seed = 10000 map = full_dist[seed] map[fullmask] = 0 vmin = 0 vmax = 160 cropped_img = [] sns.set_style('white') for (elev, azim) in [(180, 0), (180, 180)]: plot = plot_surf_stat_map(lv, lf, stat_map=map[:lv.shape[0]], bg_map=lh_sulc, bg_on_stat=True, darkness=0.4, elev=elev, azim=azim, figsize=(10, 9), threshold=1e-50, cmap='Reds_r', symmetric_cbar=False, vmin=vmin, vmax=vmax) cropped_img.append(crop_img(plot)) for (elev, azim) in [(180, 0), (180, 180)]: plot = plot_surf_stat_map(rv, rf, stat_map=map[lv.shape[0]:], bg_map=rh_sulc, bg_on_stat=True, darkness=0.4,
# load aligned A_init = h5py.File('/nobackup/kocher1/bayrak/tmp/realigned_full_n10_468.h5', 'r') A = np.array(A_init.get('aligned')) # load sulc D_in = h5py.File('/nobackup/kocher1/bayrak/tmp/sulc_full_468.h5', 'r') D = np.array(D_in.get('sulc')) # get first component A = A[:,:,0] # Correlate two datasets C = [] for i in range(np.shape(A)[1]): # covariance of one region (over subjects) in two datasets C.append(np.cov(A[:,i],D[:,i])[0][1]) print i C = np.array(C) # Vizualize data: data = np.zeros(len(vertices)) data[ind] = C plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=0) plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=90) plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=180) plt = plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, azim=270) plt.show()
#embedding_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/profiles/%s_smooth_3_embedding_10_%s.npy' % (hemi, method) #fig_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/figs/profiles_%s_%s_comp%s.png' embedding_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/coefficients/%s_smooth_3_embedding_10_%s.npy' % (hemi, method) fig_file = '/scr/ilz3/myelinconnect/all_data_on_simple_surf/embed/figs/coefficients_%s_%s_comp%s.png' v, f, d = read_vtk(mesh_file) data = np.load(embedding_file) sulc = np.load(sulc_file) for e in range(10): print method, hemi, e if hemi == 'lh': sns.set_style('white') lat=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True, elev=180,azim=180, figsize=(14,10), darkness=0.3) sns.set_style('white') med=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True, elev=180,azim=0, figsize=(14,10), darkness=0.3) elif hemi == 'rh': sns.set_style('white') lat=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True, elev=180,azim=0, figsize=(11,10), darkness=0.4) sns.set_style('white') med=plot_surf_stat_map(v, f, stat_map=data[:,e], bg_map=sulc, bg_on_stat=True, elev=180,azim=180, figsize=(11,10), darkness=0.4)
print "OVERLAP" x[n_lh[index]] = L1[n_lh[index]] x[n_lh[index_neg]] = -1 * L2[n_lh[index_neg]] print np.shape(index)[1], np.shape(index_neg)[1] if np.shape(index)[1] != 0 and np.shape(index_neg)[1] != 0: x[np.where(x==1)] = x[n_lh[index]].max() + 0.001 plotting.plot_surf_stat_map(vertices_lh, triangles_lh, fig_number, '221', stat_map = x, cmap='jet', azim=180, threshold=x[n_lh[index]].max(), figsize=(14, 10)) plotting.plot_surf_stat_map(vertices_lh, triangles_lh, fig_number, '223', stat_map = x, cmap='jet', azim=0, threshold=x[n_lh[index]].max(), figsize=(14, 10)) del index, index_neg y = np.ones(len(vertices_rh)) index = np.where(R1[n_rh] < 0.05) index_neg = np.where(R2[n_rh] < 0.05) if set(index[0]).intersection(index_neg[0]).__len__() != 0: print "OVERLAP"
#DATA = h5py.File(path + '468_embeddings.h5', 'r') #mode = 'embedding' # plot subjects individually for subject_id in subject_list[args.begin : args.end]: ## chose subject_id randomly #subject_id = choose_random_subject(subject_list) #subject_id = '100307' subject_id = ''.join(subject_id) print subject_id component = None subject_component = choose_component(DATA, subject_id, mode, component) data = np.zeros(len(vertices)) data[n] = subject_component plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, cmap='jet', azim=0) plt.title(subject_id + ' , component 1' ) plt.savefig(path_out + subject_id + '_comp_01' + '_000.png') plotting.plot_surf_stat_map(vertices, triangles, stat_map=data, cmap='jet', azim=180) plt.title(subject_id + ' , component 1' ) plt.savefig(path_out + subject_id + '_comp_01' + '_180.png') #plt.show() # save out group level results... #tmp_list = [] # plot a component over all subjects #components = np.arange(0, 10, 1) #for component in components: #tmp = get_mean(DATA, subject_list, mode, component)