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],