def test_label_io_and_time_course_estimates(): """Test IO for label + stc files """ values, times, vertices = label_time_courses(label_fname, stc_fname) assert_true(len(times) == values.shape[1]) assert_true(len(vertices) == values.shape[0])
def test_label_io_and_time_course_estimates(): """Test IO for STC files """ values, times, vertices = mne.label_time_courses(label_fname, stc_fname) assert len(times) == values.shape[1] assert len(vertices) == values.shape[0]
def test_label_time_course(): """Test extracting label data from SourceEstimate""" values, times, vertices = label_time_courses(label_fname, stc_fname) stc = read_source_estimate(stc_fname) label_lh = read_label(label_fname) stc_lh = stc.in_label(label_lh) assert_array_almost_equal(stc_lh.data, values) assert_array_almost_equal(stc_lh.times, times) assert_array_almost_equal(stc_lh.vertno[0], vertices) label_rh = read_label(label_rh_fname) stc_rh = stc.in_label(label_rh) label_bh = label_rh + label_lh stc_bh = stc.in_label(label_bh) assert_array_equal(stc_bh.data, np.vstack((stc_lh.data, stc_rh.data)))
def test_label_time_course(): """Test extracting label data from SourceEstimate""" values, times, vertices = label_time_courses(real_label_fname, stc_fname) stc = read_source_estimate(stc_fname) label_lh = read_label(real_label_fname) stc_lh = stc.in_label(label_lh) assert_array_almost_equal(stc_lh.data, values) assert_array_almost_equal(stc_lh.times, times) assert_array_almost_equal(stc_lh.vertno[0], vertices) label_rh = read_label(real_label_rh_fname) stc_rh = stc.in_label(label_rh) label_bh = label_rh + label_lh stc_bh = stc.in_label(label_bh) assert_array_equal(stc_bh.data, np.vstack((stc_lh.data, stc_rh.data)))
def test_label_time_course(): """Test extracting label data from SourceEstimate""" values, times, vertices = label_time_courses(real_label_fname, stc_fname) stc = read_source_estimate(stc_fname) label_lh = read_label(real_label_fname) stc_lh = stc.in_label(label_lh) assert_array_almost_equal(stc_lh.data, values) assert_array_almost_equal(stc_lh.times, times) assert_array_almost_equal(stc_lh.vertno[0], vertices) label_rh = read_label(real_label_rh_fname) stc_rh = stc.in_label(label_rh) label_bh = label_rh + label_lh label_bh_2 = label_lh + label_rh label_bh_3 = label_bh + label_bh_2 assert_true(repr(label_bh)) # test __repr__ for check in (label_bh, label_bh_2, label_bh_3): stc_bh = stc.in_label(check) assert_array_equal(stc_bh.data, np.vstack((stc_lh.data, stc_rh.data)))
def test_label_time_course(): """Test extracting label data from SourceEstimate""" values, times, vertices = label_time_courses(real_label_fname, stc_fname) stc = read_source_estimate(stc_fname) label_lh = read_label(real_label_fname) stc_lh = stc.in_label(label_lh) assert_array_almost_equal(stc_lh.data, values) assert_array_almost_equal(stc_lh.times, times) assert_array_almost_equal(stc_lh.vertices[0], vertices) label_rh = read_label(real_label_rh_fname) stc_rh = stc.in_label(label_rh) label_bh = label_rh + label_lh label_bh_2 = label_lh + label_rh label_bh_3 = label_bh + label_bh_2 assert_true(repr(label_bh)) # test __repr__ for check in (label_bh, label_bh_2, label_bh_3): stc_bh = stc.in_label(check) assert_array_equal(stc_bh.data, np.vstack((stc_lh.data, stc_rh.data)))
#label = 'G_front_inf-Triangul-'+hem #label = 'G_front_inf-Opercular-'+hem #label = 'G_front_inf-Orbital-'+hem #label = 'G_temp_sup-Lateral-'+hem #label = 'G_temporal_middle-'+hem #label = 'Pole_temporal-'+hem #label = 'S_temporal_sup-'+hem #label = 'G_pariet_inf-Angular-'+hem #values, times, vertices = mne.label_time_courses(label_fname, stc_fname) #vtv = [mne.label_time_courses(label_fname, stc_fname) for stc_fname in stcs_fname] valuesAll = [] for stc_fname in stcs_fname: values, times, vertices = mne.label_time_courses(label_fname, stc_fname) values = np.mean(values,0) values = values[sample1:sample2] values = np.mean(values,0) #print values valuesAll.append(values) valuesHem.append(valuesAll) print "mean",np.mean(valuesAll) outTable = [] for x in range(len(stcs_fname)): temp = [] temp.append(valuesHem[0][x]) temp.append(valuesHem[1][x]) temp.append(valuesHem[0][x]-valuesHem[1][x]) outTable.append(temp)
Extracting the time series of activations in a label ==================================================== """ # Author: Alexandre Gramfort <*****@*****.**> # # License: BSD (3-clause) print __doc__ import mne from mne.datasets import sample data_path = sample.data_path('..') stc_fname = data_path + '/MEG/sample/sample_audvis-meg-lh.stc' label = 'Aud-lh' label_fname = data_path + '/MEG/sample/labels/%s.label' % label values, times, vertices = mne.label_time_courses(label_fname, stc_fname) print "Number of vertices : %d" % len(vertices) # View source activations import pylab as pl pl.plot(1e3 * times, values.T) pl.xlabel('time (ms)') pl.ylabel('Source amplitude') pl.title('Activations in Label : %s' % label) pl.show()
picks = pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, exclude=raw.info['bads'], selection=left_temporal_channels) # Read epochs epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=(None, 0), preload=True, reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6)) evoked = epochs.average() forward = mne.read_forward_solution(fname_fwd) noise_cov = mne.read_cov(fname_cov) noise_cov = mne.cov.regularize(noise_cov, evoked.info, mag=0.05, grad=0.05, eeg=0.1, proj=True) data_cov = mne.compute_covariance(epochs, tmin=0.04, tmax=0.15) stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01) # Save result in stc files stc.save('lcmv') ############################################################################### # View activation time-series data, times, _ = mne.label_time_courses(fname_label, "lcmv-lh.stc") pl.close('all') pl.plot(1e3 * times, np.mean(data, axis=0)) pl.xlabel('time (ms)') pl.ylabel('LCMV value') pl.title('LCMV in %s' % label_name) pl.show()
pick_oris = [None, 'normal', 'max-power'] names = ['free', 'normal', 'max-power'] descriptions = [ 'Free orientation', 'Normal orientation', 'Max-power ' 'orientation' ] colors = ['b', 'k', 'r'] for pick_ori, name, desc, color in zip(pick_oris, names, descriptions, colors): stc = lcmv(evoked, forward, noise_cov, data_cov, reg=0.01, pick_ori=pick_ori) # Save result in stc files stc.save('lcmv-' + name) # View activation time-series data, times, _ = mne.label_time_courses(fname_label, "lcmv-" + name + "-lh.stc") plt.plot(1e3 * times, np.mean(data, axis=0), color, hold=True, label=desc) plt.xlabel('Time (ms)') plt.ylabel('LCMV value') plt.ylim(-0.8, 2.2) plt.title('LCMV in %s' % label_name) plt.legend() plt.show()
args = parser.parse_args() data_path = '/cluster/kuperberg/SemPrMM/MEG/results/source_space/ga_stc' if args.single_diff == 'diff': stc1_fname = data_path + '/diff/ga_' + args.prefix + '_' + args.protocol1 + '_c' + args.set2 + '-c' + args.set1 + 'M-' + args.model + '-' + args.hem1 + '.stc' stc2_fname = data_path + '/diff/ga_' + args.prefix + '_' + args.protocol2 + '_c' + args.set2 + '-c' + args.set1 + 'M-' + args.model + '-' + args.hem2 + '.stc' else: stc1_fname = data_path + '/single_condition/ga_' + args.prefix + '_' + args.protocol1 + '_c' + args.set1 + 'M-' + args.model + '-' + args.hem1 + '.stc' stc2_fname = data_path + '/single_condition/ga_' + args.prefix + '_' + args.protocol2 + '_c' + args.set2 + 'M-' + args.model + '-' + args.hem2 + '.stc' label1 = args.label1 + '-' + args.hem1 label1_fname = data_path + '/label/%s.label' % label1 values1, times1, vertices1 = mne.label_time_courses(label1_fname, stc1_fname) values1 = np.mean(values1, 0) #print values1.shape print "Number of vertices : %d" % len(vertices1) values2, times2, vertices2 = mne.label_time_courses(label1_fname, stc2_fname) values2 = np.mean(values2, 0) print "Number of vertices : %d" % len(vertices1) times1 = times1 * 1000 times2 = times2 * 1000 # 'weight' : 'bold', font = {'size': 20} pl.rc('font', **font)
if args.single_diff == 'diff': stc1_fname = data_path + '/diff/ga_'+args.prefix+'_'+args.protocol1+'_c'+args.set2+'-c'+args.set1+'M-'+args.model+'-'+args.hem1+'.stc' stc2_fname = data_path + '/diff/ga_'+args.prefix+'_'+args.protocol2+'_c'+args.set2+'-c'+args.set1+'M-'+args.model+'-'+args.hem2+'.stc' else: stc1_fname = data_path + '/single_condition/ga_'+args.prefix+'_'+args.protocol1+'_c'+args.set1+'M-'+args.model+'-'+args.hem1+'.stc' stc2_fname = data_path + '/single_condition/ga_'+args.prefix+'_'+args.protocol2+'_c'+args.set2+'M-'+args.model+'-'+args.hem2+'.stc' label1 = args.label1+'-'+args.hem1 label1_fname = data_path + '/label/%s.label' % label1 values1, times1, vertices1 = mne.label_time_courses(label1_fname, stc1_fname) values1 = np.mean(values1,0) #print values1.shape print "Number of vertices : %d" % len(vertices1) values2, times2, vertices2 = mne.label_time_courses(label1_fname, stc2_fname) values2 = np.mean(values2,0) print "Number of vertices : %d" % len(vertices1) times1=times1*1000 times2=times2*1000 # 'weight' : 'bold', font = {'size' : 20} pl.rc('font', **font)