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%load_ext autoreload
%autoreload 2
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import os
import expipe
import pathlib
import numpy as np
import septum_mec.analysis.data_processing as dp
from septum_mec.analysis.registration import store_notebook
import re
import joblib
import multiprocessing
import shutil
import psutil
import pandas as pd
import matplotlib.pyplot as plt
import quantities as pq
import exdir
from distutils.dir_util import copy_tree
from datetime import timedelta
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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project_path = dp.project_path()
project = expipe.get_project(project_path)
actions = project.actions
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output = pathlib.Path('output/identify_neurons')
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identify_neurons = project.require_action('identify-neurons')
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actions['1833-010719-2'].attributes
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data_loader = dp.Data()
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skip_actions = [
'1849-270219-1',
'1849-260219-2',
'1834-250219-1',
'1834-230219-1'
]
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sessions = []
for action in actions.values():
if action.id in skip_actions:
continue
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
baseline = False
is_i = False
is_ii = False
tag = None
stim_location = None
stim_times = data_loader.stim_times(action.id)
if stim_times is not None:
stimulated = True
freq = round(1 / np.median(np.diff(stim_times)))
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]]
what, how = tag.split(' ')
if what == 'stim':
where = [t for t in tags if 'stim-' in t]
assert len(where) == 1
stim_location = where[0].split('-')[-1]
assert stimulated
elif what == 'baseline':
baseline = True
assert not stimulated
else:
raise Exception(f'Found {tag}, what to do?')
if how == 'i':
is_i = True
elif how == 'ii':
is_ii = True
else:
raise Exception(f'Found {tag}, what to do?')
elif len(tag_i) > 1:
print(action.id, [tags[i] for i in tag_i])
sessions.append({
'tag': tag,
'action': action.id,
'stimulated': stimulated,
'baseline': baseline,
'i': is_i,
'ii': is_ii,
'frequency': float(freq),
'session': int(action.id.split('-')[-1]),
'stim_location': stim_location,
'entity': int(action.entities[0]),
})
sessions = pd.DataFrame(sessions)
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sessions.query('stimulated and frequency!=30')
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sessions.to_csv(output / 'sessions.csv', index=False)
Identify unique neurons¶
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# save graphs
for entity, values in sessions.groupby('entity'):
data_path = output / f'{entity}-graphs'
if data_path.exists():
continue
print('Processing', entity)
unit_matching = TrackMultipleSessions(
actions, values.action.values.tolist(),
progress_bar=tqdm, verbose=False, data_path=data_path
)
unit_matching.do_matching()
unit_matching.make_graphs_from_matches()
unit_matching.compute_time_delta_edges()
unit_matching.compute_depth_delta_edges()
# save graph with all dissimilarities for later use
unit_matching.save_graphs()
Plot comparisons¶
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unit_comp = TrackMultipleSessions(actions, data_path=f'output/identify_neurons/1833-graphs')
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unit_comp.load_graphs()
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max_dissimilarity = .05
max_depth_delta = 100
unit_comp.remove_edges_above_threshold('weight', max_dissimilarity)
unit_comp.remove_edges_above_threshold('depth_delta', max_depth_delta)
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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()
Store uniqe unit ids to csv¶
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max_dissimilarity = .05
max_depth_delta = 100
for entity in sessions.entity.values:
unit_matching = TrackMultipleSessions(
actions, list(sessions.query(f'entity=={entity}').action),
progress_bar=tqdm, verbose=False, data_path=output / f'{entity}-graphs'
)
unit_matching.load_graphs()
# cutoff large dissimilarities
unit_matching.remove_edges_above_threshold('weight', max_dissimilarity)
unit_matching.remove_edges_above_threshold('depth_delta', max_depth_delta)
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': action_id,
'channel_group': ch,
'max_dissimilarity': max_dissimilarity,
'max_depth_delta': max_depth_delta
}
for name in orig_unit_ids])
pd.DataFrame(units).to_csv(output / f'{entity}-units.csv', index=False)
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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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unique_units.to_csv(output / 'units.csv', index=False)
Store results in Expipe action¶
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identify_neurons.data['sessions'] = 'sessions.csv'
identify_neurons.data['units'] = 'units.csv'
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copy_tree(output, str(identify_neurons.data_path()))
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store_notebook(
identify_neurons, "00-identify-neurons.ipynb")
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