def _gen_mne(active_cov, baseline_cov, common_cov, fwd, info, method='dSPM'):
inverse_operator = make_inverse_operator(info, fwd, common_cov)
stc_act = apply_inverse_cov(active_cov, info, inverse_operator,
method=method, verbose=True)
stc_base = apply_inverse_cov(baseline_cov, info, inverse_operator,
method=method, verbose=True)
stc_act /= stc_base
return stc_act
def test_apply_inverse_cov(method, pick_ori):
"""Test MNE with precomputed inverse operator on cov."""
raw = read_raw_fif(fname_raw, preload=True)
# use 10 sec of data
raw.crop(0, 10)
raw.filter(1, None)
label_lh = read_label(fname_label % 'Aud-lh')
# test with a free ori inverse
inverse_operator = read_inverse_operator(fname_inv)
data_cov = compute_raw_covariance(raw, tstep=None)
with pytest.raises(ValueError, match='has not been prepared'):
apply_inverse_cov(data_cov, raw.info, inverse_operator,
lambda2=lambda2, prepared=True)
this_inv_op = prepare_inverse_operator(inverse_operator, nave=1,
lambda2=lambda2, method=method)
raw_ori = 'normal' if pick_ori == 'normal' else 'vector'
stc_raw = apply_inverse_raw(
raw, this_inv_op, lambda2, method, label=label_lh, nave=1,
pick_ori=raw_ori, prepared=True)
stc_cov = apply_inverse_cov(
data_cov, raw.info, this_inv_op, method=method, pick_ori=pick_ori,
label=label_lh, prepared=True, lambda2=lambda2)
n_sources = np.prod(stc_cov.data.shape[:-1])
raw_data = stc_raw.data.reshape(n_sources, -1)
exp_res = np.diag(np.cov(raw_data, ddof=1)).copy()
exp_res *= 1 if raw_ori == pick_ori else 3.
# There seems to be some precision penalty when combining orientations,
# but it's probably acceptable
rtol = 5e-4 if pick_ori is None else 1e-12
assert_allclose(exp_res, stc_cov.data.ravel(), rtol=rtol)
with pytest.raises(ValueError, match='Invalid value'):
apply_inverse_cov(
data_cov, raw.info, this_inv_op, method=method, pick_ori='vector')
fwd = mne.read_forward_solution(fname_fwd)
# make an MEG inverse operator
info = evoked.info
inverse_operator = make_inverse_operator(info,
fwd,
noise_cov,
loose=0.2,
depth=0.8)
###############################################################################
# Project our data and baseline covariance to source space:
stc_data = apply_inverse_cov(data_cov,
evoked.info,
inverse_operator,
nave=len(epochs),
method='dSPM',
verbose=True)
stc_base = apply_inverse_cov(base_cov,
evoked.info,
inverse_operator,
nave=len(epochs),
method='dSPM',
verbose=True)
###############################################################################
# And visualize power is relative to the baseline:
stc_data /= stc_base
brain = stc_data.plot(subject='sample',
subjects_dir=subjects_dir,
clim=dict(kind='percent', lims=(50, 90, 98)))