def _gen_lcmv(active_cov, baseline_cov, common_cov):
filters = make_lcmv(epochs.info, fwd, common_cov, reg=0.05,
noise_cov=None, pick_ori='max-power')
stc_base = apply_lcmv_cov(baseline_cov, filters)
stc_act = apply_lcmv_cov(active_cov, filters)
stc_act /= stc_base
return stc_act
def test_lcmv_cov(weight_norm, pick_ori):
"""Test LCMV source power computation."""
raw, epochs, evoked, data_cov, noise_cov, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
convert_forward_solution(forward, surf_ori=True, copy=False)
filters = make_lcmv(evoked.info,
forward,
data_cov,
noise_cov=noise_cov,
weight_norm=weight_norm,
pick_ori=pick_ori)
for cov in (data_cov, noise_cov):
this_cov = pick_channels_cov(cov, evoked.ch_names)
this_evoked = evoked.copy().pick_channels(this_cov['names'])
this_cov['projs'] = this_evoked.info['projs']
assert this_evoked.ch_names == this_cov['names']
stc = apply_lcmv_cov(this_cov, filters)
assert stc.data.min() > 0
assert stc.shape == (498, 1)
ev = EvokedArray(this_cov.data, this_evoked.info)
stc_1 = apply_lcmv(ev, filters)
assert stc_1.data.min() < 0
ev = EvokedArray(stc_1.data.T, this_evoked.info)
stc_2 = apply_lcmv(ev, filters)
assert stc_2.data.shape == (498, 498)
data = np.diag(stc_2.data)[:, np.newaxis]
assert data.min() > 0
assert_allclose(data, stc.data, rtol=1e-12)
def test_lcmv_fieldtrip(_get_bf_data, bf_type, weight_norm, pick_ori, pwr):
"""Test LCMV vs fieldtrip output."""
evoked, data_cov, fwd = _get_bf_data
# run the MNE-Python beamformer
filters = make_lcmv(evoked.info,
fwd,
data_cov=data_cov,
noise_cov=None,
pick_ori=pick_ori,
reg=0.05,
weight_norm=weight_norm)
if pwr:
stc_mne = apply_lcmv_cov(data_cov, filters)
else:
stc_mne = apply_lcmv(evoked, filters)
# load the FieldTrip output
ft_fname = op.join(ft_data_path, 'ft_source_' + bf_type + '-vol.mat')
stc_ft_data = read_mat(ft_fname)['stc']
if stc_ft_data.ndim == 1:
stc_ft_data.shape = (stc_ft_data.size, 1)
if stc_mne.data.ndim == 2:
signs = np.sign((stc_mne.data * stc_ft_data).sum(-1, keepdims=True))
if pwr:
assert_array_equal(signs, 1.)
stc_mne.data *= signs
assert stc_ft_data.shape == stc_mne.data.shape
if pick_ori == 'vector':
# compare norms first
assert_allclose(np.linalg.norm(stc_mne.data, axis=1),
np.linalg.norm(stc_ft_data, axis=1),
rtol=1e-6)
assert_allclose(stc_mne.data, stc_ft_data, rtol=1e-6)
def test_lcmv_fieldtrip(_get_bf_data, bf_type, weight_norm, pick_ori, pwr):
"""Test LCMV vs fieldtrip output."""
evoked, data_cov, fwd = _get_bf_data
# run the MNE-Python beamformer
filters = make_lcmv(evoked.info,
fwd,
data_cov=data_cov,
noise_cov=None,
pick_ori=pick_ori,
reg=0.05,
weight_norm=weight_norm)
if pwr is True:
stc_mne = apply_lcmv_cov(data_cov, filters)
else:
stc_mne = apply_lcmv(evoked, filters)
# take the absolute value, since orientation can be flipped
stc_mne.data[:, :] = np.abs(stc_mne.data)
# load the FieldTrip output
ft_fname = op.join(ft_data_path, 'ft_source_' + bf_type + '-vol.stc')
stc_ft = mne.read_source_estimate(ft_fname)
# calculate the Pearson correlation between the source solutions:
pearson = np.corrcoef(stc_mne.data.ravel(), stc_ft.data.ravel())[0, 1]
assert pearson >= 0.99
def test_lcmv_fieldtrip(_get_bf_data, bf_type, weight_norm, pick_ori, pwr):
"""Test LCMV vs fieldtrip output."""
evoked, data_cov, fwd = _get_bf_data
# run the MNE-Python beamformer
filters = make_lcmv(evoked.info, fwd, data_cov=data_cov,
noise_cov=None, pick_ori=pick_ori, reg=0.05,
weight_norm=weight_norm)
if pwr is True:
stc_mne = apply_lcmv_cov(data_cov, filters)
else:
stc_mne = apply_lcmv(evoked, filters)
# take the absolute value, since orientation can be flipped
stc_mne.data[:, :] = np.abs(stc_mne.data)
# load the FieldTrip output
ft_fname = op.join(ft_data_path, 'ft_source_' + bf_type + '-vol.stc')
stc_ft = mne.read_source_estimate(ft_fname)
# calculate the Pearson correlation between the source solutions:
pearson = np.corrcoef(stc_mne.data.ravel(), stc_ft.data.ravel())[0, 1]
assert pearson >= 0.99
