def baseline_GA(subject, subjects1, KIND):
#set baseline == 'fixation_cross_norisks' and GA_correction = True to build a well-behaved baseline for additional substraction in GA (remove trend this way)
run_counter = 0
fpath_events = '/home/asmyasnikova83/DATA/mio_out_{0}/{1}_run{2}_mio_corrected_{3}{4}{5}.txt'
fpath_raw = '/net/server/data/Archive/prob_learn/vtretyakova/ICA_cleaned/{}/run{}_{}_raw_ica.fif'
for run in runs:
rf = fpath_events.format(KIND, subject, run, stimulus, KIND, train)
file = pathlib.Path(rf)
if file.exists() and os.stat(rf).st_size != 0:
run_counter = run_counter + 1
print('run_counter', run_counter)
SUBJ_BASE = np.zeros(204)
BASE = np.zeros((run_counter, 204))
ind = 0
for run in runs:
rf = fpath_events.format(KIND, subject, run, stimulus, KIND, train)
file = pathlib.Path(rf)
if file.exists() and os.stat(rf).st_size != 0:
raw_file = fpath_raw.format(subject, run, subject)
raw_data = mne.io.Raw(raw_file, preload=True)
#filter 1-50 Hz
raw_data = raw_data.filter(
None, 50, fir_design='firwin'
) # for low frequencies, below the peaks of power-line noise low pass filter the data
raw_data = raw_data.filter(
1., None, fir_design='firwin') #remove slow drifts
picks = mne.pick_types(raw_data.info, meg='grad')
events_with_cross, events_of_interest = retrieve_events_for_baseline(
raw_data, rf, KIND, subject, run, picks)
print('\n\nEvents with cross extracted!')
BASELINE, b_line = compute_baseline_substraction_and_power(
raw_data, events_with_cross, events_of_interest, picks)
print('\n\nDone with the BASELINE I!')
if BASELINE.all == 0:
print('Yes, BASELINE is dummy')
continue
ind = ind + 1
print('Baseline shape', BASELINE.shape)
#average over epochs
BASELINE = np.mean(BASELINE, axis=1)
print('Baseline new shape', BASELINE.shape)
print('ind', ind)
#put here baseline for each run
BASE[ind - 1, 0:204] = BASELINE
print('BASE', BASE)
#average baseline over runs/blocks
SUBJ_BASE = np.mean(BASE, axis=0)
print('SUBJ BASE', SUBJ_BASE)
#prepare for further baseline correction
SUBJ_BASE = np.matlib.repmat(SUBJ_BASE, int(time.shape[0]), 1)
SUBJ_BASE = np.transpose(SUBJ_BASE)
print('SUBJ BASE shape', SUBJ_BASE.shape)
return SUBJ_BASE
for run in runs:
for subject in subjects:
rf = fpath_events.format(subject, run)
file = pathlib.Path(rf)
if file.exists():
print('This file is being processed: ', rf)
if mode == 'server':
raw_file = fpath_raw.format(subject, run, subject)
else:
raw_file = fpath_raw.format(subject, run)
raw_data = mne.io.Raw(raw_file, preload=True)
raw_data = raw_data.filter(None, 50, fir_design='firwin') # for low frequencies, below the peaks of power-line noise low pass filter the data
raw_data = raw_data.filter(1., None, fir_design='firwin') #remove slow drifts
picks = mne.pick_types(raw_data.info, meg = True)
events_with_cross, events_of_interest = retrieve_events_for_baseline(raw_data, rf, picks)
print('Done with the events!')
BASELINE, b_line = compute_baseline_substraction_and_power(raw_data, events_with_cross, picks)
print('\n\nDone with the BASELINE I!')
CORRECTED_DATA = correct_baseline_substraction(BASELINE, events_of_interest, raw_data, picks)
print('\n\nDone with the CORRECTED!')
plot_created_epochs_evoked = False
epochs_of_interest, evoked = create_mne_epochs_evoked(kind, subject, run, CORRECTED_DATA, events_of_interest, plot_created_epochs_evoked, raw_data, picks)
evoked_ave.append(evoked)
else:
print('This file: ', rf, 'does not exit')
continue
grand_averages = mne.grand_average(evoked_ave)
if batterfly:
GA = grand_averages.plot(spatial_colors=True, gfp=True, picks=picks, show=False)