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+In [1]:
+
+
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+
+
+
+%load_ext autoreload
+%autoreload 2
+
+
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+In [2]:
+
+
+
+
+
+
+import os
+import expipe
+import pathlib
+import numpy as np
+import spatial_maps.stats as stats
+import septum_mec.analysis.data_processing as dp
+from septum_mec.analysis.registration import store_notebook
+import head_direction.head as head
+import spatial_maps as sp
+import pnnmec.registration
+import speed_cells.speed as spd
+import re
+import joblib
+import multiprocessing
+import shutil
+import psutil
+import pandas as pd
+import matplotlib.pyplot as plt
+import pnnmec
+import scipy.ndimage.measurements
+import quantities as pq
+import exdir
+from tqdm import tqdm_notebook as tqdm
+from septum_mec.analysis.trackunitmulticomparison import TrackMultipleSessions
+import networkx as nx
+from nxpd import draw
+%matplotlib inline
+
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+In [307]:
+
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+
+project_path = dp.project_path()
+
+project = expipe.get_project(project_path)
+actions = project.actions
+
+
+
+
+
+In [308]:
+
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+
+
+identify_neurons = project.require_action('identify-neurons')
+
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+In [309]:
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+actions['1833-010719-2'].attributes
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+Out[309]:
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+In [310]:
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+actions['1833-010719-1'].attributes
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+Out[310]:
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+In [311]:
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+data_loader = dp.Data()
+
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+In [313]:
+
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+
+
+
+sessions = []
+for action in actions.values():
+ if action.type != 'Recording':
+ continue
+ action_data_path = pathlib.Path(action.data_path('main'))
+ processing = exdir.File(action_data_path)['processing']
+ if not 'electrophysiology' in processing:
+ continue
+ elphys = processing['electrophysiology']
+ if 'spikesorting' not in elphys:
+ continue
+ tags = [t.lower() for t in action.tags]
+
+ freq = np.nan
+ stimulated = False
+ control = False
+
+ stim_times = data_loader.stim_times(action.id)
+ if stim_times is not None:
+ stimulated = True
+ freq = round(1 / np.mean(np.diff(stim_times)))
+
+
+ tag = ""
+ stim_location = ""
+ tag_i = [i for i, t in enumerate(tags) if 'baseline' in t or 'stim' in t]
+ if len(tag_i) == 1:
+ tag = tags[tag_i[0]]
+ if 'stim' in tag:
+ stim_location = tag.split('-')[-1]
+ elif 'baseline' in tag:
+ control = True
+
+
+
+ sessions.append({
+ 'tag': tag,
+ 'action': action.id,
+ 'stimulated': stimulated,
+ 'control': control,
+ 'frequency': freq,
+ 'session': int(action.id.split('-')[-1]),
+ 'stim_location': stim_location,
+ 'entity': int(action.entities[0]),
+
+ })
+sessions = pd.DataFrame(sessions)
+
+
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+
+In [316]:
+
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+sessions.query('stimulated and not control')
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+Out[316]:
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+In [ ]:
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+
+
+Identify unique neurons¶
+
+
+
+
+
+In [304]:
+
+
+
+
+
+
+output = pathlib.Path('output/identify_neurons_weighted')
+
+max_dissimilarity = .035
+for entity in sessions.entity.unique():
+ unit_matching = TrackMultipleSessions(
+ actions, list(sessions.query(f'entity=={entity}').action),
+ progress_bar=tqdm, verbose=False, data_path=output / f'{entity}-graphs'
+ )
+ unit_matching.do_matching()
+ unit_matching.make_graphs_from_matches()
+ # save graph with all dissimilarities for later use
+ unit_matching.save_graphs()
+ # cutoff large dissimilarities
+ unit_matching.threshold_dissimilarity(max_dissimilarity)
+ unit_matching.remove_edges_with_duplicate_actions()
+ unit_matching.identify_units()
+ units = []
+ for ch, group in unit_matching.identified_units.items():
+ for unit_id, val in group.items():
+ for action_id, orig_unit_ids in val['original_unit_ids'].items():
+ units.extend([
+ {
+ 'unit_name': name,
+ 'unit_id': unit_id,
+ 'action_id': action_id,
+ 'channel_group': ch,
+ 'max_dissimilarity': max_dissimilarity
+ }
+ for name in orig_unit_ids])
+
+ pd.DataFrame(units).to_csv(output / f'{entity}-units.csv', index=False)
+
+
+
+
+
+In [282]:
+
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+
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+sessions.to_csv(output / 'sessions.csv', index=False)
+
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+
+In [283]:
+
+
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+
+
+
+unique_units = pd.concat([
+ pd.read_csv(p)
+ for p in output.iterdir()
+ if p.name.endswith('units.csv')])
+
+
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+
+In [284]:
+
+
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+
+
+unique_units.to_csv(output / 'unique_units.csv', index=False)
+
+
+
+
+
+yoyoyo¶
+
+
+
+
+
+In [288]:
+
+
+
+
+
+
+unit_comp = TrackMultipleSessions(actions, data_path=f'output/identify_neurons_weighted/1833-graphs')
+
+
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+
+In [299]:
+
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+
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+
+unit_comp.load_graphs()
+
+
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+
+In [300]:
+
+
+
+
+
+
+# unit_comp._compute_timedelta()
+# unit_comp.save_graphs()
+
+
+
+
+
+In [301]:
+
+
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+
+unit_comp.threshold_dissimilarity(0.08)
+
+
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+
+In [302]:
+
+
+
+
+
+
+# unit_comp.threshold_timedelta(timedelta(days=10))
+
+
+
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+
+In [303]:
+
+
+
+
+
+
+unit_comp.remove_edges_with_duplicate_actions()
+unit_comp.identify_units()
+unit_comp.plot_matches('template', chan_group=6, step_color=False)
+plt.tight_layout()
+
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+In [298]:
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+
+[d['weight'] for _,_, d in unit_comp.graphs[6].edges(data=True)]
+
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+Out[298]:
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+In [87]:
+
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+
+
+cmp = TrackMultipleSessions(actions)
+
+
+
+
+
+In [95]:
+
+
+
+
+
+
+from septum_mec.analysis.track_units_tools import plot_waveform, dissimilarity, dissimilarity_weighted
+
+
+
+
+
+In [90]:
+
+
+
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+
+
+from matplotlib import gridspec
+
+
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+
+In [230]:
+
+
+
+
+
+
+fig = plt.figure(figsize=(16, 9))
+gs = gridspec.GridSpec(1, 1)
+
+wf1 = cmp.load_waveforms('1833-050619-3', 143, 6)
+wf2 = cmp.load_waveforms('1833-200619-3', 126, 6)
+axs = plot_waveform(wf1, fig, gs[0])
+plot_waveform(wf2, fig, gs[0], axs=axs)
+
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+Out[230]:
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+In [276]:
+
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+
+
+d00 = dissimilarity(wf1.mean(), wf2.mean())
+d00
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+Out[276]:
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+In [275]:
+
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+
+
+d10 = dissimilarity_weighted(wf1, wf2)
+d10
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+Out[275]:
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+In [233]:
+
+
+
+
+
+
+fig = plt.figure(figsize=(16, 9))
+gs = gridspec.GridSpec(1, 1)
+
+wf3 = cmp.load_waveforms('1833-050619-3', 143, 6)
+wf4 = cmp.load_waveforms('1833-060619-1', 170, 6)
+axs = plot_waveform(wf3, fig, gs[0])
+plot_waveform(wf4, fig, gs[0], axs=axs)
+
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+Out[233]:
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+In [278]:
+
+
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+
+
+d01 = dissimilarity(wf3.mean(), wf4.mean())
+d01
+
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+Out[278]:
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+In [277]:
+
+
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+
+
+d11 = dissimilarity_weighted(wf3, wf4)
+d11
+
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+Out[277]:
+
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+In [236]:
+
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+
+
+d00 / d01
+
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+Out[236]:
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+In [237]:
+
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+
+
+d10 / d11
+
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+Out[237]:
+
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+
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+
+
+In [239]:
+
+
+
+
+
+
+t = abs(actions['1833-260619-2'].datetime - actions['1833-050619-3'].datetime)
+
+
+
+
+
+In [240]:
+
+
+
+
+
+
+t > timedelta(15)
+
+
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+
+
+Out[240]:
+
+
+
+
+
+
+
+
+
+
+
+Store results in Expipe action¶
+
+
+
+
+
+In [64]:
+
+
+
+
+
+
+identify_neurons.data['sessions'] = 'sessions.csv'
+identify_neurons.data['units'] = 'units.csv'
+
+
+
+
+
+In [67]:
+
+
+
+
+
+
+sessions.to_csv(identify_neurons.data_path('sessions'), index=False)
+units.to_csv(identify_neurons.data_path('units'), index=False)
+
+
+
+
+
+In [69]:
+
+
+
+
+
+
+store_notebook(
+ identify_neurons, "00-identify-neurons.ipynb")
+