task = 'stimuli'
run = '1'
nepochs = 28
nobs = 1126
#HFB_tot_face = np.empty(shape =( nepochs, 0, nobs))
#HFB_tot_place = np.empty(shape =( nepochs, 0, nobs))
HFB_tot_pref = np.empty(shape=(nepochs, 0, nobs)) # need to define time before
HFB_tot_npref = np.empty(shape=(nepochs, 0, nobs))
sub_id = [
'AnRa', 'AnRi', 'ArLa', 'BeFe', 'DiAs', 'FaWa', 'JuRo', 'NeLa', 'SoGi'
]
path_visual = cf_load.visual_path()
df_visual = pd.read_csv(path_visual)
for sub in sub_id:
subject = cf_load.Subject(name=sub, task=task, run=run)
fpath = subject.fpath(proc=preproc, suffix='lnrmv')
raw = subject.import_data(fpath)
face_chan = list(df_visual['chan_name'].loc[
df_visual['subject_id'] == sub].loc[df_visual['category'] == 'Face'])
place_chan = list(df_visual['chan_name'].loc[
df_visual['subject_id'] == sub].loc[df_visual['category'] == 'Place'])
bands = HFB_process.freq_bands() # Select Bands of interests
HFB_db = HFB_process.extract_HFB_db(raw, bands)
HFB_db = HFB_db.drop_channels(ch_names='TRIG')
import pandas as pd
%matplotlib
plt.rcParams.update({'font.size': 17})
preproc = 'preproc'
suffix2save = 'HFB_visual'
ext2save = '.mat'
cf_subjects = ['AnRa', 'AnRi', 'ArLa', 'BeFe', 'DiAs', 'FaWa', 'JuRo', 'NeLa', 'SoGi']
tasks = ['stimuli', 'rest_baseline']
runs = ['1','2']
path_visual = cf_load.visual_path() # pick visual channels for all subjects
df_visual = pd.read_csv(path_visual)
for sub in cf_subjects:
for task in tasks:
for run in runs:
#%% Import data
subject = cf_load.Subject(name=sub, task= task, run = run)
fpath = subject.fpath(preproc = preproc, suffix='lnrmv')
raw = subject.import_data(fpath)
# %% Extract HFB and save
bands = HFB_test.freq_bands() # Select Bands of interests
HFB = HFB_test.extract_HFB(raw, bands)