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)