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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_almost_equal
import mne
from mne import (
VectorSourceEstimate,
convert_forward_solution,
pick_types_forward,
read_cov,
read_evokeds,
read_forward_solution,
)
from mne.cov import regularize
from mne.datasets import testing
from mne.dipole import Dipole
from mne.inverse_sparse import gamma_map
from mne.inverse_sparse.mxne_inverse import make_stc_from_dipoles
from mne.minimum_norm.tests.test_inverse import assert_stc_res, assert_var_exp_log
from mne.utils import assert_stcs_equal, catch_logging
data_path = testing.data_path(download=False)
fname_evoked = data_path / "MEG" / "sample" / "sample_audvis-ave.fif"
fname_cov = data_path / "MEG" / "sample" / "sample_audvis-cov.fif"
fname_fwd = data_path / "MEG" / "sample" / "sample_audvis_trunc-meg-eeg-oct-6-fwd.fif"
subjects_dir = data_path / "subjects"
def _check_stc(
stc, evoked, idx, hemi, fwd, dist_limit=0.0, ratio=50.0, res=None, atol=1e-20
):
"""Check correctness."""
assert_array_almost_equal(stc.times, evoked.times, 5)
stc_orig = stc
if isinstance(stc, VectorSourceEstimate):
assert stc.data.any(1).any(1).all() # all dipoles should have some
stc = stc.magnitude()
amps = np.sum(stc.data**2, axis=1)
order = np.argsort(amps)[::-1]
amps = amps[order]
verts = np.concatenate(stc.vertices)[order]
hemi_idx = int(order[0] >= len(stc.vertices[1]))
hemis = ["lh", "rh"]
assert hemis[hemi_idx] == hemi
dist = (
np.linalg.norm(np.diff(fwd["src"][hemi_idx]["rr"][[idx, verts[0]]], axis=0)[0])
* 1000.0
)
assert dist <= dist_limit
assert amps[0] > ratio * amps[1]
if res is not None:
assert_stc_res(evoked, stc_orig, fwd, res, atol=atol)
@pytest.mark.slowtest
@testing.requires_testing_data
def test_gamma_map_standard():
"""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)
alpha = 0.5
with catch_logging() as log:
stc = gamma_map(
evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=True,
update_mode=1,
verbose=True,
)
_check_stc(stc, evoked, 68477, "lh", fwd=forward)
assert_var_exp_log(log.getvalue(), 20, 22)
with catch_logging() as log:
stc_vec, res = gamma_map(
evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=True,
update_mode=1,
pick_ori="vector",
return_residual=True,
verbose=True,
)
assert_var_exp_log(log.getvalue(), 20, 22)
assert_stcs_equal(stc_vec.magnitude(), stc)
_check_stc(stc_vec, evoked, 68477, "lh", fwd=forward, res=res)
stc, res = gamma_map(
evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=False,
update_mode=1,
pick_ori="vector",
return_residual=True,
)
_check_stc(
stc, evoked, 82010, "lh", fwd=forward, dist_limit=6.0, ratio=2.0, res=res
)
with catch_logging() as log:
dips = gamma_map(
evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=False,
update_mode=1,
return_as_dipoles=True,
verbose=True,
)
exp_var = assert_var_exp_log(log.getvalue(), 58, 60)
dip_exp_var = np.mean(sum(dip.gof for dip in dips))
assert_allclose(exp_var, dip_exp_var, atol=10) # not really equiv, close
assert isinstance(dips[0], Dipole)
stc_dip = make_stc_from_dipoles(dips, forward["src"])
assert_stcs_equal(stc.magnitude(), stc_dip)
# force fixed orientation
stc, res = gamma_map(
evoked,
forward,
cov,
alpha,
tol=1e-4,
xyz_same_gamma=False,
update_mode=2,
loose=0,
return_residual=True,
)
_check_stc(stc, evoked, 85739, "lh", fwd=forward, ratio=20.0, res=res)
@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=dict(eeg=58))
info = evoked.info
sphere = mne.make_sphere_model(r0=(0.0, 0.0, 0.0), head_radius=0.080)
src = mne.setup_volume_source_space(
subject=None,
pos=30.0,
mri=None,
sphere=(0.0, 0.0, 0.0, 0.08),
bem=None,
mindist=5.0,
exclude=2.0,
sphere_units="m",
)
fwd = mne.make_forward_solution(
info, trans=None, src=src, bem=sphere, eeg=False, meg=True
)
alpha = 0.5
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)
# Computing inverse with restricted orientations should also work, since
# we have a discrete source space.
stc = gamma_map(evoked, fwd, cov, alpha, loose=0.2, 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[0][: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[0]]
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
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