def test_ica_reset(): """Test ICA resetting""" raw = Raw(raw_fname).crop(0.5, stop, False) raw.load_data() picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] run_time_attrs = ( '_pre_whitener', 'unmixing_matrix_', 'mixing_matrix_', 'n_components_', 'n_samples_', 'pca_components_', 'pca_explained_variance_', 'pca_mean_' ) with warnings.catch_warnings(record=True): ica = ICA( n_components=3, max_pca_components=3, n_pca_components=3, method='fastica', max_iter=1).fit(raw, picks=picks) assert_true(all(hasattr(ica, attr) for attr in run_time_attrs)) ica._reset() assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs))
def test_ica_reset(method): """Test ICA resetting.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] run_time_attrs = ('pre_whitener_', 'unmixing_matrix_', 'mixing_matrix_', 'n_components_', 'n_samples_', 'pca_components_', 'pca_explained_variance_', 'pca_mean_') with pytest.warns(UserWarning, match='did not converge'): ica = ICA(n_components=3, max_pca_components=3, n_pca_components=3, method=method, max_iter=1).fit(raw, picks=picks) assert (all(hasattr(ica, attr) for attr in run_time_attrs)) assert ica.labels_ is not None ica._reset() assert (not any(hasattr(ica, attr) for attr in run_time_attrs)) assert ica.labels_ is not None
def test_ica_reset(method): """Test ICA resetting.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] run_time_attrs = ( '_pre_whitener', 'unmixing_matrix_', 'mixing_matrix_', 'n_components_', 'n_samples_', 'pca_components_', 'pca_explained_variance_', 'pca_mean_' ) with warnings.catch_warnings(record=True): # convergence ica = ICA( n_components=3, max_pca_components=3, n_pca_components=3, method=method, max_iter=1).fit(raw, picks=picks) assert_true(all(hasattr(ica, attr) for attr in run_time_attrs)) assert_not_equal(ica.labels_, None) ica._reset() assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs)) assert_not_equal(ica.labels_, None)
def test_ica_reset(method): """Test ICA resetting.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] run_time_attrs = ( 'pre_whitener_', 'unmixing_matrix_', 'mixing_matrix_', 'n_components_', 'n_samples_', 'pca_components_', 'pca_explained_variance_', 'pca_mean_' ) with pytest.warns(UserWarning, match='did not converge'): ica = ICA( n_components=3, max_pca_components=3, n_pca_components=3, method=method, max_iter=1).fit(raw, picks=picks) assert (all(hasattr(ica, attr) for attr in run_time_attrs)) assert ica.labels_ is not None ica._reset() assert (not any(hasattr(ica, attr) for attr in run_time_attrs)) assert ica.labels_ is not None
def test_ica_reset(method): """Test ICA resetting.""" _skip_check_picard(method) raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data() picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[:10] run_time_attrs = ('_pre_whitener', 'unmixing_matrix_', 'mixing_matrix_', 'n_components_', 'n_samples_', 'pca_components_', 'pca_explained_variance_', 'pca_mean_') with warnings.catch_warnings(record=True): # convergence ica = ICA(n_components=3, max_pca_components=3, n_pca_components=3, method=method, max_iter=1).fit(raw, picks=picks) assert_true(all(hasattr(ica, attr) for attr in run_time_attrs)) assert_not_equal(ica.labels_, None) ica._reset() assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs)) assert_not_equal(ica.labels_, None)