def get_Tpermutation_distribution(dat_all, nperm, time_start, time_end,
labels):
dat = dat_all[:, :,
int(time_start * 1000) + 100:int(time_end * 1000) + 100]
# permuta the data and calculate the largest sumed p values for each permutation across labels
highest_permutation_values = []
sign = np.ones((dat.shape[0], ))
sign[:np.int32(np.round(len(sign) / 2.))] = -1
for index in range(nperm): #to permute the conditions
print("permutation:" + str(index))
sign = np.random.permutation(sign)
dat_perm = sign * np.transpose(dat, [1, 2, 0])
stats_perm, pval_perm = ttest_1samp(dat_perm, 0, axis=2)
stc_perm = SourceEstimate(
-np.log10(pval_perm),
vertices=[np.arange(10242), np.arange(10242)],
tmin=time_start,
tstep=1 / 1000.,
subject='fsaverage')
means_of_thresholded_labels = []
for label in labels:
label_fname = op.join(
'/autofs/cluster/kuperberg/nonconMM/MEG/MNE/250Labels',
label + '.label')
label_name = mne.read_label(label_fname,
subject='fsaverage',
color='r')
stc_label_perm = stc_perm.in_label(label_name)
stc_label_perm = threshold_by_pvalue(stc_label_perm, 2)
means_of_thresholded_labels.append(stc_label_perm.data.mean())
highest_permutation_values.append(max(means_of_thresholded_labels))
return highest_permutation_values
# ############################################################################
# construct appropriate brain activity
print('Constructing original (simulated) sources')
tmin, tmax = -0.2, 0.8
vertices = [s['vertno'] for s in src]
n_vertices = sum(s['nuse'] for s in src)
data = np.ones((n_vertices, int((tmax - tmin) * sfreq)))
stc = SourceEstimate(data, vertices, -0.2, 1. / sfreq, subject)
# limit activation to a square pulse in time at two vertices in space
labels = [read_labels_from_annot(subject, 'aparc.a2009s', hemi,
regexp='G_temp_sup-G_T_transv')[0]
for hi, hemi in enumerate(('lh', 'rh'))]
stc = stc.in_label(labels[0] + labels[1])
stc.data.fill(0)
stc.data[:, np.where(np.logical_and(stc.times >= pulse_tmin,
stc.times <= pulse_tmax))[0]] = 10e-9
# ############################################################################
# Simulate data
# Simulate data with movement
with warnings.catch_warnings(record=True):
raw = Raw(fname_raw, allow_maxshield=True)
raw_movement = simulate_movement(raw, fname_pos_orig, stc, trans, src, bem,
interp='zero', n_jobs=6, verbose=True)
# Simulate data with no movement (use initial head position)
raw_stationary = simulate_movement(raw, None, stc, trans, src, bem,
print('Constructing original (simulated) sources')
tmin, tmax = -0.2, 0.8
vertices = [s['vertno'] for s in src]
n_vertices = sum(s['nuse'] for s in src)
data = np.zeros((n_vertices, int((tmax - tmin) * sfreq)))
stc = SourceEstimate(data, vertices, -0.2, 1. / sfreq, subject)
# limit activation to a square pulse in time at two vertices in space
labels = [
read_labels_from_annot(subject,
'aparc.a2009s',
hemi,
regexp='G_temp_sup-G_T_transv')[0]
for hi, hemi in enumerate(('lh', 'rh'))
]
stc = stc.in_label(labels[0] + labels[1])
stc.data.fill(0)
stc.data[:, (stc.times >= pulse_tmin) & (stc.times <= pulse_tmax)] = 10e-9
# ############################################################################
# Simulate data
# Simulate data with movement
with warnings.catch_warnings(record=True):
raw = Raw(fname_raw, allow_maxshield=True)
raw_movement = simulate_raw(raw,
stc,
trans,
src,
bem,
chpi=True,
