRber|37+(%xofiB`Yf=LM}oidsMdUghI$(*`vtH-Xo)s6fV1vy&^lCkiF02 z_4)jM=X*Qn{BdsQyl-`TSGca%>-l;fXHL;CThV8=;TJuYR zWatn~2)!gW@&^enUve})IR~K${0%4c0xfGGnQRtH0iRVq=KuGXG^5WB7nyf9rkLNo zdzW~_>Oo!o$Y7yXh4Zpz)xL4P=dHMw| zFp#h2u=Z4E_1}D_+g#*LmRKeDH9bEcgpEfsxcZ8ORuDDiwQpEgujnH7?03lJ>yM`U zvK95eJhw=VreS0x_VV(|O+ODNrWf=%^Q?-Na+tjR^mk1~w|fbfgan;g1+B5?R#20N zqM{;$q-#lcdvsVBT3=s3HCirEv7zF|9a}{X3e%~2FIqv9J9YJH_wEtw?CglRe#2ny zd^npfB4-=80X zH*VN%?psC3a!`mK9KSibdsPri(`7C?DoUZj`&7`Xn?Z?#qSAg`H#6>SdU|vy5D;m%}T(Pd#eBd};joli0?_=Hym9xAAjcAH$LlANooS zCEtrXUFhrUbA0|BU99_|++|faH@*E}YmS_eF%p&u0}ov>ib2-Yl$DL0-PFN>6b}!t z>-{atyLayrj?8@Okq$^rW#qh{m@!MA@f&`w!n%*`EEx|Mw`pvQ4%TIJcX$5t;|jBj z3=9k=rlwdgU%q6J@}Rn7bB|L*ByNhIl@%*h@@23*TO-B~+i7=2NBV=y>hQsAxIpiR z$2avKKQ`}56yoCJ(@wexKf1F#fM@J|nA`8%Mk{@~qg81)Lh>`^rlO;x;J<(Ws7h7b zKRo{27R#>axH&D$_qh6!r0bgRmoK+Gc9yU*c5WDyn_zi*dYahUhK+xAnEN~N6`zpM zKQfZ|_wV1E>z}9owTFj=H7)gJ3%Rbrw$sm0N#GZMwfD4YBtk@30jIUKHHcB#Q{QIn z-Sx+bnVGEPl{S=bJLjJ^2NU1tiT}4OD=YhSWw3DVaC?zYQZi+CWhmVKcTMb+p0RQI z>D2itGcPx{>BWl|ksm10%74Je%NssFKVLCo^eQs6)l64Yv&rb2TjSHGPe+G}^$zFu zm-+%u59Xqkw-*)`+AdtUFgjWH>euP*CnqPP zd8#Q?lPVk(LOwn|?-CN)2?+`HZEbmc9$0j2e75MkY-wVG$;!@7GT^r&y3A+t;K2hv zHa36i%a=dOy(STQ@#4ka&azhSqSUkB;?<5bXm9tKrl1nFbZNe8*Iv*6`6KAy;7|=a zBWP6+j=ZkE{&#qTIPbR8qut`YI@spzc@I)Ml$DkBot%V@;YrY%M;Lx}jl3lx@$uFE zU!gWy6%`fp&(9{z;Ch`Hh=_^jKi?1)wRla+Yza?g4fdh=`dF1Y?8=CW{teSIqq@<$ zSNo&QuSt`>ew92vS}luTDCX;-;WseR)zw{_X?_ha1Y1&HQea;$X&QAiMqM8g9=?IF61%LQ(9S$EkJ$>hfor6QP%|My`nCBq#t(&(l$3<3qT_xleRg23|L9TE_wNM?BVXb0;u8@C zC~+Xi$Z4^g@%s~DT3Xua-4Wy0nVFeQU$n1sa^eanU4q(BUQyw9pR>8ze*C~$N=i!p z?%hTxe9XgF(IAm~34i|D2hh|GM7m_+L9x38#F{BWGvL#Bbfo zgm;YJoBHzN8k`S7$LTvu%gZy(q%tkri#=QI%s!jrP(ZM+KdugpkEgzXjqP-Ha>&zD zXE##1xxe2YM$OBU3ulxZPI?ejvP+jPmBT(=o2+}B`NZJyTw5gUZ$_5O;Z7TqQYUA} z8~RYn;G2-*1hrdZX6*RjU^?>Vq^}YO+S;0n-{>2D#sZX!+k_?;sGv6g{%vsE^MLHj zmoNCFq>BTOs%%@&CrTdPKw;iqQ;T``E;uWT4eI&U%?56*JQg@DQ}t$|s=B)Gnrg$A zO-)Tbb*A7^3fT^F!76h7{G`p(nk;JPIM+rDy{)mO1qa&O{J@6~A55P$E&ZOoSq`h zL)!X3jqY^Ar_qVwQT5Z%my_;z`+q02lGWg#ct7NzXoPN=l$3N*`){PRr6osUWcsa| zbRz%&$MZ9n!36!!D+swsj0a+pe8fZDbVm|+}=MR!0F_`Nt&72YeysZ zG*`0_E$!FOOjJdDMqTAF#RRRP`J+WARrk=39|R32|A@}7$iN1IT8iw?`>Lwc(&=yB zP&~1++U^uRe4ney4VSnD)z{n5!$V?iecdi@BW$F^(;mm>HWem01P*cI)D-g#tL_#+ z1&y@L&>@S~eu}}4Dlw{~gEsKJv$NSM-AmUdrMFjIUS1x9!T4^(X=p(A5RZ~mRwkr1 z|Ni4gUhz=+R?GGrpZYrXvD7pIKivu3$wiIDXxVDs(A8) z;?JKya2)W$X#`50rpqF|j`y|}O)V{P0YV}t92%Xt#|{@E5s|62b%^E9WbXB4ak!_o z@z114rGq{qeSY$SmY$y1({kler8T*j!(`XXew6Qzx7X(8qGc9*I4rEJ*a3f_rWY21 z{rxYT{Oc6p;^%LBb5(^(BBJu?FN(s#LLF~@5)zWly}hFQF;S*3Uo{t1+fS(T?z~ex_C|}?OD@d-+4|rQR~7Bm)BQet_UzPg5lQ3j{dfs zdvB*VHlj-nzrx~RL$&pTCf+ieV2rH&$?5)-j`VADxFLXs-_DL!%PTA8M@kLhY)1gh z*&c^V(FnLovycjE=>c@LnUxj7j7mIx=mfoAUpnYNe26^kkuqaEl$*P|DRiKah={=4 z+^f*!4^pO~S(Mw2@Q;p;vIq-*?BC_u{&{n18bNW;V@eIaG<~KS`jcEFkyliCk?1mJ zV1N?3@LhOb)62^b^Q}H>e{q__HF~u!3Va~jVXEHk{B*ys!N=#D+t!5_i`{qO{aE9b zV^dQjEjr)nj7#@|8@~E4Gm3Ta4=6?Fu)bRj0~28 z%VcLvmWbD1SwBX-w*yD4D_0eqdEo9oMz!}Cu0nD(fn=mON) z0H3o%yZ~I{CMdx4oCB(4$Da*(#KmcUCX345xr2gNJ=pnJ+5+&%zJ!kgom{R!U;?F< z(9nR9fgw)1GwYK1C1&pf)@p~TC}W?~s``&g>SI#hihAauq0qTA8yin4OED0z=14?p z27Qm)xm;wEy$Q!k=*f4o`s3n{=DKWO!WN*ZKkTJ(Ijh2Ugw+(Ey= zTZ&1>I|BL_YzDm*YI*FJ6zC8S94$fXh%|OUWhM6G)qJ9&@pW!n{?Mc&4eLE0E>}ZK zH3yudn_LaBGQN&%b$jrM@WZ;ldPA;Ns>L;>)t$*Xks}}SR+npa8jL8XWK@fKR&^)D zcHJjdwv&Z)8}=Wl+Qsq617Ik5{F&tJ>}+YUkP?c<%-_ExN%w=H;$A^H9_`pbQ!s;G zlqhULSnIspPy0|ud15kJbfT9_a1%OW7UxXmtyZaiK&Jo?evikwf2FY z6M^zAiRv9;RqPa@fm)Y_*%;dW=4#rygQt64v`7sp&apt_b7TF?8)hi&i`@jA* z+is5{9)r(%n?F`gTT?rCSJ?Q9xJ>EH4SBlvF@IB4OK}!vXD`Gm$qRTCdcsB%*&C@e z^4w~t5j1{Aq7jFm#YJ8tUs~}=UMW>X`Q}ku+`D(2iN-WJP9gHOhxoV&Ei?B_e=b~S zm&Z>wK(Xf(7Tw#NJrOVB;qaHmiiqJ1`B8A$AD{W6AUYX8sjTF|ae~8*Y)!g8T-8#V ztfJkVy%sZ}XOrJT#q7tlzZe4UMWfL%v9W!3Zt;qGpSZzt%|dT39P-{_)ObL(`_Vo| z*|AndY3iJFgh(R==*jO|7Zp=OZSA-Y?nRIUgq&vOYieo`=E=mwBtxArRliU86S1O>?&8ygAR@kvM&yuGE7JqX3^kqawL zN0|sZd_=|;0DB!dH>9mzOIXECT!1=UYbeQjm zk5SHbbacc3523zb2W(3EWK&LDQnI?f}4HS;N`vXQ`Ke832x%*w}5+j~GYHlfhXZq78`({QB8M?ba%E=15+>QWi-`ditBsTCSyg`ZG$pa!H**LllHfSo8I3 z_QOw>)8Y>@TMpoj%zyzw4-70UEtVNyCsQ=8B` zx5uB2S0h{P4^by^xzFex*MfPjE# zY0lqSid$#7BuQlvKIB=%1@v{7`vwN&G&EwHYT=#%ccW0aq)ZV2cjP!IW>(7@+F;R8 zak^5$fq@vHVZbGHJk|h%n1ZNeJ^AfcsZkx_>{*konG1U;JJV zKEZ(i)qa1B|aFbn57a!a7ld5;H3qaog*N>ej7WC|^Az_luU6 zmLLov8cocbH~v7AP`TL!PbX^QWfa?Jfr`e&$6rL9@J)35Q_2u*%aoXyE?m9cCq{+g znXVbr!5VfwE&{T|Dk@41RBCZ#qTVaTc(TE#q5l+sNbK9U0iY_#si~>WZ-JDsx%TVE z6H7~g!PS}`Iulj^q^W8f^K)~z>`khxtC5|atC9e;Q*ZuOUVi>u-Tq`V=zM+oYRsrR zAV*P6zFZrv7_GLa2fh-v~Z5;Ho) zQG+_`@)ao$k#~F#{bADzwr#>v&n~9>w1tqfYyZ7r(LrTU=Rr+CKyVUS-v!4i4Dh-f ze*(LV3^TB9e|a`P;QA;QfONnCP`-cuJSlnj3D==+_g(+~6(}XuPk;Ht?wf|~O}PG; zEf)VRrz-l%6ONCcK4GDtb%J`Po;!yuI}``F&zR2n(QocI&d!b1r7d4A-R%7A)a_`8`)Fqw8vtsF;n&MouUs(rAI&srOLA@TA< zfS}*%>SpHVuz~!4d9la_JQy~=cC8oSc0?_OyU7OFbn?l-!d?yTfcA#vrIM18>6MkR z$Dg0gfIjA#5gr~s_X<0T{w4`PEjhrT{rhwx)?^?L2m$p4LOug!K}Sho_2KCM1rC-u zGk|>7l_o_Gf`kI>BFE+at5BV|o|*%1Nt1eo=Ahutad&f@PI0K01-!;VK>(r{8EBj+ z;AI$6nG;_?pmJ=nCV)q2r9It<%WkvEhgfrvWW~b{0%g#6RYOEXReQ{OvCx903siNa`9Q?-<>J)`2w5~ z5&Y$2HE3vrOOja(7t8Zxm8N`FT4eu6_RayIgGa(}2Z%X>+(D((R+jNT5l855X`cnZ zK@AGzCTRH=;moyznn5o-4ib8$Qouesu5&&lBm@I>32^ER2-ko+gG?jer=}u;XOGOe zfvGX@x~EV_0KK%r3(U;S_{j#~Ha0b7WvT^`HEtQVu?i7v{>A3txgs4r=POXpb^3B| zt>burHbj10#0{nL;6X4zPMHUW|Alqb7xaHou~08I^2qST0EGiC;6xJ!Wg1M2h1P9zHm(r(-Q_9WBPB4)y9yV#dS0^nlfe!zmybuzU$W!(gJG`srG7f`ag=PogESET9996 zqh-#f2axvvV&VSi_;@okdH}^-+}wrRhum7@Jw{6#D)@cxam&!oj*ip$-|&1TU;e$r z6F=;B-X6`&&ccG~e0ROrwfAf2ot&nf$-u)P)4N*I#h_q6R!C$7LEO+nbxa+`uK`tjhvt&P@PuhhU4 zDx}0)C5{r{;!hX5Q%_t$A2ES-0^s70NYmF1YGQU*N7UvIt+!w1@MHXZxK-ObX>4i( zcTOEP|34$_3#d^L;k=L6Z$AC?@uHN+&K=lQPFu5gBNw7o{;-5m=y((+x9a}dTj_Rt zY^0!oySFigRGy;Wd3i%7J?S#-g92_XOTZ;Ngz}Z+pGeeY55OWZA9uq@`08KHm%Lw|5=hZ`@;12-+ z#Qsh-M1yWSRO9tv%7njJ}yTIXOLr z=Xp{K6kHG-rgy2SKmShFb$aZsv;u~Qvuu=m3Y@$Hx=nU@cVnZUfM4C=LdqJ{gSi0^ z(G%fHRn*k1BDmjSc)}tgoD;-^ENk@fGVE6nx^Z*6UT|M?TqWGh_PAJJa9 zqVu%}Ot-@?7{rqas=W1LVH!>$fW+7RUI6}uJa(-4jO!VEPIqZgCZNB9G>ljKa)ShJ z?%V0!I5e0$;g_%Ttf$Av=k^v;`8GWV)fzw(R;c_1_(e}oA0p3|E2VaRvaNQBL7eRR zqe@>;bL-EJ?BGFd9x@=!-C>HPjn>TE{O||=K!3mEzd!fRq`>3?u`l3we}f$$Cqgiw zCQaq9k&~c9%KMyos2#lxKBT=YT;Cfq<&h8trJH9*>D$W(CKhd@&8nu)gGQ-*Ey${xN}v|Z)vhla&~rB-M}CMK&?5jU7cqQ zo`=s{+S+D)&X1V^Jy7+Z!J;9`H5U&L8U$3`5`8~JS*Y{ay`cW6sP@%9KWh5VkGJ|C zpM-XVUVVM(ihpNjNPDUfPQez(WAkXjoZ{6GNJePZYe@_9t=5 ziFf#|d$nPm@&_Um7$#}Yf~{^URSlg~Velp*pw6S&Vp({22si4FpT)N)%lJqmOR}=^ zsL*4pHJk>Kp-iDgfj7w@?udfNtq2eYg#v;H%3W|$(v`Mo=I9>j<4a}rM@9Yn6~J+z zR&MqwGB<+Mi+tPG*37^Z)Ej^zG(a!QKYm2RwK`2>um;KZBmZ|6Ab0T_$T)lkHDq9) z!-^3oL^ByKpEd#i*q`!Y=imr5wt`Xv6_=Htp9G{S_yxKcT%rI91vH@*IK-jVD)^=9 z^HuD%yh0U~R0>yxCNlOsO}xFmp@G~1;SzvMZ`NI+o%L}V#O%!5F1VabdjBRAFyblC zZQ>|KNnMAH%;G8XhUYKsbe{zk&iTX8_}N%4Sp;=N@>F zn*ea3HD!%aSQ{>_yv(U3;O^8z4KD0-;t6Jmgh-|79ma+StJ}xuk_Y}!K_8tJPlRRz z!$KSzz+FhHKy@KBMwx_BA`s3JNRtf;jA*4fcg<0^0B#A`eYWKUuX#E%y1qR};^X6w5SHwPHY6XZ=LRaHa`Zm2IL8IV)bx)vgKHR$$p9x-so zT~S1YglMq(H^=PCWP$v=b!Q74#MUuqc~|)Q2kwf_L!07~937{mCFv^cQCN?1vsOaY zg&;D`=%TL`H$#ul8thF%rx|^^8EZ;C%2NA2e0Oi)(I{Gfm6` zu7m093}?PLIW?7@&R)DW3U6IrUOxRgMOjt#L&A$}D%gD&!;NVzuXsN~*Rgmd?5a8zz28$?)S%w7MQABUgR)}l@ zLyj06J3xfy(Dp&cxnOL+h^4R|y zNh4r{kXE;o10hWQ6goMZu zeI2kC5(Pp*7NrsJ0@9~UK%{nCOnvDHb{5CgtKdn`0pvu(`iZ*Vb9tai5ffK%a(qRn z6qDDvl{x!`n{VKSnZIT}Ps_J&x4{xYH*Vf75i+X1* zOIQ^|dP00fP;Sf)HfPGKt92zJ3Dh5{es`%1a9hLN?}a0lH(5@n!(g0kn8gnS$%)msnX@1%-v1z!ZyT`USc0-^vQ?w5?7Hog`EYXgAPY zgM63Q)*?VQ@%{p&iSQ5`1$!jOWxid8&&LxOEvz^4BfzUAvc&{tHF$l6+NS5`0$^ol z0lS$*n1a4}5*UKkwb=Ei>#Umj^Cu9vd!@rvn(@oyJ$r9=kT{h0iAYHmA3Pud=x#h; zzy%ry;xh~1xIum4ZqiC^&%5|P9sa7IXMp0KRqk^It^uAB%k}G#q;cT&x)6$4*c;th zUi%6AjOts5%~~Ov&Wul~&US(_Mw9v^=1c*unzpXmrxNAIic_=6-OsuU7V)hWh=;Ux4&dT?mo_0`wka!yS0vVMw zHehG^_U{#l+6_;C_&lk3XcQp?&<;^FA-3dMrf31!S|O4y2=>t&c!WwSD$PYY#oE#o z>aHh6Yf}x(J||mvVCYpqzGVln4zzX@RDxp8sYq!a?l2$G;E%Q{jeB=+l*ch0M)IqL zhC$FX)ZRa7C3mU4MG`RZmLR*Z2ndiuJHLDX{v`?5wF2kkogHTg!jZSNwRyT2 z9Imv&3Gv))#QWaWB@f{Je(>>?8kr!uxgj@&1xTuZn@I8e z3j^TagQe_fs(vW2QQ!q5UMVE7FaY-sDwme5`3!5R-~@oswJ3Rk>pg8`OQy!Xd-p)1 zz3buOk^Su)p2Q5Oglf`d=T=R6#e+1oWQbcXEfT_r+sBqvgS8dDTljGWA zv(FcvvTXZbB!ZtR*?d%PA{)7<`d{q^>IH&qL8)(PZ|8kF2WJ7`j9!s~gxe+yWFJgz zYzTOHdBL#cy_7EROc_}zONUeCM-Vo4wH#;xH(x?r+$Q@S?Q1t~Xl1HY1_WTgdHZ%~ zY-DoMDAP`?kQofnNL?@dUim`NzR??OH{}JCcwA!t*)n2~)E}JR8Q6qKqh3`8`m_bl zZD@(MXe@8#l`-1+q51w*qIy5fKm)(wIr(;jrCD^U(*@XF*xl`|1Fh z%CXN5(NKqxb6EE6A0fi27TJS=(po27zBiJc=6C_ z#i9L_E+kzIj7iRb>R32ny)%DHT%4ClIVLNDGpIEB)rp8;Nvdb0iu2cG=%}q=M{cm-0DK1$Pfyp3Og@ zD<;lnw_az-y*J}luwJ?aE#NE8!t(OrKbE5-L}`}Mqtu9o*cS9Xq?!G$dqo4t2H`l+ zCUsZMq5LArTSpg{yU(8s;}X;1fOn15K}4Mcm0Tvu)YJ1e1gsEbQK26(-*qDCG9VNv?kj1#OObThyUAITI%=d{OOo&vbWaH zqF0?NAx<()QLL1YL3fzDAPeLQs#PsR)?|T1l|Mu|?enHRWtcv$jb+si3r14SbP>BVh{vg_(aXuXI8?e)d zAqr;s>{++;!E^wfA+P;el=ejiqk2r@{XB3A=2i55JP|NfjYsI39_tyne+cDA79S<< zb9NhCxuV#NvBa7d!re6&16{rUs%pj-RZmr!r@bM@04`$Cf1JdP;ScnH9kzxaJD36r zJYfJaG3Z$(6m2LPjle2&>e~TCB1ihY)T_%N@emHVf=+M^;K^&CQeEjX%usFGz{-n_ zi$esE88B)=OIH^+g8hIPjj&~rJ^+i;48|Zrh|NKE6bG1`J166|iDGCs9W6IS(ncW2 z_LdpTApZT~;i3K~OV!9ynnGratKW*onoq=|e8@E!tFS@bRC@4W;VXjZ7$J(2+@be& z7p@=d<^SM5K@$|%oO+Aq55VjLz0tR!p`qyr-B9H{TpCU#K@`Zwe0s?UXIzU^vL`g}B2v?xU0i-}FV{d2O z4F{wV0)bs(Xo1MNf#b&#uiOW>^Ir00l#DmrK62n8qhvZ*pkX~#F9iyS-r_3flL(us z=I+Pu(e(OWr+2OVr-kPPXd9x*5)Kd+Gn-;s0 zm(8l|t?8Cnm3rJ`$J8Z#UH<;Q0zo`P8H%L8iRfN#uU^>+J49y9p1ZjimR`@BC2#|@ z8AQZnaD1Zez!6VVQMKEq1_bItDH>5PcD z~Fg;X&r?p+LMu|z0w_kSGVP-w2p zzX6&p58x6MsdCtAICyx)zg;17s$XSG4Mc0}&ksuE@PMd97Ov6|Rpej813D(@aH(N9 zBv>JXO$5orR#+UYI9-9sT9;zoc6QNbr`$TmPNU=udwY}j%=u&yy!>z5a$O0!plbla zXob*$Xi_@ohob%_NInT!cF_XBKR7y?op?EQ_t~@3j`hW!bW*tcn%dfbpDYq|1nME8 z1tMWPsI~~UfyFTaL~lJ*M2%v(di5gkRm4Am!2{{zwHx1#R*H2Nq`0{70URPw8i1LQ zX%kNT_ycf@;5^=ipWT8q+$A~@rObf4t*h!2&tZ9xTq@-HvLH_l*q0PqGZyNGWfumD zWrb-oF2ue1qxm#*S7te-ls*^^GIi}k;OGE)d2DiW7_5f3muegODZWUeLo<1JYmNIh zC(JnvOm8rPIR_xNkip;);|^otm8jxg)_`IUL1WekSJDC|rJ9{9qJQUICFn4SXz~?D z45DI41c36|gB(090vmrG*H9Xrfv@T*0lJYI^hvsfS^_O#H`odb$Y&&LDiYWakG`<5 zF;iG-lg4A?gIfEX;WaY70vUv0NDnj^vBg^Ry>OA$36VEMpGH|fmo9C~TE~hgBC84q z(6{7+cbiv6AW?}Ui^({Utjfl`mu*aC9pwtlNp&Fr%tz)bKO5@EpLj67qf>ZKK;FI# z%`ukyI9UE?=I7Q{)Ey64+P>5@H62 zZY7P7)gN={G)>``)6&*`Z{pyPz{+Gwxq$e7aRDO++I<9+HWR=kC|`iiBW_PyKnh3B z+eCaoKma*2a~T;1syg|uL&G@G)oFl(D0PXkc34aeO-+9!_5tA$C`~JAxUBw~PAEhB ztk4RwlqPjm`4=E902xHWYe@72TF8j)6tMdsn*~Vsfm&1YoF3oltGDhB1$McNh|NH6(qna&RD;4NTLtKox)>ygT2cN_@yu z4E-&(jhQH_=1z$dx^?Rc3?$Wn2LhpSU+~Xnsz!{!F%GZIhddyK_6l1S2&XV7gNC*b z<}E7jfxGd*>pC$`Dasl5C;KsgY%mOX)9>+Al@hd-$lR+z(eTVNPtw_ z>!`9H)fRvJV{vh@eKDS_e-mf?uTC0CwI9q0uvHYLi9RZMu-_A>c_a^;43b`8Ze+k60879C zpN0`F!)&kJot^S$6B0lGwZ+;h)M_b zvkzVib;r$3{QM?sE#E!U4)fBAkC|fi#|<&B*>c4MKV-S09z)wgG$$zB-?qCYkr}&k z$C)O&4H@fR7U*fJriO>#9iBW1gcGJa)&l~~vIpqt{qoD9%7!^&gK4quNIc`#^CwlS zbm1|U)K_yjE4|w0SN>h2!_?iE>CjQ8xsYnzUK|X8#40A`qAu|1>?)(k(D;R}@1Y2GneTnrhsyUP(s-OtY(M+OyVwah0FH9`LBuMLc~UQYc2`NRz(t@1@?Y>Yraw=4 z%YkZtunRInFeJIWPj~eqtuz_YEBAof2j^{OR3W!lirxY92P)X}=g$v*CHeJ>l_x)d zO$_nd_^}>88%fsFFqRx{vLkhUloVc`y{+Me?X9gwhlW#JQ0jxo|8y#ZLqd4O8kc}I zH;@8VBTu4nFTQ=?(|txF-Wn0l;E?Vj7Zi*yOrhec-O15(QN8VG&$tDDMGJeS$8wWs%{9aWLCqvH;P&5T5~_k7oWM2BzQ; z>qvFsH3S`;P54?qQqU$8u+DdJQ%$L3ewC{Ozuf7Qel&TL>v>-Udkg5rCz{W-y8yiPjl=25^N2`SJ7aT-r zx1u=d7WIx4S2<*>XRx5)R0F@XG&9Q*OeKWD)&7`4em_8&M1ZepC7cV;)LGTe*);uS z_~_&dnfqBK1cuhwl>K#DpzuI83Wd6+miA}FM(d^r+*xg88=51~THa+{u2%g5$d~I` z;Fs-hOew-?n)KXOMPYz#RzPkd(jtP)aJ!Yp_scC73hsLC3H)27P1V@q&e4CU+T;fn zLZ_*U`zcfsQj-ME`!CK*7I8i1+*MoWX@SI|>B+&CeyuY%3InNiV0H>_ZZ&PfiXr?n z;?qCgN-$sq2x*L%K9v6It652lRSxG#zzjJUt*E$ME$^Q`b*Q~FjtP`sr_cv^6lBEK zmzg6YBakyRirklhY%FMJsGB!$mgrYxX?lQshXj@($pQlvEK*XbYG0uvLpltp5zec_ z#Ly#=ra)}D_ta!bHwV>$FJtft`3ETznh9SI;3{G|{d$OPm zkb`zuWj8WxJ@wxo8~G(}k1D8ioKiksNry(a1ra}l8X#P%xU8NKj6sMcWegv($SXamZ=xfrU^8_4 zo?x9IaZ%6(QHh*8kX%ZRW?_?9R21l*-+i@t?t+f?Z+qJmX8k~$17wcO!=ePi1^{}k zYlE8WIAz<}-VT6ifx7pkti-b84o5>(es{0dmCy|Cz0i1k*ywpm$4goDHZd^-iXNoT zAJJX)g;?MWv`!GGk-1yIFJ$!e#pS9dCM+B9jB~qH>5+S_-Dh&|e5Ey@u2@ zGEVYeW!LfL%h&fg1E2?k3P=X=V3=3|w9^CuFyO~16bPcoEHsk34R1wEnR7u=*0GWk zFIgIFeHVKZQC3RV>)cg-qJmXl`+Q)_0x7@)y!Bn?9Kv6fmePW(E*Gz=${kux<{b%(LY zP)m8N2r^A}^v2zLm1?P+l`zdGY2>x56U2fDJ&{i$$k_OX;%h6b{uF2o;tpp(U4&E8 z+M6jSnnZ(keq=Af1-X!cni)z>JeOqBlJfEx<}2A^>`o$*17VaX6oV`Bxd1q^!FcBshEU@hSEy9@3kRot;05ohV<(_5g2VqU)q(mH(=(S6lQ< z%#4mG);bxCbC@qXXgEKX0fhrMLI|1p1SfRh$s~RscWp1mSmBp^oJC`JOt5CYQOMYR z=s`I)1*Kk!>h>n*??gJ3HK`Ng3x6CENPS;LixxtgjTEMnVny$?G8_A<%x>mv5}<~f zB?{?q3B83|dGZLYa|lgwulKvbCl+YR%?IciCMFe%*S|YA6F7fQqxvEL0z5q~BnzqT z(pbE9mWGdFn;%b1T2|oBHY@*dZ!?#Ivrh!!7RC)xKq+*SN!Px|veR`M7~&T)CE&}s z*o&imfVZ+n`+p5V@=E`CZjg*aW?Ck_kL`n&wK*t46@(Q+N()Op?$H#buY^Gh0^mzf zf{dGza#GBI5a!Yy$BDnJhto&w;?=F9)Z;fqlbK-VGNTv*>tdULc{Wx5;fE&~n_E@V zMlpAPP>LjSC0#G!5gH(Tcl%+H|ogbv@1d zu*i@+;44rfk>ogN$`?>jcy)>w8%Y?%eZi#x2_I1FTz0gyrV`B7%IoXX0!zRK?+!xT z*uaE5!JLtha9Ih2fCy4VCI(n?8nyQsu1Hzs9S@bue}V%9B1j8Z;0TTpY(wT+!Ac#m zhKBV2@M+NhZAkWGIFKlXDF72_NC!PWX8|C}NPB@GN_*R_y!O|%oJJiz+krhzL%G{{ zoNnj3H?H~fo|awC-hDf!CFxrS3Iz;2-~!^uZ39HHl(BhRUk7X!WFihYHR{g)nZ5;P zly&CC^`s?)9MLP4>|lTuqEjOijs}dcoi$ z$^-<T*KDHVdosxg22B3 zW>qu!m5d_eu3AT}vQk2kttlfH1GCe3ra&X&$A)KB6@&5r(}V_}Vbj9|1~B#F@@KH5 zqIA+S zpTZ7HYwk;t7VAx1&0^+G)>&=zoSU1gfc64`P!m8iCmsO)LBwkwAAdZTr2RSuH;1E1 zeZVAXs%FGQL2=%;n_?WU6A|6BY!Jsb27Mdy!MC?ESlwhBc?Ja3XXLlnAiW zW)onh+v)G`Taiz~$Ugkht9MGVD=`>M>E|is=@+G~`6B;ddCSv+;lVV~fj_q3WAlxX zn4+gDITaM5%IheYRNMm20Uw`%je_xD2;h}eM386M1qxoyDq2hsX@rQF8ezKjmq?X; zZ4N^>f68EnKst<4GpGbVVMt(da*|fa3f4Y~*tb_fw)(0>t~&ucp+)CP zOO}8<_MIxf2j!rMWJ3u632}3C69XArm@-BN1^{T<3OfXnF$#~r60>}zRTyR+HHLKw zg_IHiG21;px%v6L_ub)BZb&@3i?P({6BQs227Zkoq%|O}kO@Nq2-JbG4$zfyew>hJ ziB@JthLQby1D$N9DF{(vk#SUz-bp~NY6XNSnsgIgscimPW9X)R;znn}@%`vyQJN5m7N`)fM0ld-VlVI2IP8`5$gqr>z#qW^-} z5eOBRCzU;dEGt#K^m*Z%BPTLejbg$IkQ5*)hRiTS?f^dgp?KMo94&I6e&N{m-+vaA z&;-gZKq+K^3L^QUsn^hzlrDbHXuTPxX)bC6*%SgZCM=Qn8%q`M>gwvvRiXJ$j73y* zex&T-Ck6H^?DDIFPCx-O$OeDrMrl`;O%urZsVVDC+$~ zg%g_si$m0we}()MzaLf$DqpkU(@{Yr@0O}77WXDRKD^(VhLv!bcu)c`hpSj zZ_EAK)#@g>8g&Z4rG}B?Yu3K3HN<|koSOiG%0(0gj+us`;rjM{a`Z`OdU5yhc-^ah zVw0B7ze!9MVh=%M*tW66XFa2&vSTc|l9PjKQzRF@VAX~_h_|5i3{NoW*L}HdA4-Zf z>+3C;PiZNfyu8f<(Z@tubWSOY_R_0*p1X&W0eL!%8iI`V+1Fq@PVBjj-)d^$a_Hu04PW=X7VY-BAg zu7bvSDfPi?IMH+P(L)$5HK`y7C$T|V9t3&x$(6v6s6v~8NuPjB()<5z zdreRk0EUvhEMxB$+(1CT3WjE^l8c6WWX#)wi;%^?>aaLfHM>h7)kmRY(nG{h`6W-Uyqpb z4SQ?JF@{Y&p%7-+b-H+5=C1H_bkIazCD6-+uhZY+Vdke9a@pX%{k^@}FN z$gauN1enGI-_T6&U zpIZ_FrY$;uKsHm|1U@AMA!l*QSEowcAk!8>C))9k^lk4s4jZuiR$ z+*j9BpK!K>)hW86e3d&#ZwCkB_Z*!kj52=~?$yR4H2F)OLmENGtr>uoeOZDgDhN%P z#mjM8LruOH8=7Ro>jVMWhTqw;A*_DMWCXD+$udvU2kkNkfzuTEk+W&=+1-k1^pK$v z?dUj&#F1ISW|ua7)ZISSd7v*q|0GMt#_K3tRDHzhwfnk%o`}RX-nRrt=bh|uL@&e7 zCkXL1J47YE+lsvqEY!^_JQWt6s}XKGcxlkG^Wnpd*G1nOW6@TAIAlYZkj^eU4(!vS z_~Hcbosiuq#`Mi$p~aE88S_r_~_25RN|ggk&ME0{U{ZQt>d`F1f28%VE4k0cqED zDojX^wN8`t)83ucc+tP^p3*E&x8G#P(}lcP5Vhs`JgtZoaS+k#Md-<#dTCmGe|y%J z*_dVY6<6+~>z?%?=^D(5kAYDogysfc?2UX+hQ^AhkzLAQ&7*rGBc8lyVem&3lSH72 zKF5C%c7*v2UnV2}NYdL#TOM?0p4I?yhrJu$tj-A#H&k)Vlh=uMK}v zu{C+3?zS~Ih1WKPQ%C%PKz#d$UjLun8YrUY!*)l9FFv5RR};_Ht*=Wc+7SnzKMqeb zj~;leCe74~Qh^VWn7WB8I#enzR~h}dVhP)vA(!ox%QuL2;89Q-j-Zuj2`s&l0Q-=+ z7Fvqy$Dmz^2(~$2HUSb;JI#I-7c_5M%bLHl%eRr!)oLeiHYb`DRZka}? z|6-9wWATNEr(By~qe*EvzWBbb;ZsMbdt}d!!#1563(w7{ckX7iD|2jEceY#j)KToW zx9K82y{h<3G~@C7*^@sQ+1_!Dn9?){hooz(p>2chcfXmQI%T>Dt!JYY+|16OC;I1Q zvn;GDGft+d$%g$OO`Ll;)M+2ae`BHprP$^|N~FU|LOE2D9K&iUd6`rbIgPNCsVSxr zB}PJV0{(hzW^}21gy0scIFJ;WB?^Xxsy*!b872n zflf34hj~h>EYr}pR9;k{IkTdq-)dg;S$bkwRsxm z2~aGuva;wT^uy6n{(z>wd52^xLyzUbV2^GpoER@xP}}~gs_JlE z39=!QlVWYNwmLbTwMk1$QzJwMm4s#hy|WBOv`hc>YUCcwIVFQP)YsS3rWeKJg)VE_ zr_Bl2#pD&l?pNgSxLb6hy>EwyUP7HdPlL6U_IO>Co{4R}#oQW+ z{aZ_Rpf`=u4V-xTd!M50$r<|Pd(UB?=k3$GXeI32^qn$<&gWIJSp@~HgS`-ryRJw= zGI+zDn;Ts=dh=P8p?Ocf(_k94F{%c0`)}ULSMb?)!K3X!1eZQBV#S)~Jrt(ztJ|2J zoy)cu6c*@idM25gaQb$h@M(L;;FMHq%k%bN=vIvw86E!=rPVeRbglF6#YYA%sXRKn zpEbJJaC%8;ik)A;)UIF!?}PU_t^r2#R;fwt*!l~8nVl>)P516_;wQG0gz24#V9-IR zCMe4+ln9#p+I`FHY3D-Dyk?8n8WstLJ*d~WB*RW`H5w^#dtdpMIvMV3#&A(}jcB~( znB263v$)Yg%ynKTTEpn}>W~hUU6;`-qKSJnSD)W6%^i?x^!S#!otQi`oyV$F8LGSV zzV+%o>9k&~SIqtAFMADS72e#TMXs#Btte{i@_elCoZTL*$oXOXhDn*cyRhZO&FNwf z^$-c$>eF(@?}n;dxJ)-Uxm*Xq#c$^5|1)7AEM=MmZRq*9@_XUHD=z)|p|P}b@ojbG zouwO?4Z0OuPkpfM7%Xw9Gt02+pjd9xq1y#DhBcF~oC7>=a{X#XUD=W`U8?B($C&J(O(TOrkg+1DdcZ2AUOQ7^dt*Ei}PJ$li4nQpNt zsIb{KeP+5V>IB&D>;eNPk)k>P6V_RjXFb-?%y@6uDglogw1~lm3WhhdJ8I zT(<;jcE$V|s!r%th|jZrT4+D~>eYRO3g$s~$SlzM7NLix5z@S-uA~@9t(vfM0mp0d zUmwwL66%y4*`e)0M>b+5styB8nFQkS@Nm?lq6cxx`GF7Ka>4@1?Dz~vPTQLYzLRp}TmjMjLk$!v{D`QFA0si|Su zAT53NIQQ}TAn421-cc#p&4GsXf;aWf4iCcZygcV5GeOkd0beEdRU`Wj5s7<187T0k z=L+2cI-N70oCSU4!5`t0JRq-#?vhUq-^yhMx3{;GZFkqMg`Bs2YpgqD2Lejxsv6FK zdF?&|7A!w>{(jfz;>C-xIXODF5Y~m~XTDb5&(DHO!#SMOFa6f|XUy~siL{Z}@OJ4e zbk*5)tzZTF_K!Va_TbFyw(xUm^ZfSCzeIFYL)u?+T{%SQ2ICp?C@p)gYjEbI$N2+u zA1`S*Yot``?HZhs88)hZ;U~ZB-h;AE6sp~l$WO-i`kR}hAnDK!Y2z*b_b>Lw{7`kj zcY+5Ikv7ql6drRt&2uhUh5j5is&FBFDEn_StARlonq1=1T!`obaW=I%rjo`LOQRmY?==B2eS9^96dT`R2dH}mY5 zAYA*OzXai?dt zb?hIi26L%Qp->SA4?i@s1IXB-W93Vba8!kr<;g570;P+B9VL-Nqd>-V1sr!IW*1O1 zfn{M0ZW`<+)(}9ML7}<;QCwVHvTxn8M#54?#C!YCMZg}O1G9*rD&VzcGMV!=G!)O; zl0yNzbX!~7GcbDtjpSD12Nm1KEzsKjjcAs^I^=LsHBn38E|Os#3_}xlddF{t+m7Q) zL6eVsqUQ?sz&iz#IP1?OO>&VLNADNE^M;1`*PA!R8}{NDK#Hsw*#(S;8xpHP!pCz* z2fME9bay`kl!&DG!ud)vmZq6Uj@+?Xs%@Q0vN@ z7UAJWrioSHAZMrgOmw0{#pqFh-#fFU!CON7BqKAKO#pcO^XzKj!}xt*VnT3RQ_sLb8~iiQ!%!jQw5dU(#SAK`hB zzX);wbAb4#BYpYl!2k}icrL9EPy#V=8Wd0kcLA|z#iKfddi!Ma33$sPbv*m%G!d7O z@BrtJzJHQWbS+XolQ2cbPJj>dhD;x!PfsYygA?mQ2*W7A-`|46Db9MKX>IrymlY#z z0{#VtQ@zgJO^%Wh|1F^}vH zUbjMX2Whu7zzQ+YqBpg+Mz^%=fX#@epExDJRRK;$xYC=EDL~iD3=En>4i)0mCsC>R yMOxRs{eIh0EVG#TTJ~+HEpTH0yT|=9J#!Dw%e*)KmOSFhDSMmk*0(KLC;tm|z$YvK diff --git a/examples/jupyter/images/mgxs.png b/examples/jupyter/images/mgxs.png deleted file mode 100644 index 3946a5b3c6d3c28579957643f8a15507de01b98e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 54562 zcmagG2RxSh|37{kAtfYHHbqw4c4kH?q)28)6v>Y4P1!W;S=rgyTUpuJvPV|--uzyd z&N<)C`TqXDf1gi}a}M`?-PiRVuh(z&ZY$xzA4j~$-tgZi&2On$qELj7kiQryV#$Um6fNqul(>>@^!$+BL(=Xe zvE|-}!IPh_uq2LSessRHB_daS`SxXnL8AL4VU4`$HT%K%&Vyo;K^=KYgTB|%WX+dz zIV+#i$SSbD>ln1GJ#j*+U~SK1F<@?J>Z+x|nXLmyVcTGCgVr*A%AZbN+y>ZnPOC@3$y_)Q$Z84ps&9 z9ps%7-XSUBjK8lZqa;Ui{0`mA$mOuBUJ+2dS4_>en7COSZywZonXV?{9=t9247D&` zh|EiB5qokI%a!pPDM~EZI5@tGTUnKdUsora+6&FJ;Rj!bhN$wNbxWwKk{6hbn3_jv zJMWyBs1K4JC@>kDp2p?p=kLnWrV(-2tRd5Obo>07%VQu$)afD`jegpn$1E%&qEKpW zVl~@w!l)-*+`=Mfrg>#$&C{MeFFHj)t$&)92 zB`;WU2&w&dH|DCVtFg4Sv}~s$43_(ivc|r>VR*YRKprb5b?NeD@kGTB+Lg{X2RyyJ zBoh@ME%fILHZ6@*23@n9dVeR1Z+Ugqy|h&5S9hA%>(@kO2b%)|cB=_pD<|JPAVr4W zm?23gCm}_x!tq+@RZZWjs_X0XKS=V8dh2Ta$Z|{u_$UR;Q8_s|ss+Xot$b=~YJJ6) zOcD|j-}8-Bl+u(5db70aGc`*&pZ~mN{NwWpA?x|?Kc)uEDsNOeAKX({_pNZ+(_J3@ z@~x$%7G8$j|08(VzCyFO8$Z&NQr+1#3P!65#XOUflZSVCje6e8gs@y;W^SM3=i)j` zM@N^USz0i2KwO<58pdxrSoNAt(){N);_dD2_clwzc=-63dZ!KAVr!>bqH^?GF0yG? zP`+cj8+yruS=gDDg5Q+VdZGX2`npy6!{W2=81HnbRX*!xnI9@8JJ{Rm-Knx)=;syQ z8ni63nmz08?#}tF>q^(B2dB0chpOA-rE`pbaHVuV%QNh(Yxk1!p5T~H6Msh}3N zt#bG9h@i!r-&?BeRU^B2u@PHbCyLLcLoGHgt}T+^%+#DhZ>lw>34V-)b6N-957Y1~ zzu0^X^M0z5@o1Hs91(U;h6cZa6Umc?wl;$djY55R6*0rj?7gj}x%`HvCcTp5%SSk&Sa}>+6pzj}A+%ca|!TTDzX{I_%CS|9~r5 z6{^IuS+dBRisBm9~4WUK_hzr%8Q%e8jD-`EbrqdLwUcYC1Jk@}io5#F3$) zm`DG|=O^3n>UP)~mzbF1)$^a(n?MCX8?;iEZS-jLKk3cXtS+-#TOPD*`v42m+|tsc zvQq4(Ke^k8^Ipr;#Nu$dP+cI^DU`R5Px~1r#Z;vt#~m(|PIEX~&?WO0w{hBd{>NDr z*zzeUc$QPm;;^Jgr>0(ohm*%x3O;E($MdY~mJpj!l`DpT^?Z1Wz|8>2Jn4tJly_3NnqLNhakGZMCv)xvmm zA0K3>KbWqZI(P2eXz5ag2NV<7{#HHZ%U@4zFBFbsTTaXSQ;VpV3X6%+z+{GT}j<0y2qxT_w>={ASRa^F7tipvh8ud zBAX@Z5}Tz!I>}eGaIxgOaW^F-FozwML}E;6#GK(%>}mP=$(;5!UxtN+SvT)ChOnaH zscjt`^!l+4H&{`{-v zW;q_SVaoeSa^lX;q8%L_L{wA(Buw%;E915H)01Cco|@@MJOwlV(uag;yg%Ql57yf5 z=7Jw=5X|aV1i5ptPFf;)QLaC74WdjmRwRc%NP1;IZa9mST`1lj$;nKI+Y^MwLnWlM zv$Fz@+nnl!rY0K}^))qBy}e{YHj6b?Zdfl~@hY)xw-8{U3!J~d7Z-5e7l2OT1s^;% zn;eI8Pqc=GjqNnlG0Bk%Cp0|JV1*L}6zA)(1aqABtk)L?dgqHfyuH1v`f~JgOos%U zqxcE^(Ud~gr=eF)=2xO2dwRTbP0-Yc3K7KYGf1%);JmY`NKpG z);pVPYhOMwFrYBD+?-eAvsrw&3Gsf4U&@>`%ZwYRsY!-kqq z4AauJu&^*5ukmRf8HwvoQwdraD7A7<=>7`IM)70}> z(zMD2g&a21TT6PiU1FeIGs=a(*W4WQqTv!J>hq#;PAMF5cgNhEX=hl76FXSH4@J{q z^M}C?4_rd4{?O3S&vU(5Mn`2jUohb}e%{_XiHV8*ll*4GT~lq%&0P)bc$o0HwY9ag z>rSwcmGhrHn3|hQ%hh&>mX*A!SyBV_JTo)%rq}rkmUBJqp9WKGT(G4a;Vr++CbC70(}Y}U-wylB$cS6 zs=*NShHxkzX_}>ctZZyN;q~EOgfyRT23*WxdGIM4`TPDot;cjAx=uMmy%z34R8-Ws z&i{fjEC_o^alF)d1T@_bWJ_vlZ0!5~K@wV%@#@cpn*$3&r79Y(c&yi`W7sXw5c#aqZONoPo;yg(=R_{S1}hDD-nd6S2c>z4f_XLY5Hxh=_=9O-|_b7GahIoduMpxU>;g%RGd#3j%bV3}LA*b=0 zlPFGskPlf|9Q_3*egIa+nj@h2VqoDQJqr+0UG}5uZ2OI89(oSE)t6$OcMUzN&#~BV zFVnTgi4nTExbT<`s+u`vX_Z@jR!+EZaph9Ww!LL%oSNm;1rwP3nUPAdUtOtKDk>@q z;Zrby@z9a3UAtyK&}TomNsEUmXtS7JxDVS4bx-u%WW&zQ!Sl_WJE-+{KK}@Iy7b#JB*^cGYp=uUg^TU-lXA6 z|5jUjBH$wL*ZCj0W6&^dhV5_x|A+$Q#YZ9I4a4(6IV}gOjqpa-eN-cjX!JEpM&uT1 zYNRePGU_f47VB?qC4BpK3qc%4y_vO8z!lWhpFKY`)LR^>Bt$K5ZIP7+OsU2u?C9ti zmlQ$mo5?f!{vN}5e?=PBh8}D@1l6P7J0I+N`}uXs@95vYX?cOCiNMLb-M>Bmag=ZR zwXnONGzzb}uHxQ@j&~a9Mf-p=EU9oA61?xl4@!yYI!t53b3yu7`l zSH55@V(NenmRxhqOx0B(!u~lut&x{iR3xwuK+QWKp!SOg?wjCXPk4ZLmiP9yw{@Un z_M?BEIB`O?%oaV>78lg;j%fmFGuLjpINs7sD-T_Chj7>9XLYAqV)$AlO;F2Fqu!^d z%T3Qg_rxiT<1@KfUjoQdC~9-TrD5NBnQRtGMs zT~((GPC!~?k}-;31p*!xj}o!_*WnUf#{6k)R;4L-Gvu{I72>%P#*g zi2KsgLI8x2U%$aHy6ky_K!@K3>Wn|ok?a9c<@{D@VK&QQR`!)t@ zLDn3wX#2+-r{D@$$?`E=5nlkfi;IgF*>AAG04EoUGB8Lo8QlPq2V0_3BlB8pfyqGR zU}1UdWLj1QJ}O+yMU-5Rao{{DDaMt`R+!Fq0D9#@Wp;LU-x?dacIm#oov^mBz_=&s z;7?akQGwDie-%i$Uga#Mh# z@`~=7nVFS7W{_%sOr4t{03hxWP@J8$=_aa|M7cMEs57>z!t0W0r`4GH6xr1-Q9M-(z95)mBZGky0dA>UHFo{x@DH6^cf}5BknZc~ctq zKcmS@+tzu{TVD>{0zof9LHyxPz-{`&T9nq;+QhgyI5^@7X+J5kcpQ+Dk>%C}P;lp~ zw`|V;(93Rt4_Vg_Mc-U|y#kaqE4M5?-CyKrf6acR-XC3HHLGa7yKd-la4>RoKx1iR zV*nsWu$PuDgdiXy6Y9iiq>kUZg%_6Pak_#v{6K`F>FACoBOgC0-|-7C+fXCJ@lezJUucBSaUhY!tR z9-yM*zI^%8b}~eT({bC}!~s+tQdVVJ78ZOGM!D(>Q@X zAOgXN%aO?8R^<_$fPm`WcQTZ5f8I$zD>ngMx2GsDZ13)lw}~CqK>HtET+HOIbK2it z*{(d=6LSP)`E;-dO;1lh3e*fh#3@`t>Y5^p2~J>%s45tQ0GKRY7?Y<%CACFN1^6JuJ5;1(Nl3vMKCM zY-}Ll3>{cyINY2JHlv8j1M@dlOQtPlYb#J+M%=kDqFezD5GON)iSNspkul zIU`P6RM_HGxHOKmKhh>oDqt6<*>M0&0pVp|mJLTT3HtU%!402RLU5 z`1?*I&m#nb7Z(=`E(B7GG&0IZe*==(0s!HQu&}Tyr~-Y!>H>yO%E`;?fB5hr9j@9u zG!(=8tjjxq^4i-N0Sk#_%}0;uG=Q5MK^tCMUPkyqx<;WX&a_KUbQV1FCT#j#m`P4; zZSASq*@hD9g+}OqS%m>2t%=t_qGV%d4bFwRZ=H8@<}b z+skA9m7Ty7a)1`*0u8%Z#ywQ-U=weX#A`ZKQj6$3JERg}C%8y9m@e3$B$bQnR^SAiK z+#0=6bdoMK9+&8d&!~s(Sn2Ufcp8p-tXHGGd<}!|R=xm8zOXH)e5ijb;y&@QLY+AA zIlQ&0N%G-CN|;=z2DnhUJUy}FP4x8i#+H@{;ZL5)fb!n5_f36$#$yQuuqmQ)AZq8+ zc6$E{+;tezlE)gjZSABnHIz5iTzvwh-~rA{*DURs$@T}hI0mZ$fjW_VCcZy^K9-V} zj)(dTd>qvn=Sc%&2hv>!f|^0fL|abDc~Ozjd0tU~)z?sHtgPH>Kj=x+WT7U9i;X=B z2o&Hk<-=0zj4##>oAY1Xuy9|$ee0f*as_oAU^_5g&kNl8Nc{ym11)KJeZ9*xSvhTN zVj@09C+@d)FImKv#LC56V7Irqu)Mk1GyI|lfWa4UVg_^IZ3`3$Eb5icAuG>SmPX z+2f%$RLat#syx_?dVJzV;?<<3!D0`X^f~q{h4@$2<@uS~m4WbTM<}dt#exzV>0kZ% ztX$mOlYw*>&FnA^x$bKju7r|}a&S+EP@6?CXXV*8IZQQQ@Ta_X76kBQUG-F0`R$pS zLdAOC9~D!J8t|9He!u+uU+l_`L208Lsuh2V}gE{Tz z(%E?bU!OIH7g7>#+Wm>NHTqgSe|bAcBYnF!rYL(y4EG3V$g zp-w=%gO5*aH=SAzg9(x~w6j^l5f*lh6$vO*P}jG-lC^HzBxjdctEZJZ$IFtGQKJX; zk%eiw16D;vRVe%B0>20q9zJFyf1z1rjc9AENE+4I+qa&KFDwKw-iZu1p76F07(gS2 z7F)a`hT@=Tgdiz7FMhb#$e|O-K(vU%d2ne$gmCEI?o0%UHJ|TNj7JQvXiu3F!sz=c zntfwxru4)uREDv`J+j{9k^B@aV})0hXUR_0H^FTY6s@$a=6+Og`(oD>rJgR+tJ z%w4m&%bxs$ty3S)3Ab>^goOM#F!0KfB2;2x(tv7|T4u6YuytU|7&`}D+4G;mo$IKN zAu||K5_o1fr>}6>cXloenCZ}R>SAZ>0J6AT5Fhs-;qzGs6QOYj*G2H#yx+ZE?eXcW z5ZfoRUA|sI=}9#H6!GD_N#2Y<5|`ce4>-{plk0*bf%oRrpVl4|AP&0!%+?%t)5bc1 z*>reenUp9%BTtz`kel5H6}&GX&&+&3=^ND-`m#xwF_@LayfR&UEh8=sL93eBmxUdz zIoHZ2GmJmy=AvKxQp$VUjtNeT2hffEgM$^2fF6Nd4-n_MYN#I>heVaY?%OtLL?GxC3tat4_JhV zh9(GTDlI!Z0cfRNMr+UnCn}E)vZ}cZ^%?p2By+sHaLe&yNqCw)kcFXC$(}SKL2?;U zIM1FvJNoOFFLX3=wgik(z*sf0A`VueCc|YH;8`WXyL_*l*8cg4EXeJ6K{R4L1ERFF zDCmB`&#Rz;fOw2mR9YH;`yC^62p{Ihj-V2@=5Rj%^xk*Ir6Ttu`&Dkq>R;kAMx<;Fqn@L^?_75;JK9(qIwqf z;FbV?yT!%PKmi$DS_*=0q6g{?&Yl7bOOi=VCk2tseBYmI0$I|<48|t<#fyBb3zSjS z8pDk`fS$l3{4)B*W2Kq9E!=qS*RM|pM-kix^n!vH5j!wNA)XT!0B7AYG;MlN!a<%D z9Kd&X1qBW!`4PCcc!Y%Y09BSjh-!}HJq508e}RboI$|_opq_Rn5r9&VuMNPpd#?92 z)TeaS987>x;XHp=oI^YuSevwf(t&kU8D*8fijJm~ixs}W zaMM5Ajt1~r4Ul+?y-kzTG@_Jdf%;3y}yJJ&q_NkR0*`vJI6t*C5}5SplN z$6>zf2iS_6EDp^{8B+isD4U7kRwPe zZ9W4OKZNxL2Vb&}0?PuZ->K859~YX9^jzg&W_~m_HU_^g07*N>jlT zS^mxfq-ST}D?KLAV3Z;75#(Lf(*5yTKZM7Ukmzwr0!;vNeEHI)88$)!f+xUA5wr_L z%P?$udit13f&^g=V$Q-UklpaXyMPV=0t+_1efJK;=5*k3>F?iPH=qHSX8>;pWD>PG zHaFJ<$gLjOM?$YC6H~p87^zQr0o97gb(^FZJhMtZC{tD1`i-pY!>gG4Pm;=NqG9`? zQ1B)u`zRnoSg2#i2p6UK@L@LM;3~0zPd;nHgohc*p?wA$O%-5`w?>D8KmCg_xL`TIDSEQGYbS75!0%gYD0df^#ueo#zZuA?v;1(^%9KE{guRar2m5wbkfnH)5X6)SVI zHjf0o6?!@G&sEZ6Q1H_fin!TuE85JC)dR>4B0a&zy9|Iz?{kK}dNJ6sZs=ElV}9jd zvst8s!9lVRG436Tk);)zWf~^lz`jtUKw0hQVkzr05U>Zea2zr7HEd?S(*=iwe9$bl z=39uTM^+cy7Sy$w_IO-?1bzTbqeMARe9-OpS>Bxbd*ybFvvcTZ>R46p-V3cru%d9pnZh3p0H8JLVI<}{&`7=w zy+E*00Exbdh)7*~1j70`1k~f7I<-;IPI;8BmWx!og;l`1WoxZMsP~rpp^J!c56)4~ zfEhSUFyyDii9oY3og@~b4v4U(2a@n7cw=VI3a9VsZ+=d&aj*XxpI2d7NG)9Vo}!{V zP#2`^gIXXCpdPe{mr?v?LfWN=mHvv1&pZwMP7Vpt*`w2PT`y;cMWCNh#c(ch`0_`jSpI{ZnSthwi@yv zaJPZ}{-~@e z%^Nr1`|lw!fFy(<$-xQq4=91+fHSzkPX`LvmD5$-0Ns560U92p4to7^kA|5c&1NU( z*4ML=b7Fh*+;VZER4@wNtyM@feOH^8Qz4f@A!5%1trY1pdSJ^#JHtVS;KZr(IuNG8 zDFl5?9I7ekC|fx>~p5&f3NJ?A5*HIow<0?)DW zPUe{5=#n3#(}#rg84VDRVDaep`#jdyZ~Ox<)oN%}?KVFbas4)MZ3@6-jWA16%r}_u z$v_?Azp8bIqPiCPL>2?rz}nUaq-8CKh7svflXl8q;^lp?hhVD{6S*2uNqpe$4wEiV0s%{Zzq3QjNb6-S&8Oj{Oo;ahQQmC z$lxZD8xuG=JY~wlhD|N}TId9wXE;(Na$dOGNl*OUOOOvE`#~I0dwwI=0BdY4Y2eBV zWfXjdgtr}nI{}~li96v*ud79z!gnnb?lcJwRyqCqgZXLuuki7mhbfV^vdSCRCJ_P< zQp}+0Sy+a0L1<%ec<-R*BO`8EUQek{WzUZhw+zgsj0`?VZX7!mDd7bQAheY~@I`pC zlsFInHD0sp!Dk89Xg!IT=)0RK$~I*2=r$$b2^v~kLxtRL*#1RbfLpq5L`+69dlp(Y z|8wKu0R4Fb@M6_4wbs)5{~4@)AAmLze~x_OB&p?=s$oRShcZv1Vr0l|qu%=b>lq$j z*5{jCepIo#YVFMG0eYP-aPyg-3<^g_N66bD^z7XLf)!zB*#fVIwtgyKJpe3e1pWX- z%V&{RP#{JY@ZrH;^~eYfsDB7t1fhkPl=LNFfpes!YTf*A$e?RL1Nkt2t-`(r@A_Eo z+p`fZabh%LNBi7m<>d-lS|X1P3?w;MQn@%d2q8Fi9e@eqdP0>4U^@Z0;9GNZ4Y+mf zkTCM&+TR>7Bj*DHk+63E28{c$kQC0S$m2aJXzJ+ZPaUjpyh{aN~7L+{dcD zCIBrUo)*ZQ%Ww(3ubwBL*VB6v)xI-|HI!(+Ehi^+u)9GFEEtSMKric@KMnyP-hjIi zg;aqD?{(1qZ|qfCFI?2J9Xl305o_kwE!U{1D0zxHa_hxGAJ8$>y7(1vE>0iLeR9D1gkd^toOgObE5kl<0^UlG!nuIsCZu!IhI?SpQOFqY$NPMK|v799b~omL0pW#7Ji`>y5;AvOduUAi>t zaH~h|jfExu}M+yqx^8Q*I0^#lVQv2fQz#YdseY#7n z;88UWyvKO3h*;ZcgA_zxOt2ad>lCS;z>3%eX+nsHA~IiTTv@~3fEFAmIc|czBqX4|1%i{!6UUP@Ji4>`5^!1%( zDgy(wufT)?MGHvF};6op5%{Q0X7y#e8`FOATE zkJLLA$!%~3OjS<^l6-VII<#FZo=zVwL|lMnSdB(>k%Po*vq;;}WDhtEQZm zkvm5sYoH23=m=5bzzm+9HIl$XyA3Oo4D^g< zzk`+y5!NveLecNc%W?LPP$&T1^NMni?U7A>jpGX3YBWAb^ed9@0#R4Uq+F^t z4+8{7@RFqz70-5bb|OizVg_Of?-pCf@F4sb5rah-`x{|{%#@k%Sl4&5+rVoy@)9nC z;2Pq?3tG*%!ZIEkIXdM3q@3XkTYnt(xS$g@4$gRpiWYHxB`8bhz$xuegD2oG*DV3Y zVm~12xJ@+O7AFn6y}ignuY3a@9TOD|lDg7)OE4%lLQ|DktQPu7;YBvTqJjp`cY;3} z&TmEy5tieX1D0X9kWJ>7yMFKs+okfY*P)@WKnnb*maA_&9b*Fl%O}riE?>S31@Y}K zBFd{wOkQb^SpQi;AWQh(mwsHZ#2G}1Y80$by0Q|yd_Ft$#SIe?C6;Q#}nc~&f9A=p^ z#?yk5UBoZ|czU^uy+?fML{>#AB6U?i)GioHc4lf6JHp^xC^ z6+NHCKiprt`)-eqx6}K>ZW3@;)O&8ZgpWW`=JK%KQ{n3x+gAd~plC5)o4VuC#JiVO zL}O5Gf~JB13E~qnFkk`MEb%sJzZ!4?xSk z27RJx<}jAB6*IV!8MJE>ciQ(*wL+;S62F+BHQ%J(wS)rU1q9*M8LCgY!i#|6|{1HwRl6OD+yCs5{y!CqwZLzrZa zO(RG51mu(uixLUboS)8OW4VDgX@u;)@93ybxuLOof$>R{fYWZ?or5h1R)MoAs0{=J zR-Ko8DP+GH=;I`cckkkJ9l_+ZCppxHZU^rI?XCy1@jK6f zok$v}iHl<(uLF9OD}4F#3CN7v{{Z87MgaLAOS=USQhzPCXF(H1R?L_|)ILIIXfeaC z`UnLMl`^~zQ?WY|rL?1LH3m(LoLk?2Gm-iFPFYLj6Vl@(HXjJ{;zS4~@h3-P0!8LS z94N34N5LemgGj@(_Crwc{h;K%hXL7Mayjw?{;UUnSvZ81#>dAw?bn}TV`J;U_2IzB zb$~TDwlW$DAfcOQI&=}>MmX0Kd`OxHLJ4KKdi4x&wf2H6#YFsJ$F)1a{XfEjL0+Qe za_|B%ghBRC(J0JprwE3j@b>qYQc^m1c(CLW4{hW)k<^p!1Kn5MN`8J8qF&#fU67q` zlmpBeY-JocHqhav6H*1!2zn129i6zDCMf%=5QN$Y)h@LTfZQEKdW^e8L74oldTHc~ z09^0)eLJQMric>7e?Cp$h5uZdLzEKvDAT9 z2cH0r>Z~JN6XdL8F$JPwz62MzJmE3W(C&M(A4&2-&qE}sG)rOU{ctF;Im^rD%CLT? zrl#s34Ua@DVf{Y=3j?xi<5^C^9KEF44UJfSUefIuhNkD=#0>!jeHXr@s;>TNEzwSA8S+^619{K7@eK_P zi(br~r-raJ2;AeKZSe_Oz9NTY2Dy+mTgC1#)_o9{uduO6K~;t}Uq?kDbs-`;2g#5gGx|Q5y3^oM!YPO^qdVVc zAm5K*p|UdJ!Ey&OxD%Y@NlU(vittR(jgaI(;lnlMc;M86f^Ut1dj9+mDZG)+z~#rD zRGWXl=Ng~1VJ|jajWxXcZ`+HTlV*GR_7J2&zY)uxr*YZO`I7|7G#f3NW}W4Zs#DQA zYz68WEu$?b4Tlzh6F?K{R;vI&y1lm-4|+V`1`P!T1bOjduBCyoNNzCli25jRa)lts z^GXaY-p!GT504|gh^bak^o3^Ev0b5MfuutNX&dZ&qTF6dPtOn%f)a=e(AWdfuOY{5 zyF7{k{2K8=k>Yag+K;JIm(b`pNrLpA=8~V>?^oC*CCd2up~yij zP8TA4&+C{xU(1Mp!X-*<1)j87wZR#LyeF!pDC`$4qSKZG6j zNnnFkjny-#{b;@7Odsek0*-N>w;Bd{V_+tL1q@cYXs@y<$qjfX+a=vsR4m&4 z()w@Rp!++)n#ujD`{;!kBc#iski!vP3-5@eJ04El{b-lubBWJvnC(AwD?+pto=w#C zifGnZYDKGcUW8jB`M&r0?#UOn7q;M8$F*t_g0z=WJ!tGRvafgJ1Di*en+JGqB#Vs7hJdj_<} zzl^yhC+GjK#oxr5G-@TKzjwY9eRiFh3i`6{^51szbx;b%7Fsm^L62aWEz*Li)%R6p zkZQ?gb(OP19SRD08EEOCNMPmAe19+1HT? zB#LU-@iosmSming2l>b%FSgwjF2{I7B#}Vdjpbc7Xu{}uC5U0f^_ESNNpN_$H>4`B z;?DwY`QOsSCp)MBdrTaJwS~D4NuGa%Te@s!WyJ#k|4n%K6+|JiriWb;@*m|xk88lD zs8-C*j(keI*K_>}V$sjxA<_+4!~d6$5&Am5o4-JLHxfHIxUcKwjp)h9*Cdl6V4qWg zX3q3cIqgq)`q!E-mo(NGevsxmt}Aqd*e&zqtOAk?=p{ZremH(lhTIg{f)d9=;`KHy zJc&M*i2ej3f`QZapFO1Gq`!mzprSR0Ta)DZhyNM*i#$(h|5$B^pFhVX%iESGK#<-@ zOJ-2a_)`k(XbmlCBJy}`M>Pgk)>F`6>meR~96*?W^N?!9#FIBb72jMad|~ungI}i> zAJOq3Hg&zZYyW;Srq>yaWci52Ahga`?9u5sw|MD*LUtC+!NJBYFq~I*_S*9YlZ0& zo+COph%!z8(FV~G?9BSC1uZQn@K41VzcI%M2<})K}--IJqu@z|9t&ML~^Uhsvt4w$m~9#JRlulfa8 zG`}~Jum_W{?B|_76~&nsW$|sab-eaWSsQpheB~V4%@c z1M*Qgk00;fALTQ`@$|x?aeO~OqX#x729tbvY~x}(!}SatMa4#KgTtS5HM}+jykewY zXHHNHqbd&j9hD`#x!UJypqH9|QOLD}1=xY)p6TQzm~)k(jQK5u>4SdRM}nxlOia@M zOWY|)pv|}DBdp&0hRz+{cc!vS9*oF5S$yyB-DOP=RN=on9<)veLqDr&RbTpn|GfJD zLchkpBhBcYckf}`xiyu6y*^-I-d{9+`-p!n0X6BrmjJ{-YR|n2{AS#wX?&nZ6xSOm z)UsJ2l#yp7aeDZ78VK3-hZ>+^>gwtuCOUE$78nL(^=Wh0fmD1-2~VMaE{56YcEBG(Dvxy zQrSM2`NpR~!;AOydMQB%*$oC=Gjq z-inFWCT*;YZ3_ccJ8LCP6^9p>*K&+YTelb;;IY;dm74V07yD(SNiKA}#YtlD0={`gY1OXZ*s9-VM*!u5i@ zaOck%sU|HOMt6ugrZR=}s(eU7x9&hOD`HPtTU{k0BlF76=7d9lpiRM~etA03*QW~b z5jlzt90@sYywM*L67mQXP4gEoUihNL+eB2H!yuE^w4~T68-PdGlbshH8WqeDzB5VI z)K4O@U2lSpdH2o(IgJT^5V%Ju)R%9S9UeFY$RR5v1Oj2E53tY?Tqq;sD_`2HS2l1NwAfWkhVga zvYlq&(cf7Bc7QY00FA&UwCr6=?mD^CM8;uKgUEqnu5gQ#&2D!-xEPw3{ zvTV`ZbJfDJ#zsXt=i^j->8VjS{fS0;{@A84(5$+QhcDq=q7caVXselfF_XZji$EWT zOe;R>DV*m6dFORpTo8Qlv&vY9#cQ{7g*pI@%4OPEVx4Dap^+7NO0?XBmjZ`-8Pz4oJUz`C?zq_y4C9Q0(*Y z+pRt>U-8h4ZE(jyRLxuVe;CW1DR|bK~<#V`q?Svihqj7SCj{&|_K}yUIcmm;P`F74)lh+(f^9lTWJ5VS zOMKG_Y<}s^|tuzT0Y;`2KoLKv7XKFCRG@ zf#ghKUz9t~2R^`ra0zl49bDk@7f3=G7IU|TGn}u7@QDW43=j5zaSLEoA+C1!X4&=> zMVZ1Pbbns#4f`2YUmRORX^%AlCL!gOIT9EJ^Ip?yD|Jc@h}~hhkp6+R>6kH^ND{ zW5_jB!ESa&!dsZd39Sk40=O~KW;?);^XFn}-&I~DPP5-w)h&@FqZOvyo@Elk<^I}V z_Iw!|pgUiQ;dwV1EqLL{eimKx-{F%fVXQfR6@oNBbdYr-M~NW0 zl$UG$bbod0@p}k?*sw@= zqjmiZjc)HKi#wPYHC6B&RT_m&Y~IDo(E_3XnCVSO$ji+nXU)gB56Od5-QYSDUK9#~VAXJ{9XS9t3X(X)e{Ujo836^Of#V{4UBc_Piif1sXV^4w z1lm(BNECf!PD-@jd#}V{g5EG7Kpz-R4Rdx4cWrJKz0@%nJ``$D38%xa-)_oox+UikipU|D80~fElSIed6I#(7L>wWVjt@{3<@z1r3T=Xmz)xzroY;2T}P3Tco zpg;B@b&n}pdNQBsxDhZjo7L);B-Gdn8F2|c3&+)YI`FjN)jcJ)@3OKjg<0Vf4TNk> zVOSc4EOU`We#f`*8-4g(Qex3*JU8k==-R7|+mn@y_VE0cs*7e%;>`18fVsaA8BzV# zgw09opuy(l!HUxp^7GI2_Hq*!TB64H7(7+m-mJ;pN1Mb$av9Wo%N3&_AgOAb zbK{uJ(I+om3JyvgE>ajIykET9@BY`Yu>#=Fk1%0=Z88uvX*cPJY9GON4m3C{ZrCktPQ&5;Z;=Qq0*7MKCF6V|_wpU3%xb3{oP6|p$ zSG8=-y0&^r*LgoTXEx$7pDT;~vS2yN=RHZ0PcFtai_bdzjyAb&vUyiw6 zF3QYE*vz?N`=BpsWiU16e*K%<#>xM_m{i7l74v-Klk>v_`}^FS_B`fWna&iKCrN)# zjZCA4y87Xxo`*;oNUf9ei^tL1y>Rs*s;ta-Y3ObGc&p^j4m(?($*+6TiXAW5aQ@+Q z^b{&8qhd!GjCZfP2&~LyP0CB%n!G%T~OmC=`dwq*^fN~YzJ79!H zCM$+nsVg635(viUCv(fYYSe-N76zf#(SrNTTG; z6I+3LQjh|W^$u)oQBDIci|#!7$*XcsX!QxYZY$Bj*^b=#)4#|2F+?VwOB2-&P=Krp zUm%f6EBf~(yFSb!5VSIg8IUyit_aQ7UN5Fcx4hpikVvZ+yD(}si!~?xO#AG(xg}%I0*x3eg z4KdzClV=?a`Y*1wa%aeS5n@pZsTZ^?4!I^L^Vs3hb$rl`G|z%Q5mvUp!^CM!Za(P? zY)2v1w>B@DaJl~oqh|H6Chx^#;-g;esvN!XeyvhQlk*bZdB<{EDBBwtceVzNM0_F| z!saO4vP1hFS?%;A-t-NYwQI_ljYc9l)xzLL+Bc|GjN4iQhgF^wTlX3i-6ZXeEJ3M0&z; zBP7NnVw7!K^=fdhJ?WBM6iZTKtMQQ+Y3*dbetH4VjY8+)cv$-pOE<2vo zJouInj)bEvF`ez@RkXjSK-x$8s+NVLT$`yeg%E?IBe`3cCm(5JyI-M&ukFHqxc5nu zh{*ihIm5v(nq5hwQ7etTWzhZOrqV;Phi5vE$} zQV^3@vs`O)G&i8V*ye-c%wqlLV6`mStu{CCZwx5}BK-#H_YU?za3RKNdD%SwV>4c|zQJxLBKhj~h9)ghrZI$M^dMNi0E z%clK~85a?E%uTtwiKOg&=CVuKkFt#~lx>`U_~L;st2123@Aq|5r%D9(rt&gdBO(e^ z67@>bh^K2fB%)(yuuA0rb1nF4B3t$^#HPPYhAvV&5;?D&Dq2@mEfYZ3hoG&=+9|}W zTk`I=(AiVhqn_1WF}24i+Ff(9b=uP|V0S)O$|y(rU)N?&;5~8T z)3~%@s;}bG@IpSlxH)d&dD=*Q!S+1~JqC(~K0)3x>!5+)6f62W+i6)}r{mHDcim|d zsCAQG<|RLo1%6>#aiK2AYqF_a`4XN_y$QETy=_m=r~Ff5sr8-tGhG2&DhH8R0`gWu~Di8xgeTG zvaU~4%51-@>q;j@lblfZJP+4Gjhxq#fk`@(|=++)HoQpXquaV<_^Y-mo2-||^`Jq=)S5aAcDj!?g z_5(i??%^_S(f;$IWr-*0F|?ooe=IcIcFaKr*EG`5dsyd&$W^1~kfPx?WNb~29H(N1>d;yX}q2_tIDMC zN0u!6@wI1bxH{!|nYg)34$AU}rIg0ZlXdx_Ms)u>Yz-o@B=ovHhOTlb%Nv8Rr!fQzd`2fnMts@>dxJ|a91Q0* zlxAEiXz?J-vUz;m}Jjf`?viu5ok`(jIx>IhT zDZ=!7REp`#SH!!+(ljyQ6D()Bna++xpt({7g5v4yhi>qfH(YPFx@ghFUWn{~)K7&5 zcSAPYOZojTo``eoSbq5FS)k|4i!i0Gs1VX2SRc{N->d{0mxnvP?I-c2@-yka@m&+R z__0^hsx#_zjU4Vuve_Z-|F%1-s|Foe^h z4ol@>r#Q-pw7DuJN%9hnRPeAz`}8h{so%Q$mMX&Zp+WypO5Pb4{7;{B*7UO;;7RPh zoy~Vu8h1Z+6*c;Q=z0sNsNe04R|N${N~BX!Q4m2wBm@BobpVx88tLxt5JXA@#i2un z?rsSIk)dJe5TsMO?;btB^FQa@dzTB>a><(c#vA+H`*}Va#BAR>X9dOUblTJw7*D9o z%g?(1CM(-gB0mj2M6J1J6j`IEAM}OFE&puMd0Js7|Df1xq68TtS@ffb(yV1v7)37V zP~V-BVUl?`cnR0yy~vJHBS@9uR`EwSx0lWQ?jnbI~DUX z=it@9eaJKcuYL|isnwNqifGx_S+#RA1gZD@jGV`ataM8Hg+ z3bi<0UEN1nU%{R!3h8hVTTD++b3#S@bGRr4(*>Cn34+gD5{mm}Gc=C-^SvQ22+)z@ z{v(3+dB_mn+E^+JvdpJedMN^*dOGBjqN?z{nQ)V{k#{&a492RQ6qJ>@0r13BaGC+= zgji}ESSe=FeB;v*^0J9}d3jyRkfg?06Lw6?^;o%H8jXZRmeJ~O8dY*m;C&^q~b@H=A0a*;!JKXrv6{u>By2uZZrDF=1+tgZwF%{rW{E__qK

%vLND}1}pwwjk{lj&T z*7!lS&)+m2GANM1m;!#LP=Oi95#Y|>j6{Wp#eod~_UXiEjkno*jbw@u&Z7clKQ9ba z3qG#Dn(%iRSihf{RIst*)z!cEdET(MKf`TV(yRepw0ECJT_;K3h<2qt00-}qN1Fn& z*`D-o4A6PI&6Wq4JPp9&2zeAQBP%d}?Non``tX;jP%vxMjx$;dP6ct(d<+5ZLW-UU zR$x^K<+}G~fD70?a&HB@dx>21ydFKuE&+@z>1tc%|3ti3Ry7w93WsDJ7FKsa_fKH!YKdtVcd0E6J2e` z{*FwX^7lhG(3p=DclguJYTI9UIx{=6Ae2Fr0kEP}IXp2Ly%r}ZNvr{NfEs@wPI(a% zdDkK?J}T<>dmiG0T`n%#&;NiZig*r z2k;EMr;)J)#g=(o)j!Z@mY|)bZbId!9Yjs0 z-Q4}+qx;fCMTu>?c3Zp!RiwH;)wldC zc5Y~C;S6Gld$x+_w~prXgWbsQuHmzW8d|=L*^A<%9$O!^UDw|@gEBg?!|lMVL5DLa zgq~2Rovxv5?rHe$g?5kf ztWz-Wd}mtP@8G-=uwlPQhd-ct+mMnQ&CK@nL5O5hhSTT!kB8UxVieckDl%|`Pu> z!OHwk$qKDM;?Q*xbCi9NksPgI7&TKRM4l3NpR_6XsceJ_b{AQ$Ibq~yos?FglEA+)j*q?t3S@mxB_z&*``@}{ z8@sfCTwPXhx#$LcEtt4!$8#lI(2&Ud*ZSXRc-$4Lu+xaqRA&Fq#(IhA_L+*{{JG#z z96XxndFe2t)OTos9wd_W2|c010-_^-1k>s(B@owVG8!n&Vj(-?s593wS-Wv^j2W|f zp~o;R@h65ue|!7=rDvL1tdS>RcCbYyfPxUGwbG%CK#s`+2nw)3eX!|x5E*S=5dc*H zEJ^u{*C{sT99=LD%dFVGeDOjI^m5@H*Qvpj++uP*q*I3Dnk>K(*IJmtu*({`e|#|6 z#!Y5DM`BB>(?!d8up>TPLMS2?h!9j<;z1|tJ)JFYpA1ERa9X$@XiCq)+xpXMgH)ET zygYHLvoyEb>hVV>Q%daKJI8+e_Lsc8_nvFUJ0a7G5JFql+|xVyb;icIi4_6X=~E(S4V z)70R5lWi{-QX|d#o+us~_O)*3qj^Ggl{WIs$1YXlye=|k@JpmvK6_%?=k{F6kI1ab zY*yBx^7RF=ibOkI6_W0=@x(b*FC;QNj!fppH6_w*tgDo6%z1u9mb4)m?vv2nE67N< zvN-*^XfD0ytyR_$9-;BR%a^MuDNt);wYvyuw28E2tsfK?cIU-x#1>nMCW6-5#nVH~ zo$K7t4_qJwN$U;mN}ddti?SE`6WT~M(=Qsj4V{;&>3{nP_MJj_FxiG`G`R!ao08m1 z0ogZxMof1EXjE$r@oWv4?j3){+189_-59v@@6a@rv@V@PAR$^qI&Ox5!GKq#(l@B7GrF70tk$B(zgjidjXGrh*74T5WG-f7Ll(_Fob=`ax7m!c z;CEz6VQ$}Bx2`r&)}XpT_Y#&(C(`d_EA*Pt8L zcxvejuhRuhss07A6~oV|BWY?gRjiTvR%7-hvd++mL6+BH{tV8!GR*0&)`$L|nufJ1 zjh=ovsuw&fIb1sLD9Ca{#<*&ErE;=?n7Q@_`Ak09zrx42NLgm^+Wh<*48IVE!YFu% zSSbP6Irp*sg=jAPqD@8yO=NIL(W(3@x(fucM#4w72LvRUnO6jdYn}&3%#hM($GsM$ z92(&`{G40Mu*VzF5xea!`|_^2+Vt!TZ3aP6SgXvP!@b~;iA6X5y`3v|JO{4NUZzD= zBjSk)4frA&FWgYv9Fo`+Fc=qZ0hTkxE%a33^+m^ta6&7)a|JpmVn(Hd!EdbyKJzv% z%=Vc_sa-RM!5TlC5BMh|z{J#@6N_P83cnC9$nsX>h!oNqb4u-!eAVxrqbePguWyD3 zTxbdcptg9t9B*|#Np<^RDY0>-y|Nx(g#+GII4T-vQZ>z56*t>lLdy1@d8C)*G8lqL zl{_xovAIg&gO_>G;NSNdAjW}i2UUI3z6!e|{Gyu+p`VRJ4QF;;Ew}pCW(RAhVQtY+xS0^2(#ZnKZv^m0#pjGl#)BZ-<0OTNi)$I_e=D*ud5aNhPU9s8{m z$Ta7<-0cW7+ufgK-9%KJo^LZ7V7!IizDgB){QKvXK5s1Vz)_#PcpD_y})4_$8A zypMp|k!R_6T-{JHr`1(8>wtHXDZiD61%f%g_>H+8fQ;|p zpgD6O2YHXVC;x@mL+Ta7bf(rv9hTWq{(_DoELj63?3)HX3Maof@^qw(jYA%>x~{}9 zIgegJ(vz4)&jW9ur`E&c0I$7G7yQXUfabfST>;K**o?g}T)0=vW$&N<#NqGH=m+bo z6yw=fS);B4rNlo9^p%>dzpma#6{`>TN$xTWDG`ON&Fm(3*I)VI#az=9y|0^J8^}WFE~5n zxfM4qcqdp!%v>>S>r9NW7=2>EQtkQxRaK(IR-F((V_ALV_3Pm`ygV1KA`4>npSKc> zNIxQQ0jP)d)=W0r1uAq_no!!dKEO9QA#mGId%^JXB-ysiMwr&2A;I;)rp%Snb_KVO z7yLp+xg$9vX6p2&qcjD%Mpc5I*}ap78Vt2T(h6s8a8z*DXV%cW}YTwFn89OJT54iuVD zrO6|cvlfH-K9Auj-DW~h^ch!W{_GKt>ipJevo^-t#-zjbDPsL#xOhFt! zEPbsyaVnu#_lq>Sr`%}0ymXDD)V{+d1NR{IZC<(G4F!66df^$DHd^q+nP<*@ZaO%q z&S29)eKa6`vK9jSs_Pi%4Q;C6t$Qg^{*`*`FLqc-oz#)trl?kw^Mcgh0k_3+hx-9A z0-=g---wVya=ih19~Qi>;q;3f3@gnLT9L6T7+xK%xI2n?*^4{6>Fpk+Gtc|X=#1a# z&RbQ>{rF@#NAkQjj5Bzr?RD9< z%5EKT5@Qcy>g~4ck54^h8U{n^C>M4nA`}ilYZq83Yiys~OkXRv@t*68FKgQbq|M&4 zg$KbyNEm?CR~wGBfNDVkI`DmJob!`5+l+i?-U{~cUKx(LRQ^vIjGYXSNpPII(ia9< z;&5yv&Au(8S8X<&@hzZEUxoU`L6i@Ag_N8Atj+k6X)#q7k&?c^eS-?UAKqyWcFgOOE|d045}hyXox!af6kWwQRzh;8fp; zUZG$BNNOA#=ek%~w|)j%j@q%>J?xAwF^e7>y|WugD5yWwC`(-(MWaYCdZDmYQa$!v zg%o#BT{dZVHJ3jV{t8JXas^0c@owAH3BOZA`Ddt{`*VlJYCS|=<+H96cqh;=4`)(L zV>AfJZt(Efk-UgBw2MNw*mBA;=lc`gA|q^XD~j!QblGVVmMue;bXzd6+((ON^x}KJ zmxd7v9R;S_1y`p50?F&K+rTLHWWo*D4RkI``2pQ5*WLrJ;O|)kMrCs{Hgi4HywGRa zr5QHs<3H8UBkoR}yB{UMXaGpt8R;0)t7sGt7M>Fzq6J4Gf_5oD1xkk9;ECitrAAhd#(_HY-T8Gr;p05KJMdz|KxS#ntrGjGJ4%>8#6BiT(-# zZ&kkx@80{k+g9X;uG1en|0!%fgH`WKX2$Cp;rj;rX~_(QK?r}^$_asO+vqd7e=nnF8pRIgx#@|z&euhtze$PQWTx;fN>F6` z%#8;;@upy(pRez)3gs%EC(%XWUG(HN5mTBfHY3n}I7 z^B?756g4z&R7X!mndDpn?0<5Iz&oAL2luA?VR?GAcp3M~H~tNT zQ#Zb_SxKOPyNi!|M$HDz8)Mu3xF~4WMOc(1ik`oQ)!IIH%9~U%h@QY<~YO6#gfYyNzP z;D96^n0|NKVg(~<&C9%iZCueMBmLmez4mX?tW&N~)tTTzZ&D*h!zbF@*crF?Q4@w* zwN%WluUqm_JF+CVJV^{`EeK^7GQ#bvJzbnOC=-*(%-ln%Hrv}9CQ23GJ2`D{6NbEIN#cnsKb5aqs(M?HLPcV5c?MA#os)o} zQ<_T|{O5~4&R78|a~&8W8$I#IJYCMXl+?nmW&hlx4cF%JZnJUm2`q$xlnq(0wh3R} z=c*y45>Uv#cX4W7{h_69g=#5taU1CT4uie8!nS#p6>iF#`&)94-sE!I`?cUcE#1`x{$(@6F*cOuAz(G8qYvs+vL;U;qM1J$ca_QvZ#$=q!!(&Q0Ce>1v zsQLC&s@a+!km=pmyscE0U!S-GWM^{LaHcRLL;EOMFRD52!Gim&>_a!RA+m&b%(Ir4 zAM)CsO6zcGKTWiflbqA;9_9bN#H?$F5?3@&Tupz!kxUL3B)Ol#Kq=XA;3{m~klT%p zNJ_d2&MY}F{sCQB;|Hm4g#G|$pXhps;6zHK!s7>({H0=-1#E#TGccS%P)t2&yL5`T zxOK@MKj(nHgr+NlqL8fK28CABXOnkX(-Ma&?Ql|zt$WP{UW~jl-f&4K#x^Gu<9ZP5 zyzGUO5$A+@E|Xb3ZdGRIBE{cxW4KgmTocmg-ZgOCgY?Pxu)5UK@Z-q;t0-p z2zi1(mCptW$iWjD9mOp`abdG3#g7+0h*y-O_x`Ovm{;AGLu>2l3(J z%PdA4ljXBoEhPHKb27r;Z-O9f7jv*ydqPk;ZhpeqYI^7Fk`Yk{(tME1I?moQN-9&y zxR#{eJ=Mj&t}Ax(&_;WZt9bcPesVf|a?oJ1?m$aRk^11Bf9G%1EN9JIGxt~+jT@@v$r1pcP0{i!}Zgg zU4M4mN#}-|j8Ms^2?P(z(%*@lDtnepl=(f5n3|gK*Y|rCembFc+s=CTa2iceHa6nS zoprv^^Dl#mvXiDKhZhzihB8edZAasDGDB-{Fx>F>Iy611pTr~Yq17e2qz@MJeqbvZ z_wV(9h#NXDgtEjrdIJx8oKVL5DHag$_rNGBfZ}*&-9#y9tC}2}sH< ztuK67+Ura@+uMI)Q(#0k@QUTKso2NCqTIn>AgbR=A}%HVNdaFL1ag&rlwt{k4vt znZNp;>Jjtv+L!ACG{v5GKg(kL%ohbR)`d^i9Cg~?P@}0VbW&T=x|y2LT*`LAV8e0W z)&gxir0K5?XUgwi^C4%5{sn$(F!K%(KJaFmL&VHeW_$X$OjKM|q;y(PFfhwF3&0U^ z)&`W!mC*)vqfr&4=7u#x$C*Yg?%43;5CaLz#vxf<0UD{H4~);oeW#bkzlW`|El{%Q zld81fdX8!j*Ax!meLc0E9&wGYC1G!eSF>7$97*bH%ap?t8h<#=f+a`2pphf-_3cW; zIsC-|0T!ookxES3cWd8>D+7qtk@K;FQscExDU0KcP|-uDyMho>LzuSC06sKxj1T>9 ze#f+LVRTDTG;>1|50urDhtlzS@|l;$M%QdGIcuX1!J$xS9KM%_DIjt<)O{h&VxJ#9 zqq%JS?E)wu_93JJzkemRR~1m2u&akH81lQ4#BT+&;EM^V*^)cecs-H4TG`VZod0^m%tlbPOG1f{vCQK^bHrY2_5mMx#n|Wo<^Qo?7y4u=JK+% z-NdQn@z8FG@mUzFIr>U4&r8P85OeP_vCOF@I9Ii~5j(DVBzWL|C`u7GSW#VS|f;np7yH ziy;=d3vu~Oah7kAD(32+$Xi~0bi0&qbiH9#MKdqYk z4RL5KtK6UT9pK>Z5kkb+KfK7I6y=Y!|9~_aHnGGZ|4AK>nZ6%>Eqi@a7tM(!P3GGdXn-1i8 z`G_XsH*l`)xnfPc=Se?b#L~n8!VX(kbC?-RtnDSJO>8~;$b_$iK%TK!Jy)yAE_)b% zvaa%c;hB1N+piKc5?lVjw{}r@keC_q@Yoy^w6p6Eo0rZFXhw`%;w%m6#l~sRAPfv( zjPHE5@8pz!PJ&Auz{XV#ZSkOyU>mfHXn#k}HURZZfd5Q0p6udb0eF7;C*8^l3Y}0o zdT$2SmXsvqn;IZq{Q8@o+w94T9os+9ZqWLEsZU~(X4}V6Na>KfQo7PyQrw9Fr!QnaoOR3N*|4Q{x=#?#g#b$ z8p{C?06^IaSO?zVi8;mTaqL=RI#>_r%qD1w1w;dVF^LKpXxjztz9=q(@}9#iMs1H* z3+k%>UY&>L37toB=!MpC5i$60-sEYCF1Yc=FRf?iD9uyv?QL&&v>F?V5Y6nO^FMOs zyYSgAT&`f};IQ785z-f6W-dYAVUM-B#RW< z7VaCgD`it5jkk&tl9IduN`q}V0B|ELGoQQ7S(@SiIxmF9$IAiH0(dg!y%!3hI;WtZ zkOz;Jc^F$ElFbXZO(O-s$nU?IuYIHbC#%0A3-Pt4rTcE=AKUMLd$2cL5vTThF>c9x zG<=BRfp}c0olSb>qd!j`OjYed+%=fYr+Zgb^((5X@{#5}=e8vbZLKFBirY{Lem1(s zuHsAMZpc~3%e%G_&tbgjdab_WRyY3;|A`~jR{`OlHs=!LoTT1JV$Sna=3H@(#NmRsYZL1wb=sbV7!0F z2rRN3K-yuLjdQt^aJ2%!I|1^Bb)Y|TY5cl6R2&E$MpRcy0rWBEeqaXi7TLnEMyyRc zKX6TuKwW~SVV2h_pd1rw)2FE1l`hW$7^v^i)omcF_2+r-?de%nj1)lwCqxN!!dWjw z3zpP=E!p0Gtk<9IvpzNN-1}6`Vf3N%R>4VkR+gWdNzbF9V1$44{5`X)(MGAbP>z<%;|?vSF=cXH}sW-~vH5&Ty_x)ZyD=Mx2vlUshm{S?@+U7^d#&3Ibe-%1M5ebKC z+0ZNsadFcktiNnHF7t|vyOpxuW)b+BC{8$=ulx8eOvK{@vVlUD&7IS1UVa;foGsyA zXI99WdmpuZb;lzL8ZoQSbx`5SO+p-R<#y@i#SaM1pzBY=0Ty~EzEz{ zSwe9U^n8-TxNt++^1TL-@ig4zAa;JetkOVr|Ji8e!|tZiqF&CgyWd{5=Sq3(`e;`j zs*Tz5Sxk&|-m7$?+_K9yg-d$lv2tIN-T&Un=# z!zyAbN%I`-!{eR)4&Gn)KEIeEyP-$+yt25>qrScEY*n7NdpuGlbb!--!3>#oCamY zXTkHPJQsTX9Q&W!&!5qBPiGK(<>@%_a0Y?E5fU^M6kA_c|w$2YrcJYOM7NTuPu#)v_1` zaU!i%`53J7fU&M?X7oTnzX_nF3cXOAs>5T{{nh%>wuH98G7Pj1r1-dLnVAINqmKbt zcl!Q%BU$x_8~EN*x_Wi74j_MRh%rgPRooNjFwv0__1eJLt`b4-AO<{+hefJ;^#Cdt zLvJ1*WteJkqL4Y+#TrFWKY}_RDUE=z*wWdzlVS6QC!B|alVY-hMN*210`G3R4arAb zjN-h4n}{TfL>~X*@c0h-+gH%J1Sr`o+}s<|U-tcI%@d$?Aa-%@QlZBx#<2Sp5c;ss z+FJL3yF*AEHiFbxdzJ8UAl|;+ULKZz`O@)=xm`v9kYPfIHQnRu{~n*)oe#ErM2<YRpyL=V*qS zcbsv&PI*1_T&+DB+uC{e15vv@WHJJ*t1B~ixw*ria#TmG)*OVuUV0s&bq#zB*p3?m znwq`9m@CHO20a%VKZl1mKoeJfXk}bJkBY{k*g$!4odh2XV+TYaReo0>8Bo}6zGb1C zJ`LSVwV@xA#*e(vdyexdk=P%Inn5HHps}S+@c*sNxDvKttNrgHFB~G0l*7VtKpf_Al4(rI=s-7O zC8dKi7Xi1l;jt>abAa-(;TXNiTR$63+Y_Orq%!-|$Byz58~12MacgCWuVQ-C5CL$!0?NYe2!vxS-LQ;v40Yw!#T(zuJZEqcOWHZ%~3z=LRE56!O97=mDQ zP-~!KDTwlSncG6Dc06p?OU7`~g>7SaBe6pJJTnyf0@W#O!~ktBdwY8=2lXPctYzqB z-VVKLUqQqGoG5#VEGB$0vB6{tU;1=HWx33KT7OT&d3%{xDUnRVN0;}v-+W@967e(g zL>pS(mai~T*5w{8VF%cAh1rhzN7+jV4UL9+aW-z?p$`sr4F|+qNlx7fx)FA9pIrH? z=85r9)XQ9KwynMdS(4Gd-YHIu{ox%FhJH%M^31qQ_}(PQLs#9uz3%=Z*cZO$jRcW@?@ z*x&ObRo~zn|Jo&`Dq`?)M*Abhhe$k45Bj|aZ7oHL?Y5S^yIy$uINMG4M-VP#ZY8;V zcAZachBPs1fATpc*yLA5AeXQ1vsbU?|2c2nc3$pDCBChKyIMo=?b{k|{-S(kw!;jf zZ!kX{3|jH7ulr4Nf{@h(PTUvijgMkL9Kyy;Z*n>*wuR?woe!Z7-48klEDg&HinjLK zZebIyynhm|@9lreeEFjjlVc)TQ+a{_gWTl*eQPWviM`7|AC(MGgh^eEbhMFQ{rRmG zcV}sEQ)g>6_XYPwoPt6F{jiH8|7Zakc|L&6vYAfjC`M7&dB+@(-L|TW{rzYaM3yzG z!H(Lm@UH!96noU{4gA&FJe$q#mFop*xL&?2TGfFn;m3ZrqT28#rNC8flon*mt0(S>BE!1_8idaLfK(1IQ-D{M|4=coZ_sj z{jcf5Qa$C?)68DvA~h!rITaUHYBkSRkv-Ok+h@GOT_|L%TTvn4Y>VM(2SsLH+THF0 ziwGGBc{4H(2&a=c zYNj|eBfvASquKm=G)Gr z%$cu=d%TGH1hqL9`f;rnES7=_dUm=5+8(8PcsQ<0%ynw-hdK8s0TCHm7}=D zr6D329?-0kW&TJ~1mV=#w^+e>F(xaXI5nsJmAbLR=au49%?P}l;3+$9{j}Sux8!^I zZuOvEo#=ctw5^c!oHHXDsL8^1JHOLP^1;iT;JO`l#iyvg^C4bN^rft5>}h`@QAI^@ zcWZqV*C*q%;=&}UljE~nX5X@R022SFzMUyWoR+09 zW8Z@sm>K4zad=$B8f@^<{im+%KroblW)i#Ta#C--bJ=|MYd|^2m*9@)_i*iz`wq=7 zGZ%4_O8F=DPUgIs@X?n772aQ@x!Q*6I-H@^ySB6Re21Fa^7r?J{&JmU@iM=`PnD{r z&SZ>rORn407a19jUheHJjB(Pu3wqSRsUY=jqQ!dcOrz%UC0UVbt0<`=>7qUxUf#r$ zh(`ia-(Mhv8vGj|pBTfRXL&_v?bPP-Vlk_z^u^bxv!WzIUuWfS6sC_7kV#b9Th7L= z&8I#JMtE$I)47;#>LB_1?Ccg8{j?im7SD$ua9oeew;*#J^QnofCq_-B*gIW*GLU0} zuhz@^Oxclr^mqln*ac=8)$X_dS!oW$n#@+)kZW5Qf+R^)wy~tYP5pVo1M{KF<#mCN zi@F`_B!3u-u;?s(F$#2<<7Hu5e-|n!%S$h?av$tNHHTdJ&6s5y7RXt)*^+goCmz~qs%HLfP%VB}qaly$2odC)Yivp_``_iwa=t=xq`m)}hu&fP1%addN3ZXPV91)7Ift!oc1 zKPp}Hz2h7{c)H6#ny|8rQ*Z%*fKRw;;N|&c=6GvVCw%#0#nSK#CfFu>X`^sRM`~1R zZ|_%mIf*g6iRzt+7#lH}rRCCwkIETlEOGcAdx06bM|-oK1t}^mPTPYE#WPmz!nDVG za$CDSaEAl#DC?*G>&0dzu2rLWJs7rsW%vMqV26w%o^!JO1IM8_aUAL;*OzbQ+o2o$S)ab)Kf+4 z*J?#HCL$Axf>#&@$@Juddpfj)ee1+Qzta8`d5aV_jbx=9@ zgT#6b+qxbaqXCr@%QA;ni=5DCL$+7-yp?retfKfW5N=|FV63N$;F($$&#eEiN=R-m zXhljVAvzFEE4FV4kY9Ri}_H{^8+l217A(iN)+E`(>>Idu7{l zFLTU!aM=0y-uVVCR*2p9HxmKf5o6c?ipsBUHr}HhQZA36t#UKH10D28m7P|-Kt#om zY4+;fzcw_i`t=Otcw^Afio*tE4W1{5saAGDs7q|t%Nx93^l+t6G6e-!L-Q3QhB#j& zioSKXsb@orobYF<6L=ikh@buR8SB=VjgvRyu5@rv)J*DJ7Dxm?>co@S`NDPqf>ZGw zuu>kCFBSaYyy)}p-37ieSrXXFnW~-N93KvutcAy46K20!=4X3 zOM2daGtw2PBGB#WvXK4aJrnmpyZ^Qexp0;8;>FP%GOVLbM#a;yiQ0e@_}>|snXhWy zA3&okDA+ShLBdxyLimc;f0a@Hj>g0&K|AZ6YJ`nzpJM(Fq3j0d_jV;`V!CG?v5*ov zR&tEz&K{4ECP$~nWvJ9sQZ|6G&#AWenF-@R-}(;G0n)cn;r+LOllO&wJ9nYe1R$)K zpBuxQs=Fs>pLSqa-V=7UC!zb4we@Q$R70lI=j_(AZZ~iHRtYIiij%x}Q--z$+Y~5< z*23a}_EomtMC-I%jQLY?XvD5@^Wu&bQk-E=rq1fHpn*zP)Txl9v}@RR;T(~vVeBpZ z$4u~g*S}G2EERU#8yW-KK#0IX+Kqrqu)=ivu*4ZU(wV}+I9D+W`y#`M+4WUC+Ac8G z9>hw#3H8EJv|8}OdC+y^3k!@mJO)S4+M{I)tR@v}258wH8d8C3%qJ2nvDe6d8@aH- zA$Bf_{pQ7^ou1$cjfv@mZY+Qgdb3~`H8V-Lj}B1i9jV}j0^TTxD4f})U`b%}?z!WQR@Ts%bN46W z&3Qmy({%ejm-?&0-Rg_t{VBXs?ZT>~a-W&}R186lK!T6EJ0_bNf8zt#)!QQ?B0xR_ zYk{nQgSjJJ%nT{rgE)RN=PhqyVSG0;M-96?WE|JG{i7v?Lz2w~o?ckNZmV4LQ<;!{ z-xGwWd>p=jAaKSu!uzKj{O>0X3cX1yOI?8h#LluwEZ6&J+6=z?t!{JArgwt+L_5?s zqsb}^g%3=3(G~zGP^7y*QA~3%XCK@7FDE^03(bE$G;^u1{>a|1>_z!R4m^5?FwlL8 zNf7=O@vJ%@OzXq-lw3DpkMWg_>fp!IBARM$SQ*vG%=iDh#)FI6s1bU2{Y#GQ9wmXb zds2SLEq@vb`bD(u&Pt-Q4${a%rYOMOMvM);*uUPt(&1#;EzlH-M^D4G_~R72-K zE>uvw{fWmou4&rX-wUEnW{`Ta@m3dgMZw}W_p)2C(J8ndrKrq|meD*fFSm@(BASZt zoOaZ6B_yOE2>K)8H^RXpqWA*vm^0Q$x$-%;LOVRPz>GM58V-xT@@F+JT z2w?8Uy^fmD!f_q3A`D%QnxEha=?z8@eE(u+(&t=dp}BUzJ`_p-&!dh+kqkD1%Quh8 z?$vO*4qADKvGcY2_p7Zb(_WR*lYA8gUKJ#>%FR>#(U26EcpRF(P`cSy{bL4QSiE zv6o@DGI_=9dCT;*~Ip+u~|u#%H`U7VQxiaYU@Lu+Ttc&9)63U}T0 z%Y+Ka30VnyPIH_~521+k;-$K%qA-RNT zdqr`hSY5DXP1G*<-Lq1|(}Sq03W5IB(WgJ6Wa-ATkF5}PwpBUh!4&*GPdM@$;HKWRcQpgo!AUENHnait*JoF3e6$JT7)Cj?Wv(xg#ko3pCl{F?>vm*(tGzm zZ`2{`VDq`Y^Hful{?M#E0m*QQq6njh5I|9!>EI4{i2qD6{jDkCFWNw zIw%AfN5p=_sybRsQn+(#6ExtNPPVg^uAPhxc(2P$VU{&(-*^buzTq!5)80&AcilRs z5Ku^)%GV=!C0O!azUIx6g^$M0{^DUt!G~CzxS3FoP3N_x#3>jgMQ;(2k&)aMV?wgR zd%8^_^gn=5&rCCBHPw{wyk!jJPZ_u&YziONXD}^8-!-cB2gW8Q={Y$l6v}U5!8ju$ z119DTX7RuiKGVCcFQrhn%K6Bqd4)epqopCm9x3vVWQ4lmQ*Ew&!MJ!@x zOiT-ZOe9{iRA#jHS1HEyvw4>)|ABQ%3PLS;d7Zw=4PkTav*dNl>@@i^r z;0M1NH3jmHP~aNuv~&QBd>UyGH`&UTi&z_U^f&)y`X}>CfwfX=tjTVKy5p|a64Ty$ zSnD1fFJhaP7$ueW9A-S7ldL*64C<6ARJ9qH^^uoTG9ipF-b6m(o?SQ zna9%Ive~#tx9su$T&(&4ugYKr6k0n`GPF&hjEn_tfR)Ai2(br*pC+kt@v`db>Mhy) z)UclVD$IBsrzSpnwb&mVU%Pnc{?tW1!>e}UZQqR?V=9ao#B9FK`sAo925+LM=C%BY z{ddOXo%*~r3=L4p`B*{8MXJu5`>x%OSx)CcWy`mA7FyJ;9UNUL0ymJ9f^a}kcNnAT5j?(XfbGCF41I~ZB-P)svax|J98jgKx-Lv4 zT5ub|MKYf`Mcc*y{9(Q|Yqc}_ZNv)ib1IoTLr-eMt<M|`uByDa?S4(>!Fm- z5YB2#qFChOF8apdMf9zzGL*ReSBW+FU{$eywUX7;<&>c-L&nSD?mV+ZsH_^tOR=`G z#R@ZyyKe9b+V46YT2*&6{1}&FBDw$boAb^J8dMMnU>zh+U857{hNfMBKoQ1LRDW!RVRnPMP#^u|a|o#VaX6nmeM)AJD1AJ=$AUAuN#pmg{T0LcLUV)O zfvPFL`Q9O27T^O2#Rm-==7vir^POBK-}qsQ4ZC*trbqOhHfz3PmdMkE{l#yx^XhwP z_qqf;aJi^6E!~iox$#o@auJOtGrxQGD+$z_IRz!9|Ji`Q)Xdl~D}28ju2nr716{Z> zn)9tu)vBs-LsKTf8d36YME+!1C2lUfp*W{IrW+rXuFIT5w;~)CdIKQLhFbzDIdexU zcUbFr6zdYIWUb2OTY#57;%TKC|VNL53{V8Y}wl zl48t<2Jva5#654I6M@rz2V-Dr`7rj9zeibZ@l(5rVpNBqYc*ePdIronxcKK*G@fCB-^7L6uo z!|dW4>Nc0oOED6j>?>dTdm(^Nf!v|5zQ1CWKhsLp4gKu;Q4xX6D zb~|G6&qFlf_0P<>Ib1`Zk&(30pNOMo8@3QM#|st{s5~586zBT!vo1>&`nIrs2P6>w zt@<*nJlz1}&P%2V4JX}h>t{C?Bh}32{mKYvBRzmjpRev#w3DgmTaX)S#1H!dfI zU#z_mhK-052es_^v(nd<{F$yrQLs-eWKPbfRx4fgW&ZnA?oIzncY*XH9f>z$~AveUS0Tm(|rE)D<0p-*D$peSHDqI1Y2;mY^r#^7cwvZ zMe#q|_7RW&A}{zCId`7cAp1>R$(@b*jfphf%V|cj#n9pYrvj(smc61@-VfHxSoM#* zP)t$bZW1pw&vUkWeKzIP3pBIr7Z?TjVhbQWu+9#+!_K~iaYFPKbog8UsQUiy^Vl&y7D69eT_Y9h*ZlqQ)6T#5>_#-Kcj)N2_X{^L z-|Nm)bQ{sz*QjV|SVVq&Vo%P?>omD_x2tXq4-gXn%sO}n4O=P_DCUo~w0FhaewbU2 z+F7D`*XQ%(B_2^T{vFMuvw7<7o6NhI+Do^a&cV0?{{r?Cp|kSV&WE#at*#P~z+8F( z@GzQLnOu!*fqPQ*ZSlgcRB>(;Io17p2Q$A!B_uZ8YK|yE1X}m5+5;{%uxU#5; zj=KYp6t=G$@n7cGn~;0|9ec0BFLIl)q`)t}Hw+PY^06f|drRf9Gcy)4_y5Xyz@0D8 z6%W6r`^i^jzaXEf^LMiGHS^O2_sIG{-dOatX~OU>76fM0_?K4&5U2kbw5C$IWZbdT z20LdD^i^`#73WgQ`P(NAgJ?dEC=ib}@li|jhTab|W`H4*{{59fU4|l|2y|2&in&h5 zO=CutXGq#8KhGnms)!TN6!)Mr(Pu+v@m*Qq^fOfgohIoO+iQQuq&L(3X)nCq>Q{rz z)L};7krY)RrkoohFEQItdAxu1@4QsfHOM@`Xvpu#B1{7*)~i#Gc!))%g>;(he}01@ ztJ#Q1CC&7%-RV8`-*=F*(?~Yyag5i_-|Up(b->@_9Gol0fktZ+3F^VKE$2G@AI~6$ z-{yag@tZxSDSU#oV;&+kQ|0k9iI`p17?YZHNlM*;FZ^xVsngRB&2w18XYXOB?|T?1 zNmFMUTU(!4+t~cinF$;w_|24e3N>n@s9rUmBbkSKb3t4IMsds|3MKMNka(HO(hB0TOwJ>OlCqx z_Lh~($VjqhDU>*b>=i}Xo6Ir}jy;Zy!~gwN_w#hu{rs=%=c=yzF3vgM&v?(*`}Hbs zW9cwG=iCvR1s?{Q!`Tk5ou{-bL{In!1zC?G2`!S(ON+HNI_A&Yi8uv$TCpe6?yWvO z+kf%W1ILRigzL9|e#dJ!nH#r?+#F_%75J!t(SU3O;XnxYLVN%K`~EOu@aDj>hBa**HC$6v4|bxBLfk%_8@?z{=f+(tqpq* zcUdsnX?7k`1Xxc#GW-BDQ>LxVMH?2Yf`Uz~gG~V`?ByOcS_ZE+9Y2!W$5pEcgu_XL z5?0oydY|-U%`m?X(YxZ9@r5yZ+Xq{$-;Spzk?m|3_MB9S&kkJ#4zt(VG?&6iv;y`+Le+s|CVdd{e1o+)YLdq2P-S0I)^T_&z z+o=_vj(gTc=4lT;2E=A#!1^dA2!`vIyCW3l&5&4ak2{Y$AnX2e-`p+QFc_<9clRi= z3pGho<1qUoRD$hEWE4}6^WUn8Vt^Ws8t|AUi+g?t@@tsa;u08iq=85lz~t_noJNo{ z_5ZWh2b~^x)QFA+K}O!=Tka8!iWylNVLQvo#N`{b$1%ot8@bQb=g60|7)4Zg8g-=A zGHD@^{mnEiNPLcisKPT4V@Bz^jYmQr4kc{^*EeVF{>&aAVr+BwV|~3CGc_>qv7d+c zS(>a^_N!N-OX3?b<``oufx!S-Y9N8oAK)Dy%5wo$( z&Y{pXv$tqY80&fOsN3p{c_POE3~Y71s;ibQ74m;eOtGZn+lo%+>Abi3B5Sv51nB8! zzylP_D7I0Lo1UKD86HZk6VSkHGI_JV(YvM-MfOyq`;1AORN5g~^$f{6vPMqrV?|F* z%9t+~>)WX$-r{13WeCAYplyW>v>-gp- zYuW{+4mR@&q4oTE%xJh=e?-KX6Xl7!9)kNpUZE9jAaPc|thY*q%mi(=0rnuq$WiMBjfZOpL(2WrU?RTZcd=s+L)CPHX{iikHDWLJ#w0ma#yfJN*D zswJ3G2?nt)?W}Gc16UHqYN#XRxGkH;gowIJM}XEa{U4vC$L?!XPEH9)N0QZQrf`h& zN}M9DdLu=OPlBx|)Dz<&&7N3JL^8BB-fHk7YuM*1Aw19Nh)*w|=B7K>4=idtv5FQj zHm=-3LqnqhvTA3zxX_D2EeS%Yifj>N6cidDSP)r=XjusEKp*cg%4uPwdV}k#&t(gZ zEBfebOL2Xf(fRr>$M~?5_-T%psF&G#YSZ=9n`^wLy_K4ko*S?5mKt4{z(;Bpa|7|^ zKG$me`@NM_(Ccn~Sm#!3-J!$l_^=Pgcd3RQw~DUqY|n3dDhFb<)?7i4Q%j>kl^)ot z*v=k7PDMgm`pcJJL6ll>WTJHnG)6#qln{nMBTKzu2U$U;20bhL#JQXLpS#tQYf^G) z{|K&smZU>S`Hq&Jx1?KdYKmu-lrvj7SQ+k^J2#Rs!7sq!?N)#&!epyAu#f`DD<6YB@R8Uea9c)U42eANb{kaY<{jv-=I_}%+7<@akod6XJ z%j+9Y_fSO>^(F|Hfk$CwZvg=b}GDkf4!wCTEE?$z^zHxy{5+3 z0b{*AGiULjt&LBjVx!D^BLaVu)2H0!{rz|n#^!{n8^#fA6=KZ-Lug}GmD#$roIC=e zEsq1w^H=HjQCOcJgu>w%22-lYb?Q_`S!M2LkboAh=GcAxnJxt#E@t#~E%5UKl`aA? zUaMVkOn`a5UANss+2wIj+S+Tr=`azINX!IWxIuK(=p^y;-7LHS8EA0xh?5>xPSC1`sm&fiM<0E=Lq&ok%Psh|yY1aJ%omEPSKxJx^7>%Hia&jdp5IDRQIQO@Uq2->b4iZ|?qPg|CA|hZtZAtFgN3niw zs$Ki6Pm`(Rr4b=YS@K$f03~eFU#>G>3Nn{EoqX_(453mEAUG~9Esdz{1_SLSBE$vt zCYWp_$m{}yJC2!yqha=e{@uvf*iRj`2dMcBd(izV5n74tH`1_Q*h{8U9GVkKFa$o< zgBBHsimxqkT~v0z@83_-`Sq)8^d4nUAi1Wd77miDB76qJ`kjXEF!Ltpfjf)Z?4l~P z9t;9l@%vjmUxCkPvi;?Llp%}*?TS#}`g9N`!r`(W0Yu105c&*hWM^UV)ycOV)FZO3 zH2D&*@YrGhxxLc547OwY4lvm`%GaE<5C{W^TPoNR=u4d9;&Ry-;s(Wa(7(E6YWfY# z4I;?|9pqZy3s*r3kMdaOP{4j`q>JZ=`)^aZ?~8Pv7$3~iL2n^p#9}X>_j8i-Osx_Sj9JaVO_@h!wT-LGK?DCk{=H_Pp->#KVYdM=5 zo-#Xg$fJSNNg{39M}+15dPS8kX*lw{T^^gWV0Bl$8Bb#c9%EuB3`>$g;5!_|#K2vq zs!g9XsxpZDbyuQ>0N*eecA7KGBZ-7*g}a^=x$`X(C-7p=9#s-|Tr=}L-js?Pg(SCFv_rjZPw4M5)tJ?eSZgImnVgLNjSCY-G zP(SKNk7jRVVj2nb-AQB4S9|?+$tJrj!>@_>{)1!vlFejfKI_ZAO7X$xNZ&{X_Y|4H znx5JRVc3q26NioCN|UeS>LzMaDA$ibpkr@xch)SC#gYVN8c5;YyQ&$5$BtCvINOux zxTO^+`A|yn?(g~!xcU2;v#>m89_yXfbt(*0cHi?&v~?YU?#bRftvU)o9mMpLtwvXIB-@y6QoRcs#)vYnc~% z1($y{NCHoX;+nm_Htpq);sQ<)D)EJ_VqLyqCx$nKeaPPA8vp!ZSAHs{MJq-bnX%AM zd&)M?owt8sw*0ZSNPlj|W zWmrl~yl&*P`tr>%uHHp6^Dz8^&e|7)M__Q|+YcXDo9#Dqyr=bImGEc4BB}3?gFAAV zKw>i%yAtOv;*#yw;FxFXRH{b zJnST+kL&8R#{Gv6=@P$k{~E9i9B@C%Mf}c+0MsnD*WncL-kfViC9}jKZ{sDY|BG)C zxUc`xVu)v!RKG2KTG>GTTES+k(UNW?iZ zDAG_vZ*v>EJ!rns9(OjEA-Oyt5|oEj*1y+ z9B@1v>C`~srGg9*qDkF_VJjr+ldK1K5Jmsh(dYfjivxYgf9V+E)xwC)!29!M;8~} zdk^Wtp>khz#nNmpVp|ui7{#CLo<%Zo5a0q~>Qd+Rd7(_}5dF7%>g$VyW?N4fj#R}j zDC+O}jF?OI!L9Sh!66kuV$k8k?N!JNq&MacP-F7E=FwTx%?*&Q&> z`RehWw`=Xe+Ee6!4VaZshdT??|9TG^hvH1kN|ot(KjOE#3a@J7`ywVPJjKZOqu{+e zQb~Y|RnjrvB`o5d7m@D`#BA<^oRFa%b$~`~^KRF1iR|>imEu*+O~bh9uwAn$`{JEi zztEgze^h#v<>baH5%j($gC6?&P08N+5OnyD&#%Q)m(BPjjsWLAjqCSil`Hd3`1xUf zu|Ob0PM4^bnW=^T-3d49i@mmvjB1~$(r<}rk^~1^?0{=%@VN4Mqd+GM0&37A@7&fY zODGC+6|!StdGhfVp>on5cbsEVQWWvs)x!%r+sU3ue3m}LTOU`eWFkI!5k%XKT5ppt zT>Ey-#Ej@D7oo!ht=@AL=8u*GO@09Z8Ptnw6|EXvThecX8AzcG)OL7jHv)eyYdwCY z1s$N}bl#eusOI66ISc-M&8&CQhYvmH01MF{M?in)c29G~j+N5Ag1+N8 zcNm|ILqDAgZ7Z)1KvoSH>i$@@9+AL6N%f~LVcaEsH)d9wiKsg7hBLVY&r!%Sz#)ZV z{O`Ugwj4Tk$5Wgv=NXM1^9p;hpxf+AFW|%EwP59y>fuLC(h(AnpBH)=36Ar$xPF|H zjp4CR%@PW?vxCZq;)Q5{MkLS8@0mca?Rk8JKco9T7T8C=8&yT z+1G8LPazU^saRizz^ylTBmOd4H5D}O#U%DcCfLCbL)QJR-=^{sgtSAzdM64P2tm({ z{I=`x3R1@N`YF>7KanswGU`Ssn)04ECI+?~+35P(>J^on#>Q>=ukeb#p#LZZ?|qhH zMwA;XikC=WKJlxq>&r~eH^ptR@pWxCT+?I6?u!~Sd|&N&7y!hwmoM)-c<^8jU<~r_ z^mk95to)ZWB({7*9#S70MK)liU}jClt&6XpJi3V^7$5hYdg2pJgHD5J7MQ4G#65*6 znZ$$7S)bExX$YjDR=rr@e5WazCjFt!w7Ov%ONxO+&t|p(35e>@3LpBuqBhcJ=36>P zb~jZ3&LjYn-x}s>!>`9rpN=^u_gQ@`vKxK;__0qy0)rnJT)6;3pic2+Pkyt0^p%!= zZu0u5#12hw$zkm>+Eb@V?;7V&bN@>@rfA)JUwqR;P(iP)tz|xWau2k^L5|@@!bGg7 z8xPd0RdscP0nOX}T@*Tx`>3eaHs2ngrl#JT*_`O>F~3*etIy3{m3pDDl7LFWYDb9Y zASj=V6k`a7X{}`brPT6CeYA{*O|c5bW^=9jcxHym>7ky82pzuB%-B^J6=9HYA1JJrhDy|T}pRaT#@%xwj1k4#RMalZ`+O@djy;vq{ zYDwi#yku<*?TCu^+vLSm_xX|Ps=ho)DPO5)Qj!CPCH%l`vlp&}5x)qRCTJU=MgU3R z<=vEPT*m^BH+2LQe`jW_(${e_eQKjN$pC83Xb_@zaBv8RGD(>Qy9Y?}o`!kfuNP)P zeH6qoKL*mUC!|%vg@YA*){$>i^YDa_&@ssV9OA4Cbi$*eCffPU+fQDMX65G2{Wx9( zSgY1^a>L*@wwL!k9KWon_-1YM%;Sden3#;&dH>Mezp)cSd3v{COBn)TCl=ktr|bp0 zbK8=&lFs?33ebw9MfF1An5KL$TXGa)!1hkIaHvGrv{|lTG?W_Px_fGc_FFf6@T2+y ze|y78=$hKBR?P)>p*NbA07ad>{>{+< zVQH|Ip)&EPC2)zGMjevPI~q6>p-R_`Sn=Fdbtjk<_!xHbEZ9%WCqK4}tp$tNVs7bAk@w;^4UzDk6gMSU0A?=OmhLCMwHI#uA zFxDe6%xx*SSkO@x0F>J!D4ZGtTq&{x$YMf3J^LH%n{|uIEN7j#AZVq?-MV!WN$lg| zrlT=Xl3;lPSk27`dT|%HOKQa@3wK>&EiiUuI3-V|=_G0R0rKYAix+!^7icgot2atJw92q%Y=o4Jhsk8EhBOGO8c$7XpQd5oG!IlZ|@G^(?y?j|!WLMRk z_|bXK&9aXi%-u9-pUdzsV{fkWGM^i#(7r}x>vhl`+EWzo zA9iwX!Vc)0RplrY{t-J(vu8oE(XGGK;&5xZ$m4`JFyRdmS=i$70jL;sKg zo~*Y*PYpH`DGmh$=lTx^iNFaKWi>u{Nce(qJ z^4K3a;=i;{{$648a^Kcl(1U3R4t+ccV#qade3qcZ5tagzezF+X7b?l(H+2ZyerW;y z@{_b^D0ql}>k~y5Rxk$HXqF`iURR$cC54WS-O|p|gklwt7YhHL{Qf-F zf4%I19sqc>si`TbUHDD8e{O7)0V*vpnjy-s)U2(uf3H3Kylqd~Sy8XD+4Sx^3+?0ZXHYKBI z0BpDsAX!nUm+F24O{w60{y*n3fL5{JiXRkQ_Cer~R1yCTg*tRsUPem&c2D;4MpIts zu?;lG!`8>d(B8j)AK^#reQWZlLlg<~?d-TA&dqheCrC&B7K5hY7_5QMZE#?==$s z!`TC_-=p4Jqf7;7!&J}d78(8z6Y^q}_+CJs8&F43Xl3gX7)o3Pen^8Xy9yBfqK8=K z0r=P#@)0N$(6b0MCy70Ju>a^y=vSSO*Yoz?w}1adZEfxGmS@kN!M;nXt3Q4Ck~=iN z67_HKN4qurnYF~FWg2W+biD_j-drDGM@3ZM;J z>7-lcstQ!0XHaD@{RTr4$RB&BIC*%k>6N***%((%*WQWERszx-AUd1kDR;tlN3Lxu z07abbOfUCcz$trqDDU2IH1q}#Q}HFKQi>vtNNJ6U61i}D1*pxI7u4hV?^q0M=)&Q4 zlR*ADyz)XuwIiVa?Y6$#0WqGSl7#)(K2!?On`9h3KoF|6@->@va%1`gbGUTuJb&3^5g&`W2m_2h9Shyvg}<~+Djt8 z4@Iu@T}^!2Vi>sdQJ(H1O48NUb$p{h`|NFarmF@T8<2o}|G?$tTVH<}Sr%+}*76)_ zexhU8nenl)3<4tJ3|M!LmrpkUzuO7qYvkGXHnjoN0LX8cBu4|TITL7mGJu`&HH_AD zPbLThNt9w_RMZ*x5fCU-hvG&TAdtzkUtgD(4|Jc1HS2_d+Q@5H%3&V$HBcZ-P14C!9%>&J{yVcr@8q~QQ;kI z;}#HZ$v_4~fPz^l4i;8citN{kug9-MR8K}~=}SpV69T56^SK!G%#`B4ti82pe?bbz z8ghQ*DQ@3BmE^Ty?l|2^iaPH!4d+#T1c)^GiyF&8EZ{&T@ZYw**2qlplIXSKF9Z3Y z`uciFScSzZA}V0FBXGrL6$UFiD10v2d#l3bFHe5;zh-&0rBnTCxn(r2U%yT(?0Blg zdDg-)=tf-_6MDeU2&`m4NXQ4snx_^=g<`tjl$9~SJS9lBZMFfZe83%>bJZWabDqcP0(J{E_dgPI;RJ(sD+?A9zQ;C^5jXwrZ~Ra{qM5cYG{4Q}59ip(kF$ zyar90a-xN!Y!={3a-q_WlnSbRi3Gj3MZb-XfFRn~Gd1py`t}D=vV6Wl@;Tf-8YSag)TEnwTI-4_sPv zb8|tLxr8=(rxN$oGawptFFo0d4$P}8CcrGO@62!^Vrp{wtcVZu={^}MZ5OA1;YqBp zA)x$?_WA1;i5&)*0Fk+JC8+Imfet|RL3bt$R)n0MpS(qMnf!Tb>h9JM9S#H3tMmNO9otuWtNJKpDogs}MNk9Ve^xsO1;_aV>V3gqwy3}xUzBH~o*YCHAQDQNclauq(l`CW*gW(T?EcDuwZ{Dra&%Cdx z(c7lp0b70gTjlCs(0sAJb8j*48QVc|zs&s;-lMPgladk~C^HTyff->^1ZP1{aSyHy zDaZ_nZ+)fmt^yzp;;T;Z^3r&3_797Ch003&S$is!%K)eSB0Zgzjg1&dBQM-O0IaLu zF0_n&ia^Rv00#ofV4CpoaOIr`*b%JFT!{x>$z>kq9T3A08t_P10FHb=K&^xnjEpKUnXP4R4)bC5{3(TJysiIQcJV-ivR7W8Ey&J%HkS)!sGcnx zW_()SweGS1MX0`ag$q&J4LwhBdcw9hqrBQ?XzT5{kf5L?EG^4{CQ9f^L$Mg-MuUcR zoK_e`oXO#cK{dzs(=F+>xk2QrjU)nLqv+?#x{IG3Hdf3JA3OF6nCzaJHpRUJKiJRO zDHlTb63{Z85ZQKa&Q}@;?EoDq24F_7(vz+sw^+l@E?3xlyKK6@5Zxko5IYCDaq8eR zRKd%r^`H&3GBvZV6%N=xI&=0c`Dgw3rZk8mQjp&jc-##>Xe_J(jeG_u=O()owQb?o zhBKi_e+Iq}=6<@Mwg;Bu=?1Rf#_z4x>6fO>3A-NXq!g;UlapNq)=sM%%jKD+J;TWqzPGyClhWqs!EH*Y=<3JiR;+GIcakpfC}XS-f&a3J>la|e2l@%(s0bXSWPj@Kkt zTOB+{SNYmdCIpSN`Q=?;jGNHuChb2S9OfEfwRB!(WSrsydfZjut02BJ531)+Ki#?p zm-mdQXxS1lkX5A2Xk2*(<>oUHpT-t5`Or>%;jOhUY*L5KzNr1Tu!nw&K%(}Qs9n$9P1qkR z%3E9{9gLq_TI68G^wLDq#xWQY;a8eJ(iS2)9g&|nirrYAsITEi@*W;07=yctgic$W zX(=fwOWTI;chg}3+ZP4DCLcnN%NZ0sGJaIA1mWQl(wKzo_n;5}A?w?Lo4XkTy?V`RUX9U{nD#Ne65K?}1fK6_m?i=DEtE;ATsb7uWDULWa-x>E-%bnMwk71?UOXO3M;b4hVX4!Y2J}c*qZTRWPl^ zPa29ujxyrVAxFOB*DMeq1@OgWhqM}@aKZW`oB#15;Y@cR`ZUe!o1OKe5nkS&)Q9m~ z_4+^-ZVaQ2Fx`i4z~|=XLS35p>6FVv^4yy^^VW^LP9<4Jdw%-d!R|X(* zV5F~&ib9zE0gkxs*Y|rbn78jiyqlC1!9-)MA7n-_qmJ3ufy^@O0-vx?K*ot_Ab0?} zYRXjJt2#Pld40ibN^yH;_&io7p~q3ufUN`nYcx<~2cP*PM0>i#-+(o^3iv;R;dhz6 zoBQhYUKD)9Qve9_7Nri4E{Z3i4wr&p7T7iY;nJUgsL9C42&wSGAmkL!Jm~!{F4Rzs zM%}QMf-uOg|4t#yEMUA7Cfs)LYsgwbo-wjm$0P?JEuD8(fq-r#_VGL#2o=;+lZL1w zr~(@m=x~F26wvw~N2NTK2G@ZAt)hDh`>bwvy*`X!!IziZ-;#r`ty;zQghRb^={x*~ zR`W%fLuV13$z?yFoq+g6NQpOA@!X97m$`vJB>DS}pZ5*}v-n20_#gzjbr57-7pfO? zo}qn6c|6E^u;gpy&Sqs>g{KG7A_)`2(E>#~68@)SAYFo@;_)Cbi&a+qhzryP2Dk0m z;cbeYC9a)&pl}6h)CZ0oJ67a{7gl6bGZ`*(Th1}8CcF!FZ4#=q;||sXMbuEq6{kuQ z(|ZV3jS61n1JIpMtmptNwHG2R2&60rJRb(sp9iC~WeS`oxLy*?0Y&AeX0weG3_i z4y25$pwwJCpw+{p3-Awn9mqofH#-e-evutW7?0o`fhgVb?krS~ZongUeUX!SLF59Z zs&+%D!+)=n*#7D$Vmso~a2FU#souYTf5tmBG*lLHe&8i0K_LjU?KSV6qp&M-aBesX zU4eYv2mqDHzX0>JaXMzJ3+*+&vJ86`2!K)}$lH`Vz2WM6v2v$Cu!;G$+tE(WR`{apax_|)3uV4=@`+;v9u&X<+ zTnG%e#f?2s?uj<%)XA>`r@RkU59S-->zZi&5IFZDgOGd~^29!lj>fEfhul{XpOBnP zg+jVXy80Z#!e30jz}8;{hY#c8K>r&6@Qe{?Gp*Q8CAz&O50$MFuLmQ2a(fFS0rJd; zfQlSQ)kzS5uh$|o3mNf%SzN%W0JxjaU~)4?*y#)-%tg~-rnWBzWopsSBY$#6}aV4lA?$N1=6GXRy6=6r$V|djz+O48uYW^l6-X8Pie+ z!A+PqzQ_9#0H?X(Z2Fw5fwm|zcU}g}wMDLrEbu;~ZxzU>RnH^U(#w9336DS$aIOO) zszAu;yEn8oW_5HRN_d}RRD*En!(anFT!%}L%>#FlCHQ|L@14zIOHc8LPxpg^$q_bc zi20;oD}iM`7ZN*2S*T6(u#Dc|bfm4U&JKC+cmjC+W%ERdYbiM-JT+*TIff~6GE~mZ z4?*t8bDu+=dp+D(Dx0QV#XuL>4e)#l^+i6?cV@_zNNyFG*8aU`vRZRnZO#n{UY}5!(cLVQap* znqs1G!4QRvjPQc}HyN1_br{G)UKS>Ip>eno(SUaua$`r<-u~SI7lvpWgF@@$NT!pf zv}%J^z48q5KSWa=h)Qh7>ZxbZ@-pg&kt%EFK4=e;!WPtkFIx28S@Z5KvSXbq#xMd` zjUlMjM=pasFosHtE09mw0XZi^S*Bmv4T%>LpIZ%A3?@IinhHwtscy>?-@$Bqic3BT z$Phlq&`FTnT3aM#@g+U2|7-0IkLc?Hl5Tb*lNEQTLF1_q*?RfA( z>6|q`^K5(xLl@9nlK~D5TElGBB_!s$N%jd>%e4%~ zl5M7BsVqY@V=Oa8hRiS+Gh;S>XWaMa``52O>dZN>b6&6Wd_9-P^YMJ1(yv}|+M}SM zux;D6Jr^(7U)#1#W_jB-*_(1ZfisxBfj);uCJu*1< zZb0Ry{EG zjXICLvcG%ZA4(=1u=5rtzev5gOV;jz?76-6%6mH&8r!D3mn1TLN%^3)DJiDJf-?T{ z#OramEz$VYAPv?eD*Ao3%B%A4rA;w*lS6cnJDj}NPR>}4*8FA9f5(T%v+t?dNsj~1 zUU;kc-}PT;^tdT^{x8zOhm%9>RT8$&AKsZP+rD*b?@x9z{#y@7+g@t2ymi0bZaJ5) zDO;D-_wM_D=j8u??zQbYvGwS#dqcAS_n5=m4gYs8Vbk6Gmt?)!O^ImJR3M=%iRm$u z>Cy)`Sy}#M*pqij|KEDUg4qcXFGUJU!XMCjcXh*a-YW_Q^47Noh@R^N9teE0PO;s*0bVav zs-bTxE)Dz)iRPKAQwLrKvqv(dQ$NfArmj1;!6l)eUidX_xKVyTr^JF@i(; zR)Ha1wLie?@01p#6FQvwwz!zDqe9NV2~G4~XyEAs@=06F1TEWDrKPhnotrwY@+qwO zOf)wyvJbR9MnSsR1Yi^EWWuKXRLIDe3)O^S&~w!z%2LtpR*~$j+-oD4v)%9luYFS* zKTYsIUyD+wOz( z7OgaS6F%VBcMtr(%gS$Mc_(zuRemJ=Q^9!RG7TFM7tkHC*QI7Vw@a!E+j))Oh>l=k z9cBYmzh|P600&#pvdMfAIt8jzb{zbHCf^KJ9sUrwL)0E)16Ng?lX8pHJ ziOJt~r;XIwGEty1Sj#C}Qm~+r@t>$1oI;Xc4SBR{Dn413UCRi(ig2=<6c5o>cURkN z$&C5eJvH4@&gC30fZ{0EE_~NodGN;JjL($@f%lgf z-Z>OK#_S!%O%o77pr{ORXS#>?)ore%Z{|&G&ZunGEx#|z-fUwNd@gy3ONZ`m3L^^8 zS$#m1nA})uW1n0nh$wgGB%T8mzFx&%gDIbdCR>{B9wK9y{L_xOqo@wZ(N3Sxn<$m9 zF^Bz28GWpclJ2C!t=H878Mu!BO3R_-$>JBH%7`-zHaYY6zI<%|^uZ6Rf8tXCvJeRVL=*Of=sDnwz1+dyq#Y z_Qr_|NJ-HD+?Ti1v=l&9b23)<1h)+i=eX_$ZH(8iwvRS)HK@iVRXSU0YuoCIT76RRR?&EdxVcJyEqQ_! z-ZG_$z9Lm;U{1#R*luboKV9sF;;|ZOQYRI)F)Zt!w$1S0MN5_M{lQWBH+g1;?Zdg; zA3?&Npsh&QWG+1Hepu$zIofn#cOF`G#(a-Kt-p`(j^O0|Er}~x+8s$Q=gPS(=3UCM zD*OO#S+?39wvZ)p=)n7r8EZfOPuI=@x)yTJ<$XEx@eRzs&y6B2bXAI<^7;Gbr2<$U z+LZdemnYYM7xZB_>v_Uw#C~?iK#h-Qnxqes8+vd9_BpWj7$f8P0HyZO8~GnkAkv-l zInRIXio66oTdyxLzwY0(=FDyWlu&$oUS&7ti(MVf`@dmZ(NYGPj5zv_F;+0`{k!P8 z8sguJA4Fi!P3bNP!(2CQ#~_T1^(1d6iB6PnUN3%WdnfB09Y*Ob-J+He6rZaVM+lnEOuwNFjH=HIAbz^%>Pp0Gzs) zeit;|fs?<@#-=0MmaJxLoGdzs{P6S@u&4Pd zAL&3a_+J1WvORL#J_Rg8r8{ZLV2@0kwwGxI(Y%zv&q$dz7aF9_WkLr)czMH?#WgP9 zJFuhyd-J{nF0EZ&!zoj;xfUf2;c+J#)q z_g>g~75H;>ieuW&_92IF+Ac#r$zb8_5&kQ%uogz1IZ?O^c#K_`jS%aZ@@hGsgnA)f zQ9}E^+31JbUf)>nwH-vZ*8GMQu%MTY-zG#re8Q&Y@SDgl(FN$^xBdGN`ZYOmhi&=n zPuC*Bb7C%V>-@mn+B|9J*6KbZ875djlE#U;%5A@lZPuQwSrH<#zw~DA*Aj!ve~p#dY5-423%|&xl2Go@uif8`?`J)~iOsnA zW^lURQh!&*3hYh(&6YCS*o&h{5nI!_0w7DTPR}gQGk7EsoAFL;@ZwkSr}V_JySqJ? zSpNYTo8f~AhYvabBZ~9kFwY1a>-xL?I9&?0zmBLX$fhMu0B$%5yXC*?kU)Sc`{Qt?ZZoKC(ehL8vK>`%J$A$hdkAG5N2-@w!Fbkz=yg`--$i? zsr<<4g!Y{;r{2&$pWoRB>H88bd!chH_z~t)JyR~6z3*d{y7Y2-e&lbQ!&j#{n%7{+ zPv2(Tsb{uSlPUFUvdaQP1VOxUmxv`gGC_#88Xq5#q+nnfoCe_=SNwLlr7E?B&br*h zY~`X*MB1DkEy9?#I_^D+-SFM=v4&G~wwsB77Cq_7h<&KG#L!8HF`AFQ{qvBQ#G4p~ z&*r{019`b@y{(QkCdnE`sDY?NpOPjKEK>2!%pwZzrXCu!sRI2d*|e z8oRJuFE2S^|6p>iA0){)vC6Gf5-5K3GOQIOO;Z`&N#m)LO?%U^4w8(6D>kAN$;-auzcjj+7Kq!+XCN6Ag(!=gzeMP zN`0Na&RT!W?^_6pAjib}KAe5!LN8C+a;RYG7RoK#{qMQ#RkO@aO|~9 zg2hO5+aDNjg&K{Rd>`unh(Wm z&Gs+X>FY2aht{FY#t1z9#yN{Barb_dT^Iv$kjsHdb;OvT;p7qKoG`D+Ln;0zq6q6IpIqbRx0L@T;s7JWNwW{5sf_qpsuH_*Nw?4I zMmw*#ijsVqS;2S@+Nk9DRQ|T5P=Cs{=bbiH9!$PVKk{33?TlsR_g7r}r#kKbv~o?y zLjK(PY6|2eQ7{lm_D4$!KgUgd_C5}qUhh*wCB8GmU z!lzax!RMa1s&CByP`oq(=iEM=QAW;)YR{T>4k<^Ucze-i=lCLRHE6TSgIDBcQ`e9C z))D0UiVx%`T^7$ptIvp4M@|RUKfh;?;#xs%RU_&BVIQnEXyQSj*yPd;~9(GV0WUtuR!80&aLtWx7aRFd} z?*MKj?b@pZK8~RxFWF(dF^E$`)O&-K$Tv}_J&R*MG2YS28(#-Z3yTlXc4crqPx?T? z%IeacHTs|TJX@+Qe_N&3`46};@*6M*x-@f2loz7K5z(T~2Y^viw(oyWoGXTSbe|d* zDM*~ZI^`js?vbLVJJ03Y_sZ^X^6xJ{ll5X+?{Aag0r3&y4dJWsOD`Pzz9XI)#`Q(j92>88@~fW#8zfRk(P6jB-ptM`S{bq_2m70E z{Nuz0*693fsVw89RPnzTLpsh=427Q@W`QI8a_B}A1KF{MGphH~&ju$5#`F zT4B&PkL5{-e90GaXndC_=x2BMQ_F0C)k(VpkVl})E+5K}2b5Vm8m644+nVIlk`tNu z+JRm0p1RMv{|+1OW74o+R$B5PxzA?#8~auOcLMlJIT@AK{O5dQ)0NDXMo+Q_sKKq) zsW8iV4YLRIubW)?D_Yum#5*%N({ov117qNxo=kv5FbP$O2Yp96hj;p^{Lou9x$kG? zuA(*d8p*%;xIw|+@Ri9*i3>%fuR+tvyvvf7kpizx zn8ClP4-T6Dq&^aukwqO%f(lZ1D+KRfE4i=@cVZClJ*SWkP=k4@0Bn^ViXQFKaScEX zdvPxem7|*m2Z?wO`mu?S$6UERg+)#yF`M3(+V%$P+7#6D^Z&&y09qdCEe!>K_#!@b z&n`C8PFCnE1o$*J)-V|`HAr;S@rt1{?|bug@JBY7BM1hUhAJGddf7}*f@|x9pDy!0 z-)nn$vE^ib2ev|=@Dj%F)71&06(CERdOpSZd7NzdKE2ADh&1dLU7{qo6{gp=_$r~!zHZIgc= zJ{#q}asVK}4E{6A;IUAX74i+_p?GWWRp}g_*HjRnIeJ^Uff8!jK%eK??V=ZOe-)aWZ!jO6*WCE*MreT+^ET-?pdpj7&wQiP zvjD%8o$gumx6{ZU`$y(3sXN&i*)sp^9$o$V;$}!fsiyq2^Y^4|)=YVxJK*L1K;p%r zh1&r`m^lj&--l42vvjq07!}r!961HpJumllQtZ)Vm3>0(iTyN13i+aC-@6*>KW#Vk zBfD@n9$yABrLMK1g9*lW2uVLY`U;7N7*u2@nQ*dwXt6b7iA^DHosJ~5e7%0@cqBo< z5RZ!r_E1`DeCE4*34%VanwoxO)lw&HsF0nVez(u&)-vf-`{t}6UH@4tfAiXU7;EDh zp3na*nCpc3S;Ae7F<YM5< zIv5KhB6V0b{Fmht?)}7if6vIEF4ua}?RI{j*bPCs2|m*eARS{DAotid)}=|=y?;fI zA+fo#+M&gC5*V9b89P!}5^mq)4EoncHVL8}IVzi!tAtrL()7RF4~VM)NC}?2a2P6E zz8!d`;1RyuWcVK&6>h#w!C>>61<1^@u0|e-HLT44@~_K+Bxy12ar2L{$3P6pSo(a~ z@C%T*f_e?<>$9K7E1vna)}nsges&E)jG(v;a(3`Wo7FZCo&TO`j`BKrgtWXfQD=x0 zSboS&yZ=VtpUi#|t~!uJmfALel~VEo$|$n6YV-cTlw;M1{?^cG?Hw`UCU3>V4=rk) z9#cQ<=@oR!Hu^p5?0%z0_bO2hm|Vy}sgJyT@xL%6`!%InjhKBkvUtczrr1Sz1TuMO zeb0_Q&$Uk0x1@GLz&)t@;*QqjyP;S{wL4+%z5TX{7k`YRucE7;fC$4K#57u^4wd^ zNO8R{7;cDK$uR#k`wY^wrgQjPOcJrrkXY5e(xb^al-BKtV%eOAFmba75&Ddqq%PsX z_j7=W={h=vbbU`t)>rU@J19T4n-T?_eB3K^?k~^mp0-i>k)41`7aZnfK&lj{R3U3^ z;?=%fvX8`52M}3g?s))B=~d(`)%TqoA=(`QIq0lBl9^Hr1SM0yYwbXgcNKS;D{NHF zX;dv;UAAimO}fNFrzb?;;(niqt_O#jipX{d{YpTHfe3(T1i6M^D|Vq#j~+@7qbA$v zsz|7yfd1=uU0uExxVn5L%bNR4To^2HZIXYBItt{Xf#M4QbXU>Je|M)%VE=?WpO*nK zDNDcGl>XK?VKVg#v<$@xBsj!2Yr7f~!&(=SX6L*34!se;bV;$#AgC6=v0Plqz-|WK zSzN{WU7>Ugh>SM-i1C7JS~@<=(qA#nGf8ClPO_$kZR(uJd)eA{Q(*XsLWmcL8(o*Y z2gxF`f4#MrGa1OY(pIGC)d}>~$HDT^YQY6C<*Q?+ay-K`qMqn9DcV(~yT4{c0PNAr0LgUf+ z_)+a}B#7Vm(Nfrm+I9F(n`Y^hQle(}{>EWtbvDJa6eqtNQY;_SjtjRG7lhmOJW3Bd zqB4biF_Dk1R@%SVuf~iFHk8hsD)p$t{N0G}l;{W=9m+K(i<(NxBNq)S&F#g***9KL ztDXFZY^{18+_Lb=qT08N;V+K@cE0|Dz-z=^e4BA`g+oCT8h{_@YO$D$HEb`yRp(<# zGZG_@WYYb_vW?Id1X>*fW1&8@|4b5bap>?n;pGS|TDhJrha-7DC8`yxG8Y@6L8btf zd@N;a%JdcE+MEJN$+ZAyBgvbn8O}o53p$Fqk9c|z?4Hh{I|qQYjK{kH%X6*H65NX4 z&zTE+572Lc@FP;fD)+dhLek^+p8FS^Jw>8|TIdKo7ejraJK2#}ogT23EMAbrR?lyB54nt$`rF{@)V6t|BJ{}=FdvB@Ay@O%IAvH7vfzhlw` zH&(wHA5<-z4YBC~NV-5oJCWI1y>u>()5Eld7uOU%mk7-X{{EX(=Ik796R{}dQu-*w z6>efxb@@QA(A0fo0s>sDV768?_UphpHiSa&AF4A|y)A6kLF*u)$=i}yE(PsRFovTx zL&Yp8lfS-B|AT!z;~cv34naS+qjz3MU_3#3K<{llB;CaOfckt}o~CBdF5HBl3&*{A zNC{Ki@9lPoZ9RV*ZWYdDfC5ws4N?FmdNuyC>`4X_bkCkUs{KO zoXhvHei`bb%`~N*!;qUCZT%m!X?BEBru|t$p6x?$-&G9|r6>=7g0v%VG?O zgE$TTyf6naR1sP8LvchZx5A{;g zSDi?og}&z*jh{F`srG;eB4`Op*?2m;#?02K#0mngtU==tsLeylZb&Ik0E*$xkDT9{ ztQ5+JwBgckJ;eB#l7iPvK-)3XljMF(I{yRaOTe_{CmpVae5||ef@zXF)(J>--)!-a zWgjhTzWqLx5u7uIzx{u`0H6=X&6wMxFNAJj5UrfOzrlGnRiW^q)04aGnINAXe;gg9 zzWC)Qrc;M~V2RBWsWk&y43bxQryajLB|Q-IhS~+T+Sr#4Cqo9bxgX6A2c@KYgZ{ub zIwPRh<8>xHJ_Ol@S&{;`JYkYlk@q79-#R^sF}qDaQTsCU(EEeB)zhYyCui8<)w6O} zMlUa}yTq!}_*6c%Oj^^QaoAL;%(Dh1;ohu1S{<*%RYQn8@0RILzX3#hJ8uCt3&lyQ ze4}O$TsG5TQuGJkvyg%9i;sT8=~cv7w05ie^s1w6dvKe-;o8gE7fMz>QRi+maUDE- z^z2g){mDYeaTq(@kH{NO($46ko#LUqk?bIeSFr~K8D63Z=K}>`cL3vdtq)0Q@q_?o zcjVFM!w)sL5eKJXOt3AZ5su&@^}{dtBg5~+7zI0jFF5140-HYQaC*G`YH*q0({DIW z?+gU8_ye8)6#|Hk}jI*XJ+vi>#fkO(1K0<1-&If!=0XrllDAjkF|bns5b3OPwEjpoJ&=%+J9f zintD;cgyO(zs|8TaRS;HiA zh#Fn-88E%&ULhC#<%UNtc92E_G9y0kQ72^y7tL-Ki1=iLKa%?;r3ew_0kOy$>;u)Ypy8=Swk}j(mdgLEXa{4{TI=LNdhV1;g2S z?wj7kkY0Z(Ug;_MW31Va9_!%-oBBc}l?gYD0T=%farFDI((rC64exv9$1Z#gHd}kI zk^KtiGEvUqxE1R;_5F_iFx`>kdkFs#?(#cUv5d21p!tX(xde2;NL8S9;RGS*5FEP@DQk=<{|&ZmmY-Y zR4%H{m8b6ok$Tz;fMwgA|ChtN{^X>i)5F*=L4o<*4Lq-js|{%aV}*qMjmrMNP-;Z$ zw>uo-hHb}h84V9c1I(GaEl}X<`uv+7>S+SYwr|VCU3`G=aQ1oUW)Q8g_({DZ|62A8(xKrXXU^Qr#7Php4ceRC4 zP}$eFBx!=fO}|aV`LzV|zZy3 zG3g-z45F-)v|5t5BY7|FS^dePjsXZM#YTD6Ic-Mr<(Kuaj$kPAYtMZf(%6cOnu9gR zBxz3dYNE&Cu(pNkoFr(X}lj_6CgjVqhm(k@XC{y85>spQ#Q@D1AH< z{TOh~g-$^7-(P4gd|V`Yz(#c7bb65}7gbhVMb zLq!{J9QNzi?s>t-%eG$LZM;HCF1CH`i2&Bb;EdVyQDrLYE@gS!dZ9L*&wsD@B;pN3 z`nP=#g7q_ywGXSk!m!{_0WzX0g_O<3NNXnQ<^byj%1|55PvbLb4CG?LtXW>jV3#@X zQ=k!e{7~2}ywU#lTCNO*Ow~{b27QotgNod~I+aj2seApZ@O?* z1aBV6K1q{c1*Ux(pK>5ia9({tJ`edPn^nvP5JMHgwU4p3+c*(a;g z$UQ%apI=mdlYKq1$|*#sU7Vh5rSh#XDj6h$`B6)m7u33L?INJk51hxQ>+)FZmyb^S zWVJuH$q0}%^7eI)CTZ+5=(N~JKo0(mS9d3xwLp0ns_JXrS)|(GDGYk!UMM;Jo>z}O$g=;IEv52TW zJ2upbOSrbB!dVACYTU$s7yCLTj?>7ToLiaBFiep-h^bqD(^!;YTCK$4#7E+_SqE%Y z>6p#_GN?2P?P-lfaSDM#&G1Z0eQ!oAdH~U$=4e&PaoL;br8J*v3bUx+@Cp4nQ|-yz z+BCVw<$~zV2c_<2+{aaW8+*z0i|z1t8weLh@50vJ0X*lv(PRsS*!?_|O<#W0NlvZH zjtEfwK=;rq`4HEmgD^Ywly*A*n+Tuma4=MWsdR+du`xqm3rLNR z&R_sGpts9Pg|ogp=sD07x0IjIn{L^+NU6V=WEKbAc%z-p3n{OoYD&vj@6Q2lZ9Z|O z*YHI=U^r(9OG4%~BD02AlT*_#@kc~eXHDJp1G=_Yd9czf+utNJ-H2e0O%lTS`wX(T zhf9HY_#;-jqa|F*Q=QHAZlIbY9{0a|oWli5g!Bo6XLG&F@OdE<=VOVBV80d!50RC| z#jT_}(}zUO9{brrVr#&Zx?(AQcZ-1>(Cvx*Ixu;}IX&X~jG7ubY-)9#S)I()qL#0h zJ_yt}XB+|3CyUCy_M8dEECdn#@8O~8W_#ZVLaMjeIVtGWcdo^ow(-r_Fp755_T{F2 zouD#Wo*c9gQ%wsiQfmL;a2V+SBfj~ig!((JmujyaQwp(HRp}0vj7m2nPaD{bzl{QX zR95Dp1a(b7i|V!0C1DHNT$|vrqnas}Dm>>`P~C~ZsE*kWE(U5$)A6udE&m5C5<^vB zMF~#RX9_k8XnQ<)3MSH#j8@FG)Q0@pzQA!4^h5{iBAs zkp%RAlrwq_#d$Rl^n0$NSsR~8q5mPV({Ag1A=BLtEb>--b zBUXLNT#H{%tuS}g3+g^vUTH%{b!*&TxoIKe;_hd)Xx%D!7*b6 z>2q3ExhX|oJo&S_f64#B>IQ4DnIb43&}WZXmLl}>@e0N!}1lm6< zGG|n}X{zan^57sDVG7=GsK5=Jdu$@PsM4iZ0NTgyS55cZ|2})U3Dywc{Jk&f9QM^N z-fk-1HG5#+=j;po>xu1ru6a4oI5RJWn@9Cxk0@h0DT>hjWBykG&TKAl!_A)#!?J&Q z?9guF4H|=z2v>_EL>{ieeWA67^j%MXBxUudb^l1X^;^ZsLGuTs4lj$8y=4csw4BB1 zL%-AXA6t48H`9tCX#a~AHIo%LL5;J0%;(lFdB2T@oZt_ES)?9w~Z+3z@W6kX}vX>bKm$wnHLIoBd4qh`d`R&bOrUNlDn6ZgU<-^}zh z(v_?tb|Z~m@;NI&j~sYDo;3zErUy1WzsG+tIZ+?iH-i(^zL!6Pg&t|CM=c+$M?KuB zQhN}TbYz7osdYTM;KNz>?5FZCP3q;67_e-TZv}Mk7dSREYnD+gBvB( zgZ;gc+dNCEA{(4Y;NTKl5kfD|C8wDYpt(&nOL3p=(K6>P09`lkK*rVmeK~M41Gay~ z6pa^n1_#CEVRofh)CB2-J;~-;fy6nQ$0*sq^t?`D=2#ch;!AXi)TEowMX7A%o;a&oG3%e>ag!@1rdBOy$xY9r^@!+2l|=JrZUZGdfN>h z`#84K=oMPRNan0S$A;$L>>pf6EF>v&#{%CUa)WGGl#9>LIDO}||9GMKBZ;Bb zJ$V|AZeIi?kjlLKm0*q*-bt|67OJQLU?5)GdIZqD7My} zkamH*v%ecFyCa;sqLGXJGMM%87K|mg=L!|(6w>oZ-Usw>kJ(e4*6kIhry{$&+|1O< zA1H3PhswN}eRa9grjzn1`gw=D>yrdP(Ewr=B(LBTpH(CuU=4bo)q3j*!ZlYXW=6T- zl^LV(K>|*m2kiAFuhkXiMx!JB$Z0Z$T1(F?;_K*|mHNk>pxITPiL4&>$hq95c-Sms z=|y40XW*TOSIg%X=uLzlj6w#AHI5UgSItO#gdd`x+rWP&Ll{=KN-mdhfFfPN#G;Tv zFG2A{@-(LDl`GwY*B|t)MaRbD((zeFVMGZ8E*1`$PaR?y5&L(=)F#dS z{mLBpWW)goX9X9|h`1MC6^=Q$(N5&nG5)P{%bK?^dT29p^<$U*XWa)PIi$NzOnGE( zb0nQ*(WCoSBo~=1AC-GV<;OhzGpU7lz%Fe!kcWqQAETVH0gw{a``Bzq&~<*l4%4Yn zo-uNYtFsUJlAZ0Ek(L$Ryk3vO7gUe%ygLrh>(`74P4>Tjj06^{}<{E^{ky6`(8Adf$>; zNoJa`^nPFyJGnJZ24mcqX-6mE+clAnhm+6#??aic#5smf#7RK4=jB+zB zH938vAXD&#Z1&qu$tf&>V9=7h_dzLOpwZ83yWuLt?1UzGhJ)LoboMJyUw3k=8l%db z9@LO{P}4bQMnmCF(PJk&D%qrNAoNy?Q7SdGFm0aNZK?Or?W|K^%Z(5`#oKn{hMk{W zUA{E)ynOn8O*Owb82_ef*JCKb)Q9W=>nP2Z_bRF_M4t{PiIE*)-Xw7y?$zeFabcLv zCxANM+f#nx&BGPb`!uMA_BYrqNi9NMb5_+Z4GiRx!`DBj`u3zw^*ds6h%%FU7G*hhP{R%X6%x9p| zZaDuvH)!n=+Cd`9}F^hh6%VCQT1m>Ey}?4J2a7% z>RwLzCu3p`csV81*VUKI*X~bKMTKF_NB%n%*a;o!MXt5 zNyS9a=pt?6sx>>AFFxIl?jIzVogpq!5I?VdTzYv=t;KoFiQjMjX=YJXQ;?XaLxXlu zRuvE1Ka`b%$iJ%P{SC8_&JEi?gf^}nNROohZfVY&HMY`ewr{q-_wAm0kluCYd6R&w zsCc&M*2>sKUN_w3v`L#EUwN*QH}ZFRV^&w&-ar#+MOfzL7j4781~BOfMe(9ZeI0U7 zV?UPL^FqREDVsZF88)6}X85@@`QiPYQzMoF8cRd-^&J7L z^`Z2EcV0;Oka)?`6ZK6`Kd0FJ|)dc*eBc^bO~G zPEV4wO4Ge=Um_kvC1|~4#a|&}KB(EHsY;t8uCdzVpcWF=j|%wyURvZ_wbkY0w^3Lv zWF77pklavMmf^HQ#<#h!M?#Kb!ZB&7S>k)CJNPb0?4ZFH|NuLCUC!GXX zSK-vpQ9}P5m)*R<&&i~Qs&?fMSKs>DC4ZVfFiJPm2VFw0% z`tUEY2F*+DaX-*TT;ur}pg);87U$8r%K8C)4={pWq;$_caKE$|$g|wu(0V9f0`5TB z<%#T$R<&*#;GCLo$N%I&>|On;!3mO(qwNcy)N%B!%ctPV;ksiYAeu`JEGy(@2z0UIFYJIW40|15;pImQt({~FsuO)wQ>WFt-=GJQW526#@A3Mf z&bI>9FtuafC1jtwC#gm??(Zjr!k|d~UA(TP-rZMT`MjW;ivFE0ntd(5;rz3UAnuz?ny8+oUMCF#c?8xEV!i@2XFsS09Cu$RxYEL(5S3VRSz-w|F0?~QBv%`W zeJ|{R+Vw0aPMZ`Z@qRyEkuwp|Q4Ji9j?cv~!!JiM_`sgMoaj-T5r_kv45PWNGXF8O9G+@$NHI~RSQ^4Z<;*gLf8?c*m=2mox5_|W<9C@ zWh0R3d;m%*#uwZ5oRlLIR$*HcQM3VGXt1I_pGRcCM=fAy$0 z+-7FaXPK$EX_I|&gLdDtQ=>?{Gh7*|c~c0Oo#x`$v%Pjjze%a&o4p~d0{M{rb2JkE zQ9^JaA@>+lb5TG3abB%ycH1L8zRbaHn;yqN?U=f8!p}bM*Teoi1@cagV@x;|!2<^E z(J+WxiTf~Z9KYAjUdrp=d%`P7b9{HGI^fVaBHTB~6yG@i>gP&Z-bHJbt_=-`=-8ZY zgptyCndR_l>w5g5vAHpawmP?!h_wSNxk}-lmANX&ygW*dzg*NwDH^;OP)0`4ZQ%>l z=!wPVr`KOzc{&k64F8A&ti-2C5b&62h9=VD{4BWrtHRe%-;#)xjZu@Aj3@#oW&Qd~ z)sk<(uiP$?I&p9@KY}P}TJ2{c8(~B;KwVFD+-)Y&nxQ2YPZe$lbL!m7YzpDzH%EF4^52W$r9%-Tcnc&4}3rs?E90JuJ0YMO`` zA^v3On5y>aVfuuIURAlTXRqS=tJDu9ir1Lj*`L>7iQ4WlA=WC^#}wj0k2?&>$9M%5$qn7k}G-b{}diJyMyjw8AQy+Fn~ETGfX zTdEBfd2+#lV-uIW_K!H9!6!rpC3=SC%mFW}nlz9s#n-o-N2e=$rNF$CMxQZd40hT0 z1OCF5reS3br2&#==ZW5=wEH}vBPEL7v3MEbn7>B%{djrh{f@xsHuz)#tge$V|^jWChr;5lkQx_r?&4_HVlCUPKufz(F)Ky z+qGwVeZeogTJ}!+0^PgnzanMBjy^b|gC)j!V_mvniCzd8y^1#U%4K+k5cePZnoaNj_P8Ty##eT52!1maL7yaL2GGx0;q3bLt5jYa`64uQ9#F zc;Z*rn$6o`W#eHFyLEHv?~?`X@bPV`Wo<2E7;Ru?sTJKjR4Tnk(_RS^EY2D3z;)>M=_Gs;MT-V2!t|!N^ zsH98Xk>&3V$67qA*s0vkF1SlS=DroXy}Za;uqJ+5Es)Zx!IWcv@;{J|z`yHMmv&U> zZ2AU0lpim!Rfsg~7xj$A?cuyb;ajVefHYlyjSUn#zIHdLHOCL6weQ^ff4u<7rV0U| zzw@t}1+W+6!_CYpdR?}6cG;*f0h+%eXGiJMv9Do!b`bbn3QsXSTZ?iEU6pQ=?Cpp0NY(2dv`J_6+p?4A31+WGA?$h2VlTwFT{5Y(m_JWqTXV{fh{bz=14hg!w?n>RJr3kPa0ACIwOEE`wk0OOu8ZFd7G>I$A} z$vBg^daLm+ou6k^2M74;T;Q{Ok{_PmiKF7-h0TQnwQ*RT#D&*SE;H8?p+KjLpDlOv z;sR5|fdvVy*{z(K)zObHFCAx+zN3xyBYhGUzF+6Gd;N@1s>Zw`&8IpewOPZPB_Cd} zf#E58=bkMMTCqoE{U1Mi;+ie5ktPV4%7E@4_etlb7Pl<$8z7 zp-xdc+ymjdr*zh|PMFrQ7B>i>X_xkyIM|dP#2si{Z!{|54z*eU6&wrzwYBnVxt)}Z z0G(cY>qm1{+IUJ}?Ma`Yc*Ki&)CYT+5_$fIWBb~13 z`XRXSxg$8IP7Nk>I)An+FQj}(chV;bpn~Oqufp^8`;R%66$#YeY+q*T6Po-d&fdh1 z?P7*k5`F#v-|Vz5jKF_P0Ogs~Ey0XziK^im`l=kMZT;rFRxh8RQj=j-ufRE0rkUt43U%;2y|r!AZ|r2LiL|Ncer07AH?Yc%~CBAefvP7 zDpa3x(KskT-^e)g0VNFh=rMUw(`T|BK!Py8)XF>w@ZROquaAAS z7-?glaJ3PP0w2wc10T&CVwKGHdXFhF?(7iVvZy^4Z+okRB`5)Ud8Y2tZ)CPjbQm&V zQFq_Nb7woP31*eq+=*s(>B{%(u4aNMBxNcFf-V-|y_=SB`8M7`;CYfTIBv0^8>opw z05mh7W*>bt7S^c_8DKCS%BXX_8lLnC{GMHXq@=h059JwA|g^fMB4u<3!pmGTI3(0)z%WiY8{8lYIEALD|4dgRl_m&s56b|DWab+(f`sO_1qc=LIhDWth8i+uVL-EJB ztj)svxpyvhOvZLB&|Yb#0(=1U3UK*8WCPN_z5ZsJMPf?UQ0;o@=V(@P^_Q;5SVYef zwE^5$++8oaA?l%}I>vv$_0$7h69UpnauUb)pGDruYB*ceB&8?0hNlSC&r#*Q&QAjW{C;_!<7EN5*phBOs}ZsXzH zz>()Sy_NWH;Y=jV41!b2yL-WU<-5vjNl6>K6wIGQlI+^2W;DjJzyv$!WNpuCFtVk{G$FfGP4P1^%Vse;rCF1O0R zhAR`o#s|%0fC?3^IYQPO#MFm2+yU%KN;tT=sk}{qSb=A7_=_EwZWQnz2b6*FwEvH( ze-C8xfB(SoBB>}zQ8}zA=^*5k*mR@=Nkz_c%G4Y}jypvniB&o{HANw(Va{wwOioJ< zbKIOK!<=U9Yc0toaC!J8OhA?Wd&H!*V zs}Kw|(0=&&UdDUGJTc2?v7xl~djEP5f{hNEL(NxmaQ+k`q4rCf%|>iLDXJ|hC=CPl z1ou)`%BH<|U=B^2;#)S&40#QuTNnXLAb3@22*CnRMjIntg{hnlS_4S95L0=cL?uE! z%Fow&_$m2nBcK>xKrpEw%qT)5p?$EE0FuulGH6Q;C(t%Pg}KI|1!xE^5CS*CSYS>; zqj3@PBhAb4pRE4?S1N67Gy=;WTTWC&;egcDSdVoGIJ%2tARISk7v; zF)%#wfOG~(C7CUZ{l^c<)3(#wGL5Yyg!TERg}Lot`2OUre2mLSZ2oxE5vIy3Ji9F{ zdnac$G!mGt05%V<`x2WQA7Eo#d}AYtwPbkP=NJSg&ef2a@le?~sPET>lx8|`Tfa7I zJaqITWa!dP7^)`Z7ws0~_QwBt{lR*%V_T@|%ZwW#WzvOk)wcd_MO1p#Y8a$8+kPZ{np#{=H<4U6EeS&zK}1deIqC(zL`%^nK5>2 z79jz8y}pMcN$b5cS5vlv8n$7|VqWU)uw|7(Hg6Gt{+{BRWvfUltgrFaFV~js%$}SZ z)JMc0m00WjhtTUUGjduhdo_8+g07Gs`q&lSCZgn$uy*|q%m69ydX+Z$I+Nd)^-#2a zX%~PY0J+=KQ<@mkjyxyrn9@QGd&$l3zf)MQlyxZMnSM=QTzu0Pb;BuFc%qzlp0Qpi zjF=zR)qs^GA3e9#I9HcGN7F#ADgN0%DYfnx*xg z!>lwOJ|j2vaNU+Q9ZlR5$2yV55OC<5ZY^zD9|Kgbg1f_hCfeb6(fm;12oD&a&DXMo z>!oD;AX~s0uHDh zOk+pt)dKKAYbyW~{_+4=?!Y9<6+nVLQ+-T-p5NCe-KMdRXKO6{uyegA+*S64w>MX5 z*#1m)Q%z|g&{CDvFAzfqE4jDpgN`SY2tP{u|8zuhIhX}hm5Z~?)7Ong(w)J(9@G7R zd>i)#z@c^A69SChxe80iHO&66^97gn6=$3)RnfTuf!w>9qKWNI3h}t=XNh{vCUv?S z8>)7Nt!xk2^Y;3$bZ2}Fqm4cbA$^ZNtWJj6U^|JMQli|=j!IF@-9A2fL`O-$RZ4S# zF*Dd|nR=t9qmb1b$dj$)yZk`!s=0A3@hN@7fpac6)TcTshiGz{ge?SJQ1c=zFLu6_ zU$#9C?X_Sora)kHfTIYMF|`|LV2@T?6LOlN-BLsJ67+^BhY|pg4K0Ojpa2-#CM}#vvJF;(C=g#64KC*iuAE=>Tze0IXC6v^py#DOlzUIUjqKDmKDj z0A@VQj|~MGsgj^^Hxw<~+FE5Pt^f|+dGzXOU|?nG=1P@-R&TcV%5PcA+|)e10_{ab z_DciO29cHJN@97qW^4KlDo-aBa*Eob{M5J^v_ClZ9cDnAdjvJst=d#RmR*^25jz75 zVm)^qrfV0`s(x>7e4DxWig``htB@7)&QNbV?5bD7Gol-`l(pZTqS>_5=yq1dkHBLc z4-Sw!@7MV5q2E)70;Ia{*f;Ilw3ZtWn@!^fOh2Z0?@Mpr(c1$UsT4_X25TKeBPs>FIx#PMH&P3co_&2CP9baKJfwj++F@L zxeD)i(R2d@Q%Hp~ljdtsHuMl>9|f$Q@_|0;<` zWSz}NF~fa7u+Xe(3H%fsKE+rLF-|+3dtm)qAQIC{Qe2ZeP(w)-Tip=>9N0^ z9sDOUf66~u_j#xQS`k2T{?ZDlxHDXOZUn0rsFv7=IivfVzV!OiNzo>`TblkEyb3CL z2(c08xrkIfOc-6hmcW&@H1^mEvXp6>lIeM+w>2@P5hlO0>_#0x;bH@8)C#!sYA@px zu9{k;{|@zjjE3}2Tc!fU)#v7;88;B-eFTJ_CDzOG%%H=}nWTA?bLc@Riz}uqGHVwUJl$fTKT5?VQ6}Mqm#urEWm5>Ye09^+N4KkE7 ziJYp3z4VuSORnXG0JyApE!&wrKp(Fn^R5Qgv=8cwwzfYup}Z*C9P$Fd(EjdUdG%LI z<$jsHK|l$mYXqBK-}c^9eQ@3AtLDcLMa*kPP3d)}AdOc^UslVSJuwYhul1?Fnb9OlOpDLJN^(jA$ce0M>~OYYHF z&fSV<6xHsxUno~Adt5|}X2K_b>ut9Lu@!8(ucJ*|Otc>`lw)jV5s58r$lwyd{W zI^$Is02h;LFuC#o92Ai*m&NdajrGlU`f!AY=kmlq8s{h~X8hJWg3{l4Hkeq4eHUp#%q6`PcF(}H5_<=p0{@%1hv;!GF}Wh zm2H251}OHls@bE6MwYBQ_%)k3aCPpA6xil=YDkTda4op?3V||zbff?6&JaL95WDWu z=E@(x#RRY*nG+wW7O6I^U4ajMIC`-J2&x8Uwi>$5SbDvbOt+?A-Y1#q>%U^ia=m{jPz1;$wvI2`ihKfoh4oSwycXL( zqn6X|5u}=ms=;3KYe%+CF-H%#9ssJm9gyb$<3bi#cHLjHA1ceeFye+@nqwv9mqjrX z3B9HzhYnSTa5Tf?T@|v%gWCNM>W0r#mA|(4W;hk2?eyiYM8`ZtTzfl@(jJXdR(wIM zHG%ftI>t>>gQe^{8hKvssT5@GOGjO7eF7l+0^;F$4C^K79_xCaslmB=njJj*>R732 zx7Hpph)h=qCG%ft+!h&*?OZnJdIjOP@Qw3jS?q_A?g6}gyys0?boOn!0x8Q5H=P6^ zKvsZ=;q}M$LxYZ+clEQ4;jL=sSO2kJG3x{G=xtMT9@<{RktOA!AO~YV*L96`vW1aS zZ!zGMHqA==Oi;JhDKmb#ts~a|oF#s8P5v(h>)W~AFYNua3oBfN^&m$6dKK5lYvBYZt1wzK#De9(q+hMc&<2r_|axqIE zxnA0D@(Dujk*&36$GV9Z^+9DH8+U4%07-o0(Pt7Y!Mn8~b0$BPVx+4Dqp6CR3&sX8E2M$YM0&Y^MhAZ&E1|rEGt@s)%)aE>0=D$zVyQju1v_zl z&j-`zFyz{(<7B5P$Ua0DW3O$TCO%#xmCMnr2_{*mLHbeo`mY}fB;!m$h^LR{MjfTP z*v;5=;~M5@;hurwO(EFQYBqEQ4>%%upE6pV9zYP7qtTywy#ZlAB36n3W4}143KD=oEQy*CtUTMvi4BnX$zqf$e$I2YoQM zGzsH9gEL<%IuS(S9y-;ze-Nm%0f7fRQEJg3ou8;p$Vej=4<)?;$v6R|R~%r2*`YeA z)@`W+OJINe4FN>N!4i6IU7GHXy8>$H?1cvNu9bk!*u7UaA^z?A32I*OuHdO?nb6v; z&3u9pzzgZyE(#xyay|tZ_jpw~wiN#{O}|mwpT5($;2{}mgYRr|-}NQCU6oCf-T4@U zrS@+#-nVQiN#a8KJ=}A~P}=di?jnPcX!!1r68aLCC3gb3@_Ck3A|X&UFXOx~99-Uk zw5QaTD{TDekyn-ni_&LUOf8qV|9b0=;c~Bw>HI6-D2LWO{b`(vmQ(jwkc5E!fELhv zbcea>EBh>Fe`u&lkWQBB-#|Sl$+z>P(V&md6IOd1Dn9A`dS4#_7vW&k{eD30&kR)Eew^<%OQVoR~{eW`$13Go~h zEX@1!JI5b#8mbwjGLN7%GH+eM5?tbfv%`|)(llm{zxu(}QB?wygn3e|DGGObaeQ)g zxSD~t-SdmzF3uu?xL)62N6X|l*Gl&`f1p23!?4!{sADVe)$2o?d`0kcj^e4eFCV6V z&bN=Z)SVl~p{gD3@=MJFR_=3E=D z5x{{U8RpkNVmwRC%C(E^Q-TN=P^z>4e=>Z$n8r8WtH&$e(u`6&l2hyv-}aNgf&dgm z5_kQ)X_wy$fo+8)x(7aJA4U9?PEMR{mRw$3x6fpkx~Z4eptv~T&4F!3Ks95a-SJRK z@vE(DjqcXeoy_DnP7m|Hr+rMl>~(@h4Rq|(5FB380A?b?h#(59J(zEr^;Ftd7E}CLLfvY)f_;w0% zb^!ggy*7FceHLkQwwJM_hsXpzI%AZ^AqyA4eKQny0tOt$59ueuC_e}0!HAX&GB!)t zi5R8YD#=E}`w}Kyz(vB03*7I}!GE8Ghi+&+jt3c%o)8g~`Sn~E__VJcD3)pY8XAk7 zJ3J?gHK>W}QPf7P&YmU5D*`TKaP%-Ryci{h2i11;9`qWf#x6N;+#Bm#bh||*$Aj$m zjZ8;h9-cLOY8v<_x+j*qV4y6&oOF(i%_o9XX~C@id%)%NYV|=Fiog468@aaz+)C!k zxEglO(TMf{%lJ!7WS(5LT8>KE8M=vcWd^QBefdYvVP)N;fYY1opz?%woVQ4gdMm}> zh31)1&vgxqx~b~Q0UpDV_vVS(;d9q`))H>L!S2{GvdAlc4@%^d*hHTLL#0l7W2dDx z%2a@s@=}-S7Sr@Z^Zukl82iqd@ox|&q4d*iQf3q|)0%tnyS#!o-GSlojOa|E| z2ez2f*GqJU!2yCdjamTik0k01lM}P`Re$zRx#a>A(0v*aa~GBdUv61l_p?i5_gv1! zU#;nj2O7-n=H|`k!WCjh%t}l-mOdV_uJ)PB{ZkobKMw9odgcv=xP=1!%IVVfi*P`j zQ_6QstTT1h+ebuY_Nh%|JVJT5P+YaOC^)yrU7P_e8PzwP`YBN`k!lO2-v9FiK2Ha9 z+GC)@GOp7he$_A*&fjY_x=28x6M`)af>!z>+R1bM)v)?9;G^$rcW-XK_qYEkAz$na zN2feN;}XlQvd9!k3jHCCHfMbX^JqjwOJ~MrMIr$QCR8iaMg9OcsyLX1JD$SnuMS?+ z7|Srls`s4wz8@ivVc?eY`IH|c{C-;ohF2s=9)yl_$;;<{f zB%r?}Lihgyv{GMQav+w9X`;&|E6x5cl-bZeFcX#?;?g?r2Pil#P985s98K$aG?!J@ zdm?MEeIu`|YU|MsG1VuQdk1r&*hr$>Poa{S|FJ|BJPfY^j^o{nYx=Cp8e=hRw=FUxg#jlYWWlZMCmrTrP)3fn>(jgo#75&gq` zhB7R-@SV1RRRxGZ9kUl$|AP_ycNKjA%!L}KH<{S}`)9mPQ8I@ivQ%5ilm)jv`=|0A zwEYf)K5Q}8DSo843nnu^dm*_@`)gcj#zA0AFivdeP)He?dVM8|E;1nwv8n@J#qmGo z@fnXo+_7QKM66YfFMy#@Glz25dicDxT}6(YGzQPaXAgg-DjLT8TDN{RHf^{)AG{kS z0+qqfo;Po`{6HGJV7N=>a~77H(^&3xCu)~Wai?FBwI3n z2{9ud-A<~Tdf36e-}eIVd5LXKPcHyZ-guw}IAbMv}C#->_(0A1VbeHtXJl0nrGw8~b+o!+l+h=ml- zFON*s&1=7nPuYbi_))j5O0Efz9JEtvl`BXTRJ@HwY}}H9f&bI559pxS7_NehTIw9_ z>XQjTir&ZJv{~E70?4#zTlm;89xJ`SK^1L3_81@}gbWX~0<R>z| ziK_p(&CB~{d#_5G8uWfjW3dzb9ybpS2Q=d`4^+F`Ya5#)QR=io07j#7TJ#jwjPh#L zHuk!R=vQBU^eou2#usb24+;O0{HGKYtVRut5pw&^Eh)F(O3niy+3J~4U&La z*_`YMGVphQ)K0UYr}Xau8Pq)SMh&0T~R41p{P=cm7|AT$Vu7B3G(a9gyV!x$b6Sh)GSho*_3>epjGIo~`!^zCS}L zF>YP*b3?s0;7M3+mOV%;*IG>LSqVz3ZKRXe9!y0;Ru2=!-9*J!MpdFeZL7^6Jl!-; zFHd{r3-Z;AT`aPNwsrX1_{>VD`AH^D`#huz1g|ayq<}pwJdCACX7@G1~a#*cfrQptv*{Jg}vt+x2JaPX5Uqoa=8gfU%KAQ`~h; zLfo@_g3;@2!eG@!+}YU;}6B04Fm9JQe=)rq7nKq|kkFn%A`<9VQ0w?56hKkLWO$xCi>^*y`kUZw{iEQZ zJ&O2SF<6Sbh$1!77mzKeo1&U_q}v^TV4M1mSV=R71`5i zZZ8!ePqSx@3;B}KQtA-j3BpEQxTfiQiGZ?+MZKwLc#_3xTYmr`O9Ct%U+>Xqv((8@ zGfDHz2UPf2SHL3AGL@6d-UpVE;arhrNjr6i`NhJ`hBC4#zs|o3t1;1v6ES)P1D{*$ z7;Ab7w1mF~bAg71!-jtxeExq9dv0T&!BVRR9yNc!$mzWYMm@BZzs?Kn;bBkmA9g(p z*{~XWaUQz)$e+9F$+Xdo_fdjCjl~zt+T46(<##Tt(RwM3!Mn<|J6C{Jhub;c9HpBG zlHn#caPBRq?NWQJ@-}7tnhf{)A8N1Qu+N@Z$i9DPIsi|R3@aT%#ToGdyQ|Bm&}~p< zN68Ny??BPZUhl78*S7Q>3jyx^)3JF}jrxU%d##j6a4+CHCO1$x-?unMwW#c0Jg65} zj=*@={+XI9)op8ZMQ*go=3|qrk9v0n*XV&X-Xz(?&ud%8C17O9WA^&T+`eHKhA|@- z*=v%wN%sCeQBVN$+?3I6#;aXC^ju#x_{6y;{G_Rp!JR!FxPA$T|FbQ`w$Wg+ zu)+YgZX0!phoDO^=}vRnj}2}ErUuJ@Mz2i16~(q;Fe!DVrKWp~?(R{#P;mrFY~SP} zD+FN{{?s2&DgRU~#3>m)eNsQ~MjZA6D0Qf+zv_MMbbkVDp)|TMtXmzkXFFk+Q|&x16%;VK67^8Kil1Ty7!ew70he>hW$%k{d?)9 z{pUn99t0N8jWqzEcc0z+EAvJ4mtgG2V+sJNXQ@BWDf0h`h`;A5SE)`%2CIg(7adZ{ zv=Op^VI}vq-ceiO*Uodp9lw0M2ij*=k_t0Gy?K+(T;CrCI{yihC;){t>3v3=`LH%7 z4{GRfZ%_@u>N1|S|6+O6Zwpqf(GI6Q=q@+=2M}hgGXSpn`w$nz^vKtx=5o+UaVe!U zSS0Ulch>v<(3vE_xcq1n6!aA1(tqI<(%yLOGh4e@yf)y;?IMSD72LAgP~S1A=AoT@}OwbGq;!Z;bzhqhN$b$PKM^$btdr^G3;obylX>>llOoU z+F=K01>nCSRuP$3J1j{#ZczEIHk<0C?W6)^61+m zL0+Ps2FgRSXPWmD=JY1>(EzEZ_xeUs=;`RyI%4rnp^)$bPuo^#%=6#f z+L0oW3&96S4c&@)wM9s>pi{+LK5Fp3`QN@~Ab0|L3%#Fp|7gXzYyzkt>j*@AEuZb8 zx9hE=_m9IBnJxXP_pOGn_h3wlbFP1XHYnmO(UkwF;vu6-vbEySb@sHk+TBq9OQhJ# zbv^JX&($l}jZuVPQLfuPR<2dIytYi|EV#!tfJr-&!Htm$eo?MsqnlD)fXm5i9g z1Fd>ags?(?+XT$EfL#6APG0U(W5kVlo`y8T^fdo=Th-89Slx=kh8%1ZwhaIl2c z+vGDfOFA?|<%U3`H}RSOX7$bX}ckeC;e?mG-XvN<%c?}9*Z>+aPs^}Ob z{2O|-TKL7MF^q)NgUo0=_%3RMyT@=lVNeI2?O$uAi~jqe0BrN40aM*MIaRigfs;o_ zyB4e(|Gmgvkz)!trSM+ef`3(0^~~SRxmsTF_l4?jpRSj91gJQf@fM-*^y##~$7 z6(#(=N`p_IPRw|pLw(B5)o=*(=Fo#Na4OuX!6*zgxiT~K+Nck4JANPgXOECk%@Xp& zdsQ=d^BlTdJEipe)Qj8)JY-(#%y>Ql%=WUR|L<#`*uT}V;0K;YXcu`@@U{4v!GPqK z+S2~QkEUw!=Of67?jOA!}>^Z-EMZWT}_7`HtC;IQXfOf5fQ~p2NlR zXDLTN3yf^a^^Q2jojoZsnecxT&2)u|LV=lpI80-Xr~~-~T#W7^nwF16l0*@9uW}+Ytg* zvzDj2G&7XWT)P)2F-2794VJ25sN3HAeBEAVekd`uJ>moQk`l@Y<9m~m14NPK9Wfx3 zCe14X;y*i3#<9lv>5s1OPDdTk#9hT-3^LD;PLn8Ja^F#JTncwIDd(& zsfo&H1e;GDydC5EvMjz_Qk2?xVQ_6A=R-oP;aQo<1--KeMsiIqZLS>HesjV-GHh+S zy^sv6IT$ZLWrW_ge9yqwxTW~w=SEYjGY3~y2Tt0URO^nGa~D+E21Lp?6`juahqu}u z?N81#g-zxoJ+Z1Q270H-W=5CC&A&w_Jw|B#Xs;_IyBFTh0exgEKFi6myemkv$-Qv= z#Q+af0WEN_!Sy6^U-!>w}yy2C9{&PE8?et?aAf3 zupAQ7h_ROoiQJ!7p(8?ZRY)}t^aOPuxC~mMOHK+CZ-)_i>{#p1zR5#FE5jbvO|c_T zaH9A;#`%Z|Z$5p$&k2$b1j@`NC4&0xre__!{SIm@OY5 zM|_zEM2El;Vs0AD3J8_j4IvKWM9Ie~pZpCK=uw3TiPyLqXQ`(!t5Tga-Klto`az(k!P^|YmQ;&?9i z+^b)ZvmWh%sf=yw2%vp^8gJ_ zkO7LjQpZiwhD}HbN2J~@Zo0rs0tZSZ@)x8w3Woxzj!OenlC@LVzvJtmjd4sfhL7E_ z6wb4RKGQfK4`B`yAGV#8qwRW!ir9{j7-C;YelMjh{b14Ka)6&YT-4e7;@Y9AJWnoq zvc)Q(rCr8X;=PLfsitH;Lg-Zc5woZJ)7kBTL|7pwlM_ZM3c*tkQovu%*M2(9-6H}T zAB9akmOXq#JGLUKfa;P>s{`EkDr={E{j%F2o6q@@q|=3G4Kl(I4l#X>w> zo$dGMt~?5zgF{cVLMe#0ip)n7!1-tu`~kcmdC0$2U0Hc)sa2!3yjI{{$-p5daEST0 zAVDYS1D)Sae@_<-(-o`m@}FyGntp_igp1E?1MnlqN+Z5px407WVu1tcFDVS-AUf_J zR>&{Rv~nvJgz4VudN{rg&?dH4BG+%ybGb9gTojcS_L#tX?3+_H$ zZ<)N&2wtmu?hpJc{6UF*;2-0jSjO|xKPSRu=QZA0mG5_~Ohq>V*LqTCK~)O*gAOYD zj@0t)&K=N=669Z)PT|!gbOO$^XP|38<=lbqy97A=-0afv=gpP3)^2b%oaQUW_h?U1 zF#_tO9C}o4;==={wJ1mR?b(-_YG*>G6qz7Ha{Wft2b4y~&EvG5i# z(&YGm=dR+4?%V#E-5R=WX2&EIeowPs;RpKediC}N$tp>v?A|QrVbKsr#sfTcM@K)I zsV%mcAQsXC<_xKb?@@T8fmv@|`YMIGjvm)XU-4*9)*aypIknI-??F}Qddq_eGKNq7 z?{5bVFckgoZ)ZAjF08MX*Q(=5+?+FxRreu_F&536M~d_lU|4`q4YoxMP4lhvt)JJr z0lR^N(vY2uPh?1>d&$>sQ-M~z;E&#G<0fZN?|b@E9PN3*Z&A0K`H;U_SR=4?v0Xk(OSMtzDA{?Mk7!|3q}@6g$^P ztg_)yUNRDJ@%h9dvQSG^RAK?UQm{HW*$NdFzkG>YYXE)RSfvZ?t)=uYV0z*nfJLD_ z1}BG1Or7^WJH#iBpZ)QOs4tCu{O|B(qp6V?zS$N4p>m`W|LKuK>t+=*MpbmAowMS5 zLQI42(!tZodz++N4vqN@S4}xcKgsZqAG0w~H$0uwW2IHdOrDf$WOUydT^Tl1l`)^z zYML3$eF$dWf3bz7_&JneNIywm|4cuLSvrAHuHE3(YJhlsw21c31&Exy&6;4WV0UA)zGy{%xKT)9RY>7wKK@OIJ~Z;a79%H7YulWSH_=$LGKH zWY@by|J2D0DnE?)UUUHjoG^Ix>I5N8kXJM3?VG^jcd(u=6aSg}?vs8i2Ss+KPFa`& z>l@Dn8CV^_i!+SWK501mK&`k4UcCX)7sc^1?=o9+!o3^~VUrKO-TB5up1Z((7`)_e z=2cqjWc+t!Fb^jGlGO(qHwaLAe~ZYNe8f$q!Suyx0ghKN0zRXTmmA7x3%!$U!u214 zMd@~$);j2!cdcesRzTrPSd?99A?xhFr4Oy=81)7y3JndB>X4Rxd+U=%DRU-Qk9`m1 zK-FEj;#h_J?cnAIs)!=$EJT|lE{BjIiw4!-#2IaGTR&}2lTHg9W6nmVi7Y%1m|E$- zy1mxgsS+9bbE?L-m$yEWT2Hs{gW5Cye%}f$86_;j?_&oOO zha6E>-aMUX!jTpBz=o6Wi^_fZk}sfd5Nm{NJY{Clgn_SG+^lR|zu(Dq&c#>6Z)p=A zY%pLS9!)zs@I16kWyH;g$RJh9`Jz;JTyny*oi3%~*}`{=N-kan=|&%iXC9pktX7R= zZGJ!J_GfVm%z6WI$l{N{T)pQ;tN$9ub4v}TbN2fjdYTV+=t7h-lXu}e$uc}Q^lHpV zDx$>S!YY^+nl%(O=)EMhu^_bsSRD_i)}5ZH1*`{`Hj~^^j0IA;ZM`6vcf6Dq9MZ30#TAi3T|NhRD4o{o{F;53Axs+u_@hG~sG_>wo#G!I*z#J)X6; zCv;q%p7>#zvcacJs{4lCDvsXVai+3GkXBjlTdVX97{5cdHFYi?$a4W;B}{JG`wcVc zEBRuTl1sC&OFE=5;SVG0+qh!;H%cf|7u9BwmF}nZcq>^oQ?h4`k+ql3VtM9luZi-2 z!_ynRR|iuHRSNA5o+yS7*b~O`_|qvlhLUYi|2p*TF+(wB0fP=cXq~139JJ$4Rt%0b zwO+X1@rkr@e4WM;&}NlTa)rr52x4h?W$?*chP$ej*s%}ykahwo%gKHMR=y)3|r}5ii&EaXBpgSt4 z^ikf;(a`y!`r61Mnl8r8AHL;0=g0aV>WmbtdM)(TN&{=iAb>~fS`A3mesI>Qst-m6 z`Aqgz227Rd5oHhYYjni9(^46F<|=X;j`wRv-Z7`}ad)Y@I-HQNcQ z;aHLpWQt5TO_d6hfNWrvoyi)&%W>!vqxR~QX8`PZs*Tk9XtLYDS9OVASZOL zMs9Gf-0RBoqL2q-0ltdfI~Gr^)(0ZZPqvKqmrBj;M8x2JFz;S`FsjQtqQflZ+Cwaj zdI@^seRAA|Qnmu$jA~y#9F#HyoTsz2X)rBZ1Z`NNCrr`5R=P0kNz4XoL!Hicq#u~# ze+6-mN++#R-REnTJMti#70LgtzsuTCmFvKB)HE5-8~SKIXDumOF1Aw9WN>gMl*n9i zmK#kB$BlQJQV;!AKS+j4%%~u?Ee_GmwgexzU2)*qp>Dt7h!FVhfDF(;)FK%;QqPwB_LY%V9U z)sw)3#f6$9hCQ_4#lLi$Dm6|j$A0_gAhMzp0a zV?ozalRn9kwAQH6V$5$au8Fs3;v!UC)}x8BWr{@}yP)#HT->|3a$1E>!r|lXp8Z|%@_fG!%DcGwy|Et zz$c@7b|S4#L^^lu#lmaik6`s=$>SI3h+;1r5I!GWtrj)jT;Tlr*_ppmGm^7AMis`{ zZ$xK%o9c=Ku-140C90zx#uKoY%U-TD7OLBEF4FDDaoZS+TvpZIC(=*-%tvidhnh12 zJKl=&Ydj`zO#a1%sJtDpk<1v{$=@tzLpOMIjWqtmiOCc?Ka<}H61+tUm{2+Cw}$f- zSI|Hmb{8#VV4R$mlc~*{6}|BWW8;i5C%~BcY5c$XA3TMK@f!+09U0LCzMa z)qA5ilY!geFPx>i)!#529*+;=gaN;7q6`~fLXE1utiZ_a|SPp)@o zL5>6V#jo0ub#uY`8$dyMugquoRJ0W6<7NXeCzbii3>iEEv~EDxpw4AcobI)D5q6CXr+_Y{4BkUJo|R;{tUnHliQ73{hP0;7~yS+fmh zwHg1a*uz_LA+0UJ#FdU<(mkMQISwo=%yB^eP~PTtI(xHBeoN=()8AR3X6`MKI7Ewi zBF8Bd@NS>L3uo{X95jnFG22gA0*+|;9SvDIy7{E(ay+1^ar7awt%1IWq^wcPFJ5Q0 zTO7yqcxD4T#C&;Hbxcl7B7^f-ovGeVlUFOE9F<-oLJl=33A&u{D-B!zQt!;)1C!sCC!2`QP**y;? zKid1e9rUer{4aPoC_3ji6S@(mJ-^v^ZpPWi%`@MYfHQ4p{);DFAa&#dEATZA)3F}w zF9b3|C4=YG1Ev1?v0VXJ$1s^ue>e?H4J$Ue7^PvfY=t-FOuJabB zdYPY4XAo9BYT36(a$mg@E0Wz}&y=J%K~7r~;H^#!Y0mRp0DjFQMc(ZxGckt74}9oq_mlWKOB<^vFhx;AhzNV zJ&HpVK1LYy;Ubpyk05k!-P=~y`)lArayqBe@-QCv(4z5)BRpT}>%Rx{ehDsLQkUJN zWLFSEYqJimks%fTQK>g7MgGxQ|s>_aBL+{4_ti^7O?~rt zp67fgV>=oGkU+t2X{n77@;8z1=}K6dyiH22GrBsSic6c+#?&Lr_Y9^(`l1oBW0e#I zTjGaru;&biLT5Cb$`m6q8Uu+8_-fIN`WKGW@&uRA&P?Eq!Ah1sWx~=By$(=K%q4gO zycJf@7xOvWnhCc=t;YIKOvKDuw*OrjA_xOIpoL#bnQXq8#P+c#?MsA$fSIg`o)Ox5 z=oxvg5pR}I?0l&|4Q{3X-;KDo1&g17Z`I+37~ETWY^EChi9G1}O;7IlNs*9`;`KO< z-olY&rhRCJkte19rH%Z%Y!-mO1Z;o09nt18oK@E^vgmo`O~UDxday0$IQ-le#jToL z8{NF!&*bD9$E!Yd4LtjX`znI$QG3+;RR4~Zu9X%)tc%USp$aHe(b08@sq)%Bk72jh zYx{XFP=zoiL4zVrB$b* zoFpOwho1*-M+sgmrpni+yeLX+ZGm|oJI`|0-+I{l;Mlsh7 z+D^U~9JM@ME&c(G`Se08@^W#GWgg>-|--n1t;8XZTYl6ems6wc00_=v6d(X0j;WH?ksVA{zYN zwFY*|QZ54KSn=di8p?%bpy3b_R2L;&dnL4y@wTpcxa^uw%A*l&mM4d_}d}QjJ2upMNSDVcx+uE$l(;X_`w^Tz17xi zD{AFSM-S?1_1P8rZY7AXhPCT;u-5%GJ`OYM2cKS*Pduv@;TRdh?m?9KIpk~~h+;En zc?v@bfpn{*-F6E;>&j>Bi+&)an(u)_Qnosc8~rf9Zq<|hU$U{>hVNY&cBt?A3v@0rsE%w%$Ms#J!jh_M+kIg)L*H@JEK@xIT7U&~!9qjdAj z!xl362jSj({4?hb{?HnCBIhTDf!PWKcjg)SQ6-h|psVGx=-iC`i3O{S^v&Q@3kFW) zyDM|4B=B3K2ohgF^=)aYgUm{&?)wnoj>9n&Ffs8I8(reKuI27BQ+5We8XakJjMW#T zzu58FQ%YM9uP6Egb}*>L>0C{$WN@Tv;H-OM4EH|v&sBkcN2;5#0(2|s@qOU+{R6q1 zfEXZxGOt*i-p&02mYWaIllX`^+Z=q2=66uyjS8mBPjc#-tql5Oa9a~oCCmCg-u>DL zJp95>z$r`2t;X%FfvIPWF4I$Lu(CV}8QGNlnsK`{AoHJs4A7w?oR;Ie?^}KN(htB# z!BSRM3HwA>e_oTA{HZNB(Jw){?EtMfA|b~XD$nPaqn+WXFz5u^Xu6lV3a4=`iyggVxLpsaIu0%od0D=zqyUUBlU2+VWN-7i2 zqpU&z8G?oeQqS6W4auKHI$G&EK9)V}V6%C5ze;Rh)9kjp3~B`(O<@+cGaFhPN;5+6 zjOHx&&?C8T;6zsi+OjUshArF4iw;d!s>Ei?F)Pz~?_RvgyL;qz%-3YMGksI+`=Ht9 z#L#sjB*2|*{qCT%)vog1)8x_QN%EU`^vTd?6g`@!je-A64ojP2q!Rmj02nLUZ_dO{ zxL>LwElt>?Rv;pi3VrcNzxKiNHiF&v%HggHrAKfAqw|b|8jw<-9*;YWc2V;9fHuY9 ziM}S_8GTN)WKzX{cP7{~-dPcDgRb~Ep!o!s%_+Eblp0q0Y^X~1Q0f?=J@!^ULQm>% zC^%Tu(n@#uKTK@DIZ3+j(Chq|3q0*Udv?vvYN}g&`=CPzF^gn)=UvUepgnzdrdD^+ zVW1v{&5EK&aax!Cef-tXE%5pRE~78~Mx`7!2wywB!@MMELGMU!z^5}3u7`X=cVU^y z4asd%DpBj62_xYf>}l9&`n5o=lkVNk3y(E^QB*8lR2`#5(nP~p=UPbp8S%;x3{OR%qBtR(4v-`=0W-q%(N8)W~={sJzz_(&Pnpp!$4#kQF+MXKYC+L^T!8%o!ac1Op{gYiK!1lr~{Ai_;wQ=3?|3}up$20x^|KoUtct**KkeK3` zP9#q`45OmfNs{wf$sr>lVKax6LP()Pn{vu=&gVHK=U8H!uBSV<-;w9ltV8@u0zYD-R{UUJVdWoOgx}3({hhwY^5NlU3g+d( zlpKc;X4Kt71y)KN`t=6qR$IDKaMd3Ey8M#)-j92c-D6j$!uE_CLi{pS57x95AR7W2 z15TwFU+tM$p<>$--&ieGCGhtc9wI!}AN1$C59_)8_PINE1796qm*}M&t^YD8WYj`a zyHo>eM#6~|O@sV&=;BL&eKfP;-jQ^ejPROx9`L^^-CY2;%|*~&NAOIDzCfY%di?2{ zd!R6?7NV{Fvo#fB&_GLmRP7re*D{jr$Y|Q~1&4gT2AOi~vKp(PGbsgCnDGCqtarf6 z1~uyIn_WwvTx96|_@NpM4Ip{O@hQ9TcwW;j(T)voa&6x%FrVF$tL+=B%2%-67c_&_ z^w_6tu$q^cNUd4&CEO*`JuIPg*t#j zH-rDaeqBcr&&O+2mZ7|ku#R_@ zM$qHM73-RZH~3p!TMz`bcIcZL%lF~sUg^tpBz47nIdgbEEc*c)u}gDK74u^#Q=IyoJa#k*tCg$};WiVTyvd(;g>j4}I@cQQ^M@k2Rw0cXA~^mXBp; zXIOzdJ>r83x_r+7*h6=ZyA8a^UOT=ev5cVS0#_RS=kPgh)NveB9MMq9yF8?OM}BSG z{hzO$aPp-72;sAi+MZup!)x_@Ty6#lSYEcF&boxumoxsX=m&2de=$<}xcu?u4~+I; zin~%`XhPpIF~!-gA|{+ga{(%yPJtl?Ym73;Oljv+Mlq2?+Kwr}ipnoPDox#Ym$b4tSvlmzWQ}p7G5- z#I}S#%hiIm1bsz;iVKTFCL!cNY#ItkA?Y`<>ldv^vOyszfay8ly)NepwKc7xV_+9lK>8)v zVQLcfJn`hMgxd8ohq1wvcHA_V5q7Ev$SV21NQxT2ThPAsxNx^g?C z<(t?VbKzD3@8W6xv84enWWK{?zh2$A(D`ofw3Oj&3(x|_aYik^$=mg^|JC4ViQ^DpPuOgDjFXDXafaadHjYo-@`#PPHR zMPwi;aHVRh_2tZUXz=IvT&VWF&jK?J&`nY^I6{ci3N~fNT^Ok5lbE^~!e^LTA%(p! zSGywP$q=T|X1{QP<1PK9fx<(Bj5mP%b;su6XJWvg?MGWvn>gw zn82VkDm!u;iw64g3s1uuDHrwa>|DkNW?w073gn&- z#83xtH##QfJ}G-G%=L%UC^@fJ^NJ5TFf7v%)8yTPGC zErsv5zwQGVmj~qL`{xP=8m#Sz9~vO!)Z{b%Dso9&*idYwLto}YrQ`4i>-rn^2f24d zQpPRgc<24-lW$npM!@-KEpl`W$~PIl#}QhI>^;Bl!ks~ASCMC~SxgAD^k~6mzZ0XM zIlM2`7l1rBL)7i8v;~NU=kW$agN=-gRl}_z4_@Y7qxguHiAL~X9Bf9(f+$e6G?-F0+eXyB@gWBl zBUnTa5j1zOmrS{Np_0t#hO}iXdx-wFLIE>)1Q`w9UqVF%+;jkrQ(4yV9#~gpEQ8x< z^;4}GgQ&u2)S7-(S!3xkE|~>U@WhID5PlG5fjI7tbFD!xP?NOH^f>Rg?S4$Wypyag zuao<7Z5oWI*R6~H{;cgiU~O0HR++5rT}3z9@;Qj^57TxnceN~Q9f`O_Y(f#p9X5$w z$z?i(xC8~e^Uim$5I;xh{Lt+zfX!@qc|V!oeLd3vn=pB8D?59JbH!uLxSIvaDiR!u z?Gxz=Z=-?a?_;XM^Hq@=huZ%%-97eS`N{iX5o-K8gq?J+}+9f9djKxy|jsVO_$o#1t!f3jyI)X@ zL=YSPmAPlC<53xZg$yT^q2BxOr$IB@6H6TK*p5T`of@juHVB{F&4Bg`=!{$^;x+Hb zkW^w+paf#)Plu42_*6ZMJv9A6Bh@v4-t_{@iRh@o8lV0l4$z0y^e24IM%|S9a&&)% zw;UtuOh8+YZ|vpzsC?L9l)CmfkUMIf;$_Z+>|%Y{0PBmYx%c*G&r0&RVz-i5PKIxd zTz11+GON3aZ15;zkG5rTS3PPY|5a|PlHrc)G4O}l7SI1PB$UV(oT|LyN=6B;8ga_z z3f&0CGWvRt+H8bKY}~gtbFXV-^R#IiICs-o+XlHNS6w$ECcRj3fgPgT8x=jya&DOh z9m_{L*jolnHjR#E6hAP|oQZ^y^mI0{=B0Hc%4*9$aR{9(*^3=!@*dtkSHX3UJ#sFg zBg=?PaYpNy)>Bhy=a5)x#F36c^#GIm+CN{8eMG=MXRwvs4|R6n^UMot=s^!m>;gm= zvLfw5&E)M)WZuF4CpPf4HhQWg*SvOYfK6O!RAG3G6i=R$BN<>H9}k%eEUF;069T{yx`84tFIfUiQJD^x%*E3_063FF@CPuf+@O*Vu*kAR~}`w`uKMiIRtg1+m?^3@~lle?=2t5)1XWYw-dBUKZbKU zgR!aS_tgNpe*9dEMG6673)?@{>r}t&oJ7ndiZLHgD(XXiTr+Y-z@8%oTk$MO#DD9m zF`lyueq-T4CwFeY9sgKAA@qK9_*g0<`5?CkL|%H%rwRn5D#t6M%Y!ZH|NqnW7ZNh+ z_yCt{4Rg=%proG91-aQqcJ%kBf#su4=bTv({TAq}vpi$aU}=3q<%e(B`=hnlt{Nr; zj%M=6IkVQROq8?R-q(lU5e@VvdV4bE9*puUHy3uYy&EJ zc4UwfI9&?oq6nABruo(<40O=hFGu3f;m}rAI3n-7j11=$LdIH{wsO>EopWL&*Tf8u zgWg{B%>eUigk|M}bS?BOc(l+a(>CCX*Vhp}2q9Dv4gnG)CY?2Lj~=i7vCZA?Z{Z3s z22_7f=AE{P_;Z6Jbx0iIoN0cXk%7)IZ>>uYN@&JPSP05k?Qz$dx;BH#)^^{vm7lsY z_=8Nu7|^S5A25Q=7ix0wkzhRO=S=@!UmngSS2PFmt(qP>GT0x6u$9L-!?g;Bsi^(zH~=sfS!kP>q~T0(CHW%HHX!ECYDb~%c|z74|p-z+hb796j+D! zIXsT{{oBbrXl^)h2$l3MJNusT?mWo%b3T3PkU{~p|_>SBmNThb4!Kx1Oqsg$l@NZKLzMn=kb3(^^{$(4Hefd1CGFxv6mwwjI_17dqZd)zJ&?wC>u+ABL z4U#}-<-9ss5zpNq(39|)oU88P(2x{8{Yk!}+||q^DuS~@rm4)W4zK)qT*Rmr`ueqm!98QnLkhgo@|fQwc;$eioLlC5U1-vz8+4j7DL zAJjV@kB-h71r(z&dwQwHP1L@SuHxzFqYp`nTj6)I}(B50pAoMRg9*rq*j2=rH2=mye9) z+%zh7C1wNJyX!+n@B3!E%^b5&v;@JrB0(~zqx%||x7l4hp>5WN{Ow;$%rR?wZ=g+N zW^Y%8)w=0oiHne{OD;^7l|J3KmvI@-QdSY~1 zzA8>dEEm{iM{^ikM%q?#jwvo76BAJMZ*$m;FegMZH!Bs(JaI$sspGKn*R9~Moyjr# zz*)}>cAtIub)wmeCt+Suh4%f$_vNWeaRV3pl3oD)u#G^GK$qw=;*A(k#|_G(xS3hv zflb=}Mh$ph8&Phn3vX2UN&O?C{-sy#qnW1CRnhos$8`jTKFGfu;zRmKxr;tgHeOL_ z(R&g@&fzP-Yis!#G;lz-tlecitF4rW3TDIQ7Nn$fh%fg{vf==@-^iKQ$TL8FpGis$ z_x)RO=Yoqg# z`9t=iJYD_ff$7R?y3@S2P&n0&B>KeEMYRgS?u?HFu&F-YH6J>4$KUlm0FV3iQFQX-B{uS70l9 zoCe6a#h;gc{(Jl{i8?Lj(3>U)eryaZ7Y19;n@WXESif~zTwA8Dd1Qgs{z}Ew*Hrb6 z=E8rwlp>2`0t@faHJw+?_Co%s?u$}Pn*E;^0KuP~YK;V7MJ_R?ybGOefRkn@T_h+SaurD z;iieXGmlfU&zkY!NTvG}8&_ska`dIcCVQPiR=lM@iXo6o-G2vZ#B2uiNiQyz#$Ay&G?h@ZF=%8 z$Q2p1L|$B`5x8f!auqgK0>&P?fP_|eS}GYq&Q)>f7en|lKgzbpAlzcSEGlJn7VsBg zo?ZZNEq<1#<+=QjJ)5`dfXjM~5IB}Jdn!JtbD-2~dE$meb=eT1G`pJfI2J{W!rt-x z6n*P0#s57OjJ6cx39U$88dT-hJKl1EIUx}%)vzEON2ro2j>|9qYiYE$V-Gz%q;KDS zMx%znjG?sXb*sDB_J&fUS&8~cfqf|MX1C%|o$T;uKj{o=zY**jEjg7lYi4sE%d;w1 z34lGOJPb5H$Tesxzh*yqbCNDopCfNh_~a(bZFR|ApJrqfs7T`Sp+UXmmTh?aJOGG( zRQj5lvH|z<$;Rr}f<M#9f*B$gt0$5ubYcNC$}7M^?y zu1vXYBj`IL6BSoZ*BTq|yMIVi7Si7rgP2J6CbX%<150Di*1HC6pBG{DS1>NDh8R}k zit^75M)D2xET8_&9-X7R#2VHbiPeF&`PWK=V!&_<7~+6D*<#|9hl-t5c^8k}IAK zqpnJ?i2>@Q*G`Z1U{zvgGX=Qpp#Eb&Qg*_H(Sdy(ZNjGp)N?zQ$lI8THS5!xTGgBV zrtvvWz{>-cOO!Rh>s zevbj~S}OmMFyDi^mQ()j>Da_y-Zp@sD<{m)fB$$-bU~jj98SXZL&ZKgX-*-fvJL z#~dGQcj%Y7UXD3ATdkK~pS*W?p9@{vOkm8Yb3CM)XF0*(B{SR5bn65u`|HEY|JW+W zjh9ZQ7Pwu8Hv1QnMC?us&8j4BuF!5D{*L1*;cJ_w>Gy-nJGgzbzec5_`I(1qh^hy^ zBVzQJT{j^yk*4_4alaE?JMp!{9glkeTYCa4-8=J6ycgfb0_WV)j{n7yWl*7lnRsNS zI*6oxd`PbP#<%Rm$0{^ZUJmD8?+qVsj-!3?@fuqXUsJIJmRXO<`tJlP-oHlI9;!;jHt9Ee4X(<|vn8U9Zg_OaF(* zwN%RAuZeG=5rmnKn!I&L=r#>ct(~oVd8p|VP|r<2_Uy)!gKs)!8kQRqO{3b@B5PY> zq(8iZoMi)nNj?Vx%GJ8QR!`z`C`-RI66(b5m!s1m>aM=-FK?$96ux|%v=4!>@tz1? zHJ)IIY)rXb%Pc{0r1Pt z0>yJS5jK3#*yP~3utAr5F{*-dOBh_LWdb%nW?aUPiOHlD$&n~=*6xsrNP+Koo(`=q zWR>X7;k)LrsmI(^e}@ew-@s2&_2-r-IgDFy1hOneJr9bEw zy^JKs{7U<;pe1za{t>Clw(us%SGq`=SKPN`hf^fW1A#yvio|NzI_PgeN$r}%T4Fh; z547N*DyBlCG8H*EHIwqqb@ewUiW=GuO?n&m&{LPtZF>MFcW1BXApxr=(u_a)R2Zu3 z(`GVt{y+7_en{<~OCr{huE-W=udT}R`_cVR=lS#(xHWvzsXsso&YMyaZ z$UJ;&qu<SR|IJOCxtIIka#{+gBumnjjYDc7VRk2-~u(HmcEV)$FrNU2{I& zs^#Oe&JAzZ5GV=dvV{~{cdJk{VCC9uTIW=Knfmeyvb$OcH=aB2hqTYe8vgpukr5w5NZX!i+o$FlxWosXIKq<`qh$4F>8D%S;s`jlIncki`AR>OHys{}a4A!l#(u@n%zY|P(Q0$9e7jmPslp%OuWR3+ zK3Jncd8%Dx1qJbhQ~Ls4xxkVcv3nAX4?N5}6VSDqjP|pH=uhA`ZPC$;WmF->^Yeck z_sSa-dv>l65sK7K<7_(6kZ{o7)smsa@6=#LT}^|gJeJ|M%Wr~b*7`T*`8%Vj;bmZm z-708@jlFCC0=q3|0M^R*6wau7ZFiuZ(l5`m7N=oC)n@gMyEE!x59}xtXt~>C{=XNb zj0`P|p7GBSdBH@B&#F4|-do^Q_6H5Y5uOthGIPt8Pk-zu8^KN0acY;#y{u^6OW02r zJe`*e#RbPw$CW5)Lz>OsmsPpCdxvMa+<}xTJNxEcPQiZAyB$0j#_*b(F04hMO%`qH z_R5_`9(A+Pt`ze*E9`m0^o38nnU{hP7%L4nVePRd{l{3Az?7dqJ z(k?c2_d_K$k-b_~zb{7Rj{MrGoc=#sQD1i|xz<(^HsH);xv;gK<)RtjX&4$9K5{+s_J-Us0hJ|LGT>()H{k?_PY3ZEiP z9$#;DA}$mi?iGsgp9jf?^NRe@L<8KCHatO^`k?NcbRZ;*nIG}Rf-aQM@4FRt5d1zr zxi9S5Ez?7DZS8*;2*y1Vldp*k8kwvN*5gxx^(G_@gLZ|hZwbHVCJUDR>>&&X#<7ir zT)u}wRg6UP1q1GN)6aZe-mH~n|D^p0;rknLxstD=?E9WOGX6-2>SY3<=E_G!wO3?k zAZWUJJjckD`@W5?bcyGk7p%Cbl{BPehFE}T^dC0JYi!@2S`N+u$BP$he7*$S9X~Yh zac@_G$N{b(u@mLCW_u}I&umvV6+V*KBGV|qF}%u~hjFnI&hon$)ko739gO7$|EC6q zaHF(8>WHnr8vfhd6Osm3`Blzoq;vWU?(1^?LQl7B!$Y_6Fj7AGnCj!{$VODzm*MOd z)voumLhO#2rpOzB!iwg`X1b0yAI!u0L?Of*feSk`<}~kMDz$(W8E}gfn+mrcY(98w zvb9^b$g{DTB!(lla9Bn{5nKtXiZhst#C8L;BO0=#V?YdxI;O8^C5>3;|pL}0eYf;VvB%(~?!J4X_;$6nay-2EBszVZ4sKPw?>9@ClKSy@0Z#;3xg z!;XE-Z{@NT>^<5ZBMEhF2BS_-vd*r*>IE9HA_OxsOSeZ{zQCSV88{s6KO8|72LdR! z;<+JTZhHE;ea?b?&_f}K^R|J4t4Dw4t)DKBr%uPpI@Ot&oXq*Rm?eD)Xly?CC5E>{ zlG;?%OAg(RJ80+p-0yp5kGD19Q$6C1%5a(6`z5m3T=@7CDJ;ia_?Um1L0rDS=xkm# z3U$7MyYM&!#!P?4Ppamo$BH~T`Z~|2OzCHiPX5xW7F--L*+TJ`y-)mn8|WZYzjENE zza2y5?+p02cec1bfwdn!qhcbhDk#mqkNpG`osLqU!ZR4=p=w;V=q@!kOt0@>_Xt+Y zLK1@Z{txCup3HuX;O#Si#l9g`87?CA<&X2N$w00zA#JF=PJ zE{km$SKJP$1v}zC9=p+*Vh7ZgBe4{*CwTa=0jQbXXNokIlhk8{Qtp#I;BXJ_QngNf z+LIAp_qUq+!08yBK)u9_N5D0pH{c`mXiLQwt=izctiH9~uUDsdQHS<=Nvw)>|IPxB zjzmt-EzZ(R`m`(yk7><_O48GN*wd`PydK-)2>wOWD{D{9W~$;n5;O4EG)b4MUtjNG z>-GV{Lzr9%4?MKcz1&EtPK!#C!=T4A^t~pQT2OY!sxA2P1h=Ohhn9fwuum(fB3ZcCSA)(Pi}eej ziJQV)7dk9GLWUQerE|g@9jbXY#5-mDUvVneN-ZQaJpM zhbfxt`xgavLQnSBm_%bC&zgEV6(ut8Ri(^AsZWPVB`$oe$*SP}eV|d@ld8Uz{%pT9 zU3;HfsRJxp_ZKq&tX5{6$uik1qZ=U2Ds5}W?dW-r(5~$sO;c`iVKYgz_zahQ1YsYK z=Xw4$Z2>o-JsY%7r*_3is6F{b^;$<#RdM@uy)U{`wICle<(S>xeheU>mT-p{Dn$d4 zyXWds2HTWFfg09Ld+0CY+q9_Pz%T(CL0_QAI(+5v?D}5@rFrTdgm+@J4U5HmvcOzr z<*hJg+~K|@34opl;GH$6^pY}@#}!B!-!+{aKI05Nzfv@T3}vKz_%akRF3~6%Q+P>= z_8@<{dat#25&Y>*(S6lEdQFf8&~t3$CF7CL zvaz5k zneSHD|AagDm-u_5qL>uqM5-zf2G~khLi-*!S-~>Xpn}rc@<~zJIoa2)a(ePR;3>9` zssHdtd}SPcU=^m>i0(D(+W!MZc^AKLy*F{n{ukyvhe7@A7%aAQLYs$JnG)maVh~ap z-$7Gsd4&cZsV>ncsJC5rG5j4_Rqs1PVE##h9(e$dKeOxnIeg{(_wzW5yL0*_3NB^# z$tzaNbEb!I1d&$X8$z2h$8)eM7E}--RJQEtZX3CoN9iN5A2&Qa4zyB3GIL`RtP{tr zwEYM^>Rl0z{?BrpJEj;tCQ1`BDYD0{dQht?a+8>Y@dFoAI`;*2A>3~+9=&^nl5_L! z?meefuog_#069I)uS^!=wBor?5~>Dbrxr0^Wt9^YNv)qq@VD-hg>C@@qI+05v%MPIJ35~g*PXfvRjy2&zx#ml|W38`^>E13?2{w2^srprOcb2uzM#d30$0FRWsARN?|* z{8|9UYWgU{HpI-XJI3MZl^7*tUg^;kXx&tktqLJQA<4?B_XBCXhO+-*l}^mDC4WfZ zW`SFOO}d9=RM4|BH_Gq)92_f0x;e*Ye3gh4T!H`>2|q{{5?BpYTJIlIe#%%Gs&TwE zl2e|4xW>ODLHk(q%+q5b7C41t1ODI`$$?n=WV1#Zxt&00ugjdpu@Db~+$lAcoQdt` zE5U<0LIX99#UO#Q0?i1h*xzgcGv82;p*Y;0h2{q0DS7(3H#8n<^?*a2{Pm>~jhoFu zfSj~ng5Fnsas$;p@1tCy(U-fH}31_TGd*kw6RIl?uwPH z^}5MNuull%eF=GX85t2Ei~V+|ZFx^WmsObG8Z$+j=AR#_o67=FMw7od-guL^-0)JhlV(Z08`-TH z&(4J*rXHWpg?Ov&v-wY>dsiA|c|~q5z@rIuVSchvQZ!|J>jKca{J7A;)SAC`SLf;a zT@)dh{(qDAJ)cyF^K+R?RF$$5)>ez|lM`m80Qpnxpe}yVUC59fs0SafYFE}0m-xU6 zQQ7116Prjvd1jfHU44t;qMtMvH8NUF_UROsif@ZGbLf-o(_Q`i+&KC_DggJhUS~uT zrqQ#0Q7V&E^K*1#_7pPxj2Ljrb~@;P_nwey{B$$_8%utTJAQi z@h_`-aOJC%L#hOLIxHEfJ`3Z8Es*zlRvlZ`IZk1mt|U(8Np!FjStW*d30eepL@~Rg zbLK7mpZel^i;;oWjiIb$eQwrrhRi90$0Nzb7Z36e33LML_e??grK1k_U`Vvp)}pfC z6FXpa>DxoNOY0XO=m+!U9tfks|7+A`SJ`*S^Z9_#$mRxec!@LVd-@aYt?ZK%TT$=* z-oTTHSQUIZa%C1y+x(f{o^;77C3!i@>y8OYZ#c(6e=rY|+IKJhxx(kQvSC!5? zI#)33li6zloV+ukdaHu2z{JL+#1S|Dp7kLN_(1V#EZtln{(E8h!pd5=m9a^5YD($M zY1BTAaHxk}^Q55L78lNFIDJ2!htLute12!u;wb-ms-jov=(%!+yxN0eCD$vhSNp&i zOu~mhbKU1bT(Tc1MnOw(e2dk&e1Sr56)McCOEWxAd)y-+V(W-Wz7JcFn3LV04TU@va#Yi167h{(ok4>G{ zCr{{);EU&kT2t?}JUd*K+UH_q&q$1zFA{fUX!G_cgSnJxUBwSv?X(%R~Izq9bywdO8@q|Qlx%;4oeIxT{cGJeG z2l-9ebpyn(rH^7X?}MCph?YYzxxuo>?^_^Aez5hv%a0PB?DbFh4U*i2Jz+`tQUP=J zSfEWFb>L;Yxp+(r?0n2ZOt)03kCf#Nrp0E^CB|z8IO0&Dj3RJbeL zin@fgET1jVSKDK##WE*=ydds#-=x!+d)#GyJ3HGU4qhJaaA=yEfch;KhBy~saI^Qs*y&9y|1ui* zm7Sga4rq2=|8A;}Ea;$-5_#HAhq-pNM3y2;?R6puOEcMr17%;A+nb+A%v>>DXnXB_ z-`%5g7A@DH`TV>=#ZhE;2C(c!xM9S5U-g?mse_FA+6c_@4Y?FwGH>|Q081pLer7K& zTcIuRR(Sh2JaiB@8s~mZ2MjD3P5{Ma*nP>8IC}MXM~l5*Rp()M{q{VSGEbdurBBz* zJ7_0M3k#5EZ$J-g9u<$iggOQkyI|t;IPHaT5O<*_rUPL*e;0mSQS?XZ?%*lR5GNKP z+hA85z~t$bL7?;|+Cjr;=7%9*KOXyPrp?}*o$KJv+I2-Ix(}9d9^s=>T5ok`2G$l; zp?;(GY>ECdSWTWH;A9oZ(W+gr+wSzEzsilJE?{QY@>VC%unSa~`J3>Nv2gq@PQ!S`6i?vqKNyeD}Flv9NS@t1_ctD1`pw7_$V{~uY zM&0+eN1yw^bn^=ut>5dv{-;QXoc-ww2g*;E42JiW=EG8`8lo@YXgz2j1on!Th5_2_ zJz{@EPe8`yU&H;Jj!z*IU;_er8#T5SJH5&uaz$RRlukPAg|Z3`o`4Qqx^No>1*F?! z^Hr+vCfywI~O$p_iLn#W;a<9+*T^Qfnfj(U$u(Y-Tf-3&sMTPDAwM zKSzkGe`s~NbW)i(t_*FTfx8d1Jd7UaI8SeIZY=<~RZxV)x$g^><%#Enl{#%$9^=Zf zTTq%U6l!vimVE@xreEx@B>Xt=W2C`bk}qwCZ4sSV}X zb~8{LS9_T9R%NCO*7UgD_-*K2M1}8aiz?7oucB-4(YrV z@3?HZpw{d_nNAJ9l=i;~FpgO6%pAK#41u4*(EiL14kASIX^h6fawbSAHE*nLk^DH) zKHub(EJp9o)riq%eRCVJ1zI8%Z_agnD02D2z5?DRzUn!TjVJmYPJ6+QXN$|81d8%i z|69ffMqx^aQJ1=W{DcoP?uT3?|7>e0^~U5G`ycP9?@EDO)?!~cY*pw2;sTln?aj4^ z&NcgQ#bt<;z#n{D^YZ-s>aRE6!2{Wis z%JZhxMxQ(loEHJ_(Yy3+`lsK*->857Wm;3Mp#-a< z46pr|5ZJx|k17uaaMtVw1N^Py1F1bHyq>JQ7=ZP58qSVIy=Y$U0M!i^N~|_327UKU z!(tFP$f*}!;LMhwjmIN}cm`md9XJW7e)Rsn#|-EG+{B?&aP zLGNtC+4&wXYA`Z(ft^tb7HsKZ=>cE2k#5$sN`9eq?SsG?XRCZE_hi&Zj;+Ka3;A?s zRM~x`FxA6`p6Ao~g>2iEbaLTQpY6p!kaAz-0Pn}T6Mu4oSz}{)7DZF6%0zy7OYNWk z#(OS?XQmQbVL(4x&9``#+wR5vH!Hv@iU+0ZNTW}liihrf_ba)Q=@BQ)AZ`p^938ZkA2Y51D;1*Ymj!+=$R-Qa`nvONPyZvKQg0L%aQP}0iD(96iU-SQkDHgiXP@QiAdXpQBSLQ%uEVK#E@37WBu$8?^^K*xi ztYOQ@&B_dw$(A8otj!TFRbA!A6>x&}rK3#DqgDA`S~f-RI@sD$F$%|OySSAmKp?uB6n}OwPl)+m zv9E%6%XY*NpPl{tV)diMnT0ReXGOR+QrXh+Qktlqcb_$H>(o3>%wUX**H8rQdVM}4 z%KahziY#a69a*-1Qoh5XIV;+HU#0GvPEk;bso0L&tn&O&r7mO!Z-C6h&6W#OQa|IF zrt>bkC$#88{eq&F4&MnerF$f4^pPEx!E@xE?OZl+v7CKjg{SOH2liJwIRC~yN+jX7 zYEVp<;<=B4Taf;p>+C?CqMca8Mw%!4mn54>_ps}ap8ZYTaAB9aIMZo8clvi)W%0Pr z!erK2nfa>MCEGH;2UNsx_od>doGpUxD6B)Kd9;rKERN;>YzC02WljdZTqY*5B9Aw0 z$?|^9hyar%bbgFjPx)ULSidnBGJS`Al)lLtM_Py4a{H_``n}PgTzheh?`Ko#ua;Q? zD(t)~HTtuHI5;dJUmN!WzxaAw8~sck-fDUhNWh}L2f~7I*xwT&@;GJlbKJ_`XfT$m z{PO^AmZ_kj{D{@t=*8%pK%xd_JCLRF;&3qRv{Mq#j*O}n{lXXf=5P`r zrpX^hIwl!ZhKT#Vn#MCFd#f)RE1|Y%RFBQD1ggBn1VtO z-J*sr?}z^&x8GdwztbJ@mEnTW2jks%YRzX0ad3#g-n0G3UUMReIeCivpXOLh_4S`LWTC^a-mZ+2(KoL8LWdgY5krwVMU|%!F zvdnXEd?k6ZI;J}|*jg*2C$O`PEq53B4i3t{osu||SPi@x)5@HN=)+NoRwqz3b*Mxt@*I^%si&hw9FWXgWIR8iW{c7>fXVKpH-zD@|^b{-qb2Wg`Jo3OVuPytP7D(4hU&{*gLVjnZ&%~lb38miT z^WIYK(+(T-A)k5JeVr12n7M!N2ZR-^QR}@4*r^y@gfOebB8WeCO)+;5#0fnUDu5>Z zNkSivP)NCEH9?zf&m(AOZKj{$DL%`eE%aI_`_y~INd)5i5u7z_*HUn$@lKV(dg-N) zCq91B;F-zNf`Yz%|TEEAYxK%>lN^8Y)de#D2D`Tl(A?HzavGu$46zZyK^cy%!{*sTFSkOz!;{_l~y zuwRX=CZ#*^JU?iKKajtso~^*3-ZZsKZ-DRf|AI1~O0Q&PS7EYugb~E+w;IZ}?2a&D zGS$!MvsSubFtn-mZL6h-&1O8Z+yM({DS0+?fY+{1X{Qz+N@K5G!CbS%?*0s(J)k}p ziW)Zq71J72ObY(i+8RX}ze{MU1vnw`zVzC|(F(_!T@Q8;LRXWuvdjb4@^5@G#Tw9= zup)zJ^Z|F4S2Ak`%Y|?8+f@F|6241gQP8NtK(MnFV&JKi_Z+1ZHO91}n~=dPns;*| zv6?k#G>WYi(-RH7&U?9!P)ejE9=7t z>HN6);cAaPi``;4hS*>(>dg`# z9^}VYUnmh$8K!1GQu1-4zi5j4|A6yV9*B%$nxBK)o;%gFMo4Roi zo4RAVGUvdksNg$(*Tm#`qD}QP4y}>+%zv@HzN^Jt=5_gIA*6-6M~iy3y0o&u^+`|` zoiFYi(6msJidKxbuf0Z4*AEGY7M|<~C39pgq!L@Y!Z&ABoHVL@OzXMGznGDP@>R|xAVs5zHY;BIxvkf8pSof%- z?Z6kxg|)sOs7}>V2ltZj*B9|RFZ6p_*C|V?w&cRH<{W8k5|vh~A+@Q+EVYwnwo;as zDC8Cx9%q3kI0sm-F&6s63M&9qRASJKMa;jXED4@o>jR1^kCvWyhUAnF)e?OJz@g2x?O4<6 zb^#k*2BNUYC;9)A62ut+_6l=1)9_@ zzEo@lDW`tA<2l#tI6TJ{pgkIr`QeZZZbO2=4{N|iC+#JSWD_{*BeT8QV$Wd5;nHO% z{)QiWeLsU~7}Twt=9-Wu^0(~VwcyTRkB}0x_wHFy!H$Igtqy;LM8-9)_(XeMO5B15 z8GDP{mp^#5cU`UK&%q+k@oO6(VApH|q;JE?biiaUI|r>fFW;(Lz15Gus4u&-FtVk& zF@xQH^a+LWeYU~oFB?m<%f(?p9hG&{5Jyu z_2hArxym}`ej+VxF4p}Y;qBi)MiG~5R8Ek6&e^_HA{l<_MRp~seU4#av$fY71lw~h ziYL~W#)}Z-^PJf23D$OB%vRssN}aXBm^Qc$%aD^{=SZjEy?}k+etVId+?Y#ycZ0Y0R_Ci}LDL8>xwvJ!{`g0Suq3 z(y%5sONkY$A(sM!uI=s)DIT)1#SPQ5+?2%H%z%5)%&)!OVyivcvqFi1{(G;H61;cC|s`Q==+9vFr z(OGlQk+a*HHJeP8-ukxrOK|HgxPTsSJ%ZSSoWNw8-Eefm_8g&|6DbMxnT^}57u>A( z2?y)??hs*$YDU@_x2(0U_x%twnJKM}Xf0(>#2$4x6U^y~b4tm4^Yg`77ZX$--+A#S zJdZTXIHaTFyyleE5p-VYF#{P8fP1q{^tJ%;YNh=wk%e7BxX^;pr9ryiT@ zo_$5mJ9rzwnuRcjf3&sFJD{2$x&^q6!RBmeIj>7-GQU}f8rHOB5^8e*@3^ulRMpLk z`dv%p4XQtaeTmZZnO_-FR+SrfaFF>&U4=}B!K#mhYR|q3+FpAT6%I`8(VnSy?4mMa zmuzc2%b0Gbe=2FKlGYs1!&B6^CjwtHs%vT06Yd_Uq<}yCc5mx8lImiyTm4x4<2vcx z08K!I`L9g1ox|10bbL^xCoKSp9VdDk1Rici{u{&2$*NoiQI7PgA7=6CyUv-W;12t| z!*2own)wz`&1(Wf4pPDcq1L9k6sxxj4X6WC)oF!=Fm#HRs_G%!)}FjF1bGehRN*`w zR~c0ZnZ|yYR7t0ty^@ldeKQN&6;`US%AM%8l^e7sK$8vK~${pz{ye6Qj{_vJk#2~}qb9o@zWn|)%Q)IM3aOpo<2m?z zX>QcSD!%#TTXTGzuzoilvuz9Z9B!>NpRQ~%s%$d4ui$F+Zq;(z9QIBhKfsV0rUWUi z9xSFQUJK~YgMJ7#Dg}QZ>aj}Q$C&poXfBfQKWjp1k3M!XkCx5NKTN%}54H>{Q+-z) z=RS|lX!qdM`~3TaE#>!zLj!IRVKSb&*O6Qn3IJ3Gt6^Z%z4mW>gzP@Gj^4HTXrw~>|RL#mWziY zvAn$8D@R^N*}lZ=)JNQX?W9JgHi>04VCq2XYTu@G)z5yy6P2m$vUJo7ZJXIy$r{b+ z#PU=0(RN{z&00S_O?#oCCQ4NO`NsU$jP|`%i4`BE?~BPt&MG@uSHnU(DSo&+UWC$} z<8UjnS@dtO5sJH_6ho&D`(AWYG+_w`2cPh}I9Jf^bP{wgGDrCD8Xba>M;#Vs9u<}) zHmukGugW{uiwWLJAiHYl$MRCY|2$9<9PLE@W6Re5db{Fjn(SeCzMq_f3)Z8Ch_cEI zn`*fA*6RGz^XQANRc60>FcKe(Qb~1)5&p$7(D{w6c&b5Y#o?hT@x@nfm!ysR7?n)x zlZQT$lPfA8`o(m3Cthziq2Ah_w|~7Tag;SEag=B&>U- z%yUd11dAf@rpmv^`@1QU^@P$Ba%q?4i%CJyo#WJMyc(7G_nCoEZ1?`lKl=ogKiet< zpqYAPyH3&c{VV>e|L3Pl7cidK^M5GsK~3%Sxc`sEdeiUqn;jY<#J9&=17?yLeB&#JB%8^YM1fvG|zZiurV`h)GGOj(DTM zUlC%80bjCK-uY9xpJ(7@Y()NTA9M|N_DtaU&@~jo~p_{^*{}uI$sD$O~x%jo=r1Ohikc!uJJ=6^gE`k ziO*{if^44D{{6${uu_10H5rxezQS;Q*KEze0sR|lIL#WHsfo0$#qZ1Syu(hU9t!-p zXw};{YL<9KFy!q(X{rF9({Mg5E$5wR<>Ozf@-w0Qk5frjFVjOq`{WimeLxKIS|W_j zvo+IuChUnOUMEp(q{LFeXRVrq++Uyc9MTr%s(J}ij-m4V`V{+4tY%Zy{SGlZ#v7qi zx6Bi5avYs11+(cLE3$!J5DYM`RT%NFm7nFlx7UgHdRpd(BfMt%kf4E-Mm7^i`Nzt+ zd1}*`;9M zpO_Jf1USSF7!>t3TZWS!ss8iSXmEF7p-Y<`JRj(|7ZpGj5&nrzh z9(wgM`tAN5E3rRwb@nYyFZvoQUyGU_Rv%D)`dah&mQicGyvwQOk+;l{OurTT`n3&( zjrreliGK_o&dFtf1|eH1YYXrG2rYo)kKf8q%}T}XA;aYniTCq9rsUER@~+12Cj@YZ zY+<@pEb?(~0h}&#+%MIOEWiP`zCvcRb&1^pY5L(UXj8QBA^Zr#*<(`hsCVPXi;Yh? zGt+(P9*YU{&c2HibxdCPMd-^-#KPB?spOZaA(Qr4dF5g7f8#X$qwb?Qv-$0v3{;UZ zZmG!X#Od&=%-g1eOKL!FeEJcnALjVgd;Wx}sR8@TE3s^-ulbM}AWuK~rzRO}YCViT z_)zK>kVglGkM{J@qC3Pcg_hpv@cIA`npJaq)RUYU-)z&zW#5Rvs<(2w=^^@)Jv5oh ziL0S>(Y6VWxg7q*tfuFdVd&?RtT~-Y9vz%~ABCGd){`CuRNIO=SP6|LAP8>`tD6Hv z{gv2=ujO&4@UL7R3y*OdNm3tPU)!D<(y_vNnqvkuimvQd1dnaos)I=bQjMh0(0sl0 z`zDs?c+|_&!$n5=LI3?p%?_TIevj!+8&`S!cj&8Kz7eZXllAE2SAUi?Rc`&cF#2X! zzf+hYQ2&l-pj!IA)(n(Sv|&qYwQ-F1iQitE1^L3=hHFTN8|>2AMO`s>bqqJ?=h~wk zAS}zjXexxvO6{!vm2{;yd(SVoXdah@Q zg=In5`EM1qwgn_H8skQ68@M%HkY6dISI1&Nz^)JOFy^o$N(^*AlwYeFUK)4E%t^w zbqFMS49?|>jn-*=uf2t%cbhP8of`6;#EdY-eDY0q&A#uBLYHIG)MLGOD&3LtF3uXU z&vz>2PsvG|dt<1TcuJln87@B~dc1}M7Q12;dU8QKjOnTQ{O6;xME zEk^^J4dmh;Wh(o39|1|sxO%U(`~6D$oQf^y)qPd!uc1~wKhP_xoYcGzdKx*xtN{r zZ>|mv>#IEQ-CMo6qWtHuKe6lRliMuvm8KvcyS(QO^u|@d53vLPM99a-#CDh<$n`*0^Ixak&r#KFPi>OGD=2GVUpXEO(^lcc`=$^12e@o?;0m6LukHBr%MQHdQf+cVZHbd69VhDj)L ztQ{w*=qGV`XGDNp!FX&7(FEVjrCU>l_Pp6=M*Z@Xv|@fTq4;z!ZM-C4Y64@&f#^?F zDD5^`9tb9ZkJ0oMxwH)Sajb3mnd)#$IC$-DrzyB1;wNI=XzJ+hg)^=2TQwfpSxPwE zn+Aeu!+6YTrP9ybxFz%HBV)`IrR_%!pkxljXmV?-skt_v$Y5C|UN`+Y*%+=SAkiJRrMaDRI{OrcJ+9><}j0ZsYDTg4|Q0&C~ z=gA4k`1hV(oAfobeIy4U>dl<)aqtd4tA`T9JOx+_h1?`FLLZXR<>^){ANXL~^aGeh0U&XuAL2 zxF_(hMXhO}OTDtwlmLdZULw2S-?TqwL3js(MAK?urL2CpL zz&YIm&Ixike8S1{SJEb1UF6Ry)NYUlQn9nX8m`JOjS<>V+w0WPUHrC!M9o6T2f77x zkc%admx?+>{c~HP4?l42lc_Fm;GTWbT*uM*7HFU%G>ZfmER2;fs;gAk;DI~gqyABA zWjS078xLPhFSmyDslCU378c+XL&hmYV_-c8csvL_AlC zPM+a-#_V&^y-Y|`7a{4`)q1b^e&bHOPhs5dQ$J$4MFZ=g{nYt9!!Y~a3?sozsIRfQ?npTgsqirIk2%v~?2Vc2IjP=M zl}!8o6r){TY5D)<0+c6I9BmYCx9yJv@7LdRjj?|q<}VjwFKkz^$f`zro^z&?oEP=J zYtgLLA@!kit`&ds^RYl`2?N0}jpV90RL#?&NH<&V2<#@*)CXs8mTKY}!y8twIb+a= z(rGUjTxgN{abUJ;5F$}UHbB_kt`p3@%!ie96ecZ&$X|i$Y47Ew3nu!M?j`xWJ1ekE zBd{4ep`+p`vTN*IsF`g1CgN?Kbh5ER1}|?G)eW$aiDL%@s#ry>UFZ?-yY6tCUA=cC zlVE|`wZ+<4vTwbTFKPDc`;S&8*UX&~M%_Ci?lO zmeV*(YHG-N?Et|n9D86g^xG;jCv&x(Uz?~^Sw)&-wJNeA(8o0(%e16o9d4$At_|^- zt3x?F%MV?<#-g^n?vOwHV_cjKGNI@Ke%=JNHv}B@5NQCe#oL^2dvzQ~S z&TIU}t>`C&5_tnbvjlm8k+B~MmhKNq&#c|%ZjVMkX@ra6erHBUo8;1`1hp`@bG9wp zR$6epeA@WlESJN^oaKy*TE|x9!s6yv0}&f(wI6KWRdKoSWme|Fgn@P|>(IuC#5{u4 z6@FYMOZ;oGHA1jlB$m)N+LDcvAGkrB$ zBM`15r$gxq&aHdRV^`k3pr#&uFKl`q0gJVCaiRC=d80-yYN_~gEU{SDS#OtW5)@h? zP#fC{0>W0Et%x8~{sF_pQEt@m@{n_JILv?=AEZ78sfd&*2Uyv=R!nirs`6Uk39aR=8N5RW4AviGbMZw2?!`OhDrBY9Cusy7@m zuJKhE*K+yWbU2tUpa*d!^=O06*lCW+7|hn?s8%P?ZsuacB(E)3+~AC_1k7?T$pWLM zdxohZ_Y1m*6skyW$W@2t=qT8 zB))t2Kwan;753PkE%OVy;G!$drCrA$4Icf$cHQE-EzwyiiP>9llgX|4U28?u$OmRk zq_zA=*5Lbx``3*3knHc-;jCAH&?gW&;AX4}TJtn*W)jIW&@Ui9NrTMHY zx(1&GO!!;0UJ1G_R%@)XrIdoGDIv31y9w@4v8Vo4pe}Ybn?@T-7PBZD5S(Q_w|zai zUD5mIU2{wV>lWg%R`z;~H}=yuod1Y5HS6a*!um|K6C_e&s(SlLIyJTW-Q~~1Rjkry zMH)uomlQ_iT(u23A4L(-nt$+oIw6+bxuq8c#v~K+RsBPY&E23c#cNDnfFdy*XYSHv zo0imvfBW!~vJMT~;uGsRdfu&%^cLYXIkXGW0dQJlF~>#p3C;&k2sL^EE;GmUQ@mV= z(|AC`Aas?UxiGHA9nyTQfZ!A*SFKNF>-+#A?H$B6FXb?W5NfHIjSN7tEPFZptg;E% zNkp5Kqb|fATi0?@%P{yk61?yUzSQB;D0eo(yAZTa>ez((>(MhL4MW=wt`plt?h}}g zPP=lNe*1v@sd4cLTK~qa`&U@Rbh&_`ed%GN{qx4EstliSw{F5*xwz6@%hH+FS(ud4 zSJW|NhF+wXN|O?USCthnLc47_rwBZeMWmWX62i<9iP;kTTD z|5*x=8$&F^pid~rN=S^!K2{(m$o8Z?ZN7Si1(KaCWk2v$J-oX{)TS|lf6bOgByq&i z&Dv>X#;m$i3ggR{%?6I(%WDb>YT*i_jEA*WgW!H>b%?1XZhq4ikzRiIPWOA*R{`#| zxuOX1edfWdKOyd}mSUnWS7<6C@n8AMCET?$pkJpLKBrU=4QOM*fuic2HiCyfl%Nv{W)ZNi>e1NDE1!k6J7z6lMT zg;#FYPr@^j=o(1rwaRBY;myII2ZG>MMl?!*k_k|u^C1j8>_*hs`X}oHHzH&LY(|-m zaz87m8c^YbG{{hM9O_wH#66lXkzu^3cU<(azlKQW`;vf-;2Fm4YsnnJ-5T}joX|l+ z`EPJtjY-^9B|bz}(x2l;D^f%=_NLs!d%s_sO8@(JS5J_NlYEIEV->^Dg}X^#T5 z%OJ}GJpNTA_$!&cuclSVr*9SW&-E*_%Mm@x_li4AaD*rs?~-g)1~Y+Ptqc~3MK1Ze z8J#8bZDk_wYe0!(IaCw&OG8c}rNg;E$U6RWQo8US`p4!(|M`Y_cF7pTmQh zN+np{f&pdAQx9hbfCMc3NxNqrq1v0iH%U2XCf>!Y-OF&jjpj@D;27%`r?=S7y;MaT zpWuC$%^7uR|D%YlqW6w9l9x8-*|$;U_&bIh>8eEvLRQ=o{_Q)r#%I*S9giY@GL-jqJAg4eDfPA)!P;08NH~(Idd%dzlbKvYJgc8b$L4B`xF*mF)A-uZnIye=+JBP~`=|sxm=o(CN zNb#gCr^%OVoRR?)2gUYx@)e7ri_>hR(V7Zg#S2d~-QH<^4_n=22tVI(&36JdD>@~3 zT^h#79)I(Q3M-4q#zC^zvBlED>;M^T>*R;o2(p{)ca~PQaBA&HG&6D7m|3C)mdSF~ zD|+XZdHa#WLl*=_grX;WwtujPb+|FfWn`nfBtk}?l9sEEi`ibd=w4bp3RhHq^NUKp z`=M}Hjr!_5x2B7>rBnmlEh2rK+(H7a%VW5R|@dkFFLUzQ7Sm+Vr%5Cj{LE^?)a|@7QtF`+(}2xuryq z<|03(ODI=a*thm=A~%(zZPqX?(s#ba3Wht9tA5UtSmiomR*I2*cf2o(XmCro997*K zk8RUIl6M^ENv8jEcC*PF7M+Di>~K>3cIeOH)k+AWBKQAsqCv}Fq>$C+{Z)>`$gWsF zm2V)-0&kzwYoN1QGa~QmQAH1bM@Ff=LtCsG*9u+Ces*9Oq*-!2e$JA?rpksZ)ztP^=zAlAIuUR2Z-W_CMp}}r}!$g-0d!zADICMU2Orc zLJ^iE#`8-jw@e%e%m%Bz=Y|GA<3E<xZmR87r zV00>6=NWdZc2)YfppHm0n~u^lUVcDhA=ak|QAHvm`tVObZYqP;-$4Mf%x}7{Qr7_` zBM%e4{uZ8XJ;gUvPgiuAQv;8KmG%>K7S6K%JAWUNtDJ_8Ed%Iva>~ zIyIC*FH3KqeWZpw&(&3?QGjucS-3V>D|Y@yc$gHL{5Y}Gfp;Hsk-=e#|BHq>mWDs3 zmZ}~t_iHO?$C*)e3K+rbokUYcIu5NZ5rv@}HrY+KKN;f32+)@p`NBkB>RxUS>q$e0 zuJDL!_ek<9a8g>cb(ds2X{I6okQ?kj093dh&bO{L67!GF>t0_h<;*NGk=xAeaZpDV z+TtwcP6Y-}fvH$U9j;i%(^$z79i%P1@(W$VXkLep%@%#$x-aZBMjjxeOl7Jd@HS0% z(Jq)r48OI#L8?C`7ZpGHt(KJ_zeyg8AWiPcPcm@d+m}CfFQtE8lrYe#3yH=E)Jzp% zN0~HZS7V+*5}{6vLnyRqVk{hnl)ku@)EXkc1m91azY({Wf*bup65}nrlKD_XOA5dp zuanrkUHyV>kaOQ)s}YR}!a^5`se)Eh3R_uh-gOSX*d3qBEv-(R1glQeCRimsj)-h! z35tC77ZLoQMHg8^bTq$l?3b@L9vj>48C z(aJ6D#)zkf-kW&%oEglaPb7cJK2yi~XN4V84Zg#qSAQXPFH3&l;+nNh*~CYK?Ygy^ zZ^-0W6<%I(EpUtK*K{KDL^VRr%d3^(bYPt(+m%}NiRV1#nb^x|>Ah|)vXmdf}m>Qk?RhOyM%C-$=0FA8~$Dlb~q7BlvGi_$;<(ghAj7Exm1>eIs<8 zVVP*o<(7bwDA%CIR`KX2s)$q3TcOl~{RgJ`mdBJ5w+1?~!nO6s5)LA_1PbHli0pyo zt7gLfkn|u~p*J!_RxDa#(_K*DUuxjz23D!)&5FK7HkHpxiH5VqY`XuaymFvt0P?lP z_Qq$sS)7@hPBG8{n8GJ9k3y$wv?~~u5WQydx%x?UeS^J(O-H|qj2oLd%Wbf6L(Y-A40O{jR{UrBq?{AH7 z*%6E|%&GH)gjCGK#nG&Bw`Ya5PBG~A43Yj%F7E=G>8G$?6(@qaO%0i&G_0!!>>2^FT2kTqLB@knFY zcE{B=LGdS&gdpZH2m`7rQL;|cY7O>kPx1px__nWa@dKi%^cMGPjetWV{sFv0<6G8> zwej)^{u9&8swmzq^>eZI;XvbsIg5_|SJEBgg|p&UQ)mg#QYG6IO<=Goig)RyE8T`r zD|q$e=jZXjoxE~sqsqG-HKIuG;Z64 z-XW^koXfLU*7NDFc9avXR$8M0M-i$d208N9-OP)Xv0;l{o1|<|kf(>&cny^lMQ717 zWiPMGE#qT2#vMt^cIm~V{99(7{xDd^K>p)f)t9W_P+yqnL7lKo7uK9)6$* zMDy>(eb5!kzR%a=o)8akRp+)XCN;H>JgP_KmQ<@PoAU@__RoX(OoJY8J^IW=U{V8H zOd{RWJO(vDEww#QNx(u>&RS$VJk6plt5Zc#;Pq2?@NeG_!J+TY9YaG|j!svE8oL#=pL;Ttapo67&+=Q6w1)B~w$;B(2U z%-Jr71omPNZ#ghvF`d3J-e`cLz-c5V9dYNPB#UyebLPtf`YXR|xKgG*-YZhlaZuS9 zs2>?q%2dSM$CRRpHVtbj%HjGwZ_lo5Y9QZTUaiv4kgUUN)(Nx^*@Ka>7fw4FygG{- zsNWgtueU(TIh`4MN~&KJP4tSr+u_v>9m0)TkK2ergo!t-t}c9yj(&cJAK+#wT*fTq z<;WZ^0*3++ZFF&_F?*Dt&zk!Z5%U)B!!MQc|0J;&6~?pZ*#$<-y4)VM7v|BY6wev0 zwE9l^Bn#J=yP^+mX1xJ~M)wj#$p2o*V-hi@1dPQ=ie1U&mUY?1i@QMk{^PMU*Ae_M zSMSKay4KxkH>5AkL07V`#(n->g8!$Kg(KcNaJ|}~ul?_!Onyays;M`S%TV`nFBFu7WRsFTb zD~O?J7qk>=-7k}*32VPwClp@TCjZFh@8pjD>(Rmo;3(?SS^pb9&?k`JplZr2+|PAz z&8?aWzrdk7XSRTzYm98j*RnKemF9*X8Ff$@CznvxnAU$5J3e}EU0`CVdK_f@dftiwT6#c^fyb)9r3eCe~4 zz?b=Ifv?O@?Y-5e_?C%D5ypw5c2PgcEO{&3=TI~k?eHCgOenu#e_QSm%-#}M$Oijt z*TOGry9%?m3rm^DuI z^(IEU%)qFh6|(jVc)8gi`^B&u;OQ<0{!h$%9hlO`(;U6*b1x-afi%9f<4o@5^c~AO zC%d=L!0$$ z{ITMPyvVYQ)6K2Q2L-h6L6eGPiX8=O-_5^8b=azH;2z0OG8;^D`$c0Mp?J0nRHVj7 zJ1g^46?&e=_=ra`>nyY<85cingqJA77_5Q>wzNXcgl4aYCCV6cvz?o59YMAYEyq-& zd~q50Gt^>d=iRODy_34;QW)n$%YN8_)}<*PRuHEjYz<>{9Q{ZM41y>=l8HA~w5vp5 zb1D2hkBh*~_V8Ev!^^K88C6u;_# zo|{)z+kq&>{vMeBx>ehEeWg;n00aMvwA;Vc_})lYt-e$(Cc7|5 z^~vZBb4LTKDY`X-@KM1vbj_5|*RWHmrAS7BT1WArDY~6#o!%wykO5qEQptH@T^*5cvfqV()Qwh2*sFw;e$p=uv`b{OH_*x>>nuB8wLv zO586P-eKjLwRZj)ate1|p()xvRTS1Mh{YkQQ-pNW9tP~%{w?ybc8*~$fOI!QQIlK3 z4R(w%!66JTw8by(U~#T6Luirl}6LHK+81C%=(5R9(T2o6>FwkHJw(ZE>muGHo}2Pbq}!R*(Zb#3 zUxL3dU#TFN1Q9J0<)d~`xtz1V0DZsZN{Cl`_Bm~_4XC@Q%RHRHsgC5yn;b8k>%f7< zlh~6`L3{*|VLtoMS9e$8UEHg}TZ$THQ9VovlmddMN1v<7+8_nhl`!L8?F|{gC5% zc;w1|E=#biQPUAIysQR;)Y%+*`|z=CcJ$ILw*NmY6u~%LO(`T+zZzN|EqVkH#qrxe zjB|8q?9uU&b`tfYDC5N0ZT5Z1VEZT^i1CeD=5O+Es(MA98TD1!_by+S+knb-nDS3u zg4lbUL0ulTPQMxT3H=q9|EzZzYUGp9wgj~LAH}Gy8-$#u^lho98(V2bE_R9tO~5e; zqku9QWbO)SZts+fw2-}M99b*@+YOp2MN|e;7^v_{q2_)lZ{Xi-|AO=ur|J@f1CgUg za2OBSF{VxFtN-*by)II)E?ywR#T#(7xNUS43JKOxKE*620)>mD1iZ7fuyi(u>eBp5 z$|&mb7UaY%oUxIa^oRs;vL(SA}Q__k!|11f4zU>`9v_)@|!7K3siuSWErqC zI@5gzTgb6JPg~CArQhdP)rS#Hk&4uqt7gTh8K33&CRnQ_v!V2w`<uRFYiAZI@9Zu~*^MOCpmDg);&L*t(=%{9v0H*D%D+ znXOCITv$Cux!GEpS+fAy7S4`GWgaE;zS=>U{tDJuXY`5$Gug*R#<+*?T*Hz(4!v|3 z-)!1%MN>!GVII)<{qH{tD`ep_-pm`TpwM;iZ$b5y4P=T_z9ejw&c6UeSFLWxf?h#F z_9Gucsn8#xUq$_^B$%oaeO)+(q+zpNII)*K<0kFeL#%TNnRB8 z_=PBuXlAim9$+~c;PLQvc=By?wquxFeHc(fO|3>j0{ImIR#B$@4q}ZPM;=Ct&f0x0 zV)P&!OH^YLWAQNlAMAy+_thz#PjeY0~HT6mwenMoPU(EnZ{>hv98>!9?B%{sn(tL@%@=-sj|nL5 zCzxNko4tmf&gnpNxB2qoib;kfd~;1o~@qK)qV9Ns8%!nZ-yV*2K{ViCgcO~ zI&+oLzyH6gb_(s3sV4QfQOc#i;KnN-{fk|7=f zwSI^j3@Wn#qpzp>H~zEE6X7YozNI$+p5AYsWxC2$;{vPv-UJjCknQPXUevE)~fPD5w+%oxV)A~uOz8|TZ&YK z9Hy>)tE*{$Bx>pWffEhzoyv}5vv%iqGB8F}A2-~1Ycz5vyt=kCphxh!eUJtr5xl#Y zhw$2GmXv3D?PfFE{G-IYIkFwgWfDL;-g)5OkLq!48}+OHWVTE<-!0O9%K_dTz=h>f zDEYs&%$tFQK{lEO%|M{?<`kSJwJ0j(;hpJy2u z1Y*=m)aLDXh7Q;O^F?5WV9hkTf4VVy@3!RY@2X&1w#b%s#%Ka!q#7nny?BP&e3(~pvTs#3Os z+G9-sD&hmV>b0YaMtYHj|3O3~`81|sUaKR^ZLpIbMdzNOmvCt*dMmGdxdI|WFq;+h z%k3<TfaQ1C3L7?%s~nU3q>B9p|d068>#frD?4jNqS6=-a9etv5D@ zL=GTx71c(nW8#ZLx!kDRs77bo95O);@-9dQVtE&K7I5$qawOc<1igeZNQUe4o1s;9 zZ}^p`Fp3S?_xM*+BKF73YU}4#`M3WQ8%YPHM`BNtmk`S8u~uvmy|Z>U#5%$TYAlN& z*o`}tLzIY23a~hbt!H1Bi&2tOHV&5jLx7EfCM#`WtDTgmjuqNbt9HBqcfWzNYF4Ir z5KfzpyTZ;bz<%tV51|@;Zgce#W$w0EptkUD=E+v5MBqL}&YMXFU^WXOa;EL4Nz?DJ z` zH!S~!VL*v0hyq41Bh!(kjNIP@HDTDMED9x9#KWdz@*UH+(SC)%S}GUa#mSWj9Tkx% zjfC=|^-*Nou>9#%G7LITt@WIZ`v>+X(l-}AZ1$^uxnSEGnaPb*#t83Zt=En!)yjW& z=UT4Gp_}!FT5};F#N=Wn0}AkTg%U1YQ9R4TKg)VaL7DM2bcnH{S3dI5dOX;qk%G#i6g$dKImZg1=^gB2;+GUS?A(`&4AfpEK`7c z6bgV!+Fb2}e;uc3BDzn}rf2D>(s}GtIlf(KdBDD<9m`Zca1kxq!Y)eTXsb%#j~df} z!g@ZIgHo1cVEtJO=%F_R?(sT(}^#G0+LzysDa2dqt>Jw*cE!ODa?5deP2mqf#Rqz74SG9Ptu^nBQm)%ok~N z=h4PFMv3aEK$?j3iRA`;r5SYnMxH_Sqp6imP%_4X5`_79+g`hkG(#yC zk5pq{gD8mPzexV2tW>~+@6lwzEoe2a=vovVOZTq3RI5*u!7dS1&7+ z(tZ{HCb`w!?X1wvMTqu24q2Cv1KKJGow79E-5gD9x3l>6Q?4zZ8$@x``|p)3Kf#*%Ugp_E z5`e!RL=NV>7c)60(a|Wh$jTIC5jJk|IR#d2>d?)vA?Ki;En=6VR~aKWH^n!d(YLj0 z{dT#gFUjP3U}BKPvLb~X(*C(S2C7kz@z4){XO5aZ&<=TCRxKcpYvBo7tY>^s_!#q- zt8I?DlgyqN%U7OuS@?CMcXvt zd23qMyW}F(XH*BYnvAS~6UpNvl%Fzqr8{q&vt^KE=ibp=g^WT`$$~9V7(kxe&uk14J_$d z^enx~2}?T7(aMCS2R*_UjTbX+fc$EP_v9K1R%tW5@IcyWuHeXIleYF))g_gq=|?1| zQ9|WfesAin6AnaJ0Uxu0(hvLepq-n9h(8#|R*QviRTxHtLPJ2^cv$uFMXIc8b`0%e z{9FQwV#mERO4FhlN~h0P>^D#EXBrkhE>G;&T`1m>`%c((nZg&&Tr#r5GFV8R)&_d9 zmQt}oeg1U;66B|B&SYJFO|0TCKN%MxzU-JB4F-c25BeIKy*T9|eX@20C)6!j?kVrc2_DYqJ#YgdLCZI1hsqlD5u@$H`Mu?|% zH(8jQ^{YHDZoNU0b9#JeM0^#!#ivZ+6FCm&KcJMIbAN@3(i=rj-ZET2$i@oHvxg<@ z!Tt1I$5FmIk(jM_G2K#6&x(iP1>+>1PcuYAO?Nw`OiwWv7@W>CxCxC(1W1Mmd}Ge4 zoeA+96rbBjKO=q>b1v>FwRVr78PQhq;(F!Xqi)V#u8H&3Yeq+qea|Md|52AX91E5TfwKA)}klU=^8uLep*R(V!gr|A7WnM#BSF9 zzxlJ7{O>HXHCWb|zo>s*r?+|Vf&L`AdBl9z<*eb_=#7)=dH@3opUr$Io!i`+GLT(X zzmcO6`H>y{<^Lwneo8~7kuz0KgzNGG)xNEa=%lL`fbe98w=Url`oO7e)Ja_4x_$%x z!eSXZvkJfsH)$)8zD@&v5vScyKW6t#huej1Ce%XBh7R<;fD6>V*Qr#Z>SOj{FAQd* zaPqgDHp1ONPQP>Y51(IpW~T9 z#v)J#&zUo4?+YM6_(h29xvrhY+v>j|GyWZS;OS<7X;B-2SC4lEn&vPdX_io-w4B@M z7r!T&$;7cXs1eT8y*BzG-Lk!(Ut`M9KOg9WKkmd(KuM5R-hL6`!p!qiN6nJ}sU1jw z{DnOj{!V>S5VX9exHIw-Iy_D5$dedfJZF$KGVyZnhtTsGfw`$+@(gZ~v-B=2u>VHi zY_(tW7jX@pU;%j|GDtx!SXE6S|6;G+!jQZ7u6gbxAz9Y@ya~h8n%?^}vgfGxJXt%6 z1_^niGnTODnV^zGPw{baEfkI}C{h*tuw)kfmJ}Ymkm}It*<-+*qk5K$k0jwEuuq)l z+tCjEo9&ufBBRQ5vfIU7!bTJRH8YgI+t!o zaclH{pUql<#*nw7bZM7brk`-?w1tyAG!UKIuhch&g%Yl>6 zTol#$H_3Ox2nNp{C_ijhE z80?V}s+ppS5NIx_T==fEg=@n$-qqs3=Ax>hqrw+M7_A+?=h1XkE%Zx973;*Vw++m6 zvcfq_PvNfw>X~nnoLPyVbPCKjdOK#xDWy?|=d+Bz(|hqXcyJH@ARgx_CQO7ZgB@}< z3o{_@eKiEQ9h5vs+l@eF(^UOT#|)6QdlTv*_M#6uB*j(8<&cF4?CejFVzp2$rlHa1 z-988sG|lA5QGTa-E31TVsy|w3A*zsmr4r0A{%34YmzQfs;&zW$+Hk5hA-oBm&^?9cOU%D_XZweJmnu zcs$S=pzL>x<5%m6`H#Om(H-ogOB)nVnD8yf>s%n_t^qHOM-6nAY6L+UBIlHAE@HoT zSkS=G9diT@QTOokrXY#klZ>4IFh3M3dE^a+qir~gY92-?@~M4?AX`~>;h^B+3ld51 zLaMgVLKYW5a=yVwSyBvJ0j+x0SktmnHM>86*>R5wPsG%^TVu1rsWRkBJIVf6$Ahf7 zX{?vO$Xio2%x7u_b(Ry~*2+)wT8n$|h3$pD&?>bQt2zMB^MA;C%YZ1qXnosOL_k82 zmKG@yh7P5X5>a9(B_xKB7&;8PhLCOqX^@siq@;63S{jCC=zRC%@0|1h@P1PnnP>Lw zz4nTGT`NM%Jd?b3G$v$$Mt-$hP|ygw-63`YOxX*WOiCb|_htT3M;KbHP%oEBNv@7s z@k4BXZ~KEAzEY2qHzaM!n2V%vLY+_GGLUvo)P$xMQQVH1jb zTmK?Q>WQgx<$Z2dn%e%Z+olKM!#m5%3RV@wl*}V^p#4|H-wnYm&$MCB6Id;HQMAxR z6?7y2q}S*EIp<_IWt_kAnEPE45!gZ!h2xcoh>Sb5t?vf8!YAY?&UCk4jx2i%S^mY2Y zk_@|ig{uU_DE1frB!umia&tHhq-5=h$xl4_REf6cYTOZ4Z>xN%<<*PS{jb#l{o9Od z9j2?&$NNLhpeso)dzXC{_eG}CW>r(Gw-nsRF(cNK@CCC7_g-s#k{8^Fg*edsFr%e& zbJG~Fp?hSoj}>47q|aY#X~hDmM8X{=8^Z2CHih}$XT&>UlHEU_>|=J}R^`#4yy+Ci z30fcoMa{-*DvJI`z3a|fCwhBfC#+*{JPPT)xWg=gB(I#R+|9P(=-pV}aBTR0I@rwq zjsjVGE+Z~H*JY^>Uc60W=<84ON0@L1MRkla;nM%x{}9tVXiQfyS)-@mo$q9wYSt3& zq!czk8sz-&FEFmUp+DLW7yqX)3Ir#o_w8v1nX0R>r3%$%pDX{*Rq z;RXE<^h4>OS)P~T_9p3dQ0x84xJ6}Gg(gpL7o);Ez-}3(O|2H_n&?A`LRmQrKreu7 zG^GvQhWFP=Hw+r&`?K!+*V2&_JOU&(y^;Twq5L?^YF3L1U)w9S@otc8ekOU7=VH zN4F4pA0c3G`wTmvRW{-nC(0_%bxE;^c{!39A)_;vuG}`LP$siH_yGAN1v%5t&9ZZc zB(RQ+>>h0T<7R*QRnOz0)NdP;wLlG=oC9F(yS<{D{o}TcDvc+To6DhsQ$ou$5bj|Q zI}!}_;}^m!GODKBg3keeDe-(aTk^W+cm+Z}^>{cN>Q;8_=52qGC}3^gmmfYJ=b@h>ktzIE_MEpbqlj zkanrnNI?O=d+jIlPlF@mO&E&F@hYuM!{R{ruz;CWsMkViIxl~U)B;!cnt2%)i94nA zxPQPC61&1KI($GTFvIP;${CAJGDxD#ncjl#wt30QRaB<y2GE*I6VzqGuL5ZEh5x80K|V!D0&RNr;cW0q9j^B)ARi6_0-FVzrRWh}xc0;5FaQs#PZcR8D5L@CM^jsIBhYNKD-Imk+&07M9cTaJB8sPt0 z{PH7k98wV+xTmG}44<4c4PZEabRIzO00B9mMf&$PUf3u}3dmlJ^ z%l{)+tdGY|xy%t@ryC{rz@Ni}7w?ZXdP~-yAFmE)tI=aI#q?+3IAv`D>Wbsf>l89e z%L89;>LW*9?{*oUM?%mCk-uwojU18vWiye>ldC3t;96o@68Ufh$cMa!z#Tv2P z`sOpuzWfL@Cw8({5Q2xK0m4f>s8OguFoR?yTF0>mhUx!BB&mD>2u{kWClgDAlZg`_ zW`2F)#!NcD{(rGRl^Gl2_7O;BaQIO!)t_wC<~ec(4@^#OHFUr>Oz-rlVL1(lE%tO^ z7G{m2?R^e^w%*=5sokte^dq3x^a_n|psBV|?4T+!Q1UyjGdg^HqUSv6=^UM6nn1Yz z_-5B1dYt?JFgsp=^oG@+$2BJ#Q2+2XeAwv6#`#F4fB^HR50`g;-lXq$2F7eB4p05e z&`Na1hqQB!i$dR#IFv^C3F${kz-$N%FrTV(9R?^8Dq(x(3d@l^n$&1;rT zCLxBb!XvEvV!WC8Vt2e_Cwum*VK8%95KM;?Oa@rJg=0vkDkEc-WOoC(GU$7ble~xm zuaw3ZxN|}1iiJ8E8Gw7@he#zdGLAF86oYZR#tA-6?m*(gv|SflbC-c|h5w4U$Dgf7 zj79qQ%ki(LuE(5?j=fY4g_cxtj6Z7AxgY6dT{6LsFF- zfPP9%pjHn4Xg07f>n}S6ql|n-$9VL=S^#tAMb27$g_4k!BTJ-(NMX^;FTdOpQW8sI znEc;bK=)~L^^0hwmBO(5%Tl)F8T-h{$iP>W z#B*?hIRNwJSnX0kK#IqX;xR7uLKzI}=2&&x!^#56HM98n;oLuliCiev>t|CeQ*NtV zj6la;!n6*U9nh8pfCK2oGBQA^l)=|4^!fN;)9~38B`}*DsQ?(WdGREf=LUJiNPtL$ z%IpBjdLVWt!nRPLc!=jfJA1cE;@QJT@oc4{YY$N?Nr-EsL$0)koV?m>7Ua&3V{duj*ZT41bYtV`@t_jVK#I6~&Z|cQ z{`6i7l7OqWY-@3F?aX z`-M~@ev3v-UT@zfV{WIKiKVrl0z*JmaE@Kh{-ukM400!It%K* zxQ8cua}@0>K2=^5L-o0QZ!_TkR2X5zjH_#}=r$dB@Uvmiu&ott>XqN?;{%uKLH<1w zY@YDGIQtoK|J<6VPx3+xaCL+oxLI4-lHOl|_%@DfCGCTNN2CJ2%xrMHQRe`i8t6wz z3!fRvQiApk;59168o1?-37b8>e35!_M1KshaL=J*us+?GO6T(T^4RycSHa?Zc01T8 zJg&4H34)9hBjeZ)JL9ij3W6L`#;lxGHRI`oRp<(NlHv$Af+^pp)afNPI9Y!f;n$uj z_x*yXhKCrB@=<3W*t;lGpSJueh3_?gWCW@)au!m_ZlRp*A^qC+*5x<%v~{^0XDUj_ z4Vc*Fb-LF?u;pSoRhHYkE_8+a{4b+kAk628TQbtY7h~5>D-YRHaGNKb&(NR$Wa$9Z zA{t_1@$D8;=(et{q-!{A<v4CCH`&>|x*QmIWIRfS=M(6>jbMok}+W z*sBoZOdU*33Jie!41TXum!V?tE?Kb#&yz)v84w1}YwI(8KR~BUEaO_Q;O$S2Z+v?z z?LV;f7L0=XPk;2=47I@KC~D50JGZL0LjukT@xrc_l0-YaI*iFP zeonSNO`U2sURi5l{!7O-^qQ;gFNX=%DPs$^fw&_Fi>kuY=>YoneCoY`3~{9!q~q4k zwqMhK`3-)b`DH#nIgU-8oE*oT5GR5X z7;ZyiS&tNtZ?#p{U*8h+Fd8T|>pqIcF8q7m7QP#za%f@4f8Y~80$TWMhmP2s9ar*g zkgXejb@Lpe8C6=34??yCm<@cVOjQYpU0=#qk1Ul3mZ_^{lKr}iRy z!x}3`OQ}44?Q=w<0ymjwqG{K_etV5<81*|)2%#kZS(!GfV?vK;I^N=}xW-(efQ5;o z)|BY`rlSzm5gq>Av@X2tBCfIFEn1PtLcU%EV{(U*i;2Nv{~CV=inl;WVSbxa_C_Rp z3Nf!e2KRw)aD58fPvL6&vS+~?UWXHjIPif>tB@Rm56t*SeaYqwQCu$vToj2MLVy|q z$W(m;$^AdZ!^MV@|L|Z?S&-W1M|!~tAONcXUfWm5GkMn?_e@RljK3au(SU|VKL7^R zDVZfBHIEMJd2}m7Ah_)S;7-0ghkYRzaavOV@U5@VdNHBIqzq5SFw4L@&tTX1cFj}# z?;T0wae?~JtbuL;EoFMgin_UG)OdxTZIB~e?!y3g$mIk&{uDp3{FIHC3mG7vbKMeI zPe=y&4D5hC8HnWEm<^aV>d*n=ZSTyJo$?(vL;KV2oIQ3f40l#^tgxw*OW|VVdJ8hi z%1ql-qw@A)lY1}Dfw_IE($mEE(Hl4y%SjHscA{I5i6tI!UKIro4EDINiGw4(*C7KS z?uq;cyrmGEy6v{ojn709X*cOT!l5q|#8hVS1x)|YS%lNh9lx~B;#^RU|7##ZAEp^(-0s<4bT;~+Uy9txz)1CEx9Q*ugS0xB} z(?4-kklkRE=({?tRyEY`x7jsbX={6!@|q{V6w$9JzIvaNUh_Of>+IrVy0qV`-xYo+ zxu#JRB7NtA!%y?i8&CUGke~?)OTab(;NAcT{Xq0_+`=X?GuAn+IRv&|j_swT6nAc7 z;MTy;R@$>uC}UK1SciA8M{)wl=5}pt2|`!GJ1!|o-%U&E|m+*qQ!F5tUukTVwX`{ zl$I=(qa;G0xoOZ1IsF}cQmdWrv^I(jFvhTxyCs;K1Grd>E0UZtl79}~>B57gI>Hzs zL?}s3V^Uc$M&^5Sl)-mGFENeEN};^0e%Ujxz@4W`M=Z+}8s$1GXz=hV0umUv0CjvC z%8WQ3uVh0sOqE(SPppPx3$c9v|6&$H>BL1Ioi})%#{T;F5Wu=~cpbjZa9HYf9C@v!4u!@b zc*z7-?;~Wo0A$c2X8M5c6WTXcMtnH1ov@nu0Dd4QGd2)wz4bC(|4D&|i(dw#%pYTs zIr6}mVg?sp&U&==-?1tRIj@JwK6(T4^D;=#d?RL(gF?T8$kDkl(J%-~d3-pB zvgnTE9R}YvcB}%SGYz70>_jzRJ+L{u`{TA+hYd%IiI4*{0QOX;WXPj|8YBXAcq!$A z`*qj$V&8VR=_*mb4<6}60$|)e*D`Qrfx?5L8tfx*x`=ubVEL!=-|mXL?WTdP@db)S zohR&WLP>(--4$tRzdPw5*TT48X!%mxghF(R>SIiQ&1{aeWRGTk591l&Jsewp{K5C0 z|GK=|AHM^tK#a+g%JQfh&i%ogqHE^<0l5V*r)0s_uY zmn4UPWqv9nLo4E#29Yd##Sio>Cu?9_I;B460012fh=uyR-|PW2ZwLTp)efe;C5efX z?K{~`CxQPX%a-g&ayBP<&ICEi&$CFN6&b6vR)TH@&;7F0EaNa?KLd|#SwLNGkcBJ> zNMt1%KZe{%ud`#&5R?Jg87L1h5JJiKh?^_~O21vF(0dBK7(}f8z{_wu{;lREH4TxQ zc@s!A8`ecDhr)l3NSc>>wmVT>k^In7x-Qz%zWCYyhcQn=*qQ$wx|h-N!Rf6Lyncj~ zaEY7aGu8ZLN7GbJwa)x?eWLSa8dd?==~&v?YK7G27g%}`8qO&P3sIM?kMwRPiL|1X zxW49S91)#ZW(K#8;8IzDJ7~KMTNlkcr!7qiFy9LQX(~Lv@u%UllhyRI&;ECcTt?25 z6=%D7g^MP1FXVV{w9UFbpXJFb5y!ks2badj$&GbUCw>pxN%fdAH%n#V|@ES*U;CnXC`pbap$P*ZXZ$M`90?Kp!`Tlmn?!Fu_{auYw zXJ9k|^%?M1bC+WzPq%(S88FDs z0kUIwKmf#abOYgLE;#&)aRlmT#bq7#+l|= z;;&Keg{;FsI1V!+&^)bs`yIy_e+!_1-bSvsg38*Mu?J6f0f!PDr63B_<3Tr7bS8~w z1liY6xj`=X@>Ux1nfBnTiwE;Ai*=tA3;n<{zPfAL6SEL$?#@eeLz5sSd2z0F#$oFs z7zmI85~$J{7~MeD!hA4O(eY>@b_gU@_5F({Y;8`*e`Q+ru2ejWP(L1}Npo>dGdv^|?ewCiSp|4a_R>4=^9dkaYwX z@1W~aGDUe$&tT z`_QKd<_X3P&7{u^d$J#ytLo%PLY+wHBB}yvetu*ynqoRgjvVycV}Kf>LI&R6S*6)Yf_D8@ z8g{ALc&Yc{QCPBQ7cVD0n3|4i7}RXrnN8DwNVG`JZ+>@uA|C2Ddy~Eh#Aa}Eh4*%S z%=dr)kn4ppqC|$>?Rh}41a1uvrHkhw;os>hR>)*wH%pd_;Y!{eu{YmRXmnQG3h+qz zU3(4isrIP`%;>w+n7TlNT%1yruEZgBp?NHrF!UP2L>n8B6IAfEUUpGuiP)hUgMCw7 zFv7T_69D!`M1NiuzCsSIPe3`VG-LO*1jRDu5l#QZGXP=_>9HG-U;Hj1{5^bWqxSk* z0Uv+7Y>@NDgnH#%#e|Z1rp_1w;36ZOby8oO5fd}ky)63ewDCu0VJYGyd@4UmbVZ@L zBKXt2+L(Y^=0A+7V(fE-k064rmUoU%lO{OM-{@}~%6Urc z7gMRw4N^X;&%A|Qm$2oaJ!v5S`g);f3bC|Nq1~4kT6DMvfFJdrpubp^!wiB z@mSL%B)FvT$ep7S=?4hB#qR4pP42vj_$3ZUg9f4+|CRZcdXqt!kLKka8lmsXf1NkR z6hGe}w8z8{T5Gd0tiNb?^P#x9mmrDm-q-Ab1Jq6$PGW7Wq71Zh7y03y1KbA}XsS`6 z-}zYw+GjP;S;^}8Y_|7>%+nrCS2$?>Q$Bz>cf;D8^2uLxk=g>HAvJ;W3O<9C!N>< zmC3|@0GjZnnN|S!R9|f82C9jPlf()r|Ea3mPU(Kj8FB!vNd#Y%P%vfHeZY5OZ`;R)T#gpHj6Isv)=zoQPkn-DA^RcTeNibkP;s8ecH8pcrde@aUjOC2blkBD974byI zD~d%UwDdyeZ?4Z&25f;P=rqh3+K*8V_N~-I$xfB{wds-V+)_-6U`8bGCD%H=j<7zD zbRTAV@ohb#!&nckgVcqR%-2^i3atDb;o{_GxgAEp=ahbF{hbn z!fByZCX~;%Yvwdl`fsV+p={UwlS>5nD7WKdfgdD!zB0E9QxLWdxa*gj6O6!u~6kiJ|&iLVo(Z z`^%*p&%}R&G|0AR6d_{!>RYayd6?La0%&)nNcPzIJ)En-_V2O->Ck_5R8Y%tcf<7v zel>>4o_;IhVeozkWypbSP&(-H)f+k2yfEUm=6^qYyjLvqiosa)2B7f_-oPnz+<%C= zyx0zhwxx<|^M{TDJi9*d%BBw=Ul*HycFpuurcFrh&RW>QhF)ImCcSZoCarUG6iGyI zUnuSWDZq*sh8D4UDiH_|^|z45C4pW*!<91m!FaGG=g zzv1zR4fP$wVYiut^iry~M1{>{LO=kNyN5m+R7?s8fO7XJv7KAu7pFM&Z9?R~&z1^= zBQxQ@7`J3hB6U%RZkeew#Ap31PlFd0+Js6#qbC1gA+bH^Vw|tZ`T*0W2);Yo$H93; zP3F;0f`5$e@MU8!4uQ780O1X4UBN{l)R77{!7yT#Gg#PdyR*`S=Uik`JdN;9Avbm@ z$$=2e)QbgIo)U-~GCWyVVJBJ8{0j#6A#wt`&=nZ$uFCkFID2Z%4ec|#$dFOJS_ zeM)U@=%~%C9mNm`z>Vo12L1-vLTz}gez3k$QxVK*e6$U8TFAgR>Q!sLfK65~)Jcgw zZFgFLC4s>gj$jr>KN!X&1@bUif>SP^|J77Mm1;T8I4u3_Y^x+wu!gNmY;pKzX9KvaM1O#?AiC%_*$1yFFuV zJ{>+nCc7jcce+y}ld#SQQ{Ow-+Ga<={3o5TT5hYJIN@_HwecbW;9pGmx0yYD7o#fP zwhb7DfaaIZ?U7QuDKk?-s)qRjS^dyBCH;q*=1HtdH_w4K;;U-12;_(@3pO4R^dt#K zfrR6aN42DCViCuCtMX8V5c23iPHv`6{MCOCU1c-549dw?M;$6}|cheK0Y;rJ~j0N?#iBBwiXjFJS`OaIYi! z(PQzrpv{M7H3w7fdGinF>OCrEX>Y$deIZf6gGi~H6}{Rphuy4Sn)?5VJlbl$%l(tB z+&NU;!__TKx#!Qvgcxs)1&t>Cg>S>qu{q}#(g#xr$#G}`0aQALQm@d*fsl=6sh*XH z6G+YgW)?_+;GF<%_isNfHbDtTdhb)-vo$lx;8I%< zaq?9uKy9g46m3`lCeRIdox@4eIE;w_cWx{U!K~Lc2NcvzObt|aF(#vo`^!wm7Jxbr zrn+>Fl?%bGRgg16Ne>hYnXAYZE7wNlpsrXVEDL8n`LDbv3}Dt0|BWzE92-?7F|}wv zr>kzfsqmx????f3h<@(?a#9{UP_w*CJaEu(AP&q`tYTOo*8A!m;sGGK$OXxb9+Jk~ zRRm$(9$NQbwx)}xk;c^f7lGiH-zTR>xE0CsIZ1PnTUXIBD*C4%ms-_kD#3_sP#vL0 z5%#D6T@e74W;V=c=$mvVQV2Iz5XOerc)Vslufz=RMy64JT zhAz{im*0rp@};>Qw5u1U^7)j`;QHhv#l8eUd+W7Q%0u3@U z;`dKkh)q&t7b&k9`95j*5H^r3h&mW%rP74T+_IWeR zEN5&KBNw7BC3E-7twzb3RmaJi*C$Eijzbd;t7YV?Df+K#ost`R>ZkSVZ3QV>CRpb( z&5KlGbr-X%;Yp^kIIigx8eCw;EpW^H0=ctof63F!7`9VxPm)$JC0f2cjCNJr+Y{ql zIgt07;|}GMYnQe_ZqDfEr|Zk(RXpJ24m3q4PyYDe+J5mx&4+V{r(Bn4yOV@TL5i#y1d?`NqN8Ds zu4b@%>L%WQ`t4NxPhuxfzWC&e`Utb$mF;tu5!+sTa?PH%Jl&`6(I@7rWU||^4@uP? zDs7fUAwKpC)r(U?=H+hnJ zr47R?mr77YAGfw+9|qZjnUm$}HgwXJzCqeUSbh78$Rus0A7w)Fge&6jv%a&%6mvvt`T03FtW|p z7wr@+6%H}4mKc7XfKn?Jh?WgrJd{)SseII2BJsak0PFE9IoS7o$)bYW?u(8%jI*a1 z>D3!|0#+MAs%|@R`t$}gd~vR9D=gIcZ?c>5%%@X9WtCy2pB$cFl)Bn6C8wGmVc|FJ z(PeAtBKU=0|1d;w&1;a9peprcL`wHtYnt+t&PRG3O6mozT-Dxz zkIwN*aY-Z%l;OBYt1&;RHr>Rif?us=0ypesVMnGk2X@cp%f!g2Sw$}OS%#dI1i;Ig2R7Q z3EZ2H+VitFrDPxGBDO`YSa2?N-d9@Im9Q?NK_uR(=B|qVAJmRe!Fq; zN_^V{TdP-&lB#P~W4N$pyW!%)J?=Wz57Nob?E9|rDX0aHo$U#DK|v38s%xy!y))x| zl}`cdhpZP%PV8H*AtNko1x&oYZ4Lv)$c;i3MQiiXQkF}9fzoK(*1cpIByKZOcRt@Kj=0_;WY9N*x#RzJ`?+bq_jo+_0#op@gCeTJ`+ zz|T9^+0|wIvF`1!8+T|$c0=ZiH;<_z9|k=(>A1^pJ4FN825@t2gPC44&@?)oJ<+w` zkAQy5avrOlEMj_odK7T??e9SrFxlX^eQE@AO8j&R)@mss9NU-6Wp8a0g)lGiEl>~X zCYo3UdJZGWMzm3krZ+)>hrQR!x{x8P%8t6e;z4-%J6>MzpQmZwE0I}tcl3GmOZjcN z4=i~3SrfZCJmH>Cs=8Cl8_IE1^Wqd7_>1FxOOaVh#{YgBr+z}*repX~6nYMM4?YC` zo!9%^gHzv9vY5}T7x14{9=qjt{<` z%7d%bjE1+3*yFfTE55OOgKhV%quXSr)NsXOrZe$J z_D4|BU1i3{ju|0mqQbU)!mjvQayVT!)ZI>C_K2#PPM_lek5B!1D0dWI7IzCzXI0_( z;C5{0{pzrSc5EKPf%9YPew@ln@m9HIZDkTgc`N>O?sY|E$9;rGxZ{^^cSS$6F!5jN z2lZmG-#F>$MV;=LnwoCM?`T?eQZSQpP05##^4S2cvK>xx$u5)eV|UI6?;*wjn`?;sKPCt{PUU!R1sHtckR4I|LhNy<3cwV&PJZ!rKU;| zzS)j|Qv1W^9Jw9m!{ym%pRQDCsua6$b}@sC;nLSMO_Y>u_~vRSmZ-K5E@(@J6C|$q zS7}z)?7K^$D+!Ah$cWIB@*XAf3-3X3F8#_oxZ+ywrG)W3gU^VRvcsA&+d#{n-ld(S z^y()>J#(%uga);*sl6qlQpRHqy(l3K%45gn7oaqM6U=vNpZVp#4JJhygO0f$n++#h z2aOlB7=342R@J1qsN&c8nmIW+O~3^Y0g2oUAVhLqO;$axiSriVegC)Vkce**6c`RC zH@*D9h%>%(Yf;tpEhc`|dWKe@x3%W}z91%?itoxkEX_C_G=LqtWlbWr_l}qw{5_@J zo*tEj;1WzfG8KmxEDW{O7NGh{T0l$db-qwo?YlWCkuN+d1PLyf*-TX55(0jp@#uXz z9m)1Yucygeb@{}#0|%3&kZ7c0VvJIks|aL{fM?kb=7IWNy-D~N%@tYN#-hbkg58Zt>P^p0_sf@{y!rM}>N%O< zq$4Sifz{n8DmV;`;nv5>nE*>BjfTgmZiJ8ujXd8>PxE<5|Z+STQLGD@4YrLj&m>epC^_UeFp_|SJC#t;S{fGp!-Gnm|^1esxQ54Orn z>_qr+bCCis8B3$o@O=q)sMo```j^+9>Fo&XYSaN4JX>3lV+`i5pzWWd zf)OouO88U0YWliK_Y>fmxu_@){UJG4^irCjsHsE}iB)Qs$P}!FgAp;l0V~IH=vU@k z+<*J%5?}~FKoR;1+ACeo_Aqt}(OM9T<_8dwG=`Jp`9+fsuQL}j{Wh9X=Z_Yes_l5tH0qMRN zYB35|!1P4%yq`BdQ=}{eofR|bAR_gZHg#au)P|1j%(q*yEIoYBJdt#1Eu)~|4k)}n z1N+x*XI`;8QIO~Y6g{_lY`unB-Dydw^Wm#*iMOY8aB{edOul*6`Wq^nnh44 zNrq;rH9M$t$``R?R6Da4Vk-RGH1%6M#O7Azz>7J<(h)Xgv$0y0v)AKYsUddpViz&Z#-*IFR);HY09{y;ZY_%$0^2bCB@^ESTV(9eXj5>T>~O7yUuO@@^G4$2w;S?%;cpS2u@6U;ec;stTXu ze)$050E3pL=L2w$h!(i6XLdwiNtP*Q{ld;Y%&x}NKSX0UfF`0H7zpbhe)si7h&f|& zTY`2BSUgh+!a#HD6xKsil_x2Za9H&1mw%tXRbkX#bsK{9F#*_E1KB#9)}D`HHA8## zR#>I5!g#cEFAcN}U0sDQ{RL(8b~6barE=V3hP~Whvw?HZRJs z4g7-D(^}TN!mxU|1l4+Ecl(&02_?4;>?2Lk)!nckyT64s>k1e`Q;S&y33bReV$ukZ zV?#-wi}UCBK&&GiMW5IB#G$i@=?1 zL1UutB^i&FnjQifY%owRloN7vaS!Y)be-J_)yVEn7Ny|OEa)f$0~Lw7v^d-Asz+@1 zM=%p|P7-VcaPCf~c1;j3zev`c)Ok~McwC@eLIY~U!Sq9a*ohi#6iifn(PbF{sdLr1 z#T#cFF~uFhIwc;yBC+i+@3iU|r+i-2l|a}$jExZ`V?*!=EcR5S_APK|ws_Sn3Y2Ql zd3lO-z-}iezEXR64*ofAQ&RP8umYh4QUdek_966=bhM&QEuc`1&B?QxqXp|nOtPtG zr6hVl$}q#}TPwikQ+c@``@7Ty^ZlB52eU^bXIJ2) zo23%2sc@U6ZXDz`Kte`nQuI)#!?<Cf(0$0dvtuGDQ z+?JyC}zpOnBsTFdOX;t<*%}FOxdzmX(4T;Sr(X3DSF4?_`xGM>j)T%x$H8DKQ ze<}!TD#O4981<#lnT;&-%?5^u0`+IJ>?MR~m+O+>T~B?6Zx zibvQSx;zxyt)!d^V~m(X&8yZb#3eKmnVzJFwyd;RI$WmMQeC+Vpx2P`$?lHly9s7h zD~BYCvR(MPwszhV_M$?RWGf|9Xcp)Z>m@FAP&tI07*yNKhN0RLl6STcJ+_5Rt1|)! zgO}WebgOhydt}rOi@m=A<)P$dg%Z6!Vw*#2{@ifME}`#J`%ZP8UnWiZnDpvBCWobE zL;rgKD|~2PmvIb>_we8>u2#`iJ2pwl6d6KCE;g9-mTyXiX{;Smx@7-+VAt(i#+ss4 zpclbbX-c;e)toTC;6c@OUnL25q(i>Rkh#3j9E<-(g=R?m*rK~Q3ot{vpsQ#8R7&8w zjudHK!i(4py*UA-B>WGh&tQZ}+Mh)C-VkF2fu` z%3ql0St;>T(X)fOy^yOI06FJ>J?6ALO`3{%`bzE^$ie(ol=yjrci$E?^ z8|tzt7WmH=s65LVO4;{vQT@Likh_j={_58On*y-o+NW=g;)X)^Ptk+Edq)~^uYa~IblwQR3Eb0 z?8J7GjfC(6;JoF|0XL`Y2KGIo8NOOMG(c5|;Hljuu~=H&K|VkG5z->UExlz7^H9db zs4T`vq@=d%N~}}~kk!$xnybXj%xa2$$_RYJ9w4o6!{vvPrnM`~r%$jg)Os#&p829G ztj(u4#b0E>*Rh4g0@eRs5*2|0jsrU~Bbr0Y4J@e!5F~{H$1_t6x=yRGyX1F? z&q;;e7|q6zQF&5UlhqvJp3z8NG;sV?Pt999V25JXo?NLRS-gGmj`Z+)&Gr1C(~~z~ zSgdO$;0HrfV}(&hNgngiK?!ibTkKZ7*kjZzg8K@WnYE)d zL-aoPDQN&ME@l{=V_h#+$vx#xldTQ%BE!PSBgRL*b!6KU?*bhNIQ%Q$PWnY`N5bGOCs#F)zj)b<>K8JDy?oV$p!-$3Ov1hc8`x|X7l*fxVI!mZNRFO{ z{)nA)2wUrqQYngel1?9qb7Bf-$^4-*l#7`y2otB*Md%of?dZbH8>B=|TBR6fHA%Pm z7bTSDbrGH1ZVcNVsyU*?BWfsu`kD7her=Hb(wNo{&t&3G5W*Nq3DeXXr!Wskd8B{k zhF47`#PVw$OEKyPk8IRxvuB_?9-1oj=7H4I@r*+!tRpmp7Mcj_WI#l|pDc6bhq{ z4qn7g)~<&|smYSb66k-9de%FCFp@V?vHat($}?Q2Y*(;*BwuUcaM_hrG;sQ1(04cF zYk4c=9`4tYMY{pNXsQyXEL0E|u7KIua;tEUGC8V<#Ge>-cxSwqH}bk~GH>MFou-rv ztV<26{bKMER}f=&g1Ay>GMIoGmg=Ft=dHKua;G>EUDo0rlhXG(U(0yEKxdoj*})G> zimNR5EI)_~qU#lx4a#=wL{tMx z{kWU#l4d_%DW(F29ir6Y z5q3d*`!%7g5A^AFG}&){Jd9;b`8%ucR9OL>4vSTDUbz0C?MOtqDq5%B63O|c!sbW0 z#V3QYd9f9y#&Qc>W&qKM(PUIz6(@siA=%EU2y+mOxOia+g%;6tHo$cuO+(SFOv8cB zNiP}T6_(N70VCi{_ayj^OcAqN@)LK-icoZ>Yu8lTrzl2Z32D>W_lv|RY7^q3KM65= zNM|GmTPyYBP8B5z_g{Eg$ktZ6K4j&;oR8!#_^W887$K_&5oyjRb=o*9Wk z!t?dto3dj81s;+P>b39osJfG2e0|)g^a7pw8~-Vqu8K2=LB*8Mmkd=SC2D4Mp=z z;|1}24yTu(AJ7Yw2io$~7qrMD@nU*1L7>GhK)D4-V|Qe)&3 z6IqE+Gsde1r9ou9_IHK@3^#^+s#)i=a;ng!vnIWnBAG52OYqIKO15By!W7e`;A7|l zon%22zKO~!U6^*omQZy{QlF=XwRI>+hI3K0CcMb@PSE})RI@n8Z*KP_HUxi!X*2s( zOD&pr$w}o6pc=Ec{$;M3J4qe<*pUQX^=) zw8qSAvcP+PbTa0XIP+E^G7=CX7|X%v(t=iOw=QrJ6d46_=jgDVT`+#))Y8`Ko$;^eFDfO6|V>@ zwsZ-dci{ZPerE3J&NlQ{6uSQ{X$=Bt-i?ZKjj*z;GA_iU@)%Bhmq29LM9|F9(CsrR zLA)_BrA&WxIO511rSV(_5Pa=`(P!s-*WCmp#nd%-miXg%nRFf$=-0A_4HZ>~_C>62 z9r7c6>=nG1CCZPbN?N`!}fMI3CTc;Lx90{8XB2G}f2{MF}>ChU-qZXbZ4v>}3tJ>g*K~Qs8d-}Ljy0#4-=;XyTeOPy zX%+bR2-9m6knK;?T?w90$uT#%qm0wL!mZq7O6U^#BMC1`{T@ z5nm1%5W}-B>pDj3S6?4A?0>xZ*!b8{M*WM~)x4JDv72-q+pV~K;|{s-$sOO>1)7E% zJqyUKi?mY%lhCAqGnjTe`@M_`1m>R;1+)TTH(T&)G{k&-I4`|^pvx%4JXTl|t&7uf zkgkrzR&IRNj+bCE+`7x$djzYB08abr*qcIi^RP^}$epfGXNeR&l~2xP@OYqg0GaZ@K1EZ%8=RoEh`E1BCg^gcxrUpg^9`peV*=Z zZyo8u8>}4fc?(A~h0s3Yn}ay-G5DZOYp2JAocU}rhS9;dt@XLWV+YYt#XGpL+9dgl zc|9&cdfh9D`0v@L7MO~=c7($;K0E)_=r6a>*j*mD3aM?NLM=!H(1^Q{`4iE%fjl<~ z1lc2xY=I=?JrJ^IhfSB7;)8bS7?B3EzLXz8C`e#xA@?&t60`f^FaTC4HTb&r>VaPf=`n$Ur3FjPs+30zzecrJT8 zF00u$(Q!*#2ejG4;L5_=T^l9!Jl_2;V*pau4|#axO-)%Iac~TP==XFdwy_0>bY8yS z2nxDg2R^{Fj}sZP_>QY+f}2EiEucx^{j}#nI{4Uu)wRe~auDQUTh~EHelx%wL{K<` zdPX|P&268-Y$omlRfR0jfDeF@#_23YW7E&iH}Pu(FB&@OnBQXx-7kUUS^Mq^-*=nI zt8qXFj4sS~n`0JjwWCKx7_UT4OY(oK5ay3f^Vq#F+fln%-O-ENhL!HrcejF3$)&~a zZ)m@%q{dLl?<0u{u3l13l;Gk??FKbyn%GM2PWn9g;{fo3U%Ov2cscsA+d@``wwr2@ zaT3<>o{I0_LN>m%jp3zJON?2X^GaJph; z2=hT8E`>;91bM)m^f@d!HIG`tO_04?LvH*CY|f~x_Hls`^3NjEm8z9WiixAc?RC(M zNK=g$`w1E%7X|eEfW`KtE9F)>kh2_tc}?fytL8eH79FB5810cjr!MT3KTB8>PUb`@ z6V$T}n=GiPXX2XY6cVS)1Lp?}1~*4F3{a?mIUUiBVe>>8Zjp{NT(+~KJQr&&V@GCW zRLfP86L6a-9iN<2?OBZyYP^Kw<0&xd`3ZnO1ix(n>I@R@YnPDA6ZG`c;hRZd7HQA3 zNIZxBi3yyX^2O|&&zL3cvkcc#QZRDF7WKHA?do~izeAC#iG%Zhp9)B25{2~-bajJX zoH#uV7A0VkOJ!es;QDK#mL0esO0dd=azI9;I84;I10Cm@RoA8c!|hrepsKC`Y-8up z2gnI&urbSl=y}a?@+Pw7uDVA}n}>+Jj0ti7PTdsUOi4w(J*;D|ce?TX|F8fIJ7)JL zEm^5cxEuxk&$WA4YIt`vD^EcBNcp=FKKjo3`JXSfUB70j$7h~#$-9ws_jUkYrIjlC zz1Q{dQsF`|zQ5U0{3l3QkeGmgg!|rd85-bVX|Pm<^1+u~Rz?|NVtb4PnHC_dexuLN z%a9QVfZ?$~AfSgv=Ph7TpqEPqPR)b-#983gE5N)N;wjs}2GHncTqfGu+R$*m8n?Fr z@j(T4s&}DT>b$ZFqrpa4d-w_VWp31-b1=nF-kjr$`{=m9nCaws{6EiILFg~$ zz6P8g1MrvX%WAd+WB}A(4!E_4+E0&qQULIR>{~04jWA+GIs~)SV5uvm=tJw7&lX`vQV!f|1KbV(2IS%@|HSjR*7yoBl(8C21P39f_CMi z0Bl$DN)7klUPCJR?=C7@Uf4RkJ<)wyWpjESGwNp*w#>K4!fv0@SN(x1+!&QATla{& zNjX%EDV8c;`FdrX75C|zd_y*(QbSS~Ovl#+xGMu6;_Opf2w2BRPM>E%>UqQP-bc z$KcD>K|}aC!bFIbb`wN+PB&^`IsmgEMAX3%I4y+J@;k?noj(l=bmBq(3**lww+xua zCJ+UY6vz}X`!q2fL+14`rhm*L<#M%O(hK6`MW74T_kHS5Y`5+u6w=~ku*RDVcK0WQ zWRh@egD};e_4@g?!0;V~WQk2lm!~zh%Y6Nr$MA1!omzWYn6gIS;2zlp#|q4FpUzIc zaaqkj`NK+Laq40uu~K$6GRdFi-u_6HyiUJ;=iyr#&Vl)owr6B{hPk==wQRkaYGLdG z(m|7$rJP!|e!0q1J&B5C`*C3#JeW>62T$=ijcw1u(#QY_(FgmSZ87j@{GqwgsRt$Llx^bjEaegAwYD)wM6j?=6$Qf&G(H zN4IoF0C!i01UHAOTqS=0WGC;G$BYbPR)0?a3cs}^L8Uu)ND;}U6kuch#OLW> zOY$q)PqDH?W*&m&lgE+lR&Oc4r+feXby9jG_f*CSAUP)Z9unRE3|R_;hy$X5R6g~Z zd0Lh1!IeqS$k7K?LgF}NK0+Vw-l{v$dq{!r^9}I2>Z5SRgV@tBMXF4NIhffz zhRSF;yRQ!d6}2kGMu0Cv|v97yc&m1b&K zBa}MG)95>Vr3<+zps+grb#ucF%y;GXe1YON1(~WGXkIqnzG`TbAe|*0<+u*%+5WkJ z2Isi;=f&3Xq%}St$2AnC?Wv^#1Pdr+<;-U9>$OQ4v+t6>{P9i;W+&wni}oiT_*b*& zUp5ThkEknM?A=&%U#*nJVtoU(>ZAwg4IFPhs4Ii5MMp>H4jBT@){BOn3s7Mwz>$a+ zGR8uNBtY;FmA}Nd03t2<<lq&018VA*^@hc zS#ti!eBR;f*2Qt}Z9@clf)uGh@H<9=1Z3@8ofnWcdQ8}Itvb0p7sHM3^gGb9d6!qTo{-z~LNY%H}6 zoO(`HQIV+@AEz0k?H0%{o2+3{F!A}AX4d_0#h)~D@^jdaHT;pl65Y?TM}=+l=#4sf z%UWZjL-NzFW*2H1&$n9;Jy@L%_+<;QQLo=D))_msT$vM&0rwKZQ~*2He-&0EX6G0l zD0P4d#6Q;7#_uZTg2vl-e|n=p*bh?#_9F=x0Wu%4c`75d9s)ZWbDL;{Amq3Ue2G9~kawnrh3x+Xb_^2UI^>VMHc>^MTQ;MfOL?)PsEe=9GMeIR zUwplJqB6y;=oo!DS7^n}Q%?S$uauTZ4l|{)jAq%L6{*b?euRqVn93PCHnUT%6a8H` zBukk1=9eC8bYcO|X7jK}aa=H~piM>j?DzN(E50Nzya8`R$}u zFWRinuLdLMCIxaD``#e#!W}9(h;TWDNM3(_?8F0Y?bm>an+C0~27tw@xy$e_`L;c4 zPATXLbFeOr1i&thp8asAO=u|+qmR~T(02}D<+WLe`=#;LSvt9%^jc@ja$I0(L-lV| z{BhM-6fqAYAe<~(KAC|MjL4ASac99%4{#`Wh}3Fp+Tk$?8XrEK+(*iCq!{m%*$iD= z9Vjr~kI^~;S&jEfI?uCjEd5^M7PlRI4nTc;b|wO6vKub`23hU%({`p&*AY8-)Ba4) zkS9Sf2}rF6_5CD*!!-~J@X`DMccB-6n7@cznGni0{Q%?!Nb6+s0L)wih9e@a(g>Pd zhWiWZzTe?o0m|3SYwkw!I=iZr4NmV1TG~)w?9!+qPc3&^iQrf6zC4VD>ac{0PMS8& z7PQ??$_|z6?|17dEj8%JzkG1M;#lFFIo*)J05;VzQqYW_d3LTDTQ+*O8Ja%;Wm!?e zFGyslwIsec0#*ynZ*U}~CUFFfwUp}WQQ89J!~@jsGi0rO;P8Hf*Lvn+rGQ>B0;Y4E zX{Pc5CPvfbw^wMO?8mRWRA)iT@W7eo?!#y5=H<2}U6zOGpeSk$0r@;ugjntJYmf>c zwRKRK3VZ&xLhy))ra}KX1*U(O_x?f2>`b1zpY-FTX36J_c}(luPws2q&Pj@~DM|j6 z(4xJA-iJk*Mg`}*m%q}#MaBXjeZ8OE^xg~@@}00|4;gUp9RjnN0Dx=Z+X|vY~y8yWRCL3 zvIyph&^4d5g{%8G3CXBq{W*77m;PADq3NK0A>pyXb+|lncn$(|aCk3?mJ$j{r<;n& zH~T|Wp;v$$8AL%r!fA%g9`dINT$Jq;LGp|0&cn3{h_=oob+Figs{_h9-Tu-iWc%$+ z9Qh#iH3R6gooAN|1?I^K2vcY91ao@9*ou2|u9ca%)9j-caINR+W?D3zrgvh~^R8@e zG9>Bm?s!!^>2iuWV{kj_(y0i`co_?)q`LXS{kTJA_HCXab3wcF1;(4YfB2&~KcC}J zH4|)|mDyGDNm7}cGqa8n2K(X;UG)wEMrX+%@0g&G@d4ZQVG0z{ZUdfP7yz1YV`9?b zCMw~xFF}$5XF_6o)EUvgfIl|_O7dWt13O%ilM+~voCObyhN>9B^DMHfNFg5u0hWD$ z&)|@&(X?jr?xX$G!3*+GF;kn3!=?8G2|}=bvOe9oSP;Yo0D~7mPa+|KYu^jYB#%=^ z>+DC7UpUY#mmcqIoOjo7GoU$Ov6I`j_9(8f-mqdOUl1ZKXBcR@8D=#vr`q5gDdeq; z(nVjl)L`AnI<2>?8*p2kzBTT`Ji15dj#w?;Ps~`o305W1MF46?)~vVaV?qexdfaEv z?ktlC84dS+k_TJNHW1FbK&OkO4oc9qqr-`#`sv0H;b(t-CCHc3-g!9cVGK|RPxCA& zW07k-^6Nj6<=H`jr@lFobLqn&j0{rK(vJHAcX=JSHIs0Y6E%Y>GZ-Q?0&&{;sU#l^ zmEbh+j3k!j@6DQ12-+)NHga9A*qOULN{uhMwN$Rp>Y5|R7>_CMdN=0I>sgX7<2&Q$ zbusS2z`ou=k&y~qZh8AG=o8sQIrzg{tdtBz9y$bh@YQO^Xjf@dBon5`&^C_83Xoie zq$cEE-VHY%m(_7r1hi)4K1FIfTucscj2#ht@%pLA{=L|`!j~k|V{;DLM>r&%z%RpH zpHt|_BF6AFw2qJ{=?*}xH3%ma9UZIPg)tx{#suikn{X-UFGsF_fR3{oua;b?ToT-e zGQSB_icAl8|II3ngyCV6_3gsY`9C5k>pxtz)#vT*{(Ed$Clue9DZK5E?O7X*3!i^0 z##=6WE7`^GE?#I=xJ}12Cd7>Q=d4UHM-}Y?{$XOV(vw`ZuiVoX-h!j7JjsZZh)jRi zorJvoKV`Lzm6)Y9ASTfj)a?F)pjMGw1U#(b?y-FX+wRGsx7MYSX@v&VHO;KjCy$TwTKs4B zk#bSU-{qqKm2KnNzIEl6o)IH+tNy()yey2I+ps15tOHU68B3-q{`m~L?NozQ)jJxh zx);}|RrmD_?JLymsu=3sL4wo2@Qh=O`=~ zvM|hW@#79Fk2W1${HU*Fv=eJ>DO#R+r{v z@uc$+TJFvK`%dAd@74)))ttf+%$S-bTfD&icq< zq2hfn!s{m2e`_LhSaxmeOH)&TnCbL~AjyH>_om$lip<9@{prTMd9KEnR8r1GmEYe^ zC^zN|>6|zJa}?S{X*bF+!Zut^1%}B#sOsQgeYrg;i0WTL%#F5aQyR!#VbQ%{H$ zkJ;k27^w?96n%iw){=hT)JbHoE594}`7Y_+D) z&bHQik#+A~z}gAdB#!+C85DF{?S8@4eY1;N3SZV<=yO; z2u>Rml>Rkbv5RbrQStCLYAz>nyHRMW^E3?5@TmPLZ0uy*M9UMOkIskA$J~#V;ONY6 z@As2vkH3aK&aD>5Fwl5VE!H0WZXO(hRAVKcC?q9LOneBU$(hcsy(1ZIQ$(DPiVQBd)tF!yF%zoTruMOpxgLaZWj@Qo$6iol~((lJUSB>A8 zs7w69ENDc|YojRE`mJE;gxx`LAzs2oy)l@!n3{K5*+gF_oVLwg^>Dkbc`;$?`$GQxY$N%v0DrIB4S4g2uTN*32|W|I*`PO&0nF4HlJK30$#x zfXRIIGz#-3IeZEA1ud3Bjv*gXKv1}GiF+T6&p70qUnsNb@=|7w;r}K*%F(TM`Muk; zhk;QR3vt?LsfeBlnwbPAXlBld%)G8gcZfk8!qVKP`5Je1{<6;QOd*T?0KOX!vk^Pq znUeN^1oq(+b5a$PHf*Wu?Ym2%0VkdA{n!*$P%voYL`8V?u%xzJHyCtUN0I+3X7xnL zg{ud@i$5{^K*uA)Jqty@ij4{ZJOP>|O62l;+{1aprsjOFIBd<20#@s(iVQ=1E-^PY zSL5E}G3rr+`@KW%MJ*i_cP7BNQ=KKF6QTg$%moE_!3fb8 z0*zm|GfNnTh-G5uQwbuFsFZkyi>;%QfvWNr#FQ?ACyxEk>j}pz{tWMnw?fxuY8b^c zBcs7TbQL?_)uiWRCu}1;Rz8! z)>!atY>RXMs_kVbCTz(WeJxrcu}NbPr4Z})g|=6DT90P4C#(IPnJ!4J6-&dIjlvy= zet9o4K2uYOkbC}Xbt%56aw-}ib9i1m-MdRZs4-_g`_0Vu?42@vmzUn@gb+u`$8A}& zg^QiR?&=C>Mnn=l1v(Z7+X@p3Fw-8kA2kd5^Mf$RYYPXo$s)^9;Zrm<^_&x=T%~&v z$;t{i@0k+r=@25B>VGgqCB4wZk=VJu`TiZthsX+1UQuDT9)xTor%XyZ6P zC4Jb3)!@ZcpIDW^@)k$k7HZT{Lh(N-$g@uLRcChSkABPJa-@5krjp}U=REB&oqAkO zKOOb(a3s5GuH{!iF@0P380yMq)TE2EEEXlpi?U}ej3mzNI;rN!z3)XQFzpK!N^QOL zHMXGe;Ie3o&R!V+vzkrQ9Z_%g2Qr2<6`RfzDd|_g@2n4G7ZPyB%=2tO+Bm;k8imu=0OhIifGh5wyK=}BWL z1BF$+dJVF96yDNX2Ar<__=|Ps!0#VdR8UWcw?PZg`VfH4y0XNfznU&*9PsbE*bE`{mMB zh|HxW{t}5E@Gx|;_}V^QVqkwNm&t-PRj+3>=Ev0P4H$m0!cM*aq3d0mf$I#BMd8d- z6wOT-jjarbk?L*B^Rm|_Yuut)&>JvV3PvgDx^DMgGK*@`Q!UXIM_IAj@=kjsRA*M0 zi-&h!ogOMep;S@@#VMWS%5>#<;Z}tM)XLUtUk5sIXi0t(9e5PNbtk2lGn_DRvwgb` zpC`(CqmAc4#LQb0thi%Ym!dql)3Ou2^tO3YgocbphD9wauu~G z4anRzCC)Huvy`C*iqw+CUBY$mp0SfWWz!(EZEdQ5#zUp6Z70HT?n9y145n4pKNrw- z#|_V5sE20KQ7?^Ar8*yOAYdZC1249M{$X<+G03*9OqC=!bcb3w$z0mw;qvBEKUu@x zS}x-^hP$~2(I)d-8|Y}>+a}Hr1?SN(_@&3Lhl}A;5{lW36_h!D1l`U3A79M$y*n3ZM$PM!}?J@I2olRw5rm7N;i0MSP zImPWLjQc#(O(S~!kY4P^E`!Tl#ZlNUKieX;%JZ!6yH|KZ?g{6^ukx`kItJV>D=q!i z!9&br5=o|T!xaLMM*@3>zM+U&h_nj-Za!R3?K@LILzN5@XE&DEEER<%wK&Jym*+f# z7p7xstB?`jM#~qx;M~-&$aP*B_RMhd<5vERnl?JKA#zzNNFluIf$U7qGF2QZzv7gB zSo9K%Ss(ZRk7Vf$V>SquK@PGL1ExxI$c__CMhhFq5@^{i?Q*m^zy0UBS-P%H!k)^%7 z(&?%&ZMZA&P|)l-(|RtsjYnR8ot^c-8_`x4jbfg$D#%I*m6LQ8OG_UesJi&b`B|Kr zs})Veqkzy}vD0|+g)|HH%m^h;GMF$-iF$_7*U&&=;QcET2)%-D%bn|U6sT)5wc;b(j7FF zJUFxqN5aRi}s_rAD0m81o$`gnH?p#eSG((Nol@Mt;y&t|sWhKBzS&q>6 zHqb>oU#pg<>=?xDSxRNA&w-h8o~M0w!2;Dy+tU|tys@@$-}pBhfT%^J@&j=>@`J=~sgbDv}H85B3Z4 z=7{N6E$tX@*#8R{&Y6keHuA zc~eFycrhZpa~9>-wSPVQqL4T+28z~VK$}Peupk4B;=iW0Ici*gdjF7l7J3(ts(x=Oy$`YVVr3LU zk7gAj6_@1m zaVdEGv{|10QcX|p5LydwL=x>(e@7)Y8KmeRD)?0-SO&S+Qy#oMwzkcHTc3cv zh7G2Lmft|-#`TWH>CNhyFN>3CjptblfAn#dZ^TaMa`a_@+Q981ysr8PZ&~Fp{{|2u zkXXthEukVd26DSv=VD4)@*CF*NwL#&F6B_7jy85&nok~lsC^pIwa^a!!*IjX1-mv@W2W?O zEf1S6Y)E5vHyk_PHtMBH-X2*|E)<*=7rVFwL@r{<*_kD0ohs9^EZmHqZEcWbe%JR zpVAxX24q_PdsI2(8(h+}k)~fcy2Q@nWnVk(?1d2iW`&aqmt$!bb2laNUPFxPi$B^7 ztfh#`@sa#A)IWQ;y$!4oqL)%QBP&j)u&DGwmJ#0CPO7NKql_%b(8JH-$ItF{xSv*k z5G^n0a<52=R&25|!=atl5j6SKgnnZSs4rc!d9=cu<+}3rDAVDRbdzQVk7O^$8IZwK@ZcF8pB8BKptdJ?YlUT z%Gsa08~q#%`MqA@k|*ny%A(Dt8(uB0j87V2mCi|G_dIFicYT{`xbQ8Mim~0cg2t~R zI`wJqFv!}xWs|mfgq8oJC4U-E71t%J480G+qbZ~crskl!uMlU9l7pMQb20Jy-Ta){ zn385pl#{jOms8~Wo?L}(cVy|Cu4BD4WO`#q$X23UD}p9bR+u-c`oxx5L>) z|G_ma7wQ!JX_K&zDon))_+CA|-BMYh`pV%VOI9CJJ*ly2YtXoa9Ov|hgx}?YWNS)%&m*P2M4AB*? zOtmE(pT*AMhpok~9v)8rjgjAtO}@Ok^1LE^PXKM3=v@%DQ?~e7>{RNrW@-)&KIs*F zH!YL?*qQ>i(+X#htNQx4e@AWG-kGovM#wu0_w`@C-z&l7c2q?hH(9Z|p=xq(0Mvms z>(tSO98HIvZEZ34naD&2$)d%2YZqCYJ}a4xW;M{|>yN)$cIRO~@?4ED%^8M^U~?qV zVbW=(IWpa80zzy2ne4bOQv#~>IV|oSy(Ig=u z>P9lTaFNnOzsU5vb3;m4ruR{ryH;%dpW3tbJmAv|k~Zc^b)D0w2m8lcD4JlY6z%(-f7E8R-Ce*pR^Lq z*yvqn5voo?F@tp$O68|YRcYpIn-rJ*u^BRJYXW*-uOmv1!cJB2k4J*V{NaOT>&2Ec z8Xsvi8l2Ic^Xob*Wlqk(7ZF`Ea1yXn&2wEoWWlvu8Ln{7LRtX{fKHjFn)RRHH4<4r zI|DKvl@&iTf_yy^xs~*~)g%uN8qr9O_p7aYO0Oc(W?zv|U+7mJTIQq|e?s^1$4q`xa&whI zn=v~!gqLM>PlU(r|KVlnE2-voRM6tj1-a7l@ETR|`1+z*{#HaU?iI2uzI5hbU5sr5 zayri5Jk+%zv#;Se&FPQM{zPq*91Ffs!?xcZ*Zx<_4oXle2NpdMsD6YXWz> zFq8ej25pbO+xm)x)@{%%41^3NWeM`Z?%)>z+=47NM&Y!%us>iPpBJ{l$vAc5|C?#d~CVxYcw9MoH7#i-SsR-n5)bp@HmC zrtUs+(KR1NQ!DiHi!u68PKQ9ifObCp{+&{nu7+(eJE1N{y-W^S75Q!)>=GPF{5L2< zXKp(B{~uE1l3T6ti5THC`!gDKnEg#Ju30x$T^ST86g2xF6!vottq0h6-a*Cq^A(x3 zWfE$L01p%)<|-5_BKoF@W|*0b6w3B@a`dY$TtUWcJOlSe@+dJ4C8{q=vF~oEy2*ze zA76ZwwVa)`(6>pfX0zv;$vTnhX?|Zr^>BPzLJR3mlln#qsOA#*l`qSkyRP(!JtmTN z*t^Wsyi6LdU@cZeMaeT%zwmm%K?A(0NtWcHDY-E+HCOt`|o ziJKV`x96|nEh{hCmtTn>Mm1HhG}9Ov)*0nmIj7(~kgr78_saKeoBguXZO9;OdeT+4 zlA>PetP C3`$@d3``DXlU!FAW0zZX4nND_FK+ZIBB0qna%|M1^>$`yd^}HSeq}4 z{_Hlax@PL$f8Ye{1$ZNM=~Yg(bEBvGyi~7ZlPE#s>ur>oU(xySf+X5bR$(vquyF4K z3)a!YpZSI!6ZFHUxJ{QNfJ1}$U?Eqm1Q_3&4dm~OJ7oQy{Lb1;zeHQ#SVNKmV=K`baC-hme&K$|Y)ji>%L13~+?#kX8RlT#+l(ywKbsI+^+v3`Q znEF+@oO>L6{@x!o9ol9tx+3HS91~1aa#JZBS#apZ(?hyE zBB}P6GB?>uKBdVvVceo8$`!`oD$YN+mxmkGK5_~!$1TGuZ_N2O0BH6RPY{~-dr8sDF1bA2&^zZG%~3dSf$>u_UK1t73b0wm#roP6eBs4DRlBhxe$u+{<4w zwdOmYpLn!85lA^;O$XdTAZ;=G_WW64@Q;(ptR|PhYRWrytKAcJ7*aN%gs-n| zCC>FI3&XDL9KtAQ6mmB6+!=&@RCCC=3neA0)EdotXc!FqLauMZPQ@Ip|4Y}+P9$uhpqX&^{Rig6Fz{j zm$eve5u0RIsyK_~%j%WA&rjBMhAVE)3HL?LJI^pVu_dE8#7TSd$z61xaWK>4cr6wm z)q!cyQlUw*u-PfZ^oEXW65L|~5vV!IbC9E@gs)VG0F;%wDMs9zFlf|y+n zJOUvDlm^Ljup0ms4WoOqWq^t)U;9pRj090)TL%!~#V8(i1UIu(bQ_0Gc%wiwYQW_z zjhV8zpK7W*7lT==_G<6)YG)5_xvbHJ*S-ox%39tExE$&W1LbWtp1g(_K~#`uTqE4T zYAPoB25!L~vI56Vl1XG%4YYxmfQjeoSOPdcND-~ARIQo<{2W1WBFdWyqU_q%pha}* zJWH&lFCZ}1fIWb3VL|})fHv*|2}D3>5C#j)cEH#&2w=A4Fq0(f$ou&5Ci3?H%RsQ` zpn@df`ilX+{TUWo-RfVIjmV%fYVHTeX;zvxQ?-G=#LP z!GxcZslwan+8q=Lm zLFZS!J)#%WIa^We zg};zcDa%QsL9x&{b5 zm{A@B*c3ojdDZ|$#3uXfk4)Yu;{S+Xt`RpH201Tx{_22p%&!6FdkjGvL7C(vrqlqB zX@m$AJ)}hx{HUs zQU$an8XR@g?}S{m8Z8%2@?7Li0IVAT+zK!q&=5(7x4Q$65{$r;0k=(wXfhc9CKLg( zAUJ-&4Nt-$X~GN`1{8QNqt-_es4S4U{zp2I+5B;%93l+@dxGA}MPy|G*+wq|E}97A zs~h~f5MqiFIA|_Y1ocFGa?N}upM9{X7rSY%Qu+_*(!16)v z1!z8#A~2~34<8Ec1xvlM@%fKps2dZD7LDqq$-#sS-id%x< zSlP0(Zw$&Y2sn)dbT=v1!NQhMd$d>U?88)NMv~%tbXYzct-l63TV?pIi!YP4ToPWt zNVYru9OovF1rsX3Qp?orp+S9+Hhk5Gh2)y~&kJW+wMC=l*c1mEB2pEdnnsP|-F107 zZ~0CtH?0+Np$g=y*nL?P10Id>&{tHk7kG9&k9(FmI2Qic`ykhY1T@0BcJ%h*tiOA1 z6T)%xBNi;P3bc(&Z;}T@U792M(h>_|O6L2~HY#7QT1egPh3yf_B1sKom=2mXP>};1 zJ2Q#N)?)t-MBmpV_EiG?r;V87fGI^n0(LMH4YiV4rCqD-zF=j92FU4rqLu>$OA-@! zTUYsVB-as{>J)%p4FWhH7A*j@7MJE8CV}BI#m)XE1o2k~^aW=pNo$;>7Hj9Gnn+IyfmfIjJ`;2ulyJi0IRI2|!9)Cjbo957ANs zC@w~$3~&mPt(vSBjKY{^p?SAeAoQvp(SeP71mS8Vxi>G(yrF zUOx&1I66TNpoB>vK0(Ng6-Tb=a2)a83-5fb9xrwd4oDcO)DbF&eSh-wLpYoWX8g`( zy6|G1JaHxpu+mM|v!1Zv-N33rq^1y|Z^&54gM*M6k)HGVt+d;j6GY{%jvCxd2o^^&DuUgJAk~_U$O6#Kn_;|EIcCCMnZyw z0))&293_X*MEMaUUm!^p^ns^=DVcS^Y=wZ~s(~!P0B&V4g^VG9v(F{DtxxJt7J4#b z#T|^)%97=HF5G!&G;h@Nmp=psy5WG@qzq#cmCB1_#qpm1(5y;5>mm@|vL>Tlmb9;Z zPA0}cU;1J?Ic9;64p9~^;O;8vDCfpTNloC<%mM+kG0dloRC)}c(X}|MnA;HVP!j}E zI2G`dpd`l%Nr#SN96{G1@?F<}SW^uOu?1yJi%e2q4uLwB0u-IBlJ%>6e2U<2+yyjG zk~)My1zzkrj=*+c`kT$l9E=g!!>l$R$TbLD7nm)lO?*FR0aO`G=1w3!E(m=CpzC<-zvxedt9f|X?m%hn57vo&l}~Z# zU`oN46*@fe7f)Pb1I+r zxQ}pt!SET$@3XVB2!r&qY2%r{0)=2%x9l)(0_n%&hy`b@Ba(XlC#xG1>U)y$roGIz zH37!BZ_w4;cIh0btEB6Kq*ANdCn~1j?|m3NA7rB{gEG|%nTjV92-hmm3L!{5Sv;W6 zXP5Ir7{Ot_ze>P)$_oI!jbQhh;O<%_2kN^4#GHR7K-7AF@p0{$j``y?rSh^!p$l$j zVMx&_KI(V^Db?VWNzN<10`ftKysaKEbD%kjsCTay_9i2RVBh(gCXjhw z2N8NI_MN)Z*Pv9=>yK|pk|jEH2~M+SO+|jkFUD=2FAI&jEXh@wS5hniLkP>D?GMT4 z%^CB|1i};UCA;4Ck5JIFr){QRTx*lac~xMi09YY33!q6sZB1S)nF6Vg5yv)?NeB^t z^FJJWFQB5@M@-!DfqTI;D5OdN0zSzBiED^PJ~-fha_0NzpKHJrWaEsye1y$K zf5-JkdW1u}13u_saL^_ZKy)8vv>0&59~)xmp8rF2^dKr4 zZE`o*Zy=1^L9mTO##?~QodzS!Va5YbupHB&`-3I)$#Ogq#A(OKE)2H4zm0$UNc|_z z>Bhh-Mp1sAZ#t#q(up%(?Upc;{CgJ~_TGCIOqkIqU^gYrK74t#5C{;+S4KFy4~>kB z3YPQ`S-f&^RbK-+&m5?Gjt=Gm%y*1U?4WK2g?fV=%DxBmM=`bgSDtnxShI1aQT{kY zI0i@*14_kQo*Sq|BgLIgkvVzNvlU6C!bNzZ>u@Du1dh=+gosn%HOqHyW9ttkv!q)6 zt(UH=NnaU6+%JGI<+$uKVfQtYUt?eBNv>I~T>^(T(K)#|#RoFqynE!W)>5i>M4ZcI z6=>(DeEr`ozS&ZKVf>>&_SNZg*S!r!SB9$S;m2UR^?=}<#69 zJ=xy=;NGsL+jvTum-%q zDl4Db8iK@D2yMHDj3xl$?oA0b7IBv=j=3g1*4OMPT*Tu$FUJjL2-7c)=9BvX>&v`t zA)mRK0g>ofy%p?+8WX`tV4Yt}uc^y!(E>5@!GsE)*`Q86K|+{e1VnhjQ1lGQEpEda z#V5$`{6_eBc8BuMEZJC<(91^-ouPM`?7%7lKR4F2ce#!4i8u!b!||sSYGe_9SGY|| zZ@!sl51}Nd+;qQi$w&869mSInj+@w5uawy?*qZ5A(N|B7ML&L`@7|q1!LCt)`d4dA zwkOm=d7@KNuGerIpEnYR?krn*Me+o+Zy&8 z7FP*Wplt@U7BL>Km??XOyv&?_*GtuD%Na%a!!CrMUM?P=Ts7G$2i1s8FX4r7a&p#p z&qGBx2$B@>)2CnELUxX|we`Jw_nuFzDq%39Sax}&$pZovBAExu15I#I(7i8Y$_gWW zY8skCaKAmvCc~f-%}-9Vl}MW0833hhdQA;mOG^vnnZrpP|K#fyXT0Rtuiv<_4i7v+ zY4GN~6ej!Y*{ssii$1jzMma_ha)It;3c1D|?An$?bhTc7y9D#du$>+EQLoK*cc4Wf zQP_2-z7XEr>)Ey#H>>l0E~hgSCEL4eyoQ`clx&5JORKevhtz+y$(b4#OA?o+rNavT zh&GCE_0M+eN;>N;Z95Nb%t|;9i|lFHJ8xiu8PDC9lprqbN;Fd8sFqjXmX}M=($c16 zW}X7KUGZI6h=zku*)*s)I6FCYK{2y8M+KFg!letJ3+zxfHs2u`l!*5Q-u3O-3bH00 zpWo!5J#);;NG>WWs+YG>x~rPDx#&R2DD{6>0RCM4vh!hKVNmKl?}n{)OfH!cNPbYB zpUtUhgZT<4(y2ubEy!9REfgpi1TG311S0(3d-v}*fSpYUkeE?>r;i^CWe`5TzP^rh z-2Yy(QUll)9oN;!7C@AZFGRmWm98=~=y_Dc<2zp~m$@=Ak{=~oacMMLY94M^%-8d2 z>a$6-z3em1Vuy$ntLuAA~uk4?$k}~-PwuVP_ z0SX*d-$%z!vtvY*!r2;;8Um^78LpcL{wWQAxwvS9uuB0vib4tqLVZ-tdYFSr{p)k( z&}c)p*{|`h%<#O3rnh~?>_tS+8+Pe6tfsMXc!o+0qM!{`w9vI@e{8)#729gNkXoVg zROVt8e;}z1W`rvgLvw=0E+{o;Cg;FKp(ZCiSTAd_s-=_bVrIm^(H0FQ05k z(oM4>_y2PwJJ1~oL@-fEKO>#G48C`BdwU~fz-#E~+g^}kka&woWTXP&-pbat0eoN9 zVdgOfq^f!ZHw6U7Nf0~^`q#I|JMh!<)qm+fA8%ipjrK4IAAtR&UIWipU+u9uoS^CC zbo~$ve9PJo$TC+sp(3R1xxEhs4nbXS9|B1Wg%g&f~ve;M1t2E#@I=PWBm9Y7xd1ds)oOGSP0tZN&9tYOXcbJVZl?;ON!*LR%Ji6oUEqe%s zfWOwZw;cQKpJmj9Nm@z@3FO8Cfx3~AWEXbRe>Yw&Y3yCMoWrDi2hByr=*##Q% za_Rp|y9bGlhe7eNv4MVmCv$;f1_9$6+=Eeso`l_9Pm-Ies^{#@vgf}|Z&wC^9rOkO(M?$Z4)o!9IZ`>sv#jWo!SNWcCn zkFgp2^FEkWf+(ISwl(uiu<7lApWz^X+nMkJtiWu(^eGQpOjTZByb6oF4~wzaIw``q zhv_HB1+tXNN+x7CLqh(}rm#SVR*FZi4S<1nHi#Ewo791;sL3_5wxZ#&WEUY*ALbT> z@`EVX=LT^2UFdmvVW0>OXyMwPEz$Cc*pT4vC-iJ=WnurHPraF?)cZDqPBNme7YW4^ zTHQgGx;w4QPVuAaJAtmcUTtC}$t)AR;{Eomw=8>!4KG7O&+Z)f_$hGk8&-<^h=!$x zKlzZEY1$So@G&nh>}qNGxE;{lkZv-ocjJkRa7_#T@#DwG-#lX>u@vqKWE`xo_-!q| za1Q{u-%nNF3h{!e+UD&5DgRr0`v)a4K`vv=|KubPZXhtv7$nbJj^v zjfgvgUeC7B{Y$}}IQ&zF+zj>fm^$S4Y!QB1MMVX|SVrjJxU7sEt}9ojknje{3<~h2 zpM3dL{DOja)zs7;?MAJTlFD%Sx!iK;cQYh4r0cF+tR7WjRb1xT7JZUEDIMf4y1jB~ zlr($tUE^o|2+TY87kZx_o3;7;V|0+{L*Ave*H5^nDsoa&-!_@ihupCq!ZO3zwCwCN ze^nH~3IGWYbzraszU2YapriC1K=H5R?JLQli+LM1A~bYOtD_P^-Mcibt$C@bsk>@@ zeU*ICVJ6DTu`HMN3(24w*kpOr_huXk-C9P$23yXBCWHL zBzS!gb4@_(2T|qW1~NrflIIz>F-#1~mg6G@1qE1`S`Jd0T<3V)wF)xsuG>6g;ju;y z|5l3c+fwxililJwq*O~3qvMm)e^QpWV@sNOKhbm#e()Cv_Df!i93e4j?c;qS=E3sh zi%H1_)t(Li&{JLQ*zmZhwaC4X(_e2SyJf1qH!b!qHTAcnAIaW1ux)JFUjIVJ_uVx6 z_>kQ~NQ;==;v?x&Yum)5K3>gG4{45Zb)vUnq3N9*u>vkGu1Vs2G7bpOzp~=8MMOpA zQ+$BW2eO7?S!h2JAwj_=p!u`x1={REL`9l5P>_^W`C1;1MwNa$gKPl}^B2FK!ap4- zMm#q4U3dI(+1PIU#`W1$X~F(E*I$O@S|stqx1~u&xc6FJr_!;UoVW)g7&{{;v{Ytk z54<#U3kz$zl9H0Eyu9S7O`_M`_46W+7w=4f-iv$dEi#0;j@;~=eyggfo$4~MTkTFi z4Ry*0oK|^I9Ow8zk*3`>+gkBYip4|rpg`Ap-q$Vpb06k%{&bgML?6A#Ib+K4QfvK} z)?iM*=a6yCE5T?6<1z7BQ|f&?s?8TS|0;FC3g``i}q9fSpvsk~>< zkK&_hHQ5bo)Nmhy_ws}JZ7k4yWeX=OI=0~rh4#C97+;*=Ha4`S$&L`$8Xxnax>o41 z`4p)!pe;Qx40n86`3rxYaeX$Tk|mmGC_NEV#buuKN-BE#(<#JbDyGL%;P4C`-~YNj zUJuHL={>M4<)@Mw&ia&+Eim|fd^eDJqOO&WPuQZBHrJq#8X-x4`E;P2uQN0%HfwJD z!X|12VzgP#0k`^l+j%Z8_#1Kxo=w_{7LOK>5MkBtUvm=O{5~2MI$g;umKjvC4S76e zrmO8d@|G8%PP4La*iM;K|FEeV9T<206dG^p*BCj@Jcj;sd*eb@0K3m%w~@Ynh{?4; z7(S)v=XdyAc=Io~7X8|*J3{D~Ac?iGaN`QT9WJU2FK*TiW4WYMMpms zGOmEmcSL+Vv-Xslii)DEYpM5XhWF4f964}Z9nT9(e)|@=0>Dvu0>unsl2G*J%YPQ# z@AU0KVtfwwQjb3Hxe%$e%h*eH}U32bf4MU^-n)@e`YWLll|av z&c=78FurZA5sdf!|NmhkmHE%`GiQ zC_UnCSe{2rVy1yxO`(4xIV$Sa%#3+86k*NH&2?ZmZ#x2Wj>x#UU}*o+B8pK41}uw< zizFo9OfI~C|Gv4UH{esyBqFpry=v1J8!EFTbAC)gt8>=IgMb8`2KaYb<0j9q!fnUfrjox%4K6 za9qK|+nFtqF7atMnWt8K;23v{#s$3Tseul8!x7%or%#`|djFLVLkuL1$BBuI(%UFK zACEpeX_zaVEaAlYuhtNODG91j_kCMh->Dn0v9%pr-&xppb8`bzfRn4+6X6q}j&86o z8=~)qoa0}-;jq-q7E+5ebxZBjrt4KZt9r*xwcAU)Qs~7cjhz^5UKntMNs3lF1#>mC zhn(#&GH!My-&;*%;vL?FJj0lG^0$v?IFR-lQW=A30%Db5M<_iFPBC?e zg&V;0D9{F<1f4hC27Gg~T;qz@be=SBJZf=R@S1jWWx~2XL!#7|OWrj(eWGCR6eeHH-S*&dC6;|XSI%_vz3xw)_$Ubv8}X^} zHyYgINfAqD_*b}j3e3^B7Mu*xX-o0A?u@VAqE6Wf^WGKM-`o!9A?=N5Q!76&nKG-( z_`$An8$UO@%o>L_dz5c7RqAs($eCvS%LPL<4|H>lr|{g3_7%Zdb@_XWpWCqL7z+;^ zoZSU%#*Y zAyvnRcM=a%ORFF0nDNJ3ubcB5>zt!E9oDnA@k{*PrB^b$*4fy@`kW>I>J^oW3uCxC zv}&A0yYEJa(0B{M{)?zmy2vDpYvJH{KA*s(2N}l+{b#(`2=zdfs%$Y1_iEC|J0*{F zQgprB4g%B`9cen$-7%*mA3%*XUP+Gb!x8J#_Y~ct-@~c#E_90=$6@SU)_Td@hAh$I z71lo$o<%H?|IN^d68IL1hHR&dLsQ7@BwtwfHnxvx%j)q`;JvF~wRcC59K8Q4uGFb)oP;m4KFr&V08% zF8|@X(B7aMdN*@Z+Xy^Fg18kMHxs%oXTNR-H1A4cQ{?)usod;sT67h4fk1Ye%X!RM zxRxYK&eGAR+%qS7+SC0fwT@FYp>LmWFGRVF!f*9RcRR}m@_IrH`+-n6W6#OxA-zf7 zg}VE=oDjLS!gpbqhB-cXlhgr<-5I+9TSnd#A;GBGz3UzV1Kv4N$;{|m62|D8NQP*z5NuzVUm9X> z)){13@m)nBe%CY9;;T{bFy?MRE>#cibM%4gxbdRJ+%nG}HH$X1@ei%gT(;*@Cpa2d zvkM9$JW@C`GoeOPjGH^n!7UZ5kV93zmc63anExY#b`!s98P8JxCXFFJE}twhP;bIR z%$2hzyId!GAwz#o0*|Y_R5Qn*Vl^Tt4kkYsbRe7V-^3<`+$i&QSr%truyvs$%^!zF zJfsvVRr5UbLs`U>Gl~U#FWhw8GjUaUYINgx34D#oc*|Q)%u-GrEe&H;;9IzlNFzxf z8obFquyo4Lc8}j2!j@2tXV_FpW!?2toN*pZ^oVGTpdmoa2CFLGE+Z|(? z*b=iBBtc8g)93Zor^`YsJS&?c|B^7D(3!pX0R|QZhO~Qi*vpmPPT7l{9lF}ac7z5o zszcva5leQ|#n@l_!)H}vsT>>`+q4rIBxlyNBS(Jb?GDp)u$BM2S#eY`B!!+j0eFF$ z#%zJtQxZjLrEL<8dKUA}ZfjRrmGu-zU9>9gq7^glV-a(!C0S&r?jD77&m0#I_zIeDzl9y3^#@&q}xN(~F@-#;u zXje)xK3dna$2-;g3O;&woC^Kz-v@o+c5qwF_*$MPCC}={IVCT3) z_$b$Sp<^%FZ|&ex#J*LR9CE7CXn&{Xw_tq9vxWRv>67bj2Xa+`uLRchie+R@D(zsM zDSfMy-x${+*|P=Fq)RnSdSSWDT7NW~I~z5_v$Ipljon7*zwYdms?jx>o0f+7;t%mG zsy;byjak;2%}7%|2qwuhW`bx z?o1G2LyX#!c6KgaviGIDefv(Ne9g{sdTDD-(dnlLaq_-Z)q=|*heg8a zCDYD+jFQnd!j}X|1m-7W1qpRLWorPxIy+ zXqGW_nKqRIU5zz%ok0&UcF%s~*}7zZY%6qabGKyArzL_A2&o6@tfhobCs8p?wfb9+ z{*g-=140ptCfMq@|SMvkzYTlTs5w*VT3e757 z8cAJ%2V#Cje7p*bD43S}H}x`T3T0;~HPtnI;WW|QpKlXCs0?+h+o8Q;gGi%ZxZ;Lm zn?z#d>VzS??Lz|t{Yrd(X-sK?qnaEpS6fnke)rw}q_Jfs)6I>mp=L}`BV5?nl&8ER z&v;GH<{|2%_#jVlO*@+1RJG8QPnA>WE}4XV;HU6RyxvP@o#`W7XFxZuPD?E8l+ff6 zp8%m`{7iDgZCJ<1o;JBagJ1|x|-o> zuc$}8RrIELR!3H@Y*mWZp1Qy$V0bbuEJgRvkhAqq8YWDQ$wfi_2@g^Zp}WW@LfvteG9`SE<1+`&Hp`& z`3ZR=Y(v;uk@G87@t)zbH@te}?M5cLVL-4+smhVbSuS@PYtvMaAIscCZfqV98Hv+R zSxpt(3n*z_wQ{vjt)0mvFigyZRc4=W$d$+|T62EIEKMJ6b6&9(gC1-52!V@S7gTM$B+f z7%=K6K%Rvi8lN$L%xJpd!SwTz-n9q$UT%<#>b|EfcNo{()M}0hsQux<)XTl1<}?sh zwlcMKlH-#)(xp^AAL`9K)FCvhggDWRN}WizZCPs&~8f zYYg>nbOyO#;+ve7~&s}vtr5lsq)JHGET^7nH-9%sG3zF^a|}!c(*Uk2Q+?7`>wvA z_dFePDV%>zH~dDJZOVL-DYih+w3-Pte_zTAE#k|JOmyT61?{4(`VTq70xx(^ZkdK% z8qNfP7`A_^rU+BoI+3;7e7pJm@4T>F2U>JpAq)Vm00aUH*m?oi=rhXLL2+)rE~md~gf9ExcsgGa?e(*_dO?)pp)x|$CQYUuol zZ<<72`XjQ_f8oY)C?Rx?x~H;@$b5K3gu=N9u8a7=NwLnFg^{-nmCRCKvdK*o-?K^I zf0g!LtiMad)z>*b3f0@_G=GP5_22t;*1oY&{6^7-POQ86>_`@I-tYIc2YBBMm8S_t z9%%YnbxsrB!NyXp560qTciVL~+-hv{Y}lOT%g9JM8l=x6t50%SP=xxeho-@nA*(pq zg9;jXacJrHNk{ab$b)W)wVF!|-KN`t_L%rQKRvT= zGd+Gs-L^g0k`NzEWgv%RGqIbUa82Z&$UWly!}ATN$>EanB<2CpV8}h4QxuhbF^XkT zDDfRnx}z&RnoFXqcU=~5OOP2GkW!4?Qrx$hVp*`%nvj^95semN$W#dwI#DZ6>h!N{ zqe!7`fv;3bQc#sYpGi9(soQJCuCtOIMJyRDlO#hliyI$0&0&|QP$1xIHI_sB%fFP| zC+f1E%f=doe_vR)@CmG|8&owS&*{#hrOs$=4BC4I8-CH7$U4NYCh)DR(|Kqm;7gQ=Ey;xZ7{@oD%v}cKTmo!q4fCY>=?9$;;_5^N+)Xz)6Z2qf;(KU|a zOK~N(B9FT+zG9TcdsR;9IFucv*XCLonAzL*Bzfm6g)>=gF<7*^cS;)l(X?n4Yb#RU zEw(G0<8O>vzuP?R0h?DJ278#1z=1$yW}X1s=bWaDpXT(LOx=iUuj! zOWRQu)v@W2h|Ul<+g>7+QWz6ssqnz$f#&P-*Nt2FlKF*_**d?jkD6wMy}1~M9@8ah z=dKprRg`Pn#uBQ)eMssRlVry*L#l3qyj3LsfC76z&z4c;(&QfJUWj`?&q8W`LI|t! zpSKG|YD<~H&LX8ugyKWY5D&Jv$q<^XaEr%YcAmVI6ICNX%8|*AgRV@~0_~-Y)@Ljl ztl82WAqLO*eyc6!VoNxZgu?Vpha_@&h;Fl7tC{soXc~u=Si1XJ`Ie0hUq@$WWJCn2 zt>`WW6AJ9eaUdcshOv?!k4Q=i10?PE^BddVa$P+=snBNz)|CXb5?}sixFkbH+u`*q z$W>F2fl#Nxi&w1V`=RC(^eHepAYUM%d#)@HQeqQqXQk9+8{4{oTb0Df-%eDSY=ef{=ra@6~* zEHZEdSJsL;Tnu*ewSNXnFU5Cu@Ckcctm3c4}aSQsCuchE&%;gA&)^~<9uo^Lra z%wFn5f6Tx0vm6OZ1rA9`Ms~k_5eNJ%4?-$7Ffj1^{qDgdFt7w_D61#H%B#7KKK$cg zSG}cl|33JENjy_#^ExhEyMJYKX($95RR~KSgk+>O2aAr!P)LUj7>>(te)sBn((t6) zQiWa+O}}P>>vp(fqv~%W9`+++AkkdnfcTz2z(7H#d%$|{;wa{pipmWW6Sm;s;L89> zBev4zV0;}J6Z0}7gXfi`!_7=MZISE06sw^9dK;0pxP1P~MH zL}7D zbTz4J+pUZ0B14x$+oR9HxS?_>_NVZ=JinM9%?A7*7ofMD%=N@yt~~-siXe`D8(mtk zipNk5jg7z}7%u`9J`kfMQv2YAqMm}{Af>R7dbw)-4DzCZC_-#)!qf>}4#X}Hb@%Sw zr}7808kqv}(8 zZyrMdVIrgZn2`Bfb`ESr*bVwJb0gj`H{a{Fkqg{-wN+PNyr+ZLM>;QZ5A*g zcMt*&4PdimWMsEAG+vG@Hv#3Q_eRO}^^JKE^X*6a(cM+D=GdX0ulbEUTeO4>5jU|C zwPJv9HXqXcEK+ZZ{Bgj^D=E%89=lzABT(8HP0ImmS*m~H>G-($x43H*$T+4~j8{2> z-$q0{g$l_6j67rI{c6(k&wVN{?=u-;o8Kqpz$O@Kr|^e3^=FWqB}(*?d39$Dbe9*^ zUdoFLXCj1n@?sTEka%Me79DJE1$Ff^h>a;3Ezc+rMn17uP122-{`a3hFpQwnCbdhU zY4YEH<|8^h(L2--v(LG54s>M%+W=K0dmgXiuN7cYI&X)svNY3JRlnt$k{S{Ld|kv; z6lvPSY>Y?SD`u0^(-z%0osW5Wn=HztYjt(#Q|xksI>sR(8X+MeB6VgMv*I-_ES2+| z2${Xs)F3xbjo7d_D0Vz~hU>7^7jIK*6SX(%+&(qqI*6rup#RMA7fSJK_vYXO#_NfsZ{(@`jA zXXll}z2ym*hPvLRLAs4J1!1IsN0l`%Lg&fr$;`}5(@2&}5?*^~>l;~QR{uiR9*7$g zjkmB= zFwu$Shq6h|px# zyr;~~%~rPJj&U@+bGS6Rwb1|kB%3D+3Uk9fn063kQvH|+W%79T0y3NX)}1?@yTNnY zAoTeKZo5Avj}wEk0XQ1cFV3_T*%2KLKUp4w87o4l2ARK+hTdQw$Ei=>f-8?%#EZ3S zWazxbygT*Lr>f6YBq8jjc~V4aryz+8O6G8TB5d=;ixa?7@P?2Fro-vF=l2eLD-RIY zUtiyv>DBnSxa8z(o-3mTyqP%I249x9(h3TwfLB;g0`}*(|LrJE#av@q51MwK8-mO6 z$E)X-yYiZ}v-hMTm!=p<(6=mCghY`J3#)cx8a_{92SD%~Ht0Hx?U1fICKID|;_idp;&P!}}Y!l}?Lv(NbobjAcj z?)7IFDNPh(8%0$fq5sfMv^pj5kmFa$I2JRFOd93_;IMFrh)^TCM1o^&==JkYk%#t{ z18N4ss)9C|w8v7zwO-@ra5|YmIp*HiC=Qnn{OZpT`JU(G2%L&iHwLxU4Rdo&q3S>X zA+O8b`@NT!8t5P4I}^1PA#(=K($S3p-MM%|nX6WdNZu57<7uaw!2&0)7l}?M1lnwd z*~Gp%*iQADM=jRzP%WZm+Qj;m?b$P^2~tEtyFtbcjk!hnLh@e83@~zyu?MIPaZ*x2 zoo@#Q3}3OyGNFF;^##JpL*OSI0ylXA^nnVH;vlqr#F;ne2-<<8>pKze-UT6~;wDgq z)GvVA?=x_p77p-uDR}B9Dry)EE1UPq4P*n9FP0J>^DM?BsmWMi2o*1TZa&m$QDF^+sU{G(+ zGBPqE)MUAXKU#>2LchvW#_((i?O?$Pjr=2(j>aX}|?*`T4W?t)9tjxqua%Y(!j!4157~6mQcu z42h^i*l!>Kuz;)gmb$tU2!)(r{Kdq8ZN4J7qnp1r%`v+Gf$dK$?pFqRsaE&kr3~hb zuRiG_UEZouXEM+2|2W?u-P07xdTl^e$b{I6}8PY-GIoQffflVZd@Pa4}SH z`oD^NyUrcEH$Q5Nl3RbjN&d0q<_)!J@(3f=^Ci+k!$x$H8jQ{zzd|LnrgI~Q{6W-r z(To9Q*5>a^mM-yCROl*bjs`L2WKf}R*(NCDjc{^0Bpaks}tN0ik{-H|P5(Du@tm+=#n-s~EX%Vu~Q@;C&(IqX1 z>i+t?3yD4Dl_l}Ja}AZ3Of#qCklf^?WMpjJPX-R`Ol0FihH%a2UmQ1IEvDLcGMJ)J zFU~M_*j-gcX~)kR=g{`V*KF7Mo{O5{ccz`7s2nt%#Qk328Q7uXMRURu!EZ}{9!XM! z7c3*Qj2|=&O=)6q@8&6!1ZrqXlhaql7-kM`XK&U-6p*see^QNmc@AeA3Z;ms4kqo} zO<$_dgwz!2>jhLs0W}(mY#LUip+<#PKju2FOV@41vT5_ z6hbK+B4#V2U;1*i9pEihTF?~OkYJ`Wvx}ZkH$;*@;5AzYIWO4Zu#I}0;E4Q;@N%e} zgCEf@OHn)0RMp)tYMYqFO*@|w_KL$+UQ*{yO&U*(Xh9Hdt?eC7ER_fLGu`wzj^`&W z)}sacs;usODjzB$0e(C=ZCxEtDM{VPCZlM}Fni9Qm}V9k>%06$`Xls}RgTbENNXxx z7n>BInpofSr&|6CNMjRtS~v}d>0A7bH*^#$`*imx{KhQSbk=|MyNM{+T~qE9p@tWG|OpeFbMw% zInX8JOS(huIM+4_GS{+=KkCGTZzy7ZQPDiv=GOh+8b3I38XgbSLhrew%Nz*z%j}0r zPaivZij=mdNNwR5sqL|+&yJnwJDBRbGA<+Ny|69lGo@ejy(lM2PLN<$QM}#aqrEER zr@Zq2t+VT8sns0L5F0bS1Iu`U8aK4@kX}G_{Lq`{KQh)iu*IwhgwK(F%!zivgm1z` zp_tI8E~4u0R(F2dTuKOb_gIuZ^8K}*s(hP^PHI7892k7@Un@P|9%aqg*{{Z||Ju;u zWy+Y7L%|d|6oF*{;M_+5n<0=6I#9gJW~+3O5Z}1Cxv0dB`tIIVbg)=TKe?d3)NiWy_SE=WuVkYx|W~x;W6W5f7b?uTj>t+)Hz$Z z^O4TgP3Bzz4&RYy+c{+(+{vb?LFxp6$=|07G#t|<$y4#9oEaI>|1eVOQ>l5cO)M&w zUW^0ZT~nv*5Od&GF{@DBDF+(_8q($`{OB)WBcdb(L!55XwF4O38RuRmqs^ZG)<^P% zga24DH|eMc35WeNhQinXqHiqqbjzZ&`+L7yUo(QUQLO3FVPB2KNmQSE=2v_$Ftfx2 z;zb&%4<;oJP9GjKT%)VNHRl0VJ2*xIh!6zc2?3U1M7v&?};F?!>Bx&o| z$?|HG!?i&DLJ5)U@213+cayml3hv;-t`6K{!6jfpZs{XfE zWx7PjcO21OJlNf|AC3kb=}%Pi>Gg-L8h^TjypXs{7*ouFOX1c@XiuWIapPXiKN1bu z`!$9cVmIB!F`Lw!mqOZzPjl8aD^=c>fUr7W+pzScb{)3*d=F$R;&=>YqcPuTf~aJxS?2p!e@Q0 zZc$K7TD+B4AUs-ZtXHq@_#fD=D20Lp|j%%qCQ~k({ zE#!Nh7All{Ywz)FnM<#buLf0z(%+^!#9vo`J%?j)0r);Wdnq&FUur1J_yL~q59{k! zQtnPDNfDjLdOrUfswEZ6b;{H@O-i!}_;TmUxn;$fd2A)#k!DNki^HzsGpY`;f8j_A zo5hq%?h$pXC&>C)X9ttP_3V6g^MS1YmUZ8WPpObTlm}`;!&%=NQVs=;kt&&mfof*2 ziVF95i3dyYe^C)J@wR)51J#vY-^=@=cgf-%PW3CpA3kwD6{8YBNUvv;7iWNWM4_Uu zxsk)f%>>ei?A{Ty2v_??3#Z;f_PBBJyd5rWt{iY~u6$bA!#ih|e!)6M$a9j@d5=u6`4uzF)R$_4)Fp|6dr>Y%Iv?$jk1D zS8z(#aTFKHquriMzu_U1Q$tI`{O{4vT<3M3x*#&%UHna{STe}&nM#7RU?IYl5z{(z zb`jff1~#ENIZ_6gH>rq*tWPfmlTZGhv?eLBGU&r-5}{h`AJXovmHyxfPr14A{d;Ts zfU}FsB8wozHU(%?F06Cv!oA23O$gY;93h6#pm!VZrJodps$rV9Y<^!Y)wplc7k|zf zZm3CnH#=xuk753%kXl$nR=vj3yzkt>08C(f-yTV(d7T)}Pa>icL*ai=D9Qf*+@9Bj(gc<#Mh~L4tdbKJ777x&L`aH~6T%@*w{2$i zeQIXYdNDsj_E26|QSl!Ik*B}Ezb`FeANakMslk?`>35^cro`HXcH0Q;3cOuz7PCCG zHp4cD8HL-dH{cQf#j#@|L7wjo&d1yR+sfLN4yUf=Y58?$1bgKBCsENzBHbHw%cTZx zJ2kj%q7GceO2V0X--It@Hi-v0W^SwR&;S62wB@Lr$*EFxdF>K$-BLI5b-z!!w<|B@ zhh-ANi&K|%X6BpDV~df1_`lV>|8uyy<;PdFy1V;&si*zJV{t#ziqsbQ%ZNzl=dZnt zezqXywDjZ0R~uQ_xr@$z72ic`b`w7@lcEPs{9VnKOK4Iv&@Hv)r(zcQ6+Wo|EvwsU z(g!1iuffYCI(L!zz>WDYz(}DIKkGCWtN%LoXn{B6Nq;YQQ}uZb9@lZx$@9~5*Tf)E z&Hi0e4r7Y;BX&s`DIPMy(D5Jm|Eh-yX*~?Ej_UlcMO{ z@_D}LqET_dmrjOKX~A}wx2BzJ}n+($WbK6T@`3EXPp?#ym*!oup?kb7z)B>Q3K)9wEy zjILBg5iyOWc2NYYcE>p`uDvz_u0U@hZujHPjPvCw;G(hhfWg7vrXs?)XV(Bc%3Oi& zSJ8})>6DzwqhOBk+|n!&)rzkc5{vVrTgNySpVMt$M6lsEZ_c7VgNWd+kr5$70>oS} zipZwhL`Nq8aMN>pfG(XE5ouqSfSFwiH*emIcjm@Y(XdtpdZ6-BO|}Ci)F)Ub|-<_5kw`3@IXI);^pNXXW@DVEnHHk`y(JJ=&ba} zzHl+x$_)Mib&s&MI6_%myagJ7oan>4`%;wIK6@bH##M)Nw=BB1k#E@t39rq=59wEu z`N~suxC30r3phqrR+hE3H3$WpLHJbYcjOCDW`^48-2D710vBu&9F@;gU5Amgzz;5f zfvW&24D{uL5(6~<;sDaWlGBh`J-)WKb{WXJ0QYc6`}1x9BpJkt-u_BJlZLpX%m`-Y z20<70KsaF?<1^(1*~~!qHja{mTTCC4feEHE>%l6v+d803^7h(RGvFiPit?OQ0RsHnteFY`HnyZS>S z?fdvpkpQT5QUT(>`rrq^VRF7J&)F3&C_@g_elu{yXK(8z&{^IZZO;li1qL_9V+djN!YED$6bg2cGUjtkbhMOEvJ)5d zdtkF`$n9_g?tn#vDZaJxe3GCR3lhTJiXn7aU2~4-+qN3ZC+L#Yq1t_CQ8BT{hW>F039q0f*45QD3Nt|&f$a|PY#3nFobvMQh@p%?TOEi~R{?TV zOwdEVBAKvxC>6)>v1h0x-`$6ZZw@jyba7)6z|8Wsul#AwPGKL*;d`0`N7+7@5gr&& zZ$S3Uz=r{`Be)E2$9?)Y9g-n%J8!Z}JRR;RqH8NEIQmS4Dj>kNw=WB#P})NNqWW>7 zonwke+t7E_`B^o|7KYTX6B`cw^!W}9IdiZRZo(n~wfhAie4s%9t0#oH-&bHZunO{3 z5XWkQt`z~{0mVf`jL4Kn#6JaTE`j`D1+h5UW~n08OL4sHOg)cK2JW{pDu^FR)DuXn)$^6yz5VxZf8c#XTtpa^#ICqE;$;||M%a2 zzq-1xh?o{?`tpe|>D1TEkRa@@a|d(okVVAHkZ$J z?Ox#JJ%)mL<5H;4M+YNEKwbY?y5}tiv&mY7KGK~%dkrW5y+we^n>^)A*DpK=1#c3CSz*|}?*WZhW#}zJ^c8WN z(}t!9OYPp!@&YYZpcHzc%x^^qPl&QT3%U?9C;;Bao}FY1yln!RgVY&J$xtqOsnk-5 zcf9*HfNG0ZOrqbDr&J-ERXDGJOnopFMj8u^(9WD3%bLLf~=Ikq1y3jy-gU8bCXPIC$_2jOeFS9ytiV{7T6& z6a>9ccUSWC5U2N3S3e{@pk$Fb=dL;X?D>eBA&?fq-yr=vrM7!5&}dY~rS3Q+&Q{&Aq@HUIBf?RYT1%+~te0SKTK;9o>W zdwYYujLUw%P~1o23sltFR`%iE;mW5_RUcmgd z6$*NR%L71NAQCnGECmThuRh#P&=jrPs@alF@cR93G44hi<)gJLE+^H65+$9+k0S#) z5e+ujFe$~m1kw-fth-5l*&6$UtOU`>%dqBIS@-}n8tvcH1APSOibys^lkCHp3!NV! zx(6Cr#PW=FoqD7-ARqw5Mb|+PHUHpZ&nlUd-ix6(@B{rvK?#R~rZp_6m6Cj))1XHO z3#d;A+Pd#|`2O6zc)WIG*~@N>U+CTj8jc)X7BDpa8Hj@Rr~M8e99L#$hEpglMFMXi z@f3u3SYnt7U(sC)f|bVAA9iyz+EMuzU$IKNe=E7gdwdfnEk-6MFRK_gOUZEzxs!!5 zKnr|gE(#hhC`5^W2v#H(Ym=>J?u9v4V2Oyx_%lylY3+W_ez@{7?+`9jIF0oGs19vCg zBQNjFd{3@R?q>+x5NcE5pL>m6P9yWnm^1s>)UXaSdzW(Hl7aN)8S}|7k&cXT0tW*g=3v;bUA4 z#g|&h>fqKJdv*24qdyFH9w2r);6pM7u7>WiW;@<;l{UgnaOM?2caF=>)-*L;j>|-3 z{17I~j+8*k1^k!epw7bV8Abk zI#3RfPtw!tU7A1|%)4Xo_Tb~k$HyPF36|Z1tZ-o2a8+R)C?CiSNTnM$h~UILAUywa z9^`KDyV`&KqT1NTO0?$+o7)Q`k}&XKL-Od8AXhWv9~*@23Jq=5I8idF6wS86#o;HO zA`>+c$=v^ZDU^>O&XPkbixs(=Ob_PtavfZu5+i% z;albMaG4YeP=GPUriMS#-9w{^$0sHt;i3ma3kygbnhw9*`<3)2a37u}0>psV&!Zsm zhm4f-(j^tb2z-*HWWQWcZ+G_?_%tcpzn|gb4Rd`$cZR$m{e#akQhVLNothh!-q4^h z(TI_Pp{@%L>$sq^Sm3vG0Ga2jC|0-;RbETTiKwhewFX~oIaHG2~a407Lv_Xb{BHpty)BIcwdte&mhzwQ*Gm~?o)>JSjxE5OdE-tH3mDuj_aC4uB zEEaaxc6YvS0JL1wGczsV17g9f1TpdC5E42gDJkg*#*aoDTX6ZPsRclt)6DMoYJcDR zf2ua<_BDg&7YPb6r2wCfzKn@=2zbOm>oH3n3igy(Y0p9RLOgP z{zMYG2R;fmFwn6qEiH@ZV*weOXgL}T zz6&M9fNzGMx`&lk%zG)!f+5Im!E%ZSB~-P7-t*DW&_HS%bP5&JKPxbWn}t$l1%(V@ z1)9jqMk?Cn%H?WRSS4EUI+*oC55*+qZ8c4!{Q(jIM_;rp9YQwcp#CUc&=aOzzR_SYEIaqXX%kr2CfyTR+}Ly4MzZg*4G!n zn&2do*xwPVXsEIM1qu3silS)H2KOxd&EG1*qUqE`EyM`aXK?R8n05Z_!L8JY8KSWL7`yp2@EknWpHDTHE4WIf0>)0jbDNoj|-zkYXOWEZyD3 zkfH7fi4JOO#6J{1$!KFy)vtcj{BL@{5Tp({5Ck!5sCprCz>^j8`##}qEBvwS+B}~= zL}8zJS^3k&6rme#yCn=LNETPXuWuBYE)V_U#moW*1_lc-gG1~F5i3&}m|e3Cj%MvP z0%aG97K1bbnRTtKKCCZSrQbp9Xd#LjyS6L#l{;}k878}UEEN(xxCvH4HS z2~h&HPl%D!qvh(Yaiqisu1`C}ad4hW0h5jgiEi|AyE9x110@tAyFF#8N%inHM1b)r z1GeOO9@nU9jYUO8NA+rV=@72MKKN19gFzqDW}7DLJ0MoVu0j&}9Mf7hWNa~#{vd1) z#J~YuEUsL+g3w33_g9<2YeVtIjT>EI`MH5V)xZ-E8Gi~n#`$xnN-srkO9OiexxEoP z3=jf~s3yrUB1Qq53tv8JrWJq@;NLmN;PPg;vG~fbz7$s3{D{+|{h?Nbp1vMV} zW=fA6!^MU~3h=V!0) zWX8TbQ?VcwM_74wgXQhzUFpTeT_*l>Q+jqN{Zczdgu)3-D)Z?di3lqbl4lX{oE?P0 z=4NFtqWg=iLaG&dX7x5pn|K?@Od1AGuk0Pbe=Z6LG`zlWX9a#0bMg{j&rJ8w_^Jb87(mo9xlC^S%E^y_ob&HIBBQz5)k zfExzp$)5eDJphV|6BI=cHCEPE-^Ro!yLvkW-tK{7>?B+`VAq2^NQ7;B;xv=xksvua zIapr~ekAh*jul+TO-)LO1y}54Gyi$}Tkx+zxs5`BRo7b|=C0STX@D&xBv2|*158ab zSgP&O=ahBBybx+(Uk)rkflblJoy`^LC@2mfgjk3NYV*M&L+v&RI8GE=w{t>CO>lQ2 z;DId!7A{>5%L{(>w>14C_kN4l2Eipr0q&V)`MFfJEsA^*Q>JGVdD@bFcP}Aqmpul> zog+hmb?G7MUUFc!5u!GMU!=W#+MVx? zD>G@~QhBrtckXhHeY^Uz0T4$}AntuJp|E&v^`4rqQ^~j4G_L^q&33!Ak*1f&G z8yLD|V9>fJcz^@2^k9pw41fIT&ib?k%z-eICj`62AeAq2KO=EeBG20KpYa6(6v9_a z1NL!SjBqo!VuS+;s;7KYr#K%@*Oz;jZ5U2vMWw?vnBTNVNzuT4$xsqO4qzl$5O30F zZ~#MAN+OSlzU?IBJ>aDF9Du6+G6&l5Bl?P29)pbFxLEEW6f4tuu=10|Lf@#zlGH5yy zXN*Tk)74ruyy8VHUZmwykwK=WsH>~nU=c)sDPp>iCqNhlR{~_OPau+c@T*|#QTBcq z4FxB-WG79u8f^Q*UH36N`_uA*3m5@>ix;Q-pM9bAg7DI4(Y-Iky+*D|{@ctUDz7`A zD>aPhtG-TsR=(}jn}>;&_7DR1cDZI;%=SY96xE2E7vgw}xb$y60TL)p=^;kE)%m7s z5g3y6{z$WxpQ}zGJg4dfgPKNwt?KQ@>2t5ITk6zY%>8t9nrjNV|K@Pn2wwFqNmhD+ zL2|Y1*8%7B3n>F%ph3wVr0N?QKEAysok)GO3?~H10T63m@nKJ{r%_Rx*1w`NgXV#s z4k;tz^;Y<>lLtZZsAanoBJxf_W&ru}1SC92P|eUtUYes2P5w6IpfalfH~Z_Ts6`{) zXmA(+$D4Y1Wue)5c)o9Xq=s!p#>;Pok1r$c^#G^V=cVo(_*TK#d1P|Bc~zq&JS%J! zatex72y9-Dzvk+xPjRa#YG)x#KbPOCrx>psclrIzX@P6WcMX{5qex-px8Lk{4{AX8 zvWSVQJ2)9`TCs(y=qhtMLuqLn^B#3iINuY3z9t4^FOQc6L=5h2z)+#YV*$$-CNYCW zIK`zagDyYKAj>nGW_9X4H##Zo97#H9tsZR=IWl`YJ!*54DwND6U6BlpXEr`I(nw8- z<`WcDgeL};+uV#W_U03f-`B2P^90MW$hU6;7_Z(x4+tqxr4`)W-LpTS#yN5R{>05s z9)N=%T*HuTj)7~YEZ7PyN+v@s0Dp$Kv@w`^iy*fVZ1m#q&6yq4QQSxo0xu@X@6ZeA zgl3TZgxOX#j+i$F)0M;PbAoFJc@>4Vt|7s}NTM@1Xk4iw%45J9uq|I;(R_wQ%Jt(A z-8RBq9>Ti;@y&6gs{zSc!=#r!Bg+9CNLJXhWNpW065v&EhH+7m_LXhZZq?93B-?FJ z^D2@7n?q&OvQ}q-HEB$cYsWC#B-uKEp0rL!rDrdY&N@6g?aiPZSOiW9X5V5(^5o^e zjyuD;*qhkuLnaE4wq1V_NwW7E8DgG+kT$_=H~A6Vp)Yibu}_Z>-|pJ|DPglzWh7GN zKcVx`sQ?db%sMT@nWn}@WNEesdTU-lLc9;&C!5~k*5X|%9gGhW#HVpexNtyX`$*lk|d7|_P;-QHTntDaacS$LtEUE1BQPvK6ICDG;IAWUZv zYsJIGDeYa2t6~G6-#54rA|cgw&73CWYv6bZ7pgQkK0KM!=h6PRQ8~b3WO}ItUFy+SU7*E z8K)jgaQ+^oFU7@hA+=;MA|j7C=9jC}r10gPJWi>Gpe?}EA%;KN@R)$Z(1`5vQE(3| zVs}PZh!%(YJ7&J!-Q7rK4&3NR{Z=t4fFuCgRV1*d**QL&gCPG12^5^YMmAEfB>#Lc zGExCN-87|8G+?lB1Jib2ARBiS;8Ag4V8)>Yb+^+c2I=7il=L2zma~qHLe;$p3!5lwSvJ0k@Nj{tXfkHp* ze+mbyPk*wW@{mf6Fa8N%{}7}WKJAa6L>&DF+(OaN(6HfPVq!v;0k&P9>(W&%aUg+n z2nwq0317QL_gO`=)M-LV?}m^!Sa7v}dhqjO&QE$I4@5E{Aw`Lor0yD0LXWApp#8;p#s$h^Hkul8>_=ZNHo!GUNP^ z#-lPGl>C24E%IEwzji@Q#UF{>qhZ$ZwPRZn49=)=sdyv zfefku>PzHA|45MBhFEYK6)R>RcQ9#sZxa zIjGxG(r_oyq4+_^uKmp;~YbbdC!&`z^N4SkO|n4HdaP>*Gp`kqeOc_FfEX^ z9_l2PiJ+(*z|ll}_dPYb&p9=R!f$HcnCI*P@_LCzrmC7+BT_?y0oOgK;1D}SD6}FV zronSrfC1<>+_d2kpU%2W|8V<$iGzb^V`Bs9&8>Fa5gy}}-$9Oe%16QY$Vgrlhwct* zKs-9$K?Fd2LXjVKtcv{!KncLj2)G1B!mCGw%A;7^+!5vIPnTs9Arql!>$8mY7vIi0 z$Ho2bFzVRXJPdqu%B@rK!hYlr#e#Ok9#pOicW~bN11}uG_anX$J85K?9;Y z^fU0wz0Z(=5EXmN1C-7Vu(Zyw>G$4W%z*8`_0xk8AF0fy`FIj3Yhq05V;YOZ$DhQO z8x=#h_sNuxbkyfEhc_25UR((wYfz~9eB7iFjsh~xWgJScGs)ubRB!v673wean0P3t zo{@(84bnT-7;`GJeZUwlLyXJJ4d8S-fTSV8;p`O{1=`Tia1Y4R<&VaQUz}p}FD@2^ zqRtEeQ6vQ{0JTzPu0!d>5X89CQLR|$he4A{t3R@xK|!X5?==hcmNV3yq~zqt^L6$$ z;A8*?L_Q2qs#hOBUaXw3tE_(h{P`-B{bgQ)j`Z+~E~+jKSBJnyg&U1FZW92+fW9;{ zXdWMWq+Nb*HAP#f@$7w*?F>cklSyN>2zKqlg@@jh6pm*X6TKWiANcntDv@!HbL(~! zYJ4j1GzY1vB@8(hX0`W!dVow6fX$A7WBj$~l*(&FKLvd%DHbCkmDg8J@kmQE3sE-FlcZjgk7bJ`ohG_92p(`0veN&0+R|X(0OSB4^t`@ zmRBJ3D3knQd4+?A2eF}ThJ>1M)&LAV5zjdGxRN;gm!#)Hl@yee9=m>ToR)O?eF{pg zQCD=LXKhi@mDun1tqfF*Y${(YQT0y2py1(%IN(qJi zdaJUh8V1=%k3u%##x9=SC_LyVJ#$9M)zvjq{=q!FJ*SP*P@2cpY;d;*;qs*0pEuEa z%J&Wqo=Q}I3G-2q7M$4?enUA+10@JR2q(X#<<^u}qWZ~PU5b##RMiICgIzevjU#?X zq3~@8vcf8_Un@Wg_x|HYQowi?n{v8s+QFJxUw|Ib96v9eFZGF0DnEaBHnrSB*yD|>A3o_J0K|3MTO zNFM~!rD91Q85tJa-aI#b%c;r9i`7>sk2x-VCPyiy71rzc{+-nUewnVj2bmeRGiRth zAW^TBbsn!N?&X@kHE&)a%Xy<@QS*~3m5aEgbX{Z48Sw2>KW8XT;k%rv+TsxCgBc4_!O{*hHR@D zj00EuA0%_|^UpuKu$=mlTJbek-dUCLaQ!C=DMgm8G=OImSLo%w`+i?$wSo3)wbQ1A z&#oxy9w@0IxXDI*_i~4RQ7R{Ax<$IdP!M&&_8E$$SIzdzY=KV_fZPHikeAaZPnc$HF%8QTYcZiN1}JuO_bxZ_OZ`U+Nn;(hV;k z;ZcAIT%pIZNq$wCG|8vRP#T3Uf3m7%a8CX&I)@o7g3&i`nq58zpdoo$J1IQf@89=x zj4NmWA=j_*_KK%hYEP`P>%TGxgBHKf9|1^*dWu|s+}y`GI5@uUS&K5UfF&!O4Ya=g zIe=gi4*z3lt>i`;S7_)jgNfdR=qgQH8qsF}I0_+KgUSwIUxOX*T=tsShPio86)LiE zP@(!G0B}YIr;JQh{WeU$!opd1Xa8%u4a<}YTNUyl_2y)}k)_*()#^RSY2756df%e2 z0gf3ixBGEOmOjYy(WA>by8Of5>uRu>0mDomD(`YWJ;XuI+RoJ*m&R5BK+OQ_#BGkL zhlLBF+idQ`d;=JJ?SiS+&n932PPIlQ0dF`;P4HU6Knu09&7ypLZ^wyo{O%?@jN)6GjoBJC!t zHw`tdwT!5yOeoLdv4y5G52fT~hQip`tQ}9`Y3q!zBQ?8Np~1s>LK)K0NiEwh5}vb9 z$4PoVU%P+oIeY)QoO|cq`@7%!effO8FZF1^3<@g~iUTJ;!(U`!(O4{aT^{8P!6DfA zMd16UK2Tf;K2<8!S!AWxA6~cd&4F79d+Qq-#()B>^f*_6X%EgoNc($|W%b=2*|E~H zZVK3;u^scg=BM6{b?^NNwZlayVs>(Jda-oKTV=?(>)MUNP91_n3J691d`qkQlo*o{?=RH?oF-#UMN{0rtfnNyuco*3>x#41qL@E z*?Y>Q$5}IZfD6sAH(5X!E#j>ijD87KBjUPPYGa`Qm1&%UlG)$lAiv9l<<9kwv^`91JBdv65XmTv#{!M3t)9`kj6 zK9Q%txhNIQ2~l(1SYKP{v<03;EOMH^N;TrWL!-4E)S8a4i~TjI)de@T0}KaztCD!N zLeXrN+HS%g&koQe>qCqfbmt}3$w6%kb6orczl3=;?m32lo3biDx_MVoZfK{$ zU=(IC{{h;S1G$#P7D&rdtwdR&7GS$|F#~gmQhRtPbHep7$BCV2<)kJiCd|HEBL*e5 z+sTeL%`!ILRun$LpG280)pLTq17mi%?T|Ic9Y2KS>NgJ1@Bwfz5=mDaYQCYrK6d-a z(b2>;uvIXF(sj#&c~s(uj-F#@^`8iZgVu(wu9mS`pim{*b^C0&>gmZ*ZAq* z2K`Zf{}g0U6SQX@Ek!H6B)M-?RQdQzq#%(bZEa0+2As<<6+P!Mpb?OzrLFCb30KI0 z-S0;qJ65sIY%_cge}8`@d)wXHf5|YslOo}^ z-SbRiIc{x5V8bSaFt9o;Y0mHfC|&BuiJuq21(*3NAY370R8`u3w~&?R=|g-^e9i}m zOev{w%7M3COXpT_T;=rA@&p$ek3_zPmQ$cb()8R3@FOnaISPeaBc!-_)=kL`?X$TI z0KG-XWdhQb^|%|7+xxGfK}MmNyznAMRiDPUiyG02s1{~h&4h=L zZAFPZa7btd0272Jrb!cRe(cbPej1;I($LBMMM42-f>a@~yKdq~to`84j-*Lx| zAzd~=^F=pO3GYi4cE2^c%Z5(htm~9240C$TL90yu*K%%PGZnxpM zEe&S4ntAwofwjCAksNxZ({xCt2DNKii5XPl0qS%H(jJguBbvlTH<`E;QAwgoS6>j)u(b zVWeLFq+cD++L8tqv$SM%biD{Kb$W;H6Wd>vbN^e$wnOp6@|9l_gH7b&Ei82pO8h^p C8hT&= diff --git a/examples/jupyter/images/teapot.jpg b/examples/jupyter/images/teapot.jpg deleted file mode 100644 index 382e8838f78819e94c62b1e577f5d9db8f2ef2d0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 103729 zcmbTedpuP6{|A1Cq|!FqRTqqwO{pkpyJ5%{ipI5zTuT?WNGi8Dq*84)N>(JJTxTS; zB}t4@A=SujDuoh*$#rHhV`ltb=bW*h-S0oY-{V(%shKh7ocDRXp0DTg`Fg$2*oU!J zzi3@nfC^FFrENBek?um1sU+v6d=0sh*~`}Q964>%NXAXIzt zA^atSy#a@pj5Q&a@YDbAzm$|nB>0Due)l`(NmMB%u$~^Z~m`~^cFAC zH(veQnzifJTmHUjvz4{Y7AI$y?K^hva^35*Z~uXVzJ5W+j-Ln)2@Q)medcWJx$_sU z#$QYLEAe_#^6m7D%&hDjChPA94+|a@J}!Fly!^$>R~402uWRe-8ydMEK7RW8t?hez z2d}fMdtgu?6b%iJh$Yx{kr3s7mW;Tt|8rf_;kuM2Oi-Snid`2;DH#4$o<2ck;mV0K zEF4t5184qX6g_Fy#w)k(Kbx$x%CUd;o}=%l%+WRe`s)C8Y50}>f4i_#|G%#6p9}ln zb$vpnDwE*mDNjeJh|H+DnVmL)F$t)mmiS&J+jJ?zErsuOyP(f{hHCGq`gNk zw~5CPec!-dzoNho2ZUKkD?7~|M*Zz$8YK-)e`8uEcd@OObHA^WEty*ST{dyaV2SZ( zHQN^=lgN$N-L2TQx${iVOOkzqnv`yS`rE&S8#m`9C!q00NeOD6Q}o z(gOUw`0ayA&jOp0FJ28@Jw52dUQte!^3|}qKLrKSyA#C9Oyz-$X0~TJtchqiL!jy{ z({GTrd{(*HRwHM$Z64@EX#=e01BLvpPs1p6QeP?KYr8C>mDVw>T;dpD79$!%=HGE+ z1Z0oYsa@t#^NdyA_Vif$n5ChNuuS4dOgI7DF(l7Q|A$JtHYZ3P(N*dtY?BVLx4)VTl zDwl|r+NpJiT;H&#t@yye+f{FdWT>RU=GWS?-IXHB^$dgb(#V7z_^b7ysV(_qh-+qc zL*X##0P)p*o}M)gcDx#grxG$(>bv`GJ|kNs(_W6fUT2#cBY+#2N4#g!a(oP-UNSE6 zOuvV~y+d(xhX^^JLG4J1Q~6pW{P^urqZr z2*0QG@#Ug1WQgsWwmjDK6}rW9RTJDhz6))|7!neBEkG|_S!}PuCSG;u2#;jT{PIdl za)>)YI#{_ud1Sy2|J!P}q9M5EbYImVhPU~gi`Tnb(x{bACpklOzKh~++jMA^q3e;t zj?qHi7{W~t;ipD~vTktFWaz6)yqZXMo4LBlGg+JDeFwRZ(#TTW5;}2wdzEK;qn@eM zoJ;|EIJV3AsUn1udL%U}EyNjrT{vVmhJ0_({8yx=sWtuU2+l+vERGAiB9F*X8it=9 zLqyDz&puOl&L^>h*Pf?-qMoNVMj7-G;b-!!4>HFIKk7Ecr3bv3_ikGRT9qkK9j5c0 zhZz>|HJ29P2ZA3QL;etrpJw@Ia=X>_4RpS_dSPLH=aQ4y3;#Ne9*!_g4a+77d-%E| z=uO8NXn{Ch=G#=&83gNJf?v$ml1N7Eh>Iy60-4Rt^`ww>rgIsBKH3bew!u4N2&_`} zdGr(Y(tJ{yE~QzA>L9KwXY7?nD{MTI!;DEX@oT%iI?Sb zq;@9O5p+@7Pt)~sq$NwfR_w9r+V$L`+e$pdcRWwNFGn-=WQQfuC#@qaAb{%!urJuv z?A1%nTWf71;OSIT6DP0i9if|Gkvg(hs9+I#o{b?kS6Ms;BW7f~JDs-G;Rz z6Q&0?G1|8*5tc~VZ`24>qpvujDt57h1)7580n1nKE}tVu=0Kn{Cj02zRTU4f-g&=w zo9WxlZYQdkf7nsU4fmFd**%dAnV)}uSc?2Oh6M*Pz+19w46!5mJnrFi*&5L9jUlGp zKj9H-w~Xc_Q(2wuB930^&D9aST_lpoDQzx)UjAsDO5DWU9T8*51Qj?PN^N|oaJE}f zPqeLJcT4OTvb(^Tog00qG9s2OHYQ>=!6{54KN}w+G|tqjmWY(z9|?Penm7g1QNc(< z01^7*pt@xYF&rQfxc43hQop-`PO9fX`rEm34OxgZr-dVL7RuQfDh&9^^fzXwSmM%d zs$sWp7Hx>V4Bawg+owpwN;Q?@czNWt=WX{mUH0l)ycWntTZ8aGNkbk{qVRKP^#yp= zpl6oEjgrwp-{^HgLJF^BMb+gWk#HsXE}twu#|v{H^3CT+TTTGVNQCo9{Kqm>cAN$X zbt(}@a_DbvKLkBpwc_Mc4Y`m;mZnK3?j@Sc!jCn@_yN3r4|Jgn3Q)?Ic+YG zs{qbZyy1vq#_OnMNz_kLwxs?xZ78-a+WAij`5oL_C^zDB5be5LOzDgWAD1GGGC<8^Ev?Kr?TgrtJ%J(H%=3#}M=<}%Mf9-7< z{1&fQFW=8nq?+M%>QmGeDR_MOj#ZnT5xH+B9hUNrTUb0)CRbm$FX&h7REFWow&G~a z>>~>M8X`XtHyru4=mOM18RR!C$J1jgQ6!Kewk*H#CJ>#(5f=&(#ESZXlwkBnKC8It zQ;q^)iTb`E8hvBNkUvor3+Jae7~@d-PdH=}_muW#3^^FNhfXZd$*7{I;ea=#am*yjmzAR8YpzqyFYKbdQET?@ zRUO3ZF3ZD9J|Oe`J@pc-ji`$hOnfv}t4%rzaPuQFUApA=)bznLwOg_UW)+YdGyyqK zrhi!^lKr$e@FV*=C697W{UlMZXfwjEG%*A(?TrBBDgD`#$vI}9?I)g#?~FW~su6ZY zg6hAUW?CU0V*Va`UsYT)F)B_H(^DbLo@{E>E}zXH7Whb9I2|2B95B!>qPT_xb(6(I zcLo-kK9i#|zmT}_m>%;5FFA+E_4SA3*?M(SA*+eYh4Vk7!u+Q=MMEN)iZdQC)`}2Y?*C`+AvIfhHiI^X zxNP>UiCmN>Q1wC+ey=^D*H4l8Z7)gW$*tAQ@Z=P%l3EIs#V%9~;vE%vllxcW81n0P zmA}i2IDDIu2-H$OTS4XsOgYUsQQEp(T{Bwhd!wmq1iiG~DD08hO}4?ca&5p2zLkE@ zk>%>5fO2Y$liKBv02P|C!gPWpZr{4kOAkos!lUcssD^x(Bu)@$hIxTadXS0xY1=U@ zNX6p9?^fK9@<0ax2DLT@QM(OHIpX20B&4UJs6xm(yK#M-Df@Z45gB8-(cy2kT83)? zSWMnGQBwV46`(VoRUSs@Ln>Kbu%gn`2Czxwgk#Km9M}#jHDeLAXrR-N8ArDeWhJ4X zC{glmBsD4KB~a`-iF^9Y1n|X)CJEo-l@N!HHSjgC8m_kj_BQv71hN zLusbwY2oQX3czXUVucuEcAyVYSI;%F{5s(I=5vDAFr}T#!oJHxSf%*qr^eJXIKbRa zwbjSVBYX)A*lyTKooV~IIa`pX7U?BSs|+WN2%M|iM%b>P!XO4Gscqg!{j+~A2TIx(s=;Mw|c5A*tncEDK)C!2~l<~tm7yg@6q<5%rR-hsBhs6S^CzJAy zsgM+&1WwsQKw)ZOP1q=~D;Mdr4-}A-;a-th0NaE}P%u)LjTKDY$h8?Qa;_Y3p8F3> zDPm|VzB~)#!s2^4qU-~o%G`})s`a&@r-eHGFC8$Nv6!et+J*smsWlMv$kF-fcx49~ z!WK&pDh&+{WhmA(yCkBb9zC;w%N0@qovU%xcim?gL|?(q%5~jn-kLY2QS#$|a0d## zmiMXNMJe(E0_n4#j-rfLiHg`}ZG;B=}%e6ebJetv`)GhA# z!a9$?MVwkYTI=D?5ap;V*46NOc(jyt7O#5Qp)sQ`B99#%L$(WhWd3-#Kz(pjV0}7{ z(Kp$$TjC3D#fL}4q@aJ`6lh3;^Kg6(Z!kSP0wwonzAnyxs@6o@rEBYce3S+`3b;MCopl%Tt-;j%bI<3J3(PTcS_@4`qN~-`95??L zAlO|}mc>Gms&sUFSQY$uHgK3I!}EaiH2ATH==9`wf`}%m-1xTVQL`eJ17CJbq?~CK zM&?MDWOD8eLp7@R@cKa>%-K}|s*BUU>(}-GcM0lwWX@D}kCy3@RIeY@%pC$#Rlc2o z`UO*c?pHtVRQr{4FKdn_M=Uv!Gc`%BZFbsuUxk0fTV$ufh9HSkFp(>u4mc=AQeU!j%-0l)5HRIHvC zUl-kdP^slcZwmv84SmfQHdS6?Z!{V&{1*RqH~?TF-!7|AX&_D#)Ei_AV5(X-n??j! zXljfoL*;?C=}!5>FZ{lLghtb+sO6zC_o%Ov-G<6oO-)=O<*T*Z@S(`HX&~fJRJ2|2 z4(z6hhgt0vMOleL$|YnM&O(esW568vetfNzS$g1bg-Cvk6!Z_dfKuqn3{8^jh_6Vy zPQF3ayX}B6;k0uRK@?N>qkoFYT{bvQB84TTpzhw|)M&PCCE2|K z+I>&}L>(AY-d38V2698llLE-#kWTO*LR|Rng!eR$P&th^;4Fc+`z2mCT?<->2_o8Z zyAtm@h7i6uCm$E<$@_%*RE-*y^l`HRux*u(#R+eoJnjWYyd^LYI%dV z^C~k}Bz+oH0%uEIX#?RV1zwo%e6r;@E>A*B`51%iUM@pD(%hD%v)kV;j60z^@QtN- zrz^`7twde@*2u5Vc@z5qepvE$%^PYnCDYO9c-w%UxK>dr?z+f+ZRg7(7lzo#Yk2i^ zel5X86Rh4~I#W`kYZgWKb94_7hmz1x)47z_mvG(AQjjK`SLP_malAJv{}<{LcYLSX zS=vw(IF)oqD0(jADW5}06y?(G&{^OKv;Z!YdTFj93q&jrf+S(K$PNKpNUXsBs;_fl z;eAY{mnuw#fTgl#k0^JHR0%0>9Q)S3`%3p%>3s5kh6-khWVQ!fl~LZPmHzL`ofV4> zcu()L(>4%e!X!I17BFhHB@z3@IFephmtro*){X;rL%QDmLB%{wa078lU!yXHK;ClN ztQ1+yoJ{>hIZtU+(D^$m1DMZ)=B>5qCAZ7;W3SU}DFU!lOxdo>ib>!?kR*X{}?(`eh)lZxMeCiGPnl!vXL)sOR2N-wr#8QOixGC>J|5kWNmpCK7r-_$Dh- zuLBoUloYqzzel6kIM(@sCxp7D=?>|1`aTB$ep=i9sD-6$4wlCdPvUf%(W5NVZXg*x zk(;2dN8(MRB~WdDIw|Kh)%-+WF-)mVd<$OG&Hduon!VED?DMLa`&XaWBx!n}{QK5k ztNQ(^y2Gl2@`O}fvoyJ9ErU4m8y~ycC1Ircd8@i3v#t}SX|}>K11}abWA#$=Wl5?` zDnkT+2gbaeKmy}pNh?NP* zcCSr*JZG35B#(SZ3dY&nlHzmSAEc;6NJbvI zM{vH%S1+&`LDxqW4Z(euKRKTgLE}nlCbU<0Giu`r&f#3tdxUC;*8n^z&G7_FK9j*= znq_C2gDQ~73l><1r;r<2hMs(((z7^8bWa6bA_PaKlL5wV^a$i*^|X%|F7;R8D9ELa z!Yz!w2i`aCz}?D4%cBoH#(V)VRxsb(X(4uM-|N7SjO}t%d5eo^aOISxtVD(9kO9>K zoq7Rz>^6i%KCI;xRdhK^%fh~6I7t{xQOgpQOCwX@O9C2maRpzlH)sUMXET zD;1X)i8p%Lidu(4KyN@jxjE_>dG<&1Fe#9&ct5VXmpbhrh=v_X?}I*&By}gTFn6)k zHnlNU)M=MYf5xf7-f^?EZV~kyBWt{#Ftb3YP&p-&4ZEuc>(%x<^;#7vj1qgJ1NE{H z${EG_GY5Od5LGGn`$XbGu}`^jh^R132JfpcT|TSGv1?fRw5;Ucv_Sz8p#+I$7$%Cc z86xo+8Kx-nR#eHkcrA$hx2`m2?mptKH^w-ea`T|xj$1JGg<-07^YiRz)isT%Y1KeL90Qf zxY**Fq;6{LhX|;BkpacPndvLg0wCp?xYcrP9mduioATJ1?XpOV00P%)fj-C+>?=d7 z_kK8@g&!fSpnIJ8Dtr$n{sjt*+o^VnmLr+(N$?j(ypnBwni@+@m}Bk+oDXwUY89?d zZb=DUJVeN`I;F;eJ~$h9K3_uVXQg5ug@V;0u3-4z!bHz1S-LoxInIK^G{%rrA?X0H zBf-df1(2}LaJ(>5h*gX&)%Vm-Dhxban)%U_Fo7Ya?wlUZ=m}oY)@X6jN!piC0Bi-R;0JTu>3%s71X-^P_ zzExjhxm2*jpg81hc%z*4RUTPjZX)JhMGT8T1oR!;BU1S@}pn(&WT zG<=i@Flh$WhubhGce5q+=S0 z`P?Jro-~A#w_j190OED{P612Nt=s|gh zAmj6JKn<#6t=5bQhZWVms&&a3K0_RE_C-tu#Y9|3<(Vd$Rq9HM`{;+%fO zU>@02RhVNNV_GF;DKA2wMzZBkE+DM%5ZZ@Z<}v8cy1kyb!)Bq|HL zg_n^SyF3syP|&D?@6YsK7iE zjK4xCO9~?` zCDGPC!+Wuh=J*IHj1i};g?Fj?%bRHY8n9V#WR21OL2i5LCc^o|F54IYoF$N@K|X(Q!Dh-#X6Q&DEclh4lhAvmsg?e@Q_`kp)Cw&0IY=Kfe>& z8W!RPfKimwX47$CB(_5pY}m~k*Y{97#}IctMQvpEL4yhK^lX&Dyj7pcwi@cH@1dmxJU&=e+^`WhIccjXFQ}H%GgHCvvw+n*1JX@DDNZX7ur~uk+YBgh|FKS- zLCXR>2Petj%Y37Q|BoCbN>9&Yc{+xhm|>79K)N17@pEjUf1{jN6~FJ;I}+Q8#sN;@ z#U~LjR1P=?(!zOo0UQx)j?w|;B@J-5F;cKNj$sLnauM)~0M*=?;=2EKkJL@iQo-vd zyqj?cy<&h2HsnHV)!3PcJE1#nTgc%>vsXEN|72%vtmzHv6*%AhDBL`Bz55u_gSNHE zpp!3wT0fJ&0j@5#;Wl{&$cg{d zoJ_^~P+y=%)SF=PV{IVgp4MY~sOxWQ40)kC+opv_9aQQh6oSdRAzZuoeg*Fa%QtiN zn9I`0hsC(d=KSKc@ENuuH!;V8bY#VkFA)P0r}9*(JYn1lI)ZMNp|{ee<=`FgWY4?1 zMeG^3D%wjv=q2mcuLng(ffD;vN5m z4rSB{v*ocuQ5@;6e|2;W`C5$GI{)~zU+zm>Xscv>f)R{-P+uoR-E%v^6Xv`rZ4IC1 z_rRtbCCSkBbb@>P`i6T-fKq51gcivSYv|vXn0!X3OV7*qjH~uIIQ80_iP>Mit2{6) zj)bb)vLZE|N(aPXg3Byrzi^rwC5i9b*24V)Hl`qgi!qd%eHF)Lv_Zf|Jjp`|UU!q7 zUo4G!ynk34J5s-Yn>51C#fakFCAVAgxEDCwmKj^Umci9hw~ZkQYY288fVE1K-6YZS zh|a>pNFfoaxVRzO?qFYiqUIU$^AqFLPnLf`T`yL3wjvbD zF)0|YPqh&IdGD)llA24a4rL#td;t5g*i&TxZ)5$O2(Weu)RiF8hdG7}jqo&uMMy^1wC z8kveVze&pwn42>o+`y{6Fkbq+1XtR3Y46p3-R4ew%C+9hR! z&D~5y+kaXIc(D2A)A%$he-NlEJ|m*|ggHKZV?(TJrY1FCplpdv31M&hMa65L!|Vt{ zMS+cvfs`1pRUklNjLZ4t=e7=NKL7H=uX>}F0~Mki1*OWIL3X5a3KXIQkCKBj^I{xT zui13s#w!!|e2f)ml>Gst)#l(~+{734pRQIgJrMI26qd}9I5dRr4`vW1zSe?qCIvFr zR0)+3Z?D1~2$kQ?IZw`^@;=lmG=|v;r4i_Viczc);ey*L;Gbup3_o*l0Rw^8Z)2>~ zs#tAq(-Pc)-eM}Jwi%N`HiYp8<=6UF3`5KCCw?znjR`gV*lV++xd#aB zG?i%FEf+|`k9)acEoHo2i~H4t7m{47#p|c=GSz5?#X|F4n9I&7(F27ied$Q}iV9HL z39bU;I^#w1f$JN{T`sn<^<;l)o*s^jkPzY6@E2{xkg^zx)2L-XjEi|g>Y-!EF6h-A z7_}5;C2GE-6|Qz7(ix79`RCq1nQ=?qrTR)kl;oSDJVq^75AgiHdH_ZNaTyEm|3`Cg_5@4U>pgx4qYOf$ zL{*?ZT(JZF6A(!q%3M9TY_H`>rn^l*xA2{amZ^&X zB*jg_rJf^@mx6zQ+vf`0&BhNGgWWM5iQ}U*H)2`N(!5NoSc^a? z4qs4rdv)8WNVDu&sRsc)OWBf!aVse1vAiI&xCQ9usfn?Ky~i{u|3Ky`2ILfnX1Rss z^ZUFYL9r3gQVe{-xh0~+QJh_a4L$rxH7rwmWgA9@x_LKgT=+4rhm-ksMASQO&6Pea zLG$em6}J0ibr zp~4)R^Tgf!sjlhKNz#=tG=vp?FnM~g$;TWb&}hqB01})%xV#vn<)L+ctUB<2sBT+nibdbKq0l)+VzR) z{h3uPIosSAXc8nYv~j?K*?&H$GvW!QF%`Glk5ao^I$*A9LyWYOAW4>r18l<^qZE0` z($(*w!z^d#DfQ!{oXqh<<=|<2vx5=<6T2Dg{KR8;zX6!qwi(l#v>vZ!bZE6n9Knt^ zYmq-Gn8nWDR^~f7qEb?S%ChsP{pU?5nN{R%(Us5zJ@$<)hWvCTISktJKBemZSb&k< z%lhZyQcqK=MT&+na!}Q*0z5%67Ru&pC6o%hH$bVNHYVFeV@wF+am08?gQWqG)8EmB zwlE{YhA@p&hX(9Ix;gsJ�}CnyIkz7YTkq(=BQl4CH|J5I!I2t`Mwt!zeF|5aw<6 ztiw*kUgdThHbOxN02a~aGbTRd8nryptXw+$cGbA^z)B>>{6I_32WqI5f$fiIf9jt?Rq+&>f*vE#*uzq^ zZej0-Ax(5kj$1fGP?zkB&&ZrsDQOk$LDz5O_k#1q+7H$dppRfBw4q>BxE_35=w)X? z9b_r;(>J~wP2_JJW#_M0o)jpx9E2lzCw^g1qvkmo?X7|S$i89fSru2CToC&kv<*XJ zTCfYj=HR;NioA@gVmOJ6i_rX$aMH2Le@*=9C;l66>{Z$ffHuNF;6gjxhJXn>EI}@< z!O9G|T2hlx_QZ$MLlN})PQ^U7*;~g^(m|2m%)!#K-NfZ9+Ju=3yLCK0Hbp&h8(xd~ zQLDi5alPYX8q0$o=p!?R%x~HMnX^tOiefQH3i+|4rJP=+zVY*|Ssz6Oa_5N8=wp*J z)@oQnRhSok6X_Dl3 zujQqw9*5M<1{{rya#S$f?(^}oyQOfEH2iqJ%c*ypJ-Of2r3%}5{A2X-Rt(5JY$wA} z$Q&{1*%%$JFxvY(v%kt`-|S$Z?-CV=;P4uB%E%Gpsj1MZF>Flt4g5^@&mLfH_E^t& zF#sakp4mLr^a_rRtB*1?_4ME|Q)xR2(&Ld0`xoco!z|9#|J-2E z>{z1Jjr$Y|%yhskpmcnC_y4{lpf+P5x!_qh?d~++!z}~&2i{{FBwP#$?+#-O*(kpk zTlf-4N}`%O3eZn7Xeg!PHuV9nAkxEJ;b4rXoCiji4;qlSb0USced6dtRlVub(PU)? zOsPOjfbM`{RH6eV=~n4cjAdd*`z@Fs5)WkuD5p;s*UF#3%GQksmEw{d@zA#b4}vH2 zO~Z_I47_eC!uXD&kEAg7y?n<73>GO)&L)JNs+Szj6TC$f3`R=<6lR3UYC<@AVC@k_ zWt&3fHT;D8GY)#x5cxV7X$v36dtyC+FF+PX?z!|`=!NPd?`QDp#FWO28RS4HGp=u^ zg$w$cGk2Lzvx52dpL;%xAv#;3YXY-UFl7ZMz5%{fV)k$Df2D$K!0OH7z+U}0X#2dO z^S4clfc{EWLevvo4R1RCLNLyF)5jZED#*dfBkCA74~=0Uv7_PH2XqTD$+j~(2)knW!!s33u!g}mh8-oJ>rN8#b$pa$ zADH~hp z{acABl`c;I5WL!ZbE;uxP||S&f{}@jqtpe%;GH9yfVdij#nWqV8-)e+M)(y0>xZ@w zh#r{UyM%|AV)hwCprtSa8^)o?{7ytfJ@f(Vuno8c^Vs)anfR;l^l z4ziOW{Tnrj`kLr3BklwxS(yS*8R$HW4Z=esdOmW`N3&>nyieeXO}ZU>g>IKpCC~G) zwx`!Fqv@Z?egT;G>URsDU{(kyzsAOb(mq3bgK@(=;5wcq&fu0stjkcO+#MKE==z9R zIhxUO=emVLB)q}WaMY@Qt~_BLd>!Kx~i%LqSDfpVRLMW{Pm4dpS*j^A- z0_g&id*hK!`B$5Irnt6HG#rY0#wjjxJUxg8GvOp`V&|QF3ZND@K3=x~Mx{ezYUu>* zI=oKYG>kq|8pdgOh7q{lt(zQ;!bu|I7TgH8=N_h1S92xOjbE{u$&+;ip?<^ep_J`O z_8HRa_JiP~Rb!JsxS3&GC9k2_y3AZ;icKdfhGg!jlXWgiD)2AJyG@Jgk5LR*#9gIAY?0;l) zYH3^lJo#}Td7hY|iBFYy8O)=xdGuPkF}R;CR#N@**d%_kKOQ(RS`S2@33ey1e=aN4 zO9=Y}yd3@@1^ge_up(ws1(Uk1+Ae?%Lv(|36rf@SJUD2f%^cK+)TIsT`Vo4YH* zq;#BJFH!JfxLqdLJ%>DpdyX5U4HU(#B9>^iywn5bcBlj4#i{}!sk8rOa#9*O)v(BC zVX%$Z|BxvN>89#1UPG(()TN#tTWALmjzQvE5Ctzt(wG0pk;jzRQHJ=EWzNt_asuUP z3Hz8}a)q#YVAIif{~H^Yr4x}))Q2>+GqT;Hms*gvf`5F+n8(z2Q3n2;0T_WbuG zg>yegSiy9q@~sM^CCAcw4iQ0%};#{Wn;qSvpKOk`P6qXZy$$p;7G+KCCq2f_|Ks9HrTKGag<7JOwCUv z!}z^Q`^h7rl)No@pMm-5U$_s>kdSG4xk9z&0`k!)Xqf($(Pd{eb=V9BUmiG1i95Vz zoEq*}t9}xjAGY;f*-3R8aj`TkqHa_9y>>JjwuQQnA)e5v^w9!iBAK>!nvvBbbC3{~ zxHPzfPnkU2(#S3B`mEMrAAWHZ8$5yOmGL1gh5lYv;Mq`o&j5EI6uUN)f=EI@_zRKm{8cP8*Gx9lCcl=_sM9`ki@D+!EXZCiPOL~loxFF5zZT4?OYkQ z&q^S(TTw}N1Vaa?Q>kZTt1FN)Fw!4_DuB)thys|z$G6q^J>+K#i=+R5+=J|$r+Q1T znbimC1Ns}=n%IT|N|_%^#}K;zIkj*+(c+3km?3XFexX=Lq-!y#IS)^6v&sWKfZVqW zuMK?}NpYEJ$o>w48da$;WOl9>z!QDt<~=p%2MS-~`Nd zj>5iI+M2FI16#8;8IO}lQDC7+!AIkH>%uXWEE z9(0@9lcw_uYY)$uF_Bwz(k6JN=&z$i>(;e;Zw_(Twe8^^@%F!^Q3E$1f*V(HURwL;z2R2sLb_IgwJ zF-@C0n!8FL36OWe4;@Ij$YD}B99^WFKE{F4(}C=J%*LNoQ-u1my6C;=#Z-zfa_MPNS|bi>sP> zEn%vEi9>xlnN|2pN$%0MbrkO4>IC$_zQ`Mf3wNzB+cj~NACf)*VBxC~7gd@gT2r37AK)i+(Hcz=gewL*Wv<-A1*h*Gt8Y2`{HS+S~s-x&m8`y{T+orqhI&D~_s~AXm)lNqg}Xugz5k?{rJl3e!oMX=;Aoq0Zzm zEjYjAE~%VQpA)LnWNeBgY?~yBc4~-BSf4_V6q3A-l0-^>-6mh2#1Sw_6MXC^lw$A! za|y5sl(jE5NQNe?!T77oyRs*4ZTE(As-uAdhADc3z||U~^zgkb;LSdYIJ41*|CjOy z=S?zI0ItSdntPtipmmqgv@*HL_%17!x@i;T+^jln`2b9u<^~slz7*b5pL$(Lags96 z2Ef4dB`2bhaHbN5N6&1zSO}d62Fjsi>S5+9j9;ICpEN#e(M!WTFXnRgUDyeO^&w!u zkT8E-4F5xzPAKtiFuutq$u}m$U?}YWycIV}>a#V1ee>wKS-NqsOB|FEZH0sk_LPpc z=zmW=pkG(VJDMQbQ|_C6Zou4i$OMTDQ26wjM5x!w^CaJ(l|WE(g+6crZ9`0GtbsBHg%=fdDt*K$!S8Ot zx-$Kuz|ZmYS1@f9%Y%Jcn7nHIi=JJ5r~8u!{gBnCr+6VG&}3yt6v`{j@DHf3NPPp0lX5c)@_WLnbJ4d^JB}XF?<>eh)GnBe}jq1fAq3M0M&m(ShOL^c( za@Xgpza0|7uCX&I-=klx9nR4$a^bA480>rN6yC`-p$B-nB9C7dRnR=xc$@C_oucDA z>nYa*`zp3n-`H&-H9y|FmipChQ%RiWf;Z>ZU#L#)3F4ZD9T&sG~)O)j`GlQBW-+K=HcB(rvi%xpm>_UMhA#TgUCTIqsW1s)fW z`&+NBpFy3Uj{)drkQ}hhC7{tWd z6);$XV65#_3WY1o=}enENZv?p+iI_5QJIbHqcWf2V7~6VgZa!;lPWWFTpls@HfE`s zTtM6yPU2*9>#j|+NJfwPaQhK>qSL1^D3Pd{&fpvTh$uTI*uv4dLP7oZ3~Ha(FIan?b>l2+x|s)I5Cmu0b|! zQqU9OuItk>;>Y!Ku2L1)=wRl$8v{q4iEVO3w`}|_ zghAH{S+>XbGQgs0%sQ{iq%f!nmUhcj!;lB~005Xn@WdCt`?$TLXsdzekIlm2<2nw4 zk}9dHDE%!5G*p+lx*9gRF}{D1iGM}hWBygt1U$Zj?A*wC@O*{lQV>85==j#&JY<{O z&|FE~JoZzRb1!G>{XTl;in#SD_7j@8Q0Ez8gi~r!63LE*2h60FO@V#}?C}l3Cm-$- zdjaR{g^wDxo6p%=cn=iX!UkGzEd#Xww~JKBL#ulV?rrgE(4YT`&5r zY46^^AZ~|s<;A_1_np*F4NvYfZaE`)+t^t8xUjCK^;$>hy|7DZEMDiUET_}KCs3}r zNef;*YBXjbQ*G8FQ?Js@Cx)&TYQ~mR$h*$`(O6WktE%_z+Ox?+)5{+wNbcW##I<^F z{PLITZGRVfslL^i9vYi9`AYO2bbb~0!pGlK-!vxADMDj>Gg@r3)Bg;eLmeIIo<%;% z-Fmp6_hNW~?S0o>Og``P(iO@L+B?<_<)>-2s}@x#&!xuo$XC1f&gJ==oxa$1IWU#M zTRrXR6J7StCbwI@{z1C&*5`mmUfx~v)?-(9t3SeYdFGh|E01LUCZSz2(K);%;hX)c$F1bL zx%sE6?;()qCJ5C-%B^Kgl`j(y>>k50v7}lWV$k~~}K_MzPFSmX{Wucr#hH81I<-R)8RaZv#KisCd zQ_DX&p}p?g>b)sLx;p~D%9QQv_tRIFPF9Yx=4mB#hS5$f-@8v%xwa>UYx-_&m;2>W zyLCV1UC=(c$L5*xqba`!*3}R9bO&8)e6RF0Y%?Put2DtVw0eC`F{ydA*PXeWo~&_n zo;^Vj6Sc0RD3?AVU;wY|5%k*S{d;mr5`3AaXG`WL)2vQ&;v&FHt*xG2hT{Vq`o ztkUo~-Wmo3c;mJ7{;IUKRZmIEWTE@EogXeW>7ChUaj9tg)kSWU8}!7K{r3${s@>U4 zyRtm<)8($Eo4acir@pUTtB-Q>X< z-Jx7zU_#B?t+cPs|541T_nQwm^tx)6O{wxZw&(7hi|ccjY^T^;&F)h>x%QWhp}PB` z4;|Bv3T>;sVffMi+MhSVLkCwT?YZ=9!ii=hGWDzTeKJdG-XFHK(f{Pi+wS{rU;K2{ z=F5Z(L*4ju9}<*1*WNwKe-@Os-1k`gwBIt$y{<5gJf^yK$U}5HZ(hLCu;>?cF;}>Z ze9w3JZe6Zt?w;fy_*7J#rE77yXk~oiz)Sln+ajKHPtnnKdaoMo?!O_g3N1s~OJQbiNr+Cp=a8f}G!22U?LjY(3yV(+k{_hD(Z z(#zSC_IGhbOw!(Zus{;k7kK7OyM18=%_7-+-R5oj_G!l`TygDUWZH`Gx*X*8g`^$I zsoBzRQNKJBQm|L${JJbGloot zd(0=(Ceb`pVW_5&sT3^|Wu_tUcBZnfa&zXC^g`ZSME@c=Qur`!W{u;I0B;fONuRb| zw^h(5U;XQ9XTu^9(_!1uFmhv*25WOrOMz{So@uOj*6SpaGQ$ny7)QQG^PQrjqd#50 z5XOLqjU9Iz41>o*!I^YPyPw|;?die}opnYpn^v3cvK9&Ilhmlr#;<^}|K zgAPv8W4=Z3xhB}}fIs%FVNcm{K1@1pm=*y;c*pW`VA~u1tS;D1T*Iq_qG>;a=45M- z-qK~Oro!c7JL!lq1H}^{V2mzJ4U^==q!B5oiQbrWH7un@B?WAxkvbb?CRhM0oWRuL z&1`s{!sRa!envr6t0@@OpRC7}Zm^$_Ss%=S9dKAf;ZY-3J^t5%jG69k)<@!+UI?r! zKI%CBQDY3VO7ca;E?0XuL>T-wZw%R*c86`a30hMcnq(D*|*(PpmTLtFoCTI_ri%ZY=s%j-6@I&f9#Iu|%iPJKsA z4&skeu)+H}++14!=r+~i9b9JDWAl+-l9oMBOB5g2I%Dz*ze84QXY8hXoUSl*3wLty zl6)YWeXb~yd%nKwrX%`OLlL>GA%!kp4B@drtlMe`0 zSG4Z-ZG4!?_`azfvc-*z!j@6IcVWx?<-)G-*4~$GGOwJnnq^&Uqm!z1d-1Q=Q_CDs zZG0*)$H+GGZuzM$*t6V#a#qsvY6EhVkoq{%ziTUxG`6C&jAw>dsJ8evtI zFa0Cs@}ywR`uUdO~?EB;uw_t@ig zzt-Q1)fI=IyneR#ncKjnhR+tWw&yrD>YgVrv-H1eS@~tz>GpTDAR(*n%P&*4LMqbA zbdQ_0Mjn5w|A67)(hfwtJbM4)p2Hh6RJ*@nXQ(?aaruaM!>odS!*jh;tByWzDt~hA-Hkk+wm8h? z#c76-y7!|wdW*kXz5e@$kT!E9w5QqLUSnpZato8PWhA}qg!(7m1>>9XRr?PAk@L~L z`oyim7k>$KU*1}H#OGJUt$#9SwM3;&bIc44m_&wUAfe5!h`B%M}M}| zf8G0X=it2P3RAM{g_f7+%J$VC{(AKFnx(<7!e@?zHR!wQ-AxO>(lG0kazancslay@ z&)}&NyB#?LbDlk`(z*DZ#HoFHoBFfryk>KXS>z$_%X57slyY&y!-nA~OXkqK$v<73 z`7ZFS#-I1JF3B<`>jW9l`cFlsUAY+47}p#>d_8XRyhCYbjF3^ryT`iqmo4_U<(s4i zHmx}Wk#Dt2~E6G0!N(3kVA8oL-kTl z1*S^d|EB7I{U_Di+Qgsy6T1B9*^85&s|*RbNC(2HLtO2x{hv2PuyIe zt|*n-so#T!8ziEf+mDt1((cjq3gXraHCe(P>$F!XAuwjGxpYZz?|xj98?*d_$8XOyv>bjtIok_8Q*qM&jyUf?VO)jr@>wygCq23+w8xMc zlId~b^E(}B`&Be%*35L&kFzjA*WcJ17J2(-dnym+XV$w>EXDKD#6B2->T3OY-MX|X z?VCe2CPL@$iQ&Nlv3Ji6iybNN$VPFOi;?AHzlf37NtHFzDMNr!rjahrZ!e4gUQ*c3gSH9 zagvPI?sSfGPpiJvlig)L9YM;nS@9eTr%5I3neth(*!oEJS2!c(7XDjX=4ZKQ+m`z& zFXqb4$<+ksMZtkVcK7_dmD zByg5R`*uLzO~`Cq_&KnTJD9ulXN^>Nrb=zpQkK>Ra^s!b!phcT!*I^dxs)_1O~Cw% z`a*sxft#jfRYXU-q)a0)g8VQqHVx)l4EwVtT=SWhJ$w4mFfbCXk#x*LC^xM(%|aY! zs5)KPOZA4`3umm%XQ&G)=d*xO*=#~))s{s>FdLMo&Xk7<%*h5pWl{;ZU_`aG@&31S zw;EToH(1yBkiITB;n}@-v*m7>Ded?$Fv?XuLj9$%Su^{Gtbd=0M{Nk_#b0llHR|q~ zS>w^1WYg2XokM19$(i4A!hYiDpJx|z z1ocKnP00_MXhdUY{qf$vC%V#Q>LgwY>T&&TP~rC758}usQwLWv95}11PJC%9Mnm-& zZm%qK=1n+akSi++P|8v}az7ztUnN8lKnp_CVD<-U2JlbGpan?03cUeoWS#z3%&wpct;^ zTDVb-&QC%)1JgUVS!v!2BMZ8Agh#)wjD4(XI&d*%Z~OG<+s-~PS z_n%&7)m@F%)6{n~k!Xu=4>neK~JeOoWk1bcp zRD<1hms3*0g0nIj-<5^7b4~}rPTs=QjAa9Zf!zZPH}og3|A(e4k7qLQ|C?cw3{ljW zno_77#jqJ2&Y0XGcd3;7zUHWOu(_2Ol3XEjN60Zpnfu6XG)L|un_0i7@Av!r$IHv> zwY?tC_B_w~^S(aTdqJj^Cu6GL$fft75{TY2QS+1aI!R>)V9DG^{3BujFn4=ceEX9Q zoOJD&I5lf|V5qcE`u&c&|1w$PrrCA>5g&7-+)ewNC8n>n&e>n~(EX?oWon;Bh3_y` zoY#v>nlpH|e_REZEPv{-e=qG;QAxNaSXSg_=b8+jGd z#k??GWYx;M*(gA{8D8pBaOdvh*~Ts{Zi1_;;`WVc{LypDCATVH>8x*&-}@Wm5{!mi zaq+`_QXP2Dk!Jc0O_}MFw_jfzG&Q>OoPB_FcH`FBvFp7Tj-lSne(v|4P?H76n_J98 z2xrfvzYbs0bBfowGPQ?qzO|>@8q@TW+G5_3ZnR<{|1&#wYIMjG&vVW+xDDPuot9_< zIW(A95hz)XHv?`M16-HuIWAXO6_D3a1e^YR(j1nHCR^ zAC(E13zIicru1cBB*$O-3p(cSfD>&(F(0Nkr}4>FD|h?GVoHZg0>eZ+eAQKT49F4K z)7q;c(xloik0-8q^ou)w^;Bq8nY|}6vE?XXNAgF)1*EzCB$Yd9poq|Qvi%+_5v)2s zH?N7&?!X#U|1fch>}W?v$W@{Lf)W^m(3Fz|?IjJzDiRRB6*A3%6d(5!=>Y9^W7QIW z?}PJq#*&d2Q0@|#X-u4IY@fk`{@eMvlHouwi?I!2oKTD4twe@KO4fZsFzN|<%LU|KXE zNV&jp=pbz1t&vQ)j-9Lqk2d?p#0E*$f5mjBo_!i+&8E)oi0R-7^k#b97LX_8=CzA^ z)=-g95$v2pmX4y_<7uzodVGhFhXIX1N8)4HP6O}W`}<`O;qiS9=MHOXQiqX87}Rp* z3gYE3>{0LKtWZsm1sN*MKvK;rDUkT$L|We|8OylT8|O(CBDmf(ausxSJt(DSBZG>HL7xMaPh=7tP0- z)Ta4Ri}XPA%q6)iZF3T3a}m5I=Osj2h?&c4)~36!e${k76A`z+oydpcC?zxxY~2y0 z3RVuYgkgxfA)m9c%swx&Ly9fx)B`-47C^=7dfkAC)^aRI0L9GVwzAH}?ojEo$Z-lCKQ*0@~`3r~+p zoA%_6=RJhnm@QUJD2)ieq8 z$VC3Q2URvuUr;)aJY6g8QYpoCMnnA3g(_s2+J?lwT_{9|ko@w!^LMg-QM|G;S=@_Z zmL@CPHuMwf;NK>fQ6D*+n&NyAkg0U4_8u~(F{I~q=%NVfCmABKv%P4k@Kx80;8nEV|B>N^k=Y8sBR7aPv|9)-Gysxa?#!$ z3?qn#g#DH?G_PBbXz<>SwrrN2@~hhuXLRV#oFfsS?wwlb?6HERz;MDGxGJN0IQ3I; ze%Q@08dAz#g9@T1`~bPWuWtIS++>H73=cPd_ZEc?k4$6J3J&RA+cT5Gil`>W!>qmf zh3z}8+*6cN=N+d;J_C-OvGD2SqMUVb7jZRNWCsO0YMz}4=T8_Rcm`6dFj{?|l1

E`hr*FP_v zJ@$6}_R{6!eAk~Z9Y6kZRPlcP^n2Hi$p`qHoc~Du*1ARY8NMIJ5Ap%ve4b;|=dpS| z|6TF{sOR^cE*}8S_p?nt0G#jh3HbnUzW-lq7xn+!zsuk2CHVl<^Yh_gz!@0C4I4QT_k+IeDM1kqy&V2hz`2dX1{2U(u&V2ql`2cX{{{^WJ$i`>A zpuQpFtS|5Z7>D(U`jV`k^~>tCelgB^2OofOSRXBu4*+L9g%1E{{iQxE8(+GA#0Q{W zx_`t6fU_RN2Y|DFTqYj?F5N%k1Hf6I;sd~?`$v2LIO|{a|3^6MWqbfQ>uY=fIO}nI z0JwDjhz|gl?jP|1;7%WiaP$Ox066*sJ^&oO0v`a5zJU(_m+l|&0pRE-_yBO}{t+Jl zF5N%k1HjR9@B!fHKllJ}>HZNP0507heJR479%Wp*f5Zo%Ub=t82Y{oG;RC?Y)9?Y{ z()}Yo09?9%#0P+*@8JW$(F5@T;OK|=0C4I45g!09-9O?3z@_^~d;qv~|A-F&M=!+( zfJ^s}_yBOH$40o*Z;ea$kN5!8OZSiX0C4I45g!09-5>qg#+U9N@d2nu-^K@kOZP_) zw{g(V@d2op?jP|1;OO)C0C4I4^!3~Lm!|%I^nc^h{UbgA<4E_9Po;eZRxjN@;sa1G z-9JuD`xvZV{r~tK#`#{fPr|r#|A-I3_}X_7wGYGUwNE4dEgyh-{;vF;;amq?7x3d! z|9>2k`u}bG5^0}FoR#*S7%wM(YIE|ZjIWZPRWt2tG0t_*eUMZ1Q-JOt@d4n{{UbgA zocoab5-#08Y9CL8tN%Z$|KB*z1)dYsOZSiX0C4R)!hf`Jr29wp{~PZse^dSc#*dUA ziVwi}+BX&P0pO>meOCCbHqP?m{;YB7{t+L5aisf4_5WLaskBcl&Pn^mjDIA5`Ih7_ z8(05-#0Ow}>HhfFR^Kz}{t+L5dftn?C*kV(oDh4*>6(bpQB$+IMK<%u#>BgtSl5xb`v9&tP1-Km8BJ zzf`{jK0r>JUXZ7ry#ahzs<_D@7C(2`_rFe{HUb+ zN9Og6(?;|D@o68h)eqD<;nK8k*!UHx|33~*`;3jR(K@7Z+Lvs6ht@6Iw2sLs`YDLl zXr1$I>i@U;FSIVg2cZ5Ft)rgQx(fcQ)?KBv4uenCI_;{oZ`;Nnt^Tg_(>`zGowW|! zqje$UbW;D=Z)qR7)wj?(vuoOSZoEnH{xswDw2s}XbuHt!RR3F@v=83u8>?S#kbD5@ zzfyl)iL}q&>R;75d~Dj6Z@jP8?FCxLGfpq9^AC{^057q(C>J1eUdjouaW0XL&?Myw z7{5}w!`zfZV0?Y?ensOSslO2)AgAc3AYLLJ>@n?(o_Zsg~ydT*3ci?&|-T1!Z{qV*=F5W+HyuWmIboZR1pMtpO*P>h=`tz;6Vex)`4PlFEt?6xo#m7H`{gwGYEx1UP^5m2e7H6#2gvwv`F8&PZ7rVgW7elFwW{@?>J&Q|%v_yF)Psgr8e{znk$n#rMY< zUnL(MAAoT>72khn_3h=;`}g%O`TF<(jMG(p0Q3dGdl%n7XycDp?vI~ue5SsHl5T!d zPkjvZH86ft<^K5jPj2!3lJ@U5F229g_zCL6xYEtX+N*EF&)+U6zTeZvd02fR9o_u! zHSH($^U00M{qgh957ejP=c}I;-`{HgZujE*VU7Qxz8OD%cKdCu{xa>)eZbAn?@;bf z7dM}8rTxEt{_pk++xXJ`!`zeuWc(cUA^G*lD)lY-^^4o@Y~!p{Ulcw-PSH<6ET=vy zd;qxHe{JJjrQ9EU0P0JsuWMP_-);47Ke%z}{sA9=asH=1GkgGeUF}cz>r=O%-Nq;P z2OofOHmgq#9{@g6`|BsV^)!{kc8m02!C=AMgPfN4kH&2Y}NTs~jL3N4kIb zPwM|SF5N$LS066pNcRu;0Pr`}*L!v9|F?0Z`-h`a|G#nR{sA9=alTaE$d=UqZ}rms z13m!t()|NI0KB?#f4Zmse;a3|a({B2URp-^D!)2?HAlHWvz;E>Q28yxoPIk*c`pM~ z-i!UaYtKz-=cOZN}>0PxGz*IqN_+t|O`Q+YT+eeu+j zpYyWQ&!zhZd;scODEDW))8}2DkNvy!{o?~Lj&%Qk4*(yhd?D{IxI7{oU%G$52Vk5w z#q*A=-sK}1C-(;*fN|z4_osCFJ6OGRfAX4)v#$dmfN=&W_h)FjF0%E?{lN#IUb=t4 z2Y^fW5BLCZ>HYy90M5P_d;mDPKkEOFaB_d}0pRSz!3Tho--QnVC-(;*0M5Q3d;qv~ z|9}qwXWtP%0G!+(d;mDPKllJ}>HYy908Z`?J^-A3R`>vL_GRG%z@_^Kd;mDPKllJ} z>HYy90507>-~+(P{lN!-vu_O_0507>-~+(f7l#i3C-(;*0507>-~+&=`v-ggxOD%3 z4*)0k2Oj`V?hifyT)Kb22Y^fW5BLCZa)0mv;Ou)GsC|%d>HYy90IqpeI7U7IoZKIL z0QfBBH{t`p+2@H50B2t)J^)<0f4~QTOZN}Wv=0@|K2>}GxOD%34*+K$?3=0oKf*O{ z4nMm78O^gp2l)WhvoH5*?bC&m`-2Yv*StRj*Z(8kKi~sUFWo0PxMqkHrUo zOZN}>0PtUwPutA(r%Cq@_yE*v-5c-$;L`mAJ^)<0f4~QTYaJdYyM8?B{^1VSzbD;4 z-~%v@bpKFFJ^)-gLg?c938njo?ymokoPv$6UyNRm%IK?aw5)h{i5VccXIuu{ixEd!uPI! zm7I&_uHThhjIpjimK=?H%Wa#;Gier$4El;3XO1351> zl>5T>NiNJ7*B?%f%onboT)K0}l@Gu;-FXDYTOYo1?U(!VgU&B7?#??f z?#@Rr?#@#%?#^E@?#^p4?#_2G?#_cSUQqn}h%6u4xM_*s^MlTtFz(K$Fz(K?Fz(L3 zFz(LFFz(LRFz(LdFz(LpFz(L#Fz(I=G49S2G49SEG49SQG49ScF+O_k_n$SIpU-(H zSw7^kJI{D0Ka{TeWS?sH{AJvow_@C#&tlx2=VIKQ|6<&o7h~L=FJs)DM`PTbUlY0S z+<7;~3rd{+!t}fIgU-h>?#|OO?#|ya?#}Bm?#}ly?#=@;?#>S~?#>%B?#?GN?#?qZ z?#@3l?#@dx?#@>-?#^Q}?#^#A?#_EM?#_oY?#`1k?#`bw?#`<+?#{O|?#{z9?#|CL z?#|mX?#|~j?#}Zv?#}-*?#>G{?#>r8?#?4K?#?eW?#??i?#@Ru?#@#)?#^E`?#^p7 z?#_2J?#_cV?#_=h?#`Pt?#`z(?#{C_?#{n6?#|0I?#|aU?#|;g?#}Ns?#}x&?#>4^ z?#>f5?#>@H?#?ST?#?$f?#@Fr?#@p%?#^2@?#^d4?#^>G?#_QS?#_!e?#`Dq?#`n$ z?#{0??#{b3?#{?#>T2?#>%E?#?GQ?#?qc?#@3o z?#@d!?#@>=?#^R1?#^#D?#_EP?#_ob?#`1n?#`bz?#`<yN$c^zKy%{!Hv7~#ErZ2$Bn!5%8k48&5gHyd*IHO z=T;4RjvD72Up;q?yYtqKyYtzNbB?dx1I9VWSMLqu?!0*8?tFRUoa3wam~qbW)qBr4 z=lJS9X?*;GaYK5|tQ_=SHNN@kb4TsEzjDxf*f{6->b-58bA0ulH_kb}nim)^^Yy%W zqZS+)mWfS z-IKE84Ba&Jw%)rc1zGTPs1!8MGT!ImLw_pQt5P^` zS<4E|53gtZ&cBa8`T1s*g646?E3N(X#EQEshKe`bw0L~IZ2iY)9a81-Cn|<*7qoe( zMw2Y(9AC{VZJa9~tW$R8Clxr?*Lc}R6>~4_RUv5JYW%$otC~;PQ$A>(Yn*d@H7_>a zs%+g`ZfH`TbA633*m2_{-+xptXx?p{b9^;VH{R~u319xTqioQ;-uRe%hwXX(k+MPS z0OPd>cWs!{plr~(!T1Bi&%C_u{4zo74C6J%mws$_=Q2U-662SSuJ!ekzZ?;?jxql2 zt%I9vx$lT@Q|Vi~U9uwkJ-=6O!!zZp9}%=pvijct8e4bs8>NHRRmN8i?fKy&XO<3H zhZ%qEn2*M6`|9wZb(`@^N`5l3?k$H0t@Dgux%H5_6%U*@rgfq51)aw={N%|}LF-84 z9iDFc&3TPW1+6=c-!-HB94O*8QKlh~4)5;Yb8nli#UgGkvkJz!WM9{k5IOq6ECosNd zN1dgoZ$2bQS1^A6M`v{zfA1kdI)w3=6@HoiMWsW6bPMD2ckKLQ^~9Wzd;f`VbQ+p( z{I+3p>t_mbf^-q%m)H9Iovowx7fMGlUa@L}3u;aOyHL7|@t;aw*LeG~eTC9#jB}2! zbRFZLuiAO`(i(dUr2`qiX3MmXmbcndDBZ|-<*##dpX|1~u0ZW1I&y5{CV8QQ&(kYFP8L_PN)V;qJO4l@=*);X>=T6&PxNcH| zDNBFJexFa2di~C`2W~2qZff;!?E1CrReyr^GbGnZTZ&qh0xBu?gmp^Rw zbD?xx-> z>Cnax+3@4$!ShxYO1CzC_u6*#?`XNAP&&8q8%vfd*X)fi3Z;u1KX>`lvj?B^S)p`v z9auHF1X>GZ~Dt!-JiS-UBP()Epx*f8L)O{+dozR%IdtET#luWymY z&-n4*>UZ_yJqkX`_|H=}eg2I9{9V%ToAJNz4e9&I`0r=)`l9bMHE+4?|)VL zdu9CJYgGFCX8hlGXSyCT{(1=M`pNj~XJ)$I4m`3(R=#3O_PU*vuFs6WJ|9lkbH-oK zv(xpTkq=ZoBHdpZe}Cy*?To)4-%0mx#^1m9r29SN@AsqA^C9D(4`-+6 zNya}Icjf#Y8UOs5o}O13|GcW3o^Kide4Ct}hZ+AooSmMZnK8)+uG_}W&;QRCpK|*HJN9P$`(jOczhwOTrEGd1W&Hc-lJx${ z`1jW>>3tXd`)+P}KSucg_WMpx@6+htr}w4zZ}jiq)#-g5{rkGtzP~F@yP`z&@AsTE zABcWFa88;(L_dF+nC2VN&o?FvIQio#;pYL6i=7-VG4_`_1$>`^k zE7JTk`uS(CG+&K=zM4t%+vw-F-=z6)ln-FvPvh1M46%tC&^Svro?fQqLX>7 z3?U)$KBgp@LWhtJnKM*om0#Ch_vhKyetuW`pL3nhdCq!wdw=)$yViX_>s}V;{|Dyv zf*rQ;)w}eXSe~Ev*rqt6PQO|f*B6%8BNo>qd*}6w$p`R%m)1KL*E{p_`p6Dhd-5*l zolyR}_49hl?i_pIo0E<#^S*igW%2=h{5y_0Ws_HTtYdM#_Htg|nS21Rub_KDD?${VA_!Ev{!r=k>3}_3tfty=?LUeEeVY`r6|9TI+F( z>+wzV`rYIM_&D4A@#xrTKh(3$S5!Uw*zV=^xby*w`hfHVi+aL0S%0voKir)43cIS; z!2SO7VoU$K&+WC@vA2C%-=e-DJ;b6OGG^v&&t21{fkpk~^2JS0>2zKL+j!E8-CsVW zJpLoI`qX{$&IUHK&KJ8h+@Z|BtGmk~8^7JaqMq}~Zp-dyTWDzSpTG0>3%}pm-{%L7 zinrF@si6)1Y*pU_-YoOyPw%r}-U$sY^%=0=phwDFdXz;yYPYOk+0f-ZCVVucT)+E| zJ!;MSs^Pl(L;VM~>rv(dj+#AoR->)#n$K$w?zlsl_uAyt!np@-Wh>sgzxdS_Wxo4u zm#+Th+^wv`Z!3=I`%QVDX0z50*FO06t?aehk2kz#UYU2S+V+(J3%0V33;Vw_;|8rg^$FO5I-q%wct?@LCWcT^+my~~^XUbtVG zKe)lem(Co~$ZoiDaHp=#%e;NoGwrK0FZ|&3Uz_{qeEt5@uKD+(M%H2W1;gvUTjq~k zwng>dHf(HBFFj#U?VYaMp|RaO^wE0rt}WL~kF}`BO24%}V@JMzc9(MfL+>8=c<+ZB zn|uI&KhlRS>ci5L?bLgZU;NL7&AeXvv)!}tQ#JqdV42_Cdg_}?dNncm0A4SB+a6r{ zS@-t+%k@_*`TKUyOl@LImY>k-sy)kG`ng5@e8BA^8{hM16YF^VMVnpwZBrlrz^fka zzEAt6CLh4L^nCk&dVbdbO+J9v+hcpS{nwPH_S3R<=TAJeJU-(_e?fkPouc%q58#~ZhU$hvP^oOojx3nHh|22Mk{RYl?F3JaR z&U19Ko~w+{b5}lq*Ylk2|Hb9s_CK<|bDrxw=NSjxe>=?!;OPGH0el?h8T)1*z&Z00 z`2fzD$4u6|hVhyA$OrKH_KS|#zx(1=_Os?q)HAP=58(Ca{sT2Hqn>%2d;qU!p4U+G zKI+l^=WCt_pL%G&69(wj z{O6x};-R6f?Lf_=sb`-3g67?D=H*9go(@O%@2+`0oO!=|0E4qmVBG*`T_GR9>sg1? z*Sdsy)-8u=9Rp{b!@38~x@fl6NpRLt{j{!vv+iOY24|fnAHd+O>w0RP2WK6~x)9E~ z(X@_)v(7wH>rOc9QuzP|XC2GB7S6g?K7iN1*<-`IXYSv|KGnLJde+tVX`Kyc9nQKO z&bnPbfRF$5Z%5sJP5(Bwnb!T(qYIoWodAxGuvoeR9Nl5GbO<;)#VqL-aCHCYq;tU0 zLC%ye0!KHI58(Zi=q$~oyHJlV^SN{yI6BUI(skhIJ|9R2f}<0ipM3!TyXr5sZKN|% zj}BEQ^rNEtzabrqdUUQ=rF+5A#rBX+21iFT>1uFvw_(!Z;OKOxNVkKd>+LO_4~`Ca zopeDsy5U>W5#i{JKV~1mKOc0-{iIV;kB)htbWJ$A|4Y(A;pn7SOE-n1t3D>3)wy)o zhosBG(QP|O$AzQwu8{5vM;E?8Ix!p_`BCZ0aCGOo(xKt#)U~n?;GYk=_Lb7PsYeGt zT)H?M-TXM|=x}uQPUmLb9gZ&lfOL8|I{s&$lzaexKly)eYuN{QXHKlYZ%OtI;Ecbi zddTjg^ZYtOJ~o`^-D&c@;pqNP%O{8PJbhfgI-KY42>I}Ep4W@y+rxRj z%LhnsbpIOa3xFRywZrI!$F;F8>LY+N-?&YE2XN*m@&OVY-G8b28sN--yL{5xW~t8w&V2Y?^~Jys z*x{W{N>g$0s{~xD5AUNv<`i9`FFXRIxIO`Gm zlHfaQ{gTwj1ZTZN-xHkm(OmUO!C6nq2S{+%U-V(Y(f#EEcs;tmd;sUH2jv4eXZ?7- z`p6g`-CsU{*RwvA58xc#Up|0y*1wmj503F!FUtq;de+zS0i3fQr!S9j(Ea5Dcs;tm zd;sS`A8?MIKwlx_qd&+8@OtzL`2fz*H{=62NB5^MlJU_`GBnoTHD)2XKy_CLh2#`kQ(b9w;BcIl8}m0O#ob@&TNq`^yJ#j_xlXz&X0Vd;sU@rSbusqx;JTa31tn z=Rv=9j_xlXz&W}GBnydHgCK7ey{fBO0v=io~H-|Nx+GBnynffQEst-sLla{k2K6UM_aBtMH?I%=j`QFLIluel z{tfQAys`Dm{*m(yM@=2_MDND-)#Y8E+xN8Q&U;JupOXD4=hNh89r&+FgKN(y*9ZU0 z>(Tw$H&ZCRiiYmbJ{x%O<9r-+fA;ZEul|2y-w!z}LiXPu+_%Lj0d?$16j#$O@7 zc=^qH-qWOZORt|Le|bjsmz}Hs-`JPNIOzU*j(PnN(*5NFI1m1~bM^ll`|21U-M_|^ zIS;+FwERBN{V%`w#pTs&%6!7y-I`2kUEjXx(qiU`&06{R>i;+P1v35?>K~8~;Pv}S z_kZt;OD{clNO_!J)Ss~G_B8#YYs&mTmHiAp4!XbQLC$}X?k^v}`9@1i{r|Sb=%)X< zGBnync9P|AzBMmHi&hU##p8aej~bN#p}KNB7@p=aaX;=^w3~H>&JcasG~U z|L(cJ#W}ja=Ecs@{WXtv{+e|EE`PuKw%Vi0e;3_f^K`F2Ou9e&dJCmj(I%_^XV}@} z?%C(g@;DvUFVyFuZgu{cUgne3Uv$jk&c|LqtIS(U_wV`dV^03v%KSv>{`=>3jK806 z)$eqA?*DiGVr74n^K+#8_s{Dp=U=M->XW<z|kIfA+97KThpduAixXyr-MDoHX>vGCxlJdtLK7 z-p6S!-T$fV12{ihx_^tycK+y{)5_y4m+pVen7P}m8dByrN%y~C@6m(5JipAJuIyLz z@#jhRmk;24gmnL37d5=>`D@DK^sVfl^!lwU`z@XSA>IG#?z^{XG^ISw;>vzbum4cG zf7|wd4m|(ag_3udcC&z;N(C_N?+g0|*I)ACM zpVs*?mHoHQyH@t=I`3H7-|KuxWk0a}`SpmAy+1C0KB3>(>;Ifw>i?%d+4+FV zerD(QRQ5kRzpk=h+WD-?{%YsfSN3B&-?6fP+j;2scHUdM|H@XaH#_Fn^1pX>Wk0#s zH?8bHcfOT$f9clF7ghGRJMUB35AS?%W&gbML#6vmcX$5i?WO*I`tzOdTG`L<{E*{! z9oB!|$I|`VIzL9bKRG~!(yM4M+*Yx_!0X?W?jPUJFAFR7FL-@R>HhKooKKPNFCW19 zV(I?z^IRg`Up|1>kCN^$AHexu(*5K2`+ntq5U;N%-CsU{^K+#8lLJ&Jy^3~=bbt9& z<$CG<@&TM*CEcGKAjat`-CsU{*DuMxug>>9bX~vYpOvrAh0^^uI_U6ApITbxw@der zzu$h+{pADr_${RS%Lj12yL5l~0L~AT?!V-tp^aO=QU1GOzmnHCk?t=a!1-k9{age7Hcy4d4_cVIA5J3-JcwwLg`hs zWtID3{r&Wo?jPsRVZW`{kJtX(USWQ|jdcIx!+icm>Hcy4kM1AW3+Voj{JYKDU9T))HTU!EKw#$ToV^}mGm z^jOzXfOB+z`2fzptjr&9j_!X=?*DiGbngF8 zzijaC7me;K>k{bx@&UXa-M`CSjqm>3pmIIBzkC3%5BUtv(fwBhJ?Ep`|DQh0{r_H% z?k^v}`JAjTr2}&RzwGBnoTK~82XL;w*hCIcq4X*my8o)& z|L^ta{vC7wzjJhd`2fzh&3(OTm)!sF9Nqt>-2d+!-CsU{^LuiiaXLQt|2s$bmk;0^ z-CsU{^Rc=AKXuIg|IQ!CdTHt!^wM5gUro0MeYKGF*z`@%V{gm)ZR#KN+xv3=f9ja~ zzWwhWnES%h$+=J5dGD+zr@=u_{#(|c)3n@&?)B*Yj_!YL(9hBR9tPZ*5?xa5p;iYfT({!pZm`G9^mNy@&OV&(Tw?12{+bmk;0^-CsU{bNOE>`d{e&GBnoTK~82XHRGHOU8XE`K)3 z2XKz=FCV};x<5HUh0?2N=>GBnydK?OK7ey{e{z5r2i>0>AUL|ed;lLu{&agT)96L_0Iz3WRYUWvLg`gBbbt8(Uf-nqLtXx8&^pNnaISujBp<+e$cJ=}?k^v}IlBKz z$^j~rUPbn)CiwtfkM1uYz&Z2aZFB#>bLP!mL;no(>^C&;F8$fUy8EZ*ag9Nk|&fb)sDKP_Dm`qR+;t6W)&e8ql12{+bmk;2ab@-E^9}nIC_|U(H?k^v}$3gc$S2{tV^eP%U!uO${5Z!-b z=s!fKm>v2R(KTKT{f+4Uqe4HV`X^Jr&_DT~7fw8+YGRwzD)d{T`>ziDndmqlg?>(S z|BpleC%XSdpUT{KhJIIcu@0d>R{gYTZs?~~|843O z`ft(c$N}PeMAzFf^!K6zo)h|k)jymb4gJIFH%`k!zp?t0(}tlx869&_=x0XvoF4k0 z(MiXLera^o*`dE09d<(K$40l^E%a}z-#e`i{od*iPklpwI6Cr+p`RSxxvg@5_?*$H zTPg<#j;{S_=x;{{9~k=K)jyv`hyHnV_Ws!i@cv}==coT>AHX>}{wU=D@qWnvKRM_B zQ=d35U_w4Gz&Ym&w9vV}8Ygic!NWS&7tZ+wt@HT>J`U#{)YrMb)N?+?Jp2dAS*B8$D7h`m;FP!r-F4DQaaL(7zIotj|IgjH(o$E_I=Xac|bA92Q z_i??>^@WG?L41796KRmo6LHS@Bj@GwN1StB$-i{2FXMB*Nm1we!Z{D+b)D-A=lqnu zI@cG@c`My@t}mSPSvu-mU%1ZkO`Pis=lqx7b*?X5=lG_%I@cGjb9~c&I@cG@c{Iax zt}p!Hd~SW(P3QW;NB;Awn>XFBd3spq`ocLM=f67F7yisQty=7~ee<-3&h>@w*lz6n z6IwP;d+S_ZxX$rSKmPf@CZn%i?cYm{?scwQxk>Z1kIwa_e)n|o{NAgZr5-xh7e3*j zLoU4J$7bnzo$Cv)na}Y}Q+2K{{F2A#U%UFh&C;ei*B9RE^zZ(5+-uEJd!6eG|EW`- zNB;R-v$U1Y^@V?<^OCmHxxVne`5fQWM(6s%f7E$QU39K5eDxvA+8uIrv(#PZ`oeqU zb9~dKI@cH8f9A>8K6GZYbeqogg>O3R*k!BxHcLx&t}lGz5zBVoqDQmzg3k4Yb6(Z! zI@cHe$%s!oow8H2bhOU(h5xkus?LwLYL>pyxxVm*pT7V0oI1_YqMJHjH~7LD34T>R z$2WD;xxVmY^EtlhES>8Mzjg0Jd-hw>H0`c)ec_z{)#QdBu0EoA;{@ltu;cT2Va^vf z8h85h)0(FJbgnP;4eGYvqxLOL(+4`&7tZ-*oa+m3efF#?z8Tsyy{U73;Y&~XsNY2= zH%*)9TwnN@9iO=AwZogH?eckQ39fT|)5$v57aq=Q^ZKQCp0!W6MorUZtq1o1VacY> zb&hX(>)I{m-m$}G&UKD&`a$RVGXA!w{QCMWi<_jzI@cHe`JWpGkm|cGSEf^*YBl>HIsdzdfJho1W3RzSQd+-^97TaGm3u2IyR0csRe$$JaT&={cS2 zOZ_1Ow|VN(W=#_3`of2$VIS>Uqe=Q$=la6K`GY<_=M_GsbA74buU_lhj;?N;IM){* z&O`KZI6v{$wYRMNymM{mI>$FX_t(oqUb&{sXXkT#(}pjf{_hjsDf4iiqmMK1>q8D6 zyi?=!@Hf}|bk8YuoL`yG@lA_$u5Y3ADkjdCEb3fecsP&J$Eh=Uzz!R|)+jaTHmpn6 z1L``z>%s%iJ^s-~Y4A;ZKmXUMWggDQ^!ncU9N(n#G@XywV$H!@4QQ0ET|H=vPH&gT z>66d#O*3__Z=v)mrf|Nek8?@m{)Hv=8>KtDuKw(gJ?lCDKA+>87VBJJ#tG+*dVM&b z)OqiGj&EwNbA1_St9*`c`b6jY!oSGp_@;U~*BAa+KF2rJ(7C?wQ96(HZ#vf(exA;6 zy-Mf$!ozv53BEd?bE(n+soH}+aPhSFP!sy`A;}?{QuyL%inki&P*Cp2}oa>nD8qRgk?*OiId=tMLxX$rS{LbJX z>37KQ5`MRSw-@Vo4A(in{=0{BU+As-1f2T__Z9d~UH-A=w}As;;-ZKJf9)6X*KEhvjp86Z0VW zo0=yvZ-O_|yoz}ie5K}Loa+l;q3ffD@B5g1 z9?86t`VN|RG7p7srgjwBCT34{nfPbKM2-PTHu!~F*Rjrnchx$Obs>B|ts7ZK!dq&c$+{E1jn<{CQ{fG@j%8g7XWh#> z7+yo`WY*1a*43=D;jF`1m&1?Jx}9}AoOM3yemJ@SIstqi=?Lfw@V%rvphLhvl1_nc z0e@V&2Iu<1_mU2RE&{()x(PZ8{2$U;&|ToKOP4{XfurM~>%hC`b9_6a1HpHaPK0g* z?+TDl}UC48K8Omt27qtZP`6^{Mk z;*ZOp=RE18=%&=)DP0wv75;DOu;{YzS9%Q{b;(6bJEP;mo8@zSJEQx;zm_hHP7Gfu z9hq}|;jN`RqeH`|OQ%M+hS!p=jm`~kBpn=G96nXLIXXHVogLjBK1I4bIz4=xbbNGu z_~v&G_~3<4pW7$@_ZA6WSLgo5`V)0da2$WlgQqU5Gs3Fl`x&cqh~xYJWx}LG>R$3% zb^Q0Pp8vnOy)S*MI(|M=bS`rIJnQNl<@ou3s&kj)_w|9!X^!7-EuHHeuLsN5Pj$S0 z#_HVYc)d;0In(j_d|u~L$Lo2T&auV^;PbyC|6Z!&@8x2hlO2Cw(*3LB?{S*W;f}xG zAv(7^{@$D=*nzZ$D^%H#cQN1baP?}u}B z4*G6w+Z{iAV)_2iQ|G3~`|SZbXFcAZrTbUM`}sjS$35Qvf62Okb$njT&(D|Y_=y#6lh{?+mM z-YM(;)p0%`-M>1{AFj>1e|4O1+?RF#>Nr0soX@n)y1&KwPyIY!vi~<<%JVCW z^Q#tlK4x(~wojhFS)9KenCE*I=X=BR{Lte3P`bax`Q#^A_qRCzd@j#dEzVb^`&*pf zO7}N>0KRwW{ubxY|IPDli}P*i{ubxwgR<^#aXv5I-{SmVy1yO&=1F&*@p^e3)F;4wkBhvjXu3xm?vAEuOH|ze^NpQ~-V4T`{J!o+~_-DT z12F#VyuP-$zSer&;(Gj~ynZ))0LFQBr$M79eX)hTQFns{tGhNyaOndU^#SP#7WD+_ z{ucFz{U>k#+9g|UX;-{m{oL^H8vFQ9WZmDQz9Butq8>8*#hu=H`sXbz>L=r~?r&fI zd*;>$-cTNYcGmsvoXIC_e#-DNU;9!?_qV9$^x0#@J@>b$W9PiSe8BGqmFw@zy1y-u z55VWMDC_=qwR`}$^eBsZ)X1z~*@Tn#eek?xjr?<{o%JqjARmBn&d9pI-7OygzA)=) z_RP*ZJbCe?@;Du`{$_pT15kfu*8S}=`2g@4S@*ZTLEoGGUAvxpHm+w|-?943w|6Ly ze_PfMZA8!y-_LrZ{U{%R@qe5C(ROP`)U$DSo?GYWkG4wiPPa`dcD}Qo&3yUsadWK9 zn`PbK`p5^M{^P9sThvSIWPR1Xk`F+=^jM2}tn^!p`mJ<-!v|m->BAQFVd=^ChkO9) zr9WHLpKH8S?~1*@uW$GO)JxyCsBfQ^^>B-Nxb$<2`uQ1IZ?^?OZ!c!u-=aP*J>R09 zFWujw{@*_P1-A2rf8E&op$3WfuW=0@04_hm&Xf-T*ZZ~uuexy19X-n9?3s0c)4$ut z*XLq=&EZ_;JVHX9{?`h-=aUI>)7xCsF&_<_yBO}{)P_#m+o))0C4I47X3K=ZteEq-|2U5 zbMo&#F^=vF_E_)-N9Fs7T^;?zeBZH-@&On}_bGcvJ^)x%W2cTZ_4C^N!0Iqq7;RC=mkFg)+1Hd)!F?;~{vfM9W3q!wz=2eCd zK)rN-`!V!mXx?V{0Mu)qXLUorhjf3dEgyjTi3^VFy=0%J7W+vw@3b94|B2?QRyXvk zXkKfJLVt_q!8ZN%PdEHv-FdV8-Fmry#+HWu8O^h;OX#=JyxdL*{W;S8t!3!v(Y)XA z0r&wGOe^kEC^reHr?fw9c{E@1%8+%@6%iT1VOJ&`+gxmuVg5 z?^EkE!w2AfYF%d+hyE_D15N8fA4ls(OQC;E>r87G`pvX1HGBa6uGX=pb*+!Db+6$A zP=8VGm$NNGzns?9_F(9*(>mO=F897Zt=kPBfbloV{eIRp^!rH{u%AMIpmYS=BlHtW zcd&gz|DkjW`z-V;O82)XLVu%l5R3hg(oGB>fcGz*#d?H(OX)K9rhEYErQ_J2p`TN_ zj~y)^fO_df);aWxs=w5Fh5l0MP=N>-3 zS7GZF{{2dKv$moCRyv(6%DSD`OV?XBPyXwSQ>wmqE*;RW4E?~;4ejmFKP;WmzLF2X z=O$g!t_=Oj(lPBe`2f^Q_qUm$|5-YzS?HIRu4?y%{%Yy4c2(%dmTqeg%Lm}^O6Rqj z@&Vw|g>81|50{Q?FNS_{>CRRw^q)(owwa+{UAnf#{&wl$_FdM+{dEE>*MEtcMHotz#H-bii!G_Elc&)d_Ihy)0MzsSPL>Y< z=X&@e_y7AiTtBzV2cVwo?E?7#aIViu@&Vvn&okr$z@_`!z48Iz{9dk?4*=))HB3GL zoZsX7`SNj?C)Q||w_LvkO1kI#H#jC=ssh~il-DoLS?}NjFb?aZh4KO5tf%k+;HZSV|J^-BcAU*(`_2WYM0C4I4h7SN|eTokNm+o))0C3j7>i;)5>t%cZ zIO}VC066P$d;qv~f5QiWOZPW?0C>;`433_F4**Ahzz2Y%SKtG{(Kqk`;L`mK9{`Si zf)4_yBO}{)P_#m+o))0C4I4 zh7SNoFU1FdOZPW?0C>=24IcDc=hFQRAAox4{)P_#m+o))0C4I4=+8dBbbrGKpdNi2 z9{?`hA3fa1K|jX_pkBJa;RC?Y=kWpH()|q|0DjCdkK8hLK?C%EgG=`}d;qv~f18;1 z8F;;Pf5Qi$Ub?^CmG?1tz54(0JDl^mXrF|0>HdZf!1&sCVcLh`^>woDZ)@cPP|x?u z_YCJc;JScspZou9bngH6@i)x-Ol)S}cjCO3{Hf*HpK`ufepZvbuf;jnJ->rO=~Y~p zo0I)Ad;qv~f5QiW^E>2s3776~+Q(yX_5Yjp{W#~ozHdZf0N1`F{6`;0y1%La z-}w;vo9h2}UQd1~J^d<(IPX;Yv(Baa8$JNzNcT7O|9gGSyid%Y z%KOHgzbt?Gtn4p4SO34^12DdHfBb8&@1J#l!v~XWidE*>wAV^ggdV&Qt17 zxHIol^uMcpjPx@&m+nvhgY$RQFM$uh-)*b@imJTN((9%B8$JN_?^pJ3czylKeh=rH zRrZHCAE>K2)!-U)itX{A0~)x6b`7&ZYa)593_AzcG(4lwQTuP5n0S z=Y6+cFWsO19OtdF?r+TN8D~Gu`={i6z+OLG>x5(SzG3Ib<^F%WJnu7hzC`Pg`gvcn z^PjbDS*3MMq4X-IleEtHF!%p^{o7g>;R8@VPwS|gw621Gr*&6Nt;66CYn|3F@7wnA zuUCK9A$gy-^P{v5{8Q^f#_6H{vG4Ofa&)JH-?{T0D*Mx%x7Ip#rPj5K|9ADj zZJqbQdwmD>%UviRfci!1uiG&1v-kS@wGJPj_vJeuqIG*w>v+aFS?l}_Egj^boQvS~ zqbmC+ogZG=Z|QuAbeVs=I`!`p2A1Fd+RA=TukR_{r^&g$y?5ZSa($9cgl<$Qy^85j z=}PEK@Bx+ms6PJ3(yh?3s1N<4x}MY`eQGhhC+@%-}V6Z(z4{>aMyWano{$3)jGlwQR&rLzCo>tC$wmv%m_vcKB- zkjj2+=Z{tPZ#xhD-p;RvZ-EcMICseBzz2Z0uiPKu@8^2?DEI)>f01?n&iDZE zDe`IX0pQ2W*NNZnC(8Z72cZ5x@{RBT;JxKD;RC>LlrM!30Dn+E7Cr#{3i)360Pv&a zlf~c5Uh>r*Tv5I6(eIY8&!^=|8TDi2YvKdI+sX&M$10QkG|mGJ@K2grxU2Y?T)+`neci+0=40K|w-M)WhgI(P^l`3L zU&x_he)xd)lg9bvSIYf~^UoL6rxNF@3o7@w`rqwaxgXZ~kLsI=^XIVN*6Vv|f9@4w zetw>Ee|m=b{GQtX8|VLFzp#%l-9OEqdc;3=o72?!Kh%d5*CUJ7w-nbeVZXDF^S=6` z@Bs>?S25L69~C|TJnX;rar!Cu2Oog?8tUs>nD=*keb^7~T)Kb42Vk6U)n|qe0B@=N z>2ZA;_Otu=9UQAJhK&yTbaK+#h@Z>QBt;_k<4se?@(M_yF)1l>37Z z01tTr24Ae)AAA6~bpLc!?*I4thn4%Y>w}FZUB0NX^N@$&T)KaH;iURoetmzrUb=t6 z2jK70mr0){T)KaHCg?fG=KlY5Ztnm0zboB8;R7(vTgv^pCink){VvM=Irp?Wk4#eYlJx-9O<2z#mdy?}@qp-^Y>e zpIYVqf9KNu6FvarysNyC6}kW4>!tfAd;sdD`zL$=cq8Th^v(VMKF<5f{b?5T(#@5x z@?FqZ3(EbO74+C`l;1KY=(nSl_cA=^z4+fwKvK6HvV_}D-S2BFP?hxbM6iLxpe=84?z7c%Ke!V^!bqIOZQLs0F1L=W!{n3hkPXG4~i*mhkfA9gQm+qhN0pQa86FvZ3x_`n4fV1xf9{^774?X~#+#h@Z zIQww$0pR3!;RC=!-j~7I7laQ0m+qhN0pRRA!Uuqp`-2YvC-(;*0507>;RC?Q{lN!- zv(E}20M5QFd;qv~|AY?!C-(;*0507>;RC>>`zL$=IJrOg0C4uL;RC>>`zL$=IQ!!8 z0pR5R-~+&=`zL$=xOD%74*-|$pYQ?THY~H08Z`?J^-A3 zkHfVO5-#08;RC=muS)IY1Hj4s!3TiP{MVMx-nn7Zgbx5`pC>*5oPDAA0C4I42_FD1 z-9PQ5eW-Busp12`rTZs*066<#AIkmz2G_hfeH!{_G|x`m#l?k z0M|M#Jrer0r2D5EYT zm+qhN0pQa86FvZ3>+m!!^y5kQPv?dHJ?Z`lAAoVB`=^@n0pQXRQqRy&DBVBx4gH7Y z6nqu>70LaXD<6RIrTeFzp&ydmgwdgYa^xMI&+m17^K@kBx0LRmmdXcUe9v)sX4KI7 zSN`JthtmDiv7!HyoQN)=UzB|59-+UK9EyRVA62?l`XuzPl5??h=yxR-V|?h3B}Zd~ zd;tDk)SLjccj+usp zer9q{?hO6UhabW5BEIi(az%b%THs^zHuF&zugBXq=TM!-g4fmXSP_!_uZ{` zm+9ZU>ioAukALB?wN+_iuhi_@u5UY^z5m29$E~SK`|p=l_PqH$=XJXORb!{sRq2dh zreAjL*B?0V_Vuu?Z~Re}E^2mFo6GiE;{2eC2OPS`?^WrhF;8zb@gM(lUZ>-u%?ABi zm5!gVY_$#k%=tFozx~9iKUbx5CoW#~%uSy=pLF)KZFg8zm4-ZY{{xpj`=#>-wp?-Q zvp-g)#|C`(YOT+|cHZ}uv#)OaeN~#Z;HH|rYb|#^amN>~+xy$9H1Ey{wfp_u_tDs& z$NqBZ_Fq+{hj(oKYONi9^!l-{-roOz%c|1i>3#3)l2$nX_O$=*JK%p+Y1gw?es$Q< zE1eHLdGD5wd|Z_lRqgxx-%efS{EMxB*!<-Ws?w4zH|@B1_)pH4ZvCGv?|rW-)f+kC z!=Bgv?7Y@@XYG9W+g0iBL+?0w%B{aRU%gS|5ic&NO4rpJ++zA8zdC>G;kP!}a9&l~ zyTi)^M?U|X^N}lhzOwm?Rq3vmH@dmeJHI>sX3&?5KYq3<)w!!#gCWcQa6WnW?%gha zswzGI#La)d{O3QNcYXbpR-ZjqmA0KRa6#uKA2wR+e4p-b z>{I{#s`SEr3%6;u?O)E1oIB!<5ALW+LofgDyepa)64&jPD{j8;%*j>hxGTP`?sa#W zKUL?Ce)Dcz_c`Bn_0+W;Ht_mCmv6ZEhjCS@?~q@2zvQtpA3S5ycAs8Rm1=GDb<;QY z+|cWPnc1rE?IWtvFS}nG|!}`HQEo8GT|^D%79;)3guEeEelU9lFP{RcYjk z`^J2DlIO8oy+KLqBfVwJI%LJ+8s6gKPTx8FJ`te>UB> zD%I%m#-)E-Rp#eC*7)|-f3HewADem5Wy5{{sZG6c;)-bGsJ1tqNy0u1fE>*`Ze3O*}8%#&mw*+e@~rO3%$cWz^NzmihUU zi=X$|s49Iurp9-V)bU(&d-BbfHs1L6_UWT~zict-#xh@h&4JFo3?pS4fDI^J=}?UT!V_3O2de*OLS>9SeJe)jCvz7N`JZ1r%1sjs(BN9=cM z^GSD<`IFbZKJv&H+Nb#|f1I&>JKvXW&s=iG<8x=VPoEz0+DSw2E%WoQUOj))>Fraa zs*9GNzpdwr+q375{H*cq?bB0>cCWj`)G{CN_UX-j8s9z*J#6K;n>%=}xLy77gV)`9 z<+{Jys&~65r!LowL>M*G!pM=B=JTuVwe6+oww!-n{VIMP>d?+fV-e>p$A3eOGLM!;y_V-`hsM zG5N3aI<-&t-TUwM-}Nl>CdU`9nbVTg+@0R(q z_e>r(yLS6@WSui!o72Ga7Ock0-?Tcr&^|3|{^6}tk1F${KW%pQ$`$QW?Mo)@b;iHT zeCt=gzUsiw+NJqF%&+<9n`OS~uRr@zMydeYc3@3r8a`|iH4UD|k$qc%HhUYUQf=kWJ8nA9%4Ik&ad zsp+}aHvG-4XYO=WyL7>v<+Vp2Q09Ms|MmVmT+l9!-lu857ep&o^k7 zx-8msuMfBNycg^D&lz=3`>}2MV#rSC4_lWXUnsqb_Ugr#b)LPXZThm+4d*Sst;}B> zT;r1^3)-fJGw!&(=HfE{X!^m;-<#bwwZ66gpg$UVu8xh}@~zglKF~J(czK`wpE$hC z`=5J#i#;Z`O^07~?~S{UE%UlPzTW%p%iE?Gp7`ReOJ6GUi@H4d;Q!8Po30*w)EQ&f zmiZ0$biQ@v32jsV>W5xGzLVz~S*tpG?X#$R+tlE#!_QuEZkb>A{p(MS+oNqdsZ%@a zKE2E@tu?+&qxNmngaP;OdfKuwAJlBzX8)|)Ha)m}#hzWa_FOF6e(#<4yL)w;wBq+i zx_#NR%zItizux^{wMhdfZM&%3^<_TufwN|gcy}GIv*$5`=a%{SPW6uKu=#F7v+zbUS$4(QVTG z&z!#M!h6d6@^9OG*M4xDbogftn)Law%%8ny!uVg0ZIk{OGVZru>v_(eoz(Huvu^Lw zCJmoZr}ohYm-)OWX3l7_!@Bo>?8t?KMwEH2Yg*lScH=gw{<{NgznNveX5?+%Zr-3x zs&UQoqu%(s%sY+VYV%3owoVt{wqoO^tvr9ywk^E1`C0F^PWN{Fxo68`%DndAQKK7I zw@w?4YHuH3UFKUq)%k&29%!9f^gZr{UeA{K*)NTIeC5Aer#JR#{^8Xt%6#jkhkVrb z;@0Wmo9?*gpH*A?=Xus%bNh4{*g8$C^VVUFjw|!|y{DeG*Yu~c`cmMkO z&~HC(m1b_e=~kpI?3*EM|Y)%m*rOTPnn9sN#f z>vsb`Nx!T4`kleg)9>(P{Vw6Z>33VL-!Z(Ke&<)}cMrcy_k|4y=KBQvVckbI*L?-v zMfaV*bRU9$r2Eta-M8Q;>b^Eh_c`af5BAo55x!ja&A;hB3cpYH*)w(Dh5uXkA44QuIJ(sJtyI{^&Fk6=PLYDJ$GNza~Qr%&*}bpZo~K0bNvQA=i#Sn9&nfD1@IFz zZ@5nL2>8L8XPl;a2fUW%CHH8a0-vpU%=Vhszz^2Ehj|cugyu;_&70u=)Vzv$7W_5M z!@knI48DQpZM`**gKw^R9`iow62BE z(z=&*F#Ij8lb34U48LCM>Lprd!?)BroOL;TmDcV5)H)u%`mnstKS%3+_I4EOE*DBfiIWN@~dgKsSz5M2=d=ZLHuekvUi zK2JL1m(m^KCSCG;>6Gvm(lOCB;V((|LFF1(#|V)(AokRA7H9=|${-#+X9 z)$#p|&F{ZDzW-v@{j1}@w{QM@s^jN#cK$r8zN-J<_m#ij>iGSh zlCOvAcs)$Z*H3l4ejdu#TXnqNUdY#Hb-X?w%-3^uyq*{3>%Tf)|2yX2OLhFcEX=>J zYJ32`uf_TISd9+=-!kj|)$#YcbN;bZP>tB&`#_W6ETjSs*$ujcz_b-aICzTZ~I`|Wx8{%rUFjB{+hpIf}2@0{=d zh7Uk}pZvVA_`KLWKVJ+Vfcl^F^T^`!Xi9#5S$uxIo1b@v55PD(Hh8mP%quz;(T>hp5M9;K)rN-!v}y%_jexvF5Tbo0pQ={`MLW5aOwVr4*{*B9;sFwXsXJz{Y^a!g*oSX{pxk=HvG*E`bv-3MTN>HZeiQ}gor%kTlH zm+o))0C4I4?gPN3`&(QOO82+8ew6NS_yCMEEU!=92Y^fWH+%s2A$k34as4aZ-{N{% zy1(HAFphM8_W|J2{S6-gF5Tbo0pQa84Icn5-QRrxxO9K_0pQa84Icn5-QVy5;L`ow z2Y^fWH+%rNbbrGKfJ^r`d;qv~f5QiWOZRsl0509%@B!e`{S6-gF5Tbo0pQa8-3Ne6 z_cwe1=UE>!d;qv~f5QiWOZPW?0JwC2!v}y%_cwe1xO9KR2Y^fWU-vuBJ^)<0zxx1i z>HdZf0GIA>_yBO}{)P_#m+o))0C4I4?gPN3`x`z0T)MyE1Hj+Sdas=o^j_)yh7Ul! zbbrGKfFGCjXL~j1&(i%3AAox4{)P_#za#76WZSV|J^)<0zxx32AM&^s{RQd%h7Umfko>+49{{d@r`;9&j=Qq%Z_yu-?r-=2 zj59BP?$$T>FK1-k-yRBnjlNg=fBsIs4oufYV*F-V_qVpeKdO~=f2$e%ruJF)w`swj z+AZt;HY)g8y6!FdU()>zAAt8G-QVy5;PT@v`f<|z4IhB|FHdZf zK)voeh7SP0G3)+@4*-8M>;Bd^_?y!G?Zn`RO7}N>0LIaM)}{u(Rrh6^82nl3{)P|0 zIBl}-Z*_zJJ3j0Fh7UmfC0X~kQNdr9?r-=2)a$ur_yBO}{)P_#*K^ULKmJnI{S6<0 zdg=bQEcoxz{S6<0dg=bQSMc|x``c-uA3(al^$Yz2(*3Pl=r_14>;6_h^e1Q@W6y+s z2F-g6AAt8M-QPxreu;~6e}$bL`YWXS+k()Kp?RC(12DekdA3F9_fUU`rO+QD-QRu; z{Up-;4IhBNGs>;5(%^a~Bjy1y+6{YBFKZJW@KbZzcmvWG+e(y3YZx8p*;({s5$ z%I*#QQNwdTmHpYivY+a?+<#?rL;uy&xnIj34EPPU*!WZ z{;=HNXw5@^qjZ0JGW0`AH?f;S|D<#l`#$trO82+lHmvN=l#XLZg?>)y{?;M%e@Z8^ zBSOEZbR|1K^p{G9vd2O{s&p%RD)g^P=du*~U8RfJE1^GDI-0GP55VUs-OZj0{kPKT z?3mE6D_zfS4E?>*0qxMx53K%S>k#^fr8C;n&~GeV(tZg2$JPUILw~q*WZNV3lS_BDb3^~R zbZXl*^s7tPwpV_x>~EJ2ZokO~;QN(sZij{bdFkvnT|NNy(&g>z(4Q|I-#Wfo+0UQ< zf7{sAYxll#=z;Ic2Phi#-{<=3IDYr-Oa1@Oc|Tjo2cVw!KSDkLoPY0j@&Vv{KELJ9 z$H(XM94H@vdOrWL@&Vv{Uq{FXfb;#1mk$8vdgw170M7Muk9+_)*W0`D0pMJpbL0cS zxt^!W2Y_Fb`~U50`2cW!FUQIUfb;wMQ$7Hk-{W`k0pR?8cb5+U=l6cBd;mE2huh=> zz`38CC?5dM{b!zh066!nn(_hQ+}{?;2Y_=wJVZVKocrfX@&Vx7Z=aP90O$UEv3vkH z_w!!z0pQ&KcajeP=XudvJ^-BO%i8>WF*whopX39;d4Am@9{|qtZjO8axO9JOEgt~R z^K`X*065RzQ{@A|d0yWl9{|qteW82+xcUI>`#c{oxcUa{bNK*p<{Jh10C46f=jZu} zkE1>Yvpk=1&iv=TJpXa7J_&m#&zGD__qR9X0~AWHqA?#EmFHt#&-^Xr`I~d~dDxkG zzUQ3z;ZE`aO2e<~hh#qan0x^EvzmV{$n#GhXPD-zh1|E|TzxGzT|NNgFdy!h=fhrq zjpolY^ZeO4^X={OeA_wm^QH0u7@zt4DS1Bc_00dzmk&Ta>xF%C-;md{zQ6~dp7qF` z@&VwiU+@9otatDM;H-~6$?GG7v!1Gz*Hg|}f8hgAFWui(*YClz3la@uN&s| zwR6_v-Sc|fxpaR!DX-t1OZT@ydA;vE=mXBt6Yv2FrB~6=AMgR-=oR&H-=dF$zR@oC zIXYMWzkQecBAuh3-~%webbqVPeV1OZzD!e}rgQY16LMdtbMzm40LGW@Z}e2V6 zJyYuc_j>feLvvrSbM!-e0LD2n_Zi#8x$oHPrTZH`0QJ)S4Ico$I`=(W&D{U*rVA3fYT`Z+!T<4E^6d;mE5JU#$iy1(HAz@_`6{~KKW z|Ar3$m+o);9k_IV!v}y%_c#6>aOwW|9R|lA;d6pZ_cwe1xO9KR2Y^fWH+%qi@OKOz z{2=Gj{f+C0dg=bgbqAO3Z}Hfxb4G;d8!KM2fJ^)<0zu^PGgCFPPNcT5>$J9&r zH-7i<;1BvZ()|q|fO_fvejkEM_c!iaaOwWWeGV?&-|zw8()|q|0507hzt!N<{qbj= zOZPW?0P3at8_xl_bbsTy0hjJ?_yBO}{)P_#m+p^$ZE)%Sh7SM_{HdZf zK)rN-pJ&0P`x`z0d_&FKn8(4T`_u1XaOwW^hd7t+Z}9I zWu8mDbbtC`y#8?Y&-BjwXr14oc{cNI#*yw%e~#C8(mZ~7-q-8=pPKix4qzPC39K97 z(*2Ef27G(1Ls*x0REhG z5Ofjv#LE6jALpCOeoNp*)Qt#XGvF5|G)ETmHnvB z@0D(~an7-D9{OFK&#df^b-pU={`Av2pH|s_>wNdheqHCID*Jn#A6VHB?7WF|Lv+MK z=~cAQZ|wD}D*Kb2A6VJX?EHU~{m;&)RrX6ef3UK@+W7~S{n*Z}vVYro==XL$O}a2T zaiR1o+8LGo|&fk-+jm}*fe%-oYr^hYnoG0G`AAoTVR_+fz z0Q`ITBJuO-B_9PJfcjhHyWj)B&zDbw4*-8szE1pp&#BxGV${DT-v}Q7zDzz7J^*~C zd?|bY_zd}2_yF)PE?*5F0RD-5ID7zjOZj&3_uEZAA3gx}SI8H{ z2Y{3NgAV|oA>R=n03P-$8T@Jan)m?l4)Q_q0pQc+o8kk&ACb?B4*(w`UpC&KyUWML z2cUjC`M&Y~-%CC*J^=Nr%Kd%DIA5p_AkGJ*`=>a6*iico<9y>h^(Dml$!zs8#QBVL{}ksx ziz@d^`umjbpYQ>A|HIXX5$9w3s&6CC--cA~_w?~QsxKtY52gF3IG=n&eJ64LxtaE> z#`)^w>TAIV;QgOexgXZw&m8s5#QAgBZ|n8a{ZpKupQb*ZIG>m9pW^(VJ|TPn-sj!g zUyKg`rw=KvM|!AlDXw4WbHWE;oW<&k+9dCf_J7wV>Z3X)=KwjU?<%gpr2D71UfW-N zUHAa}-LN0r$CvJ(;`;Gz^_k%VFphNpgbx4@``LYb>HY~Hfck3n$tefO>!tgrxW1+j zFRsU>`zL$=#wYg&9{^5YVAKcbBaC_ixj*;-j6?2^`v3j?OZQLs0Mye58TAlyfA9gQ zC-(;*03Pxg{O?NlPf^by_eVKEUO!NI5!dFt2bzw;5w8^H%ClwL)2|AY?!C-(;*0507>;RC>>`=_Xv&QM=9J^=OfVMjfd zzU`>rO7~Cr0E|OlIDO)9`p5^#2Y}Oe9`$E(fA9gQr>{Ng+v@*M^u<$8-+a{1PgdT} z6*+ImsF&`aqCOw;e7v5%e|!MOA@>I#0IqRU^cUEN5d8?f?}QJ)I4@M@9r^oVUj#k? z_2m9UKZSi4?8Bg5-xGOFK0f<8@Byf29|%4GyoGXqTIK$KA4k_&!Uv#U*JXub>)B_84^Sw*il+N=(tX_dAg_|fcJi~cpaKllKpx4%vYU|(GH$Js~6zB=m3{lN!->p7kD-1dK$ zp6dx8fO_@;vM&%$?oa>FKcIO=QVx)huX#yo5c(6?$H=}$#%JH7`u}|#a(^xf{Sum2 zr6Hleg4~}s^1RH)(Y!7FwR_LFGdnO>3)P$<2Mrg>_r z8TwT;uT7fg`uOaF#Rp&<&6^WG09^CzqCV~5ix0p!n)fGs0Jzo(N$Un5 zN9&4&4?sP+Kd~Q4>z0HMK)rPTgbx5G_Xi&Uu60zZ75k}_zp8bZQLlAc!UuqBU6*zZ z{axh#3=REYS~sQxLjRc7nd#EdZ^pj#w?ls#xj*lRem1RpQ|y1!IytHT-~U}&S0}Br zoogMQv@Ul}?hifypM%!~Yf0dkz9YVh=xfpXoe=Io~>i_q?XmU5C!#O9XL%N-Fay>?e{$6rGx`uvW z>4vFU=pQC$1RsFUhg_1Cp+A`%lLJCOGr1>ALjN;4DffkbX>wKW4E@#Qu)GoavB_;w ze!G7TM2 z%K3NBeM~w3&bjX?=ifQ^N#*=I=f0|(f9KqXmGjSiJ8|Du&cAc=eU$U>oacga{+*NW zqnv-|A?M#Y&ne~nJ15^qIseYd_fgKjbDo>Z`FBpfk8=K`595TKf3GLskh~x2$@d}e2Oe_%eH`+A$oru_+q_$@fvtzjM|#%K3L5a{ir@??c{C zq4X*y@_m%^@Ac&SkoQCVde(7?d>`_Ds1G^+K0f(Aq0c{&KdYR7uP5J!ydTDAovWOG zuMau@&dK*7?}u?h&cD}_??c`X_3K&JC-QyB`=LJM{QLMJ=im8ybP4o*a{j$O%IP|wN$@d9;{wd`Adwt0HcOG*7orj!%=OO3cdC2*99&-Mjhn#=sA?M#Y`97h~KauYf z`utPK`S)?i_X&OeiF_aB{Cj=K`FBpfPw4Yc{fkn>;W@;yVJe|wNL(aeRkn`_6%IP z`VzK#@f{(YQ~^Y45;eI_a7{Cj=K`FFmazLyko{=Gir{5vP#M>+q_ z*VDI?Le9U}hn#=sA?M%udisu1$ocpBkn`_6&cE}J^Y1+5{5uah|IT}; zPfR(n&O^?>^N{oJJmma44>|wNL(aeRkn`_6&cE}J^Y1+5{5uah|IS0s zzw?mu?>yxEI}bVk&O^?>^N{oJJmma44>|wNL(aeRkn`_6&cE}J^Y45; zeX}X#{Cj=K`F9?2{+)-Mf9E0R-+9RScOG*7orj!%=OO3cdC2*99&-Mjhn#=sA?M$D z$oY33a{ir%oPXyb=ihnA`F9?2{+%zc%=vd7a{ir%oPXyb=ihnA`F9?2{+)-Mf9E0R z-+9RScOG*7orj!%=OO3cdC2*99&-Mjhn#=sXUe}(&cE~Z?E6R|=ilo?&cE}J^Y1+5 z{5uah|IS0szw?mu?;O7^^!cZd^Y8T`=ihnA`F9?2{+)-Mf9E0R-+9RScfOu|PbuX5 zdwt0HcOG*7orj!%=OO3cdC2*99&-Mjhn#=sA?M$D$oY33a{ir%oPXyb=ihnA`F9?2 z{+*-y%Li~Ca{ir%oPX!&{_+8whn#=s=>GBnoTK~82XG#8{+*-y%Li~Ca{ir%oPXyb z=ihnA`FD=)FCV};y1#q?=OO3cIl8}m0O#ob@&TNq`^yJ#9&-Mjqx;JTa2|60oum7Q zKL13%Pw4YcA?M%6A>W6*pF-(XOyv87K7Y>lCFkGALH8%`hjGaFkq_YY=>FvWP#<#s zy&m14ydUaA&cD~A`-eXN6mtH(9^Ie3AI2x&C$4|#ha>NYdUSvJ06u=m`FD=)ANu?` z-Ef9Uh)d|z_@eH{8DL!UqA`;znT_2~Zc z0i2`z%Li~yzE9}$=X_ss{=FXEpFHV8=~d)>-2%A2_-{c|UOaeM6r==lhcL?|*kaxgwnJOU}R7)2|%*{5jv3oPV!J z_m>agJmma4NB5Tx;2hmwK7e!jxz*q8oc{07=kNXE@&UY_{&Mx3J4g2qeg2&9OU}R7 zhn#=s=>GBnoYPNF-cOe2l}pMN6XC-nLI{sHoS7zf>-ydOCG z6GETA?`I(IhkEuugg*a-?oZwi_3W=8?*~r44|zXuzE{3yIM)H!1)TjMFvWz|sB5`+`_D;Owspeg294c%jcfv41c0`6u#y z$opY@@_oqrfwP~GydOCG56Sz1Gmjzf2hO}F^!X?9eaQQvp8b>L{lLlhA@2u{?k^v} z;LP*L`+yxE zI}bVk&e@NxoPX!+-zM*;Pe+9voPQrbxIe8Px z`S*TQ@+p+_@0>gf<@`G*|3W$c&dJRVeg5>{lJoEN{+*K-qnv-|o1f9HkL%X9vf_ft&NE9bv+t}D|2Z_4?1&ihf$zjNNda{it3?^Vvfb3PyKgK*C0 zshof3eE!P$ch2{voPXzhzsmV{&h?<2f9G63%K3NB^`@ME=Uku4`FGCstek)60$$U{^`dadcEiSDDTJXb&i#C{=J_0u=c$= zC*Mao|IV3jYhSH%=I7-76iTmRVm`0@POoSFPu>sptQVB?@Aa%Nm%j-J7+zmoPX!6zvKfrNB1Z1r%-wo6Zt;M`S*I(gUb1L&iawOAI3rV zmk;3etWU}Np&s2|IsabI`j@;P>RB%<=ilpDUz7JkJ?nAh{ChpRKY2gYlkcOPf3FYv zfOGT&`2fz*AISS*eDn(C{ChpRKY2gYhn#<}M?WF&hkEjTl=JWPA?M#YdJcI%jD!9| z-VZ$F{QEfM`-F2OQ_!Qlo_rtW{5wbYSI)n4^fCHm3#C^vp{FS~!|T!Cl=JT#-CsU{ zbM!ss{5wYvRL;M1^h5RkJ15^qxgyTV_fZasb98_5ehQ^m(er)C`+=jElJ^5A-$ywr z2_E!V=Rv=9PQDL$Ka3M{{=Gir{5uah|IX3<qx+NhLp}Rm z*arhg_m>agptfk-CsU{b98_C0M60<*>_bay^1O1{ChpRzkC4a=>B>RI7jy<@252Ux_K$O zzkC3%NB5Tx;2hnbeQb<_?oZwiJmmcQIOzU*t~y8eC+~-G(EZ8#fusAA_X9`wmk;3M zqx+NhLp{1bc|UM;fAW6d=>FvWz(dY|f}{JB_X9`wC+`Q2?oZwiJmmZ*IJ!UkKH(wf z-|Nx+$@`%`spA?M%g$@d}ehkEjT$oqkl@54T6c*yzp@k7qPbMk%2`(Ye(fAW6d zA?M%6LH8%`hx(B7@AV<)-+9RScaH8)-Vfu4oPV!J_b2a%`jGSQ_2m1I_d|Wi`S*Hs zfAW5)4>|u{PreU%Kh%euf3FWY|IS0szw?mu?>yxEI}bVk&O^?>^N{oJoO~a0BMPNg zF@>CeuMau@&O^?>^U&|=Jmma44>|wNL(aeRkn`_6&cE}}Z|pqe{5uah z|IS0szw?mu?>yxEI}bVk&O^?>^U&|@Jmma44>|wNL(aeRkn`_6&cE}J z^Y1+5{5uah|IS0szw?mu?>yxEI}bVk&O^?>^N{oJJmma44>|wNL(aeRkn`_6&cE}J^Y1+5{5uah|IS0szw?mu?>yxEI}bVk&O^?>^N{oJJmma44>|wNL(aeR zuwTh}$oY33a{ir%oPXyb=ihnA`F9?2{+)-Mf9E0R-+9RScOG*7oum7c15_xziYes$ zdwt0HcOG*7orj!%=OO3cdC2*99&-Mjqx;ACL&*8}`jGSQJmma4NB58OpOEwK_2~ZO zDHckvVhTC`ULSJ)orj!%=OO3cIl6zGPllX-uMau@&O^?>^N{oJJmma45BqJMqx;AC zdC2+qdUXFd{}20xy*}jpI}bVk&O^?>^RVC9dC2*99&-MjhyB;i(f#9kE#&-reb^7~ z9NnM1*h1-5Od;pr>(Tw=`ZVlk_j+`H`2fyC&cAbX|G2&mIsaad?oSR-q4X-!|DVVK zf`^=cA1CAqI7j!7`a{V1_j+`H@{IYrArHap(fy--5_0~%KIAhvNB56<4!S=%K)j!j z^Y7z?oPXyb=ihnA`F9?2{+)-Mf9E0R-+9RScOG*7orj!%=OO3cdC2*99&-Mjqx+Ks zR4BcQ3EiI@AUL`|dFOC+|EQOSoc~09$oY33a{is8`^yJ#E`2yfeK_R&dwt0HcaH8) z4p5==Dxz%z(dZz*N1#0=ji@?u8b3M{=Gir{5$7+<$Govbbt8(ULSJ)oum76-7!wc`S*IR zW3FrJx$gNL!1zP+6=ifQHzjFSaGjCJQzw?mu?>yu~I%giqys}Vw6;sIh z_xh0Y?>yxEJ4g3tUR)Y}o!^A+FCW0`L(ac*bbsdQj6^?;PDqE}JbM;>()?tOxtH^n5&cAbX6XpCn4>|wNL(ac*@|@9i3Z>V&>!i*f{pQ_R zMb5w1qx;JTa8ACoa{ir@N3EQH??)xSS~>sD$-7q0zjN}jmGkeMJZ<&=J12h|9j^4x zT6fEzavmpr!S-fG4f*?QhP z=da@vzVGtvq%}`CKkLiqw|;eP^Zz63%;U5g|35sEH7aSHX&XzDN@*;S=9WaszNLkb zorF-fZyHUG=GJ+Ap6=8x_$KWvhea`ezkJ&#`~30W@r_pRHTbW^ zF&{ALfoE&|RuWdtK4-uIvzEmC^2e6V9J;n7ywUKbiP@mfV&47nCJUbZxg@MVdQihE z`!0`pyOU1)rkcJQJ;Pp^CzF%^}##7DGB!+ zb?7#~@B4Mk=fCjl9#4N+5@tWQL!S%Btc>~fMJN4y)#oMQiEAdToVxCtnAe{7;F@hd zEeXThWaZhh-^TozH5CUQ_fbi>@`s7l@0s#l%sZ?&<%^RRl!OKI_gmYt`VTQ*a{ilN z)tgrmCUySk=Hci6FXkt_^zHF?y-^Y_K6Au1_r1M3<~Ck9bIdQjeYXYYO)CjI-ZQ&z^^1Oq`9&N581>OJCE=3Kp5MQy z(VCc_{oS5L8zz*5+v-;R?5%l){Ncef%RhUtB$U0k^6oPRt&PXG+iB-sgT|DE_dfaa zXvR$iP}7evij@dFSbNclRp^ zb2s16wEJFv#C*sj<(HkG}_zrC&I zVI^Vj-cuSru=k%azwXkJ_if$rzu%X!WVe zt9Gj~V(nHXp+lDKan0U;$9$h~)w~`Xi^Gu@z1XtN7lr)pLk}N(^)JO?boCm8@4xY% zc>IT(^9Jao3%mp_tE#ytk`Eiev=NE@v{$sjL-!Q&ANKir@9tBJL-S*9m^k|TLf*Uc-(P(7SaB%5 z^Y!8r4%{rB=a)As-rsjjap*Mo?#8IcP?D@svq2uo#@XDw{zV(cgR&MNF99F#iTeBxWFXUs6UfHJF zk;UP)4<{addYjGTdCuJOyL*;&EDrB=y0`SFp@sa5Yrop>%sq?4aigoh-*s^zU$^_O z>9=4<9o_)y>M_LA9>4~Iye5?Aavbv!?tDf3;9Vi z+n4S5bAxcf;?DcO)o_b=o(B);e!?ls8-#s6T5?RY{)PPH1*biF?t2YF{o8-O@#u;| z{?gM&AF}uC2BFtxhtxZy=9ck1OY7cp&Z`p}gpMN`?7jJEg?z`FyX;&2{s!UG1?@^k zO)caTKYU|Z$;bxbsrSCB^66$<#q%`1XvPgc3~Ue<-23mYKOa-bSDd-*lq*kf5Vq>^ z^PKtP3;FQYFWfW#s0N{V+sC)=zowAi`|im-mUn0nPTzGx$Higmc>WK1U$c1H?hV2= ze-%AgbbBFRG5&%MUF-grH|^K==jDZb#%d+)H1>xG$9&KNnoZz1ovL(_+jtEd-F9z5!;gP$qn z*=e=5|NhB(;p*4-T=dd!g?!Z=(;IcZyn5C8$VWWz=pRqktrwPlR{ZYqlMDGpXV-Y{>A&lSvQ~!=n(=!fzw`71Ctb3#ZWub? zpvU?(sTt2-W9%8NHlJTN?AZ14PF)Tz-JHFyhq*NI`+G!ZfM=+KM~&a3P7Em~fyZW!NX3we)$)B5}} zp-w2-aCUb5>xH~dan-key0K39qTjBAI(}2gSAF-|_A@W26LxrNx2-pBSu1{>(^}nf zMfaoYgsMYYH@dZXAs;f~x^9!&)d_o?dv?DyT?_do^EbOX*T#lLqhs~u{bJ-lu8XAAkPlN)v1xuSO1_Nc$MpZ#7T-~IeE zyAK&(J8b#J4mTX|Qz5Tgb>~Yb4yzq{o_1fCTdMCEzy1z0fBkFxnYBZ|DvPR2Ze7S9 z`*%&7vk$EuI*lBB-XljA^7+?I@4caU?Qs5mGh3h0zmVT|+!6D7ZCg9+bM9W7{eEj9 zZ+2|gH*fymj-mD7fA8w?Y#~4P_~-5)_3n-V#;=Ft0{?$8=e&WxC*~LCUq^T+|GHw% z*B$tG#Qd!M-x>DL|J^a?`x0vB-=~=KeGJX>?`zDr%fIhoX8v=C`40KdDe!ZPIX~CH z&pGBd=C6ZrasIl9d8_<&6Ryo)M=|Gh7I@voe8>EC8F-z>oY!&SbsckF_krJonDcuQ z_`QiazgL0ZvzRZ-e-8t{moa}V|Gf=Y)!X!Y9CLop1Hbn%=Y1jc$loVo&ihEjGycLuIQW4=v(of^1q zjXBq~f$Q9ua~&MGE{^#p`E_&PIy&ZDX9upkW6pJX;5t3##rbu7;JQBM`{mdDfptL4 zStkV64KY7FUsnXy88QDfUxx(NB{5%}uUi7^n3$iCuXDoZ`MM|O3-WbQV4W26EMG?j z)>ScQ-4$4e#r%@?NR@ z+`MOsd5^q@DyNr<`MkWhDyPSa`CfU?RZi~}^IP*?tel=K<^%E`t(;yh=Jamm^l&k! zrz@woi}{^-uUAgb7xRI64_Hnw81rd)Z&*%`81s+wp0S+XG3G<_Ub38?GUm7BJ!Uz* zX3VGNy=VE5ya$c>+j&n~PH!6X@p-RWPR|5 z#{B!d7cQqKj``ZWM=qyVj(O9(cP^)gj`=frPhC!L9rNw-Ub~#0JLYxs9=x1hJmyd5 zy?Hr3dd%tB%jw-?J}&R&%jxN3eoNltm(%OVeB1G7eEQl~b9lZ_!XW@7*{r@BP#7&p3Mj^!r(p-aq~Re?{+~{=7b=_fLPm z+vTr^iuCo6(fg;bpHcMw>FaGAy?^@pe3jlmeLYX4_h$|ezyCMT`=`GzSJC^YzhCzL z>F?u2djItIw?Dmq`ulz}y?^@t@F=~1`hHS_-amc+xq#k3eZLw-@1MTEwWjw^-w)@~ z`|q940pj=P1bYAU{q|sb|MdOY-amamKa}1-egFTR-aq}km`CrQe!d(;@1K4i4W{=` zKfm^+_fJ3XrqKH{2Z;aPCG`I3=c&Da`uW?Q-aq}k{*m54{e0h#-aow_u=h`|A8w}i zPp>zgp!ZL&Pm1XM)9aa?=>5~{pIY?(|L=N<-aoy*YDn*&UXOL4_fN0iI@9~7*L#EL z{nP71d;j!$@(X(Z^!jrSy?=VWYVV(3-`e{#2Z%p+d;j$M`4f8o^m^OgKfOLbkKRAM zp11c;umA1+d*yS0`1SOp_fOXsf!;q|kJ$UC>lb_fbiMNty?>K@4iNv|>h%80^Ep7w zThjZd>#x4_{^@$H8ofVrfOwwj^#193@FRNvbp7}vy+3n+c%BCI{^|Pk1A6~-Jv)%z zKVAR+MDNcWAfA5~y??sCW<8#z>+#d*{h0&A^Sndv|9(CPh`GIg+7H`Pny?@%D+)3~MZ9WHx=buIIKOmn2#QdK(^4>q~b9&PI z56I^L@%Sg`{ol>!05SiV-v7pY4iIyD|Fn;~j^6+7d=3zguTJm3V?GCn`MLD|k0t%s zLVEv~^Ep5~z6HI1&wLIL^AYs^*XMJ9m`|bi@168}v*`UB<#T{|{6qBq*Czep5_?fug}mi{(N z`&)Z|<^b_L_Wo%$mam@c>3oo?VqdC`!ffK$J_g-{r1`P{%Id>@1OSP=hFMX zoAm8v^!{l-Z||S>`S$*@Xw#pvuuDEK$l4_H0?(Vy7nnz6={&-(yK^$XXix9&zuUjp z?@RAwK4R~m&QoU4`}=e7JnQ*6?U>AKhUD{|?3-l1WAD!#AoD!FZhRe?`#SS=XKwGG z&Zm4Gr}Hd(|8)Lk@1M@g?ETaEn!SHIkMsBT;bea2@A>Rx-sk(mv}8Uwg!hpf(|IEA zJ1vv>qwiDcywdlz^OO1J4!jT6Oy;5X{zH=asqeGC@B00>_xF9;d>-%P6O(za@B8We z*UyRnH!tRMC7mzZ`;STH(SB~F^Xm@zygTci%)9+uOy}c%j&@Gw>3;4q2gv;Q`Z+x{ znb-Td?&rM6+xx$p><6&-XATh0?{&s0$$kT`OPB-1KzIzeKh$ z*)QRB6?1@iyuJS~$$kv4+n58y$I1Q{uY;dS_QULu@1Mz5B>QK)&hC`#xAD5XPqIJ9-oJ6OpU3Nd<^b{g<2u20 zL(!%`WwG~X4iJxb9g^-xa^3Q6vVX~Sj_V%J@49GSvOmgo)U0GbmFq6oVV=iz8gqd7 zb-J#*D%s!VI?#2Y=W*Q_lKo?@Gj~q*o4GD!4iNud*Rif^J-_STbpP9x`F^>qTC!ix zb@ilVf1T@a*X5qybvtu_xF2uxe7|4TCE4$1FR(7zA83!TZ?d1z-l0RX|InV|>tw&8 zz5ffz{ziL{bU&oM33Gt>_1m)?ne4Z;msvnhPdz=^uWqlM?r*mT|B+tY zugBi}#bp1yJ$t`ozrDTu>|}qwJ^uGiD(~md|DJ|fJ_mSzwtuh3uWh{P_;Pd4ADA~ow*JI3W zJl>!0Smr$Dz8+TP`~Ty4eEmGk9LeK-y+$#Na^`Fv@9*Oh=5pr#{=Ui_&)nblLCpQkeSet1oY36&lehEtllb-c{?jmj z|B1QpSM2|fx$kddm~(o5-wy+GQFGrv7cxgR_x-jfb60cUpO0crYwr8`e#~{vegE&y z9N65?i*@*wim=IS2r z=kMOk;m!TLZo}N(+|PIB0P+0x{vp2}i1~i`{{QT7_7Qjdq>{r_2Y_Dz_t&iDUkJF(Bg-0QI>?8`9s`i(h2F#jjt|DPSiz7KP+ z519kRz8HhV={NWnY{{F?aptJ}l2;@6Q|{9&hi@93bYd2blxJ-1Xx^ z_K|shdw=Et@p#v#%mHF<@6Q|{=B|I){~vSL%gg~{?)sWJK+Ii_yDzV3)1R_n@6Q|{ z9&hi@93bXNKM-^K1ostgI{kn5liEKp2Z+bpS1<>Nx%~!nfSB9+yD!r7+n+E8h{xOe zGY5#dy+3n+nA_(t2Z*`-4|9N++xs&Ih`GH#{Zh=6J}Tz+{>%YlZtu?=Am;XC%mHF< zpT-;@=Jx)~0b*|N&m17;_Iu0$Vs0PE93bZQhs*(DZtu?=Am;Y|%mHF<@6Q|{=Jx)~ z0b*`n${Zl(_WsNPVxIJ|F;Dv2nA`g^2Z*`7zx%|CHvK6J_WtfGH@Ek9AG*1{KXZU! zZoln5cXNAx`tW$X{ki+-J>K4*IY2z#ex5l%%c&V485+cBS7&3!87OPOcY=e`#6Wd0S*?ft!P#@ycD z`)tgUd0ae?y}$SIc)a)dc;AnCG9QfRvG->V5OeQ4;(aLQ_Ws_t%JbQn+xs&Ih`GJL_knr-ExAu@Cijh*zsY?00`4<2 zXa9fZeQDhbpeTo=dV?ftoqj(Jyl|0Uda>v`<`xlWJAH=+0UzFv>-$bG-#xDVKT5c`FW z=e}X{lew>W821^QFK0heE$&M;|Bc@NSMFmrKaKmGKjXe<^AG9$y-(WwZF>KExUbs$ zNA_QB$$i-7letg(6zYWM-oFa>*_%Jfe!M%mFWZ@mR^ERe^Oq~{SC9FzJlBSv zJLZ2^-VY!1w<_Gb~0 z0b<@MKfm`E#JnE8fBN;DNAJ%ZARhk+y+3n+nD0{g{)m{5ruSzK5c5^^{>%YlK91g> zIY7*N(fg-A-!JI>nFGY*AE5VV4iNJm^#05NVtyCBKXZVXPono{4iNJj=>2^TP|>D8 zW#L$Q|Md4|e|rB(JO{|*XVUw3%Y_#}A?RXAThaTj>3n1H^pe z-J72KQ+^rG0rEV_{Yvrp;q?B@0b;%rz5gRT2gvh$NbjG%f1XJ1&m170C(!$||3Bts zmG9?@InVv^{;!y?rT1qJ5c7}d{h0&A{9t;2<^VAtSo!|7m>*jCez%yHC8M^wJwGoI&W?hoyr zTpvC~@1I^zen;=0UVpw$@1I_;zFYbJ*7*1Ku6#di%zvTxPp_Yo`)%X#Cvt!84axQS zCG`F$B-iup>HX8|e|!IQy%Yl{y*+F_c=hG zr!n`Zr|Z+?e)f1Cd;fI(yPEsu)AjN+?yr9|Szq(qAMeMH=RY-HzdHwr`CIuuzrY+I z=C9@bfX@erxxGJgfS50>{QQBK+xw5?IY335{`~jfi_G``hrM_Xkh#6T&jB*G_kWG& z0GZqSGY1If$@3XvZtwqc(&wC%@Ba@M^Bf@0WAD!#AfD%gyk818@f;wJZ=3f~;bNWx zWNz=z93Y;@-k&)@%-I(kd=8N3vG;$4=Kz`8`}gKKK<4)T%mL#0pUwC6hEsVCkjLBm zH{m%z=Jx)~0pfW+;&~%$cn*-q+xs&Ih{xOeGY5!y-F*Ll=*@G0JkOH6FAc>>U%D;N zSNSpNSBvsKHq1!+*k(MxWq8ux4&`|-gLn>*fA5NXUw9b7bAZgZ<9RawCVlc2Jbz{m z&jIpydw-t;WPVb%a5@m^Q$ z%ypK>+xs&Ih{sRo`Hjp0V$Ob#z#Jgv_WsNPVs7ux93bZQ{(JHqprTEG%7WLa%mL!@ z_WsNPV(xYDv-$r2n0wv)RkDA^>+Eh^cW?S@|J#pY?|*8(|399`>v-k>G55MZB>R8t z{h0&AvCi=CwfAQZ5RZ4=!W%YlZtu?=Am-omeA+#e{b~08%mL!@u6vmS#N6JW zIY8VmXYbD(Am*;aCno#x?ENoE_V3yIGY5$0vG?CHp993)9^r&!KcT&U?_~d>J;is) zenor#x%3=GoBosqd;b%X{gCXR3`3Lslh^UQ~UV+^gQSI@3Qwl zDcS#N@82oeFKX|9WU{~19;#omAJyLKi)8;Q`&~nuWWTGu*qzD#SoYI~Yk3Zk|1S35 zhHaDmxAt^C2gu{?^~NRpd+h;xB>REcKO7!S_7B_pf0XPuW`A-xG1;GNkJ&%j&us5G zKH2|lPdYc*FKw?nIoV%r4?8f~k8N+u^V{Rk-JZ7|&jIrL!~XDad$K>=9{Iy$Ke@eg zG0y?=JoePDCHvLw{d>}Ld%QjP_B;p3+}`}-WdFQ9`|xDHy}f^fWPiRrem|ZAe@nx)E7~>Qv&*FT{dr*Mi!Go0FCX~Gl^wS0GCSr!^f_jiq74iY~ic z(r$6gw;xzu@bbk6(85)M3At zhP^-E?eRDE{3hmQMWbq+yrwifUA6mDi`K3xf%jKXT#LlQ;et^T(gP z^_10Lmxk64+&Sd(IX}m|%ArSHGjm00SkeB@W7k*w67yBxUwd7T&r8G1qbq9O^2nN) zf7Wlnh3|e^8ip*Idi1&>Yhzx!>&l~Qe_R@NJ$}Y_txx+k=AVAFRhu>oOT%H+Mm^X5 zpmi}{wrpU%rtg=A(^~btx>o6LF~5C!+nOuiDGf6^e%tKDqTgeFcAcraT>a*M=RfPn zk-M%ay zd(NzL&7U#v^ys!1G@JI{>*15);(K~+i20cIOJ{sNsWkLn^W2+@+W!^vop<@G-(^ph zhWSf>KX^gCjWKWW>7rqujV}$0=I?v)4Zjugo4V~&T6bJ&SX%Y07V8%N9giRP+Np={ zI;J#iS$o$b8cg{o<_%w6Uvrz=|9ky6c>l$L=UyF3+g(}6=MP?x-QB)4tUPwgx)G}i`Jm4RoYJjrY4~GU z*L|Kmc8hqPd-v{j`{L%M;p(Xees<@aLSFZ`^UvR>X=ym>-p-#M)?&+e{MdiKt9oI* z((u}`W1GEwPa*$j@Q_OeS1%2xtlex@jm@@-$A2>Juz9_?;E!sv~@hsX+2(SyVdti!&z6|*?9Fcg?#<2 z_Vqe_(KJ+S_w9Y#*WD%_fBx1N_Bw8H)9~qqLt3r5u8?qK7p6?6! ziFX`-`kz_TP;1=QcWgLz+jyRnmX@D1dPdVQY{Wq)HJ)9_dq3N?#)fB_hK56WG%MP* zT0H)Wi*A|FVSLjt`r6@tJ#=d!@4m&2ZBHE2G<1Gx{6~NOR>&vcQTkz*o12DXnw3qd zamse_JO{UX?4jyInua;QoqOvSZx-^lufF`kvjdxk1FAK?;G9<5$K&4_aqV8aoZB=s zy?kl+r|&N0-Tyhg?InGhhF8B`+GhM;h5XS`ofq7DOw+JU^BToRonAejr%jvFcfaS5 zrlH4;jjBz1yO2-peoCM7+BXf49)IRm@3gKFk6(90$D8W4Y8qbewAqe#-Cf9Qo^#&( zaZQ_sYAfE^tHy>x-k{sFoi^;)Gz|P_k9{86vt~R`*M;q>x7n&`IOFEczuxZjLcaab z1`k}lu1VPV#Z@~WcXuJ*?c<{l>bA5=SXZ->S#`}d7x!-x_Wx$sE(hOQ z$QO+K`_-ApHVNB&^76~K%qiqst=aXJY5Oz@yKk5}YU0m@e08UW(@!mK5^kNlcl~iq zYsJ6!hR0uDUGaD0&}Y@K|MfklkZ(C|_OpvtG!8$Udgv|duP@}oPwdz6f!7;{adXBF zJ$+gsU$@yutLi?{IAr^8bJ9a!74jB!SNz=N#>S!hiremdrq+(}?+uUEdu)&M8ix_n zADVMhr$S!k-q*Xmd3fV6YH5Qr_q(K!Pph%2ZS7W#!_*l)YrZ(XkpFaF`w8`{Hx577 zYtA~xyr5C|yl&Zp)%Psq9ebY9@x136 zh1bvN`R?0&3VG@KJAQfSZH>aCuU32c=vxZ;C999Tds+WRVgDBf{r>o@Lf)rGt*4qF z*(eXPwqCEa=&}|DU_ojbG1+Z5JFkxMRaGvEB0P zE;+W4j~;R1eqBl$hRK^vzGnKhg?!cdgFk)mzyBUnv-U$a%$Zooo6ovpP|g4TyG3^# zH1Vc8J}%_tx7~YT^(iG`>h;ws_NY=X{=H)cPTk?{n@hr}=T+bH)>egl)vzve4m`Ug zR2}kk{}*}`^4_Dad+(x7CE@YY`|dblSRrrLqUG3A8kK}+KkEE^kI9Anlnt+5x9$4k z&^7yG<-#R}{G7*oExmMMap-fv#PV93*N_&%BYeSC!Pt9cpU_X+$Q%unU#9ApQ}G-^C`RzcII_q{v@xPp1h9C zeVzHbGe3>jrLR+SU&p?#&3)bbdtmPGiN81I{$BZeW46^v%c?| z`@Zb^w7Ku&zOS46zVGLNxt|k$ZkYSI;^&O{NIr-BTr&4_%g-@$Kj-}1Gxu}R&q;GX zNBvwiU(M&PpTp*UPW!oS?&rFn^X6U$cwJz=E!Pe2aUEgaf$I#fJIpt5UE+0$`8cj) zR&rfqK8))guY=5gv*s0&2Q(r-*tew>jc*g<~OjeaGhaZk9COa67%C(x44cm|BQ9cd8~WPUu9k7I?22r z>nPV%=B~S3hnYXcI?Z*P`Eji4T<4khWF6?b(0mT-M%R(%-B@S3?lixWb*bxA^L4Ca zUDuks?sXk(zMOTk>t=J;)vmM6U5C$NU2c9T>vq@i=C1Qy_nX@b*b|s9r$?|?Fu$JO z!5+eV8a;)*h52lHjbeHZ^NZ*~>_yB^qBpTeF|R?-V(()9HNA{Ijk!IJy^i_&^gi}L z=BLpU*&CVHqF3rn&tzVe9%?kbl=&=rD|;;S$@EJ3Xeorg;Uu=Sq4|^SkLu z?M=;}p;s-VXElGB9@bvgyeqw}J+Aq`^t|@I=40rE?TO8QqDQt@Hg8VvY!7X|*|&L5 zZEtOUHNCbyxB0pB;P&F?htiwdqnq2a+q;_&p_jL(H=jVL`u(h( zfB!4e@Bh;L=T(vZyl%;Vz7^@u_xJquP?5eKLjL-xNMAqG^4Hsc)onfp@cT9)e|=V@ zug{zF*Kg z{e9mle}AY*-yaUnd;f~`{iILc`&XpzKU4DetBUmfs&W4QR*}BHP0Zg9E7JGF8TtEX z#qG=i{QmsAHmgk=@73t?o zwfys_BK3Q#;rJwIbdGDX4*8}waS$h5OXx{s0>Gj6=y!X%2>yu^q^-Pvt&wQ2l{#kncvo^n8 z`tLu*ozDUMJ`BpQud?*|>ahHJEK9G)X6M&$S$h4}Bfs9u((ApW^XtPby*{M(&(iD3 z#d+_arPrUQ=hv%Qdc8{TpQYEg^!}N10RO%8{#kncTqnQY&eH2`djBlFJ}=ID|17