def test_lcmv_ctf_comp():
"""Test interpolation with compensated CTF data."""
ctf_dir = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
raw = mne.io.read_raw_ctf(raw_fname, preload=True)
events = mne.make_fixed_length_events(raw, duration=0.2)[:2]
epochs = mne.Epochs(raw, events, tmin=-0.1, tmax=0.2)
evoked = epochs.average()
with pytest.warns(RuntimeWarning,
match='Too few samples .* estimate may be unreliable'):
data_cov = mne.compute_covariance(epochs)
fwd = mne.make_forward_solution(evoked.info, None,
mne.setup_volume_source_space(pos=15.0),
mne.make_sphere_model())
with pytest.raises(ValueError, match='reduce_rank'):
make_lcmv(evoked.info, fwd, data_cov)
filters = make_lcmv(evoked.info, fwd, data_cov, reduce_rank=True)
assert 'weights' in filters
# test whether different compensations throw error
info_comp = evoked.info.copy()
set_current_comp(info_comp, 1)
with pytest.raises(RuntimeError, match='Compensation grade .* not match'):
make_lcmv(info_comp, fwd, data_cov)
def test_lcmv_ctf_comp():
"""Test interpolation with compensated CTF data."""
raw = mne.io.read_raw_ctf(ctf_fname, preload=True)
raw.pick(raw.ch_names[:70])
events = mne.make_fixed_length_events(raw, duration=0.2)[:2]
epochs = mne.Epochs(raw, events, tmin=-0.1, tmax=0.2)
evoked = epochs.average()
data_cov = mne.compute_covariance(epochs)
fwd = mne.make_forward_solution(evoked.info, None,
mne.setup_volume_source_space(pos=30.0),
mne.make_sphere_model())
with pytest.raises(ValueError, match='reduce_rank'):
make_lcmv(evoked.info, fwd, data_cov)
filters = make_lcmv(evoked.info, fwd, data_cov, reduce_rank=True)
assert 'weights' in filters
# test whether different compensations throw error
info_comp = evoked.info.copy()
set_current_comp(info_comp, 1)
with pytest.raises(RuntimeError, match='Compensation grade .* not match'):
make_lcmv(info_comp, fwd, data_cov)
def test_lcmv_ctf_comp():
"""Test interpolation with compensated CTF data."""
ctf_dir = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
raw = mne.io.read_raw_ctf(raw_fname, preload=True)
events = mne.make_fixed_length_events(raw, duration=0.2)[:2]
epochs = mne.Epochs(raw, events, tmin=0., tmax=0.2)
evoked = epochs.average()
with pytest.warns(RuntimeWarning,
match='Too few samples .* estimate may be unreliable'):
data_cov = mne.compute_covariance(epochs)
fwd = mne.make_forward_solution(evoked.info, None,
mne.setup_volume_source_space(pos=15.0),
mne.make_sphere_model())
filters = make_lcmv(evoked.info, fwd, data_cov)
assert 'weights' in filters
# test whether different compensations throw error
info_comp = evoked.info.copy()
set_current_comp(info_comp, 1)
with pytest.raises(RuntimeError, match='Compensation grade .* not match'):
make_lcmv(info_comp, fwd, data_cov)
