def test_generate_sparse_stc(): """ Test generation of sparse source estimate """ 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)
def test_generate_sparse_stc(): """ Test generation of sparse source estimate """ 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)
def test_generate_sparse_stc(): """ Test generation of sparse source estimate """ n_times = 10 tmin = 0 tstep = 1e-3 stc_data = np.ones((len(labels), n_times)) stc_1 = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, 0) assert_true(np.all(stc_1.data == 1.0)) 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 = 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)
def test_generate_sparse_stc(): """ Test generation of sparse source estimate """ n_times = 10 tmin = 0 tstep = 1e-3 stc_data = np.ones((len(labels), n_times)) stc_1 = generate_sparse_stc(fwd["src"], labels, stc_data, tmin, tstep, 0) assert_true(np.all(stc_1.data == 1.0)) 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 = 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)
def test_generate_sparse_stc_single_hemi(): """ Test generation of sparse source estimate """ fwd = read_forward_solution_meg(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.vertices[hemi_idx], label.vertices) idx = np.searchsorted(stc_1.vertices[hemi_idx], idx) if hemi_idx == 1: idx += len(stc_1.vertices[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)