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# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: Simplified BSD
import os.path as op
import pytest
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_allclose
import mne
from mne.datasets import testing
from mne import (read_cov, read_forward_solution, read_evokeds,
convert_forward_solution)
from mne.cov import regularize
from mne.inverse_sparse import gamma_map
from mne.inverse_sparse.mxne_inverse import make_stc_from_dipoles
from mne import pick_types_forward
from mne.utils import run_tests_if_main
from mne.dipole import Dipole
data_path = testing.data_path(download=False)
fname_evoked = 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')
subjects_dir = op.join(data_path, 'subjects')
def _check_stc(stc, evoked, idx, ratio=50.):
"""Check correctness."""
assert_array_almost_equal(stc.times, evoked.times, 5)
amps = np.sum(stc.data ** 2, axis=1)
order = np.argsort(amps)[::-1]
amps = amps[order]
verts = np.concatenate(stc.vertices)[order]
assert idx == verts[0], str(list(verts))
assert amps[0] > ratio * amps[1]
def _check_stcs(stc1, stc2):
"""Check 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_gamma_map():
"""Test Gamma MAP inverse."""
forward = read_forward_solution(fname_fwd)
forward = convert_forward_solution(forward, surf_ori=True)
forward = pick_types_forward(forward, meg=False, eeg=True)
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.14) # crop to window around peak
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info, rank=None)
alpha = 0.5
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=True, update_mode=1)
_check_stc(stc, evoked, 68477)
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1)
_check_stc(stc, evoked, 82010)
dips = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=1,
return_as_dipoles=True)
assert (isinstance(dips[0], Dipole))
stc_dip = make_stc_from_dipoles(dips, forward['src'])
_check_stcs(stc, stc_dip)
# force fixed orientation
stc = gamma_map(evoked, forward, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=2,
loose=0, return_residual=False)
_check_stc(stc, evoked, 85739, 20)
@pytest.mark.slowtest
@testing.requires_testing_data
def test_gamma_map_vol_sphere():
"""Gamma MAP with a sphere forward and volumic source space."""
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0),
proj=False)
evoked.resample(50, npad=100)
evoked.crop(tmin=0.1, tmax=0.16) # crop to window around peak
cov = read_cov(fname_cov)
cov = regularize(cov, evoked.info, rank=None)
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=30., 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 = 0.5
pytest.raises(ValueError, gamma_map, evoked, fwd, cov, alpha,
loose=0, return_residual=False)
pytest.raises(ValueError, gamma_map, evoked, fwd, cov, alpha,
loose=0.2, return_residual=False)
stc = gamma_map(evoked, fwd, cov, alpha, tol=1e-4,
xyz_same_gamma=False, update_mode=2,
return_residual=False)
assert_array_almost_equal(stc.times, evoked.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_gmap = gamma_map(evoked_dip, fwd, cov, 0.1, return_as_dipoles=True)
amp_max = [np.max(d.amplitude) for d in dip_gmap]
dip_gmap = dip_gmap[np.argmax(amp_max)]
assert (dip_gmap[0].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_gmap.ori[0])) > 0.99)
run_tests_if_main()
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