def test_generate_stc():
""" Test generation of source estimate """
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)
assert_true(np.all(stc.data == 1.0))
assert_true(stc.data.shape[1] == n_times)
# test with function
fun = lambda x: x**2
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
print stc.data
# the first label has value 0, the second value 2
assert_array_almost_equal(stc.data[0], np.zeros(n_times))
assert_array_almost_equal(stc.data[-1], 4 * np.ones(n_times))
def test_generate_stc():
""" Test generation of source estimate """
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)
assert_true(np.all(stc.data == 1.0))
assert_true(stc.data.shape[1] == n_times)
# test with function
fun = lambda x: x ** 2
stc = generate_stc(fwd["src"], mylabels, stc_data, tmin, tstep, fun)
print stc.data
# the first label has value 0, the second value 2
assert_array_almost_equal(stc.data[0], np.zeros(n_times))
assert_array_almost_equal(stc.data[-1], 4 * np.ones(n_times))
def test_generate_stc():
""" Test generation of source estimate """
mylabels = copy.deepcopy(labels)
for i, label in enumerate(mylabels):
label['values'] = 2 * i * np.ones(len(label['values']))
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)
assert_true(np.all(stc.data == 1.0))
assert_true(stc.data.shape[1] == n_times)
# test with function
fun = lambda x: x ** 2
stc = generate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
print stc.data
# the first label has value 0, the second value 2
assert_array_almost_equal(stc.data[0], np.zeros(n_times))
assert_array_almost_equal(stc.data[-1], 4 * np.ones(n_times))
def test_generate_stc():
""" Test generation of source estimate """
fwd = read_forward_solution_meg(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.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[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.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
res = ((2. * i)**2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
def test_generate_stc_single_hemi():
""" Test generation of source estimate """
fwd = read_forward_solution_meg(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.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
assert_true(np.all(stc.data[idx] == 1.0))
assert_true(stc.data[idx].shape[1] == n_times)
# test with function
def fun(x):
return 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.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
res = ((2. * i) ** 2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
def test_generate_stc():
""" Test generation of source estimate """
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)
# idx_tmp = np.intersect1d(stc.vertno[hemi_idx], label.vertices)
# idx = idx_tmp.copy()
# for k in range(len(idx_tmp)):
# idx[k] = np.where(stc.vertno[hemi_idx] == idx_tmp[k])[0]
if hemi_idx == 1:
idx += len(stc.vertno[0])
assert_array_almost_equal(stc.data[idx],
((2. * i) ** 2.) * np.ones((len(idx), n_times)))
def test_generate_stc():
""" Test generation of source estimate """
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])
assert_array_almost_equal(stc.data[idx], ((2. * i)**2.) * np.ones(
(len(idx), n_times)))
