diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000..222dac5c --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "PythonScripts/iblapps-master"] + path = PythonScripts/iblapps-master + url = git@github.com:braincogs/iblapps.git diff --git a/PythonScripts/iblapps-master b/PythonScripts/iblapps-master new file mode 160000 index 00000000..3c378286 --- /dev/null +++ b/PythonScripts/iblapps-master @@ -0,0 +1 @@ +Subproject commit 3c3782863f23928a1fcaf67263ee60fdb43a2d7f diff --git a/PythonScripts/iblapps-master/.flake8 b/PythonScripts/iblapps-master/.flake8 deleted file mode 100644 index 9ebe579a..00000000 --- a/PythonScripts/iblapps-master/.flake8 +++ /dev/null @@ -1,11 +0,0 @@ -[flake8] -max-line-length = 99 -ignore = W504 -exclude = - .git, - __pycache__, - __init__.py, - data_exploration_gui/ - launch_phy/ - histology/ - qt_matplotlib.py diff --git a/PythonScripts/iblapps-master/.gitignore b/PythonScripts/iblapps-master/.gitignore deleted file mode 100644 index 7640b7a5..00000000 --- a/PythonScripts/iblapps-master/.gitignore +++ /dev/null @@ -1,137 +0,0 @@ -# IBL apps stuff -dlc/*.mp4 - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# VSCode -.vscode/ - -scratch/ diff --git a/PythonScripts/iblapps-master/.travis.yml b/PythonScripts/iblapps-master/.travis.yml deleted file mode 100644 index 6324eff6..00000000 --- a/PythonScripts/iblapps-master/.travis.yml +++ /dev/null @@ -1,25 +0,0 @@ -language: python -python: - - "3.7" -# command to install dependencies -install: - - pip install -r requirements.txt - - pip install -e . - # install develop branch of ibllib - - pip install git+https://github.com/int-brain-lab/ibllib.git@develop - - # command to run tests -before_script: - - echo "exec('from oneibl.one import ONE; ONE(silent=True)')" | python - - sed -i 's|null|"'$IBLFLATIRONPASS'"|g' /home/travis/.one_params - # command to run tests - -script: - - bash ./run_tests - - flake8 . - -# only build master and develop (PRs are built for all branches) -branches: - only: - - master - - develop diff --git a/PythonScripts/iblapps-master/LICENSE b/PythonScripts/iblapps-master/LICENSE deleted file mode 100644 index 1d8cb4bb..00000000 --- a/PythonScripts/iblapps-master/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2020 International Brain Laboratory - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/PythonScripts/iblapps-master/README.md b/PythonScripts/iblapps-master/README.md deleted file mode 100644 index 4fe47369..00000000 --- a/PythonScripts/iblapps-master/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# iblapps -IBL related applications that rely on unsupported libraries such as pyqt5. See package README files for more details. \ No newline at end of file diff --git a/PythonScripts/iblapps-master/__init__.py b/PythonScripts/iblapps-master/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/AdaptedAxisItem.py b/PythonScripts/iblapps-master/atlaselectrophysiology/AdaptedAxisItem.py deleted file mode 100644 index 1b422c22..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/AdaptedAxisItem.py +++ /dev/null @@ -1,48 +0,0 @@ -import pyqtgraph as pg -from pyqtgraph import debug as debug - - -class AdaptedAxisItem(pg.AxisItem): - def __init__(self, orientation, parent=None,): - pg.AxisItem.__init__(self, orientation, parent=parent) - - def drawPicture(self, p, axisSpec, tickSpecs, textSpecs): - profiler = debug.Profiler() - - p.setRenderHint(p.Antialiasing, False) - p.setRenderHint(p.TextAntialiasing, True) - - # draw long line along axis - pen, p1, p2 = axisSpec - p.setPen(pen) - p.drawLine(p1, p2) - p.translate(0.5, 0) # resolves some damn pixel ambiguity - - # draw ticks - for pen, p1, p2 in tickSpecs: - p.setPen(pen) - p.drawLine(p1, p2) - profiler('draw ticks') - - # Draw all text - if self.style['tickFont'] is not None: - p.setFont(self.style['tickFont']) - p.setPen(self.textPen()) - for rect, flags, text in textSpecs: - p.drawText(rect, int(flags), text) - - profiler('draw text') - - -def replace_axis(plot_item, orientation='left', pos=(2, 0)): - - new_axis = AdaptedAxisItem(orientation, parent=plot_item) - oldAxis = plot_item.axes[orientation]['item'] - plot_item.layout.removeItem(oldAxis) - oldAxis.unlinkFromView() - # - new_axis.linkToView(plot_item.vb) - plot_item.axes[orientation] = {'item': new_axis, 'pos': pos} - plot_item.layout.addItem(new_axis, *pos) - new_axis.setZValue(-1000) - new_axis.setFlag(new_axis.ItemNegativeZStacksBehindParent) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/ColorBar.py b/PythonScripts/iblapps-master/atlaselectrophysiology/ColorBar.py deleted file mode 100644 index 4166a9cd..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/ColorBar.py +++ /dev/null @@ -1,89 +0,0 @@ -from PyQt5 import QtCore, QtGui -import pyqtgraph as pg -import matplotlib -import numpy as np -from pyqtgraph.functions import makeARGB - - -class ColorBar(pg.GraphicsWidget): - - def __init__(self, cmap_name, cbin=256, parent=None, data=None): - pg.GraphicsWidget.__init__(self) - - # Create colour map from matplotlib colourmap name - self.cmap_name = cmap_name - cmap = matplotlib.cm.get_cmap(self.cmap_name) - if type(cmap) == matplotlib.colors.LinearSegmentedColormap: - cbins = np.linspace(0.0, 1.0, cbin) - colors = (cmap(cbins)[np.newaxis, :, :3][0]).tolist() - else: - colors = cmap.colors - colors = [(np.array(c) * 255).astype(int).tolist() + [255.] for c in colors] - positions = np.linspace(0, 1, len(colors)) - self.map = pg.ColorMap(positions, colors) - self.lut = self.map.getLookupTable() - self.grad = self.map.getGradient() - - def getBrush(self, data, levels=None): - if levels is None: - levels = [np.min(data), np.max(data)] - brush_rgb, _ = makeARGB(data[:, np.newaxis], levels=levels, lut=self.lut, useRGBA=True) - brush = [QtGui.QColor(*col) for col in np.squeeze(brush_rgb)] - return brush - - def getColourMap(self): - return self.lut - - def makeColourBar(self, width, height, fig, min=0, max=1, label='', lim=False): - self.cbar = HorizontalBar(width, height, self.grad) - ax = fig.getAxis('top') - ax.setPen('k') - ax.setTextPen('k') - ax.setStyle(stopAxisAtTick=((True, True))) - # labelStyle = {'font-size': '8pt'} - ax.setLabel(label) - ax.setHeight(30) - if lim: - ax.setTicks([[(0, str(np.around(min, 2))), (width, str(np.around(max, 2)))]]) - else: - ax.setTicks([[(0, str(np.around(min, 2))), (width / 2, - str(np.around(min + (max - min) / 2, 2))), - (width, str(np.around(max, 2)))], - [(width / 4, str(np.around(min + (max - min) / 4, 2))), - (3 * width / 4, str(np.around(min + (3 * (max - min) / 4), 2)))]]) - fig.setXRange(0, width) - fig.setYRange(0, height) - - return self.cbar - - -class HorizontalBar(pg.GraphicsWidget): - def __init__(self, width, height, grad): - pg.GraphicsWidget.__init__(self) - self.width = width - self.height = height - self.grad = grad - QtGui.QPainter() - - def paint(self, p, *args): - p.setPen(QtCore.Qt.NoPen) - self.grad.setStart(0, self.height / 2) - self.grad.setFinalStop(self.width, self.height / 2) - p.setBrush(pg.QtGui.QBrush(self.grad)) - p.drawRect(QtCore.QRectF(0, 0, self.width, self.height)) - - -class VerticalBar(pg.GraphicsWidget): - def __init__(self, width, height, grad): - pg.GraphicsWidget.__init__(self) - self.width = width - self.height = height - self.grad = grad - QtGui.QPainter() - - def paint(self, p, *args): - p.setPen(QtCore.Qt.NoPen) - self.grad.setStart(self.width / 2, self.height) - self.grad.setFinalStop(self.width / 2, 0) - p.setBrush(pg.QtGui.QBrush(self.grad)) - p.drawRect(QtCore.QRectF(0, 0, self.width, self.height)) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/README.md b/PythonScripts/iblapps-master/atlaselectrophysiology/README.md deleted file mode 100644 index 4cbd96ce..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Ephys Atlas GUI - -GUI to allow user to align electrophysiology data with histology data. Please refer to this wiki page for information on installation and usage https://github.com/int-brain-lab/iblapps/wiki - - diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/__init__.py b/PythonScripts/iblapps-master/atlaselectrophysiology/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/alignment_with_easyqc.py b/PythonScripts/iblapps-master/atlaselectrophysiology/alignment_with_easyqc.py deleted file mode 100644 index 924b8905..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/alignment_with_easyqc.py +++ /dev/null @@ -1,274 +0,0 @@ -from easyqc.gui import viewseis -from neurodsp import voltage -import neuropixel -from viewspikes.data import stream -from viewspikes.plots import overlay_spikes -import scipy -from PyQt5 import QtCore, QtGui -import numpy as np -import pyqtgraph as pg -import qt -from one.api import ONE -from iblutil.util import Bunch - -import atlaselectrophysiology.ephys_atlas_gui as alignment_window -import data_exploration_gui.gui_main as trial_window - - -class AlignmentWindow(alignment_window.MainWindow): - def __init__(self, probe_id=None, one=None, histology=False, spike_collection=None): - - self.ap = None # spikeglx.Reader for ap band - self.lf = None # spikeglx.Reader for lf band - self.line_x = None - self.trial_curve = None - self.time_plot = None - self.trial_gui = None - self.clicked = None - self.eqc = {} # handles for viewdata windows - - super(AlignmentWindow, self).__init__(probe_id=probe_id, one=one, histology=histology, - spike_collection=spike_collection) - # remove the lines from the plots - self.remove_lines_points() - self.lines_features = [] - self.lines_tracks = [] - self.points = [] - - self.plotdata.channels = Bunch() - self.plotdata.channels['localCoordinates'] = self.plotdata.chn_coords_all - self.plotdata.channels['rawInd'] = self.plotdata.chn_ind_all - - def on_mouse_double_clicked(self, event): - if not self.offline: - if event.double() and event.modifiers() and QtCore.Qt.ShiftModifier: - pos = self.data_plot.mapFromScene(event.scenePos()) - if self.line_x is not None: - self.fig_img.removeItem(self.line_x) - - self.line_x = pg.InfiniteLine(pos=pos.x() * self.x_scale, angle=90, - pen=self.kpen_dot, movable=False) - self.line_x.setZValue(100) - self.fig_img.addItem(self.line_x) - self.stream_ap(pos.x() * self.x_scale) - self.stream_lf(pos.x() * self.x_scale) - - return - - def plot_image(self, data): - super().plot_image(data) - self.remove_trial_curve(data['xaxis']) - self.remove_line_x(data['xaxis']) - if 'Time' in data['xaxis']: - self.time_plot = True - else: - self.time_plot = False - - def plot_scatter(self, data): - super().plot_scatter(data) - self.remove_trial_curve(data['xaxis']) - self.remove_line_x(data['xaxis']) - if 'Time' in data['xaxis']: - self.time_plot = True - else: - self.time_plot = False - - def add_trials(self, trial_key='feedback_times'): - self.selected_trials = self.plotdata.trials[trial_key] - x, y = self.vertical_lines(self.selected_trials, 0, 3840) - self.trial_curve = pg.PlotCurveItem() - self.trial_curve.setData(x=x, y=y, pen=self.rpen_dot, connect='finite') - self.trial_curve.setClickable(True) - self.fig_img.addItem(self.trial_curve) - self.fig_img.scene().sigMouseClicked.connect(self.on_mouse_clicked) - self.trial_curve.sigClicked.connect(self.trial_line_clicked) - - def remove_trials(self): - self.fig_img.removeItem(self.trial_curve) - - def vertical_lines(self, x, ymin, ymax): - - x = np.tile(x, (3, 1)) - x[2, :] = np.nan - y = np.zeros_like(x) - y[0, :] = ymin - y[1, :] = ymax - y[2, :] = np.nan - - return x.T.flatten(), y.T.flatten() - - def trial_line_clicked(self, ev): - self.clicked = ev - - def on_mouse_clicked(self, event): - if self.trial_gui is not None: - if not event.double() and type(self.clicked) == pg.PlotCurveItem: - self.pos = self.data_plot.mapFromScene(event.scenePos()) - x = self.pos.x() * self.x_scale - trial_id = np.argmin(np.abs(self.selected_trials - x)) - print(trial_id) - - idx = np.where(self.trial_gui.data.y == 10 * trial_id) - self.trial_scat = pg.ScatterPlotItem() - self.trial_gui.plots.fig4_raster.fig.addItem(self.trial_scat) - self.trial_scat.setData(self.trial_gui.data.x[idx], self.trial_gui.data.y[idx], - brush='r', size=5) - - self.clicked = None - - def stream_lf(self, t): - if self.lf is not None: - self.lf.close() - - self.lf, dsets, t0 = stream( - self.loaddata.probe_id, t=t, one=self.loaddata.one, cache=True, typ='lf') - sos = scipy.signal.butter(3, 5 / self.lf.fs / 2, btype='highpass', output='sos') - butt = scipy.signal.sosfiltfilt(sos, self.lf[:, :-1].T) - h = neuropixel.trace_header() - self.eqc['raw_lf'] = viewseis( - butt.T, si=1 / self.lf.fs, h=h, t0=t0, title='raw_lf', taxis=0) - self.lf.close() - - def stream_ap(self, t): - if self.ap is not None: - self.ap.close() - - self.ap, dsets, t0 = stream( - self.loaddata.probe_id, t=t, one=self.loaddata.one, cache=True) - raw = self.ap[:, :-1].T - h = neuropixel.trace_header() - sos = scipy.signal.butter(3, 300 / self.ap.fs / 2, btype='highpass', output='sos') - butt = scipy.signal.sosfiltfilt(sos, raw) - destripe = voltage.destripe(raw, fs=self.ap.fs) - - self.eqc['butterworth'] = viewseis(butt.T, si=1 / self.ap.fs, h=h, t0=t0, title='butt', - taxis=0) - self.eqc['destripe'] = viewseis(destripe.T, si=1 / self.ap.fs, h=h, t0=t0, title='destr', - taxis=0) - - overlay_spikes(self.eqc['butterworth'], self.plotdata.spikes, self.plotdata.clusters, - self.plotdata.channels) - overlay_spikes(self.eqc['destripe'], self.plotdata.spikes, self.plotdata.clusters, - self.plotdata.channels) - overlay_spikes(self.eqc['ks2'], self.plotdata.spikes, self.plotdata.clusters, - self.plotdata.channels) - self.ap.close() - - def remove_line_x(self, xaxis): - """ - If we have any horizontal lines to indicate the time points delete them if the x axis is - not time - :param xaxis: - :return: - """ - if self.line_x is not None: - self.fig_img.removeItem(self.line_x) - if 'Time' in xaxis: - self.fig_img.addItem(self.line_x) - - def remove_trial_curve(self, xaxis): - if self.trial_curve is not None: - self.fig_img.removeItem(self.trial_curve) - if 'Time' in xaxis: - self.fig_img.addItem(self.trial_curve) - - def remove_lines_points(self): - super().remove_lines_points() - self.remove_line_x('la') - self.remove_trial_curve('la') - - def add_lines_points(self): - super().add_lines_points() - if self.time_plot: - self.remove_line_x('Time') - self.remove_trial_curve('Time') - - def closeEvent(self, event): - """ - Close the spikeglx file when window is closed - """ - super().closeEvent(event) - if self.ap is not None: - self.ap.close() - - def complete_button_pressed(self): - QtGui.QMessageBox.information(self, 'Status', ("Not going to upload any results, to do" - " an alignment, launch normally")) - - -class TrialWindow(trial_window.MainWindow): - def __init__(self): - super(TrialWindow, self).__init__() - self.alignment_gui = None - self.scat = None - - def on_scatter_plot_clicked(self, scatter, point): - super().on_scatter_plot_clicked(scatter, point) - self.add_clust_scatter() - - def on_cluster_list_clicked(self): - super().on_cluster_list_clicked() - self.add_clust_scatter() - - def on_next_cluster_clicked(self): - super().on_next_cluster_clicked() - self.add_clust_scatter() - - def on_previous_cluster_clicked(self): - super().on_previous_cluster_clicked() - self.add_clust_scatter() - - def add_clust_scatter(self): - if not self.scat: - self.scat = pg.ScatterPlotItem() - self.alignment_gui.fig_img.addItem(self.scat) - - self.scat.setData(self.data.spikes.times[self.data.clus_idx], - self.data.spikes.depths[self.data.clus_idx], brush='g', size=5) - - -def load_extra_data(probe_id, one=None, spike_collection=None): - one = one or ONE() - eid, probe = one.pid2eid(probe_id) - if spike_collection == '': - collection = f'alf/{probe}' - elif spike_collection: - collection = f'alf/{probe}/{spike_collection}' - else: - # Pykilosort is default, if not present look for normal kilosort - # Find all collections - all_collections = one.list_collections(eid) - - if f'alf/{probe}/pykilosort' in all_collections: - collection = f'alf/{probe}/pykilosort' - else: - collection = f'alf/{probe}' - - _ = one.load_object(eid, obj='spikes', collection=collection, - attribute='samples') - trials = one.load_object(eid, obj='trials') - - return trials - - -def viewer(probe_id=None, one=None, data_explore=False, spike_collection=None): - """ - """ - qt.create_app() - trials = load_extra_data(probe_id, one=one, spike_collection=spike_collection) - av = AlignmentWindow(probe_id=probe_id, one=one, spike_collection=spike_collection) - av.plotdata.trials = trials - av.show() - - if data_explore: - data = Bunch() - data['spikes'] = av.plotdata.spikes - data['clusters'] = av.plotdata.clusters - data['trials'] = av.plotdata.trials - bv = TrialWindow() - bv.on_data_given(data) - av.trial_gui = bv - bv.alignment_gui = av - bv.show() - - return av diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/compare_alignments.py b/PythonScripts/iblapps-master/atlaselectrophysiology/compare_alignments.py deleted file mode 100644 index d09de309..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/compare_alignments.py +++ /dev/null @@ -1,159 +0,0 @@ -# import modules -from one.api import ONE -from ibllib.pipes.ephys_alignment import EphysAlignment -import numpy as np -import matplotlib.pyplot as plt -import matplotlib -import ibllib.atlas as atlas -from pathlib import Path -# Instantiate brain atlas and one -brain_atlas = atlas.AllenAtlas(25) -one = ONE() - -fig_path = Path('C:/Users/Mayo/Documents/PYTHON/alignment_figures/scale_factor') -# Find eid of interest -aligned_sess = one.alyx.rest('trajectories', 'list', provenance='Ephys aligned histology track', - django='probe_insertion__session__project__name__icontains,' - 'ibl_neuropixel_brainwide_01,' - 'probe_insertion__session__qc__lt,30') -eids = np.array([s['session']['id'] for s in aligned_sess]) -probes = np.array([s['probe_name'] for s in aligned_sess]) - -json = [s['json'] for s in aligned_sess] -idx_none = [i for i, val in enumerate(json) if val is None] -json_val = np.delete(json, idx_none) -keys = [list(s.keys()) for s in json_val] - -eids = np.delete(eids, idx_none) -probes = np.delete(probes, idx_none) - -# Find index of json fields with 2 or more keys -len_key = [len(s) for s in keys] -idx_several = [i for i, val in enumerate(keys) if len(val) >= 2] -eid_several = eids[idx_several] -probe_several = probes[idx_several] - - -# subject = 'KS023' -# date = '2019-12-10' -# sess_no = 1 -# probe_label = 'probe00' -# eid = one.search(subject=subject, date=date, number=sess_no)[0] -# eid = 'e2448a52-2c22-4ecc-bd48-632789147d9c' - -small_scaling = [["CSHL045", "2020-02-25", "probe00", "2020-06-12T10:40:22_noam.roth"], - ["CSHL045", "2020-02-25", "probe01", "2020-09-12T23:43:03_petrina.lau"], - ["CSHL047", "2020-01-20", "probe00", "2020-09-28T08:18:19_noam.roth"], - ["CSHL047", "2020-01-27", "probe00", "2020-09-13T15:51:21_petrina.lau"], - ["CSHL049", "2020-01-08", "probe00", "2020-09-14T15:44:56_nate"], - ["CSHL051", "2020-02-05", "probe00", "2020-06-12T14:05:49_guido"], - ["CSHL055", "2020-02-18", "probe00", "2020-08-13T12:07:08_jeanpaul"], - ["CSH_ZAD_001", "2020-01-13", "probe00", "2020-09-22T17:23:50_petrina.lau"], - ["KS014", "2019-12-03", "probe01", "2020-06-17T19:42:02_Karolina_Socha"], - ["KS016", "2019-12-05", "probe01", "2020-06-18T10:26:54_Karolina_Socha"], - ["KS020", "2020-02-06", "probe00", "2020-09-13T15:08:15_petrina.lau"], - ["NYU-11", "2020-02-21", "probe01", "2020-09-13T11:19:59_petrina.lau"], - ["SWC_014", "2019-12-15", "probe01", "2020-07-26T22:24:39_noam.roth"], - ["ZM_2240", "2020-01-23", "probe00", "2020-06-05T14:57:46_guido"]] - -big_scaling = [["CSHL045", "2020-02-25", "probe00", "2020-06-12T10:40:22_noam.roth"], - ["CSHL045", "2020-02-25", "probe01", "2020-09-12T23:43:03_petrina.lau"], - ["CSH_ZAD_001", "2020-01-13", "probe00", "2020-09-22T17:23:50_petrina.lau"], - ["KS014", "2019-12-03", "probe00", "2020-06-17T15:15:01_Karolina_Socha"], - ["KS014", "2019-12-03", "probe01", "2020-06-17T19:42:02_Karolina_Socha"], - ["KS014", "2019-12-04", "probe00", "2020-09-12T16:39:14_petrina.lau"], - ["KS014", "2019-12-06", "probe00", "2020-06-17T13:40:00_Karolina_Socha"], - ["KS014", "2019-12-07", "probe00", "2020-06-17T16:21:35_Karolina_Socha"], - ["KS016", "2019-12-04", "probe00", "2020-08-13T14:02:44_jeanpaul"], - ["KS016", "2019-12-05", "probe00", "2020-06-18T10:49:53_Karolina_Socha"], - ["KS023", "2019-12-07", "probe00", "2020-09-09T14:21:35_nate"], - ["KS023", "2019-12-07", "probe01", "2020-06-18T15:50:55_Karolina_Socha"], - ["KS023", "2019-12-10", "probe01", "2020-06-12T13:59:02_guido"], - ["KS023", "2019-12-11", "probe01", "2020-06-18T16:04:18_Karolina_Socha"], - ["NYU-11", "2020-02-21", "probe01", "2020-09-13T11:19:59_petrina.lau"], - ["NYU-12", "2020-01-22", "probe00", "2020-09-13T19:53:43_petrina.lau"], - ["SWC_014", "2019-12-12", "probe00", "2020-07-27T11:27:16_noam.roth"], - ["SWC_038", "2020-08-01", "probe01", "2020-08-31T12:32:05_nate"], - ["ibl_witten_14", "2019-12-11", "probe00", "2020-06-14T15:33:45_noam.roth"]] - -for sess in small_scaling: - eid = one.search(subject=sess[0], date=sess[1])[0] - probe_label = sess[2] -# for eid, probe_label in zip(eid_several, probe_several): - trajectory = one.alyx.rest('trajectories', 'list', provenance='Ephys aligned histology track', - session=eid, probe=probe_label) - - subject = trajectory[0]['session']['subject'] - date = trajectory[0]['session']['start_time'][0:10] - chn_coords = one.load(eid, dataset_types=['channels.localCoordinates'])[0] - depths = chn_coords[:, 1] - - insertion = one.alyx.rest('insertions', 'list', session=eid, name=probe_label) - xyz_picks = np.array(insertion[0]['json']['xyz_picks']) / 1e6 - - alignments = trajectory[0]['json'] - - def plot_regions(region, label, colour, ax): - for reg, col in zip(region, colour): - height = np.abs(reg[1] - reg[0]) - color = col / 255 - ax.bar(x=0.5, height=height, width=1, color=color, bottom=reg[0], edgecolor='w') - - ax.set_yticks(label[:, 0].astype(int)) - ax.set_yticklabels(label[:, 1]) - ax.yaxis.set_tick_params(labelsize=10) - ax.tick_params(axis="y", direction="in", pad=-50) - ax.set_ylim([20, 3840]) - ax.get_xaxis().set_visible(False) - - def plot_scaling(region, scale, mapper, ax): - for reg, col in zip(region_scaled, scale_factor): - height = np.abs(reg[1] - reg[0]) - color = np.array(mapper.to_rgba(col, bytes=True)) / 255 - ax.bar(x=1.1, height=height, width=0.2, color=color, bottom=reg[0], edgecolor='w') - - sec_ax = ax.secondary_yaxis('right') - sec_ax.set_yticks(np.mean(region, axis=1)) - sec_ax.set_yticklabels(np.around(scale, 2)) - sec_ax.tick_params(axis="y", direction="in") - sec_ax.set_ylim([20, 3840]) - - fig, ax = plt.subplots(1, len(alignments) + 1, figsize=(15, 15)) - ephysalign = EphysAlignment(xyz_picks, depths, brain_atlas=brain_atlas) - feature, track, _ = ephysalign.get_track_and_feature() - channels_orig = ephysalign.get_channel_locations(feature, track) - region, region_label = ephysalign.scale_histology_regions(feature, track) - region_scaled, scale_factor = ephysalign.get_scale_factor(region) - region_colour = ephysalign.region_colour - - norm = matplotlib.colors.Normalize(vmin=0.5, vmax=1.5, clip=True) - mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=matplotlib.cm.seismic) - - ax_i = fig.axes[0] - plot_regions(region, region_label, region_colour, ax_i) - plot_scaling(region_scaled, scale_factor, mapper, ax_i) - ax_i.set_title('Original') - - for iK, key in enumerate(alignments): - # Location of reference lines used for alignmnet - feature = np.array(alignments[key][0]) - track = np.array(alignments[key][1]) - user = key[20:] - # Instantiate EphysAlignment object - ephysalign = EphysAlignment(xyz_picks, depths, track_prev=track, feature_prev=feature, - brain_atlas=brain_atlas) - - channels = ephysalign.get_channel_locations(feature, track) - avg_dist = np.mean(np.sqrt(np.sum((channels - channels_orig) ** 2, axis=1)), axis=0) - region, region_label = ephysalign.scale_histology_regions(feature, track) - region_scaled, scale_factor = ephysalign.get_scale_factor(region) - - ax_i = fig.axes[iK + 1] - plot_regions(region, region_label, region_colour, ax_i) - plot_scaling(region_scaled, scale_factor, mapper, ax_i) - ax_i.set_title(user + '\n Avg dist = ' + str(np.around(avg_dist * 1e6, 2))) - - fig.suptitle(subject + '_' + str(date) + '_' + probe_label, fontsize=16) - plt.show() - fig.savefig(fig_path.joinpath(subject + '_' + str(date) + '_' + probe_label + '.png'), dpi=100) - plt.close(fig) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/create_overview_plots.py b/PythonScripts/iblapps-master/atlaselectrophysiology/create_overview_plots.py deleted file mode 100644 index 95d78836..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/create_overview_plots.py +++ /dev/null @@ -1,86 +0,0 @@ -import matplotlib.pyplot as plt -from pathlib import Path -import glob - - -def make_overview_plot(folder, sess_info, save_folder=None): - - image_folder = folder - image_info = sess_info - if not save_folder: - save_folder = image_folder - - def load_image(image_name, ax): - with image_name as ifile: - image = plt.imread(ifile) - - ax.spines['right'].set_visible(False) - ax.spines['top'].set_visible(False) - ax.spines['left'].set_visible(False) - ax.spines['bottom'].set_visible(False) - ax.set_axis_off() - ax.set_aspect('equal') - ax.imshow(image) - return image - - fig = plt.figure(constrained_layout=True, figsize=(18, 9)) - gs = fig.add_gridspec(3, 18) - gs.update(wspace=0.025, hspace=0.05) - - ignore_img_plots = ['leftGabor', 'rightGabor', 'noiseOn', 'valveOn', 'toneOn'] - img_row_order = [0, 0, 0, 0, 0, 0, 1, 1, 1] - img_column_order = [0, 3, 6, 9, 12, 15, 0, 3, 6] - img_idx = [0, 5, 4, 6, 7, 8, 1, 2, 3] - img_files = glob.glob(str(image_folder.joinpath(image_info + 'img_*.png'))) - img_files = [img for img in img_files if not any([ig in img for ig in ignore_img_plots])] - img_files_sort = [img_files[idx] for idx in img_idx] - - for iF, file in enumerate(img_files_sort): - ax = fig.add_subplot(gs[img_row_order[iF], img_column_order[iF]:img_column_order[iF] + 3]) - load_image(Path(file), ax) - - ignore_probe_plots = ['RF Map'] - probe_row_order = [1, 1, 1, 1, 1, 1, 2, 2, 2] - probe_column_order = [9, 10, 11, 12, 13, 14, 12, 13, 14] - probe_idx = [0, 3, 1, 2, 4, 5, 6] - probe_files = glob.glob(str(image_folder.joinpath(image_info + 'probe_*.png'))) - probe_files = [probe for probe in probe_files if not any([pr in probe for pr in - ignore_probe_plots])] - probe_files_sort = [probe_files[idx] for idx in probe_idx] - line_files = glob.glob(str(image_folder.joinpath(image_info + 'line_*.png'))) - - for iF, file in enumerate(probe_files_sort + line_files): - ax = fig.add_subplot(gs[probe_row_order[iF], probe_column_order[iF]]) - load_image(Path(file), ax) - - slice_files = glob.glob(str(image_folder.joinpath(image_info + 'slice_*.png'))) - slice_row_order = [2, 2, 2, 2] - slice_idx = [0, 1, 2, 3] - slice_column_order = [0, 3, 6, 9] - slice_files_sort = [slice_files[idx] for idx in slice_idx] - - for iF, file in enumerate(slice_files_sort): - ax = fig.add_subplot(gs[slice_row_order[iF], - slice_column_order[iF]:slice_column_order[iF] + 3]) - load_image(Path(file), ax) - - slice_files = glob.glob(str(image_folder.joinpath(image_info + 'slice_zoom*.png'))) - slice_row_order = [2, 2, 2, 2] - slice_idx = [0, 1, 2, 3] - slice_column_order = [2, 5, 8, 11] - slice_files_sort = [slice_files[idx] for idx in slice_idx] - - for iF, file in enumerate(slice_files_sort): - ax = fig.add_subplot(gs[slice_row_order[iF], slice_column_order[iF]]) - load_image(Path(file), ax) - - hist_files = glob.glob(str(image_folder.joinpath(image_info + 'hist*.png'))) - for iF, file in enumerate(hist_files): - ax = fig.add_subplot(gs[1:3, 15:18]) - load_image(Path(file), ax) - - ax.text(0.5, 0, image_info[:-1], va="center", ha="center", transform=ax.transAxes) - plt.savefig(save_folder.joinpath(image_info + "overview.png"), - bbox_inches='tight', pad_inches=0) - # plt.close() - # plt.show() diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_gui.py b/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_gui.py deleted file mode 100644 index 04062144..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_gui.py +++ /dev/null @@ -1,2157 +0,0 @@ -import os -import platform - -if platform.system() == 'Darwin': - if platform.release().split('.')[0] == '20': - os.environ["QT_MAC_WANTS_LAYER"] = "1" - -from PyQt5 import QtWidgets, QtCore, QtGui -import pyqtgraph as pg -import pyqtgraph.exporters -import numpy as np -from random import randrange -from atlaselectrophysiology.load_data import LoadData -from atlaselectrophysiology.load_data_local import LoadDataLocal -from ibllib.pipes.ephys_alignment import EphysAlignment -import atlaselectrophysiology.plot_data as pd -import atlaselectrophysiology.ColorBar as cb -import atlaselectrophysiology.ephys_gui_setup as ephys_gui -from atlaselectrophysiology.create_overview_plots import make_overview_plot -from pathlib import Path -import PyQt5 as qt -import matplotlib.pyplot as mpl # noqa # This is needed to make qt show properly :/ - - -class MainWindow(QtWidgets.QMainWindow, ephys_gui.Setup): - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, MainWindow)] - - @staticmethod - def _get_or_create(title='Electrophysiology Atlas', **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - MainWindow._instances()), None) - if av is None: - av = MainWindow(**kwargs) - av.setWindowTitle(title) - return av - - def __init__(self, offline=False, probe_id=None, one=None, histology=True, - spike_collection=None, remote=False): - super(MainWindow, self).__init__() - - self.init_variables() - self.init_layout(self, offline=offline) - self.configure = True - one_mode = 'remote' if remote else 'auto' - if not offline and probe_id is None: - self.loaddata = LoadData(mode=one_mode) - self.populate_lists(self.loaddata.get_subjects(), self.subj_list, self.subj_combobox) - self.offline = False - elif not offline and probe_id is not None: - self.loaddata = LoadData(probe_id=probe_id, one=one, load_histology=histology, - spike_collection=spike_collection, mode=one_mode) - self.current_shank_idx = 0 - _, self.histology_exists = self.loaddata.get_info(0) - self.feature_prev, self.track_prev = self.loaddata.get_starting_alignment(0) - self.data_status = False - self.data_button_pressed() - self.offline = False - else: - self.loaddata = LoadDataLocal() - self.offline = True - self.histology_exists = True - - self.allen = self.loaddata.get_allen_csv() - self.init_region_lookup(self.allen) - - def init_variables(self): - """ - Initialise variables - """ - # Line styles and fonts - self.kpen_dot = pg.mkPen(color='k', style=QtCore.Qt.DotLine, width=2) - self.rpen_dot = pg.mkPen(color='r', style=QtCore.Qt.DotLine, width=2) - self.kpen_solid = pg.mkPen(color='k', style=QtCore.Qt.SolidLine, width=2) - self.bpen_solid = pg.mkPen(color='b', style=QtCore.Qt.SolidLine, width=3) - self.bar_colour = QtGui.QColor(160, 160, 160) - - # Padding to add to figures to make sure always same size viewbox - self.pad = 0.05 - - # Variables to do with probe dimension - self.probe_tip = 0 - self.probe_top = 3840 - self.probe_extra = 100 - self.view_total = [-2000, 6000] - self.depth = np.arange(self.view_total[0], self.view_total[1], 20) - self.extend_feature = 1 - - # Initialise with linear fit scaling as default - self.lin_fit = True - - # Variables to keep track of number of fits (max 10) - self.max_idx = 10 - self.idx = 0 - self.current_idx = 0 - self.total_idx = 0 - self.last_idx = 0 - self.diff_idx = 0 - - # Variables to keep track of reference lines and points added - self.line_status = True - self.label_status = True - self.channel_status = True - self.hist_bound_status = True - self.lines_features = np.empty((0, 3)) - self.lines_tracks = np.empty((0, 1)) - self.points = np.empty((0, 1)) - self.y_scale = 1 - self.x_scale = 1 - - # Variables to keep track of plots and colorbars - self.img_plots = [] - self.line_plots = [] - self.probe_plots = [] - self.img_cbars = [] - self.probe_cbars = [] - self.scale_regions = np.empty((0, 1)) - self.slice_lines = [] - self.slice_items = [] - self.probe_bounds = [] - - # Variables to keep track of popup plots - self.cluster_popups = [] - self.label_popup = [] - self.popup_status = True - - self.hist_data = { - 'region': [0] * (self.max_idx + 1), - 'axis_label': [0] * (self.max_idx + 1), - 'colour': [] - } - - self.hist_data_ref = { - 'region': [], - 'axis_label': [], - 'colour': [] - } - - self.scale_data = { - 'region': [0] * (self.max_idx + 1), - 'scale': [0] * (self.max_idx + 1) - } - - self.hist_nearby_x = None - self.hist_nearby_y = None - self.hist_nearby_col = None - self.hist_nearby_parent_x = None - self.hist_nearby_parent_y = None - self.hist_nearby_parent_col = None - - self.track = [0] * (self.max_idx + 1) - self.features = [0] * (self.max_idx + 1) - - self.nearby = None - - def set_axis(self, fig, ax, show=True, label=None, pen='k', ticks=True): - """ - Show/hide and configure axis of figure - :param fig: figure associated with axis - :type fig: pyqtgraph PlotWidget - :param ax: orientation of axis, must be one of 'left', 'right', 'top' or 'bottom' - :type ax: string - :param show: 'True' to show axis, 'False' to hide axis - :type show: bool - :param label: axis label - :type label: string - :parm pen: colour on axis - :type pen: string - :param ticks: 'True' to show axis ticks, 'False' to hide axis ticks - :param ticks: bool - :return axis: axis object - :type axis: pyqtgraph AxisItem - """ - if not label: - label = '' - if type(fig) == pg.PlotItem: - axis = fig.getAxis(ax) - else: - axis = fig.plotItem.getAxis(ax) - if show: - axis.show() - axis.setPen(pen) - axis.setTextPen(pen) - axis.setLabel(label) - if not ticks: - axis.setTicks([[(0, ''), (0.5, ''), (1, '')]]) - else: - axis.hide() - - return axis - - def set_font(self, fig, ax, ptsize=8, width=None, height=None): - - if type(fig) == pg.PlotItem: - axis = fig.getAxis(ax) - else: - axis = fig.plotItem.getAxis(ax) - - font = QtGui.QFont() - font.setPointSize(ptsize) - axis.setStyle(tickFont=font) - labelStyle = {'font-size': f'{ptsize}pt'} - axis.setLabel(**labelStyle) - - if width: - axis.setWidth(width) - if height: - axis.setHeight(height) - - def set_lims(self, min, max): - self.probe_tip = min - self.probe_top = max - - [top_line.setY(self.probe_top) for top_line in self.probe_top_lines] - [tip_line.setY(self.probe_tip) for tip_line in self.probe_tip_lines] - - def populate_lists(self, data, list_name, combobox): - """ - Populate drop down lists with subject/session/alignment options - :param data: list of options to add to widget - :type data: 1D array of strings - :param list_name: widget object to which to add data to - :type list_name: QtGui.QStandardItemModel - :param combobox: combobox object to which to add data to - :type combobox: QtWidgets.QComboBox - """ - list_name.clear() - for dat in data: - item = QtGui.QStandardItem(dat) - item.setEditable(False) - list_name.appendRow(item) - - # This makes sure the drop down menu is wide enough to showw full length of string - min_width = combobox.fontMetrics().width(max(data, key=len)) - min_width += combobox.view().autoScrollMargin() - min_width += combobox.style().pixelMetric(QtWidgets.QStyle.PM_ScrollBarExtent) - combobox.view().setMinimumWidth(min_width) - - # Set the default to be the first option - combobox.setCurrentIndex(0) - - def set_view(self, view=1, configure=False): - """ - Layout of ephys data figures, can be changed using Shift+1, Shift+2, Shift+3 - :param view: from left to right - 1: img plot, line plot, probe plot - 2: img plot, probe plot, line plot - 3: probe plot, line plot, img_plot - :type view: int - :param configure: Returns the width of each image, set to 'True' once during the setup to - ensure figures are always the same width - :type configure: bool - """ - if configure: - self.fig_ax_width = self.fig_data_ax.width() - self.fig_img_width = self.fig_img.width() - self.fig_ax_width - self.fig_line_width = self.fig_line.width() - self.fig_probe_width = self.fig_probe.width() - self.slice_width = self.fig_slice.width() - self.slice_height = self.fig_slice.height() - self.slice_rect = self.fig_slice.viewRect() - - if view == 1: - self.fig_data_layout.removeItem(self.fig_img_cb) - self.fig_data_layout.removeItem(self.fig_probe_cb) - self.fig_data_layout.removeItem(self.fig_img) - self.fig_data_layout.removeItem(self.fig_line) - self.fig_data_layout.removeItem(self.fig_probe) - self.fig_data_layout.addItem(self.fig_img_cb, 0, 0) - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 1, 1, 2) - self.fig_data_layout.addItem(self.fig_img, 1, 0) - self.fig_data_layout.addItem(self.fig_line, 1, 1) - self.fig_data_layout.addItem(self.fig_probe, 1, 2) - - self.set_axis(self.fig_img_cb, 'left', pen='w') - self.set_axis(self.fig_probe_cb, 'left', show=False) - self.set_axis(self.fig_img, 'left', label='Distance from probe tip (um)') - self.set_axis(self.fig_probe, 'left', show=False) - self.set_axis(self.fig_line, 'left', show=False) - - self.fig_img.setPreferredWidth(self.fig_img_width + self.fig_ax_width) - self.fig_line.setPreferredWidth(self.fig_line_width) - self.fig_probe.setFixedWidth(self.fig_probe_width) - - self.fig_data_layout.layout.setColumnStretchFactor(0, 6) - self.fig_data_layout.layout.setColumnStretchFactor(1, 2) - self.fig_data_layout.layout.setColumnStretchFactor(2, 1) - self.fig_data_layout.layout.setRowStretchFactor(0, 1) - self.fig_data_layout.layout.setRowStretchFactor(1, 10) - - self.fig_img.update() - # Manually force the axis to shift and then reset axis as axis not always correct - # TO DO: find a better way! - self.fig_img.setXRange(min=self.xrange[0] - 10, max=self.xrange[1] + 10, padding=0) - self.reset_axis_button_pressed() - self.fig_line.update() - self.fig_probe.update() - - if view == 2: - self.fig_data_layout.removeItem(self.fig_img_cb) - self.fig_data_layout.removeItem(self.fig_probe_cb) - self.fig_data_layout.removeItem(self.fig_img) - self.fig_data_layout.removeItem(self.fig_line) - self.fig_data_layout.removeItem(self.fig_probe) - self.fig_data_layout.addItem(self.fig_img_cb, 0, 0) - - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 1, 1, 2) - self.fig_data_layout.addItem(self.fig_img, 1, 0) - self.fig_data_layout.addItem(self.fig_probe, 1, 1) - self.fig_data_layout.addItem(self.fig_line, 1, 2) - - self.set_axis(self.fig_img_cb, 'left', pen='w') - self.set_axis(self.fig_probe_cb, 'left', show=False) - self.set_axis(self.fig_img, 'left', label='Distance from probe tip (um)') - self.set_axis(self.fig_probe, 'left', show=False) - self.set_axis(self.fig_line, 'left', show=False) - - self.fig_img.setPreferredWidth(self.fig_img_width + self.fig_ax_width) - self.fig_line.setPreferredWidth(self.fig_line_width) - self.fig_probe.setFixedWidth(self.fig_probe_width) - - self.fig_data_layout.layout.setColumnStretchFactor(0, 6) - self.fig_data_layout.layout.setColumnStretchFactor(1, 1) - self.fig_data_layout.layout.setColumnStretchFactor(2, 2) - self.fig_data_layout.layout.setRowStretchFactor(0, 1) - self.fig_data_layout.layout.setRowStretchFactor(1, 10) - - self.fig_img.update() - self.fig_img.setXRange(min=self.xrange[0] - 10, max=self.xrange[1] + 10, padding=0) - self.reset_axis_button_pressed() - self.fig_line.update() - self.fig_probe.update() - - if view == 3: - self.fig_data_layout.removeItem(self.fig_img_cb) - self.fig_data_layout.removeItem(self.fig_probe_cb) - self.fig_data_layout.removeItem(self.fig_img) - self.fig_data_layout.removeItem(self.fig_line) - self.fig_data_layout.removeItem(self.fig_probe) - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 0, 1, 2) - self.fig_data_layout.addItem(self.fig_img_cb, 0, 2) - self.fig_data_layout.addItem(self.fig_probe, 1, 0) - self.fig_data_layout.addItem(self.fig_line, 1, 1) - self.fig_data_layout.addItem(self.fig_img, 1, 2) - - self.set_axis(self.fig_probe_cb, 'left', pen='w') - self.set_axis(self.fig_img_cb, 'left', show=False) - self.set_axis(self.fig_line, 'left', show=False) - self.set_axis(self.fig_img, 'left', pen='w') - self.set_axis(self.fig_img, 'left', show=False) - self.set_axis(self.fig_probe, 'left', label='Distance from probe tip (um)') - - self.fig_data_layout.layout.setColumnStretchFactor(0, 1) - self.fig_data_layout.layout.setColumnStretchFactor(1, 2) - self.fig_data_layout.layout.setColumnStretchFactor(2, 6) - self.fig_data_layout.layout.setRowStretchFactor(0, 1) - self.fig_data_layout.layout.setRowStretchFactor(1, 10) - - self.fig_probe.setFixedWidth(self.fig_probe_width + self.fig_ax_width) - self.fig_img.setPreferredWidth(self.fig_img_width) - self.fig_line.setPreferredWidth(self.fig_line_width) - - self.fig_img.update() - self.fig_img.setXRange(min=self.xrange[0] - 10, max=self.xrange[1] + 10, padding=0) - self.reset_axis_button_pressed() - self.fig_line.update() - self.fig_probe.update() - - def save_plots(self, save_path=None): - """ - Saves all plots from the GUI into folder - """ - # make folder to save plots to - try: - sess_info = (self.loaddata.subj + '_' + str(self.loaddata.date) + '_' + - self.loaddata.probe_label + '_') - image_path_overview = self.probe_path.joinpath('GUI_plots') - image_path = image_path_overview.joinpath(sess_info[:-1]) - except Exception: - sess_info = '' - image_path_overview = self.probe_path.joinpath('GUI_plots') - image_path = image_path_overview - - if save_path: - image_path_overview = Path(save_path) - - os.makedirs(image_path_overview, exist_ok=True) - os.makedirs(image_path, exist_ok=True) - # Reset all axis, put view back to 1 and remove any reference lines - self.reset_axis_button_pressed() - self.set_view(view=1, configure=False) - self.remove_lines_points() - - xlabel_img = self.fig_img.getAxis('bottom').label.toPlainText() - xlabel_line = self.fig_line.getAxis('bottom').label.toPlainText() - - # First go through all the image plots - self.fig_data_layout.removeItem(self.fig_probe) - self.fig_data_layout.removeItem(self.fig_probe_cb) - self.fig_data_layout.removeItem(self.fig_line) - - width1 = self.fig_data_area.width() - height1 = self.fig_data_area.height() - ax_width = self.fig_img.getAxis('left').width() - ax_height = self.fig_img_cb.getAxis('top').height() - - self.set_font(self.fig_img, 'left', ptsize=15, width=ax_width + 20) - self.set_font(self.fig_img, 'bottom', ptsize=15) - self.set_font(self.fig_img_cb, 'top', ptsize=15, height=ax_height + 15) - - self.fig_data_area.resize(700, height1) - - plot = None - start_plot = self.img_options_group.checkedAction() - - while plot != start_plot: - self.set_font(self.fig_img_cb, 'top', ptsize=15, height=ax_height + 15) - exporter = pg.exporters.ImageExporter(self.fig_data_layout.scene()) - exporter.export(str(image_path.joinpath(sess_info + 'img_' + - self.img_options_group.checkedAction() - .text() + '.png'))) - self.toggle_plots(self.img_options_group) - self.remove_lines_points() - plot = self.img_options_group.checkedAction() - - self.set_font(self.fig_img, 'left', ptsize=8, width=ax_width) - self.set_font(self.fig_img, 'bottom', ptsize=8) - self.set_font(self.fig_img_cb, 'top', ptsize=8, height=ax_height) - self.set_axis(self.fig_img, 'bottom', label=xlabel_img) - self.fig_data_layout.removeItem(self.fig_img) - self.fig_data_layout.removeItem(self.fig_img_cb) - - # Next go over probe plots - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 0, 1, 2) - self.fig_data_layout.addItem(self.fig_probe, 1, 0) - self.set_axis(self.fig_probe, 'left', label='Distance from probe tip (uV)') - self.fig_probe.setFixedWidth(self.fig_probe_width + self.fig_ax_width + 20) - self.set_font(self.fig_probe, 'left', ptsize=15, width=ax_width + 20) - self.set_font(self.fig_probe_cb, 'top', ptsize=15, height=ax_height + 15) - self.fig_data_area.resize(250, height1) - - plot = None - start_plot = self.probe_options_group.checkedAction() - - while plot != start_plot: - self.set_font(self.fig_probe_cb, 'top', ptsize=15, height=ax_height + 15) - exporter = pg.exporters.ImageExporter(self.fig_data_layout.scene()) - exporter.export(str(image_path.joinpath(sess_info + 'probe_' + - self.probe_options_group.checkedAction(). - text() + '.png'))) - self.toggle_plots(self.probe_options_group) - plot = self.probe_options_group.checkedAction() - - self.fig_probe.setFixedWidth(self.fig_probe_width + self.fig_ax_width) - self.set_font(self.fig_probe, 'left', ptsize=8, width=ax_width) - self.set_font(self.fig_probe_cb, 'top', ptsize=8, height=ax_height) - self.set_axis(self.fig_probe, 'bottom', pen='w', label='blank') - self.fig_data_layout.removeItem(self.fig_probe) - self.fig_data_layout.removeItem(self.fig_probe_cb) - - # Next go through the line plots - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 0, 1, 2) - self.fig_probe_cb.clear() - text = self.fig_probe_cb.getAxis('top').label.toPlainText() - self.set_axis(self.fig_probe_cb, 'top', pen='w') - self.fig_data_layout.addItem(self.fig_line, 1, 0) - - self.set_axis(self.fig_line, 'left', label='Distance from probe tip (um)') - self.set_font(self.fig_line, 'left', ptsize=15, width=ax_width + 20) - self.set_font(self.fig_line, 'bottom', ptsize=15) - self.fig_data_area.resize(200, height1) - - plot = None - start_plot = self.line_options_group.checkedAction() - while plot != start_plot: - exporter = pg.exporters.ImageExporter(self.fig_data_layout.scene()) - exporter.export(str(image_path.joinpath(sess_info + 'line_' + - self.line_options_group.checkedAction(). - text() + '.png'))) - self.toggle_plots(self.line_options_group) - plot = self.line_options_group.checkedAction() - - [self.fig_probe_cb.addItem(cbar) for cbar in self.probe_cbars] - self.set_axis(self.fig_probe_cb, 'top', pen='k', label=text) - self.set_font(self.fig_line, 'left', ptsize=8, width=ax_width) - self.set_font(self.fig_line, 'bottom', ptsize=8) - self.set_axis(self.fig_line, 'bottom', label=xlabel_line) - self.fig_data_layout.removeItem(self.fig_line) - self.fig_data_layout.removeItem(self.fig_probe_cb) - self.fig_data_area.resize(width1, height1) - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 0, 1, 2) - self.fig_data_layout.addItem(self.fig_img_cb, 0, 2) - self.fig_data_layout.addItem(self.fig_probe, 1, 0) - self.fig_data_layout.addItem(self.fig_line, 1, 1) - self.fig_data_layout.addItem(self.fig_img, 1, 2) - - self.set_view(view=1, configure=False) - - # Save slice images - plot = None - start_plot = self.slice_options_group.checkedAction() - while plot != start_plot: - self.toggle_channel_button_pressed() - self.traj_line.setData(x=self.xyz_channels[:, 0], y=self.xyz_channels[:, 2], - pen=self.rpen_dot) - self.fig_slice.addItem(self.traj_line) - slice_name = self.slice_options_group.checkedAction().text() - exporter = pg.exporters.ImageExporter(self.fig_slice) - exporter.export(str(image_path.joinpath(sess_info + 'slice_' + slice_name + '.png'))) - self.toggle_plots(self.slice_options_group) - plot = self.slice_options_group.checkedAction() - - plot = None - start_plot = self.slice_options_group.checkedAction() - while plot != start_plot: - self.toggle_channel_button_pressed() - self.traj_line.setData(x=self.xyz_channels[:, 0], y=self.xyz_channels[:, 2], - pen=self.rpen_dot) - self.fig_slice.addItem(self.traj_line) - slice_name = self.slice_options_group.checkedAction().text() - self.fig_slice.setXRange(min=np.min(self.xyz_channels[:, 0]) - 200 / 1e6, - max=np.max(self.xyz_channels[:, 0]) + 200 / 1e6) - self.fig_slice.setYRange(min=np.min(self.xyz_channels[:, 2]) - 500 / 1e6, - max=np.max(self.xyz_channels[:, 2]) + 500 / 1e6) - self.fig_slice.resize(50, self.slice_height) - exporter = pg.exporters.ImageExporter(self.fig_slice) - exporter.export( - str(image_path.joinpath(sess_info + 'slice_zoom_' + slice_name + '.png'))) - self.fig_slice.resize(self.slice_width, self.slice_height) - self.fig_slice.setRange(rect=self.slice_rect) - self.toggle_plots(self.slice_options_group) - plot = self.slice_options_group.checkedAction() - - # Save the brain regions image - self.set_axis(self.fig_hist_extra_yaxis, 'left') - # Add labels to show which ones are aligned - self.set_axis(self.fig_hist, 'bottom', label='aligned') - self.set_font(self.fig_hist, 'bottom', ptsize=12) - self.set_axis(self.fig_hist_ref, 'bottom', label='original') - self.set_font(self.fig_hist_ref, 'bottom', ptsize=12) - exporter = pg.exporters.ImageExporter(self.fig_hist_layout.scene()) - exporter.export(str(image_path.joinpath(sess_info + 'hist.png'))) - self.set_axis(self.fig_hist_extra_yaxis, 'left', pen=None) - self.set_font(self.fig_hist, 'bottom', ptsize=8) - self.set_axis(self.fig_hist, 'bottom', pen='w', label='blank') - self.set_font(self.fig_hist_ref, 'bottom', ptsize=8) - self.set_axis(self.fig_hist_ref, 'bottom', pen='w', label='blank') - - make_overview_plot(image_path, sess_info, save_folder=image_path_overview) - - self.add_lines_points() - - def toggle_plots(self, options_group): - """ - Allows user to toggle through image, line, probe and slice plots using keyboard shortcuts - Alt+1, Alt+2, Alt+3 and Alt+4 respectively - :param options_group: Set of plots to toggle through - :type options_group: QtGui.QActionGroup - """ - - current_act = options_group.checkedAction() - actions = options_group.actions() - current_idx = [iA for iA, act in enumerate(actions) if act == current_act][0] - next_idx = np.mod(current_idx + 1, len(actions)) - actions[next_idx].setChecked(True) - actions[next_idx].trigger() - - """ - Plot functions - """ - def plot_histology(self, fig, ax='left', movable=True): - """ - Plots histology figure - brain regions that intersect with probe track - :param fig: figure on which to plot - :type fig: pyqtgraph PlotWidget - :param ax: orientation of axis, must be one of 'left' (fig_hist) or 'right' (fig_hist_ref) - :type ax: string - :param movable: whether probe reference lines can be moved, True for fig_hist, False for - fig_hist_ref - :type movable: Bool - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - fig.clear() - self.hist_regions = np.empty((0, 1)) - axis = fig.getAxis(ax) - axis.setTicks([self.hist_data['axis_label'][self.idx]]) - axis.setZValue(10) - self.set_axis(self.fig_hist, 'bottom', pen='w', label='blank') - - # Plot each histology region - for ir, reg in enumerate(self.hist_data['region'][self.idx]): - colour = QtGui.QColor(*self.hist_data['colour'][ir]) - region = pg.LinearRegionItem(values=(reg[0], reg[1]), - orientation=pg.LinearRegionItem.Horizontal, - brush=colour, movable=False) - # Add a white line at the boundary between regions - bound = pg.InfiniteLine(pos=reg[0], angle=0, pen='w') - fig.addItem(region) - fig.addItem(bound) - # Need to keep track of each histology region for label pressed interaction - self.hist_regions = np.vstack([self.hist_regions, region]) - - self.selected_region = self.hist_regions[-2] - - # Boundary for final region - bound = pg.InfiniteLine(pos=self.hist_data['region'][self.idx][-1][1], angle=0, - pen='w') - fig.addItem(bound) - # Add dotted lines to plot to indicate region along probe track where electrode - # channels are distributed - self.tip_pos = pg.InfiniteLine(pos=self.probe_tip, angle=0, pen=self.kpen_dot, - movable=movable) - self.top_pos = pg.InfiniteLine(pos=self.probe_top, angle=0, pen=self.kpen_dot, - movable=movable) - - # Lines can be moved to adjust location of channels along the probe track - # Ensure distance between bottom and top channel is always constant at 3840um and that - # lines can't be moved outside interpolation bounds - # Add offset of 1um to keep within bounds of interpolation - offset = 1 - self.tip_pos.setBounds((self.track[self.idx][0] * 1e6 + offset, - self.track[self.idx][-1] * 1e6 - - (self.probe_top + offset))) - self.top_pos.setBounds((self.track[self.idx][0] * 1e6 + (self.probe_top + offset), - self.track[self.idx][-1] * 1e6 - offset)) - self.tip_pos.sigPositionChanged.connect(self.tip_line_moved) - self.top_pos.sigPositionChanged.connect(self.top_line_moved) - - # Add lines to figure - fig.addItem(self.tip_pos) - fig.addItem(self.top_pos) - - def plot_histology_ref(self, fig, ax='right', movable=False): - """ - Plots histology figure - brain regions that intersect with probe track - :param fig: figure on which to plot - :type fig: pyqtgraph PlotWidget - :param ax: orientation of axis, must be one of 'left' (fig_hist) or 'right' (fig_hist_ref) - :type ax: string - :param movable: whether probe reference lines can be moved, True for fig_hist, False for - fig_hist_ref - :type movable: Bool - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - - fig.clear() - self.hist_ref_regions = np.empty((0, 1)) - axis = fig.getAxis(ax) - axis.setTicks([self.hist_data_ref['axis_label']]) - axis.setZValue(10) - self.set_axis(self.fig_hist_ref, 'bottom', pen='w', label='blank') - - # Plot each histology region - for ir, reg in enumerate(self.hist_data_ref['region']): - colour = QtGui.QColor(*self.hist_data['colour'][ir]) - region = pg.LinearRegionItem(values=(reg[0], reg[1]), - orientation=pg.LinearRegionItem.Horizontal, - brush=colour, movable=False) - bound = pg.InfiniteLine(pos=reg[0], angle=0, pen='w') - fig.addItem(region) - fig.addItem(bound) - self.hist_ref_regions = np.vstack([self.hist_ref_regions, region]) - - bound = pg.InfiniteLine(pos=self.hist_data_ref['region'][-1][1], angle=0, - pen='w') - fig.addItem(bound) - # Add dotted lines to plot to indicate region along probe track where electrode - # channels are distributed - self.tip_pos = pg.InfiniteLine(pos=self.probe_tip, angle=0, pen=self.kpen_dot, - movable=movable) - self.top_pos = pg.InfiniteLine(pos=self.probe_top, angle=0, pen=self.kpen_dot, - movable=movable) - # Add lines to figure - fig.addItem(self.tip_pos) - fig.addItem(self.top_pos) - - def plot_histology_nearby(self, fig, ax='right', movable=False): - """ - Plots histology figure - brain regions that intersect with probe track - :param fig: figure on which to plot - :type fig: pyqtgraph PlotWidget - :param ax: orientation of axis, must be one of 'left' (fig_hist) or 'right' (fig_hist_ref) - :type ax: string - :param movable: whether probe reference lines can be moved, True for fig_hist, False for - fig_hist_ref - :type movable: Bool - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - - fig.clear() - self.hist_ref_regions = np.empty((0, 1)) - axis = fig.getAxis(ax) - axis.setTicks([self.hist_data_ref['axis_label']]) - axis.setZValue(10) - - self.set_axis(fig, 'bottom', label='dist to boundary (um)') - fig.setXRange(min=0, max=100) - fig.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + self.probe_extra, - padding=self.pad) - - # Plot nearby regions - for ir, (x, y, c) in enumerate(zip(self.hist_nearby_x, self.hist_nearby_y, - self.hist_nearby_col)): - colour = QtGui.QColor(c) - plot = pg.PlotCurveItem() - plot.setData(x=x, y=y * 1e6, fillLevel=10, fillOutline=True) - plot.setBrush(colour) - plot.setPen(colour) - fig.addItem(plot) - - for ir, (x, y, c) in enumerate(zip(self.hist_nearby_parent_x, self.hist_nearby_parent_y, - self.hist_nearby_parent_col)): - colour = QtGui.QColor(c) - colour.setAlpha(70) - plot = pg.PlotCurveItem() - plot.setData(x=x, y=y * 1e6, fillLevel=10, fillOutline=True) - plot.setBrush(colour) - plot.setPen(colour) - fig.addItem(plot) - - # Add dotted lines to plot to indicate region along probe track where electrode - # channels are distributed - self.tip_pos = pg.InfiniteLine(pos=self.probe_tip, angle=0, pen=self.kpen_dot, - movable=movable) - self.top_pos = pg.InfiniteLine(pos=self.probe_top, angle=0, pen=self.kpen_dot, - movable=movable) - # Add lines to figure - fig.addItem(self.tip_pos) - fig.addItem(self.top_pos) - - def offset_hist_data(self): - """ - Offset location of probe tip along probe track - """ - # If no histology we can't do alignment - if not self.histology_exists: - return - - self.track[self.idx] = (self.track[self.idx_prev] + self.tip_pos.value() / 1e6) - self.features[self.idx] = (self.features[self.idx_prev]) - - self.hist_data['region'][self.idx], self.hist_data['axis_label'][self.idx] \ - = self.ephysalign.scale_histology_regions(self.features[self.idx], - self.track[self.idx]) - self.scale_data['region'][self.idx], self.scale_data['scale'][self.idx] \ - = self.ephysalign.get_scale_factor(self.hist_data['region'][self.idx]) - - def scale_hist_data(self): - """ - Scale brain regions along probe track - """ - - # If no histology we can't do alignment - if not self.histology_exists: - return - - # Track --> histology plot - line_track = np.array([line[0].pos().y() for line in self.lines_tracks]) / 1e6 - # Feature --> ephys data plots - line_feature = np.array([line[0].pos().y() for line in self.lines_features]) / 1e6 - depths_track = np.sort(np.r_[self.track[self.idx_prev][[0, -1]], line_track]) - - self.track[self.idx] = self.ephysalign.feature2track(depths_track, - self.features[self.idx_prev], - self.track[self.idx_prev]) - - self.features[self.idx] = np.sort(np.r_[self.features[self.idx_prev] - [[0, -1]], line_feature]) - - if (self.features[self.idx].size >= 5) & self.lin_fit: - self.features[self.idx], self.track[self.idx] = \ - self.ephysalign.adjust_extremes_linear(self.features[self.idx], - self.track[self.idx], self.extend_feature) - - else: - self.track[self.idx] = self.ephysalign.adjust_extremes_uniform(self.features[self.idx], - self.track[self.idx]) - self.hist_data['region'][self.idx], self.hist_data['axis_label'][self.idx] \ - = self.ephysalign.scale_histology_regions(self.features[self.idx], - self.track[self.idx]) - self.scale_data['region'][self.idx], self.scale_data['scale'][self.idx] \ - = self.ephysalign.get_scale_factor(self.hist_data['region'][self.idx]) - - # to automatically have lines go to correct position - # self.loaddata.track2feature(line_track, self.idx) - - def plot_scale_factor(self): - """ - Plots the scale factor applied to brain regions along probe track, displayed - alongside histology figure - """ - - # If no histology we can't do alignment - if not self.histology_exists: - return - - self.fig_scale.clear() - self.scale_regions = np.empty((0, 1)) - self.scale_factor = self.scale_data['scale'][self.idx] - scale_factor = self.scale_data['scale'][self.idx] - 0.5 - color_bar = cb.ColorBar('seismic') - cbar = color_bar.makeColourBar(20, 5, self.fig_scale_cb, min=0.5, max=1.5, - label='Scale Factor') - colours = color_bar.map.mapToQColor(scale_factor) - - for ir, reg in enumerate(self.scale_data['region'][self.idx]): - region = pg.LinearRegionItem(values=(reg[0], reg[1]), - orientation=pg.LinearRegionItem.Horizontal, - brush=colours[ir], movable=False) - bound = pg.InfiniteLine(pos=reg[0], angle=0, pen=colours[ir]) - - self.fig_scale.addItem(region) - self.fig_scale.addItem(bound) - self.scale_regions = np.vstack([self.scale_regions, region]) - - bound = pg.InfiniteLine(pos=self.scale_data['region'][self.idx][-1][1], angle=0, - pen=colours[-1]) - - self.fig_scale.addItem(bound) - - self.fig_scale.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.set_axis(self.fig_scale, 'bottom', pen='w', label='blank') - self.fig_scale_cb.addItem(cbar) - - def plot_fit(self): - """ - Plots the scale factor and offset applied to channels along depth of probe track - relative to orignal position of channels - """ - - # If no histology we can't do alignment - if not self.histology_exists: - return - - self.fit_plot.setData(x=self.features[self.idx] * 1e6, - y=self.track[self.idx] * 1e6) - self.fit_scatter.setData(x=self.features[self.idx] * 1e6, - y=self.track[self.idx] * 1e6) - - depth_lin = self.ephysalign.feature2track_lin(self.depth / 1e6, self.features[self.idx], - self.track[self.idx]) - if np.any(depth_lin): - self.fit_plot_lin.setData(x=self.depth, y=depth_lin * 1e6) - else: - self.fit_plot_lin.setData() - - def plot_slice(self, data, img_type): - - # If no histology we can't do alignment - if not self.histology_exists: - return - - self.fig_slice.clear() - self.slice_chns = [] - self.slice_lines = [] - img = pg.ImageItem() - img.setImage(data[img_type]) - transform = [data['scale'][0], 0., 0., 0., data['scale'][1], 0., data['offset'][0], - data['offset'][1], 1.] - img.setTransform(QtGui.QTransform(*transform)) - - if img_type == 'label': - self.fig_slice_layout.removeItem(self.slice_item) - self.fig_slice_layout.addItem(self.fig_slice_hist_alt, 0, 1) - self.slice_item = self.fig_slice_hist_alt - else: - color_bar = cb.ColorBar('cividis') - lut = color_bar.getColourMap() - img.setLookupTable(lut) - - self.fig_slice_layout.removeItem(self.slice_item) - self.fig_slice_hist = pg.HistogramLUTItem() - self.fig_slice_hist.axis.hide() - self.fig_slice_hist.setImageItem(img) - self.fig_slice_hist.gradient.setColorMap(color_bar.map) - self.fig_slice_hist.autoHistogramRange() - self.fig_slice_layout.addItem(self.fig_slice_hist, 0, 1) - hist_levels = self.fig_slice_hist.getLevels() - hist_val, hist_count = img.getHistogram() - upper_idx = np.where(hist_count > 10)[0][-1] - upper_val = hist_val[upper_idx] - if hist_levels[0] != 0: - self.fig_slice_hist.setLevels(min=hist_levels[0], max=upper_val) - self.slice_item = self.fig_slice_hist - - self.fig_slice.addItem(img) - self.traj_line = pg.PlotCurveItem() - self.traj_line.setData(x=self.xyz_track[:, 0], y=self.xyz_track[:, 2], pen=self.kpen_solid) - self.fig_slice.addItem(self.traj_line) - self.plot_channels() - - def plot_channels(self): - - # If no histology we can't do alignment - if not self.histology_exists: - return - - self.channel_status = True - self.xyz_channels = self.ephysalign.get_channel_locations(self.features[self.idx], - self.track[self.idx]) - if not self.slice_chns: - self.slice_lines = [] - self.slice_chns = pg.ScatterPlotItem() - self.slice_chns.setData(x=self.xyz_channels[:, 0], y=self.xyz_channels[:, 2], pen='r', - brush='r') - self.fig_slice.addItem(self.slice_chns) - track_lines = self.ephysalign.get_perp_vector(self.features[self.idx], - self.track[self.idx]) - - for ref_line in track_lines: - line = pg.PlotCurveItem() - line.setData(x=ref_line[:, 0], y=ref_line[:, 2], pen=self.kpen_dot) - self.fig_slice.addItem(line) - self.slice_lines.append(line) - - else: - for line in self.slice_lines: - self.fig_slice.removeItem(line) - self.slice_lines = [] - track_lines = self.ephysalign.get_perp_vector(self.features[self.idx], - self.track[self.idx]) - - for ref_line in track_lines: - line = pg.PlotCurveItem() - line.setData(x=ref_line[:, 0], y=ref_line[:, 2], pen=self.kpen_dot) - self.fig_slice.addItem(line) - self.slice_lines.append(line) - self.slice_chns.setData(x=self.xyz_channels[:, 0], y=self.xyz_channels[:, 2], pen='r', - brush='r') - - def plot_scatter(self, data): - """ - Plots a 2D scatter plot with electrophysiology data - param data: dictionary of data to plot - {'x': x coordinate of data, np.array((npoints)), float - 'y': y coordinate of data, np.array((npoints)), float - 'size': size of data points, np.array((npoints)), float - 'colour': colour of data points, np.array((npoints)), QtGui.QColor - 'xrange': range to display of x axis, np.array([min range, max range]), float - 'xaxis': label for xaxis, string - } - type data: dict - """ - if not data: - print('data for this plot not available') - return - else: - [self.fig_img.removeItem(plot) for plot in self.img_plots] - [self.fig_img_cb.removeItem(cbar) for cbar in self.img_cbars] - - self.img_plots = [] - self.img_cbars = [] - - size = data['size'].tolist() - symbol = data['symbol'].tolist() - - color_bar = cb.ColorBar(data['cmap']) - cbar = color_bar.makeColourBar(20, 5, self.fig_img_cb, min=np.min(data['levels'][0]), - max=np.max(data['levels'][1]), label=data['title']) - self.fig_img_cb.addItem(cbar) - self.img_cbars.append(cbar) - - if type(np.any(data['colours'])) == QtGui.QColor: - brush = data['colours'].tolist() - plot = pg.ScatterPlotItem() - plot.setData(x=data['x'], y=data['y'], - symbol=symbol, size=size, brush=brush, pen=data['pen']) - - else: - brush = color_bar.getBrush(data['colours'], - levels=[data['levels'][0], data['levels'][1]]) - plot = pg.ScatterPlotItem() - plot.setData(x=data['x'], y=data['y'], - symbol=symbol, size=size, brush=brush, pen=data['pen']) - - self.fig_img.addItem(plot) - self.fig_img.setXRange(min=data['xrange'][0], max=data['xrange'][1], - padding=0) - self.fig_img.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.set_axis(self.fig_img, 'bottom', label=data['xaxis']) - self.y_scale = 1 - self.img_plots.append(plot) - self.data_plot = plot - self.xrange = data['xrange'] - - if data['cluster']: - self.data = data['x'] - self.data_plot.sigClicked.connect(self.cluster_clicked) - - def plot_line(self, data): - """ - Plots a 1D line plot with electrophysiology data - param data: dictionary of data to plot - {'x': x coordinate of data, np.array((npoints)), float - 'y': y coordinate of data, np.array((npoints)), float - 'xrange': range to display of x axis, np.array([min range, max range]), float - 'xaxis': label for xaxis, string - } - type data: dict - """ - if not data: - print('data for this plot not available') - return - else: - [self.fig_line.removeItem(plot) for plot in self.line_plots] - self.line_plots = [] - line = pg.PlotCurveItem() - line.setData(x=data['x'], y=data['y']) - line.setPen(self.kpen_solid) - self.fig_line.addItem(line) - self.fig_line.setXRange(min=data['xrange'][0], max=data['xrange'][1], padding=0) - self.fig_line.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.set_axis(self.fig_line, 'bottom', label=data['xaxis']) - self.line_plots.append(line) - - def plot_probe(self, data, bounds=None): - """ - Plots a 2D image with probe geometry - param data: dictionary of data to plot - {'img': image data for each channel bank, list of np.array((1,ny)), list - 'scale': scaling to apply to each image, list of np.array([xscale,yscale]), list - 'offset': offset to apply to each image, list of np.array([xoffset,yoffset]), list - 'level': colourbar extremes np.array([min val, max val]), float - 'cmap': colourmap to use, string - 'xrange': range to display of x axis, np.array([min range, max range]), float - 'title': description to place on colorbar, string - } - type data: dict - """ - if not data: - print('data for this plot not available') - return - else: - [self.fig_probe.removeItem(plot) for plot in self.probe_plots] - [self.fig_probe_cb.removeItem(cbar) for cbar in self.probe_cbars] - [self.fig_probe.removeItem(line) for line in self.probe_bounds] - self.set_axis(self.fig_probe_cb, 'top', pen='w') - self.probe_plots = [] - self.probe_cbars = [] - self.probe_bounds = [] - color_bar = cb.ColorBar(data['cmap']) - lut = color_bar.getColourMap() - for img, scale, offset in zip(data['img'], data['scale'], data['offset']): - image = pg.ImageItem() - image.setImage(img) - transform = [scale[0], 0., 0., 0., scale[1], 0., offset[0], - offset[1], 1.] - image.setTransform(QtGui.QTransform(*transform)) - image.setLookupTable(lut) - image.setLevels((data['levels'][0], data['levels'][1])) - self.fig_probe.addItem(image) - self.probe_plots.append(image) - - cbar = color_bar.makeColourBar(20, 5, self.fig_probe_cb, min=data['levels'][0], - max=data['levels'][1], label=data['title'], lim=True) - self.fig_probe_cb.addItem(cbar) - self.probe_cbars.append(cbar) - - self.fig_probe.setXRange(min=data['xrange'][0], max=data['xrange'][1], padding=0) - self.fig_probe.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - # so stupid!!!!! - self.set_axis(self.fig_probe, 'bottom', pen='w', label='blank') - if bounds is not None: - # add some infinite line stuff - for bound in bounds: - line = pg.InfiniteLine(pos=bound, angle=0, pen='w') - self.fig_probe.addItem(line) - self.probe_bounds.append(line) - - def plot_image(self, data): - """ - Plots a 2D image with with electrophysiology data - param data: dictionary of data to plot - {'img': image data, np.array((nx,ny)), float - 'scale': scaling to apply to each axis, np.array([xscale,yscale]), float - 'level': colourbar extremes np.array([min val, max val]), float - 'cmap': colourmap to use, string - 'xrange': range to display of x axis, np.array([min range, max range]), float - 'xaxis': label for xaxis, string - 'title': description to place on colorbar, string - } - type data: dict - """ - if not data: - print('data for this plot not available') - return - else: - [self.fig_img.removeItem(plot) for plot in self.img_plots] - [self.fig_img_cb.removeItem(cbar) for cbar in self.img_cbars] - self.set_axis(self.fig_img_cb, 'top', pen='w') - self.img_plots = [] - self.img_cbars = [] - - image = pg.ImageItem() - image.setImage(data['img']) - transform = [data['scale'][0], 0., 0., 0., data['scale'][1], 0., data['offset'][0], - data['offset'][1], 1.] - image.setTransform(QtGui.QTransform(*transform)) - cmap = data.get('cmap', []) - if cmap: - color_bar = cb.ColorBar(data['cmap']) - lut = color_bar.getColourMap() - image.setLookupTable(lut) - image.setLevels((data['levels'][0], data['levels'][1])) - cbar = color_bar.makeColourBar(20, 5, self.fig_img_cb, min=data['levels'][0], - max=data['levels'][1], label=data['title']) - self.fig_img_cb.addItem(cbar) - self.img_cbars.append(cbar) - else: - image.setLevels((1, 0)) - - self.fig_img.addItem(image) - self.img_plots.append(image) - self.fig_img.setXRange(min=data['xrange'][0], max=data['xrange'][1], padding=0) - self.fig_img.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - # TODO need to make this work, at the moment messes things up! - # self.fig_img.setLimits(xMin=data['xrange'][0], xMax=data['xrange'][1]) - # yMin=self.probe_tip - self.probe_extra - self.pad, - # yMax=self.probe_top + self.probe_extra + self.pad) - self.set_axis(self.fig_img, 'bottom', label=data['xaxis']) - self.y_scale = data['scale'][1] - self.x_scale = data['scale'][0] - self.data_plot = image - self.xrange = data['xrange'] - - """ - Interaction functions - """ - - def on_subject_selected(self, idx): - """ - Triggered when subject is selected from drop down list options - :param idx: index chosen subject (item) in drop down list - :type idx: int - """ - self.data_status = False - self.sess_list.clear() - sessions = self.loaddata.get_sessions(idx) - self.populate_lists(sessions, self.sess_list, self.sess_combobox) - self.prev_alignments, self.histology_exists = self.loaddata.get_info(0) - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.feature_prev, self.track_prev = self.loaddata.get_starting_alignment(0) - # For IBL case at the moment we only using single shank - self.current_shank_idx = 0 - - def on_session_selected(self, idx): - """ - Triggered when session is selected from drop down list options - :param idx: index of chosen session (item) in drop down list - :type idx: int - """ - self.data_status = False - self.prev_alignments, self.histology_exists = self.loaddata.get_info(idx) - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.feature_prev, self.track_prev = self.loaddata.get_starting_alignment(0) - - def on_folder_selected(self): - """ - Triggered in offline mode when folder button is clicked - """ - self.data_status = False - folder_path = Path(QtWidgets.QFileDialog.getExistingDirectory(None, "Select Folder")) - self.folder_line.setText(str(folder_path)) - self.prev_alignments, shank_options = self.loaddata.get_info(folder_path) - self.populate_lists(shank_options, self.shank_list, self.shank_combobox) - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.on_shank_selected(0) - - def on_shank_selected(self, idx): - """ - Triggered in offline mode for selecting shank when using NP2.0 - """ - self.data_status = False - self.current_shank_idx = idx - # Update prev_alignments - self.prev_alignments = self.loaddata.get_previous_alignments(self.current_shank_idx) - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.feature_prev, self.track_prev = self.loaddata.get_starting_alignment(0) - - def on_alignment_selected(self, idx): - self.feature_prev, self.track_prev = self.loaddata.get_starting_alignment(idx) - - def data_button_pressed(self): - """ - Triggered when Get Data button pressed, uses subject and session info to find eid and - downloads and computes data needed for GUI display - """ - - # Clear all plots from previous session - [self.fig_img.removeItem(plot) for plot in self.img_plots] - [self.fig_img.removeItem(cbar) for cbar in self.img_cbars] - [self.fig_line.removeItem(plot) for plot in self.line_plots] - [self.fig_probe.removeItem(plot) for plot in self.probe_plots] - [self.fig_probe.removeItem(cbar) for cbar in self.probe_cbars] - self.fig_slice.clear() - self.fig_hist.clear() - self.ax_hist.setTicks([]) - self.fig_hist_ref.clear() - self.ax_hist_ref.setTicks([]) - self.fig_scale.clear() - self.fit_plot.setData() - self.fit_scatter.setData() - self.remove_lines_points() - self.init_variables() - - # Only run once - if not self.data_status: - self.probe_path, ephys_path, alf_path, self.chn_depths, self.sess_notes = \ - self.loaddata.get_data() - if not self.probe_path: - return - - # Only get histology specific stuff if the histology tracing exists - if self.histology_exists: - self.xyz_picks = self.loaddata.get_xyzpicks() - - if np.any(self.feature_prev): - self.ephysalign = EphysAlignment(self.xyz_picks, self.chn_depths, - track_prev=self.track_prev, - feature_prev=self.feature_prev, - brain_atlas=self.loaddata.brain_atlas) - else: - self.ephysalign = EphysAlignment(self.xyz_picks, self.chn_depths, - brain_atlas=self.loaddata.brain_atlas) - - self.features[self.idx], self.track[self.idx], self.xyz_track \ - = self.ephysalign.get_track_and_feature() - - self.hist_data['region'][self.idx], self.hist_data['axis_label'][self.idx] \ - = self.ephysalign.scale_histology_regions(self.features[self.idx], - self.track[self.idx]) - self.hist_data['colour'] = self.ephysalign.region_colour - self.scale_data['region'][self.idx], self.scale_data['scale'][self.idx] \ - = self.ephysalign.get_scale_factor(self.hist_data['region'][self.idx]) - - self.hist_data_ref['region'], self.hist_data_ref['axis_label'] \ - = self.ephysalign.scale_histology_regions(self.ephysalign.track_extent, - self.ephysalign.track_extent) - self.hist_data_ref['colour'] = self.ephysalign.region_colour - - # If we have not loaded in the data before then we load eveything we need - if not self.data_status: - self.plotdata = pd.PlotData(self.probe_path, ephys_path, alf_path, - self.current_shank_idx) - self.set_lims(np.min([0, self.plotdata.chn_min]), self.plotdata.chn_max) - self.scat_drift_data = self.plotdata.get_depth_data_scatter() - (self.scat_fr_data, self.scat_p2t_data, - self.scat_amp_data) = self.plotdata.get_fr_p2t_data_scatter() - self.img_corr_data = self.plotdata.get_correlation_data_img() - self.img_fr_data = self.plotdata.get_fr_img() - self.img_rms_APdata, self.probe_rms_APdata = self.plotdata.get_rms_data_img_probe('AP') - self.img_rms_LFPdata, self.probe_rms_LFPdata = self.plotdata.get_rms_data_img_probe( - 'LF') - self.img_lfp_data, self.probe_lfp_data = self.plotdata.get_lfp_spectrum_data() - self.line_fr_data, self.line_amp_data = self.plotdata.get_fr_amp_data_line() - self.probe_rfmap, self.rfmap_boundaries = self.plotdata.get_rfmap_data() - self.img_stim_data = self.plotdata.get_passive_events() - - if not self.offline: - self.img_raw_data = self.plotdata.get_raw_data_image(self.loaddata.probe_id, one=self.loaddata.one) - else: - self.img_raw_data = {} - - if self.histology_exists: - self.slice_data = self.loaddata.get_slice_images(self.ephysalign.xyz_samples) - else: - # probably need to return an empty array of things - self.slice_data = {} - - self.data_status = True - self.init_menubar() - else: - self.set_lims(np.min([0, self.plotdata.chn_min]), self.plotdata.chn_max) - - # Initialise checked plots - self.img_init.setChecked(True) - self.line_init.setChecked(True) - self.probe_init.setChecked(True) - self.unit_init.setChecked(True) - self.slice_init.setChecked(True) - - # Initialise ephys plots - self.plot_image(self.img_fr_data) - self.plot_probe(self.probe_rms_APdata) - self.plot_line(self.line_fr_data) - - # Initialise histology plots - self.plot_histology_ref(self.fig_hist_ref) - self.plot_histology(self.fig_hist) - self.label_status = False - self.toggle_labels_button_pressed() - self.plot_scale_factor() - if np.any(self.feature_prev): - self.create_lines(self.feature_prev[1:-1] * 1e6) - # Initialise slice and fit images - self.plot_fit() - self.plot_slice(self.slice_data, 'hist_rd') - - # Only configure the view the first time the GUI is launched - self.set_view(view=1, configure=self.configure) - self.configure = False - - def compute_nearby_boundaries(self): - - # If no histology we can't plot histology - if not self.histology_exists: - return - - nearby_bounds = self.ephysalign.get_nearest_boundary(self.ephysalign.xyz_samples, - self.allen, steps=6, - brain_atlas=self.loaddata.brain_atlas) - [self.hist_nearby_x, self.hist_nearby_y, - self.hist_nearby_col] = self.ephysalign.arrange_into_regions( - self.ephysalign.sampling_trk, nearby_bounds['id'], nearby_bounds['dist'], - nearby_bounds['col']) - - [self.hist_nearby_parent_x, - self.hist_nearby_parent_y, - self.hist_nearby_parent_col] = self.ephysalign.arrange_into_regions( - self.ephysalign.sampling_trk, nearby_bounds['parent_id'], nearby_bounds['parent_dist'], - nearby_bounds['parent_col']) - - def toggle_histology_button_pressed(self): - self.hist_bound_status = not self.hist_bound_status - - if not self.hist_bound_status: - if self.hist_nearby_x is None: - self.compute_nearby_boundaries() - - self.plot_histology_nearby(self.fig_hist_ref) - else: - self.plot_histology_ref(self.fig_hist_ref) - - def filter_unit_pressed(self, type): - self.plotdata.filter_units(type) - self.scat_drift_data = self.plotdata.get_depth_data_scatter() - (self.scat_fr_data, self.scat_p2t_data, - self.scat_amp_data) = self.plotdata.get_fr_p2t_data_scatter() - self.img_corr_data = self.plotdata.get_correlation_data_img() - self.img_fr_data = self.plotdata.get_fr_img() - self.line_fr_data, self.line_amp_data = self.plotdata.get_fr_amp_data_line() - self.probe_rfmap, self.rfmap_boundaries = self.plotdata.get_rfmap_data() - self.img_stim_data = self.plotdata.get_passive_events() - self.img_init.setChecked(True) - self.line_init.setChecked(True) - self.probe_init.setChecked(True) - self.plot_image(self.img_fr_data) - self.plot_probe(self.probe_rms_APdata) - self.plot_line(self.line_fr_data) - - def fit_button_pressed(self): - """ - Triggered when fit button or Enter key pressed, applies scaling factor to brain regions - according to locations of reference lines on ephys and histology plots. Updates all plots - and indices after scaling has been applied - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - - # Use a cyclic buffer of length self.max_idx to hold information about previous moves, - # when a new move is initiated ensures indexes are all correct so user can only access - # fixed number of previous or next moves - if self.current_idx < self.last_idx: - self.total_idx = np.copy(self.current_idx) - self.diff_idx = (np.mod(self.last_idx, self.max_idx) - np.mod(self.total_idx, - self.max_idx)) - if self.diff_idx >= 0: - self.diff_idx = self.max_idx - self.diff_idx - else: - self.diff_idx = np.abs(self.diff_idx) - else: - self.diff_idx = self.max_idx - 1 - - self.total_idx += 1 - self.current_idx += 1 - self.idx_prev = np.copy(self.idx) - self.idx = np.mod(self.current_idx, self.max_idx) - self.scale_hist_data() - self.plot_histology(self.fig_hist) - self.plot_scale_factor() - self.plot_fit() - self.plot_channels() - self.remove_lines_points() - self.add_lines_points() - self.update_lines_points() - self.fig_hist.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.update_string() - - def offset_button_pressed(self): - """ - Triggered when offset button or o key pressed, applies offset to brain regions according to - locations of probe tip line on histology plot. Updates all plots and indices after offset - has been applied - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - - if self.current_idx < self.last_idx: - self.total_idx = np.copy(self.current_idx) - self.diff_idx = (np.mod(self.last_idx, self.max_idx) - np.mod(self.total_idx, - self.max_idx)) - if self.diff_idx >= 0: - self.diff_idx = self.max_idx - self.diff_idx - else: - self.diff_idx = np.abs(self.diff_idx) - else: - self.diff_idx = self.max_idx - 1 - - self.total_idx += 1 - self.current_idx += 1 - self.idx_prev = np.copy(self.idx) - self.idx = np.mod(self.current_idx, self.max_idx) - self.offset_hist_data() - self.plot_histology(self.fig_hist) - self.plot_scale_factor() - self.plot_fit() - self.plot_channels() - self.remove_lines_points() - self.add_lines_points() - self.update_lines_points() - self.fig_hist.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.update_string() - - def movedown_button_pressed(self): - """ - Triggered when Shift+down key pressed. Moves probe tip down by 50um and offsets data - """ - # If no histology we can't plot histology - if not self.histology_exists: - return - - if self.track[self.idx][-1] - 50 / 1e6 >= np.max(self.chn_depths) / 1e6: - self.track[self.idx] -= 50 / 1e6 - self.offset_button_pressed() - - def moveup_button_pressed(self): - """ - Triggered when Shift+down key pressed. Moves probe tip up by 50um and offsets data - """ - # If no histology we can't plot histology - if not self.histology_exists: - return - - if self.track[self.idx][0] + 50 / 1e6 <= np.min(self.chn_depths) / 1e6: - self.track[self.idx] += 50 / 1e6 - self.offset_button_pressed() - - def toggle_labels_button_pressed(self): - """ - Triggered when Shift+A key pressed. Shows/hides labels Allen atlas labels on brain regions - in histology plots - """ - self.label_status = not self.label_status - if not self.label_status: - self.ax_hist_ref.setPen(None) - self.ax_hist_ref.setTextPen(None) - self.ax_hist.setPen(None) - self.ax_hist.setTextPen(None) - self.fig_hist_ref.update() - self.fig_hist.update() - - else: - self.ax_hist_ref.setPen('k') - self.ax_hist_ref.setTextPen('k') - self.ax_hist.setPen('k') - self.ax_hist.setTextPen('k') - self.fig_hist_ref.update() - self.fig_hist.update() - - def toggle_line_button_pressed(self): - """ - Triggered when Shift+L key pressed. Shows/hides reference lines on ephys and histology - plots - """ - self.line_status = not self.line_status - if not self.line_status: - self.remove_lines_points() - else: - self.add_lines_points() - - def toggle_channel_button_pressed(self): - """ - Triggered when Shift+C key pressed. Shows/hides channels and trajectory on slice image - """ - # If no histology we can't plot histology - if not self.histology_exists: - return - - self.channel_status = not self.channel_status - if not self.channel_status: - self.fig_slice.removeItem(self.traj_line) - self.fig_slice.removeItem(self.slice_chns) - for line in self.slice_lines: - self.fig_slice.removeItem(line) - - else: - self.fig_slice.addItem(self.traj_line) - self.fig_slice.addItem(self.slice_chns) - for line in self.slice_lines: - self.fig_slice.addItem(line) - - def delete_line_button_pressed(self): - """ - Triggered when mouse hovers over reference line and Del key pressed. Deletes a reference - line from the ephys and histology plots - """ - - if self.selected_line: - line_idx = np.where(self.lines_features == self.selected_line)[0] - if line_idx.size == 0: - line_idx = np.where(self.lines_tracks == self.selected_line)[0] - line_idx = line_idx[0] - - self.fig_img.removeItem(self.lines_features[line_idx][0]) - self.fig_line.removeItem(self.lines_features[line_idx][1]) - self.fig_probe.removeItem(self.lines_features[line_idx][2]) - self.fig_hist.removeItem(self.lines_tracks[line_idx, 0]) - self.fig_fit.removeItem(self.points[line_idx, 0]) - self.lines_features = np.delete(self.lines_features, line_idx, axis=0) - self.lines_tracks = np.delete(self.lines_tracks, line_idx, axis=0) - self.points = np.delete(self.points, line_idx, axis=0) - - def describe_labels_pressed(self): - - # if no histology don't show - if not self.histology_exists: - return - - if self.selected_region: - idx = np.where(self.hist_regions == self.selected_region)[0] - if not np.any(idx): - idx = np.where(self.hist_ref_regions == self.selected_region)[0] - if not np.any(idx): - idx = np.array([0]) - - description, lookup = self.loaddata.get_region_description( - self.ephysalign.region_id[idx[0]][0]) - item = self.struct_list.findItems(lookup, flags=QtCore.Qt.MatchRecursive) - model_item = self.struct_list.indexFromItem(item[0]) - self.struct_view.collapseAll() - self.struct_view.scrollTo(model_item) - self.struct_view.setCurrentIndex(model_item) - self.struct_description.setText(description) - - if not self.label_popup: - self.label_win = ephys_gui.PopupWindow(title='Structure Information', - size=(500, 700), graphics=False) - self.label_win.layout.addWidget(self.struct_view) - self.label_win.layout.addWidget(self.struct_description) - self.label_win.layout.setRowStretch(0, 7) - self.label_win.layout.setRowStretch(1, 3) - self.label_popup.append(self.label_win) - self.label_win.closed.connect(self.label_closed) - self.label_win.moved.connect(self.label_moved) - self.activateWindow() - else: - self.label_win.show() - self.activateWindow() - - def label_closed(self, popup): - self.label_win.hide() - - def label_moved(self): - self.activateWindow() - - def label_pressed(self, item): - idx = int(item.model().itemFromIndex(item).accessibleText()) - description, lookup = self.loaddata.get_region_description(idx) - item = self.struct_list.findItems(lookup, - flags=QtCore.Qt.MatchRecursive) - model_item = self.struct_list.indexFromItem(item[0]) - self.struct_view.setCurrentIndex(model_item) - self.struct_description.setText(description) - - def next_button_pressed(self): - """ - Triggered when right key pressed. Updates all plots and indices with next move. Ensures - user cannot go past latest move - """ - # If no histology we can't plot histology - if not self.histology_exists: - return - - if (self.current_idx < self.total_idx) & (self.current_idx > - self.total_idx - self.max_idx): - self.current_idx += 1 - self.idx = np.mod(self.current_idx, self.max_idx) - self.remove_lines_points() - self.add_lines_points() - self.plot_histology(self.fig_hist) - self.plot_scale_factor() - self.remove_lines_points() - self.add_lines_points() - self.plot_fit() - self.plot_channels() - self.update_string() - - def prev_button_pressed(self): - """ - Triggered when left key pressed. Updates all plots and indices with previous move. - Ensures user cannot go back more than self.max_idx moves - """ - - # If no histology we can't plot histology - if not self.histology_exists: - return - - if self.total_idx > self.last_idx: - self.last_idx = np.copy(self.total_idx) - - if (self.current_idx > np.max([0, self.total_idx - self.diff_idx])): - self.current_idx -= 1 - self.idx = np.mod(self.current_idx, self.max_idx) - self.remove_lines_points() - self.add_lines_points() - self.plot_histology(self.fig_hist) - self.plot_scale_factor() - self.remove_lines_points() - self.add_lines_points() - self.plot_fit() - self.plot_channels() - self.update_string() - - def reset_button_pressed(self): - """ - Triggered when reset button or Shift+R key pressed. Resets channel locations to orignal - location - """ - # If no histology we can't plot histology - if not self.histology_exists: - return - - self.remove_lines_points() - self.lines_features = np.empty((0, 3)) - self.lines_tracks = np.empty((0, 1)) - self.points = np.empty((0, 1)) - if self.current_idx < self.last_idx: - self.total_idx = np.copy(self.current_idx) - self.diff_idx = (np.mod(self.last_idx, self.max_idx) - np.mod(self.total_idx, - self.max_idx)) - if self.diff_idx >= 0: - self.diff_idx = self.max_idx - self.diff_idx - else: - self.diff_idx = np.abs(self.diff_idx) - else: - self.diff_idx = self.max_idx - 1 - - self.total_idx += 1 - self.current_idx += 1 - self.idx = np.mod(self.current_idx, self.max_idx) - self.track[self.idx] = np.copy(self.ephysalign.track_init) - self.features[self.idx] = np.copy(self.ephysalign.feature_init) - self.hist_data['region'][self.idx], self.hist_data['axis_label'][self.idx] \ - = self.ephysalign.scale_histology_regions(self.features[self.idx], - self.track[self.idx]) - self.hist_data['colour'] = self.ephysalign.region_colour - self.scale_data['region'][self.idx], self.scale_data['scale'][self.idx] \ - = self.ephysalign.get_scale_factor(self.hist_data['region'][self.idx]) - self.plot_histology(self.fig_hist) - self.plot_scale_factor() - if np.any(self.feature_prev): - self.create_lines(self.feature_prev[1:-1] * 1e6) - self.plot_fit() - self.plot_channels() - self.fig_hist.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.update_string() - - def complete_button_pressed(self): - """ - Triggered when complete button or Shift+F key pressed. Uploads final channel locations to - Alyx - """ - # If no histology we can't upload alignment - if not self.histology_exists: - return - - upload = QtWidgets.QMessageBox.question(self, '', "Upload alignment?", - QtWidgets.QMessageBox.Yes | - QtWidgets.QMessageBox.No) - - if upload == QtWidgets.QMessageBox.Yes: - upload_channels = self.loaddata.upload_data(self.xyz_channels) - self.loaddata.update_alignments(self.features[self.idx], self.track[self.idx]) - self.prev_alignments = self.loaddata.get_previous_alignments() - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.loaddata.get_starting_alignment(0) - resolved = self.loaddata.update_qc() - - if upload_channels and resolved == 0: - # channels saved alignment not resolved - QtWidgets.QMessageBox.information(self, 'Status', - ("Channels locations saved to Alyx. " - "Alignment not resolved")) - if upload_channels and resolved == 1: - # channels saved alignment resolved, writen to flatiron - QtWidgets.QMessageBox.information(self, 'Status', - ("Channel locations saved to Alyx. " - "Alignment resolved and channels " - "datasets written to flatiron")) - if not upload_channels and resolved == 1: - # alignment already resolved, save alignment but channels not written - QtWidgets.QMessageBox.information(self, 'Status', - ("Channel locations not saved to Alyx" - " as alignment has already been " - "resolved. New user reference lines" - " have been saved")) - else: - pass - QtWidgets.QMessageBox.information(self, 'Status', "Channels not saved") - - def complete_button_pressed_offline(self): - """ - Triggered when complete button or Shift+F key pressed. Uploads final channel locations to - json file - """ - upload = QtWidgets.QMessageBox.question(self, '', "Upload alignment?", - QtWidgets.QMessageBox.Yes | - QtWidgets.QMessageBox.No) - - if upload == QtWidgets.QMessageBox.Yes: - self.loaddata.upload_data(self.features[self.idx], self.track[self.idx], - self.xyz_channels) - self.prev_alignments = self.loaddata.get_previous_alignments() - self.populate_lists(self.prev_alignments, self.align_list, self.align_combobox) - self.loaddata.get_starting_alignment(0) - QtWidgets.QMessageBox.information(self, 'Status', "Channels locations saved") - else: - pass - QtWidgets.QMessageBox.warning(self, 'Status', "Channels not saved") - - def display_qc_options(self): - - # If not histology don't show - if not self.histology_exists: - return - - self.qc_dialog.open() - - def qc_button_clicked(self): - - # If no histology we can't plot histology - if not self.histology_exists: - return - - align_qc = self.align_qc.currentText() - ephys_qc = self.ephys_qc.currentText() - ephys_desc = [] - for button in self.desc_buttons.buttons(): - if button.isChecked(): - ephys_desc.append(button.text()) - - if ephys_qc != 'Pass' and len(ephys_desc) == 0: - QtWidgets.QMessageBox.warning(self, 'Status', "You must select a reason for qc choice") - self.display_qc_options() - return - - self.loaddata.upload_dj(align_qc, ephys_qc, ephys_desc) - self.complete_button_pressed() - - def reset_axis_button_pressed(self): - self.fig_hist.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.fig_hist_ref.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - self.fig_img.setXRange(min=self.xrange[0], max=self.xrange[1], padding=0) - self.fig_img.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, padding=self.pad) - - def display_session_notes(self): - self.notes_win = ephys_gui.PopupWindow(title='Session notes from Alyx', size=(200, 100), - graphics=False) - notes = QtWidgets.QTextEdit() - notes.setReadOnly(True) - notes.setLineWrapMode(QtWidgets.QTextEdit.WidgetWidth) - notes.setText(self.sess_notes) - self.notes_win.layout.addWidget(notes) - - def display_nearby_sessions(self): - - # If no histology we can't get nearby sessions - if not self.histology_exists: - return - - if not self.nearby: - self.nearby, self.dist, self.dist_mlap = self.loaddata.get_nearby_trajectories() - - self.nearby_win = ephys_gui.PopupWindow(title='Nearby Sessions', size=(400, 300), - graphics=False) - - self.nearby_table = QtWidgets.QTableWidget() - self.nearby_table.setRowCount(10) - self.nearby_table.setColumnCount(3) - - self.nearby_table.setHorizontalHeaderItem(0, QtWidgets.QTableWidgetItem('Session')) - self.nearby_table.setHorizontalHeaderItem(1, QtWidgets.QTableWidgetItem('dist')) - self.nearby_table.setHorizontalHeaderItem(2, QtWidgets.QTableWidgetItem('dist_mlap')) - self.nearby_table.setSortingEnabled(True) - for iT, (near, dist, dist_mlap) in enumerate(zip(self.nearby, self.dist, self.dist_mlap)): - sess_item = QtWidgets.QTableWidgetItem(near) - dist_item = QtWidgets.QTableWidgetItem() - dist_item.setData(0, int(dist)) - dist_mlap_item = QtWidgets.QTableWidgetItem() - dist_mlap_item.setData(0, int(dist_mlap)) - self.nearby_table.setItem(iT, 0, sess_item) - self.nearby_table.setItem(iT, 1, dist_item) - self.nearby_table.setItem(iT, 2, dist_mlap_item) - - self.nearby_win.layout.addWidget(self.nearby_table) - - def popup_closed(self, popup): - popup_idx = [iP for iP, pop in enumerate(self.cluster_popups) if pop == popup][0] - self.cluster_popups.pop(popup_idx) - - def popup_moved(self): - self.activateWindow() - - def close_popups(self): - for pop in self.cluster_popups: - pop.blockSignals(True) - pop.close() - self.cluster_popups = [] - - def minimise_popups(self): - self.popup_status = not self.popup_status - if self.popup_status: - for pop in self.cluster_popups: - pop.showNormal() - self.activateWindow() - else: - for pop in self.cluster_popups: - pop.showMinimized() - self.activateWindow() - - def lin_fit_option_changed(self, state): - if state == 0: - self.lin_fit = False - self.fit_button_pressed() - else: - self.lin_fit = True - self.fit_button_pressed() - - def cluster_clicked(self, item, point): - point_pos = point[0].pos() - clust_idx = np.argwhere(self.data == point_pos.x())[0][0] - - autocorr, clust_no = self.plotdata.get_autocorr(clust_idx) - autocorr_plot = pg.PlotItem() - autocorr_plot.setXRange(min=np.min(self.plotdata.t_autocorr), - max=np.max(self.plotdata.t_autocorr)) - autocorr_plot.setYRange(min=0, max=1.05 * np.max(autocorr)) - self.set_axis(autocorr_plot, 'bottom', label='T (ms)') - self.set_axis(autocorr_plot, 'left', label='Number of spikes') - plot = pg.BarGraphItem() - plot.setOpts(x=self.plotdata.t_autocorr, height=autocorr, width=0.24, - brush=self.bar_colour) - autocorr_plot.addItem(plot) - - template_wf = self.plotdata.get_template_wf(clust_idx) - template_plot = pg.PlotItem() - plot = pg.PlotCurveItem() - template_plot.setXRange(min=np.min(self.plotdata.t_template), - max=np.max(self.plotdata.t_template)) - self.set_axis(template_plot, 'bottom', label='T (ms)') - self.set_axis(template_plot, 'left', label='Amplitude (a.u.)') - plot.setData(x=self.plotdata.t_template, y=template_wf, pen=self.kpen_solid) - template_plot.addItem(plot) - - clust_layout = pg.GraphicsLayout() - clust_layout.addItem(autocorr_plot, 0, 0) - clust_layout.addItem(template_plot, 1, 0) - - self.clust_win = ephys_gui.PopupWindow(title=f'Cluster {clust_no}') - self.clust_win.closed.connect(self.popup_closed) - self.clust_win.moved.connect(self.popup_moved) - self.clust_win.popup_widget.addItem(autocorr_plot, 0, 0) - self.clust_win.popup_widget.addItem(template_plot, 1, 0) - self.cluster_popups.append(self.clust_win) - self.activateWindow() - - return clust_no - - def on_mouse_double_clicked(self, event): - """ - Triggered when a double click event is detected on ephys of histology plots. Adds reference - line on ephys and histology plot that can be moved to align ephys signatures with brain - regions. Also adds scatter point on fit plot - :param event: double click event signals - :type event: pyqtgraph mouseEvents - """ - # If no histology no point adding lines - if not self.histology_exists: - return - - if event.double(): - pos = self.data_plot.mapFromScene(event.scenePos()) - pen, brush = self.create_line_style() - line_track = pg.InfiniteLine(pos=pos.y() * self.y_scale, angle=0, pen=pen, - movable=True) - line_track.sigPositionChanged.connect(self.update_lines_track) - line_track.setZValue(100) - line_feature1 = pg.InfiniteLine(pos=pos.y() * self.y_scale, angle=0, pen=pen, - movable=True) - line_feature1.setZValue(100) - line_feature1.sigPositionChanged.connect(self.update_lines_features) - line_feature2 = pg.InfiniteLine(pos=pos.y() * self.y_scale, angle=0, pen=pen, - movable=True) - line_feature2.setZValue(100) - line_feature2.sigPositionChanged.connect(self.update_lines_features) - line_feature3 = pg.InfiniteLine(pos=pos.y() * self.y_scale, angle=0, pen=pen, - movable=True) - line_feature3.setZValue(100) - line_feature3.sigPositionChanged.connect(self.update_lines_features) - self.fig_hist.addItem(line_track) - self.fig_img.addItem(line_feature1) - self.fig_line.addItem(line_feature2) - self.fig_probe.addItem(line_feature3) - - self.lines_features = np.vstack([self.lines_features, [line_feature1, line_feature2, - line_feature3]]) - self.lines_tracks = np.vstack([self.lines_tracks, line_track]) - - point = pg.PlotDataItem() - point.setData(x=[line_track.pos().y()], y=[line_feature1.pos().y()], - symbolBrush=brush, symbol='o', symbolSize=10) - self.fig_fit.addItem(point) - self.points = np.vstack([self.points, point]) - - def on_mouse_hover(self, items): - """ - Returns the pyqtgraph items that the mouse is hovering over. Used to identify reference - lines so that they can be deleted - """ - if len(items) > 1: - self.selected_line = [] - if type(items[0]) == pg.InfiniteLine: - self.selected_line = items[0] - elif (items[0] == self.fig_scale) & (type(items[1]) == pg.LinearRegionItem): - idx = np.where(self.scale_regions == items[1])[0][0] - self.fig_scale_ax.setLabel('Scale Factor = ' + - str(np.around(self.scale_factor[idx], 2))) - elif (items[0] == self.fig_hist) & (type(items[1]) == pg.LinearRegionItem): - self.selected_region = items[1] - elif (items[0] == self.fig_hist_ref) & (type(items[1]) == pg.LinearRegionItem): - self.selected_region = items[1] - - def update_lines_features(self, line): - """ - Triggered when reference line on ephys data plots is moved. Moves all three lines on the - img_plot, line_plot and probe_plot and adjusts the corresponding point on the fit plot - :param line: selected line - :type line: pyqtgraph InfiniteLine - """ - idx = np.where(self.lines_features == line) - line_idx = idx[0][0] - fig_idx = np.setdiff1d(np.arange(0, 3), idx[1][0]) - - self.lines_features[line_idx][fig_idx[0]].setPos(line.value()) - self.lines_features[line_idx][fig_idx[1]].setPos(line.value()) - - self.points[line_idx][0].setData(x=[self.lines_features[line_idx][0].pos().y()], - y=[self.lines_tracks[line_idx][0].pos().y()]) - - def update_lines_track(self, line): - """ - Triggered when reference line on histology plot is moved. Adjusts the corresponding point - on the fit plot - :param line: selected line - :type line: pyqtgraph InfiniteLine - """ - line_idx = np.where(self.lines_tracks == line)[0][0] - - self.points[line_idx][0].setData(x=[self.lines_features[line_idx][0].pos().y()], - y=[self.lines_tracks[line_idx][0].pos().y()]) - - def tip_line_moved(self): - """ - Triggered when dotted line indicating probe tip on self.fig_hist moved. Gets the y pos of - probe tip line and ensures the probe top line is set to probe tip line y pos + 3840 - """ - self.top_pos.setPos(self.tip_pos.value() + self.probe_top) - - def top_line_moved(self): - """ - Triggered when dotted line indicating probe top on self.fig_hist moved. Gets the y pos of - probe top line and ensures the probe tip line is set to probe top line y pos - 3840 - """ - self.tip_pos.setPos(self.top_pos.value() - self.probe_top) - - def remove_lines_points(self): - """ - Removes all reference lines and scatter points from the ephys, histology and fit plots - """ - for line_feature, line_track, point in zip(self.lines_features, self.lines_tracks, - self.points): - self.fig_img.removeItem(line_feature[0]) - self.fig_line.removeItem(line_feature[1]) - self.fig_probe.removeItem(line_feature[2]) - self.fig_hist.removeItem(line_track[0]) - self.fig_fit.removeItem(point[0]) - - def add_lines_points(self): - """ - Adds all reference lines and scatter points from the ephys, histology and fit plots - """ - for line_feature, line_track, point in zip(self.lines_features, self.lines_tracks, - self.points): - self.fig_img.addItem(line_feature[0]) - self.fig_line.addItem(line_feature[1]) - self.fig_probe.addItem(line_feature[2]) - self.fig_hist.addItem(line_track[0]) - self.fig_fit.addItem(point[0]) - - def update_lines_points(self): - """ - Updates position of reference lines on histology plot after fit has been applied. Also - updates location of scatter point - """ - for line_feature, line_track, point in zip(self.lines_features, self.lines_tracks, - self.points): - line_track[0].setPos(line_feature[0].getYPos()) - point[0].setData(x=[line_feature[0].pos().y()], y=[line_feature[0].pos().y()]) - - def create_lines(self, positions): - for pos in positions: - - pen, brush = self.create_line_style() - line_track = pg.InfiniteLine(pos=pos, angle=0, pen=pen, movable=True) - line_track.sigPositionChanged.connect(self.update_lines_track) - line_track.setZValue(100) - line_feature1 = pg.InfiniteLine(pos=pos, angle=0, pen=pen, - movable=True) - line_feature1.setZValue(100) - line_feature1.sigPositionChanged.connect(self.update_lines_features) - line_feature2 = pg.InfiniteLine(pos=pos, angle=0, pen=pen, - movable=True) - line_feature2.setZValue(100) - line_feature2.sigPositionChanged.connect(self.update_lines_features) - line_feature3 = pg.InfiniteLine(pos=pos, angle=0, pen=pen, - movable=True) - line_feature3.setZValue(100) - line_feature3.sigPositionChanged.connect(self.update_lines_features) - self.fig_hist.addItem(line_track) - self.fig_img.addItem(line_feature1) - self.fig_line.addItem(line_feature2) - self.fig_probe.addItem(line_feature3) - - self.lines_features = np.vstack([self.lines_features, [line_feature1, line_feature2, - line_feature3]]) - self.lines_tracks = np.vstack([self.lines_tracks, line_track]) - - point = pg.PlotDataItem() - point.setData(x=[line_track.pos().y()], y=[line_feature1.pos().y()], - symbolBrush=brush, symbol='o', symbolSize=10) - self.fig_fit.addItem(point) - self.points = np.vstack([self.points, point]) - - def create_line_style(self): - """ - Create random choice of colour and style for reference line - :return pen: style to use for the line - :type pen: pyqtgraph Pen - :return brush: colour use for the line - :type brush: pyqtgraph Brush - """ - colours = ['#000000', '#cc0000', '#6aa84f', '#1155cc', '#a64d79'] - style = [QtCore.Qt.SolidLine, QtCore.Qt.DashLine, QtCore.Qt.DashDotLine] - col = QtGui.QColor(colours[randrange(len(colours))]) - sty = style[randrange(len(style))] - pen = pg.mkPen(color=col, style=sty, width=3) - brush = pg.mkBrush(color=col) - return pen, brush - - def update_string(self): - """ - Updates text boxes to indicate to user which move they are looking at - """ - self.idx_string.setText(f"Current Index = {self.current_idx}") - self.tot_idx_string.setText(f"Total Index = {self.total_idx}") - - -def viewer(probe_id, one=None, histology=False, spike_collection=None, title=None): - """ - """ - qt.create_app() - av = MainWindow._get_or_create(probe_id=probe_id, one=one, histology=histology, - spike_collection=spike_collection, title=title) - av.show() - return av - -def launch_gui(data_directory): - - folder_path = Path(data_directory) - mainapp.folder_line.setText(str(folder_path)) - mainapp.prev_alignments, shank_options = mainapp.loaddata.get_info(folder_path) - mainapp.populate_lists(shank_options, mainapp.shank_list, mainapp.shank_combobox) - mainapp.populate_lists(mainapp.prev_alignments, mainapp.align_list, mainapp.align_combobox) - mainapp.on_shank_selected(0) - - mainapp.data_button_pressed() - - # mainapp = MainWindow(offline=True) - mainapp.show() - - app.exec_() - - -if __name__ == '__main__': - - import argparse - - parser = argparse.ArgumentParser(description='Offline vs online mode') - parser.add_argument('-o', '--offline', default=False, required=False, help='Offline mode') - parser.add_argument('-r', '--remote', default=False, required=False, action='store_true', help='Remote mode') - parser.add_argument('-i', '--insertion', default=None, required=False, help='Insertion mode') - parser.add_argument('-d', '--directory', default=None, required=False, help='Data directory') - args = parser.parse_args() - - app = QtWidgets.QApplication([]) - mainapp = MainWindow(offline=args.offline, probe_id=args.insertion, remote=args.remote) - - folder_path = Path(args.directory) - mainapp.folder_line.setText(str(folder_path)) - mainapp.prev_alignments, shank_options = mainapp.loaddata.get_info(folder_path) - mainapp.populate_lists(shank_options, mainapp.shank_list, mainapp.shank_combobox) - mainapp.populate_lists(mainapp.prev_alignments, mainapp.align_list, mainapp.align_combobox) - mainapp.on_shank_selected(0) - - mainapp.data_button_pressed() - - # mainapp = MainWindow(offline=True) - mainapp.show() - - app.exec_() diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_image.png b/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_image.png deleted file mode 100644 index 279ae587..00000000 Binary files a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_atlas_image.png and /dev/null differ diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_gui_setup.py b/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_gui_setup.py deleted file mode 100644 index 3a6d8645..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/ephys_gui_setup.py +++ /dev/null @@ -1,829 +0,0 @@ -from PyQt5 import QtCore, QtGui, QtWidgets -import pyqtgraph as pg -import pyqtgraph.exporters -import numpy as np -from random import randrange -from atlaselectrophysiology.AdaptedAxisItem import replace_axis -from ibllib.qc.critical_reasons import REASONS_INS_CRIT_GUI - -pg.setConfigOption('background', 'w') -pg.setConfigOption('foreground', 'k') - - -class Setup(): - def init_layout(self, main_window, offline=False): - self.resize(1600, 800) - self.setWindowTitle('Electrophysiology Atlas') - self.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) - self.offline = offline - main_widget = QtWidgets.QWidget() - self.setCentralWidget(main_widget) - - self.init_interaction_features() - self.init_figures() - - main_layout = QtWidgets.QGridLayout() - main_layout.addWidget(self.fig_data_area, 0, 0, 10, 1) - main_layout.addWidget(self.fig_hist_area, 0, 1, 10, 1) - main_layout.addLayout(self.interaction_layout3, 0, 2, 1, 1) - main_layout.addWidget(self.fig_slice_area, 1, 2, 3, 1) - main_layout.addLayout(self.interaction_layout1, 4, 2, 1, 1) - main_layout.addWidget(self.fig_fit, 5, 2, 3, 1) - main_layout.addLayout(self.interaction_layout2, 8, 2, 2, 1) - main_layout.setColumnStretch(0, 5) - main_layout.setColumnStretch(1, 2) - main_layout.setColumnStretch(2, 3) - - main_widget.setLayout(main_layout) - - def init_menubar(self): - """ - Create menu bar and add all possible menu options. These are: - - Image Plots: possible 2D image/scatter plots - - Line Plots: possible 1D line plots - - Probe Plots: possible 2D plots arranged according to probe geometry - - Slice Plots: possible coronal slice images - - Filter Units: filter displayed plots by unit type (All, Good, MUA) - - Fit Options: possible keyboard interactions for applying alignment - - Display Options: possible keyboard interactions to what is displayed on GUI - - Session Information: extra info, session notes and Allen brain regions description - """ - # Create menubar widget and add it to the main GUI window - menu_bar = QtWidgets.QMenuBar(self) - menu_bar.setNativeMenuBar(False) - self.setMenuBar(menu_bar) - - # IMAGE PLOTS MENU BAR - # Define all 2D scatter/ image plot options - scatter_drift = QtWidgets.QAction('Amplitude', self, checkable=True, checked=False) - scatter_drift.triggered.connect(lambda: self.plot_scatter(self.scat_drift_data)) - scatter_fr = QtWidgets.QAction('Cluster Amp vs Depth vs FR', self, checkable=True, - checked=False) - scatter_fr.triggered.connect(lambda: self.plot_scatter(self.scat_fr_data)) - scatter_p2t = QtWidgets.QAction('Cluster Amp vs Depth vs Duration', self, checkable=True, - checked=False) - scatter_p2t.triggered.connect(lambda: self.plot_scatter(self.scat_p2t_data)) - scatter_amp = QtWidgets.QAction('Cluster FR vs Depth vs Amp', self, checkable=True, - checked=False) - scatter_amp.triggered.connect(lambda: self.plot_scatter(self.scat_amp_data)) - img_fr = QtWidgets.QAction('Firing Rate', self, checkable=True, checked=True) - img_fr.triggered.connect(lambda: self.plot_image(self.img_fr_data)) - img_corr = QtWidgets.QAction('Correlation', self, checkable=True, checked=False) - img_corr.triggered.connect(lambda: self.plot_image(self.img_corr_data)) - img_rmsAP = QtWidgets.QAction('rms AP', self, checkable=True, checked=False) - img_rmsAP.triggered.connect(lambda: self.plot_image(self.img_rms_APdata)) - img_rmsLFP = QtWidgets.QAction('rms LFP', self, checkable=True, checked=False) - img_rmsLFP.triggered.connect(lambda: self.plot_image(self.img_rms_LFPdata)) - img_LFP = QtWidgets.QAction('LFP Spectrum', self, checkable=True, checked=False) - img_LFP.triggered.connect(lambda: self.plot_image(self.img_lfp_data)) - - # Initialise with firing rate 2D plot - self.img_init = img_fr - - # Add menu bar for 2D scatter/ image plot options - img_options = menu_bar.addMenu('Image Plots') - # Add action group so we can toggle through 2D scatter/ image plot options - self.img_options_group = QtWidgets.QActionGroup(img_options) - # Only allow one to plot to be selected at any one time - self.img_options_group.setExclusive(True) - img_options.addAction(img_fr) - self.img_options_group.addAction(img_fr) - img_options.addAction(scatter_drift) - self.img_options_group.addAction(scatter_drift) - img_options.addAction(img_corr) - self.img_options_group.addAction(img_corr) - img_options.addAction(img_rmsAP) - self.img_options_group.addAction(img_rmsAP) - img_options.addAction(img_rmsLFP) - self.img_options_group.addAction(img_rmsLFP) - img_options.addAction(img_LFP) - self.img_options_group.addAction(img_LFP) - img_options.addAction(scatter_fr) - self.img_options_group.addAction(scatter_fr) - img_options.addAction(scatter_p2t) - self.img_options_group.addAction(scatter_p2t) - img_options.addAction(scatter_amp) - self.img_options_group.addAction(scatter_amp) - - raw_type = list(self.img_raw_data.keys()) - for raw in raw_type: - img = QtWidgets.QAction(raw, self, checkable=True, checked=False) - img.triggered.connect(lambda checked, item=raw: self.plot_image( - self.img_raw_data[item])) - img_options.addAction(img) - self.img_options_group.addAction(img) - - stim_type = list(self.img_stim_data.keys()) - for stim in stim_type: - img = QtWidgets.QAction(stim, self, checkable=True, checked=False) - img.triggered.connect(lambda checked, item=stim: self.plot_image( - self.img_stim_data[item])) - img_options.addAction(img) - self.img_options_group.addAction(img) - - # LINE PLOTS MENU BAR - # Define all 1D line plot options - line_fr = QtWidgets.QAction('Firing Rate', self, checkable=True, checked=True) - line_fr.triggered.connect(lambda: self.plot_line(self.line_fr_data)) - line_amp = QtWidgets.QAction('Amplitude', self, checkable=True, checked=False) - line_amp.triggered.connect(lambda: self.plot_line(self.line_amp_data)) - # Initialise with firing rate 1D plot - self.line_init = line_fr - # Add menu bar for 1D line plot options - line_options = menu_bar.addMenu('Line Plots') - # Add action group so we can toggle through 2D scatter/ image plot options - self.line_options_group = QtWidgets.QActionGroup(line_options) - # Only allow one to plot to be selected at any one time - self.line_options_group.setExclusive(True) - line_options.addAction(line_fr) - self.line_options_group.addAction(line_fr) - line_options.addAction(line_amp) - self.line_options_group.addAction(line_amp) - - # PROBE PLOTS MENU BAR - # Define all 2D probe plot options - # In two stages 1) RMS plots manually, 2) frequency plots in for loop - probe_rmsAP = QtWidgets.QAction('rms AP', self, checkable=True, checked=True) - probe_rmsAP.triggered.connect(lambda: self.plot_probe(self.probe_rms_APdata)) - probe_rmsLFP = QtWidgets.QAction('rms LFP', self, checkable=True, checked=False) - probe_rmsLFP.triggered.connect(lambda: self.plot_probe(self.probe_rms_LFPdata)) - - # Initialise with rms of AP probe plot - self.probe_init = probe_rmsAP - - # Add menu bar for 2D probe plot options - probe_options = menu_bar.addMenu("Probe Plots") - # Add action group so we can toggle through probe plot options - self.probe_options_group = QtWidgets.QActionGroup(probe_options) - self.probe_options_group.setExclusive(True) - probe_options.addAction(probe_rmsAP) - self.probe_options_group.addAction(probe_rmsAP) - probe_options.addAction(probe_rmsLFP) - self.probe_options_group.addAction(probe_rmsLFP) - - # Add the different frequency band options in a loop. These bands must be the same as - # defined in plot_data - freq_bands = np.vstack(([0, 4], [4, 10], [10, 30], [30, 80], [80, 200])) - for iF, freq in enumerate(freq_bands): - band = f"{freq[0]} - {freq[1]} Hz" - probe = QtWidgets.QAction(band, self, checkable=True, checked=False) - probe.triggered.connect(lambda checked, item=band: self.plot_probe( - self.probe_lfp_data[item])) - probe_options.addAction(probe) - self.probe_options_group.addAction(probe) - - sub_types = list(self.probe_rfmap.keys()) - for sub in sub_types: - probe = QtWidgets.QAction(f'RF Map - {sub}', self, checkable=True, checked=False) - probe.triggered.connect(lambda checked, item=sub: self.plot_probe( - self.probe_rfmap[item], bounds=self.rfmap_boundaries)) - probe_options.addAction(probe) - self.probe_options_group.addAction(probe) - - # SLICE PLOTS MENU BAR - # Define all coronal slice plot options - slice_hist_rd = QtWidgets.QAction('Histology Red', self, checkable=True, checked=True) - slice_hist_rd.triggered.connect(lambda: self.plot_slice(self.slice_data, 'hist_rd')) - slice_hist_gr = QtWidgets.QAction('Histology Green', self, checkable=True, checked=False) - slice_hist_gr.triggered.connect(lambda: self.plot_slice(self.slice_data, 'hist_gr')) - slice_ccf = QtWidgets.QAction('CCF', self, checkable=True, checked=False) - slice_ccf.triggered.connect(lambda: self.plot_slice(self.slice_data, 'ccf')) - slice_label = QtWidgets.QAction('Annotation', self, checkable=True, checked=False) - slice_label.triggered.connect(lambda: self.plot_slice(self.slice_data, 'label')) - if not self.offline: - slice_hist_cb = QtWidgets.QAction('Histology cerebellar example', self, checkable=True, checked=False) - slice_hist_cb.triggered.connect(lambda: self.plot_slice(self.slice_data, 'hist_cb')) - # Initialise with raw histology image - self.slice_init = slice_hist_rd - - # Add menu bar for slice plot - - slice_options = menu_bar.addMenu("Slice Plots") - # Add action group so we can toggle through slice plot options - self.slice_options_group = QtWidgets.QActionGroup(slice_options) - self.slice_options_group.setExclusive(True) - slice_options.addAction(slice_hist_rd) - self.slice_options_group.addAction(slice_hist_rd) - slice_options.addAction(slice_hist_gr) - self.slice_options_group.addAction(slice_hist_gr) - slice_options.addAction(slice_ccf) - self.slice_options_group.addAction(slice_ccf) - slice_options.addAction(slice_label) - self.slice_options_group.addAction(slice_label) - if not self.offline: - slice_options.addAction(slice_hist_cb) - self.slice_options_group.addAction(slice_hist_cb) - - # FILTER UNITS MENU BAR - # Define unit filtering options - all_units = QtWidgets.QAction('All', self, checkable=True, checked=True) - all_units.triggered.connect(lambda: self.filter_unit_pressed('all')) - good_units = QtWidgets.QAction('KS good', self, checkable=True, checked=False) - good_units.triggered.connect(lambda: self.filter_unit_pressed('KS good')) - mua_units = QtWidgets.QAction('KS mua', self, checkable=True, checked=False) - mua_units.triggered.connect(lambda: self.filter_unit_pressed('KS mua')) - ibl_units = QtWidgets.QAction('IBL good', self, checkable=True, checked=False) - ibl_units.triggered.connect(lambda: self.filter_unit_pressed('IBL good')) - # Initialise with all units being shown - self.unit_init = all_units - - # Add menu bar for slice plot options - unit_filter_options = menu_bar.addMenu("Filter Units") - # Add action group so we can toggle through unit options - unit_filter_options_group = QtWidgets.QActionGroup(unit_filter_options) - unit_filter_options_group.setExclusive(True) - unit_filter_options.addAction(all_units) - unit_filter_options_group.addAction(all_units) - unit_filter_options.addAction(good_units) - unit_filter_options_group.addAction(good_units) - unit_filter_options.addAction(mua_units) - unit_filter_options_group.addAction(mua_units) - unit_filter_options.addAction(ibl_units) - unit_filter_options_group.addAction(ibl_units) - - # FIT OPTIONS MENU BAR - # Define all possible keyboard shortcut interactions for GUI - # Shortcut to apply interpolation - fit_option = QtWidgets.QAction('Fit', self) - fit_option.setShortcut('Return') - fit_option.triggered.connect(self.fit_button_pressed) - # Shortcuts to apply offset - offset_option = QtWidgets.QAction('Offset', self) - offset_option.setShortcut('O') - offset_option.triggered.connect(self.offset_button_pressed) - moveup_option = QtWidgets.QAction('Offset + 50um', self) - moveup_option.setShortcut('Shift+Up') - moveup_option.triggered.connect(self.moveup_button_pressed) - movedown_option = QtWidgets.QAction('Offset - 50um', self) - movedown_option.setShortcut('Shift+Down') - movedown_option.triggered.connect(self.movedown_button_pressed) - # Shortcut to remove a reference line - delete_line_option = QtWidgets.QAction('Remove Line', self) - delete_line_option.setShortcut('Shift+D') - delete_line_option.triggered.connect(self.delete_line_button_pressed) - # Shortcut to move between previous/next moves - next_option = QtWidgets.QAction('Next', self) - next_option.setShortcut('Right') - next_option.triggered.connect(self.next_button_pressed) - prev_option = QtWidgets.QAction('Previous', self) - prev_option.setShortcut('Left') - prev_option.triggered.connect(self.prev_button_pressed) - # Shortcut to reset GUI to initial state - reset_option = QtWidgets.QAction('Reset', self) - reset_option.setShortcut('Shift+R') - reset_option.triggered.connect(self.reset_button_pressed) - # Shortcut to upload final state to Alyx/to local file - complete_option = QtWidgets.QAction('Upload', self) - complete_option.setShortcut('Shift+U') - if not self.offline: - complete_option.triggered.connect(self.display_qc_options) - else: - complete_option.triggered.connect(self.complete_button_pressed_offline) - - # Add menu bar with all possible keyboard interactions - fit_options = menu_bar.addMenu("Fit Options") - fit_options.addAction(fit_option) - fit_options.addAction(offset_option) - fit_options.addAction(moveup_option) - fit_options.addAction(movedown_option) - fit_options.addAction(delete_line_option) - fit_options.addAction(next_option) - fit_options.addAction(prev_option) - fit_options.addAction(reset_option) - fit_options.addAction(complete_option) - - # DISPLAY OPTIONS MENU BAR - # Define all possible keyboard shortcut for visualisation features - # Shortcuts to toggle between plots options - toggle1_option = QtWidgets.QAction('Toggle Image Plots', self) - toggle1_option.setShortcut('Alt+1') - toggle1_option.triggered.connect(lambda: self.toggle_plots(self.img_options_group)) - toggle2_option = QtWidgets.QAction('Toggle Line Plots', self) - toggle2_option.setShortcut('Alt+2') - toggle2_option.triggered.connect(lambda: self.toggle_plots(self.line_options_group)) - toggle3_option = QtWidgets.QAction('Toggle Probe Plots', self) - toggle3_option.setShortcut('Alt+3') - toggle3_option.triggered.connect(lambda: self.toggle_plots(self.probe_options_group)) - toggle4_option = QtWidgets.QAction('Toggle Slice Plots', self) - toggle4_option.setShortcut('Alt+4') - toggle4_option.triggered.connect(lambda: self.toggle_plots(self.slice_options_group)) - - # Shortcuts to switch order of 3 panels in ephys plot - view1_option = QtWidgets.QAction('View 1', self) - view1_option.setShortcut('Shift+1') - view1_option.triggered.connect(lambda: self.set_view(view=1)) - view2_option = QtWidgets.QAction('View 2', self) - view2_option.setShortcut('Shift+2') - view2_option.triggered.connect(lambda: self.set_view(view=2)) - view3_option = QtWidgets.QAction('View 3', self) - view3_option.setShortcut('Shift+3') - view3_option.triggered.connect(lambda: self.set_view(view=3)) - - # Shortcut to reset axis on figures - axis_option = QtWidgets.QAction('Reset Axis', self) - axis_option.setShortcut('Shift+A') - axis_option.triggered.connect(self.reset_axis_button_pressed) - - # Shortcut to hide/show region labels - toggle_labels_option = QtWidgets.QAction('Hide/Show Labels', self) - toggle_labels_option.setShortcut('Shift+L') - toggle_labels_option.triggered.connect(self.toggle_labels_button_pressed) - - # Shortcut to hide/show reference lines - toggle_lines_option = QtWidgets.QAction('Hide/Show Lines', self) - toggle_lines_option.setShortcut('Shift+H') - toggle_lines_option.triggered.connect(self.toggle_line_button_pressed) - - # Shortcut to hide/show reference lines and channels on slice image - toggle_channels_option = QtWidgets.QAction('Hide/Show Channels', self) - toggle_channels_option.setShortcut('Shift+C') - toggle_channels_option.triggered.connect(self.toggle_channel_button_pressed) - - # Shortcut to change default histology reference image - toggle_histology_option = QtWidgets.QAction('Hide/Show Nearby Boundaries', self) - toggle_histology_option.setShortcut('Shift+N') - toggle_histology_option.triggered.connect(self.toggle_histology_button_pressed) - - # Shortcuts for cluster popup window - popup_minimise = QtWidgets.QAction('Minimise/Show Cluster Popup', self) - popup_minimise.setShortcut('Alt+M') - popup_minimise.triggered.connect(self.minimise_popups) - popup_close = QtWidgets.QAction('Close Cluster Popup', self) - popup_close.setShortcut('Alt+X') - popup_close.triggered.connect(self.close_popups) - - # Option to save all plots - save_plots = QtWidgets.QAction('Save Plots', self) - save_plots.triggered.connect(self.save_plots) - - # Add menu bar with all possible display options - display_options = menu_bar.addMenu('Display Options') - display_options.addAction(toggle1_option) - display_options.addAction(toggle2_option) - display_options.addAction(toggle3_option) - display_options.addAction(toggle4_option) - display_options.addAction(view1_option) - display_options.addAction(view2_option) - display_options.addAction(view3_option) - display_options.addAction(axis_option) - display_options.addAction(toggle_labels_option) - display_options.addAction(toggle_lines_option) - display_options.addAction(toggle_channels_option) - display_options.addAction(toggle_histology_option) - display_options.addAction(popup_minimise) - display_options.addAction(popup_close) - display_options.addAction(save_plots) - - # SESSION INFORMATION MENU BAR - # Define all session information options - # Display any notes associated with recording session - session_notes = QtWidgets.QAction('Session Notes', self) - session_notes.triggered.connect(self.display_session_notes) - # Shortcut to show label information - region_info = QtWidgets.QAction('Region Info', self) - region_info.setShortcut('Shift+I') - region_info.triggered.connect(self.describe_labels_pressed) - - # Add menu bar with all possible session info options - info_options = menu_bar.addMenu('Session Information') - info_options.addAction(session_notes) - info_options.addAction(region_info) - - # Display other sessions that are closeby if online mode - if not self.offline: - nearby_info = QtWidgets.QAction('Nearby Sessions', self) - nearby_info.triggered.connect(self.display_nearby_sessions) - info_options.addAction(nearby_info) - - def init_interaction_features(self): - """ - Create all interaction widgets that will be added to the GUI - """ - # Button to apply interpolation - self.fit_button = QtWidgets.QPushButton('Fit') - self.fit_button.clicked.connect(self.fit_button_pressed) - # Button to apply offset - self.offset_button = QtWidgets.QPushButton('Offset') - self.offset_button.clicked.connect(self.offset_button_pressed) - # Button to go to next move - self.next_button = QtWidgets.QPushButton('Next') - self.next_button.clicked.connect(self.next_button_pressed) - # Button to go to previous move - self.prev_button = QtWidgets.QPushButton('Previous') - self.prev_button.clicked.connect(self.prev_button_pressed) - # String to display current move index - self.idx_string = QtWidgets.QLabel() - # String to display total number of moves - self.tot_idx_string = QtWidgets.QLabel() - # Button to reset GUI to initial state - self.reset_button = QtWidgets.QPushButton('Reset') - self.reset_button.clicked.connect(self.reset_button_pressed) - # Button to upload final state to Alyx/ to local file - self.complete_button = QtWidgets.QPushButton('Upload') - if not self.offline: - self.complete_button.clicked.connect(self.display_qc_options) - else: - self.complete_button.clicked.connect(self.complete_button_pressed_offline) - - if not self.offline: - # If offline mode is False, read in Subject and Session options from Alyx - # Drop down list to choose subject - self.subj_list = QtGui.QStandardItemModel() - self.subj_combobox = QtWidgets.QComboBox() - # Add line edit and completer to be able to search for subject - self.subj_combobox.setLineEdit(QtWidgets.QLineEdit()) - subj_completer = QtWidgets.QCompleter() - subj_completer.setCaseSensitivity(QtCore.Qt.CaseInsensitive) - self.subj_combobox.setCompleter(subj_completer) - self.subj_combobox.setModel(self.subj_list) - self.subj_combobox.completer().setModel(self.subj_list) - self.subj_combobox.activated.connect(self.on_subject_selected) - - # Drop down list to choose session - self.sess_list = QtGui.QStandardItemModel() - self.sess_combobox = QtWidgets.QComboBox() - self.sess_combobox.setModel(self.sess_list) - self.sess_combobox.activated.connect(self.on_session_selected) - else: - # If offline mode is True, provide dialog to select local folder that holds data - self.folder_line = QtWidgets.QLineEdit() - self.folder_button = QtWidgets.QToolButton() - self.folder_button.setText('...') - self.folder_button.clicked.connect(self.on_folder_selected) - - # Drop down list to choose previous alignments - self.align_list = QtGui.QStandardItemModel() - self.align_combobox = QtWidgets.QComboBox() - self.align_combobox.setModel(self.align_list) - self.align_combobox.activated.connect(self.on_alignment_selected) - - # Drop down list to select shank - self.shank_list = QtGui.QStandardItemModel() - self.shank_combobox = QtWidgets.QComboBox() - self.shank_combobox.setModel(self.shank_list) - self.shank_combobox.activated.connect(self.on_shank_selected) - - # Button to get data to display in GUI - self.data_button = QtWidgets.QPushButton('Get Data') - self.data_button.clicked.connect(self.data_button_pressed) - - # Arrange interaction features into three different layout groups - # Group 1 - hlayout1 = QtWidgets.QHBoxLayout() - hlayout2 = QtWidgets.QHBoxLayout() - hlayout1.addWidget(self.fit_button, stretch=1) - hlayout1.addWidget(self.offset_button, stretch=1) - hlayout1.addWidget(self.tot_idx_string, stretch=2) - hlayout2.addWidget(self.prev_button, stretch=1) - hlayout2.addWidget(self.next_button, stretch=1) - hlayout2.addWidget(self.idx_string, stretch=2) - self.interaction_layout1 = QtWidgets.QVBoxLayout() - self.interaction_layout1.addLayout(hlayout1) - self.interaction_layout1.addLayout(hlayout2) - # Group 2 - self.interaction_layout2 = QtWidgets.QHBoxLayout() - self.interaction_layout2.addWidget(self.reset_button) - self.interaction_layout2.addWidget(self.complete_button) - - # Group 3 will depend on online/ offline mode - self.interaction_layout3 = QtWidgets.QHBoxLayout() - if not self.offline: - self.interaction_layout3.addWidget(self.subj_combobox, stretch=1) - self.interaction_layout3.addWidget(self.sess_combobox, stretch=2) - self.interaction_layout3.addWidget(self.align_combobox, stretch=2) - self.interaction_layout3.addWidget(self.data_button, stretch=1) - else: - self.interaction_layout3.addWidget(self.folder_line, stretch=2) - self.interaction_layout3.addWidget(self.folder_button, stretch=1) - self.interaction_layout3.addWidget(self.shank_combobox, stretch=1) - self.interaction_layout3.addWidget(self.align_combobox, stretch=2) - self.interaction_layout3.addWidget(self.data_button, stretch=1) - - # Pop up dialog for qc results to datajoint, only for online mode - if not self.offline: - align_qc_label = QtWidgets.QLabel("Confidence of alignment") - self.align_qc = QtWidgets.QComboBox() - self.align_qc.addItems(["High", "Medium", "Low"]) - ephys_qc_label = QtWidgets.QLabel("QC for ephys recording") - self.ephys_qc = QtWidgets.QComboBox() - self.ephys_qc.addItems(["Pass", "Warning", "Critical"]) - - self.desc_buttons = QtWidgets.QButtonGroup() - self.desc_group = QtWidgets.QGroupBox("Describe problem with recording") - self.desc_layout = QtWidgets.QVBoxLayout() - self.desc_layout.setSpacing(5) - self.desc_buttons.setExclusive(False) - options = REASONS_INS_CRIT_GUI - for i, val in enumerate(options): - - button = QtWidgets.QCheckBox(val) - button.setCheckState(QtCore.Qt.Unchecked) - - self.desc_buttons.addButton(button, id=i) - self.desc_layout.addWidget(button) - - self.desc_group.setLayout(self.desc_layout) - - self.qc_dialog = QtWidgets.QDialog(self) - self.qc_dialog.setWindowTitle('QC assessment') - self.qc_dialog.resize(300, 150) - self.qc_dialog.accepted.connect(self.qc_button_clicked) - buttonBox = QtWidgets.QDialogButtonBox( - QtWidgets.QDialogButtonBox.Ok | QtWidgets.QDialogButtonBox.Cancel) - buttonBox.accepted.connect(self.qc_dialog.accept) - buttonBox.rejected.connect(self.qc_dialog.reject) - # - dialog_layout = QtWidgets.QVBoxLayout() - dialog_layout.addWidget(align_qc_label) - dialog_layout.addWidget(self.align_qc) - dialog_layout.addWidget(ephys_qc_label) - dialog_layout.addWidget(self.ephys_qc) - dialog_layout.addWidget(self.desc_group) - dialog_layout.addWidget(buttonBox) - self.qc_dialog.setLayout(dialog_layout) - - def init_region_lookup(self, allen): - """ - Create Allen Atlas structure tree - """ - - # Remove the first row which corresponds to 'Void' - allen = allen.drop([0]).reset_index(drop=True) - - # Find the parent path of each structure by removing the structure id from path - def parent_path(struct_path): - return struct_path.rsplit('/', 2)[0] + '/' - allen['parent_path'] = allen['structure_id_path'].apply(parent_path) - - # Create standard model view - self.struct_list = QtGui.QStandardItemModel() - self.struct_view = QtWidgets.QTreeView() - self.struct_view.setModel(self.struct_list) - self.struct_view.clicked.connect(self.label_pressed) - - # Defin - self.struct_view.setHeaderHidden(True) - unique_levels = np.unique(allen['depth']).astype(int) - parent_info = {} - idx = np.where(allen['depth'] == unique_levels[0])[0] - item = QtGui.QStandardItem(allen['acronym'][idx[0]] + ': ' + allen['name'][idx[0]]) - icon = QtGui.QPixmap(20, 20) - icon.fill(QtGui.QColor('#' + allen['color_hex_triplet'][idx[0]])) - item.setIcon(QtGui.QIcon(icon)) - item.setAccessibleText(str(allen['id'][idx[0]])) - item.setEditable(False) - self.struct_list.appendRow(item) - parent_info.update({allen['structure_id_path'][idx[0]]: item}) - - for level in unique_levels[1:]: - idx_levels = np.where(allen['depth'] == level)[0] - for idx in idx_levels: - parent = allen['parent_path'][idx] - parent_item = parent_info[parent] - item = QtGui.QStandardItem(allen['acronym'][idx] + ': ' + allen['name'][idx]) - icon.fill(QtGui.QColor('#' + allen['color_hex_triplet'][idx])) - item.setIcon(QtGui.QIcon(icon)) - item.setAccessibleText(str(allen['id'][idx])) - item.setEditable(False) - parent_item.appendRow(item) - parent_info.update({allen['structure_id_path'][idx]: item}) - - self.struct_description = QtWidgets.QTextEdit() - - def init_figures(self): - """ - Create all figures that will be added to the GUI - """ - # Lists to store the position of probe top and tip - self.probe_top_lines = [] - self.probe_tip_lines = [] - - # Figures to show ephys data - # 2D scatter/ image plot - self.fig_img = pg.PlotItem() - self.fig_img.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + - self.probe_extra, padding=self.pad) - self.probe_tip_lines.append(self.fig_img.addLine(y=self.probe_tip, pen=self.kpen_dot, - z=50)) - self.probe_top_lines.append(self.fig_img.addLine(y=self.probe_top, pen=self.kpen_dot, - z=50)) - self.set_axis(self.fig_img, 'bottom') - self.fig_data_ax = self.set_axis(self.fig_img, 'left', - label='Distance from probe tip (uV)') - - self.fig_img_cb = pg.PlotItem() - self.fig_img_cb.setMaximumHeight(70) - self.fig_img_cb.setMouseEnabled(x=False, y=False) - self.set_axis(self.fig_img_cb, 'bottom', show=False) - self.set_axis(self.fig_img_cb, 'left', pen='w') - self.set_axis(self.fig_img_cb, 'top', pen='w') - - # 1D line plot - self.fig_line = pg.PlotItem() - self.fig_line.setMouseEnabled(x=False, y=False) - self.fig_line.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + - self.probe_extra, padding=self.pad) - self.probe_tip_lines.append(self.fig_line.addLine(y=self.probe_tip, pen=self.kpen_dot, - z=50)) - self.probe_top_lines.append(self.fig_line.addLine(y=self.probe_top, pen=self.kpen_dot, - z=50)) - self.set_axis(self.fig_line, 'bottom') - self.set_axis(self.fig_line, 'left', show=False) - - # 2D probe plot - self.fig_probe = pg.PlotItem() - self.fig_probe.setMouseEnabled(x=False, y=False) - self.fig_probe.setMaximumWidth(50) - self.fig_probe.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + - self.probe_extra, padding=self.pad) - self.probe_tip_lines.append(self.fig_probe.addLine(y=self.probe_tip, pen=self.kpen_dot, - z=50)) - self.probe_top_lines.append(self.fig_probe.addLine(y=self.probe_top, pen=self.kpen_dot, - z=50)) - self.set_axis(self.fig_probe, 'bottom', pen='w') - self.set_axis(self.fig_probe, 'left', show=False) - - self.fig_probe_cb = pg.PlotItem() - self.fig_probe_cb.setMouseEnabled(x=False, y=False) - self.fig_probe_cb.setMaximumHeight(70) - self.set_axis(self.fig_probe_cb, 'bottom', show=False) - self.set_axis(self.fig_probe_cb, 'left', show=False) - self.set_axis(self.fig_probe_cb, 'top', pen='w') - - # Add img plot, line plot, probe plot, img colourbar and probe colourbar to a graphics - # layout widget so plots can be arranged and moved easily - self.fig_data_area = pg.GraphicsLayoutWidget() - self.fig_data_area.scene().sigMouseClicked.connect(self.on_mouse_double_clicked) - self.fig_data_area.scene().sigMouseHover.connect(self.on_mouse_hover) - self.fig_data_layout = pg.GraphicsLayout() - - self.fig_data_layout.addItem(self.fig_img_cb, 0, 0) - self.fig_data_layout.addItem(self.fig_probe_cb, 0, 1, 1, 2) - self.fig_data_layout.addItem(self.fig_img, 1, 0) - self.fig_data_layout.addItem(self.fig_line, 1, 1) - self.fig_data_layout.addItem(self.fig_probe, 1, 2) - self.fig_data_layout.layout.setColumnStretchFactor(0, 6) - self.fig_data_layout.layout.setColumnStretchFactor(1, 2) - self.fig_data_layout.layout.setColumnStretchFactor(2, 1) - self.fig_data_layout.layout.setRowStretchFactor(0, 1) - self.fig_data_layout.layout.setRowStretchFactor(1, 10) - - self.fig_data_area.addItem(self.fig_data_layout) - - # Figures to show histology data - # Histology figure that will be updated with user input - self.fig_hist = pg.PlotItem() - self.fig_hist.setContentsMargins(0, 0, 0, 0) - self.fig_hist.setMouseEnabled(x=False) - self.fig_hist.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + - self.probe_extra, padding=self.pad) - self.set_axis(self.fig_hist, 'bottom', pen='w') - - # This is the solution from pyqtgraph people, but doesn't show ticks - # self.fig_hist.showGrid(False, True, 0) - - replace_axis(self.fig_hist) - self.ax_hist = self.set_axis(self.fig_hist, 'left', pen=None) - self.ax_hist.setWidth(0) - self.ax_hist.setStyle(tickTextOffset=-70) - - self.fig_scale = pg.PlotItem() - self.fig_scale.setMaximumWidth(50) - self.fig_scale.setMouseEnabled(x=False) - self.scale_label = pg.LabelItem(color='k') - self.set_axis(self.fig_scale, 'bottom', pen='w') - self.set_axis(self.fig_scale, 'left', show=False) - (self.fig_scale).setYLink(self.fig_hist) - - # Figure that will show scale factor of histology boundaries - self.fig_scale_cb = pg.PlotItem() - self.fig_scale_cb.setMouseEnabled(x=False, y=False) - self.fig_scale_cb.setMaximumHeight(70) - self.set_axis(self.fig_scale_cb, 'bottom', show=False) - self.set_axis(self.fig_scale_cb, 'left', show=False) - self.fig_scale_ax = self.set_axis(self.fig_scale_cb, 'top', pen='w') - self.set_axis(self.fig_scale_cb, 'right', show=False) - - # Histology figure that will remain at initial state for reference - self.fig_hist_ref = pg.PlotItem() - self.fig_hist_ref.setMouseEnabled(x=False) - self.fig_hist_ref.setYRange(min=self.probe_tip - self.probe_extra, max=self.probe_top + - self.probe_extra, padding=self.pad) - self.set_axis(self.fig_hist_ref, 'bottom', pen='w') - self.set_axis(self.fig_hist_ref, 'left', show=False) - replace_axis(self.fig_hist_ref, orientation='right', pos=(2, 2)) - self.ax_hist_ref = self.set_axis(self.fig_hist_ref, 'right', pen=None) - self.ax_hist_ref.setWidth(0) - self.ax_hist_ref.setStyle(tickTextOffset=-70) - - self.fig_hist_area = pg.GraphicsLayoutWidget() - self.fig_hist_area.setMouseTracking(True) - self.fig_hist_area.scene().sigMouseClicked.connect(self.on_mouse_double_clicked) - self.fig_hist_area.scene().sigMouseHover.connect(self.on_mouse_hover) - - self.fig_hist_extra_yaxis = pg.PlotItem() - self.fig_hist_extra_yaxis.setMouseEnabled(x=False, y=False) - self.fig_hist_extra_yaxis.setMaximumWidth(2) - self.fig_hist_extra_yaxis.setYRange(min=self.probe_tip - self.probe_extra, - max=self.probe_top + self.probe_extra, - padding=self.pad) - - self.set_axis(self.fig_hist_extra_yaxis, 'bottom', pen='w') - self.ax_hist2 = self.set_axis(self.fig_hist_extra_yaxis, 'left', pen=None) - self.ax_hist2.setWidth(10) - - self.fig_hist_layout = pg.GraphicsLayout() - self.fig_hist_layout.addItem(self.fig_scale_cb, 0, 0, 1, 4) - self.fig_hist_layout.addItem(self.fig_hist_extra_yaxis, 1, 0) - self.fig_hist_layout.addItem(self.fig_hist, 1, 1) - self.fig_hist_layout.addItem(self.fig_scale, 1, 2) - self.fig_hist_layout.addItem(self.fig_hist_ref, 1, 3) - self.fig_hist_layout.layout.setColumnStretchFactor(0, 1) - self.fig_hist_layout.layout.setColumnStretchFactor(1, 4) - self.fig_hist_layout.layout.setColumnStretchFactor(2, 1) - self.fig_hist_layout.layout.setColumnStretchFactor(3, 4) - self.fig_hist_layout.layout.setRowStretchFactor(0, 1) - self.fig_hist_layout.layout.setRowStretchFactor(1, 10) - self.fig_hist_area.addItem(self.fig_hist_layout) - - # Figure to show coronal slice through the brain - self.fig_slice_area = pg.GraphicsLayoutWidget() - self.fig_slice_layout = pg.GraphicsLayout() - self.fig_slice_hist_alt = pg.ViewBox() - self.fig_slice = pg.ViewBox() - self.fig_slice_layout.addItem(self.fig_slice, 0, 0) - self.fig_slice_layout.addItem(self.fig_slice_hist_alt, 0, 1) - self.fig_slice_layout.layout.setColumnStretchFactor(0, 3) - self.fig_slice_layout.layout.setColumnStretchFactor(1, 1) - self.fig_slice_area.addItem(self.fig_slice_layout) - self.slice_item = self.fig_slice_hist_alt - - # Figure to show fit and offset applied by user - self.fig_fit = pg.PlotWidget(background='w') - self.fig_fit.setMouseEnabled(x=False, y=False) - self.fig_fit_exporter = pg.exporters.ImageExporter(self.fig_fit.plotItem) - self.fig_fit.sigDeviceRangeChanged.connect(self.on_fig_size_changed) - self.fig_fit.setXRange(min=self.view_total[0], max=self.view_total[1]) - self.fig_fit.setYRange(min=self.view_total[0], max=self.view_total[1]) - self.set_axis(self.fig_fit, 'bottom', label='Original coordinates (um)') - self.set_axis(self.fig_fit, 'left', label='New coordinates (um)') - plot = pg.PlotCurveItem() - plot.setData(x=self.depth, y=self.depth, pen=self.kpen_dot) - self.fit_plot = pg.PlotCurveItem(pen=self.bpen_solid) - self.fit_scatter = pg.ScatterPlotItem(size=7, symbol='o', brush='w', pen='b') - self.fit_plot_lin = pg.PlotCurveItem(pen=self.rpen_dot) - self.fig_fit.addItem(plot) - self.fig_fit.addItem(self.fit_plot) - self.fig_fit.addItem(self.fit_plot_lin) - self.fig_fit.addItem(self.fit_scatter) - - self.lin_fit_option = QtWidgets.QCheckBox('Linear fit', self.fig_fit) - self.lin_fit_option.setChecked(True) - self.lin_fit_option.stateChanged.connect(self.lin_fit_option_changed) - self.on_fig_size_changed() - - def on_fig_size_changed(self): - # fig_width = self.fig_fit_exporter.getTargetRect().width() - # fig_height = self.fig_fit_exporter.getTargetRect().width() - self.lin_fit_option.move(70, 10) - - -class PopupWindow(QtWidgets.QMainWindow): - closed = QtCore.pyqtSignal(QtWidgets.QMainWindow) - moved = QtCore.pyqtSignal() - - def __init__(self, title, parent=None, size=(300, 300), graphics=True): - super(PopupWindow, self).__init__() - self.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint) - self.resize(size[0], size[1]) - self.move(randrange(30) + 1000, randrange(30) + 200) - if graphics: - self.popup_widget = pg.GraphicsLayoutWidget() - else: - self.popup_widget = QtWidgets.QWidget() - self.layout = QtWidgets.QGridLayout() - self.popup_widget.setLayout(self.layout) - self.setCentralWidget(self.popup_widget) - self.setWindowTitle(title) - self.show() - - def closeEvent(self, event): - self.closed.emit(self) - self.close() - - def leaveEvent(self, event): - self.moved.emit() - - -class CheckableComboBox(QtWidgets.QComboBox): - def __init__(self): - super(CheckableComboBox, self).__init__() - self.view().pressed.connect(self.handleItemPressed) - self.setModel(QtGui.QStandardItemModel(self)) - - def handleItemPressed(self, index): - item = self.model().itemFromIndex(index) - if item.checkState() == QtCore.Qt.Checked: - item.setCheckState(QtCore.Qt.Unchecked) - else: - item.setCheckState(QtCore.Qt.Checked) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/example_code.py b/PythonScripts/iblapps-master/atlaselectrophysiology/example_code.py deleted file mode 100644 index 82a6b003..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/example_code.py +++ /dev/null @@ -1,8 +0,0 @@ -from one.api import ONE -from atlaselectrophysiology.alignment_with_easyqc import viewer - -one = ONE() -probe_id = 'ce397420-3cd2-4a55-8fd1-5e28321981f4' - - -av = viewer(probe_id, one=one) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/extract_files.py b/PythonScripts/iblapps-master/atlaselectrophysiology/extract_files.py deleted file mode 100644 index b449262f..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/extract_files.py +++ /dev/null @@ -1,147 +0,0 @@ -from pathlib import Path -import logging - -from tqdm import tqdm -import spikeglx -import numpy as np -from neurodsp import fourier, utils -from scipy import signal -import one.alf.io as alfio -import ibllib.ephys.ephysqc as ephysqc - -from phylib.io import alf - -_logger = logging.getLogger('ibllib') - - -RMS_WIN_LENGTH_SECS = 3 -WELCH_WIN_LENGTH_SAMPLES = 1024 - - -def rmsmap(fbin, spectra=True): - """ - Computes RMS map in time domain and spectra for each channel of Neuropixel probe - - :param fbin: binary file in spike glx format (will look for attached metatdata) - :type fbin: str or pathlib.Path - :param spectra: whether to compute the power spectrum (only need for lfp data) - :type: bool - :return: a dictionary with amplitudes in channeltime space, channelfrequency space, time - and frequency scales - """ - if not isinstance(fbin, spikeglx.Reader): - sglx = spikeglx.Reader(fbin) - sglx.open() - rms_win_length_samples = 2 ** np.ceil(np.log2(sglx.fs * RMS_WIN_LENGTH_SECS)) - # the window generator will generates window indices - wingen = utils.WindowGenerator(ns=sglx.ns, nswin=rms_win_length_samples, overlap=0) - # pre-allocate output dictionary of numpy arrays - win = {'TRMS': np.zeros((wingen.nwin, sglx.nc)), - 'nsamples': np.zeros((wingen.nwin,)), - 'fscale': fourier.fscale(WELCH_WIN_LENGTH_SAMPLES, 1 / sglx.fs, one_sided=True), - 'tscale': wingen.tscale(fs=sglx.fs)} - win['spectral_density'] = np.zeros((len(win['fscale']), sglx.nc)) - # loop through the whole session - with tqdm(total=100) as pbar: - for first, last in wingen.firstlast: - D = sglx.read_samples(first_sample=first, last_sample=last)[0].transpose() - # remove low frequency noise below 1 Hz - D = fourier.hp(D, 1 / sglx.fs, [0, 1]) - iw = wingen.iw - win['TRMS'][iw, :] = utils.rms(D) - win['nsamples'][iw] = D.shape[1] - if spectra: - # the last window may be smaller than what is needed for welch - if last - first < WELCH_WIN_LENGTH_SAMPLES: - continue - # compute a smoothed spectrum using welch method - _, w = signal.welch( - D, fs=sglx.fs, window='hann', nperseg=WELCH_WIN_LENGTH_SAMPLES, - detrend='constant', return_onesided=True, scaling='density', axis=-1 - ) - win['spectral_density'] += w.T - # print at least every 20 windows - #if (iw % min(20, max(int(np.floor(wingen.nwin / 75)), 1))) == 0: - if iw % 10: - pbar.update(iw) - sglx.close() - return win - - -def extract_rmsmap(fbin, out_folder=None, spectra=True): - """ - Wrapper for rmsmap that outputs _ibl_ephysRmsMap and _ibl_ephysSpectra ALF files - - :param fbin: binary file in spike glx format (will look for attached metatdata) - :param out_folder: folder in which to store output ALF files. Default uses the folder in which - the `fbin` file lives. - :param spectra: whether to compute the power spectrum (only need for lfp data) - :type: bool - :return: None - """ - _logger.info(f"Computing QC for {fbin}") - sglx = spikeglx.Reader(fbin) - # check if output ALF files exist already: - if out_folder is None: - out_folder = Path(fbin).parent - else: - out_folder = Path(out_folder) - alf_object_time = f'ephysTimeRms{sglx.type.upper()}' - alf_object_freq = f'ephysSpectralDensity{sglx.type.upper()}' - - # crunch numbers - rms = rmsmap(fbin, spectra=spectra) - # output ALF files, single precision with the optional label as suffix before extension - if not out_folder.exists(): - out_folder.mkdir() - tdict = {'rms': rms['TRMS'].astype(np.single), 'timestamps': rms['tscale'].astype(np.single)} - alfio.save_object_npy(out_folder, object=alf_object_time, dico=tdict, namespace='iblqc') - if spectra: - fdict = {'power': rms['spectral_density'].astype(np.single), - 'freqs': rms['fscale'].astype(np.single)} - alfio.save_object_npy( - out_folder, object=alf_object_freq, dico=fdict, namespace='iblqc') - - -def _sample2v(ap_file): - """ - Convert raw ephys data to Volts - """ - md = spikeglx.read_meta_data(ap_file.with_suffix('.meta')) - s2v = spikeglx._conversion_sample2v_from_meta(md) - return s2v['ap'][0] - - -def ks2_to_alf(ks_path, bin_path, out_path, bin_file=None, ampfactor=1, label=None, force=True): - """ - Convert Kilosort 2 output to ALF dataset for single probe data - :param ks_path: - :param bin_path: path of raw data - :param out_path: - :return: - """ - m = ephysqc.phy_model_from_ks2_path(ks2_path=ks_path, bin_path=bin_path, bin_file=bin_file) - ephysqc.spike_sorting_metrics_ks2(ks_path, m, save=True, save_path=out_path) - ac = alf.EphysAlfCreator(m) - ac.convert(out_path, label=label, force=force, ampfactor=ampfactor) - - -def extract_data(ks_path, ephys_path, out_path): - efiles = spikeglx.glob_ephys_files(ephys_path) - - for efile in efiles: - if efile.get('ap') and efile.ap.exists(): - ks2_to_alf(ks_path, ephys_path, out_path, bin_file=efile.ap, - ampfactor=_sample2v(efile.ap), label=None, force=True) - - extract_rmsmap(efile.ap, out_folder=out_path, spectra=False) - if efile.get('lf') and efile.lf.exists(): - extract_rmsmap(efile.lf, out_folder=out_path) - - -# if __name__ == '__main__': -# -# ephys_path = Path('C:/Users/Mayo/Downloads/raw_ephys_data') -# ks_path = Path('C:/Users/Mayo/Downloads/KS2') -# out_path = Path('C:/Users/Mayo/Downloads/alf') -# extract_data(ks_path, ephys_path, out_path) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/get_scale_factor.py b/PythonScripts/iblapps-master/atlaselectrophysiology/get_scale_factor.py deleted file mode 100644 index a2ceb737..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/get_scale_factor.py +++ /dev/null @@ -1,91 +0,0 @@ -''' -Extract channel locations from reference points of previous alignments saved in json field of -trajectory object -Create plot showing histology regions which channels pass through as well as coronal slice with -channel locations shown -''' - -# import modules -from oneibl.one import ONE -from ibllib.pipes.ephys_alignment import EphysAlignment -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import seaborn as sns -import ibllib.atlas as atlas - - -# Instantiate brain atlas and one -brain_atlas = atlas.AllenAtlas(25) -one = ONE() - -# Find eid of interest -subject = 'KS022' - -# Find the ephys aligned trajectory for eid probe combination -trajectory = one.alyx.rest('trajectories', 'list', provenance='Ephys aligned histology track', - subject=subject) - -# Load in channels.localCoordinates dataset type -chn_coords = one.load(trajectory[0]['session']['id'], - dataset_types=['channels.localCoordinates'])[0] -depths = chn_coords[:, 1] - -subject_summary = pd.DataFrame(columns={'Session', 'User', 'Scale Factor', 'Avg Scale Factor'}) -sf = [] -sess = [] -user = [] -for traj in trajectory: - alignments = traj['json'] - - insertion = one.alyx.rest('insertions', 'list', session=traj['session']['id'], - name=traj['probe_name']) - xyz_picks = np.array(insertion[0]['json']['xyz_picks']) / 1e6 - - session_info = traj['session']['start_time'][:10] + '_' + traj['probe_name'] - - for iK, key in enumerate(alignments): - # Location of reference lines used for alignmnet - feature = np.array(alignments[key][0]) - track = np.array(alignments[key][1]) - user = key[:19] - # Instantiate EphysAlignment object - ephysalign = EphysAlignment(xyz_picks, depths, track_prev=track, feature_prev=feature) - region_scaled, _ = ephysalign.scale_histology_regions(feature, track) - _, scale_factor = ephysalign.get_scale_factor(region_scaled) - - if np.all(np.round(np.diff(scale_factor), 3) == 0): - # Case where there is no scaling but just an offset - scale_factor = np.array([1]) - avg_sf = 1 - else: - if feature.size > 4: - # Case where 3 or more reference lines have been placed so take gradient of - # linear fit to represent average scaling factor - avg_sf = scale_factor[0] - else: - # Case where 2 reference lines have been used. Only have local scaling between - # two reference lines, everywhere else scaling is 1. Use the local scaling as the - # average scaling factor - avg_sf = np.mean(scale_factor[1:-1]) - - for iS, sf in enumerate(scale_factor): - if iS == 0: - subject_summary = subject_summary.append( - {'Session': session_info, 'User': user, 'Scale Factor': sf, - 'Avg Scale Factor': avg_sf}, ignore_index=True) - else: - subject_summary = subject_summary.append( - {'Session': session_info, 'User': user, 'Scale Factor': sf, - 'Avg Scale Factor': np.NaN}, ignore_index=True) - -fig, ax = plt.subplots(figsize=(10, 8)) -sns.swarmplot(x='Session', y='Scale Factor', hue='User', data=subject_summary, ax=ax) -sns.swarmplot(x='Session', y='Avg Scale Factor', hue='User', size=8, linewidth=1, - data=subject_summary, ax=ax) -# ensures value in legend isn't repeated -handles, labels = ax.get_legend_handles_labels() -by_label = dict(zip(labels, handles)) -ax.legend(by_label.values(), by_label.keys()) - -plt.show() diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/load_data.py b/PythonScripts/iblapps-master/atlaselectrophysiology/load_data.py deleted file mode 100644 index 0787699d..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/load_data.py +++ /dev/null @@ -1,527 +0,0 @@ -import logging -import numpy as np -from datetime import datetime -import ibllib.pipes.histology as histology -from neuropixel import SITES_COORDINATES -import ibllib.atlas as atlas -from ibllib.qc.alignment_qc import AlignmentQC -from one.api import ONE -from pathlib import Path -import one.alf as alf -from one import params -import glob -from atlaselectrophysiology.load_histology import download_histology_data, tif2nrrd -import ibllib.qc.critical_reasons as usrpmt - -logger = logging.getLogger('ibllib') -ONE_BASE_URL = "https://alyx.internationalbrainlab.org" - - -class LoadData: - def __init__(self, one=None, brain_atlas=None, testing=False, probe_id=None, - load_histology=True, spike_collection=None, mode='auto'): - self.one = one or ONE(base_url=ONE_BASE_URL, mode=mode) - self.brain_atlas = brain_atlas or atlas.AllenAtlas(25) - self.download_hist = load_histology # whether or not to look for the histology files - self.spike_collection = spike_collection - - if testing: - self.probe_id = probe_id - self.chn_coords = SITES_COORDINATES - self.chn_depths = SITES_COORDINATES[:, 1] - self.probe_collection = None - else: - self.brain_regions = self.one.alyx.rest('brain-regions', 'list') - self.chn_coords = None - self.chn_depths = None - - # Initialise all variables that get assigned - self.sess_with_hist = None - self.traj_ids = None - self.traj_coords = None - self.subjects = None - self.sess = None - self.eid = None - self.lab = None - self.n_sess = None - self.probe_label = None - self.traj_id = None - self.date = None - self.subj = None - self.alignments = {} - self.prev_align = None - self.sess_path = None - self.allen_id = None - self.cluster_chns = None - self.resolved = None - self.alyx_str = None - self.sr = None - - if probe_id is not None: - self.sess = self.one.alyx.rest('insertions', 'list', id=probe_id) - - def get_subjects(self): - """ - Finds all subjects that a probe insertions with spike data - :return subjects: list of subjects - :type: list of strings - """ - - self.sess_ins = self.one.alyx.rest('insertions', 'list', dataset_type='spikes.times') - self.subj_ins = [sess['session_info']['subject'] for sess in self.sess_ins] - - self.subjects = np.unique(self.subj_ins) - - return self.subjects - - def get_sessions(self, idx): - """ - Finds all sessions for a particular subject - :param idx: index of chosen subject from drop-down list - :type idx: int - :return session: list of sessions associated with subject, displayed as date + probe - :type: list of strings - """ - subj = self.subjects[idx] - sess_idx = [i for i, e in enumerate(self.subj_ins) if e == subj] - self.sess = [self.sess_ins[idx] for idx in sess_idx] - session = [(sess['session_info']['start_time'][:10] + ' ' + sess['name']) for sess in - self.sess] - return session - - def get_info(self, idx): - """ - Reads in all information about the chosen sessions and looks to see if there are any - previous alignments associated with the session, also sees if we have histology or not - :param idx: index of chosen session from drop-down list - :type idx: int - :return prev_align: list of previous alignments associated with session, if none just has - option for 'original' alignment - :type: list of strings - """ - self.n_sess = self.sess[idx]['session_info']['number'] - self.date = self.sess[idx]['session_info']['start_time'][:10] - self.probe_label = self.sess[idx]['name'] - self.probe_id = self.sess[idx]['id'] - self.lab = self.sess[idx]['session_info']['lab'] - self.eid = self.sess[idx]['session'] - self.subj = self.sess[idx]['session_info']['subject'] - - histology_exists = False - - # Make sure there is a histology track - hist_traj = self.one.alyx.rest('trajectories', 'list', probe_insertion=self.probe_id, - provenance='Histology track') - if len(hist_traj) == 1: - if hist_traj[0]['x'] is not None: - histology_exists = True - self.traj_id = hist_traj[0]['id'] - - return self.get_previous_alignments(), histology_exists - - def get_previous_alignments(self): - """ - Find out if there are any previous alignments associated with probe insertion - :return: - """ - - # Looks for any previous alignments - ephys_traj_prev = self.one.alyx.rest('trajectories', 'list', probe_insertion=self.probe_id, - provenance='Ephys aligned histology track', - no_cache=True) - - if ephys_traj_prev: - self.alignments = ephys_traj_prev[0]['json'] or {} - self.prev_align = [*self.alignments.keys()] - self.prev_align = sorted(self.prev_align, reverse=True) - self.prev_align.append('original') - else: - self.alignments = {} - self.prev_align = ['original'] - - return self.prev_align - - def get_starting_alignment(self, idx): - """ - Finds all sessions for a particular subject that have a histology track trajectory - registered - :param idx: index of chosen alignment from drop-down list - :type idx: int - :return feature: reference points in feature space - :type: np.array - :return track: reference points in track space - :type: np.array - """ - self.current_align = self.prev_align[idx] - - if self.current_align == 'original': - feature = None - track = None - else: - feature = np.array(self.alignments[self.current_align][0]) - track = np.array(self.alignments[self.current_align][1]) - - return feature, track - - def get_nearby_trajectories(self): - """ - Find sessions that have trajectories close to the currently selected session - :return close_session: list of nearby sessions ordered by absolute distance, displayed as - subject + date + probe - :type: list of strings - :return close_dist: absolute distance to nearby sessions - :type: list of float - :return close_dist_mlap: absolute distance to nearby sessions, only using ml and ap - directions - :type: list of float - """ - - if self.traj_ids is None: - self.sess_with_hist = self.one.alyx.rest('trajectories', 'list', - provenance='Histology track', - django='x__isnull,False,probe_insertion__' - 'datasets__name__icontains,spikes.times') - # Some do not have tracing, exclude these ones - - depths = np.arange(200, 4100, 20) / 1e6 - trajectories = [atlas.Insertion.from_dict(sess) for sess in self.sess_with_hist] - self.traj_ids = [sess['id'] for sess in self.sess_with_hist] - self.traj_coords = np.empty((len(self.traj_ids), len(depths), 3)) - for iT, traj in enumerate(trajectories): - self.traj_coords[iT, :] = (histology.interpolate_along_track - (np.vstack([traj.tip, traj.entry]), depths)) - - chosen_traj = self.traj_ids.index(self.traj_id) - avg_dist = np.mean(np.sqrt(np.sum((self.traj_coords - self.traj_coords[chosen_traj]) ** 2, - axis=2)), axis=1) - avg_dist_mlap = np.mean(np.sqrt(np.sum((self.traj_coords[:, :, 0:2] - - self.traj_coords[chosen_traj][:, 0:2]) ** 2, - axis=2)), axis=1) - - closest_traj = np.argsort(avg_dist) - close_dist = avg_dist[closest_traj[0:10]] * 1e6 - close_dist_mlap = avg_dist_mlap[closest_traj[0:10]] * 1e6 - - close_sessions = [] - for sess_idx in closest_traj[0:10]: - close_sessions.append((self.sess_with_hist[sess_idx]['session']['subject'] + ' ' + - self.sess_with_hist[sess_idx]['session']['start_time'][:10] + - ' ' + self.sess_with_hist[sess_idx]['probe_name'])) - - return close_sessions, close_dist, close_dist_mlap - - def get_data(self): - """ - Load/ Download all data and info associated with session - :return alf_path: path to folder containing alf format files - :type: Path - :return ephys_path: path to folder containing ephys files - :type: Path - :return chn_depths: depths of electrode channel on probe relative to tip - :type: np.array(384,1) - :return sess_notes: user notes associated with session - :type: str - """ - - self.sess_path = self.one.eid2path(self.eid) - - # THIS IS STUPID - if self.spike_collection == '': - self.probe_collection = f'alf/{self.probe_label}' - probe_path = Path(self.sess_path, 'alf', self.probe_label) - elif self.spike_collection: - self.probe_collection = f'alf/{self.probe_label}/{self.spike_collection}' - probe_path = Path(self.sess_path, 'alf', self.probe_label, self.spike_collection) - else: - # Pykilosort is default, if not present look for normal kilosort - # Find all collections - all_collections = self.one.list_collections(self.eid) - - if f'alf/{self.probe_label}/pykilosort' in all_collections: - self.probe_collection = f'alf/{self.probe_label}/pykilosort' - probe_path = Path(self.sess_path, 'alf', self.probe_label, 'pykilosort') - else: - self.probe_collection = f'alf/{self.probe_label}' - probe_path = Path(self.sess_path, 'alf', self.probe_label) - - try: - _ = self.one.load_object(self.eid, 'spikes', collection=self.probe_collection, - attribute=['depths', 'amps', 'times', 'clusters'], - download_only=True) - - _ = self.one.load_object(self.eid, 'clusters', collection=self.probe_collection, - attribute=['metrics', 'peakToTrough', 'waveforms', - 'channels'], - download_only=True) - - _ = self.one.load_object(self.eid, 'channels', collection=self.probe_collection, - attribute=['rawInd', 'localCoordinates'], download_only=True) - except alf.exceptions.ALFObjectNotFound: - logger.error(f'Could not load spike sorting for probe insertion {self.probe_id}, GUI' - f' will not work') - return [None] * 5 - - dtypes_raw = [ - '_iblqc_ephysTimeRmsAP.rms.npy', - '_iblqc_ephysTimeRmsAP.timestamps.npy', - '_iblqc_ephysSpectralDensityAP.freqs.npy', - '_iblqc_ephysSpectralDensityAP.power.npy', - '_iblqc_ephysTimeRmsLF.rms.npy', - '_iblqc_ephysTimeRmsLF.timestamps.npy', - '_iblqc_ephysSpectralDensityLF.freqs.npy', - '_iblqc_ephysSpectralDensityLF.power.npy', - ] - - collection_raw = [f'raw_ephys_data/{self.probe_label}'] * len(dtypes_raw) - - dtypes_alf = [ - '_ibl_passiveGabor.table.csv', - '_ibl_passivePeriods.intervalsTable.csv', - '_ibl_passiveRFM.times.npy', - '_ibl_passiveStims.table.csv'] - - collection_alf = ['alf'] * len(dtypes_alf) - - dtypes_passive = [ - '_iblrig_RFMapStim.raw.bin'] - - collection_passive = ['raw_passive_data'] * len(dtypes_passive) - - dtypes = dtypes_raw + dtypes_alf + dtypes_passive - collections = collection_raw + collection_alf + collection_passive - - print(self.subj) - print(self.probe_label) - print(self.date) - print(self.eid) - print(self.probe_collection) - - _ = self.one.load_datasets(self.eid, datasets=dtypes, collections=collections, - download_only=True, assert_present=False) - - ephys_path = Path(self.sess_path, 'raw_ephys_data', self.probe_label) - alf_path = Path(self.sess_path, 'alf') - - self.chn_coords = np.load(Path(probe_path, 'channels.localCoordinates.npy')) - self.chn_depths = self.chn_coords[:, 1] - self.cluster_chns = np.load(Path(probe_path, 'clusters.channels.npy')) - - sess = self.one.alyx.rest('sessions', 'read', id=self.eid) - sess_notes = None - if sess['notes']: - sess_notes = sess['notes'][0]['text'] - if not sess_notes: - sess_notes = sess['narrative'] - if not sess_notes: - sess_notes = 'No notes for this session' - - return probe_path, ephys_path, alf_path, self.chn_depths, sess_notes - - def get_allen_csv(self): - """ - Load in allen csv file - :return allen: dataframe containing all information in csv file - :type: pd.Dataframe - """ - allen_path = Path(Path(atlas.__file__).parent, 'allen_structure_tree.csv') - allen = alf.io.load_file_content(allen_path) - - self.allen_id = allen['id'] - - return allen - - def get_xyzpicks(self): - """ - Load in user chosen track tracing points - :return xyz_picks: 3D coordinates of points relative to bregma - :type: np.array(n_picks, 3) - """ - insertion = self.one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - self.xyz_picks = np.array(insertion['json']['xyz_picks']) / 1e6 - self.resolved = (insertion.get('json', {'temp': 0}).get('extended_qc', {'temp': 0}). - get('alignment_resolved', False)) - - return self.xyz_picks - - def get_slice_images(self, xyz_channels): - - index = self.brain_atlas.bc.xyz2i(xyz_channels)[:, self.brain_atlas.xyz2dims] - ccf_slice = self.brain_atlas.image[index[:, 0], :, index[:, 2]] - ccf_slice = np.swapaxes(ccf_slice, 0, 1) - - label_slice = self.brain_atlas._label2rgb(self.brain_atlas.label[index[:, 0], :, - index[:, 2]]) - label_slice = np.swapaxes(label_slice, 0, 1) - - width = [self.brain_atlas.bc.i2x(0), self.brain_atlas.bc.i2x(456)] - height = [self.brain_atlas.bc.i2z(index[0, 2]), self.brain_atlas.bc.i2z(index[-1, 2])] - - hist_path_rd = None - hist_path_gr = None - hist_path_cb = None - # First see if the histology file exists before attempting to connect with FlatIron and - # download - if self.download_hist: - hist_dir = Path(self.sess_path.parent.parent, 'histology') - if hist_dir.exists(): - path_to_rd_image = glob.glob(str(hist_dir) + '/*RD.tif') - if path_to_rd_image: - hist_path_rd = tif2nrrd(Path(path_to_rd_image[0])) - else: - files = download_histology_data(self.subj, self.lab) - if files is not None: - hist_path_rd = files[1] - - path_to_gr_image = glob.glob(str(hist_dir) + '/*GR.tif') - if path_to_gr_image: - hist_path_gr = tif2nrrd(Path(path_to_gr_image[0])) - else: - files = download_histology_data(self.subj, self.lab) - if files is not None: - hist_path_gr = files[0] - - else: - files = download_histology_data(self.subj, self.lab) - if files is not None: - hist_path_gr = files[0] - hist_path_rd = files[1] - - files = download_histology_data('MB059', 'hausserlab') - if files is not None: - hist_path_cb = files[1] - - if hist_path_rd: - hist_atlas_rd = atlas.AllenAtlas(hist_path=hist_path_rd) - hist_slice_rd = hist_atlas_rd.image[index[:, 0], :, index[:, 2]] - hist_slice_rd = np.swapaxes(hist_slice_rd, 0, 1) - else: - print('Could not find red histology image for this subject') - hist_slice_rd = np.copy(ccf_slice) - - if hist_path_gr: - hist_atlas_gr = atlas.AllenAtlas(hist_path=hist_path_gr) - hist_slice_gr = hist_atlas_gr.image[index[:, 0], :, index[:, 2]] - hist_slice_gr = np.swapaxes(hist_slice_gr, 0, 1) - else: - print('Could not find green histology image for this subject') - hist_slice_gr = np.copy(ccf_slice) - - if hist_path_cb: - hist_atlas_cb = atlas.AllenAtlas(hist_path=hist_path_cb) - hist_slice_cb = hist_atlas_cb.image[index[:, 0], :, index[:, 2]] - hist_slice_cb = np.swapaxes(hist_slice_cb, 0, 1) - else: - print('Could not find example cerebellar histology image') - hist_slice_cb = np.copy(ccf_slice) - - slice_data = { - 'hist_rd': hist_slice_rd, - 'hist_gr': hist_slice_gr, - 'hist_cb': hist_slice_cb, - 'ccf': ccf_slice, - 'label': label_slice, - 'scale': np.array([(width[-1] - width[0]) / ccf_slice.shape[0], - (height[-1] - height[0]) / ccf_slice.shape[1]]), - 'offset': np.array([width[0], height[0]]) - } - - return slice_data - - def get_region_description(self, region_idx): - struct_idx = np.where(self.allen_id == region_idx)[0][0] - description = self.brain_regions[struct_idx]['description'] - region_lookup = (self.brain_regions[struct_idx]['acronym'] + - ': ' + self.brain_regions[struct_idx]['name']) - - if region_lookup == 'void: void': - region_lookup = 'root: root' - - if not description: - description = region_lookup + '\nNo information available on Alyx for this region' - else: - description = region_lookup + '\n' + description - - return description, region_lookup - - def upload_data(self, xyz_channels, channels=True): - if not self.resolved: - channel_upload = True - # Create new trajectory and overwrite previous one - histology.register_aligned_track(self.probe_id, xyz_channels, - chn_coords=self.chn_coords, one=self.one, - overwrite=True, channels=channels, brain_atlas=self.brain_atlas) - else: - channel_upload = False - - return channel_upload - - def update_alignments(self, feature, track, key_info=None, user_eval=None): - if not key_info: - user = params.get().ALYX_LOGIN - date = datetime.now().replace(microsecond=0).isoformat() - data = {date + '_' + user: [feature.tolist(), track.tolist(), self.alyx_str]} - else: - user = key_info[20:] - if user_eval: - data = {key_info: [feature.tolist(), track.tolist(), user_eval]} - else: - data = {key_info: [feature.tolist(), track.tolist()]} - - old_user = [key for key in self.alignments.keys() if user in key] - # Only delete duplicated if trajectory is not resolved - if len(old_user) > 0 and not self.resolved: - self.alignments.pop(old_user[0]) - - self.alignments.update(data) - self.update_json(self.alignments) - - def update_json(self, json_data): - # Get the new trajectory - ephys_traj = self.one.alyx.rest('trajectories', 'list', probe_insertion=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - patch_dict = {'json': json_data} - self.one.alyx.rest('trajectories', 'partial_update', id=ephys_traj[0]['id'], - data=patch_dict) - - def upload_dj(self, align_qc, ephys_qc, ephys_desc): - # Upload qc results to datajoint table - # Check the FTP patcher credentials are in the params, so we can upload filed - self.check_FTP_patcher_credentials() - - if len(ephys_desc) == 0: - ephys_desc_str = 'None' - else: - ephys_desc_str = ", ".join(ephys_desc) - - self.alyx_str = ephys_qc.upper() + ': ' + ephys_desc_str - - if ephys_qc.upper() == 'CRITICAL': - usrpmt.main_gui(eid=self.probe_id, reasons_selected=ephys_desc, one=self.one) - - def update_qc(self, upload_alyx=True, upload_flatiron=True): - # if resolved just update the alignment_number - align_qc = AlignmentQC(self.probe_id, one=self.one, brain_atlas=self.brain_atlas, - collection=self.probe_collection) - align_qc.load_data(prev_alignments=self.alignments, xyz_picks=self.xyz_picks, - depths=self.chn_depths, cluster_chns=self.cluster_chns, - chn_coords=self.chn_coords) - results = align_qc.run(update=True, upload_alyx=upload_alyx, - upload_flatiron=upload_flatiron) - align_qc.update_experimenter_evaluation(prev_alignments=self.alignments) - - self.resolved = results['alignment_resolved'] - - return self.resolved - - def check_FTP_patcher_credentials(self): - par = self.one.alyx._par - if not params._get_current_par('FTP_DATA_SERVER_LOGIN', par) \ - or not params._get_current_par('FTP_DATA_SERVER_PWD', par): - par_dict = par.as_dict() - par_dict['FTP_DATA_SERVER_LOGIN'] = 'iblftp' - par_dict['FTP_DATA_SERVER_PWD'] = params._get_current_par('HTTP_DATA_SERVER_PWD', par) - - from one.params import _PAR_ID_STR # noqa - from iblutil.io import params as iopar # noqa - iopar.write(f'{_PAR_ID_STR}/{params._key_from_url(self.one.alyx.base_url)}', par_dict) - self.one.alyx._par = params.get(client=self.one.alyx.base_url) diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/load_data_local.py b/PythonScripts/iblapps-master/atlaselectrophysiology/load_data_local.py deleted file mode 100644 index 542f6433..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/load_data_local.py +++ /dev/null @@ -1,269 +0,0 @@ -import numpy as np -from datetime import datetime -import ibllib.atlas as atlas -from pathlib import Path -import one.alf.io as alfio -import glob -import json -from one.api import ONE -from atlaselectrophysiology.load_histology import tif2nrrd - -# temporarily add this in for neuropixel course until figured out fix to problem on win32 -import ssl -ssl._create_default_https_context = ssl._create_unverified_context - - -class LoadDataLocal: - def __init__(self): - ONE(silent=True, password='international') - self.brain_atlas = atlas.AllenAtlas(25) - self.folder_path = None - self.chn_coords = None - self.chn_coords_all = None - self.sess_path = None - self.shank_idx = 0 - self.n_shanks = 1 - - def get_info(self, folder_path): - """ - Read in the local json file to see if any previous alignments exist - """ - self.folder_path = Path(folder_path) - shank_list = self.get_nshanks() - prev_aligns = self.get_previous_alignments() - return prev_aligns, shank_list - - def get_previous_alignments(self, shank_idx=0): - - self.shank_idx = shank_idx - # If previous alignment json file exists, read in previous alignments - prev_align_filename = 'prev_alignments.json' if self.n_shanks == 1 else \ - f'prev_alignments_shank{self.shank_idx + 1}.json' - - if self.folder_path.joinpath(prev_align_filename).exists(): - with open(self.folder_path.joinpath(prev_align_filename), "r") as f: - self.alignments = json.load(f) - self.prev_align = [] - if self.alignments: - self.prev_align = [*self.alignments.keys()] - self.prev_align = sorted(self.prev_align, reverse=True) - self.prev_align.append('original') - else: - self.alignments = [] - self.prev_align = ['original'] - - return self.prev_align - - def get_starting_alignment(self, idx): - """ - Find out the starting alignmnet - """ - align = self.prev_align[idx] - - if align == 'original': - feature = None - track = None - else: - feature = np.array(self.alignments[align][0]) - track = np.array(self.alignments[align][1]) - - return feature, track - - def get_nshanks(self): - """ - Find out the number of shanks on the probe, either 1 or 4 - """ - self.chn_coords_all = np.load(self.folder_path.joinpath('channels.localCoordinates.npy')) - chn_x = np.unique(self.chn_coords_all[:, 0]) - chn_x_diff = np.diff(chn_x) - self.n_shanks = np.sum(chn_x_diff > 100) + 1 - - if self.n_shanks == 1: - shank_list = ['1/1'] - else: - shank_list = [f'{iShank + 1}/{self.n_shanks}' for iShank in range(self.n_shanks)] - - return shank_list - - def get_data(self): - # Define alf_path and ephys_path (a bit redundant but so it is compatible with plot data) - alf_path = self.folder_path - ephys_path = self.folder_path - - chn_x = np.unique(self.chn_coords_all[:, 0]) - if self.n_shanks > 1: - shanks = {} - for iShank in range(self.n_shanks): - shanks[iShank] = [chn_x[iShank * 2], chn_x[(iShank * 2) + 1]] - - shank_chns = np.bitwise_and(self.chn_coords_all[:, 0] >= shanks[self.shank_idx][0], - self.chn_coords_all[:, 0] <= shanks[self.shank_idx][1]) - self.chn_coords = self.chn_coords_all[shank_chns, :] - else: - self.chn_coords = self.chn_coords_all - - chn_depths = self.chn_coords[:, 1] - - # Read in notes for this experiment see if file exists in directory - if self.folder_path.joinpath('session_notes.txt').exists(): - with open(self.folder_path.joinpath('session_notes.txt'), "r") as f: - sess_notes = f.read() - else: - sess_notes = 'No notes for this session' - - return alf_path, ephys_path, alf_path, chn_depths, sess_notes - - def get_allen_csv(self): - allen_path = Path(Path(atlas.__file__).parent, 'allen_structure_tree.csv') - self.allen = alfio.load_file_content(allen_path) - - return self.allen - - def get_xyzpicks(self): - # Read in local xyz_picks file - # This file must exist, otherwise we don't know where probe was - xyz_file_name = '*xyz_picks.json' if self.n_shanks == 1 else \ - f'*xyz_picks_shank{self.shank_idx + 1}.json' - xyz_file = sorted(self.folder_path.glob(xyz_file_name)) - - assert (len(xyz_file) == 1) - with open(xyz_file[0], "r") as f: - user_picks = json.load(f) - - xyz_picks = np.array(user_picks['xyz_picks']) / 1e6 - - return xyz_picks - - def get_slice_images(self, xyz_channels): - # First see if the histology file exists before attempting to connect with FlatIron and - # download - - path_to_rd_image_nrrd = glob.glob(str(self.folder_path) + '/*RD.nrrd') - path_to_rd_image_tif = glob.glob(str(self.folder_path) + '/*RD.tif') - if path_to_rd_image_nrrd: - hist_path_rd = Path(path_to_rd_image_nrrd[0]) - elif path_to_rd_image_tif: - hist_path_rd = tif2nrrd(path_to_rd_image_tif[0]) - else: - hist_path_rd = [] - - path_to_gr_image_nrrd = glob.glob(str(self.folder_path) + '/*GR.nrrd') - path_to_gr_image_tif = glob.glob(str(self.folder_path) + '/*GR.tif') - if path_to_gr_image_nrrd: - hist_path_gr = Path(path_to_gr_image_nrrd[0]) - elif path_to_gr_image_tif: - hist_path_gr = tif2nrrd(path_to_gr_image_tif[0]) - else: - hist_path_gr = [] - - index = self.brain_atlas.bc.xyz2i(xyz_channels)[:, self.brain_atlas.xyz2dims] - ccf_slice = self.brain_atlas.image[index[:, 0], :, index[:, 2]] - ccf_slice = np.swapaxes(ccf_slice, 0, 1) - - label_slice = self.brain_atlas._label2rgb(self.brain_atlas.label[index[:, 0], :, - index[:, 2]]) - label_slice = np.swapaxes(label_slice, 0, 1) - - width = [self.brain_atlas.bc.i2x(0), self.brain_atlas.bc.i2x(456)] - height = [self.brain_atlas.bc.i2z(index[0, 2]), self.brain_atlas.bc.i2z(index[-1, 2])] - - if hist_path_rd: - hist_atlas_rd = atlas.AllenAtlas(hist_path=hist_path_rd) - hist_slice_rd = hist_atlas_rd.image[index[:, 0], :, index[:, 2]] - hist_slice_rd = np.swapaxes(hist_slice_rd, 0, 1) - else: - print('Could not find red histology image for this subject') - hist_slice_rd = np.copy(ccf_slice) - - if hist_path_gr: - hist_atlas_gr = atlas.AllenAtlas(hist_path=hist_path_gr) - hist_slice_gr = hist_atlas_gr.image[index[:, 0], :, index[:, 2]] - hist_slice_gr = np.swapaxes(hist_slice_gr, 0, 1) - else: - print('Could not find green histology image for this subject') - hist_slice_gr = np.copy(ccf_slice) - - slice_data = { - 'hist_rd': hist_slice_rd, - 'hist_gr': hist_slice_gr, - 'ccf': ccf_slice, - 'label': label_slice, - 'scale': np.array([(width[-1] - width[0]) / ccf_slice.shape[0], - (height[-1] - height[0]) / ccf_slice.shape[1]]), - 'offset': np.array([width[0], height[0]]) - } - - return slice_data - - def get_region_description(self, region_idx): - struct_idx = np.where(self.allen['id'] == region_idx)[0][0] - # Haven't yet incorporated how to have region descriptions when not on Alyx - # For now always have this as blank - description = '' - region_lookup = self.allen['acronym'][struct_idx] + ': ' + self.allen['name'][struct_idx] - - if region_lookup == 'void: void': - region_lookup = 'root: root' - - if not description: - description = region_lookup + '\nNo information available for this region' - else: - description = region_lookup + '\n' + description - - return description, region_lookup - - def upload_data(self, feature, track, xyz_channels): - - brain_regions = self.brain_atlas.regions.get(self.brain_atlas.get_labels - (xyz_channels)) - brain_regions['xyz'] = xyz_channels - brain_regions['lateral'] = self.chn_coords[:, 0] - brain_regions['axial'] = self.chn_coords[:, 1] - assert np.unique([len(brain_regions[k]) for k in brain_regions]).size == 1 - channel_dict = self.create_channel_dict(brain_regions) - bregma = atlas.ALLEN_CCF_LANDMARKS_MLAPDV_UM['bregma'].tolist() - origin = {'origin': {'bregma': bregma}} - channel_dict.update(origin) - # Save the channel locations - chan_loc_filename = 'channel_locations.json' if self.n_shanks == 1 else \ - f'channel_locations_shank{self.shank_idx + 1}.json' - - with open(self.folder_path.joinpath(chan_loc_filename), "w") as f: - json.dump(channel_dict, f, indent=2, separators=(',', ': ')) - original_json = self.alignments - date = datetime.now().replace(microsecond=0).isoformat() - data = {date: [feature.tolist(), track.tolist()]} - if original_json: - original_json.update(data) - else: - original_json = data - # Save the new alignment - prev_align_filename = 'prev_alignments.json' if self.n_shanks == 1 else \ - f'prev_alignments_shank{self.shank_idx + 1}.json' - with open(self.folder_path.joinpath(prev_align_filename), "w") as f: - json.dump(original_json, f, indent=2, separators=(',', ': ')) - - @staticmethod - def create_channel_dict(brain_regions): - """ - Create channel dictionary in form to write to json file - :param brain_regions: information about location of electrode channels in brain atlas - :type brain_regions: Bunch - :return channel_dict: - :type channel_dict: dictionary of dictionaries - """ - channel_dict = {} - for i in np.arange(brain_regions.id.size): - channel = { - 'x': np.float64(brain_regions.xyz[i, 0] * 1e6), - 'y': np.float64(brain_regions.xyz[i, 1] * 1e6), - 'z': np.float64(brain_regions.xyz[i, 2] * 1e6), - 'axial': np.float64(brain_regions.axial[i]), - 'lateral': np.float64(brain_regions.lateral[i]), - 'brain_region_id': int(brain_regions.id[i]), - 'brain_region': brain_regions.acronym[i] - } - data = {'channel_' + str(i): channel} - channel_dict.update(data) - - return channel_dict diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/load_histology.py b/PythonScripts/iblapps-master/atlaselectrophysiology/load_histology.py deleted file mode 100644 index 8f57c48a..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/load_histology.py +++ /dev/null @@ -1,83 +0,0 @@ - -from pathlib import Path -import requests -import re -from one import params -from one.webclient import http_download_file -import SimpleITK as sitk - - -def download_histology_data(subject, lab): - - if lab == 'hoferlab': - lab_temp = 'mrsicflogellab' - elif lab == 'churchlandlab_ucla': - lab_temp = 'churchlandlab' - else: - lab_temp = lab - - par = params.get() - - try: - FLAT_IRON_HIST_REL_PATH = Path('histology', lab_temp, subject, - 'downsampledStacks_25', 'sample2ARA') - baseurl = (par.HTTP_DATA_SERVER + '/' + '/'.join(FLAT_IRON_HIST_REL_PATH.parts)) - r = requests.get(baseurl, auth=(par.HTTP_DATA_SERVER_LOGIN, par.HTTP_DATA_SERVER_PWD)) - r.raise_for_status() - except Exception as err: - print(err) - try: - subject_rem = subject.replace("_", "") - FLAT_IRON_HIST_REL_PATH = Path('histology', lab_temp, subject_rem, - 'downsampledStacks_25', 'sample2ARA') - baseurl = (par.HTTP_DATA_SERVER + '/' + '/'.join(FLAT_IRON_HIST_REL_PATH.parts)) - r = requests.get(baseurl, auth=(par.HTTP_DATA_SERVER_LOGIN, par.HTTP_DATA_SERVER_PWD)) - r.raise_for_status() - except Exception as err: - print(err) - path_to_nrrd = None - return path_to_nrrd - - tif_files = [] - for line in r.text.splitlines(): - result = re.findall('href="(.*).tif"', line) - if result: - tif_files.append(result[0] + '.tif') - - CACHE_DIR = params.get_cache_dir().joinpath(lab, 'Subjects', subject, 'histology') - CACHE_DIR.mkdir(exist_ok=True, parents=True) - path_to_files = [] - for file in tif_files: - path_to_image = Path(CACHE_DIR, file) - if not path_to_image.exists(): - url = (baseurl + '/' + file) - http_download_file(url, target_dir=CACHE_DIR, - username=par.HTTP_DATA_SERVER_LOGIN, - password=par.HTTP_DATA_SERVER_PWD) - - path_to_nrrd = tif2nrrd(path_to_image) - path_to_files.append(path_to_nrrd) - - if len(path_to_files) > 3: - path_to_files = path_to_files[1:3] - - return path_to_files - - -def tif2nrrd(path_to_image): - path_to_nrrd = Path(path_to_image).with_suffix('.nrrd') - if not path_to_nrrd.exists(): - reader = sitk.ImageFileReader() - reader.SetImageIO("TIFFImageIO") - reader.SetFileName(str(path_to_image)) - img = reader.Execute() - - new_img = sitk.PermuteAxes(img, [2, 1, 0]) - new_img = sitk.Flip(new_img, [True, False, False]) - new_img.SetSpacing([1, 1, 1]) - writer = sitk.ImageFileWriter() - writer.SetImageIO("NrrdImageIO") - writer.SetFileName(str(path_to_nrrd)) - writer.Execute(new_img) - - return path_to_nrrd diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/plot_data.py b/PythonScripts/iblapps-master/atlaselectrophysiology/plot_data.py deleted file mode 100644 index 0da75e88..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/plot_data.py +++ /dev/null @@ -1,754 +0,0 @@ -from matplotlib import cm -from pathlib import Path -import numpy as np -import one.alf as alf -from brainbox.processing import bincount2D -from brainbox.io.spikeglx import stream -from brainbox.population.decode import xcorr -from brainbox.task import passive -from neurodsp import voltage -import scipy -from PyQt5 import QtGui - -BNK_SIZE = 10 -AUTOCORR_BIN_SIZE = 0.25 / 1000 -AUTOCORR_WIN_SIZE = 10 / 1000 - -FS = 30000 -np.seterr(divide='ignore', invalid='ignore') - - -class PlotData: - def __init__(self, probe_path, ephys_path, alf_path, shank_idx): - - self.probe_path = probe_path - self.ephys_path = ephys_path - self.alf_path = alf_path - - channels = alf.io.load_object(self.probe_path, 'channels') - self.chn_coords_all = channels['localCoordinates'] - self.chn_ind_all = channels['rawInd'].astype(int) - - self.chn_min = np.min(self.chn_coords_all[:, 1]) - self.chn_max = np.max(self.chn_coords_all[:, 1]) - self.chn_diff = np.min(np.abs(np.diff(np.unique(self.chn_coords_all[:, 1])))) - - self.chn_full = np.arange(self.chn_min, self.chn_max + self.chn_diff, self.chn_diff) - - chn_x = np.unique(self.chn_coords_all[:, 0]) - chn_x_diff = np.diff(chn_x) - n_shanks = np.sum(chn_x_diff > 100) + 1 - - if n_shanks > 1: - shanks = {} - for iShank in range(n_shanks): - shanks[iShank] = [chn_x[iShank * 2], chn_x[(iShank * 2) + 1]] - - shank_chns = np.bitwise_and(self.chn_coords_all[:, 0] >= shanks[shank_idx][0], - self.chn_coords_all[:, 0] <= shanks[shank_idx][1]) - self.chn_coords = self.chn_coords_all[shank_chns, :] - self.chn_ind = self.chn_ind_all[shank_chns] - else: - self.chn_coords = self.chn_coords_all - self.chn_ind = self.chn_ind_all - - self.N_BNK = len(np.unique(self.chn_coords[:, 0])) - self.idx_full = np.where(np.isin(self.chn_full, self.chn_coords[:, 1]))[0] - - # See if spike data is available - try: - self.spikes = alf.io.load_object(self.probe_path, 'spikes') - self.max_spike_time = np.max(self.spikes['times']) - self.spike_data_status = True - except Exception: - print('spike data was not found, some plots will not display') - self.spike_data_status = False - - try: - self.clusters = alf.io.load_object(self.probe_path, 'clusters') - shank_spikes = np.isin(self.chn_ind_all[self.clusters.channels[self.spikes.clusters]], - self.chn_ind) - for key in self.spikes.keys(): - self.spikes[key] = self.spikes[key][shank_spikes] - - self.filter_units('all') - self.cluster_data_status = True - self.compute_timescales() - - except Exception as err: - print(err) - print('cluster data was not found, some plots will not display') - self.cluster_data_status = False - - try: - lfp_spectrum = alf.io.load_object(self.ephys_path, 'ephysSpectralDensityLF', - namespace='iblqc') - if len(lfp_spectrum) == 2: - self.lfp_freq = lfp_spectrum.get('freqs') - self.lfp_power = lfp_spectrum.get('power', []) - if not np.any(self.lfp_power): - self.lfp_power = lfp_spectrum.get('amps') - self.lfp_data_status = True - else: - print('lfp data was not found, some plots will not display') - self.lfp_data_status = False - except Exception: - print('lfp data was not found, some plots will not display') - self.lfp_data_status = False - - try: - print(self.alf_path) - rf_map_times = alf.io.load_object(self.alf_path, object='passiveRFM', - namespace='ibl') - # This needs to go into brainbox!! - rf_map_frames_path = (self.alf_path.parent. - joinpath('raw_passive_data', '_iblrig_RFMapStim.raw.bin')) - rf_map_frames = np.fromfile(rf_map_frames_path, dtype="uint8") - y_pix, x_pix = 15, 15 - frames = np.transpose(np.reshape(rf_map_frames, [y_pix, x_pix, -1], order="F"), - [2, 1, 0]) - - self.rf_map = dict() - self.rf_map['times'] = rf_map_times['times'] - self.rf_map['frames'] = frames - if len(self.rf_map) == 2: - self.rfmap_data_status = True - else: - print('rfmap data was not found, some plots will not display') - self.rfmap_data_status = False - except Exception as err: - print(err) - print('rfmp data was not found, some plots will not display') - self.rfmap_data_status = False - - try: - self.aud_stim = alf.io.load_object(self.alf_path, object='passiveStims', - namespace='ibl') - if len(self.aud_stim) > 0: - self.passive_data_status = True - except Exception as err: - print(err) - print('passive stim data was not found, some plots will not display') - self.passive_data_status = False - - try: - gabor = alf.io.load_object(self.alf_path, object='passiveGabor', - namespace='ibl') - self.vis_stim = dict() - self.vis_stim['leftGabor'] = gabor['start'][(gabor['position'] == 35) & - (gabor['contrast'] > 0.1)] - self.vis_stim['rightGabor'] = gabor['start'][(gabor['position'] == -35) & - (gabor['contrast'] > 0.1)] - self.gabor_data_status = True - except Exception as err: - print(err) - print('passive gabor data was not found, some plots will not display') - self.gabor_data_status = False - - def filter_units(self, type): - if type == 'all': - self.spike_idx = np.arange(self.spikes['clusters'].size) - - elif type == 'KS good': - clust = np.where(self.clusters.metrics.ks2_label == 'good') - self.spike_idx = np.where(np.isin(self.spikes['clusters'], clust))[0] - - elif type == 'KS mua': - clust = np.where(self.clusters.metrics.ks2_label == 'mua') - self.spike_idx = np.where(np.isin(self.spikes['clusters'], clust))[0] - - elif type == 'IBL good': - try: - clust = np.where(self.clusters.metrics.label == 1) - self.spike_idx = np.where(np.isin(self.spikes['clusters'], clust))[0] - except Exception: - print('IBL metrics not implemented will return ks good units instead') - clust = np.where(self.clusters.metrics.ks2_label == 'good') - self.spike_idx = np.where(np.isin(self.spikes['clusters'], clust))[0] - - # Filter for nans in depths and also in amps - self.kp_idx = np.where(~np.isnan(self.spikes['depths'][self.spike_idx]) & - ~np.isnan(self.spikes['amps'][self.spike_idx]))[0] - -# Plots that require spike and cluster data - def get_depth_data_scatter(self): - if not self.spike_data_status: - data_scatter = None - return data_scatter - else: - A_BIN = 10 - amp_range = np.quantile(self.spikes['amps'][self.spike_idx][self.kp_idx], [0, 0.9]) - amp_bins = np.linspace(amp_range[0], amp_range[1], A_BIN) - colour_bin = np.linspace(0.0, 1.0, A_BIN + 1) - colours = (cm.get_cmap('BuPu')(colour_bin)[np.newaxis, :, :3][0]) * 255 - spikes_colours = np.empty(self.spikes['amps'][self.spike_idx][self.kp_idx].size, - dtype=object) - spikes_size = np.empty(self.spikes['amps'][self.spike_idx][self.kp_idx].size) - for iA in range(amp_bins.size): - if iA == (amp_bins.size - 1): - idx = np.where((self.spikes['amps'][self.spike_idx][self.kp_idx] > - amp_bins[iA]))[0] - # Make saturated spikes a very dark purple - spikes_colours[idx] = QtGui.QColor('#400080') - else: - idx = np.where((self.spikes['amps'][self.spike_idx][self.kp_idx] > - amp_bins[iA]) & - (self.spikes['amps'][self.spike_idx][self.kp_idx] <= - amp_bins[iA + 1]))[0] - - spikes_colours[idx] = QtGui.QColor(*colours[iA]) - - spikes_size[idx] = iA / (A_BIN / 4) - - data_scatter = { - 'x': self.spikes['times'][self.spike_idx][self.kp_idx][0:-1:100], - 'y': self.spikes['depths'][self.spike_idx][self.kp_idx][0:-1:100], - 'levels': amp_range * 1e6, - 'colours': spikes_colours[0:-1:100], - 'pen': None, - 'size': spikes_size[0:-1:100], - 'symbol': np.array('o'), - 'xrange': np.array([np.min(self.spikes['times'][self.spike_idx][self.kp_idx] - [0:-1:100]), - np.max(self.spikes['times'][self.spike_idx][self.kp_idx] - [0:-1:100])]), - 'xaxis': 'Time (s)', - 'title': 'Amplitude (uV)', - 'cmap': 'BuPu', - 'cluster': False - } - - return data_scatter - - def get_fr_p2t_data_scatter(self): - if not self.spike_data_status: - data_fr_scatter = None - data_p2t_scatter = None - data_amp_scatter = None - return data_fr_scatter, data_p2t_scatter, data_amp_scatter - else: - (clu, - spike_depths, - spike_amps, - n_spikes) = self.compute_spike_average((self.spikes['clusters'][self.spike_idx] - [self.kp_idx]), (self.spikes['depths'] - [self.spike_idx][self.kp_idx]), - (self.spikes['amps'][self.spike_idx] - [self.kp_idx])) - spike_amps = spike_amps * 1e6 - fr = n_spikes / np.max(self.spikes['times']) - fr_levels = np.quantile(fr, [0, 1]) - - data_fr_scatter = { - 'x': spike_amps, - 'y': spike_depths, - 'colours': fr, - 'pen': 'k', - 'size': np.array(8), - 'symbol': np.array('o'), - 'levels': fr_levels, - 'xrange': np.array([0.9 * np.min(spike_amps), - 1.1 * np.max(spike_amps)]), - 'xaxis': 'Amplitude (uV)', - 'title': 'Firing Rate (Sp/s)', - 'cmap': 'hot', - 'cluster': True - } - - p2t = self.clusters['peakToTrough'][clu] - - # Define the p2t levels so always same colourbar across sessions - p2t_levels = [-1.5, 1.5] - data_p2t_scatter = { - 'x': spike_amps, - 'y': spike_depths, - - 'colours': p2t, - 'pen': 'k', - 'size': np.array(8), - 'symbol': np.array('o'), - 'levels': p2t_levels, - 'xrange': np.array([0.9 * np.min(spike_amps), - 1.1 * np.max(spike_amps)]), - 'xaxis': 'Amplitude (uV)', - 'title': 'Peak to Trough duration (ms)', - 'cmap': 'RdYlGn', - 'cluster': True - } - - spike_amps_levels = np.quantile(spike_amps, [0, 1]) - - data_amp_scatter = { - 'x': fr, - 'y': spike_depths, - - 'colours': spike_amps, - 'pen': 'k', - 'size': np.array(8), - 'symbol': np.array('o'), - 'levels': spike_amps_levels, - 'xrange': np.array([0.9 * np.min(fr), - 1.1 * np.max(fr)]), - 'xaxis': 'Firing Rate (Sp/s)', - 'title': 'Amplitude (uV)', - 'cmap': 'magma', - 'cluster': True - } - - return data_fr_scatter, data_p2t_scatter, data_amp_scatter - - def get_fr_img(self): - if not self.spike_data_status: - data_img = None - return data_img - else: - T_BIN = 0.05 - D_BIN = 5 - chn_min = np.min(np.r_[self.chn_min, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - chn_max = np.max(np.r_[self.chn_max, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - n, times, depths = bincount2D(self.spikes['times'][self.spike_idx][self.kp_idx], - self.spikes['depths'][self.spike_idx][self.kp_idx], - T_BIN, D_BIN, ylim=[chn_min, chn_max]) - img = n.T / T_BIN - xscale = (times[-1] - times[0]) / img.shape[0] - yscale = (depths[-1] - depths[0]) / img.shape[1] - - data_img = { - 'img': img, - 'scale': np.array([xscale, yscale]), - 'levels': np.quantile(np.mean(img, axis=0), [0, 1]), - 'offset': np.array([0, 0]), - 'xrange': np.array([times[0], times[-1]]), - 'xaxis': 'Time (s)', - 'cmap': 'binary', - 'title': 'Firing Rate' - } - - return data_img - - def get_fr_amp_data_line(self): - if not self.spike_data_status: - data_fr_line = None - data_amp_line = None - return data_fr_line, data_amp_line - else: - T_BIN = np.max(self.spikes['times']) - D_BIN = 10 - chn_min = np.min(np.r_[self.chn_min, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - chn_max = np.max(np.r_[self.chn_max, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - nspikes, times, depths = bincount2D(self.spikes['times'][self.spike_idx][self.kp_idx], - self.spikes['depths'][self.spike_idx][self.kp_idx], - T_BIN, D_BIN, - ylim=[chn_min, chn_max]) - - amp, times, depths = bincount2D(self.spikes['amps'][self.spike_idx][self.kp_idx], - self.spikes['depths'][self.spike_idx][self.kp_idx], - T_BIN, D_BIN, ylim=[chn_min, chn_max], - weights=self.spikes['amps'][self.spike_idx] - [self.kp_idx]) - mean_fr = nspikes[:, 0] / T_BIN - mean_amp = np.divide(amp[:, 0], nspikes[:, 0]) * 1e6 - mean_amp[np.isnan(mean_amp)] = 0 - remove_bins = np.where(nspikes[:, 0] < 50)[0] - mean_amp[remove_bins] = 0 - - data_fr_line = { - 'x': mean_fr, - 'y': depths, - 'xrange': np.array([0, np.max(mean_fr)]), - 'xaxis': 'Firing Rate (Sp/s)' - } - - data_amp_line = { - 'x': mean_amp, - 'y': depths, - 'xrange': np.array([0, np.max(mean_amp)]), - - 'xaxis': 'Amplitude (uV)' - } - - return data_fr_line, data_amp_line - - def get_correlation_data_img(self): - if not self.spike_data_status: - data_img = None - return data_img - else: - T_BIN = 0.05 - D_BIN = 40 - chn_min = np.min(np.r_[self.chn_min, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - chn_max = np.max(np.r_[self.chn_max, self.spikes['depths'][self.spike_idx][self.kp_idx]]) - R, times, depths = bincount2D(self.spikes['times'][self.spike_idx][self.kp_idx], - self.spikes['depths'][self.spike_idx][self.kp_idx], - T_BIN, D_BIN, ylim=[chn_min, chn_max]) - corr = np.corrcoef(R) - corr[np.isnan(corr)] = 0 - scale = (np.max(depths) - np.min(depths)) / corr.shape[0] - data_img = { - 'img': corr, - 'scale': np.array([scale, scale]), - 'levels': np.array([np.min(corr), np.max(corr)]), - 'offset': np.array([0, 0]), - 'xrange': np.array([self.chn_min, self.chn_max]), - 'cmap': 'viridis', - 'title': 'Correlation', - 'xaxis': 'Distance from probe tip (um)' - } - return data_img - - def get_rms_data_img_probe(self, format): - # Finds channels that are at equivalent depth on probe and averages rms values for each - # time point at same depth togehter - try: - rms_amps = alf.io.load_file_content(Path(self.ephys_path, '_iblqc_ephysTimeRms' + - format + '.rms.npy')) - except Exception: - try: - rms_amps = alf.io.load_file_content(Path(self.ephys_path, '_iblqc_ephysTimeRms' + - format + '.amps.npy')) - except Exception: - print('rms data was not found, some plots will not display') - data_img = None - data_probe = None - return data_img, data_probe - - try: - rms_times = alf.io.load_file_content(Path(self.ephys_path, '_iblqc_ephysTimeRms' + - format + '.timestamps.npy')) - xaxis = 'Time (s)' - except Exception: - rms_times = np.array([0, rms_amps.shape[0]]) - xaxis = 'Time samples' - - _rms = np.take(rms_amps, self.chn_ind, axis=1) - _, self.chn_depth, chn_count = np.unique(self.chn_coords[:, 1], return_index=True, - return_counts=True) - self.chn_depth_eq = np.copy(self.chn_depth) - self.chn_depth_eq[np.where(chn_count == 2)] += 1 - - def avg_chn_depth(a): - return(np.mean([a[self.chn_depth], a[self.chn_depth_eq]], axis=0)) - - def get_median(a): - return(np.median(a)) - - def median_subtract(a): - return(a - np.median(a)) - img = np.apply_along_axis(avg_chn_depth, 1, _rms * 1e6) - median = np.mean(np.apply_along_axis(get_median, 1, img)) - # Medium subtract to remove bands, but add back average median so values make sense - img = np.apply_along_axis(median_subtract, 1, img) + median - - img_full = np.full((img.shape[0], self.chn_full.shape[0]), np.nan) - img_full[:, self.idx_full] = img - - levels = np.quantile(img, [0.1, 0.9]) - xscale = (rms_times[-1] - rms_times[0]) / img_full.shape[0] - yscale = (self.chn_max - self.chn_min) / img_full.shape[1] - - if format == 'AP': - cmap = 'plasma' - else: - cmap = 'inferno' - - data_img = { - 'img': img_full, - 'scale': np.array([xscale, yscale]), - 'levels': levels, - 'offset': np.array([0, 0]), - 'cmap': cmap, - 'xrange': np.array([rms_times[0], rms_times[-1]]), - 'xaxis': xaxis, - 'title': format + ' RMS (uV)' - } - - # Probe data - rms_avg = (np.mean(rms_amps, axis=0)[self.chn_ind]) * 1e6 - probe_levels = np.quantile(rms_avg, [0.1, 0.9]) - probe_img, probe_scale, probe_offset = self.arrange_channels2banks(rms_avg) - - data_probe = { - 'img': probe_img, - 'scale': probe_scale, - 'offset': probe_offset, - 'levels': probe_levels, - 'cmap': cmap, - 'xrange': np.array([0 * BNK_SIZE, (self.N_BNK) * BNK_SIZE]), - 'title': format + ' RMS (uV)' - } - - return data_img, data_probe - - # only for IBL sorry - def get_raw_data_image(self, pid, t0=(1000, 2000, 3000), one=None): - - def gain2level(gain): - return 10 ** (gain / 20) * 4 * np.array([-1, 1]) - data_img = dict() - - times = [t for t in t0 if t < self.max_spike_time] - - for t in times: - - sr, t = stream(pid, t, one=one) - raw = sr[:, :-sr.nsync].T - channel_labels, channel_features = voltage.detect_bad_channels(raw, sr.fs) - raw = voltage.destripe(raw, fs=sr.fs, channel_labels=channel_labels) - raw_image = raw[:, int((450 / 1e3) * sr.fs):int((500 / 1e3) * sr.fs)].T - x_range = np.array([0, raw_image.shape[0] - 1]) / sr.fs * 1e3 - levels = gain2level(-90) - xscale = (x_range[1] - x_range[0]) / raw_image.shape[0] - yscale = (self.chn_max - self.chn_min) / raw_image.shape[1] - - data_raw = { - 'img': raw_image, - 'scale': np.array([xscale, yscale]), - 'levels': levels, - 'offset': np.array([0, 0]), - 'cmap': 'bone', - 'xrange': x_range, - 'xaxis': 'Time (ms)', - 'title': 'Power (uV)' - } - data_img[f'Raw data t={t}'] = data_raw - - return data_img - - def get_lfp_spectrum_data(self): - freq_bands = np.vstack(([0, 4], [4, 10], [10, 30], [30, 80], [80, 200])) - data_probe = {} - if not self.lfp_data_status: - data_img = None - for freq in freq_bands: - lfp_band_data = {f"{freq[0]} - {freq[1]} Hz": None} - data_probe.update(lfp_band_data) - - return data_img, data_probe - else: - # Power spectrum image - freq_range = [0, 300] - freq_idx = np.where((self.lfp_freq >= freq_range[0]) & - (self.lfp_freq < freq_range[1]))[0] - _lfp = np.take(self.lfp_power[freq_idx], self.chn_ind, axis=1) - _lfp_dB = 10 * np.log10(_lfp) - _, self.chn_depth, chn_count = np.unique(self.chn_coords[:, 1], return_index=True, - return_counts=True) - self.chn_depth_eq = np.copy(self.chn_depth) - self.chn_depth_eq[np.where(chn_count == 2)] += 1 - - def avg_chn_depth(a): - return(np.mean([a[self.chn_depth], a[self.chn_depth_eq]], axis=0)) - - img = np.apply_along_axis(avg_chn_depth, 1, _lfp_dB) - img_full = np.full((img.shape[0], self.chn_full.shape[0]), np.nan) - img_full[:, self.idx_full] = img - - levels = np.quantile(img, [0.1, 0.9]) - xscale = (freq_range[-1] - freq_range[0]) / img_full.shape[0] - yscale = (self.chn_max - self.chn_min) / img_full.shape[1] - - data_img = { - 'img': img_full, - 'scale': np.array([xscale, yscale]), - 'levels': levels, - 'offset': np.array([0, 0]), - 'cmap': 'viridis', - 'xrange': np.array([freq_range[0], freq_range[-1]]), - 'xaxis': 'Frequency (Hz)', - 'title': 'PSD (dB)' - } - - # Power spectrum in bands on probe - for freq in freq_bands: - freq_idx = np.where((self.lfp_freq >= freq[0]) & (self.lfp_freq < freq[1]))[0] - lfp_avg = np.mean(self.lfp_power[freq_idx], axis=0)[self.chn_ind] - lfp_avg_dB = 10 * np.log10(lfp_avg) - probe_img, probe_scale, probe_offset = self.arrange_channels2banks(lfp_avg_dB) - probe_levels = np.quantile(lfp_avg_dB, [0.1, 0.9]) - - lfp_band_data = {f"{freq[0]} - {freq[1]} Hz": { - 'img': probe_img, - 'scale': probe_scale, - 'offset': probe_offset, - 'levels': probe_levels, - 'cmap': 'viridis', - 'xaxis': 'Time (s)', - 'xrange': np.array([0 * BNK_SIZE, (self.N_BNK) * BNK_SIZE]), - 'title': f"{freq[0]} - {freq[1]} Hz (dB)"} - } - data_probe.update(lfp_band_data) - - return data_img, data_probe - - def get_rfmap_data(self): - data_img = dict() - if not self.rfmap_data_status: - return data_img, None - else: - - (rf_map_times, rf_map_pos, - rf_stim_frames) = passive.get_on_off_times_and_positions(self.rf_map) - - rf_map, _ = \ - passive.get_rf_map_over_depth(rf_map_times, rf_map_pos, rf_stim_frames, - self.spikes['times'][self.spike_idx][self.kp_idx], - self.spikes['depths'][self.spike_idx][self.kp_idx], - d_bin=160) - rfs_svd = passive.get_svd_map(rf_map) - img = dict() - img['on'] = np.vstack(rfs_svd['on']) - img['off'] = np.vstack(rfs_svd['off']) - yscale = ((np.max(self.chn_coords[:, 1]) - np.min( - self.chn_coords[:, 1])) / img['on'].shape[0]) - xscale = 1 - levels = np.quantile(np.c_[img['on'], img['off']], [0, 1]) - - depths = np.linspace(0, 3840, len(rfs_svd['on']) + 1) - - sub_type = ['on', 'off'] - for sub in sub_type: - sub_data = {sub: { - 'img': [img[sub].T], - 'scale': [np.array([xscale, yscale])], - 'levels': levels, - 'offset': [np.array([0, 0])], - 'cmap': 'viridis', - 'xrange': np.array([0, 15]), - 'xaxis': 'Position', - 'title': 'rfmap (dB)'} - } - data_img.update(sub_data) - - return data_img, depths - - def get_passive_events(self): - stim_keys = ['valveOn', 'toneOn', 'noiseOn', 'leftGabor', 'rightGabor'] - data_img = dict() - if not self.passive_data_status and not self.gabor_data_status: - return data_img - elif not self.passive_data_status and self.gabor_data_status: - stim_types = ['leftGabor', 'rightGabor'] - stims = self.vis_stim - elif self.passive_data_status and not self.gabor_data_status: - stim_types = ['valveOn', 'toneOn', 'noiseOn'] - stims = {stim_type: self.aud_stim[stim_type] for stim_type in stim_types} - else: - stim_types = stim_keys - stims = {stim_type: self.aud_stim[stim_type] for stim_type in stim_types[0:3]} - stims.update(self.vis_stim) - - base_stim = 1 - pre_stim = 0.4 - post_stim = 1 - stim_events = passive.get_stim_aligned_activity(stims, self.spikes['times'][self.spike_idx] - [self.kp_idx], self.spikes['depths'] - [self.spike_idx][self.kp_idx], - pre_stim=pre_stim, post_stim=post_stim, - base_stim=base_stim) - - for stim_type, z_score in stim_events.items(): - xscale = (post_stim + pre_stim) / z_score.shape[1] - yscale = ((np.max(self.chn_coords[:, 1]) - np.min( - self.chn_coords[:, 1])) / z_score.shape[0]) - - levels = [-10, 10] - - stim_data = {stim_type: { - 'img': z_score.T, - 'scale': np.array([xscale, yscale]), - 'levels': levels, - 'offset': np.array([-1 * pre_stim, 0]), - 'cmap': 'bwr', - 'xrange': [-1 * pre_stim, post_stim], - 'xaxis': 'Time from Stim Onset (s)', - 'title': 'Firing rate (z score)'} - } - data_img.update(stim_data) - - return data_img - - def get_autocorr(self, clust_idx): - idx = np.where(self.spikes['clusters'] == self.clust_id[clust_idx])[0] - autocorr = xcorr(self.spikes['times'][idx], self.spikes['clusters'][idx], - AUTOCORR_BIN_SIZE, AUTOCORR_WIN_SIZE) - - return autocorr[0, 0, :], self.clust_id[clust_idx] - - def get_template_wf(self, clust_idx): - template_wf = (self.clusters['waveforms'][self.clust_id[clust_idx], :, 0]) - return template_wf * 1e6 - - def arrange_channels2banks(self, data): - bnk_data = [] - bnk_scale = np.empty((self.N_BNK, 2)) - bnk_offset = np.empty((self.N_BNK, 2)) - for iX, x in enumerate(np.unique(self.chn_coords[:, 0])): - bnk_idx = np.where(self.chn_coords[:, 0] == x)[0] - - bnk_ycoords = self.chn_coords[bnk_idx, 1] - bnk_diff = np.min(np.diff(bnk_ycoords)) - - # NP1.0 checkerboard - if bnk_diff != self.chn_diff: - bnk_full = np.arange(np.min(bnk_ycoords), np.max(bnk_ycoords) + bnk_diff, bnk_diff) - _bnk_vals = np.full((bnk_full.shape[0]), np.nan) - idx_full = np.where(np.isin(bnk_full, bnk_ycoords)) - _bnk_vals[idx_full] = data[bnk_idx] - - # Detect where the nans are, whether it is odd or even - _bnk_data = _bnk_vals[np.newaxis, :] - - _bnk_yscale = ((self.chn_max - - self.chn_min) / _bnk_data.shape[1]) - _bnk_xscale = BNK_SIZE / _bnk_data.shape[0] - - _bnk_yoffset = np.min(bnk_ycoords) - _bnk_xoffset = BNK_SIZE * iX - - else: # NP2.0 - _bnk_vals = np.full((self.chn_full.shape[0]), np.nan) - idx_full = np.where(np.isin(self.chn_full, bnk_ycoords)) - _bnk_vals[idx_full] = data[bnk_idx] - - _bnk_data = _bnk_vals[np.newaxis, :] - - _bnk_yscale = ((self.chn_max - - self.chn_min) / _bnk_data.shape[1]) - _bnk_xscale = BNK_SIZE / _bnk_data.shape[0] - _bnk_yoffset = 0 - _bnk_xoffset = BNK_SIZE * iX - - bnk_data.append(_bnk_data) - bnk_scale[iX, :] = np.array([_bnk_xscale, _bnk_yscale]) - bnk_offset[iX, :] = np.array([_bnk_xoffset, _bnk_yoffset]) - - return bnk_data, bnk_scale, bnk_offset - - def compute_spike_average(self, spike_clusters, spike_depth, spike_amp): - clust, inverse, counts = np.unique(spike_clusters, return_inverse=True, return_counts=True) - _spike_depth = scipy.sparse.csr_matrix((spike_depth, (inverse, - np.zeros(inverse.size, dtype=int)))) - _spike_amp = scipy.sparse.csr_matrix((spike_amp, (inverse, - np.zeros(inverse.size, dtype=int)))) - spike_depth_avg = np.ravel(_spike_depth.toarray()) / counts - spike_amp_avg = np.ravel(_spike_amp.toarray()) / counts - self.clust_id = clust - return clust, spike_depth_avg, spike_amp_avg, counts - - def compute_timescales(self): - self.t_autocorr = 1e3 * np.arange((AUTOCORR_WIN_SIZE / 2) - AUTOCORR_WIN_SIZE, - (AUTOCORR_WIN_SIZE / 2) + AUTOCORR_BIN_SIZE, - AUTOCORR_BIN_SIZE) - n_template = self.clusters['waveforms'][0, :, 0].size - self.t_template = 1e3 * (np.arange(n_template)) / FS - - def normalise_data(self, data, lquant=0, uquant=1): - levels = np.quantile(data, [lquant, uquant]) - if np.min(data) < 0: - data = data + np.abs(np.min(data)) - norm_data = data / np.max(data) - norm_levels = np.quantile(norm_data, [lquant, uquant]) - norm_data[np.where(norm_data < norm_levels[0])] = 0 - norm_data[np.where(norm_data > norm_levels[1])] = 1 - - return norm_data, levels diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/qc_table.py b/PythonScripts/iblapps-master/atlaselectrophysiology/qc_table.py deleted file mode 100644 index bee8f08a..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/qc_table.py +++ /dev/null @@ -1,15 +0,0 @@ -import datajoint as dj - -schema = dj.schema('group_shared_ephys') - - -@schema -class EphysQC(dj.Imported): - definition = """ - probe_insertion_uuid: uuid # probe insertion uuid - -> reference.LabMember # user name - --- - alignment_qc=null: enum('high', 'medium', 'low') # confidence in alignment - ephys_qc=null: enum('pass', 'critical', 'warning') # quality of ephys - ephys_qc_description=null: varchar(255) #Description for ephys_qc - """ diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/rendering.py b/PythonScripts/iblapps-master/atlaselectrophysiology/rendering.py deleted file mode 100644 index cbd71978..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/rendering.py +++ /dev/null @@ -1,99 +0,0 @@ -from pathlib import Path - -import numpy as np -import cv2 -import vtk -from matplotlib import pyplot as plt # noqa -import mayavi.mlab as mlab - - -def add_mesh(fig, obj_file, color=(1., 1., 1.), opacity=0.4): - """ - Adds a mesh object from an *.obj file to the mayavi figure - :param fig: mayavi figure - :param obj_file: full path to a local *.obj file - :param color: rgb tuple of floats between 0 and 1 - :param opacity: float between 0 and 1 - :return: vtk actor - """ - reader = vtk.vtkOBJReader() - reader.SetFileName(str(obj_file)) - reader.Update() - mapper = vtk.vtkPolyDataMapper() - mapper.SetInputConnection(reader.GetOutputPort()) - actor = vtk.vtkActor() - actor.SetMapper(mapper) - actor.GetProperty().SetOpacity(opacity) - actor.GetProperty().SetColor(color) - fig.scene.add_actor(actor) - fig.scene.render() - return mapper, actor - - -def figure(grid=False, **kwargs): - """ - Creates a mayavi figure with the brain atlas mesh - :return: mayavi figure - """ - fig = mlab.figure(bgcolor=(1, 1, 1), **kwargs) - # engine = mlab.get_engine() # Returns the running mayavi engine. - obj_file = Path(__file__).parent.joinpath("root.obj") - mapper, actor = add_mesh(fig, obj_file) - - if grid: - # https://vtk.org/Wiki/VTK/Examples/Python/Visualization/CubeAxesActor - cubeAxesActor = vtk.vtkCubeAxesActor() - cubeAxesActor.SetMapper(mapper) - cubeAxesActor.SetBounds(mapper.GetBounds()) - cubeAxesActor.SetCamera(fig.scene.renderer._vtk_obj.GetActiveCamera()) - cubeAxesActor.SetXTitle("AP (um)") - cubeAxesActor.SetYTitle("DV (um)") - cubeAxesActor.SetZTitle("ML (um)") - cubeAxesActor.GetTitleTextProperty(0).SetColor(1.0, 0.0, 0.0) - cubeAxesActor.GetLabelTextProperty(0).SetColor(1.0, 0.0, 0.0) - cubeAxesActor.GetTitleTextProperty(1).SetColor(0.0, 1.0, 0.0) - cubeAxesActor.GetLabelTextProperty(1).SetColor(0.0, 1.0, 0.0) - cubeAxesActor.GetTitleTextProperty(2).SetColor(0.0, 0.0, 1.0) - cubeAxesActor.GetLabelTextProperty(2).SetColor(0.0, 0.0, 1.0) - cubeAxesActor.DrawXGridlinesOn() - cubeAxesActor.DrawYGridlinesOn() - cubeAxesActor.DrawZGridlinesOn() - fig.scene.add_actor(cubeAxesActor) - - mlab.view(azimuth=180, elevation=0) - mlab.view(azimuth=210, elevation=210, reset_roll=False) - - return fig - - -def rotating_video(output_file, mfig, fps=12, secs=6): - # ffmpeg -i input.webm -pix_fmt rgb24 output.gif - # ffmpeg -i certification.webm -vf scale=640:-1 -r 10 -f image2pipe -vcodec ppm - | convert -delay 5 -loop 0 - certification.gif # noqa - - file_video = Path(output_file) - if file_video.suffix == '.avi': - fourcc = cv2.VideoWriter_fourcc(*'XVID') - elif file_video.suffix == '.webm': - fourcc = cv2.VideoWriter_fourcc(*'VP80') - else: - NotImplementedError(f"Extension {file_video.suffix} not supported") - - mlab.view(azimuth=180, elevation=0) - mfig.scene.render() - mfig.scene._lift() - frame = mlab.screenshot(figure=mfig, mode='rgb', antialiased=True) - w, h, _ = frame.shape - video = cv2.VideoWriter(str(file_video), fourcc, float(fps), (h, w)) - - # import time - for e in np.linspace(-180, 180, secs * fps): - # frame = np.random.randint(0, 256, (w, h, 3), dtype=np.uint8) - mlab.view(azimuth=0, elevation=e, reset_roll=False) - mfig.scene.render() - frame = mlab.screenshot(figure=mfig, mode='rgb', antialiased=True) - print(e, (h, w), frame.shape) - video.write(np.flip(frame, axis=2)) # bgr instead of rgb... - # time.sleep(0.05) - - video.release() - cv2.destroyAllWindows() diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/root.obj b/PythonScripts/iblapps-master/atlaselectrophysiology/root.obj deleted file mode 100644 index 784233c9..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/root.obj +++ /dev/null @@ -1,197292 +0,0 @@ -# wavefront obj file written by the visualization toolkit - - -v 7896.56 3384.15 503.781 -v 7777.44 3414.98 501.766 -v 7903 3480.7 492.858 -v 8032.95 3442.49 500.912 -v 7640.92 3486 503.112 -v 7764.88 3520.89 492.379 -v 8001.34 3514.57 491.054 -v 8074.36 3552.96 494.143 -v 8159.48 3526.6 503.367 -v 7669.63 3607.29 492.381 -v 7845.27 3639.25 485.729 -v 7983.04 3599.35 488.57 -v 7742.93 3626.77 488.26 -v 7572.5 3601.73 501.662 -v 8149.11 3622.35 498.444 -v 8078.31 3664.19 492.388 -v 7621.63 3695.96 493.536 -v 7723.33 3718.64 488.004 -v 7971.02 3723.37 486.934 -v 7554.82 3721.25 500.572 -v 8155.81 3761.01 500.571 -v 7829.55 3772.8 485.968 -v 8078.54 3744.76 493.918 -v 8094.98 3789.84 496.692 -v 7629.62 3782.91 495.031 -v 7728 3855.41 494.546 -v 8010.07 3823.55 494.046 -v 7574.77 3827.31 501.773 -v 7901.5 3817.59 488.196 -v 8088.96 3853.56 500.735 -v 7828.71 3862.83 491.906 -v 7907.74 3898.42 497.278 -v 7641.74 3870.88 500.15 -v 8024.87 3924.87 502.095 -v 7934.64 3984.14 503.913 -v 7730.86 3967.29 503.404 -v 7834.73 3941.76 499.955 -v 7842.93 3202.06 531.739 -v 7831.3 3310.08 516.615 -v 8018.04 3266.06 523.432 -v 7665.78 3378.28 514.586 -v 7676.34 3257.34 530.631 -v 7990.49 3352.41 511.417 -v 8137.91 3374.02 514.72 -v 8150.44 3262.29 529.059 -v 7549.95 3342.26 528.107 -v 8310.59 3347.76 528.278 -v 7556.56 3434.78 516.894 -v 8132.73 3442.22 506.893 -v 8211.48 3478.2 509.218 -v 8309.22 3469.72 518.58 -v 7448.96 3461.03 525.72 -v 8432.32 3455.51 528.982 -v 7511.91 3535.63 512.175 -v 8411.19 3532.45 523.845 -v 8274.73 3607.89 510.12 -v 8362.65 3593.13 518.197 -v 7414.92 3646.09 515.611 -v 7354.19 3611.82 526.586 -v 8202.18 3671.61 504.091 -v 7493.78 3666.79 507.751 -v 8433.38 3661.36 524.172 -v 7446.03 3741.34 509.74 -v 8353.08 3756.44 518.182 -v 7260.92 3730.11 530.615 -v 7342.01 3774.99 520.341 -v 7505.48 3817.31 506.12 -v 8243.62 3774.69 508.958 -v 7428.29 3813 512.024 -v 8444.64 3796.33 527.846 -v 7383.73 3918.7 518.399 -v 8272.09 3908.48 516.998 -v 7259.5 3874.68 529.776 -v 8375.93 3901.65 526.729 -v 7617.64 3926.27 504.744 -v 8147.1 3914.11 508.371 -v 7525.02 3947.59 510.903 -v 7321.02 3993.22 527.17 -v 8197.21 3985.88 516.712 -v 8290.15 4010.79 526.45 -v 7447.89 4021.36 520.381 -v 8018.62 4010.28 508.582 -v 7375.66 4074.65 529.75 -v 7700.55 4033.76 509.74 -v 7872.19 4022.29 507.505 -v 8148.94 4069.22 523.313 -v 7584.62 4052.62 516.392 -v 7535.54 4122.06 526.298 -v 7997.51 4089.7 518.329 -v 7689.49 4108.94 518.975 -v 7854.27 4130.55 522.265 -v 8019.08 4177.48 534.101 -v 7705.65 4179.18 529.264 -v 7843.6 4218.35 536.192 -v 8044.1 3150.71 544.75 -v 7842.84 3086.59 556.974 -v 8253.15 3215.85 543.196 -v 8178.01 3107.94 559.121 -v 7718.05 3156.1 546.885 -v 7605.13 3141.08 557.57 -v 7509.14 3196.35 558.374 -v 8389.02 3290.99 540.757 -v 8391.55 3199.61 556.202 -v 8502.56 3277.98 553.245 -v 7378.33 3301.24 558.823 -v 8467.1 3366.44 539.796 -v 7297.6 3400.87 555.486 -v 7332.73 3487.58 540.826 -v 8586.74 3368.45 551.268 -v 8539.09 3466.04 539.629 -v 8652.6 3490.21 550.656 -v 7207.61 3537.7 553.854 -v 8492.15 3580.5 530.902 -v 7267 3611.92 538.237 -v 8584.45 3611.64 540.853 -v 8671.29 3595.15 550.827 -v 7188.71 3662.88 544.944 -v 7066.74 3676.5 564.379 -v 8628.64 3741.24 548.947 -v 8515.59 3712.69 534.791 -v 7152.49 3797.23 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-v 9429.37 1610.59 1842.8 -v 9667.24 1769.61 1833.18 -v 5298.7 1987.3 1752.01 -v 10007.6 2038.85 1811.14 -v 4993.75 2125.74 1793.01 -v 4838.71 2262.99 1774.52 -v 10171.2 2239 1794.24 -v 4438.03 2587.4 1786.77 -v 10346.5 2653.91 1784.16 -v 10914.4 2656.48 1767.31 -v 10877.4 2714.27 1751.63 -v 11371.4 2618.76 1803.99 -v 11426 2691.74 1768.6 -v 11472.6 2701.44 1783.16 -v 10834.9 2734.21 1771.85 -v 11522.4 2726.78 1799.21 -v 10399.7 2838.06 1803.34 -v 11571.1 2859.98 1775.39 -v 11747.3 2850.48 1814.6 -v 10719.3 2977.76 1760.66 -v 11593.3 2947.75 1743.78 -v 11628.3 2922.33 1762.99 -v 11831.2 2928.63 1764.03 -v 11891.6 2953.02 1767.91 -v 4157.8 2939.35 1798.63 -v 4114.45 3094.63 1770.83 -v 10416.7 2955.31 1800.41 -v 10427.5 3114.71 1786.53 -v 10654.2 3310.15 1775.53 -v 12111.1 3241.99 1774.38 -v 10648.8 3413 1787.42 -v 10435.4 3396.22 1779.1 -v 10642.2 3505.74 1812.12 -v 12218.3 3533.04 1776.02 -v 10683.7 3633.4 1787.35 -v 10730 3673.17 1763.41 -v 3861.97 3662.32 1791.82 -v 10798.6 3755.76 1764.08 -v 11139.1 3781.61 1750.16 -v 10396.8 3805.4 1793.34 -v 11153.3 3800.88 1765 -v 3900.86 3782.71 1763.11 -v 10934.8 3830.42 1791.75 -v 11016.3 3822.8 1793.91 -v 11174.6 3813.18 1763.8 -v 11193.3 3863.88 1778.16 -v 3863.96 3868.05 1791.96 -v 10384.1 3888.82 1765.11 -v 12225.5 3868.13 1788.11 -v 11273.9 3932.18 1761.08 -v 12184.7 3943.47 1791.61 -v 10361.6 4013.29 1788.7 -v 11347.8 3989.76 1758.82 -v 11375.8 4040.17 1783.65 -v 12086.2 4028.63 1784.99 -v 10329.6 4157 1777.67 -v 11534.5 4111.12 1788.7 -v 11969.7 4101.85 1811.19 -v 11623.8 4119.04 1780.06 -v 11733.4 4122.75 1785.3 -v 3880.77 4147.36 1809.39 -v 10801.9 4390.39 1788.52 -v 10866.7 4409.04 1761.75 -v 11043.5 4439.29 1771.03 -v 3995 4492.37 1797.07 -v 10593.9 4509.49 1734.03 -v 11246.7 4523.83 1763.5 -v 10556.1 4521.07 1775.96 -v 10196.6 4660.49 1754.94 -v 10496.1 4634.1 1836.83 -v 10475.1 4749.39 1802.85 -v 10143.9 4839.53 1804.84 -v 11586.8 4821.34 1783.18 -v 10105.1 4919.01 1799.39 -v 4308.32 4993.11 1753.48 -v 4360.73 5048.1 1748.08 -v 10072.8 4977.39 1792.29 -v 10418.2 4875.84 1897.97 -v 11758.1 5076.77 1790.03 -v 10409.2 5146.3 1777.23 -v 4408.68 5252.56 1800.91 -v 11815.9 5258.64 1764.13 -v 4457.24 5324.06 1786.16 -v 10411.2 5304.9 1758.47 -v 4511.98 5400.66 1766.94 -v 11847.5 5392.81 1780.15 -v 10433.4 5476.71 1759.92 -v 4530.1 5503.67 1792.59 -v 4584.01 5566.61 1781.63 -v 4662.74 5624.48 1762.03 -v 11864.3 5581.62 1770.59 -v 10491 5722.88 1774.89 -v 11873.2 5690.59 1793.93 -v 4787.85 5740.07 1759.88 -v 9400.97 5972.15 1783.58 -v 10572.1 6048.03 1753.57 -v 11849.6 5933.28 1791.88 -v 11792 6180.77 1792.66 -v 9076.64 6260.11 1855.26 -v 11759.9 6240.35 1776.42 -v 10814.4 6479.33 1787.26 -v 8549.72 6647.36 1771.08 -v 6204.17 6682.02 1799.72 -v 11146.8 6648.98 1789.7 -v 8384.02 6789.65 1788.42 -v 8266.56 6863.06 1778.87 -v 8182.43 6913.29 1784.87 -v 8098.69 6940.1 1756.54 -v 6835.21 7090.52 1789.23 -v 6934.83 7152.73 1809.99 -v 7699.67 7087.39 1752.12 -v 7784.05 7089.43 1780.64 -v 7482.87 7135.28 1763.29 -v 7373.91 7146.02 1764.97 -v 7883.2 1270.48 1793.03 -v 8033.1 1246.56 1821.99 -v 8526.26 1252.83 1872.22 -v 8787.4 1344.74 1834.97 -v 8984.82 1406.22 1839.61 -v 9186.37 1499.55 1832.02 -v 5543.38 1809.24 1801.56 -v 9765.94 1819.97 1848.07 -v 10066.7 2066.83 1838.22 -v 4869.49 2150.91 1843.87 -v 10219.5 2287.15 1820.09 -v 4623.82 2365.45 1820.94 -v 10318.2 2500.52 1825.4 -v 4471.36 2479.51 1829.98 -v 10957.1 2597.56 1791.53 -v 11241.7 2534.68 1839.65 -v 10876.9 2652.63 1793.11 -v 11458.4 2628.82 1827.89 -v 10377.9 2709.02 1822.44 -v 10775.7 2784.97 1800.02 -v 11556.9 2780.72 1797.99 -v 10719.8 2865.77 1818.13 -v 11662.3 2861.98 1805.43 -v 11599.4 2904.37 1767.45 -v 11624.2 2890.03 1782.13 -v 11883.4 2887 1801.62 -v 11982.4 2953.51 1808.12 -v 12028.1 3043.54 1793.25 -v 10686.7 3058.52 1775.85 -v 12079.7 3138.57 1789.34 -v 4003.71 3179.65 1804.54 -v 10436.2 3215.27 1817.53 -v 10643.9 3223.07 1804.46 -v 12188.5 3316.13 1805.19 -v 12250 3426.93 1822.71 -v 3888.46 3461.8 1796.33 -v 10431.1 3497.08 1805.78 -v 10646 3620.53 1831.86 -v 12278.8 3612.9 1807.14 -v 12277.1 3693.01 1802.93 -v 10408.5 3694.29 1822.27 -v 10739.1 3739.71 1789.36 -v 10775.5 3770.9 1787.44 -v 12258.1 3780.89 1794.52 -v 11113.1 3817.56 1789.86 -v 10879.6 3844.2 1811.25 -v 11059.6 3847.44 1828.83 -v 11229.5 3901.27 1772.25 -v 12179.8 3994.73 1822.18 -v 12244.8 3968.79 1852.14 -v 10343.3 4099.22 1790.99 -v 12134.2 4012.67 1805.53 -v 12058.2 4061.63 1813.92 -v 11892 4117.46 1805.17 -v 10315.8 4225.36 1806.84 -v 10850.4 4388.35 1813.41 -v 10257.5 4475.64 1788.49 -v 10902.8 4401.25 1812.4 -v 11119.9 4458.1 1808.54 -v 10619.5 4433.43 1792.08 -v 10587.4 4453.17 1801.95 -v 11185.2 4489.08 1787.75 -v 4019.3 4606.24 1807.3 -v 10528.8 4554.01 1800.14 -v 11431.2 4658.45 1822.46 -v 11334.5 4583.55 1823.77 -v 11512.5 4738.44 1805.7 -v 4027.74 4752.98 1847.39 -v 10173.9 4754.38 1820.09 -v 4219.28 5004.9 1806.62 -v 4288.45 5071.79 1793.54 -v 11723.4 5000.4 1833.98 -v 4357.48 5124.21 1778.55 -v 11791.9 5165.99 1779.71 -v 9886.95 5307 1877.95 -v 10433.5 5360.24 1793.99 -v 11864.9 5506.94 1786.95 -v 4492.07 5496.52 1822.26 -v 10468.6 5457.27 1830.57 -v 9677.57 5702.46 1865.73 -v 9718.06 5648.13 1918.39 -v 4697.2 5727.75 1796.03 -v 10483.8 5659.05 1796.4 -v 4923.57 5887.48 1796.26 -v 10526.3 5840.26 1790.08 -v 5035.39 5947.38 1801.33 -v 11873.6 5818.15 1818.44 -v 10563.5 5952.17 1816.16 -v 5410.76 6094.66 1800.79 -v 9274.88 6106.66 1842.35 -v 5605.4 6192.6 1805.59 -v 10623.6 6172.5 1800.7 -v 5698.08 6248.71 1802.74 -v 5788.6 6326.98 1808.68 -v 11717.2 6307.27 1774.53 -v 5924.9 6434.83 1803.42 -v 10715.2 6361.55 1816.93 -v 6055.28 6527.43 1790.63 -v 10781.9 6430.09 1843.37 -v 10889 6533.83 1834.38 -v 11524.4 6512.53 1774.9 -v 8722.49 6503.9 1802.94 -v 10951.9 6579.39 1800.21 -v 8641.15 6585.44 1832.77 -v 11017.1 6616.13 1800.81 -v 11342.1 6628.39 1802.32 -v 8451.94 6744.07 1814.44 -v 6346.08 6781.92 1788.42 -v 8327.54 6817.84 1767.62 -v 6582.03 6930.65 1775.17 -v 8041.94 6999.46 1801.35 -v 7970.75 7029.17 1795.04 -v 6712.03 7041.86 1798.06 -v 7891.23 7087.59 1830.61 -v 7730.58 1231 1847.88 -v 7257.62 1288.3 1829.24 -v 7432.23 1248.3 1852.19 -v 8320.72 1233.73 1860.6 -v 6614.22 1399.99 1843.51 -v 5800.96 1632.52 1871.09 -v 9524.92 1658.8 1850.11 -v 5563.5 1724.71 1877.2 -v 9593.01 1667.99 1882.96 -v 9854.76 1795.96 1929.87 -v 5240.54 1945.63 1820.04 -v 5106.08 2008.4 1834.95 -v 10135.9 2100.7 1877.31 -v 10272.4 2350.57 1857.88 -v 11018.5 2550.35 1812.54 -v 11100.5 2550.44 1808.4 -v 4379.09 2547.05 1840.44 -v 4314.8 2640.1 1830.37 -v 10914.9 2600.48 1807.17 -v 10845.2 2649.53 1817.61 -v 10810.2 2666.93 1839.35 -v 4239.32 2772 1815.79 -v 11576.6 2710.22 1844.95 -v 11614.7 2869.17 1790.04 -v 11853.3 2828.78 1839.64 -v 11931.9 2905.29 1809.32 -v 10683 2949.21 1828.13 -v 4017.47 3062.12 1836.6 -v 12135.8 3055.48 1857.05 -v 10647.2 3112.12 1833.37 -v 3884.08 3245.55 1849.88 -v 10623.1 3327.94 1840.53 -v 10438.1 3299.21 1857.63 -v 3874.46 3368.57 1824.86 -v 10628.1 3428.33 1830.19 -v 3810.05 3454.26 1846.46 -v 3759.07 3535.32 1869.51 -v 10690.1 3735.69 1827.63 -v 10732.4 3785.96 1823.8 -v 11145.1 3842.05 1797.39 -v 12324.3 3824.5 1851.8 -v 10836.5 3840.23 1819.2 -v 10929.6 3885.13 1867.29 -v 11149.6 3890.03 1825.53 -v 11197.8 3936.92 1813.78 -v 3802.63 3983.92 1845.91 -v 11259.8 3990.07 1804.45 -v 11317.8 4055.75 1822.63 -v 11434.3 4083.85 1798.11 -v 11577.6 4141.41 1813.33 -v 11666.7 4150.5 1817.16 -v 11749.7 4146.05 1819.8 -v 11832.3 4140.68 1825.15 -v 3835.47 4286.05 1865.29 -v 10284.1 4374.71 1815.54 -v 10727.4 4374.81 1806.74 -v 10666 4405.05 1791.74 -v 10982.5 4419.43 1826.62 -v 11053.6 4438.8 1841.68 -v 10217.5 4614.86 1822.28 -v 11630.9 4867.38 1835.87 -v 10435.1 4858.91 1839.11 -v 4260.48 5103.09 1819.76 -v 4308.75 5134.82 1809.06 -v 4341.21 5200.18 1821.66 -v 10415.7 5038.35 1889.36 -v 10010.1 5104.44 1875.28 -v 4370.27 5211.46 1809.78 -v 11830 5241.31 1834.37 -v 10429 5224.69 1822.12 -v 11845.5 5328.62 1814.6 -v 4453.08 5445.79 1842.69 -v 9838.49 5400.05 1904.27 -v 11871.1 5443.49 1837.53 -v 9785.8 5520.18 1946.84 -v 10494.3 5599.53 1836.07 -v 11880.6 5578.4 1823.3 -v 4619.03 5667.56 1807.5 -v 10503.3 5689.06 1823.41 -v 4801.56 5820.85 1803.81 -v 5080.77 6065.03 1875.67 -v 5265.15 6097.83 1847.38 -v 11845.7 6024.38 1827.81 -v 11827.2 6111.11 1839.2 -v 10670.3 6277.79 1811.22 -v 9013.29 6319.04 1885.9 -v 11701.5 6356 1823.73 -v 8891.71 6399.62 1879.47 -v 11642.8 6421.93 1822.12 -v 8764.18 6480.17 1842.5 -v 10856.5 6489.13 1885.52 -v 11567.2 6496.89 1834.92 -v 11485.1 6551.39 1808.25 -v 6053.26 6695.21 1871.79 -v 8572.8 6646.97 1826.09 -v 11398.6 6615.26 1842.39 -v 11091.9 6641.04 1829 -v 11207.4 6657.89 1848.48 -v 11273.8 6644.94 1810.66 -v 6422.29 6885.85 1812.3 -v 8316.65 6846.38 1818.35 -v 8105.72 6970.81 1813.03 -v 7982.44 7055.85 1848.16 -v 7623.64 7162.72 1814.34 -v 7301.76 7213.02 1816.44 -v 7141.73 7217.19 1820.38 -v 7208.41 7229.4 1828.3 -v 7397.47 7218.69 1833.11 -v 8162.47 1231.58 1846.13 -v 7551.59 1203.16 1897.83 -v 8679.86 1280.69 1876.49 -v 6738.54 1357.82 1854.82 -v 6163.17 1504.85 1873.41 -v 9318.16 1503.89 1890.19 -v 5693.64 1654.7 1893.59 -v 9976.99 1943.11 1879.88 -v 10157.4 2173.43 1835.41 -v 4720.92 2187.73 1902.29 -v 10946.6 2539.58 1839.54 -v 11059.2 2480.58 1862.51 -v 11136.6 2433.43 1905 -v 10887.1 2574.89 1843.67 -v 11528.7 2603.7 1880.96 -v 11579.5 2635.24 1890.35 -v 4181.19 2695.84 1884.57 -v 11630.3 2821.31 1822.84 -v 11619.8 2752.69 1855.18 -v 10701.1 2776.83 1897.01 -v 10734.1 2753 1870.24 -v 11670.8 2793.91 1855.03 -v 11939.3 2850.7 1846.64 -v 11985.2 2883.33 1846.86 -v 12092.2 2962.01 1870.85 -v 10659.2 3005.43 1849.35 -v 12199.3 3189.69 1856.1 -v 10435.9 3021.03 1872.33 -v 10621.3 3224.31 1868.33 -v 12261.6 3328.73 1858.89 -v 10432.8 3444.73 1870.44 -v 12339.1 3539.25 1867.51 -v 10416.9 3587.65 1876.57 -v 10652 3698.43 1850.11 -v 12355.6 3654.27 1865.81 -v 12342.8 3745.39 1857.46 -v 10693.4 3801.38 1860.46 -v 10777.8 3818.51 1824.35 -v 11099.6 3860.44 1834.09 -v 11126.6 3868.16 1827.73 -v 10372.1 3942.74 1834.3 -v 12327.6 3875.1 1871.05 -v 12295.1 3928.62 1873.03 -v 12287.5 3968.84 1890.7 -v 11211.6 3986.05 1829.93 -v 11217.1 4035.51 1860.7 -v 12170 4038.85 1866.48 -v 10337.7 4118.86 1827.73 -v 11938.8 4129.1 1841.2 -v 11503.4 4149.53 1833.82 -v 11599 4166.68 1833.49 -v 11756.9 4163.13 1837.39 -v 10308.1 4255.91 1847.72 -v 10773.8 4356.23 1833.66 -v 10830 4361.4 1853.48 -v 10636.3 4377.7 1832.47 -v 10605 4394.45 1840.47 -v 10939.4 4403.13 1867.39 -v 10253.9 4494.55 1853.01 -v 11248.1 4522.97 1824.19 -v 11193 4493.67 1850.94 -v 10513.4 4555.47 1846.05 -v 11588 4818.64 1864.44 -v 4117.16 4922.38 1845.13 -v 10155.6 4819.67 1865.88 -v 10451.4 4729.52 1904.26 -v 10123.2 4898.55 1888.44 -v 11677.4 4929.59 1851.35 -v 10060.8 5010.81 1860.2 -v 10412.6 4877.27 1969.39 -v 11773.2 5089.28 1865.3 -v 11804.8 5165.97 1868.71 -v 4286.35 5193.07 1846.36 -v 4369.27 5254.67 1830.16 -v 9935.32 5236.54 1920.27 -v 10454.7 5288.6 1857.2 -v 4400.99 5377.84 1865 -v 9811.63 5460.51 1945.6 -v 4510.64 5593.1 1847.18 -v 9751.56 5597.52 1975.19 -v 4561.18 5675.6 1847.24 -v 11896.7 5671.62 1860.02 -v 11889.9 5754.11 1846.38 -v 9602.21 5836.21 1929.08 -v 10527.1 5770.16 1837.1 -v 4697.18 5807.81 1843.22 -v 4861.39 5914.22 1840.67 -v 9494.41 5933.09 1869.23 -v 10564.9 5874.83 1871.06 -v 4954.39 5975.67 1850.71 -v 9367.64 6036.06 1850.46 -v 10618.8 6083.03 1868.76 -v 5373.56 6149.98 1851.93 -v 10663.8 6218.86 1868.8 -v 5468.71 6199.61 1855.78 -v 11814.5 6177.97 1864.7 -v 5587.45 6276.08 1861.79 -v 11785.1 6246.18 1887.24 -v 11747.6 6279.97 1823.89 -v 5677.58 6301.62 1840.69 -v 10708.1 6304.32 1884.41 -v 5799.59 6420.32 1850.63 -v 5902.87 6529.23 1858.11 -v 11447.1 6586.45 1841.59 -v 6162.61 6733.88 1842.83 -v 8513.11 6700.92 1843.91 -v 11150.6 6652.41 1842.94 -v 6231.9 6799.84 1847.86 -v 8356.03 6826.72 1852.33 -v 8239.2 6901.73 1833.29 -v 6534.1 7004.54 1840.11 -v 8063.93 7017.67 1860.06 -v 6784.95 7173.35 1865.78 -v 7699.96 7174.53 1852.12 -v 7511.22 7175.64 1807.24 -v 7038.29 7231.84 1847.34 -v 7899.89 1211.63 1864.57 -v 8010.73 1179.44 1905.41 -v 8420.41 1175.99 1945.22 -v 7089.27 1237.22 1919.11 -v 6912.57 1296.55 1882.42 -v 8900.08 1305.21 1915.43 -v 6497.88 1355.05 1929.79 -v 9147.16 1397.62 1915.08 -v 5961.97 1560.36 1887.76 -v 9671.01 1636.65 1963.7 -v 5370.54 1811.18 1883.88 -v 9907.68 1868.98 1898.15 -v 5191.14 1891.1 1901.59 -v 10058.1 1992.12 1901.52 -v 4974.72 2039.38 1883.76 -v 10195.1 2142.46 1916.22 -v 10245 2184.79 1956.12 -v 4455.79 2383.65 1900.84 -v 4379.09 2466.86 1889.4 -v 11312.7 2478.09 1904.44 -v 11482 2544.7 1904.75 -v 10363.6 2545.68 1896.57 -v 10846.6 2595.71 1857.35 -v 10397.5 2691.42 1891.91 -v 10767.2 2661.25 1894.57 -v 4101.48 2825.18 1877.68 -v 10412.9 2827.31 1858.11 -v 11646.1 2781.62 1853.55 -v 11796.7 2784.29 1868.54 -v 11736.3 2782.94 1869.94 -v 11966.2 2778.43 1907.93 -v 10678.9 2830.3 1905.17 -v 10655.4 2888.74 1923.12 -v 12050.4 2884.36 1882.66 -v 10424.8 2927.67 1862.53 -v 3906.01 3115.48 1881.29 -v 10438.6 3089.29 1904.94 -v 3805.51 3282.23 1884.83 -v 3746.81 3347.45 1907.24 -v 12348.4 3410.78 1907.57 -v 10598.3 3554.53 1897.84 -v 3708 3628.75 1897.96 -v 10604.2 3642.92 1900.77 -v 3799.57 3731.6 1833.72 -v 10405 3695.84 1878.21 -v 10639.8 3760.52 1888.81 -v 10391.9 3802.76 1867.87 -v 10726.3 3834.55 1863.96 -v 10855.7 3876.76 1857.53 -v 10985.4 3891.37 1884.11 -v 11097.2 3892.81 1861.07 -v 12380.5 3847.14 1906.71 -v 11126.4 3937.01 1865.45 -v 11178.5 3982.41 1853.02 -v 12136.7 4077.5 1886.87 -v 11379.2 4135.33 1859.31 -v 12069.5 4104.24 1874.1 -v 12006.1 4119.53 1859.57 -v 11675.3 4183.03 1844.42 -v 11836 4166.91 1852.66 -v 3773.36 4156.57 1888.51 -v 11584.4 4207.77 1856.8 -v 10673.6 4346.76 1842.57 -v 10632.4 4340.43 1867.34 -v 10746.6 4333.31 1849.96 -v 10796.4 4319.48 1877.51 -v 3872.07 4433.32 1870.41 -v 10715.6 4308.9 1864.39 -v 10894.8 4383.85 1868.93 -v 10567.8 4423.05 1855.14 -v 11017.6 4428.92 1874.19 -v 11120.7 4470.47 1900.38 -v 11276.2 4545.3 1883.36 -v 3937.43 4589.3 1863.3 -v 11483 4712.39 1901.53 -v 10181.2 4737.32 1888.81 -v 11540.9 4772.89 1907.57 -v 11706.6 4970.41 1903.64 -v 4104.35 5062.69 1909.38 -v 4216.64 5138.28 1864.31 -v 10425.2 5123.98 1861.37 -v 9966.24 5192.25 1954.9 -v 4327.72 5289.61 1871.46 -v 4373.87 5305.75 1851.06 -v 10443.7 5222.68 1858.59 -v 11855 5331.46 1860.13 -v 11874.6 5395.44 1898.76 -v 11889.6 5486.26 1893.94 -v 4461.55 5545.45 1874.38 -v 11896.1 5565.77 1890.93 -v 10504.2 5493.88 1888.59 -v 10535.6 5691.62 1883.82 -v 4618.98 5760.98 1858.93 -v 9556.02 5846.7 1833.8 -v 4768.12 5899.21 1873.05 -v 11877.2 5902.96 1873.25 -v 11887.2 5814.04 1857.94 -v 11836.2 6127.19 1921.69 -v 9152.89 6254.72 1950.4 -v 5665.48 6369.74 1883.07 -v 11732.5 6327.76 1885.96 -v 10776.5 6377.08 1928.05 -v 11681.6 6394.12 1884.33 -v 8808.79 6463.35 1892.04 -v 11626.5 6451.57 1884.63 -v 8736.95 6528.58 1929.42 -v 11506.4 6552.77 1881.9 -v 10984.2 6580.43 1909.6 -v 8581.89 6648.04 1885.88 -v 11034.7 6616.23 1862.86 -v 11352.4 6634.99 1872.52 -v 11279.9 6650.89 1885.4 -v 8429.75 6774.37 1903.72 -v 6339.12 6925.71 1871.42 -v 8263.98 6898.48 1881.21 -v 8148.31 6966.72 1859.39 -v 6414.04 7001.19 1884.63 -v 6474.63 7059.63 1898.92 -v 6591.15 7134.04 1909.1 -v 7791.91 7140.78 1850.55 -v 6869.82 7219.89 1874 -v 6932.52 7242.89 1877.29 -v 7580.26 7230.61 1883.5 -v 7310.74 7258.48 1859.88 -v 7245.6 7271.65 1868.15 -v 7469.87 7251.23 1883.05 -v 7178.38 1193.47 1957.24 -v 7331.94 1197.15 1930.54 -v 6786.84 1288.53 1923.06 -v 8795.32 1236.72 1959.88 -v 6663.89 1332.94 1905.83 -v 8968.54 1301.23 1943.23 -v 6312.28 1424.04 1914.01 -v 9515.42 1570.45 1931.49 -v 5070.35 1940.45 1922.68 -v 4852.93 2055.62 1941.69 -v 4570.85 2264.44 1925.67 -v 10325.3 2349.26 1965.06 -v 10993.5 2469.27 1879.98 -v 10919.6 2481 1896.71 -v 4273.28 2556.04 1901.78 -v 10865 2538.5 1886.81 -v 11233.7 2420.14 1934.47 -v 11412.9 2492.84 1923.25 -v 10817.9 2557.88 1915.55 -v 11642 2646.65 1929.87 -v 11657 2693.35 1914.71 -v 11663.4 2759.79 1874.46 -v 11854.7 2751.14 1899.19 -v 11932.7 2731.61 1935.12 -v 12014.4 2823.26 1900.16 -v 3953.72 2945.11 1919.02 -v 12141.7 2860.18 1957.08 -v 10634.5 2999.79 1926.49 -v 10618.1 3128.28 1920.07 -v 3782.39 3175.1 1931.79 -v 10438.8 3152.95 1937.97 -v 10603.2 3329.7 1898.59 -v 10593.8 3447.28 1911.77 -v 10434.2 3320.59 1924.85 -v 10428.1 3452.19 1902.38 -v 12412 3712.15 1911.61 -v 3684.82 3820.1 1919.23 -v 10655.8 3800.14 1893.1 -v 10682.9 3842.45 1898.66 -v 10366.6 3942.69 1892.18 -v 3717.27 4027.08 1912.28 -v 10759.3 3873.05 1885.21 -v 11039.3 3902.32 1892.52 -v 10854.3 3915.84 1913.64 -v 10347.9 4045.37 1878.22 -v 12274.7 4001.49 1918.3 -v 11140.8 4002.1 1895.48 -v 11173.9 4050.45 1900.58 -v 11280.3 4099.2 1874.79 -v 10322.8 4151.92 1899.76 -v 11912.8 4152.3 1858.51 -v 11975.7 4138.95 1867.89 -v 11471.9 4193.97 1867.05 -v 11774.1 4194.78 1858.57 -v 11869.5 4185.37 1868.32 -v 11691.2 4214.61 1859.23 -v 11524.7 4229.04 1871.51 -v 11744.5 4241.29 1866.91 -v 11837.4 4238.92 1872.57 -v 11579.3 4273.5 1874.49 -v 11680 4275.95 1869.15 -v 11785.7 4288.46 1875.07 -v 11648.2 4346.56 1883.63 -v 11723.7 4333.88 1880.69 -v 10674.4 4287.87 1889.64 -v 10756 4291.31 1882.92 -v 10848.8 4337.9 1895.99 -v 10880.1 4360.18 1897.95 -v 10281.7 4362.04 1906.36 -v 10993.3 4420.22 1902.02 -v 10244 4516.18 1906.85 -v 10529 4444.9 1904.35 -v 10554.6 4340.54 1944.69 -v 11051.7 4444.57 1908.16 -v 10213.8 4623.92 1900.6 -v 10491.9 4526.71 1945.89 -v 11351.5 4601 1902.71 -v 11411.6 4651.82 1901.75 -v 3897.51 4689.85 1920.96 -v 3889.25 4816.78 1970.48 -v 4016.13 4925.19 1918.99 -v 11644.5 4889.35 1898.1 -v 10413.6 4964.52 1929.18 -v 11751.4 5046.23 1902.36 -v 4156.98 5184.29 1925.02 -v 10445.7 5192.27 1879.16 -v 4240.82 5257.97 1905.82 -v 11851.8 5299.39 1920.51 -v 4365.55 5336.42 1872.32 -v 10483.4 5357.81 1892.98 -v 11894.1 5490.33 1934.06 -v 10527.7 5598.5 1900.23 -v 4507.41 5686.65 1892.3 -v 4539.74 5750.5 1903.86 -v 11898.2 5766.65 1890.63 -v 4666 5887.71 1918.14 -v 4862.95 5983.45 1893.69 -v 4963.37 6060.82 1915.1 -v 10599.6 5962.72 1904.36 -v 11853.5 6039.6 1898.81 -v 9237.17 6181.68 1927.34 -v 5334.26 6213.53 1910.85 -v 5382.68 6280.75 1942.27 -v 5473.61 6332.82 1941.61 -v 5749.53 6529.18 1926.86 -v 11434.7 6598.88 1902.6 -v 11108.2 6634.77 1937.37 -v 11155.1 6649.54 1891.26 -v 8520.85 6702.63 1921.08 -v 8194.76 6948.16 1892.51 -v 7931.33 7107.83 1895.41 -v 7843.35 7151.95 1902.05 -v 7737.08 7194.15 1901.13 -v 7618.63 7247.44 1923.55 -v 7132.09 7274.33 1873.04 -v 7651.31 1141.92 1968.45 -v 7837.37 1142.03 1958.92 -v 8179.08 1135.21 1971.71 -v 8648.55 1193.2 1972.47 -v 6933.54 1251.63 1934.52 -v 8933.49 1233.53 2008.3 -v 9246.26 1368.95 1991.88 -v 9415.56 1460.42 1986.38 -v 5613.28 1632.36 1953.76 -v 5477.46 1677.75 1972.4 -v 9766.87 1671.76 1992.59 -v 10117.5 1991.99 1959.63 -v 10961.5 2416.7 1926.87 -v 11002.9 2400.8 1928.82 -v 11049.2 2414.24 1916.35 -v 4153.98 2619.64 1940.73 -v 10388 2567.19 1951.12 -v 10724.5 2634 1972.77 -v 11674 2722.49 1904.59 -v 11815.5 2730.15 1918.29 -v 11762.4 2711.93 1936.95 -v 10417.6 2748.46 1955.52 -v 11702.4 2748.23 1894.57 -v 12217 2976.66 1957.31 -v 12213.8 3089.68 1905.68 -v 12313.2 3122.23 1978.6 -v 10594.8 3240.11 1962.74 -v 12335.6 3276 1942.14 -v 3666.22 3425.54 1946.74 -v 3633.8 3533.34 1956.07 -v 12434.8 3618.13 1938.86 -v 10613.1 3729.26 1912.37 -v 10396.5 3727.42 1908.53 -v 12391.7 3791.15 1905.28 -v 10712.2 3859.83 1895.13 -v 11077.5 3918.48 1889.62 -v 12374.6 3888.07 1922.33 -v 10780.6 3915.43 1930.05 -v 10904.5 3925.45 1930.66 -v 11084.9 3956.79 1911.2 -v 12335.9 3963.64 1940.54 -v 11219.1 4105.79 1914.53 -v 12199 4070 1926.01 -v 3668.69 4133.65 1962.12 -v 12025.5 4143.3 1886.99 -v 11315.7 4172.23 1907.86 -v 11946.8 4167.95 1875.87 -v 11406.1 4207.05 1889.7 -v 11932 4220.93 1881.97 -v 11998.7 4190.78 1891.19 -v 10301.2 4240.57 1926.82 -v 11506.3 4269.94 1884.14 -v 11464.7 4297.13 1903.2 -v 12028.6 4235.67 1905.26 -v 11916 4288.69 1890.23 -v 12027.2 4287.29 1912.16 -v 3737.91 4345.65 1951.79 -v 10628.2 4301.75 1901.54 -v 10745.5 4240.36 1915.26 -v 11587 4341.79 1888.63 -v 11809.8 4347.66 1890 -v 11984 4327.81 1909.99 -v 10595.6 4338.37 1898.99 -v 10841.6 4298.04 1917.29 -v 10895 4338.17 1933.53 -v 11541.5 4373.49 1907.71 -v 11888.8 4389.75 1915.94 -v 11721.2 4400.73 1899.8 -v 11784.6 4433.79 1916.92 -v 10949.9 4399.48 1909.06 -v 11634.8 4434.99 1914.53 -v 10247.8 4472.3 1958.43 -v 3818.88 4547.12 1940.41 -v 11197.8 4511.4 1931.18 -v 11327.1 4595.54 1964.76 -v 10477 4640.19 1897.94 -v 10150.7 4822.66 1944.08 -v 4012.09 5018.82 1959.09 -v 10435.6 5086.4 1927.19 -v 11789.5 5122.39 1927.32 -v 11823.6 5206.41 1951.17 -v 10465.6 5250.22 1896.34 -v 4305.97 5344.64 1914.23 -v 4355.69 5365.12 1893.79 -v 4367.78 5438.29 1921.45 -v 4412.92 5518.55 1911.9 -v 10500.3 5424.27 1910.22 -v 4447.9 5630.85 1927.27 -v 11904.3 5669.2 1910.64 -v 9697.26 5704.16 1985.44 -v 9645.46 5795.08 1978.31 -v 9672.77 5752.32 2007.76 -v 10562 5792.51 1902.11 -v 4590.66 5820.25 1914.54 -v 9438.29 6016.66 1936.48 -v 5050.02 6161.82 1965.76 -v 9324.69 6124.34 1953.9 -v 10663.1 6147.83 1932.87 -v 11818 6182.47 1948.37 -v 11789.4 6244.39 1971.34 -v 5568.39 6354.49 1915.82 -v 10707.2 6232.1 1957.33 -v 10839.2 6439.13 1955.37 -v 8882.38 6437.66 1961.14 -v 11580.6 6497.8 1934.67 -v 10928.1 6529.15 1941.66 -v 5886.53 6665.86 1931.91 -v 8639.52 6609.83 1948.85 -v 11054.2 6609.62 1944.88 -v 11230.3 6649.41 1937.99 -v 11331.1 6640.05 1946.78 -v 8314.17 6868.64 1958.77 -v 6232.88 6969.38 1940.87 -v 8109.83 7015.25 1935.18 -v 6331.16 7050.42 1952.19 -v 8073.65 7030.84 1908.65 -v 8006.07 7068.16 1902.9 -v 6679.64 7236.06 1950.8 -v 7768.27 7209.29 1950.59 -v 6966.14 7296.46 1917.05 -v 7323.36 7286.57 1894.56 -v 7177.79 7316.89 1916.52 -v 7059.01 7324.37 1929.24 -v 8342.73 1138.94 1982.13 -v 6981.26 1166.62 2032.14 -v 7438.76 1177.59 1942.85 -v 8534.31 1161.78 1985.25 -v 6070.12 1472.14 1943.55 -v 5778.55 1557.48 1966.39 -v 9869.43 1701.88 2030.62 -v 5244.72 1807.38 1956.15 -v 4786.45 2037.63 2000.42 -v 4680.31 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5280.26 1926.48 -v 4146.06 5289.37 1987.39 -v 10494.9 5384.89 1929.25 -v 11883.8 5425.28 1954.59 -v 9868.97 5367.87 1997.96 -v 4323.61 5396.62 1928.64 -v 10509 5461.95 1925.73 -v 10524.5 5534.67 1925.72 -v 10517.7 5501.8 1955.22 -v 10546.7 5614.96 1947.31 -v 10552 5669.5 1928.85 -v 4469.33 5749.94 1968.75 -v 10571.8 5754.61 1940.55 -v 11909.5 5722.48 1960.92 -v 4538.21 5822.33 1952.99 -v 11896.2 5840.48 1941.47 -v 4593.37 5896.12 1967.9 -v 9525.06 5927.95 1938.71 -v 10593.8 5859.1 1946.77 -v 11876.3 5958.83 1946.47 -v 4769.57 5998 1951.14 -v 4874.34 6060.02 1954.24 -v 10634.7 5987.37 1974.38 -v 11861.4 6040.69 1977.61 -v 5191.29 6246.75 1983.5 -v 11745.2 6314.92 1954.67 -v 11702.8 6372.16 1939.01 -v 11649.9 6430.22 1946.4 -v 10890.8 6483.12 1974.11 -v 11506.9 6553.17 1975.9 -v 11050.6 6591 2006.02 -v 11385.3 6622.01 1941.21 -v 11287.2 6644.86 1948.39 -v 8568.96 6667.46 1954.15 -v 6139.26 6859.27 1920.75 -v 8185.61 6964.56 1977.16 -v 6413.6 7141.07 1983.69 -v 7961.54 7109.48 1939.4 -v 7903.04 7145.43 1949.6 -v 7662.15 7258.12 1965.64 -v 6810.38 7262.99 1930.11 -v 7502.41 7293.21 1947.23 -v 7382.54 7320.22 1950.92 -v 6938.6 7358.6 1983.79 -v 7236.52 7348.09 1963.5 -v 7363.87 1096.72 2061.4 -v 7507.4 1096.15 2048.24 -v 9030.91 1273.53 1999.26 -v 6301.93 1309.14 2055.03 -v 9114.16 1337.09 1966.05 -v 6129.73 1408.91 1998.4 -v 5967.43 1478.62 1978.86 -v 9964.31 1806.8 1999.92 -v 5117.5 1856.29 1980.28 -v 10065.8 1892.51 2004.3 -v 4962.64 1938.98 1990.94 -v 10291.6 2169.75 2053.8 -v 4436.63 2283.73 1987.4 -v 10983.5 2334.07 1988.12 -v 11042.2 2341.19 1975.28 -v 10922.4 2382.42 1974.76 -v 11085.3 2364.47 1957.29 -v 4331.81 2404.83 1962.51 -v 10354.2 2402.73 1989.55 -v 11294.7 2346.14 2017.05 -v 4191.77 2511.47 1979.36 -v 11433.7 2393.47 2021.79 -v 11516.1 2428.96 2022.18 -v 11582.4 2483.73 2007.44 -v 4075.15 2614.96 1995.39 -v 10378.9 2438.33 2050.37 -v 11696.4 2671.83 1951.25 -v 11738.2 2675.7 1965.48 -v 11902.2 2690.26 1965.89 -v 10418.5 2689.17 2060.91 -v 10422.2 2789.97 2041.22 -v 12424.3 3237.88 2036.18 -v 10431.8 3156.19 1994.43 -v 12428.9 3355.56 2000.87 -v 10395.4 3602.04 1980.26 -v 10572.7 3806.6 2006.31 -v 12420.9 3835.5 1949.09 -v 10658.1 3909.03 1990.34 -v 11012.2 3947.43 1943.69 -v 11016 3980.94 1970.82 -v 12354.6 3992.78 1992.04 -v 11166.3 4138.62 1990.94 -v 12138.4 4163.51 1932.69 -v 10311.8 4138.08 1966.75 -v 10692.1 4190.46 1969.04 -v 10744.8 4184.83 1961.5 -v 12177.1 4209.68 1957.43 -v 12204.1 4158.1 1963.15 -v 10635 4215.55 1979.34 -v 10802 4186.22 1964.51 -v 10866.7 4232.9 1960.05 -v 10838.4 4262.5 1934.31 -v 11344.1 4261.83 1945.23 -v 10895.6 4284.4 1959.26 -v 12101.6 4349.92 1960.31 -v 10957 4347.03 1986.5 -v 11329.1 4300.13 1982.09 -v 11407.7 4361.75 1960.17 -v 10945.1 4364.98 1963.65 -v 12023.5 4439.61 1973.5 -v 10265 4353.85 1999.72 -v 10988.9 4410.26 1953.6 -v 11495.3 4454.77 1960.49 -v 11930.3 4473.82 1963.89 -v 11035.8 4437.88 1960.13 -v 11577.5 4517.25 1957.26 -v 10213.8 4592.83 1972.27 -v 11640.8 4530.07 1952.25 -v 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49292//49292 49321//49321 -f 49320//49320 49296//49296 49319//49319 -f 49321//49321 49292//49292 49320//49320 -f 49292//49292 49296//49296 49320//49320 -f 49296//49296 49291//49291 49319//49319 diff --git a/PythonScripts/iblapps-master/atlaselectrophysiology/sandbox.py b/PythonScripts/iblapps-master/atlaselectrophysiology/sandbox.py deleted file mode 100644 index 3b6f5b7d..00000000 --- a/PythonScripts/iblapps-master/atlaselectrophysiology/sandbox.py +++ /dev/null @@ -1,8 +0,0 @@ -from brainbox.io.one import load_spike_sorting, load_channel_locations -from oneibl.one import ONE - -one = ONE(base_url="https://dev.alyx.internationalbrainlab.org") -eids = one.search(subject='ZM_2407', task_protocol='ephys') - -channels = load_channel_locations(eids[0], one=one) -spikes, clusters = load_spike_sorting(eids[0], one=one) diff --git a/PythonScripts/iblapps-master/atlasview/__init__.py b/PythonScripts/iblapps-master/atlasview/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/atlasview/atlasview.py b/PythonScripts/iblapps-master/atlasview/atlasview.py deleted file mode 100644 index efb8d186..00000000 --- a/PythonScripts/iblapps-master/atlasview/atlasview.py +++ /dev/null @@ -1,371 +0,0 @@ -""" -TopView is the main Widget with the related ControllerTopView Class -There are several SliceView windows (sagittal, coronal, possibly tilted etc...) that each have -a SliceController object -The underlying data model object is an ibllib.atlas.AllenAtlas object - - TopView(QMainWindow) - ControllerTopView(PgImageController) - - SliceView(QWidget) - SliceController(PgImageController) - -""" -from dataclasses import dataclass, field -from pathlib import Path - -import numpy as np -from PyQt5 import QtWidgets, uic -from PyQt5.QtGui import QTransform -import pyqtgraph as pg -import matplotlib - -from ibllib.atlas import AllenAtlas -import qt - - -class TopView(QtWidgets.QMainWindow): - """ - Main Window of the application. - This is a top view of the brain with 2 movable lines allowing to select sagittal and coronal - slices. - """ - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, TopView)] - - @staticmethod - def _get_or_create(title=None, **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - TopView._instances()), None) - if av is None: - av = TopView(**kwargs) - av.setWindowTitle(title) - return av - - def __init__(self, **kwargs): - super(TopView, self).__init__() - self.ctrl = ControllerTopView(self, **kwargs) - self.ctrl.image_layers = [ImageLayer()] - uic.loadUi(Path(__file__).parent.joinpath('topview.ui'), self) - self.plotItem_topview.setAspectLocked(True) - self.plotItem_topview.addItem(self.ctrl.imageItem) - # setup one horizontal and one vertical line that can be moved - line_kwargs = {'movable': True, 'pen': pg.mkPen((0, 255, 0), width=3)} - self.line_coronal = pg.InfiniteLine(angle=0, pos=0, **line_kwargs) - self.line_sagittal = pg.InfiniteLine(angle=90, pos=0, **line_kwargs) - self.line_coronal.sigDragged.connect(self._refresh_coronal) # sigPositionChangeFinished - self.line_sagittal.sigDragged.connect(self._refresh_sagittal) - self.plotItem_topview.addItem(self.line_coronal) - self.plotItem_topview.addItem(self.line_sagittal) - # connect signals and slots: mouse moved - s = self.plotItem_topview.getViewBox().scene() - self.proxy = pg.SignalProxy(s.sigMouseMoved, rateLimit=60, slot=self.mouseMoveEvent) - # combobox for the atlas remapping choices - self.comboBox_mappings.addItems(self.ctrl.atlas.regions.mappings.keys()) - self.comboBox_mappings.currentIndexChanged.connect(self._refresh) - # slider for transparency between image and labels - self.slider_alpha.sliderMoved.connect(self.slider_alpha_move) - self.ctrl.set_top() - - def add_scatter_feature(self, data): - self.ctrl.scatter_data = data / 1e6 - self.ctrl.scatter_data_ind = self.ctrl.atlas.bc.xyz2i(self.ctrl.scatter_data) - self.ctrl.fig_coronal.add_scatter() - self.ctrl.fig_sagittal.add_scatter() - self.line_coronal.sigDragged.connect( - lambda: self.ctrl.set_scatter(self.ctrl.fig_coronal, self.line_coronal.value())) - self.line_sagittal.sigDragged.connect( - lambda: self.ctrl.set_scatter(self.ctrl.fig_sagittal, self.line_sagittal.value())) - self.ctrl.set_scatter(self.ctrl.fig_coronal) - self.ctrl.set_scatter(self.ctrl.fig_sagittal) - - def add_image_layer(self, **kwargs): - """ - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, - 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :return: - """ - self.ctrl.fig_sagittal.add_image_layer(**kwargs) - self.ctrl.fig_coronal.add_image_layer(**kwargs) - - def add_regions_feature(self, values, cmap, opacity=1.0): - self.ctrl.values = values - # creat cmap look up table - colormap = matplotlib.cm.get_cmap(cmap) - colormap._init() - lut = (colormap._lut * 255).view(np.ndarray) - lut = np.insert(lut, 0, [0, 0, 0, 0], axis=0) - self.add_image_layer(pg_kwargs={'lut': lut, 'opacity': opacity}, slice_kwargs={ - 'volume': 'value', 'region_values': values, 'mode': 'clip'}) - self._refresh() - - def slider_alpha_move(self): - annotation_alpha = self.slider_alpha.value() / 100 - self.ctrl.fig_coronal.ctrl.image_layers[0].pg_kwargs['opacity'] = 1 - annotation_alpha - self.ctrl.fig_sagittal.ctrl.image_layers[0].pg_kwargs['opacity'] = 1 - annotation_alpha - self.ctrl.fig_coronal.ctrl.image_layers[1].pg_kwargs['opacity'] = annotation_alpha - self.ctrl.fig_sagittal.ctrl.image_layers[1].pg_kwargs['opacity'] = annotation_alpha - self._refresh() - - def mouseMoveEvent(self, scenepos): - if isinstance(scenepos, tuple): - scenepos = scenepos[0] - else: - return - pass - # qpoint = self.imageItem.mapFromScene(scenepos) - - def _refresh(self): - self._refresh_sagittal() - self._refresh_coronal() - - def _refresh_coronal(self): - self.ctrl.set_slice(self.ctrl.fig_coronal, self.line_coronal.value(), - mapping=self.comboBox_mappings.currentText()) - - def _refresh_sagittal(self): - self.ctrl.set_slice(self.ctrl.fig_sagittal, self.line_sagittal.value(), - mapping=self.comboBox_mappings.currentText()) - - -class SliceView(QtWidgets.QWidget): - """ - Window containing a volume slice - """ - - def __init__(self, topview: TopView, waxis, haxis, daxis): - super(SliceView, self).__init__() - self.topview = topview - self.ctrl = SliceController(self, waxis, haxis, daxis) - uic.loadUi(Path(__file__).parent.joinpath('sliceview.ui'), self) - self.add_image_layer(slice_kwargs={'volume': 'image', 'mode': 'clip'}, - pg_kwargs={'opacity': 0.8}) - self.add_image_layer(slice_kwargs={'volume': 'annotation', 'mode': 'clip'}, - pg_kwargs={'opacity': 0.2}) - # init the image display - self.plotItem_slice.setAspectLocked(True) - # connect signals and slots - s = self.plotItem_slice.getViewBox().scene() - self.proxy = pg.SignalProxy(s.sigMouseMoved, rateLimit=60, slot=self.mouseMoveEvent) - s.sigMouseClicked.connect(self.mouseClick) - - def add_scatter(self): - self.scatterItem = pg.ScatterPlotItem() - self.plotItem_slice.addItem(self.scatterItem) - - def add_image_layer(self, **kwargs): - """ - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, - 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :return: - """ - il = ImageLayer(**kwargs) - self.ctrl.image_layers.append(il) - self.plotItem_slice.addItem(il.image_item) - - def closeEvent(self, event): - self.destroy() - - def keyPressEvent(self, e): - pass - - def mouseClick(self, event): - if not event.double(): - return - - def mouseMoveEvent(self, scenepos): - if isinstance(scenepos, tuple): - scenepos = scenepos[0] - else: - return - qpoint = self.ctrl.image_layers[0].image_item.mapFromScene(scenepos) - iw, ih, w, h, v, region = self.ctrl.cursor2xyamp(qpoint) - self.label_x.setText(f"{w:.4f}") - self.label_y.setText(f"{h:.4f}") - self.label_ix.setText(f"{iw:.0f}") - self.label_iy.setText(f"{ih:.0f}") - if isinstance(v, np.ndarray): - self.label_v.setText(str(v)) - else: - self.label_v.setText(f"{v:.4f}") - if region is None: - self.label_region.setText("") - self.label_acronym.setText("") - else: - self.label_region.setText(region['name'][0]) - self.label_acronym.setText(region['acronym'][0]) - - def replace_image_layer(self, index, **kwargs): - if index and len(self.imageItem) >= index: - il = self.image_layers.pop(index) - self.plotItem_slice.removeItem(il.image_item) - self.add_image_layer(**kwargs) - - -class PgImageController: - """ - Abstract class that implements mapping fr`om axes to voxels for any window. - Not instantiated directly. - """ - def __init__(self, win, res=25): - self.qwidget = win - self.transform = None # affine transform image indices 2 data domain - self.image_layers = [] - - def cursor2xyamp(self, qpoint): - """Used for the mouse hover function over image display""" - iw, ih = self.cursor2ind(qpoint) - v = self.im[iw, ih] - w, h, _ = np.matmul(self.transform, np.array([iw, ih, 1])) - return iw, ih, w, h, v - - def cursor2ind(self, qpoint): - """ image coordinates over the image display""" - iw = np.max((0, np.min((int(np.floor(qpoint.x())), self.nw - 1)))) - ih = np.max((0, np.min((int(np.round(qpoint.y())), self.nh - 1)))) - return iw, ih - - @property - def imageItem(self): - """returns the first image item""" - return self.image_layers[0].image_item - - def set_image(self, pg_image_item, im, dw, dh, w0, h0, **pg_kwargs): - """ - :param im: - :param dw: - :param dh: - :param w0: - :param h0: - :param pgkwargs: og.ImageItem.setImage() parameters: level=None, lut=None, opacity=1 - :return: - """ - self.im = im - self.nw, self.nh = self.im.shape[0:2] - pg_image_item.setImage(self.im, **pg_kwargs) - transform = [dw, 0., 0., 0., dh, 0., w0, h0, 1.] - self.transform = np.array(transform).reshape((3, 3)).T - pg_image_item.setTransform(QTransform(*transform)) - - def set_points(self, x=None, y=None): - # at the moment brush and size are fixed! These need to be arguments - # For the colour need to convert the colour to QtGui.QColor - self.qwidget.scatterItem.setData(x=x, y=y, brush='b', size=5) - - -class ControllerTopView(PgImageController): - """ - TopView ControllerTopView - """ - def __init__(self, qmain: TopView, res: int = 25, volume='image', brainmap='Allen'): - super(ControllerTopView, self).__init__(qmain) - self.volume = volume - self.atlas = AllenAtlas(res) - self.fig_top = self.qwidget = qmain - # Setup Coronal slice: width: ml, height: dv, depth: ap - self.fig_coronal = SliceView(qmain, waxis=0, haxis=2, daxis=1) - self.fig_coronal.setWindowTitle('Coronal Slice') - self.set_slice(self.fig_coronal) - self.fig_coronal.show() - # Setup Sagittal slice: width: ap, height: dv, depth: ml - self.fig_sagittal = SliceView(qmain, waxis=1, haxis=2, daxis=0) - self.fig_sagittal.setWindowTitle('Sagittal Slice') - self.set_slice(self.fig_sagittal) - self.fig_sagittal.show() - - def set_slice(self, fig, coord=0, mapping="Allen"): - waxis, haxis, daxis = (fig.ctrl.waxis, fig.ctrl.haxis, fig.ctrl.daxis) - # construct the transform matrix image 2 ibl coordinates - dw = self.atlas.bc.dxyz[waxis] - dh = self.atlas.bc.dxyz[haxis] - wl = self.atlas.bc.lim(waxis) - dw / 2 - hl = self.atlas.bc.lim(haxis) - dh / 2 - # the ImageLayer object carries slice kwargs and pyqtgraph ImageSet kwargs - # reversed order so the self.im is set with the base layer - for layer in reversed(fig.ctrl.image_layers): - _slice = self.atlas.slice(coord, axis=daxis, mapping=mapping, **layer.slice_kwargs) - fig.ctrl.set_image(layer.image_item, _slice, dw, dh, wl[0], hl[0], **layer.pg_kwargs) - fig.ctrl.slice_coord = coord - - def set_top(self): - self.atlas.compute_surface() - img = self.atlas.top.transpose() - img[np.isnan(img)] = np.nanmin(img) # img has dims ml, ap - dw, dh = (self.atlas.bc.dxyz[0], self.atlas.bc.dxyz[1]) - wl, hl = (self.atlas.bc.xlim, self.atlas.bc.ylim) - self.set_image(self.image_layers[0].image_item, img, dw, dh, wl[0], hl[0]) - - def set_scatter(self, fig, coord=0): - waxis = fig.ctrl.waxis - # dealing with coronal slice - if waxis == 0: - idx = np.where(self.scatter_data_ind[:, 1] == self.atlas.bc.y2i(coord))[0] - x = self.scatter_data[idx, 0] - y = self.scatter_data[idx, 2] - else: - idx = np.where(self.scatter_data_ind[:, 0] == self.atlas.bc.x2i(coord))[0] - x = self.scatter_data[idx, 1] - y = self.scatter_data[idx, 2] - - fig.ctrl.set_points(x, y) - - def set_volume(self, volume): - self.volume = volume - - -class SliceController(PgImageController): - - def __init__(self, fig, waxis=None, haxis=None, daxis=None): - """ - :param waxis: brain atlas axis corresponding to display abscissa (coronal: 0, sagittal: 1) - :param haxis: brain atlas axis corresponding to display ordinate (coronal: 2, sagittal: 2) - :param daxis: brain atlas axis corresponding to display abscissa (coronal: 1, sagittal: 0) - """ - super(SliceController, self).__init__(fig) - self.waxis = waxis - self.haxis = haxis - self.daxis = daxis - - def cursor2xyamp(self, qpoint): - """ - Extends the superclass method to also get the brain region from the model - :param qpoint: - :return: - """ - iw, ih, w, h, v = super(SliceController, self).cursor2xyamp(qpoint) - ba = self.qwidget.topview.ctrl.atlas - xyz = np.zeros(3) - xyz[np.array([self.waxis, self.haxis, self.daxis])] = [w, h, self.slice_coord] - mapping = self.qwidget.topview.comboBox_mappings.currentText() - try: - region = ba.regions.get(ba.get_labels(xyz, mapping=mapping)) - except ValueError: - region = None - return iw, ih, w, h, v, region - - -@dataclass -class ImageLayer: - """ - Class for keeping track of image layers. - :param image_item - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :param - """ - image_item: pg.ImageItem = field(default_factory=pg.ImageItem) - pg_kwargs: dict = field(default_factory=lambda: {}) - slice_kwargs: dict = field(default_factory=lambda: {'volume': 'image', 'mode': 'clip'}) - - -def view(res=25, title=None, brainmap='Allen'): - """ - """ - qt.create_app() - av = TopView._get_or_create(title=title, res=res, brainmap=brainmap) - av.show() - return av diff --git a/PythonScripts/iblapps-master/atlasview/channels_test.npy b/PythonScripts/iblapps-master/atlasview/channels_test.npy deleted file mode 100644 index 67fa0ddd..00000000 Binary files a/PythonScripts/iblapps-master/atlasview/channels_test.npy and /dev/null differ diff --git a/PythonScripts/iblapps-master/atlasview/launcher.py b/PythonScripts/iblapps-master/atlasview/launcher.py deleted file mode 100644 index fecbabb9..00000000 --- a/PythonScripts/iblapps-master/atlasview/launcher.py +++ /dev/null @@ -1,24 +0,0 @@ -from pathlib import Path -import numpy as np -from atlasview import atlasview # mouais il va falloir changer ça -av = atlasview.view() # need to have an output argument here or the garbage collector will clean -# it up and boom - -""" Roadmap - - swap volumes combox (label RGB option / density) - - overlay brain regions with transparency - - overlay volumes (for example coverage with transparency) - - overlay plots: probes channels - - tilted slices - - coordinate swaps: add Allen / Needles / Voxel options - - should we add horizontal slices ? -""" - -# add brain regions feature: -reg_values = np.load(Path(atlasview.__file__).parent.joinpath('region_values.npy')) -av.add_regions_feature(reg_values, 'Blues', opacity=0.7) - -# add scatter feature: -chans = np.load( - Path(atlasview.__file__).parent.joinpath('channels_test.npy'), allow_pickle=True) -av.add_scatter_feature(chans) diff --git a/PythonScripts/iblapps-master/atlasview/region_values.npy b/PythonScripts/iblapps-master/atlasview/region_values.npy deleted file mode 100644 index 68276103..00000000 Binary files a/PythonScripts/iblapps-master/atlasview/region_values.npy and /dev/null differ diff --git a/PythonScripts/iblapps-master/atlasview/sliceview.ui b/PythonScripts/iblapps-master/atlasview/sliceview.ui deleted file mode 100644 index df59b8f8..00000000 --- a/PythonScripts/iblapps-master/atlasview/sliceview.ui +++ /dev/null @@ -1,313 +0,0 @@ - - - SliceWindow - - - - 0 - 0 - 1101 - 737 - - - - MainWindow - - - - - - - - - - - - 100 - 0 - - - - - 120 - 16777215 - - - - QFrame::StyledPanel - - - QFrame::Raised - - - - - - - 70 - 0 - - - - - 80 - 20 - - - - - - - - - - - - 15 - 0 - - - - - 10 - 20 - - - - y - - - - - - - - 15 - 0 - - - - - 10 - 20 - - - - ix - - - - - - - - 15 - 0 - - - - - 10 - 20 - - - - iy - - - - - - - - 70 - 0 - - - - - 80 - 20 - - - - - - - - - - - - 15 - 0 - - - - - 10 - 20 - - - - x - - - - - - - - 70 - 0 - - - - - 80 - 20 - - - - - - - - - - - - 70 - 0 - - - - - 80 - 20 - - - - - - - - - - - Qt::Vertical - - - - 20 - 40 - - - - - - - - - - - - - - - v - - - - - - - - - - - - - - - - - Qt::Vertical - - - QSizePolicy::Fixed - - - - 20 - 5 - - - - - - - - - - - Qt::Horizontal - - - QSizePolicy::Maximum - - - - 5 - 17 - - - - - - - - Qt::Horizontal - - - QSizePolicy::Fixed - - - - 5 - 20 - - - - - - - - - - - - - - - - - - 0 - 0 - 3 - 22 - - - - - - - PlotWidget - QGraphicsView -
pyqtgraph
- - - - - diff --git a/PythonScripts/iblapps-master/atlasview/topview.ui b/PythonScripts/iblapps-master/atlasview/topview.ui deleted file mode 100644 index 7b32cf79..00000000 --- a/PythonScripts/iblapps-master/atlasview/topview.ui +++ /dev/null @@ -1,125 +0,0 @@ - - - TopView - - - - 0 - 0 - 435 - 461 - - - - MainWindow - - - - - - - - - - - 100 - 0 - - - - QFrame::StyledPanel - - - QFrame::Raised - - - - - 20 - 20 - 67 - 17 - - - - Annotation - - - - - - 40 - 50 - 16 - 160 - - - - 20 - - - Qt::Vertical - - - - - - 10 - 220 - 67 - 17 - - - - Image - - - - - - 0 - 350 - 93 - 27 - - - - - - - 10 - 330 - 67 - 17 - - - - Mapping - - - - - - - - - - 0 - 0 - 435 - 24 - - - - - - - - PlotWidget - QGraphicsView -
pyqtgraph
- - - - - diff --git a/PythonScripts/iblapps-master/data_exploration_gui/README.md b/PythonScripts/iblapps-master/data_exploration_gui/README.md deleted file mode 100644 index f2f38b6e..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# Data Exploration GUI - -GUI to allow user to explore ephys data from IBL task - -## Setup - -Install ibl environment following [these instructions](https://github.com/int-brain-lab/iblenv#iblenv-installation-guide) - -Go to the ```data_exploration_gui``` folder - -``` -cd iblapps/data_exploration_gui -``` - -### Using GUI -To launch the gui you should run the following from the command line. You can specify either a probe insertion id -e.g -``` -python data_explore_gui.py -pid 9657af01-50bd-4120-8303-416ad9e24a51 -``` - -or an eid and probe name, e.g -``` -python data_explore_gui.py -eid 7f6b86f9-879a-4ea2-8531-294a221af5d0 -name probe00 -``` diff --git a/PythonScripts/iblapps-master/data_exploration_gui/cluster.py b/PythonScripts/iblapps-master/data_exploration_gui/cluster.py deleted file mode 100644 index bbc42be9..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/cluster.py +++ /dev/null @@ -1,116 +0,0 @@ -from PyQt5 import QtGui, QtWidgets -from data_exploration_gui import utils - - -class ClusterGroup: - - def __init__(self): - - self.cluster_buttons = QtWidgets.QButtonGroup() - self.cluster_group = QtWidgets.QGroupBox('Sort Clusters By:') - self.cluster_layout = QtWidgets.QHBoxLayout() - for i, val in enumerate(utils.SORT_CLUSTER_OPTIONS): - button = QtWidgets.QRadioButton(val) - if i == 0: - button.setChecked(True) - else: - button.setChecked(False) - self.cluster_buttons.addButton(button) - self.cluster_layout.addWidget(button) - - self.cluster_group.setLayout(self.cluster_layout) - - self.cluster_colours = QtWidgets.QGroupBox() - h_layout_col = QtWidgets.QHBoxLayout() - h_layout_lab = QtWidgets.QHBoxLayout() - for val in utils.UNIT_OPTIONS: - img = QtWidgets.QLabel() - label = QtWidgets.QLabel(val) - pix = QtGui.QPixmap(40, 5) - pix.fill(utils.colours[val]) - img.setPixmap(pix) - h_layout_lab.addWidget(img) - h_layout_col.addWidget(label) - - v_layout = QtWidgets.QVBoxLayout() - v_layout.addLayout(h_layout_lab) - v_layout.addLayout(h_layout_col) - - self.cluster_colours.setLayout(v_layout) - - self.cluster_list = QtWidgets.QListWidget() - - self.cluster_next_button = QtWidgets.QPushButton('Next') - self.cluster_next_button.setFixedSize(90, 30) - self.cluster_previous_button = QtWidgets.QPushButton('Previous') - self.cluster_previous_button.setFixedSize(90, 30) - - self.cluster_list_group = QtWidgets.QGroupBox() - - group_layout = QtWidgets.QGridLayout() - group_layout.addWidget(self.cluster_group, 0, 0, 1, 3) - group_layout.addWidget(self.cluster_colours, 1, 0, 1, 3) - group_layout.addWidget(self.cluster_list, 2, 0, 3, 3) - group_layout.addWidget(self.cluster_previous_button, 5, 0) - group_layout.addWidget(self.cluster_next_button, 5, 2) - self.cluster_list_group.setLayout(group_layout) - - self.reset() - - def reset(self): - self.cluster_list.clear() - self.clust_colour_ks = None - self.clust_colour_ibl = None - self.clust_ids = None - self.clust = None - self.clust_prev = None - - def populate(self, clust_ids, clust_colour_ks, clust_colour_ibl): - self.clust_ids = clust_ids - self.clust_colour_ks = clust_colour_ks - self.clust_colour_ibl = clust_colour_ibl - - for idx, val in enumerate(clust_ids): - item = QtWidgets.QListWidgetItem('Cluster Number ' + str(val)) - icon = utils.get_icon(self.clust_colour_ibl[idx], self.clust_colour_ks[idx], 20) - item.setIcon(QtGui.QIcon(icon)) - self.cluster_list.addItem(item) - self.cluster_list.setCurrentRow(0) - - def update_list_icon(self): - item = self.cluster_list.item(self.clust_prev) - icon = utils.get_icon(self.clust_colour_ibl[self.clust_prev], - self.clust_colour_ks[self.clust_prev], 12) - item.setIcon(QtGui.QIcon(icon)) - item.setText(' Cluster Number ' + str(self.clust_ids[self.clust_prev])) - - def update_current_row(self, clust): - self.clust = clust - self.cluster_list.setCurrentRow(self.clust) - - def on_next_cluster_clicked(self): - self.clust += 1 - self.cluster_list.setCurrentRow(self.clust) - return self.clust - - def on_previous_cluster_clicked(self): - self.clust -= 1 - self.cluster_list.setCurrentRow(self.clust) - return self.clust - - def on_cluster_list_clicked(self): - self.clust = self.cluster_list.currentRow() - return self.clust - - def update_cluster_index(self): - self.clust_prev = self.clust - return self.clust_prev - - def initialise_cluster_index(self): - self.clust = 0 - self.clust_prev = 0 - - return self.clust, self.clust_prev - - def get_selected_sort(self): - return self.cluster_buttons.checkedButton().text() diff --git a/PythonScripts/iblapps-master/data_exploration_gui/cluster_class.py b/PythonScripts/iblapps-master/data_exploration_gui/cluster_class.py deleted file mode 100644 index fdf04386..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/cluster_class.py +++ /dev/null @@ -1,101 +0,0 @@ -from PyQt5 import QtCore, QtGui, QtWidgets - - -class ClusterGroup: - - def __init__(self): - - self.cluster_buttons = QtWidgets.QButtonGroup() - self.cluster_group = QtWidgets.QGroupBox('Sort Clusters By:') - self.cluster_layout = QtWidgets.QHBoxLayout() - cluster_options = ['cluster no.', 'no. of spikes', 'good units'] - for i, val in enumerate(cluster_options): - button = QtWidgets.QRadioButton(val) - if i == 0: - button.setChecked(True) - else: - button.setChecked(False) - self.cluster_buttons.addButton(button, id=i) - self.cluster_layout.addWidget(button) - - self.cluster_group.setLayout(self.cluster_layout) - - self.cluster_list = QtWidgets.QListWidget() - self.cluster_list.SingleSelection - - self.cluster_next_button = QtWidgets.QPushButton('Next') - self.cluster_next_button.setFixedSize(90, 30) - self.cluster_previous_button = QtWidgets.QPushButton('Previous') - self.cluster_previous_button.setFixedSize(90, 30) - - self.cluster_list_group = QtWidgets.QGroupBox() - self.cluster_list_group.setFixedSize(400, 200) - self.group_widget() - - self.clust_colour = [] - self.clust_ids = [] - self.clust = [] - self.clust_prev = [] - - - def group_widget(self): - group_layout = QtWidgets.QGridLayout() - group_layout.addWidget(self.cluster_group, 0, 0, 1, 3) - group_layout.addWidget(self.cluster_list, 1, 0, 3, 3) - group_layout.addWidget(self.cluster_previous_button, 4, 0) - group_layout.addWidget(self.cluster_next_button, 4, 2) - self.cluster_list_group.setLayout(group_layout) - - def reset(self): - self.cluster_list.clear() - #self.cluster_option1.setChecked(True) - self.clust_colour = [] - self.clust_ids = [] - self.clust = [] - self.clust_prev = [] - - def populate(self, clust_ids, clust_colour): - self.clust_ids = clust_ids - self.clust_colour = clust_colour - icon = QtGui.QPixmap(20, 20) - for idx, val in enumerate(clust_ids): - item = QtWidgets.QListWidgetItem('Cluster Number ' + str(val)) - icon.fill(self.clust_colour[idx]) - item.setIcon(QtGui.QIcon(icon)) - self.cluster_list.addItem(item) - self.cluster_list.setCurrentRow(0) - - def update_list_icon(self): - item = self.cluster_list.item(self.clust_prev) - icon = QtGui.QPixmap(10, 10) - icon.fill(self.clust_colour[self.clust_prev]) - item.setIcon(QtGui.QIcon(icon)) - item.setText(' Cluster Number ' + str(self.clust_ids[self.clust_prev])) - - def update_current_row(self, clust): - self.clust = clust - self.cluster_list.setCurrentRow(self.clust) - - def on_next_cluster_clicked(self): - self.clust += 1 - self.cluster_list.setCurrentRow(self.clust) - return self.clust - - def on_previous_cluster_clicked(self): - self.clust -= 1 - self.cluster_list.setCurrentRow(self.clust) - return self.clust - - def on_cluster_list_clicked(self): - self.clust = self.cluster_list.currentRow() - return self.clust - - def update_cluster_index(self): - self.clust_prev = self.clust - return self.clust_prev - - def initialise_cluster_index(self): - self.clust = 0 - self.clust_prev = 0 - - return self.clust, self.clust_prev diff --git a/PythonScripts/iblapps-master/data_exploration_gui/data_class.py b/PythonScripts/iblapps-master/data_exploration_gui/data_class.py deleted file mode 100644 index 2172c9ad..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/data_class.py +++ /dev/null @@ -1,235 +0,0 @@ -from PyQt5 import QtGui, QtWidgets -import numpy as np -import os -import one.alf.io as alfio -from brainbox.processing import get_units_bunch, compute_cluster_average -from brainbox.population.decode import xcorr -from brainbox.singlecell import calculate_peths -from brainbox.io.spikeglx import extract_waveforms -from pathlib import Path - - -class DataGroup: - def __init__(self): - - self.waveform_button = QtWidgets.QPushButton('Generate Waveform') - self.waveform_list = QtWidgets.QListWidget() - self.waveform_list.SingleSelection - self.waveform_text = QtWidgets.QLabel('No. of spikes =') - - waveform_layout = QtWidgets.QGridLayout() - waveform_layout.addWidget(self.waveform_button, 0, 0) - waveform_layout.addWidget(self.waveform_text, 1, 0) - waveform_layout.addWidget(self.waveform_list, 0, 1, 3, 1) - - self.waveform_group = QtWidgets.QGroupBox() - self.waveform_group.setLayout(waveform_layout) - - - #For peths and rasters - self.t_before = 0.4 - self.t_after = 1 - - #For autocorrolelogram - self.autocorr_window = 0.1 - self.autocorr_bin = 0.001 - - - #For waveform (N.B in ms) - self.waveform_window = 2 - self.CAR = False - - def load(self, folder_path): - self.folder_path = folder_path - self.find_files() - self.load_data() - self.compute_timescales() - - return self.ephys_file_path, self.gui_path - - def find_files(self): - self.probe_path = self.folder_path - self.alf_path = self.folder_path.parent - dir_path = self.folder_path.parent.parent - self.gui_path = os.path.join(self.probe_path, 'gui') - probe = os.path.split(self.probe_path)[1] - ephys_path = os.path.join(dir_path, 'raw_ephys_data', probe) - - self.ephys_file_path = [] - - try: - for i in os.listdir(ephys_path): - if 'ap' in i and 'bin' in i: - self.ephys_file_path = os.path.join(ephys_path, i) - except: - self.ephys_file_path = [] - - def load_data(self): - self.spikes = alfio.load_object(self.probe_path, 'spikes') - self.trials = alfio.load_object(self.alf_path, 'trials') - self.clusters = alfio.load_object(self.probe_path, 'clusters') - self.prepare_data(self.spikes, self.clusters, self.trials) - # self.ids = np.unique(self.spikes.clusters) - # self.metrics = np.array(self.clusters.metrics.ks2_label[self.ids]) - # self.colours = np.array(self.clusters.metrics.ks2_label[self.ids]) - # self.colours[np.where(self.colours == 'mua')[0]] = QtGui.QColor('#fdc086') - # self.colours[np.where(self.colours == 'good')[0]] = QtGui.QColor('#7fc97f') - # - # file_count = 0 - # if os.path.isdir(self.gui_path): - # for i in os.listdir(self.gui_path): - # if 'depth' in i: - # self.depths = np.load(Path(self.gui_path + '/cluster_depths.npy')) - # file_count += 1 - # elif 'amp' in i: - # self.amps = np.load(Path(self.gui_path + '/cluster_amps.npy')) - # file_count += 1 - # elif 'nspikes' in i: - # self.nspikes = np.load(Path(self.gui_path + '/cluster_nspikes.npy')) - # file_count += 1 - # if file_count != 3: - # self.compute_depth_and_amplitudes() - # else: - # os.mkdir(self.gui_path) - # self.compute_depth_and_amplitudes() - # - # self.sort_by_id = np.arange(len(self.ids)) - # self.sort_by_nspikes = np.argsort(self.nspikes) - # self.sort_by_nspikes = self.sort_by_nspikes[::-1] - # self.sort_by_good = np.append(np.where(self.metrics == 'good')[0], np.where(self.metrics == 'mua')[0]) - # self.n_trials = len(self.trials['contrastLeft']) - - def prepare_data(self, spikes, clusters, trials): - self.spikes = spikes - self.clusters = clusters - self.trials = trials - self.ids = np.unique(spikes.clusters) - self.metrics = np.array(clusters.metrics.ks2_label[self.ids]) - self.colours = np.array(clusters.metrics.ks2_label[self.ids]) - self.colours[np.where(self.colours == 'mua')[0]] = QtGui.QColor('#fdc086') - self.colours[np.where(self.colours == 'good')[0]] = QtGui.QColor('#7fc97f') - _, self.depths, self.nspikes = compute_cluster_average(spikes.clusters, spikes.depths) - _, self.amps, _ = compute_cluster_average(spikes.clusters, spikes.amps) - self.amps = self.amps * 1e6 - self.sort_by_id = np.arange(len(self.ids)) - self.sort_by_nspikes = np.argsort(self.nspikes) - self.sort_by_nspikes = self.sort_by_nspikes[::-1] - self.sort_by_good = np.append(np.where(self.metrics == 'good')[0], - np.where(self.metrics == 'mua')[0]) - self.n_trials = len(trials['contrastLeft']) - - - def compute_depth_and_amplitudes(self): - units_b = get_units_bunch(self.spikes) - self.depths = [] - self.amps = [] - self.nspikes = [] - for clu in self.ids: - self.depths = np.append(self.depths, np.nanmean(units_b.depths[str(clu)])) - self.amps = np.append(self.amps, np.nanmean(units_b.amps[str(clu)]) * 1e6) - self.nspikes = np.append(self.nspikes, len(units_b.amps[str(clu)])) - - np.save((self.gui_path + '/cluster_depths'), self.depths) - np.save((self.gui_path + '/cluster_amps'), self.amps) - np.save((self.gui_path + '/cluster_nspikes'), self.nspikes) - - def sort_data(self, order): - self.clust_ids = self.ids[order] - self.clust_amps = self.amps[order] - self.clust_depths = self.depths[order] - self.clust_colours = self.colours[order] - - return self.clust_ids, self.clust_amps, self.clust_depths, self.clust_colours - - def reset(self): - self.waveform_list.clear() - - def populate(self, clust): - self.reset() - self.clus_idx = np.where(self.spikes.clusters == self.clust_ids[clust])[0] - - if len(self.clus_idx) <= 500: - self.spk_intervals = [0, len(self.clus_idx)] - else: - self.spk_intervals = np.arange(0, len(self.clus_idx), 500) - self.spk_intervals = np.append(self.spk_intervals, len(self.clus_idx)) - - for idx in range(0, len(self.spk_intervals) - 1): - item = QtWidgets.QListWidgetItem(str(self.spk_intervals[idx]) + ':' + str(self.spk_intervals[idx+1])) - self.waveform_list.addItem(item) - - self.waveform_list.setCurrentRow(0) - self.n_waveform = 0 - - def compute_peth(self, trial_type, clust, trials_id): - peths, bin = calculate_peths(self.spikes.times, self.spikes.clusters, - [self.clust_ids[clust]], self.trials[trial_type][trials_id], self.t_before, self.t_after) - - peth_mean = peths.means[0, :] - peth_std = peths.stds[0, :] / np.sqrt(len(trials_id)) - t_peth = peths.tscale - - return t_peth, peth_mean, peth_std - - def compute_rasters(self, trial_type, clust, trials_id): - self.x = np.empty(0) - self.y = np.empty(0) - spk_times = self.spikes.times[self.spikes.clusters == self.clust_ids[clust]] - for idx, val in enumerate(self.trials[trial_type][trials_id]): - spks_to_include = np.bitwise_and(spk_times >= val - self.t_before, spk_times <= val + self.t_after) - trial_spk_times = spk_times[spks_to_include] - trial_spk_times_aligned = trial_spk_times - val - trial_no = (np.ones(len(trial_spk_times_aligned))) * idx * 10 - self.x = np.append(self.x, trial_spk_times_aligned) - self.y = np.append(self.y, trial_no) - - return self.x, self.y, self.n_trials - - def compute_autocorr(self, clust): - self.clus_idx = np.where(self.spikes.clusters == self.clust_ids[clust])[0] - - x_corr = xcorr(self.spikes.times[self.clus_idx], self.spikes.clusters[self.clus_idx], - self.autocorr_bin, self.autocorr_window) - - corr = x_corr[0, 0, :] - - return self.t_autocorr, corr - - def compute_template(self, clust): - template = (self.clusters.waveforms[self.clust_ids[clust], :, 0]) * 1e6 - return self.t_template, template - - def compute_waveform(self, clust): - if len(self.ephys_file_path) != 0: - spk_times = self.spikes.times[self.clus_idx][self.spk_intervals[self.n_waveform]:self.spk_intervals[self.n_waveform + 1]] - max_ch = self.clusters['channels'][self.clust_ids[clust]] - wf = extract_waveforms(self.ephys_file_path, spk_times, max_ch, t = self.waveform_window, car = self.CAR) - wf_mean = np.mean(wf[:,:,0], axis = 0) - wf_std = np.std(wf[:,:,0], axis = 0) - - return self.t_waveform, wf_mean, wf_std - - def compute_timescales(self): - self.t_autocorr = np.arange((self.autocorr_window/2) - self.autocorr_window, (self.autocorr_window/2)+ self.autocorr_bin, self.autocorr_bin) - sr = 30000 - n_template = len(self.clusters.waveforms[self.ids[0], :, 0]) - self.t_template = 1e3 * (np.arange(n_template)) / sr - - n_waveform = int(sr / 1000 * (self.waveform_window)) - self.t_waveform = 1e3 * (np.arange(n_waveform)) / sr - #self.t_waveform = 1e3 * (np.arange(n_waveform/2 - n_waveform, n_waveform/2, 1))/sr - - - - - - - - - - - - - - - diff --git a/PythonScripts/iblapps-master/data_exploration_gui/data_explore_gui.py b/PythonScripts/iblapps-master/data_exploration_gui/data_explore_gui.py deleted file mode 100644 index d3aff437..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/data_explore_gui.py +++ /dev/null @@ -1,320 +0,0 @@ -from PyQt5 import QtWidgets, QtCore - -import data_exploration_gui.cluster as clust -import data_exploration_gui.scatter as scatt -import data_exploration_gui.filter as filt -import data_exploration_gui.plot as plt -import data_exploration_gui.misc as misc -import data_exploration_gui.data_model as dat -from data_exploration_gui import utils - -import qt -import matplotlib.pyplot as mpl # noqa # This is needed to make qt show properly :/ -from one.api import ONE - - -class MainWindow(QtWidgets.QMainWindow): - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, MainWindow)] - - @staticmethod - def _get_or_create(title='Data Exploration GUI', **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - MainWindow._instances()), None) - if av is None: - av = MainWindow(**kwargs) - av.setWindowTitle(title) - - return av - - def __init__(self, eid, probe, one=None, spike_collection=None): - super(MainWindow, self).__init__() - - self.data = dat.DataModel(eid, probe, one=one, spike_collection=spike_collection) - self.plot = plt.PlotGroup(self.data) - - # Initialise cluster group - self.cluster = clust.ClusterGroup() - # Connect signals - self.cluster.cluster_list.clicked.connect(self.on_cluster_list_clicked) - self.cluster.cluster_next_button.clicked.connect(self.on_next_cluster_clicked) - self.cluster.cluster_previous_button.clicked.connect(self.on_previous_cluster_clicked) - self.cluster.cluster_buttons.buttonClicked.connect(self.on_cluster_sort_clicked) - - # Initialise scatter group - self.scatter = scatt.ScatterGroup() - # connect signals - self.scatter.scatter_plot.sigClicked.connect(self.on_scatter_plot_clicked) - - # Initialise filter group - self.filter = filt.FilterGroup() - # Connect signals - self.filter.contrast_buttons.buttonToggled.connect(self.on_filters_changed) - self.filter.hold_button.stateChanged.connect(self.on_hold_button_clicked) - self.filter.trial_buttons.buttonToggled.connect(self.on_trial_set_changed) - self.filter.order_buttons.buttonClicked.connect(self.on_filters_changed) - self.filter.sort_buttons.buttonClicked.connect(self.on_filters_changed) - self.filter.reset_button.clicked.connect(self.on_reset_filters_clicked) - - self.filter.populate(self.data.trial_events, self.data.behav_events) - self.filter.event_combobox.activated.connect(self.on_event_selected) - self.filter.behav_combobox.activated.connect(self.on_behav_selected) - - self.misc = misc.MiscGroup() - - self.resize(1800, 1000) - main_widget = QtWidgets.QWidget() - self.setCentralWidget(main_widget) - - main_widget_layout = QtWidgets.QGridLayout() - main_widget_layout.setHorizontalSpacing(20) - main_widget_layout.setVerticalSpacing(20) - - main_widget_layout.addWidget(self.cluster.cluster_list_group, 0, 0, 3, 1) - main_widget_layout.addWidget(self.scatter.fig_scatter, 3, 0, 7, 1) - main_widget_layout.addWidget(self.filter.filter_options_group, 0, 1, 10, 1) - main_widget_layout.addWidget(self.plot.fig_area, 0, 2, 8, 1) - main_widget_layout.addWidget(self.misc.qc_group, 8, 2, 2, 1) - - main_widget_layout.setColumnStretch(0, 2) - main_widget_layout.setColumnStretch(1, 1) - main_widget_layout.setColumnStretch(2, 5) - - - - - main_widget.setLayout(main_widget_layout) - - self.initialise_gui() - - def initialise_gui(self): - - self.cluster.reset() - self.scatter.reset() - self.plot.reset() - - # Reset all filters - self.filter.trial_buttons.blockSignals(True) - self.filter.reset_filters() - self.filter.trial_buttons.blockSignals(False) - - # cluster sort, trial event, behav_event, sort, order, hold, contrasts, trial_set - self.filter.trial_buttons.setExclusive(False) - # Get filter options - self.contrasts, self.order, self.sort, self.hold = self.filter.get_selected_filters() - # Get trial event to align to - self.filter.set_selected_event(event='goCue_times') - self.trial_event = self.filter.get_selected_event() - # Get behaviour event to display - self.behav = self.filter.get_selected_behaviour() - # Get initial trial_set to show - self.trial_set = self.filter.get_selected_trials()[0] - # Get the order to sort clusters - self.cluster_sort = self.cluster.get_selected_sort() - - # Sort the clusters - self.clust_data = self.data.sort_clusters(self.cluster_sort) - # Populate cluster list - self.cluster.populate(self.clust_data.ids, self.clust_data.colours_ks, - self.clust_data.colours_ibl) - self.clust, self.clust_prev = self.cluster.initialise_cluster_index() - - # Populate scatter plot - self.scatter.populate(self.clust_data.amps, self.clust_data.depths, self.clust_data.ids, - self.clust_data.colours_ks, self.clust_data.colours_ibl) - self.scatter.initialise_scatter_index() - - # Get the relevant data and plot - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.data._get_behaviour_data_for_selection(self.behav, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - - self.misc.set_sess_qc_text(self.data.sess_qc) - self.misc.set_dlc_label(self.data.dlc_aligned) - self.misc.set_clust_qc_text(self.data.get_cluster_metrics()) - - def on_event_selected(self, idx): - self.trial_event = self.filter.event_combobox.itemText(idx) - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.data._get_behaviour_data_for_selection(self.behav, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - - def on_behav_selected(self, idx): - self.behav = self.filter.behav_combobox.itemText(idx) - self.data._get_behaviour_data_for_selection(self.behav, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - - def on_filters_changed(self): - self.contrasts, self.order, self.sort, self.hold = self.filter.get_selected_filters() - self.plot.change_plots(self.contrasts, self.trial_set, self.order, self.sort, self.hold, - self.trial_event) - - def on_hold_button_clicked(self): - self.hold = self.filter.get_hold_status() - if self.hold: - self.filter.trial_buttons.setExclusive(False) - else: - self.filter.trial_buttons.blockSignals(True) - self.filter.reset_filters() - self.filter.trial_buttons.setExclusive(True) - self.plot.reset() - self.filter.trial_buttons.blockSignals(False) - - self.contrasts, self.order, self.sort, self.hold = self.filter.get_selected_filters() - self.trial_set = self.filter.get_selected_trials()[0] - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - - def on_trial_set_changed(self, button, signal): - self.trial_set = button.text() - if self.hold is False: - self.plot.change_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - else: - if signal is True: - self.sort = utils.MAP_SORT_OPTIONS[self.trial_set] - self.filter.set_sorted_button(self.sort) - self.plot.change_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - else: - # removing a plot - self.plot.remove_plots(self.trial_set) - - def on_cluster_list_clicked(self): - # Get new cluster index - self.clust = self.cluster.on_cluster_list_clicked() - - # Update cluster list and scatter plot - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - - # Change plots - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - self.misc.set_clust_qc_text(self.data.get_cluster_metrics()) - - def on_next_cluster_clicked(self): - - if self.clust != len(self.clust_data.ids) - 1: - self.clust = self.cluster.on_next_cluster_clicked() - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - self.misc.set_clust_qc_text(self.data.get_cluster_metrics()) - - def on_previous_cluster_clicked(self): - - if self.clust != 0: - self.clust = self.cluster.on_previous_cluster_clicked() - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - self.misc.set_clust_qc_text(self.data.get_cluster_metrics()) - - def on_scatter_plot_clicked(self, scatter, point): - - self.clust = self.scatter.on_scatter_plot_clicked(point) - self.cluster.update_current_row(self.clust) - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data._get_spike_data_for_selection(self.clust, self.trial_event) - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - self.misc.set_clust_qc_text(self.data.get_cluster_metrics()) - - def on_cluster_sort_clicked(self): - self.initialise_gui() - - def on_reset_filters_clicked(self): - - self.filter.trial_buttons.blockSignals(True) - self.filter.reset_filters() - self.filter.hold_button.setCheckState(QtCore.Qt.Checked) - self.filter.trial_buttons.blockSignals(False) - - # cluster sort, trial event, behav_event, sort, order, hold, contrasts, trial_set - self.filter.trial_buttons.setExclusive(False) - # Get filter options - self.contrasts, self.order, self.sort, self.hold = self.filter.get_selected_filters() - self.trial_set = self.filter.get_selected_trials()[0] - self.plot.reset() - - self.plot.change_all_plots(self.contrasts, self.trial_set, self.order, self.sort, - self.hold, self.trial_event) - - - -def viewer(pid, one=None, spike_collection=None, title=None): - """ - """ - one = one or ONE() - eid, probe = one.pid2eid(pid) - qt.create_app() - av = MainWindow._get_or_create(title=title, eid=eid, probe=probe, one=one, - spike_collection=spike_collection) - av.show() - return av - - -if __name__ == '__main__': - - from argparse import ArgumentParser - import numpy as np - - one = ONE() - app = QtWidgets.QApplication([]) - - parser = ArgumentParser() - parser.add_argument('-pid', '--pid', default=False, required=False, - help='Probe id') - parser.add_argument('-eid', '--eid', default=False, required=False, - help='Experiment id') - parser.add_argument('-name', '--probe_name', default=False, required=False, - help='Probe name') - parser.add_argument('-c', '--collection', default='', required=False, - help='spike sorting collection') - - args = parser.parse_args() - - if args.pid: - eid, probe = one.pid2eid(str(args.pid)) - mainapp = MainWindow(eid, probe, one=one, spike_collection=args.collection) - else: - if not np.all(np.array([args.eid, args.probe_name], - dtype=object)): - print('Must provide eid and probe name') - else: - mainapp = MainWindow(str(args.eid), str(args.probe_name), one=one, - spike_collection=args.collection) - - # pid = 'ce397420-3cd2-4a55-8fd1-5e28321981f4' - # eid, probe = one.pid2eid(pid) - # eid = '934dd7a4-fbdc-459c-8830-04fe9033bc28' - # probe='probe00' - # mainapp = MainWindow(eid, probe, one=one) - - mainapp.show() - app.exec_() diff --git a/PythonScripts/iblapps-master/data_exploration_gui/data_model.py b/PythonScripts/iblapps-master/data_exploration_gui/data_model.py deleted file mode 100644 index 18aa9b8b..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/data_model.py +++ /dev/null @@ -1,358 +0,0 @@ -import logging -from one.api import ONE -import one.alf as alf -import numpy as np -from brainbox.processing import compute_cluster_average -from brainbox.task.trials import find_trial_ids, get_event_aligned_raster -from brainbox.behavior.wheel import velocity -from brainbox.behavior.dlc import get_dlc_everything -from brainbox.population.decode import xcorr -from iblutil.util import Bunch -from data_exploration_gui.utils import colours, SESS_QC, CLUSTER_QC - -logger = logging.getLogger('ibllib') - -EPOCH = [-0.4, 1] -TBIN = 0.02 -AUTOCORR_WINDOW = 0.1 -AUTOCORR_BIN = 0.001 -FS = 30000 - - -class DataModel: - def __init__(self, eid, probe, one=None, spike_collection=None): - one = one or ONE() - - if spike_collection: - collection = f'alf/{probe}/{spike_collection}' - else: - # Pykilosort is default, if not present look for normal kilosort - all_collections = one.list_collections(eid) - - if f'alf/{probe}/pykilosort' in all_collections: - collection = f'alf/{probe}/pykilosort' - else: - collection = f'alf/{probe}' - - print(collection) - #collection = f'alf/{probe}' - - try: - self.spikes = one.load_object(eid, obj='spikes', collection=collection, - attribute=['clusters', 'times', 'amps', 'depths']) - self.clusters = one.load_object(eid, obj='clusters', collection=collection, - attribute=['metrics', 'waveforms']) - - except alf.exceptions.ALFObjectNotFound: - logger.error(f'Could not load spike sorting for session: {eid} and probe: {probe}, GUI' - f' will not work') - raise - - # Get everything we need for the clusters - # need to get rid of nans in amps and depths - self.clusters.clust_ids, self.clusters.depths, n_spikes = \ - compute_cluster_average(self.spikes.clusters[~np.isnan(self.spikes.depths)], - self.spikes.depths[~np.isnan(self.spikes.depths)]) - _, self.clusters.amps, _ = compute_cluster_average( - self.spikes.clusters[~np.isnan(self.spikes.amps)], - self.spikes.amps[~np.isnan(self.spikes.amps)]) - - # If we don't have metrics file, we can't assign colours - if not any(self.clusters.get('metrics', [None])): - col = np.full((len(self.clusters.clust_ids)), colours['no metric']) - self.clusters.colours_ks = col - self.clusters.colours_ibl = col - self.clusters['KS good'] = np.arange(len(self.clusters.clust_ids)) - self.clusters['IBL good'] = np.arange(len(self.clusters.clust_ids)) - elif not any(self.clusters.metrics.get('ks2_label', [None])): - col = np.full((len(self.clusters.clust_ids)), colours['no metric']) - self.clusters.colours_ks = col - - colours_ibl = np.full((len(self.clusters.clust_ids)), colours['no metric']) - good_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] == 1)[0] - colours_ibl[good_ibl] = colours['IBL good'] - bad_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] != 1)[0] - colours_ibl[bad_ibl] = colours['IBL bad'] - self.clusters.colours_ibl = colours_ibl - self.clusters.metrics['amp_median'] *= 1e6 - - self.clusters['KS good'] = np.arange(len(self.clusters.clust_ids)) - self.clusters['IBL good'] = np.r_[good_ibl, bad_ibl] - else: - # KS2 good mua colours - # Bug in the KS2 units, in some cases, the clusters that do not exist in - # spikes.clusters have not been filled with nans like the other features in metrics - # Signature of these is for last values in ks_label to be None - if len(np.where(self.clusters.metrics.ks2_label.values == None)[0]) > 0: # noqa - colours_ks = np.array(self.clusters.metrics.ks2_label - [:len(self.clusters.clust_ids)]) - else: - colours_ks = np.array(self.clusters.metrics.ks2_label[self.clusters.clust_ids]) - good_ks = np.where(colours_ks == 'good')[0] - colours_ks[good_ks] = colours['KS good'] - mua_ks = np.where(colours_ks == 'mua')[0] - colours_ks[mua_ks] = colours['KS mua'] - self.clusters.colours_ks = colours_ks - - # IBL good bad colours - colours_ibl = np.array(self.clusters.metrics.ks2_label[self.clusters.clust_ids]) - good_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] == 1)[0] - colours_ibl[good_ibl] = colours['IBL good'] - bad_ibl = np.where(self.clusters.metrics.label[self.clusters.clust_ids] != 1)[0] - colours_ibl[bad_ibl] = colours['IBL bad'] - self.clusters.colours_ibl = colours_ibl - self.clusters.metrics['amp_median'] *= 1e6 - - self.clusters['KS good'] = np.r_[good_ks, mua_ks] - self.clusters['IBL good'] = np.r_[good_ibl, bad_ibl] - - # Some extra info for sorting clusters - self.clusters['ids'] = np.arange(len(self.clusters.clust_ids)) - self.clusters['n spikes'] = np.argsort(n_spikes)[::-1] - - # Get trial data - self.trials = one.load_object(eid, obj='trials', collection='alf') - self.n_trials = self.trials['probabilityLeft'].shape[0] - self.trial_events = [key for key in self.trials.keys() if 'time' in key] - - # Get behaviour data - wheel = one.load_object(eid, obj='wheel', collection='alf') - dlc_left = one.load_object(eid, obj='leftCamera', collection='alf', - attribute=['times', 'dlc']) - dlc_right = one.load_object(eid, obj='rightCamera', collection='alf', - attribute=['times', 'dlc']) - - (self.behav, self.behav_events, - self.dlc_aligned) = self.combine_behaviour_data(wheel, dlc_left, dlc_right) - - self.sess_qc = self.get_qc_info(eid, one) - - self.spikes_raster = Bunch() - self.spikes_raster_psth = Bunch() - self.behav_raster = Bunch() - self.behav_raster_psth = Bunch() - - def get_cluster_metrics(self): - data = Bunch() - # Only if we have the metrics - if any(self.clusters.get('metrics', [None])): - for qc in CLUSTER_QC: - data[qc] = round((self.clusters.metrics[qc][self.clust_ids[self.clust]]), 3) - else: - for qc in CLUSTER_QC: - data[qc] = None - - return data - - def get_qc_info(self, eid, one): - data = Bunch() - sess = one.alyx.rest('sessions', 'read', id=eid)['extended_qc'] - for qc in SESS_QC: - data[qc] = sess.get(qc, '') - - return data - - def combine_behaviour_data(self, wheel, dlc_left, dlc_right): - behav = Bunch() - behav_options = [] - aligned = [True] - - if all([val in wheel.keys() for val in ['timestamps', 'position']]): - behav['wheel'] = wheel - behav_options = behav_options + ['wheel'] - - if all([val in dlc_left.keys() for val in ['times', 'dlc']]): - dlc = get_dlc_everything(dlc_left, 'left') - behav['leftCamera'] = dlc - keys = [f'leftCamera_{key}' for key in dlc.dlc.keys() if 'speed' in key] - behav_options = behav_options + keys - keys = [f'leftCamera_{key}' for key in dlc.keys() if - any([key in name for name in ['licks', 'sniffs']])] - behav_options = behav_options + keys - aligned.append(dlc['aligned']) - - if all([val in dlc_right.keys() for val in ['times', 'dlc']]): - dlc = get_dlc_everything(dlc_right, 'right') - behav['rightCamera'] = dlc - keys = [f'rightCamera_{key}' for key in dlc.dlc.keys() if 'speed' in key] - behav_options = behav_options + keys - keys = [f'rightCamera_{key}' for key in dlc.keys() if - any([key in name for name in ['licks', 'sniffs']])] - behav_options = behav_options + keys - aligned.append(dlc['aligned']) - - aligned = all(aligned) - - return behav, behav_options, aligned - - def sort_clusters(self, sort): - clust_sort = Bunch() - self.clust_ids = self.clusters.clust_ids[self.clusters[sort]] - clust_sort['ids'] = self.clust_ids - clust_sort['amps'] = self.clusters.amps[self.clusters[sort]] * 1e6 - clust_sort['depths'] = self.clusters.depths[self.clusters[sort]] - clust_sort['colours_ks'] = self.clusters.colours_ks[self.clusters[sort]] - clust_sort['colours_ibl'] = self.clusters.colours_ibl[self.clusters[sort]] - - return clust_sort - - def _get_spike_data_for_selection(self, clust, trial_event='goCue_times'): - """ - Triggered when one of the filters is changed, inputs should only be the filter options - :return: - """ - # Store the clust id and trial event - self.clust = clust - self.trial_event = trial_event - raster, t = get_event_aligned_raster(self.spikes.times[self.spikes.clusters == - self.clust_ids[clust]], - self.trials[trial_event], tbin=TBIN, epoch=EPOCH) - self.spikes_raster_psth['vals'] = raster - self.spikes_raster_psth['time'] = t - self.spikes_raster_psth['ylabel'] = 'Firing Rate / Hz' - - def _get_spike_raster(self, trial_ids): - # Ain't most efficient but it will do for now! - - data = Bunch() - - x = np.empty(0) - y = np.empty(0) - - epoch = [0.4, 1] - spk_times = self.spikes.times[self.spikes.clusters == self.clust_ids[self.clust]] - - for idx, val in enumerate(self.trials[self.trial_event][trial_ids]): - spks_to_include = np.bitwise_and(spk_times >= val - epoch[0], - spk_times <= val + epoch[1]) - trial_spk_times = spk_times[spks_to_include] - val - x = np.append(x, trial_spk_times) - y = np.append(y, np.ones(len(trial_spk_times)) * idx) - - data['raster'] = np.c_[x, y] - data['time'] = EPOCH - data['n_trials'] = self.n_trials - - return data - - def get_autocorr_for_selection(self): - data = Bunch() - x_corr = xcorr(self.spikes.times[self.spikes.clusters == self.clust_ids[self.clust]], - self.spikes.clusters[self.spikes.clusters == self.clust_ids[self.clust]], - AUTOCORR_BIN, AUTOCORR_WINDOW) - t_corr = np.arange(0, AUTOCORR_WINDOW + AUTOCORR_BIN, AUTOCORR_BIN) - AUTOCORR_WINDOW / 2 - - data['vals'] = x_corr[0, 0, :] - data['time'] = t_corr - - return data - - def get_template_for_selection(self): - data = Bunch() - template = (self.clusters.waveforms[self.clust_ids[self.clust], :, 0]) * 1e6 - t_template = 1e3 * np.arange(template.shape[0]) / FS - - data['vals'] = template - data['time'] = t_template - - return data - - def _get_behaviour_data_for_selection(self, behav, trial_event='goCue_times'): - """ - Triggered when one of the filters is changed, inputs should only be the filter options - :return: - """ - if behav == 'wheel': - v = velocity(self.behav[behav].timestamps, self.behav[behav].position) - raster, t = get_event_aligned_raster(self.behav[behav].timestamps, - self.trials[trial_event], values=v, tbin=TBIN, - epoch=EPOCH) - self.behav_raster['vals'] = raster - self.behav_raster['time'] = t - self.behav_raster['cmap'] = 'viridis' - self.behav_raster['clevels'] = np.nanquantile(raster, [0, 1]) - self.behav_raster['ylabel'] = 'rad / s' - - else: - camera = behav.split('_')[0] - behav = '_'.join(behav.split('_')[1:]) - - if behav == 'licks' or behav == 'sniffs': - raster, t = get_event_aligned_raster(self.behav[camera][behav], - self.trials[trial_event], tbin=TBIN, - epoch=EPOCH) - self.behav_raster['vals'] = raster - self.behav_raster['time'] = t - self.behav_raster['cmap'] = 'binary' - self.behav_raster['clevels'] = [0, 0.1] - self.behav_raster['ylabel'] = 'Rate (Hz)' - - else: - raster, t = get_event_aligned_raster(self.behav[camera]['times'], - self.trials[trial_event], - values=self.behav[camera]['dlc'][behav], - tbin=TBIN, epoch=EPOCH) - self.behav_raster['vals'] = raster - self.behav_raster['time'] = t - self.behav_raster['cmap'] = 'viridis' - self.behav_raster['clevels'] = np.nanquantile(raster, [0, 0.9]) - self.behav_raster['ylabel'] = 'px / s' - - def _get_psth(self, raster, trials_id, tbin=1): - data = Bunch() - if len(trials_id) == 0: - data['psth_mean'] = np.zeros((raster.vals.shape[1])) - data['psth_std'] = np.zeros((raster.vals.shape[1])) - data['time'] = raster.time - data['ylabel'] = raster.ylabel - else: - - data['psth_mean'] = np.nanmean(raster.vals[trials_id], axis=0) / tbin - data['psth_std'] = ((np.nanstd(raster.vals[trials_id], axis=0) / tbin) / - np.sqrt(trials_id.shape[0])) - data['time'] = raster.time - data['ylabel'] = raster.ylabel - - return data - - def _get_raster(self, raster, trials_id): - data = Bunch() - data['raster'] = np.r_[raster.vals[trials_id], - np.full((self.n_trials - trials_id.shape[0], - raster.vals.shape[1]), np.nan)] - data['time'] = raster.time - data['cmap'] = raster.cmap - data['clevels'] = raster.clevels - - return data - - def get_psths_for_selection(self, side, choice, order, sort, contrast, event): - trial_ids, _ = find_trial_ids(self.trials, side=side, choice=choice, order=order, - sort=sort, contrast=contrast, event=event) - spike_psth = self._get_psth(self.spikes_raster_psth, trial_ids, tbin=TBIN) - behav_psth = self._get_psth(self.behav_raster, trial_ids) - - return spike_psth, behav_psth - - def get_rasters_for_selection(self, side, choice, order, sort, contrast, event): - trial_ids, div = find_trial_ids(self.trials, side=side, choice=choice, order=order, - sort=sort, contrast=contrast, event=event) - dividers = self._get_dividers(div, trial_ids.shape[0]) - spike_raster = self._get_spike_raster(trial_ids) - spike_raster['dividers'] = dividers - behav_raster = self._get_raster(self.behav_raster, trial_ids) - behav_raster['dividers'] = dividers - - return spike_raster, behav_raster - - def _get_dividers(self, div, n_trials): - if len(div) == 0: - return div - else: - div.insert(0, 0) - div.append(n_trials) - dividers = [] - for i in range(len(div) - 1): - dividers.append([div[i], div[i + 1]]) - - return dividers diff --git a/PythonScripts/iblapps-master/data_exploration_gui/filter.py b/PythonScripts/iblapps-master/data_exploration_gui/filter.py deleted file mode 100644 index e2dc415f..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/filter.py +++ /dev/null @@ -1,210 +0,0 @@ -from PyQt5 import QtCore, QtWidgets, QtGui -import numpy as np -from data_exploration_gui import utils - - -class FilterGroup: - def __init__(self): - - # button to reset filters to default - self.reset_button = QtWidgets.QPushButton('Reset Filters') - - # checkboxes for contrasts - self.contrasts = utils.CONTRAST_OPTIONS - self.contrast_buttons = QtWidgets.QButtonGroup() - self.contrast_buttons.setExclusive(False) - self.contrast_group = QtWidgets.QGroupBox('Stimulus Contrast') - self.contrast_layout = QtWidgets.QVBoxLayout() - for val in self.contrasts: - button = QtWidgets.QCheckBox(str(val * 100)) - button.setCheckState(QtCore.Qt.Checked) - self.contrast_buttons.addButton(button) - self.contrast_layout.addWidget(button) - - self.contrast_group.setLayout(self.contrast_layout) - - # button for whether to overlay trial plots - self.hold_button = QtWidgets.QCheckBox('Hold') - self.hold_button.setCheckState(QtCore.Qt.Checked) - - # checkboxes for trial options - self.trial_buttons = QtWidgets.QButtonGroup() - # Just for now - self.trial_group = QtWidgets.QGroupBox('Trial Options') - self.trial_layout = QtWidgets.QVBoxLayout() - for val in utils.TRIAL_OPTIONS: - button = QtWidgets.QCheckBox(val) - if val == utils.TRIAL_OPTIONS[0]: - button.setCheckState(QtCore.Qt.Checked) - else: - button.setCheckState(QtCore.Qt.Unchecked) - self.trial_buttons.addButton(button) - self.trial_layout.addWidget(button) - - self.trial_colours = QtWidgets.QVBoxLayout() - for val in utils.TRIAL_OPTIONS: - img = QtWidgets.QLabel() - pix = QtGui.QPixmap(40, 5) - pix.fill(utils.colours[val]) - img.setPixmap(pix) - self.trial_colours.addWidget(img) - - hlayout = QtWidgets.QHBoxLayout() - hlayout.addLayout(self.trial_layout) - hlayout.addLayout(self.trial_colours) - - self.trial_group.setLayout(hlayout) - - # radio buttons for order options - self.order_buttons = QtWidgets.QButtonGroup() - self.order_group = QtWidgets.QGroupBox('Order Trials By:') - self.order_layout = QtWidgets.QVBoxLayout() - for val in utils.ORDER_OPTIONS: - button = QtWidgets.QRadioButton(val) - if val == utils.ORDER_OPTIONS[0]: - button.setChecked(True) - else: - button.setChecked(False) - self.order_buttons.addButton(button) - self.order_layout.addWidget(button) - - self.order_group.setLayout(self.order_layout) - - # radio buttons for sort options - self.sort_buttons = QtWidgets.QButtonGroup() - self.sort_group = QtWidgets.QGroupBox('Sort Trials By:') - self.sort_layout = QtWidgets.QVBoxLayout() - for val in utils.SORT_OPTIONS: - button = QtWidgets.QRadioButton(val) - if val == utils.SORT_OPTIONS[0]: - button.setChecked(True) - else: - button.setChecked(False) - self.sort_buttons.addButton(button) - self.sort_layout.addWidget(button) - self.sort_group.setLayout(self.sort_layout) - - self.event_group = QtWidgets.QGroupBox('Align to:') - self.event_list = QtGui.QStandardItemModel() - self.event_combobox = QtWidgets.QComboBox() - self.event_combobox.setModel(self.event_list) - self.event_layout = QtWidgets.QVBoxLayout() - self.event_layout.addWidget(self.event_combobox) - self.event_group.setLayout(self.event_layout) - - self.behav_group = QtWidgets.QGroupBox('Show behaviour:') - self.behav_list = QtGui.QStandardItemModel() - self.behav_combobox = QtWidgets.QComboBox() - self.behav_combobox.setModel(self.behav_list) - self.behav_layout = QtWidgets.QVBoxLayout() - self.behav_layout.addWidget(self.behav_combobox) - self.behav_group.setLayout(self.behav_layout) - - # Now group everything into one big box - self.filter_options_group = QtWidgets.QGroupBox() - group_layout = QtWidgets.QVBoxLayout() - group_layout.addWidget(self.reset_button) - group_layout.addWidget(self.contrast_group) - group_layout.addWidget(self.hold_button) - group_layout.addWidget(self.trial_group) - group_layout.addWidget(self.order_group) - group_layout.addWidget(self.sort_group) - group_layout.addWidget(self.event_group) - group_layout.addWidget(self.behav_group) - self.filter_options_group.setLayout(group_layout) - - def populate(self, event_options, behav_options): - self.populate_lists(event_options, self.event_list, self.event_combobox) - self.populate_lists(behav_options, self.behav_list, self.behav_combobox) - - def populate_lists(self, data, list_name, combobox): - - list_name.clear() - for dat in data: - item = QtGui.QStandardItem(dat) - item.setEditable(False) - list_name.appendRow(item) - - # This makes sure the drop down menu is wide enough to show full length of string - min_width = combobox.fontMetrics().width(max(data, key=len)) - min_width += combobox.view().autoScrollMargin() - min_width += combobox.style().pixelMetric(QtWidgets.QStyle.PM_ScrollBarExtent) - combobox.view().setMinimumWidth(min_width) - - # Set the default to be the first option - combobox.setCurrentIndex(0) - - def set_selected_event(self, event): - for it in range(self.event_combobox.count()): - if self.event_combobox.itemText(it) == event: - self.event_combobox.setCurrentIndex(it) - - def get_selected_event(self): - return self.event_combobox.currentText() - - def get_selected_behaviour(self): - return self.behav_combobox.currentText() - - def get_selected_contrasts(self): - contrasts = [] - for button in self.contrast_buttons.buttons(): - if button.isChecked(): - contrasts.append(np.float(button.text()) / 100) - - return contrasts - - def get_selected_trials(self): - trials = [] - for button in self.trial_buttons.buttons(): - if button.isChecked(): - trials.append(button.text()) - return trials - - def get_selected_order(self): - return self.order_buttons.checkedButton().text() - - def get_selected_sort(self): - return self.sort_buttons.checkedButton().text() - - def get_hold_status(self): - return self.hold_button.isChecked() - - def get_selected_filters(self): - contrasts = self.get_selected_contrasts() - order = self.get_selected_order() - sort = self.get_selected_sort() - hold = self.get_hold_status() - - return contrasts, order, sort, hold - - def set_sorted_button(self, sort): - for button in self.sort_buttons.buttons(): - if button.text() == sort: - button.setChecked(True) - else: - button.setChecked(False) - - def reset_filters(self, contrasts=True): - - if contrasts: - for button in self.contrast_buttons.buttons(): - button.setChecked(True) - - for button in self.trial_buttons.buttons(): - if button.text() == utils.TRIAL_OPTIONS[0]: - #if not button.isChecked(): - button.setChecked(True) - else: - button.setChecked(False) - - for button in self.order_buttons.buttons(): - if button.text() == utils.ORDER_OPTIONS[0]: - button.setChecked(True) - else: - button.setChecked(False) - - for button in self.sort_buttons.buttons(): - if button.text() == utils.SORT_OPTIONS[0]: - button.setChecked(True) - else: - button.setChecked(False) \ No newline at end of file diff --git a/PythonScripts/iblapps-master/data_exploration_gui/filter_class.py b/PythonScripts/iblapps-master/data_exploration_gui/filter_class.py deleted file mode 100644 index b38eba0e..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/filter_class.py +++ /dev/null @@ -1,486 +0,0 @@ -from PyQt5 import QtCore, QtWidgets, QtGui -import numpy as np -from iblutil.util import Bunch - - -class FilterGroup: - - def __init__(self): - - self.reset_filter_button = QtWidgets.QPushButton('Reset Filters') - - self.contrast_options_text = QtWidgets.QLabel('Stimulus Contrast') - self.contrasts = [1, 0.25, 0.125, 0.0625, 0] - self.contrast_options = QtWidgets.QListWidget() - for val in self.contrasts: - item = QtWidgets.QListWidgetItem(str(val * 100) + ' %') - item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable) - item.setCheckState(QtCore.Qt.Checked) - self.contrast_options.addItem(item) - - self.hold_button = QtWidgets.QCheckBox('Hold') - self.hold_button.setCheckState(QtCore.Qt.Checked) - - self.filter_buttons = QtWidgets.QButtonGroup() - self.filter_group = QtWidgets.QGroupBox('Filter Options') - self.filter_layout = QtWidgets.QVBoxLayout() - self.filter_layout.setSpacing(5) - #self.filter_buttons.setExclusive(False) - filter_options = ['all', 'correct', 'incorrect', 'left', 'right', 'left correct', 'left incorrect', 'right correct', 'right incorrect'] - for i, val in enumerate(filter_options): - button = QtWidgets.QCheckBox(val) - if val == 'all': - button.setCheckState(QtCore.Qt.Checked) - else: - button.setCheckState(QtCore.Qt.Unchecked) - self.filter_buttons.addButton(button, id=i) - self.filter_layout.addWidget(button) - - self.filter_group.setLayout(self.filter_layout) - - self.trial_buttons = QtWidgets.QButtonGroup() - self.trial_group = QtWidgets.QGroupBox('Sort Trials By:') - self.trial_layout = QtWidgets.QHBoxLayout() - trial_options = ['trial no.', 'correct vs incorrect', 'left vs right', 'correct vs incorrect and left vs right'] - for i, val in enumerate(trial_options): - button = QtWidgets.QRadioButton(val) - if i == 0: - button.setChecked(True) - else: - button.setChecked(False) - self.trial_buttons.addButton(button, id = i) - self.trial_layout.addWidget(button) - - self.trial_group.setLayout(self.trial_layout) - - # Print out no. of trials for each filter condition - self.ntrials_text = QtWidgets.QLabel('No. of trials = ') - - self.filter_options_group = QtWidgets.QGroupBox() - self.group_filter_widget() - self.filter_options_group.setFixedSize(250, 380) - - def group_filter_widget(self): - group_layout = QtWidgets.QVBoxLayout() - group_layout.addWidget(self.reset_filter_button) - group_layout.addWidget(self.contrast_options_text) - group_layout.addWidget(self.contrast_options) - group_layout.addWidget(self.hold_button) - group_layout.addWidget(self.filter_group) - group_layout.addWidget(self.ntrials_text) - self.filter_options_group.setLayout(group_layout) - - def get_checked_contrasts(self): - - ''' - Finds the contrast options that are selected. Called by on_contrast_list_changed in gui_main. - - Returns - ---------- - - stim_contrast: list - A list of the contrast options that are selected - - ''' - - stim_contrast = [] - for idx in range(self.contrast_options.count()): - if self.contrast_options.item(idx).checkState() == QtCore.Qt.Checked: - stim_contrast.append(self.contrasts[idx]) - - return stim_contrast - - - def compute_and_sort_trials(self, stim_contrast): - #Precompute trials for a given contrast set - #All - - all_trials = Bunch() - all_trials['colour'] = QtGui.QColor('#808080') - all_trials['fill'] = QtGui.QColor('#808080') - all_trials['linestyle'] = QtGui.QPen(QtCore.Qt.SolidLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - for c in stim_contrast: - idx = np.where((self.trials['contrastLeft'] == c) | (self.trials['contrastRight'] == c))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - all_trials['trial no.'] = trials_no - - - trials_ic = Bunch() - correct = np.intersect1d(trials_id, self.correct_idx) - incorrect = np.intersect1d(trials_id, self.incorrect_idx) - trials_ic['trials'] = np.append(correct, incorrect) - trials_ic['lines'] = [[0, len(correct)], [len(correct), len(trials_ic['trials'])]] - trials_ic['linecolours'] = [QtGui.QColor('#1f77b4'), QtGui.QColor('#d62728')] - trials_ic['text'] = ['correct', 'incorrect'] - all_trials['correct vs incorrect'] = trials_ic - - trials_lf = Bunch() - left = np.intersect1d(trials_id, self.left_idx) - right = np.intersect1d(trials_id, self.right_idx) - trials_lf['trials'] = np.append(left, right) - trials_lf['lines'] = [[0, len(left)], [len(left), len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#2ca02c'), QtGui.QColor('#bcbd22')] - trials_lf['text'] = ['left', 'right'] - all_trials['left vs right'] = trials_lf - - trials_iclf = Bunch() - correct_right = np.intersect1d(trials_id, self.correct_right_idx) - correct_left = np.intersect1d(trials_id, self.correct_left_idx) - incorrect_right = np.intersect1d(trials_id, self.incorrect_right_idx) - incorrect_left = np.intersect1d(trials_id, self.incorrect_left_idx) - trials_iclf['trials'] = np.concatenate((correct_left, correct_right, incorrect_left, incorrect_right)) - trials_iclf['lines'] = [[0, len(correct_left)], [len(correct_left), len(correct_left) - + len(correct_right)], [len(correct_left) + len(correct_right), len(correct_left) - + len(correct_right) + len(incorrect_left)],[len(correct_left) + len(correct_right) - + len(incorrect_left), len(trials_iclf['trials'])]] - trials_iclf['linecolours'] = [QtGui.QColor('#17becf'), QtGui.QColor('#9467bd'), QtGui.QColor('#8c564b'), QtGui.QColor('#ff7f0e')] - trials_iclf['text'] = ['left correct', 'right correct', 'left incorrect', 'right incorrect'] - all_trials['correct vs incorrect and left vs right'] = trials_iclf - - - #Correct - correct_trials = Bunch() - correct_trials['colour'] = QtGui.QColor('#1f77b4') - correct_trials['fill'] = QtGui.QColor('#1f77b4') - correct_trials['linestyle'] = QtGui.QPen(QtCore.Qt.SolidLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - for c in stim_contrast: - idx = np.where(((self.trials['contrastLeft'] == c) | (self.trials['contrastRight'] == c)) & (self.trials['feedbackType'] == 1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - correct_trials['trial no.'] = trials_no - - - trials_ic = Bunch() - trials_ic['trials'] = trials_id - trials_ic['lines'] = [[0, len(trials_ic['trials'])]] - trials_ic['linecolours'] = [QtGui.QColor('#1f77b4')] - trials_ic['text'] = ['correct'] - correct_trials['correct vs incorrect'] = trials_ic - - trials_lf = Bunch() - left = np.intersect1d(trials_id, self.correct_left_idx) - right = np.intersect1d(trials_id, self.correct_right_idx) - trials_lf['trials'] = np.append(left, right) - trials_lf['lines'] = [[0, len(left)], [len(left), len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#17becf'), QtGui.QColor('#9467bd')] - trials_lf['text'] = ['left correct', 'right correct'] - correct_trials['left vs right'] = trials_lf - correct_trials['correct vs incorrect and left vs right'] = trials_lf - - #Incorrect - incorrect_trials = Bunch() - incorrect_trials['colour'] = QtGui.QColor('#d62728') - incorrect_trials['fill'] = QtGui.QColor('#d62728') - incorrect_trials['linestyle'] = QtGui.QPen(QtCore.Qt.SolidLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - for c in stim_contrast: - idx = np.where(((self.trials['contrastLeft'] == c) | (self.trials['contrastRight'] == c)) & (self.trials['feedbackType'] == -1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - incorrect_trials['trial no.'] = trials_no - - trials_ic = Bunch() - trials_ic['trials'] = trials_id - trials_ic['lines'] = [[0, len(trials_ic['trials'])]] - trials_ic['linecolours'] = [QtGui.QColor('#d62728')] - trials_ic['text'] = ['incorrect'] - incorrect_trials['correct vs incorrect'] = trials_ic - - trials_lf = Bunch() - trials_iclf = Bunch() - left = np.intersect1d(trials_id, self.incorrect_left_idx) - right = np.intersect1d(trials_id, self.incorrect_right_idx) - trials_lf['trials'] = np.append(left, right) - trials_lf['lines'] = [[0, len(left)], [len(left), len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#8c564b'), QtGui.QColor('#ff7f0e')] - trials_lf['text'] = ['left incorrect', 'right incorrect'] - incorrect_trials['left vs right'] = trials_lf - incorrect_trials['correct vs incorrect and left vs right'] = trials_lf - - #Left - left_trials = Bunch() - left_trials['colour'] = QtGui.QColor('#2ca02c') - left_trials['fill'] = QtGui.QColor('#2ca02c') - left_trials['linestyle'] = QtGui.QPen(QtCore.Qt.SolidLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - for c in stim_contrast: - idx = np.where(self.trials['contrastLeft'] == c)[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - left_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#2ca02c')] - trials_lf['text'] = ['left'] - left_trials['left vs right'] = trials_lf - - trials_ic = Bunch() - correct = np.intersect1d(trials_id, self.correct_left_idx) - incorrect = np.intersect1d(trials_id, self.incorrect_left_idx) - trials_ic['trials'] = np.append(correct, incorrect) - trials_ic['lines'] = [[0, len(correct)], [len(correct), len(trials_ic['trials'])]] - trials_ic['linecolours'] = [QtGui.QColor('#17becf'), QtGui.QColor('#8c564b')] - trials_ic['text'] = ['left correct', 'left incorrect'] - left_trials['correct vs incorrect'] = trials_ic - left_trials['correct vs incorrect and left vs right'] = trials_ic - - #Right - right_trials = Bunch() - right_trials['colour'] = QtGui.QColor('#bcbd22') - right_trials['fill'] = QtGui.QColor('#bcbd22') - right_trials['linestyle'] = QtGui.QPen(QtCore.Qt.SolidLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - for c in stim_contrast: - idx = np.where(self.trials['contrastRight'] == c)[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - right_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#bcbd22')] - trials_lf['text'] = ['right'] - right_trials['left vs right'] = trials_lf - - trials_ic = Bunch() - correct = np.intersect1d(trials_id, self.correct_right_idx) - incorrect = np.intersect1d(trials_id, self.incorrect_right_idx) - trials_ic['trials'] = np.append(correct, incorrect) - trials_ic['lines'] = [[0, len(correct)], [len(correct), len(trials_ic['trials'])]] - trials_ic['linecolours'] = [QtGui.QColor('#9467bd'), QtGui.QColor('#ff7f0e')] - trials_ic['text'] = ['right correct', 'right incorrect'] - right_trials['correct vs incorrect'] = trials_ic - right_trials['correct vs incorrect and left vs right'] = trials_ic - - - #Left Correct - left_correct_trials = Bunch() - left_correct_trials['colour'] = QtGui.QColor('#17becf') - left_correct_trials['fill'] = QtGui.QColor('#17becf') - left_correct_trials['linestyle'] = QtGui.QPen(QtCore.Qt.DashLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - - for c in stim_contrast: - idx = np.where((self.trials['contrastLeft'] == c) & (self.trials['feedbackType'] == 1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - left_correct_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#17becf')] - trials_lf['text'] = ['left correct'] - - left_correct_trials['left vs right'] = trials_lf - left_correct_trials['correct vs incorrect'] = trials_lf - left_correct_trials['correct vs incorrect and left vs right'] = trials_lf - - - #Left Incorrect - left_incorrect_trials = Bunch() - left_incorrect_trials['colour'] = QtGui.QColor('#8c564b') - left_incorrect_trials['fill'] = QtGui.QColor('#8c564b') - left_incorrect_trials['linestyle'] = QtGui.QPen(QtCore.Qt.DashLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - - for c in stim_contrast: - idx = np.where((self.trials['contrastLeft'] == c) & (self.trials['feedbackType'] == -1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - left_incorrect_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#8c564b')] - trials_lf['text'] = ['left incorrect'] - left_incorrect_trials['left vs right'] = trials_lf - left_incorrect_trials['correct vs incorrect'] = trials_lf - left_incorrect_trials['correct vs incorrect and left vs right'] = trials_lf - - #Right Correct - right_correct_trials = Bunch() - right_correct_trials['colour'] = QtGui.QColor('#9467bd') - right_correct_trials['fill'] = QtGui.QColor('#9467bd') - right_correct_trials['linestyle'] = QtGui.QPen(QtCore.Qt.DashLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - - for c in stim_contrast: - idx = np.where((self.trials['contrastRight'] == c) & (self.trials['feedbackType'] == 1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - right_correct_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#9467bd')] - trials_lf['text'] = ['right correct'] - right_correct_trials['left vs right'] = trials_lf - right_correct_trials['correct vs incorrect'] = trials_lf - right_correct_trials['correct vs incorrect and left vs right'] = trials_lf - - #Right Incorrect - right_incorrect_trials = Bunch() - right_incorrect_trials['colour'] = QtGui.QColor('#ff7f0e') - right_incorrect_trials['fill'] = QtGui.QColor('#ff7f0e') - right_incorrect_trials['linestyle'] = QtGui.QPen(QtCore.Qt.DashLine) - trials_id = np.empty(0,int) - idx= np.empty(0, int) - - for c in stim_contrast: - idx = np.where((self.trials['contrastRight'] == c) & (self.trials['feedbackType'] == -1))[0] - trials_id = np.append(trials_id, idx) - trials_id = np.setdiff1d(trials_id, self.nan_trials) - - trials_no = Bunch() - trials_no['trials'] = trials_id - trials_no['lines'] = [] - trials_no['linecolours'] = [] - trials_no['text'] = [] - right_incorrect_trials['trial no.'] = trials_no - - trials_lf = Bunch() - trials_lf['trials'] = trials_id - trials_lf['lines'] = [[0, len(trials_lf['trials'])]] - trials_lf['linecolours'] = [QtGui.QColor('#ff7f0e')] - trials_lf['text'] = ['right incorrect'] - right_incorrect_trials['left vs right'] = trials_lf - right_incorrect_trials['correct vs incorrect'] = trials_lf - right_incorrect_trials['correct vs incorrect and left vs right'] = trials_lf - - - trials = Bunch() - trials['all'] = all_trials - trials['correct'] = correct_trials - trials['incorrect'] = incorrect_trials - trials['left'] = left_trials - trials['right'] = right_trials - trials['left correct'] = left_correct_trials - trials['left incorrect'] = left_incorrect_trials - trials['right correct'] = right_correct_trials - trials['right incorrect'] = right_incorrect_trials - - return trials - - def get_sort_method(self, case): - if case == 'all': - sort_method = 'trial no.' - id = 0 - elif (case == 'correct') | (case == 'incorrect'): - sort_method = 'correct vs incorrect' - id = 1 - elif (case == 'left') | (case == 'right'): - sort_method = 'left vs right' - id = 2 - else: - sort_method = 'correct vs incorrect and left vs right' - id = 3 - - return sort_method, id - - - - def compute_trial_options(self, trials): - self.trials = trials - nan_feedback = np.where(np.isnan(self.trials['feedback_times']))[0] - nan_goCue = np.where(np.isnan(self.trials['goCue_times']))[0] - self.nan_trials = np.unique(np.append(nan_feedback, nan_goCue)) - self.n_trials = len(np.setdiff1d(np.arange(len(self.trials['feedbackType'])), self.nan_trials)) - self.correct_idx = np.setdiff1d(np.where(self.trials['feedbackType'] == 1)[0], self.nan_trials) - self.incorrect_idx = np.setdiff1d(np.where(self.trials['feedbackType'] == -1)[0], self.nan_trials) - self.right_idx = np.setdiff1d(np.where(np.isfinite(self.trials['contrastRight']))[0], self.nan_trials) - self.left_idx = np.setdiff1d(np.where(np.isfinite(self.trials['contrastLeft']))[0], self.nan_trials) - self.correct_right_idx = np.setdiff1d(np.intersect1d(self.correct_idx, self.right_idx), self.nan_trials) - self.correct_left_idx = np.setdiff1d(np.intersect1d(self.correct_idx, self.left_idx), self.nan_trials) - self.incorrect_right_idx = np.setdiff1d(np.intersect1d(self.incorrect_idx, self.right_idx), self.nan_trials) - self.incorrect_left_idx = np.setdiff1d(np.intersect1d(self.incorrect_idx, self.left_idx), self.nan_trials) - - return self.nan_trials - - def reset_filters(self, stim = True): - stim_contrast = [1, 0.25, 0.125, 0.0625, 0] - case = 'all' - sort_method = 'trial no.' - if stim is True: - for idx in range(self.contrast_options.count()): - item = self.contrast_options.item(idx) - item.setCheckState(QtCore.Qt.Checked) - - for idx, but in enumerate(self.filter_buttons.buttons()): - if idx == 0: - but.setCheckState(QtCore.Qt.Checked) - else: - but.setCheckState(QtCore.Qt.Unchecked) - - for idx, but in enumerate(self.trial_buttons.buttons()): - if idx == 0: - but.setChecked(True) - else: - but.setChecked(False) - - return stim_contrast, case, sort_method - diff --git a/PythonScripts/iblapps-master/data_exploration_gui/gui_main.py b/PythonScripts/iblapps-master/data_exploration_gui/gui_main.py deleted file mode 100644 index 9490c212..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/gui_main.py +++ /dev/null @@ -1,428 +0,0 @@ -from PyQt5 import QtWidgets, QtCore -import pyqtgraph as pg -import pyqtgraph.exporters - -import data_exploration_gui.cluster_class as clust -import data_exploration_gui.scatter_class as scatt -import data_exploration_gui.filter_class as filt -import data_exploration_gui.plot_class as plt -import data_exploration_gui.data_class as dat -import data_exploration_gui.misc_class as misc - -from pathlib import Path -import numpy as np - -import qt -import matplotlib.pyplot as mpl # noqa # This is needed to make qt show properly :/ - - -class MainWindow(QtWidgets.QMainWindow): - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, MainWindow)] - - @staticmethod - def _get_or_create(title='Data Exploration GUI', **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - MainWindow._instances()), None) - if av is None: - av = MainWindow(**kwargs) - av.setWindowTitle(title) - - return av - - def __init__(self): - super(MainWindow, self).__init__() - - # Initialise cluster group - self.cluster = clust.ClusterGroup() - self.cluster.cluster_list.clicked.connect(self.on_cluster_list_clicked) - self.cluster.cluster_next_button.clicked.connect(self.on_next_cluster_clicked) - self.cluster.cluster_previous_button.clicked.connect(self.on_previous_cluster_clicked) - self.cluster.cluster_buttons.buttonClicked.connect(self.on_cluster_sort_clicked) - - # Initialise scatter group - self.scatter = scatt.ScatterGroup() - self.scatter.fig_scatter.scene().sigMouseMoved.connect(self.on_mouse_hover) - self.scatter.scatter_plot.sigClicked.connect(self.on_scatter_plot_clicked) - self.scatter.scatter_reset_button.clicked.connect(self.on_scatter_reset_button_clicked) - self.scatter_exporter = pg.exporters.ImageExporter(self.scatter.fig_scatter.plotItem) - self.scatter.fig_scatter.sigDeviceRangeChanged.connect(lambda: self.on_fig_size_changed( - self.scatter_exporter, self.scatter.scatter_reset_button)) - - # Initialise filter group - self.filter = filt.FilterGroup() - self.filter.reset_filter_button.clicked.connect(self.on_reset_filter_button_clicked) - self.filter.contrast_options.itemClicked.connect(self.on_contrast_list_changed) - self.filter.hold_button.stateChanged.connect(self.hold_button_clicked) - self.filter.filter_buttons.buttonToggled.connect(self.on_button) - self.filter.trial_buttons.buttonClicked.connect(self.on_trial_sort_change) - - # Initialise figure group - self.plots = plt.PlotGroup() - self.plots.fig1_button.clicked.connect(self.on_fig1_button_clicked) - self.plots.fig2_button.clicked.connect(self.on_fig2_button_clicked) - self.plots.fig3_button.clicked.connect(lambda: self.on_raster_button_clicked(self.plots.fig3_raster)) - self.plots.fig4_button.clicked.connect(lambda: self.on_raster_button_clicked(self.plots.fig4_raster)) - - self.fig1_exporter = pg.exporters.ImageExporter(self.plots.fig1_peth.fig.plotItem) - self.plots.fig1_peth.fig.sigDeviceRangeChanged.connect(lambda: self.on_fig_size_changed(self.fig1_exporter, self.plots.fig1_button)) - self.fig2_exporter = pg.exporters.ImageExporter(self.plots.fig2_peth.fig.plotItem) - self.plots.fig2_peth.fig.sigDeviceRangeChanged.connect(lambda: self.on_fig_size_changed(self.fig2_exporter, self.plots.fig2_button)) - self.fig3_exporter = pg.exporters.ImageExporter(self.plots.fig3_raster.fig.plotItem) - self.plots.fig3_raster.fig.sigDeviceRangeChanged.connect(lambda: self.on_fig_size_changed(self.fig3_exporter, self.plots.fig3_button)) - self.fig4_exporter = pg.exporters.ImageExporter(self.plots.fig4_raster.fig.plotItem) - self.plots.fig4_raster.fig.sigDeviceRangeChanged.connect(lambda: self.on_fig_size_changed(self.fig4_exporter, self.plots.fig4_button)) - - # Intitialise data group - self.data = dat.DataGroup() - self.data.waveform_list.clicked.connect(self.on_waveform_list_clicked) - self.data.waveform_button.clicked.connect(self.on_waveform_button_clicked) - - - # Initialise misc group - self.misc = misc.MiscGroup() - self.misc.folder_button.clicked.connect(self.on_folder_button_clicked) - self.misc.remove_clust_button1.clicked.connect(lambda: self.on_remove_cluster_button_clicked(self.misc.clust_list1)) - self.misc.remove_clust_button2.clicked.connect(lambda: self.on_remove_cluster_button_clicked(self.misc.clust_list2)) - self.misc.save_clust_button.clicked.connect(self.on_save_button_clicked) - self.misc.terminal_button.clicked.connect(self.on_terminal_button_clicked) - - # Setup main figure and add all widgets to display - self.resize(1800, 1000) - main_widget = QtWidgets.QWidget() - self.setCentralWidget(main_widget) - - main_widget_layout = QtWidgets.QGridLayout() - main_widget_layout.setHorizontalSpacing(20) - main_widget_layout.setVerticalSpacing(20) - main_widget_layout.setColumnStretch(0, 5) - main_widget_layout.setColumnStretch(1, 1) - main_widget_layout.addWidget(self.misc.folder_group, 0, 0, 1, 2) - main_widget_layout.addWidget(self.cluster.cluster_list_group, 1, 0, 2, 1) - main_widget_layout.addWidget(self.scatter.fig_scatter, 3, 0, 4, 1) - main_widget_layout.addWidget(self.misc.terminal, 7, 0, 1, 2) - main_widget_layout.addWidget(self.filter.filter_options_group, 1, 1, 3, 1) - main_widget_layout.addWidget(self.misc.clust_interest, 4, 1, 3, 1) - main_widget_layout.addWidget(self.plots.fig_area, 0, 2, 7, 8) - main_widget_layout.addWidget(self.filter.trial_group, 7, 6) - main_widget_layout.addWidget(self.data.waveform_group, 7, 9) - main_widget.setLayout(main_widget_layout) - - - - def reset_gui(self): - self.filter.filter_buttons.blockSignals(True) - [self.stim_contrast, self.case, self.sort_method] = \ - self.filter.reset_filters() - self.filter.filter_buttons.blockSignals(False) - self.filter.hold_button.setCheckState(QtCore.Qt.Checked) - self.filter.filter_buttons.setExclusive(False) - self.cluster.reset() - self.scatter.reset() - self.plots.reset() - self.data.reset() - - def initialise_gui(self): - self.cluster.populate(self.clust_ids, self.clust_colours) - [self.clust, self.clust_prev] = self.cluster.initialise_cluster_index() - self.scatter.populate(self.clust_amps, self.clust_depths, self.clust_ids, - self.clust_colours) - self.scatter.initialise_scatter_index() - self.data.populate(self.clust) - self.plot_status = np.zeros(9, dtype=bool) - self.hold = True - - def on_folder_button_clicked(self): - - self.reset_gui() - folder_path = Path(QtWidgets.QFileDialog.getExistingDirectory(None, "Select Folder")) - - if str(folder_path) == '.': - self.misc.terminal.append('>> Cancelling loading') - else: - self.misc.folder_line.setText(str(folder_path)) - [self.ephys_file_path, self.gui_path] = self.data.load(folder_path) - self.misc.terminal.append('>> Loaded data for ' + str(folder_path)) - if len(self.ephys_file_path) != 0: - self.misc.terminal.append('>> Found ephys file: ' + str(self.ephys_file_path)) - else: - self.misc.terminal.append('>> Ephys file not found... WARNING Figure 7 will not work') - - order = self.data.sort_by_id - [self.clust_ids, self.clust_amps, self.clust_depths, self.clust_colours] = \ - self.data.sort_data(order) - - self.initialise_gui() - nan_trials = self.filter.compute_trial_options(self.data.trials) - self.misc.terminal.append('>> Found ' + str(len(nan_trials)) + ' nan trials in data ...... these will be removed') - - self.trials = self.filter.compute_and_sort_trials(self.stim_contrast) - self.trials_id = self.trials[self.case][self.sort_method]['trials'] - - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - - self.update_display_string() - - def on_data_given(self, data): - self.reset_gui() - self.data.prepare_data(data.spikes, data.clusters, data.trials) - self.data.compute_timescales() - - order = self.data.sort_by_id - [self.clust_ids, self.clust_amps, self.clust_depths, self.clust_colours] = \ - self.data.sort_data(order) - - self.initialise_gui() - nan_trials = self.filter.compute_trial_options(self.data.trials) - self.misc.terminal.append('>> Found ' + str( - len(nan_trials)) + ' nan trials in data ...... these will be removed') - - self.trials = self.filter.compute_and_sort_trials(self.stim_contrast) - self.trials_id = self.trials[self.case][self.sort_method]['trials'] - - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, - self.sort_method, self.hold) - - self.update_display_string() - - - - # All commands associated with cluster list - def on_cluster_list_clicked(self): - # Get new cluster index - self.clust = self.cluster.on_cluster_list_clicked() - print(self.clust) - - # Update cluster list and scatter plot - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - - # Repopulate spike list for current cluster - self.data.populate(self.clust) - # Change plots - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - - self.update_display_string() - - - def hold_button_clicked(self, state): - if state == QtCore.Qt.Checked: - self.hold = True - self.filter.filter_buttons.setExclusive(False) - else: - self.hold = False - self.filter.filter_buttons.blockSignals(True) - [ _, self.case, self.sort_method] = \ - self.filter.reset_filters(stim=False) - self.filter.filter_buttons.setExclusive(True) - self.plots.reset() - self.filter.filter_buttons.blockSignals(False) - - - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - - - def on_button(self, button, signal): - - if self.hold is False: - self.case = button.text() - self.plots.change_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - else: - if signal is True: - self.case = button.text() - [self.sort_method, id] = self.filter.get_sort_method(self.case) - self.filter.trial_buttons.button(id).setChecked(True) - self.plots.change_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - else: - #if self.filter.filter_buttons.checkedButton(): - self.case = button.text() - self.plots.remove_plot_item(self.case) - - def on_next_cluster_clicked(self): - - if self.clust != len(self.clust_ids) - 1: - self.clust = self.cluster.on_next_cluster_clicked() - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data.populate(self.clust) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - else: - self.misc.terminal.append('>> Already on last cluster ... cannot move to next cluster') - - def on_previous_cluster_clicked(self): - - if self.clust != 0: - self.clust = self.cluster.on_previous_cluster_clicked() - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data.populate(self.clust) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - else: - self.misc.terminal.append('>> Already on first cluster ... cannot move to previous cluster') - - def on_cluster_sort_clicked(self, button): - - option = button.text() - if option == 'cluster no.': - order = self.data.sort_by_id - elif option == 'no. of spikes': - order = self.data.sort_by_nspikes - else: - order = self.data.sort_by_good - - [self.clust_ids, self.clust_amps, self.clust_depths, self.clust_colours] = \ - self.data.sort_data(order) - - self.reset_gui() - self.initialise_gui() - - self.trials = self.filter.compute_and_sort_trials(self.stim_contrast) - self.trials_id = self.trials[self.case][self.sort_method]['trials'] - - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - - self.update_display_string() - self.misc.terminal.append('>> Clusters sorted by ' + str(option)) - - def on_mouse_hover(self, pos): - self.scatter.on_mouse_hover(pos) - - def on_scatter_plot_clicked(self, scatter, point): - - self.clust = self.scatter.on_scatter_plot_clicked(point) - self.cluster.update_current_row(self.clust) - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data.populate(self.clust) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - - def on_scatter_reset_button_clicked(self): - self.scatter.on_scatter_plot_reset() - - def on_contrast_list_changed(self): - - self.stim_contrast = self.filter.get_checked_contrasts() - self.trials = self.filter.compute_and_sort_trials(self.stim_contrast) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.check_n_trials() - self.update_display_string() - - def on_cluster_chosen(self, cluster): - self.clust = np.where(self.data.ids == cluster)[0][0] - print(self.clust) - print(self.clust_prev) - self.cluster.update_current_row(self.clust) - self.cluster.update_list_icon() - self.scatter.set_current_point(self.clust) - self.scatter.update_scatter_icon(self.clust_prev) - self.scatter.update_prev_point() - self.clust_prev = self.cluster.update_cluster_index() - self.data.populate(self.clust) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - - - def on_trial_sort_change(self, button): - - self.sort_method = button.text() - if self.hold == False: - self.plots.change_rasters(self.data, self.clust, self.trials, self.case, self.sort_method) - else: - self.plots.change_rasters(self.data, self.clust, self.trials, 'all', self.sort_method) - - - def on_reset_filter_button_clicked(self): - - [self.stim_contrast, self.case, self.sort_method] = self.filter.reset_filters() - self.trials = self.filter.compute_and_sort_trials(self.stim_contrast) - self.plots.change_all_plots_final(self.data, self.clust, self.trials, self.case, self.sort_method, self.hold) - self.update_display_string() - self.misc.terminal.append('>> All filters reset') - - def on_fig1_button_clicked(self): - item = QtWidgets.QListWidgetItem('Cluster ' + str(self.clust_ids[self.clust])) - self.misc.clust_list1.addItem(item) - - def on_fig2_button_clicked(self): - item = QtWidgets.QListWidgetItem('Cluster ' + str(self.clust_ids[self.clust])) - self.misc.clust_list2.addItem(item) - - def on_raster_button_clicked(self, fig): - fig.reset_axis() - - def on_remove_cluster_button_clicked(self, clust_list): - item = clust_list.currentRow() - clust_list.takeItem(item) - - def on_save_button_clicked(self): - file = self.misc.on_save_button_clicked(self.gui_path) - if file: - self.misc.terminal.append('>> Clusters saved to file ' + file) - else: - self.misc.terminal.append('>> Cancelled saving') - - def on_waveform_list_clicked(self): - self.data.n_waveform = self.data.waveform_list.currentRow() - - def on_waveform_button_clicked(self): - if len(self.ephys_file_path) != 0: - self.plots.plot_waveform(self.data, self.clust) - else: - self.misc.terminal.append('>> WARNING Did not find ephys file... cannot generate waveform') - - def on_terminal_button_clicked(self): - self.misc.terminal.clear() - - def on_fig_size_changed(self, exporter, button): - fig_width = exporter.getTargetRect().width() - button_width = 0.2 * fig_width - button.setFixedWidth(button_width) - # button_width = button.width() - button.move(fig_width - button_width, 0) - - def check_n_trials(self): - self.trials_id = self.trials[self.case][self.sort_method]['trials'] - if len(self.trials_id) == 0: - self.misc.terminal.append('>> No trials for this filter combination') - - def update_display_string(self): - self.trials_id = self.trials[self.case][self.sort_method]['trials'] - self.filter.ntrials_text.setText('No. of trials = ' + str(len(self.trials_id))) - self.data.waveform_text.setText('No. of spikes = ' + str(len(self.data.clus_idx))) - - -def viewer(data=None): - """ - """ - qt.create_app() - av = MainWindow._get_or_create() - av.show() - if data is not None: - av.on_data_given(data) - return av - - -if __name__ == '__main__': - app = QtWidgets.QApplication([]) - mainapp = MainWindow() - mainapp.show() - app.exec_() diff --git a/PythonScripts/iblapps-master/data_exploration_gui/load_data.py b/PythonScripts/iblapps-master/data_exploration_gui/load_data.py deleted file mode 100644 index 00855dd9..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/load_data.py +++ /dev/null @@ -1,36 +0,0 @@ -import argparse -from one.api import ONE -import numpy as np - -parser = argparse.ArgumentParser(description='Load in subject info') - -parser.add_argument('-s', '--subject', default=False, required=False, - help='Subject Name') -parser.add_argument('-d', '--date', default=False, required=False, - help='Date of session YYYY-MM-DD') -parser.add_argument('-n', '--session_no', default=1, required=False, - help='Session Number', type=int) -parser.add_argument('-e', '--eid', default=False, required=False, - help='Session eid') -parser.add_argument('-p', '--probe_label', default=False, required=True, - help='Probe Label') - -args = parser.parse_args() - -one = ONE() -if not args.eid: - if not np.all(np.array([args.subject, args.date, args.session_no], - dtype=object)): - print('Must give Subject, Date and Session number') - else: - eid = one.search(subject=str(args.subject), date=str(args.date), number=args.session_no)[0] - print(eid) -else: - eid = str(args.eid) - -_ = one.load_object(eid, obj='trials', collection='alf', - download_only=True) -_ = one.load_object(eid, obj='spikes', attribute=['times', 'clusters', 'amps', 'depths'], - collection=f'alf/{str(args.probe_label)}', download_only=True) -_ = one.load_object(eid, obj='clusters', collection=f'alf/{str(args.probe_label)}', - download_only=True) diff --git a/PythonScripts/iblapps-master/data_exploration_gui/misc.py b/PythonScripts/iblapps-master/data_exploration_gui/misc.py deleted file mode 100644 index ae2b8fb3..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/misc.py +++ /dev/null @@ -1,45 +0,0 @@ -from PyQt5 import QtWidgets -from data_exploration_gui import utils - -class MiscGroup: - def __init__(self): - - self.qc_group = QtWidgets.QGroupBox() - self.dlc_warning_label = QtWidgets.QLabel() - self.sess_qc_group = QtWidgets.QHBoxLayout() - self.clust_qc_group = QtWidgets.QHBoxLayout() - - self.sess_qc_labels = [] - self.clust_qc_labels = [] - - for qc in utils.SESS_QC: - qc_label = QtWidgets.QLabel(f'{qc}:') - self.sess_qc_group.addWidget(qc_label) - self.sess_qc_labels.append(qc_label) - - for qc in utils.CLUSTER_QC: - qc_label = QtWidgets.QLabel(f'{qc}: ') - self.clust_qc_group.addWidget(qc_label) - self.clust_qc_labels.append(qc_label) - - vlayout = QtWidgets.QVBoxLayout() - vlayout.addWidget(self.dlc_warning_label) - vlayout.addLayout(self.sess_qc_group) - vlayout.addLayout(self.clust_qc_group) - self.qc_group.setLayout(vlayout) - - def set_sess_qc_text(self, data): - for label in self.sess_qc_labels: - title = label.text().split(':')[0] - text = title + ': ' + str(data[title]) - label.setText(text) - - def set_clust_qc_text(self, data): - for label in self.clust_qc_labels: - title = label.text().split(':')[0] - text = title + ': ' + str(data[title]) - label.setText(text) - - def set_dlc_label(self, aligned): - if not aligned: - self.dlc_warning_label.setText(utils.dlc_warning) \ No newline at end of file diff --git a/PythonScripts/iblapps-master/data_exploration_gui/misc_class.py b/PythonScripts/iblapps-master/data_exploration_gui/misc_class.py deleted file mode 100644 index fcc413c5..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/misc_class.py +++ /dev/null @@ -1,80 +0,0 @@ -from PyQt5 import QtCore, QtGui, QtWidgets - -import pandas as pd - - -class MiscGroup: - def __init__(self): - - folder_prompt = QtWidgets.QLabel('Select Folder') - self.folder_line = QtWidgets.QLineEdit() - self.folder_button = QtWidgets.QToolButton() - self.folder_button.setText('...') - - folder_layout = QtWidgets.QHBoxLayout() - folder_layout.addWidget(folder_prompt) - folder_layout.addWidget(self.folder_line) - folder_layout.addWidget(self.folder_button) - - self.folder_group = QtWidgets.QGroupBox() - self.folder_group.setLayout(folder_layout) - - clust_list1_title = QtWidgets.QLabel('Go Cue Aligned Clusters') - self.clust_list1 = QtWidgets.QListWidget() - self.remove_clust_button1 = QtWidgets.QPushButton('Remove') - - clust_list2_title = QtWidgets.QLabel('Feedback Aligned Clusters') - self.clust_list2 = QtWidgets.QListWidget() - self.remove_clust_button2 = QtWidgets.QPushButton('Remove') - self.save_clust_button = QtWidgets.QPushButton('Save Clusters') - - clust_interest_layout = QtWidgets.QGridLayout() - clust_interest_layout.addWidget(clust_list1_title, 0, 0) - clust_interest_layout.addWidget(self.remove_clust_button1, 0, 1) - clust_interest_layout.addWidget(self.clust_list1, 1, 0, 1, 2) - clust_interest_layout.addWidget(clust_list2_title, 2, 0) - clust_interest_layout.addWidget(self.remove_clust_button2, 2, 1) - clust_interest_layout.addWidget(self.clust_list2, 3, 0, 1, 2) - clust_interest_layout.addWidget(self.save_clust_button, 4, 0) - - self.clust_interest = QtWidgets.QGroupBox() - self.clust_interest.setLayout(clust_interest_layout) - self.clust_interest.setFixedSize(250, 350) - - self.terminal = QtWidgets.QTextBrowser() - self.terminal_button = QtWidgets.QPushButton('Clear') - self.terminal_button.setFixedSize(70, 30) - self.terminal_button.setParent(self.terminal) - self.terminal_button.move(580, 0) - - - def on_save_button_clicked(self, gui_path): - save_file = QtWidgets.QFileDialog() - save_file.setAcceptMode(QtWidgets.QFileDialog.AcceptSave) - file = save_file.getSaveFileName(None, 'Save File', gui_path, 'CSV (*.csv)') - print(file[0]) - if file[0]: - save_clusters = pd.DataFrame(columns=['go_Cue_aligned', 'feedback_aligned']) - - go_cue_clusters = [] - for idx in range(self.clust_list1.count()): - cluster_no = self.clust_list1.item(idx).text() - go_cue_clusters.append(int(cluster_no[8:])) - - go_cue = pd.Series(go_cue_clusters) - save_clusters['go_Cue_aligned'] = go_cue - - feedback_clusters = [] - for idx in range(self.clust_list2.count()): - cluster_no = self.clust_list2.item(idx).text() - feedback_clusters.append(int(cluster_no[8:])) - - feedback = pd.Series(feedback_clusters) - save_clusters['feedback_aligned'] = feedback - - save_clusters.to_csv(file[0], index=None, header=True) - - return file[0] - - - diff --git a/PythonScripts/iblapps-master/data_exploration_gui/plot.py b/PythonScripts/iblapps-master/data_exploration_gui/plot.py deleted file mode 100644 index 82534ae1..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/plot.py +++ /dev/null @@ -1,373 +0,0 @@ -from PyQt5 import QtGui -import pyqtgraph as pg -import pyqtgraph.dockarea -from data_exploration_gui.utils import (MAP_SORT_OPTIONS, MAP_SIDE_OPTIONS, MAP_CHOICE_OPTIONS, - PSTH_OPTIONS, RASTER_OPTIONS, colours) -import atlaselectrophysiology.ColorBar as cb -import numpy as np - - -class PlotGroup: - - def __init__(self, data_model): - - self.data = data_model - self.create_dock_area() - - self.fig_spike_psth = PlotTemplate('Time After Event (s)', 'Firing Rate (Hz)') - self.fig1_area.addWidget(self.fig_spike_psth.fig) - self.fig_behav_psth = PlotTemplate('Time After Event (s)', 'Firing Rate (Hz)') - self.fig2_area.addWidget(self.fig_behav_psth.fig) - - self.fig_spike_raster = ScatterTemplate('Time After Event (s)', 'No. of Trials') - self.fig3_area.addWidget(self.fig_spike_raster.fig) - self.fig_behav_raster = ImageTemplate('Time After Event (s)', 'No. of Trials') - self.fig4_area.addWidget(self.fig_behav_raster.fig) - - self.fig_autocorr = BarTemplate('Time (ms)', 'Corr') - self.fig5_area.addWidget(self.fig_autocorr.fig) - - self.fig_template = PlotTemplate('Time (ms)', 'V (uV)') - self.fig6_area.addWidget(self.fig_template.fig) - - self.plot_status = {key: False for key in MAP_SORT_OPTIONS.keys()} - - def create_dock_area(self): - self.fig_area = pg.dockarea.DockArea() - self.fig1_area = pg.dockarea.Dock('', autoOrientation='horizontal') - self.fig2_area = pg.dockarea.Dock('', autoOrientation='horizontal') - self.fig3_area = pg.dockarea.Dock('', autoOrientation='horizontal') - self.fig4_area = pg.dockarea.Dock('', autoOrientation='horizontal') - self.fig5_area = pg.dockarea.Dock('', autoOrientation='horizontal') - self.fig6_area = pg.dockarea.Dock('', autoOrientation='horizontal') - - self.fig_area.addDock(self.fig2_area, 'top') - self.fig_area.addDock(self.fig1_area, 'left', self.fig2_area) - self.fig_area.addDock(self.fig3_area, 'bottom', self.fig1_area) - self.fig_area.addDock(self.fig4_area, 'bottom', self.fig2_area) - self.fig_area.addDock(self.fig5_area, 'right') - self.fig_area.addDock(self.fig6_area, 'bottom', self.fig5_area) - - self.fig3_area.setStretch(x=1, y=18) - self.fig4_area.setStretch(x=1, y=18) - - self.fig5_area.setStretch(x=10, y=1) - self.fig6_area.setStretch(x=10, y=1) - - def change_rasters(self, trial_set, contrast, order, sort, hold, event): - side = MAP_SIDE_OPTIONS[trial_set] - choice = MAP_CHOICE_OPTIONS[trial_set] - - if hold and trial_set != 'all': - spike_raster, behav_raster = self.data.get_rasters_for_selection('all', 'all', order, - sort, contrast, event) - raster_options = RASTER_OPTIONS['all'][sort] - else: - spike_raster, behav_raster = self.data.get_rasters_for_selection(side, choice, order, - sort, contrast, event) - raster_options = RASTER_OPTIONS[trial_set][sort] - - self.fig_spike_raster.remove_regions() - self.fig_spike_raster.plot(spike_raster.raster, spike_raster.time, spike_raster.n_trials) - self.fig_spike_raster.add_regions(spike_raster.dividers, raster_options) - - self.fig_behav_raster.remove_regions() - self.fig_behav_raster.plot(behav_raster.raster, behav_raster.time, cmap=behav_raster.cmap, - clevels=behav_raster.clevels) - self.fig_behav_raster.add_regions(behav_raster.dividers, raster_options) - - def change_psths(self, trial_set, contrast, order, sort, event): - side = MAP_SIDE_OPTIONS[trial_set] - choice = MAP_CHOICE_OPTIONS[trial_set] - - spike_psth, behav_psth = self.data.get_psths_for_selection(side, choice, order, - sort, contrast, event) - self.fig_spike_psth.plot(trial_set, spike_psth.time, spike_psth.psth_mean, - spike_psth.psth_std, PSTH_OPTIONS[trial_set], - ylabel=spike_psth.ylabel) - self.fig_behav_psth.plot(trial_set, behav_psth.time, behav_psth.psth_mean, - behav_psth.psth_std, PSTH_OPTIONS[trial_set], - ylabel=behav_psth.ylabel) - - def change_plots(self, contrast, trial_set, order, sort, hold, event): - - self.plot_status[trial_set] = True - - if not hold: - self.remove_plots(self.prev_trial_set) - - self.change_rasters(trial_set, contrast, order, sort, hold, event) - self.change_psths(trial_set, contrast, order, sort, event) - - self.prev_trial_set = trial_set - - def change_all_plots(self, contrast, trial_set, order, sort, hold, event): - - n_plots = np.sum(list(self.plot_status.values())) - if n_plots == 0: - self.change_psths(trial_set, contrast, order, sort, event) - self.plot_status[trial_set] = True - else: - for key, val in self.plot_status.items(): - if val: - self.remove_plots(key) - self.change_psths(key, contrast, order, sort, event) - self.plot_status[key] = True - - self.change_rasters(trial_set, contrast, order, sort, hold, event) - autocorr = self.data.get_autocorr_for_selection() - self.fig_autocorr.plot(autocorr.time, autocorr.vals) - - template = self.data.get_template_for_selection() - self.fig_template.plot_line(template.time, template.vals) - - self.prev_trial_set = trial_set - - def remove_plots(self, trial_set): - self.plot_status[trial_set] = False - self.fig_spike_psth.remove_item(trial_set) - self.fig_behav_psth.remove_item(trial_set) - - def reset(self): - for key, val in self.plot_status.items(): - if val: - self.remove_plots(key) - self.fig_spike_raster.reset() - self.fig_behav_raster.reset() - self.fig_autocorr.reset() - - -class PlotTemplate: - def __init__(self, xlabel, ylabel, single=True): - self.fig = pg.PlotWidget(background='w') - self.fig.setMouseEnabled(x=False, y=False) - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.plot_items = dict() - if single: - self.plot_item = pg.PlotCurveItem() - self.fig.addItem(self.plot_item) - else: - self.fig.plotItem.addLine(x=0, pen=colours['line']) - - def add_item(self, trial_set, yrange): - - if trial_set not in self.plot_items.keys(): - - curve = {'centre': pg.PlotCurveItem(), - 'upper': pg.PlotCurveItem(), - 'lower': pg.PlotCurveItem(), - 'fill': pg.FillBetweenItem(), - 'yrange': yrange} - - self.plot_items[trial_set] = curve - self.fig.addItem(self.plot_items[trial_set]['centre']) - self.fig.addItem(self.plot_items[trial_set]['fill']) - - def remove_item(self, trial_set): - self.fig.removeItem(self.plot_items[trial_set]['centre']) - self.fig.removeItem(self.plot_items[trial_set]['fill']) - self.plot_items.pop(trial_set) - - def plot(self, trial_set, x, y, se, plot_info, ylabel=None): - self.add_item(trial_set, [np.nanmin(y - se), np.nanmax(y + se)]) - - self.plot_items[trial_set]['centre'].setData(x=x, y=y) - self.plot_items[trial_set]['centre'].setPen(plot_info['colour']) - self.plot_items[trial_set]['lower'].setData(x=x, y=y - se) - self.plot_items[trial_set]['upper'].setData(x=x, y=y + se) - self.plot_items[trial_set]['fill'].setCurves(self.plot_items[trial_set]['upper'], - self.plot_items[trial_set]['lower']) - plot_info['fill'].setAlpha(50) - self.plot_items[trial_set]['fill'].setBrush(plot_info['fill']) - - # find the correct y range based on all the lines on the plot - y_min = np.nanmin([val['yrange'][0] for _, val in self.plot_items.items()]) - y_max = np.nanmax([val['yrange'][1] for _, val in self.plot_items.items()]) - - self.fig.setXRange(min=np.min(x), max=np.max(x)) - self.fig.setYRange(min=0.95 * y_min, max=1.05 * y_max) - - if ylabel is not None: - self.fig.setLabel('left', ylabel) - - def plot_line(self, x, y): - self.plot_item.setData(x=x, y=y) - self.plot_item.setPen('b') - self.fig.setXRange(min=np.min(x), max=np.max(x)) - self.fig.setYRange(min=0.95 * np.min(y), max=1.05 * np.max(y)) - - -class ImageTemplate: - def __init__(self, xlabel, ylabel): - self.fig = pg.PlotWidget(background='w') - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.fig.scene().sigMouseMoved.connect(self.on_mouse_hover) - - self.image = pg.ImageItem() - self.fig.addItem(self.image) - self.fig.plotItem.addLine(x=0, pen=colours['line']) - - self.text_popup = pg.TextItem(color=colours['line']) - font = QtGui.QFont() - font.setPointSize(8) - font.setBold(True) - self.text_popup.setFont(font) - self.text_popup.hide() - self.fig.addItem(self.text_popup) - - self.region_items = [] - self.regions = None - self.region_text = None - - def reset(self): - self.image.setImage() - - def plot(self, image, t, cmap='binary', clevels=(0, 1)): - - # TODO makes sure this is in the order that you expect!!! - self.image.setImage(image.T) - self.x_range = [np.min(t), np.max(t)] - self.x_scale = (np.max(t) - np.min(t)) / image.shape[1] - transform = [self.x_scale, 0., 0., 0., 1., 0., np.min(t), - 0., 1.] - self.image.setTransform(QtGui.QTransform(*transform)) - - color_bar = cb.ColorBar(cmap) - lut = color_bar.getColourMap() - self.image.setLookupTable(lut) - self.image.setLevels(clevels) - self.fig.setXRange(min=np.min(t), max=np.max(t)) - self.fig.setYRange(min=0, max=image.shape[0]) - - def add_regions(self, regions, region_info): - self.regions = regions - self.region_text = region_info['text'] - for reg, col in zip(self.regions, region_info['colours']): - # Hack so we can easily revert to the coloured option if users prefer - col.setAlpha(0) - region = pg.LinearRegionItem(values=(reg[0], reg[1]), brush=col, pen=colours['line'], - movable=False, orientation='horizontal') - self.fig.plotItem.addItem(region) - self.region_items.append(region) - - def remove_regions(self): - for reg in self.region_items: - self.fig.plotItem.removeItem(reg) - - def on_mouse_hover(self, pos): - if len(self.regions) > 0: - pos = self.image.mapFromScene(pos) - - # only show if mouse is in x range - if ((pos.x() * self.x_scale) + self.x_range[0] > self.x_range[0]) & \ - ((pos.x() * self.x_scale) + self.x_range[0] < self.x_range[1]): - if (pos.y() > np.min(np.min(self.regions))) & \ - (pos.y() < np.max(np.max(self.regions))): - text = self.find_text(pos.y()) - self.text_popup.setText(text) - self.text_popup.setPos((pos.x() * self.x_scale) + self.x_range[0] + 0.05, - pos.y() - 10) - self.text_popup.show() - else: - self.text_popup.hide() - else: - self.text_popup.hide() - else: - self.text_popup.hide() - - def find_text(self, y): - idx = [i for i, val in enumerate(self.regions) if (y > val[0]) & (y < val[1])][0] - return self.region_text[idx] - - -class ScatterTemplate: - def __init__(self, xlabel, ylabel): - - self.fig = pg.PlotWidget(background='w') - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.fig.scene().sigMouseMoved.connect(self.on_mouse_hover) - - self.scatter = pg.ScatterPlotItem() - self.fig.addItem(self.scatter) - self.fig.plotItem.addLine(x=0, pen=colours['line']) - - self.text_popup = pg.TextItem(color=colours['line']) - font = QtGui.QFont() - font.setPointSize(8) - font.setBold(True) - self.text_popup.setFont(font) - self.text_popup.hide() - self.fig.addItem(self.text_popup) - - self.region_items = [] - self.regions = None - self.region_text = None - - def reset(self): - self.scatter.setData() - - def plot(self, scatter, t, n_trials): - self.scatter.setData(x=scatter[:, 0], y=scatter[:, 1], size=1, symbol='s') - self.scatter.setPen('k') - self.fig.setXRange(min=t[0], max=t[1]) - self.fig.setYRange(min=0, max=n_trials) - self.x_range = t - - def add_regions(self, regions, region_info): - self.regions = regions - self.region_text = region_info['text'] - for reg, col in zip(self.regions, region_info['colours']): - # Hack so we can easily revert to the coloured option if users prefer - col.setAlpha(0) - region = pg.LinearRegionItem(values=(reg[0], reg[1]), brush=col, pen=colours['line'], - movable=False, orientation='horizontal') - self.fig.plotItem.addItem(region) - self.region_items.append(region) - - def remove_regions(self): - for reg in self.region_items: - self.fig.plotItem.removeItem(reg) - - def on_mouse_hover(self, pos): - if len(self.regions) > 0: - pos = self.scatter.mapFromScene(pos) - - # only show if mouse is in x range - if (pos.x() > self.x_range[0]) & (pos.x() < self.x_range[1]): - if (pos.y() > np.min(np.min(self.regions))) & \ - (pos.y() < np.max(np.max(self.regions))): - text = self.find_text(pos.y()) - self.text_popup.setText(text) - self.text_popup.setPos(pos.x() + 0.05, pos.y() - 10) - self.text_popup.show() - else: - self.text_popup.hide() - else: - self.text_popup.hide() - else: - self.text_popup.hide() - - def find_text(self, y): - idx = [i for i, val in enumerate(self.regions) if (y > val[0]) & (y < val[1])][0] - return self.region_text[idx] - - -class BarTemplate: - def __init__(self, xlabel, ylabel): - self.fig = pg.PlotWidget(background='w') - self.fig.setMouseEnabled(x=False, y=False) - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.bar = pg.BarGraphItem(x=[0], height=[0], width=0) - self.fig.addItem(self.bar) - - def reset(self): - self.bar.setOpts(x=[0], height=[0], width=0) - - def plot(self, x, y): - self.fig.setXRange(min=np.min(x), max=np.max(x)) - self.fig.setYRange(min=0, max=1.05 * np.max(y)) - self.bar.setOpts(x=x, height=y, width=0.0009, brush='b') diff --git a/PythonScripts/iblapps-master/data_exploration_gui/plot_class.py b/PythonScripts/iblapps-master/data_exploration_gui/plot_class.py deleted file mode 100644 index f9b3f00f..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/plot_class.py +++ /dev/null @@ -1,393 +0,0 @@ -from PyQt5 import QtWidgets, QtCore, QtGui -import pyqtgraph as pg -import pyqtgraph.dockarea -import copy - - -class PlotGroup: - - def __init__(self): - - self.create_dock_area() - - self.fig1_peth = PlotTemplate('Time After Event (s)', 'Firing Rate (Hz)') - self.fig1_button = QtWidgets.QPushButton('Add') - self.fig1_button.setParent(self.fig1_peth.fig) - self.fig1_button.setFixedSize(30, 30) - self.fig1_area.addWidget(self.fig1_peth.fig) - - self.fig2_peth = PlotTemplate('Time After Event (s)', 'Firing Rate (Hz)') - self.fig2_button = QtWidgets.QPushButton('Add') - self.fig2_button.setParent(self.fig2_peth.fig) - self.fig2_button.setFixedSize(30, 30) - self.fig2_area.addWidget(self.fig2_peth.fig) - - self.fig3_raster = ScatterTemplate('Time After Event (s)', 'No. of Trials') - self.fig3_button = QtWidgets.QPushButton('Reset Axis') - self.fig3_button.setParent(self.fig3_raster.fig) - self.fig3_button.setFixedSize(60, 30) - self.fig3_area.addWidget(self.fig3_raster.fig) - - self.fig4_raster = ScatterTemplate('Time After Event (s)', 'No. of Trials') - self.fig4_button = QtWidgets.QPushButton('Reset Axis') - self.fig4_button.setParent(self.fig4_raster.fig) - self.fig4_button.setFixedSize(60, 30) - self.fig4_area.addWidget(self.fig4_raster.fig) - - self.fig5_autocorr = BarTemplate('Time (ms)', 'Corr') - self.fig5_area.addWidget(self.fig5_autocorr.fig) - - self.fig6_template = PlotTemplate('Time (ms)', 'V (uV)') - self.fig6_area.addWidget(self.fig6_template.fig) - - self.fig7_waveform = PlotTemplate('Time(ms)', 'V (uV)') - self.fig7_area.addWidget(self.fig7_waveform.fig) - - self.plot_status = {} - self.y_range = {} - self.case_prev = [] - self.raster_x = [] - self.init_flag = False - - def create_dock_area(self): - self.fig_area = pg.dockarea.DockArea() - self.fig1_area = pg.dockarea.Dock('Figure 1: Go Cue PETH', autoOrientation='horizontal') - self.fig2_area = pg.dockarea.Dock('Figure 2: Feedback PETH', autoOrientation='horizontal') - self.fig3_area = pg.dockarea.Dock('Figure 3: Go Cue Raster', autoOrientation='horizontal') - self.fig4_area = pg.dockarea.Dock('Figure 4: Feedback Raster', autoOrientation='horizontal') - self.fig5_area = pg.dockarea.Dock('Figure 5: Autocorrelogram', autoOrientation='horizontal') - self.fig6_area = pg.dockarea.Dock('Figure 6: Template Waveform', autoOrientation='horizontal') - self.fig7_area = pg.dockarea.Dock('Figure 7: Waveform', autoOrientation='horizontal') - - self.fig_area.addDock(self.fig2_area, 'top') - self.fig_area.addDock(self.fig1_area, 'left', self.fig2_area) - self.fig_area.addDock(self.fig3_area, 'bottom', self.fig1_area) - self.fig_area.addDock(self.fig4_area, 'bottom', self.fig2_area) - self.fig_area.addDock(self.fig5_area, 'right') - self.fig_area.addDock(self.fig6_area, 'bottom', self.fig5_area) - self.fig_area.addDock(self.fig7_area, 'bottom', self.fig6_area) - - self.fig3_area.setStretch(x=1, y=18) - self.fig4_area.setStretch(x=1, y=18) - - self.fig5_area.setStretch(x=7, y=1) - self.fig6_area.setStretch(x=7, y=1) - self.fig7_area.setStretch(x=7, y=1) - - def reset(self): - - self.fig1_peth.fig.plotItem.clear() - self.fig2_peth.fig.plotItem.clear() - self.plot_status = {} - self.y_range = {} - self.fig3_raster.reset() - self.fig4_raster.reset() - self.fig5_autocorr.reset() - self.fig6_template.reset() - self.fig7_waveform.reset() - - - def change_rasters(self, data, clust, trials, case, sort_method): - option = trials[case] - trial_id = option[sort_method]['trials'] - - if len(trial_id) != 0: - self.fig3_raster.remove_lines() - [x, y, n_trials] = data.compute_rasters('goCue_times', clust, trial_id) - if len(x) != 0: - self.fig3_raster.plot(x, y, n_trials, self.raster_x) - self.fig3_raster.add_lines(option[sort_method]['lines'], option[sort_method]['linecolours'], option[sort_method]['text']) - self.fig3_raster.fig.plotItem.addLine(x = 0, pen = 'm') - else: - self.fig3_raster.reset() - self.fig4_raster.remove_lines() - [x, y, n_trials] = data.compute_rasters('feedback_times', clust, trial_id) - if len(x) != 0: - self.fig4_raster.plot(x, y, n_trials, self.raster_x) - self.fig4_raster.add_lines(option[sort_method]['lines'], option[sort_method]['linecolours'], option[sort_method]['text']) - self.fig4_raster.fig.plotItem.addLine(x = 0, pen = 'm') - else: - self.fig4_raster.reset() - else: - self.fig3_raster.reset() - self.fig4_raster.reset() - - - - def plot_psth(self, data, clust, trials, case, sort_method): - option = trials[case] - trial_id = option[sort_method]['trials'] - - if len(trial_id) != 0: - plot_item1 = self.fig1_peth.plot_item() - [x1, y1, errbar1] = data.compute_peth('goCue_times', clust, trial_id) - y_range1 = [(y1-errbar1).min(), (y1 + errbar1).max()] - [x2, y2, errbar2] = data.compute_peth('feedback_times', clust, trial_id) - y_range2 = [(y2-errbar2).min(), (y2 + errbar2).max()] - self.y_range[case] = [y_range1, y_range2] - if self.init_flag is False: - self.raster_x = x1 - self.init_flag = True - - plot_item1 = self.fig1_peth.plot_item() - self.fig1_peth.plot_errbar_hold(x1, y1, errbar1, plot_item1, option['colour'], option['fill'], option['linestyle'], self.y_range, 0) - self.fig1_peth.fig.plotItem.addLine(x = 0, pen = 'm') - - plot_item2 = self.fig2_peth.plot_item() - self.fig2_peth.plot_errbar_hold(x2, y2, errbar2, plot_item2, option['colour'], option['fill'], option['linestyle'], self.y_range, 1) - self.fig2_peth.fig.plotItem.addLine(x = 0, pen = 'm') - - plots = [plot_item1, plot_item2] - self.plot_status[case] = plots - else: - plot_item1 = self.fig1_peth.plot_item() - plot_item2 = self.fig2_peth.plot_item() - plots = [plot_item1, plot_item2] - self.plot_status[case] = plots - y_range1 = [0, 0] - y_range2 = [0, 0] - self.y_range[case] = [y_range1, y_range2] - - - def change_all_plots_final(self, data, clust, trials, case, sort_method, hold): - bla = copy.copy(list(self.plot_status.keys())) - if len(bla) == 0: - self.plot_psth(data, clust, trials, case, sort_method) - else: - for key in bla: - self.remove_plot_item(key) - self.plot_psth(data, clust, trials, key, sort_method) - - if hold is True: - self.change_rasters(data, clust, trials, 'all', sort_method) - else: - self.change_rasters(data, clust, trials, case, sort_method) - - [x, y] = data.compute_autocorr(clust) - self.fig5_autocorr.plot(x, y, data.autocorr_bin) - [x, y] = data.compute_template(clust) - self.fig6_template.plot_line(x, y) - self.fig7_waveform.reset() - self.case_prev = case - - - def change_plots_final(self, data, clust, trials, case, sort_method, hold): - if hold is False: - self.remove_plot_item(self.case_prev) - - self.plot_psth(data, clust, trials, case, sort_method) - - if hold is True: - self.change_rasters(data, clust, trials, 'all', sort_method) - else: - self.change_rasters(data, clust, trials, case, sort_method) - - [x, y] = data.compute_autocorr(clust) - self.fig5_autocorr.plot(x, y, data.autocorr_bin) - [x, y] = data.compute_template(clust) - self.fig6_template.plot_line(x, y) - self.fig7_waveform.reset() - - self.case_prev = case - - def remove_plot_item(self, case): - plot_item = self.plot_status[case] - - self.fig1_peth.remove_item(plot_item[0]) - self.fig2_peth.remove_item(plot_item[1]) - - self.plot_status.pop(case) - self.y_range.pop(case) - - #return self.plot_status - -class PlotTemplate: - def __init__(self, xlabel, ylabel): - self.fig = pg.PlotWidget(background='w') - self.fig.setMouseEnabled(x=False, y=False) - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.plot = pg.PlotCurveItem() - self.plot_lb = pg.PlotCurveItem() - self.plot_ub = pg.PlotCurveItem() - self.plot_fill = pg.FillBetweenItem() - self.fig.addItem(self.plot) - self.fig.addItem(self.plot_fill) - #self.fig.plotItem.legend - - def plot_item(self): - plot_items = [] - #plot - plot_items.append(pg.PlotCurveItem()) - #plot_lb - plot_items.append(pg.PlotCurveItem()) - #plot_ub - plot_items.append(pg.PlotCurveItem()) - #plot_fill - plot_items.append(pg.FillBetweenItem()) - self.fig.addItem(plot_items[0]) - self.fig.addItem(plot_items[3]) - - return plot_items - - def reset(self): - self.plot.setData() - self.plot_lb.setData() - self.plot_ub.setData() - self.plot_fill.setCurves(self.plot_ub, self.plot_lb) - - def remove_item(self, plot_item): - self.fig.removeItem(plot_item[0]) - self.fig.removeItem(plot_item[3]) - - - def plot_errbar(self, x, y, errbar, colour, fill, linestyle): - self.plot.setData(x=x, y=y) - #self.plot.setBrush(colour) - self.plot.setPen(colour) - self.plot_lb.setData(x=x, y=(y - errbar)) - self.plot_ub.setData(x=x, y=(y + errbar)) - self.plot_fill.setCurves(self.plot_ub, self.plot_lb) - fill.setAlpha(50) - self.plot_fill.setBrush(fill) - self.fig.setXRange(min=x.min(), max=x.max()) - self.fig.setYRange(min=0.95 * (y - errbar).min(), max=1.05 * (y + errbar).max()) - - def plot_errbar_hold(self, x, y, errbar, plot_item, colour, fill, linestyle, y_range, id): - min_val = min([val[id][0] for val in y_range.values()]) - max_val = max([val[id][1] for val in y_range.values()]) - case_min = [idx for idx, val in y_range.items() if val[id][0] == min_val][0] - case_max = [idx for idx, val in y_range.items() if val[id][1] == max_val][0] - y_min = y_range[case_min][id][0] - y_max = y_range[case_max][id][1] - - plot_item[0].setData(x=x, y=y) - #plot_item[0].setBrush('k') - #plot_item[0].setBrush(colour) - plot_item[0].setPen(colour) - plot_item[1].setData(x=x, y=(y - errbar)) - plot_item[2].setData(x=x, y=(y + errbar)) - #plot_item[3].setCurves(self.plot_ub, self.plot_lb) - plot_item[3].setCurves(plot_item[2], plot_item[1]) - fill.setAlpha(50) - plot_item[3].setBrush(fill) - #plot_item[3].setBrush('b') - self.fig.setXRange(min=x.min(), max=x.max()) - self.fig.setYRange(min=0.95 * y_min, max=1.05 * y_max) - - return y_range - - def plot_line(self, x, y): - self.plot.setData(x=x, y=y) - self.plot.setPen('b') - self.fig.setXRange(min=x.min(), max=x.max()) - self.fig.setYRange(min=0.95 * y.min(), max=1.05 * y.max()) - - -class ScatterTemplate: - def __init__(self, xlabel, ylabel): - - self.viewbox = pg.ViewBox() - self.viewbox.setMouseMode(self.viewbox.RectMode) - self.fig = pg.PlotWidget(background='w', viewBox=self.viewbox) - self.fig.setMouseEnabled(x=False, y=False) - self.fig.setMouseTracking(True) - #self.fig.setMouseEnabled(y=False) - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.scatter = pg.ScatterPlotItem() - self.scatter_text = pg.TextItem(color='k') - font = QtGui.QFont() - font.setPointSize(10) - font.setBold(True) - self.scatter_text.setFont(font) - self.scatter_text.hide() - self.fig.addItem(self.scatter) - self.fig.addItem(self.scatter_text) - self.axis = self.fig.plotItem.getAxis('left') - self.axis.setScale(0.1) - self.line_item = [] - #self.fig. - self.fig.scene().sigMouseMoved.connect(self.on_mouse_hover) - self.lines = [] - self.text = [] - self.x = [] - self.n_trials = [] - - - def reset(self): - self.scatter.setData() - - def reset_axis(self): - self.fig.setXRange(min=self.x.min(), max=self.x.max()) - self.fig.setYRange(min=0, max=(1.00 * self.n_trials * 10)) - - def plot(self, x, y, n_trials, x_axis): - self.fig.setXRange(min=x_axis.min(), max=x_axis.max()) - self.fig.setYRange(min=0, max=(1.00 * n_trials * 10)) - self.scatter.setData(x=x, y=y, size=1, symbol='s') - self.scatter.setPen('k') - self.x = x - self.n_trials = n_trials - - return self.x, self.n_trials - - def add_lines(self, lines, line_colour, text): - self.lines = lines - self.text = text - for idx, val in enumerate(lines): - orient = pg.LinearRegionItem.Horizontal - colour = line_colour[idx] - colour.setAlpha(50) - region = pg.LinearRegionItem(values = (val[0]*10, val[1]*10), brush = colour, - movable = False, orientation = orient) - self.fig.plotItem.addItem(region) - self.line_item.append(region) - - def on_mouse_hover(self, pos): - if len(self.lines) > 0: - pos = self.scatter.mapFromScene(pos) - - if (pos.y() > min(min(self.lines))*10) & (pos.y() < max(max(self.lines))*10): - text = self.find_text(pos.y()) - self.scatter_text.setText(text) - self.scatter_text.setPos(pos.x() + 0.1, pos.y() - 10) - #self.scatter_text.setPos(pos.x(), pos.y()) - self.scatter_text.show() - - else: - self.scatter_text.hide() - else: - self.scatter_text.hide() - - - def find_text(self, y): - idx = [x for x, val in enumerate(self.lines) if (y > val[0]*10) & (y < val[1]*10)][0] - - return self.text[idx] - - def remove_lines(self): - for val in self.line_item: - self.fig.plotItem.removeItem(val) - - - -class BarTemplate: - def __init__(self, xlabel, ylabel): - self.fig = pg.PlotWidget(background='w') - self.fig.setMouseEnabled(x=False, y=False) - self.fig.setLabel('bottom', xlabel) - self.fig.setLabel('left', ylabel) - self.bar = pg.BarGraphItem(x=[0], height=[0], width=0) - self.fig.addItem(self.bar) - - def reset(self): - self.bar.setOpts(x=[0], height=[0], width=0) - - def plot(self, x, y, n_bin): - self.fig.setXRange(min=x.min(), max=x.max()) - self.fig.setYRange(min=0, max=1.05 * y.max()) - self.bar.setOpts(x=x, height=y, width=0.0009, brush='b') - - - diff --git a/PythonScripts/iblapps-master/data_exploration_gui/scatter.py b/PythonScripts/iblapps-master/data_exploration_gui/scatter.py deleted file mode 100644 index bc40fdc5..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/scatter.py +++ /dev/null @@ -1,121 +0,0 @@ -from PyQt5 import QtCore -import pyqtgraph as pg -import numpy as np - - -class ScatterGroup: - - def __init__(self): - - self.fig_scatter = pg.PlotWidget(background='w') - self.fig_scatter.setMouseTracking(True) - self.fig_scatter.scene().sigMouseMoved.connect(self.on_mouse_hover) - self.fig_scatter.setLabel('bottom', 'Cluster Amplitude (uV)') - self.fig_scatter.setLabel('left', 'Cluster Distance From Tip (uV)') - - self.scatter_plot = pg.ScatterPlotItem() - self.scatter_text = pg.TextItem(color='k') - self.scatter_text.hide() - - self.fig_scatter.addItem(self.scatter_plot) - self.fig_scatter.addItem(self.scatter_text) - - self.reset() - - def reset(self): - self.scatter_plot.setData() - self.clust_amps = None - self.clust_depths = None - self.clust_color_ks = None - self.clust_color_ibl = None - self.clust_ids = None - self.point_pos = None - self.point_pos_prev = None - - def populate(self, clust_amps, clust_depths, clust_ids, clust_color_ks, clust_color_ibl): - self.clust_amps = clust_amps - self.clust_depths = clust_depths - self.clust_color_ks = clust_color_ks - self.clust_color_ibl = clust_color_ibl - self.clust_ids = clust_ids - self.scatter_plot.setData(x=self.clust_amps, y=self.clust_depths, size=10) - self.scatter_plot.setBrush(clust_color_ibl) - self.scatter_plot.setPen(clust_color_ks) - self.x_min = np.min(self.clust_amps) - 2 - self.x_max = np.max(self.clust_amps) + 2 - self.fig_scatter.setXRange(min=self.x_min, max=self.x_max) - self.fig_scatter.setYRange(min=0, max=4000) - - def on_scatter_plot_clicked(self, point): - self.point_pos = point[0].pos() - clust = np.argwhere(self.scatter_plot.data['x'] == self.point_pos.x())[0][0] - return clust - - def on_mouse_hover(self, pos): - mouse_pos = self.scatter_plot.mapFromScene(pos) - point = self.scatter_plot.pointsAt(mouse_pos) - - if len(point) != 0: - point_pos = point[0].pos() - clust = np.argwhere(self.scatter_plot.data['x'] == point_pos.x())[0][0] - self.scatter_text.setText('Cluster no. ' + str(self.clust_ids[clust])) - self.scatter_text.setPos(mouse_pos.x(), (mouse_pos.y())) - self.scatter_text.show() - else: - self.scatter_text.hide() - - def update_scatter_icon(self, clust_prev): - point = self.scatter_plot.pointsAt(self.point_pos) - if len(point) > 1: - p_diff = [] - for p in point: - p_diff.append(np.abs(p.pos().x() - self.point_pos.x())) - idx = np.argmin(p_diff) - point[idx].setBrush('k') - point[idx].setPen('k') - else: - point[0].setBrush('k') - point[0].setPen('k') - - point_prev = self.scatter_plot.pointsAt(self.point_pos_prev) - if len(point_prev) > 1: - p_diff = [] - for p in point_prev: - p_diff.append(np.abs(p.pos().x() - self.point_pos_prev.x())) - idx = np.argmin(p_diff) - point_prev[idx].setPen(self.clust_color_ks[clust_prev]) - point_prev[idx].setBrush(self.clust_color_ibl[clust_prev]) - point_prev[idx].setSize(8) - else: - point_prev[0].setPen(self.clust_color_ks[clust_prev]) - point_prev[0].setBrush(self.clust_color_ibl[clust_prev]) - point_prev[0].setSize(8) - - def set_current_point(self, clust): - self.point_pos.setX(self.clust_amps[clust]) - self.point_pos.setY(self.clust_depths[clust]) - - def update_prev_point(self): - self.point_pos_prev.setX(self.point_pos.x()) - self.point_pos_prev.setY(self.point_pos.y()) - - def initialise_scatter_index(self): - self.point_pos = QtCore.QPointF() - self.point_pos_prev = QtCore.QPointF() - self.point_pos.setX(self.clust_amps[0]) - self.point_pos.setY(self.clust_depths[0]) - self.point_pos_prev.setX(self.clust_amps[0]) - self.point_pos_prev.setY(self.clust_depths[0]) - - point = self.scatter_plot.pointsAt(self.point_pos) - - if len(point) > 1: - p_diff = [] - for p in point: - p_diff.append(np.abs(p.pos().x() - self.point_pos.x())) - idx = np.argmin(p_diff) - point[idx].setBrush('k') - point[idx].setPen('k') - else: - point[0].setBrush('k') - point[0].setPen('k') diff --git a/PythonScripts/iblapps-master/data_exploration_gui/scatter_class.py b/PythonScripts/iblapps-master/data_exploration_gui/scatter_class.py deleted file mode 100644 index 78e8e848..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/scatter_class.py +++ /dev/null @@ -1,126 +0,0 @@ -from PyQt5 import QtCore, QtGui, QtWidgets -import pyqtgraph as pg -import numpy as np - -class ScatterGroup: - - def __init__(self): - - self.fig_scatter = pg.PlotWidget(background='w') - self.fig_scatter.setMouseTracking(True) - self.fig_scatter.setLabel('bottom', 'Cluster Amplitude (uV)') - self.fig_scatter.setLabel('left', 'Cluster Distance From Tip (uV)') - - self.scatter_plot = pg.ScatterPlotItem() - self.scatter_text = pg.TextItem(color='k') - self.scatter_text.hide() - - self.scatter_reset_button = QtWidgets.QPushButton('Reset Axis') - self.scatter_reset_button.setFixedSize(70, 30) - self.scatter_reset_button.setParent(self.fig_scatter) - - self.fig_scatter.addItem(self.scatter_plot) - self.fig_scatter.addItem(self.scatter_text) - self.fig_scatter.setFixedSize(400, 580) - - self.clust_amps = [] - self.clust_depths = [] - self.clust_color = [] - self.clust_ids = [] - self.point_pos = [] - self.point_pos_prev = [] - - def reset(self): - self.scatter_plot.setData() - self.clust_amps = [] - self.clust_depths = [] - self.clust_color = [] - self.clust_ids = [] - self.point_pos = [] - self.point_pos_prev = [] - - def populate(self, clust_amps, clust_depths, clust_ids, clust_color): - self.clust_amps = clust_amps - self.clust_depths = clust_depths - self.clust_color = clust_color - self.clust_ids = clust_ids - self.scatter_plot.setData(x=self.clust_amps, y=self.clust_depths, size=8) - self.scatter_plot.setBrush(clust_color) - self.scatter_plot.setPen(QtGui.QColor(30, 50, 2)) - self.x_min = self.clust_amps.min() - 2 - self.x_max = self.clust_amps.max() - 2 - self.fig_scatter.setXRange(min=self.x_min, max=self.x_max) - self.fig_scatter.setYRange(min=0, max=4000) - - def on_scatter_plot_clicked(self, point): - self.point_pos = point[0].pos() - clust = np.argwhere(self.scatter_plot.data['x'] == self.point_pos.x())[0][0] - return clust - - def on_mouse_hover(self, pos): - mouse_pos = self.scatter_plot.mapFromScene(pos) - point = self.scatter_plot.pointsAt(mouse_pos) - - if len(point) != 0: - point_pos = point[0].pos() - clust = np.argwhere(self.scatter_plot.data['x'] == point_pos.x())[0][0] - self.scatter_text.setText('Cluster no. ' + str(self.clust_ids[clust])) - self.scatter_text.setPos(mouse_pos.x(), (mouse_pos.y())) - self.scatter_text.show() - else: - self.scatter_text.hide() - - def on_scatter_plot_reset(self): - self.fig_scatter.setXRange(min=self.x_min, max=self.x_max) - self.fig_scatter.setYRange(min=0, max=4000) - - def update_scatter_icon(self, clust_prev): - point = self.scatter_plot.pointsAt(self.point_pos) - if len(point) > 1: - p_diff = [] - for p in point: - p_diff.append(np.abs(p.pos().x() - self.point_pos.x())) - idx = np.argmin(p_diff) - point[idx].setBrush('b') - point[idx].setPen('b') - else: - point[0].setBrush('b') - point[0].setPen('b') - - point_prev = self.scatter_plot.pointsAt(self.point_pos_prev) - if len(point_prev) > 1: - p_diff = [] - for p in point_prev: - p_diff.append(np.abs(p.pos().x() - self.point_pos_prev.x())) - idx = np.argmin(p_diff) - point_prev[idx].setPen('w') - point_prev[idx].setBrush(self.clust_color[clust_prev]) - else: - point_prev[0].setPen('w') - point_prev[0].setBrush(self.clust_color[clust_prev]) - - def set_current_point(self, clust): - self.point_pos.setX(self.clust_amps[clust]) - self.point_pos.setY(self.clust_depths[clust]) - - return self.point_pos - - def update_prev_point(self): - self.point_pos_prev.setX(self.point_pos.x()) - self.point_pos_prev.setY(self.point_pos.y()) - - return self.point_pos_prev - - def initialise_scatter_index(self): - self.point_pos = QtCore.QPointF() - self.point_pos_prev = QtCore.QPointF() - self.point_pos.setX(self.clust_amps[0]) - self.point_pos.setY(self.clust_depths[0]) - self.point_pos_prev.setX(self.clust_amps[0]) - self.point_pos_prev.setY(self.clust_depths[0]) - - point = self.scatter_plot.pointsAt(self.point_pos) - point[0].setBrush('b') - point[0].setPen('b') - - return self.point_pos, self.point_pos_prev diff --git a/PythonScripts/iblapps-master/data_exploration_gui/utils.py b/PythonScripts/iblapps-master/data_exploration_gui/utils.py deleted file mode 100644 index fb0cebd1..00000000 --- a/PythonScripts/iblapps-master/data_exploration_gui/utils.py +++ /dev/null @@ -1,262 +0,0 @@ -from PyQt5 import QtGui, QtCore -from iblutil.util import Bunch -import copy - -colours = {'all': QtGui.QColor('#808080'), - 'correct': QtGui.QColor('#1f77b4'), - 'incorrect': QtGui.QColor('#d62728'), - 'left': QtGui.QColor('#2ca02c'), - 'right': QtGui.QColor('#bcbd22'), - 'left correct': QtGui.QColor('#17becf'), - 'right correct': QtGui.QColor('#9467bd'), - 'left incorrect': QtGui.QColor('#8c564b'), - 'right incorrect': QtGui.QColor('#ff7f0e'), - 'KS good': QtGui.QColor('#1f77b4'), - 'KS mua': QtGui.QColor('#fdc086'), - 'IBL good': QtGui.QColor('#7fc97f'), - 'IBL bad': QtGui.QColor('#d62728'), - 'line': QtGui.QColor('#7732a8'), - 'no metric': QtGui.QColor('#989898')} - - -all = Bunch() -idx = Bunch() -idx['colours'] = [] -idx['text'] = [] -side = Bunch() -side['colours'] = [colours['left'], colours['right']] -side['text'] = ['left', 'right'] -choice = Bunch() -choice['colours'] = [colours['correct'], colours['incorrect']] -choice['text'] = ['correct', 'incorrect'] -choice_and_side = Bunch() -choice_and_side['colours'] = [colours['left correct'], colours['right correct'], - colours['left incorrect'], colours['right incorrect']] -choice_and_side['text'] = ['left correct', 'right correct', 'left incorrect', 'right incorrect'] -all['idx'] = idx -all['side'] = side -all['choice'] = choice -all['choice and side'] = choice_and_side - - -correct = Bunch() -side = Bunch() -side['colours'] = [colours['left correct'], colours['right correct']] -side['text'] = ['left correct', 'right correct'] -choice = Bunch() -choice['colours'] = [colours['correct']] -choice['text'] = ['correct'] -correct['idx'] = idx -correct['side'] = side -correct['choice'] = choice -correct['choice and side'] = side - -incorrect = Bunch() -side = Bunch() -side['colours'] = [colours['left incorrect'], colours['right incorrect']] -side['text'] = ['left incorrect', 'right incorrect'] -choice = Bunch() -choice['colours'] = [colours['incorrect']] -choice['text'] = ['incorrect'] -incorrect['idx'] = idx -incorrect['side'] = side -incorrect['choice'] = choice -incorrect['choice and side'] = side - - -left = Bunch() -side = Bunch() -side['colours'] = [colours['left']] -side['text'] = ['left'] -choice = Bunch() -choice['colours'] = [colours['left correct'], colours['left incorrect']] -choice['text'] = ['left correct', 'left incorrect'] -left['idx'] = idx -left['side'] = side -left['choice'] = choice -left['choice and side'] = choice - - -right = Bunch() -side = Bunch() -side['colours'] = [colours['right']] -side['text'] = ['right'] -choice = Bunch() -choice['colours'] = [colours['right correct'], colours['right incorrect']] -choice['text'] = ['right correct', 'right incorrect'] -right['idx'] = idx -right['side'] = side -right['choice'] = choice -right['choice and side'] = choice - -left_correct = Bunch() -side = Bunch() -side['colours'] = [colours['left correct']] -side['text'] = ['left correct'] -left_correct['idx'] = idx -left_correct['side'] = side -left_correct['choice'] = side -left_correct['choice and side'] = side - -right_correct = Bunch() -side = Bunch() -side['colours'] = [colours['right correct']] -side['text'] = ['right correct'] -right_correct['idx'] = idx -right_correct['side'] = side -right_correct['choice'] = side -right_correct['choice and side'] = side - -left_incorrect = Bunch() -side = Bunch() -side['colours'] = [colours['left incorrect']] -side['text'] = ['left incorrect'] -left_incorrect['idx'] = idx -left_incorrect['side'] = side -left_incorrect['choice'] = side -left_incorrect['choice and side'] = side - -right_incorrect = Bunch() -side = Bunch() -side['colours'] = [colours['right incorrect']] -side['text'] = ['right incorrect'] -right_incorrect['idx'] = idx -right_incorrect['side'] = side -right_incorrect['choice'] = side -right_incorrect['choice and side'] = side - -RASTER_OPTIONS = Bunch() -RASTER_OPTIONS['all'] = all -RASTER_OPTIONS['left'] = left -RASTER_OPTIONS['right'] = right -RASTER_OPTIONS['correct'] = correct -RASTER_OPTIONS['incorrect'] = incorrect -RASTER_OPTIONS['left correct'] = left_correct -RASTER_OPTIONS['right correct'] = right_correct -RASTER_OPTIONS['left incorrect'] = left_incorrect -RASTER_OPTIONS['right incorrect'] = right_incorrect - -all = Bunch() -all['colour'] = copy.copy(colours['all']) -all['fill'] = colours['all'] -all['text'] = 'all' - -left = Bunch() -left['colour'] = copy.copy(colours['left']) -left['fill'] = colours['left'] -left['text'] = 'left' - -right = Bunch() -right['colour'] = copy.copy(colours['right']) -right['fill'] = colours['right'] -right['text'] = 'right' - -correct = Bunch() -correct['colour'] = copy.copy(colours['correct']) -correct['fill'] = colours['correct'] -correct['text'] = 'correct' - -incorrect = Bunch() -incorrect['colour'] = copy.copy(colours['incorrect']) -incorrect['fill'] = colours['incorrect'] -incorrect['text'] = 'incorrect' - -left_correct = Bunch() -left_correct['colour'] = copy.copy(colours['left correct']) -left_correct['fill'] = colours['left correct'] -left_correct['text'] = 'left correct' - -right_correct = Bunch() -right_correct['colour'] = copy.copy(colours['right correct']) -right_correct['fill'] = colours['right correct'] -right_correct['text'] = 'right correct' - -left_incorrect = Bunch() -left_incorrect['colour'] = copy.copy(colours['left incorrect']) -left_incorrect['fill'] = colours['left incorrect'] -left_incorrect['text'] = 'left incorrect' - -right_incorrect = Bunch() -right_incorrect['colour'] = copy.copy(colours['right incorrect']) -right_incorrect['fill'] = colours['right incorrect'] -right_incorrect['text'] = 'right incorrect' - -PSTH_OPTIONS = Bunch() -PSTH_OPTIONS['all'] = all -PSTH_OPTIONS['left'] = left -PSTH_OPTIONS['right'] = right -PSTH_OPTIONS['correct'] = correct -PSTH_OPTIONS['incorrect'] = incorrect -PSTH_OPTIONS['left correct'] = left_correct -PSTH_OPTIONS['right correct'] = right_correct -PSTH_OPTIONS['left incorrect'] = left_incorrect -PSTH_OPTIONS['right incorrect'] = right_incorrect - -MAP_SIDE_OPTIONS = Bunch() -MAP_SIDE_OPTIONS['all'] = 'all' -MAP_SIDE_OPTIONS['left'] = 'left' -MAP_SIDE_OPTIONS['right'] = 'right' -MAP_SIDE_OPTIONS['correct'] = 'all' -MAP_SIDE_OPTIONS['incorrect'] = 'all' -MAP_SIDE_OPTIONS['left correct'] = 'left' -MAP_SIDE_OPTIONS['right correct'] = 'right' -MAP_SIDE_OPTIONS['left incorrect'] = 'left' -MAP_SIDE_OPTIONS['right incorrect'] = 'right' - -MAP_CHOICE_OPTIONS = Bunch() -MAP_CHOICE_OPTIONS['all'] = 'all' -MAP_CHOICE_OPTIONS['left'] = 'all' -MAP_CHOICE_OPTIONS['right'] = 'all' -MAP_CHOICE_OPTIONS['correct'] = 'correct' -MAP_CHOICE_OPTIONS['incorrect'] = 'incorrect' -MAP_CHOICE_OPTIONS['left correct'] = 'correct' -MAP_CHOICE_OPTIONS['right correct'] = 'correct' -MAP_CHOICE_OPTIONS['left incorrect'] = 'incorrect' -MAP_CHOICE_OPTIONS['right incorrect'] = 'incorrect' - -MAP_SORT_OPTIONS = Bunch() -MAP_SORT_OPTIONS['all'] = 'idx' -MAP_SORT_OPTIONS['left'] = 'side' -MAP_SORT_OPTIONS['right'] = 'side' -MAP_SORT_OPTIONS['correct'] = 'choice' -MAP_SORT_OPTIONS['incorrect'] = 'choice' -MAP_SORT_OPTIONS['left correct'] = 'choice and side' -MAP_SORT_OPTIONS['right correct'] = 'choice and side' -MAP_SORT_OPTIONS['left incorrect'] = 'choice and side' -MAP_SORT_OPTIONS['right incorrect'] = 'choice and side' - - -TRIAL_OPTIONS = ['all', 'correct', 'incorrect', 'left', 'right', 'left correct', 'left incorrect', - 'right correct', 'right incorrect'] -CONTRAST_OPTIONS = [1, 0.25, 0.125, 0.0625, 0] -ORDER_OPTIONS = ['trial num', 'reaction time'] -SORT_OPTIONS = ['idx', 'choice', 'side', 'choice and side'] -UNIT_OPTIONS = ['IBL good', 'IBL bad', 'KS good', 'KS mua'] -SORT_CLUSTER_OPTIONS = ['ids', 'n spikes', 'IBL good', 'KS good'] - -SESS_QC = ['task', 'behavior', 'dlcLeft', 'dlcRight', 'videoLeft', 'videoRight'] -CLUSTER_QC = ['noise_cutoff', 'amp_median', 'slidingRP_viol'] - -dlc_warning = 'WARNING: dlc points and timestamps differ in length, dlc points are ' \ - 'not aligned correctly' - - -def get_icon(col_outer, col_inner, pix_size): - - p1 = QtGui.QPixmap(pix_size, pix_size) - p1.fill(col_outer) - p2 = QtGui.QPixmap(pix_size, pix_size) - p2.fill(QtCore.Qt.transparent) - p = QtGui.QPainter(p2) - p.fillRect(int(pix_size / 4), int(pix_size / 4), int(pix_size / 2), - int(pix_size / 2), col_inner) - p.end() - - result = QtGui.QPixmap(p1) - painter = QtGui.QPainter(result) - painter.drawPixmap(QtCore.QPoint(), p1) - painter.setCompositionMode(QtGui.QPainter.CompositionMode_SourceOver) - painter.drawPixmap(result.rect(), p2, p2.rect()) - painter.end() - - return result diff --git a/PythonScripts/iblapps-master/dlc/DLC_labeled_video.py b/PythonScripts/iblapps-master/dlc/DLC_labeled_video.py deleted file mode 100644 index babec53e..00000000 --- a/PythonScripts/iblapps-master/dlc/DLC_labeled_video.py +++ /dev/null @@ -1,287 +0,0 @@ -import numpy as np -from one.api import ONE -from pathlib import Path -import cv2 -import os,fnmatch -import matplotlib -import pandas as pd -# conda install -c conda-forge pyarrow -import os - -def Find(pattern, path): - - ''' - find a local video like so: - flatiron='/home/mic/Downloads/FlatIron' - vids = Find('*.mp4', flatiron) - ''' - result = [] - for root, dirs, files in os.walk(path): - for name in files: - if fnmatch.fnmatch(name, pattern): - result.append(os.path.join(root, name)) - return result - -def find_nearest(array, value): - array = np.asarray(array) - idx = (np.abs(array - value)).argmin() - return idx - - -def Viewer(eid, video_type, trial_range, save_video=True, eye_zoom=False): - ''' - eid: session id, e.g. '3663d82b-f197-4e8b-b299-7b803a155b84' - video_type: one of 'left', 'right', 'body' - trial_range: first and last trial number of range to be shown, e.g. [5,7] - save_video: video is saved this local folder - - Example usage to view and save labeled video with wheel angle: - Viewer('3663d82b-f197-4e8b-b299-7b803a155b84', 'left', [5,7]) - 3D example: 'cb2ad999-a6cb-42ff-bf71-1774c57e5308', [5,7] - ''' - - save_vids_here = Path.home() - - one = ONE() - alf_path = one.eid2path(eid) - - # Download a single video - video_path = (alf_path / - f'raw_video_data/_iblrig_{video_type}Camera.raw.mp4') - - if not os.path.isfile(video_path): - print('mp4 not found locally, downloading it ...') - video_path = one.load_dataset(eid, - f'raw_video_data/_iblrig_{video_type}Camera.raw.mp4', - download_only=True) - - # Get trials info - trials = one.load_object(eid, 'trials', download_only=True) - - - # Download DLC traces and stamps - Times = one.load_dataset(eid,f'alf/_ibl_{video_type}Camera.times.npy') - cam = one.load_dataset(eid,f'alf/_ibl_{video_type}Camera.dlc.pqt') - - # get video info - cap = cv2.VideoCapture(video_path.as_uri()) - length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - fps = cap.get(cv2.CAP_PROP_FPS) - size = (int(cap.get(3)), int(cap.get(4))) - - - print(eid, - ', ', - video_type, - ', fsp:', - fps, - ', #frames:', - length, - ', #stamps:', - len(Times), - ', #frames - #stamps = ', - length - len(Times)) - - # pick trial range for which to display stuff - trials = one.load_object(eid, 'trials') - num_trials = len(trials['intervals']) - if trial_range[-1] > num_trials - 1: - print('There are only %s trials' % num_trials) - print('There are %s trials' % num_trials) - frame_start = find_nearest(Times, - [trials['intervals'][trial_range[0]][0]]) - frame_stop = find_nearest(Times, - [trials['intervals'][trial_range[-1]][1]]) - - ''' - load wheel - ''' - - wheel = one.load_object(eid, 'wheel') - import brainbox.behavior.wheel as wh - try: - pos, t = wh.interpolate_position( - wheel['timestamps'], wheel['position'], freq=1000) - except BaseException: - pos, t = wh.interpolate_position( - wheel['times'], wheel['position'], freq=1000) - - w_start = find_nearest(t, trials['intervals'][trial_range[0]][0]) - w_stop = find_nearest(t, trials['intervals'][trial_range[-1]][1]) - - # confine to interval - pos_int = pos[w_start:w_stop] - t_int = t[w_start:w_stop] - - # alignment of cam stamps and interpolated wheel stamps - wheel_pos = [] - kk = 0 - for wt in Times[frame_start:frame_stop]: - wheel_pos.append(pos_int[find_nearest(t_int, wt)]) - kk += 1 - if kk % 3000 == 0: - print('iteration', kk) - - ''' - DLC related stuff - ''' - Times = Times[frame_start:frame_stop] - - # some exception for inconsisitent data formats - - - - points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()]) - if len(points) == 1: - points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()]) - - if video_type != 'body': - d = list(points) - d.remove('tube_top') - d.remove('tube_bottom') - points = np.array(d) - - # Set values to nan if likelyhood is too low # for pqt: .to_numpy() - XYs = {} - for point in points: - x = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_x']) - x = x.filled(np.nan) - y = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_y']) - y = y.filled(np.nan) - XYs[point] = np.array( - [x[frame_start:frame_stop], y[frame_start:frame_stop]]) - - # Just for 3D testing - # return XYs - - # Zoom at eye - if eye_zoom: - pivot = np.nanmean(XYs['pupil_top_r'], axis=1) - x0 = int(pivot[0]) - 33 - x1 = int(pivot[0]) + 33 - y0 = int(pivot[1]) - 28 - y1 = int(pivot[1]) + 38 - size = (66, 66) - dot_s = 1 # [px] for painting DLC dots - - else: - x0 = 0 - x1 = size[0] - y0 = 0 - y1 = size[1] - if video_type == 'left': - dot_s = 10 # [px] for painting DLC dots - else: - dot_s = 5 - - if save_video: - loc = (save_vids_here / - f'{eid}_trials_{trial_range[0]}_{trial_range[-1]}_{video_type}.mp4') - - out = cv2.VideoWriter(str(loc), - cv2.VideoWriter_fourcc(*'mp4v'), - fps, - size) # put , 0 if grey scale - - # writing stuff on frames - font = cv2.FONT_HERSHEY_SIMPLEX - - if video_type == 'left': - bottomLeftCornerOfText = (20, 1000) - fontScale = 4 - else: - bottomLeftCornerOfText = (10, 500) - fontScale = 2 - - lineType = 2 - - # assign a color to each DLC point (now: all points red) - cmap = matplotlib.cm.get_cmap('Set1') - CR = np.arange(len(points)) / len(points) - - block = np.ones((2 * dot_s, 2 * dot_s, 3)) - - # set start frame - cap.set(1, frame_start) - - k = 0 - while(cap.isOpened()): - ret, frame = cap.read() - gray = frame - - # print wheel angle - fontColor = (255, 255, 255) - Angle = round(wheel_pos[k], 2) - Time = round(Times[k], 3) - cv2.putText(gray, - 'Wheel angle: ' + str(Angle), - bottomLeftCornerOfText, - font, - fontScale / 2, - fontColor, - lineType) - - a, b = bottomLeftCornerOfText - bottomLeftCornerOfText0 = (int(a * 10 + b / 2), b) - cv2.putText(gray, - ' time: ' + str(Time), - bottomLeftCornerOfText0, - font, - fontScale / 2, - fontColor, - lineType) - - # print DLC dots - ll = 0 - for point in points: - - # Put point color legend - fontColor = (np.array([cmap(CR[ll])]) * 255)[0][:3] - a, b = bottomLeftCornerOfText - if video_type == 'right': - bottomLeftCornerOfText2 = (a, a * 2 * (1 + ll)) - else: - bottomLeftCornerOfText2 = (b, a * 2 * (1 + ll)) - fontScale2 = fontScale / 4 - cv2.putText(gray, point, - bottomLeftCornerOfText2, - font, - fontScale2, - fontColor, - lineType) - - X0 = XYs[point][0][k] - Y0 = XYs[point][1][k] - # transform for opencv? - X = Y0 - Y = X0 - - if not np.isnan(X) and not np.isnan(Y): - col = (np.array([cmap(CR[ll])]) * 255)[0][:3] - # col = np.array([0, 0, 255]) # all points red - X = X.astype(int) - Y = Y.astype(int) -# cv2.imshow('frame', gray) -# print(gray.shape) -# print(X - dot_s,X + dot_s, Y - dot_s,Y + dot_s) - gray[X - dot_s:X + dot_s, Y - dot_s:Y + dot_s] = block * col - ll += 1 - - gray = gray[y0:y1, x0:x1] - if save_video: - out.write(gray) - #cv2.imshow('frame', gray) - #cv2.waitKey(1) - k += 1 - if k == (frame_stop - frame_start) - 1: - break - - if save_video: - out.release() - cap.release() - #cv2.destroyAllWindows() - - print(eid, video_type, frame_stop, frame_start) - #return XYs, Times diff --git a/PythonScripts/iblapps-master/dlc/Example_DLC_access.ipynb b/PythonScripts/iblapps-master/dlc/Example_DLC_access.ipynb deleted file mode 100644 index d077336b..00000000 --- a/PythonScripts/iblapps-master/dlc/Example_DLC_access.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook will illustrate how to access DeepLabCut(DLC) results for IBL sessions and how to create short videos with DLC labels printed onto, as well as wheel angle, starting by downloading data from the IBL flatiron server. It requires ibllib, a ONE account and the following script: https://github.com/int-brain-lab/iblapps/blob/master/DLC_labeled_video.py" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "run '/home/mic/Dropbox/scripts/IBL/DLC_labeled_video.py'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connected to https://alyx.internationalbrainlab.org as michael.schartner\n" - ] - } - ], - "source": [ - "one = ONE()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first find IBL ephys sessions with DLC results:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "eids= one.search(task_protocol='ephysChoiceworld', dataset_types=['camera.dlc'], details=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "47" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(eids)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a particular session, we can create a short labeled video by calling the function Viewer, specifying the eid of the desired session, the video type (there's 'left', 'right' and 'body' videos), and a range of trials for which the video should be created. Most sesions have around 700 trials. In the following, this is illustrated with session '3663d82b-f197-4e8b-b299-7b803a155b84', video type 'left', trials range [10,13] and without a zoom for the eye, such that nose, paw and tongue tracking is visible. The eye-zoom option shows only the four points delineating the pupil edges, which are too small to be visible in the normal view. Note that this automatically starts the download of the video from flatiron (in case it is not locally stored already), which may take a while since these videos are about 8 GB large. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "eid = eids[6]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connected to https://alyx.internationalbrainlab.org as michael.schartner\n", - "Connected to https://alyx.internationalbrainlab.org as michael.schartner\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Inconsistent dimensions for object:_ibl_leftCamera\n", - "(379271,), times\n", - "(379265,), nose_tip_x\n", - "(379265,), nose_tip_y\n", - "(379265,), nose_tip_likelihood\n", - "(379265,), pupil_top_r_x\n", - "(379265,), pupil_top_r_y\n", - "(379265,), pupil_top_r_likelihood\n", - "(379265,), pupil_right_r_x\n", - "(379265,), pupil_right_r_y\n", - "(379265,), pupil_right_r_likelihood\n", - "(379265,), pupil_bottom_r_x\n", - "(379265,), pupil_bottom_r_y\n", - "(379265,), pupil_bottom_r_likelihood\n", - "(379265,), pupil_left_r_x\n", - "(379265,), pupil_left_r_y\n", - "(379265,), pupil_left_r_likelihood\n", - "(379265,), paw_l_x\n", - "(379265,), paw_l_y\n", - "(379265,), paw_l_likelihood\n", - "(379265,), paw_r_x\n", - "(379265,), paw_r_y\n", - "(379265,), paw_r_likelihood\n", - "(379265,), tube_top_x\n", - "(379265,), tube_top_y\n", - "(379265,), tube_top_likelihood\n", - "(379265,), tube_bottom_x\n", - "(379265,), tube_bottom_y\n", - "(379265,), tube_bottom_likelihood\n", - "(379265,), tongue_end_l_x\n", - "(379265,), tongue_end_l_y\n", - "(379265,), tongue_end_l_likelihood\n", - "(379265,), tongue_end_r_x\n", - "(379265,), tongue_end_r_y\n", - "(379265,), tongue_end_r_likelihood\n", - "Inconsistent dimensions for object:_ibl_trials\n", - "(893,), itiDuration\n", - "(767,), stimOn_times\n", - "(767, 2), intervals\n", - "(893,), feedbackType\n", - "(893,), choice\n", - "(893,), rewardVolume\n", - "(767,), feedback_times\n", - "(893,), contrastRight\n", - "(893,), probabilityLeft\n", - "(893,), contrastLeft\n", - "(767,), goCue_times\n" - ] - } - ], - "source": [ - "Viewer(eid, 'left', [10,13], save_video=True, eye_zoom=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As usual when downloading IBL data from flatiron, the dimensions are listed. Below is one frame of the video for illustration. One can see one point for each paw, two points for the edges of the tongue, one point for the nose and there are 4 points close together around the pupil edges. All points for which the DLC network had a confidence probability of below 0.9 are hidden. For instance when the mouse is not licking, there is no tongue and so the network cannot detect it, and no points are shown. \n", - "\n", - "The script will display and save the short video in your local folder. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![alt text](video_frame.png \"Example frame of video with DLC labels\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sections of the script DLC_labeled_video.py can be recycled to analyse DLC traces. For example let's plot the x coordinate for the right paw in a 'left' cam video for a given trial. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connected to https://alyx.internationalbrainlab.org as michael.schartner\n" - ] - } - ], - "source": [ - "one = ONE()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_types = ['camera.times','trials.intervals','camera.dlc']\n", - "video_type = 'left'" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# get paths to load in data\n", - "D = one.load('3663d82b-f197-4e8b-b299-7b803a155b84',dataset_types=dataset_types, dclass_output=True)\n", - "alf_path = Path(D.local_path[0]).parent.parent / 'alf'\n", - "video_data = alf_path.parent / 'raw_video_data'" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Inconsistent dimensions for object:_ibl_trials\n", - "(893,), itiDuration\n", - "(767,), stimOn_times\n", - "(767, 2), intervals\n", - "(893,), feedbackType\n", - "(893,), choice\n", - "(893,), rewardVolume\n", - "(767,), feedback_times\n", - "(893,), contrastRight\n", - "(893,), probabilityLeft\n", - "(893,), contrastLeft\n", - "(767,), goCue_times\n", - "Inconsistent dimensions for object:_ibl_leftCamera\n", - "(379271,), times\n", - "(379265,), nose_tip_x\n", - "(379265,), nose_tip_y\n", - "(379265,), nose_tip_likelihood\n", - "(379265,), pupil_top_r_x\n", - "(379265,), pupil_top_r_y\n", - "(379265,), pupil_top_r_likelihood\n", - "(379265,), pupil_right_r_x\n", - "(379265,), pupil_right_r_y\n", - "(379265,), pupil_right_r_likelihood\n", - "(379265,), pupil_bottom_r_x\n", - "(379265,), pupil_bottom_r_y\n", - "(379265,), pupil_bottom_r_likelihood\n", - "(379265,), pupil_left_r_x\n", - "(379265,), pupil_left_r_y\n", - "(379265,), pupil_left_r_likelihood\n", - "(379265,), paw_l_x\n", - "(379265,), paw_l_y\n", - "(379265,), paw_l_likelihood\n", - "(379265,), paw_r_x\n", - "(379265,), paw_r_y\n", - "(379265,), paw_r_likelihood\n", - "(379265,), tube_top_x\n", - "(379265,), tube_top_y\n", - "(379265,), tube_top_likelihood\n", - "(379265,), tube_bottom_x\n", - "(379265,), tube_bottom_y\n", - "(379265,), tube_bottom_likelihood\n", - "(379265,), tongue_end_l_x\n", - "(379265,), tongue_end_l_y\n", - "(379265,), tongue_end_l_likelihood\n", - "(379265,), tongue_end_r_x\n", - "(379265,), tongue_end_r_y\n", - "(379265,), tongue_end_r_likelihood\n" - ] - } - ], - "source": [ - "# get trials start and end times, camera time stamps (one for each frame, synced with DLC trace)\n", - "trials = alf.io.load_object(alf_path, '_ibl_trials')\n", - "cam0 = alf.io.load_object(alf_path, '_ibl_%sCamera' % video_type)\n", - "cam1 = alf.io.load_object(video_data, '_ibl_%sCamera' % video_type)\n", - "cam = {**cam0,**cam1}" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['times', 'nose_tip_x', 'nose_tip_y', 'nose_tip_likelihood', 'pupil_top_r_x', 'pupil_top_r_y', 'pupil_top_r_likelihood', 'pupil_right_r_x', 'pupil_right_r_y', 'pupil_right_r_likelihood', 'pupil_bottom_r_x', 'pupil_bottom_r_y', 'pupil_bottom_r_likelihood', 'pupil_left_r_x', 'pupil_left_r_y', 'pupil_left_r_likelihood', 'paw_l_x', 'paw_l_y', 'paw_l_likelihood', 'paw_r_x', 'paw_r_y', 'paw_r_likelihood', 'tube_top_x', 'tube_top_y', 'tube_top_likelihood', 'tube_bottom_x', 'tube_bottom_y', 'tube_bottom_likelihood', 'tongue_end_l_x', 'tongue_end_l_y', 'tongue_end_l_likelihood', 'tongue_end_r_x', 'tongue_end_r_y', 'tongue_end_r_likelihood'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# for each tracked point there's x,y in [px] in the frame and a likelihood that indicates the network's confidence\n", - "cam.keys()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is also 'times' in this dictionary, the time stamps for each frame that we'll use to sync it with other events in the experiment. Let's get rid of it briefly to have only DLC points and set coordinates to nan when the likelihood is below 0.9. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "Times = cam['times'] \n", - "del cam['times']\n", - "points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()])\n", - "cam['times'] = Times" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# A helper function to find closest time stamps\n", - "def find_nearest(array, value):\n", - " array = np.asarray(array)\n", - " idx = (np.abs(array - value)).argmin()\n", - " return idx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's pick say the 5th trial and find all DLC traces for it." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "frame_start = find_nearest(cam['times'], trials['intervals'][4][0])\n", - "frame_stop = find_nearest(cam['times'], trials['intervals'][4][1])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "XYs = {}\n", - "for point in points:\n", - " x = np.ma.masked_where(\n", - " cam[point + '_likelihood'] < 0.9, cam[point + '_x'])\n", - " x = x.filled(np.nan)\n", - " y = np.ma.masked_where(\n", - " cam[point + '_likelihood'] < 0.9, cam[point + '_y'])\n", - " y = y.filled(np.nan)\n", - " XYs[point] = np.array(\n", - " [x[frame_start:frame_stop], y[frame_start:frame_stop]])" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'x location of right paw [px]')" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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raidQNmRdI07rpaHbKvBxL8c1JhVlBwNUzyi0OweTEbzcOfxRRL4oIgtFZIGIfAF4yO3MFmurJWMy2vJZxWx9o83GWDJpz8udw2Xu348NWX85TkujWOofjMloJ84q5r5NtTS091JZnJvscIyZMC+tlRYlIhBjMkF0pbQlB5POvBQrGWM8WjbThtEwmcGSgzFxNC0/RGlBiJqGzmSHYsykWHIwJs6qygssOZi056UT3GNe1hljHFUVBdQcseRg0tuoyUFEct2mquUiMj3SdFVEFjLCgHjGGEdVRSFHOnpp6+kff2NjUtRYrZU+BvwTTq/oTYC469uA7/kclzFpq6q8AICahk5OnjctydEYMzGj3jmo6q1uM9bPqWqVqi5yl5NU1ZKDMaOoqogkh44kR2LMxHnp5/BdETkbWBi9var+1Me4jElb80sLCAaEPVbvYNLYuMlBRH4GLAZeAgbd1QpYcjBmBKGsAPOm51mLJZPWvAyfsQZYrjZYjDGeVVUUstuKlUwa89LP4VWc4bqNMR5VlRewt7GTcNh+U5n0NOqdg4j8Hqf4qAjYKiLPA72R11V1nf/hGZOeFlUU0NMf5lBbD3Om5SU7HGNiNlax0rcSFoUxGaaqvBBwWixZcjDpaNTkoKpPJDIQYzLJ4opjfR3Ora5IcjTGxM5La6V2nOKlaK3ARuCzqlrjR2DGpLOKohwKQkH2NlqLJZOevLRW+i+gFmcuacGZ5Gcx8CLwY+AtfgVnTLoSEWaU5FLf1jv+xsakIC+tldap6g9UtV1V21R1PfAOVf0VMN3n+IxJWzOKcqlr60l2GMZMiJfk0CUil4lIwF0uAyKf+DHb6YnINBG5X0S2i8g2ETlLRH4lIi+5y14RecnddqGIdEe9dsck35sxSVVZnEN9u905mPTkpVjpSuBW4DacZLAB+DsRyQM+Mc6+twIPq+r7RSQE5KvqByMvish/4tRfROxW1ZNjeQPGpKoZxc6dg6oiIuPvYEwK8TK2Ug3w7lFefnq0/USkBDgPuNo9Th/QF/W6AJcBF3gP15j0UVmUQ+9AmLbuAUrys5MdjjExGasT3BdU9T9E5LuMUHykqp8a59iLgAbgLhE5CWfY70+raqT5xrlAnarujN5HRDbjDAv+ZVV9Kob3YkxKmVGcC0Bde48lB5N2xqpz2Ob+3YjzxT50GU8WcCpwu6qeAnQCX4x6/QrgF1HPDwHz3W1vAO4VkeKhBxWR60Rko4hsbGho8BCGMclRWZQDYC2WTFoaqxPc70UkCKxS1c9N4Ni1QK2qPuc+vx83OYhIFvA+4E1R5+vFHZ5DVTeJyG5gKU5yio5rPbAeYM2aNTZwjUlZR+8crMWSSUNjtlZS1UHgnIkcWFUPAwdE5AR31Vpgq/v4bcB2Va2NbC8iFW4yQkSqgGrAOtiZtFVZ7Nw51LVbcjDpx0trpZdE5EHgPpyiIQBU9Tce9v0kcI/bUqkGuMZdfznHFymBU3l9k4j0A2HgelVt8nAOY1JSfiiLotwsK1YyaclLcsgFGjm+VZEC4yYHVX0JZz6IoeuvHmHdA8ADHuIxJm3MKM6l3u4cTBry0pT1mvG2McaMrLIohzq7czBpyEsPaWPMBEU6whmTbiw5GOOjyuIc6tt6sVl2TboZNTmIyKfdvxNqrWSMcQbf6xsM09rdn+xQjInJWHcOkbqG7yYiEGMy0dHmrFbvYNLMWBXS20RkJzBbRLZErRdAVXW1v6EZk/6iO8KdMLMoydEY491YPaSvEJGZwJ+AdYkLyZjMMaPIekmb9DRmU1a3l/NJbie2pe7qHapqBajGeBApVrJ5HUy68TKH9PnAT4G9OEVK80TkKlV90ufYjEl7udlBinOzqLc7B5NmvPSQ/jbwdlXdASAiS3GGvnjTmHsZYwCn3uGwJQeTZrz0c8iOJAYAVX0dsMHpjfFo6YwiXqlttb4OJq14SQ4bReROEXmLu/yQIcNoG2NGd2ZVKW+09nCgqTvZoRjjmZfk8A84Q21/yl22uuuMMR6cWVUGwIaaxiRHYox3Xgbe68Wpd/i2/+EYk3mWVBZSVhBiQ00jl502L9nhGOOJja1kjM9EhDOqSnluT5PVO5i0YcnBmAQ4s6qMgy3d1DZbvYNJD5YcjEmASL3Ds1bvYNLEuMlBRJaKyA9F5M8i8nhkSURwxmSK6spCSt16B2PSgZdOcPcBdwA/BAb9DceYzCQinFlVylM7j9A/GCY7aDftJrV5+YQOqOrtqvq8qm6KLL5HZkyGef+b5tLQ3stDrxxKdijGjMtLcvi9iPyjiMwSkdLI4ntkxmSYtyytpKqigB89vcdaLZmU5yU5XAV8Hvg/YJO7eOohLSLTROR+EdkuIttE5CwR+aqIHBSRl9zlkqjtvyQiu0Rkh4i8YyJvyJhUFQgI15yziC21rWzc15zscIwZ07jJQVUXjbBUeTz+rcDDqroMOAnY5q7/jqqe7C4PAYjIcuByYAVwEXCbiARjfkfGpLC/OXUOJXnZ/OipPckOxZgxeRmyOxtnuIzz3FV/BX4w3pwOIlLi7nM1gKr2AX0iMtoulwK/dHtk7xGRXcDpwLPjvgtj0kR+KIsrTp/P+id3c6Sjl/LCHN/Ota+xk/s31RKOKsLKyw7y0XOryM22311mbF5aK92OMwrrbe7zD7nrPjrOfouABuAuETkJpzjq0+5rnxCRD+MUT31WVZuBOcCGqP1r3XXHEZHrgOsA5s+f7yF8Y1LLu1bP4o4ndvOX7fV8YI0/w2kMDIa57qeb2FHXTlbA+UGmwGBYmT0tj/edOteX85rM4aXO4TRVvUpVH3eXa4DTPOyXBZwK3K6qpwCdwBdxEsti4GTgEPCfsQSsqutVdY2qrqmoqIhlV2NSworZxcwqyeXRbXW+nePXG2vZUdfObVeeyq5vXMKub1zCzq9fTEVRDo9tr/ftvCZzeEkOgyKyOPJERKrw1t+hFqhV1efc5/cDp6pqnaoOqmoYp+/E6e7rB4Hon1Fz3XXGZBQRYe2JlTy18wg9/fHvOtTe08+3H9nB6QtLuXjlzKPrAwHhghMqeXJHA/2D4bif12QWL8nh88BfROSvIvIE8Djw2fF2cuefPiAiJ7ir1gJbRWRW1GbvBV51Hz8IXC4iOSKyCKgGnvf4PoxJK2tPnEFX36Avw2nc8cRujnT08eV3ncjQOr4LTqykvXeAF/Y2xf28JrN4GbL7MRGpBiJf8jvcSmMvPgncIyIhoAa4BvhvETkZpwh0L/Ax9zyvicivceaLGAA+rqrWI9tkpLOqysgPBXlsWx1vPaEyrsd+8vUjnL24jNVzpw177c1LygkFAzy+rZ6zF5fH9bwms4x65yAiF7h/3we8E1jiLu90141LVV9y6wdWq+p7VLVZVT+kqqvcdetU9VDU9jer6mJVPUFV/zi5t2ZM6srNDnJedQWPbauPe4e4fY2dLK4oHPG1gpwszlpcxuNW72DGMVax0vnu33ePsLzL57iMyXhvWz6DQ609PL8nfkU8rV39tPUMsKAsf9Rt1p5YSc2RTmoaOuJ2XpN5Ri1WUtX/5z68SVWP67Hj1gkYYybhklUzueWP2/jvx3dyjzuk92Tta+oEYF7p6MnhLUsrgdf4v92NVI1yh5EpuvsGeX5vExv3NnGw5fi5NIIiVBbnsKC0gHUnz056349wWOnsG6C9Z4CO3gHae5xE394zQEeP87y9Z4CuvkG6+wdYMbuEvztzgW/xeOnn8ABOk9Ro9wNvin84xkwd+aEsrj9/MV//3208v6eJ0xdNfsiy/U1dAGPeOcydnkdWQHijJbMnHuruG+TS7z/N63UdBARmleQRXT/fPxjmSEcfg2Fl84Fm/u19qxMWW/9gmH/+zStsqW11vvR7nYQwXgmjCBSEssjNDpIV8Hdk31GTg4gswxnKomRIHUMxkOtrVMZMEVeesYAfPFnDdx55nV9cd+akj7ev0UkO86aPnhwCAWFGcS6HW3smfb5Udssft/F6XQffvuwk3r5iJoU5w7/uBsPKfzy8nR88WcP5Syu4aOWsEY4Uf9/80w7u21TLBcsqmZ5fQlFuFsW5WRTlZlOYm0VR5HHO8esLQsFhLdD8Mtadwwk4dQvTcOoZItqBv/czKGOmirxQkH84fzE3/WErP9+wjyvPmD+p//z7G7soL8yhYIQvwmgzS3I5lMHJ4amdDdz97D6uOWfhmL3BgwHhs28/gWdrGrnxgVcIZQWoriyivDCH7KAQDEjcv4wf3VrH+idr+NCZC/jae1bG9djxNFadw++A34nIWapq4xsZ45O/PWM+f9lRz5f/51U272/h6+9ZSV5oYuXf+5u6xixSiphZksvWN9omdI5U1D8YZvP+Fp7e2cCL+1vYtK+ZxRUF3HjRsnH3DWUFuPXyU1j3vae59ifHDzgtAqFggFBWgJysIDlZAXKyox5nBcjJdh6X5GVTVhiiIDTy12pP/yDbD7ezoaaRFbOL+Zd3nhiX9+4XL3UOm0Xk4zhFTEeLk1T1Wt+iMmYKyc0O8pNrTufWx3by3cd3kh0UbvmbiZV/72/q8lR3Mas4l8e21aGqCSumiMWh1m6+8dB26tp6GAwrA4NhBsLqPHb/9g+Gjz7v6Bmgu3+QYEBYNrOI9506h7+PYYDBReUFPH3jBWw/1EbNkU5au/vpHwjTN+gsvf3H/vYODNI7EHaW/kHauvvp6R/kte5+jnT00TdK7/OAwJLKQi5aOZMbLlya9Arw8XhJDj8DtgPvAG4CruTY0NvGmDgIBoQbLlxKZ+8AP35mDx8+ayHLZxfHdIzegUHeaO1m/hgtlSJmluTS0x+mtbufafmhiYbtiw01jXz8nhfp6R9k1dwSQtlBgjlZZAWcYp7sYIBgQI4+zwoKudlBTl9YyjnV5RTnZk/ovCV52ZxRVcYZk2g5puokrpESruDU96QLL8lhiap+QEQuVdW7ReRe4Cm/AzNmKvrUBdU88GItNz+0lZ9/5IyYftUfbO5GFU/JYVZJHgCHWntSKjnUNHRw5Z3PsaAsn1996CyWVKZXU1sRJ1llAi9toSLzNrSIyEqgBIhvf39jDAAl+dl8em01z+xqjLkX8z4PzVgjZpY4JcSp1mJp475mBsPK+g+tSbvEkGm8JIf1IjId+DLO4Hhbgf/wNSpjprC/O3MBiysK+Ox9L7PjcLvn/fa7zVjne0gOs9zkkGotlnbVdxDKCrDQw3sw/vIyTeid7phIT6pqlapWquodiQjOmKkoOxjgx1efRk5WgCvvfM7zMBf7m7rIyw5S4WF2uYqiHAICh1tTqyPczrp2qsoLyAr628HLjG/cfwER+YaITIt6Pl1Evu5vWMZMbQvKCrjno2eiqtz4wBZP++xr7GJ+ab6neorsYICKohwOt6XWncPO+g6qZxQlOwyDt2Kli1W1JfLEndLzEv9CMsaA0+zxHStnsruh09P2B5q6xhxTaaiZJXkpVazU1TdAbXM3S62uISV4SQ5BETl6nyoieYB/s6IbY46aWZxLU2cfvQPjT21S195ztC7Bi1kpNoTG7nonCVbPsOSQCrwkh3uAx0TkIyLyEeAR4G5/wzLGgJMcAOrbxp5fKxxWWrv7mZ7vvY3/zJLUSg47653K9yWVVqyUCrzMBPfvIvIy8DZ31ddU9U/+hmWMAagsdm7S69p6xiwyauvpRxVKYuizMKskl3Z3aOiiCXYci6ed9R1kB8VTU1zjPy+d4AA2A9k4U3tu9i8cY0y0SH+EunHuHFq6nO5Isd45OMfuSY3kUNfOovICsq2lUkrw0lrpMuB54P3AZcBzIvJ+vwMzxhwrVhqvVVFzVx8A02JIDtG9pFPBzvoOqq1IKWV4uXP4F+A0Va0HEJEK4FGcCX+MMT4qycsmlBWgbpzk0NLt3DnEMhRGKnWE6+kfZH9TF+85eU6yQzEuL/dvgUhicDV63M8YM0kiwkwPrYpaIncOed7vHCL1GalQKb27oQNVWGp9HFKGlzuHh0XkT8Av3OcfBB7ycnC389ydwEqc+oprgffhTB7UB+wGrlHVFhFZiDPa6w539w2qer23t2FM5ppZnDv+ncPROgfvdw45WUHmTMtj++Hkz+vw59fqAGw8pRTiZfiMzwPrgdXusl5Vbxt5wmUAABBjSURBVPR4/FuBh1V1GXASzpf/I8BKVV0NvA58KWr73ap6srtYYjAG5xf+eMmhuasfESiO4c4B4OzFZTyzq5HB8DiTF/tEVfnmn7Zz62M7efvyGVRbckgZnoqHVPUBVb3BXX7rZR8RKQHOA37kHqNPVVtU9c+qOuButgEYfQ4/Y4xTrNTWg44x+3xrVx/FudkEY5wv4M3V5bR29/PqwdbJhjkhP3p6D9//y26uOH0et115alrNd5DpRk0OItIuIm0jLO0i4uU+dBHQANwlIptF5E4RKRiyzbXAH6P3cbd9QkTOHSWu60Rko4hsbGho8BCGMektMjFPW8/AqNs0d/XH1FIp4s1LygFnzuVk+NNrh1k5p5hvvHeVDbaXYkb911DVIlUtHmEpUlUvU1RlAacCt6vqKUAn8MXIiyLyL8AATg9sgEPAfHfbG4B7RWTYeVR1vaquUdU1FRUVnt+oMemqsvhYf4TRtExwRreywhxWzinmyZ1HJhzfRHX3DfLygVbOWVyeklOVTnV+pupaoFZVn3Of34+TLBCRq4F3AVeqe6+sqr2q2ug+3oRTWb3Ux/iMSQtH+zqM0aqopasvppZK0d68pIIX9zXT0Tv6nYkfNu9vpm8wzBlV4895bRLPt+SgqoeBAyJygrtqLbBVRC4CvgCsU9WuyPYiUiEiQfdxFVAN1PgVnzHpYqaXO4eu2MZVinZedTkDYeW5msYJ7T9RG/Y0ERBYs9CSQyryOnzGRH0SuEdEQjhf9NcAL+CM6vqIeysZabJ6HnCTiPQDYeB6VW3yOT5jUl70+Eqjae7qm/Bc0G9aOJ3c7ABPvt7A2hNnTOgYE7GhppEVs0soToGhO8xw4yYHEVmuqluHrHuLqv51vH1V9SVgzZDVS0bZ9gHggfGOacxUk5sdZFp+9qhDaAwMhmnvGZhQhTQ4/R1Wz5nGtkPepySdrJ7+QV7a38JVZy9I2DlNbLwUK/1aRG4UR56IfBf4N78DM8Yc43SEG3nwvdbI0BkTrHMAZ9rQIx1jD+4XT5v3tzj1DYvKEnZOExsvyeEMYB7wfzhFQm8A5/gZlDHmeJVj9JKOjKs0vWBixUoA5YWhhCaHDTWNiMBpi6y+IVV5SQ79QDeQB+QCe1Q17GtUxpjjzCzOGbW1UmRcpZJJ3DmUF+bQ1jPgaca5eHhyZwMrZ5dMKmbjLy/J4QWc5HAacC5whYjc52tUxpjjzJmWT0NHL50jNDedyLhKQ5UVOpXeTZ19Ez6GV/sbu9i8v4VLVs3y/Vxm4rwkh4+o6ldUtV9VD6nqpcCDfgdmjDlm5ZxiVGHroeGDEzR3RYbrnsydg5NYjrT7nxx+v+UNAN59kiWHVOZl4L2NI6z7mT/hGGNGsmpuCQAvH2gZ9trR4boncedQXuTcOfhd76Cq/M/mg5y2cDpzp9t0oKnMBjMxJg1UFuUyqySXLbXDB8hr6eonIFCUM/FuS+UFiUkO2w61s7O+g3U2qU/Ks+RgTJpYNaeEV0YYPbWl2+kAN5kRTcuL3GKlDn+LlX730kGyAsI7rb4h5VlyMCZNnDRvGnuOdB7t1xDR3NU/qT4OAPmhLPJDQRp9vHNQVf6w5RDnVpdTOolmtyYxLDkYkyZWu/UOQ+deaJ3gcN1Dlfnc1+G1N9o42NLNxXbXkBYsORiTJlbNcSula4+vlJ7MuErRygtzfC1W+vPWOgICa5dV+nYOEz+WHIxJE9PyQywoy2fLgePvHFridOfgJAf/7hwe2VrHmxZMP9qnwqQ2Sw7GpJGRKqWduRzicecQ8u3O4UBTF9sOtXHh8sSN+momx+8hu40xcXTS3Gn8Ycsh1nz9USKTp3X2DU54Lodo5YU5NHX2MhjWmOeiHs+j2+oAuHD5zLge1/jHkoMxaeTSU2ZzsKWb3oFjw5tlBYR1J8+e9LHLCkKE1bkTiXfRz59fq6O6spBF5UOnkTepypKDMWmksiiXr65b4cuxj/WSjm9yeHZ3I8/vbeL686vidkzjP6tzMMYATrESENe+Drvq2/nYzzayqLyA685dHLfjGv9ZcjDGAMcG32uIU3Kobe7i6rteIJQV5K6rT6MkDvUiJnGsWMkYAxy7c4hHi6Xth9u46sfP09U3yD0fPYN5pTbIXrqx5GCMAZzJgrICMqlipZ117fxhyyF+/Mwe8kNB7rv+LJbNLI5jlCZRfE0OIjINuBNYCShwLbAD+BWwENgLXKaqzSIiwK3AJUAXcLWqvuhnfMaYY0RkUkNofO/xnXzrz68jAucsLueWv1llw3KnMb/vHG4FHlbV94tICMgH/hl4TFVvEZEvAl8EbgQuBqrd5QzgdvevMSZBJjqExqsHW/nOozu5ZNVMvvruFVQW5/oQnUkk3yqkRaQEOA/4EYCq9qlqC3ApcLe72d3Ae9zHlwI/VccGYJqI2AhdxiRQeWFOzMVKfQNhPnffy5QVhPi39662xJAh/GyttAhoAO4Skc0icqeIFAAzVPWQu81hINKffg5wIGr/WnedMSZBygtzqGuLLTmsf3I32w+3c/N7V1mLpAziZ3LIAk4FblfVU4BOnCKko1RVceoiPBOR60Rko4hsbGhoiFuwxhionlHI4bYemju9FS31DYS565m9XLCs0sZNyjB+JodaoFZVn3Of34+TLOoixUXu33r39YPAvKj957rrjqOq61V1jaquqaio8C14Y6aiFbOdlkVbD7V52v6RrXU0dvbxobMW+BmWSQLfkoOqHgYOiMgJ7qq1wFbgQeAqd91VwO/cxw8CHxbHmUBrVPGTMSYBls9yk8Mb3pLDL57fz5xpeZxXbT/UMo3frZU+CdzjtlSqAa7BSUi/FpGPAPuAy9xtH8JpxroLpynrNT7HZowZoqwwh5nFubz2xvC5qofa19jJ07uOcMOFS+M+iqtJPl+Tg6q+BKwZ4aW1I2yrwMf9jMcYM74Vs4t5zcOdwy9fOEBA4LI188bd1qQfG1vJGHOcFbOL2d3QQXff4KjbtPf088vn97P2xBnMLLGmq5nIkoMx5jjLZ5cQVmd8pNHc9cxemrv6+cRblyQwMpNIlhyMMceJtFgarWiptaufHz5Vw9tOnMFJ86YlMjSTQJYcjDHHmTs9j+LcrFGbs975dA3tPQPccOHSBEdmEslGZTXGHEdEWD5CpXRP/yC3/XU3dzyxm0tWzWT5bBttNZNZcjDGDLNidgk/+b+9XPjtJ46ua+7q40hHH5eePJuvvtufqUpN6rDkYIwZ5rI186hr6yGsx0a3yQoEuPy0eZy9pDyJkZlEseRgjBnmhJlFfO9vT012GCaJrELaGGPMMJYcjDHGDGPJwRhjzDCWHIwxxgxjycEYY8wwlhyMMcYMY8nBGGPMMJYcjDHGDCMa1QMy3YhIA85scuXAkSSHk2x2DewagF2Dqf7+wds1WKCqY87tmtbJIUJENqrqSDPOTRl2DewagF2Dqf7+IX7XwIqVjDHGDGPJwRhjzDCZkhzWJzuAFGDXwK4B2DWY6u8f4nQNMqLOwRhjTHxlyp2DMcaYOLLkYIwxZpiUTw4iEhSRzSLyB/f5j0TkZRHZIiL3i0jhCPssFJFuEXnJXe5IfOTxM/QaRK3/bxHpGGO/L4nILhHZISLv8D9S/0zkGmT650BEfiIie6Le38mj7HeViOx0l6sSG3V8TeIaDEZt82Bio46vEa6BiMjNIvK6iGwTkU+Nsl9Mn4N0mAnu08A2IDKb+WdUtQ1ARL4NfAK4ZYT9dqvqiB+UNDT0GiAia4Dpo+0gIsuBy4EVwGzgURFZqqqDPsfql5ivgSujPwfA51X1/tF2EJFS4P8BawAFNonIg6ra7Guk/on5Gri6M/hzcDUwD1imqmERqRy6w0Q+Byl95yAic4F3AndG1kUlBgHycN5oxhrpGohIEPgm8IUxdr0U+KWq9qrqHmAXcLqfsfplEtcgY4x0DTx6B/CIqja5XwSPABfFO75EmMQ1yBijXIN/AG5S1TCAqtaPsGvMn4OUTg7Af+H85w9HrxSRu4DDwDLgu6Psu8i99XpCRM71N0xfjXQNPgE8qKqHxthvDnAg6nmtuy4dTfQaQGZ/DgBudotYvyMiOSPsl+mfAxj/GgDkishGEdkgIu/xN0xfjXQNFgMfdN/fH0WkeoT9Yv4cpGxyEJF3AfWqumnoa6p6DU5RyTbggyPsfgiYr6qnADcA94pI8QjbpbSRroGIzAY+wOhJMaNM8hpk7OfA9SWcH0inAaXAjYmOLVHicA0WuENK/C3wXyKy2LdgfTLGNcgBetz390Pgx/E4X8omB+AcYJ2I7AV+CVwgIj+PvOiWnf8S+JuhO7pFKY3u403AbmBpIoKOs2HXAHgNWALsctfni8iuEfY9iFMOGTHXXZduJnwNMvlzICI/V9VD6ugF7mLkYsOM/RzEcA1Q1YPu3xrgr8ApCYk6vkb7TqwFfuNu81tg9Qj7xv45UNWUX4C3AH8ABFjirhPgW8C3Rti+Agi6j6vci1Ca7PcRj2swwvqOUbZfAbyM86tiEVATuSbpukzgGmT05wCY5f4VnOKGW0bYvhTYg1NxP919PNWuwXQgx31cDuwElif7fcTxGtwCXBu1/oV4fA7SobVSNAHudosGBOfL7x8ARGQdsEZVvwKcB9wkIv04ZXPXq2pTkmJOmOhroKqvicivga3AAPBxTd+WSp5Nsc/BPSJSgfN/4SXgejjaiut6Vf2oqjaJyNeAF9x9bppq1wA4EfiBiIRxSktuUdWtyQrYB7fgXIfPAB3AR2HynwMbPsMYY8wwqVznYIwxJkksORhjjBnGkoMxxphhLDkYY4wZxpKDMcaYYSw5GGOMGcaSg5mSRGSaiPxj1PPZIjLeyJ4TOc9XReSgiNwUx2N+RkT2i8j34nVMY4ayfg5mShKRhTg9TFf6fJ6v4vTg/lacj3s1Tme/T8TzuMZE2J2DmapuARa7k798U5yJgV4F54tXRP5HRB4Rkb0i8gkRucEd3XWDOzY+IrJYRB4WkU0i8pSILBvvpCJyvhybdGaziBS56z8vIi+4o4v+a9T2H3bXvSwiP/PpWhgzTLoNn2FMvHwRWKnuBDDunUS0lTiDs+XizIVxo6qeIiLfAT6MM47PepzhCXaKyBnAbTgDA47lczhDmTwjziyGPSLydqAaZ9A4AR4UkfOARuDLwNmqeiSSlIxJBEsOxozsL6raDrSLSCvwe3f9K8Bq94v9bOA+Z94pwBnkcDzPAN8WkXuA36hqrZsc3g5sdrcpxEkWJwH3qeoRgAwbE8mkOEsOxoysN+pxOOp5GOf/TQBo0RinnlTVW0Tkf4FLgGfEmdtbgH9T1R9Ebysin5xo8MZMltU5mKmqHSia6M7qTFe7R0Q+AEcneT9pvP1EZLGqvqKq/44zQuYy4E/Ate7dCCIyR5x5gB8HPiAiZe56K1YyCWPJwUxJ6kwC9IyIvCoi35zgYa4EPiIiL+NMQHSph33+yT3nFqAf+KOq/hm4F3hWRF4B7geKVPU14GbgCfcc355gnMbEzJqyGuMja8pq0pXdORjjrw7gunh3gsOZO7ktXsc0Zii7czDGGDOM3TkYY4wZxpKDMcaYYSw5GGOMGcaSgzHGmGH+PwSDCqcpHoBFAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(cam['times'][frame_start:frame_stop],XYs['paw_r'][0])\n", - "plt.xlabel('time [sec]')\n", - "plt.ylabel('x location of right paw [px]')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (base)", - "language": "python", - "name": "base" - }, - "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/PythonScripts/iblapps-master/dlc/README.md b/PythonScripts/iblapps-master/dlc/README.md deleted file mode 100644 index a2db61a1..00000000 --- a/PythonScripts/iblapps-master/dlc/README.md +++ /dev/null @@ -1,57 +0,0 @@ -## How to get example frames with DLC labeles painted on top -Avoiding to download lengthy videos, with `stream_dlc_labeled_frames.py` one can stream 5 example frames randomly picked across a whole video, specified by eid and video type ('body', 'left' or 'right'). - -To use it, start an ipython session, then type `run /path/to/stream_dlc_labeled_frames.py` to load the script. Then type `stream_save_labeled_frames(eid, video_type)` after setting the path where the labelled example frames should be saved as png images locally, `save_images_folder`. - -## How to make DLC-labeled video -With the script DLC_labeled_video.py one can make DLC-labeled videos. The script downloads a specific IBL video ('body', 'left' or 'right') for some session (eid). It also downloads the wheel info and will print the wheel angle onto each frame. -To use it, start an ipython session, then type `run /path/to/DLC_labeled_video.py` to load the script. Next type - -`Viewer(eid, video_type, trial_range, save_video=True, eye_zoom=False)` - -This will display and save a labeled video for a particular trial range. E.g. `Viewer('3663d82b-f197-4e8b-b299-7b803a155b84', 'left', [5,7])` will display and save a DLC labeled video for the left cam video of session with eid '3663d82b-f197-4e8b-b299-7b803a155b84' and range from trial 5 to trial 7. There's further the option to show a zoom of the pupil only. - -See `Example_DLC_access.ipynb` for more intructions and an example how to load DLC results for other potential applications. - -## Wheel and DLC live viewer -Plotting the wheel data along side the video and DLC can be done withe `wheel_dlc_viewer`. The viewer loops over the -video frames for a given trial and shows the wheel position plotted against time, indicating the -detected wheel movements. __NB__: Besides the IBL enviroment dependencies, this module also -requires cv2. - -### Running from command line -You can run the viewer from the terminal. In Windows, running from the Anaconda prompt within -iblenv ensures the paths are correctly set (`conda activate iblenv`). - -The below code will show the wheel data for a particular session, with the tongue and paw DLC -features overlaid: - -```python -python wheel_dlc_viewer.py --eid 77224050-7848-4680-ad3c-109d3bcd562c --dlc tongue,paws -``` -Pressing the space bar will toggle playing of the video and the left and right arrows allow you -to step frame by frame. Key bindings also allow you to move between trials and toggle the legend. - -When called without the `--eid` flag will cause a random session to be selected. You can find a -full list of input arguments and key bindings by calling the function with the `--help` flag: -```python -python wheel_dlc_viewer.py -h -``` - -### Running from within Python -You can also import the Viewer from within Python... - -Example 1 - inspect trial 100 of a given session, looking at the right camera -```python -from wheel_dlc_viewer import Viewer -eid = '77224050-7848-4680-ad3c-109d3bcd562c' -v = Viewer(eid=eid, trial=100, camera='right') -``` - -Example 2 - pick a random session to inspect, showing all DLC -```python -from wheel_dlc_viewer import Viewer -Viewer(dlc_features='all') - ``` - -For more details, see the docstring for the Viewer. diff --git a/PythonScripts/iblapps-master/dlc/__init__.py b/PythonScripts/iblapps-master/dlc/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/dlc/get_dlc_traces.py b/PythonScripts/iblapps-master/dlc/get_dlc_traces.py deleted file mode 100644 index 91744795..00000000 --- a/PythonScripts/iblapps-master/dlc/get_dlc_traces.py +++ /dev/null @@ -1,37 +0,0 @@ -import alf.io -import numpy as np -from oneibl.one import ONE - -def get_DLC(eid,video_type): - '''load dlc traces - load dlc traces for a given session and - video type. - - :param eid: A session eid - :param video_type: string in 'left', 'right', body' - :return: array of times and dict with dlc points - as keys and x,y coordinates as values, - for each frame id - ''' - one = ONE() - D = one.load(eid, dataset_types = ['camera.dlc', 'camera.times']) - alf_path = one.path_from_eid(eid) / 'alf' - cam0 = alf.io.load_object( - alf_path, - '%sCamera' % - video_type, - namespace='ibl') - Times = cam0['times'] - cam = cam0['dlc'] - points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()]) - XYs = {} - for point in points: - x = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_x']) - x = x.filled(np.nan) - y = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_y']) - y = y.filled(np.nan) - XYs[point] = np.array([x, y]) - - return Times, XYs diff --git a/PythonScripts/iblapps-master/dlc/overview_plot_dlc.py b/PythonScripts/iblapps-master/dlc/overview_plot_dlc.py deleted file mode 100644 index 48c4bc39..00000000 --- a/PythonScripts/iblapps-master/dlc/overview_plot_dlc.py +++ /dev/null @@ -1,1366 +0,0 @@ -import numpy as np -import alf.io -#pip install ONE-api -from one.api import ONE -from pathlib import Path -import pandas as pd -import matplotlib.pyplot as plt -import matplotlib.image as mpimg -#os.environ['CUDA_VISIBLE_DEVICES'] = str(gputouse) -from ibllib.io.video import get_video_meta, get_video_frames_preload -import ibllib.io.video as vidio -import brainbox.behavior.wheel as wh -import math -from brainbox.processing import bincount2D -import time -import copy -import matplotlib.patches as patches -import string -from scipy.interpolate import interp1d -from scipy.stats import zscore - -#Generate scatterplots, variances, 2-state AR-HMMs to summarize differences in behavior in different sessions. Develop a 1-page figure per session that provides a behavioral overview. Would be great to generate these figs for the RS sessions noted in repro-ephys slides above -#lickogram -import matplotlib -# https://github.com/lindermanlab/ssm -import os - - -def get_all_sess_with_ME(): - one = ONE() - # get all bwm sessions with dlc - all_sess = one.alyx.rest('sessions', 'list', - project='ibl_neuropixel_brainwide_01', - task_protocol="ephys", - dataset_types='camera.ROIMotionEnergy') - - eids = [s['url'].split('/')[-1] for s in all_sess] - - return eids - - -def get_repeated_sites(): - one = ONE() - STR_QUERY = 'probe_insertion__session__project__name__icontains,ibl_neuropixel_brainwide_01,' \ - 'probe_insertion__session__qc__lt,50,' \ - '~probe_insertion__json__qc,CRITICAL,' \ - 'probe_insertion__session__n_trials__gte,400' - all_sess = one.alyx.rest('trajectories', 'list', provenance='Planned', - x=-2243, y=-2000, theta=15, - django=STR_QUERY) - eids = [s['session']['id'] for s in all_sess] - - return eids - - -def get_bwm_sessions(): - one = ONE() - traj_traced = one.alyx.rest('trajectories', 'list', provenance='Planned', - django='probe_insertion__session__project__name__' - 'icontains,ibl_neuropixel_brainwide_01,' - 'probe_insertion__session__qc__lt,50,' - 'probe_insertion__session__extended_qc__behavior,1,' - 'probe_insertion__json__extended_qc__tracing_exists,True,' - '~probe_insertion__session__extended_qc___task_stimOn_goCue_delays__lt,0.9,' - '~probe_insertion__session__extended_qc___task_response_feedback_delays__lt,0.9,' - '~probe_insertion__session__extended_qc___task_response_stimFreeze_delays__lt,0.9,' - '~probe_insertion__session__extended_qc___task_wheel_move_before_feedback__lt,0.9,' - '~probe_insertion__session__extended_qc___task_wheel_freeze_during_quiescence__lt,0.9,' - '~probe_insertion__session__extended_qc___task_error_trial_event_sequence__lt,0.9,' - '~probe_insertion__session__extended_qc___task_correct_trial_event_sequence__lt,0.9,' - '~probe_insertion__session__extended_qc___task_n_trial_events__lt,0.9,' - '~probe_insertion__session__extended_qc___task_reward_volumes__lt,0.9,' - '~probe_insertion__session__extended_qc___task_reward_volume_set__lt,0.9,' - '~probe_insertion__session__extended_qc___task_stimulus_move_before_goCue__lt,0.9,' - '~probe_insertion__session__extended_qc___task_audio_pre_trial__lt,0.9') - eids = [[x['session']['id'],x['probe_name']] for x in traj_traced] - return eids - - -def check_progress(): - - one = ONE() - eids = get_repeated_sites() - - s = {} - comp = [] - for eid in eids: - - task = one.alyx.rest('tasks', 'list', session=eid, name='EphysDLC')[0] - try: - s[eid] = task['version'] - print(task['version']) - if task['version'][:3] != '1.1': - comp.append(eid) - except: - print(eid, 'has no task version') - - return s - - -def download_all_dlc(): - - eids = get_repeated_sites() - one = ONE() - dataset_types = ['camera.dlc', 'camera.times'] - - for eid in eids: - try: - - a = one.list(eid,'dataset-types') - # for newer iblib version do [x['dataset_type'] for x in a] - if not all([x['dataset_type'] for x in a]): - print('not all data available') - continue - - - one.load(eid, dataset_types = dataset_types) - except: - continue - - -def constant_reaction_time(eid, rt, st,stype='stim'): - - - ''' - getting trial numbers, feedback time interval - ''' - - one = ONE() - if stype == 'motion': - wheelMoves = one.load_object(eid, 'wheelMoves') - trials = one.load_object(eid, 'trials') - d = {} # dictionary, trial number and still interval - evts = ['goCue_times', 'feedback_times', 'probabilityLeft', - 'choice', 'feedbackType'] - - for tr in range(len(trials['intervals'])): - if stype == 'motion': - a = wheelMoves['intervals'][:,0] - b = trials['goCue_times'][tr] - c = trials['feedback_times'][tr] - ch = trials['choice'][tr] - pl = trials['probabilityLeft'][tr] - ft = trials['feedbackType'][tr] - - - if any(np.isnan([trials[k][tr] for k in evts])): - continue - - - if c-b>10: # discard too long trials - continue - - if stype == 'motion': - # making sure the motion onset time is in a coupled interval - ind = np.where((a > b) & (a < c), True, False) - if all(~ind): - #print(f'non-still start, trial {tr} and eid {eid}') - continue - - a = a[ind][0] - react = np.round(a - b,3) - - if np.isnan(trials['contrastLeft'][tr]): - cont = trials['contrastRight'][tr] - side = 0 - else: - cont = trials['contrastLeft'][tr] - side = 1 - - if stype == 'feedback': - d[tr] = [c + st,rt, cont, side, ch, ft] - if stype == 'stim': - d[tr] = [b + st,rt, cont, side, ch, ft] - if stype == 'motion': - d[tr] = [a + st,rt, cont, side, ch, ft] - - - print(f"cut {len(d)} of {len(trials['intervals'])} full trials segments") - return d - - -def get_dlc_XYs(eid, video_type): - - #video_type = 'left' - one = ONE() - Times = one.load_dataset(eid,f'alf/_ibl_{video_type}Camera.times.npy') - cam = one.load_dataset(eid,f'alf/_ibl_{video_type}Camera.dlc.pqt') - points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()]) - - # Set values to nan if likelyhood is too low # for pqt: .to_numpy() - XYs = {} - for point in points: - x = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_x']) - x = x.filled(np.nan) - y = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_y']) - y = y.filled(np.nan) - XYs[point] = np.array( - [x, y]) - - return Times, XYs - -# a = one.list_datasets(eid) -# if not all([u in a for u in dataset_types]): -# print('not all data available') -# return - -def get_ME(eid, video_type): - - #video_type = 'left' - one = ONE() - - - Times = one.load_dataset(eid,f'alf/_ibl_{video_type}Camera.times.npy') - ME = one.load_dataset(eid,f'alf/{video_type}Camera.ROIMotionEnergy.npy') - - return Times, ME - - - -def get_example_images(eid): - - -# eids = get_repeated_sites() -## eid = eids[23] -## video_type = 'body' - -# #eids = ['15f742e1-1043-45c9-9504-f1e8a53c1744'] -# eids = ['4a45c8ba-db6f-4f11-9403-56e06a33dfa4'] - frts = {'body':30, 'left':60,'right':150} - - one=ONE() - - - #for eid in eids: - urls = vidio.url_from_eid(eid, one=one) - for video_type in frts: - - frame_idx = [5 * 60 * frts[video_type]] - try: - - - video_path = urls[video_type] - frames = get_video_frames_preload(video_path, - frame_idx, - mask=np.s_[:, :, 0]) - np.save('/home/mic/reproducible_dlc/example_images/' - f'{eid}_{video_type}.npy', frames) - print(eid, video_type, 'done') - except: - print(eid, video_type,'error') - continue - - - #'/home/mic/reproducible_dlc/example_images' - -def get_mean_positions(XYs): - mloc = {} # mean locations - for point in XYs: - if point in ['tube_bottom', 'tube_top']: - continue - mloc[point] = [int(np.nanmean(XYs[point][0])), - int(np.nanmean(XYs[point][1]))] - return mloc - - -def plot_paw_on_image(eid, video_type='left', XYs = None): - - #fig = plt.figure(figsize=(8,4)) - - Cs = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', - '#FFA15A', '#19D3F3', '#FF6692', '#B6E880', - '#FF97FF', '#FECB52','cyan'] - - try: - r = np.load(f'/home/mic/reproducible_dlc/example_images/' - f'{eid}_{video_type}.npy')[0] - except: - get_example_images(eid) - r = np.load(f'/home/mic/reproducible_dlc/example_images/' - f'{eid}_{video_type}.npy')[0] - - try: - if XYs == None: - _, XYs = get_dlc_XYs(eid, video_type) - - Cs = dict(zip(XYs.keys(),Cs)) - - ds = {'body':3,'left':6,'right':15} - - - for point in XYs:# ['paw_l','paw_r']: - if point in ['tube_bottom', 'tube_top']: - continue - - # downsample; normalise number of points to be the same - # across all sessions - xs = XYs[point][0][0::ds[video_type]] - ys = XYs[point][1][0::ds[video_type]] - - plt.scatter(xs,ys, alpha = 0.05, s = 2, - label = point, c = Cs[point]) - - plt.axis('off') - plt.tight_layout() - plt.imshow(r,cmap='gray') - plt.tight_layout() - - - if video_type != 'body': - - if video_type == 'left': - fa = 1 - elif video_type == 'right': - fa = 0.5 - - # plot whisker pad rectangle - mloc = get_mean_positions(XYs) - p_nose = np.array(mloc['nose_tip']) - p_pupil = np.array(mloc['pupil_top_r']) - - # heuristic to find whisker area in side videos: - # square with side length half the distance - # between nose and pupil and anchored on midpoint - - p_anchor = np.mean([p_nose,p_pupil],axis=0) - squared_dist = np.sum((p_nose-p_pupil)**2, axis=0) - dist = np.sqrt(squared_dist) - whxy = [int(dist/2), int(dist/3), - int(p_anchor[0] - dist/4), int(p_anchor[1])] - - rect = patches.Rectangle((whxy[2], whxy[3]), whxy[0], whxy[1], - linewidth=1, - edgecolor='lime', - facecolor='none') - ax = plt.gca() - ax.add_patch(rect) - - - - # plot eye region zoom as inset in upper right corner - pivot = np.nanmean(XYs['pupil_top_r'], axis=1) - x0 = int(pivot[0] - 33 * fa) - x1 = int(pivot[0] + 33 * fa) - y0 = int(pivot[1] - 28 * fa) - y1 = int(pivot[1] + 38 * fa) - - if video_type == 'right': - axins = ax.inset_axes([0, -0.5, 0.5, 0.5]) - elif video_type == 'left': - axins = ax.inset_axes([0.5, -0.5, 0.5, 0.5]) - - axins.spines['bottom'].set_color('white') - axins.spines['top'].set_color('white') - axins.spines['right'].set_color('white') - axins.spines['left'].set_color('white') - axins.imshow(r, cmap='gray', origin="lower") - - for point in XYs:# ['paw_l','paw_r']: - if point in ['tube_bottom', 'tube_top']: - continue - - # downsample; normalise number of points to be the same - # across all sessions - xs = XYs[point][0][0::ds[video_type]] - ys = XYs[point][1][0::ds[video_type]] - - axins.scatter(xs,ys, alpha = 1, s = 0.001, - label = point, c = Cs[point]) - axins.set_xlim(x0, x1) - axins.set_ylim(y1, y0) - axins.set_xticklabels('') - axins.set_yticklabels('') - #ax.indicate_inset_zoom(axins, edgecolor="white") - - # plot tongue region zoom as inset in llower right corner - p1 = np.nanmean(XYs['tube_top'], axis=1) - p2 = np.nanmean(XYs['tube_bottom'], axis=1) - pivot = np.nanmean([p1,p2], axis=0) - x0 = int(pivot[0] - 60 * fa) - x1 = int(pivot[0] + 100 * fa) - y0 = int(pivot[1] - 100 * fa) - y1 = int(pivot[1] + 60 * fa) - - if video_type == 'right': - axins = ax.inset_axes([0.5, -0.5, 0.5, 0.5]) - elif video_type == 'left': - axins = ax.inset_axes([0, -0.5, 0.5, 0.5]) - - axins.spines['bottom'].set_color('white') - axins.spines['top'].set_color('white') - axins.spines['right'].set_color('white') - axins.spines['left'].set_color('white') - axins.imshow(r, cmap='gray', origin="upper") - - for point in XYs:# ['paw_l','paw_r']: - if point in ['tube_bottom', 'tube_top']: - continue - # downsample; normalise number of points to be the same - # across all sessions - xs = XYs[point][0][0::ds[video_type]] - ys = XYs[point][1][0::ds[video_type]] - - axins.scatter(xs,ys, alpha = 1, s = 0.001, - label = point, color = Cs[point]) - axins.set_xlim(x0, x1) - axins.set_ylim(y1, y0) - axins.set_xticklabels('') - axins.set_yticklabels('') - #ax.indicate_inset_zoom(axins, edgecolor="white") - - except: - - plt.imshow(r,cmap='gray') - ax = plt.gca() - plt.text(.5, .5,'DLC is nan',color='r',fontweight='bold', - bbox=dict(facecolor='white', alpha=0.5), - fontsize=10,transform=ax.transAxes) - plt.tight_layout() - #plt.show() - plt.title(video_type) - - -def plot_paw_on_imageL(eid): - plot_paw_on_image(eid, video_type='left') - -def plot_paw_on_imageR(eid): - plot_paw_on_image(eid, video_type='right') - -def plot_paw_on_imageB(eid): - plot_paw_on_image(eid, video_type='body') - - -def paw_speed_PSTH(eid): - - rt = 2 - st = -0.5 - fs = {'right':150,'left':60} - - cols = {'left':{'1':['darkred','--'],'-1':['#1f77b4','--']}, - 'right':{'1':['darkred','-'],'-1':['#1f77b4','-']}} - # take speed from right paw only, i.e. closer to cam - # for each video - speeds = {} - for video_type in ['right','left']: - times, XYs = get_dlc_XYs(eid, video_type) - x = XYs['paw_r'][0] - y = XYs['paw_r'][1] - if video_type == 'left': #make resolution same - x = x/2 - y = y/2 - - # get speed in px/sec [half res] - s = ((np.diff(x)**2 + np.diff(y)**2)**.5)*fs[video_type] - - - speeds[video_type] = [times,s] - - - # that's centered at feedback time - d = constant_reaction_time(eid, rt, st,stype='stim') - - sc = {'left':{'1':[],'-1':[]},'right':{'1':[],'-1':[]}} - for video_type in ['right','left']: - - times,s = speeds[video_type] - for i in d: - - start_idx = find_nearest(times,d[i][0]) - end_idx = find_nearest(times,d[i][0]+d[i][1]) - - sc[video_type][str(d[i][4])].append(s[start_idx:end_idx]) - - # trim on e frame if necessary - for video_type in ['right','left']: - for choice in ['-1','1']: - m = min([len(x) for x in sc[video_type][choice]]) - q = [x[:m] for x in sc[video_type][choice]] - xs = np.arange(m) / fs[video_type] - xs = np.concatenate([ - -1*np.array(list(reversed(xs[:int(abs(st)*fs[video_type])]))), - np.array(xs[:int((rt - abs(st))*fs[video_type])])]) - m = min(len(xs),m) - - c = cols[video_type][choice][0] - ls = cols[video_type][choice][1] - - qm = np.nanmean(q,axis=0) - plt.plot(xs[:m], qm[:m], c = c,linestyle=ls, - linewidth = 1, - label = f'paw {video_type[0]},' + ' choice ' + choice) - - ax = plt.gca() - ax.axvline(x=0, label='stimOn', linestyle = '--', c='g') - plt.title('paw speed PSTH') - plt.xlabel('time [sec]') - plt.ylabel('speed [px/sec]') - plt.legend()#bbox_to_anchor=(1.05, 1), loc='upper left') - - -def nose_speed_PSTH(eid,vtype='right'): - - ''' - nose speed PSTH - eid = 'd0ea3148-948d-4817-94f8-dcaf2342bbbe' is good - ''' - - rt = 2 - st = -0.5 - fs = {'right':150,'left':60} - cts = {'1':'correct trials', '-1':'incorrect trials'} - - - cols = {'left':{'1':['k','--'],'-1':['gray','--']}, - 'right':{'1':['k','-'],'-1':['gray','-']}} - # take speed from right paw only, i.e. closer to cam - # for each video - speeds = {} - for video_type in ['right','left']: - times, XYs = get_dlc_XYs(eid, video_type) - x = XYs['nose_tip'][0] - y = XYs['nose_tip'][1] - if video_type == 'left': #make resolution same - x = x/2 - y = y/2 - - # get speed in px/sec [half res] - s = ((np.diff(x)**2 + np.diff(y)**2)**.5)*fs[video_type] - - - speeds[video_type] = [times,s] - - # average nose tip speeds across cams - - - - # that's centered at feedback time - d = constant_reaction_time(eid, rt, st,stype='feedback') - - sc = {'left':{'1':[],'-1':[]},'right':{'1':[],'-1':[]}} - for video_type in ['right','left']: - - times,s = speeds[video_type] - for i in d: - - start_idx = find_nearest(times,d[i][0]) - end_idx = find_nearest(times,d[i][0]+d[i][1]) - - sc[video_type][str(d[i][5])].append(s[start_idx:end_idx]) - - # trim on e frame if necessary - for video_type in [vtype]: # 'right','left' - for choice in ['-1','1']: - m = min([len(x) for x in sc[video_type][choice]]) - q = [x[:m] for x in sc[video_type][choice]] - xs = np.arange(m) / fs[video_type] - xs = np.concatenate([ - -1*np.array(list(reversed(xs[:int(abs(st)*fs[video_type])]))), - np.array(xs[:int((rt - abs(st))*fs[video_type])])]) - m = min(len(xs),m) - - c = cols[video_type][choice][0] - ls = cols[video_type][choice][1] - - qm = np.nanmean(q,axis=0) - plt.plot(xs[:m], qm[:m], c = c,linestyle=ls, - linewidth = 1, - label = cts[choice]) - - ax = plt.gca() - ax.axvline(x=0, label='stimOn', linestyle = '--', c='r') - plt.title('nose tip speed PSTH, ' - f'{vtype} vid') - plt.xlabel('time [sec]') - plt.ylabel('speed [px/sec]') - plt.legend(loc='lower right') - - -def get_licks(XYs): - - ''' - define a frame as a lick frame if - x or y for left or right tongue point - change more than half the sdt of the diff - ''' - - licks = [] - for point in ['tongue_end_l', 'tongue_end_r']: - for c in XYs[point]: - thr = np.nanstd(np.diff(c))/4 - licks.append(set(np.where(abs(np.diff(c))>thr)[0])) - return sorted(list(set.union(*licks))) - - -def plot_licks(eid, combine=True): - - ''' - lick PSTH - eid = 'd0ea3148-948d-4817-94f8-dcaf2342bbbe' is good - ''' - T_BIN = 0.02 - rt = 2 - st = -0.5 - - if combine: - # combine licking events from left and right cam - lick_times = [] - for video_type in ['right','left']: - times, XYs = get_dlc_XYs(eid, video_type) - # for the case that there are less times than DLC points - r = get_licks(XYs) - idx = np.where(np.array(r) len(D): - break - - # split by feedback type) - if d[i][5] == 1: - licks_pos.append(D[start_idx:end_idx]) - if d[i][5] == -1: - licks_neg.append(D[start_idx:end_idx]) - - - licks_pos_ = np.array(licks_pos).mean(axis=0) - licks_neg_ = np.array(licks_neg).mean(axis=0) - - xs = np.arange(int(rt/T_BIN)) - xs = np.concatenate([ - -1*np.array(list(reversed(xs[:int(len(xs)*abs(st/rt))]))), - np.array(xs[:int(len(xs)*(1 - abs(st/rt)))])]) - xs = xs*T_BIN - plt.plot(xs, licks_pos_, c = 'k', label = 'correct trial') - plt.plot(xs, licks_neg_, c = 'gray', label = 'incorrect trial') - ax = plt.gca() - ax.axvline(x=0, label='feedback time', linestyle = '--', c='r') - plt.title('lick PSTH') - plt.xlabel('time [sec]') - plt.ylabel('lick events \n [a.u.]') - plt.legend(loc='lower right') - - - -def lick_raster(eid, combine=True): - - #plt.figure(figsize=(4,4)) - - T_BIN = 0.02 - rt = 2 - st = -0.5 - - if combine: - # combine licking events from left and right cam - lick_times = [] - for video_type in ['right','left']: - times, XYs = get_dlc_XYs(eid, video_type) - r = get_licks(XYs) - idx = np.where(np.array(r) len(D): - break - - # split by feedback type) - if d[i][5] == 1: - licks_pos.append(D[start_idx:end_idx]) - - licks_pos_ = np.array(licks_pos).mean(axis=0) - - y_dims, x_dims = len(licks_pos), len(licks_pos[0]) - plt.imshow(licks_pos,aspect='auto', extent=[-0.5,1.5,y_dims,0], - cmap='gray_r') - - ax = plt.gca() - ax.set_xticks([-0.5,0,0.5,1,1.5]) - ax.set_xticklabels([-0.5,0,0.5,1,1.5]) - plt.ylabel('trials') - plt.xlabel('time [sec]') - ax.axvline(x=0, label='feedback time', linestyle = '--', c='r') - plt.title('lick events per correct trial') - plt.tight_layout() - - - -def find_nearest(array,value): - idx = np.searchsorted(array, value, side="left") - if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])): - return idx-1 - else: - return idx - - -def plot_wheel_position(eid): - - ''' - illustrate wheel position next to distance plot - ''' - T_BIN = 0.02 - rt = 2 - st = -0.5 - - d = constant_reaction_time(eid, rt, st) - - one = ONE() - wheel = one.load_object(eid, 'wheel') - pos, t = wh.interpolate_position(wheel.timestamps, - wheel.position, - freq=1/T_BIN) - whe_left = [] - whe_right = [] - - for i in d: - - start_idx = find_nearest(t,d[i][0]) - end_idx = start_idx + int(d[i][1]/T_BIN) - - wheel_pos = pos[start_idx:end_idx] - if len(wheel_pos) == 1: - print(i, [start_idx,end_idx]) - - wheel_pos = wheel_pos - wheel_pos[0] - - if d[i][4] == -1: - whe_left.append(wheel_pos) - if d[i][4] == 1: - whe_right.append(wheel_pos) - - xs = np.arange(len(whe_left[0]))*T_BIN - times = np.concatenate([ - -1*np.array(list(reversed(xs[:int(len(xs)*abs(st/rt))]))), - np.array(xs[:int(len(xs)*(1 - abs(st/rt)))])]) - - - for i in range(len(whe_left)): - plt.plot(times, whe_left[i],c='#1f77b4', alpha =0.5, linewidth = 0.05) - for i in range(len(whe_right)): - plt.plot(times, whe_right[i],c='darkred', alpha =0.5, linewidth = 0.05) - - plt.plot(times, np.mean(whe_left,axis=0),c='#1f77b4', - linewidth = 2, label = 'left') - plt.plot(times, np.mean(whe_right,axis=0),c='darkred', - linewidth = 2, label = 'right') - - plt.axhline(y=0.26, linestyle = '--', c = 'k') - plt.axhline(y=-0.26, linestyle = '--', c = 'k', label = 'reward boundary') - plt.axvline(x=0, linestyle = '--', c = 'g', label = 'stimOn') - axes = plt.gca() - #axes.set_xlim([0,rt]) - axes.set_ylim([-0.27,0.27]) - plt.xlabel('time [sec]') - plt.ylabel('wheel position [rad]') - plt.legend(loc='lower right') - plt.title('wheel positions colored by choice') - plt.tight_layout() - - -def get_sniffs(XYs): - - ''' - define a frame as a sniff frame if - x or y for left or right tongue point - change more than half the sdt of the diff - ''' - - c = XYs['nose_tip'][1] # on - thr = np.nanstd(np.diff(c))/4 - sniffs = np.where(abs(np.diff(c))>thr)[0] - return sniffs - - -def plot_sniffPSTH(eid, combine=False): - - ''' - sniff PSTH - eid = 'd0ea3148-948d-4817-94f8-dcaf2342bbbe' is good - ''' - T_BIN = 0.02 - rt = 2 - st = -0.5 - - if combine: - # combine licking events from left and right cam - lick_times = [] - for video_type in ['right','left']: - times, XYs = get_dlc_XYs(eid, video_type) - lick_times.append(times[get_sniffs(XYs)]) - - lick_times = sorted(np.concatenate(lick_times)) - - else: - times, XYs = get_dlc_XYs(eid, 'left') - lick_times = times[get_sniffs(XYs)] - - - R, t, _ = bincount2D(lick_times, np.ones(len(lick_times)), T_BIN) - D = R[0] - - # that's centered at feedback time - d = constant_reaction_time(eid, rt, st,stype='feedback') - - licks_pos = [] - licks_neg = [] - - for i in d: - - start_idx = find_nearest(t,d[i][0]) - end_idx = start_idx + int(d[i][1]/T_BIN) - - # split by feedback type) - if d[i][5] == 1: - licks_pos.append(D[start_idx:end_idx]) - if d[i][5] == -1: - licks_neg.append(D[start_idx:end_idx]) - - - licks_pos = np.array(licks_pos).mean(axis=0) - licks_neg = np.array(licks_neg).mean(axis=0) - - xs = np.arange(int(rt/T_BIN)) - - xs = np.concatenate([ - -1*np.array(list(reversed(xs[:int(len(xs)*abs(st/rt))]))), - np.array(xs[:int(len(xs)*(1 - abs(st/rt)))])]) - - xs = xs*T_BIN - plt.plot(xs, licks_pos, c = 'k', label = 'correct trial') - plt.plot(xs, licks_neg, c = 'gray', label = 'incorrect trial') - ax = plt.gca() - ax.axvline(x=0, label='feedback time', linestyle = '--', c='r') - plt.title('sniff PSTH') - plt.xlabel('time [sec]') - plt.ylabel('sniff events \n [a.u.]') - plt.legend(loc='lower right') - - -def get_pupil_diameter(XYs, smooth=True): - """get mean of pupil diameter in two different ways: - d1 = top - bottom, d2 = left - right - and in addition assume it's a circle and - estimate diameter from other pairs of points - Author: Michael Schartner - """ - - # direct diameters - t = XYs['pupil_top_r'].T - b = XYs['pupil_bottom_r'].T - l = XYs['pupil_left_r'].T - r = XYs['pupil_right_r'].T - - ds = [] - def distance(p1, p2): - return ((p1[:, 0] - p2[:, 0]) ** 2 + (p1[:, 1] - p2[:, 1]) ** 2) ** 0.5 - # get diameter via top-bottom and left-right - ds.append(distance(t, b)) - ds.append(distance(l, r)) - def dia_via_circle(p1, p2): - # only valid for non-crossing edges - u = distance(p1, p2) - return u * (2 ** 0.5) - # estimate diameter via circle assumption - for side in [[t, l], [t, r], [b, l], [b, r]]: - ds.append(dia_via_circle(side[0], side[1])) - - - diam = np.nanmedian(ds, axis=0) - if smooth: - return smooth_pupil_diameter(diam) - else: - return diam - - -def smooth_pupil_diameter(diam, window=31, order=3, interp_kind='cubic'): - - signal_noisy_w_nans = np.copy(diam) - timestamps = np.arange(signal_noisy_w_nans.shape[0]) - good_idxs = np.where(~np.isnan(signal_noisy_w_nans)) - # perform savitzky-golay filtering on non-nan points - signal_smooth_nonans = non_uniform_savgol( - timestamps[good_idxs], signal_noisy_w_nans[good_idxs], window=window, polynom=order) - signal_smooth_w_nans = np.copy(signal_noisy_w_nans) - signal_smooth_w_nans[good_idxs] = signal_smooth_nonans - # interpolate nan points - interpolater = interp1d( - timestamps[good_idxs], signal_smooth_nonans, kind=interp_kind, fill_value='extrapolate') - diam = interpolater(timestamps) - return diam - - -def non_uniform_savgol(x, y, window, polynom): - """ - Applies a Savitzky-Golay filter to y with non-uniform spacing - as defined in x - This is based on https://dsp.stackexchange.com/questions/1676/savitzky-golay-smoothing-filter-for-not-equally-spaced-data - The borders are interpolated like scipy.signal.savgol_filter would do - https://dsp.stackexchange.com/a/64313 - Parameters - ---------- - x : array_like - List of floats representing the x values of the data - y : array_like - List of floats representing the y values. Must have same length - as x - window : int (odd) - Window length of datapoints. Must be odd and smaller than x - polynom : int - The order of polynom used. Must be smaller than the window size - Returns - ------- - np.array of float - The smoothed y values - """ - if len(x) != len(y): - raise ValueError('"x" and "y" must be of the same size') - if len(x) < window: - raise ValueError('The data size must be larger than the window size') - if type(window) is not int: - raise TypeError('"window" must be an integer') - if window % 2 == 0: - raise ValueError('The "window" must be an odd integer') - if type(polynom) is not int: - raise TypeError('"polynom" must be an integer') - if polynom >= window: - raise ValueError('"polynom" must be less than "window"') - half_window = window // 2 - polynom += 1 - # Initialize variables - A = np.empty((window, polynom)) # Matrix - tA = np.empty((polynom, window)) # Transposed matrix - t = np.empty(window) # Local x variables - y_smoothed = np.full(len(y), np.nan) - # Start smoothing - for i in range(half_window, len(x) - half_window, 1): - # Center a window of x values on x[i] - for j in range(0, window, 1): - t[j] = x[i + j - half_window] - x[i] - # Create the initial matrix A and its transposed form tA - for j in range(0, window, 1): - r = 1.0 - for k in range(0, polynom, 1): - A[j, k] = r - tA[k, j] = r - r *= t[j] - # Multiply the two matrices - tAA = np.matmul(tA, A) - # Invert the product of the matrices - tAA = np.linalg.inv(tAA) - # Calculate the pseudoinverse of the design matrix - coeffs = np.matmul(tAA, tA) - # Calculate c0 which is also the y value for y[i] - y_smoothed[i] = 0 - for j in range(0, window, 1): - y_smoothed[i] += coeffs[0, j] * y[i + j - half_window] - # If at the end or beginning, store all coefficients for the polynom - if i == half_window: - first_coeffs = np.zeros(polynom) - for j in range(0, window, 1): - for k in range(polynom): - first_coeffs[k] += coeffs[k, j] * y[j] - elif i == len(x) - half_window - 1: - last_coeffs = np.zeros(polynom) - for j in range(0, window, 1): - for k in range(polynom): - last_coeffs[k] += coeffs[k, j] * y[len(y) - window + j] - # Interpolate the result at the left border - for i in range(0, half_window, 1): - y_smoothed[i] = 0 - x_i = 1 - for j in range(0, polynom, 1): - y_smoothed[i] += first_coeffs[j] * x_i - x_i *= x[i] - x[half_window] - # Interpolate the result at the right border - for i in range(len(x) - half_window, len(x), 1): - y_smoothed[i] = 0 - x_i = 1 - for j in range(0, polynom, 1): - y_smoothed[i] += last_coeffs[j] * x_i - x_i *= x[i] - x[-half_window - 1] - return y_smoothed - - -#def align_left_right_pupil(eid): - -# timesL, XYsL = get_dlc_XYs(eid, 'left') -# dL=get_pupil_diameter(XYsL) -# timesR, XYsR = get_dlc_XYs(eid, 'right') -# dR=get_pupil_diameter(XYsR) - -# # align time series left/right -# interpolater = interp1d( -# timesR, -# np.arange(len(timesR)), -# kind="cubic", -# fill_value="extrapolate",) - -# idx_aligned = np.round(interpolater(timesL)).astype(np.int) -# dRa = dR[idx_aligned] -# -# plt.plot(timesL,zscore(dL),label='left') -# plt.plot(timesL,zscore(dRa),label='right') -# plt.title('smoothed, aligned, z-scored right/left pupil diameter \n' -# f'{eid}') -# plt.legend(loc='lower right') -# plt.ylabel('pupil diameter') -# plt.xlabel('time [sec]') - - - -def pupil_diameter_PSTH(eid, s=None, times=None, per_trial_norm=True): - - ''' - nose speed PSTH - eid = 'd0ea3148-948d-4817-94f8-dcaf2342bbbe' is good - ''' - - rt = 2 # duration of window - st = -0.5 # lag of window wrt to stype - - if (s is None) or (times is None): - times, XYs = get_dlc_XYs(eid, 'left') - s = get_pupil_diameter(XYs) - - D = {} - - xs = np.arange(rt*60) # number of frames - xs[:int(abs(st)*60)] = -1*np.array(list(reversed(xs[:int(abs(st)*60)]))) - xs[int(abs(st)*60):] = np.arange(rt*60)[1:1+len(xs[int(abs(st)*60):])] - xs = xs /60. - - cols = {'stim':'r','feedback':'b','motion':'g'} - - - for stype in ['stim','feedback']:#,'motion' - # that's centered at feedback time - d = constant_reaction_time(eid, rt, st,stype=stype) - - D[stype] = [] - - for i in d: - - start_idx = find_nearest(times,d[i][0]) - end_idx = start_idx + rt*60 - - if per_trial_norm: - # per trial baseline - at start = 0 - dia = s[start_idx:end_idx] - dia = zscore(dia,nan_policy='omit') - dia = dia - dia[0] - else: - dia = s[start_idx:end_idx] - - - D[stype].append(dia) - - - MEAN = np.mean(D[stype],axis=0) - STD = np.std(D[stype],axis=0)/np.sqrt(len(d)) - - plt.plot(xs, MEAN, label=stype, color = cols[stype]) - plt.fill_between(xs, MEAN + STD, MEAN - STD, color = cols[stype], - alpha=0.5) - - ax = plt.gca() - ax.axvline(x=0, label='align event', linestyle = '--', c='k') - plt.title('left cam pupil diameter PSTH') - plt.xlabel('time [sec]') - plt.ylabel('pupil diameter [px]') - plt.legend(loc='lower right') - - -def motion_energy_PSTH(eid): - - ''' - ME PSTH - canonical session - eid = '15f742e1-1043-45c9-9504-f1e8a53c1744' - ''' - - rt = 2 # duration of window - st = -0.5 # lag of window wrt to stype - stype = 'stimOn_times' - - ME = {} - one = ONE() - trials = one.load_object(eid,'trials') - ts = trials.intervals[0][0] - te = trials.intervals[-1][1] - - try: - for video_type in ['left','right','body']: - t,m = get_ME(eid, video_type) - m = zscore(m,nan_policy='omit') - - sta, end = find_nearest(t,ts), find_nearest(t,te) - t = t[sta:end] - m = m[sta:end] - - ME[video_type] = [t,m] - - # align to body cam - for video_type in ['left','right']: - - # align time series camera/neural - interpolater = interp1d( - ME[video_type ][0], - np.arange(len(ME[video_type ][0])), - kind="cubic", - fill_value="extrapolate") - - idx_aligned = np.round(interpolater(ME['body'][0])).astype(int) - ME[video_type] = [ME['body'][0], ME[video_type][1][idx_aligned]] - - - D = {} - - fs = 30 - xs = np.arange(rt*fs) # number of frames - xs = np.concatenate([-1*np.array(list(reversed(xs[:int(abs(st)*fs)]))), - np.arange(rt*fs)[1:1+len(xs[int(abs(st)*fs):])]]) - xs = xs /float(fs) - - cols = {'left':'r','right':'b','body':'g'} - - for video_type in ME: - # that's centered at feedback time - - - D[video_type] = [] - - times,s = ME[video_type] - - trs = trials[stype][20:-20] - for i in trs: - - start_idx = int(find_nearest(times,i) + st*30) - end_idx = int(start_idx + rt*30) - - D[video_type].append(s[start_idx:end_idx]) - - - MEAN = np.mean(D[video_type],axis=0) - STD = np.std(D[video_type],axis=0)/np.sqrt(len(trs)) - - - plt.plot(xs, MEAN, label=video_type, - color = cols[video_type], linewidth = 2) - plt.fill_between(xs, MEAN + STD, MEAN - STD, color = cols[video_type], - alpha=0.2) - - ax = plt.gca() - ax.axvline(x=0, label='stimOn', linestyle = '--', c='k') - plt.title('Motion Energy PSTH') - plt.xlabel('time [sec]') - plt.ylabel('z-scored motion energy [a.u.]') - plt.legend(loc='lower right') - - except: - plt.title('No motion energy available!') - - -def interp_nans(y): - - def nan_helper(y): - return np.isnan(y), lambda z: z.nonzero()[0] - nans, yy = nan_helper(y) - y2 = copy.deepcopy(y) - y2[nans] = np.interp(yy(nans), yy(~nans), y[~nans]) - return y2 - - -def add_panel_letter(k): - - ''' - k is the number of the subplot - ''' - L = string.ascii_uppercase[k-1] - ax = plt.gca() - ax.text(-0.1, 1.15, L, transform=ax.transAxes, - fontsize=16, fontweight='bold', va='top', ha='right') - - -def plot_all(eid): - matplotlib.rcParams.update({'font.size': 10}) - # report eid = '4a45c8ba-db6f-4f11-9403-56e06a33dfa4' - - panels = {'plot_paw_on_imageL':plot_paw_on_imageL, - 'plot_paw_on_imageR':plot_paw_on_imageR, - 'plot_paw_on_imageB':plot_paw_on_imageB, - 'plot_wheel_position':plot_wheel_position, - 'paw_speed_PSTH':paw_speed_PSTH, - 'plot_licks':plot_licks, - 'lick_raster':lick_raster, - 'nose_speed_PSTH':nose_speed_PSTH, - 'pupil_diameter_PSTH':pupil_diameter_PSTH, - 'motion_energy_PSTH':motion_energy_PSTH} - - nrows = 2 - ncols = int(np.ceil(len(panels)/2)) - - plt.ioff() - - plt.figure(figsize=(17,10)) - - k = 1 - for panel in panels: - plt.subplot(nrows,ncols,k) - add_panel_letter(k) - try: - panels[panel](eid) - #continue - except: - ax = plt.gca() - plt.text(.5, .5,f'error in \n {panel}',color='r',fontweight='bold', - bbox=dict(facecolor='white', alpha=0.5), - fontsize=10, transform=ax.transAxes) - k += 1 - - - plt.tight_layout() - - - # print QC outcome in title and DLC task version - one = ONE() - - det = one.get_details(eid, True)['extended_qc'] - p = one.eid2path(eid) - s1 = '_'.join([str(p).split('/')[i] for i in [4,6,7,8]]) - - dlc_qcs = [ 'time_trace_length_match', - 'trace_all_nan', - 'mean_in_bbox', - 'pupil_blocked', - 'lick_detection'] - - raw_qcs = ['focus','position','brightness', - 'resolution','timestamps','file_headers', - 'wheel_alignment','camera_times','framerate', - 'pin_state','dropped_frames'] - - - qcs = ['task','behavior','videoLeft','videoRight','videoBody', - 'dlcLeft','dlcRight','dlcBody'] - - l = [] - for q in qcs: - try: - if det[q] == 'FAIL': - - if ('dlc' in q): - l.append('\n') - l.append(q+':'+str(det[q])+'-->') - video_type = q[3:] - for dlc_qc in dlc_qcs: - w = f'_dlc{video_type}_{dlc_qc}' - if not det[w]: - l.append(w+':'+str(det[w])+',') - elif ('video' in q): - l.append('\n') - l.append(q+':'+str(det[q])+'-->') - video_type = q[5:] - for raw_qc in raw_qcs: - w = f'_video{video_type}_{raw_qc}' - if type(det[w]) == bool: - if (not det[w]): - l.append(w+':'+str(det[w])+',') - if type(det[w]) != bool: - if not det[w][0]: - l.append(w+':'+str(det[w])+',') - - else: - l.append(q+':'+str(det[q])+',') - except: - continue - - s2 = ' '.join(l) - - ntrials = len(one.load_object(eid, 'trials')['goCue_times']) - - task = one.alyx.rest('tasks', 'list', session=eid, name='EphysDLC') - - try: - plt.suptitle(s1+'#Trials:'+str(ntrials)+', DLC version: ' - +str(task[0]['version'])+' \n '+s2, - backgroundcolor= 'white', fontsize=6) - except: - plt.suptitle(s1+'#Trials:'+str(ntrials)+', DLC version: ??' - +' \n '+s2, - backgroundcolor= 'white', fontsize=6) - - plt.tight_layout(rect=[0, 0.03, 1, 0.95]) - #plt.savefig(f'/home/mic/reproducible_dlc/overviewJune/{eid}.png') - #plt.savefig(f'/home/mic/reproducible_dlc/all_DLC/{s1}_{eid}.png') - plt.savefig(f'/home/mic/reproducible_dlc/motor_correlate/{s1}_{eid}.png') - plt.close() - - -def inspection(): - ''' - go through computed png images - to report QC issues - ''' - one = ONE() - a = os.listdir('/home/mic/reproducible_dlc/overviewJune/') - - for i in a: - plt.figure(figsize=(15,10)) - eid = i.split('.')[0] - p = one.path_from_eid(eid) - s1 = '/'.join([str(p).split('/')[i] for i in [4,5,6,7,8]]) - print(s1) - print(eid) - img = mpimg.imread('/home/mic/reproducible_dlc/overviewJune/'+i) - plt.imshow(img) - plt.tight_layout() - plt.show() - input("Press Enter to continue...") - plt.close() - - - diff --git a/PythonScripts/iblapps-master/dlc/stream_dlc_labeled_frames.py b/PythonScripts/iblapps-master/dlc/stream_dlc_labeled_frames.py deleted file mode 100644 index 4bb1fa81..00000000 --- a/PythonScripts/iblapps-master/dlc/stream_dlc_labeled_frames.py +++ /dev/null @@ -1,207 +0,0 @@ -from oneibl.one import ONE -from ibllib.io.video import get_video_meta, get_video_frames_preload -import numpy as np -from pathlib import Path -import cv2 -import alf.io -import matplotlib -from random import sample -import time - - -def stream_save_labeled_frames(eid, video_type): - - startTime = time.time() - ''' - For a given eid and camera type, stream - sample frames, print DLC labels on them - and save - ''' - - # eid = '5522ac4b-0e41-4c53-836a-aaa17e82b9eb' - # video_type = 'left' - - n_frames = 5 # sample 5 random frames - - save_images_folder = '/home/mic/DLC_QC/example_frames/' - one = ONE() - info = '_'.join(np.array(str(one.path_from_eid(eid)).split('/'))[[5,7,8]]) - print(info, video_type) - - r = one.list(eid, 'dataset_types') - - dtypes_DLC = ['_ibl_rightCamera.times.npy', - '_ibl_leftCamera.times.npy', - '_ibl_bodyCamera.times.npy', - '_iblrig_leftCamera.raw.mp4', - '_iblrig_rightCamera.raw.mp4', - '_iblrig_bodyCamera.raw.mp4', - '_ibl_leftCamera.dlc.pqt', - '_ibl_rightCamera.dlc.pqt', - '_ibl_bodyCamera.dlc.pqt'] - - dtype_names = [x['name'] for x in r] - - assert all([i in dtype_names for i in dtypes_DLC] - ), 'For this eid, not all data available' - - - D = one.load(eid, - dataset_types=['camera.times', 'camera.dlc'], - dclass_output=True) - alf_path = Path(D.local_path[0]).parent.parent / 'alf' - - cam0 = alf.io.load_object( - alf_path, - '%sCamera' % - video_type, - namespace='ibl') - - Times = cam0['times'] - - cam = cam0['dlc'] - points = np.unique(['_'.join(x.split('_')[:-1]) for x in cam.keys()]) - - XYs = {} - for point in points: - x = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_x']) - x = x.filled(np.nan) - y = np.ma.masked_where( - cam[point + '_likelihood'] < 0.9, cam[point + '_y']) - y = y.filled(np.nan) - XYs[point] = np.array( - [x, y]) - - - - if video_type != 'body': - d = list(points) - d.remove('tube_top') - d.remove('tube_bottom') - points = np.array(d) - - # stream frames - recs = [x for x in r if f'{video_type}Camera.raw.mp4' in x['name']][0]['file_records'] - video_path = [x['data_url'] for x in recs if x['data_url'] is not None][0] - vid_meta = get_video_meta(video_path) - - frame_idx = sample(range(vid_meta['length']), n_frames) - print('frame indices:', frame_idx) - frames = get_video_frames_preload(video_path, - frame_idx, - mask=np.s_[:, :, 0]) - size = [vid_meta['width'], vid_meta['height']] - #return XYs, frames - - x0 = 0 - x1 = size[0] - y0 = 0 - y1 = size[1] - if video_type == 'left': - dot_s = 10 # [px] for painting DLC dots - else: - dot_s = 5 - - # writing stuff on frames - font = cv2.FONT_HERSHEY_SIMPLEX - - if video_type == 'left': - bottomLeftCornerOfText = (20, 1000) - fontScale = 4 - else: - bottomLeftCornerOfText = (10, 500) - fontScale = 2 - - lineType = 2 - - # assign a color to each DLC point (now: all points red) - cmap = matplotlib.cm.get_cmap('Set1') - CR = np.arange(len(points)) / len(points) - - block = np.ones((2 * dot_s, 2 * dot_s, 3)) - - k = 0 - for frame in frames: - - gray = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB) - - - # print session info - fontColor = (255, 255, 255) - cv2.putText(gray, - info, - bottomLeftCornerOfText, - font, - fontScale / 4, - fontColor, - lineType) - - # print time - Time = round(Times[frame_idx[k]], 3) - a, b = bottomLeftCornerOfText - bottomLeftCornerOfText0 = (int(a * 10 + b / 2), b) - - - - a, b = bottomLeftCornerOfText - bottomLeftCornerOfText0 = (int(a * 10 + b / 2), b) - cv2.putText(gray, - ' time: ' + str(Time), - bottomLeftCornerOfText0, - font, - fontScale / 2, - fontColor, - lineType) - - - - # print DLC dots - ll = 0 - for point in points: - - # Put point color legend - fontColor = (np.array([cmap(CR[ll])]) * 255)[0][:3] - a, b = bottomLeftCornerOfText - if video_type == 'right': - bottomLeftCornerOfText2 = (a, a * 2 * (1 + ll)) - else: - bottomLeftCornerOfText2 = (b, a * 2 * (1 + ll)) - fontScale2 = fontScale / 4 - cv2.putText(gray, point, - bottomLeftCornerOfText2, - font, - fontScale2, - fontColor, - lineType) - - X0 = XYs[point][0][frame_idx[k]] - Y0 = XYs[point][1][frame_idx[k]] - - X = Y0 - Y = X0 - - #print(point,X,Y) - if not np.isnan(X) and not np.isnan(Y): - try: - col = (np.array([cmap(CR[ll])]) * 255)[0][:3] - # col = np.array([0, 0, 255]) # all points red - X = X.astype(int) - Y = Y.astype(int) - - uu = block * col - gray[X - dot_s:X + dot_s, Y - dot_s:Y + dot_s] = uu - - except Exception as e: - print('frame', frame_idx[k]) - print(e) - ll += 1 - - gray = gray[y0:y1, x0:x1] - # cv2.imshow('frame', gray) - cv2.imwrite(f'{save_images_folder}{eid}_frame_{frame_idx[k]}.png', - gray) - cv2.waitKey(1) - k += 1 - - print(f'{n_frames} frames done in', np.round(time.time() - startTime)) diff --git a/PythonScripts/iblapps-master/dlc/wheel_dlc_viewer.py b/PythonScripts/iblapps-master/dlc/wheel_dlc_viewer.py deleted file mode 100644 index 7e488143..00000000 --- a/PythonScripts/iblapps-master/dlc/wheel_dlc_viewer.py +++ /dev/null @@ -1,692 +0,0 @@ -""" -Wheel trace viewer. Requires cv2 - -Example 1 - inspect trial 100 of a given session - from wheel_dlc_viewer import Viewer - eid = '77224050-7848-4680-ad3c-109d3bcd562c' - v = Viewer(eid=eid, trial=100) - -Example 2 - pick a random session to inspect - from wheel_dlc_viewer import Viewer - Viewer() - -""" -import time -import random -import json -import sys -import logging -from itertools import cycle -from more_itertools import chunked - -import cv2 -import numpy as np -import matplotlib as mpl -import matplotlib.animation as animation -import matplotlib.pyplot as plt -from matplotlib.collections import LineCollection -from matplotlib.lines import Line2D - -from alf.io import is_uuid_string -from oneibl.one import ONE -from oneibl.webclient import http_download_file_list -import brainbox.behavior.wheel as wh -from ibllib.misc.exp_ref import eid2ref - - -def get_video_frame(video_path, frame_number): - """ - Obtain numpy array corresponding to a particular video frame in video_path - :param video_path: local path to mp4 file - :param frame_number: video frame to be returned - :return: numpy array corresponding to frame of interest. Dimensions are (1024, 1280, 3) - """ - cap = cv2.VideoCapture(str(video_path)) - # fps = cap.get(cv2.CAP_PROP_FPS) - # print("Frame rate = " + str(fps)) - cap.set(1, frame_number) # 0-based index of the frame to be decoded/captured next. - ret, frame_image = cap.read() - cap.release() - return frame_image - - -def get_video_frames_preload(video_path, frame_numbers): - """ - Obtain numpy array corresponding to a particular video frame in video_path - :param video_path: local path to mp4 file - :param frame_numbers: video frame to be returned - :return: numpy array corresponding to frame of interest. Dimensions are (1024, 1280, - 3). Also returns the frame rate and total number of frames - """ - cap = cv2.VideoCapture(str(video_path)) - total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - fps = cap.get(cv2.CAP_PROP_FPS) - if len(frame_numbers) == 0: - return None, fps, total_frames - elif 0 < frame_numbers[-1] >= total_frames: - raise IndexError('frame numbers must be between 0 and ' + str(total_frames)) - cap.set(cv2.CAP_PROP_POS_FRAMES, frame_numbers[0]) - frame_images = [] - for i in frame_numbers: - sys.stdout.write(f'\rloading frame {i}/{frame_numbers[-1]}') - sys.stdout.flush() - ret, frame = cap.read() - frame_images.append(frame) - cap.release() - sys.stdout.write('\x1b[2K\r') # Erase current line in stdout - return np.array(frame_images), fps, total_frames - - -class Viewer: - def __init__(self, eid=None, trial=None, camera='left', dlc_features=None, quick_load=True, - t_win=3, one=None, start=True): - """ - Plot the wheel trace alongside the video frames. Below is list of key bindings: - :key n: plot movements of next trial - :key p: plot movements of previous trial - :key r: plot movements of a random trial - :key t: prompt for a trial number to plot - :key l: toggle between legend for wheel and trial events - :key space: pause/play frames - :key left: move to previous frame - :key right: move to next frame - - :param eid: uuid of experiment session to load - :param trial: the trial id to plot - :param camera: the camera position to load, options: 'left' (default), 'right', 'body' - :param plot_dlc: tuple of dlc features overlay onto frames - :param quick_load: when true, move onset detection is performed on individual trials - instead of entire session - :param t_win: the window in seconds over which to plot the wheel trace - :param start: if False, the Viewer must be started by calling the `run` method - :return: Viewer object - """ - self._logger = logging.getLogger('ibllib') - - self.t_win = t_win # Time window of wheel plot - self.one = one or ONE() - self.quick_load = quick_load - - # Input validation - if camera not in ['left', 'right', 'body']: - raise ValueError("camera must be one of 'left', 'right', or 'body'") - - # If None, randomly pick a session to load - if not eid: - self._logger.info('Finding random session') - eids = self.find_sessions(dlc=dlc_features is not None) - eid = random.choice(eids) - ref = eid2ref(eid, as_dict=False, one=self.one) - self._logger.info('using session %s (%s)', eid, ref) - elif not is_uuid_string(eid): - raise ValueError('f"{eid}" is not a valid session uuid') - - # Store complete session data: trials, timestamps, etc. - ref = eid2ref(eid, one=self.one, parse=False) - self._session_data = {'eid': eid, 'ref': ref, 'dlc': None} - self._plot_data = {} # Holds data specific to current plot, namely data for single trial - - # Download the DLC data if required - if dlc_features: - self._session_data['dlc'] = self.get_dlc(dlc_features, camera=camera) - - # These are for the dict returned by ONE - trial_data = self.get_trial_data('ONE') - total_trials = trial_data['intervals'].shape[0] - trial = random.randint(0, total_trials) if not trial else trial - self._session_data['total_trials'] = total_trials - self._session_data['trials'] = trial_data - - # Check for local first movement times - first_moves = self.one.path_from_eid(eid) / 'alf' / '_ibl_trials.firstMovement_times.npy' - if first_moves.exists() and 'firstMovement_times' not in trial_data: - # Load file if exists locally - self._session_data['trials']['firstMovement_times'] = np.load(first_moves) - - # Download the raw video for left camera only - self.video_path, = self.download_raw_video(camera) - cam_ts = self.one.load(self._session_data['eid'], ['camera.times'], dclass_output=True) - cam_ts, = [ts for ts, url in zip(cam_ts.data, cam_ts.url) if camera in url] - # _, cam_ts, _ = one.load(eid, ['camera.times']) # leftCamera is in the middle of the list - Fs = 1 / np.diff(cam_ts).mean() # Approx. frequency of camera timestamps - # Verify video frames and timestamps agree - _, fps, count = get_video_frames_preload(self.video_path, []) - - if count != cam_ts.size: - assert count <= cam_ts.size, 'fewer camera timestamps than frames' - msg = 'number of timestamps does not match number video file frames: ' - self._logger.warning(msg + '%i more timestamps than frames', cam_ts.size - count) - - assert Fs - fps < 1, 'camera timestamps do not match reported frame rate' - self._logger.info("Frame rate = %.0fHz", fps) - # cam_ts = cam_ts[-count:] # Remove extraneous timestamps - self._session_data['camera_ts'] = cam_ts - - # Load wheel data - self._session_data['wheel'] = self.one.load_object(self._session_data['eid'], 'wheel') - if 'firstMovement_times' in self._session_data['trials']: - pos, t = wh.interpolate_position( - self._session_data['wheel']['timestamps'], - self._session_data['wheel']['position'], freq=1000) - - # Plot the first frame in the upper subplot - fig, axes = plt.subplots(nrows=2) - fig.canvas.mpl_disconnect(fig.canvas.manager.key_press_handler_id) # Disable defaults - fig.canvas.mpl_connect('key_press_event', self.process_key) # Connect our own key press fn - - self._plot_data['figure'] = fig - self._plot_data['axes'] = axes - self._trial_num = trial - - self.anim = animation.FuncAnimation(fig, self.animate, init_func=self.init_plot, - frames=cycle(range(60)), interval=20, blit=False, - repeat=True, cache_frame_data=False) - self.anim.running = False - self.trial_num = trial # Set trial and prepare plot/frame data - if start: - self.run() - - def run(self): - self._logger.debug('Running Viewer') - plt.show() # Start animation - - @property - def trial_num(self): - return self._trial_num - - @trial_num.setter - def trial_num(self, trial): - """ - Setter for the trial_num property. Loads frames for trial, extracts onsets - for trial and reinitializes plot - :param trial: the trial number to select. Must be > 0, <= len(trials['intervals']) - :return: None - """ - # Validate input: trial must be within range (1, total trials) - trial = int(trial) - total_trials = self._session_data['total_trials'] - if not 0 < trial <= total_trials: - raise IndexError( - 'Trial number must be between 1 and {}'.format(total_trials)) - self._trial_num = trial - sys.stdout.write('\rLoading trial ' + str(self._trial_num)) - - # Our plot data, e.g. data that falls within trial - frame_ids = self.frames_for_period(self._session_data['camera_ts'], trial - 1) - try: - data = { - 'frames': frame_ids, - 'camera_ts': self._session_data['camera_ts'][frame_ids], - 'frame_images': get_video_frames_preload(self.video_path, frame_ids)[0] - } - except cv2.error as ex: - # Log errors when out of memory; occurs when there are too many frames to load - if ex.func.endswith('OutOfMemoryError'): - self._logger.error(f'{ex.func}: {ex.err}') - return - else: - raise ex - except MemoryError: - self._logger.error('Out of memory; please try loading another trial') - return - # frame = get_video_frame(video_path, frames[0]) - - on, off, ts, pos, units = self.extract_onsets_for_trial() - data['moves'] = {'intervals': np.c_[on, off]} - data['wheel'] = {'ts': ts, 'pos': pos, 'units': units} - - # Update title - ref = '{date:s}_{sequence:s}_{subject:s}'.format(**self._session_data['ref']) - if 'firstMovement_times' in self._session_data['trials']: - first_move = self._session_data['trials']['firstMovement_times'][trial - 1] - go_cue = self._session_data['trials']['goCue_times'][trial - 1] - rt = (first_move - go_cue) * 1000 - title = '%s #%i rt = %.2f ms' % (ref, trial, rt) - else: - title = '%s #%i' % (ref, trial) - - self._plot_data['axes'][0].set_title(title) - - # Get the sample numbers for each onset and offset - onoff_samps = np.c_[np.searchsorted(ts, on), np.searchsorted(ts, off)] - data['moves']['onoff_samps'] = np.array(onoff_samps) - # Points to split trace - data['moves']['indicies'] = np.sort(np.hstack(onoff_samps)) - data['frame_num'] = 0 - data['figure'] = self._plot_data['figure'] - data['axes'] = self._plot_data['axes'] - if 'im' in self._plot_data.keys(): - # Copy over artists - data['im'] = self._plot_data['im'] - data['ln'] = self._plot_data['ln'] - data['dlc'] = self._plot_data['dlc'] - - # Stop running so we have to to reinitialize the plot after swapping out the plot data - if self.anim.running: - self.anim.running = False - if self.anim: # deals with issues on cleanup - self.anim.event_source.stop() - self._plot_data = data - - def find_sessions(self, dlc=False): - """ - Compile list of eids with required files, i.e. raw camera and wheel data - :param dlc: search for sessions with DLC output - :return: list of session eids - """ - datasets = ['_iblrig_Camera.raw', 'camera.times', - 'wheel.timestamps', 'wheel.position'] - if dlc: - datasets.append('camera.dlc') - return self.one.search(dataset_types=datasets) - - def download_raw_video(self, cameras=None): - """ - Downloads the raw video from FlatIron or cache dir. This allows you to download just one - of the three videos - :param cameras: the specific camera to load (i.e. 'left', 'right', or 'body') If None all - three videos are downloaded. - :return: the file path(s) of the raw videos - FIXME Currently returns if only one video already downloaded - """ - one = self.one - eid = self._session_data['eid'] - if cameras: - cameras = [cameras] if isinstance(cameras, str) else cameras - cam_files = ['_iblrig_{}Camera.raw.mp4'.format(cam) for cam in cameras] - datasets = one.alyx.rest('sessions', 'read', id=eid)['data_dataset_session_related'] - urls = [ds['data_url'] for ds in datasets if ds['name'] in cam_files] - cache_dir = one.path_from_eid(eid).joinpath('raw_video_data') - cache_dir.mkdir(exist_ok=True) # Create folder if doesn't already exist - # Check if file already downloaded - cam_files = [file[:-4] for file in cam_files] # Remove ext - filenames = [f for f in cache_dir.iterdir() - if any([cam in str(f) for cam in cam_files])] - if filenames: - return [cache_dir.joinpath(file) for file in filenames] - return http_download_file_list(urls, username=one._par.HTTP_DATA_SERVER_LOGIN, - password=one._par.HTTP_DATA_SERVER_PWD, - cache_dir=str(cache_dir)) - else: - return one.load(eid, ['_iblrig_Camera.raw'], download_only=True) - - def get_trial_data(self, mode='ONE'): - """ - Obtain dict of trial data - :param mode: get data from ONE (default) or DataJoint - :return: dict of ALF trials object - """ - DJ2ONE = {'trial_stim_on_time': 'stimOn_times', - 'trial_feedback_time': 'feedback_times', - 'trial_go_cue_time': 'goCue_times'} - if mode == 'DataJoint': - # raise NotImplementedError('DataJoint support has been removed') - from uuid import UUID - from ibl_pipeline import acquisition, behavior - restriction = acquisition.Session & {'session_uuid': UUID(self._session_data['eid'])} - query = (behavior.TrialSet.Trial & restriction).proj( - 'trial_response_time', - 'trial_stim_on_time', - 'trial_go_cue_time', - 'trial_feedback_time', - 'trial_start_time', - 'trial_end_time') - data = query.fetch(order_by='trial_id', format='frame') - data = {DJ2ONE.get(k, k): data[k].values for k in data.columns.values} - data['intervals'] = np.c_[data.pop('trial_start_time'), data.pop('trial_end_time')] - return data - else: - return self.one.load_object(self._session_data['eid'], 'trials') - - def get_dlc(self, features='all', camera='left'): - """ - Load the DLC data from file and discard features we don't need. - :param features: tuple of features to load, or 'all' to load all available - :param camera: which camera dlc data to load ('left', 'right', 'body') - :return: None; data saved to self._session_data as dict with keys ('columns, 'data') - """ - files = self.one.load(self._session_data['eid'], ['camera.dlc'], download_only=True) - filename = '_ibl_{}Camera.dlc'.format(camera) - if not [f for f in files if f and camera in str(f)]: - self._logger.warning('No DLC found for %s camera', camera) - return - dlc_path = self.one.path_from_eid(self._session_data['eid']) / 'alf' / filename - with open(str(dlc_path) + '.metadata.json', 'r') as meta_file: - meta = meta_file.read() - columns = json.loads(meta)['columns'] # parse file - dlc = np.load(str(dlc_path) + '.npy') - - # Discard unused columns - if features != 'all': - incl = np.array([col.startswith(tuple(features)) for col in columns]) - else: - incl = np.ones(len(columns), dtype=bool) - - dlc = { - 'labels': [col for col, keep in zip(columns, incl) if keep], - 'features': dlc[:, incl]} - assert len(dlc['labels']) % 3 == 0, \ - 'should be three columns per feature in the form (x, y, likelihood)' - return dlc - - def update_dlc_plot(self, frame_idx=0): - """ - Update the coordinates and alpha values of DLC overlay markers - :param frame_idx: the index in the trial's frame list - :return: None - """ - i = self._plot_data['frames'][frame_idx] - dlc = self._session_data['dlc'] - # Loop through labels list range in threes, i.e. [0, 1, 2], [3, 4, 5] - for j, ln in zip(chunked(range(len(dlc['labels'])), 3), self._plot_data['dlc']): - x, y, p = dlc['features'][i, j] - ln.set_offsets(np.c_[x, y]) # Update marker position - ln.set_alpha(p) # Update alpha based on likelihood - - def frames_for_period(self, cam_ts, start_time=None, end_time=None): - """ - Load video frames between two events - :param cam_ts: a camera.times numpy array - :param start_time: a timestamp for the start of the period. If an int, the trial - interval start at that index is used. If None, period starts at first frame - :param end_time: a timestamp for the end of the period. If an int, the trial - interval end at that index is used. If None, period ends at last frame, unless start_time - is an int, in which case the trial interval at the start_time index is used - :return: numpy bool mask the same size as cam_ts - """ - if isinstance(start_time, int): - trial_ends = self._session_data['trials']['intervals'][:, 1] - trial_starts = self._session_data['trials']['intervals'][:, 0] - end_time = trial_ends[start_time] if not end_time else trial_ends[end_time] - start_time = trial_starts[start_time] - else: - if not start_time: - start_time = cam_ts[0] - if not end_time: - end_time = cam_ts[-1] - mask = np.logical_and(cam_ts >= start_time, cam_ts <= end_time) - return np.where(mask)[0] - - def extract_onsets_for_trial(self): - """ - Extracts the movement onsets and offsets for the current trial - :return: tuple of onsets, offsets on, interpolated timestamps, interpolated positions, - and position units - """ - wheel = self._session_data['wheel'] - trials = self._session_data['trials'] - trial_idx = self.trial_num - 1 # Trials num starts at 1 - # Check the values and units of wheel position - res = np.array([wh.ENC_RES, wh.ENC_RES / 2, wh.ENC_RES / 4]) - # min change in rad and cm for each decoding type - # [rad_X4, rad_X2, rad_X1, cm_X4, cm_X2, cm_X1] - min_change = np.concatenate([2 * np.pi / res, wh.WHEEL_DIAMETER * np.pi / res]) - pos_diff = np.median(np.abs(np.ediff1d(wheel['position']))) - - # find min change closest to min pos_diff - idx = np.argmin(np.abs(min_change - pos_diff)) - if idx < len(res): - # Assume values are in radians - units = 'rad' - encoding = idx - else: - units = 'cm' - encoding = idx - len(res) - thresholds = wh.samples_to_cm(np.array([8, 1.5]), resolution=res[encoding]) - if units == 'rad': - thresholds = wh.cm_to_rad(thresholds) - kwargs = {'pos_thresh': thresholds[0], 'pos_thresh_onset': thresholds[1]} - # kwargs = {'make_plots': True, **kwargs} # Uncomment for plot - - # Interpolate and get onsets - pos, t = wh.interpolate_position(wheel['timestamps'], wheel['position'], freq=1000) - # Get the positions and times between our trial start and the next trial start - if self.quick_load or not self.trial_num: - try: - # End of previous trial to beginning of next - t_mask = np.logical_and(t >= trials['intervals'][trial_idx - 1, 1], - t <= trials['intervals'][trial_idx + 1, 0]) - except IndexError: # We're on the last trial - # End of previous trial to end of current - t_mask = np.logical_and(t >= trials['intervals'][trial_idx - 1, 1], - t <= trials['intervals'][trial_idx, 1]) - else: - t_mask = np.ones_like(t, dtype=bool) - wheel_ts = t[t_mask] - wheel_pos = pos[t_mask] - on, off, *_ = wh.movements(wheel_ts, wheel_pos, freq=1000, **kwargs) - return on, off, wheel_ts, wheel_pos, units - - def init_plot(self): - """ - Plot the wheel data for the current trial - :return: None - """ - self._logger.debug('Initializing plot') - data = self._plot_data - trials = self._session_data['trials'] - trial_idx = self.trial_num - 1 - if 'im' in data: - data['im'].set_data(data['frame_images'][0]) - else: - data['im'] = data['axes'][0].imshow(data['frame_images'][0]) - - # Plot DLC features - dlc = self._session_data.get('dlc') - dlc_paths = [] # DLC marker PathsCollections - if dlc: # DLC data loaded - if data.get('dlc'): # Already initialized - self.update_dlc_plot() # Update positions for first frame - else: # Plot new elements - i = data['frames'][0] # First frame - cmap = ['b', 'g', 'r', 'c', 'm', 'y'] - # Each feature has three dimensions (x, y, likelihood) - labels = chunked(range(len(dlc['labels'])), 3) - for j, colour in zip(labels, cycle(sorted(cmap * 2))): - x, y, p = dlc['features'][i, j] - mkr = data['axes'][0].scatter(x, y, marker='+', c=colour, s=100, alpha=p) - dlc_paths.append(mkr) - data['dlc'] = dlc_paths or None - - data['axes'][0].axis('off') - - indicies = data['moves']['indicies'] - on = data['moves']['intervals'][:, 0] - off = data['moves']['intervals'][:, 1] - onoff_samps = data['moves']['onoff_samps'] - wheel_pos = data['wheel']['pos'] - wheel_ts = data['wheel']['ts'] - cam_ts = data['camera_ts'] - - # Plot the wheel position - ax = data['axes'][1] - ax.clear() - ax.plot(on, wheel_pos[onoff_samps[:, 0]], 'go', label='move onset') - ax.plot(off, wheel_pos[onoff_samps[:, 1]], 'bo', label='move offset') - if 'firstMovement_times' in trials: - first_move = trials['firstMovement_times'][trial_idx] - if ~np.isnan(first_move): - first_move_pos = wheel_pos[np.where(wheel_ts > first_move)[0][0]] - ax.plot(first_move, first_move_pos, 'ro', label='first move') - - t_split = np.split(np.vstack((wheel_ts, wheel_pos)).T, indicies, axis=0) - ax.add_collection(LineCollection(t_split[0::2], colors='k', label='wheel position')) - ax.add_collection(LineCollection(t_split[1::2], colors='r', label='in movement')) # Moving - ax.set_ylabel('position / ' + data['wheel']['units']) - wheel_legend = data['legends'][0] if 'legends' in data else plt.legend(title='Wheel') - - # Plot some trial events - t1 = trials['intervals'][trial_idx, 0] - t2 = trials['feedback_times'][trial_idx] - t3 = trials['goCue_times'][trial_idx] - t4 = trials['stimOn_times'][trial_idx] - pos_rng = [wheel_pos.min(), wheel_pos.max()] # The range for vertical lines on plot - ax.vlines([t1, t2, t3, t4], pos_rng[0], pos_rng[1], - colors=['r', 'b', 'y', 'g'], linewidth=0.5) - - # Create legend for the trial events - if 'legends' not in data: - labels = ['trial start', 'feedback', 'stim on', 'go cue'] - custom_lines = [Line2D([0], [0], color='r', lw=.5), - Line2D([0], [0], color='b', lw=.5), - Line2D([0], [0], color='y', lw=.5), - Line2D([0], [0], color='g', lw=.5)] - trial_legend = plt.legend(custom_lines, labels, title="Trial Events") - trial_legend.set_visible(False) - data['legends'] = (wheel_legend, trial_legend) - else: - trial_legend = data['legends'][1] - - # Add the legends to the plot - ax.add_artist(wheel_legend) - ax.add_artist(trial_legend) - - # Set limits and add frame marker - ax.set_ylim(pos_rng) - data['ln'] = ax.axvline(x=cam_ts[0], color='k') - ax.set_xlim([cam_ts[0] - (self.t_win / 2), cam_ts[0] + (self.t_win / 2)]) - - self._plot_data = data - - return data['im'], data['ln'], data['dlc'] - - def animate(self, i): - """ - Callback for figure animation. Sets image data for current frame and moves pointer - along axis - :param i: unused; the current timestep of the calling method - :return: None - """ - t_start = time.time() - data = self._plot_data - if i < 0: - self._plot_data['frame_num'] -= 1 - if self._plot_data['frame_num'] < 0: - self._plot_data['frame_num'] = len(data['frame_images']) - 1 - else: - self._plot_data['frame_num'] += 1 - if self._plot_data['frame_num'] >= len(data['frame_images']): - self._plot_data['frame_num'] = 0 - i = self._plot_data['frame_num'] # NB: This is index for current trial's frame list - # Print current frame number to terminal - sys.stdout.flush() - sys.stdout.write('\rFrame {} / {}'.format(i, len(data['frame_images']))) - - frame = data['frame_images'][i] - t_x = data['camera_ts'][i] - data['ln'].set_xdata([t_x, t_x]) - data['axes'][1].set_xlim([t_x - (self.t_win / 2), t_x + (self.t_win / 2)]) - data['im'].set_data(frame) - if data['dlc']: - self.update_dlc_plot(i) - self._logger.debug('Render time: %.3f', time.time() - t_start) - - return data['im'], data['ln'] - - def process_key(self, event): - """ - Callback for key presses. - :param event: a figure key_press_event - :return: None - """ - print(event.key) - total_trials = self._session_data['total_trials'] - if event.key.isspace(): - if self.anim.running: - self.anim.event_source.stop() - else: - self.anim.event_source.start() - self.anim.running = ~self.anim.running - elif event.key == 'right': - if self.anim.running: - self.anim.event_source.stop() - self.anim.running = False - self.animate(1) - self._plot_data['figure'].canvas.draw() - elif event.key == 'left': - if self.anim.running: - self.anim.event_source.stop() - self.anim.running = False - self.animate(-1) - self._plot_data['figure'].canvas.draw() - elif event.key == 'r': - # Pick random trial - self.trial_num = random.randint(0, total_trials) - self.init_plot() - self.anim.event_source.start() - self.anim.running = True - elif event.key == 't': - if self.anim.running: - self.anim.event_source.stop() - self.anim.running = False - # Select trial - trial = input(f'\rInput a trial within range (1, {total_trials}): \n') - if trial: - self.trial_num = int(trial) - self.init_plot() - self.anim.event_source.start() - self.anim.running = True - elif event.key == 'n': - # Next trial - self.trial_num = self.trial_num + 1 if self.trial_num < total_trials else 1 - self.init_plot() - self.anim.event_source.start() - self.anim.running = True - elif event.key == 'p': - # Previous trial - self.trial_num = self.trial_num - 1 if self.trial_num > 1 else total_trials - self.init_plot() - self.anim.event_source.start() - self.anim.running = True - elif event.key == 'l': - # Toggle legend - legends = self._plot_data['legends'] - for leg in legends: - leg.set_visible(not leg.get_visible()) - if not self.anim.running: - pass - - -if __name__ == "__main__": - import textwrap - import argparse - - docstring = """ - Viewer to see the wheel data along label the video and DLC. - - Below is list of key bindings:\n - n plot movements of next trial - p plot movements of previous trial - r plot movements of a random trial - t prompt for a trial number to plot - l toggle between for wheel and for trial events - pause/play frames - move to previous frame - move to next frame - """ - mpl.use('TKAgg') # Different backend required for event callbacks in main - - parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, - description=textwrap.dedent(docstring)) - parser.add_argument('--eid', help='session uuid') - parser.add_argument('-v', '--verbose', help='increase output verbosity', action='store_true') - parser.add_argument('--camera', default='left', help='the camera to load, choices are ' - '"left" (default), "right" and "body"') - parser.add_argument('-t', '--trial', type=int, help="trial number") - parser.add_argument('--dlc', help='a comma separated list of DLC features to plot, or "all"') - parser.add_argument('-w', '--window', type=int, default=3, - help="length of time window in wheel plot, default=3") - args = vars(parser.parse_args()) - verbose = args.pop('verbose') - - dlc_features = args.pop('dlc') - if dlc_features and dlc_features != 'all': - dlc_features = dlc_features.split(',') - args['t_win'] = args.pop('window') - args['dlc_features'] = dlc_features - - v = Viewer(**args, start=False) - if verbose: - v._logger.setLevel(logging.DEBUG) - # Start the viewer - v.run() diff --git a/PythonScripts/iblapps-master/histology/__init__.py b/PythonScripts/iblapps-master/histology/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/histology/atlas_mpl.py b/PythonScripts/iblapps-master/histology/atlas_mpl.py deleted file mode 100644 index f51ec01b..00000000 --- a/PythonScripts/iblapps-master/histology/atlas_mpl.py +++ /dev/null @@ -1,107 +0,0 @@ -from pathlib import Path -import matplotlib -import numpy as np -from PyQt5 import QtCore, QtWidgets, uic - -from iblapps import qt -from iblapps.qt_matplotlib import BaseMplCanvas -import ibllib.atlas as atlas - -# Make sure that we are using QT5 -matplotlib.use('Qt5Agg') - - -class Model: - """ - Container for Data and variables of the application - """ - brain_atlas: atlas.BrainAtlas - ap_um: float - - def __init__(self, brain_atlas=None, ap_um=0): - self.ap_um = ap_um - # load the brain atlas - if brain_atlas is None: - self.brain_atlas = atlas.AllenAtlas(res_um=25) - else: - self.brain_atlas = brain_atlas - - -class MyStaticMplCanvas(BaseMplCanvas): - - def __init__(self, *args, **kwargs): - super(MyStaticMplCanvas, self).__init__(*args, **kwargs) - self.mpl_connect("motion_notify_event", self.on_move) - - def on_move(self, event): - mw = qt.get_main_window() - ap = mw._model.ap_um - xlab = '' if event.xdata is None else '{: 6.0f}'.format(event.xdata) - ylab = '' if event.ydata is None else '{: 6.0f}'.format(event.ydata) - mw.label_ml.setText(xlab) - mw.label_dv.setText(ylab) - mw.label_ap.setText('{: 6.0f}'.format(ap)) - if event.xdata is None or event.ydata is None: - return - # if whithin the bounds of the Atlas, label with the current strucure hovered onto - xyz = np.array([[event.xdata, ap, event.ydata]]) / 1e6 - regions = mw._model.brain_atlas.regions - id_label = mw._model.brain_atlas.get_labels(xyz) - il = np.where(regions.id == id_label)[0] - mw.label_acronym.setText(regions.acronym[il][0]) - mw.label_structure.setText(regions.name[il][0]) - mw.label_id.setText(str(id_label)) - - def update_slice(self, volume='image'): - mw = qt.get_main_window() - ap_um = mw._model.ap_um - im = mw._model.brain_atlas.slice(ap_um / 1e6, axis=1, volume=volume) - im = np.swapaxes(im, 0, 1) - self.axes.images[0].set_data(im) - self.draw() - - -class AtlasViewer(QtWidgets.QMainWindow): - def __init__(self, brain_atlas=None, ap_um=0): - # init the figure - super(AtlasViewer, self).__init__() - uic.loadUi(str(Path(__file__).parent.joinpath('atlas_mpl.ui')), self) - self.setAttribute(QtCore.Qt.WA_DeleteOnClose) - self.file_menu = QtWidgets.QMenu('&File', self) - self.file_menu.addAction('&Quit', self.fileQuit, - QtCore.Qt.CTRL + QtCore.Qt.Key_Q) - self.menuBar().addMenu(self.file_menu) - self.pb_toggle.clicked.connect(self.update_slice) - - # init model - self._model = Model(brain_atlas=brain_atlas, ap_um=ap_um) - # display the coronal slice in the mpl widget - self._model.brain_atlas.plot_cslice(ap_coordinate=ap_um / 1e6, - ax=self.mpl_widget.axes) - - def fileQuit(self): - self.close() - - def closeEvent(self, ce): - self.fileQuit() - - def update_slice(self, ce): - volume = self.pb_toggle.text() - if volume == 'Annotation': - self.pb_toggle.setText('Image') - elif volume == 'Image': - self.pb_toggle.setText('Annotation') - self.mpl_widget.update_slice(volume.lower()) - - -def viewatlas(brain_atlas=None, title=None, ap_um=0): - qt.create_app() - av = AtlasViewer(brain_atlas, ap_um=ap_um) - av.setWindowTitle(title) - av.show() - return av, av.mpl_widget.axes - - -if __name__ == "__main__": - w = viewatlas(title="Allen Atlas - IBL") - qt.run_app() diff --git a/PythonScripts/iblapps-master/histology/atlas_mpl.ui b/PythonScripts/iblapps-master/histology/atlas_mpl.ui deleted file mode 100644 index 5a6d9ec8..00000000 --- a/PythonScripts/iblapps-master/histology/atlas_mpl.ui +++ /dev/null @@ -1,270 +0,0 @@ - - - AtlasViewer - - - - 0 - 0 - 986 - 860 - - - - - 0 - 0 - - - - MainWindow - - - background-color: rgb(255, 255, 255); - - - - - 0 - 0 - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 16777215 - 20 - - - - dv (um) - - - - - - - - 16777215 - 20 - - - - structure - - - - - - - - 16777215 - 20 - - - - ap (um) - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 16777215 - 20 - - - - ml (um) - - - - - - - - 16777215 - 20 - - - - atlas id - - - - - - - - 16777215 - 20 - - - - acronym - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 16777215 - 20 - - - - - - - - - - - - 188 - 0 - - - - - 10 - 16777215 - - - - Annotation - - - - - - - - 0 - 316 - - - - - - - - - - 0 - 0 - 986 - 22 - - - - - - - toolBar - - - TopToolBarArea - - - false - - - - - - MyStaticMplCanvas - QWidget -
iblapps.histology.atlas_mpl
- 1 - - - - - - pb_toggle - released() - mpl_widget - update() - - - 956 - 255 - - - 524 - 563 - - - - - diff --git a/PythonScripts/iblapps-master/histology/transform-tracks.py b/PythonScripts/iblapps-master/histology/transform-tracks.py deleted file mode 100644 index 05c3462c..00000000 --- a/PythonScripts/iblapps-master/histology/transform-tracks.py +++ /dev/null @@ -1,233 +0,0 @@ -"""Re-Scale and Transform Traced Electrode Tracks - -This script will convert traced tracks from full resolution stacks, into -the downsampled stack and finally transform them into Standard (ARA) -space. - - -DEPENDENCIES - -For this script to run, elastix must be installed. See instruction at -elastix.isi.uu.nl/download.php. - -Check elastix is installed by running at command line: - -$ elastix --version -elastix version: 4.800 - - -GENERATING TRACED TRACKS - -The tracks must be generated using Lasagna and the add_line_plugin. - - -The resulting .csv files must be saved into a directory titled -sparsedata/ inside the Sample ROOT Directory: - - - [KS006]: - - sparsedata: - - track01.csv - - track02.csv - -NB: [KS006] can be any sample ID. - -The Sample ROOT Directory must also have the following structure and -files: - - - [KS006]: - - downsampledStacks_25: - - dsKS006_190508_112940_25_25_GR.txt - - ARA2sample: - - TransformParameters.0.txt - - TransformParameters.1.txt - -The script uses information in dsKS006_190508_112940_25_25_GR.txt, -TransformParameters.0.txt and TransformParameters.1.txt to perform the -transformation. - -The output of the script is a series of new csv files, with the track -coordinates now in the registered ARA space. - -- [KS006]: - - downsampledStacks_25: - - rescaled-transformed-sparsedata: - - track01.csv - - track02.csv - - -""" - -import sys -import os -import re -import glob -import csv -import subprocess -from os import listdir -from os.path import isfile, join -from pathlib import Path -import platform - -#print("Python version") -#print (sys.version) -#print("Version info.") -#print (sys.version_info) - -#print( os.getcwd() ) - -xy = 0.0 # num to hold x/y resolution -z = 0.0 # num to hold z resolution - - -rescaleFile = glob.glob("downsampledStacks_25" + os.sep + "ds*GR.txt")[0] - -print(rescaleFile) - - -with open (rescaleFile, 'rt') as myfile: - - for line in myfile: - - #print(line) - - if line.lower().find("x/y:") != -1: - for t in line.split(): - try: - xy = float(t) - except ValueError: - pass - - if line.lower().find("z:") != -1: - for t in line.split(): - try: - z = float(t) - except ValueError: - pass - -#print(xy) - -#print(z) - - -# NEXT - change the path in TransformParameters.1.txt file to point to correct ABSOLUTE path to TransformParameters.0.txt file: - -tp = os.getcwd() + os.sep + "downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.0.txt" - -#print(tp) - - -#tp1 = "downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.1.txt" - -#for line in fileinput.input(tp1, inplace = 1): -# print line.replace("foo", "bar"), - -fp = open("downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.1.txt","r+") - -fg = open("downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "new_TransformParameters.1.txt","w") - -for line in fp: - - if line.find("(InitialTransformParametersFileName") != -1: - new_line = line.replace(line,'(InitialTransformParametersFileName '+tp+')\n') - fg.write(new_line) - - else: - fg.write(line) - -fg.close() - -fp.close() - -os.remove("downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.1.txt") -os.rename("downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "new_TransformParameters.1.txt","downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.1.txt") - - -# have extracted the resolution change - now open each CSV file of track coords, and transform them: - -# Read the CSV files: - -# get list of files: -csvFiles = [f for f in listdir("sparsedata" + os.sep) if isfile(join("sparsedata" + os.sep, f))] - -for csvFilePath in csvFiles: - - print(csvFilePath) - # get the total number of lines in the file first: - csvfile = open("sparsedata" + os.sep + csvFilePath) - linenum = len(csvfile.readlines()) - csvfile.close() - - csvFilePathNoExt = csvFilePath[:-4] - - - with open( ("sparsedata" + os.sep + csvFilePath), newline='') as csvfile: - # write to rescaled-file: - csvOut = open( ('downsampledStacks_25' + os.sep + 'rescaled-' + csvFilePath), 'w') - pts = open('downsampledStacks_25' + os.sep + 'rescaled-'+csvFilePathNoExt+'.txt', 'w') - pts.write("point\n") - pts.write("%s\n" % linenum ) - spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') - for row in spamreader: - # NB: CSV file is laid out ZXY! - row[0] = round( (float(row[0])/ z), 6) - - row[1] = round( (float(row[1])/ xy), 6) - - row[2] = round( (float(row[2])/ xy), 6) - - # re-write as PTS: XYZ! - pts.write("%s " % row[1]) - pts.write("%s " % row[2]) - pts.write("%s\n" % row[0]) - - # also re-write as csv: ZXY! - csvOut.write("%s," % row[0]) - csvOut.write("%s," % row[1]) - csvOut.write("%s\n" % row[2]) - - pts.close() - csvOut.close() - - - # next, use system call to convert the points into ARA space: - # Use the INVERSE of sample2ARA -> ARA2sample! - - pts = "downsampledStacks_25" + os.sep + "rescaled-"+csvFilePathNoExt+'.txt' - out = "downsampledStacks_25" + os.sep + "rescaled-transformed-sparsedata" + os.sep - # NOTE - need to use TransformParameters.1.txt - which points to TP0! - tp = "downsampledStacks_25" + os.sep + "ARA2sample" + os.sep + "TransformParameters.1.txt" - - # make output DIR: - if os.path.isdir(out)==False: - os.mkdir(out) - - cmd = "transformix -def " + pts + " -out " + out + " -tp " + tp - - print(cmd) - - returned_value = subprocess.call(cmd, shell=True) # returns the exit code in unix - print('returned value:', returned_value) - - # finally, want to convert outputpoints.txt into the ORIGINAL CSV format: - outputPts = open(out+"outputpoints.txt", "r") - - outPtsCsv = open( (r"" + out + csvFilePathNoExt + "_transformed.csv"), 'w') - - for aline in outputPts: - values = aline.split() - # XYZ: - # print("XYZ: ", values[22], values[23], values[24] ) - # NB: CSV file is laid out ZXY! - outPtsCsv.write("%s," % values[24]) - outPtsCsv.write("%s," % values[22]) - outPtsCsv.write("%s\n" % values[23]) - - outputPts.close() - - # remove the temp files: - os.remove('downsampledStacks_25' + os.sep + 'rescaled-' + csvFilePath) - os.remove('downsampledStacks_25' + os.sep + 'rescaled-'+csvFilePathNoExt+'.txt') - os.remove(out+"outputpoints.txt") - os.remove(out+"transformix.log") - - diff --git a/PythonScripts/iblapps-master/launch_phy/README.md b/PythonScripts/iblapps-master/launch_phy/README.md deleted file mode 100644 index 084f3f19..00000000 --- a/PythonScripts/iblapps-master/launch_phy/README.md +++ /dev/null @@ -1,52 +0,0 @@ -## Launch Phy with an eid and probe_name - -To launch Phy, first follow [these instructions](https://github.com//int-brain-lab/iblenv) for setting up a unified IBL conda environment. - -Then in your terminal, make sure you are on the 'develop' branch of this repository, activate your unified conda environment, and run either: - -`python -s subject -d date -n session_no -p probe_name` -e.g `python int-brain-lab\iblapps\launch_phy\phy_launcher.py -s KS022 -d 2019-12-10 -n 1 -p probe00` - -or: - -`python -s subject -e eid -p probe_name` -e.g. `python int-brain-lab\iblapps\launch_phy\phy_launcher.py -e a3df91c8-52a6-4afa-957b-3479a7d0897c -p probe00` - - -or: -`python phys_launcher.py -pid 2aea57ac-d3d0-4a09-b6fa-0aa9d58d1e11` - -Quality metrics will be computed within phy and displayed automatically. -Description of metrics can be found here: https://docs.google.com/document/d/1ba_krsfm4epiAd0zbQ8hdvDN908P9VZOpTxkkH3P_ZY/edit# - -## Manual Curation -Manual curation comprises three different steps - -1) **Labelling clusters** (assigning each cluster with a label Good, Mua or Noise) - * Select a cluster within Cluster View and click on `Edit -> Move best to` and assign your chosen label. - * Alternatively you can use the shortcuts alt+G, alt+M, alt+N to label the cluster as good, mua or noise respectively - -2) **Additional notes associated with clusters** (extra information about clusters, for example if it looks like artifact or drift) - * Select a cluster within Cluster View - * Ensure snippet mode is enabled by clicking `File-> Enable Snippet Mode` - * Type `:l notes your_note` and then hit enter (when typing you should see the text appear in the lower left - hand corner of the main window) - * A new column, with the heading **notes** should have been created in the Cluster View window - -Make sure you hit the save button frequently during manual curation so your results are saved and can be -recovered in case Phy freezes or crashes! - - -## Upload manual labels to datajoint - -Once you have completed manual curation, the labels that you assigned clusters and any additional notes that you -added can be uploaded and stored in a datajoint table by running either: - -`python -s subject -d date -n session_no -p probe_name` -e.g `python int-brain-lab\iblapps\launch_phy\populate_cluster_table.py -s KS022 -d 2019-12-10 -n 1 -p probe00` - -or: - -`python -s subject -e eid -p probe_name` -e.g. `python int-brain-lab\iblapps\launch_phy\populate_cluster_table.py -e a3df91c8-52a6-4afa-957b-3479a7d0897c -p probe00` - diff --git a/PythonScripts/iblapps-master/launch_phy/cluster_table.py b/PythonScripts/iblapps-master/launch_phy/cluster_table.py deleted file mode 100644 index 3bb2f5ea..00000000 --- a/PythonScripts/iblapps-master/launch_phy/cluster_table.py +++ /dev/null @@ -1,26 +0,0 @@ -import datajoint as dj -from ibl_pipeline import reference - -schema = dj.schema('group_shared_ephys') -dj.config["enable_python_native_blobs"] = True - - -@schema -class ClusterLabel(dj.Imported): - definition = """ - cluster_uuid: uuid # uuid of cluster - -> reference.LabMember #user name - --- - label_time: datetime # date on which labelling was done - cluster_label=null: varchar(255) # user assigned label - cluster_note=null: varchar(255) # user note about cluster - """ - - -@schema -class MergedClusters(dj.Imported): - definition = """ - cluster_uuid: uuid # uuid of merged cluster - --- - merged_uuid: longblob # array of uuids of original clusters that form merged cluster - """ diff --git a/PythonScripts/iblapps-master/launch_phy/defined_metrics.py b/PythonScripts/iblapps-master/launch_phy/defined_metrics.py deleted file mode 100644 index 8a8e3f03..00000000 --- a/PythonScripts/iblapps-master/launch_phy/defined_metrics.py +++ /dev/null @@ -1,697 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Apr 1 22:01:32 2020 - -@authors: Noam Roth, Jai Bhagat - -Computes metrics for assessing quality of single units. - -""" - - - -import time -import numpy as np -import brainbox as bb -from phylib.stats import correlograms -import scipy.stats as stats -import scipy.ndimage.filters as filters -from ibllib.io import spikeglx -from iblutil.util import Bunch - - -def FP_RP(ts): - binSize=0.25 #in ms - b= np.arange(0,10.25,binSize)/1000 + 1e-6 #bins in seconds - bTestIdx = [5, 6, 7, 8, 10, 12, 14, 16, 18, 20, 24, 28, 32, 36, 40] - bTest = [b[i] for i in bTestIdx] - - thresh = 0.2 - acceptThresh=0.1 - if(len(ts)>0 and ts[-1]>ts[0]): #only do this for units with samples - recDur = (ts[-1]-ts[0]) - c0 = correlograms(ts,np.zeros(len(ts),dtype='int8'),cluster_ids=[0],bin_size=binSize/1000,sample_rate=20000,window_size=2,symmetrize=False) #compute acg - cumsumc0 = np.cumsum(c0[0,0,:]) #cumulative sum of acg, i.e. number of total spikes occuring from 0 to end of that bin - res = cumsumc0[bTestIdx] #cumulative sum at each of the testing bins - total_spike_count = len(ts) - - bin_count_normalized = c0[0,0]/total_spike_count/binSize*1000 #divide each bin's count by the unit's total spike count and the bin size - num_bins_2s = len(c0[0,0]) #number of total bins that equal 2 seconds - num_bins_1s = int(num_bins_2s/2) #number of bins that equal 1 second - fr = np.sum(bin_count_normalized[num_bins_1s:num_bins_2s])/num_bins_1s #compute fr based on the mean of bin_count_normalized from 1 to 2 s instead of as before (len(ts)/recDur) for a better estimate - mfunc =np.vectorize(max_acceptable_cont) - m = mfunc(fr,bTest,recDur,fr*acceptThresh,thresh) #compute the maximum allowed number of spikes per testing bin - didpass = int(np.any(np.less_equal(res,m))) #did the unit pass (resulting number of spikes less than maximum allowed spikes) at any of the testing bins? - else: - didpass=0 - - return didpass - - - -def noise_cutoff(amps,quartile_length=.2, nbins=100, end_low = 2): - if(len(amps)>1): - bins_list= np.linspace(0, np.max(amps), nbins) - n,bins = np.histogram(amps,bins = bins_list) - dx = np.diff(n) - idx_nz = np.nonzero(dx) #indices of nonzeros - length_nonzeros = idx_nz[0][-1]-idx_nz[0][0] #length of the entire stretch, from first nonzero to last nonzero - high_quartile = 1-quartile_length - idx_peak = np.argmax(n) - length_top_half = idx_nz[0][-1]-idx_peak - high_quartile = 1-(2*quartile_length) - - high_quartile_start_ind = int(np.ceil(high_quartile*length_top_half + idx_peak)) - xx=idx_nz[0][idx_nz[0]>high_quartile_start_ind] - if len(n[xx])>0: - mean_high_quartile = np.mean(n[xx]) - std_high_quartile = np.std(n[xx]) - first_low_quartile = np.mean(n[idx_nz[0][1:end_low]]) - if std_high_quartile>0: - cutoff=(first_low_quartile-mean_high_quartile)/std_high_quartile - else: - cutoff=np.float64(np.nan) - else: - cutoff=np.float64(np.nan) - else: - cutoff=np.float64(np.nan) - return cutoff - - -def peak_to_peak_amp(ephys_file, samp_inds, nsamps): - - # read raw ephys file - with spikeglx.Reader(ephys_file) as sr: - # take a subset (nsamps) of the spike samples - samples = np.random.choice(samp_inds,nsamps) - # initialize arrays - amps=np.zeros(len(samples)) - wfs=np.zeros((384,len(samples))) - wfs_baseline=np.zeros((384,len(samples))) - cnt=0 - for i in samples: - wf = sr.data[int(i)] - wf_baseline = wf[:-1]-np.median(wf[:-1]) # subtract median baseline - # plt.plot(wf_baseline) - wfs[:,cnt] = wf[:-1] - wfs_baseline[:,cnt] = wf_baseline - amps[cnt] = np.max(wf_baseline)-np.min(wf_baseline) - cnt+=1 - amps = np.max(wfs_baseline,axis=0)-np.min(wfs_baseline,axis=0) - mean_amp = np.mean(amps) - return mean_amp - - -def max_acceptable_cont(FR, RP, rec_duration,acceptableCont, thresh ): - trueContRate = np.linspace(0,FR,100) - timeForViol = RP*2*FR*rec_duration - - - expectedCountForAcceptableLimit = acceptableCont*timeForViol - - max_acceptable = stats.poisson.ppf(thresh,expectedCountForAcceptableLimit) - - if max_acceptable==0 and stats.poisson.pmf(0,expectedCountForAcceptableLimit)>0: - max_acceptable=-1 - - return max_acceptable - - -def genST(rate, duration): - ''' - For use in False Positive estimate (sliding refractory period violation metric) - Generates a spike train with specified rate and duration. Rate and - duration should have the same units. - ''' - - mu = 1/rate - n = rate*duration - isi = np.random.exponential(mu, int(np.ceil(n*2))) # generate twice as many spikes as likely required - - while sum(isi)>> p_vals_b, variances_b = bb.metrics.unit_stability(units_b) - # Plot histograms of variances color-coded by depth of channel of max amplitudes - >>> fig = bb.plot.feat_vars(units_b, feat_name='amps') - # Get all unit IDs which have amps variance > 50 - >>> var_vals = np.array(tuple(variances_b['amps'].values())) - >>> bad_units = np.where(var_vals > 50) - ''' - - # Get units. - if not(units is None): # we're using a subset of all units - unit_list = list(units_b[feat_names[0]].keys()) - # for each `feat` and unit in `unit_list`, remove unit from `units_b` if not in `units` - for feat in feat_names: - [units_b[feat].pop(unit) for unit in unit_list if not(int(unit) in units)] - unit_list = list(units_b[feat_names[0]].keys()) # get new `unit_list` after removing units - - # Initialize `p_vals` and `variances`. - p_vals_b = Bunch() - cv_b = Bunch() - - # Set the test as a lambda function (in future, more tests can be added to this dict) - tests = \ - { - 'ks': lambda x, y: stats.kstest(x, y) - } - test_fun = tests[test] - - # Compute the statistical tests and variances. For each feature, iteratively get each unit's - # p-values and variances, and add them as keys to the respective bunches `p_vals_feat` and - # `variances_feat`. After iterating through all units, add these bunches as keys to their - # respective parent bunches, `p_vals` and `variances`. - for feat in feat_names: - p_vals_feat = Bunch((unit, 0) for unit in unit_list) - cv_feat = Bunch((unit, 0) for unit in unit_list) - for unit in unit_list: - # If we're missing units/features, create a NaN placeholder and skip them: - if len(units_b['times'][str(unit)]) == 0: - p_val = np.nan - cv = np.nan - else: - # compute p_val and var for current feature - _, p_val = test_fun(units_b[feat][unit], dist) - cv = np.var(units_b[feat][unit]) / np.mean(units_b[feat][unit]) - # Append current unit's values to list of units' values for current feature: - p_vals_feat[str(unit)] = p_val - cv_feat[str(unit)] = cv - p_vals_b[feat] = p_vals_feat - cv_b[feat] = cv_feat - - return p_vals_b, cv_b - - -def feat_cutoff(feat, spks_per_bin=20, sigma=5, min_num_bins=50): - ''' - Computes the approximate fraction of spikes missing from a spike feature distribution for a - given unit, assuming the distribution is symmetric. - - Inspired by metric described in Hill et al. (2011) J Neurosci 31: 8699-8705. - - Parameters - ---------- - feat : ndarray - The spikes' feature values. - spks_per_bin : int (optional) - The number of spikes per bin from which to compute the spike feature histogram. - sigma : int (optional) - The standard deviation for the gaussian kernel used to compute the pdf from the spike - feature histogram. - min_num_bins : int (optional) - The minimum number of bins used to compute the spike feature histogram. - - Returns - ------- - fraction_missing : float - The fraction of missing spikes (0-0.5). *Note: If more than 50% of spikes are missing, an - accurate estimate isn't possible. - pdf : ndarray - The computed pdf of the spike feature histogram. - cutoff_idx : int - The index for `pdf` at which point `pdf` is no longer symmetrical around the peak. (This - is returned for plotting purposes). - - See Also - -------- - plot.feat_cutoff - - Examples - -------- - 1) Determine the fraction of spikes missing from unit 1 based on the recorded unit's spike - amplitudes, assuming the distribution of the unit's spike amplitudes is symmetric. - # Get unit 1 amplitudes from a unit bunch, and compute fraction spikes missing. - >>> feat = units_b['amps']['1'] - >>> fraction_missing = bb.plot.feat_cutoff(feat) - ''' - - # Ensure minimum number of spikes requirement is met. - error_str = 'The number of spikes in this unit is {0}, ' \ - 'but it must be at least {1}'.format(len(feat), spks_per_bin * min_num_bins) - assert (len(feat) > (spks_per_bin * min_num_bins)), error_str - - # compute the spike feature histogram and pdf: - num_bins = int(len(feat) / spks_per_bin) - hist, bins = np.histogram(feat, num_bins, density=True) - pdf = filters.gaussian_filter1d(hist, sigma) - - # Find where the distribution stops being symmetric around the peak: - peak_idx = np.argmax(pdf) - max_idx_sym_around_peak = np.argmin(np.abs(pdf[peak_idx:] - pdf[0])) - cutoff_idx = peak_idx + max_idx_sym_around_peak - - # compute fraction missing from the tail of the pdf (the area where pdf stops being - # symmetric around peak). - fraction_missing = np.sum(pdf[cutoff_idx:]) / np.sum(pdf) - fraction_missing = 0.5 if (fraction_missing > 0.5) else fraction_missing - - return fraction_missing, pdf, cutoff_idx - - -def wf_similarity(wf1, wf2): - ''' - Computes a unit normalized spatiotemporal similarity score between two sets of waveforms. - This score is based on how waveform shape correlates for each pair of spikes between the - two sets of waveforms across space and time. The shapes of the arrays of the two sets of - waveforms must be equal. - - Parameters - ---------- - wf1 : ndarray - An array of shape (#spikes, #samples, #channels). - wf2 : ndarray - An array of shape (#spikes, #samples, #channels). - - Returns - ------- - s: float - The unit normalized spatiotemporal similarity score. - - See Also - -------- - io.extract_waveforms - plot.single_unit_wf_comp - - Examples - -------- - 1) Compute the similarity between the first and last 100 waveforms for unit1, across the 20 - channels around the channel of max amplitude. - # Get the channels around the max amp channel for the unit, two sets of timestamps for the - # unit, and the two corresponding sets of waveforms for those two sets of timestamps. - # Then compute `s`. - >>> max_ch = clstrs_b['channels'][1] - >>> if max_ch < 10: # take only channels greater than `max_ch`. - >>> ch = np.arange(max_ch, max_ch + 20) - >>> elif (max_ch + 10) > 385: # take only channels less than `max_ch`. - >>> ch = np.arange(max_ch - 20, max_ch) - >>> else: # take `n_c_ch` around `max_ch`. - >>> ch = np.arange(max_ch - 10, max_ch + 10) - >>> ts1 = units_b['times']['1'][:100] - >>> ts2 = units_b['times']['1'][-100:] - >>> wf1 = bb.io.extract_waveforms(path_to_ephys_file, ts1, ch) - >>> wf2 = bb.io.extract_waveforms(path_to_ephys_file, ts2, ch) - >>> s = bb.metrics.wf_similarity(wf1, wf2) - - TODO check `s` calculation - ''' - - # Remove warning for dividing by 0 when calculating `s` (this is resolved by using - # `np.nan_to_num`) - import warnings - warnings.filterwarnings('ignore', r'invalid value encountered in true_divide') - assert wf1.shape == wf2.shape, ('The shapes of the sets of waveforms are inconsistent ({})' - '({})'.format(wf1.shape, wf2.shape)) - - # Get number of spikes, samples, and channels of waveforms. - n_spks = wf1.shape[0] - n_samples = wf1.shape[1] - n_ch = wf1.shape[2] - - # Create a matrix that will hold the similarity values of each spike in `wf1` to `wf2`. - # Iterate over both sets of spikes, computing `s` for each pair. - similarity_matrix = np.zeros((n_spks, n_spks)) - for spk1 in range(n_spks): - for spk2 in range(n_spks): - s_spk = \ - np.sum(np.nan_to_num( - wf1[spk1, :, :] * wf2[spk2, :, :] / - np.sqrt(wf1[spk1, :, :]**2 * wf2[spk2, :, :]**2))) / (n_samples * n_ch) - similarity_matrix[spk1, spk2] = s_spk - - # Return mean of similarity matrix - s = np.mean(similarity_matrix) - return s - - -def firing_rate_coeff_var(ts, hist_win=0.01, fr_win=0.5, n_bins=10): - ''' - Computes the coefficient of variation of the firing rate: the ratio of the standard - deviation to the mean. - - Parameters - ---------- - ts : ndarray - The spike timestamps from which to compute the firing rate. - hist_win : float - The time window (in s) to use for computing spike counts. - fr_win : float - The time window (in s) to use as a moving slider to compute the instantaneous firing rate. - n_bins : int (optional) - The number of bins in which to compute a coefficient of variation of the firing rate. - - Returns - ------- - cv : float - The mean coefficient of variation of the firing rate of the `n_bins` number of coefficients - computed. - cvs : ndarray - The coefficients of variation of the firing for each bin of `n_bins`. - fr : ndarray - The instantaneous firing rate over time (in hz). - - See Also - -------- - singlecell.firing_rate - plot.firing_rate - - Examples - -------- - 1) Compute the coefficient of variation of the firing rate for unit 1 from the time of its - first to last spike, and compute the coefficient of variation of the firing rate for unit 2 - from the first to second minute. - >>> ts_1 = units_b['times']['1'] - >>> ts_2 = units_b['times']['2'] - >>> ts_2 = np.intersect1d(np.where(ts_2 > 60)[0], np.where(ts_2 < 120)[0]) - >>> cv, cvs, fr = bb.metrics.firing_rate_coeff_var(ts_1) - >>> cv_2, cvs_2, fr_2 = bb.metrics.firing_rate_coeff_var(ts_2) - ''' - - # Compute overall instantaneous firing rate and firing rate for each bin. - fr = bb.singlecell.firing_rate(ts, hist_win=hist_win, fr_win=fr_win) - bin_sz = int(fr.size / n_bins) - fr_binned = np.array([fr[(b * bin_sz):(b * bin_sz + bin_sz)] for b in range(n_bins)]) - - # Compute coefficient of variations of firing rate for each bin, and the mean c.v. - cvs = np.std(fr_binned, axis=1) / np.mean(fr_binned, axis=1) - cv = np.mean(cvs) - - return cv, cvs, fr - - -def isi_viol(ts, rp=0.002): - ''' - Computes the fraction of isi violations for a unit. - - Parameters - ---------- - ts : ndarray - The spike timestamps from which to compute the firing rate. - rp : float - The refractory period (in s). - - Returns - ------- - frac_isi_viol : float - The fraction of isi violations. - n_isi_viol : int - The number of isi violations. - isis : ndarray - The isis. - - See Also - -------- - - Examples - -------- - 1) Get the fraction of isi violations, the total number of isi violations, and the array of - isis for unit 1. - >>> unit_idxs = np.where(spks_b['clusters'] == 1)[0] - >>> ts = spks_b['times'][unit_idxs] - >>> frac_isi_viol, n_isi_viol, isi = bb.metrics.isi_viol(ts) - ''' - - isis = np.diff(ts) - v = np.where(isis < rp)[0] # violations - frac_isi_viol = len(v) / len(ts) - return frac_isi_viol, len(v), isis - - -def max_drift(feat): - ''' - Computes the maximum drift (max - min) of a spike feature array. - - Parameters - ---------- - feat : ndarray - The spike feature values from which to compute the maximum drift. - - Returns - ------- - md : float - The maxmimum drift of the unit. - - See Also - -------- - cum_drift - - Examples - -------- - 1) Get the maximum depth and amp drift for unit 1. - >>> unit_idxs = np.where(spks_b['clusters'] == 1)[0] - >>> depths = spks_b['depths'][unit_idxs] - >>> amps = spks_b['amps'][unit_idxs] - >>> depth_md = bb.metrics.max_drift(depths) - >>> amp_md = bb.metrics.max_drift(amps) - ''' - - md = np.max(feat) - np.min(feat) - return md - - -def cum_drift(feat): - ''' - Computes the cumulative drift (normalized by the total number of spikes) of a spike feature - array. - - Parameters - ---------- - feat : ndarray - The spike feature values from which to compute the maximum drift. - - Returns - ------- - cd : float - The cumulative drift of the unit. - - See Also - -------- - max_drift - - Examples - -------- - 1) Get the cumulative depth drift for unit 1. - >>> unit_idxs = np.where(spks_b['clusters'] == 1)[0] - >>> depths = spks_b['depths'][unit_idxs] - >>> amps = spks_b['amps'][unit_idxs] - >>> depth_cd = bb.metrics.cum_drift(depths) - >>> amp_cd = bb.metrics.cum_drift(amps) - ''' - - cd = np.sum(np.abs(np.diff(feat))) / len(feat) - return cd - - -def pres_ratio(ts, hist_win=10): - ''' - Computes the presence ratio of spike counts: the number of bins where there is at least one - spike, over the total number of bins, given a specified bin width. - - Parameters - ---------- - ts : ndarray - The spike timestamps from which to compute the presence ratio. - hist_win : float - The time window (in s) to use for computing the presence ratio. - - Returns - ------- - pr : float - The presence ratio. - spks_bins : ndarray - The number of spks in each bin. - - See Also - -------- - plot.pres_ratio - - Examples - -------- - 1) Compute the presence ratio for unit 1, given a window of 10 s. - >>> ts = units_b['times']['1'] - >>> pr, pr_bins = bb.metrics.pres_ratio(ts) - ''' - - bins = np.arange(0, ts[-1] + hist_win, hist_win) - spks_bins, _ = np.histogram(ts, bins) - pr = len(np.where(spks_bins)[0]) / len(spks_bins) - return pr, spks_bins - - -def ptp_over_noise(ephys_file, ts, ch, t=2.0, sr=30000, n_ch_probe=385, dtype='int16', offset=0, - car=True): - ''' - For specified channels, for specified timestamps, computes the mean (peak-to-peak amplitudes / - the MADs of the background noise). - - Parameters - ---------- - ephys_file : string - The file path to the binary ephys data. - ts : ndarray_like - The timestamps (in s) of the spikes. - ch : ndarray_like - The channels on which to extract the waveforms. - t : numeric (optional) - The time (in ms) of the waveforms to extract to compute the ptp. - sr : int (optional) - The sampling rate (in hz) that the ephys data was acquired at. - n_ch_probe : int (optional) - The number of channels of the recording. - dtype: str (optional) - The datatype represented by the bytes in `ephys_file`. - offset: int (optional) - The offset (in bytes) from the start of `ephys_file`. - car: bool (optional) - A flag to perform common-average-referencing before extracting waveforms. - - Returns - ------- - ptp_sigma : ndarray - An array containing the mean ptp_over_noise values for the specified `ts` and `ch`. - - Examples - -------- - 1) Compute ptp_over_noise for all spikes on 20 channels around the channel of max amplitude - for unit 1. - >>> ts = units_b['times']['1'] - >>> max_ch = max_ch = clstrs_b['channels'][1] - >>> if max_ch < 10: # take only channels greater than `max_ch`. - >>> ch = np.arange(max_ch, max_ch + 20) - >>> elif (max_ch + 10) > 385: # take only channels less than `max_ch`. - >>> ch = np.arange(max_ch - 20, max_ch) - >>> else: # take `n_c_ch` around `max_ch`. - >>> ch = np.arange(max_ch - 10, max_ch + 10) - >>> p = bb.metrics.ptp_over_noise(ephys_file, ts, ch) - ''' - - # Ensure `ch` is ndarray - ch = np.asarray(ch) - ch = ch.reshape((ch.size, 1)) if ch.size == 1 else ch - - # Get waveforms. - wf = bb.io.extract_waveforms(ephys_file, ts, ch, t=t, sr=sr, n_ch_probe=n_ch_probe, - dtype=dtype, offset=offset, car=car) - - # Initialize `mean_ptp` based on `ch`, and compute mean ptp of all spikes for each ch. - mean_ptp = np.zeros((ch.size,)) - for cur_ch in range(ch.size,): - mean_ptp[cur_ch] = np.mean(np.max(wf[:, :, cur_ch], axis=1) - - np.min(wf[:, :, cur_ch], axis=1)) - - # Compute MAD for `ch` in chunks. - with spikeglx.Reader(ephys_file) as s_reader: - file_m = s_reader.data # the memmapped array - n_chunk_samples = 5e6 # number of samples per chunk - n_chunks = np.ceil(file_m.shape[0] / n_chunk_samples).astype('int') - # Get samples that make up each chunk. e.g. `chunk_sample[1] - chunk_sample[0]` are the - # samples that make up the first chunk. - chunk_sample = np.arange(0, file_m.shape[0], n_chunk_samples, dtype=int) - chunk_sample = np.append(chunk_sample, file_m.shape[0]) - # Give time estimate for computing MAD. - t0 = time.perf_counter() - stats.median_absolute_deviation(file_m[chunk_sample[0]:chunk_sample[1], ch], axis=0) - dt = time.perf_counter() - t0 - print('Performing MAD computation. Estimated time is {:.2f} mins.' - ' ({})'.format(dt * n_chunks / 60, time.ctime())) - # Compute MAD for each chunk, then take the median MAD of all chunks. - mad_chunks = np.zeros((n_chunks, ch.size), dtype=np.int16) - for chunk in range(n_chunks): - mad_chunks[chunk, :] = stats.median_absolute_deviation( - file_m[chunk_sample[chunk]:chunk_sample[chunk + 1], ch], axis=0, scale=1) - print('Done. ({})'.format(time.ctime())) - - # Return `mean_ptp` over `mad` - mad = np.median(mad_chunks, axis=0) - ptp_sigma = mean_ptp / mad - return ptp_sigma - - -def fp_est(ts, rp=0.002): - ''' - An estimate of the fraction of false positive spikes in a unit - (see Hill et al. (2011) J Neurosci 31: 8699-8705). - - Parameters - ---------- - ts : ndarray_like - The timestamps (in s) of the spikes. - rp : float - The refractory period (in s). - - Returns - ------- - fp : float - An estimate of the fraction of false positives. - - Examples - -------- - 1) Compute false positive estimate for unit 1. - >>> ts = units_b['times']['1'] - >>> fp = bb.metrics.fp_est(ts) - ''' - - # Get number of spikes, number of isi violations, and time from first to final spike. - n_spks = len(ts) - #bug in below line fixed (original had parenthesis error, returning incorrect n_isi_viol) - n_isi_viol = len(np.where(np.diff(ts) < rp)[0]) - t = ts[-1] - ts[0] - - # `fp` is min of roots of solved quadratic equation. - c = (t * n_isi_viol) / (2 * rp * n_spks**2) # 3rd term in quadratic - fp = np.min(np.abs(np.roots([-1, 1, c]))) # solve quadratic - return fp diff --git a/PythonScripts/iblapps-master/launch_phy/metrics.py b/PythonScripts/iblapps-master/launch_phy/metrics.py deleted file mode 100644 index 81335dfd..00000000 --- a/PythonScripts/iblapps-master/launch_phy/metrics.py +++ /dev/null @@ -1,310 +0,0 @@ -# -*- coding: utf-8 -*- -r""" -Created on Wed Apr 1 21:46:59 2020 - -@author: Noam Roth - -This code generates quality metrics for a session given an eid and probe name. -Then computes a label based on: - 1) refractor period violations - 2) amplitude distribution Cutoff - 3) mean amplitudes - -And adds these metrics and labels to a csv file to load in phy. - -""" - - -import time -import os -from oneibl.one import ONE -from pathlib import Path -import numpy as np -import alf.io as aio -import matplotlib.pyplot as plt -import brainbox as bb -from phylib.stats import correlograms -import pandas as pd -from defined_metrics import * - - - - - -def gen_metrics_labels(eid,probe_name): - one = ONE() - ses_path=one.path_from_eid(eid) - alf_probe_dir = os.path.join(ses_path, 'alf', probe_name) - ks_dir = alf_probe_dir - spks_b = aio.load_object(alf_probe_dir, 'spikes') - units_b = bb.processing.get_units_bunch(spks_b) - units = list(units_b.amps.keys()) - lengths_samples = [len(v) for k, v in units_b.samples.items()] - units_nonzeros=[i for i,d in enumerate(lengths_samples) if d>0] - n_units = len(units_nonzeros) #only compute metrics for units with no samples - - - #for cases where raw data is available locally: - ephys_file_dir = os.path.join(ses_path, 'raw_ephys_data', probe_name) - ephys_file = os.path.join(ses_path, 'raw_ephys_data', probe_name,'_iblrig_ephysData.raw_g0_t0.imec.ap.cbin') - #create params.py file - params_file = os.path.join(ks_dir,'params.py') - if os.path.exists(ephys_file) and not os.path.exists(params_file): - f = open(params_file,"w+") - f.write('dat_path = ' + 'r"' + ephys_file + '"\n' + '''n_channels_dat = 385 - dtype = 'int16' - offset = 0 - sample_rate = 30000 - hp_filtered = False - uidx=0''' ) - f.close() - - # Initialize metrics - cum_amp_drift = np.full((n_units,), np.nan) - cum_depth_drift = np.full((n_units,), np.nan) - cv_amp = np.full((n_units,), np.nan) - cv_fr = np.full((n_units,), np.nan) - frac_isi_viol = np.full((n_units,), np.nan) - frac_missing_spks = np.full((n_units,), np.nan) - fp_estimate = np.full((n_units,), np.nan) - presence_ratio = np.full((n_units,), np.nan) - pres_ratio_std = np.full((n_units,), np.nan) - ptp_sigma = np.full((n_units,), np.nan) - - units_missing_metrics = set() - label=np.empty([len(units)]) - RefPViol = np.empty([len(units)]) - NoiseCutoff = np.empty([len(units)]) - MeanAmpTrue = np.empty([len(units)]) - - for idx,unit in enumerate(units_nonzeros): - if unit == units_nonzeros[0]: - t0 = time.perf_counter() # used for computation time estimate - - print('computing metrics for unit ' + str(unit) + '...' ) - - #load relevant data for unit - ts = units_b['times'][str(unit)] - amps = units_b['amps'][str(unit)] - samples = units_b['samples'][str(unit)] - depths = units_b['depths'][str(unit)] - - - RefPViol[idx] = FP_RP(ts) - NoiseCutoff[idx] = noise_cutoff(amps,quartile_length=.25) - - #create 'label' based on RPviol,NoiseCutoff, and MeanAmp - if len(samples>50): #only compute mean amplitude for units with more than 50 samples - try: - MeanAmpTrue[int(unit)] = peak_to_peak_amp(ephys_file, samples, nsamps=20) - - if (FP_RP(ts) and noise_cutoff(amps,quartile_length=.25)<20 and MeanAmpTrue[int(unit)]>50) : - label[idx] = 1 - else: - label[idx] = 0 - except: - if (FP_RP(ts) and noise_cutoff(amps,quartile_length=.25)<20) : - label[idx] = 1 - else: - label[idx] = 0 - - else: #no ephys file, do not include true mean amps - if (FP_RP(ts) and noise_cutoff(amps,quartile_length=.25)<20) : - label[idx] = 1 - else: - label[idx] = 0 - - - - #now compute additional metrics that label does not depend on: - - # Cumulative drift of spike amplitudes, normalized by total number of spikes. - try: - cum_amp_drift[idx] = cum_drift(amps) - except Exception as err: - print("Failed to compute 'cum_drift(amps)' for unit {}. Details: \n {}" - .format(unit, err)) - units_missing_metrics.add(unit) - - # Cumulative drift of spike depths, normalized by total number of spikes. - try: - cum_depth_drift[idx] = cum_drift(depths) - except Exception as err: - print("Failed to compute 'cum_drift(depths)' for unit {}. Details: \n {}" - .format(unit, err)) - units_missing_metrics.add(unit) - - # Coefficient of variation of spike amplitudes. - try: - cv_amp[idx] = np.std(amps) / np.mean(amps) - except Exception as err: - print("Failed to compute 'cv_amp' for unit {}. Details: \n {}".format(unit, err)) - units_missing_metrics.add(unit) - - # Coefficient of variation of computed instantaneous firing rate. - try: - fr = bb.singlecell.firing_rate(ts, hist_win=0.01, fr_win=0.25) - cv_fr[idx] = np.std(fr) / np.mean(fr) - except Exception as err: - print("Failed to compute 'cv_fr' for unit {}. Details: \n {}".format(unit, err)) - units_missing_metrics.add(unit) - - # Fraction of isi violations. - try: - frac_isi_viol[idx], _, _ = isi_viol(ts, rp=0.002) - except Exception as err: - print("Failed to compute 'frac_isi_viol' for unit {}. Details: \n {}" - .format(unit, err)) - units_missing_metrics.add(unit) - - # Estimated fraction of missing spikes. - try: - frac_missing_spks[idx], _, _ = feat_cutoff( - amps, spks_per_bin=10, sigma=4, min_num_bins=50) - except Exception as err: - print("Failed to compute 'frac_missing_spks' for unit {}. Details: \n {}" - .format(unit, err)) - units_missing_metrics.add(unit) - - # Estimated fraction of false positives. - try: - fp_estimate[idx] = fp_est(ts, rp=0.002) - except Exception as err: - print("Failed to compute 'fp_est' for unit {}. Details: \n {}".format(unit, err)) - units_missing_metrics.add(unit) - - # Presence ratio - try: - presence_ratio[idx], _ = pres_ratio(ts, hist_win=10) - except Exception as err: - print("Failed to compute 'pres_ratio' for unit {}. Details: \n {}".format(unit, err)) - units_missing_metrics.add(unit) - - # Presence ratio over the standard deviation of spike counts in each bin - try: - pr, pr_bins = pres_ratio(ts, hist_win=10) - pres_ratio_std[idx] = pr / np.std(pr_bins) - except Exception as err: - print("Failed to compute 'pres_ratio_std' for unit {}. Details: \n {}" - .format(unit, err)) - units_missing_metrics.add(unit) - - - #append metrics to the current clusters.metrics - metrics_read = pd.read_csv(Path(alf_probe_dir,'clusters.metrics.csv')) - - if not 'label' in metrics_read.columns: - try: - label_df = pd.DataFrame(label) - pd.DataFrame.insert(metrics_read,1,'label',label_df) - except ValueError: - pd.DataFrame.drop(metrics_read,columns = 'label') - pd.DataFrame.insert(metrics_read,1,'label',label_df) - except: - print("Could not save 'label' to .csv.") - - try: - df_cum_amp_drift = pd.DataFrame(cum_amp_drift.round(2)) - metrics_read['cum_amp_drift'] = df_cum_amp_drift - except Exception as err: - print("Could not save 'cum_amp_drift' to .csv. Details: \n {}".format(err)) - - try: - df_cum_depth_drift = pd.DataFrame(cum_depth_drift.round(2)) - metrics_read['cum_depth_drift'] = df_cum_depth_drift - except Exception as err: - print("Could not save 'cum_depth_drift' to .tsv. Details: \n {}".format(err)) - - try: - df_cv_amp = pd.DataFrame(cv_amp.round(2)) - metrics_read['cv_amp'] = df_cv_amp - except Exception as err: - print("Could not save 'cv_amp' to .tsv. Details: \n {}".format(err)) - - try: - df_cv_fr = pd.DataFrame(cv_fr.round(2)) - metrics_read['cv_fr'] = df_cv_fr - except Exception as err: - print("Could not save 'cv_fr' to .tsv. Details: \n {}".format(err)) - - try: - df_frac_isi_viol = pd.DataFrame(frac_isi_viol.round(2)) - metrics_read['frac_isi_viol'] = df_frac_isi_viol - except Exception as err: - print("Could not save 'frac_isi_viol' to .tsv. Details: \n {}".format(err)) - - try: - df_frac_missing_spks = pd.DataFrame(frac_missing_spks.round(2)) - metrics_read['frac_missing_spks'] = df_frac_missing_spks - except Exception as err: - print("Could not save 'frac_missing_spks' to .tsv. Details: \n {}".format(err)) - - try: - df_fp_est = pd.DataFrame(fp_estimate.round(2)) - metrics_read['fp_est'] = df_fp_est - except Exception as err: - print("Could not save 'fp_est' to .tsv. Details: \n {}".format(err)) - - try: - df_pres_ratio = pd.DataFrame(presence_ratio.round(2)) - metrics_read['pres_ratio'] = df_pres_ratio - except Exception as err: - print("Could not save 'pres_ratio' to .tsv. Details: \n {}".format(err)) - - try: - df_pres_ratio_std = pd.DataFrame(pres_ratio_std.round(2)) - metrics_read['pres_ratio_std'] = df_pres_ratio_std - except Exception as err: - print("Could not save 'pres_ratio_std' to .tsv. Details: \n {}".format(err)) - - - try: - df_refp_viol = pd.DataFrame(RefPViol) - pd.DataFrame.insert(metrics_read,2,'refp_viol',df_refp_viol) - except ValueError: - pd.DataFrame.drop(metrics_read,columns = 'refp_viol') - pd.DataFrame.insert(metrics_read,2,'refp_viol', df_refp_viol) - except Exception as err: - print("Could not save 'RefPViol' to .tsv. Details: \n {}".format(err)) - - try: - df_noise_cutoff = pd.DataFrame(NoiseCutoff) - pd.DataFrame.insert(metrics_read,3,'noise_cutoff',df_noise_cutoff) - except ValueError: - pd.DataFrame.drop(metrics_read,columns = 'noise_cutoff') - pd.DataFrame.insert(metrics_read,3,'noise_cutoff',df_noise_cutoff) - except Exception as err: - print("Could not save 'NoiseCutoff' to .tsv. Details: \n {}".format(err)) - - try: - df_mean_amp_true = pd.DataFrame(MeanAmpTrue) - pd.DataFrame.insert(metrics_read,4,'mean_amp_true',df_mean_amp_true) - except ValueError: - pd.DataFrame.drop(metrics_read,columns = 'mean_amp_true') - pd.DataFrame.insert(metrics_read,4,'mean_amp_true',df_mean_amp_true) - except Exception as err: - print("Could not save 'Mean Amp True' to .tsv. Details: \n {}".format(err)) - - - - #now add df to csv - metrics_read.to_csv(Path(alf_probe_dir,'clusters.metrics.csv')) - print('Launching phy') - else: - print('Launching phy') - - try: - numpass=int(sum(label)) - print("\n Number of units that pass: ", numpass) - - numpassRP=int(sum(RefPViol)) - numpassAC=int(sum(NoiseCutoff[~np.isnan(NoiseCutoff)]<2.5)) - ntot = len(label) - - print("Number of units that pass RP threshold: ", numpassRP) - print("Number of units that pass Amp Cutoff threshold: ", numpassAC) - print("Number of total units: ", ntot) - except Exception as err: - print ("Could not compute number of units that pass. Details \n {}".format(err)) - - return metrics_read diff --git a/PythonScripts/iblapps-master/launch_phy/phy_launcher.py b/PythonScripts/iblapps-master/launch_phy/phy_launcher.py deleted file mode 100644 index d0925c06..00000000 --- a/PythonScripts/iblapps-master/launch_phy/phy_launcher.py +++ /dev/null @@ -1,122 +0,0 @@ -import glob -import logging -import os -import pandas as pd - -from phy.apps.template import TemplateController, template_gui -from phy.gui.qt import create_app, run_app -from phylib import add_default_handler -from one.api import ONE -from brainbox.metrics.single_units import quick_unit_metrics -from pathlib import Path -from brainbox.io.one import SpikeSortingLoader - - -def launch_phy(probe_name=None, eid=None, pid=None, subj=None, date=None, sess_no=None, one=None): - """ - Launch phy given an eid and probe name. - """ - - # This is a first draft, no error handling and a draft dataset list. - - # Load data from probe # - # -------------------- # - - from one.api import ONE - from ibllib.atlas import AllenAtlas - from brainbox.io.one import SpikeSortingLoader - from ibllib.io import spikeglx - one = ONE() - ba = AllenAtlas() - - datasets = [ - 'spikes.times', - 'spikes.clusters', - 'spikes.amps', - 'spikes.templates', - 'spikes.samples', - 'spikes.depths', - 'clusters.uuids', - 'clusters.metrics', - 'clusters.waveforms', - 'clusters.waveformsChannels', - 'clusters.depths', - 'clusters.amps', - 'clusters.channels'] - - if pid is None: - ssl = SpikeSortingLoader(eid=eid, pname=probe_name, one=one, atlas=ba) - else: - ssl = SpikeSortingLoader(pid=pid, one=one, atlas=ba) - ssl.download_spike_sorting(dataset_types=datasets) - ssl.download_spike_sorting_object('templates') - ssl.download_spike_sorting_object('spikes_subset') - - alf_dir = ssl.session_path.joinpath(ssl.collection) - - if not alf_dir.joinpath('clusters.metrics.pqt').exists(): - spikes, clusters, channels = ssl.load_spike_sorting() - ssl.merge_clusters(spikes, clusters, channels, cache_dir=alf_dir) - - raw_file = next(ssl.session_path.joinpath('raw_ephys_folder', ssl.pname).glob('*.ap.*bin'), None) - - if raw_file is not None: - sr = spikeglx.Reader(raw_file) - sample_rate = sr.fs - n_channel_dat = sr.nc - sr.nsync - else: - sample_rate = 30000 - n_channel_dat = 384 - - # Launch phy # - # -------------------- # - add_default_handler('DEBUG', logging.getLogger("phy")) - add_default_handler('DEBUG', logging.getLogger("phylib")) - create_app() - controller = TemplateController(dat_path=raw_file, dir_path=alf_dir, dtype=np.int16, - n_channels_dat=n_channel_dat, sample_rate=sample_rate, - plugins=['IBLMetricsPlugin'], - plugin_dirs=[Path(__file__).resolve().parent / 'plugins']) - gui = controller.create_gui() - gui.show() - run_app() - gui.close() - controller.model.close() - - -if __name__ == '__main__': - """ - `python int-brain-lab\iblapps\launch_phy\phy_launcher.py -e a3df91c8-52a6-4afa-957b-3479a7d0897c -p probe00` - `python int-brain-lab\iblapps\launch_phy\phy_launcher.py -pid c07d13ed-e387-4457-8e33-1d16aed3fd92` - """ - from argparse import ArgumentParser - import numpy as np - - parser = ArgumentParser() - parser.add_argument('-s', '--subject', default=False, required=False, - help='Subject Name') - parser.add_argument('-d', '--date', default=False, required=False, - help='Date of session YYYY-MM-DD') - parser.add_argument('-n', '--session_no', default=1, required=False, - help='Session Number', type=int) - parser.add_argument('-e', '--eid', default=False, required=False, - help='Session eid') - parser.add_argument('-p', '--probe_label', default=False, required=False, - help='Probe Label') - parser.add_argument('-pid', '--pid', default=False, required=False, - help='Probe ID') - - args = parser.parse_args() - if args.eid: - launch_phy(probe_name=str(args.probe_label), eid=str(args.eid)) - elif args.pid: - launch_phy(pid=str(args.pid)) - else: - if not np.all(np.array([args.subject, args.date, args.session_no], - dtype=object)): - print('Must give Subject, Date and Session number') - else: - launch_phy(probe_name=str(args.probe_label), subj=str(args.subject), - date=str(args.date), sess_no=args.session_no) - # launch_phy('probe00', subj='KS022', - # date='2019-12-10', sess_no=1) diff --git a/PythonScripts/iblapps-master/launch_phy/plugins/phy_plugin.py b/PythonScripts/iblapps-master/launch_phy/plugins/phy_plugin.py deleted file mode 100644 index 3985d78d..00000000 --- a/PythonScripts/iblapps-master/launch_phy/plugins/phy_plugin.py +++ /dev/null @@ -1,96 +0,0 @@ -# import from plugins/cluster_metrics.py -"""Show how to add a custom cluster metrics.""" - -import logging -import numpy as np -from phy import IPlugin -import pandas as pd -from pathlib import Path -from brainbox.metrics.single_units import quick_unit_metrics - - -class IBLMetricsPlugin(IPlugin): - def attach_to_controller(self, controller): - """Note that this function is called at initialization time, *before* the supervisor is - created. The `controller.cluster_metrics` items are then passed to the supervisor when - constructing it.""" - - clusters_file = Path(controller.dir_path.joinpath('clusters.metrics.pqt')) - if clusters_file.exists(): - self.metrics = pd.read_parquet(clusters_file) - else: - self.metrics = None - return - - def amplitudes(cluster_id): - amps = controller.get_cluster_amplitude(cluster_id) - return amps * 1e6 - - def amp_max(cluster_id): - return self.metrics['amp_max'].iloc[cluster_id] * 1e6 - - def amp_min(cluster_id): - return self.metrics['amp_min'].iloc[cluster_id] * 1e6 - - def amp_median(cluster_id): - return self.metrics['amp_median'].iloc[cluster_id] * 1e6 - - def amp_std_dB(cluster_id): - return self.metrics['amp_std_dB'].iloc[cluster_id] - - def contamination(cluster_id): - return self.metrics['contamination'].iloc[cluster_id] - - def contamination_alt(cluster_id): - return self.metrics['contamination_alt'].iloc[cluster_id] - - def drift(cluster_id): - return self.metrics['drift'].iloc[cluster_id] - - def missed_spikes_est(cluster_id): - return self.metrics['missed_spikes_est'].iloc[cluster_id] - - def noise_cutoff(cluster_id): - return self.metrics['noise_cutoff'].iloc[cluster_id] - - def presence_ratio(cluster_id): - return self.metrics['presence_ratio'].iloc[cluster_id] - - def presence_ratio_std(cluster_id): - return self.metrics['presence_ratio_std'].iloc[cluster_id] - - def slidingRP_viol(cluster_id): - return self.metrics['slidingRP_viol'].iloc[cluster_id] - - def spike_count(cluster_id): - return self.metrics['spike_count'].iloc[cluster_id] - - def firing_rate(cluster_id): - return self.metrics['firing_rate'].iloc[cluster_id] - - def label(cluster_id): - return self.metrics['label'].iloc[cluster_id] - - def ks2_label(cluster_id): - if 'ks2_label' in self.metrics.columns: - return self.metrics['ks2_label'].iloc[cluster_id] - else: - return 'nan' - - controller.cluster_metrics['amplitudes'] = controller.context.memcache(amplitudes) - controller.cluster_metrics['amp_max'] = controller.context.memcache(amp_max) - controller.cluster_metrics['amp_min'] = controller.context.memcache(amp_min) - controller.cluster_metrics['amp_median'] = controller.context.memcache(amp_median) - controller.cluster_metrics['amp_std_dB'] = controller.context.memcache(amp_std_dB) - controller.cluster_metrics['contamination'] = controller.context.memcache(contamination) - controller.cluster_metrics['contamination_alt'] = controller.context.memcache(contamination_alt) - controller.cluster_metrics['drift'] = controller.context.memcache(drift) - controller.cluster_metrics['missed_spikes_est'] = controller.context.memcache(missed_spikes_est) - controller.cluster_metrics['noise_cutoff'] = controller.context.memcache(noise_cutoff) - controller.cluster_metrics['presence_ratio'] = controller.context.memcache(presence_ratio) - controller.cluster_metrics['presence_ratio_std'] = controller.context.memcache(presence_ratio_std) - controller.cluster_metrics['slidingRP_viol'] = controller.context.memcache(slidingRP_viol) - controller.cluster_metrics['spike_count'] = controller.context.memcache(spike_count) - controller.cluster_metrics['firing_rate'] = controller.context.memcache(firing_rate) - controller.cluster_metrics['label'] = controller.context.memcache(label) - controller.cluster_metrics['ks2_label'] = controller.context.memcache(ks2_label) diff --git a/PythonScripts/iblapps-master/launch_phy/populate_cluster_table.py b/PythonScripts/iblapps-master/launch_phy/populate_cluster_table.py deleted file mode 100644 index 5177ed2f..00000000 --- a/PythonScripts/iblapps-master/launch_phy/populate_cluster_table.py +++ /dev/null @@ -1,207 +0,0 @@ -import sys -from pathlib import Path -import uuid -from datetime import datetime - -from tqdm import tqdm -from one import alf -import pandas as pd -from one.api import ONE -from one import params - -from launch_phy import cluster_table - - -def populate_dj_with_phy(probe_label, eid=None, subj=None, date=None, - sess_no=None, one=None): - if one is None: - one = ONE() - - if eid is None: - eid = one.search(subject=subj, date=date, number=sess_no)[0] - - sess_path = one.eid2path(eid) - - cols = one.list_collections(eid) - if f'alf/{probe_label}/pykilosort' in cols: - collection = f'alf/{probe_label}/pykilosort' - else: - collection = f'alf/{probe_label}' - - - alf_path = sess_path.joinpath(collection) - - cluster_path = Path(alf_path, 'spikes.clusters.npy') - template_path = Path(alf_path, 'spikes.templates.npy') - - # Compare spikes.clusters with spikes.templates to find which clusters have been merged - phy_clusters = np.load(cluster_path) - id_phy = np.unique(phy_clusters) - orig_clusters = np.load(template_path) - id_orig = np.unique(orig_clusters) - - uuid_list = alf.io.load_file_content(alf_path.joinpath('clusters.uuids.csv')) - - # First deal with merged clusters and make sure they have cluster uuids assigned - # Find the original cluster ids that have been merged into a new cluster - merged_idx = np.setdiff1d(id_orig, id_phy) - - # See if any clusters have been merged, if not skip to the next bit - if np.any(merged_idx): - # Make association between original cluster and new cluster id and save in dict - merge_list = {} - for m in merged_idx: - idx = phy_clusters[np.where(orig_clusters == m)[0][0]] - if idx in merge_list: - merge_list[idx].append(m) - else: - merge_list[idx] = [m] - - # Create a dataframe from the dict - merge_clust = pd.DataFrame(columns={'cluster_idx', 'merged_uuid', 'merged_id'}) - for key, value in merge_list.items(): - value_uuid = uuid_list['uuids'][value] - merge_clust = merge_clust.append({'cluster_idx': key, 'merged_uuid': tuple(value_uuid), - 'merged_idx': tuple(value)}, - ignore_index=True) - - # Get the dj table that has previously stored merged clusters and store in frame - merge = cluster_table.MergedClusters() - merge_dj = pd.DataFrame(columns={'cluster_uuid', 'merged_uuid'}) - merge_dj['cluster_uuid'] = merge.fetch('cluster_uuid').astype(str) - merge_dj['merged_uuid'] = tuple(map(tuple, merge.fetch('merged_uuid'))) - - # Merge the two dataframe to see if any merge combinations already have a cluster_uuid - merge_comb = pd.merge(merge_dj, merge_clust, on=['merged_uuid'], how='outer') - - # Find the merged clusters that do not have a uuid assigned - no_uuid = np.where(pd.isnull(merge_comb['cluster_uuid']))[0] - - # Assign new uuid to new merge pairs and add to the merge table - for nid in no_uuid: - new_uuid = str(uuid.uuid4()) - merge_comb['cluster_uuid'].iloc[nid] = new_uuid - merge.insert1( - dict(cluster_uuid=new_uuid, merged_uuid=merge_comb['merged_uuid'].iloc[nid]), - allow_direct_insert=True) - - # Add all the uuids to the cluster_uuid frame with index according to cluster id from phy - for idx, c_uuid in zip(merge_comb['cluster_idx'].values, - merge_comb['cluster_uuid'].values): - uuid_list.loc[idx] = c_uuid - - csv_path = Path(alf_path, 'merge_info.csv') - merge_comb = merge_comb.reindex(columns=['cluster_idx', 'cluster_uuid', 'merged_idx', - 'merged_uuid']) - - try: - merge_comb.to_csv(csv_path, index=False) - except Exception as err: - print(err) - print('Close merge_info.csv file and then relaunch script') - sys.exit(1) - else: - print('No merges detected, continuing...') - - # Now populate datajoint with cluster labels - user = params.get().ALYX_LOGIN - current_date = datetime.now().replace(microsecond=0) - - try: - cluster_group = alf.io.load_file_content(alf_path.joinpath('cluster_group.tsv')) - except Exception as err: - print(err) - print('Could not find cluster group file output from phy') - sys.exit(1) - - try: - cluster_notes = alf.io.load_file_content(alf_path.joinpath('cluster_notes.tsv')) - cluster_info = pd.merge(cluster_group, cluster_notes, on=['cluster_id'], how='outer') - except Exception as err: - cluster_info = cluster_group - cluster_info['notes'] = None - - cluster_info = cluster_info.where(cluster_info.notnull(), None) - cluster_info['cluster_uuid'] = uuid_list['uuids'][cluster_info['cluster_id']].values - - # dj table that holds data - cluster = cluster_table.ClusterLabel() - - # Find clusters that have already been labelled by user - old_clust = cluster & cluster_info & {'user_name': user} - - dj_clust = pd.DataFrame() - dj_clust['cluster_uuid'] = (old_clust.fetch('cluster_uuid')).astype(str) - dj_clust['cluster_label'] = old_clust.fetch('cluster_label') - - # First find the new clusters to insert into datajoint - idx_new = np.where(np.isin(cluster_info['cluster_uuid'], - dj_clust['cluster_uuid'], invert=True))[0] - cluster_uuid = cluster_info['cluster_uuid'][idx_new].values - cluster_label = cluster_info['group'][idx_new].values - cluster_note = cluster_info['notes'][idx_new].values - - if idx_new.size != 0: - print('Populating dj with ' + str(idx_new.size) + ' new labels') - else: - print('No new labels to add') - for iClust, iLabel, iNote in zip(tqdm(cluster_uuid), cluster_label, cluster_note): - cluster.insert1(dict(cluster_uuid=iClust, user_name=user, - label_time=current_date, - cluster_label=iLabel, - cluster_note=iNote), - allow_direct_insert=True) - - # Next look through clusters already on datajoint and check if any labels have - # been changed - comp_clust = pd.merge(cluster_info, dj_clust, on='cluster_uuid') - idx_change = np.where(comp_clust['group'] != comp_clust['cluster_label'])[0] - - cluster_uuid = comp_clust['cluster_uuid'][idx_change].values - cluster_label = comp_clust['group'][idx_change].values - cluster_note = comp_clust['notes'][idx_change].values - - # Populate table - if idx_change.size != 0: - print('Replacing label of ' + str(idx_change.size) + ' clusters') - else: - print('No labels to change') - for iClust, iLabel, iNote in zip(tqdm(cluster_uuid), cluster_label, cluster_note): - prev_clust = cluster & {'user_name': user} & {'cluster_uuid': iClust} - cluster.insert1(dict(*prev_clust.proj(), - label_time=current_date, - cluster_label=iLabel, - cluster_note=iNote), - allow_direct_insert=True, replace=True) - - print('Upload to datajoint complete') - - -if __name__ == '__main__': - from argparse import ArgumentParser - import numpy as np - - parser = ArgumentParser() - parser.add_argument('-s', '--subject', default=False, required=False, - help='Subject Name') - parser.add_argument('-d', '--date', default=False, required=False, - help='Date of session YYYY-MM-DD') - parser.add_argument('-n', '--session_no', default=1, required=False, - help='Session Number', type=int) - parser.add_argument('-e', '--eid', default=False, required=False, - help='Session eid') - parser.add_argument('-p', '--probe_label', default=False, required=True, - help='Probe Label') - args = parser.parse_args() - - if args.eid: - populate_dj_with_phy(str(args.probe_label), eid=str(args.eid)) - else: - if not np.all(np.array([args.subject, args.date, args.session_no], - dtype=object)): - print('Must give Subject, Date and Session number') - else: - populate_dj_with_phy(str(args.probe_label), subj=str(args.subject), - date=str(args.date), sess_no=args.session_no) - - #populate_dj_with_phy('probe00', subj='KS022', date='2019-12-10', sess_no=1) diff --git a/PythonScripts/iblapps-master/needles2/coverageUI.ui b/PythonScripts/iblapps-master/needles2/coverageUI.ui deleted file mode 100644 index 3bc2bbac..00000000 --- a/PythonScripts/iblapps-master/needles2/coverageUI.ui +++ /dev/null @@ -1,398 +0,0 @@ - - - Form - - - - 0 - 0 - 320 - 255 - - - - - 0 - 0 - - - - Form - - - - - 10 - 10 - 291 - 171 - - - - - 75 - true - - - - QFrame::StyledPanel - - - QFrame::Raised - - - - - 10 - 10 - 40 - 30 - - - - ml: - - - - - - 10 - 40 - 40 - 30 - - - - ap: - - - - - - 10 - 70 - 40 - 30 - - - - depth: - - - - - - 10 - 100 - 40 - 30 - - - - theta: - - - - - - 10 - 130 - 30 - 30 - - - - phi: - - - - - - 220 - 15 - 60 - 20 - - - - - 50 - false - - - - - - - 220 - 45 - 60 - 20 - - - - - 50 - false - - - - - - - 220 - 75 - 60 - 20 - - - - - 50 - false - - - - - - - 220 - 105 - 60 - 20 - - - - - 50 - false - - - - - - - 220 - 135 - 60 - 20 - - - - - 50 - false - - - - - - - 140 - 45 - 30 - 20 - - - - + - - - - - - 140 - 75 - 30 - 20 - - - - + - - - - - - 140 - 15 - 30 - 20 - - - - + - - - - - - 140 - 105 - 30 - 20 - - - - + - - - - - - 140 - 135 - 30 - 20 - - - - + - - - - - - 180 - 15 - 30 - 20 - - - - - - - - - - - 180 - 45 - 30 - 20 - - - - - - - - - - - 180 - 75 - 30 - 20 - - - - - - - - - - - 180 - 105 - 30 - 20 - - - - - - - - - - - 180 - 135 - 30 - 20 - - - - - - - - - - - 50 - 15 - 61 - 20 - - - - - - - 50 - 45 - 61 - 20 - - - - - - - 50 - 75 - 61 - 20 - - - - - - - 50 - 105 - 61 - 20 - - - - - - - 50 - 135 - 61 - 20 - - - - - - - - 10 - 180 - 75 - 23 - - - - Add - - - - - - 20 - 210 - 271 - 30 - - - - Coverage: - - - - - - diff --git a/PythonScripts/iblapps-master/needles2/layerUI.ui b/PythonScripts/iblapps-master/needles2/layerUI.ui deleted file mode 100644 index a13f18bd..00000000 --- a/PythonScripts/iblapps-master/needles2/layerUI.ui +++ /dev/null @@ -1,29 +0,0 @@ - - - Form - - - - 0 - 0 - 400 - 82 - - - - Form - - - - - 20 - 20 - 341 - 41 - - - - - - - diff --git a/PythonScripts/iblapps-master/needles2/mainUI.ui b/PythonScripts/iblapps-master/needles2/mainUI.ui deleted file mode 100644 index b1aa8a82..00000000 --- a/PythonScripts/iblapps-master/needles2/mainUI.ui +++ /dev/null @@ -1,170 +0,0 @@ - - - MainWindow - - - - 0 - 0 - 1896 - 917 - - - - - 0 - 0 - - - - MainWindow - - - - - - 350 - 10 - 500 - 400 - - - - - - - 10 - 10 - 321 - 230 - - - - - - - 10 - 250 - 321 - 331 - - - - - - - 350 - 450 - 500 - 400 - - - - - - - 880 - 450 - 500 - 400 - - - - - - - 880 - 10 - 500 - 400 - - - - - - - 1400 - 10 - 101 - 841 - - - - - - - 1530 - 60 - 351 - 781 - - - - 50 - - - - - - 10 - 610 - 321 - 71 - - - - - - - 1530 - 20 - 121 - 23 - - - - - - - - - - - - 0 - 0 - 1896 - 21 - - - - - - - true - - - true - - - Image - - - - - true - - - Annotation - - - - - - PlotWidget - QGraphicsView -
pyqtgraph
- - - - - diff --git a/PythonScripts/iblapps-master/needles2/needles_viewer.py b/PythonScripts/iblapps-master/needles2/needles_viewer.py deleted file mode 100644 index 8e3fab25..00000000 --- a/PythonScripts/iblapps-master/needles2/needles_viewer.py +++ /dev/null @@ -1,87 +0,0 @@ -from iblviewer.atlas_controller import AtlasController -import vedo -from iblviewer.atlas_model import AtlasModel, AtlasUIModel, CameraModel -from iblviewer.slicer_model import SlicerModel - -from iblviewer.atlas_view import AtlasView -from iblviewer.volume_view import VolumeView -from iblviewer.slicer_view import SlicerView -import iblviewer.utils as utils - -from ipyvtk_simple.viewer import ViewInteractiveWidget - - -class NeedlesViewer(AtlasController): - def __init__(self): - super(NeedlesViewer, self).__init__() - - def initialize(self, plot, resolution=25, mapping='Allen', volume_mode=None, num_windows=1, render=False): - vedo.settings.allowInteraction = False - self.plot = plot - self.plot_window_id = 0 - self.model = AtlasModel() - self.model.initialize(resolution) - self.model.load_allen_volume(mapping, volume_mode) - self.model.initialize_slicers() - - self.view = AtlasView(self.plot, self.model) - self.view.initialize() - self.view.volume = VolumeView(self.plot, self.model.volume, self.model) - - #pn = SlicerModel.NAME_XYZ_POSITIVE - nn = SlicerModel.NAME_XYZ_NEGATIVE - - #pxs_model = self.model.find_model(pn[0], self.model.slicers) - #self.px_slicer = SlicerView(self.plot, self.view.volume, pxs_model, self.model) - #pys_model = self.model.find_model(pn[1], self.model.slicers) - #self.py_slicer = SlicerView(self.plot, self.view.volume, pys_model, self.model) - #pzs_model = self.model.find_model(pn[2], self.model.slicers) - #self.pz_slicer = SlicerView(self.plot, self.view.volume, pzs_model, self.model) - - nxs_model = self.model.find_model(nn[0], self.model.slicers) - self.nx_slicer = SlicerView(self.plot, self.view.volume, nxs_model, self.model) - nys_model = self.model.find_model(nn[1], self.model.slicers) - self.ny_slicer = SlicerView(self.plot, self.view.volume, nys_model, self.model) - nzs_model = self.model.find_model(nn[2], self.model.slicers) - self.nz_slicer = SlicerView(self.plot, self.view.volume, nzs_model, self.model) - - self.slicers = [self.nx_slicer, self.ny_slicer, self.nz_slicer] - - vedo.settings.defaultFont = self.model.ui.font - self.initialize_embed_ui(slicer_target=self.view.volume) - - self.plot.show(interactive=False) - self.handle_transfer_function_update() - # By default, the atlas volume is our target - self.model.camera.target = self.view.volume.actor - # We start with a sagittal view - self.set_left_view() - - self.render() - - def initialize_embed_ui(self, slicer_target=None): - s_kw = self.model.ui.slider_config - d = self.view.volume.model.dimensions - if d is None: - return - - #self.add_slider('px', self.update_px_slicer, 0, int(d[0]), 0, pos=(0.05, 0.065, 0.12), - # title='+X', **s_kw) - #self.add_slider('py', self.update_py_slicer, 0, int(d[1]), 0, pos=(0.2, 0.065, 0.12), - # title='+Y', **s_kw) - #self.add_slider('pz', self.update_pz_slicer, 0, int(d[2]), 0, pos=(0.35, 0.065, 0.12), - # title='+Z', **s_kw) - - def update_slicer(self, slicer_view, value): - """ - Update a given slicer with the given value - :param slicer_view: SlicerView instance - :param value: Value - """ - volume = self.view.volume - model = slicer_view.model - model.set_value(value) - model.clipping_planes = volume.get_clipping_planes(model.axis) - slicer_view.update(add_to_scene=self.model.slices_visible) - - diff --git a/PythonScripts/iblapps-master/needles2/probe_model.py b/PythonScripts/iblapps-master/needles2/probe_model.py deleted file mode 100644 index e7a778a0..00000000 --- a/PythonScripts/iblapps-master/needles2/probe_model.py +++ /dev/null @@ -1,490 +0,0 @@ -import time -import copy -import numpy as np -import pandas as pd -from scipy.signal import fftconvolve -from one.api import ONE -from iblutil.numerical import ismember - -from ibllib.pipes import histology -from ibllib.atlas import AllenAtlas, atlas -from neuropixel import TIP_SIZE_UM, SITES_COORDINATES -from ibllib.pipes.ephys_alignment import EphysAlignment -from neurodsp.utils import fcn_cosine - -PROV_2_VAL = { - 'Resolved': 90, - 'Ephys aligned histology track': 70, - 'Histology track': 50, - 'Micro-manipulator': 30, - 'Planned': 10} - -VAL_2_PROV = {v: k for k, v in PROV_2_VAL.items()} - - -class ProbeModel: - def __init__(self, one=None, ba=None, lazy=False, res=25, verbose=False): - - self.one = one or ONE() - self.ba = ba or AllenAtlas(res_um=res) - self.ba.compute_surface() - self.traj = {'Planned': {}, - 'Micro-manipulator': {}, - 'Histology track': {}, - 'Ephys aligned histology track': {}, - 'Resolved': {}, - 'Best': {}} - self.ins = {} - self.cvol = None - self.cvol_flat = None - self.initialised = False - self.mirror = False - self.verbose = verbose - - if not lazy: - self.initialise() - - def initialise(self): - self.get_traj_for_provenance(provenance='Histology track', django=['x__isnull,False']) - self.get_traj_for_provenance(provenance='Ephys aligned histology track') - self.get_traj_for_provenance(provenance='Ephys aligned histology track', - django=['probe_insertion__json__extended_qc__' - 'alignment_resolved,True'], prov_dict='Resolved') - self.find_traj_is_best(provenance='Histology track') - self.find_traj_is_best(provenance='Ephys aligned histology track') - self.traj['Resolved']['is_best'] = np.arange(len(self.traj['Resolved']['traj'])) - - self.get_insertions_with_xyz() - self.initialised = True - - @staticmethod - def get_traj_info(traj): - return traj['probe_insertion'], traj['x'], traj['y'] - - def get_traj_for_provenance(self, provenance='Histology track', django=None, - prov_dict=None): - start = time.time() - if django is None: - django = [] - - if prov_dict is None: - prov_dict = provenance - - django_base = ['probe_insertion__session__project__name__icontains,' - 'ibl_neuropixel_brainwide_01,probe_insertion__session__json__IS_MOCK,False', - 'probe_insertion__session__qc__lt,50', - '~probe_insertion__json__qc,CRITICAL', - 'probe_insertion__session__extended_qc__behavior,1'] - - django_str = ','.join(django_base + django) - - self.traj[prov_dict]['traj'] = np.array(self.one.alyx.rest('trajectories', 'list', - provenance=provenance, - django=django_str)) - - if self.mirror: - # if we want to mirror all insertions onto one hemisphere - for ip, p in enumerate(self.traj[prov_dict]['traj']): - if p['x'] < 0: - continue - elif p['y'] < -4400: - self.traj[prov_dict]['traj'][ip]['x'] = -1 * p['x'] - if p['phi'] == 180: - self.traj[prov_dict]['traj'][ip]['phi'] = 0 - else: - self.traj[prov_dict]['traj'][ip]['phi'] = 180 - - - ins_ids, x, y = zip(*[self.get_traj_info(traj) for traj in self.traj[prov_dict]['traj']]) - self.traj[prov_dict]['ins'] = np.array(ins_ids) - self.traj[prov_dict]['x'] = np.array(x) - self.traj[prov_dict]['y'] = np.array(y) - end = time.time() - if self.verbose is True: - print(end-start) - - def get_insertions_with_xyz(self): - start = time.time() - django_str = 'session__project__name__icontains,ibl_neuropixel_brainwide_01,' \ - 'json__has_key,xyz_picks' - self.ins['insertions'] = self.one.alyx.rest('insertions', 'list', django=django_str) - self.ins['ids'] = np.array([ins['id'] for ins in self.ins['insertions']]) - - end = time.time() - print(end-start) - - def compute_best_for_provenance(self, provenance='Histology track'): - val = PROV_2_VAL[provenance] - prov_to_include = [] - for k, v in VAL_2_PROV.items(): - if k >= val: - prov_to_include.append(v) - - for iP, prov in enumerate(prov_to_include): - if not 'is_best' in self.traj[prov].keys(): - self.find_traj_is_best(prov) - - if iP == 0: - self.traj['Best']['traj'] = self.traj[prov]['traj'][self.traj[prov]['is_best']] - self.traj['Best']['ins'] = self.traj[prov]['ins'][self.traj[prov]['is_best']] - self.traj['Best']['x'] = self.traj[prov]['x'][self.traj[prov]['is_best']] - self.traj['Best']['y'] = self.traj[prov]['y'][self.traj[prov]['is_best']] - else: - self.traj['Best']['traj'] = np.r_[self.traj['Best']['traj'], - (self.traj[prov]['traj'] - [self.traj[prov]['is_best']])] - self.traj['Best']['ins'] = np.r_[self.traj['Best']['ins'], - (self.traj[prov]['ins'] - [self.traj[prov]['is_best']])] - self.traj['Best']['x'] = np.r_[self.traj['Best']['x'], - self.traj[prov]['x'][self.traj[prov]['is_best']]] - self.traj['Best']['y'] = np.r_[self.traj['Best']['y'], - self.traj[prov]['y'][self.traj[prov]['is_best']]] - - def find_traj_is_best(self, provenance='Histology track'): - val = PROV_2_VAL[provenance] - next_provenance = VAL_2_PROV[val + 20] - - if not 'traj' in self.traj[provenance].keys(): - self.get_traj_for_provenance(provenance) - if not 'traj' in self.traj[next_provenance].keys(): - self.get_traj_for_provenance(next_provenance) - - isin, _ = ismember(self.traj[provenance]['ins'], - self.traj[next_provenance]['ins']) - self.traj[provenance]['is_best'] = np.where(np.invert(isin))[0] - - # Special exception for planned provenance - if provenance == 'Planned': - next_provenance = VAL_2_PROV[val + 40] - if not 'traj' in self.traj[next_provenance].keys(): - self.get_traj_for_provenance(next_provenance) - isin, _ = ismember(self.traj[provenance]['ins'][self.traj[provenance]['is_best']], - self.traj[next_provenance]['ins']) - self.traj[provenance]['is_best'] = (self.traj[provenance]['is_best'] - [np.where(np.invert(isin))[0]]) - - def get_all_channels(self, provenance): - - depths = SITES_COORDINATES[:, 1] - for iT, traj in enumerate(self.traj[provenance]['traj']): - try: - xyz_channels = self.get_channels(traj, depths=depths) - if iT == 0: - all_channels = xyz_channels - else: - all_channels = np.r_[all_channels, xyz_channels] - except Exception as err: - print(err) - print(traj['id']) - - iii = self.ba.bc.xyz2i(all_channels) - keep_idx = np.setdiff1d(np.arange(all_channels.shape[0]), np.unique(np.where(iii < 0)[0])) - return all_channels[keep_idx, :] - - def compute_channel_coverage(self, all_channels): - - start = time.time() - cvol = np.zeros(self.ba.image.shape, dtype=np.float) - val, counts = np.unique(self.ba._lookup(all_channels), return_counts=True) - #cvol[np.unravel_index(val, cvol.shape)] = counts - cvol[np.unravel_index(val, cvol.shape)] = 1 - - DIST_FCN = np.array([100, 150]) / 1e6 - dx = self.ba.bc.dx - template = np.arange(- np.max(DIST_FCN) - dx, np.max(DIST_FCN) + 2 * dx, dx) ** 2 - kernel = sum(np.meshgrid(template, template, template)) - kernel = 1 - fcn_cosine(DIST_FCN)(np.sqrt(kernel)) - # - cvol = fftconvolve(cvol, kernel, mode='same') - end = time.time() - print(end-start) - self.cvol = cvol - self.cvol_flat = cvol.flatten() - - return cvol - - def grid_coverage(self, all_channels, spacing): - cov, bc = histology.coverage_grid(all_channels, spacing, self.ba) - - return cov, bc - - def add_coverage(self, traj): - - cov, xyz, flatixyz = histology.coverage([traj], self.ba) - if self.cvol_flat is not None: - idx = np.where(cov.flatten()[flatixyz] > 0.1)[0] - idx_sig = np.where(self.cvol_flat[flatixyz][idx] > 0.1)[0].shape[0] - per_new_coverage = (1 - idx_sig / idx.shape[0]) * 100 - else: - per_new_coverage = np.nan - - return cov, xyz, per_new_coverage - - def insertion_by_id(self, ins_id): - traj = self.one.alyx.rest('trajectories', 'list', probe_insertion=ins_id) - ins = self.one.alyx.rest('insertions', 'list', id=ins_id)[0] - val = [PROV_2_VAL[tr['provenance']] for tr in traj] - best_traj = traj[np.argmax(val)] - - return best_traj, ins - - def get_channels(self, traj, ins=None, depths=None): - if depths is None: - depths = SITES_COORDINATES[:, 1] - if traj['provenance'] == 'Planned' or traj['provenance'] == 'Micro-manipulator': - ins = atlas.Insertion.from_dict(traj, brain_atlas=self.ba) - # Deepest coordinate first - xyz = np.c_[ins.tip, ins.entry].T - xyz_channels = histology.interpolate_along_track(xyz, (depths + - TIP_SIZE_UM) / 1e6) - else: - if ins is None: - ins_idx = np.where(traj['probe_insertion'] == self.ins['ids'])[0][0] - xyz = np.array(self.ins['insertions'][ins_idx]['json']['xyz_picks']) / 1e6 - else: - xyz = np.array(ins['json']['xyz_picks']) / 1e6 - if traj['provenance'] == 'Histology track': - xyz = xyz[np.argsort(xyz[:, 2]), :] - xyz_channels = histology.interpolate_along_track(xyz, (depths + - TIP_SIZE_UM) / 1e6) - else: - if ins is None: - align_key = (self.ins['insertions'][ins_idx]['json']['extended_qc'] - ['alignment_stored']) - else: - align_key = ins['json']['extended_qc']['alignment_stored'] - feature = traj['json'][align_key][0] - track = traj['json'][align_key][1] - ephysalign = EphysAlignment(xyz, depths, track_prev=track, - feature_prev=feature, - brain_atlas=self.ba, speedy=True) - xyz_channels = ephysalign.get_channel_locations(feature, track) - - return xyz_channels - - def get_brain_regions(self, traj, ins=None, mapping='Allen'): - depths = SITES_COORDINATES[:, 1] - xyz_channels = self.get_channels(traj, ins=ins, depths=depths) - region_ids = self.ba.get_labels(xyz_channels, mapping=mapping) - if all(region_ids == 0): - region = [[0, 3840]] - region_label = [[3840/2, 'VOID']] - region_colour = [[0, 0, 0]] - else: - (region, region_label, - region_colour, _) = EphysAlignment.get_histology_regions(xyz_channels, depths, - brain_atlas=self.ba, - mapping=mapping) - return region, region_label, region_colour - - def report_coverage(self, provenance, dist): - coverage, _ = self.compute_coverage(self.traj[provenance]['traj'], - dist_fcn=[dist, dist + 1]) - coverage[coverage > 2] = 2 - return coverage - - def compute_coverage(self, trajs, dist_fcn=[50, 100], limit=True, coverage=None, pl_voxels=None, factor=1, - entire_voxel=True): - """ - Computes coverage of the brain atlas associated with the probe model given a set of trajectories. - - Parameters - ---------- - trajs: list of dictionary of trajectories from Alyx rest endpoint - dist_fcn: list of two values in micro meter, the first one marks the distance to the probe, below which - voxels will be marked covered (=1). Coverage will then decrease with a cosine taper until the - second value, after which coverage will be 0. - limit: boolean, if True, the effect of the cosine taper will be negated, i.e. all coverage values below 1 - will be set to 0. - coverage: np.array of the same size as the brain atlas image associated with the probe insertion. - If given, the coverage computed by this function will be added to this input. - pl_voxels: np.array with flat indices into the brain atlas image volume. If given, only these voxels are - considered for the computation of coverage percentages. Note that the coverage itself is always - computed for the entire volume - factor: Number by which to multiply coverage of each trajectory, e.g. to count trajectories twice - entire_voxel: boolean, if True, consider every voxel as covered, that is touched by the radius if dist_fcn[0] - around the probe. If False, only consider those as covered where the center of the voxel falls in - this radius. - - Returns - 3D np.array the same size as the brain atlas with coverage values for every voxel in the brain - - If pl_voxels is given: - Three lists of values indicating the percent coverage of these voxels after adding each trajectory (increases - incrementally). The first list indicates percent covered by 0 probes, second list percent covered by 1 probe, - and the third list percent covered by 2 probes. - """ - - ba = self.ba - ACTIVE_LENGTH_UM = 3.84 * 1e3 # This is the length of the NP1 probe with electrodes - MAX_DIST_UM = dist_fcn[1] # max distance around the probe to be searched for - - # Covered_length_um are two values which indicate on the path from tip to entry of a given insertion - # where the region that is considered to be covered by this insertion begins and ends in micro meter - # Note that the second value is negative, because the covered regions extends beyond the tip of the probe - # We multiply by sqrt(2) to translate the radius given by dist_fcn[1] into the side length of a square - # that is contained in the circle with radius dist_fcn[1] - covered_length_um = TIP_SIZE_UM + np.array([ACTIVE_LENGTH_UM + MAX_DIST_UM * np.sqrt(2), - -MAX_DIST_UM * np.sqrt(2)]) - - # Horizontal slice of voxels to be considered around each trajectory is only dependent on MAX_DIST_UM - # and the voxel resolution, so can be defined here. We translate max dist in voxels and add 1 for safety - # Unit of atlas is m so divide um by 1e6 - nx = int(np.ceil(MAX_DIST_UM / 1e6 / np.abs(ba.bc.dxyz[0]))) + 1 - ny = int(np.ceil(MAX_DIST_UM / 1e6 / np.abs(ba.bc.dxyz[1]))) + 1 - - def crawl_up_from_tip(ins, covered_length): - straight_trajectory = ins.entry - ins.tip # Straight line from entry to tip of the probe - # scale generic covered region to the length of the trajectory - covered_length_scaled = covered_length[:, np.newaxis] / np.linalg.norm(straight_trajectory) - # starting from tip crawl scaled covered region - covered_region = ins.tip + (straight_trajectory * covered_length_scaled) - return covered_region - - # Coverage that we start from, is either given or instantiated with all zeros. Values outside brain set to nan - if coverage is None: - full_coverage = np.zeros(ba.image.shape, dtype=np.float32) - else: - full_coverage = copy.deepcopy(coverage) - full_coverage[ba.label == 0] = np.nan - full_coverage = full_coverage.flatten() - # There are lists to collect the updated coverage with 0, 1 and 2 probes after each trajectory - per2 = [] - per1 = [] - per0 = [] - - for p in np.arange(len(trajs)): - if len(trajs) > 20 and self.verbose is True: - if p % 20 == 0: - print(p / len(trajs)) - # Get one trajectory from the list and create an insertion in the brain atlas - # x and y coordinates of entry are translated to the atlas voxel space - # z is locked to surface of the brain at these x,y coordinates (disregarding actual z value of trajectory) - traj = trajs[p] - ins = atlas.Insertion.from_dict(traj, brain_atlas=ba) - # Don't use probes that have same entry and tip, something is wrong - set_nan = False - if np.linalg.norm(ins.entry - ins.tip) == 0: - print(f"Insertion entry and tip are identical for insertion {traj['probe_insertion']}. " - "Skipping for coverage computation.") - set_nan = True - else: - # Translate the top and bottom of the region considered covered from abstract to current insertion - # Unit of atlas is m so divide um by 1e6 - top_bottom = crawl_up_from_tip(ins, covered_length_um / 1e6) - # Check that z is the axis with the biggest deviation, don't use probes that are more shallow than deep - axis = np.argmax(np.abs(np.diff(top_bottom, axis=0))) - if axis != 2: - print(f"Z is not the longest axis for insertion {traj['probe_insertion']}. " - "Skipping for coverage computation.") - set_nan = True - # Skip insertions with length zero or where z is not the longest axis - if set_nan is True: - if pl_voxels is not None: - per2.append(np.nan) - per1.append(np.nan) - per0.append(np.nan) - continue - # To sample the active track path along the longest axis, first get top and bottom voxel - # If these lay outside of the atlas volume, clip to the nearest voxel in the volume - tbi = ba.bc.xyz2i(top_bottom, mode='clip') - # Number of voxels along the longest axis between top and bottom - nz = tbi[1, axis] - tbi[0, axis] + 1 - # Create a set of nz voxels that track the path between top and bottom by equally spacing between the - # x, y and z coordinates and then rounding - ishank = np.round( - np.array([np.linspace(tbi[0, i], tbi[1, i], nz) for i in np.arange(3)]).T).astype(np.int32) - # Around each of the voxels along this shank, get a horizontal slice of voxels to consider - # nx and ny are defined outside the loop as they don't depend on the trajectory - # Instead of a set of slices, flatten these voxels. ixyz is of size (n_voxels, 3) - ixyz = np.stack([v.flatten() for v in np.meshgrid( - np.arange(-nx, nx + 1), np.arange(-ny, ny + 1), np.arange(nz))]).T - # Add the x and y coordinates of the respective slice center voxel (defined by z coordinate) - # So that the slice actually is at the right spot in the volume - ixyz[:, 0] = ishank[ixyz[:, 2], 0] + ixyz[:, 0] - ixyz[:, 1] = ishank[ixyz[:, 2], 1] + ixyz[:, 1] - ixyz[:, 2] = ishank[ixyz[:, 2], 2] - # If any, remove indices that lie outside the volume bounds - iok = np.logical_and(0 <= ixyz[:, 0], ixyz[:, 0] < ba.bc.nx) - iok &= np.logical_and(0 <= ixyz[:, 1], ixyz[:, 1] < ba.bc.ny) - iok &= np.logical_and(0 <= ixyz[:, 2], ixyz[:, 2] < ba.bc.nz) - ixyz = ixyz[iok, :] - - # Get the minimum distance of each of the considered voxels to the insertion - # Translate voxel indices into distance from origin in atlas space - xyz = np.c_[ba.bc.xscale[ixyz[:, 0]], ba.bc.yscale[ixyz[:, 1]], ba.bc.zscale[ixyz[:, 2]]] - # This is the active region that we want to calculate the distance TO (here without MAX_DIST_UM) - sites_bounds = crawl_up_from_tip(ins, (np.array([ACTIVE_LENGTH_UM, 0]) + TIP_SIZE_UM) / 1e6) - # Calculate the minimum distance of each voxel in the search voxels to the active region - mdist = ins.trajectory.mindist(xyz, bounds=sites_bounds) - - # Now we calculate the actual coverage - # mdist gives us for each voxel in the search volume the distance of the CENTER of that voxel to the probe - # If we want to include voxels where only part of the voxel, but not the center, falls into the radius - # we consider covered, we need to increase the radius by the distance of a voxel's center to its corners - # which is given by (sqrt(3) * side_length / 2). - if entire_voxel is True: - dist_adjusted = np.array(dist_fcn) / 1e6 + (np.abs(ba.bc.dxyz).max() * np.sqrt(3) / 2) - else: - dist_adjusted = np.array(dist_fcn) / 1e6 - # We then compute coverage using a cosine taper - # Anything below the minimum distance will be 1 - # Anything above the maximum distance will be 0 - # Anything between minimum and maximum distance will slowly decrease from 1 to 0 with cosine taper - coverage = 1 - fcn_cosine(dist_adjusted)(mdist) - # If limit is set to True, remove effect of cosine taper and set everything under 1 to 0 - if limit: - coverage[coverage < 1] = 0 - # Translate the flat coverage values to the volume - flat_ind = ba._lookup_inds(ixyz) - full_coverage[flat_ind] += (coverage * factor) - - # If a mask in which coverage should be calculated is given, restrict to this mask - if pl_voxels is not None: - n_pl_voxels = pl_voxels.shape[0] - fp_voxels_2 = np.where(full_coverage[pl_voxels] >= 2)[0].shape[0] - fp_voxels_1 = np.where(full_coverage[pl_voxels] == 1)[0].shape[0] - fp_voxels_0 = np.where(full_coverage[pl_voxels] == 0)[0].shape[0] - - per2.append((fp_voxels_2 / n_pl_voxels) * 100) - per1.append((fp_voxels_1 / n_pl_voxels) * 100) - per0.append((fp_voxels_0 / n_pl_voxels) * 100) - - full_coverage = full_coverage.reshape(ba.image.shape) - full_coverage[ba.label == 0] = np.nan - - if pl_voxels is not None: - return full_coverage, per0, per1, per2 - else: - return full_coverage, np.mean(xyz, 0) - - -def coverage_with_insertions(csv_file, second_pass_volume, res=25): - pr = ProbeModel(res=res) - pr.initialise() - pr.compute_best_for_provenance(provenance='Histology track') - first_pass_coverage = pr.report_coverage(provenance='Best', dist=354) - - second_pass_coverage = np.load(second_pass_volume) - second_pass_coverage = second_pass_coverage.flatten() - second_pass_coverage[second_pass_coverage == 0] = np.nan - ixyz_second = np.where(~np.isnan(second_pass_coverage.flatten()))[0] - - insertions = pd.read_csv(csv_file).to_dict(orient='records') - - # want to make it twice for each insertion - - new_coverage, per0, per1, per2 = pr.compute_coverage(insertions, dist_fcn=[354, 355], coverage=first_pass_coverage, - pl_voxels=ixyz_second, factor=1) - - return new_coverage, per0, per1, per2 - - - - - - - - - diff --git a/PythonScripts/iblapps-master/needles2/regionUI.ui b/PythonScripts/iblapps-master/needles2/regionUI.ui deleted file mode 100644 index 0cec8fd5..00000000 --- a/PythonScripts/iblapps-master/needles2/regionUI.ui +++ /dev/null @@ -1,210 +0,0 @@ - - - Form - - - - 0 - 0 - 291 - 210 - - - - - 0 - 0 - - - - Form - - - - - 10 - 10 - 291 - 210 - - - - - 0 - 0 - - - - QFrame::StyledPanel - - - QFrame::Raised - - - - - 10 - 10 - 100 - 30 - - - - - 75 - true - - - - Selected Region: - - - - - - 10 - 40 - 250 - 20 - - - - QComboBox::AdjustToContentsOnFirstShow - - - 0 - - - - - - 10 - 70 - 90 - 30 - - - - - 75 - true - - - - Current Region: - - - - - - 110 - 70 - 80 - 30 - - - - - - - - - - 10 - 100 - 30 - 30 - - - - - 75 - true - - - - ML: - - - - - - 50 - 100 - 60 - 30 - - - - - - - - - - 10 - 130 - 30 - 30 - - - - - 75 - true - - - - AP: - - - - - - 50 - 130 - 60 - 30 - - - - - - - - - - 10 - 160 - 50 - 30 - - - - - 75 - true - - - - DV: - - - - - - 50 - 160 - 50 - 30 - - - - - - - - - - - diff --git a/PythonScripts/iblapps-master/needles2/run_needles2.py b/PythonScripts/iblapps-master/needles2/run_needles2.py deleted file mode 100644 index b7608382..00000000 --- a/PythonScripts/iblapps-master/needles2/run_needles2.py +++ /dev/null @@ -1,1308 +0,0 @@ -from dataclasses import dataclass, field -from pathlib import Path - -import numpy as np -import pandas as pd -from PyQt5 import QtWidgets, QtGui, QtCore, uic -from PyQt5.QtGui import QTransform -import pyqtgraph as pg -import matplotlib - -from ibllib.atlas import AllenAtlas -from ibllib.atlas.regions import BrainRegions -from iblutil.numerical import ismember -import qt - -from one.api import ONE -from needles2.probe_model import ProbeModel -import copy - -pg.setConfigOption('background', 'w') -pg.setConfigOption('foreground', 'k') - -class MainWindow(QtWidgets.QMainWindow): - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, MainWindow)] - - @staticmethod - def _get_or_create(title=None, **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - MainWindow._instances()), None) - if av is None: - av = MainWindow(**kwargs) - av.setWindowTitle(title) - return av - - def __init__(self, lazy=False, res=25): - super(MainWindow, self).__init__() - uic.loadUi(Path(__file__).parent.joinpath('mainUI.ui'), self) - - self.atlas = AllenAtlas(res_um=res) - self.atlas.compute_surface() - one = ONE() - self.dist = 354 - - # Configure the Menu bar - menu_bar = QtWidgets.QMenuBar(self) - menu_bar.setNativeMenuBar(False) - self.setMenuBar(menu_bar) - - # Add menu bar for mappings - self.map_menu = menu_bar.addMenu('Mappings') - self.map_group = QtWidgets.QActionGroup(self.map_menu) - # Only allow one to plot to be selected at any one time - self.map_group.setExclusive(True) - self.add_menu_bar(self.map_menu, self.map_group, list(self.atlas.regions.mappings.keys()), - callback=self.change_mapping, default='Allen') - - # Add menu bar for base image - self.img_menu = menu_bar.addMenu('Images') - self.img_group = QtWidgets.QActionGroup(self.img_menu) - self.img_group.setExclusive(True) - images = ['Image', 'Annotation'] - self.add_menu_bar(self.img_menu, self.img_group, images, callback=self.change_image, - default='Image') - - # Add menu bar for coverage - self.coverage_menu = menu_bar.addMenu('Coverage') - self.coverage_group = QtWidgets.QActionGroup(self.coverage_menu) - self.coverage_group.setExclusive(True) - # coverages = ['Coverage cosine', 'Coverage grid 500', 'Coverage grid 250', - # 'Coverage grid 100', 'Coverage 354', 'Coverage 250', 'Coverage 120'] - coverages = ['Coverage 354', 'Coverage 250', 'Coverage 120'] - self.add_menu_bar(self.coverage_menu, self.coverage_group, coverages, - callback=self.add_coverage, default='Coverage 354') - - # Add menubar for insertions - self.insertion_menu = menu_bar.addMenu('Insertions') - self.insertion_group = QtWidgets.QActionGroup(self.insertion_menu) - self.insertion_group.setExclusive(True) - insertions = ['Resolved', 'Ephys aligned histology track', 'Histology track', - 'Histology track (best)', 'Micro-manipulator', 'Micro-manipulator (best)', - 'Planned', 'Planned (best)'] - self.add_menu_bar(self.insertion_menu, self.insertion_group, insertions, - callback=self.add_insertions, default='Histology track (best)') - - # Get all the views - self.coronal = SliceView(self, self.fig_coronal, self.atlas, slice='coronal', - waxis=0, haxis=2, daxis=1) - self.sagittal = SliceView(self, self.fig_sagittal, self.atlas, slice='sagittal', - waxis=1, haxis=2, daxis=0) - self.horizontal = SliceView(self, self.fig_horizontal, self.atlas, slice='horizontal', - waxis=1, haxis=0, daxis=2) - self.horizontal.fig_slice.getViewBox().invertY(True) - - self.top = TopView(self, self.fig_top, self.atlas) - self.probe = ProbeView(self, self.fig_probe) - self.coverage = CoverageView(self) - self.probe_model = ProbeModel(one=one, ba=self.atlas, lazy=lazy) - self.region = RegionView(self, self.atlas) - self.table = InsertionTableView(self) - self.layers = LayersView(self) - self.change_mapping() - - layout = QtWidgets.QGridLayout() - layout.addWidget(self.coverage) - self.coverage_placeholder.setLayout(layout) - - layout = QtWidgets.QGridLayout() - layout.addWidget(self.region) - self.region_placeholder.setLayout(layout) - - if not lazy: - self.initialise() - - def initialise(self, second_pass_map=None): - self.add_insertions() - self.add_coverage() - self.missing_coverage(second_pass_map=second_pass_map) - self.coverage_increase() - self.layers.initialise_table() - - def missing_coverage(self, second_pass_map=None): - # get the coverage volume layer from the coverage - cov = self.coronal.ctrl.get_image_layer('coverage').slice_kwargs['region_values'] - self.second_pass = self.load_second_pass_volume(second_pass_map=second_pass_map) - self.second_pass_missing = np.copy(self.second_pass) - self.second_pass_missing[cov >= 1] = np.nan - self.ixyz_second = np.where(~np.isnan(self.second_pass.flatten()))[0] - self.ixyz_second_missing = np.where(~np.isnan(self.second_pass_missing.flatten()))[0] - self.add_volume_layer(self.second_pass_missing, name='missing', cmap='Reds', levels=(0, 1)) - - - def change_mapping(self): - data = np.unique(self.atlas.regions.name[self.atlas._get_mapping( - mapping=self.get_mapping())]) - self.region.populate_combobox(data) - self._refresh() - - def get_mapping(self): - return self.map_group.checkedAction().text() - - def change_image(self): - self.coronal.ctrl.change_base_layer(self.img_group.checkedAction().text()) - self.sagittal.ctrl.change_base_layer(self.img_group.checkedAction().text()) - self.horizontal.ctrl.change_base_layer(self.img_group.checkedAction().text()) - self._refresh() - - def add_coverage(self): - provenance = self.insertion_group.checkedAction().text() - coverage_choice = self.coverage_group.checkedAction().text() - if not self.probe_model.initialised: - self.probe_model.initialise() - - if '(best)' in provenance: - provenance = provenance[:-7] - self.probe_model.compute_best_for_provenance(provenance) - self.provenance = 'Best' - else: - self.provenance = provenance - - if 'cosine' in coverage_choice: - all_channels = self.probe_model.get_all_channels(self.provenance) - cov = self.probe_model.compute_coverage(all_channels) - bc = None - elif 'grid' in coverage_choice: - all_channels = self.probe_model.get_all_channels(self.provenance) - bin_size = int(coverage_choice[-3:]) - cov, bc = self.probe_model.grid_coverage(all_channels, bin_size) - else: - self.dist = int(coverage_choice[-3:]) - # self.dist = 354 - cov = self.probe_model.report_coverage(self.provenance, self.dist) - bc = None - - self.add_volume_layer(cov, 'coverage', bc=bc) - self._refresh() - - def add_extra_coverage(self, traj, ins=None): - # This had an ins option, need to look into that ra - # when double clicked - self.top.ctrl.set_scatter_layer('active_insertion', x=[traj['x'] / 1e6], - y=[traj['y'] / 1e6]) - self.do_this_thing(traj, ins=ins) - - def coverage_added(self): - # when add button pressed - # get out the pandas df - df = self.coverage.ctrl.model.to_df() - # Add the insertions twice - self.table.ctrl.model.insertRow(df) - self.table.ctrl.model.insertRow(df) - - self.get_coverage_from_table() - - self.table.tableview.selectionModel().blockSignals(True) - self.table.tableview.clearSelection() - self.table.tableview.selectionModel().blockSignals(False) - - def get_coverage_from_table(self): - - if self.table.ctrl.model.rowCount() > 0: - cov, _ = self.probe_model.compute_coverage(self.table.ctrl.model.to_dict(), - dist_fcn=[self.dist, self.dist + 1]) - self.add_volume_layer(cov, name='planned_insertions', cmap='Purples', levels=(0, 1)) - self.top.ctrl.set_scatter_layer('planned_insertions', - x=self.table.ctrl.model.arraydata.x.values / 1e6, - y=self.table.ctrl.model.arraydata.y.values / 1e6) - - else: - self.remove_volume_layer('planned_insertions') - self.top.ctrl.set_scatter_layer('planned_insertions') - - self.remove_volume_layer('selected_insertion') - self.top.ctrl.set_scatter_layer('active_insertion') - self.top.ctrl.set_scatter_layer('selected_insertion') - self._refresh() - self.layers.update_table('planned_insertions') - self.coverage_increase() - - - def add_trajectory(self, x, y): - self.coverage.ctrl.model.initialise_data() - self.coverage.ctrl.set_value(x, 'x') - self.coverage.ctrl.set_value(y, 'y') - self.coverage.update_view() - - def add_volume_layer(self, volume, name, cmap='viridis', opacity=0.8, levels=None, - bc=None): - self.coronal.ctrl.add_volume_layer(volume, name=name, cmap=cmap, opacity=opacity, - levels=levels, bc=bc) - self.sagittal.ctrl.add_volume_layer(volume, name=name, cmap=cmap, opacity=opacity, - levels=levels, bc=bc) - self.horizontal.ctrl.add_volume_layer(volume, name=name, cmap=cmap, opacity=opacity, - levels=levels, bc=bc) - - def remove_volume_layer(self, name): - self.coronal.ctrl.remove_image_layer(name) - self.sagittal.ctrl.remove_image_layer(name) - self.horizontal.ctrl.remove_image_layer(name) - - def toggle_volume_layer(self, name, checked): - self.coronal.ctrl.toggle_image_layer(name, checked) - self.sagittal.ctrl.toggle_image_layer(name, checked) - self.horizontal.ctrl.toggle_image_layer(name, checked) - self._refresh() - - - def add_insertion_by_id(self, ins_id): - traj, ins = self.probe_model.insertion_by_id(ins_id) - self.add_extra_coverage(traj, ins) - - def add_insertions(self): - provenance = self.insertion_group.checkedAction().text() - if not self.probe_model.initialised: - self.probe_model.initialise() - - if '(best)' in provenance: - provenance = provenance[:-7] - self.probe_model.compute_best_for_provenance(provenance) - self.provenance = 'Best' - else: - if not 'traj' in self.probe_model.traj[provenance].keys(): - - self.probe_model.get_traj_for_provenance(provenance) - self.provenance = provenance - - self.top.ctrl.set_scatter_layer('coverage', - x=self.probe_model.traj[self.provenance]['x']/1e6, - y=self.probe_model.traj[self.provenance]['y']/1e6) - - def on_insertion_clicked(self, scatter, point): - idx = np.argwhere(self.probe_model.traj[self.provenance]['x']/1e6 == - point[0].pos().x())[0][0] - traj = self.probe_model.traj[self.provenance]['traj'][idx] - - self.do_this_thing(traj) - - def on_first_pass_insertion_clicked(self, scatter, point): - idx = np.argwhere(self.first_pass_map.x.values/1e6 == - point[0].pos().x())[0][0] - traj = self.first_pass_map.iloc[idx].to_dict() - - self.do_this_thing(traj) - - -# TODO need to catch for dodgey coordinates that return rubbish - - def do_this_thing(self, traj, ins=None): - self.top.ctrl.set_scatter_layer('selected_insertion', x=[traj['x'] / 1e6], - y=[traj['y'] / 1e6]) - - - (region, region_lab, region_col) = self.probe_model.get_brain_regions( - traj, ins=ins, mapping=self.get_mapping()) - self.probe.plot_region_along_probe(region, region_lab, region_col) - - cov, xyz_cnt = self.probe_model.compute_coverage([traj], limit=False, - dist_fcn=[self.dist, self.dist + 1]) - self.add_volume_layer(cov, name='selected_insertion', cmap='Wistia') - - self.refresh_slices('horizontal', 'x', xyz_cnt[1]) - self.refresh_slices('horizontal', 'y', xyz_cnt[0]) - self.refresh_slices('sagittal', 'y', xyz_cnt[2]) - self._refresh() -# TODO figure this out - # self.table.tableview.selectionModel().blockSignals(True) - # self.table.tableview.clearSelection() - # self.table.tableview.selectionModel().blockSignals(False) - - def on_planned_insertion_clicked(self, scatter, point): - idx = np.argwhere(self.table.ctrl.model.arraydata.x.values/1e6 == - point[0].pos().x())[0][0] - traj = self.table.ctrl.model.arraydata.iloc[idx].to_dict() - - self.do_this_thing(traj) - - # also highlight the table - # self.table.tableview.selectionModel().blockSignals(True) - # self.table.tableview.selectRow(idx) - # self.table.tableview.selectionModel().blockSignals(False) - - def on_active_insertion_clicked(self, scatter, point): - traj = self.coverage.ctrl.model.get_traj() - self.do_this_thing(traj) - - - def add_menu_bar(self, menu, group, items, callback=None, default=None): - for item in items: - if item == default: - _item = QtWidgets.QAction(item, self, checkable=True, checked=True) - else: - _item = QtWidgets.QAction(item, self, checkable=True, checked=False) - if callback: - _item.triggered.connect(callback) - menu.addAction(_item) - group.addAction(_item) - - return menu, group - - def _refresh(self): - self._refresh_sagittal() - self._refresh_coronal() - self._refresh_horizontal() - - def _refresh_coronal(self): - self.coronal.ctrl.set_slice() - - def _refresh_sagittal(self): - self.sagittal.ctrl.set_slice() - - def _refresh_horizontal(self): - self.horizontal.ctrl.set_slice() - - def refresh_highlighted_region(self, region, mlapdv): - if (region is None) or (region['acronym'][0] == 'void'): - self._reset_region('non_locked') - self.region.update_labels(mlapdv[0], mlapdv[1], mlapdv[2], None) - else: - self._refresh_non_locked_region(region['id'][0]) - self.region.update_labels(mlapdv[0], mlapdv[1], mlapdv[2], region) - - def refresh_locked_region(self, region): - region_values = np.zeros_like(self.atlas.regions.id) - self._change_region_value('non_locked', region_values) - - region_idx = np.where(self.atlas.regions.id == region)[0] - region_values[region_idx] = 100 - self._change_region_value('locked', region_values) - self._refresh() - self.region.update_selected_region(region) - - def _reset_region(self, name): - region_values = np.zeros_like(self.atlas.regions.id) - self._change_region_value(name, region_values) - self._refresh() - - def _refresh_non_locked_region(self, region): - region_values = np.zeros_like(self.atlas.regions.id) - region_idx = np.where(self.atlas.regions.id == region)[0] - region_values[region_idx] = 100 - self._change_region_value('non_locked', region_values) - self._refresh() - - def _change_region_value(self, name, values): - image = self.coronal.ctrl.get_image_layer(name=name) - image.slice_kwargs['region_values'] = values - image = self.sagittal.ctrl.get_image_layer(name=name) - image.slice_kwargs['region_values'] = values - image = self.horizontal.ctrl.get_image_layer(name=name) - image.slice_kwargs['region_values'] = values - - def refresh_slices(self, slice, orientation, value, move_top=True): - if slice == 'coronal' and orientation == 'x': - self.sagittal.slice_coord = value - self.horizontal.line_y.setValue(value) - self.coronal.line_x.setValue(value) - if move_top: - self.top.line_x.setValue(value) - self._refresh_sagittal() - - if slice == 'coronal' and orientation == 'y': - self.horizontal.slice_coord = value - self.sagittal.line_y.setValue(value) - self.coronal.line_y.setValue(value) - self._refresh_horizontal() - - if slice == 'sagittal' and orientation == 'x': - self.coronal.slice_coord = value - self.horizontal.line_x.setValue(value) - self.sagittal.line_x.setValue(value) - if move_top: - self.top.line_y.setValue(value) - self._refresh_coronal() - - if slice == 'sagittal' and orientation == 'y': - self.horizontal.slice_coord = value - self.coronal.line_y.setValue(value) - self.sagittal.line_y.setValue(value) - self._refresh_horizontal() - - if slice == 'horizontal' and orientation == 'x': - self.coronal.slice_coord = value - self.sagittal.line_x.setValue(value) - self.horizontal.line_x.setValue(value) - self.top.line_y.setValue(value) - self._refresh_coronal() - - if slice == 'horizontal' and orientation == 'y': - self.sagittal.slice_coord = value - self.coronal.line_x.setValue(value) - self.horizontal.line_y.setValue(value) - self.top.line_x.setValue(value) - self._refresh_sagittal() - - def load_first_pass_map(self): - first_pass_map_path = r'C:\Users\Mayo\iblenv\ibllib-matlab\needles\maps\first_pass_map.csv' - self.first_pass_map = pd.read_csv(first_pass_map_path) - self.first_pass_map = self.first_pass_map.rename(columns={'ml_um': 'x', 'ap_um': 'y', - 'dv_um': 'z', - 'depth_um': 'depth'}) - self.first_pass_map = self.first_pass_map.drop(columns=['coronal_index', 'sagittal_index', - 'index']) - self.first_pass_map['provenance'] = 'Planned' - - cov, _ = self.probe_model.compute_coverage(self.first_pass_map.to_dict(orient='records'), - dist_fcn=[self.dist, self.dist + 1]) - self.add_volume_layer(cov, name='first_pass', cmap='YlGnBu', levels=(0, 1)) - self.top.ctrl.set_scatter_layer('first_pass', x=self.first_pass_map.x.values / 1e6, - y=self.first_pass_map.y.values / 1e6) - - def load_second_pass_volume(self, second_pass_map=None): - - if second_pass_map: - reg_volume2 = np.load(second_pass_map) - reg_volume2[reg_volume2 == 0] = np.nan - else: - flat_ind = np.arange(self.probe_model.ba.image.shape[0] * - self.probe_model.ba.image.shape[1] * - self.probe_model.ba.image.shape[2]) - ixyz = np.unravel_index(flat_ind, - shape=(self.probe_model.ba.image.shape[0], - self.probe_model.ba.image.shape[1], - self.probe_model.ba.image.shape[2])) - ixyz = (np.c_[ixyz[1], ixyz[0], ixyz[2]]) - xyz = self.probe_model.ba.bc.i2xyz(ixyz.astype(np.float)) - - ra = self.probe_model.ba.label.flatten().astype(np.float64) - flat = np.zeros_like(ra) - - # because - rh = int((np.max(ra) + 1) / 2) - t = np.bitwise_and(ra >= rh, xyz[:, 1] < 3800 / 1e6) - # t = np.where(ra >= rh) - flat[t] = 1 - flat[ra == 0] = 0 - - reg_volume2 = flat.reshape(self.probe_model.ba.image.shape[0], - self.probe_model.ba.image.shape[1], - self.probe_model.ba.image.shape[2]).astype(np.float32) - reg_volume2[reg_volume2 == 0] = np.nan - - return reg_volume2 - - def coverage_increase(self): - - layer = None - layer_planned = self.coronal.ctrl.get_image_layer('planned_insertions') - if layer_planned is not None: - layer = (copy.deepcopy(layer_planned.slice_kwargs['region_values'])) - - layer_active = self.coronal.ctrl.get_image_layer('selected_insertion') - if layer_active is not None: - if layer is not None: - layer += (copy.deepcopy(layer_active.slice_kwargs['region_values']) * 2) - else: - layer = (copy.deepcopy(layer_active.slice_kwargs['region_values']) * 2) - - layer_covered = self.coronal.ctrl.get_image_layer('coverage') - if layer_covered is not None: - if layer is not None: - layer += copy.deepcopy(layer_covered.slice_kwargs['region_values']) - else: - layer = copy.deepcopy(layer_covered.slice_kwargs['region_values']) - - self.layer_val = layer - if layer is not None: - la = layer.flatten() - # percentage of the second volume that is covered, - # so we need to add the coverage to the main coverage - - bla = la[self.ixyz_second] - per0 = (np.where(bla == 0)[0].shape[0] / self.ixyz_second.shape[0]) * 100 - per1 = (np.where(bla == 1)[0].shape[0] / self.ixyz_second.shape[0]) * 100 - per2 = (np.where(bla > 1)[0].shape[0] / self.ixyz_second.shape[0]) * 100 - - self.coverage.coverage_label.setText(f'0%: {np.round(per0, 2)} %, ' - f'1%: {np.round(per1, 2)} %, ' - f'2%: {np.round(per2, 2)} % ') - - -class LayersView(QtWidgets.QWidget): - def __init__(self, qmain: MainWindow): - self.qmain = qmain - super(LayersView, self).__init__() - self.layer_list = self.qmain.layer_list - self.ignore_layers = ['base', 'locked', 'non_locked'] - - self.layer_list.itemClicked.connect(self.layer_clicked) - - def initialise_table(self): - for layer in self.qmain.coronal.ctrl.image_layers: - if not layer.name in self.ignore_layers: - item = QtWidgets.QListWidgetItem() - item.setText(layer.name) - item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable) - item.setCheckState(QtCore.Qt.Checked) - self.layer_list.addItem(item) - - def layer_clicked(self, item): - if item.checkState() == QtCore.Qt.Checked: - self.qmain.toggle_volume_layer(item.text(), True) - self.qmain.top.ctrl.toggle_scatter_layer(item.text(), True) - else: - self.qmain.toggle_volume_layer(item.text(), False) - self.qmain.top.ctrl.toggle_scatter_layer(item.text(), False) - # need to add layers - # if they are not ticked then you keep them unticked - - def update_table(self, name): - # Check if it already on the list - for x in range(self.layer_list.count()): - _item = self.layer_list.item(x) - if _item.text() == name: - - if _item.checkState() != QtCore.Qt.Checked: - _item.setCheckState(QtCore.Qt.Checked) - self.qmain.top.ctrl.toggle_scatter_layer(_item.text(), True) - return - - # Otherwise add to the list - item = QtWidgets.QListWidgetItem() - item.setText(name) - item.setFlags(item.flags() | QtCore.Qt.ItemIsUserCheckable) - item.setCheckState(QtCore.Qt.Checked) - self.layer_list.addItem(item) - - -class RegionView(QtWidgets.QWidget): - def __init__(self, qmain: MainWindow, atlas: AllenAtlas): - self.atlas = atlas - self.qmain = qmain - super(RegionView, self).__init__() - uic.loadUi(Path(__file__).parent.joinpath('regionUI.ui'), self) - - self.region_list = QtGui.QStandardItemModel() - self.region_comboBox.setLineEdit(QtWidgets.QLineEdit()) - region_completer = QtWidgets.QCompleter() - region_completer.setCaseSensitivity(QtCore.Qt.CaseInsensitive) - self.region_comboBox.setCompleter(region_completer) - self.region_comboBox.setModel(self.region_list) - self.region_comboBox.completer().setModel(self.region_list) - self.region_comboBox.activated.connect(self.on_region_selected) - - def populate_combobox(self, data): - self.region_list.clear() - for dat in data: - item = QtGui.QStandardItem(dat) - item.setEditable(False) - self.region_list.appendRow(item) - - # Set the default to be the first option - self.region_comboBox.setCurrentIndex(0) - - def update_labels(self, ml, ap, dv, region): - self.ml_label.setText(f"{ml * 1e6:.0f}") - self.ap_label.setText(f"{ap * 1e6:.0f}") - self.dv_label.setText(f"{dv * 1e6:.0f}") - if region is None: - self.current_region_label.setText("") - else: - self.current_region_label.setText(region['acronym'][0]) - - def update_selected_region(self, region): - region_idx = np.where(self.atlas.regions.id == region)[0][0] - region_name = self.atlas.regions.name[region_idx] - item = self.region_list.findItems(region_name) - self.region_comboBox.setCurrentIndex(self.region_list.indexFromItem(item[0]).row()) - - def on_region_selected(self, idx): - region_name = self.region_list.item(idx).text() - region_idx = np.where(self.atlas.regions.name == region_name)[0][0] - self.qmain.refresh_locked_region(self.atlas.regions.id[region_idx]) - - -class CoverageView(QtWidgets.QWidget): - def __init__(self, qmain: MainWindow): - self.qmain = qmain - super(CoverageView, self).__init__() - uic.loadUi(Path(__file__).parent.joinpath('coverageUI.ui'), self) - - self.ctrl = CoverageController(self) - - self.populate_combobox(self.x_comboBox, self.ctrl.model.x_steps) - self.populate_combobox(self.y_comboBox, self.ctrl.model.y_steps) - self.populate_combobox(self.d_comboBox, self.ctrl.model.d_steps) - self.populate_combobox(self.t_comboBox, self.ctrl.model.t_steps) - self.populate_combobox(self.p_comboBox, self.ctrl.model.p_steps) - self.x_comboBox.currentIndexChanged.connect(lambda: self.update_step(self.x_comboBox, 'x')) - self.y_comboBox.currentIndexChanged.connect(lambda: self.update_step(self.y_comboBox, 'y')) - self.d_comboBox.currentIndexChanged.connect(lambda: self.update_step(self.d_comboBox, 'd')) - self.t_comboBox.currentIndexChanged.connect(lambda: self.update_step(self.t_comboBox, 't')) - self.p_comboBox.currentIndexChanged.connect(lambda: self.update_step(self.p_comboBox, 'p')) - - self.xplus_pushButton.clicked.connect(lambda: self.ctrl.increase_value('x')) - self.yplus_pushButton.clicked.connect(lambda: self.ctrl.increase_value('y')) - self.dplus_pushButton.clicked.connect(lambda: self.ctrl.increase_value('d')) - self.tplus_pushButton.clicked.connect(lambda: self.ctrl.increase_value('t')) - self.pplus_pushButton.clicked.connect(lambda: self.ctrl.increase_value('p')) - - self.xminus_pushButton.clicked.connect(lambda: self.ctrl.decrease_value('x')) - self.yminus_pushButton.clicked.connect(lambda: self.ctrl.decrease_value('y')) - self.dminus_pushButton.clicked.connect(lambda: self.ctrl.decrease_value('d')) - self.tminus_pushButton.clicked.connect(lambda: self.ctrl.decrease_value('t')) - self.pminus_pushButton.clicked.connect(lambda: self.ctrl.decrease_value('p')) - - self.x_label.returnPressed.connect(self.set_values) - self.y_label.returnPressed.connect(self.set_values) - self.d_label.returnPressed.connect(self.set_values) - self.t_label.returnPressed.connect(self.set_values) - self.p_label.returnPressed.connect(self.set_values) - - self.add_pushButton.clicked.connect(self.qmain.coverage_added) - - self.update_labels() - - def populate_combobox(self, combobox, values): - for val in values: - combobox.addItem(str(val)) - - def update_step(self, combobox, target): - index = combobox.currentIndex() - step = int(combobox.itemText(index)) - self.ctrl.set_step(step, target) - - def update_labels(self): - self.x_label.setText(f"{self.ctrl.model.data['x']['value']:.0f}") - self.y_label.setText(f"{self.ctrl.model.data['y']['value']:.0f}") - self.d_label.setText(f"{self.ctrl.model.data['d']['value']:.0f}") - self.t_label.setText(f"{self.ctrl.model.data['t']['value']:.0f}") - self.p_label.setText(f"{self.ctrl.model.data['p']['value']:.0f}") - - def update_view(self): - self.update_labels() - self.qmain.add_extra_coverage(self.ctrl.model.get_traj()) - self.qmain.coverage_increase() - - def set_values(self): - self.ctrl.set_value(int(self.x_label.text()), 'x') - self.ctrl.set_value(int(self.y_label.text()), 'y') - self.ctrl.set_value(int(self.d_label.text()), 'd') - self.ctrl.set_value(int(self.t_label.text()), 't') - self.ctrl.set_value(int(self.p_label.text()), 'p') - self.update_view() - - -class InsertionTableView(QtWidgets.QWidget): - def __init__(self, qmain: MainWindow): - super(InsertionTableView, self).__init__() - self.qmain = qmain - data = pd.DataFrame(columns=['x', 'y', 'z', 'phi', 'theta', 'depth', 'provenance']) - self.ctrl = InsertionTableController(self, data) - - self.tableview = self.qmain.tableview - self.tableview.setModel(self.ctrl.model) - - # self.delete_button = QtWidgets.QPushButton('-') - self.delete_button = self.qmain.delete_button - self.delete_button.clicked.connect(self.on_delete_pressed) - self.tableview.selectionModel().selectionChanged.connect(self.row_selected) - self.ctrl.model.dataChanged.connect(self.data_changed) - - def on_delete_pressed(self): - if len(self.tableview.selectedIndexes()) == self.ctrl.model.columnCount(): - row = self.tableview.selectedIndexes()[0].row() - self.ctrl.model.removeRow(row) - self.qmain.get_coverage_from_table() - - def row_selected(self): - if len(self.tableview.selectedIndexes()) == self.ctrl.model.columnCount(): - row = self.tableview.selectedIndexes()[0].row() - traj = self.ctrl.model.arraydata.iloc[row].to_dict() - self.qmain.do_this_thing(traj) - - def initialise_data(self, data): - self.ctrl.model.setDataFrame(data) - self.qmain.get_coverage_from_table() - - def data_changed(self): - self.qmain.get_coverage_from_table() - - -class InsertionTableController: - def __init__(self, view: InsertionTableView, data): - self.view = view - self.model = InsertionTableModel(data) - - -class InsertionTableModel(QtCore.QAbstractTableModel): - def __init__(self, data=None): - super(InsertionTableModel, self).__init__() - - self.arraydata = data - - def setDataFrame(self, data): - self.beginResetModel() - self.arraydata = data.copy() - self.endResetModel() - - def rowCount(self, parent=QtCore.QModelIndex()): - if parent.isValid(): - return 0 - return len(self.arraydata.index) - - def columnCount(self, parent=QtCore.QModelIndex()): - if parent.isValid(): - return 0 - return self.arraydata.columns.size - - def data(self, index, role): - if (not index.isValid() or not (0 <= index.row() < self.rowCount() and - 0 <= index.column() < self.columnCount())): - return QtCore.QVariant() - row = self.arraydata.index[index.row()] - col = self.arraydata.columns[index.column()] - - val = self.arraydata.iloc[row][col] - if role == QtCore.Qt.DisplayRole: - return str(val) - else: - return QtCore.QVariant() - - def headerData(self, x, orientation, role): - if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole: - return self.arraydata.columns[x] - if orientation == QtCore.Qt.Vertical and role == QtCore.Qt.DisplayRole: - return self.arraydata.index[x] - return None - - def setData(self, index, value, role): - if not index.isValid(): - return False - if role != QtCore.Qt.EditRole: - return False - row = index.row() - if row < 0 or row >= len(self.arraydata.values): - return False - column = index.column() - if column < 0 or column >= self.arraydata.columns.size: - return False - - self.arraydata.iloc[row, column] = int(value) - self.dataChanged.emit(index, index) - return True - - def flags(self, index): - return QtCore.Qt.ItemIsEditable | QtCore.Qt.ItemIsEnabled | QtCore.Qt.ItemIsSelectable - - def insertRow(self, data): - self.beginInsertRows(QtCore.QModelIndex(), self.rowCount(), self.rowCount()) - self.arraydata = self.arraydata.append(data, ignore_index=True) - self.endInsertRows() - return True - - def removeRow(self, position): - - self.beginRemoveRows(QtCore.QModelIndex(), position, position) - self.arraydata = self.arraydata.drop([position]) - self.arraydata = self.arraydata.reset_index(drop=True) - self.endRemoveRows() - - return True - - def to_dict(self): - return self.arraydata.to_dict(orient='records') - - -class CoverageController: - def __init__(self, view: CoverageView): - self.view = view - self.model = CoverageModel() - - def increase_value(self, target): - self.model.data[target]['value'] += self.model.data[target]['step'] - if self.model.data[target]['max'] is not None: - if self.model.data[target]['value'] > self.model.data[target]['max']: - self.model.data[target]['value'] = self.model.data[target]['max'] - - self.view.update_view() - - def decrease_value(self, target): - self.model.data[target]['value'] -= self.model.data[target]['step'] - if self.model.data[target]['min'] is not None: - if self.model.data[target]['value'] < self.model.data[target]['min']: - self.model.data[target]['value'] = self.model.data[target]['min'] - - self.view.update_view() - - def set_step(self, value, target): - self.model.data[target]['step'] = value - - def set_value(self, value, target): - self.model.data[target]['value'] = value - - -class CoverageModel: - def __init__(self): - self.data = {'x': {}, 'y': {}, 'd': {}, 't': {}, 'p': {}} - self.x_steps = [100, 500, 50, 10] - self.y_steps = [100, 500, 50, 10] - self.d_steps = [100, 500, 50, 10] - self.t_steps = [5, 1, 10] - self.p_steps = [5, 1, 10] - self.x_minmax = [None, None] - self.y_minmax = [None, None] - self.d_minmax = [None, None] - self.t_minmax = [-30, 30] - self.p_minmax = [-30, 30] - - self.initialise_data() - - def initialise_data(self): - self._initialise_data('x', self.x_steps[0], self.x_minmax, start_value=0) - self._initialise_data('y', self.y_steps[0], self.y_minmax, start_value=0) - self._initialise_data('d', self.d_steps[0], self.d_minmax, start_value=4000) - self._initialise_data('t', self.t_steps[0], self.t_minmax, start_value=15) - self._initialise_data('p', self.p_steps[0], self.p_minmax, start_value=0) - - def _initialise_data(self, target, step, minmax=(None, None), start_value=None): - self.data[target]['value'] = start_value - self.data[target]['step'] = step - self.data[target]['min'] = minmax[0] - self.data[target]['max'] = minmax[1] - - def get_traj(self): - traj = {'x': float(self.data['x']['value']), - 'y': float(self.data['y']['value']), - 'z': 0.0, - 'phi': float(self.data['p']['value']), - 'theta': float(self.data['t']['value']), - 'depth': float(self.data['d']['value']), - 'provenance': 'Planned'} - - return traj - - def to_df(self): - return pd.DataFrame([self.get_traj()]) - - -class ProbeView: - def __init__(self, qmain: MainWindow, fig: pg.PlotWidget): - self.qmain = qmain - self.fig_probe = fig - self.fig_probe.setYRange(min=0, max=4000) - - def plot_region_along_probe(self, region, region_label, region_color): - self.fig_probe.clear() - axis = self.fig_probe.getAxis('left') - axis.setTicks([region_label]) - axis.setZValue(10) - - # Plot each histology region - for reg, reg_col in zip(region, region_color): - colour = QtGui.QColor(*reg_col) - region = pg.LinearRegionItem(values=(reg[0], reg[1]), - orientation=pg.LinearRegionItem.Horizontal, - brush=colour, movable=False) - self.fig_probe.addItem(region) - - self.fig_probe.setYRange(min=0, max=4000) - axis = self.fig_probe.getAxis('bottom') - axis.setTicks([[(0, ''), (0.5, ''), (1, '')]]) - - -class TopView: - def __init__(self, qmain: MainWindow, fig: pg.PlotWidget, atlas: AllenAtlas, **kwargs): - self.qmain = qmain - self.atlas = atlas - self.fig_top = fig - self.ctrl = TopController(self, qmain, atlas, **kwargs) - - self.fig_top.setAspectLocked(True) - self.ctrl.add_image_layer(name='top') - - line_kwargs = {'movable': True, 'pen': pg.mkPen((0, 255, 0), width=1)} - self.line_x = pg.InfiniteLine(angle=90, pos=0, **line_kwargs) - self.line_y = pg.InfiniteLine(angle=0, pos=0, **line_kwargs) - self.line_x.setValue(0) - self.line_y.setValue(0) - - self.line_x.sigDragged.connect( - lambda: self.qmain.refresh_slices('coronal', 'x', self.line_x.value(), False)) - self.line_y.sigDragged.connect( - lambda: self.qmain.refresh_slices('sagittal', 'x', self.line_y.value(), False)) - self.fig_top.addItem(self.line_x) - self.fig_top.addItem(self.line_y) - - # selected insertion - self.ctrl.add_scatter_layer('coverage', pen='y', brush='y', - callback=self.qmain.on_insertion_clicked) - - # planned - self.ctrl.add_scatter_layer('planned_insertions', pen='m', brush='m', - callback=self.qmain.on_planned_insertion_clicked) - - # coverage - self.ctrl.add_scatter_layer('active_insertion', pen='r', brush='r', - callback=self.qmain.on_active_insertion_clicked) - - self.ctrl.add_scatter_layer('selected_insertion', pen='k', brush=pg.mkBrush(None)) - - # First pass - self.ctrl.add_scatter_layer('first_pass', pen='b', brush='b', - callback=self.qmain.on_first_pass_insertion_clicked) - - s = self.fig_top.scene() - s.sigMouseClicked.connect(self.mouseClick) - - self.ctrl.set_top() - - def mouseClick(self, event): - if event.double(): - qpoint = self.ctrl.image_layers[0].image_item.mapFromScene(event.scenePos()) - iw, ih, w, h, v = self.ctrl.cursor2xyamp(qpoint) - #TODO check within bounds - self.ctrl.set_scatter_layer('selected_insertion', x=[w], y=[h]) - self.qmain.add_trajectory(w * 1e6, h * 1e6) - - -class SliceView: - """ - Window containing a volume slice - """ - def __init__(self, qmain: MainWindow, fig: pg.PlotWidget, atlas: AllenAtlas, - slice, waxis, haxis, daxis): - self.qmain = qmain - self.atlas = atlas - self.slice = slice - self.slice_coord = 0 - self.fig_slice = fig - - self.ctrl = SliceController(self, qmain, atlas, waxis, haxis, daxis) - self.fig_slice.setAspectLocked(True) - - self.ctrl.add_image_layer(slice_kwargs={'volume': 'image', 'mode': 'clip'}, - pg_kwargs={'opacity': 0.8}) - self.ctrl.add_mask_layer(name='locked', cmap='Blues') - self.ctrl.add_mask_layer(name='non_locked', cmap='Greens') - - # connect signals and slots - s = self.fig_slice.scene() - self.proxy = pg.SignalProxy(s.sigMouseMoved, rateLimit=60, slot=self.mouseMoveEvent) - s.sigMouseClicked.connect(self.mouseClick) - - line_kwargs = {'movable': True, 'pen': pg.mkPen((0, 125, 0), width=1)} - self.line_x = pg.InfiniteLine(angle=90, pos=0, **line_kwargs) - self.line_y = pg.InfiniteLine(angle=0, pos=0, **line_kwargs) - self.line_x.sigDragged.connect(lambda: self.line_dragged(self.line_x, 'x')) - self.line_y.sigDragged.connect(lambda: self.line_dragged(self.line_y, 'y')) - self.fig_slice.addItem(self.line_x) - self.fig_slice.addItem(self.line_y) - - def line_dragged(self, line, orientation): - value = line.value() - self.qmain.refresh_slices(self.slice, orientation, value) - - def mouseClick(self, event): - if event.double(): - self.qmain._reset_region('locked') - else: - qpoint = self.ctrl.image_layers[0].image_item.mapFromScene(event.scenePos()) - iw, ih, w, h, v, region, _ = self.ctrl.cursor2xyamp(qpoint) - if region and region['acronym'][0] != 'void': - self.qmain.refresh_locked_region(region['id'][0]) - - def mouseMoveEvent(self, scenepos): - if isinstance(scenepos, tuple): - scenepos = scenepos[0] - else: - return - qpoint = self.ctrl.image_layers[0].image_item.mapFromScene(scenepos) - iw, ih, w, h, v, region, xyz = self.ctrl.cursor2xyamp(qpoint) - - self.qmain.refresh_highlighted_region(region, xyz) - - -class BaseController: - """ - Abstract class that implements mapping from axes to voxels for any window. - Not instantiated directly. - """ - def __init__(self, atlas: AllenAtlas, fig): - self.transform = None # affine transform image indices 2 data domain - self.image_layers = [] - self.atlas = atlas - self.fig = fig - - def cursor2xyamp(self, qpoint): - """Used for the mouse hover function over image display""" - iw, ih = self.cursor2ind(qpoint) - v = self.im[iw, ih] - w, h, _ = np.matmul(self.transform, np.array([iw, ih, 1])) - return iw, ih, w, h, v - - def cursor2ind(self, qpoint): - """ image coordinates over the image display""" - iw = np.max((0, np.min((int(np.floor(qpoint.x())), self.nw - 1)))) - ih = np.max((0, np.min((int(np.round(qpoint.y())), self.nh - 1)))) - return iw, ih - - def set_image(self, image_item, im, dw, dh, w0, h0, **pg_kwargs): - """ - :param im: - :param dw: - :param dh: - :param w0: - :param h0: - :param pgkwargs: pg.ImageItem.setImage() parameters: level=None, lut=None, opacity=1 - :return: - """ - self.im = im - self.nw, self.nh = self.im.shape[0:2] - image_item.setImage(self.im, **pg_kwargs) - transform = [dw, 0., 0., 0., dh, 0., w0, h0, 1.] - self.transform = np.array(transform).reshape((3, 3)).T - image_item.setTransform(QTransform(*transform)) - - def add_image_layer(self, idx=None, **kwargs): - """ - :param name: name of the image item to keep track of layers - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, - 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :return: - """ - il = ImageLayer(**kwargs) - if idx is not None: - self.image_layers.insert(idx, il) - else: - self.image_layers.append(il) - self.fig.addItem(il.image_item) - - def add_mask_layer(self, name='locked', cmap='Blues'): - colormap = matplotlib.cm.get_cmap(cmap) - colormap._init() - # The last one is [0, 0, 0, 0] so remove this - lut = (colormap._lut * 255).view(np.ndarray)[:-1] - lut = np.insert(lut, 0, [0, 0, 0, 0], axis=0) - self.add_image_layer(name=name, pg_kwargs={'lut': lut, 'opacity': 1}, slice_kwargs={ - 'volume': 'value', 'region_values': np.zeros_like(self.atlas.regions.id), - 'mode': 'clip', 'bc': None}) - - def add_volume_layer(self, volume, name='coverage', cmap='viridis', opacity=0.8, - bc=None, levels=None): - # If there is a layer with the same name remove it - self.remove_image_layer(name) - colormap = matplotlib.cm.get_cmap(cmap) - colormap._init() - # The last one is [0, 0, 0, 0] so remove this - lut = (colormap._lut * 255).view(np.ndarray)[:-1] - lut = np.insert(lut, 0, [0, 0, 0, 0], axis=0) - if levels is None: - levels = (0, np.nanmax(volume)) - self.add_image_layer(name=name, pg_kwargs={'lut': lut, 'opacity': opacity, 'levels': levels}, - slice_kwargs={'volume': 'volume', 'region_values': volume, - 'mode': 'clip', 'bc': bc}) - - def change_base_layer(self, image='Image'): - base_layer = self.get_image_layer(name='base') - if image == 'Image': - base_layer.slice_kwargs = {'volume': 'image', 'mode': 'clip', 'bc': None} - elif image == 'Annotation': - base_layer.slice_kwargs = {'volume': 'annotation', 'mode': 'clip', 'bc': None} - - def remove_image_layer(self, name): - im_idx = np.where([im.name == name for im in self.image_layers])[0] - if len(im_idx) != 0: - il = self.image_layers.pop(im_idx[0]) - self.fig.removeItem(il.image_item) - - def get_image_layer(self, name=None): - """Returns the either the first image item or the image item with specified name""" - if not name: - return self.image_layers[0] - else: - im_idx = np.where([im.name == name for im in self.image_layers])[0] - if len(im_idx) == 1: - return self.image_layers[im_idx[0]] - else: - return None - - def toggle_image_layer(self, name, checked=True): - layer = self.get_image_layer(name=name) - if checked: - self.fig.addItem(layer.image_item) - else: - self.fig.removeItem(layer.image_item) - - -class TopController(BaseController): - """ - TopView ControllerTopView - """ - def __init__(self, topview: TopView, qmain: MainWindow, atlas: AllenAtlas): - super(TopController, self).__init__(atlas, topview.fig_top) - self.fig = topview.fig_top - self.atlas = atlas - self.scatter_layers = [] - - def set_top(self): - self.atlas.compute_surface() - img = self.atlas.top.transpose() - img[np.isnan(img)] = np.nanmin(img) # img has dims ml, ap - dw, dh = (self.atlas.bc.dxyz[0], self.atlas.bc.dxyz[1]) - wl, hl = (self.atlas.bc.xlim, self.atlas.bc.ylim) - self.set_image(self.image_layers[0].image_item, img, dw, dh, wl[0], hl[0]) - - def set_scatter_layer(self, name, x=None, y=None): - layer = self.get_scatter_layer(name) - if x is not None: - layer.scatter_item.setData(x=x, y=y, **layer.pg_kwargs) - else: - layer.scatter_item.setData() - - def add_scatter_layer(self, name, pen='g', brush='g', callback=None): - self.remove_scatter_layer(name) - - sc = self.add_scatter(name=name, pg_kwargs={'pen': pen, 'brush': brush}) - if callback: - sc.scatter_item.sigClicked.connect(callback) - - def get_scatter_layer(self, name=None): - """Returns the either the first image item or the image item with specified name""" - if not name: - return self.scatter_layers[0] - else: - sc_idx = np.where([sc.name == name for sc in self.scatter_layers])[0] - if len(sc_idx) == 1: - return self.scatter_layers[sc_idx[0]] - else: - return None - - - def add_scatter(self, idx=None, **kwargs): - """ - :param name: name of the image item to keep track of layers - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, - 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :return: - """ - sc = ScatterLayer(**kwargs) - if idx is not None: - self.scatter_layers.insert(idx, sc) - else: - self.scatter_layers.append(sc) - self.fig.addItem(sc.scatter_item) - return sc - - def remove_scatter_layer(self, name): - sc_idx = np.where([sc.name == name for sc in self.scatter_layers])[0] - if len(sc_idx) != 0: - sc = self.image_layers.pop(sc_idx[0]) - self.fig.removeItem(sc.scatter_item) - - def toggle_scatter_layer(self, name, checked=True): - layer = self.get_scatter_layer(name=name) - if layer: - if checked: - self.fig.addItem(layer.scatter_item) - else: - self.fig.removeItem(layer.scatter_item) - - - -class SliceController(BaseController): - - def __init__(self, sliceview: SliceView, qmain: MainWindow, atlas: AllenAtlas, - waxis=None, haxis=None, daxis=None): - """ - :param waxis: brain atlas axis corresponding to display abscissa (coronal: 0, sagittal: 1) - :param haxis: brain atlas axis corresponding to display ordinate (coronal: 2, sagittal: 2) - :param daxis: brain atlas axis corresponding to display abscissa (coronal: 1, sagittal: 0) - """ - self.view = sliceview - self.fig = self.view.fig_slice - super(SliceController, self).__init__(atlas, self.fig) - self.waxis = waxis - self.haxis = haxis - self.daxis = daxis - self.atlas = atlas - self.qmain = qmain - - def cursor2xyamp(self, qpoint): - """ - Extends the superclass method to also get the brain region from the model - :param qpoint: - :return: - """ - iw, ih, w, h, v = super(SliceController, self).cursor2xyamp(qpoint) - xyz = np.zeros(3) - xyz[np.array([self.waxis, self.haxis, self.daxis])] = [w, h, self.view.slice_coord] - try: - region = self.atlas.regions.get( - self.atlas.get_labels(xyz, mapping=self.qmain.get_mapping())) - except ValueError: - region = None - return iw, ih, w, h, v, region, xyz - - def set_slice(self): - # construct the transform matrix image 2 ibl coordinates - # the ImageLayer object carries slice kwargs and pyqtgraph ImageSet kwargs - # reversed order so the self.im is set with the base layer - # - for layer in reversed(self.image_layers): - bc = layer.slice_kwargs.get('bc', None) or self.atlas.bc - dw = bc.dxyz[self.waxis] - dh = bc.dxyz[self.haxis] - wl = bc.lim(self.waxis) - dw / 2 - hl = bc.lim(self.haxis) - dh / 2 - - _slice = self.atlas.slice(self.view.slice_coord, axis=self.daxis, - mapping=self.qmain.get_mapping(), - **layer.slice_kwargs) - self.set_image(layer.image_item, _slice, dw, dh, wl[0], hl[0], **layer.pg_kwargs) - # needs to have it's own bc - # bc is on the image levell - - -@dataclass -class ImageLayer: - """ - Class for keeping track of image layers. - :param image_item - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :param - """ - name: str = field(default='base') - image_item: pg.ImageItem = field(default_factory=pg.ImageItem) - pg_kwargs: dict = field(default_factory=lambda: {}) - slice_kwargs: dict = field(default_factory=lambda: {'volume': 'image', 'mode': 'clip', - 'bc': None}) - -@dataclass -class ScatterLayer: - """ - Class for keeping track of scatter layers. - :param scatter_item - :param pg_kwargs: pyqtgraph scatter arguments: {'pen': None, 'brush': None, 'size': None} - :param callback: callback function - :param - """ - name: str = field(default='base') - scatter_item: pg.ImageItem = field(default_factory=pg.ScatterPlotItem) - pg_kwargs: dict = field(default_factory=lambda: {}) - scatter_kwargs: dict = field(default_factory=lambda: {'x': None, 'y': None}) - - -def view(title=None, lazy=False, res=25): - """ - """ - qt.create_app() - av = MainWindow._get_or_create(title=title, lazy=lazy, res=res) - av.show() - return av - - -if __name__ == '__main__': - - app = QtWidgets.QApplication([]) - mainapp = MainWindow() - # mainapp = MainWindow(offline=True) - mainapp.show() - app.exec_() - diff --git a/PythonScripts/iblapps-master/needles2/spike_features.py b/PythonScripts/iblapps-master/needles2/spike_features.py deleted file mode 100644 index 6c408262..00000000 --- a/PythonScripts/iblapps-master/needles2/spike_features.py +++ /dev/null @@ -1,507 +0,0 @@ -from PyQt5 import QtWidgets, QtGui, QtCore -import pyqtgraph as pg -import pyqtgraph.exporters -import qt -import atlaselectrophysiology.ColorBar as cb - -from dataclasses import dataclass, field -from pathlib import Path -import numpy as np - -import alf.io -from brainbox import ephys_plots -from brainbox.plot_base import LinePlot -pg.setConfigOption('background', 'w') -pg.setConfigOption('foreground', 'k') - -DATAPATH = r'C:\Users\Mayo\Downloads\FlatIron\SpikeSorting' -spikeSorters = ['ks2', 'pyks2.5', 'ks3'] - -PLOT_DICT = {'Firing Rate Raster Plot': 'frRaster', - 'Amplitude Raster Plot': 'ampRaster', - 'Cluster Raster Plot': 'clustRaster', - 'Cluster Amp vs Depth vs FR': 'ampDepthFr', - 'Cluster count': 'clustCount'} - - -# TODO seperate windows, describe plots - -class MainWindow(QtWidgets.QMainWindow): - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, MainWindow)] - - @staticmethod - def _get_or_create(eid, title=None, **kwargs): - av = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - MainWindow._instances()), None) - if av is None: - av = MainWindow(eid, **kwargs) - av.setWindowTitle(title) - return av - - def __init__(self, eid): - super(MainWindow, self).__init__() - self.resize(1650, 700) - self.eid = eid - self.view = SpikeSortView(self, eid) - mainWidget = QtWidgets.QWidget() - self.setCentralWidget(mainWidget) - mainLayout = QtWidgets.QGridLayout() - - menuBar = QtWidgets.QMenuBar(self) - menuBar.setNativeMenuBar(False) - self.setMenuBar(menuBar) - - plotOptions = menuBar.addMenu('Plots') - self.plotOptionsGroup = QtGui.QActionGroup(plotOptions) - self.plotOptionsGroup.setExclusive(True) - plotFrRaster = QtGui.QAction('Firing Rate Raster Plot', self, checkable=True, checked=True) - plotFrRaster.triggered.connect(self.view.ctrl.plotFrRaster) - plotOptions.addAction(plotFrRaster) - self.plotOptionsGroup.addAction(plotFrRaster) - - plotClustRaster = QtGui.QAction('Cluster Raster Plot', self, checkable=True, checked=False) - plotClustRaster.triggered.connect(self.view.ctrl.plotClusterRaster) - plotOptions.addAction(plotClustRaster) - self.plotOptionsGroup.addAction(plotClustRaster) - - plotAmpRaster = QtGui.QAction('Amplitude Raster Plot', self, checkable=True, checked=False) - plotAmpRaster.triggered.connect(self.view.ctrl.plotAmpRaster) - plotOptions.addAction(plotAmpRaster) - self.plotOptionsGroup.addAction(plotAmpRaster) - - plotAmpDepthFr = QtGui.QAction('Cluster Amp vs Depth vs FR', self, checkable=True, - checked=False) - plotAmpDepthFr.triggered.connect(self.view.ctrl.plotAmpDepthFr) - plotOptions.addAction(plotAmpDepthFr) - self.plotOptionsGroup.addAction(plotAmpDepthFr) - - plotclustCount = QtGui.QAction('Cluster count', self, checkable=True, - checked=False) - plotclustCount.triggered.connect(self.view.ctrl.plotClusterCount) - plotOptions.addAction(plotclustCount) - self.plotOptionsGroup.addAction(plotclustCount) - - for iw, wind in enumerate(self.view.spikeSortWindows): - mainLayout.addWidget(wind.widget, 0, iw) - - mainWidget.setLayout(mainLayout) - - def keyPressEvent(self, event): - if event.key() == QtCore.Qt.Key_N: - self.view.normalisePlots() - if event.key() == QtCore.Qt.Key_U: - self.view.unNormalisePlots() - if event.key() == QtCore.Qt.Key_P: - self.view.printPlots() - -# ONE DAY -#class SpikeSortWidget(QtWidgets.QWidget): -# def __init__(self, qmain: MainWindow, eid: str): - - -# View is going to have the main figures -class SpikeSortView: - def __init__(self, qmain: MainWindow, eid: str): - self.qmain = qmain - self.eid = eid - self.spikeSortWindows = [] - - # Add a figure window for each spikesorter - for iS, spikeSort in enumerate(spikeSorters): - widget, fig, fig_cb = self.makePlotWindow() - if iS == 0: - vb = fig.getViewBox() - else: - fig.getViewBox().setXLink(vb) - fig.getViewBox().setYLink(vb) - - self.spikeSortWindows.append(SpikeSortFigures(name=spikeSort, widget=widget, - fig=fig, cb=fig_cb)) - self.ctrl = SpikeSortController(self, eid) - - def makePlotWindow(self): - widget = pg.GraphicsLayoutWidget() - layout = pg.GraphicsLayout() - fig = pg.PlotItem() - fig_cb = pg.PlotItem() - fig_cb.setMouseEnabled(x=False, y=False) - fig.getViewBox().setAspectLocked(False) - self.setAxis(fig_cb, 'bottom', show=False) - self.setAxis(fig_cb, 'left', pen='w', label='blank') - layout.addItem(fig_cb, 0, 0) - layout.addItem(fig, 1, 0) - layout.layout.setRowStretchFactor(0, 1) - layout.layout.setRowStretchFactor(1, 10) - widget.addItem(layout) - return widget, fig, fig_cb - - def plotImage(self, fig, fig_cb, data): - [fig.removeItem(item) for item in fig.items] - [fig_cb.removeItem(item) for item in fig_cb.items] - - self.setAxis(fig_cb, 'top', pen='w') - - image = pg.ImageItem() - image.setImage(data.data['c']) - transform = [data.scale[0], 0., 0., 0., data.scale[1], 0., data.offset[0], - data.offset[1], 1.] - image.setTransform(QtGui.QTransform(*transform)) - - color_bar = cb.ColorBar(data.cmap) - lut = color_bar.getColourMap() - image.setLookupTable(lut) - image.setLevels((data.clim[0], data.clim[1])) - cbar = color_bar.makeColourBar(20, 5, fig_cb, min=data.clim[0], - max=data.clim[1], label=data.labels['clabel']) - fig_cb.addItem(cbar) - fig.addItem(image) - fig.setLimits(xMin=data.xlim[0], xMax=data.xlim[1], yMin=data.ylim[0], yMax=data.ylim[1]) - fig.setXRange(min=data.xlim[0], max=data.xlim[1], padding=0) - fig.setYRange(min=data.ylim[0], max=data.ylim[1], padding=0) - self.setAxis(fig, 'bottom', label=data.labels['xlabel']) - self.setAxis(fig, 'left', label=data.labels['ylabel']) - - def plotScatter(self, fig, fig_cb, data): - [fig.removeItem(item) for item in fig.items] - [fig_cb.removeItem(item) for item in fig_cb.items] - - self.setAxis(fig_cb, 'top', pen='w') - - size = data.marker_size.tolist() if not isinstance(data.marker_size, int) else \ - data.marker_size - symbol = data.marker_type.tolist() if not isinstance(data.marker_type, str) else \ - data.marker_type - color_bar = cb.ColorBar(data.cmap) - brush = color_bar.getBrush(data.data['c'], levels=[data.clim[0], data.clim[1]]) - cbar = color_bar.makeColourBar(20, 5, fig_cb, min=data.clim[0], - max=data.clim[1], label=data.labels['clabel']) - plot = pg.ScatterPlotItem() - plot.setData(x=data.data['x'], y=data.data['y'], - symbol=symbol, size=size, brush=brush, pen=data.line_color) - - fig_cb.addItem(cbar) - fig.addItem(plot) - fig.setLimits(xMin=data.xlim[0], xMax=data.xlim[1], yMin=data.ylim[0], yMax=data.ylim[1]) - fig.setXRange(min=data.xlim[0], max=data.xlim[1], padding=0) - fig.setYRange(min=data.ylim[0], max=data.ylim[1], padding=0) - self.setAxis(fig, 'bottom', label=data.labels['xlabel']) - self.setAxis(fig, 'left', label=data.labels['ylabel']) - - def plotBar(self, fig, fig_cb, data): - [fig.removeItem(item) for item in fig.items] - [fig_cb.removeItem(item) for item in fig_cb.items] - - self.setAxis(fig_cb, 'top', pen='w') - - plot = pg.BarGraphItem(x=data.data['x'], height=data.data['y'], width=0.6, brush='r') - fig.addItem(plot) - fig.setLimits(xMin=data.xlim[0], xMax=data.xlim[1], yMin=data.ylim[0], yMax=data.ylim[1]) - fig.setXRange(min=data.xlim[0], max=data.xlim[1], padding=0) - fig.setYRange(min=data.ylim[0], max=data.ylim[1], padding=0) - self.setAxis(fig, 'bottom', label=data.labels['xlabel']) - self.setAxis(fig, 'left', label=data.labels['ylabel']) - - # For bar plot also add a label - fig_cb.setTitle(f'N clusters = {np.max(data.data["x"])}, N spikes = {np.sum(data.data["y"])}') - - - def setAxis(self, fig, ax, show=True, label=None, pen='k', fontsize=12, ticks=True): - """ - Show/hide and configure axis of figure - :param fig: figure associated with axis - :type fig: pyqtgraph PlotWidget - :param ax: orientation of axis, must be one of 'left', 'right', 'top' or 'bottom' - :type ax: string - :param show: 'True' to show axis, 'False' to hide axis - :type show: bool - :param label: axis label - :type label: string - :parm pen: colour on axis - :type pen: string - :param ticks: 'True' to show axis ticks, 'False' to hide axis ticks - :param ticks: bool - :return axis: axis object - :type axis: pyqtgraph AxisItem - """ - if not label: - label = '' - if type(fig) == pg.PlotItem: - axis = fig.getAxis(ax) - else: - axis = fig.plotItem.getAxis(ax) - if show: - axis.show() - labelStyle = {'font-size': f'{fontsize}pt'} - axis.setLabel(label, **labelStyle) - font = QtGui.QFont() - font.setPixelSize(fontsize) - axis.setTickFont(font) - axis.setPen(pen) - axis.setTextPen(pen) - - if not ticks: - axis.setTicks([[(0, ''), (0.5, ''), (1, '')]]) - else: - axis.hide() - - return axis - - def getPlotType(self): - return self.qmain.plotOptionsGroup.checkedAction() - - - def normalisePlots(self): - selectedPlot = self.getPlotType() - self.ctrl.model.normaliseAcrossSorters(PLOT_DICT[selectedPlot.text()]) - selectedPlot.trigger() - - def unNormalisePlots(self): - selectedPlot = self.getPlotType() - self.ctrl.model.unNormaliseAcrossSorters(PLOT_DICT[selectedPlot.text()]) - selectedPlot.trigger() - - def printPlots(self): - image_path = Path(DATAPATH).joinpath('figures') - image_path.mkdir(exist_ok=True, parents=True) - for spikeSort in self.spikeSortWindows: - exporter = pg.exporters.ImageExporter(spikeSort.widget.scene()) - exporter.export(str(image_path.joinpath(self.eid + '_' + spikeSort.name + '_' + - self.getPlotType().text() + '.png'))) - - -# Controller is going to change the plots etc -class SpikeSortController: - def __init__(self, view: SpikeSortView, eid: str): - - self.view = view - self.model = SpikeSortModel(self, DATAPATH, eid) - self.plotFrRaster() - - def plotFrRaster(self): - for spikeSort in spikeSorters: - data = self.model.getFrRaster(spikeSort) - window = self.get_spikeSorterWindow(spikeSort) - self.view.plotImage(window.fig, window.cb, data) - - def plotAmpRaster(self): - for spikeSort in spikeSorters: - data = self.model.getAmpRaster(spikeSort) - window = self.get_spikeSorterWindow(spikeSort) - self.view.plotScatter(window.fig, window.cb, data) - - def plotAmpDepthFr(self): - for spikeSort in spikeSorters: - data = self.model.getAmpDepthFr(spikeSort) - window = self.get_spikeSorterWindow(spikeSort) - self.view.plotScatter(window.fig, window.cb, data) - - def plotClusterRaster(self): - for spikeSort in spikeSorters: - data = self.model.getClusterRaster(spikeSort) - window = self.get_spikeSorterWindow(spikeSort) - self.view.plotScatter(window.fig, window.cb, data) - - def plotClusterCount(self): - for spikeSort in spikeSorters: - data = self.model.getClusterCount(spikeSort) - window = self.get_spikeSorterWindow(spikeSort) - self.view.plotBar(window.fig, window.cb, data) - - - def get_spikeSorterRawData(self, name=None): - """Returns the either the first image item or the image item with specified name""" - if not name: - return self.model.spikeSortRawData[0] - else: - idx = np.where([sp.name == name for sp in self.model.spikeSortRawData])[0][0] - return self.model.spikeSortRawData[idx] - - def get_spikeSorterPlotData(self, name=None): - """Returns the either the first image item or the image item with specified name""" - if not name: - return self.model.spikeSortPlotData[0] - else: - idx = np.where([sp.name == name for sp in self.model.spikeSortPlotData])[0][0] - return self.model.spikeSortPlotData[idx] - - def get_spikeSorterWindow(self, name=None): - """Returns the either the first image item or the image item with specified name""" - if not name: - return self.view.spikeSortWindows[0] - else: - idx = np.where([sp.name == name for sp in self.view.spikeSortWindows])[0][0] - return self.view.spikeSortWindows[idx] - - -class SpikeSortModel: - def __init__(self, ctrl: SpikeSortController, dataPath, eid): - self.ctrl = ctrl - - self.spikeSortRawData = [] - self.spikeSortPlotData = [] - - for spikeSort in spikeSorters: - spikeSortPath = Path(dataPath).joinpath(spikeSort, eid, 'alf') - spikes = alf.io.load_object(spikeSortPath, object='spikes') - spikes = self.removeNans(spikes) - clusters = alf.io.load_object(spikeSortPath, object='clusters') - channels = alf.io.load_object(spikeSortPath, object='channels') - self.spikeSortRawData.append(SpikeSortRawData(name=spikeSort, spikes=spikes, - clusters=clusters, channels=channels)) - self.spikeSortPlotData.append(SpikeSortPlotData(name=spikeSort)) - - def getFrRaster(self, spikeSort): - plotData = self.ctrl.get_spikeSorterPlotData(name=spikeSort) - if plotData.frRaster is None: - rawData = self.ctrl.get_spikeSorterRawData(name=spikeSort) - data = ephys_plots.image_fr_plot(rawData.spikes.depths, rawData.spikes.times, - rawData.channels.localCoordinates, t_bin=0.2) - data.set_offset() - data.set_scale() - self.setStartingRange(data) - plotData.frRaster = data - - return plotData.frRaster - - def getAmpRaster(self, spikeSort): - plotData = self.ctrl.get_spikeSorterPlotData(name=spikeSort) - if plotData.ampRaster is None: - rawData = self.ctrl.get_spikeSorterRawData(name=spikeSort) - data = ephys_plots.scatter_raster_plot(rawData.spikes.amps, rawData.spikes.depths, - rawData.spikes.times) - self.setStartingRange(data) - plotData.ampRaster = data - - return plotData.ampRaster - - def getAmpDepthFr(self, spikeSort): - plotData = self.ctrl.get_spikeSorterPlotData(name=spikeSort) - if plotData.ampDepthFr is None: - rawData = self.ctrl.get_spikeSorterRawData(name=spikeSort) - data = (ephys_plots. - scatter_amp_depth_fr_plot(rawData.spikes.amps, rawData.spikes.clusters, - rawData.spikes.depths, rawData.spikes.times)) - self.setStartingRange(data) - data.set_line_color('k') - plotData.ampDepthFr = data - - return plotData.ampDepthFr - - def getClusterRaster(self, spikeSort): - plotData = self.ctrl.get_spikeSorterPlotData(name=spikeSort) - if plotData.clustRaster is None: - rawData = self.ctrl.get_spikeSorterRawData(name=spikeSort) - data = ephys_plots.scatter_cluster_plot(rawData.spikes.clusters, rawData.spikes.depths, - rawData.spikes.times) - self.setStartingRange(data) - plotData.clustRaster = data - - return plotData.clustRaster - - def getClusterCount(self, spikeSort): - plotData = self.ctrl.get_spikeSorterPlotData(name=spikeSort) - if plotData.clustCount is None: - rawData = self.ctrl.get_spikeSorterRawData(name=spikeSort) - data = LinePlot(*np.unique(rawData.spikes.clusters, return_counts=True)) - data.set_labels(xlabel='Cluster number', ylabel='Cluster count') - self.setStartingRange(data) - plotData.clustCount = data - - return plotData.clustCount - - def removeNans(self, spikes): - kp_idx = np.bitwise_and(~np.isnan(spikes['depths']), ~np.isnan(spikes['amps'])) - for key in spikes.keys(): - spikes[key] = spikes[key][kp_idx] - - return spikes - - def setStartingRange(self, data): - data.clim_start = data.clim - data.xlim_start = data.xlim - data.ylim_start = data.ylim - - def normaliseAcrossSorters(self, plotType): - # have to go through this twice - min_val = {'x': [], 'y': [], 'c': []} - max_val = {'x': [], 'y': [], 'c': []} - for spikeSort in self.spikeSortPlotData: - plotData = spikeSort.__getattribute__(plotType) - min_val['x'].append(plotData.xlim[0]) - max_val['x'].append(plotData.xlim[1]) - min_val['y'].append(plotData.ylim[0]) - max_val['y'].append(plotData.ylim[1]) - min_val['c'].append(plotData.clim[0]) if plotData.clim[0] else min_val['c'].append(0) - max_val['c'].append(plotData.clim[1]) if plotData.clim[1] else max_val['c'].append(1) - - for spikeSort in self.spikeSortPlotData: - plotData = spikeSort.__getattribute__(plotType) - plotData.set_clim(clim=[np.min(min_val['c']), np.max(max_val['c'])]) - plotData.set_xlim(xlim=[np.min(min_val['x']), np.max(max_val['x'])]) - plotData.set_ylim(ylim=[np.min(min_val['y']), np.max(max_val['y'])]) - - def unNormaliseAcrossSorters(self, plotType): - for spikeSort in self.spikeSortPlotData: - plotData = spikeSort.__getattribute__(plotType) - plotData.set_clim(plotData.clim_start) - plotData.set_xlim(plotData.xlim_start) - plotData.set_ylim(plotData.ylim_start) - - -@dataclass -class SpikeSortFigures: - """ - Class for keeping track of image layers. - :param image_item - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :param - """ - name: str = field(default='base') - widget: pg.GraphicsLayoutWidget = field(default_factory=pg.GraphicsLayoutWidget) - fig: pg.PlotItem = field(default_factory=pg.PlotItem) - cb: pg.PlotItem = field(default_factory=pg.PlotItem) - - -@dataclass -class SpikeSortRawData: - """ - Class for keeping track of image layers. - :param image_item - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :param - """ - name: str = field(default='base') - spikes: dict = field(default=dict) - clusters: dict = field(default=dict) - channels: dict = field(default=dict) - - -@dataclass -class SpikeSortPlotData: - """ - Class for keeping track of image layers. - :param image_item - :param pg_kwargs: pyqtgraph setImage arguments: {'levels': None, 'lut': None, 'opacity': 1.0} - :param slice_kwargs: ibllib.atlas.slice arguments: {'volume': 'image', 'mode': 'clip'} - :param - """ - name: str = field(default='base') - frRaster: dict = field(default=None) - ampRaster: dict = field(default=None) - ampDepthFr: dict = field(default=None) - clustRaster: dict = field(default=None) - clustCount: dict = field(default=None) - -def view(eid, title=None): - """ - """ - qt.create_app() - av = MainWindow._get_or_create(eid, title=title) - av.show() - return av diff --git a/PythonScripts/iblapps-master/needles2/tableUI.ui b/PythonScripts/iblapps-master/needles2/tableUI.ui deleted file mode 100644 index 3816f5f2..00000000 --- a/PythonScripts/iblapps-master/needles2/tableUI.ui +++ /dev/null @@ -1,29 +0,0 @@ - - - Form - - - - 0 - 0 - 400 - 300 - - - - Form - - - - - 10 - 10 - 321 - 271 - - - - - - - diff --git a/PythonScripts/iblapps-master/prepare_ephys_data_ibl.py b/PythonScripts/iblapps-master/prepare_ephys_data_ibl.py deleted file mode 100755 index 1ccfccd7..00000000 --- a/PythonScripts/iblapps-master/prepare_ephys_data_ibl.py +++ /dev/null @@ -1,45 +0,0 @@ -from pathlib import Path -from ibllib.io import spikeglx -from atlaselectrophysiology.extract_files import extract_data, extract_rmsmap, _sample2v -import ibllib.ephys.ephysqc as ephysqc -from phylib.io import alf -import numpy as np -import os -import sys - -# Paths -# Path to KS2 output -#ks_path = Path(r'/Volumes/braininit/Data/Processed/electrophysiology/ms81/ms81_651/20220715/T4_Zhihao_g0/T4_Zhihao_g0_imec0/job_id_203/kilosort_output') -def prepare_data(ephys_path, ks_path, output_path): - - - ks_path = Path(ks_path) - ephys_path = Path(ephys_path) - # Save path - out_path = Path(output_path) - - # STEP 1: Load KS data and convert to ALF files - ap_bin_path = list(ephys_path.glob('*.ap.bin'))[0] - #ap_bin_path = list(ephys_path.glob('tmp_wh.dat'))[0] - lf_bin_path = list(ephys_path.glob('*.lf.bin'))[0] - m = ephysqc.phy_model_from_ks2_path(ks_path, ephys_path, ap_bin_path) - ac = alf.EphysAlfCreator(m) - ac.convert(out_path, force=True, ampfactor=_sample2v(ap_bin_path)) - - # STEP 2: SAVE AP & LF RMSMAP - extract_rmsmap(ap_bin_path, out_folder=out_path, spectra=False) - extract_rmsmap(lf_bin_path, out_folder=out_path) - - # STEP 3: Cluster metrics, using the spike depths computed already - spike_depths = np.load(out_path / 'spikes.depths.npy'); - m.depths = spike_depths - ephysqc.spike_sorting_metrics_ks2(ks_path, m, save=True) - os.rename(ks_path.joinpath('cluster_metrics.csv'), out_path.joinpath('cluster_metrics.csv')) - - print('All done') - -if __name__ == "__main__": - args = sys.argv[1:] - print(args) - - prepare_data(args[0], args[1], args[2]) \ No newline at end of file diff --git a/PythonScripts/iblapps-master/qt.py b/PythonScripts/iblapps-master/qt.py deleted file mode 100644 index 5b2ca4d7..00000000 --- a/PythonScripts/iblapps-master/qt.py +++ /dev/null @@ -1,44 +0,0 @@ -import logging -import sys -from functools import wraps - -from PyQt5 import QtWidgets - -_logger = logging.getLogger('ibllib') - - -def get_main_window(): - """ Get the Main window of a QT application""" - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, QtWidgets.QMainWindow)][0] - - -def create_app(): - """Create a Qt application.""" - global QT_APP - QT_APP = QtWidgets.QApplication.instance() - if QT_APP is None: # pragma: no cover - QT_APP = QtWidgets.QApplication(sys.argv) - return QT_APP - - -def require_qt(func): - """Function decorator to specify that a function requires a Qt application. - Use this decorator to specify that a function needs a running - Qt application before it can run. An error is raised if that is not - the case. - """ - @wraps(func) - def wrapped(*args, **kwargs): - if not QtWidgets.QApplication.instance(): # pragma: no cover - _logger.warning("Creating a Qt application.") - create_app() - return func(*args, **kwargs) - return wrapped - - -@require_qt -def run_app(): # pragma: no cover - """Run the Qt application.""" - global QT_APP - return QT_APP.exit(QT_APP.exec_()) diff --git a/PythonScripts/iblapps-master/qt_matplotlib.py b/PythonScripts/iblapps-master/qt_matplotlib.py deleted file mode 100644 index d3064d71..00000000 --- a/PythonScripts/iblapps-master/qt_matplotlib.py +++ /dev/null @@ -1,32 +0,0 @@ -import matplotlib -# Make sure that we are using QT5 -matplotlib.use('Qt5Agg') -from PyQt5 import QtCore, QtWidgets, uic - -from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas -from matplotlib.figure import Figure - - -class BaseMplCanvas(FigureCanvas): - """ - ui - model - """ - def __init__(self, parent=None, ui=None, model=None, width=5, height=4, dpi=100): - fig = Figure(figsize=(width, height), dpi=dpi) - self.axes = fig.add_subplot(111) - self._ui = ui - self._model = model - self.compute_initial_figure() - - FigureCanvas.__init__(self, fig) - self.setParent(parent) - - FigureCanvas.setSizePolicy(self, - QtWidgets.QSizePolicy.Expanding, - QtWidgets.QSizePolicy.Expanding) - FigureCanvas.updateGeometry(self) - self.setCursor(QtCore.Qt.CrossCursor) - - def compute_initial_figure(self): - pass diff --git a/PythonScripts/iblapps-master/requirements.txt b/PythonScripts/iblapps-master/requirements.txt deleted file mode 100644 index d20bf163..00000000 --- a/PythonScripts/iblapps-master/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -easyqc -ibllib -pyqtgraph -simpleITK -PyQt5==5.12.3 diff --git a/PythonScripts/iblapps-master/run_tests b/PythonScripts/iblapps-master/run_tests deleted file mode 100644 index 23296e6d..00000000 --- a/PythonScripts/iblapps-master/run_tests +++ /dev/null @@ -1 +0,0 @@ -python -m unittest discover diff --git a/PythonScripts/iblapps-master/setup.py b/PythonScripts/iblapps-master/setup.py deleted file mode 100644 index ef3f9ee6..00000000 --- a/PythonScripts/iblapps-master/setup.py +++ /dev/null @@ -1,12 +0,0 @@ -from setuptools import setup, find_packages - -with open('requirements.txt') as f: - require = [x.strip() for x in f.readlines() if not x.startswith('git+')] - -setup( - name='iblapps', - version='0.1', - packages=find_packages(), - include_package_data=True, - install_requires=require -) diff --git a/PythonScripts/iblapps-master/task_qc_viewer/README.md b/PythonScripts/iblapps-master/task_qc_viewer/README.md deleted file mode 100644 index e413ef5c..00000000 --- a/PythonScripts/iblapps-master/task_qc_viewer/README.md +++ /dev/null @@ -1,63 +0,0 @@ -# Task QC Viewer -This will download the TTL pulses and data collected on Bpod and/or FPGA and plot the results -alongside an interactive table. -The UUID is the session id. - -## Setup -Needs the iblenv installed properly. Follow this guide for setup: https://github.com/int-brain-lab/iblenv#iblenv - - -## Usage: command line -If on the server PC, activate the environment by typing: -``` -conda activate iblenv -``` -Otherwise, activate the iblenv as described in the guide above. - -Go into the iblapps directory that you cloned: -``` -cd /home/olivier/Documents/PYTHON/iblapps/task_qc_viewer -git checkout develop -git pull -``` -Launch the Viewer by typing `ipython task_qc.py session_UUID` , example: -``` -python task_qc.py c9fec76e-7a20-4da4-93ad-04510a89473b -# or with ipython -ipython task_qc.py -- c9fec76e-7a20-4da4-93ad-04510a89473b -``` - -Or just using a local path (on a local server for example): -``` -python task_qc.py /mnt/s0/Subjects/KS022/2019-12-10/001 --local -# or with ipython -ipython task_qc.py -- /mnt/s0/Subjects/KS022/2019-12-10/001 --local -``` - -## Usage: from ipython prompt -``` python -from iblapps.task_qc_viewer.task_qc import show_session_task_qc -session_path = "/datadisk/Data/IntegrationTests/ephys/choice_world_init/KS022/2019-12-10/001" -show_session_task_qc(session_path, local=True) -``` - -## Plots -1) Sync pulse display: -- TTL sync pulses (as recorded on the Bpod or FPGA for ephys sessions) for some key apparatus (i -.e. frame2TTL, audio signal). TTL pulse trains are displayed in black (time on x-axis, voltage on y-axis), offset by an increment of 1 each time (e.g. audio signal is on line 3, cf legend). -- trial event types, vertical lines (marked in different colours) - -2) Wheel display: -- the wheel position in radians -- trial event types, vertical lines (marked in different colours) - -3) Interactive table: -Each row is a trial entry. Each column is a trial event - -When double-clicking on any field of that table, the Sync pulse display time (x-) axis is adjusted so as to visualise the corresponding trial selected. - -### What to look for -Tests are defined in the SINGLE METRICS section of ibllib/qc/task_metrics.py: https://github.com/int-brain-lab/ibllib/blob/master/ibllib/qc/task_metrics.py#L148-L149 - -### Exit -Close the GUI window containing the interactive table to exit. diff --git a/PythonScripts/iblapps-master/task_qc_viewer/ViewEphysQC.py b/PythonScripts/iblapps-master/task_qc_viewer/ViewEphysQC.py deleted file mode 100644 index 03de6e0d..00000000 --- a/PythonScripts/iblapps-master/task_qc_viewer/ViewEphysQC.py +++ /dev/null @@ -1,179 +0,0 @@ -import logging - -from PyQt5 import QtCore, QtWidgets -from matplotlib.figure import Figure -from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg, NavigationToolbar2QT -import pandas as pd -import numpy as np - -import qt as qt - -_logger = logging.getLogger('ibllib') - - -class DataFrameModel(QtCore.QAbstractTableModel): - DtypeRole = QtCore.Qt.UserRole + 1000 - ValueRole = QtCore.Qt.UserRole + 1001 - - def __init__(self, df=pd.DataFrame(), parent=None): - super(DataFrameModel, self).__init__(parent) - self._dataframe = df - - def setDataFrame(self, dataframe): - self.beginResetModel() - self._dataframe = dataframe.copy() - self.endResetModel() - - def dataFrame(self): - return self._dataframe - - dataFrame = QtCore.pyqtProperty(pd.DataFrame, fget=dataFrame, fset=setDataFrame) - - @QtCore.pyqtSlot(int, QtCore.Qt.Orientation, result=str) - def headerData(self, section: int, orientation: QtCore.Qt.Orientation, - role: int = QtCore.Qt.DisplayRole): - if role == QtCore.Qt.DisplayRole: - if orientation == QtCore.Qt.Horizontal: - return self._dataframe.columns[section] - else: - return str(self._dataframe.index[section]) - return QtCore.QVariant() - - def rowCount(self, parent=QtCore.QModelIndex()): - if parent.isValid(): - return 0 - return len(self._dataframe.index) - - def columnCount(self, parent=QtCore.QModelIndex()): - if parent.isValid(): - return 0 - return self._dataframe.columns.size - - def data(self, index, role=QtCore.Qt.DisplayRole): - if (not index.isValid() or not (0 <= index.row() < self.rowCount() and - 0 <= index.column() < self.columnCount())): - return QtCore.QVariant() - row = self._dataframe.index[index.row()] - col = self._dataframe.columns[index.column()] - dt = self._dataframe[col].dtype - - val = self._dataframe.iloc[row][col] - if role == QtCore.Qt.DisplayRole: - return str(val) - elif role == DataFrameModel.ValueRole: - return val - if role == DataFrameModel.DtypeRole: - return dt - return QtCore.QVariant() - - def roleNames(self): - roles = { - QtCore.Qt.DisplayRole: b'display', - DataFrameModel.DtypeRole: b'dtype', - DataFrameModel.ValueRole: b'value' - } - return roles - - def sort(self, col, order): - """ - Sort table by given column number - :param col: the column number selected (between 0 and self._dataframe.columns.size) - :param order: the order to be sorted, 0 is descending; 1, ascending - :return: - """ - self.layoutAboutToBeChanged.emit() - col_name = self._dataframe.columns.values[col] - # print('sorting by ' + col_name) - self._dataframe.sort_values(by=col_name, ascending=not order, inplace=True) - self._dataframe.reset_index(inplace=True, drop=True) - self.layoutChanged.emit() - - -class PlotCanvas(FigureCanvasQTAgg): - - def __init__(self, parent=None, width=5, height=4, dpi=100, wheel=None): - fig = Figure(figsize=(width, height), dpi=dpi) - - FigureCanvasQTAgg.__init__(self, fig) - self.setParent(parent) - - FigureCanvasQTAgg.setSizePolicy( - self, - QtWidgets.QSizePolicy.Expanding, - QtWidgets.QSizePolicy.Expanding) - FigureCanvasQTAgg.updateGeometry(self) - if wheel: - self.ax, self.ax2 = fig.subplots( - 2, 1, gridspec_kw={'height_ratios': [2, 1]}, sharex=True) - else: - self.ax = fig.add_subplot(111) - self.draw() - - -class PlotWindow(QtWidgets.QWidget): - def __init__(self, parent=None, wheel=None): - QtWidgets.QWidget.__init__(self, parent=None) - self.canvas = PlotCanvas(wheel=wheel) - self.vbl = QtWidgets.QVBoxLayout() # Set box for plotting - self.vbl.addWidget(self.canvas) - self.setLayout(self.vbl) - self.vbl.addWidget(NavigationToolbar2QT(self.canvas, self)) - - -class GraphWindow(QtWidgets.QWidget): - def __init__(self, parent=None, wheel=None): - QtWidgets.QWidget.__init__(self, parent=None) - vLayout = QtWidgets.QVBoxLayout(self) - hLayout = QtWidgets.QHBoxLayout() - self.pathLE = QtWidgets.QLineEdit(self) - hLayout.addWidget(self.pathLE) - self.loadBtn = QtWidgets.QPushButton("Select File", self) - hLayout.addWidget(self.loadBtn) - vLayout.addLayout(hLayout) - self.pandasTv = QtWidgets.QTableView(self) - vLayout.addWidget(self.pandasTv) - self.loadBtn.clicked.connect(self.loadFile) - self.pandasTv.setSortingEnabled(True) - self.pandasTv.doubleClicked.connect(self.tv_double_clicked) - self.wplot = PlotWindow(wheel=wheel) - self.wplot.show() - self.wheel = wheel - - def loadFile(self): - fileName, _ = QtWidgets.QFileDialog.getOpenFileName(self, "Open File", "", - "CSV Files (*.csv)") - self.pathLE.setText(fileName) - df = pd.read_csv(fileName) - self.update_df(df) - - def update_df(self, df): - model = DataFrameModel(df) - self.pandasTv.setModel(model) - self.wplot.canvas.draw() - - def tv_double_clicked(self): - df = self.pandasTv.model()._dataframe - ind = self.pandasTv.currentIndex() - start = df.loc[ind.row()]['intervals_0'] - finish = df.loc[ind.row()]['intervals_1'] - dt = finish - start - if self.wheel: - idx = np.searchsorted(self.wheel['re_ts'], np.array([start - dt / 10, - finish + dt / 10])) - max_val = np.max(self.wheel['re_pos'][idx[0]:idx[1]]) - min_val = np.min(self.wheel['re_pos'][idx[0]:idx[1]]) - self.wplot.canvas.ax2.set_ylim(min_val - 1, max_val + 1) - self.wplot.canvas.ax2.set_xlim(start - dt / 10, finish + dt / 10) - self.wplot.canvas.ax.set_xlim(start - dt / 10, finish + dt / 10) - - self.wplot.canvas.draw() - - -def viewqc(qc=None, title=None, wheel=None): - qt.create_app() - qcw = GraphWindow(wheel=wheel) - qcw.setWindowTitle(title) - if qc is not None: - qcw.update_df(qc) - qcw.show() - return qcw diff --git a/PythonScripts/iblapps-master/task_qc_viewer/__init__.py b/PythonScripts/iblapps-master/task_qc_viewer/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/task_qc_viewer/requirements.txt b/PythonScripts/iblapps-master/task_qc_viewer/requirements.txt deleted file mode 100644 index de93d2ba..00000000 --- a/PythonScripts/iblapps-master/task_qc_viewer/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -flake8 -ibllib -matplotlib -pandas -pyqt5 -pyqtgraph -ipython - diff --git a/PythonScripts/iblapps-master/task_qc_viewer/task_qc.py b/PythonScripts/iblapps-master/task_qc_viewer/task_qc.py deleted file mode 100644 index 846ced42..00000000 --- a/PythonScripts/iblapps-master/task_qc_viewer/task_qc.py +++ /dev/null @@ -1,225 +0,0 @@ -import logging -import argparse -from itertools import cycle -import random -from collections.abc import Sized - -import pandas as pd -import qt as qt -from matplotlib.colors import TABLEAU_COLORS - -from one.api import ONE -import ibllib.plots as plots -from ibllib.io.extractors import ephys_fpga -from ibllib.qc.task_metrics import TaskQC -from ibllib.qc.task_extractors import TaskQCExtractor - -from task_qc_viewer import ViewEphysQC - -EVENT_MAP = {'goCue_times': ['#2ca02c', 'solid'], # green - 'goCueTrigger_times': ['#2ca02c', 'dotted'], # green - 'errorCue_times': ['#d62728', 'solid'], # red - 'errorCueTrigger_times': ['#d62728', 'dotted'], # red - 'valveOpen_times': ['#17becf', 'solid'], # cyan - 'stimFreeze_times': ['#0000ff', 'solid'], # blue - 'stimFreezeTrigger_times': ['#0000ff', 'dotted'], # blue - 'stimOff_times': ['#9400d3', 'solid'], # dark violet - 'stimOffTrigger_times': ['#9400d3', 'dotted'], # dark violet - 'stimOn_times': ['#e377c2', 'solid'], # pink - 'stimOnTrigger_times': ['#e377c2', 'dotted'], # pink - 'response_times': ['#8c564b', 'solid'], # brown - } -cm = [EVENT_MAP[k][0] for k in EVENT_MAP] -ls = [EVENT_MAP[k][1] for k in EVENT_MAP] -CRITICAL_CHECKS = ( - 'check_audio_pre_trial', - 'check_correct_trial_event_sequence', - 'check_error_trial_event_sequence', - 'check_n_trial_events', - 'check_response_feedback_delays', - 'check_response_stimFreeze_delays', - 'check_reward_volume_set', - 'check_reward_volumes', - 'check_stimOn_goCue_delays', - 'check_stimulus_move_before_goCue', - 'check_wheel_move_before_feedback', - 'check_wheel_freeze_during_quiescence' -) - - -_logger = logging.getLogger('ibllib') - - -class QcFrame(TaskQC): - - def __init__(self, session, bpod_only=False, local=False, one=None): - """ - Loads and extracts the QC data for a given session path - :param session: A str or Path to a session, or a session eid - :param bpod_only: When True all data is extracted from Bpod instead of FPGA for ephys - """ - if not isinstance(session, TaskQC): - one = one or ONE() - super().__init__(session, one=one, log=_logger) - - if local: - dsets, out_files = ephys_fpga.extract_all(session, save=True) - self.extractor = TaskQCExtractor(session, lazy=True, one=one) - # Extract extra datasets required for QC - self.extractor.data = dsets - self.extractor.extract_data() - # Aggregate and update Alyx QC fields - self.run(update=False) - else: - self.load_data(bpod_only=bpod_only) - self.compute() - else: - assert session.extractor and session.metrics, 'Please run QC before passing to QcFrame' - super().__init__(session.eid or session.session_path, one=session.one, log=session.log) - for attr in ('criteria', 'criteria', '_outcome', - 'extractor', 'metrics', 'passed', 'fcns_value2status'): - setattr(self, attr, getattr(session, attr)) - self.n_trials = self.extractor.data['intervals'].shape[0] - self.wheel_data = {'re_pos': self.extractor.data.pop('wheel_position'), - 're_ts': self.extractor.data.pop('wheel_timestamps')} - - # Print failed - outcome, results, outcomes = self.compute_session_status() - map = {k: [] for k in set(outcomes.values())} - for k, v in outcomes.items(): - map[v].append(k[6:]) - for k, v in map.items(): - if k == 'PASS': - continue - print(f'The following checks were labelled {k}:') - print('\n'.join(v), '\n') - - print('The following *critical* checks did not pass:') - critical_checks = [f'_{x.replace("check", "task")}' for x in CRITICAL_CHECKS] - for k, v in outcomes.items(): - if v != 'PASS' and k in critical_checks: - print(k[6:]) - - # Make DataFrame from the trail level metrics - def get_trial_level_failed(d): - new_dict = {k[6:]: v for k, v in d.items() if - isinstance(v, Sized) and len(v) == self.n_trials} - return pd.DataFrame.from_dict(new_dict) - - self.frame = get_trial_level_failed(self.metrics) - self.frame['intervals_0'] = self.extractor.data['intervals'][:, 0] - self.frame['intervals_1'] = self.extractor.data['intervals'][:, 1] - self.frame.insert(loc=0, column='trial_no', value=self.frame.index) - - def create_plots(self, axes, - wheel_axes=None, trial_events=None, color_map=None, linestyle=None): - """ - Plots the data for bnc1 (sound) and bnc2 (frame2ttl) - :param axes: An axes handle on which to plot the TTL events - :param wheel_axes: An axes handle on which to plot the wheel trace - :param trial_events: A list of Bpod trial events to plot, e.g. ['stimFreeze_times'], - if None, valve, sound and stimulus events are plotted - :param color_map: A color map to use for the events, default is the tableau color map - linestyle: A line style map to use for the events, default is random. - :return: None - """ - color_map = color_map or TABLEAU_COLORS.keys() - if trial_events is None: - # Default trial events to plot as vertical lines - trial_events = [ - 'goCue_times', - 'goCueTrigger_times', - 'feedback_times', - 'stimFreeze_times', - 'stimOff_times', - 'stimOn_times' - ] - - plot_args = { - 'ymin': 0, - 'ymax': 4, - 'linewidth': 2, - 'ax': axes - } - - bnc1 = self.extractor.frame_ttls - bnc2 = self.extractor.audio_ttls - trial_data = self.extractor.data - - plots.squares(bnc1['times'], bnc1['polarities'] * 0.4 + 1, ax=axes, color='k') - plots.squares(bnc2['times'], bnc2['polarities'] * 0.4 + 2, ax=axes, color='k') - linestyle = linestyle or random.choices(('-', '--', '-.', ':'), k=len(trial_events)) - - if self.extractor.bpod_ttls is not None: - bpttls = self.extractor.bpod_ttls - plots.squares(bpttls['times'], bpttls['polarities'] * 0.4 + 3, ax=axes, color='k') - plot_args['ymax'] = 4 - ylabels = ['', 'frame2ttl', 'sound', 'bpod', ''] - else: - plot_args['ymax'] = 3 - ylabels = ['', 'frame2ttl', 'sound', ''] - - for event, c, l in zip(trial_events, cycle(color_map), linestyle): - plots.vertical_lines(trial_data[event], label=event, color=c, linestyle=l, **plot_args) - - axes.legend(loc='upper left', fontsize='xx-small', bbox_to_anchor=(1, 0.5)) - axes.set_yticklabels(ylabels) - axes.set_yticks(list(range(plot_args['ymax'] + 1))) - axes.set_ylim([0, plot_args['ymax']]) - - if wheel_axes: - wheel_plot_args = { - 'ax': wheel_axes, - 'ymin': self.wheel_data['re_pos'].min(), - 'ymax': self.wheel_data['re_pos'].max()} - plot_args = {**plot_args, **wheel_plot_args} - - wheel_axes.plot(self.wheel_data['re_ts'], self.wheel_data['re_pos'], 'k-x') - for event, c, ln in zip(trial_events, cycle(color_map), linestyle): - plots.vertical_lines(trial_data[event], - label=event, color=c, linestyle=ln, **plot_args) - - -def show_session_task_qc(qc_or_session=None, bpod_only=False, local=False, one=None): - """ - Displays the task QC for a given session - :param qc_or_session: session_path or TaskQC object - :param bpod_only: (no FPGA) - :param local: set True for local extraction - :return: The QC object - """ - if isinstance(qc_or_session, QcFrame): - qc = qc_or_session - elif isinstance(qc_or_session, TaskQC): - qc = QcFrame(qc_or_session, one=one) - else: - qc = QcFrame(qc_or_session, bpod_only=bpod_only, local=local, one=one) - # Run QC and plot - w = ViewEphysQC.viewqc(wheel=qc.wheel_data) - qc.create_plots(w.wplot.canvas.ax, - wheel_axes=w.wplot.canvas.ax2, - trial_events=EVENT_MAP.keys(), - color_map=cm, - linestyle=ls) - # Update table and callbacks - w.update_df(qc.frame) - qt.run_app() - return qc - - -if __name__ == "__main__": - """Run TaskQC viewer with wheel data - For information on the QC checks see the QC Flags & failures document: - https://docs.google.com/document/d/1X-ypFEIxqwX6lU9pig4V_zrcR5lITpd8UJQWzW9I9zI/edit# - ipython task_qc.py c9fec76e-7a20-4da4-93ad-04510a89473b - ipython task_qc.py ./KS022/2019-12-10/001 --local - """ - # Parse parameters - parser = argparse.ArgumentParser(description='Quick viewer to see the behaviour data from' - 'choice world sessions.') - parser.add_argument('session', help='session uuid') - parser.add_argument('--bpod', action='store_true', help='run QC on Bpod data only (no FPGA)') - parser.add_argument('--local', action='store_true', help='run from disk location (lab server') - args = parser.parse_args() # returns data from the options specified (echo) - - show_session_task_qc(qc_or_session=args.session, bpod_only=args.bpod, local=args.local) diff --git a/PythonScripts/iblapps-master/task_qc_viewer/version.py b/PythonScripts/iblapps-master/task_qc_viewer/version.py deleted file mode 100644 index 1f356cc5..00000000 --- a/PythonScripts/iblapps-master/task_qc_viewer/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '1.0.0' diff --git a/PythonScripts/iblapps-master/tests/__init__.py b/PythonScripts/iblapps-master/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/PythonScripts/iblapps-master/tests/fixtures/data_alignmentqc_gui.npz b/PythonScripts/iblapps-master/tests/fixtures/data_alignmentqc_gui.npz deleted file mode 100644 index eb536b58..00000000 Binary files a/PythonScripts/iblapps-master/tests/fixtures/data_alignmentqc_gui.npz and /dev/null differ diff --git a/PythonScripts/iblapps-master/tests/test_alignment_qc_gui.py b/PythonScripts/iblapps-master/tests/test_alignment_qc_gui.py deleted file mode 100644 index 7977bd51..00000000 --- a/PythonScripts/iblapps-master/tests/test_alignment_qc_gui.py +++ /dev/null @@ -1,292 +0,0 @@ -import unittest -from pathlib import Path -import re -from inspect import getmembers, isfunction - -import numpy as np - -from one.api import ONE -from ibllib.atlas import AllenAtlas -from atlaselectrophysiology.load_data import LoadData -from ibllib.pipes.ephys_alignment import EphysAlignment -from ibllib.pipes.misc import create_alyx_probe_insertions -from ibllib.pipes.histology import register_track - - -EPHYS_SESSION = 'b1c968ad-4874-468d-b2e4-5ffa9b9964e9' -one = ONE(base_url='https://test.alyx.internationalbrainlab.org') -brain_atlas = AllenAtlas(25) - - -class TestsAlignmentQcGUI(unittest.TestCase): - @classmethod - def setUpClass(cls) -> None: - probe = ['probe00', 'probe01'] - insertions = create_alyx_probe_insertions(session_path=EPHYS_SESSION, model='3B2', - labels=probe, one=one, force=True) - print(insertions) - cls.probe_id = one.alyx.rest('insertions', 'list', session=EPHYS_SESSION, - name='probe00', no_cache=True)[0]['id'] - cls.probe_id2 = one.alyx.rest('insertions', 'list', session=EPHYS_SESSION, - name='probe01', no_cache=True)[0]['id'] - data = np.load(Path(Path(__file__).parent. - joinpath('fixtures', 'data_alignmentqc_gui.npz')), allow_pickle=True) - cls.xyz_picks = data['xyz_picks'] - cls.alignments = data['alignments'].tolist() - cls.cluster_chns = data['cluster_chns'] - register_track(cls.probe_id, picks=cls.xyz_picks, one=one, overwrite=True, - channels=False, brain_atlas=brain_atlas) - register_track(cls.probe_id2, picks=cls.xyz_picks, one=one, overwrite=True, - channels=False, brain_atlas=brain_atlas) - - def setUp(self) -> None: - self.resolved_key = '2020-09-14T15:44:56_nate' - self.ld = LoadData(one=one, brain_atlas=brain_atlas, testing=True, probe_id=self.probe_id) - _ = self.ld.get_xyzpicks() - self.ld.cluster_chns = self.cluster_chns - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - self.ephysalign = EphysAlignment(self.ld.xyz_picks, self.ld.chn_depths, - brain_atlas=self.ld.brain_atlas) - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - if traj: - self.prev_traj_id = traj[0]['id'] - - def test_alignments(self): - checks = getmembers(TestsAlignmentQcGUI, - lambda x: isfunction(x) and re.match(r'^_\d{2}_.*', x.__name__)) - # Run each function in order - for name, fn in sorted(checks, key=lambda x: x[0]): - if not name.startswith('_01_'): - self.setUp() - fn(self) - - def _01_no_alignment(self): - - prev_align = self.ld.get_previous_alignments() - assert (len(prev_align) == 1) - assert (prev_align[0] == 'original') - feature, track = self.ld.get_starting_alignment(0) - assert (not feature) - assert (not track) - assert (not self.ld.alignments) - assert (self.ld.resolved == 0) - - def _02_one_alignment(self): - key = '2020-07-26T17:06:58_alejandro' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert (len(traj[0]['json']) == 1) - # Not added user evaluation so should just contain feature and track lists - assert(len(traj[0]['json'][key]) == 2) - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['extended_qc']['alignment_count'] == 1) - assert (insertion['json']['extended_qc']['alignment_stored'] == key) - assert (not insertion['json']['extended_qc'].get('experimenter', None)) - assert (insertion['json']['qc'] == 'NOT_SET') - assert (self.ld.resolved == 0) - - def _03_same_user(self): - key = '2020-08-26T17:06:58_alejandro' - eval_str = 'PASS: Noise and artifact' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key, - user_eval=eval_str) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - traj_id = traj[0]['id'] - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert (len(traj[0]['json']) == 1) - assert(len(traj[0]['json'][key]) == 3) - assert (traj_id != self.prev_traj_id) - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['extended_qc']['alignment_count'] == 1) - assert (insertion['json']['extended_qc']['alignment_stored'] == key) - assert (insertion['json']['extended_qc']['alignment_resolved'] == 0) - assert(insertion['json']['extended_qc']['experimenter'] == 'PASS') - assert (insertion['json']['qc'] == 'PASS') - assert (self.ld.resolved == 0) - - def _04_two_alignments(self): - key = '2020-09-14T15:42:22_guido' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - traj_id = traj[0]['id'] - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert (len(traj[0]['json']) == 2) - assert(len(traj[0]['json'][key]) == 2) - assert (traj_id != self.prev_traj_id) - # Also assert all the keys match - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['qc'] == 'PASS') - assert (insertion['json']['extended_qc']['experimenter'] == 'PASS') - assert (insertion['json']['extended_qc']['alignment_count'] == 2) - assert (insertion['json']['extended_qc']['alignment_stored'] == key) - assert (insertion['json']['extended_qc']['alignment_resolved'] == 0) - assert (insertion['json']['extended_qc']['alignment_qc'] < 0.8) - assert (self.ld.resolved == 0) - - def _05_three_alignments(self): - - key = '2020-09-14T15:44:56_nate' - eval_str = 'WARNING: Drift' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key, - user_eval=eval_str) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - traj_id = traj[0]['id'] - assert (len(traj[0]['json']) == 3) - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert(len(traj[0]['json'][key]) == 3) - assert (traj_id != self.prev_traj_id) - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['qc'] == 'WARNING') - assert (insertion['json']['extended_qc']['experimenter'] == 'WARNING') - assert (insertion['json']['extended_qc']['alignment_count'] == 3) - assert (insertion['json']['extended_qc']['alignment_stored'] == key) - assert (insertion['json']['extended_qc']['alignment_resolved'] == 1) - assert (insertion['json']['extended_qc']['alignment_resolved_by'] == 'qc') - assert (insertion['json']['extended_qc']['alignment_qc'] > 0.8) - assert(self.ld.resolved == 1) - - def _06_new_user_after_resolved(self): - key = '2020-09-16T15:44:56_mayo' - eval_str = 'PASS: Drift' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key, - user_eval=eval_str) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - traj_id = traj[0]['id'] - assert (len(traj[0]['json']) == 4) - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert (traj_id == self.prev_traj_id) - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['qc'] == 'WARNING') - assert (insertion['json']['extended_qc']['experimenter'] == 'WARNING') - assert (insertion['json']['extended_qc']['alignment_count'] == 4) - assert (insertion['json']['extended_qc']['alignment_stored'] == self.resolved_key) - assert (insertion['json']['extended_qc']['alignment_resolved'] == 1) - assert (insertion['json']['extended_qc']['alignment_resolved_by'] == 'qc') - assert (insertion['json']['extended_qc']['alignment_qc'] > 0.8) - assert (self.ld.resolved == 1) - - def _07_same_user_after_resolved(self): - key = '2020-10-14T15:44:56_nate' - eval_str = 'CRITICAL: Brain Damage' - feature = self.alignments[key][0] - track = self.alignments[key][1] - xyz_channels = self.ephysalign.get_channel_locations(feature, track) - self.ld.upload_data(xyz_channels, channels=False) - self.ld.update_alignments(np.array(feature), np.array(track), key_info=key, - user_eval=eval_str) - _ = self.ld.get_previous_alignments() - _ = self.ld.get_starting_alignment(0) - - assert (self.ld.current_align == key) - - traj = one.alyx.rest('trajectories', 'list', probe_id=self.probe_id, - provenance='Ephys aligned histology track', no_cache=True) - traj_id = traj[0]['id'] - assert (sorted(list(traj[0]['json'].keys()), reverse=True)[0] == key) - assert (len(traj[0]['json']) == 5) - assert (traj_id == self.prev_traj_id) - - self.ld.update_qc(upload_flatiron=False) - insertion = one.alyx.rest('insertions', 'read', id=self.probe_id, no_cache=True) - assert (insertion['json']['qc'] == 'CRITICAL') - assert (insertion['json']['extended_qc']['experimenter'] == 'CRITICAL') - assert (insertion['json']['extended_qc']['alignment_count'] == 5) - assert (insertion['json']['extended_qc']['alignment_stored'] == self.resolved_key) - assert (insertion['json']['extended_qc']['alignment_resolved'] == 1) - assert (insertion['json']['extended_qc']['alignment_resolved_by'] == 'qc') - assert (insertion['json']['extended_qc']['alignment_qc'] > 0.8) - assert (self.ld.resolved == 1) - - def _08_starting_alignments(self): - key = '2020-10-14T15:44:56_nate' - # Starting from original - self.ld.probe_id = self.probe_id2 - prev_align = self.ld.get_previous_alignments() - assert(len(prev_align) == 1) - assert(prev_align[0] == 'original') - assert(self.ld.alignments == {}) - - # Loading in one with alignments - self.ld.probe_id = self.probe_id - prev_align = self.ld.get_previous_alignments() - assert(len(prev_align) == 6) - assert(prev_align[-1] == 'original') - assert(len(self.ld.alignments) == 5) - assert(sorted(self.ld.alignments, reverse=True)[0] == key) - - # Now loading back one with nothing - self.ld.probe_id = self.probe_id2 - prev_align = self.ld.get_previous_alignments() - assert(len(prev_align) == 1) - assert(prev_align[0] == 'original') - assert(self.ld.alignments == {}) - - @classmethod - def tearDownClass(cls) -> None: - one.alyx.rest('insertions', 'delete', id=cls.probe_id) - one.alyx.rest('insertions', 'delete', id=cls.probe_id2) - - -if __name__ == "__main__": - unittest.main(exit=False) diff --git a/PythonScripts/iblapps-master/tests/test_task_qc_viewer.py b/PythonScripts/iblapps-master/tests/test_task_qc_viewer.py deleted file mode 100644 index a4e054dc..00000000 --- a/PythonScripts/iblapps-master/tests/test_task_qc_viewer.py +++ /dev/null @@ -1,16 +0,0 @@ -import os -import unittest -from pathlib import Path - - -class TestTaskQCViewer(unittest.TestCase): - def setUp(self): - iblapps_path = Path(__file__).parts[:-2] - self.task_qc_path = Path(*iblapps_path) / 'task_qc_viewer' / 'task_qc.py' - self.UUID = '1211f4af-d3e4-4c4e-9d0b-75a0bc2bf1f0' - - def test_build(self): - os.system(f'python {self.task_qc_path} {self.UUID}') - - def test_build_fail(self): - os.system(f'python {self.task_qc_path} "BAD_ID"') diff --git a/PythonScripts/iblapps-master/viewspikes/README.md b/PythonScripts/iblapps-master/viewspikes/README.md deleted file mode 100644 index a7154fc7..00000000 --- a/PythonScripts/iblapps-master/viewspikes/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# View Spikes -Electrophysiology display tools for IBL insertions. - -## Run instructions -Look at the examples file at the root of the package. - -## Install Instructions - -Pre-requisites: -- IBL environment installed https://github.com/int-brain-lab/iblenv -- ONE parameters setup to download data - -``` -conda activate iblenv -pip install easyqc -pip install -U pyqtgraph -``` diff --git a/PythonScripts/iblapps-master/viewspikes/data.py b/PythonScripts/iblapps-master/viewspikes/data.py deleted file mode 100644 index cd0e93b9..00000000 --- a/PythonScripts/iblapps-master/viewspikes/data.py +++ /dev/null @@ -1,110 +0,0 @@ -from pathlib import Path -import shutil - -from ibllib.io import spikeglx -from one.webclient import dataset_record_to_url - -import numpy as np -import scipy.signal - -from one import alf - - -CHUNK_DURATION_SECS = 1 -OUTPUT_TO_TEST = True - - -def get_ks2_batch(ks2memmap, ibatch): - BATCH_SIZE = 65600 - NTR = 384 - offset = BATCH_SIZE * NTR * ibatch - from_to = np.array([0, BATCH_SIZE * NTR]) - slic = slice(from_to[0] + offset, from_to[1] + offset) - - ks2 = np.reshape(ks2memmap[slice(from_to[0] + offset, from_to[1] + offset)], (NTR, BATCH_SIZE)) - return ks2 - - -# ks2 proc -def get_ks2(raw, dsets, one): - kwm = next(dset for dset in dsets if dset['dataset_type'] == 'kilosort.whitening_matrix') - kwm = np.load(one._download_dataset(kwm)) - channels = [dset for dset in dsets if dset['dataset_type'].startswith('channels')] - malf_path = next(iter(one._download_datasets(channels))).parent - channels = alf.io.load_object(malf_path, 'channels') - _car = raw[channels['rawInd'], :] - np.mean(raw[channels.rawInd, :], axis=0) - sos = scipy.signal.butter(3, 300 / 30000 / 2, btype='highpass', output='sos') - ks2 = np.zeros_like(raw) - ks2[channels['rawInd'], :] = scipy.signal.sosfiltfilt(sos, _car) - std_carbutt = np.std(ks2) - ks2[channels['rawInd'], :] = np.matmul(kwm, ks2[channels['rawInd'], :]) - ks2 = ks2 * std_carbutt / np.std(ks2) - return ks2 - - -def get_spikes(dsets, one): - dtypes_spikes = ['spikes.clusters', 'spikes.amps', 'spikes.times', 'clusters.channels', - 'spikes.samples', 'spikes.depths'] - dsets_spikes = [dset for dset in dsets if dset['dataset_type'] in dtypes_spikes] - malf_path = next(iter(one._download_datasets(dsets_spikes))).parent - channels = alf.io.load_object(malf_path, 'channels') - clusters = alf.io.load_object(malf_path, 'clusters') - spikes = alf.io.load_object(malf_path, 'spikes') - return spikes, clusters, channels - - -def stream(pid, t, one=None, cache=True, dsets=None, typ='ap', tlen=1): - """ - NB: returned Reader object must be closed after use - :param pid: Probe UUID - :param t: - :param one: An instance of ONE - :param cache: - :param dsets: - :param typ: 'ap' or 'lf' - :param tlen: no. of seconds to stream - :return: sr, dsets, t0 - """ - - assert one - assert typ in ['lf', 'ap'] - t0 = np.floor(t / CHUNK_DURATION_SECS) * CHUNK_DURATION_SECS - if cache: - samples_folder = Path(one.alyx._par.CACHE_DIR).joinpath('cache', typ) - sample_file_name = Path(f"{pid}_{str(int(t0)).zfill(5)}.meta") - if dsets is None: - dsets = one.alyx.rest('datasets', 'list', probe_insertion=pid) - if cache and samples_folder.joinpath(sample_file_name).exists(): - print(f'loading {sample_file_name} from cache') - sr = spikeglx.Reader(samples_folder.joinpath(sample_file_name).with_suffix('.bin'), - open=True) - return sr, dsets, t0 - - dset_ch = next(dset for dset in dsets if dset['dataset_type'] == "ephysData.raw.ch" and - f'.{typ}.' in dset['name']) - dset_meta = next(dset for dset in dsets if dset['dataset_type'] == "ephysData.raw.meta" and - f'.{typ}.' in dset['name']) - dset_cbin = next(dset for dset in dsets if dset['dataset_type'] == f"ephysData.raw.{typ}" and - f'.{typ}.' in dset['name']) - - file_ch, file_meta = one._download_datasets([dset_ch, dset_meta]) - - first_chunk = int(t0 / CHUNK_DURATION_SECS) - last_chunk = int((t0 + tlen) / CHUNK_DURATION_SECS) - 1 - - sr = spikeglx.download_raw_partial( - one=one, - url_cbin=dataset_record_to_url(dset_cbin)[0], - url_ch=file_ch, - first_chunk=first_chunk, - last_chunk=last_chunk) - - if cache: - samples_folder.mkdir(exist_ok=True, parents=True) - out_meta = samples_folder.joinpath(sample_file_name) - shutil.copy(sr.file_meta_data, out_meta) - with open(out_meta.with_suffix('.bin'), 'wb') as fp: - sr.open() - sr._raw[:].tofile(fp) - - return sr, dsets, t0 diff --git a/PythonScripts/iblapps-master/viewspikes/datoviz.py b/PythonScripts/iblapps-master/viewspikes/datoviz.py deleted file mode 100644 index 14278e2f..00000000 --- a/PythonScripts/iblapps-master/viewspikes/datoviz.py +++ /dev/null @@ -1,75 +0,0 @@ -from dataclasses import dataclass - -import numpy as np - -import datoviz as dviz - -# ------------------------------------------------------------------------------------------------- -# Raster viewer -# ------------------------------------------------------------------------------------------------- - - -class RasterView: - def __init__(self): - self.canvas = dviz.canvas(show_fps=True) - self.panel = self.canvas.panel(controller='axes') - self.visual = self.panel.visual('point') - self.pvars = {'ms': 2., 'alpha': .03} - self.gui = self.canvas.gui('XY') - self.gui.control("label", "Coords", value="(0, 0)") - - def set_spikes(self, spikes): - pos = np.c_[spikes.times, spikes.depths, np.zeros_like(spikes.times)] - color = dviz.colormap(20 * np.log10(spikes.amps), cmap='cividis', alpha=self.pvars['alpha']) - self.visual.data('pos', pos) - self.visual.data('color', color) - self.visual.data('ms', np.array([self.pvars['ms']])) - - -class RasterController: - _time_select_cb = None - - def __init__(self, model, view): - self.m = model - self.v = view - self.v.canvas.connect(self.on_mouse_move) - self.v.canvas.connect(self.on_key_press) - self.redraw() - - def redraw(self): - print('redraw', self.v.pvars) - self.v.set_spikes(self.m.spikes) - - def on_mouse_move(self, x, y, modifiers=()): - p = self.v.canvas.panel_at(x, y) - if not p: - return - # Then, we transform into the data coordinate system - # Supported coordinate systems: - # target_cds='data' / 'scene' / 'vulkan' / 'framebuffer' / 'window' - xd, yd = p.pick(x, y) - self.v.gui.set_value("Coords", f"({xd:0.2f}, {yd:0.2f})") - - def on_key_press(self, key, modifiers=()): - print(key, modifiers) - if key == 'a' and modifiers == ('control',): - self.v.pvars['alpha'] = np.minimum(self.v.pvars['alpha'] + 0.1, 1.) - elif key == 'z' and modifiers == ('control',): - self.v.pvars['alpha'] = np.maximum(self.v.pvars['alpha'] - 0.1, 0.) - elif key == 'page_up': - self.v.pvars['ms'] = np.minimum(self.v.pvars['ms'] * 1.1, 20) - elif key == 'page_down': - self.v.pvars['ms'] = np.maximum(self.v.pvars['ms'] / 1.1, 1) - else: - return - self.redraw() - - -@dataclass -class RasterModel: - spikes: dict - - -def raster(spikes): - rm = RasterController(RasterModel(spikes), RasterView()) - dviz.run() diff --git a/PythonScripts/iblapps-master/viewspikes/examples_local.py b/PythonScripts/iblapps-master/viewspikes/examples_local.py deleted file mode 100644 index dfe38152..00000000 --- a/PythonScripts/iblapps-master/viewspikes/examples_local.py +++ /dev/null @@ -1,111 +0,0 @@ -from pathlib import Path -import numpy as np -import scipy -import matplotlib.pyplot as plt -from easyqc.gui import viewseis - -import one.alf.io as alfio -from one.api import ONE -import spikeglx -import neuropixel -from neurodsp import voltage -from brainbox.plot import driftmap - -from needles2 import run_needles2 -from viewspikes.data import stream, get_ks2, get_spikes -from viewspikes.plots import plot_insertion, show_psd, overlay_spikes - -RAW_PATH = Path("/datadisk/Data/spike_sorting/benchmark/raw") -SORT_PATH = Path("/datadisk/team_drives/WG-Neural-Analysis/Spike-Sorting-Analysis/benchmarks") -SORTERS = ['ks2','ks3', 'pyks2.5'] - -"8413c5c6-b42b-4ec6-b751-881a54413628", -"8ca1a850-26ef-42be-8b28-c2e2d12f06d6", -"ce24bbe9-ae70-4659-9e9c-564d1a865de8", -"ce397420-3cd2-4a55-8fd1-5e28321981f4", - -# Example 1 -pid, t0 = ("ce24bbe9-ae70-4659-9e9c-564d1a865de8", 810) -bin_file = next(RAW_PATH.joinpath(pid).rglob("*.ap.bin")) -sr = spikeglx.Reader(bin_file) -sel = slice(int(t0 * sr.fs), int((t0 + 4) * sr.fs)) -raw = sr[sel, :-1].T - - -# Example 2: Plot Insertion for a given PID -av = run_needles2.view(lazy=True) -av.add_insertion_by_id(pid) - -# Example 3: Show the PSD -fig, ax = plt.subplots() -fig.set_size_inches(14, 7) -show_psd(raw, sr.fs, ax=ax) - -# Example 4: Display the raw / pre-proc -h = neuropixel.trace_header() -sos = scipy.signal.butter(3, 300 / sr.fs / 2, btype='highpass', output='sos') -butt = scipy.signal.sosfiltfilt(sos, raw) -fk_kwargs ={'dx': 1, 'vbounds': [0, 1e6], 'ntr_pad': 160, 'ntr_tap': 0, 'lagc': .01, 'btype': 'lowpass'} -destripe = voltage.destripe(raw, fs=sr.fs, fk_kwargs=fk_kwargs, tr_sel=np.arange(raw.shape[0])) -eqc_butt = viewseis(butt.T, si=1 / sr.fs, h=h, t0=t0, title='butt', taxis=0) -eqc_dest = viewseis(destripe.T, si=1 / sr.fs, h=h, t0=t0, title='destr', taxis=0) -eqc_dest_ = viewseis(destripe.T, si=1 / sr.fs, h=h, t0=t0, title='destr_', taxis=0) -# Example 5: overlay the spikes on the existing easyqc instances -from ibllib.plots import color_cycle -ss = {} -symbols = 'x+o' -eqcsort = {} -for i, sorter in enumerate(SORTERS): - alf_path = SORT_PATH.joinpath(sorter, pid,'alf') - ss[sorter] = {} - for k in ['spikes', 'clusters', 'channels']: - ss[sorter][k] = alfio.load_object(alf_path, k) - col = (np.array(color_cycle(i)) * 255).astype(np.uint8) - eqcsort[sorter] = viewseis(destripe.T, si=1 / sr.fs, h=h, t0=t0, title=sorter, taxis=0) - _, _, _ = overlay_spikes( - eqcsort[sorter], ss[sorter]['spikes'], ss[sorter]['clusters'], ss[sorter]['channels'], - label=sorter, symbol=symbols[i]) - _, _, _ = overlay_spikes( - eqc_dest_, ss[sorter]['spikes'], ss[sorter]['clusters'], ss[sorter]['channels'], - rgb=tuple(col), label=sorter, symbol=symbols[i]) - # overlay_spikes(eqc_dest, ss[sorter]['spikes'], ss[sorter]['clusters'], ss[sorter]['channels']) - # sc.setPen(pg.mkPen((0, 255, 0, 155), width=1)) - -## -from ibllib.dsp.fourier import fshift -from ibllib.dsp.voltage import destripe -eqc_butt = viewseis(butt.T, si=1 / sr.fs, h=h, t0=t0, title='butt', taxis=0) -bshift = fshift(butt, h['sample_shift'], axis=1) -eqc_buts = viewseis(bshift.T, si=1 / sr.fs, h=h, t0=t0, title='shift', taxis=0) - - - -## -from one.api import ONE -pid = "8413c5c6-b42b-4ec6-b751-881a54413628" -one = ONE() - -dtypes = ['spikes.amps', 'spikes.clusters', 'spikes.times', - 'clusters.channels', - 'clusters.mlapdv'] - -from ibllib.atlas import atlas -from ibllib.pipes import histology -from ibllib.ephys import neuropixel - -import numpy as np -neuropixel.TIP_SIZE_UM -neuropixel.SITES_COORDINATES - -len(one.alyx.rest('datasets', 'list', insertion=pid)) - -# if we don't have the data on the flatiron -pi = one.alyx.rest('insertions', 'read', id=pid) -traj = one.alyx.rest('trajectories', 'list', probe_insertion=pid)[-1] -ins = atlas.Insertion.from_dict(traj) -xyz_channels = histology.interpolate_along_track( - ins.xyz, (neuropixel.SITES_COORDINATES[:, 1] + neuropixel.TIP_SIZE_UM) / 1e6) - - - - diff --git a/PythonScripts/iblapps-master/viewspikes/examples_reproducible.py b/PythonScripts/iblapps-master/viewspikes/examples_reproducible.py deleted file mode 100644 index e1e8f6a7..00000000 --- a/PythonScripts/iblapps-master/viewspikes/examples_reproducible.py +++ /dev/null @@ -1,60 +0,0 @@ -from one.api import ONE, alfio -from atlaselectrophysiology.alignment_with_easyqc import viewer - -one = ONE() - - -pids = ['ce397420-3cd2-4a55-8fd1-5e28321981f4', - 'e31b4e39-e350-47a9-aca4-72496d99ff2a', - 'f8d0ecdc-b7bd-44cc-b887-3d544e24e561', - '6fc4d73c-2071-43ec-a756-c6c6d8322c8b', - 'c17772a9-21b5-49df-ab31-3017addea12e', - '0851db85-2889-4070-ac18-a40e8ebd96ba', - 'eeb27b45-5b85-4e5c-b6ff-f639ca5687de', - '69f42a9c-095d-4a25-bca8-61a9869871d3', - 'f03b61b4-6b13-479d-940f-d1608eb275cc', - 'f2ee886d-5b9c-4d06-a9be-ee7ae8381114', - 'f26a6ab1-7e37-4f8d-bb50-295c056e1062', - 'c4f6665f-8be5-476b-a6e8-d81eeae9279d', - '9117969a-3f0d-478b-ad75-98263e3bfacf', - 'febb430e-2d50-4f83-87a0-b5ffbb9a4943', - '8413c5c6-b42b-4ec6-b751-881a54413628', - '8b7c808f-763b-44c8-b273-63c6afbc6aae', - 'f936a701-5f8a-4aa1-b7a9-9f8b5b69bc7c', - '63517fd4-ece1-49eb-9259-371dc30b1dd6', - '8d59da25-3a9c-44be-8b1a-e27cdd39ca34', - '19baa84c-22a5-4589-9cbd-c23f111c054c', - '143dd7cf-6a47-47a1-906d-927ad7fe9117', - '84bb830f-b9ff-4e6b-9296-f458fb41d160', - 'b749446c-18e3-4987-820a-50649ab0f826', - '36362f75-96d8-4ed4-a728-5e72284d0995', - '9657af01-50bd-4120-8303-416ad9e24a51', - 'dab512bd-a02d-4c1f-8dbc-9155a163efc0'] - - - -INDEX = 2 -pid = pids[INDEX] -av = viewer(pid, one=one) -## -eid, pname = one.pid2eid(pid) -session_path = one.eid2path(eid) -probe_path = session_path.joinpath('alf', pname) - -from pathlib import Path - -local_path = Path("/datadisk/Data/spike_sorting/mic").joinpath(pid, pname) -spikes = alfio.load_object(probe_path, 'spikes') -spikes_ = alfio.load_object(local_path, 'spikes') - - -# from ibllib.plots import vertical_lines -# vertical_lines(rewards, ymin=0, ymax=3800, ax=ax) -from brainbox.plot import driftmap -import matplotlib.pyplot as plt -fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True) -driftmap(spikes['times'], spikes['depths'], t_bin=0.1, d_bin=5, ax=ax[0]) -driftmap(spikes_['times'], spikes_['depths'], t_bin=0.1, d_bin=5, ax=ax[1]) - - - diff --git a/PythonScripts/iblapps-master/viewspikes/examples_stream.py b/PythonScripts/iblapps-master/viewspikes/examples_stream.py deleted file mode 100644 index 9ecc8048..00000000 --- a/PythonScripts/iblapps-master/viewspikes/examples_stream.py +++ /dev/null @@ -1,13 +0,0 @@ -from one.api import ONE -from atlaselectrophysiology.alignment_with_easyqc import viewer - -one = ONE() - - -"da8dfec1-d265-44e8-84ce-6ae9c109b8bd", # SWC_043_2020-09-21_probe00 ok -"b749446c-18e3-4987-820a-50649ab0f826", # KS023_2019-12-10_probe01 ok -"f86e9571-63ff-4116-9c40-aa44d57d2da9", # CSHL049_2020-01-08_probe00 a bit stripy but fine -"675952a4-e8b3-4e82-a179-cc970d5a8b01", # CSH_ZAD_029_2020-09-19_probe01 a bit stripy as well - -pid = "af2a0072-e17e-4368-b80b-1359bf6d4647" -av = viewer(pid, one=one) diff --git a/PythonScripts/iblapps-master/viewspikes/gui.py b/PythonScripts/iblapps-master/viewspikes/gui.py deleted file mode 100644 index c84b766e..00000000 --- a/PythonScripts/iblapps-master/viewspikes/gui.py +++ /dev/null @@ -1,238 +0,0 @@ -from pathlib import Path - -import numpy as np -import scipy.signal - -from PyQt5 import QtWidgets, QtCore, QtGui, uic -import pyqtgraph as pg -from brainbox.processing import bincount2D - -from easyqc.gui import viewseis -import one.alf.io as alfio -from one.alf.files import get_session_path -from iblutil.numerical import ismember -import spikeglx -from neurodsp import voltage - -import qt -from brainbox.io.one import SpikeSortingLoader - -T_BIN = .007 # time bin size in secs -D_BIN = 10 # depth bin size in um - -SNS_PALETTE = [(0.12156862745098039, 0.4666666666666667, 0.7058823529411765), - (1.0, 0.4980392156862745, 0.054901960784313725), - (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), - (0.8392156862745098, 0.15294117647058825, 0.1568627450980392), - (0.5803921568627451, 0.403921568627451, 0.7411764705882353), - (0.5490196078431373, 0.33725490196078434, 0.29411764705882354), - (0.8901960784313725, 0.4666666666666667, 0.7607843137254902), - (0.4980392156862745, 0.4980392156862745, 0.4980392156862745), - (0.7372549019607844, 0.7411764705882353, 0.13333333333333333), - (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)] - -YMAX = 4000 - - -def view_raster(bin_file): - bin_file = Path(bin_file) - pname = bin_file.parent.name - session_path = get_session_path(bin_file) - ssl = SpikeSortingLoader(session_path=session_path, pname=pname) - spikes, clusters, channels = ssl.load_spike_sorting(dataset_types=['spikes.samples']) - trials = alfio.load_object(ssl.session_path.joinpath('alf'), 'trials') - return RasterView(bin_file, spikes, clusters, trials=trials) - - -class RasterView(QtWidgets.QMainWindow): - def __init__(self, bin_file, spikes, clusters, channels=None, trials=None, *args, **kwargs): - self.sr = spikeglx.Reader(bin_file) - self.spikes = spikes - self.clusters = clusters - self.channels = channels - self.trials = trials - self.eqcs = [] - super(RasterView, self).__init__(*args, **kwargs) - # wave by Diana Militano from the Noun Projectp - uic.loadUi(Path(__file__).parent.joinpath('raster.ui'), self) - background_color = self.palette().color(self.backgroundRole()) - self.plotItem_raster.setAspectLocked(False) - self.imageItem_raster = pg.ImageItem() - self.plotItem_raster.setBackground(background_color) - self.plotItem_raster.addItem(self.imageItem_raster) - self.viewBox_raster = self.plotItem_raster.getPlotItem().getViewBox() - s = self.viewBox_raster.scene() - # vb.scene().sigMouseMoved.connect(self.mouseMoveEvent) - s.sigMouseClicked.connect(self.mouseClick) - ################################################### set image - iok = ~np.isnan(spikes.depths) - self.raster, self.rtimes, self.depths = bincount2D( - spikes.times[iok], spikes.depths[iok], T_BIN, D_BIN) - self.imageItem_raster.setImage(np.flip(self.raster.T)) - transform = [T_BIN, 0., 0., 0., D_BIN, 0., - .5, - .5, 1.] - self.transform = np.array(transform).reshape((3, 3)).T - self.imageItem_raster.setTransform(QtGui.QTransform(*transform)) - self.plotItem_raster.setLimits(xMin=0, xMax=self.rtimes[-1], yMin=0, yMax=self.depths[-1]) - # set colormap - cm = pg.colormap.get('Greys', source='matplotlib') # prepare a linear color map - bar = pg.ColorBarItem(values=(0, .5), colorMap=cm) # prepare interactive color bar - # Have ColorBarItem control colors of img and appear in 'plot': - bar.setImageItem(self.imageItem_raster) - ################################################## plot location - # self.view.layers[label] = {'layer': new_scatter, 'type': 'scatter'} - self.line_eqc = pg.PlotCurveItem() - self.plotItem_raster.addItem(self.line_eqc) - # self.plotItem_raster.removeItem(new_curve) - ################################################## plot trials - if self.trials is not None: - trial_times = dict( - goCue_times=trials['goCue_times'], - error_times=trials['feedback_times'][trials['feedbackType'] == -1], - reward_times=trials['feedback_times'][trials['feedbackType'] == 1]) - self.trial_lines = {} - for i, k in enumerate(trial_times): - self.trial_lines[k] = pg.PlotCurveItem() - self.plotItem_raster.addItem(self.trial_lines[k]) - x = np.tile(trial_times[k][:, np.newaxis], (1, 2)).flatten() - y = np.tile(np.array([0, 1, 1, 0]), int(trial_times[k].shape[0] / 2 + 1))[ - :trial_times[k].shape[0] * 2] * YMAX - self.trial_lines[k].setData(x=x.flatten(), y=y.flatten(), pen=pg.mkPen(np.array(SNS_PALETTE[i]) * 256)) - - self.show() - - def mouseClick(self, event): - """Draws a line on the raster and display in EasyQC""" - if not event.double(): - return - qxy = self.imageItem_raster.mapFromScene(event.scenePos()) - x = qxy.x() - self.show_ephys(t0=self.rtimes[int(x - .5)]) - ymax = np.max(self.depths) + 50 - self.line_eqc.setData(x=x + np.array([-.5, -.5, .5, .5]), - y=np.array([0, ymax, ymax, 0]), - pen=pg.mkPen((0, 255, 0))) - - - def keyPressEvent(self, e): - """ - page-up / ctrl + a : gain up - page-down / ctrl + z : gain down - :param e: - """ - k, m = (e.key(), e.modifiers()) - # page up / ctrl + a - if k == QtCore.Qt.Key_PageUp or ( - m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_A): - self.imageItem_raster.setLevels([0, self.imageItem_raster.levels[1] / 1.4]) - # page down / ctrl + z - elif k == QtCore.Qt.Key_PageDown or ( - m == QtCore.Qt.ControlModifier and k == QtCore.Qt.Key_Z): - self.imageItem_raster.setLevels([0, self.imageItem_raster.levels[1] * 1.4]) - - def show_ephys(self, t0, tlen=1): - - first = int(t0 * self.sr.fs) - last = first + int(self.sr.fs * tlen) - - raw = self.sr[first:last, : - self.sr.nsync].T - - butter_kwargs = {'N': 3, 'Wn': 300 / self.sr.fs * 2, 'btype': 'highpass'} - sos = scipy.signal.butter(**butter_kwargs, output='sos') - butt = scipy.signal.sosfiltfilt(sos, raw) - destripe = voltage.destripe(raw, fs=self.sr.fs) - - self.eqc_raw = viewephys(butt, self.sr.fs, channels=None, br=None, title='butt', t0=t0, t_scalar=1) - self.eqc_des = viewephys(destripe, self.sr.fs, channels=None, br=None, title='destripe', t0=t0, t_scalar=1) - - eqc_xrange = [t0 + tlen / 2 - 0.01, t0 + tlen / 2 + 0.01] - self.eqc_des.viewBox_seismic.setXRange(*eqc_xrange) - self.eqc_raw.viewBox_seismic.setXRange(*eqc_xrange) - - # eqc2 = viewephys(butt - destripe, self.sr.fs, channels=None, br=None, title='diff') - # overlay spikes - tprobe = self.spikes.samples / self.sr.fs - slice_spikes = slice(np.searchsorted(tprobe, t0), np.searchsorted(tprobe, t0 + tlen)) - t = tprobe[slice_spikes] - c = self.clusters.channels[self.spikes.clusters[slice_spikes]] - self.eqc_raw.ctrl.add_scatter(t, c) - self.eqc_des.ctrl.add_scatter(t, c) - - - - -def viewephys(data, fs, channels=None, br=None, title='ephys', t0=0, t_scalar=1e3): - """ - :param data: [nc, ns] - :param fs: - :param channels: - :param br: - :param title: - :return: - """ - width = 40 - height = 800 - nc, ns = data.shape - # ih = np.linspace(0, nc - 1, height).astype(np.int32) - # image = br.rgb[channels['ibr'][ih]].astype(np.uint8) - # image = np.tile(image[:, np.newaxis, :], (1, width, 1)) - # image = np.tile(image[np.newaxis, :, :], (width, 1, 1)) - from ibllib.ephys.neuropixel import trace_header - if channels is None or br is None: - channels = trace_header(version = 1) - eqc = viewseis(data.T, si=1 / fs * t_scalar, h=channels, title=title, taxis=0, t0=t0) - return eqc - else: - _, ir = ismember(channels['atlas_id'], br.id) - image = br.rgb[ir].astype(np.uint8) - image = image[np.newaxis, :, :] - - - eqc = viewseis(data.T, si=1 / fs * t_scalar, h=channels, title=title, taxis=0) - imitem = pg.ImageItem(image) - eqc.plotItem_header_v.addItem(imitem) - transform = [1, 0, 0, 0, 1, 0, -0.5, 0, 1.] - imitem.setTransform(QtGui.QTransform(*transform)) - eqc.plotItem_header_v.setLimits(xMin=-.5, xMax=.5) - # eqc.comboBox_header.setVisible(False) - - return eqc - - -COLOR_PLOTS = (pg.mkColor((31, 119, 180)),) - - -def view2p(tiff_file, title=None): - qt.create_app() - v2p = View2p._get_or_create(title=title) - v2p.update_tiff(tiff_file) - v2p.show() - return v2p - - -class View2p(QtWidgets.QMainWindow): - """ - This is the view in the MVC approach - """ - layers = None # used for additional scatter layers - - @staticmethod - def _instances(): - app = QtWidgets.QApplication.instance() - return [w for w in app.topLevelWidgets() if isinstance(w, View2p)] - - @staticmethod - def _get_or_create(title=None): - v2p = next(filter(lambda e: e.isVisible() and e.windowTitle() == title, - View2p._instances()), None) - if v2p is None: - v2p = View2p() - v2p.setWindowTitle(title) - return v2p - - def __init__(self, *args, **kwargs): - super(View2p, self).__init__(*args, **kwargs) - # wave by Diana Militano from the Noun Projectp - uic.loadUi(Path(__file__).parent.joinpath('view2p.ui'), self) - - def update_tiff(self, tiff_file): - pass \ No newline at end of file diff --git a/PythonScripts/iblapps-master/viewspikes/load_ma_data.py b/PythonScripts/iblapps-master/viewspikes/load_ma_data.py deleted file mode 100644 index 7ad6a27e..00000000 --- a/PythonScripts/iblapps-master/viewspikes/load_ma_data.py +++ /dev/null @@ -1,136 +0,0 @@ -from one.api import ONE -import one.alf.io as alfio -from iblutil.util import Bunch -import qt -import numpy as np -import pyqtgraph as pg - - -import atlaselectrophysiology.ephys_atlas_gui as alignment_window -import data_exploration_gui.gui_main as trial_window - - -# some extra controls - -class AlignmentWindow(alignment_window.MainWindow): - def __init__(self, offline=False, probe_id=None, one=None): - super(AlignmentWindow, self).__init__(probe_id=probe_id, one=one) - self.trial_gui = None - - - def cluster_clicked(self, item, point): - clust = super().cluster_clicked(item, point) - print(clust) - self.trial_gui.on_cluster_chosen(clust) - - def add_trials_to_raster(self, trial_key='feedback_times'): - self.selected_trials = self.trial_gui.data.trials[trial_key] - x, y = self.vertical_lines(self.selected_trials, 0, 3840) - trial_curve = pg.PlotCurveItem() - trial_curve.setData(x=x, y=y, pen=self.rpen_dot, connect='finite') - trial_curve.setClickable(True) - self.fig_img.addItem(trial_curve) - self.fig_img.scene().sigMouseClicked.connect(self.on_mouse_clicked) - trial_curve.sigClicked.connect(self.trial_line_clicked) - - - def vertical_lines(self, x, ymin, ymax): - - x = np.tile(x, (3, 1)) - x[2, :] = np.nan - y = np.zeros_like(x) - y[0, :] = ymin - y[1, :] = ymax - y[2, :] = np.nan - - return x.T.flatten(), y.T.flatten() - - def trial_line_clicked(self, ev): - self.clicked = ev - - def on_mouse_clicked(self, event): - if not event.double() and type(self.clicked) == pg.PlotCurveItem: - self.pos = self.data_plot.mapFromScene(event.scenePos()) - x = self.pos.x() * self.x_scale - trial_id = np.argmin(np.abs(self.selected_trials - x)) - - - - - - - - - # highlight the trial in the trial gui - - - - self.clicked = None - - - - -class TrialWindow(trial_window.MainWindow): - def __init__(self): - super(TrialWindow, self).__init__() - self.alignment_gui = None - self.scat = None - - def on_scatter_plot_clicked(self, scatter, point): - super().on_scatter_plot_clicked(scatter, point) - self.add_clust_scatter() - - def on_cluster_list_clicked(self): - super().on_cluster_list_clicked() - self.add_clust_scatter() - - def on_next_cluster_clicked(self): - super().on_next_cluster_clicked() - self.add_clust_scatter() - - def on_previous_cluster_clicked(self): - super().on_previous_cluster_clicked() - self.add_clust_scatter() - - def add_clust_scatter(self): - if not self.scat: - self.scat = pg.ScatterPlotItem() - self.alignment_gui.fig_img.addItem(self.scat) - - self.scat.setData(self.data.spikes.times[self.data.clus_idx], - self.data.spikes.depths[self.data.clus_idx], brush='r') - - # need to get out spikes.times and spikes.depths - - - - - - -def load_data(eid, probe, one=None): - one = one or ONE() - session_path = one.path_from_eid(eid).joinpath('alf') - probe_path = session_path.joinpath(probe) - data = Bunch() - data['trials'] = alfio.load_object(session_path, 'trials', namespace='ibl') - data['spikes'] = alfio.load_object(probe_path, 'spikes') - data['clusters'] = alfio.load_object(probe_path, 'clusters') - - return data - -def viewer(probe_id=None, one=None): - """ - """ - probe = one.alyx.rest('insertions', 'list', id=probe_id)[0] - data = load_data(probe['session'], probe['name'], one=one) - qt.create_app() - av = AlignmentWindow(probe_id=probe_id, one=one) - bv = TrialWindow() - bv.on_data_given(data) - av.trial_gui = bv - bv.alignment_gui = av - - av.show() - bv.show() - return av, bv - diff --git a/PythonScripts/iblapps-master/viewspikes/main.py b/PythonScripts/iblapps-master/viewspikes/main.py deleted file mode 100644 index 6c986e28..00000000 --- a/PythonScripts/iblapps-master/viewspikes/main.py +++ /dev/null @@ -1,93 +0,0 @@ -from pathlib import Path - -import scipy.signal -import numpy as np - -import spikeglx -from neurodsp import voltage -import neuropixel -from one.api import ONE -from easyqc.gui import viewseis - -from viewspikes.plots import plot_insertion, show_psd, overlay_spikes -from viewspikes.data import stream, get_spikes, get_ks2 - -folder_samples = Path('/datadisk/Data/spike_sorting/short_samples') -files_samples = list(folder_samples.rglob('*.bin')) - -one = ONE() -SIDE_BY_SIDE = False -# -# pins = one.alyx.rest('insertions', 'list', django=('json__extended_qc__alignment_count__gt,0')) -# pid, t0 = ('3e7618b8-34ca-4e48-ba3a-0e0f88a43131', 1002) # SWC_054_2020-10-10_probe01__ - sync w/ spikes !!! -# pid, t0 = ('04c9890f-2276-4c20-854f-305ff5c9b6cf', 1002.) # SWC_054_2020-10-10_probe00__04c9890f-2276-4c20-854f-305ff5c9b6cf - sync w/ spikes !!! -# pid, t0 = ('0925fb1b-cf83-4f55-bfb7-aa52f993a404', 500.) # DY_013_2020-03-06_probe00__0925fb1b-cf83-4f55-bfb7-aa52f993a404 -# pid, t0 = ('0ece5c6a-7d1e-4365-893d-ac1cc04f1d7b', 750.) # CSHL045_2020-02-27_probe01__0ece5c6a-7d1e-4365-893d-ac1cc04f1d7b -# pid, t0 = ('0ece5c6a-7d1e-4365-893d-ac1cc04f1d7b', 3000.) # CSHL045_2020-02-27_probe01__0ece5c6a-7d1e-4365-893d-ac1cc04f1d7b -pid, t0 = ('10ef1dcd-093c-4839-8f38-90a25edefb49', 2400.) -# pid, t0 = ('1a6a17cc-ba8c-4d79-bf20-cc897c9500dc', 5000) -# pid, t0 = ('2dd99c91-292f-44e3-bbf2-8cfa56015106', 2500) # NYU-23_2020-10-14_probe01__2dd99c91-292f-44e3-bbf2-8cfa56015106 -# pid, t0 = ('2dd99c91-292f-44e3-bbf2-8cfa56015106', 6000) # NYU-23_2020-10-14_probe01__2dd99c91-292f-44e3-bbf2-8cfa56015106 -# pid, t0 = ('30dfb8c6-9202-43fd-a92d-19fe68602b6f', 2400.) # ibl_witten_27_2021-01-16_probe00__30dfb8c6-9202-43fd-a92d-19fe68602b6f -# pid, t0 = ('31dd223c-0c7c-48b5-a513-41feb4000133', 3000.) # really good one : striping on not all channels -# pid, t0 = ('39b433d0-ec60-460f-8002-a393d81620a4', 2700.) # ZFM-01577_2020-10-27_probe01 needs FDNAT -# pid, t0 = ('47da98a8-f282-4830-92c2-af0e1d4f00e2', 2700.) - -# 67 frequency spike -# 458 /datadisk/Data/spike_sorting/short_samples/b45c8f3f-6361-41df-9bc1-9df98b3d30e6_01210.bin ERROR dans le chargement de la whitening matrix -# 433 /datadisk/Data/spike_sorting/short_samples/8d59da25-3a9c-44be-8b1a-e27cdd39ca34_04210.bin Cortex complètement silencieux. -# 531 /datadisk/Data/spike_sorting/short_samples/47be9ae4-290f-46ab-b047-952bc3a1a509_00010.bin Sympa pour le spike sorting, un bon example de trace pourrie à enlever avec FDNAT / Cadzow. Il y a du striping à la fin mais pas de souci pour KS2 ou pour le FK. -# 618 5b9ce60c-dcc9-4789-b2ff-29d873829fa5_03610.bin: gros cabossage plat laissé par le FK !! Tester un filtre K tout bête # spikes tous petits en comparaison. Le spike sorting a l'air décalé -# 681 /datadisk/Data/spike_sorting/short_samples/eab93ab0-26e3-4bd9-9c53-9f81c35172f4_02410.bin !! Spikes décalés. Superbe example de layering dans le cerveau avec 3 niveaux très clairement définis -# 739 /datadisk/Data/spike_sorting/short_samples/f03b61b4-6b13-479d-940f-d1608eb275cc_04210.bin: Autre example de layering ou les charactéristiques spectrales / spatiales sont très différentes. Spikes alignés -# 830 /datadisk/Data/spike_sorting/short_samples/b02c0ce6-2436-4fc0-9ea0-e7083a387d7e_03010.bin, très mauvaise qualité - spikes sont décalés ?!? - - - -file_ind = np.random.randint(len(files_samples)) -file_ind = 739 # very good quality spike sorting -print(file_ind, files_samples[file_ind]) - -pid, t0 = ('47da98a8-f282-4830-92c2-af0e1d4f00e2', 1425.) - -pid = files_samples[file_ind] -# pid, t0 = ("01c6065e-eb3c-49ba-9c25-c1f17b18d529", 500) -if isinstance(pid, Path): - file_sample = pid - pid, t0 = file_sample.stem.split('_') - t0 = float(t0) - sr = spikeglx.Reader(file_sample, open=True) - dsets = one.alyx.rest('datasets', 'list', probe_insertion=pid) -else: - sr, dsets, t0 = stream(pid, t0, one=one, samples_folder=folder_samples) - -# -plot_insertion(pid, one) - - -h = neuropixel.trace_header() -raw = sr[:, :-1].T - -sos = scipy.signal.butter(3, 300 / sr.fs / 2, btype='highpass', output='sos') -butt = scipy.signal.sosfiltfilt(sos, raw) -# show_psd(butt, sr.fs) - -fk_kwargs ={'dx': 1, 'vbounds': [0, 1e6], 'ntr_pad': 160, 'ntr_tap': 0, 'lagc': .01, 'btype': 'lowpass'} -destripe = voltage.destripe(raw, fs=sr.fs, fk_kwargs=fk_kwargs, tr_sel=np.arange(raw.shape[0])) -ks2 = get_ks2(raw, dsets, one) - -# get the spikes corresponding to current chunk, here needs to go through samples for sync reasons -spikes, clusters, channels = get_spikes(dsets, one) - -if SIDE_BY_SIDE: - hhh = {k: np.tile(h[k], 3) for k in h} - eqc_concat = viewseis(np.r_[butt, destripe, ks2], si=1 / sr.fs, h=hhh, t0=t0, title='concat') - overlay_spikes(eqc_concat, spikes, clusters, channels) -else: - eqc_butt = viewseis(butt.T, si=1 / sr.fs, h=h, t0=t0, title='butt', taxis=0) - eqc_dest = viewseis(destripe.T, si=1 / sr.fs, h=h, t0=t0, title='destr', taxis=0) - eqc_ks2 = viewseis(ks2.T, si=1 / sr.fs, h=h, t0=t0, title='ks2', taxis=0) - overlay_spikes(eqc_butt, spikes, clusters, channels) - overlay_spikes(eqc_dest, spikes, clusters, channels) - overlay_spikes(eqc_ks2, spikes, clusters, channels) -sr.close() diff --git a/PythonScripts/iblapps-master/viewspikes/plots.py b/PythonScripts/iblapps-master/viewspikes/plots.py deleted file mode 100644 index 611e92dc..00000000 --- a/PythonScripts/iblapps-master/viewspikes/plots.py +++ /dev/null @@ -1,178 +0,0 @@ -# import modules -import numpy as np -import matplotlib.pyplot as plt -import scipy.signal -import pyqtgraph as pg - -import ibllib.atlas as atlas -from neuropixel import SITES_COORDINATES -from ibllib.pipes.ephys_alignment import EphysAlignment -from ibllib.plots import wiggle, color_cycle - -brain_atlas = atlas.AllenAtlas() -# Instantiate brain atlas and one - - -def show_psd(data, fs, ax=None): - psd = np.zeros((data.shape[0], 129)) - for tr in np.arange(data.shape[0]): - f, psd[tr, :] = scipy.signal.welch(data[tr, :], fs=fs) - - if ax is None: - fig, ax = plt.subplots() - ax.plot(f, 10 * np.log10(psd.T), color='gray', alpha=0.1) - ax.plot(f, 10 * np.log10(np.mean(psd, axis=0).T), color='red') - ax.set_xlabel('Frequency (Hz)') - ax.set_ylabel('PSD (dB rel V/Hz)') - ax.set_ylim(-150, -110) - ax.set_xlim(0, fs / 2) - plt.show() - - -def plot_insertion(pid, one=None): - # Find eid of interest - assert one - insertion = one.alyx.rest('insertions', 'list', id=pid)[0] - probe_label = insertion['name'] - eid = insertion['session'] - - # Load in channels.localCoordinates dataset type - chn_coords = one.load(eid, dataset_types=['channels.localCoordinates'])[0] - depths = chn_coords[:, 1] - - # Find the ephys aligned trajectory for eid probe combination - trajs = one.alyx.rest('trajectories', 'list', session=eid, probe=probe_label) - #provenance=, - traj_aligned = next(filter(lambda x: x['provenance'] == 'Ephys aligned histology track', trajs), None) - if traj_aligned is None: - raise NotImplementedError(f"Plots only aligned insertions so far - TODO") - else: - plot_alignment(insertion, traj_aligned, -1) - # Extract all alignments from the json field of object - # Load in the initial user xyz_picks obtained from track traccing - - -def plot_alignment(insertion, traj, ind=None): - depths = SITES_COORDINATES[:, 1] - xyz_picks = np.array(insertion['json']['xyz_picks']) / 1e6 - - alignments = traj['json'].copy() - k = list(alignments.keys())[-1] # if only I had a Walrus available ! - alignments = {k: alignments[k]} - # Create a figure and arrange using gridspec - widths = [1, 2.5] - heights = [1] * len(alignments) - gs_kw = dict(width_ratios=widths, height_ratios=heights) - fig, axis = plt.subplots(len(alignments), 2, constrained_layout=True, - gridspec_kw=gs_kw, figsize=(8, 9)) - - # Iterate over all alignments for trajectory - # 1. Plot brain regions that channel pass through - # 2. Plot coronal slice along trajectory with location of channels shown as red points - # 3. Save results for each alignment into a dict - channels - channels = {} - for iK, key in enumerate(alignments): - - # Location of reference lines used for alignmnet - feature = np.array(alignments[key][0]) - track = np.array(alignments[key][1]) - chn_coords = SITES_COORDINATES - # Instantiate EphysAlignment object - ephysalign = EphysAlignment(xyz_picks, depths, track_prev=track, feature_prev=feature) - - # Find xyz location of all channels - xyz_channels = ephysalign.get_channel_locations(feature, track) - # Find brain region that each channel is located in - brain_regions = ephysalign.get_brain_locations(xyz_channels) - # Add extra keys to store all useful information as one bunch object - brain_regions['xyz'] = xyz_channels - brain_regions['lateral'] = chn_coords[:, 0] - brain_regions['axial'] = chn_coords[:, 1] - - # Store brain regions result in channels dict with same key as in alignment - channel_info = {key: brain_regions} - channels.update(channel_info) - - # For plotting -> extract the boundaries of the brain regions, as well as CCF label and colour - region, region_label, region_colour, _ = ephysalign.get_histology_regions(xyz_channels, depths) - - # Make plot that shows the brain regions that channels pass through - ax_regions = fig.axes[iK * 2] - for reg, col in zip(region, region_colour): - height = np.abs(reg[1] - reg[0]) - bottom = reg[0] - color = col / 255 - ax_regions.bar(x=0.5, height=height, width=1, color=color, bottom=reg[0], edgecolor='w') - ax_regions.set_yticks(region_label[:, 0].astype(int)) - ax_regions.yaxis.set_tick_params(labelsize=8) - ax_regions.get_xaxis().set_visible(False) - ax_regions.set_yticklabels(region_label[:, 1]) - ax_regions.spines['right'].set_visible(False) - ax_regions.spines['top'].set_visible(False) - ax_regions.spines['bottom'].set_visible(False) - ax_regions.hlines([0, 3840], *ax_regions.get_xlim(), linestyles='dashed', linewidth=3, - colors='k') - # ax_regions.plot(np.ones(channel_depths_track.shape), channel_depths_track, '*r') - - # Make plot that shows coronal slice that trajectory passes through with location of channels - # shown in red - ax_slice = fig.axes[iK * 2 + 1] - brain_atlas.plot_tilted_slice(xyz_channels, axis=1, ax=ax_slice) - ax_slice.plot(xyz_channels[:, 0] * 1e6, xyz_channels[:, 2] * 1e6, 'r*') - ax_slice.title.set_text(insertion['id'] + '\n' + str(key)) - - # Make sure the plot displays - plt.show() - - -def overlay_spikes(self, spikes, clusters, channels, rgb=None, label='default', symbol='x', size=8): - first_sample = self.ctrl.model.t0 / self.ctrl.model.si - last_sample = first_sample + self.ctrl.model.ns - - ifirst, ilast = np.searchsorted(spikes['samples'], [first_sample, last_sample]) - tspi = spikes['samples'][ifirst:ilast].astype(np.float64) * self.ctrl.model.si - xspi = channels['rawInd'][clusters['channels'][spikes['clusters'][ifirst:ilast]]] - - n_side_by_side = int(self.ctrl.model.ntr / 384) - print(n_side_by_side) - if n_side_by_side > 1: - addx = (np.zeros([1, xspi.size]) + np.array([self.ctrl.model.ntr * r for r in range(n_side_by_side)])[:, np.newaxis]).flatten() - xspi = np.tile(xspi, n_side_by_side) + addx - yspi = np.tile(tspi, n_side_by_side) - if self.ctrl.model.taxis == 1: - self.ctrl.add_scatter(xspi, tspi, label=label) - else: - self.ctrl.add_scatter(tspi, xspi, label=label) - sc = self.layers[label]['layer'] - sc.setSize(size) - sc.setSymbol(symbol) - # sc.setPen(pg.mkPen((0, 255, 0, 155), width=1)) - if rgb is None: - rgbs = [list((rgb * 255).astype(np.uint8)) for rgb in color_cycle(spikes['clusters'][ifirst:ilast])] - sc.setBrush([pg.mkBrush(rgb) for rgb in rgbs]) - sc.setPen([pg.mkPen(rgb) for rgb in rgbs]) - else: - sc.setBrush(pg.mkBrush(rgb)) - sc.setPen(pg.mkPen(rgb)) - return sc, tspi, xspi - - # sc.setData(x=xspi, y=tspi, brush=pg.mkBrush((255, 0, 0))) - def callback(sc, points, evt): - NTR = 12 - NS = 128 - qxy = self.imageItem_seismic.mapFromScene(evt.scenePos()) - # tr, _, _, s, _ = self.ctrl.cursor2timetraceamp(qxy) - itr, itime = self.ctrl.cursor2ind(qxy) - print(itr, itime) - h = self.ctrl.model.header - x = self.ctrl.model.data - trsel = np.arange(np.max([0, itr - NTR]), np.min([self.ctrl.model.ntr, itr + NTR])) - ordre = np.lexsort((h['x'][trsel], h['y'][trsel])) - trsel = trsel[ordre] - w = x[trsel, int(itime) - NS:int(itime) + NS] - wiggle(-w.T * 10000, fs=1 / self.ctrl.model.si, t0=(itime - NS) * self.ctrl.model.si) - # hw = {k:h[k][trsel] for k in h} - - # s.sigMouseClicked.disconnect() - sc.sigClicked.connect(callback) - # self.ctrl.remove_all_layers() diff --git a/PythonScripts/iblapps-master/viewspikes/view2p.ui b/PythonScripts/iblapps-master/viewspikes/view2p.ui deleted file mode 100644 index f5b68196..00000000 --- a/PythonScripts/iblapps-master/viewspikes/view2p.ui +++ /dev/null @@ -1,67 +0,0 @@ - - - MainWindow - - - - 0 - 0 - 810 - 606 - - - - MainWindow - - - - - - -1 - -1 - 801 - 551 - - - - - - - - - - - - -10 - 550 - 799 - 15 - - - - Qt::Horizontal - - - - - - - 0 - 0 - 810 - 22 - - - - - - - - PlotWidget - QGraphicsView -
pyqtgraph
- - - - -