def make_raw(self, data_duration=None, apply_ica=True, first_samp=0,
verbose=None):
"""Create instance of mne.io.RawArray.
Parameters
----------
data_duration : int, float
Duration of previous data to use. If data=10, returns instance of
mne.io.RawArray of the previous 10 seconds of data.
apply_ica : bool (defaults to True)
If True and self.ica has been fitted, will apply the ICA to the
requested data. If False, will not fit ICA to the data.
first_samp : int (defaults to 0)
Sample offset.
Returns
-------
raw : mne.io.RawArray
The EEG data.
"""
raw_data = self.get_data(data_duration=data_duration)
raw_data[-1, :] = 0 # Make row of timestamps a row of events 0.
if not apply_ica:
return io.RawArray(raw_data, self.info, first_samp=first_samp,
verbose=verbose)
elif apply_ica and self.ica.current_fit == 'unfitted':
return io.RawArray(raw_data, self.info, first_samp=first_samp,
verbose=verbose)
elif apply_ica and self.ica.current_fit != 'unfitted':
raw = io.RawArray(raw_data, self.info, first_samp=first_samp,
verbose=verbose)
return self.ica.apply(raw)
def test_interpolation_meg():
"""Test interpolation of MEG channels."""
# speed accuracy tradeoff: channel subselection is faster but the
# correlation drops
thresh = 0.68
raw, epochs_meg = _load_data('meg')
# check that interpolation works when non M/EEG channels are present
# before MEG channels
raw.crop(0, 0.1).load_data().pick_channels(epochs_meg.ch_names)
raw.info.normalize_proj()
with pytest.warns(RuntimeWarning, match='unit .* changed from .* to .*'):
raw.set_channel_types({raw.ch_names[0]: 'stim'})
raw.info['bads'] = [raw.ch_names[1]]
raw.load_data()
raw.interpolate_bads(mode='fast')
del raw
# check that interpolation works for MEG
epochs_meg.info['bads'] = ['MEG 0141']
evoked = epochs_meg.average()
pick = pick_channels(epochs_meg.info['ch_names'], epochs_meg.info['bads'])
# MEG -- raw
raw_meg = io.RawArray(data=epochs_meg._data[0], info=epochs_meg.info)
raw_meg.info['bads'] = ['MEG 0141']
data1 = raw_meg[pick, :][0][0]
raw_meg.info.normalize_proj()
data2 = raw_meg.interpolate_bads(reset_bads=False,
mode='fast')[pick, :][0][0]
assert np.corrcoef(data1, data2)[0, 1] > thresh
# the same number of bads as before
assert len(raw_meg.info['bads']) == len(raw_meg.info['bads'])
# MEG -- epochs
data1 = epochs_meg.get_data()[:, pick, :].ravel()
epochs_meg.info.normalize_proj()
epochs_meg.interpolate_bads(mode='fast')
data2 = epochs_meg.get_data()[:, pick, :].ravel()
assert np.corrcoef(data1, data2)[0, 1] > thresh
assert len(epochs_meg.info['bads']) == 0
# MEG -- evoked (plus auto origin)
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads(origin='auto').data[pick]
assert np.corrcoef(data1, data2)[0, 1] > thresh
# MEG -- with exclude
evoked.info['bads'] = ['MEG 0141', 'MEG 0121']
pick = pick_channels(evoked.ch_names, evoked.info['bads'], ordered=True)
evoked.data[pick[-1]] = 1e10
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads(origin='auto',
exclude=['MEG 0121']).data[pick]
assert np.corrcoef(data1[0], data2[0])[0, 1] > thresh
assert np.all(data2[1] == 1e10)
def make_epochs(self,
marker_stream,
end_index,
marker_end,
trial_num,
data_duration=None,
events=None,
filter_range: tuple=(0.5, 15),
event_duration=0, event_id=None, tmin=-0.2, tmax=1.0, baseline=(None, 0), picks=None, preload=False,
reject=None, flat=None, proj=True, decim=1, reject_tmin=None, reject_tmax=None, detrend=None,
on_missing='error', reject_by_annotation=False, verbose=None):
"""Create instance of mne.Epochs. If events are not supplied, this script must be connected to a Markers stream.
Args:
marker_stream: stream_rt.MarkerStream; stream of marker data.
end_index: Last index in data that is included.
marker_end: index of the last marker in a set of trials
trial_num: number of trials in a set
data_duration: int, float; duration of previous data to use. If data=10, returns instance of mne.Epochs of
the previous 10 seconds of data.
events: ndarray; array of events of the shape (n_events, 3).
filter_range: tuple containing bandpass filter range in Hz
everything else: Copy parameters from mne.Epochs.
Returns:
epochs: mne.Epochs; contains time segments of data_duration after stimuli for training and prediciton.
identities: list containing the row/column that was flashed in order.
targets: list containing target values for training; i.e. 0 or 1.
"""
targets = []
raw_data = self.get_data(end_index, data_duration=data_duration)
if events is None:
events, targets = make_events(raw_data, marker_stream, marker_end, trial_num, event_duration)
# Replace timestamps with zeros.
raw_data[-1, :] = 0
# Create raw array
raw = io.RawArray(raw_data, self.info)
# Populate events in event channel
raw.add_events(events, self.event_channel_name)
if targets[0][0] == 0:
event_id = {'Non-Target': 0}
else:
event_id = {'Target': 1}
# bandpass filter the data between two ends of the filter_range
raw_filter(raw, filter_range[0], filter_range[1], picks=[0, 1, 2, 3])
return Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
baseline=baseline, picks=picks,
preload=preload, reject=reject, flat=flat, proj=proj, decim=decim,
reject_tmin=reject_tmin,
reject_tmax=reject_tmax, detrend=detrend, on_missing=on_missing,
reject_by_annotation=reject_by_annotation,
verbose=verbose), targets
def test_interplation():
"""Test interpolation"""
epochs.info['bads'] = []
goods_idx = np.ones(len(epochs.ch_names), dtype=bool)
goods_idx[epochs.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked = epochs.average()
ave_before = evoked.data[bads_idx]
pos = epochs.get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert_equal(interpolation.shape, (1, len(epochs.ch_names) - 1))
ave_after = np.dot(interpolation, evoked.data[goods_idx])
assert_allclose(ave_before, ave_after, atol=2e-6)
epochs.info['bads'] = []
assert_raises(ValueError, epochs.interpolate_bads_eeg)
epochs.info['bads'] = ['EEG 012']
epochs.preload = False
assert_raises(ValueError, epochs.interpolate_bads_eeg)
epochs.preload = True
epochs2.info['bads'] = ['EEG 012', 'MEG 1711']
epochs2.interpolate_bads_eeg()
ave_after2 = epochs2.average().data[n_meg + np.where(bads_idx)[0]]
assert_array_almost_equal(ave_after, ave_after2, decimal=16)
raw = io.RawArray(data=epochs._data[0], info=epochs.info)
raw_before = raw._data[bads_idx]
raw.interpolate_bads_eeg()
raw_after = raw._data[bads_idx]
assert_equal(np.all(raw_before == raw_after), False)
evoked = epochs.average()
evoked.interpolate_bads_eeg()
assert_array_equal(ave_after, evoked.data[bads_idx])
for inst in [raw, epochs]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
assert_raises(ValueError, inst.interpolate_bads_eeg)
def make_epochs(self, marker_stream, data_duration=None, events=None,
event_duration=0, event_id=None, apply_ica=True, tmin=-0.2,
tmax=1.0, baseline=(None, 0), picks=None, name='Unknown',
preload=False, reject=None, flat=None, proj=True, decim=1,
reject_tmin=None, reject_tmax=None, detrend=None,
add_eeg_ref=None, on_missing='error',
reject_by_annotation=True, verbose=None):
"""Create instance of mne.Epochs. If events are not supplied, this
script must be connected to a Markers stream.
Parameters
----------
marker_stream : rteeg.MarkerStream
Stream of marker data.
data_duration : int, float
Duration of previous data to use. If data=10, returns instance of
mne.Epochs of the previous 10 seconds of data.
events : ndarray
Array of events of the shape (n_events, 3)
Copy parameters from mne.Epochs
apply_ica : bool (defaults to True)
If True and if self.ica has been fitted, will apply the ICA to the
requested data. If False, will not fit ICA to the data.
Returns
-------
epochs : mne.Epochs
"""
raw_data = self.get_data(data_duration=data_duration)
if events is None:
events = make_events(raw_data, marker_stream, event_duration)
raw_data[-1, :] = 0 # Replace timestamps with zeros.
raw = io.RawArray(raw_data, self.info)
# If user wants to apply ICA and if ICA has been fitted ...
if apply_ica and self.ica.current_fit != 'unfitted':
raw = self.ica.apply(raw)
# Should this be changed? ICA.apply() works in-place on raw.
return Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
baseline=baseline, picks=picks, name=name,
preload=preload, reject=reject, flat=flat, proj=proj,
decim=decim, reject_tmin=reject_tmin,
reject_tmax=reject_tmax, detrend=detrend,
add_eeg_ref=add_eeg_ref, on_missing=on_missing,
reject_by_annotation=reject_by_annotation,
verbose=verbose)
def test_interpolation_eeg(offset, avg_proj, ctol, atol, method):
"""Test interpolation of EEG channels."""
raw, epochs_eeg = _load_data('eeg')
epochs_eeg = epochs_eeg.copy()
assert not _has_eeg_average_ref_proj(epochs_eeg.info['projs'])
# Offsetting the coordinate frame should have no effect on the output
for inst in (raw, epochs_eeg):
for ch in inst.info['chs']:
if ch['kind'] == io.constants.FIFF.FIFFV_EEG_CH:
ch['loc'][:3] += offset
ch['loc'][3:6] += offset
for d in inst.info['dig']:
d['r'] += offset
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
kw = dict(method=method)
with pytest.warns(RuntimeWarning, match='Doing nothing'):
evoked_eeg.interpolate_bads(**kw)
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
pos = epochs_eeg._get_channel_positions()
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
assert_allclose(evoked_eeg.data.mean(0), 0., atol=1e-20)
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
good_picks = pick_types(evoked_eeg.info, meg=False, eeg=True)
assert_allclose(evoked_eeg.data[good_picks].mean(0), 0., atol=1e-20)
evoked_eeg_bad = evoked_eeg.copy()
bads_picks = pick_channels(epochs_eeg.ch_names,
include=epochs_eeg.info['bads'],
ordered=True)
evoked_eeg_bad.data[bads_picks, :] = 1e10
# Test first the exclude parameter
evoked_eeg_2_bads = evoked_eeg_bad.copy()
evoked_eeg_2_bads.info['bads'] = ['EEG 004', 'EEG 012']
evoked_eeg_2_bads.data[pick_channels(evoked_eeg_bad.ch_names,
['EEG 004', 'EEG 012'])] = 1e10
evoked_eeg_interp = evoked_eeg_2_bads.interpolate_bads(origin=(0., 0., 0.),
exclude=['EEG 004'],
**kw)
assert evoked_eeg_interp.info['bads'] == ['EEG 004']
assert np.all(evoked_eeg_interp.get_data('EEG 004') == 1e10)
assert np.all(evoked_eeg_interp.get_data('EEG 012') != 1e10)
# Now test without exclude parameter
evoked_eeg_bad.info['bads'] = ['EEG 012']
evoked_eeg_interp = evoked_eeg_bad.copy().interpolate_bads(origin=(0., 0.,
0.),
**kw)
if avg_proj:
assert_allclose(evoked_eeg_interp.data.mean(0), 0., atol=1e-6)
interp_zero = evoked_eeg_interp.data[bads_idx]
if method is None: # using
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
interp_manual = np.dot(interpolation, evoked_eeg_bad.data[goods_idx])
assert_array_equal(interp_manual, interp_zero)
del interp_manual, interpolation, pos, pos_good, pos_bad
assert_allclose(ave_before, interp_zero, atol=atol)
assert ctol[0] < np.corrcoef(ave_before, interp_zero)[0, 1] < ctol[1]
interp_fit = evoked_eeg_bad.copy().interpolate_bads(**kw).data[bads_idx]
assert_allclose(ave_before, interp_fit, atol=2.5e-6)
assert ctol[1] < np.corrcoef(ave_before, interp_fit)[0, 1] # better
# check that interpolation fails when preload is False
epochs_eeg.preload = False
with pytest.raises(RuntimeError, match='requires epochs data to be loade'):
epochs_eeg.interpolate_bads(**kw)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads(**kw)._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs_eeg]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
with pytest.raises(RuntimeError, match='requires.*data to be loaded'):
inst.interpolate_bads(**kw)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with _record_warnings() as w:
raw_few.interpolate_bads(reset_bads=False, **kw)
assert len([ww for ww in w if 'more than' not in str(ww.message)]) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.2
def test_interpolation():
"""Test interpolation"""
raw, epochs, epochs_eeg, epochs_meg = _load_data()
# It's a trade of between speed and accuracy. If every second channel is
# selected the tests are more than 3x faster but the correlation
# drops to 0.8
thresh = 0.80
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked_eeg = epochs_eeg.average()
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
pos = epochs_eeg._get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert_equal(interpolation.shape, (1, len(epochs_eeg.ch_names) - 1))
ave_after = np.dot(interpolation, evoked_eeg.data[goods_idx])
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
assert_array_equal(ave_after, evoked_eeg.interpolate_bads().data[bads_idx])
assert_allclose(ave_before, ave_after, atol=2e-6)
# check that interpolation fails when preload is False
epochs_eeg.preload = False
assert_raises(ValueError, epochs_eeg.interpolate_bads)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads()._data[bads_idx]
assert_equal(np.all(raw_before == raw_after), False)
# check that interpolation fails when preload is False
for inst in [raw, epochs]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
assert_raises(ValueError, inst.interpolate_bads)
# check that interpolation works for MEG
epochs_meg.info['bads'] = ['MEG 0141']
evoked = epochs_meg.average()
pick = pick_channels(epochs_meg.info['ch_names'], epochs_meg.info['bads'])
# MEG -- raw
raw_meg = io.RawArray(data=epochs_meg._data[0], info=epochs_meg.info)
raw_meg.info['bads'] = ['MEG 0141']
data1 = raw_meg[pick, :][0][0]
# reset_bads=False here because epochs_meg appears to share the same info
# dict with raw and we want to test the epochs functionality too
raw_meg.info.normalize_proj()
data2 = raw_meg.interpolate_bads(reset_bads=False)[pick, :][0][0]
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
# the same number of bads as before
assert_true(len(raw_meg.info['bads']) == len(raw_meg.info['bads']))
# MEG -- epochs
data1 = epochs_meg.get_data()[:, pick, :].ravel()
epochs_meg.info.normalize_proj()
epochs_meg.interpolate_bads()
data2 = epochs_meg.get_data()[:, pick, :].ravel()
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
assert_true(len(raw_meg.info['bads']) == 0)
# MEG -- evoked
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads().data[pick]
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
def test_interpolation_eeg():
"""Test interpolation of EEG channels."""
raw, epochs_eeg = _load_data('eeg')
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
with pytest.warns(RuntimeWarning, match='Doing nothing'):
evoked_eeg.interpolate_bads()
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked_eeg = epochs_eeg.average()
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
pos = epochs_eeg._get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
ave_after = np.dot(interpolation, evoked_eeg.data[goods_idx])
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
assert_array_equal(ave_after, evoked_eeg.interpolate_bads().data[bads_idx])
assert_allclose(ave_before, ave_after, atol=2e-6)
# check that interpolation fails when preload is False
epochs_eeg.preload = False
pytest.raises(RuntimeError, epochs_eeg.interpolate_bads)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads()._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs_eeg]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
pytest.raises(RuntimeError, inst.interpolate_bads)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with pytest.warns(None) as w:
raw_few.interpolate_bads(reset_bads=False)
assert len(w) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.1
def test_interpolation_eeg(offset):
"""Test interpolation of EEG channels."""
raw, epochs_eeg = _load_data('eeg')
epochs_eeg = epochs_eeg.copy()
# Offsetting the coordinate frame should have no effect on the output
for inst in (raw, epochs_eeg):
for ch in inst.info['chs']:
if ch['kind'] == io.constants.FIFF.FIFFV_EEG_CH:
ch['loc'][:3] += offset
ch['loc'][3:6] += offset
for d in inst.info['dig']:
d['r'] += offset
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
with pytest.warns(RuntimeWarning, match='Doing nothing'):
evoked_eeg.interpolate_bads()
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked_eeg = epochs_eeg.average()
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
pos = epochs_eeg._get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
interp_manual = np.dot(interpolation, evoked_eeg.data[goods_idx])
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
interp_zero = evoked_eeg.interpolate_bads(
origin=(0., 0., 0.)).data[bads_idx]
assert_array_equal(interp_manual, interp_zero)
assert_allclose(ave_before, interp_zero, atol=3e-6)
assert 0.985 < np.corrcoef(ave_before, interp_zero)[0, 1] < 0.99
evoked_eeg.info['bads'] = ['EEG 012']
interp_fit = evoked_eeg.interpolate_bads().data[bads_idx]
assert_allclose(ave_before, interp_fit, atol=2e-6)
assert 0.99 < np.corrcoef(ave_before, interp_fit)[0, 1] # better
# check that interpolation fails when preload is False
epochs_eeg.preload = False
pytest.raises(RuntimeError, epochs_eeg.interpolate_bads)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads()._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs_eeg]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
pytest.raises(RuntimeError, inst.interpolate_bads)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with pytest.warns(None) as w:
raw_few.interpolate_bads(reset_bads=False)
assert len([ww for ww in w if 'more than' not in str(ww.message)]) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.2
def test_sns():
"""Test sensor noise suppression"""
# artificial (IID) data
data = np.random.RandomState(0).randn(102, 5000)
info = create_info(len(data), 1000., 'mag')
raw = io.RawArray(data, info)
assert_raises(ValueError, SensorNoiseSuppression, 'foo')
assert_raises(TypeError, SensorNoiseSuppression(10).fit, 'foo')
assert_raises(ValueError, SensorNoiseSuppression, -1)
raw.info['bads'] = [raw.ch_names[1]]
assert_raises(ValueError, SensorNoiseSuppression(101).fit, raw)
for n_neighbors, bounds in ((2, (17, 20)),
(5, (11, 15),),
(10, (9, 12)),
(20, (7, 10)),
(50, (5, 9)),
(100, (5, 8)),
):
sns = SensorNoiseSuppression(n_neighbors)
sns.fit(raw)
raw_sns = sns.apply(raw.copy())
operator = sns.operator
# bad channels are modified but not used
assert_allclose(operator[:, 1], 0.)
assert_true(np.sum(np.abs(operator)) > 0)
assert_equal(operator[0].astype(bool).sum(), n_neighbors)
assert_allclose(np.diag(operator), 0.)
picks = pick_types(raw.info)
orig_power = np.linalg.norm(raw[picks][0])
# Test the suppression factor
factor = orig_power / np.linalg.norm(raw_sns[picks][0])
assert_true(bounds[0] < factor < bounds[1],
msg='%s: %s < %s < %s'
% (n_neighbors, bounds[0], factor, bounds[1]))
# degenerate conditions
assert_raises(TypeError, sns.apply, 'foo')
sub_raw = raw.copy().pick_channels(raw.ch_names[:-1])
assert_raises(RuntimeError, sns.apply, sub_raw) # not all orig chs
sub_sns = SensorNoiseSuppression(8)
sub_sns.fit(sub_raw)
assert_raises(RuntimeError, sub_sns.apply, raw) # not all new chs
# sample data
raw = io.read_raw_fif(raw_fname)
n_neighbors = 8
sns = SensorNoiseSuppression(n_neighbors=n_neighbors)
sns.fit(raw)
raw_sns = sns.apply(raw.copy().load_data())
operator = sns.operator
# bad channels are modified
assert_equal(len(raw.info['bads']), 2)
for pick in pick_channels(raw.ch_names, raw.info['bads']):
expected = np.zeros(operator.shape[0])
sub_pick = sns._used_chs.index(raw.ch_names[pick])
expected[sub_pick] = 1.
assert_raises(AssertionError, assert_allclose, operator[sub_pick],
expected)
assert_raises(AssertionError, assert_allclose, raw[pick][0],
raw_sns[pick][0])
assert_equal(operator[0].astype(bool).sum(), n_neighbors)
assert_equal(operator[0, 0], 0.)
picks = pick_types(raw.info)
orig_power = np.linalg.norm(raw[picks][0])
# Test the suppression factor
factor = orig_power / np.linalg.norm(raw_sns[picks][0])
bounds = (1.3, 1.7)
assert_true(bounds[0] < factor < bounds[1],
msg='%s: %s < %s < %s'
% (n_neighbors, bounds[0], factor, bounds[1]))
# degenerate conditions
assert_raises(RuntimeError, sns.apply, raw) # not preloaded
# Test against NoiseTools
rng = np.random.RandomState(0)
n_channels = 9
x = rng.randn(n_channels, 10000)
# make some correlations
x = np.dot(rng.randn(n_channels, n_channels), x)
x -= x.mean(axis=-1, keepdims=True)
# savemat('test.mat', dict(x=x)) # --> run through nt_sns in MATLAB
nt_op = np.array([ # Obtained from NoiseTools 18-Nov-2016
[0, 0, -0.3528, 0, 0.6152, 0, 0, -0.3299, 0.1914],
[0, 0, 0, 0.0336, 0, 0, -0.4284, 0, 0],
[-0.2928, 0.2463, 0, 0, -0.0891, 0, 0, 0.2200, 0],
[0, 0.0191, 0, 0, -0.3756, -0.3253, 0.4047, -0.4608, 0],
[0.3184, 0, -0.0878, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.5865, 0, -0.2137],
[0, -0.5190, 0, 0.5059, 0, 0.8271, 0, 0, -0.0771],
[-0.3953, 0, 0.3092, -0.5018, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, -0.2050, 0, 0, 0]]).T
raw = RawArray(x, create_info(n_channels, 1000.,
['grad', 'grad', 'mag'] * 3))
sns = SensorNoiseSuppression(3)
sns.fit(raw)
# this isn't perfect, but it's close
np.testing.assert_allclose(sns.operator, nt_op, rtol=5e-2, atol=1e-3)
def to_mne_eeg(eegstream=None,
line_freq=None,
filenames=None,
nasion=None,
lpa=None,
rpa=None):
'''Convert recordings to MNE format.
Args:
eegstream : array
EEG streams previously imported.
line_freq : int
Powerline frequency (50 or 60).
filenames : array
Full path of XDF files. Used for recording identification.
nasion : array, shape(3,)
Position of the nasion fiducial point.
If specified, the array must have the same lenght of eegstream.
Format for every recording (X, Y, Z) in meters: [0,0,0]
lpa : array, shape(3,)
Position of the left periauricular fiducial point.
If specified, the array must have the same lenght of eegstream.
Format for every recording (X, Y, Z) in meters: [0,0,0]
rpa : array, shape(3,)
Position of the right periauricular fiducial point.
If specified, the array must have the same lenght of eegstream.
Format for every recording (X, Y, Z) in meters: [0,0,0]
Returns:
Array of MNE RawArray instances with the recordings specified in eegstream.
Raises:
ValueError: if no stream is specified in eegstream or powerline frequency is not 50 or 60.
See also:
read_raw_xdf
read_raw_xdf_dir
'''
if eegstream is None:
raise (ValueError('Enter parameter array of EEG recordings.'))
if line_freq is None or line_freq not in [50, 60]:
raise (ValueError(
'Enter the powerline frequency of your region (50 Hz or 60 Hz).'))
eegstream = [eegstream] if not isinstance(eegstream, list) else eegstream
raweeg = []
# Get the names of the channels
ch_names = [
eegstream[0]['info']['desc'][0]['channels'][0]['channel'][i]['label']
[0] for i in range(len(eegstream[0]['time_series'][0]))
]
# Define sensor coordinates
sensor_coord = [[-0.0856192, -0.0465147, -0.0457070],
[-0.0548397, 0.0685722, -0.0105900],
[0.0557433, 0.0696568, -0.0107550],
[0.0861618, -0.0470353, -0.0458690]]
for index, stream in enumerate(eegstream):
# Get channels position
dig_montage = channels.make_dig_montage(
ch_pos=dict(zip(ch_names, sensor_coord)),
nasion=nasion[index] if nasion is not None else None,
lpa=lpa[index] if lpa is not None else None,
rpa=rpa[index] if rpa is not None else None,
coord_frame='head')
# Create raw info for processing
info = create_info(ch_names=dig_montage.ch_names,
sfreq=float(stream['info']['nominal_srate'][0]),
ch_types='eeg')
# Add channels position to info
info.set_montage(dig_montage)
# Convert data from microvolts to volts
conv_data = stream["time_series"] * 1e-6
# Reorder channels
ord_data = [[sublist[1][item] for item in [1, 2, 3, 0]]
for sublist in enumerate(conv_data)]
# Create raw data for mne
raw = io.RawArray(np.array(ord_data).T, info)
# Get the information of each stream
stream_info = stream['info']['name'][0][:9] + ' ' + (
filenames[index] if filenames is not None else '')
# Print the information of each stream
print('\nInfo: ' + str(index) + ' ' + stream_info + '\n')
# Add the powerline frequency of each stream
raw.info['line_freq'] = line_freq
# Create annotation to store the name of the device and the filenames
annotations = Annotations(0, 0, stream_info)
# Add the annotations to raw data
raw.set_annotations(annotations)
# Add the RawArray object to list of eeg recordings
raweeg.append(raw)
return raweeg
#%% # for i, ch in enumerate(raw_edf.ch_names): # if i == 10: # print(ch) # plt.plot(raw_edf[ch][0][0]) # continue # plt.show() # ch_names = raw_edf.ch_names # ch_types = ['eeg'] * len(ch_names) # info = mne.create_info(ch_names=ch_names, sfreq=fs, ch_types=ch_types) #raw_edf.plot(scalings="auto", alpha=0.7) #%% Using the .easy raw_easy = pd.read_csv(data_path + ".easy", sep='\t', names=raw_edf.ch_names) data = raw_easy.values.T filtered_data = bandpass(data=data, f1=1, f2=40, fs=fs, axis=1) # for i, row in enumerate(filtered_data): # plt.plot(row, alpha=0.7, label=i) ch_names = raw_edf.ch_names ch_types = ['eeg'] * len(ch_names) info = mne.create_info(ch_names=ch_names, sfreq=fs, ch_types=ch_types) raw_mne = io.RawArray(raw_easy.values.T, info) raw_edf.plot(scalings="auto")
def test_ssd():
"""Test Common Spatial Patterns algorithm on raw data."""
X, A, S = simulate_data()
sf = 250
n_channels = X.shape[0]
info = create_info(ch_names=n_channels, sfreq=sf, ch_types='eeg')
n_components_true = 5
# Init
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info, filt_params_signal, filt_params_noise)
# freq no int
freq = 'foo'
filt_params_signal = dict(l_freq=freq,
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
with pytest.raises(TypeError, match='must be an instance '):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# Wrongly specified noise band
freq = 2
filt_params_signal = dict(l_freq=freq,
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
with pytest.raises(ValueError, match='Wrongly specified '):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# filt param no dict
filt_params_signal = freqs_sig
filt_params_noise = freqs_noise
with pytest.raises(ValueError, match='must be defined'):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# Data type
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info, filt_params_signal, filt_params_noise)
raw = io.RawArray(X, info)
pytest.raises(TypeError, ssd.fit, raw)
# More than 1 channel type
ch_types = np.reshape([['mag'] * 10, ['eeg'] * 10], n_channels)
info_2 = create_info(ch_names=n_channels, sfreq=sf, ch_types=ch_types)
with pytest.raises(ValueError, match='At this point SSD'):
ssd = SSD(info_2, filt_params_signal, filt_params_noise)
# Number of channels
info_3 = create_info(ch_names=n_channels + 1, sfreq=sf, ch_types='eeg')
ssd = SSD(info_3, filt_params_signal, filt_params_noise)
pytest.raises(ValueError, ssd.fit, X)
# Fit
n_components = 10
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=n_components)
# Call transform before fit
pytest.raises(AttributeError, ssd.transform, X)
# Check outputs
ssd.fit(X)
assert (ssd.filters_.shape == (n_channels, n_channels))
assert (ssd.patterns_.shape == (n_channels, n_channels))
# Transform
X_ssd = ssd.fit_transform(X)
assert (X_ssd.shape[0] == n_components)
# back and forward
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=None,
sort_by_spectral_ratio=False)
ssd.fit(X)
X_denoised = ssd.inverse_transform(X)
assert_array_almost_equal(X_denoised, X)
# Power ratio ordering
spec_ratio, _ = ssd.get_spectral_ratio(ssd.transform(X))
# since we now that the number of true components is 5, the relative
# difference should be low for the first 5 components and then increases
index_diff = np.argmax(-np.diff(spec_ratio))
assert index_diff == n_components_true - 1
# Check detected peaks
# fit ssd
n_components = n_components_true
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=n_components,
sort_by_spectral_ratio=False)
ssd.fit(X)
out = ssd.transform(X)
psd_out, _ = psd_array_welch(out[0], sfreq=250, n_fft=250)
psd_S, _ = psd_array_welch(S[0], sfreq=250, n_fft=250)
corr = np.abs(np.corrcoef((psd_out, psd_S))[0, 1])
assert np.abs(corr) > 0.95
# Check pattern estimation
# Since there is no exact ordering of the recovered patterns
# a pair-wise greedy search will be done
error = list()
for ii in range(n_channels):
corr = np.abs(np.corrcoef(ssd.patterns_[ii, :].T, A[:, 0])[0, 1])
error.append(1 - corr)
min_err = np.min(error)
assert min_err < 0.3 # threshold taken from SSD original paper
def make_epochs(self,
marker_stream,
end_index,
data_duration=None,
events=None,
event_duration=0,
event_id=None,
tmin=-0.2,
tmax=1.0,
baseline=(None, 0),
picks=None,
preload=False,
reject=None,
flat=None,
proj=True,
decim=1,
reject_tmin=None,
reject_tmax=None,
detrend=None,
on_missing='error',
reject_by_annotation=False,
verbose=None):
"""Create instance of mne.Epochs. If events are not supplied, this script must be connected to a Markers stream.
Args:
marker_stream: stream_rt.MarkerStream; stream of marker data.
data_duration: int, float; duration of previous data to use. If data=10, returns instance of mne.Epochs of the
previous 10 seconds of data.
end_index: Last index in data that is included.
events: ndarray; array of events of the shape (n_events, 3).
everything else: Copy parameters from mne.Epochs.
Returns:
epochs: mne.Epochs; contains time segments of data_duration after stimuli for training and prediciton.
identities: list containing the row/column that was flashed in order.
targets: list containing target values for training; i.e. 0 or 1.
"""
identities = []
targets = []
raw_data = self.get_data(end_index, data_duration=data_duration)
if events is None:
events, identities, targets = make_events(raw_data, marker_stream,
event_duration)
# Replace timestamps with zeros.
raw_data[-1, :] = 0
# Create raw array
raw = io.RawArray(raw_data, self.info)
# Populate events in event channel
raw.add_events(events, 'P300')
event_id = {'Non-Target': 0, 'Target': 1}
# Plot power spectral density
# plot_psd(raw, 'std', picks=[0, 1, 2, 3])
# Notch filter
# notch_filter(raw, np.arange(50, 101, 50), picks=[0, 1, 2, 3])
# filter the data between 0.5 and 15 Hz
raw_filter(raw, 0.5, 15, picks=[0, 1, 2, 3])
# plot raw data for visualization/validation
plot(raw,
events,
duration=data_duration,
n_channels=5,
scalings='auto')
return Epochs(raw,
events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
baseline=baseline,
picks=picks,
preload=preload,
reject=reject,
flat=flat,
proj=proj,
decim=decim,
reject_tmin=reject_tmin,
reject_tmax=reject_tmax,
detrend=detrend,
on_missing=on_missing,
reject_by_annotation=reject_by_annotation,
verbose=verbose), identities, targets
def test_interpolation():
"""Test interpolation"""
raw, epochs, epochs_eeg, epochs_meg = _load_data()
# It's a trade of between speed and accuracy. If every second channel is
# selected the tests are more than 3x faster but the correlation
# drops to 0.8
thresh = 0.80
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
with warnings.catch_warnings(record=True) as w:
evoked_eeg.interpolate_bads()
assert_true(any('Doing nothing' in str(ww.message) for ww in w))
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked_eeg = epochs_eeg.average()
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
pos = epochs_eeg._get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert_equal(interpolation.shape, (1, len(epochs_eeg.ch_names) - 1))
ave_after = np.dot(interpolation, evoked_eeg.data[goods_idx])
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
assert_array_equal(ave_after, evoked_eeg.interpolate_bads().data[bads_idx])
assert_allclose(ave_before, ave_after, atol=2e-6)
# check that interpolation fails when preload is False
epochs_eeg.preload = False
assert_raises(RuntimeError, epochs_eeg.interpolate_bads)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads()._data[bads_idx]
assert_equal(np.all(raw_before == raw_after), False)
# check that interpolation fails when preload is False
for inst in [raw, epochs]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
assert_raises(RuntimeError, inst.interpolate_bads)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert_equal(len(raw_few.ch_names), 2)
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with warnings.catch_warnings(record=True) as w:
raw_few.interpolate_bads(reset_bads=False)
assert len(w) == 0
new_data = raw_few[1][0]
assert_true((new_data == 0).mean() < 0.5)
assert_true(np.corrcoef(new_data, orig_data)[0, 1] > 0.1)
# check that interpolation works when non M/EEG channels are present
# before MEG channels
with warnings.catch_warnings(record=True): # change of units
raw.rename_channels({'MEG 0113': 'TRIGGER'})
raw.set_channel_types({'TRIGGER': 'stim'})
raw.info['bads'] = [raw.info['ch_names'][1]]
raw.load_data()
raw.interpolate_bads()
# check that interpolation works for MEG
epochs_meg.info['bads'] = ['MEG 0141']
evoked = epochs_meg.average()
pick = pick_channels(epochs_meg.info['ch_names'], epochs_meg.info['bads'])
# MEG -- raw
raw_meg = io.RawArray(data=epochs_meg._data[0], info=epochs_meg.info)
raw_meg.info['bads'] = ['MEG 0141']
data1 = raw_meg[pick, :][0][0]
raw_meg.info.normalize_proj()
data2 = raw_meg.interpolate_bads(reset_bads=False)[pick, :][0][0]
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
# the same number of bads as before
assert_true(len(raw_meg.info['bads']) == len(raw_meg.info['bads']))
# MEG -- epochs
data1 = epochs_meg.get_data()[:, pick, :].ravel()
epochs_meg.info.normalize_proj()
epochs_meg.interpolate_bads()
data2 = epochs_meg.get_data()[:, pick, :].ravel()
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
assert_true(len(epochs_meg.info['bads']) == 0)
# MEG -- evoked
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads().data[pick]
assert_true(np.corrcoef(data1, data2)[0, 1] > thresh)
def fit_ica(self, data, when='next', warm_start=False):
"""Conduct Independent Components Analysis (ICA) on a segment of data.
The fitted ICA object is stored in the variable ica. Noisy components
can be selected in the ICA, and then the ICA can be applied to incoming
data to remove noise. Once fitted, ICA is applied by default to data
when using the methods make_raw() or make_epochs().
Components marked for removal can be accessed with self.ica.exclude.
data : int, float, mne.RawArray
The duration of previous or incoming data to use to fit the ICA, or
an mne.RawArray object of data.
when : {'previous', 'next'} (defaults to 'next')
Whether to compute ICA on the previous or next X seconds of data.
Can be 'next' or 'previous'. If data is type mne.RawArray, this
parameter is ignored.
warm_start : bool (defaults to False)
If True, will include the EEG data from the previous fit. If False,
will only use the data specified in the parameter data.
"""
# Re-define ICA variable to start ICA from scratch if the ICA was
# already fitted and user wants to fit again.
if self.ica.current_fit != 'unfitted':
self.ica = ICA(method='extended-infomax')
if isinstance(data, io.RawArray):
self.raw_for_ica = data
elif isinstance(data, numbers.Number):
user_index = int(data * self.info['sfreq'])
if when.lower() not in ['previous', 'next']:
raise ValueError("when must be 'previous' or 'next'. {} was "
"passed.".format(when))
elif when == 'previous':
end_index = len(self.data)
start_index = end_index - user_index
# TODO: Check if out of bounds.
elif when == 'next':
start_index = len(self.data)
end_index = start_index + user_index
# Wait until the data is available.
pbar = ProgressBar(end_index - start_index,
mesg="Collecting data")
while len(self.data) <= end_index:
# Sometimes sys.stdout.flush() raises ValueError. Is it
# because the while loop iterates too quickly for I/O?
try:
pbar.update(len(self.data) - start_index)
except ValueError:
pass
print("") # Get onto new line after progress bar finishes.
_data = np.array([r[:] for r in
self.data[start_index:end_index]]).T
# Now we have the data array in _data. Use it to make instance of
# mne.RawArray, and then we can compute the ICA on that instance.
_data[-1, :] = 0
# Use previous data in addition to the specified data when fitting
# the ICA, if the user requested this.
if warm_start and self.raw_for_ica is not None:
self.raw_for_ica = concatenate_raws(
[self.raw_for_ica, io.RawArray(_data, self.info)])
else:
self.raw_for_ica = io.RawArray(_data, self.info)
logger.info("Computing ICA solution ...")
t_0 = local_clock()
self.ica.fit(self.raw_for_ica.copy()) # Fits in-place.
logger.info("Finished in {:.2f} s".format(local_clock() - t_0))
def test_interpolation():
"""Test interpolation."""
raw, epochs, epochs_eeg, epochs_meg = _load_data()
# speed accuracy tradeoff: channel subselection is faster but the
# correlation drops
thresh = 0.7
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
with pytest.warns(RuntimeWarning, match='Doing nothing'):
evoked_eeg.interpolate_bads()
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
evoked_eeg = epochs_eeg.average()
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
pos = epochs_eeg._get_channel_positions()
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
ave_after = np.dot(interpolation, evoked_eeg.data[goods_idx])
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
assert_array_equal(ave_after, evoked_eeg.interpolate_bads().data[bads_idx])
assert_allclose(ave_before, ave_after, atol=2e-6)
# check that interpolation fails when preload is False
epochs_eeg.preload = False
pytest.raises(RuntimeError, epochs_eeg.interpolate_bads)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads()._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
pytest.raises(RuntimeError, inst.interpolate_bads)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with pytest.warns(None) as w:
raw_few.interpolate_bads(reset_bads=False)
assert len(w) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.1
# check that interpolation works when non M/EEG channels are present
# before MEG channels
raw.rename_channels({'MEG 0113': 'TRIGGER'})
with pytest.warns(RuntimeWarning, match='unit .* changed from .* to .*'):
raw.set_channel_types({'TRIGGER': 'stim'})
raw.info['bads'] = [raw.info['ch_names'][1]]
raw.load_data()
raw.interpolate_bads(mode='fast')
# check that interpolation works for MEG
epochs_meg.info['bads'] = ['MEG 0141']
evoked = epochs_meg.average()
pick = pick_channels(epochs_meg.info['ch_names'], epochs_meg.info['bads'])
# MEG -- raw
raw_meg = io.RawArray(data=epochs_meg._data[0], info=epochs_meg.info)
raw_meg.info['bads'] = ['MEG 0141']
data1 = raw_meg[pick, :][0][0]
raw_meg.info.normalize_proj()
data2 = raw_meg.interpolate_bads(reset_bads=False,
mode='fast')[pick, :][0][0]
assert np.corrcoef(data1, data2)[0, 1] > thresh
# the same number of bads as before
assert len(raw_meg.info['bads']) == len(raw_meg.info['bads'])
# MEG -- epochs
data1 = epochs_meg.get_data()[:, pick, :].ravel()
epochs_meg.info.normalize_proj()
epochs_meg.interpolate_bads(mode='fast')
data2 = epochs_meg.get_data()[:, pick, :].ravel()
assert np.corrcoef(data1, data2)[0, 1] > thresh
assert len(epochs_meg.info['bads']) == 0
# MEG -- evoked (plus auto origin)
data1 = evoked.data[pick]
evoked.info.normalize_proj()
data2 = evoked.interpolate_bads(origin='auto').data[pick]
assert np.corrcoef(data1, data2)[0, 1] > thresh