def LFP_to_dict(LFP, visual_chan, tmin=-0.5, tmax=1.75, sfreq=250):
"""Return dictionary with all category specific LFP and visual channels
information"""
visual_LFP = LFP.copy().pick(visual_chan['chan_name'].tolist())
LFP_dict = visual_chan.to_dict(orient='list')
categories = ['Rest', 'Face', 'Place']
for cat in categories:
epochs, events = hf.epoch_category(visual_LFP,
cat=cat,
tmin=-0.5,
tmax=1.75)
epochs = epochs.resample(sfreq)
X = epochs.copy().get_data()
X = np.transpose(X, axes=(1, 2, 0))
LFP_dict[cat] = X
LFP_dict['time'] = epochs.times
population_to_channel = hf.parcellation_to_indices(visual_chan,
parcellation='group')
DK_to_channel = hf.parcellation_to_indices(visual_chan, parcellation='DK')
LFP_dict['population_to_channel'] = population_to_channel
LFP_dict['DK_to_channel'] = DK_to_channel
return LFP_dict
from scipy.io import savemat, loadmat
#%% Plot mvgc
subject = "DiAs"
# Anatomical info
ecog = hf.Ecog(args.cohort_path,
subject=subject,
proc=args.proc,
stage=args.stage,
epoch=args.epoch)
df_visual = ecog.read_channels_info(fname=args.channels)
functional_group = df_visual["group"].unique()
functional_indices = hf.parcellation_to_indices(df_visual,
'group',
matlab=True)
visual_chan = df_visual['chan_name'].to_list()
# Read results
result_path = Path('~', 'neocortex', 'results').expanduser()
fname = subject + '_GC_sliding.mat'
gc_path = result_path.joinpath(fname)
gc = loadmat(gc_path)
fb = gc['Fb']
sig = gc['sig']
z = gc['z']
pval = gc['pval']
LFP_dict = LFP_to_dict(LFP, visual_chan, tmin=-0.5, tmax=1.75, sfreq=250)
#%%
LFP_dict = visual_chan.to_dict(orient='list')
for cat in categories:
epochs, events = hf.epoch_category(visual_LFP,
cat=cat,
tmin=-0.5,
tmax=1.75)
epochs = epochs.resample(sfreq)
X = epochs.copy().get_data()
X = np.transpose(X, axes=(1, 2, 0))
LFP_dict[cat] = X
LFP_dict['time'] = epochs.times
population_to_channel = hf.parcellation_to_indices(visual_chan,
parcellation='group')
DK_to_channel = hf.parcellation_to_indices(visual_chan, parcellation='DK')
LFP_dict['population_to_channel'] = population_to_channel
LFP_dict['DK_to_channel'] = DK_to_channel
#%% Save dictionary
fname = sub_id + '_visual_LFP_all_categories.mat'
fpath = datadir.joinpath(fname)
# Save data in Rest x Face x Place array of time series
savemat(fpath, LFP_dict)
# -Check that there are output visual_data X is correct with HFB_visual (i.e. check that # permutation works) # - Create a module for category specific electrodes # - Rearrange HFB module consequently #%% pd.options.display.max_rows = 999 sub_id = 'DiAs' visual_chan_table = 'visual_channels_BP_montage.csv' proc = 'preproc' cat = 'Face' sfreq = 100 picks = ['LGRD60-LGRD61', 'LTo1-LTo2'] tmin_crop = 0.5 tmax_crop = 1.5 suffix = 'preprocessed_raw' ext = '.fif' #%%v subject = cf.Subject(name=sub_id) datadir = subject.processing_stage_path(proc=proc) visual_chan = subject.pick_visual_chan() sorted_visual_chan = visual_chan.sort_values(by='latency') # %% group = visual_chan['group'].unique().tolist() anatomical_indices = hf.parcellation_to_indices(visual_chan, parcellation='DK')
visual_chan = subject.pick_visual_chan()
HFB = hf.visually_responsive_HFB(sub_id=sub_id)
ts[s], time = fun.ts_all_categories(HFB,
tmin_crop=tmin_crop,
tmax_crop=tmax_crop)
df_sorted = df_sorted.append(
df.loc[df['subject_id'] == sub_id].sort_values(by='latency'))
df_sorted = df_sorted.reset_index(drop=True)
#%%
X = np.concatenate(tuple(ts), axis=0)
#%%
pop_to_idx = hf.parcellation_to_indices(df_sorted, parcellation='group')
pop = df_sorted['group'].unique()
npop = len(pop)
ts_pop = [0] * npop
for i, p in enumerate(pop):
idx = pop_to_idx[p]
X_p = np.take(X, idx, axis=0)
ts_pop[i] = np.average(X_p, axis=0)
X_pop = np.stack(ts_pop)
ntrial = X_p.shape[2]
std_pop = np.std(X_pop, axis=2)
X_pop = np.average(X_pop, axis=2)
#%% Plot result