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import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_true
from mne.datasets import sample
from mne import read_label, read_forward_solution
from mne.label import Label
from mne.simulation.source import generate_stc, generate_sparse_stc
data_path = sample.data_path(download=False)
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis-meg-oct-6-fwd.fif')
label_names = ['Aud-lh', 'Aud-rh', 'Vis-rh']
label_names_single_hemi = ['Aud-rh', 'Vis-rh']
@sample.requires_sample_data
def test_generate_stc():
""" Test generation of source estimate """
fwd = read_forward_solution(fname_fwd, force_fixed=True)
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
mylabels = []
for i, label in enumerate(labels):
new_label = Label(vertices=label.vertices,
pos=label.pos,
values=2 * i * np.ones(len(label.values)),
hemi=label.hemi,
comment=label.comment)
mylabels.append(new_label)
n_times = 10
tmin = 0
tstep = 1e-3
stc_data = np.ones((len(labels), n_times))
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep)
for label in labels:
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertno[0])
assert_true(np.all(stc.data[idx] == 1.0))
assert_true(stc.data[idx].shape[1] == n_times)
# test with function
fun = lambda x: x ** 2
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
# the first label has value 0, the second value 2, the third value 6
for i, label in enumerate(labels):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertno[0])
res = ((2. * i) ** 2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
@sample.requires_sample_data
def test_generate_sparse_stc():
""" Test generation of sparse source estimate """
fwd = read_forward_solution(fname_fwd, force_fixed=True)
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
n_times = 10
tmin = 0
tstep = 1e-3
stc_data = (np.ones((len(labels), n_times))
* np.arange(len(labels))[:, None])
stc_1 = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, 0)
for i, label in enumerate(labels):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc_1.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc_1.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc_1.vertno[0])
assert_true(np.all(stc_1.data[idx] == float(i)))
assert_true(stc_1.data.shape[0] == len(labels))
assert_true(stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, 0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
@sample.requires_sample_data
def test_generate_stc_single_hemi():
""" Test generation of source estimate """
fwd = read_forward_solution(fname_fwd, force_fixed=True)
labels_single_hemi = [read_label(op.join(data_path, 'MEG', 'sample',
'labels', '%s.label' % label))
for label in label_names_single_hemi]
mylabels = []
for i, label in enumerate(labels_single_hemi):
new_label = Label(vertices=label.vertices,
pos=label.pos,
values=2 * i * np.ones(len(label.values)),
hemi=label.hemi,
comment=label.comment)
mylabels.append(new_label)
n_times = 10
tmin = 0
tstep = 1e-3
stc_data = np.ones((len(labels_single_hemi), n_times))
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep)
for label in labels_single_hemi:
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertno[0])
assert_true(np.all(stc.data[idx] == 1.0))
assert_true(stc.data[idx].shape[1] == n_times)
# test with function
fun = lambda x: x ** 2
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
# the first label has value 0, the second value 2, the third value 6
for i, label in enumerate(labels_single_hemi):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertno[0])
res = ((2. * i) ** 2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
@sample.requires_sample_data
def test_generate_sparse_stc_single_hemi():
""" Test generation of sparse source estimate """
fwd = read_forward_solution(fname_fwd, force_fixed=True)
n_times = 10
tmin = 0
tstep = 1e-3
labels_single_hemi = [read_label(op.join(data_path, 'MEG', 'sample',
'labels', '%s.label' % label))
for label in label_names_single_hemi]
stc_data = (np.ones((len(labels_single_hemi), n_times))
* np.arange(len(labels_single_hemi))[:, None])
stc_1 = generate_sparse_stc(fwd['src'], labels_single_hemi, stc_data,
tmin, tstep, 0)
for i, label in enumerate(labels_single_hemi):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc_1.vertno[hemi_idx], label.vertices)
idx = np.searchsorted(stc_1.vertno[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc_1.vertno[0])
assert_true(np.all(stc_1.data[idx] == float(i)))
assert_true(stc_1.data.shape[0] == len(labels_single_hemi))
assert_true(stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = generate_sparse_stc(fwd['src'], labels_single_hemi, stc_data,
tmin, tstep, 0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
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