def test_simulate_sparse_stc_single_hemi():
"""Test generation of sparse source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
labels_single_hemi = [
read_label(
op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label))
for label in label_names_single_hemi
]
stc_1 = simulate_sparse_stc(fwd['src'],
len(labels_single_hemi),
times,
labels=labels_single_hemi,
random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'],
len(labels_single_hemi),
times,
labels=labels_single_hemi,
random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
def test_simulate_sparse_stc_single_hemi(_get_fwd_labels):
"""Test generation of sparse source estimate."""
fwd, labels = _get_fwd_labels
labels_single_hemi = labels[1:] # keep only labels in one hemisphere
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
stc_1 = simulate_sparse_stc(fwd['src'],
len(labels_single_hemi),
times,
labels=labels_single_hemi,
random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'],
len(labels_single_hemi),
times,
labels=labels_single_hemi,
random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
def test_simulate_sparse_stc(_get_fwd_labels):
"""Test generation of sparse source estimate."""
fwd, labels = _get_fwd_labels
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='sample',
subjects_dir=subjects_dir) # no non-zero values
mylabels = []
for label in labels:
this_label = label.copy()
this_label.values.fill(1.)
mylabels.append(this_label)
for location in ('random', 'center'):
random_state = 0 if location == 'random' else None
stc_1 = simulate_sparse_stc(fwd['src'], len(mylabels), times,
labels=mylabels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_equal(stc_1.subject, 'sample')
assert (stc_1.data.shape[0] == len(mylabels))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(mylabels), times,
labels=mylabels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
# Degenerate cases
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='center', subject='foo',
subjects_dir=subjects_dir) # wrong subject
del fwd['src'][0]['subject_his_id'] # remove subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='center',
subjects_dir=subjects_dir) # no subject
fwd['src'][0]['subject_his_id'] = 'sample' # put back subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='foo') # bad location
err_str = 'Number of labels'
with pytest.raises(ValueError, match=err_str):
simulate_sparse_stc(
fwd['src'], len(mylabels) + 1, times, labels=mylabels,
random_state=random_state, location=location,
subjects_dir=subjects_dir)
def test_simulate_sparse_stc(_get_fwd_labels):
"""Test generation of sparse source estimate."""
fwd, labels = _get_fwd_labels
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='sample',
subjects_dir=subjects_dir) # no non-zero values
mylabels = []
for label in labels:
this_label = label.copy()
this_label.values.fill(1.)
mylabels.append(this_label)
for location in ('random', 'center'):
random_state = 0 if location == 'random' else None
stc_1 = simulate_sparse_stc(fwd['src'], len(mylabels), times,
labels=mylabels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_equal(stc_1.subject, 'sample')
assert (stc_1.data.shape[0] == len(mylabels))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(mylabels), times,
labels=mylabels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
# Degenerate cases
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='center', subject='foo',
subjects_dir=subjects_dir) # wrong subject
del fwd['src'][0]['subject_his_id'] # remove subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='center',
subjects_dir=subjects_dir) # no subject
fwd['src'][0]['subject_his_id'] = 'sample' # put back subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(mylabels),
times, labels=mylabels, location='foo') # bad location
err_str = 'Number of labels'
with pytest.raises(ValueError, match=err_str):
simulate_sparse_stc(
fwd['src'], len(mylabels) + 1, times, labels=mylabels,
random_state=random_state, location=location,
subjects_dir=subjects_dir)
def test_simulate_sparse_stc():
"""Test generation of sparse source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=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
times = np.arange(n_times, dtype=np.float) * tstep + tmin
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='sample',
subjects_dir=subjects_dir) # no non-zero values
for label in labels:
label.values.fill(1.)
for location in ('random', 'center'):
random_state = 0 if location == 'random' else None
stc_1 = simulate_sparse_stc(fwd['src'], len(labels), times,
labels=labels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_equal(stc_1.subject, 'sample')
assert (stc_1.data.shape[0] == len(labels))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels), times,
labels=labels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
# Degenerate cases
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='foo',
subjects_dir=subjects_dir) # wrong subject
del fwd['src'][0]['subject_his_id']
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center',
subjects_dir=subjects_dir) # no subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='foo') # bad location
err_str = 'Number of labels'
with pytest.raises(ValueError, match=err_str):
simulate_sparse_stc(
fwd['src'], len(labels) + 1, times, labels=labels,
random_state=random_state, location=location,
subjects_dir=subjects_dir)
def test_simulate_sparse_stc():
""" Test generation of sparse 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]
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
stc_1 = simulate_sparse_stc(fwd["src"], len(labels), times, labels=labels, random_state=0)
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 = simulate_sparse_stc(fwd["src"], len(labels), times, labels=labels, random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
def test_simulate_sparse_stc_single_hemi(_get_fwd_labels):
"""Test generation of sparse source estimate."""
fwd, labels = _get_fwd_labels
labels_single_hemi = labels[1:] # keep only labels in one hemisphere
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
stc_1 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
def test_simulate_sparse_stc_single_hemi():
"""Test generation of sparse source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
labels_single_hemi = [read_label(op.join(data_path, 'MEG', 'sample',
'labels', '%s.label' % label))
for label in label_names_single_hemi]
stc_1 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
