1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214
|
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: Simplified BSD
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
import pytest
import mne
from mne.datasets import testing
from mne.label import read_label
from mne import (read_cov, read_forward_solution, read_evokeds,
convert_forward_solution)
from mne.inverse_sparse import mixed_norm, tf_mixed_norm
from mne.inverse_sparse.mxne_inverse import make_stc_from_dipoles
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.utils import run_tests_if_main
from mne.dipole import Dipole
from mne.source_estimate import VolSourceEstimate
data_path = testing.data_path(download=False)
# NOTE: These use the ave and cov from sample dataset (no _trunc)
fname_data = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif')
label = 'Aud-rh'
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
def _check_stcs(stc1, stc2):
"""Check STC correctness."""
assert_allclose(stc1.times, stc2.times)
assert_allclose(stc1.data, stc2.data)
assert_allclose(stc1.vertices[0], stc2.vertices[0])
assert_allclose(stc1.vertices[1], stc2.vertices[1])
assert_allclose(stc1.tmin, stc2.tmin)
assert_allclose(stc1.tstep, stc2.tstep)
@pytest.mark.slowtest
@testing.requires_testing_data
def test_mxne_inverse():
"""Test (TF-)MxNE inverse computation."""
# Read noise covariance matrix
cov = read_cov(fname_cov)
# Handling average file
loose = 0.0
depth = 0.9
evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0))
evoked.crop(tmin=-0.05, tmax=0.2)
evoked_l21 = evoked.copy()
evoked_l21.crop(tmin=0.081, tmax=0.1)
label = read_label(fname_label)
forward = read_forward_solution(fname_fwd)
forward = convert_forward_solution(forward, surf_ori=True)
# Reduce source space to make test computation faster
inverse_operator = make_inverse_operator(evoked_l21.info, forward, cov,
loose=loose, depth=depth,
fixed=True, use_cps=True)
stc_dspm = apply_inverse(evoked_l21, inverse_operator, lambda2=1. / 9.,
method='dSPM')
stc_dspm.data[np.abs(stc_dspm.data) < 12] = 0.0
stc_dspm.data[np.abs(stc_dspm.data) >= 12] = 1.
weights_min = 0.5
# MxNE tests
alpha = 70 # spatial regularization parameter
stc_prox = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose,
depth=depth, maxit=300, tol=1e-8,
active_set_size=10, weights=stc_dspm,
weights_min=weights_min, solver='prox')
with pytest.warns(None): # CD
stc_cd = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose,
depth=depth, maxit=300, tol=1e-8,
active_set_size=10, weights=stc_dspm,
weights_min=weights_min, solver='cd')
stc_bcd = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose,
depth=depth, maxit=300, tol=1e-8, active_set_size=10,
weights=stc_dspm, weights_min=weights_min,
solver='bcd')
assert_array_almost_equal(stc_prox.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_cd.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_bcd.times, evoked_l21.times, 5)
assert_allclose(stc_prox.data, stc_cd.data, rtol=1e-3, atol=0.0)
assert_allclose(stc_prox.data, stc_bcd.data, rtol=1e-3, atol=0.0)
assert_allclose(stc_cd.data, stc_bcd.data, rtol=1e-3, atol=0.0)
assert stc_prox.vertices[1][0] in label.vertices
assert stc_cd.vertices[1][0] in label.vertices
assert stc_bcd.vertices[1][0] in label.vertices
with pytest.warns(None): # CD
dips = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose,
depth=depth, maxit=300, tol=1e-8, active_set_size=10,
weights=stc_dspm, weights_min=weights_min,
solver='cd', return_as_dipoles=True)
stc_dip = make_stc_from_dipoles(dips, forward['src'])
assert isinstance(dips[0], Dipole)
assert stc_dip.subject == "sample"
_check_stcs(stc_cd, stc_dip)
with pytest.warns(None): # CD
stc, _ = mixed_norm(evoked_l21, forward, cov, alpha, loose=loose,
depth=depth, maxit=300, tol=1e-8,
active_set_size=10, return_residual=True,
solver='cd')
assert_array_almost_equal(stc.times, evoked_l21.times, 5)
assert stc.vertices[1][0] in label.vertices
# irMxNE tests
with pytest.warns(None): # CD
stc = mixed_norm(evoked_l21, forward, cov, alpha,
n_mxne_iter=5, loose=loose, depth=depth,
maxit=300, tol=1e-8, active_set_size=10,
solver='cd')
assert_array_almost_equal(stc.times, evoked_l21.times, 5)
assert stc.vertices[1][0] in label.vertices
assert stc.vertices == [[63152], [79017]]
# Do with TF-MxNE for test memory savings
alpha = 60. # overall regularization parameter
l1_ratio = 0.01 # temporal regularization proportion
stc, _ = tf_mixed_norm(evoked, forward, cov,
loose=loose, depth=depth, maxit=100, tol=1e-4,
tstep=4, wsize=16, window=0.1, weights=stc_dspm,
weights_min=weights_min, return_residual=True,
alpha=alpha, l1_ratio=l1_ratio)
assert_array_almost_equal(stc.times, evoked.times, 5)
assert stc.vertices[1][0] in label.vertices
pytest.raises(ValueError, tf_mixed_norm, evoked, forward, cov,
alpha=101, l1_ratio=0.03)
pytest.raises(ValueError, tf_mixed_norm, evoked, forward, cov,
alpha=50., l1_ratio=1.01)
@pytest.mark.slowtest
@testing.requires_testing_data
def test_mxne_vol_sphere():
"""Test (TF-)MxNE with a sphere forward and volumic source space."""
evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0))
evoked.crop(tmin=-0.05, tmax=0.2)
cov = read_cov(fname_cov)
evoked_l21 = evoked.copy()
evoked_l21.crop(tmin=0.081, tmax=0.1)
info = evoked.info
sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=0.080)
src = mne.setup_volume_source_space(subject=None, pos=15., mri=None,
sphere=(0.0, 0.0, 0.0, 80.0),
bem=None, mindist=5.0,
exclude=2.0)
fwd = mne.make_forward_solution(info, trans=None, src=src,
bem=sphere, eeg=False, meg=True)
alpha = 80.
pytest.raises(ValueError, mixed_norm, evoked, fwd, cov, alpha,
loose=0.0, return_residual=False,
maxit=3, tol=1e-8, active_set_size=10)
pytest.raises(ValueError, mixed_norm, evoked, fwd, cov, alpha,
loose=0.2, return_residual=False,
maxit=3, tol=1e-8, active_set_size=10)
# irMxNE tests
stc = mixed_norm(evoked_l21, fwd, cov, alpha,
n_mxne_iter=1, maxit=30, tol=1e-8,
active_set_size=10)
assert isinstance(stc, VolSourceEstimate)
assert_array_almost_equal(stc.times, evoked_l21.times, 5)
# Compare orientation obtained using fit_dipole and gamma_map
# for a simulated evoked containing a single dipole
stc = mne.VolSourceEstimate(50e-9 * np.random.RandomState(42).randn(1, 4),
vertices=stc.vertices[:1],
tmin=stc.tmin,
tstep=stc.tstep)
evoked_dip = mne.simulation.simulate_evoked(fwd, stc, info, cov, nave=1e9,
use_cps=True)
dip_mxne = mixed_norm(evoked_dip, fwd, cov, alpha=80,
n_mxne_iter=1, maxit=30, tol=1e-8,
active_set_size=10, return_as_dipoles=True)
amp_max = [np.max(d.amplitude) for d in dip_mxne]
dip_mxne = dip_mxne[np.argmax(amp_max)]
assert dip_mxne.pos[0] in src[0]['rr'][stc.vertices]
dip_fit = mne.fit_dipole(evoked_dip, cov, sphere)[0]
assert np.abs(np.dot(dip_fit.ori[0], dip_mxne.ori[0])) > 0.99
# Do with TF-MxNE for test memory savings
alpha = 60. # overall regularization parameter
l1_ratio = 0.01 # temporal regularization proportion
stc, _ = tf_mixed_norm(evoked, fwd, cov, maxit=3, tol=1e-4,
tstep=16, wsize=32, window=0.1, alpha=alpha,
l1_ratio=l1_ratio, return_residual=True)
assert isinstance(stc, VolSourceEstimate)
assert_array_almost_equal(stc.times, evoked.times, 5)
run_tests_if_main()
|