def get_explained(subject_no, n_comp, n_bads): subject = subjects[subject_no] raw_fname = root_dir + '%s/passive_raw.fif' % subject room_fname = root_dir + '%s/emptyroom_%s.fif' % (subject, subject) raw = mne.io.read_raw_fif(raw_fname, preload=True) raw_room = mne.io.read_raw_fif(room_fname, preload=True) raw.filter(0.5, 70) raw_room.filter(1, 70) mne.channels.fix_mag_coil_types(raw.info) mne.channels.fix_mag_coil_types(raw_room.info) picks = mne.pick_types(raw.info, meg='mag', eeg=False, eog=False, stim=False, exclude='bads') events = mne.find_events(raw) tmin, tmax = -0.1, 0.3 expes = list(event_id.keys()) expe = expes[2] # reject = dict(grad=4000e-13, mag=1e-12, eog=150e-6) reject = None # Fit smica freqs = np.linspace(1, 70, 41) smica = ICA(n_components=n_comp, freqs=freqs, rng=0).fit(raw, picks, tol=1e-9, verbose=100, n_it_min=3000, em_it=3000, n_it_lbfgs=3000) ica = ICA_mne(n_comp).fit(raw, picks) X_room = raw_room.get_data(picks=picks) X_room /= np.linalg.norm(X_room) Ss = smica.compute_sources(X_room) As = smica.A del smica Ai = ica.get_components() Si = np.linalg.pinv(Ai).dot(X_room) def greedy_comp(X_room, A, S, n_comp=5): bads = [] vals = [] Xc = X_room.copy() idxs = np.arange(S.shape[0]) for i in range(n_comp): print(i) diffs = [np.linalg.norm(Xc - a[:, None] * s) for a, s in zip(A.T[idxs], S[idxs])] j = idxs[np.argmin(diffs)] bads.append(j) Xc -= A[:, j][:, None] * S[j] vals.append(np.linalg.norm(Xc)) idxs = np.delete(idxs, np.where(idxs == j)) return bads, vals bads, vals = greedy_comp(X_room, As, Ss, n_bads) badi, vali = greedy_comp(X_room, Ai, Si, n_bads) np.save('results/expl_%d_%d_%d.npy' % (subject_no, n_comp, n_bads), np.array((vals, vali))) return vals, vali
def test_ica_additional(method): """Test additional ICA functionality.""" _skip_check_picard(method) tempdir = _TempDir() stop2 = 500 raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() raw.del_proj() # avoid warnings raw.set_annotations(Annotations([0.5], [0.5], ['BAD'])) # XXX This breaks the tests :( # raw.info['bads'] = [raw.ch_names[1]] test_cov = read_cov(test_cov_name) events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[1::2] epochs = Epochs(raw, events, None, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, proj=False) epochs.decimate(3, verbose='error') assert len(epochs) == 4 # test if n_components=None works ica = ICA(n_components=None, max_pca_components=None, n_pca_components=None, random_state=0, method=method, max_iter=1) with pytest.warns(UserWarning, match='did not converge'): ica.fit(epochs) # for testing eog functionality picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)]) epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2, baseline=(None, 0), preload=True) del picks2 test_cov2 = test_cov.copy() ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4, n_pca_components=4, method=method) assert (ica.info is None) with pytest.warns(RuntimeWarning, match='normalize_proj'): ica.fit(raw, picks[:5]) assert (isinstance(ica.info, Info)) assert (ica.n_components_ < 5) ica = ICA(n_components=3, max_pca_components=4, method=method, n_pca_components=4, random_state=0) pytest.raises(RuntimeError, ica.save, '') ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2) # check passing a ch_name to find_bads_ecg with pytest.warns(RuntimeWarning, match='longer'): _, scores_1 = ica.find_bads_ecg(raw) _, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1]) assert scores_1[0] != scores_2[0] # test corrmap ica2 = ica.copy() ica3 = ica.copy() corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True, ch_type="mag") corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False) assert (ica.labels_["blinks"] == ica2.labels_["blinks"]) assert (0 in ica.labels_["blinks"]) # test retrieval of component maps as arrays components = ica.get_components() template = components[:, 0] EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s') corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True, ch_type="mag") assert (ica2.labels_["blinks"] == ica3.labels_["blinks"]) plt.close('all') # make sure a single threshold in a list works corrmap([ica, ica3], template, threshold=[0.5], label='blinks', plot=True, ch_type="mag") ica_different_channels = ICA(n_components=2, random_state=0).fit(raw, picks=[2, 3, 4, 5]) pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0)) # test warnings on bad filenames ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz') with pytest.warns(RuntimeWarning, match='-ica.fif'): ica.save(ica_badname) with pytest.warns(RuntimeWarning, match='-ica.fif'): read_ica(ica_badname) # test decim ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) raw_ = raw.copy() for _ in range(3): raw_.append(raw_) n_samples = raw_._data.shape[1] with pytest.warns(UserWarning, match='did not converge'): ica.fit(raw, picks=picks[:5], decim=3) assert raw_._data.shape[1] == n_samples # test expl var ica = ICA(n_components=1.0, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) with pytest.warns(UserWarning, match='did not converge'): ica.fit(raw, picks=None, decim=3) assert (ica.n_components_ == 4) ica_var = _ica_explained_variance(ica, raw, normalize=True) assert (np.all(ica_var[:-1] >= ica_var[1:])) # test ica sorting ica.exclude = [0] ica.labels_ = dict(blink=[0], think=[1]) ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True) assert_equal(ica_sorted.exclude, [3]) assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2])) # epochs extraction from raw fit pytest.raises(RuntimeError, ica.get_sources, epochs) # test reading and writing test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif') for cov in (None, test_cov): ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) with pytest.warns(None): # ICA does not converge ica.fit(raw, picks=picks[:10], start=start, stop=stop2) sources = ica.get_sources(epochs).get_data() assert (ica.mixing_matrix_.shape == (2, 2)) assert (ica.unmixing_matrix_.shape == (2, 2)) assert (ica.pca_components_.shape == (4, 10)) assert (sources.shape[1] == ica.n_components_) for exclude in [[], [0], np.array([1, 2, 3])]: ica.exclude = exclude ica.labels_ = {'foo': [0]} ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert (list(ica.exclude) == ica_read.exclude) assert_equal(ica.labels_, ica_read.labels_) ica.apply(raw) ica.exclude = [] ica.apply(raw, exclude=[1]) assert (ica.exclude == []) ica.exclude = [0, 1] ica.apply(raw, exclude=[1]) assert (ica.exclude == [0, 1]) ica_raw = ica.get_sources(raw) assert (ica.exclude == [ ica_raw.ch_names.index(e) for e in ica_raw.info['bads'] ]) # test filtering d1 = ica_raw._data[0].copy() ica_raw.filter(4, 20, fir_design='firwin2') assert_equal(ica_raw.info['lowpass'], 20.) assert_equal(ica_raw.info['highpass'], 4.) assert ((d1 != ica_raw._data[0]).any()) d1 = ica_raw._data[0].copy() ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin') assert ((d1 != ica_raw._data[0]).any()) ica.n_pca_components = 2 ica.method = 'fake' ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert (ica.n_pca_components == ica_read.n_pca_components) assert_equal(ica.method, ica_read.method) assert_equal(ica.labels_, ica_read.labels_) # check type consistency attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ ' 'pca_explained_variance_ pre_whitener_') def f(x, y): return getattr(x, y).dtype for attr in attrs.split(): assert_equal(f(ica_read, attr), f(ica, attr)) ica.n_pca_components = 4 ica_read.n_pca_components = 4 ica.exclude = [] ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) for attr in [ 'mixing_matrix_', 'unmixing_matrix_', 'pca_components_', 'pca_mean_', 'pca_explained_variance_', 'pre_whitener_' ]: assert_array_almost_equal(getattr(ica, attr), getattr(ica_read, attr)) assert (ica.ch_names == ica_read.ch_names) assert (isinstance(ica_read.info, Info)) sources = ica.get_sources(raw)[:, :][0] sources2 = ica_read.get_sources(raw)[:, :][0] assert_array_almost_equal(sources, sources2) _raw1 = ica.apply(raw, exclude=[1]) _raw2 = ica_read.apply(raw, exclude=[1]) assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0]) os.remove(test_ica_fname) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(raw, target='EOG 061', score_func=func, start=0, stop=10) assert (ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew) # check exception handling pytest.raises(ValueError, ica.score_sources, raw, target=np.arange(1)) params = [] params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params params += [(None, 'MEG 1531')] # ECG / EOG channel params for idx, ch_name in product(*params): ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name, eog_ch=ch_name, skew_criterion=idx, var_criterion=idx, kurt_criterion=idx) # Make sure detect_artifacts marks the right components. # For int criterion, the doc says "E.g. range(2) would return the two # sources with the highest score". Assert that's what it does. # Only test for skew, since it's always the same code. ica.exclude = [] ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None, eog_ch=None, skew_criterion=0, var_criterion=None, kurt_criterion=None) assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores)) evoked = epochs.average() evoked_data = evoked.data.copy() raw_data = raw[:][0].copy() epochs_data = epochs.get_data().copy() with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_ecg(raw, method='ctps') assert_equal(len(scores), ica.n_components_) with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_ecg(raw, method='correlation') assert_equal(len(scores), ica.n_components_) with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(epochs, method='ctps') assert_equal(len(scores), ica.n_components_) pytest.raises(ValueError, ica.find_bads_ecg, epochs.average(), method='ctps') pytest.raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling') with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202 with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert (isinstance(scores, list)) assert_equal(len(scores[0]), ica.n_components_) idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(evoked, method='correlation') assert_equal(len(scores), ica.n_components_) assert_array_equal(raw_data, raw[:][0]) assert_array_equal(epochs_data, epochs.get_data()) assert_array_equal(evoked_data, evoked.data) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(epochs_eog, target='EOG 061', score_func=func) assert (ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(epochs, score_func=stats.skew) # check exception handling pytest.raises(ValueError, ica.score_sources, epochs, target=np.arange(1)) # ecg functionality ecg_scores = ica.score_sources(raw, target='MEG 1531', score_func='pearsonr') with pytest.warns(RuntimeWarning, match='longer'): ecg_events = ica_find_ecg_events(raw, sources[np.abs(ecg_scores).argmax()]) assert (ecg_events.ndim == 2) # eog functionality eog_scores = ica.score_sources(raw, target='EOG 061', score_func='pearsonr') with pytest.warns(RuntimeWarning, match='longer'): eog_events = ica_find_eog_events(raw, sources[np.abs(eog_scores).argmax()]) assert (eog_events.ndim == 2) # Test ica fiff export ica_raw = ica.get_sources(raw, start=0, stop=100) assert (ica_raw.last_samp - ica_raw.first_samp == 100) assert_equal(len(ica_raw._filenames), 1) # API consistency ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch] assert (ica.n_components_ == len(ica_chans)) test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif') ica.n_components = np.int32(ica.n_components) ica_raw.save(test_ica_fname, overwrite=True) ica_raw2 = read_raw_fif(test_ica_fname, preload=True) assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4) ica_raw2.close() os.remove(test_ica_fname) # Test ica epochs export ica_epochs = ica.get_sources(epochs) assert (ica_epochs.events.shape == epochs.events.shape) ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch] assert (ica.n_components_ == len(ica_chans)) assert (ica.n_components_ == ica_epochs.get_data().shape[1]) assert (ica_epochs._raw is None) assert (ica_epochs.preload is True) # test float n pca components ica.pca_explained_variance_ = np.array([0.2] * 5) ica.n_components_ = 0 for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]: ncomps_ = ica._check_n_pca_components(ncomps) assert (ncomps_ == expected) ica = ICA(method=method) with pytest.warns(None): # sometimes does not converge ica.fit(raw, picks=picks[:5]) with pytest.warns(RuntimeWarning, match='longer'): ica.find_bads_ecg(raw) ica.find_bads_eog(epochs, ch_name='MEG 0121') assert_array_equal(raw_data, raw[:][0]) raw.drop_channels(['MEG 0122']) pytest.raises(RuntimeError, ica.find_bads_eog, raw) with pytest.warns(RuntimeWarning, match='longer'): pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
def test_ica_additional(method): """Test additional ICA functionality.""" _skip_check_picard(method) tempdir = _TempDir() stop2 = 500 raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() raw.del_proj() # avoid warnings raw.set_annotations(Annotations([0.5], [0.5], ['BAD'])) # XXX This breaks the tests :( # raw.info['bads'] = [raw.ch_names[1]] test_cov = read_cov(test_cov_name) events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')[1::2] epochs = Epochs(raw, events, None, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, proj=False) epochs.decimate(3, verbose='error') assert len(epochs) == 4 # test if n_components=None works ica = ICA(n_components=None, max_pca_components=None, n_pca_components=None, random_state=0, method=method, max_iter=1) with pytest.warns(UserWarning, match='did not converge'): ica.fit(epochs) # for testing eog functionality picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)]) epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2, baseline=(None, 0), preload=True) del picks2 test_cov2 = test_cov.copy() ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4, n_pca_components=4, method=method) assert (ica.info is None) with pytest.warns(RuntimeWarning, match='normalize_proj'): ica.fit(raw, picks[:5]) assert (isinstance(ica.info, Info)) assert (ica.n_components_ < 5) ica = ICA(n_components=3, max_pca_components=4, method=method, n_pca_components=4, random_state=0) pytest.raises(RuntimeError, ica.save, '') ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2) # check passing a ch_name to find_bads_ecg with pytest.warns(RuntimeWarning, match='longer'): _, scores_1 = ica.find_bads_ecg(raw) _, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1]) assert scores_1[0] != scores_2[0] # test corrmap ica2 = ica.copy() ica3 = ica.copy() corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True, ch_type="mag") corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False) assert (ica.labels_["blinks"] == ica2.labels_["blinks"]) assert (0 in ica.labels_["blinks"]) # test retrieval of component maps as arrays components = ica.get_components() template = components[:, 0] EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s') corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True, ch_type="mag") assert (ica2.labels_["blinks"] == ica3.labels_["blinks"]) plt.close('all') ica_different_channels = ICA(n_components=2, random_state=0).fit( raw, picks=[2, 3, 4, 5]) pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0)) # test warnings on bad filenames ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz') with pytest.warns(RuntimeWarning, match='-ica.fif'): ica.save(ica_badname) with pytest.warns(RuntimeWarning, match='-ica.fif'): read_ica(ica_badname) # test decim ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) raw_ = raw.copy() for _ in range(3): raw_.append(raw_) n_samples = raw_._data.shape[1] with pytest.warns(UserWarning, match='did not converge'): ica.fit(raw, picks=picks[:5], decim=3) assert raw_._data.shape[1] == n_samples # test expl var ica = ICA(n_components=1.0, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) with pytest.warns(UserWarning, match='did not converge'): ica.fit(raw, picks=None, decim=3) assert (ica.n_components_ == 4) ica_var = _ica_explained_variance(ica, raw, normalize=True) assert (np.all(ica_var[:-1] >= ica_var[1:])) # test ica sorting ica.exclude = [0] ica.labels_ = dict(blink=[0], think=[1]) ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True) assert_equal(ica_sorted.exclude, [3]) assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2])) # epochs extraction from raw fit pytest.raises(RuntimeError, ica.get_sources, epochs) # test reading and writing test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif') for cov in (None, test_cov): ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4, n_pca_components=4, method=method, max_iter=1) with pytest.warns(None): # ICA does not converge ica.fit(raw, picks=picks[:10], start=start, stop=stop2) sources = ica.get_sources(epochs).get_data() assert (ica.mixing_matrix_.shape == (2, 2)) assert (ica.unmixing_matrix_.shape == (2, 2)) assert (ica.pca_components_.shape == (4, 10)) assert (sources.shape[1] == ica.n_components_) for exclude in [[], [0], np.array([1, 2, 3])]: ica.exclude = exclude ica.labels_ = {'foo': [0]} ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert (list(ica.exclude) == ica_read.exclude) assert_equal(ica.labels_, ica_read.labels_) ica.apply(raw) ica.exclude = [] ica.apply(raw, exclude=[1]) assert (ica.exclude == []) ica.exclude = [0, 1] ica.apply(raw, exclude=[1]) assert (ica.exclude == [0, 1]) ica_raw = ica.get_sources(raw) assert (ica.exclude == [ica_raw.ch_names.index(e) for e in ica_raw.info['bads']]) # test filtering d1 = ica_raw._data[0].copy() ica_raw.filter(4, 20, fir_design='firwin2') assert_equal(ica_raw.info['lowpass'], 20.) assert_equal(ica_raw.info['highpass'], 4.) assert ((d1 != ica_raw._data[0]).any()) d1 = ica_raw._data[0].copy() ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin') assert ((d1 != ica_raw._data[0]).any()) ica.n_pca_components = 2 ica.method = 'fake' ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert (ica.n_pca_components == ica_read.n_pca_components) assert_equal(ica.method, ica_read.method) assert_equal(ica.labels_, ica_read.labels_) # check type consistency attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ ' 'pca_explained_variance_ pre_whitener_') def f(x, y): return getattr(x, y).dtype for attr in attrs.split(): assert_equal(f(ica_read, attr), f(ica, attr)) ica.n_pca_components = 4 ica_read.n_pca_components = 4 ica.exclude = [] ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_', 'pca_mean_', 'pca_explained_variance_', 'pre_whitener_']: assert_array_almost_equal(getattr(ica, attr), getattr(ica_read, attr)) assert (ica.ch_names == ica_read.ch_names) assert (isinstance(ica_read.info, Info)) sources = ica.get_sources(raw)[:, :][0] sources2 = ica_read.get_sources(raw)[:, :][0] assert_array_almost_equal(sources, sources2) _raw1 = ica.apply(raw, exclude=[1]) _raw2 = ica_read.apply(raw, exclude=[1]) assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0]) os.remove(test_ica_fname) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(raw, target='EOG 061', score_func=func, start=0, stop=10) assert (ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew) # check exception handling pytest.raises(ValueError, ica.score_sources, raw, target=np.arange(1)) params = [] params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params params += [(None, 'MEG 1531')] # ECG / EOG channel params for idx, ch_name in product(*params): ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name, eog_ch=ch_name, skew_criterion=idx, var_criterion=idx, kurt_criterion=idx) # Make sure detect_artifacts marks the right components. # For int criterion, the doc says "E.g. range(2) would return the two # sources with the highest score". Assert that's what it does. # Only test for skew, since it's always the same code. ica.exclude = [] ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None, eog_ch=None, skew_criterion=0, var_criterion=None, kurt_criterion=None) assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores)) evoked = epochs.average() evoked_data = evoked.data.copy() raw_data = raw[:][0].copy() epochs_data = epochs.get_data().copy() with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_ecg(raw, method='ctps') assert_equal(len(scores), ica.n_components_) with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_ecg(raw, method='correlation') assert_equal(len(scores), ica.n_components_) with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(epochs, method='ctps') assert_equal(len(scores), ica.n_components_) pytest.raises(ValueError, ica.find_bads_ecg, epochs.average(), method='ctps') pytest.raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling') with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202 with pytest.warns(RuntimeWarning, match='longer'): idx, scores = ica.find_bads_eog(raw) assert (isinstance(scores, list)) assert_equal(len(scores[0]), ica.n_components_) idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(evoked, method='correlation') assert_equal(len(scores), ica.n_components_) assert_array_equal(raw_data, raw[:][0]) assert_array_equal(epochs_data, epochs.get_data()) assert_array_equal(evoked_data, evoked.data) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(epochs_eog, target='EOG 061', score_func=func) assert (ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(epochs, score_func=stats.skew) # check exception handling pytest.raises(ValueError, ica.score_sources, epochs, target=np.arange(1)) # ecg functionality ecg_scores = ica.score_sources(raw, target='MEG 1531', score_func='pearsonr') with pytest.warns(RuntimeWarning, match='longer'): ecg_events = ica_find_ecg_events( raw, sources[np.abs(ecg_scores).argmax()]) assert (ecg_events.ndim == 2) # eog functionality eog_scores = ica.score_sources(raw, target='EOG 061', score_func='pearsonr') with pytest.warns(RuntimeWarning, match='longer'): eog_events = ica_find_eog_events( raw, sources[np.abs(eog_scores).argmax()]) assert (eog_events.ndim == 2) # Test ica fiff export ica_raw = ica.get_sources(raw, start=0, stop=100) assert (ica_raw.last_samp - ica_raw.first_samp == 100) assert_equal(len(ica_raw._filenames), 1) # API consistency ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch] assert (ica.n_components_ == len(ica_chans)) test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif') ica.n_components = np.int32(ica.n_components) ica_raw.save(test_ica_fname, overwrite=True) ica_raw2 = read_raw_fif(test_ica_fname, preload=True) assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4) ica_raw2.close() os.remove(test_ica_fname) # Test ica epochs export ica_epochs = ica.get_sources(epochs) assert (ica_epochs.events.shape == epochs.events.shape) ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch] assert (ica.n_components_ == len(ica_chans)) assert (ica.n_components_ == ica_epochs.get_data().shape[1]) assert (ica_epochs._raw is None) assert (ica_epochs.preload is True) # test float n pca components ica.pca_explained_variance_ = np.array([0.2] * 5) ica.n_components_ = 0 for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]: ncomps_ = ica._check_n_pca_components(ncomps) assert (ncomps_ == expected) ica = ICA(method=method) with pytest.warns(None): # sometimes does not converge ica.fit(raw, picks=picks[:5]) with pytest.warns(RuntimeWarning, match='longer'): ica.find_bads_ecg(raw) ica.find_bads_eog(epochs, ch_name='MEG 0121') assert_array_equal(raw_data, raw[:][0]) raw.drop_channels(['MEG 0122']) pytest.raises(RuntimeError, ica.find_bads_eog, raw) with pytest.warns(RuntimeWarning, match='longer'): pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
def process_one(dipole_no, amp, n_components, crop_idx, crop_len=None): raw_fname = "%sAmp%d_Dip%d_IASoff.fif" % (get_root_dir(), amp, dipole_no) raw = read_raw_fif(raw_fname) raw.load_data() raw.crop(5, None) if crop_len is not None: raw.crop(crop_idx * crop_len, (crop_idx + 1) * crop_len) raw.fix_mag_coil_types() events = find_events(raw, "SYS201") picks = mne.pick_types(raw.info, meg="mag", eeg=False, eog=False, stim=False, exclude="bads") n_bins = 40 freqs = np.linspace(1, 70, n_bins + 1) smica = ICA(n_components, freqs, rng=0).fit( raw, picks=picks, em_it=50000, n_it_lbfgs=200, tol=1e-8, verbose=100, n_it_min=10000, ) ica = ICA_mne(n_components, method="picard").fit(raw.copy().filter(1, 70), picks=picks) jdiag = JDIAG_mne(n_components, freqs, rng=0).fit(raw, picks=picks) tmin, tmax = -0.1, 0.1 event_id = [dipole_no] # pows = np.linalg.norm(smica.A, axis=0) * # np.linalg.norm(smica.powers, axis=0) ** .5 raw_max = raw.copy().filter(1, 40) raw_max = maxwell_filter(raw_max, origin=(0.0, 0.0, 0.0)) epochs = mne.Epochs( raw_max, events, event_id, tmin, tmax, baseline=(None, -0.01), preload=True, picks=picks, ) cov = mne.compute_covariance(epochs, tmax=0) e1 = get_error(epochs, cov) e_2 = get_error_A(smica.A, cov, epochs.info) good = np.argmin(e_2[0]) e2 = [e_2[0][good], e_2[1][good]] e_3 = get_error_A(jdiag.A, cov, epochs.info) good = np.argmin(e_3[0]) e3 = [e_3[0][good], e_3[1][good]] e_4 = get_error_A(ica.get_components(), cov, epochs.info) good = np.argmin(e_4[0]) e4 = [e_4[0][good], e_4[1][good]] crop_str = crop_len if crop_len is not None else 0 np.save( "results/phantom_%d_%d_%d_%d_%d.npy" % (dipole_no, amp, n_components, crop_idx, crop_str), np.array([e1, e2, e3, e4]), ) print(e1, e2, e3, e4) return e1, e2, e3, e4
bad_sources = [17, 18, 19] X_filtered = smica.filter( raw._data[picks], bad_sources=bad_sources, method="wiener" ) raw_filtered = raw.copy() raw_filtered._data[picks] = X_filtered raw.filter(1, 70) ica = ICA_mne(n_components=20, method="fastica", random_state=0) ica.fit(raw, picks=picks) sources = ica.get_sources(raw).get_data() ica_mne = transfer_to_ica( raw, picks, freqs, ica.get_sources(raw).get_data(), ica.get_components() ) # ica_mne.plot_extended(sources, sort=False) bads_infomax = [0, 1, 2] X_ifmx = ica_mne.filter( raw._data[picks], bad_sources=bads_infomax, method="pinv" ) raw_ifmx = raw.copy() raw_ifmx._data[picks] = X_ifmx # We identify that clusters 6, 7, 8, 9 correspond to noise max_raw = raw.copy() max_raw = mne.preprocessing.maxwell_filter(max_raw) setups = [] setups.append({"raw": raw, "proj": False, "name": "Unfiltered"})
# noise_sources = [6, 8, 9] # muscle_source = [7] # f, ax = plt.subplots(figsize=(4, 2)) # plot_powers(smica.powers, noise_sources, muscle_source, ax, 'smica') # plt.show() # # # # # sobi = SOBI_mne(100, n_components, freqs, rng=0) # sobi.fit(raw, picks=picks) raw.filter(2, 70) ica = ICA_mne(n_components=n_components, method='fastica', random_state=0) ica.fit(raw, picks=picks) ica_mne = transfer_to_ica(raw, picks, freqs, ica.get_sources(raw).get_data(), ica.get_components()) brain_sources = smica.compute_sources() K, W, _ = picard(brain_sources, ortho=False, verbose=True, random_state=0, max_iter=1000) picard_mix = np.linalg.pinv(W @ K) A_wiener = smica.A.dot(picard_mix) gof_wiener = dipolarity(A_wiener, raw, picks, fname_bem, n_jobs=3)[0] brain_sources = smica.compute_sources(method='pinv') K, W, _ = picard(brain_sources, ortho=False, verbose=True,
n_bins = 40 freqs = np.linspace(1, 70, n_bins + 1) n_comp = 40 smica_room = ICA(10, freqs=freqs, rng=0).fit(raw_room, picks) jdiag_room = JDIAG_mne(10, freqs=freqs, rng=0).fit(raw_room, picks=picks) smica = ICA(n_comp, freqs=freqs, rng=0).fit(raw, picks=picks) to_plot_smica = [37, 38, 39, 20, 4, 3, 19, 8, 12, 2] raw.filter(1, 70) jdiag = JDIAG_mne(n_comp, freqs=freqs, rng=0).fit(raw, picks=picks) to_plot_jdiag = [2, 1, 0, 13, 34, 3, 20, 30, 5, 10] sobi = SOBI_mne(100, n_comp, freqs).fit(raw, picks=picks) to_plot_sobi = [2, 0, 8, 12, 20, 31, 3, 17, 9, 10] ifmx_ = ICA_mne(n_comp, random_state=0).fit(raw, picks=picks) ifmx = transfer_to_ica(raw, picks, freqs, ifmx_.get_sources(raw).get_data(), ifmx_.get_components()) to_plot_ifmx = [1, 2, 0, 35, 33, 19, 3, 32, 25, 9] plot_args = dict(number=True, t_min=2, t_max=4) pow_lims = [(np.min(algo.powers), np.max(algo.powers)) for algo in [smica, jdiag, sobi, ifmx]] y_lim = 0.0001, 0.21 for algo, sort, method, name in zip( [smica, jdiag, sobi, ifmx], [to_plot_smica, to_plot_jdiag, to_plot_sobi, to_plot_ifmx], ['wiener', 'pinv', 'pinv', 'pinv'], ['smica', 'jdiag', 'sobi', 'ifmx']): algo.plot_extended(sources=algo.compute_sources(method=method), sort=sort, save=name,
def test_ica_additional(): """Test additional ICA functionality.""" import matplotlib.pyplot as plt tempdir = _TempDir() stop2 = 500 raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() # XXX This breaks the tests :( # raw.info['bads'] = [raw.ch_names[1]] test_cov = read_cov(test_cov_name) events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads') epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True) # test if n_components=None works with warnings.catch_warnings(record=True): ica = ICA(n_components=None, max_pca_components=None, n_pca_components=None, random_state=0) ica.fit(epochs, picks=picks, decim=3) # for testing eog functionality picks2 = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=True, exclude='bads') epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2, baseline=(None, 0), preload=True) test_cov2 = test_cov.copy() ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4, n_pca_components=4) assert_true(ica.info is None) with warnings.catch_warnings(record=True): ica.fit(raw, picks[:5]) assert_true(isinstance(ica.info, Info)) assert_true(ica.n_components_ < 5) ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4) assert_raises(RuntimeError, ica.save, '') with warnings.catch_warnings(record=True): ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2) # test corrmap ica2 = ica.copy() ica3 = ica.copy() corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True, ch_type="mag") corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False) assert_true(ica.labels_["blinks"] == ica2.labels_["blinks"]) assert_true(0 in ica.labels_["blinks"]) # test retrieval of component maps as arrays components = ica.get_components() template = components[:, 0] EvokedArray(components, ica.info, tmin=0.).plot_topomap([0]) corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True, ch_type="mag") assert_true(ica2.labels_["blinks"] == ica3.labels_["blinks"]) plt.close('all') # test warnings on bad filenames with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz') ica.save(ica_badname) read_ica(ica_badname) assert_naming(w, 'test_ica.py', 2) # test decim ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4) raw_ = raw.copy() for _ in range(3): raw_.append(raw_) n_samples = raw_._data.shape[1] with warnings.catch_warnings(record=True): ica.fit(raw, picks=None, decim=3) assert_true(raw_._data.shape[1], n_samples) # test expl var ica = ICA(n_components=1.0, max_pca_components=4, n_pca_components=4) with warnings.catch_warnings(record=True): ica.fit(raw, picks=None, decim=3) assert_true(ica.n_components_ == 4) ica_var = _ica_explained_variance(ica, raw, normalize=True) assert_true(np.all(ica_var[:-1] >= ica_var[1:])) # test ica sorting ica.exclude = [0] ica.labels_ = dict(blink=[0], think=[1]) ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True) assert_equal(ica_sorted.exclude, [3]) assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2])) # epochs extraction from raw fit assert_raises(RuntimeError, ica.get_sources, epochs) # test reading and writing test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif') for cov in (None, test_cov): ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4, n_pca_components=4) with warnings.catch_warnings(record=True): # ICA does not converge ica.fit(raw, picks=picks, start=start, stop=stop2) sources = ica.get_sources(epochs).get_data() assert_true(ica.mixing_matrix_.shape == (2, 2)) assert_true(ica.unmixing_matrix_.shape == (2, 2)) assert_true(ica.pca_components_.shape == (4, len(picks))) assert_true(sources.shape[1] == ica.n_components_) for exclude in [[], [0]]: ica.exclude = exclude ica.labels_ = {'foo': [0]} ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert_true(ica.exclude == ica_read.exclude) assert_equal(ica.labels_, ica_read.labels_) ica.exclude = [] ica.apply(raw, exclude=[1]) assert_true(ica.exclude == []) ica.exclude = [0, 1] ica.apply(raw, exclude=[1]) assert_true(ica.exclude == [0, 1]) ica_raw = ica.get_sources(raw) assert_true( ica.exclude == [ica_raw.ch_names.index(e) for e in ica_raw.info['bads']]) # test filtering d1 = ica_raw._data[0].copy() ica_raw.filter(4, 20, l_trans_bandwidth='auto', h_trans_bandwidth='auto', filter_length='auto', phase='zero', fir_window='hamming') assert_equal(ica_raw.info['lowpass'], 20.) assert_equal(ica_raw.info['highpass'], 4.) assert_true((d1 != ica_raw._data[0]).any()) d1 = ica_raw._data[0].copy() ica_raw.notch_filter([10], filter_length='auto', trans_bandwidth=10, phase='zero', fir_window='hamming') assert_true((d1 != ica_raw._data[0]).any()) ica.n_pca_components = 2 ica.method = 'fake' ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert_true(ica.n_pca_components == ica_read.n_pca_components) assert_equal(ica.method, ica_read.method) assert_equal(ica.labels_, ica_read.labels_) # check type consistency attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ ' 'pca_explained_variance_ _pre_whitener') def f(x, y): return getattr(x, y).dtype for attr in attrs.split(): assert_equal(f(ica_read, attr), f(ica, attr)) ica.n_pca_components = 4 ica_read.n_pca_components = 4 ica.exclude = [] ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) for attr in [ 'mixing_matrix_', 'unmixing_matrix_', 'pca_components_', 'pca_mean_', 'pca_explained_variance_', '_pre_whitener' ]: assert_array_almost_equal(getattr(ica, attr), getattr(ica_read, attr)) assert_true(ica.ch_names == ica_read.ch_names) assert_true(isinstance(ica_read.info, Info)) sources = ica.get_sources(raw)[:, :][0] sources2 = ica_read.get_sources(raw)[:, :][0] assert_array_almost_equal(sources, sources2) _raw1 = ica.apply(raw, exclude=[1]) _raw2 = ica_read.apply(raw, exclude=[1]) assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0]) os.remove(test_ica_fname) # check scrore funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(raw, target='EOG 061', score_func=func, start=0, stop=10) assert_true(ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(raw, score_func=stats.skew) # check exception handling assert_raises(ValueError, ica.score_sources, raw, target=np.arange(1)) params = [] params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params params += [(None, 'MEG 1531')] # ECG / EOG channel params for idx, ch_name in product(*params): ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name, eog_ch=ch_name, skew_criterion=idx, var_criterion=idx, kurt_criterion=idx) evoked = epochs.average() evoked_data = evoked.data.copy() raw_data = raw[:][0].copy() epochs_data = epochs.get_data().copy() with warnings.catch_warnings(record=True): idx, scores = ica.find_bads_ecg(raw, method='ctps') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(raw, method='correlation') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(epochs, method='ctps') assert_equal(len(scores), ica.n_components_) assert_raises(ValueError, ica.find_bads_ecg, epochs.average(), method='ctps') assert_raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling') raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202 idx, scores = ica.find_bads_eog(raw) assert_true(isinstance(scores, list)) assert_equal(len(scores[0]), ica.n_components_) idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(evoked, method='correlation') assert_equal(len(scores), ica.n_components_) assert_array_equal(raw_data, raw[:][0]) assert_array_equal(epochs_data, epochs.get_data()) assert_array_equal(evoked_data, evoked.data) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(epochs_eog, target='EOG 061', score_func=func) assert_true(ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(epochs, score_func=stats.skew) # check exception handling assert_raises(ValueError, ica.score_sources, epochs, target=np.arange(1)) # ecg functionality ecg_scores = ica.score_sources(raw, target='MEG 1531', score_func='pearsonr') with warnings.catch_warnings(record=True): # filter attenuation warning ecg_events = ica_find_ecg_events(raw, sources[np.abs(ecg_scores).argmax()]) assert_true(ecg_events.ndim == 2) # eog functionality eog_scores = ica.score_sources(raw, target='EOG 061', score_func='pearsonr') with warnings.catch_warnings(record=True): # filter attenuation warning eog_events = ica_find_eog_events(raw, sources[np.abs(eog_scores).argmax()]) assert_true(eog_events.ndim == 2) # Test ica fiff export ica_raw = ica.get_sources(raw, start=0, stop=100) assert_true(ica_raw.last_samp - ica_raw.first_samp == 100) assert_equal(len(ica_raw._filenames), 1) # API consistency ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch] assert_true(ica.n_components_ == len(ica_chans)) test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif') ica.n_components = np.int32(ica.n_components) ica_raw.save(test_ica_fname, overwrite=True) ica_raw2 = read_raw_fif(test_ica_fname, preload=True) assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4) ica_raw2.close() os.remove(test_ica_fname) # Test ica epochs export ica_epochs = ica.get_sources(epochs) assert_true(ica_epochs.events.shape == epochs.events.shape) ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch] assert_true(ica.n_components_ == len(ica_chans)) assert_true(ica.n_components_ == ica_epochs.get_data().shape[1]) assert_true(ica_epochs._raw is None) assert_true(ica_epochs.preload is True) # test float n pca components ica.pca_explained_variance_ = np.array([0.2] * 5) ica.n_components_ = 0 for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]: ncomps_ = ica._check_n_pca_components(ncomps) assert_true(ncomps_ == expected) ica = ICA() ica.fit(raw, picks=picks[:5]) ica.find_bads_ecg(raw) ica.find_bads_eog(epochs, ch_name='MEG 0121') assert_array_equal(raw_data, raw[:][0]) raw.drop_channels(['MEG 0122']) assert_raises(RuntimeError, ica.find_bads_eog, raw) assert_raises(RuntimeError, ica.find_bads_ecg, raw)
def test_ica_additional(): """Test additional ICA functionality.""" import matplotlib.pyplot as plt tempdir = _TempDir() stop2 = 500 raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data() # XXX This breaks the tests :( # raw.info['bads'] = [raw.ch_names[1]] test_cov = read_cov(test_cov_name) events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads') epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True) # test if n_components=None works with warnings.catch_warnings(record=True): ica = ICA(n_components=None, max_pca_components=None, n_pca_components=None, random_state=0) ica.fit(epochs, picks=picks, decim=3) # for testing eog functionality picks2 = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=True, exclude='bads') epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2, baseline=(None, 0), preload=True) test_cov2 = test_cov.copy() ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4, n_pca_components=4) assert_true(ica.info is None) with warnings.catch_warnings(record=True): ica.fit(raw, picks[:5]) assert_true(isinstance(ica.info, Info)) assert_true(ica.n_components_ < 5) ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4) assert_raises(RuntimeError, ica.save, '') with warnings.catch_warnings(record=True): ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2) # test corrmap ica2 = ica.copy() ica3 = ica.copy() corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True, ch_type="mag") corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False) assert_true(ica.labels_["blinks"] == ica2.labels_["blinks"]) assert_true(0 in ica.labels_["blinks"]) # test retrieval of component maps as arrays components = ica.get_components() template = components[:, 0] EvokedArray(components, ica.info, tmin=0.).plot_topomap([0]) corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True, ch_type="mag") assert_true(ica2.labels_["blinks"] == ica3.labels_["blinks"]) plt.close('all') # test warnings on bad filenames with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz') ica.save(ica_badname) read_ica(ica_badname) assert_naming(w, 'test_ica.py', 2) # test decim ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4) raw_ = raw.copy() for _ in range(3): raw_.append(raw_) n_samples = raw_._data.shape[1] with warnings.catch_warnings(record=True): ica.fit(raw, picks=None, decim=3) assert_true(raw_._data.shape[1], n_samples) # test expl var ica = ICA(n_components=1.0, max_pca_components=4, n_pca_components=4) with warnings.catch_warnings(record=True): ica.fit(raw, picks=None, decim=3) assert_true(ica.n_components_ == 4) ica_var = _ica_explained_variance(ica, raw, normalize=True) assert_true(np.all(ica_var[:-1] >= ica_var[1:])) # test ica sorting ica.exclude = [0] ica.labels_ = dict(blink=[0], think=[1]) ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True) assert_equal(ica_sorted.exclude, [3]) assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2])) # epochs extraction from raw fit assert_raises(RuntimeError, ica.get_sources, epochs) # test reading and writing test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif') for cov in (None, test_cov): ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4, n_pca_components=4) with warnings.catch_warnings(record=True): # ICA does not converge ica.fit(raw, picks=picks, start=start, stop=stop2) sources = ica.get_sources(epochs).get_data() assert_true(ica.mixing_matrix_.shape == (2, 2)) assert_true(ica.unmixing_matrix_.shape == (2, 2)) assert_true(ica.pca_components_.shape == (4, len(picks))) assert_true(sources.shape[1] == ica.n_components_) for exclude in [[], [0]]: ica.exclude = exclude ica.labels_ = {'foo': [0]} ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert_true(ica.exclude == ica_read.exclude) assert_equal(ica.labels_, ica_read.labels_) ica.exclude = [] ica.apply(raw, exclude=[1]) assert_true(ica.exclude == []) ica.exclude = [0, 1] ica.apply(raw, exclude=[1]) assert_true(ica.exclude == [0, 1]) ica_raw = ica.get_sources(raw) assert_true(ica.exclude == [ica_raw.ch_names.index(e) for e in ica_raw.info['bads']]) # test filtering d1 = ica_raw._data[0].copy() ica_raw.filter(4, 20, l_trans_bandwidth='auto', h_trans_bandwidth='auto', filter_length='auto', phase='zero', fir_window='hamming') assert_equal(ica_raw.info['lowpass'], 20.) assert_equal(ica_raw.info['highpass'], 4.) assert_true((d1 != ica_raw._data[0]).any()) d1 = ica_raw._data[0].copy() ica_raw.notch_filter([10], filter_length='auto', trans_bandwidth=10, phase='zero', fir_window='hamming') assert_true((d1 != ica_raw._data[0]).any()) ica.n_pca_components = 2 ica.method = 'fake' ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) assert_true(ica.n_pca_components == ica_read.n_pca_components) assert_equal(ica.method, ica_read.method) assert_equal(ica.labels_, ica_read.labels_) # check type consistency attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ ' 'pca_explained_variance_ _pre_whitener') def f(x, y): return getattr(x, y).dtype for attr in attrs.split(): assert_equal(f(ica_read, attr), f(ica, attr)) ica.n_pca_components = 4 ica_read.n_pca_components = 4 ica.exclude = [] ica.save(test_ica_fname) ica_read = read_ica(test_ica_fname) for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_', 'pca_mean_', 'pca_explained_variance_', '_pre_whitener']: assert_array_almost_equal(getattr(ica, attr), getattr(ica_read, attr)) assert_true(ica.ch_names == ica_read.ch_names) assert_true(isinstance(ica_read.info, Info)) sources = ica.get_sources(raw)[:, :][0] sources2 = ica_read.get_sources(raw)[:, :][0] assert_array_almost_equal(sources, sources2) _raw1 = ica.apply(raw, exclude=[1]) _raw2 = ica_read.apply(raw, exclude=[1]) assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0]) os.remove(test_ica_fname) # check scrore funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(raw, target='EOG 061', score_func=func, start=0, stop=10) assert_true(ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(raw, score_func=stats.skew) # check exception handling assert_raises(ValueError, ica.score_sources, raw, target=np.arange(1)) params = [] params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params params += [(None, 'MEG 1531')] # ECG / EOG channel params for idx, ch_name in product(*params): ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name, eog_ch=ch_name, skew_criterion=idx, var_criterion=idx, kurt_criterion=idx) evoked = epochs.average() evoked_data = evoked.data.copy() raw_data = raw[:][0].copy() epochs_data = epochs.get_data().copy() with warnings.catch_warnings(record=True): idx, scores = ica.find_bads_ecg(raw, method='ctps') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(raw, method='correlation') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_eog(raw) assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(epochs, method='ctps') assert_equal(len(scores), ica.n_components_) assert_raises(ValueError, ica.find_bads_ecg, epochs.average(), method='ctps') assert_raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling') raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202 idx, scores = ica.find_bads_eog(raw) assert_true(isinstance(scores, list)) assert_equal(len(scores[0]), ica.n_components_) idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441') assert_equal(len(scores), ica.n_components_) idx, scores = ica.find_bads_ecg(evoked, method='correlation') assert_equal(len(scores), ica.n_components_) assert_array_equal(raw_data, raw[:][0]) assert_array_equal(epochs_data, epochs.get_data()) assert_array_equal(evoked_data, evoked.data) # check score funcs for name, func in get_score_funcs().items(): if name in score_funcs_unsuited: continue scores = ica.score_sources(epochs_eog, target='EOG 061', score_func=func) assert_true(ica.n_components_ == len(scores)) # check univariate stats scores = ica.score_sources(epochs, score_func=stats.skew) # check exception handling assert_raises(ValueError, ica.score_sources, epochs, target=np.arange(1)) # ecg functionality ecg_scores = ica.score_sources(raw, target='MEG 1531', score_func='pearsonr') with warnings.catch_warnings(record=True): # filter attenuation warning ecg_events = ica_find_ecg_events(raw, sources[np.abs(ecg_scores).argmax()]) assert_true(ecg_events.ndim == 2) # eog functionality eog_scores = ica.score_sources(raw, target='EOG 061', score_func='pearsonr') with warnings.catch_warnings(record=True): # filter attenuation warning eog_events = ica_find_eog_events(raw, sources[np.abs(eog_scores).argmax()]) assert_true(eog_events.ndim == 2) # Test ica fiff export ica_raw = ica.get_sources(raw, start=0, stop=100) assert_true(ica_raw.last_samp - ica_raw.first_samp == 100) assert_equal(len(ica_raw._filenames), 1) # API consistency ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch] assert_true(ica.n_components_ == len(ica_chans)) test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif') ica.n_components = np.int32(ica.n_components) ica_raw.save(test_ica_fname, overwrite=True) ica_raw2 = read_raw_fif(test_ica_fname, preload=True) assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4) ica_raw2.close() os.remove(test_ica_fname) # Test ica epochs export ica_epochs = ica.get_sources(epochs) assert_true(ica_epochs.events.shape == epochs.events.shape) ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch] assert_true(ica.n_components_ == len(ica_chans)) assert_true(ica.n_components_ == ica_epochs.get_data().shape[1]) assert_true(ica_epochs._raw is None) assert_true(ica_epochs.preload is True) # test float n pca components ica.pca_explained_variance_ = np.array([0.2] * 5) ica.n_components_ = 0 for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]: ncomps_ = ica._check_n_pca_components(ncomps) assert_true(ncomps_ == expected) ica = ICA() ica.fit(raw, picks=picks[:5]) ica.find_bads_ecg(raw) ica.find_bads_eog(epochs, ch_name='MEG 0121') assert_array_equal(raw_data, raw[:][0]) raw.drop_channels(['MEG 0122']) assert_raises(RuntimeError, ica.find_bads_eog, raw) assert_raises(RuntimeError, ica.find_bads_ecg, raw)
if ICA_exclude_EOG: #eog_inds, scores = ica.find_bads_eog(raw) eog_epochs = mne.preprocessing.create_eog_epochs( filt_raw2) # get single EOG trials eog_inds, scores = ica.find_bads_eog(eog_epochs) ica.exclude += eog_inds # I don't want to save ECG here, better I select it by hand in jupyter if ICA_exclude_ECG: icacomp = ica.get_sources(filt_raw2) ecg_inds, ratios, ecg_intervals = upre.getECGindsICAcomp(icacomp) ica.exclude += ecg_inds ica.save(icafname_full) nonexcluded = list( set(range(ica.get_components().shape[1])) - set(ica.exclude)) #print( sorted(nonexcluded) ) s = ','.join(map(str, ica.exclude)) exclStr = 'excl_' + s print(exclStr) compinds = range(ica.get_components().shape[1]) #all components #nr = len(compinds); nc = 2 if plot_ICA_prop: print('Start plotting ICA properties') with PdfPages( os.path.join( dir_fig, '{}_ica_components_{}.pdf'.format( fname_noext, exclStr))) as pdf: filt_raw_tmp = filt_raw2.copy()