def source_estimate(name, save_dir, lowpass, method, overwrite):
inverse_operator = io.read_inverse_operator(name, save_dir, lowpass)
to_reconstruct = io.read_evokeds(name, save_dir, lowpass)
evokeds = io.read_evokeds(name, save_dir, lowpass)
stcs = dict()
for to_reconstruct_index, evoked in enumerate(evokeds):
stc_name = name + filter_string(lowpass) + '_' + evoked.comment + \
'_' + method + '-lh.stc'
stc_path = join(save_dir, stc_name)
if overwrite or not isfile(stc_path):
trial_type = evoked.comment
stcs[trial_type] = mne.minimum_norm.apply_inverse(
to_reconstruct[to_reconstruct_index],
inverse_operator,
method=method)
else:
print('source estimates for: '+ stc_path + \
' already exists')
for stc in stcs:
stc_name = name + filter_string(lowpass) + '_' + stc + '_' + method
stc_path = join(save_dir, stc_name)
if overwrite or not isfile(stc_path + '-lh.stc'):
stcs[stc].save(stc_path)
def plot_evokeds(name, save_dir, lowpass, subject, save_plots, figures_path):
evokeds = io.read_evokeds(name, save_dir, lowpass)
order = [
'standard_1', 'standard_2', 'standard_3', 'standard_4', 'standard_5',
'omission_4', 'omission_5', 'omission_6', 'non_stimulation'
]
colours = [
'white', 'blue', 'green', 'purple', 'yellow', 'red', 'orange', 'pink',
'grey'
]
# sort evokeds
plot_evokeds = []
plot_standards = []
plot_omissions = []
for evoked_type in order:
for evoked in evokeds:
if evoked.comment == evoked_type:
plot_evokeds.append(evoked)
if evoked.comment == evoked_type and \
(evoked.comment[0] == 's' or evoked.comment[0] == 'n'):
plot_standards.append(evoked)
if evoked.comment == evoked_type and \
(evoked.comment[0] == 'o' or evoked.comment[0] == 'n'):
plot_omissions.append(evoked)
plt.close('all')
evoked_figure = mne.viz.plot_evoked_topo(plot_evokeds,
color=colours,
show=False)
evoked_figure.comment = 'all_evokeds_'
standards_figure = mne.viz.plot_evoked_topo(plot_standards,
color=colours[:5] +
colours[8:9],
show=False)
standards_figure.comment = 'standards_evokeds_'
omissions_figure = mne.viz.plot_evoked_topo(plot_omissions,
color=colours[5:9],
show=False)
omissions_figure.comment = 'omissions_evokeds_'
figures = [evoked_figure, standards_figure, omissions_figure]
if save_plots:
for figure in figures:
save_path = join(figures_path, subject, 'evokeds',
figure.comment + name + \
filter_string(lowpass) + '.jpg')
figure.savefig(save_path, dpi=600)
print 'figure: ' + save_path + ' has been saved'
else:
print 'Not saving plots; set "save_plots" to "True" to save'
def plot_butterfly_evokeds(name, save_dir, lowpass, subject, save_plots,
figures_path):
evokeds = io.read_evokeds(name, save_dir, lowpass)
for evoked in evokeds:
figure = evoked.plot(show=False)
if save_plots:
save_path = join(figures_path, subject, 'evokeds',
'butterfly_' + evoked.comment + '_' + name + \
filter_string(lowpass) + '.jpg')
figure.savefig(save_path, dpi=600)
print 'figure: ' + save_path + ' has been saved'
else:
print 'Not saving plots; set "save_plots" to "True" to save'
# MORPH TO FSAVERAGE
#==========================================================================
if operations_to_apply['morph_to_fsaverage']:
stcs = operations.morph_data_to_fsaverage(name, save_dir,
subjects_dir, subject,
lowpass,
method=method,
overwrite=overwrite)
#==========================================================================
# GRAND AVERAGE EVOKEDS (within-subject part)
#==========================================================================
if operations_to_apply['grand_averages_evokeds']:
evoked_data = io.read_evokeds(name, save_dir, lowpass)
for evoked in evoked_data:
trial_type = evoked.comment
evoked_data_all[trial_type].append(evoked)
#==========================================================================
# GRAND AVERAGE MORPHED DATA (within-subject part)
#==========================================================================
if operations_to_apply['average_morphed_data'] or \
operations_to_apply['statistics_source_space']:
morphed_data = io.read_source_estimates(name, save_dir, lowpass,
method=method,
morphed=True)
for trial_type in morphed_data: