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evoked_analysis.py
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import matplotlib.pyplot as plt
import mne
import numpy as np
from scipy.stats import zscore
from controls import session_name, check_area
from directories import epochs_dir
def collect_evoked(subject, condition, session, event='trigger', time_window=[-0.5, 0.5], picks=None):
# cue_interval = [-2.0, -1.0]
# trigger_interval = [-0.5, 0.5]
# Correct session name and read the associate epochs file
trial_num = session_name(session)
epochs_fname = epochs_dir.format(subject, condition, trial_num) + '{0}_{1}-epo.fif'.format(trial_num, event)
epochs = mne.read_epochs(epochs_fname, preload=True)
if isinstance(picks, list):
epochs.pick_channels(picks)
# cue_epochs = epochs.copy().crop(cue_interval[0], cue_interval[1])
# trigger_epochs = epochs.copy().crop(trigger_interval[0], trigger_interval[1])
event_epochs = epochs.copy().crop(time_window[0], time_window[1])
# cue_evoked = cue_epochs.copy().average()
# cue_sem = cue_epochs.copy().standard_error()
# trigger_evoked = trigger_epochs.copy().average()
# trigger_sem = trigger_epochs.copy().standard_error()
event_evoked = event_epochs.copy().average()
event_sem = event_epochs.copy().standard_error()
# fig = mne.viz.plot_compare_evokeds(cue_evoked, picks=[0], vlines=[-1.5])
# return cue_evoked, cue_sem, trigger_evoked, trigger_sem
return event_evoked, event_sem
def collect_avg_evoked(subject, condition, session, area, event='trigger', time_window=[-0.5, 0.5], picks=None):
all_epochs = []
for ses in session:
trial_n = check_area(subject, condition, ses, area)
if isinstance(trial_n, str):
trial_num = session_name(ses)
epochs_fname = epochs_dir.format(subject, condition, trial_num) + '{0}_{1}-epo.fif'.format(trial_num, event)
epochs = mne.read_epochs(epochs_fname, preload=True)
if isinstance(picks, list):
epochs.pick_channels(picks)
event_epochs = epochs.copy().crop(time_window[0], time_window[1])
all_epochs.append(event_epochs)
all_epochs = mne.concatenate_epochs(all_epochs)
event_evoked = all_epochs.copy().average()
event_sem = all_epochs.copy().standard_error()
return event_evoked, event_sem
def plot_evoked(subject, condition, session, events_struct, picks=None, aligned=True, show=True):
cm = plt.get_cmap('Set1')
col = 0.11
# fig, ax = plt.subplots()
if show == True: fig = plt.figure()
for k in events_struct.keys():
time_window = events_struct[k][0]
event = events_struct[k][1]
event_evoked, event_sem = collect_evoked(subject, condition, session, event, time_window, picks)
times = event_evoked.times
if aligned == True: times -= np.average(times)
average = event_evoked.data.squeeze() * 1000
error = event_sem.data.squeeze() * 1000
plt.plot(times, average, color=cm(col), label=k)
plt.fill_between(times, average-error, average+error, color=cm(col), alpha=0.2)
plt.axvline(np.average(times), color='k', linestyle=':')
plt.axhline(0, color='k')
col += 0.11
if show == True:
plt.title('Average evoked', fontsize=15)
plt.xlabel('Time')
plt.ylabel('mV')
plt.legend()
plt.tight_layout()
plt.show()
# return ax
def plot_area_evoked(subject, condition, session, area, events_struct, mode='all'):
good_trials = []
for ses in session:
trial_n = check_area(subject, condition, ses, area)
if isinstance(trial_n, str):
good_trials.append(trial_n)
if mode == 'all':
fig = plt.figure()
fig.suptitle(area, fontsize=18)
for t in range(len(good_trials)):
ax = fig.add_subplot(np.ceil(len(good_trials)/2.), 2, t+1)
ax = plot_evoked(subject, condition, good_trials[t], events_struct, picks=['lfp'], aligned=True, show=False)
plt.title('Average evoked', fontsize=15)
plt.xlabel('Time')
plt.ylabel('mV')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
elif mode == 'single':
for t in range(len(good_trials)):
plot_evoked(subject, condition, good_trials[t], events_struct, picks=['lfp'], aligned=True, show=True)
def plot_avg_evoked(subject, condition, session, area, events_struct, picks=None, aligned=True):
cm = plt.get_cmap('Set1')
col = 0.11
for k in events_struct.keys():
time_window = events_struct[k][0]
event = events_struct[k][1]
event_evoked, event_sem = collect_avg_evoked(subject, condition, session, area, event, time_window, picks)
times = event_evoked.times
if aligned == True: times -= np.average(times)
average = event_evoked.data.squeeze() * 1000
error = event_sem.data.squeeze() * 1000
plt.plot(times, average, color=cm(col), label=k)
plt.fill_between(times, average-error, average+error, color=cm(col), alpha=0.2)
plt.axvline(np.average(times), color='k', linestyle=':')
plt.axhline(0, color='k')
col += 0.11
plt.title('Average evoked, all session, area {0}'.format(area), fontsize=15)
plt.xlabel('Time')
plt.ylabel('mV')
plt.legend()
plt.tight_layout()
plt.show()
def plot_zscore_evoked(subject, condition, session, area, events_struct, picks=None, mode='max'):
good_trials = []
for ses in session:
trial_n = check_area(subject, condition, ses, area)
if isinstance(trial_n, str):
good_trials.append(trial_n)
if mode == 'single':
for t in range(len(good_trials)):
for ev in events_struct.keys():
evk_avg, evk_sem = collect_evoked(subject, condition, good_trials[t], events_struct[ev][1],
events_struct[ev][0], picks)
times = evk_avg.times
times -= np.average(times)
evk_zscore = zscore(evk_avg.data.squeeze())
plt.plot(times, evk_zscore, label=ev)
plt.axvline(np.average(times), color='k', linestyle=':')
plt.axhline(0, color='k')
plt.title('z-scores evoked', fontsize=15)
plt.xlabel('Time')
plt.ylabel('zscore(V)')
plt.legend()
plt.tight_layout()
plt.show()
if mode == 'max':
ev_mex_zscore = {key: [] for key in events_struct.keys()}
for t in range(len(good_trials)):
for ev in events_struct.keys():
evk_avg, evk_sem = collect_evoked(subject, condition, good_trials[t], events_struct[ev][1],
events_struct[ev][0], picks)
max_zscore = max(zscore(evk_avg.data.squeeze()))
ev_mex_zscore[ev].append(max_zscore)
for ev in ev_mex_zscore.keys():
plt.hist(ev_mex_zscore[ev], bins=len(ev_mex_zscore[ev])/2)
plt.axvline(2.58, color='g', linestyle='-')
plt.title('Max z-scores for {0}'.format(ev), fontsize=15)
plt.xlabel('max zscore')
plt.ylabel('N')
plt.tight_layout()
plt.show()
# plot_evoked(subject, condition, good_trials[t], events_struct, picks=picks, aligned=True, show=True)
if __name__ == '__main__':
# collect_evoked('freddie', 'easy', 'fneu0437', picks=['lfp'])
events_struct = {'trigger':([-0.5, 0.5], 'trigger'), 'cue':([-2.0, -1.0], 'trigger')}
# plot_evoked('freddie', 'easy', 'fneu0437', events, picks=['lfp'])
# plot_area_evoked('freddie', 'easy', ['fneu0437', 'fneu0772', 'fneu0773', 'fneu0779'],
# 'associative striatum', events_struct, mode='all')
# plot_avg_evoked('freddie', 'easy', ['fneu0437', 'fneu0772', 'fneu0773', 'fneu0779'],
# 'associative striatum', events_struct, ['lfp'])
plot_zscore_evoked('freddie', 'easy', ['fneu0437', 'fneu0772', 'fneu0773', 'fneu0779'],
'associative striatum', events_struct, picks=['lfp'], mode='max')