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())
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])
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))
