def test_orientation_prior(bias_params_free, method, looses, vmin, vmax,
nmin, nmax):
"""Test that orientation priors are handled properly."""
evoked, fwd, noise_cov, _, _ = bias_params_free
stcs = list()
vec_stc = None
for loose in looses:
inv = make_inverse_operator(evoked.info, fwd, noise_cov, loose=loose)
if looses[0] == 0.:
pick_ori = None if loose == 0 else 'normal'
else:
pick_ori = 'vector'
stcs.append(apply_inverse(
evoked, inv, method=method, pick_ori=pick_ori))
if loose in (1., 0.2):
assert vec_stc is None
vec_stc = apply_inverse(
evoked, inv, method=method, pick_ori='vector')
assert vec_stc is not None
rot = _normal_orth(np.concatenate(
[_get_src_nn(s) for s in inv['src']]))
vec_stc_surf = np.matmul(rot, vec_stc.data)
if 0. in looses:
vec_stc_normal = vec_stc.normal(inv['src'])
assert_allclose(stcs[1].data, vec_stc_normal.data)
del vec_stc
assert_allclose(vec_stc_normal.data, vec_stc_surf[:, 2])
assert_allclose(vec_stc_normal.data, stcs[1].data)
# Ensure that our relative strengths are reasonable
# (normal should be much larger than tangential)
normal = np.linalg.norm(vec_stc_surf[:, 2].ravel())
for ii in range(2):
tangential = np.linalg.norm(vec_stc_surf[:, ii].ravel())
ratio = normal / tangential
assert nmin < ratio < nmax
assert stcs[0].data.shape == stcs[1].data.shape
R2 = 1. - (
np.linalg.norm(stcs[0].data.ravel() - stcs[1].data.ravel()) /
np.linalg.norm(stcs[0].data.ravel()))
assert vmin < R2 < vmax
def test_normal_orth():
"""Test _normal_orth."""
nns = np.eye(3)
for nn in nns:
ori = _normal_orth(nn)
assert_allclose(ori[2], nn, atol=1e-12)
