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# Authors: Mark Wronkiewicz <wronk@uw.edu>
# Yousra Bekhti <yousra.bekhti@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import warnings
from copy import deepcopy
import numpy as np
from numpy.testing import assert_allclose, assert_array_equal
from nose.tools import assert_true, assert_raises, assert_equal
from mne import (read_source_spaces, pick_types, read_trans, read_cov,
make_sphere_model, create_info, setup_volume_source_space,
find_events, Epochs, fit_dipole, transform_surface_to,
make_ad_hoc_cov, SourceEstimate, setup_source_space)
from mne.chpi import (_calculate_chpi_positions, read_head_pos,
_get_hpi_info, head_pos_to_trans_rot_t)
from mne.tests.test_chpi import _compare_positions
from mne.datasets import testing
from mne.simulation import simulate_sparse_stc, simulate_raw
from mne.io import read_raw_fif, RawArray
from mne.time_frequency import psd_welch
from mne.utils import _TempDir, run_tests_if_main, requires_version, slow_test
warnings.simplefilter('always')
data_path = testing.data_path(download=False)
raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif')
cov_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-cov.fif')
trans_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-trans.fif')
subjects_dir = op.join(data_path, 'subjects')
bem_path = op.join(subjects_dir, 'sample', 'bem')
src_fname = op.join(bem_path, 'sample-oct-2-src.fif')
bem_fname = op.join(bem_path, 'sample-320-320-320-bem-sol.fif')
raw_chpi_fname = op.join(data_path, 'SSS', 'test_move_anon_raw.fif')
pos_fname = op.join(data_path, 'SSS', 'test_move_anon_raw_subsampled.pos')
def _make_stc(raw, src):
"""Helper to make a STC."""
seed = 42
sfreq = raw.info['sfreq'] # Hz
tstep = 1. / sfreq
n_samples = len(raw.times) // 10
times = np.arange(0, n_samples) * tstep
stc = simulate_sparse_stc(src, 10, times, random_state=seed)
return stc
def _get_data():
"""Helper to get some starting data."""
# raw with ECG channel
raw = read_raw_fif(raw_fname, add_eeg_ref=False)
raw.crop(0., 5.0, copy=False).load_data()
data_picks = pick_types(raw.info, meg=True, eeg=True)
other_picks = pick_types(raw.info, meg=False, stim=True, eog=True)
picks = np.sort(np.concatenate((data_picks[::16], other_picks)))
raw = raw.pick_channels([raw.ch_names[p] for p in picks])
raw.info.normalize_proj()
ecg = RawArray(np.zeros((1, len(raw.times))),
create_info(['ECG 063'], raw.info['sfreq'], 'ecg'))
for key in ('dev_head_t', 'buffer_size_sec', 'highpass', 'lowpass',
'filename', 'dig'):
ecg.info[key] = raw.info[key]
raw.add_channels([ecg])
src = read_source_spaces(src_fname)
trans = read_trans(trans_fname)
sphere = make_sphere_model('auto', 'auto', raw.info)
stc = _make_stc(raw, src)
return raw, src, stc, trans, sphere
@testing.requires_testing_data
def test_simulate_raw_sphere():
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = _get_data()
assert_true(len(pick_types(raw.info, meg=False, ecg=True)) == 1)
# head pos
head_pos_sim = dict()
# these will be at 1., 2., ... sec
shifts = [[0.001, 0., -0.001], [-0.001, 0.001, 0.]]
for time_key, shift in enumerate(shifts):
# Create 4x4 matrix transform and normalize
temp_trans = deepcopy(raw.info['dev_head_t'])
temp_trans['trans'][:3, 3] += shift
head_pos_sim[time_key + 1.] = temp_trans['trans']
#
# Test raw simulation with basic parameters
#
raw_sim = simulate_raw(raw, stc, trans, src, sphere, read_cov(cov_fname),
head_pos=head_pos_sim,
blink=True, ecg=True, random_state=seed)
raw_sim_2 = simulate_raw(raw, stc, trans_fname, src_fname, sphere,
cov_fname, head_pos=head_pos_sim,
blink=True, ecg=True, random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
# Test IO on processed data
tempdir = _TempDir()
test_outname = op.join(tempdir, 'sim_test_raw.fif')
raw_sim.save(test_outname)
raw_sim_loaded = read_raw_fif(test_outname, preload=True, proj=False,
add_eeg_ref=False)
assert_allclose(raw_sim_loaded[:][0], raw_sim[:][0], rtol=1e-6, atol=1e-20)
del raw_sim, raw_sim_2
# with no cov (no noise) but with artifacts, most time periods should match
# but the EOG/ECG channels should not
for ecg, eog in ((True, False), (False, True), (True, True)):
raw_sim_3 = simulate_raw(raw, stc, trans, src, sphere,
cov=None, head_pos=head_pos_sim,
blink=eog, ecg=ecg, random_state=seed)
raw_sim_4 = simulate_raw(raw, stc, trans, src, sphere,
cov=None, head_pos=head_pos_sim,
blink=False, ecg=False, random_state=seed)
picks = np.arange(len(raw.ch_names))
diff_picks = pick_types(raw.info, meg=False, ecg=ecg, eog=eog)
these_picks = np.setdiff1d(picks, diff_picks)
close = np.isclose(raw_sim_3[these_picks][0],
raw_sim_4[these_picks][0], atol=1e-20)
assert_true(np.mean(close) > 0.7)
far = ~np.isclose(raw_sim_3[diff_picks][0],
raw_sim_4[diff_picks][0], atol=1e-20)
assert_true(np.mean(far) > 0.99)
del raw_sim_3, raw_sim_4
# make sure it works with EEG-only and MEG-only
raw_sim_meg = simulate_raw(raw.copy().pick_types(meg=True, eeg=False),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
raw_sim_eeg = simulate_raw(raw.copy().pick_types(meg=False, eeg=True),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
raw_sim_meeg = simulate_raw(raw.copy().pick_types(meg=True, eeg=True),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
assert_allclose(np.concatenate((raw_sim_meg[:][0], raw_sim_eeg[:][0])),
raw_sim_meeg[:][0], rtol=1e-7, atol=1e-20)
del raw_sim_meg, raw_sim_eeg, raw_sim_meeg
# check that different interpolations are similar given small movements
raw_sim_cos = simulate_raw(raw, stc, trans, src, sphere,
head_pos=head_pos_sim,
random_state=seed)
raw_sim_lin = simulate_raw(raw, stc, trans, src, sphere,
head_pos=head_pos_sim, interp='linear',
random_state=seed)
assert_allclose(raw_sim_cos[:][0], raw_sim_lin[:][0],
rtol=1e-5, atol=1e-20)
del raw_sim_cos, raw_sim_lin
# Make impossible transform (translate up into helmet) and ensure failure
head_pos_sim_err = deepcopy(head_pos_sim)
head_pos_sim_err[1.][2, 3] -= 0.1 # z trans upward 10cm
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
ecg=False, blink=False, head_pos=head_pos_sim_err)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src,
bem_fname, ecg=False, blink=False,
head_pos=head_pos_sim_err)
# other degenerate conditions
assert_raises(TypeError, simulate_raw, 'foo', stc, trans, src, sphere)
assert_raises(TypeError, simulate_raw, raw, 'foo', trans, src, sphere)
assert_raises(ValueError, simulate_raw, raw, stc.copy().crop(0, 0),
trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
assert_raises(ValueError, simulate_raw, raw, stc_bad, trans, src, sphere)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
chpi=True) # no cHPI info
assert_raises(ValueError, simulate_raw, raw, stc, trans, src, sphere,
interp='foo')
assert_raises(TypeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=1.)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=pos_fname) # ends up with t>t_end
head_pos_sim_err = deepcopy(head_pos_sim)
head_pos_sim_err[-1.] = head_pos_sim_err[1.] # negative time
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
assert_raises(RuntimeError, simulate_raw, raw_bad, stc, trans, src, sphere,
blink=True)
@testing.requires_testing_data
def test_simulate_raw_bem():
"""Test simulation of raw data with BEM."""
raw, src, stc, trans, sphere = _get_data()
src = setup_source_space('sample', None, 'oct1', subjects_dir=subjects_dir)
# use different / more complete STC here
vertices = [s['vertno'] for s in src]
stc = SourceEstimate(np.eye(sum(len(v) for v in vertices)), vertices,
0, 1. / raw.info['sfreq'])
raw_sim_sph = simulate_raw(raw, stc, trans, src, sphere, cov=None)
raw_sim_bem = simulate_raw(raw, stc, trans, src, bem_fname, cov=None,
n_jobs=2)
# some components (especially radial) might not match that well,
# so just make sure that most components have high correlation
assert_array_equal(raw_sim_sph.ch_names, raw_sim_bem.ch_names)
picks = pick_types(raw.info, meg=True, eeg=True)
n_ch = len(picks)
corr = np.corrcoef(raw_sim_sph[picks][0], raw_sim_bem[picks][0])
assert_array_equal(corr.shape, (2 * n_ch, 2 * n_ch))
assert_true(np.median(np.diag(corr[:n_ch, -n_ch:])) > 0.65)
# do some round-trip localization
for s in src:
transform_surface_to(s, 'head', trans)
locs = np.concatenate([s['rr'][s['vertno']] for s in src])
tmax = (len(locs) - 1) / raw.info['sfreq']
cov = make_ad_hoc_cov(raw.info)
# The tolerance for the BEM is surprisingly high (28) but I get the same
# result when using MNE-C and Xfit, even when using a proper 5120 BEM :(
for use_raw, bem, tol in ((raw_sim_sph, sphere, 1),
(raw_sim_bem, bem_fname, 28)):
events = find_events(use_raw, 'STI 014')
assert_equal(len(locs), 12) # oct1 count
evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None,
add_eeg_ref=False).average()
assert_equal(len(evoked.times), len(locs))
fits = fit_dipole(evoked, cov, bem, trans, min_dist=1.)[0].pos
diffs = np.sqrt(np.sum((locs - fits) ** 2, axis=-1)) * 1000
assert_true(np.median(diffs) < tol)
@slow_test
@requires_version('numpy', '1.7')
@requires_version('scipy', '0.12')
@testing.requires_testing_data
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI."""
raw = read_raw_fif(raw_chpi_fname, allow_maxshield='yes',
add_eeg_ref=False)
sphere = make_sphere_model('auto', 'auto', raw.info)
# make sparse spherical source space
sphere_vol = tuple(sphere['r0'] * 1000.) + (sphere.radius * 1000.,)
src = setup_volume_source_space('sample', sphere=sphere_vol, pos=70.)
stc = _make_stc(raw, src)
# simulate data with cHPI on
raw_sim = simulate_raw(raw, stc, None, src, sphere, cov=None, chpi=False)
# need to trim extra samples off this one
raw_chpi = simulate_raw(raw, stc, None, src, sphere, cov=None, chpi=True,
head_pos=pos_fname)
# test cHPI indication
hpi_freqs, _, hpi_pick, hpi_ons = _get_hpi_info(raw.info)[:4]
assert_allclose(raw_sim[hpi_pick][0], 0.)
assert_allclose(raw_chpi[hpi_pick][0], hpi_ons.sum())
# test that the cHPI signals make some reasonable values
picks_meg = pick_types(raw.info, meg=True, eeg=False)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
for picks in [picks_meg, picks_eeg]:
psd_sim, freqs_sim = psd_welch(raw_sim, picks=picks)
psd_chpi, freqs_chpi = psd_welch(raw_chpi, picks=picks)
assert_array_equal(freqs_sim, freqs_chpi)
freq_idx = np.sort([np.argmin(np.abs(freqs_sim - f))
for f in hpi_freqs])
if picks is picks_meg:
assert_true((psd_chpi[:, freq_idx] >
100 * psd_sim[:, freq_idx]).all())
else:
assert_allclose(psd_sim, psd_chpi, atol=1e-20)
# test localization based on cHPI information
quats_sim = _calculate_chpi_positions(raw_chpi)
trans_sim, rot_sim, t_sim = head_pos_to_trans_rot_t(quats_sim)
trans, rot, t = head_pos_to_trans_rot_t(read_head_pos(pos_fname))
t -= raw.first_samp / raw.info['sfreq']
_compare_positions((trans, rot, t), (trans_sim, rot_sim, t_sim),
max_dist=0.005)
run_tests_if_main()
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