def test_psf_ctf():
"""Test computation of PSFs and CTFs for linear estimators
"""
forward = read_forward_solution(fname_fwd)
labels = [mne.read_label(ss) for ss in fname_label]
method = 'MNE'
n_svd_comp = 2
# make sure it works for both types of inverses
for fname_inv in (fname_inv_meg, fname_inv_meeg):
inverse_operator = read_inverse_operator(fname_inv)
# Test PSFs (then CTFs)
for mode in ('sum', 'svd'):
stc_psf, psf_ev = point_spread_function(inverse_operator,
forward,
method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp,
use_cps=True)
n_vert, n_samples = stc_psf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
n_chan, n_samples = psf_ev.data.shape
assert_true(n_chan == forward['nchan'])
# Test CTFs
for mode in ('sum', 'svd'):
stc_ctf = cross_talk_function(inverse_operator,
forward,
labels,
method=method,
lambda2=lambda2,
signed=False,
mode=mode,
n_svd_comp=n_svd_comp,
use_cps=True)
n_vert, n_samples = stc_ctf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
def test_psf_ctf():
"""Test computation of PSFs and CTFs for linear estimators
"""
inverse_operator = read_inverse_operator(fname_inv)
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
forward = pick_types_forward(forward, meg=True, eeg=False)
labels = [mne.read_label(ss) for ss in fname_label]
method = 'MNE'
n_svd_comp = 2
# Test PSFs (then CTFs)
for mode in ('sum', 'svd'):
stc_psf, psf_ev = point_spread_function(inverse_operator,
forward,
method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
n_vert, n_samples = stc_psf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
n_chan, n_samples = psf_ev.data.shape
assert_true(n_chan == forward['nchan'])
forward = read_forward_solution(fname_fwd, force_fixed=True, surf_ori=True)
forward = pick_types_forward(forward, meg=True, eeg=False)
# Test CTFs
for mode in ('sum', 'svd'):
stc_ctf = cross_talk_function(inverse_operator, forward,
labels, method=method,
lambda2=lambda2,
signed=False, mode=mode,
n_svd_comp=n_svd_comp)
n_vert, n_samples = stc_ctf.shape
should_n_vert = (inverse_operator['src'][1]['vertno'].shape[0] +
inverse_operator['src'][0]['vertno'].shape[0])
if mode == 'svd':
should_n_samples = len(labels) * n_svd_comp + 1
else:
should_n_samples = len(labels) + 1
assert_true(n_vert == should_n_vert)
assert_true(n_samples == should_n_samples)
inverse_operator_meg = read_inverse_operator(fname_inv_meg)
# read label(s)
labels = [mne.read_label(ss) for ss in fname_label]
# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr**2
method = 'MNE' # can be 'MNE' or 'sLORETA'
mode = 'svd'
n_svd_comp = 1
stc_psf_eegmeg, _ = point_spread_function(inverse_operator_eegmeg,
forward,
method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
stc_psf_meg, _ = point_spread_function(inverse_operator_meg,
forward,
method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
# save for viewing in mne_analyze in order of labels in 'labels'
# last sample is average across PSFs
inverse_operator_meg = read_inverse_operator(fname_inv_meg)
# read label(s)
labels = [mne.read_label(ss) for ss in fname_label]
# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr ** 2
method = 'MNE' # can be 'MNE' or 'sLORETA'
mode = 'svd'
n_svd_comp = 1
stc_psf_eegmeg, _ = point_spread_function(inverse_operator_eegmeg,
forward, method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
stc_psf_meg, _ = point_spread_function(inverse_operator_meg,
forward, method=method,
labels=labels,
lambda2=lambda2,
pick_ori='normal',
mode=mode,
n_svd_comp=n_svd_comp)
# save for viewing in mne_analyze in order of labels in 'labels'
# last sample is average across PSFs
# stc_psf_eegmeg.save('psf_eegmeg')
