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13-grand_averages-source.py
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13-grand_averages-source.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 11 10:29:39 2019
@author: wexu
"""
import os.path as op
import mne
from mne.parallel import parallel_func
from mne.minimum_norm import (make_inverse_operator, apply_inverse)
from config_GP_Learn import MEG_data_path,MRI_data_path,group_name,Ids
cond_day1_AV=list([])
cond_day2_AV=list([])
cond_day1_FB=list([])
cond_day2_FB=list([])
conditions_AV=['/A','/V','/AV'] #'/UB/A','/UB/V','/UB/AVX','/LB/A','/LB/V','/LB/AVC','/LB/AVI',
conditions_FB=['/YES','/NO','/UNKNOWN']
for cond in conditions_AV:
cond_day1_AV.append(cond)
cond_day2_AV.append(cond)
for cond in conditions_FB:
cond_day1_FB.append(cond)
cond_day2_FB.append(cond)
x
def run_inverse(subject_id):
tasks=['AVLearn','AVLearn']
days=[100,200]
for task,day in zip(tasks,days):
subject = group_name+"%d" % subject_id
print("processing subject: %s" % subject)
fname=op.join(MEG_data_path,subject,task+'_%d'%(day+subject_id)+'_tsss_mc.fif')
if day==100:
evokeds_AV = mne.read_evokeds(fname.replace("_tsss_mc", "_GA-ave"),cond_day1_AV)
# evokeds_FB = mne.read_evokeds(fname.replace("_tsss_mc", "_GA-ave"),cond_day1_FB)
elif day==200:
evokeds_AV = mne.read_evokeds(fname.replace("_tsss_mc", "_GA-ave"),cond_day2_AV)
# evokeds_FB = mne.read_evokeds(fname.replace("_tsss_mc", "_GA-ave"),cond_day2_FB)
evokeds_AV=[evk for evk in evokeds_AV ]
# evokeds_FB=[evk for evk in evokeds_FB ]
cov_AV = mne.read_cov(fname.replace('_tsss_mc.fif','_AV-cov.fif'))
# cov_FB = mne.read_cov(fname.replace('_tsss_mc.fif','_FB-cov.fif'))
forward = mne.read_forward_solution(fname.replace('_tsss_mc.fif','-meg-ico5-fwd.fif'))
inverse_operator_AV = make_inverse_operator(evokeds_AV[0].info, forward, cov_AV, loose=1, depth=0.8)
# inverse_operator_FB = make_inverse_operator(evokeds_FB[0].info, forward, cov_FB, loose=1, depth=0.8)
snr = 3.0
lambda2 = 1.0 / snr ** 2
methods=['dSPM']
pick_ori=None
for method in methods:
for evoked in evokeds_AV:
stc = apply_inverse(evoked, inverse_operator_AV, lambda2, method=method, pick_ori=pick_ori)
stc_fsaverage = mne.compute_source_morph(stc,subjects_dir=MRI_data_path).apply(stc)
stc_fsaverage.save(fname.replace('tsss_mc.fif',evoked.comment.replace('/','_')+'-'+method))
# for evoked in evokeds_FB:
# stc = apply_inverse(evoked, inverse_operator_FB, lambda2, method=method, pick_ori=pick_ori)
# stc_fsaverage = mne.compute_source_morph(stc,subjects_dir=MRI_data_path).apply(stc)
# stc_fsaverage.save(fname.replace('tsss_mc.fif',evoked.comment.replace('/','_')+'-'+method))
parallel, run_func, _ = parallel_func(run_inverse, n_jobs=10)
parallel(run_func(subject_id) for subject_id in Ids)
# %% Visualization
import os.path as op
import mne
from mne.parallel import parallel_func
from mne.minimum_norm import (make_inverse_operator, apply_inverse)
from config_GP_Learn import MEG_data_path,MRI_data_path,group_name,Ids
cond_day1_AV=list([])
cond_day2_AV=list([])
cond_day1_FB=list([])
cond_day2_FB=list([])
conditions_AV=['/A','/V','/AV'] #'/UB/A','/UB/V','/UB/AVX','/LB/A','/LB/V','/LB/AVC','/LB/AVI',
conditions_FB=['/YES','/NO','/UNKNOWN']
for cond in conditions_AV:
cond_day1_AV.append(cond)
cond_day2_AV.append(cond)
for cond in conditions_FB:
cond_day1_FB.append(cond)
cond_day2_FB.append(cond)
import numpy as np
from config_GP_Learn import Ids
task='AVLearn'
method='dSPM'
conditions=['/A','/V','/AV']
timess=[[0.117,0.209,0.360,0.482],[0.107,0.180,0.293,0.520],[0.120,0.185,0.320,0.569]]
limss=[(2.5,4,9),(2.5,4,9),(6,9,15)]
views=['med','lat','caudal']
for condition,times, lims in zip(conditions,timess,limss):
stcs_D1 = list()
stcs_D2 = list()
for subject_id in Ids:
subject = group_name+"%d" % subject_id
print("processing subject: %s" % subject)
fname_D1=op.join(MEG_data_path,subject,task+'_%d'%(100+subject_id)+'_tsss_mc.fif')
stc_D1=mne.read_source_estimate(fname_D1.replace('tsss_mc.fif',condition.replace('/','_'))+'-'+method)
stcs_D1.append(stc_D1)
fname_D2=op.join(MEG_data_path,subject,task+'_%d'%(200+subject_id)+'_tsss_mc.fif')
stc_D2=mne.read_source_estimate(fname_D2.replace('tsss_mc.fif',condition.replace('/','_'))+'-'+method)
stcs_D2.append(stc_D2)
stcs=stcs_D1+stcs_D2
data = np.average([s.data for s in stcs], axis=0)
stc_average = mne.SourceEstimate(data, stcs[0].vertices, stcs[0].tmin, stcs[0].tstep)
for time in times:
for view in views:
brain = stc_average.plot(views=view, hemi='both', subject='fsaverage',subjects_dir=MRI_data_path,
size=300,time_label='',
background='white',
colorbar=False,
smoothing_steps=2,
clim=dict(kind='value', lims=lims),
initial_time=time, time_unit='s')
brain.save_single_image('/nashome1/wexu/Results/MNE_Results/AVLearn/'+condition+str(time)+'_'+view+'.pdf')
brain.close()
for condition,times, lims in zip(conditions,timess,limss):
stcs_D1 = list()
stcs_D2 = list()
for subject_id in Ids:
subject = group_name+"%d" % subject_id
print("processing subject: %s" % subject)
fname_D1=op.join(MEG_data_path,subject,task+'_%d'%(100+subject_id)+'_tsss_mc.fif')
stc_D1=mne.read_source_estimate(fname_D1.replace('tsss_mc.fif',condition.replace('/','_'))+'-'+method)
stcs_D1.append(stc_D1)
fname_D2=op.join(MEG_data_path,subject,task+'_%d'%(200+subject_id)+'_tsss_mc.fif')
stc_D2=mne.read_source_estimate(fname_D2.replace('tsss_mc.fif',condition.replace('/','_'))+'-'+method)
stcs_D2.append(stc_D2)
stcs=stcs_D1+stcs_D2
data = np.average([s.data for s in stcs], axis=0)
stc_average = mne.SourceEstimate(data, stcs[0].vertices, stcs[0].tmin, stcs[0].tstep)
brain = stc_average.plot(views='lat', hemi='both', subject='fsaverage',subjects_dir=MRI_data_path,
size=600,time_label='',
#background='white',
#foreground='black',
colorbar=True,
smoothing_steps=2,
clim=dict(kind='value', lims=lims),
initial_time=times[0], time_unit='s')
brain.save_single_image('/nashome1/wexu/Results/MNE_Results/AVLearn/'+condition+str(time)+'_'+view+'_colorbar2.pdf')
brain.close()