def test_io_w():
"""Test IO for w files
"""
w_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis-meg-oct-6-fwd-sensmap')
src = SourceEstimate(w_fname)
src.save('tmp', ftype='w')
src2 = SourceEstimate('tmp-lh.w')
assert_array_almost_equal(src.data, src2.data)
assert_array_almost_equal(src.lh_vertno, src2.lh_vertno)
assert_array_almost_equal(src.rh_vertno, src2.rh_vertno)
# Store a nice visualization of the cluster by summing across time (in ms)
data = np.sign(data) * np.logical_not(data == 0) * data1.tstep
data_summary[:, ii + 1] = 0.25e3 * np.sum(data, axis=1)
# save as stc
for i, cluster_ind in enumerate(good_cluster_inds):
v_inds = clusters[cluster_ind][1]
t_inds = clusters[cluster_ind][0]
data[v_inds, t_inds] = T_obs[t_inds, v_inds]
stc_cluster_vis = SourceEstimate(data,
fsave_vertices,
tmin=dataface.tmin,
tstep=dataface.tstep)
stc_cluster_vis.save(
'/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/GROUP/plots/clusters/test_west_par'
)
########################################################################################"
# Implement gave variable
erfAAgave[s] = np.mean(erfAA_trial, axis=0)
erfAVgave[s] = np.mean(erfAV_trial, axis=0)
erfVAgave[s] = np.mean(erfVA_trial, axis=0)
erfVVgave[s] = np.mean(erfVV_trial, axis=0)
# Compute statistic between subjects
#threshold = 6.0
T_obs_Aon_inter, clusters_Aon_inter, cluster_p_values_Aon_inter, H0_Aon_inter = \
permutation_cluster_test([erfAAgave, erfAVgave],
n_permutations=1000, threshold=None, tail=1,
data.fill(0)
v_inds = clusters[cluster_ind][1]
t_inds = clusters[cluster_ind][0]
data[v_inds, t_inds] = T_obs[t_inds, v_inds]
# Store a nice visualization of the cluster by summing across time (in ms)
data = np.sign(data) * np.logical_not(data == 0) * dataface.tstep
data_summary[:, ii + 1] = 1e3 * np.sum(data, axis=1)
# save as stc
for i, cluster_ind in enumerate(good_cluster_inds):
v_inds = clusters[cluster_ind][1]
t_inds = clusters[cluster_ind][0]
data[v_inds, t_inds] = T_obs[t_inds, v_inds]
stc_cluster_vis = SourceEstimate(data, fsave_vertices, tmin=dataface.tmin, tstep=dataface.tstep)
stc_cluster_vis.save('/neurospin/meg/meg_tmp/ResonanceMeg_Baptiste_2009/MEG/inter_subject/processed/STC_face_vs_house_clust')
########################################################################################"
# Implement gave variable
erfAAgave[s] = np.mean(erfAA_trial,axis=0)
erfAVgave[s] = np.mean(erfAV_trial,axis=0)
erfVAgave[s] = np.mean(erfVA_trial,axis=0)
erfVVgave[s] = np.mean(erfVV_trial,axis=0)
# Compute statistic between subjects
#threshold = 6.0
T_obs_Aon_inter, clusters_Aon_inter, cluster_p_values_Aon_inter, H0_Aon_inter = \
permutation_cluster_test([erfAAgave, erfAVgave],
