def test_spatio_temporal_src_connectivity():
"""Test spatio-temporal connectivity from source spaces."""
tris = np.array([[0, 1, 2], [3, 4, 5]])
src = [dict(), dict()]
connectivity = spatio_temporal_tris_connectivity(tris, 2)
src[0]['use_tris'] = np.array([[0, 1, 2]])
src[1]['use_tris'] = np.array([[0, 1, 2]])
src[0]['vertno'] = np.array([0, 1, 2])
src[1]['vertno'] = np.array([0, 1, 2])
src[0]['type'] = 'surf'
src[1]['type'] = 'surf'
connectivity2 = spatio_temporal_src_connectivity(src, 2)
assert_array_equal(connectivity.todense(), connectivity2.todense())
# add test for dist connectivity
src[0]['dist'] = np.ones((3, 3)) - np.eye(3)
src[1]['dist'] = np.ones((3, 3)) - np.eye(3)
src[0]['vertno'] = [0, 1, 2]
src[1]['vertno'] = [0, 1, 2]
src[0]['type'] = 'surf'
src[1]['type'] = 'surf'
connectivity3 = spatio_temporal_src_connectivity(src, 2, dist=2)
assert_array_equal(connectivity.todense(), connectivity3.todense())
# add test for source space connectivity with omitted vertices
inverse_operator = read_inverse_operator(fname_inv)
src_ = inverse_operator['src']
with pytest.warns(RuntimeWarning, match='will have holes'):
connectivity = spatio_temporal_src_connectivity(src_, n_times=2)
a = connectivity.shape[0] / 2
b = sum([s['nuse'] for s in inverse_operator['src']])
assert (a == b)
assert_equal(grade_to_tris(5).shape, [40960, 3])
def test_spatio_temporal_src_connectivity():
"""Test spatio-temporal connectivity from source spaces"""
tris = np.array([[0, 1, 2], [3, 4, 5]])
src = [dict(), dict()]
connectivity = spatio_temporal_tris_connectivity(tris, 2)
src[0]['use_tris'] = np.array([[0, 1, 2]])
src[1]['use_tris'] = np.array([[0, 1, 2]])
src[0]['vertno'] = np.array([0, 1, 2])
src[1]['vertno'] = np.array([0, 1, 2])
connectivity2 = spatio_temporal_src_connectivity(src, 2)
assert_array_equal(connectivity.todense(), connectivity2.todense())
# add test for dist connectivity
src[0]['dist'] = np.ones((3, 3)) - np.eye(3)
src[1]['dist'] = np.ones((3, 3)) - np.eye(3)
src[0]['vertno'] = [0, 1, 2]
src[1]['vertno'] = [0, 1, 2]
connectivity3 = spatio_temporal_src_connectivity(src, 2, dist=2)
assert_array_equal(connectivity.todense(), connectivity3.todense())
# add test for source space connectivity with omitted vertices
inverse_operator = read_inverse_operator(fname_inv)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
src_ = inverse_operator['src']
connectivity = spatio_temporal_src_connectivity(src_, n_times=2)
assert len(w) == 1
a = connectivity.shape[0] / 2
b = sum([s['nuse'] for s in inverse_operator['src']])
assert_true(a == b)
assert_equal(grade_to_tris(5).shape, [40960, 3])
def test_spatio_temporal_src_connectivity():
"""Test spatio-temporal connectivity from source spaces"""
tris = np.array([[0, 1, 2], [3, 4, 5]])
src = [dict(), dict()]
connectivity = spatio_temporal_tris_connectivity(tris, 2)
src[0]['use_tris'] = np.array([[0, 1, 2]])
src[1]['use_tris'] = np.array([[0, 1, 2]])
src[0]['vertno'] = np.array([0, 1, 2])
src[1]['vertno'] = np.array([0, 1, 2])
connectivity2 = spatio_temporal_src_connectivity(src, 2)
assert_array_equal(connectivity.todense(), connectivity2.todense())
# add test for dist connectivity
src[0]['dist'] = np.ones((3, 3)) - np.eye(3)
src[1]['dist'] = np.ones((3, 3)) - np.eye(3)
src[0]['vertno'] = [0, 1, 2]
src[1]['vertno'] = [0, 1, 2]
connectivity3 = spatio_temporal_src_connectivity(src, 2, dist=2)
assert_array_equal(connectivity.todense(), connectivity3.todense())
# add test for source space connectivity with omitted vertices
inverse_operator = read_inverse_operator(fname_inv)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
src_ = inverse_operator['src']
connectivity = spatio_temporal_src_connectivity(src_, n_times=2)
assert len(w) == 1
a = connectivity.shape[0] / 2
b = sum([s['nuse'] for s in inverse_operator['src']])
assert_true(a == b)
assert_equal(grade_to_tris(5).shape, [40960, 3])
def test_spatio_temporal_src_connectivity():
"""Test spatio-temporal connectivity from source spaces."""
tris = np.array([[0, 1, 2], [3, 4, 5]])
src = [dict(), dict()]
connectivity = spatio_temporal_tris_connectivity(tris, 2)
src[0]['use_tris'] = np.array([[0, 1, 2]])
src[1]['use_tris'] = np.array([[0, 1, 2]])
src[0]['vertno'] = np.array([0, 1, 2])
src[1]['vertno'] = np.array([0, 1, 2])
src[0]['type'] = 'surf'
src[1]['type'] = 'surf'
connectivity2 = spatio_temporal_src_connectivity(src, 2)
assert_array_equal(connectivity.todense(), connectivity2.todense())
# add test for dist connectivity
src[0]['dist'] = np.ones((3, 3)) - np.eye(3)
src[1]['dist'] = np.ones((3, 3)) - np.eye(3)
src[0]['vertno'] = [0, 1, 2]
src[1]['vertno'] = [0, 1, 2]
src[0]['type'] = 'surf'
src[1]['type'] = 'surf'
connectivity3 = spatio_temporal_src_connectivity(src, 2, dist=2)
assert_array_equal(connectivity.todense(), connectivity3.todense())
# add test for source space connectivity with omitted vertices
inverse_operator = read_inverse_operator(fname_inv)
src_ = inverse_operator['src']
with pytest.warns(RuntimeWarning, match='will have holes'):
connectivity = spatio_temporal_src_connectivity(src_, n_times=2)
a = connectivity.shape[0] / 2
b = sum([s['nuse'] for s in inverse_operator['src']])
assert (a == b)
assert_equal(grade_to_tris(5).shape, [40960, 3])
def test_vol_connectivity():
"""Test volume connectivity."""
vol = read_source_spaces(fname_vsrc)
pytest.raises(ValueError, spatial_src_connectivity, vol, dist=1.)
connectivity = spatial_src_connectivity(vol)
n_vertices = vol[0]['inuse'].sum()
assert_equal(connectivity.shape, (n_vertices, n_vertices))
assert (np.all(connectivity.data == 1))
assert (isinstance(connectivity, sparse.coo_matrix))
connectivity2 = spatio_temporal_src_connectivity(vol, n_times=2)
assert_equal(connectivity2.shape, (2 * n_vertices, 2 * n_vertices))
assert (np.all(connectivity2.data == 1))
def test_vol_connectivity():
"""Test volume connectivity."""
vol = read_source_spaces(fname_vsrc)
pytest.raises(ValueError, spatial_src_connectivity, vol, dist=1.)
connectivity = spatial_src_connectivity(vol)
n_vertices = vol[0]['inuse'].sum()
assert_equal(connectivity.shape, (n_vertices, n_vertices))
assert (np.all(connectivity.data == 1))
assert (isinstance(connectivity, sparse.coo_matrix))
connectivity2 = spatio_temporal_src_connectivity(vol, n_times=2)
assert_equal(connectivity2.shape, (2 * n_vertices, 2 * n_vertices))
assert (np.all(connectivity2.data == 1))
#mean_stc1.data = np.mean(mean_stc1.data[:, :, time_mask], axis=2)[:, :, None]
#mean_stc2.data = np.mean(mean_stc2.data[:, :, time_mask], axis=2)[:, :, None]
X1 = np.mean(X1[:, :, time_mask], axis=2)[:, :, None]
X2 = np.mean(X2[:, :, time_mask], axis=2)[:, :, None]
template_stc = copy.deepcopy(template_stc)
template_stc.crop(tmin, tmin + template_stc.tstep)
assert X1.shape == X2.shape
n_samples, n_vertices, n_times = X1.shape
X1 = np.ascontiguousarray(np.swapaxes(X1, 1, 2).reshape(n_samples, -1))
X2 = np.ascontiguousarray(np.swapaxes(X2, 1, 2).reshape(n_samples, -1))
connectivity = mne.spatio_temporal_src_connectivity(src, n_times)
for t in thresholds:
from time import time
t0 = time()
T_obs, clusters, cluster_pv, H0 = mem.cache(permutation_cluster_1samp_test,
ignore=['n_jobs'])(X1 - X2,
threshold=t,
n_permutations=n_permutations,
tail=0,
stat_fun=stat_fun,
connectivity=connectivity,
n_jobs=n_jobs, seed=0)
print "Time elapsed : %s (s)" % (time() - t0)
clusters = [c.reshape(n_times, n_vertices).T for c in clusters]
