%load_ext autoreload
-%autoreload 2
-import matplotlib.pyplot as plt
-%matplotlib inline
-import spatial_maps as sp
-import septum_mec.analysis.data_processing as dp
-import septum_mec.analysis.registration
-import expipe
-import os
-import pathlib
-import numpy as np
-import exdir
-import pandas as pd
-import optogenetics as og
-import quantities as pq
-import shutil
-from distutils.dir_util import copy_tree
-
-from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response
-
-from tqdm import tqdm_notebook as tqdm
-from tqdm._tqdm_notebook import tqdm_notebook
-tqdm_notebook.pandas()
-std_gaussian_kde = 0.04
-window_size = 0.03
-data_loader = dp.Data()
-actions = data_loader.actions
-project = data_loader.project
-output = pathlib.Path('output/stimulus-response')
-(output / 'figures').mkdir(parents=True, exist_ok=True)
-identify_neurons = actions['identify-neurons']
-units = pd.read_csv(identify_neurons.data_path('units'))
-def process(row):
-
- action_id = row['action']
- channel_id = int(row['channel_group'])
- unit_id = int(row['unit_name'])
-
- spike_times = data_loader.spike_train(action_id, channel_id, unit_id)
-
- spike_times = np.array(spike_times)
-
- stim_times = data_loader.stim_times(action_id)
-
- nan_series = pd.Series({
- 't_e_peak': np.nan,
- 'p_e_peak': np.nan,
- 't_i_peak': np.nan,
- 'p_i_peak': np.nan
- })
-
- if stim_times is None:
- return nan_series
-
- stim_times = np.array(stim_times)
-
- times, spikes, kernel, p_e, p_i = stimulus_response_latency(
- spike_times, stim_times, window_size, std_gaussian_kde)
-
- # if no spikes detected after stimulus nan is returned
- if all(np.isnan([p_e, p_i])):
- return nan_series
-
- t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(
- spike_times, stim_times, times, kernel, p_e, p_i)
-
- return pd.Series({
- 't_e_peak': t_e_peak,
- 'p_e_peak': p_e_peak,
- 't_i_peak': t_i_peak,
- 'p_i_peak': p_i_peak
- })
-results = units.merge(
- units.progress_apply(process, axis=1),
- left_index=True, right_index=True)
-results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()
-plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')
-action = project.require_action("stimulus-response")
-action.modules['parameters'] = {
- 'window_size': window_size,
- 'std_gaussian_kde': std_gaussian_kde
-}
-action.data['results'] = 'results.csv'
-results.to_csv(action.data_path('results'), index=False)
-stuff = {
- "figures": "figures",
-# "statistics": "statistics"
-}
-
-for key, value in stuff.items():
- action.data[key] = value
- data_path = action.data_path(key)
- data_path.parent.mkdir(exist_ok=True, parents=True)
- source = output / value
- if source.is_file():
- shutil.copy(source, data_path)
- else:
- copy_tree(str(source), str(data_path))
-septum_mec.analysis.registration.store_notebook(action, "10-calculate-stimulus-response.ipynb")
-%load_ext autoreload
+%autoreload 2
+import matplotlib.pyplot as plt
+%matplotlib inline
+import spatial_maps as sp
+import septum_mec.analysis.data_processing as dp
+import septum_mec.analysis.registration
+import expipe
+import os
+import pathlib
+import numpy as np
+import exdir
+import pandas as pd
+import optogenetics as og
+import quantities as pq
+import shutil
+from distutils.dir_util import copy_tree
+
+from septum_mec.analysis.stimulus_response import stimulus_response_latency, compute_response
+
+from tqdm import tqdm_notebook as tqdm
+from tqdm._tqdm_notebook import tqdm_notebook
+tqdm_notebook.pandas()
+std_gaussian_kde = 0.04
+window_size = 0.03
+data_loader = dp.Data()
+actions = data_loader.actions
+project = data_loader.project
+output = pathlib.Path('output/stimulus-response')
+(output / 'figures').mkdir(parents=True, exist_ok=True)
+identify_neurons = actions['identify-neurons']
+units = pd.read_csv(identify_neurons.data_path('units'))
+def process(row):
+
+ action_id = row['action']
+ channel_id = int(row['channel_group'])
+ unit_id = int(row['unit_name'])
+
+ spike_times = data_loader.spike_train(action_id, channel_id, unit_id)
+
+ spike_times = np.array(spike_times)
+
+ stim_times = data_loader.stim_times(action_id)
+
+ nan_series = pd.Series({
+ 't_e_peak': np.nan,
+ 'p_e_peak': np.nan,
+ 't_i_peak': np.nan,
+ 'p_i_peak': np.nan
+ })
+
+ if stim_times is None:
+ return nan_series
+
+ stim_times = np.array(stim_times)
+
+ times, spikes, kernel, p_e, p_i = stimulus_response_latency(
+ spike_times, stim_times, window_size, std_gaussian_kde)
+
+ # if no spikes detected after stimulus nan is returned
+ if all(np.isnan([p_e, p_i])):
+ return nan_series
+
+ t_e_peak, p_e_peak, t_i_peak, p_i_peak = compute_response(
+ spike_times, stim_times, times, kernel, p_e, p_i)
+
+ return pd.Series({
+ 't_e_peak': t_e_peak,
+ 'p_e_peak': p_e_peak,
+ 't_i_peak': t_i_peak,
+ 'p_i_peak': p_i_peak
+ })
+results = units.merge(
+ units.progress_apply(process, axis=1),
+ left_index=True, right_index=True)
+results.loc[:, ['t_e_peak', 't_i_peak', 'p_e_peak', 'p_i_peak']].hist()
+plt.gcf().savefig(output / 'figures' / 'summary_histogram.png')
+action = project.require_action("stimulus-response")
+action.modules['parameters'] = {
+ 'window_size': window_size,
+ 'std_gaussian_kde': std_gaussian_kde
+}
+action.data['results'] = 'results.csv'
+results.to_csv(action.data_path('results'), index=False)
+copy_tree(output, str(action.data_path()))
+septum_mec.analysis.registration.store_notebook(action, "10-calculate-stimulus-response.ipynb")
+
+