def make_report(subject_id):
subject = "sub%03d" % subject_id
print("processing %s" % subject)
meg_path = op.join(meg_dir, subject)
ave_fname = op.join(meg_path, "%s-ave.fif" % subject)
rep = Report(info_fname=ave_fname, subject=subject,
subjects_dir=subjects_dir)
rep.parse_folder(meg_path)
evokeds = mne.read_evokeds(op.join(meg_path, '%s-ave.fif' % subject))
fam = evokeds[0]
scramb = evokeds[1]
unfam = evokeds[2]
figs = list()
captions = list()
fig = fam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Famous faces')
fig = unfam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Unfamiliar faces')
fig = scramb.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Scrambled faces')
if 'EEG070' in fam.ch_names:
idx = fam.ch_names.index('EEG070')
fig = mne.viz.plot_compare_evokeds({'Famous': fam, 'Unfamiliar': unfam,
'Scrambled': scramb}, idx,
show=False)
figs.append(fig)
captions.append('Famous, unfamliliar and scrambled faces on EEG070')
fname_trans = op.join(study_path, 'ds117', subject, 'MEG',
'%s-trans.fif' % subject)
mne.viz.plot_trans(fam.info, fname_trans, subject=subject,
subjects_dir=subjects_dir, meg_sensors=True,
eeg_sensors=True)
fig = mlab.gcf()
figs.append(fig)
captions.append('Coregistration')
rep.add_figs_to_section(figs, captions)
for cond in ['faces', 'famous', 'unfamiliar', 'scrambled', 'contrast']:
fname = op.join(meg_path, 'mne_dSPM_inverse-%s' % cond)
stc = mne.read_source_estimate(fname, subject)
brain = stc.plot(views=['ven'], hemi='both')
brain.set_data_time_index(112)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False, overwrite=True)
def make_report(subject_id):
subject = "sub%03d" % subject_id
print("processing %s" % subject)
meg_path = op.join(meg_dir, subject)
ave_fname = op.join(meg_path,
"%s_highpass-%sHz-ave.fif" % (subject, l_freq))
rep = Report(info_fname=ave_fname,
subject=subject,
subjects_dir=subjects_dir)
rep.parse_folder(meg_path)
evokeds = mne.read_evokeds(ave_fname)
fam = evokeds[0]
scramb = evokeds[1]
unfam = evokeds[2]
figs = list()
captions = list()
fig = fam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Famous faces')
fig = unfam.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Unfamiliar faces')
fig = scramb.plot(spatial_colors=True, show=False, gfp=True)
figs.append(fig)
captions.append('Scrambled faces')
if 'EEG070' in fam.ch_names:
idx = fam.ch_names.index('EEG070')
fig = mne.viz.plot_compare_evokeds(
{
'Famous': fam,
'Unfamiliar': unfam,
'Scrambled': scramb
},
idx,
show=False)
figs.append(fig)
captions.append('Famous, unfamliliar and scrambled faces on EEG070')
fname_trans = op.join(study_path, 'ds117', subject, 'MEG',
'%s-trans.fif' % subject)
mne.viz.plot_trans(fam.info,
fname_trans,
subject=subject,
subjects_dir=subjects_dir,
meg_sensors=True,
eeg_sensors=True)
fig = mlab.gcf()
figs.append(fig)
captions.append('Coregistration')
rep.add_figs_to_section(figs, captions)
for cond in ['faces', 'famous', 'unfamiliar', 'scrambled', 'contrast']:
fname = op.join(meg_path,
'mne_dSPM_inverse_highpass-%sHz-%s' % (l_freq, cond))
stc = mne.read_source_estimate(fname, subject)
brain = stc.plot(views=['ven'], hemi='both')
brain.set_data_time_index(112)
fig = mlab.gcf()
rep._add_figs_to_section(fig, cond)
rep.save(fname=op.join(meg_dir, 'report%s.html' % subject),
open_browser=False,
overwrite=True)
