def test_utils(): """Test utils.""" event_id = {'Visual/Left': 3} tmin, tmax = -0.2, 0.5 events = mne.find_events(raw) picks = mne.pick_channels(raw.info['ch_names'], ['MEG 2443', 'MEG 2442', 'MEG 2441']) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=None, preload=True) this_epoch = epochs.copy() epochs_clean = clean_by_interp(this_epoch) assert_array_equal(this_epoch.get_data(), epochs.get_data()) assert_raises(AssertionError, assert_array_equal, epochs_clean.get_data(), this_epoch.get_data()) picks_meg = mne.pick_types(evoked.info, meg='grad', eeg=False, exclude=[]) picks_eeg = mne.pick_types(evoked.info, meg=False, eeg=True, exclude=[]) picks_bad_meg = mne.pick_channels(evoked.ch_names, include=['MEG 2443']) picks_bad_eeg = mne.pick_channels(evoked.ch_names, include=['EEG 053']) evoked_orig = evoked.copy() for picks, picks_bad in zip([picks_meg, picks_eeg], [picks_bad_meg, picks_bad_eeg]): evoked_autoreject = interpolate_bads(evoked, picks=picks, reset_bads=False) evoked.interpolate_bads(reset_bads=False) assert_array_equal(evoked.data[picks_bad], evoked_autoreject.data[picks_bad]) assert_raises(AssertionError, assert_array_equal, evoked_orig.data[picks_bad], evoked.data[picks_bad])
def test_utils(): """Test utils.""" event_id = {'Visual/Left': 3} tmin, tmax = -0.2, 0.5 events = mne.find_events(raw) picks = mne.pick_channels(raw.info['ch_names'], ['MEG 2443', 'MEG 2442', 'MEG 2441']) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=None, preload=True) this_epoch = epochs.copy() assert this_epoch.info['bads'] == ['MEG 2443'] epochs_clean = clean_by_interp(this_epoch) assert this_epoch.info['bads'] == ['MEG 2443'] assert_array_equal(this_epoch.get_data(), epochs.get_data()) pytest.raises(AssertionError, assert_array_equal, epochs_clean.get_data(), this_epoch.get_data()) picks_meg = mne.pick_types(evoked.info, meg='grad', eeg=False, exclude=[]) picks_eeg = mne.pick_types(evoked.info, meg=False, eeg=True, exclude=[]) picks_bad_meg = mne.pick_channels(evoked.ch_names, include=['MEG 2443']) picks_bad_eeg = mne.pick_channels(evoked.ch_names, include=['EEG 053']) evoked_orig = evoked.copy() for picks, picks_bad in zip([picks_meg, picks_eeg], [picks_bad_meg, picks_bad_eeg]): evoked_autoreject = interpolate_bads(evoked, picks=picks, reset_bads=False) evoked.interpolate_bads(reset_bads=False) assert_array_equal(evoked.data[picks_bad], evoked_autoreject.data[picks_bad]) pytest.raises(AssertionError, assert_array_equal, evoked_orig.data[picks_bad], evoked.data[picks_bad]) # test that autoreject EEG interpolation code behaves the same as MNE evoked_ar = evoked_orig.copy() evoked_mne = evoked_orig.copy() origin = _check_origin('auto', evoked_ar.info) _interpolate_bads_eeg(evoked_ar, picks=None) mne.channels.interpolation._interpolate_bads_eeg(evoked_mne, origin=origin) assert_array_almost_equal(evoked_ar.data, evoked_mne.data)
def test_utils(): """Test utils.""" event_id = {'Visual/Left': 3} tmin, tmax = -0.2, 0.5 events = mne.find_events(raw) picks = mne.pick_channels(raw.info['ch_names'], ['MEG 2443', 'MEG 2442', 'MEG 2441']) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=None, preload=True) this_epoch = epochs.copy() epochs_clean = clean_by_interp(this_epoch) assert_array_equal(this_epoch.get_data(), epochs.get_data()) assert_raises(AssertionError, assert_array_equal, epochs_clean.get_data(), this_epoch.get_data())
event_fname = data_path + ('/MEG/sample/sample_audvis_filt-0-40_raw-' 'eve.fif') event_id = {'Auditory/Left': 1} tmin, tmax = -0.2, 0.5 events = mne.read_events(event_fname) include = [] picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=False, include=include, exclude='bads') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=None, verbose=False, detrend=True) epochs.load_data() epochs_gt = clean_by_interp(epochs) picks = mne.pick_types(epochs.info, meg='grad', eeg=False, stim=False, eog=False, include=include, exclude='bads') X = epochs.get_data() X_gt = epochs_gt.get_data() X = np.concatenate((X, X_gt), axis=0) np.random.seed(42) cv = KFold(X.shape[0], 10, random_state=42) low, high = 4e-13, 900e-13 best_threshes = np.zeros((len(picks), )) for idx, pick in enumerate(picks): est = ChannelAutoReject() param_dist = dict(thresh=uniform(low, high))