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_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_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)
