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# Authors: Yousra Bekhti <yousra.bekhti@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import linalg
from numpy.testing import assert_array_equal, assert_equal
import mne
from mne.datasets import testing
from mne.beamformer import rap_music
from mne.cov import regularize
from mne.utils import run_tests_if_main
data_path = testing.data_path(download=False)
fname_ave = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-cov.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
def _get_data(ch_decim=1):
"""Read in data used in tests."""
# Read evoked
evoked = mne.read_evokeds(fname_ave, 0, baseline=(None, 0))
evoked.info['bads'] = ['MEG 2443']
evoked.info['lowpass'] = 20 # fake for decim
evoked.decimate(12)
evoked.crop(0.0, 0.3)
picks = mne.pick_types(evoked.info, meg=True, eeg=False)
picks = picks[::ch_decim]
evoked.pick_channels([evoked.ch_names[pick] for pick in picks])
evoked.info.normalize_proj()
noise_cov = mne.read_cov(fname_cov)
noise_cov['projs'] = []
noise_cov = regularize(noise_cov, evoked.info, rank='full', proj=False)
return evoked, noise_cov
def simu_data(evoked, forward, noise_cov, n_dipoles, times, nave=1):
"""Simulate an evoked dataset with 2 sources.
One source is put in each hemisphere.
"""
# Generate the two dipoles data
mu, sigma = 0.1, 0.005
s1 = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(times - mu) ** 2 /
(2 * sigma ** 2))
mu, sigma = 0.075, 0.008
s2 = -1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(times - mu) ** 2 /
(2 * sigma ** 2))
data = np.array([s1, s2]) * 1e-9
src = forward['src']
rng = np.random.RandomState(42)
rndi = rng.randint(len(src[0]['vertno']))
lh_vertno = src[0]['vertno'][[rndi]]
rndi = rng.randint(len(src[1]['vertno']))
rh_vertno = src[1]['vertno'][[rndi]]
vertices = [lh_vertno, rh_vertno]
tmin, tstep = times.min(), 1 / evoked.info['sfreq']
stc = mne.SourceEstimate(data, vertices=vertices, tmin=tmin, tstep=tstep)
sim_evoked = mne.simulation.simulate_evoked(forward, stc, evoked.info,
noise_cov, nave=nave,
random_state=rng)
return sim_evoked, stc
def _check_dipoles(dipoles, fwd, stc, evoked, residual=None):
src = fwd['src']
pos1 = fwd['source_rr'][np.where(src[0]['vertno'] ==
stc.vertices[0])]
pos2 = fwd['source_rr'][np.where(src[1]['vertno'] ==
stc.vertices[1])[0] +
len(src[0]['vertno'])]
# Check the position of the two dipoles
assert (dipoles[0].pos[0] in np.array([pos1, pos2]))
assert (dipoles[1].pos[0] in np.array([pos1, pos2]))
ori1 = fwd['source_nn'][np.where(src[0]['vertno'] ==
stc.vertices[0])[0]][0]
ori2 = fwd['source_nn'][np.where(src[1]['vertno'] ==
stc.vertices[1])[0] +
len(src[0]['vertno'])][0]
# Check the orientation of the dipoles
assert (np.max(np.abs(np.dot(dipoles[0].ori[0],
np.array([ori1, ori2]).T))) > 0.99)
assert (np.max(np.abs(np.dot(dipoles[1].ori[0],
np.array([ori1, ori2]).T))) > 0.99)
if residual is not None:
picks_grad = mne.pick_types(residual.info, meg='grad')
picks_mag = mne.pick_types(residual.info, meg='mag')
rel_tol = 0.02
for picks in [picks_grad, picks_mag]:
assert (linalg.norm(residual.data[picks], ord='fro') <
rel_tol * linalg.norm(evoked.data[picks], ord='fro'))
@testing.requires_testing_data
def test_rap_music_simulated():
"""Test RAP-MUSIC with simulated evoked."""
evoked, noise_cov = _get_data(ch_decim=16)
forward = mne.read_forward_solution(fname_fwd)
forward = mne.pick_channels_forward(forward, evoked.ch_names)
forward_surf_ori = mne.convert_forward_solution(forward, surf_ori=True)
forward_fixed = mne.convert_forward_solution(forward, force_fixed=True,
surf_ori=True, use_cps=True)
n_dipoles = 2
sim_evoked, stc = simu_data(evoked, forward_fixed, noise_cov,
n_dipoles, evoked.times, nave=evoked.nave)
# Check dipoles for fixed ori
dipoles = rap_music(sim_evoked, forward_fixed, noise_cov,
n_dipoles=n_dipoles)
_check_dipoles(dipoles, forward_fixed, stc, sim_evoked)
assert (0.97 < dipoles[0].gof.max() < 1.)
assert (dipoles[0].gof.min() >= 0.)
assert_array_equal(dipoles[0].gof, dipoles[1].gof)
nave = 100000 # add a tiny amount of noise to the simulated evokeds
sim_evoked, stc = simu_data(evoked, forward_fixed, noise_cov,
n_dipoles, evoked.times, nave=nave)
dipoles, residual = rap_music(sim_evoked, forward_fixed, noise_cov,
n_dipoles=n_dipoles, return_residual=True)
_check_dipoles(dipoles, forward_fixed, stc, sim_evoked, residual)
# Check dipoles for free ori
dipoles, residual = rap_music(sim_evoked, forward, noise_cov,
n_dipoles=n_dipoles, return_residual=True)
_check_dipoles(dipoles, forward_fixed, stc, sim_evoked, residual)
# Check dipoles for free surface ori
dipoles, residual = rap_music(sim_evoked, forward_surf_ori, noise_cov,
n_dipoles=n_dipoles, return_residual=True)
_check_dipoles(dipoles, forward_fixed, stc, sim_evoked, residual)
@testing.requires_testing_data
def test_rap_music_sphere():
"""Test RAP-MUSIC with real data, sphere model, MEG only."""
evoked, noise_cov = _get_data(ch_decim=8)
sphere = mne.make_sphere_model(r0=(0., 0., 0.04))
src = mne.setup_volume_source_space(subject=None, pos=10.,
sphere=(0.0, 0.0, 40, 65.0),
mindist=5.0, exclude=0.0)
forward = mne.make_forward_solution(evoked.info, trans=None, src=src,
bem=sphere)
dipoles = rap_music(evoked, forward, noise_cov, n_dipoles=2)
# Test that there is one dipole on each hemisphere
pos = np.array([dip.pos[0] for dip in dipoles])
assert_equal(pos.shape, (2, 3))
assert_equal((pos[:, 0] < 0).sum(), 1)
assert_equal((pos[:, 0] > 0).sum(), 1)
# Check the amplitude scale
assert (1e-10 < dipoles[0].amplitude[0] < 1e-7)
# Check the orientation
dip_fit = mne.fit_dipole(evoked, noise_cov, sphere)[0]
assert (np.max(np.abs(np.dot(dip_fit.ori, dipoles[0].ori[0]))) > 0.99)
assert (np.max(np.abs(np.dot(dip_fit.ori, dipoles[1].ori[0]))) > 0.99)
@testing.requires_testing_data
def test_rap_music_picks():
"""Test RAP-MUSIC with picking."""
evoked = mne.read_evokeds(fname_ave, condition='Right Auditory',
baseline=(None, 0))
evoked.crop(tmin=0.05, tmax=0.15) # select N100
evoked.pick_types(meg=True, eeg=False)
forward = mne.read_forward_solution(fname_fwd)
noise_cov = mne.read_cov(fname_cov)
dipoles = rap_music(evoked, forward, noise_cov, n_dipoles=2)
assert len(dipoles) == 2
run_tests_if_main()
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