def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI"""
with warnings.catch_warnings(record=True): # MaxShield
raw = Raw(raw_chpi_fname, allow_maxshield=True)
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 that the cHPI signals make some reasonable values
psd_sim, freqs_sim = compute_raw_psd(raw_sim)
psd_chpi, freqs_chpi = compute_raw_psd(raw_chpi)
assert_array_equal(freqs_sim, freqs_chpi)
hpi_freqs = _get_hpi_info(raw.info)[0]
freq_idx = np.sort([np.argmin(np.abs(freqs_sim - f)) for f in hpi_freqs])
picks_meg = pick_types(raw.info, meg=True, eeg=False)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
assert_allclose(psd_sim[picks_eeg], psd_chpi[picks_eeg], atol=1e-20)
assert_true((psd_chpi[picks_meg][:, freq_idx] >
100 * psd_sim[picks_meg][:, freq_idx]).all())
# test localization based on cHPI information
trans_sim, rot_sim, t_sim = _calculate_chpi_positions(raw_chpi)
trans, rot, t = get_chpi_positions(pos_fname)
t -= raw.first_samp / raw.info['sfreq']
_compare_positions((trans, rot, t), (trans_sim, rot_sim, t_sim),
max_dist=0.005)
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI"""
with warnings.catch_warnings(record=True): # MaxShield
raw = Raw(raw_chpi_fname, allow_maxshield=True)
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)
raw_chpi = simulate_raw(raw, stc, None, src, sphere, cov=None, chpi=True)
# XXX we need to test that the cHPI signals are actually in the correct
# place, but that should be a subsequent enhancement (not trivial to do so)
psd_sim, freqs_sim = compute_raw_psd(raw_sim)
psd_chpi, freqs_chpi = compute_raw_psd(raw_chpi)
assert_array_equal(freqs_sim, freqs_chpi)
hpi_freqs = np.array([x['custom_ref'][0]
for x in raw.info['hpi_meas'][0]['hpi_coils']])
freq_idx = np.sort([np.argmin(np.abs(freqs_sim - f)) for f in hpi_freqs])
picks_meg = pick_types(raw.info, meg=True, eeg=False)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
assert_allclose(psd_sim[picks_eeg], psd_chpi[picks_eeg])
assert_true((psd_chpi[picks_meg][:, freq_idx] >
100 * psd_sim[picks_meg][:, freq_idx]).all())
def test_simulate_raw_bem():
"""Test simulation of raw data with BEM"""
seed = 42
raw, src, stc, trans, sphere = _get_data()
raw_sim_sph = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
ecg=True,
blink=True,
random_state=seed)
raw_sim_bem = simulate_raw(raw,
stc,
trans,
src,
bem_fname,
cov=None,
ecg=True,
blink=True,
random_state=seed,
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:])) > 0.9)
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI."""
raw = read_raw_fif(raw_chpi_fname, allow_maxshield='yes')
picks = np.arange(len(raw.ch_names))
picks = np.setdiff1d(picks, pick_types(raw.info, meg=True, eeg=True)[::4])
raw.load_data().pick_channels([raw.ch_names[pick] for pick in picks])
raw.info.normalize_proj()
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,
interp='zero',
use_cps=True)
# 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,
interp='zero',
use_cps=True)
# test cHPI indication
hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(raw.info)
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[:3], picks_eeg[:3]]:
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, t_step_min=10.)
quats = read_head_pos(pos_fname)
_assert_quats(quats, quats_sim, dist_tol=5e-3, angle_tol=3.5)
def test_simulate_raw_bem(raw_data):
"""Test simulation of raw data with BEM."""
raw, src, stc, trans, sphere = raw_data
src = setup_source_space('sample', 'oct1', subjects_dir=subjects_dir)
for s in src:
s['nuse'] = 3
s['vertno'] = src[1]['vertno'][:3]
s['inuse'].fill(0)
s['inuse'][s['vertno']] = 1
# 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'])
with pytest.deprecated_call():
raw_sim_sph = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
verbose=True)
with pytest.deprecated_call():
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))
med_corr = np.median(np.diag(corr[:n_ch, -n_ch:]))
assert med_corr > 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, 2), (raw_sim_bem,
bem_fname, 31)):
events = find_events(use_raw, 'STI 014')
assert len(locs) == 6
evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None).average()
assert 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
med_diff = np.median(diffs)
assert med_diff < tol, '%s: %s' % (bem, med_diff)
def test_simulate_round_trip():
"""Test simulate_raw round trip calculations."""
# Check a diagonal round-trip
raw, src, stc, trans, sphere = _get_data()
raw.pick_types(meg=True, stim=True)
bem = read_bem_solution(bem_1_fname)
old_bem = bem.copy()
old_src = src.copy()
old_trans = trans.copy()
fwd = make_forward_solution(raw.info, trans, src, bem)
# no omissions
assert (sum(len(s['vertno'])
for s in src) == sum(len(s['vertno'])
for s in fwd['src']) == 36)
# make sure things were not modified
assert (
old_bem['surfs'][0]['coord_frame'] == bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
data = np.eye(fwd['nsource'])
raw.crop(0, (len(data) - 1) / raw.info['sfreq'])
stc = SourceEstimate(data, [s['vertno'] for s in fwd['src']], 0,
1. / raw.info['sfreq'])
for use_fwd in (None, fwd):
if use_fwd is None:
use_trans, use_src, use_bem = trans, src, bem
else:
use_trans = use_src = use_bem = None
for use_cps in (False, True):
this_raw = simulate_raw(raw,
stc,
use_trans,
use_src,
use_bem,
cov=None,
use_cps=use_cps,
forward=use_fwd)
this_raw.pick_types(meg=True, eeg=True)
assert (old_bem['surfs'][0]['coord_frame'] == bem['surfs'][0]
['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
this_fwd = convert_forward_solution(fwd,
force_fixed=True,
use_cps=use_cps)
assert_allclose(this_raw[:][0],
this_fwd['sol']['data'],
atol=1e-12,
rtol=1e-6)
with pytest.raises(ValueError, match='If forward is not None then'):
simulate_raw(raw, stc, trans, src, bem, forward=fwd)
fwd['info']['dev_head_t']['trans'][0, 0] = 1.
with pytest.raises(ValueError, match='dev_head_t.*does not match'):
simulate_raw(raw, stc, None, None, None, forward=fwd)
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI"""
with warnings.catch_warnings(record=True): # MaxShield
raw = Raw(raw_chpi_fname, allow_maxshield=True)
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_on, _ = _get_hpi_info(raw.info)
assert_allclose(raw_sim[hpi_pick][0], 0.)
assert_allclose(raw_chpi[hpi_pick][0], hpi_on)
# 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
trans_sim, rot_sim, t_sim = _calculate_chpi_positions(raw_chpi)
trans, rot, t = get_chpi_positions(pos_fname)
t -= raw.first_samp / raw.info['sfreq']
_compare_positions((trans, rot, t), (trans_sim, rot_sim, t_sim),
max_dist=0.005)
def test_simulate_raw_bem():
"""Test simulation of raw data with BEM"""
seed = 42
raw, src, stc, trans, sphere = _get_data()
raw_sim_sph = simulate_raw(raw, stc, trans, src, sphere, cov=None, ecg=True, blink=True, random_state=seed)
raw_sim_bem = simulate_raw(
raw, stc, trans, src, bem_fname, cov=None, ecg=True, blink=True, random_state=seed, 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:])) > 0.9)
def test_simulate_round_trip():
"""Test simulate_raw round trip calculations."""
# Check a diagonal round-trip
raw, src, stc, trans, sphere = _get_data()
raw.pick_types(meg=True, stim=True)
bem = read_bem_solution(bem_1_fname)
old_bem = bem.copy()
old_src = src.copy()
old_trans = trans.copy()
fwd = make_forward_solution(raw.info, trans, src, bem)
# no omissions
assert (sum(len(s['vertno']) for s in src) ==
sum(len(s['vertno']) for s in fwd['src']) ==
36)
# make sure things were not modified
assert (old_bem['surfs'][0]['coord_frame'] ==
bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
data = np.eye(fwd['nsource'])
raw.crop(0, (len(data) - 1) / raw.info['sfreq'])
stc = SourceEstimate(data, [s['vertno'] for s in fwd['src']],
0, 1. / raw.info['sfreq'])
for use_cps in (False, True):
this_raw = simulate_raw(raw, stc, trans, src, bem, cov=None,
use_cps=use_cps)
this_raw.pick_types(meg=True, eeg=True)
assert (old_bem['surfs'][0]['coord_frame'] ==
bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
this_fwd = convert_forward_solution(fwd, force_fixed=True,
use_cps=use_cps)
assert_allclose(this_raw[:][0], this_fwd['sol']['data'],
atol=1e-12, rtol=1e-6)
def test_simulate_raw_bem(raw_data):
"""Test simulation of raw data with BEM."""
raw, src, stc, trans, sphere = raw_data
src = setup_source_space('sample', 'oct1', subjects_dir=subjects_dir)
for s in src:
s['nuse'] = 3
s['vertno'] = src[1]['vertno'][:3]
s['inuse'].fill(0)
s['inuse'][s['vertno']] = 1
# 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'])
with pytest.deprecated_call():
raw_sim_sph = simulate_raw(raw, stc, trans, src, sphere, cov=None,
verbose=True)
with pytest.deprecated_call():
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))
med_corr = np.median(np.diag(corr[:n_ch, -n_ch:]))
assert med_corr > 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, 2),
(raw_sim_bem, bem_fname, 31)):
events = find_events(use_raw, 'STI 014')
assert len(locs) == 6
evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None).average()
assert 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
med_diff = np.median(diffs)
assert med_diff < tol, '%s: %s' % (bem, med_diff)
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI."""
raw = read_raw_fif(raw_chpi_fname, allow_maxshield='yes')
picks = np.arange(len(raw.ch_names))
picks = np.setdiff1d(picks, pick_types(raw.info, meg=True, eeg=True)[::4])
raw.load_data().pick_channels([raw.ch_names[pick] for pick in picks])
raw.info.normalize_proj()
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(sphere=sphere_vol, pos=70.)
stc = _make_stc(raw, src)
# simulate data with cHPI on
with pytest.deprecated_call():
raw_sim = simulate_raw(raw, stc, None, src, sphere, cov=None,
head_pos=pos_fname, interp='zero')
# need to trim extra samples off this one
with pytest.deprecated_call():
raw_chpi = simulate_raw(raw, stc, None, src, sphere, cov=None,
chpi=True, head_pos=pos_fname, interp='zero')
# test cHPI indication
hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(raw.info)
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[:3], picks_eeg[:3]]:
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 (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, t_step_min=10.)
quats = read_head_pos(pos_fname)
_assert_quats(quats, quats_sim, dist_tol=5e-3, angle_tol=3.5)
def generate_combined_simulation(raw_fname,
fwd,
subject=None,
subjects_dir=None,
topdir=None,
label_index=None,
sine_amplitude=None,
sine_frequency=None):
"""Create a combined dataset of simulated plus real data and save
to the topdir/(subjid)_(AMP)_nAm_(HZ)_hz folder"""
os.chdir(topdir)
if label_index == None:
print('Must provide a label index for simulation')
exit(1)
raw = mne.io.read_raw_fif(raw_fname)
rng = np.random.RandomState(0) # random state (make reproducible)
#Labels for simulation
labels = mne.read_labels_from_annot(subject, subjects_dir=subjects_dir)
labels_sim = [labels[label_index]]
times = raw.times #[:int(raw.info['sfreq'] * epoch_duration)]
src = fwd['src']
sig_generator = partial(data_fun,
amplitude=sine_amplitude,
freq=sine_frequency)
stc = simulate_sparse_stc(src,
n_dipoles=1,
times=times,
data_fun=sig_generator,
labels=labels_sim,
location='center',
subjects_dir=subjects_dir)
# Simulate raw data
raw_sim = simulate_raw(raw.info, [stc] * 1, forward=fwd, verbose=True)
#Load raw and save to outfolder
raw.load_data()
#Combine simulation w/raw
comb_out_fname = '{}_{}_label_{}_nAm_{}_hz_meg.fif'.format(
subject, str(label_index), sine_amplitude, sine_frequency)
# comb_out_fname = op.join(outfolder, outfolder+'_meg.fif')
combined = raw.copy()
combined._data += raw_sim.get_data()
combined.save(comb_out_fname)
print('Saved {}'.format(comb_out_fname))
#Save stc for later use
stc_out_fname = op.join('{}_{}_label_{}_nAm_{}_hz-stc.fif'.format(
subject, str(label_index), sine_amplitude, sine_frequency))
stc.save(stc_out_fname)
def test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI."""
raw = read_raw_fif(raw_chpi_fname, allow_maxshield='yes')
picks = np.arange(len(raw.ch_names))
picks = np.setdiff1d(picks, pick_types(raw.info, meg=True, eeg=True)[::4])
raw.load_data().pick_channels([raw.ch_names[pick] for pick in picks])
raw.info.normalize_proj()
sphere = make_sphere_model('auto', 'auto', raw.info)
# make sparse spherical source space
sphere_vol = tuple(sphere['r0']) + (sphere.radius, )
src = setup_volume_source_space(sphere=sphere_vol,
pos=70.,
sphere_units='m')
stcs = [_make_stc(raw, src)] * 15
# simulate data with cHPI on
raw_sim = simulate_raw(raw.info,
stcs,
None,
src,
sphere,
head_pos=pos_fname,
interp='zero',
first_samp=raw.first_samp)
# need to trim extra samples off this one
raw_chpi = add_chpi(raw_sim.copy(), head_pos=pos_fname, interp='zero')
# test cHPI indication
hpi_freqs, hpi_pick, hpi_ons = get_chpi_info(raw.info, on_missing='raise')
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[:3], picks_eeg[:3]]:
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 (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
chpi_amplitudes = compute_chpi_amplitudes(raw, t_step_min=10.)
coil_locs = compute_chpi_locs(raw.info, chpi_amplitudes)
quats_sim = compute_head_pos(raw_chpi.info, coil_locs)
quats = read_head_pos(pos_fname)
_assert_quats(quats,
quats_sim,
dist_tol=5e-3,
angle_tol=3.5,
vel_atol=0.03) # velicity huge because of t_step_min above
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)
def gen_x_raw(n_trials, raw_template, stc_sim, s_dict):
"""Helper to simulate multiple trials of raw data"""
scaled_cov = deepcopy(s_dict['inv']['noise_cov'])
scaled_cov['data'] = scaled_cov['data'] * s_dict['noise_scale']
# XXX: blink rate: 9 to 21.5 blinks/min (higher than found experimentally)
return simulate_raw(raw_template, stc_sim, s_dict['inv']['mri_head_t'],
src=s_dict['inv']['src'], bem=s_dict['bem_fname'],
cov=scaled_cov, blink=True, n_jobs=n_jobs,
verbose=False)
def test_simulate_raw_sphere(raw_data, tmpdir):
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = raw_data
assert len(pick_types(raw.info, meg=False, ecg=True)) == 1
tempdir = str(tmpdir)
# head pos
head_pos_sim = _get_head_pos_sim(raw)
#
# Test raw simulation with basic parameters
#
raw.info.normalize_proj()
cov = read_cov(cov_fname)
cov['projs'] = raw.info['projs']
raw.info['bads'] = raw.ch_names[:1]
with pytest.deprecated_call(match='cov is deprecated'):
raw_sim = simulate_raw(raw, stc, trans, src, sphere, cov,
head_pos=head_pos_sim,
blink=True, ecg=True, random_state=seed,
verbose=True)
with pytest.warns(RuntimeWarning, match='applying projector with'):
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)
with pytest.raises(RuntimeError, match='Maximum number of STC iterations'):
simulate_raw(raw.info, [stc] * 5, trans_fname, src_fname, sphere,
cov=None, max_iter=1)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
std = dict(grad=2e-13, mag=10e-15, eeg=0.1e-6)
with pytest.deprecated_call():
raw_sim = simulate_raw(raw, stc, trans, src, sphere,
make_ad_hoc_cov(raw.info, std=std),
head_pos=head_pos_sim, blink=True, ecg=True,
random_state=seed)
with pytest.deprecated_call():
raw_sim_2 = simulate_raw(raw, stc, trans_fname, src_fname, sphere,
cov=std, head_pos=head_pos_sim, blink=True,
ecg=True, random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
sphere_norad = make_sphere_model('auto', None, raw.info)
raw_meg = raw.copy().pick_types()
with pytest.deprecated_call():
raw_sim = simulate_raw(raw_meg, stc, trans, src, sphere_norad,
cov=None,
head_pos=head_pos_sim, blink=True, ecg=True,
random_state=seed)
with pytest.deprecated_call():
raw_sim_2 = simulate_raw(raw_meg, stc, trans_fname, src_fname,
sphere_norad, cov=None, 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
test_outname = op.join(tempdir, 'sim_test_raw.fif')
raw_sim.save(test_outname)
raw_sim_loaded = read_raw_fif(test_outname, preload=True)
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)):
with pytest.deprecated_call():
raw_sim_3 = simulate_raw(raw, stc, trans, src, sphere,
cov=None, head_pos=head_pos_sim,
blink=eog, ecg=ecg, random_state=seed)
with pytest.deprecated_call():
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 np.mean(close) > 0.7
far = ~np.isclose(raw_sim_3[diff_picks][0],
raw_sim_4[diff_picks][0], atol=1e-20)
assert np.mean(far) > 0.99
del raw_sim_3, raw_sim_4
# make sure it works with EEG-only and MEG-only
with pytest.deprecated_call():
raw_sim_meg = simulate_raw(raw.copy().pick_types(meg=True, eeg=False),
stc, trans, src, sphere, cov=None)
raw_sim_eeg = simulate_raw(raw.copy().pick_types(meg=False, eeg=True),
stc, trans, src, sphere, cov=None)
raw_sim_meeg = simulate_raw(raw.copy().pick_types(meg=True, eeg=True),
stc, trans, src, sphere, cov=None)
for this_raw in (raw_sim_meg, raw_sim_eeg, raw_sim_meeg):
add_eog(this_raw, random_state=seed)
for this_raw in (raw_sim_meg, raw_sim_meeg):
add_ecg(this_raw, random_state=seed)
with pytest.raises(RuntimeError, match='only add ECG artifacts if MEG'):
add_ecg(raw_sim_eeg)
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 raw-as-info is supported
n_samp = len(stc.times)
raw_crop = raw.copy().crop(0., (n_samp - 1.) / raw.info['sfreq'])
assert len(raw_crop.times) == len(stc.times)
with pytest.deprecated_call():
raw_sim = simulate_raw(raw_crop, stc, trans, src, sphere, cov=None)
with catch_logging() as log:
raw_sim_2 = simulate_raw(raw_crop.info, stc, trans, src, sphere,
cov=None, verbose=True)
log = log.getvalue()
assert '1 STC iteration provided' in log
assert len(raw_sim_2.times) == n_samp
assert_allclose(raw_sim[:, :n_samp][0],
raw_sim_2[:, :n_samp][0], rtol=1e-5, atol=1e-30)
del raw_sim, raw_sim_2
# check that different interpolations are similar given small movements
with pytest.deprecated_call():
raw_sim = simulate_raw(raw, stc, trans, src, sphere, cov=None,
head_pos=head_pos_sim, interp='linear')
with pytest.deprecated_call():
raw_sim_hann = simulate_raw(raw, stc, trans, src, sphere, cov=None,
head_pos=head_pos_sim, interp='hann')
assert_allclose(raw_sim[:][0], raw_sim_hann[:][0], rtol=1e-1, atol=1e-14)
del raw_sim, raw_sim_hann
np.intersect1d(l.vertices, s['vertno']) for l, s in zip(labels, src)
]
data = np.zeros([sum(len(v) for v in vertices), int(raw.info['sfreq'])])
activation = np.hanning(int(raw.info['sfreq'] * 0.2)) * 1e-9 # nAm
t_offset = int(np.ceil(0.2 * raw.info['sfreq'])) # 200 ms in (after baseline)
data[:, t_offset:t_offset + len(activation)] = activation
stc = mne.SourceEstimate(data,
vertices,
tmin=-0.2,
tstep=1. / raw.info['sfreq'])
# Simulate the movement
raw = simulate_raw(raw,
stc,
trans_fname,
src,
bem_fname,
head_pos=pos,
interp='zero',
n_jobs=-1)
raw_stat = simulate_raw(raw,
stc,
trans_fname,
src,
bem_fname,
head_pos=None,
n_jobs=-1)
# Save the results
raw.save('simulated_movement_raw.fif', buffer_size_sec=1.)
raw_stat.save('simulated_stationary_raw.fif', buffer_size_sec=1.)
mne.chpi.write_head_quats('simulated_quats.pos', pos)
stc.save('simulated_activation')
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)
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)
]
stc = stc.in_label(labels[0] + labels[1])
stc.data.fill(0)
stc.data[:, (stc.times >= pulse_tmin) & (stc.times <= pulse_tmax)] = 10e-9
# ############################################################################
# Simulate data
# Simulate data with movement
with warnings.catch_warnings(record=True):
raw = Raw(fname_raw, allow_maxshield=True)
raw_movement = simulate_raw(raw,
stc,
trans,
src,
bem,
chpi=True,
head_pos=fname_pos_orig,
n_jobs=6,
verbose=True)
# Simulate data with no movement (use initial head position)
raw_stationary = simulate_raw(raw,
stc,
trans,
src,
bem,
chpi=True,
n_jobs=6,
verbose=True)
def test_simulate_raw_sphere():
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = _get_data()
assert 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])
std = dict(grad=2e-13, mag=10e-15, eeg=0.1e-6)
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
make_ad_hoc_cov(raw.info, std=std),
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=std,
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
sphere_norad = make_sphere_model('auto', None, raw.info)
raw_meg = raw.copy().pick_types()
raw_sim = simulate_raw(raw_meg,
stc,
trans,
src,
sphere_norad,
make_ad_hoc_cov(raw.info, std=None),
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed)
raw_sim_2 = simulate_raw(raw_meg,
stc,
trans_fname,
src_fname,
sphere_norad,
cov='simple',
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)
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 np.mean(close) > 0.7
far = ~np.isclose(
raw_sim_3[diff_picks][0], raw_sim_4[diff_picks][0], atol=1e-20)
assert 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 raw-as-info is supported
raw_sim = simulate_raw(raw, stc, trans, src, sphere, cov=None)
n_samp = int(round(raw.info['sfreq']))
for use_raw in (raw, raw.info):
raw_sim_2 = simulate_raw(use_raw,
stc,
trans,
src,
sphere,
cov=None,
duration=1.)
assert len(raw_sim_2.times) == n_samp
assert_allclose(raw_sim[:, :n_samp][0],
raw_sim_2[:, :n_samp][0],
rtol=1e-5,
atol=1e-30)
del raw_sim, raw_sim_2
# check that different interpolations are similar given small movements
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
head_pos=head_pos_sim,
interp='linear')
raw_sim_hann = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
head_pos=head_pos_sim,
interp='hann')
assert_allclose(raw_sim[:][0], raw_sim_hann[:][0], rtol=1e-1, atol=1e-14)
del raw_sim, raw_sim_hann
# 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
pytest.raises(RuntimeError,
simulate_raw,
raw,
stc,
trans,
src,
sphere,
ecg=False,
blink=False,
head_pos=head_pos_sim_err)
pytest.raises(RuntimeError,
simulate_raw,
raw,
stc,
trans,
src,
bem_fname,
ecg=False,
blink=False,
head_pos=head_pos_sim_err)
# other degenerate conditions
pytest.raises(TypeError, simulate_raw, 'foo', stc, trans, src, sphere)
pytest.raises(TypeError, simulate_raw, raw, 'foo', trans, src, sphere)
pytest.raises(ValueError, simulate_raw, raw,
stc.copy().crop(0, 0), trans, src, sphere)
with pytest.raises(ValueError, match='duration cannot be None'):
simulate_raw(raw.info, stc, trans, src, sphere)
with pytest.raises(TypeError, match='must be an instance of Raw or Info'):
simulate_raw(0, stc, trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
pytest.raises(ValueError, simulate_raw, raw, stc_bad, trans, src, sphere)
pytest.raises(TypeError, simulate_raw, raw, stc, trans, src, sphere,
cov=0) # wrong covariance type
pytest.raises(RuntimeError,
simulate_raw,
raw,
stc,
trans,
src,
sphere,
chpi=True) # no cHPI info
pytest.raises(ValueError,
simulate_raw,
raw,
stc,
trans,
src,
sphere,
interp='foo')
pytest.raises(TypeError,
simulate_raw,
raw,
stc,
trans,
src,
sphere,
head_pos=1.)
pytest.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
pytest.raises(RuntimeError,
simulate_raw,
raw,
stc,
trans,
src,
sphere,
head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
pytest.raises(RuntimeError,
simulate_raw,
raw_bad,
stc,
trans,
src,
sphere,
blink=True)
def run():
t0 = time.time()
parser = get_optparser(__file__)
parser.add_option("--raw",
dest="raw_in",
help="Input raw FIF file",
metavar="FILE")
parser.add_option("--pos",
dest="pos",
default=None,
help="Position definition text file. Can be 'constant' "
"to hold the head position fixed",
metavar="FILE")
parser.add_option("--dipoles",
dest="dipoles",
default=None,
help="Dipole definition file",
metavar="FILE")
parser.add_option("--cov",
dest="cov",
help="Covariance to use for noise generation. Can be "
"'simple' to use a diagonal covariance, or 'off' to "
"omit noise",
metavar="FILE",
default='simple')
parser.add_option("--duration",
dest="duration",
default=None,
help="Duration of each epoch (sec). If omitted, the last"
" time point in the dipole definition file plus 200 ms "
"will be used",
type="float")
parser.add_option("-j",
"--jobs",
dest="n_jobs",
help="Number of jobs to"
" run in parallel",
type="int",
default=1)
parser.add_option("--out",
dest="raw_out",
help="Output raw filename",
metavar="FILE")
parser.add_option("--plot-dipoles",
dest="plot_dipoles",
help="Plot "
"input dipole positions",
action="store_true")
parser.add_option("--plot-raw",
dest="plot_raw",
help="Plot the resulting "
"raw traces",
action="store_true")
parser.add_option("--plot-evoked",
dest="plot_evoked",
help="Plot evoked "
"data",
action="store_true")
parser.add_option("-p",
"--plot",
dest="plot",
help="Plot dipoles, raw, "
"and evoked",
action="store_true")
parser.add_option("--overwrite",
dest="overwrite",
help="Overwrite the"
"output file if it exists",
action="store_true")
options, args = parser.parse_args()
raw_in = options.raw_in
pos = options.pos
raw_out = options.raw_out
dipoles = options.dipoles
n_jobs = options.n_jobs
plot = options.plot
plot_dipoles = options.plot_dipoles or plot
plot_raw = options.plot_raw or plot
plot_evoked = options.plot_evoked or plot
overwrite = options.overwrite
duration = options.duration
cov = options.cov
# check parameters
if not (raw_out or plot_raw or plot_evoked):
raise ValueError('data must either be saved (--out) or '
'plotted (--plot-raw or --plot_evoked)')
if raw_out and op.isfile(raw_out) and not overwrite:
raise ValueError('output file exists, use --overwrite (%s)' % raw_out)
if raw_in is None or pos is None or dipoles is None:
parser.print_help()
sys.exit(1)
s = 'Simulate raw data with head movements'
print('\n%s\n%s\n%s\n' % ('-' * len(s), s, '-' * len(s)))
# setup the simulation
with printer('Reading dipole definitions'):
if not op.isfile(dipoles):
raise IOError('dipole file not found:\n%s' % dipoles)
dipoles = np.loadtxt(dipoles, skiprows=1, dtype=float)
n_dipoles = dipoles.shape[0]
if dipoles.shape[1] != 8:
raise ValueError('dipoles must have 8 columns')
rr = dipoles[:, :3] * 1e-3
nn = dipoles[:, 3:6]
t = dipoles[:, 6:8]
duration = t.max() + 0.2 if duration is None else duration
if (t[:, 0] > t[:, 1]).any():
raise ValueError('found tmin > tmax in dipole file')
if (t < 0).any():
raise ValueError('found t < 0 in dipole file')
if (t > duration).any():
raise ValueError('found t > duration in dipole file')
amp = np.sqrt(np.sum(nn * nn, axis=1)) * 1e-9
mne.surface._normalize_vectors(nn)
nn[(nn == 0).all(axis=1)] = (1, 0, 0)
src = mne.SourceSpaces([
dict(rr=rr,
nn=nn,
inuse=np.ones(n_dipoles, int),
coord_frame=FIFF.FIFFV_COORD_HEAD)
])
for key in ['pinfo', 'nuse_tri', 'use_tris', 'patch_inds']:
src[0][key] = None
trans = {
'from': FIFF.FIFFV_COORD_HEAD,
'to': FIFF.FIFFV_COORD_MRI,
'trans': np.eye(4)
}
if (amp > 100e-9).any():
print('')
warnings.warn('Largest dipole amplitude %0.1f > 100 nA' %
(amp.max() * 1e9))
if pos == 'constant':
print('Holding head position constant')
pos = None
else:
with printer('Loading head positions'):
pos = mne.get_chpi_positions(pos)
with printer('Loading raw data file'):
with warnings.catch_warnings(record=True):
raw = mne.io.Raw(raw_in,
preload=False,
allow_maxshield=True,
verbose=False)
if cov == 'simple':
print('Using diagonal covariance for brain noise')
elif cov == 'off':
print('Omitting brain noise in the simulation')
cov = None
else:
with printer('Loading covariance file for brain noise'):
cov = mne.read_cov(cov)
with printer('Setting up spherical model'):
bem = mne.bem.make_sphere_model('auto',
'auto',
raw.info,
verbose=False)
# check that our sources are reasonable
rad = bem['layers'][0]['rad']
r0 = bem['r0']
outside = np.sqrt(np.sum((rr - r0)**2, axis=1)) >= rad
n_outside = outside.sum()
if n_outside > 0:
print('')
raise ValueError(
'%s dipole%s outside the spherical model, are your positions '
'in mm?' % (n_outside, 's were' if n_outside != 1 else ' was'))
with printer('Constructing source estimate'):
tmids = t.mean(axis=1)
t = np.round(t * raw.info['sfreq']).astype(int)
t[:, 1] += 1 # make it inclusive
n_samp = int(np.ceil(duration * raw.info['sfreq']))
data = np.zeros((n_dipoles, n_samp))
for di, (t_, amp_) in enumerate(zip(t, amp)):
data[di, t_[0]:t_[1]] = amp_ * np.hanning(t_[1] - t_[0])
stc = mne.VolSourceEstimate(data, np.arange(n_dipoles), 0,
1. / raw.info['sfreq'])
# do the simulation
print('')
raw_mv = simulate_raw(raw,
stc,
trans,
src,
bem,
cov=cov,
head_pos=pos,
chpi=True,
n_jobs=n_jobs,
verbose=True)
print('')
if raw_out:
with printer('Saving data'):
raw_mv.save(raw_out, overwrite=overwrite)
# plot results -- must be *after* save because we low-pass filter
if plot_dipoles:
with printer('Plotting dipoles'):
fig, axs = plt.subplots(1, 3, figsize=(10, 3), facecolor='w')
fig.canvas.set_window_title('Dipoles')
meg_info = mne.pick_info(
raw.info, mne.pick_types(raw.info, meg=True, eeg=False))
helmet_rr = [
ch['coil_trans'][:3, 3].copy() for ch in meg_info['chs']
]
helmet_nn = np.zeros_like(helmet_rr)
helmet_nn[:, 2] = 1.
surf = dict(rr=helmet_rr,
nn=helmet_nn,
coord_frame=FIFF.FIFFV_COORD_DEVICE)
helmet_rr = mne.surface.transform_surface_to(
surf, 'head', meg_info['dev_head_t'])['rr']
p = np.linspace(0, 2 * np.pi, 40)
x_sphere, y_sphere = rad * np.sin(p), rad * np.cos(p)
for ai, ax in enumerate(axs):
others = np.setdiff1d(np.arange(3), [ai])
ax.plot(helmet_rr[:, others[0]],
helmet_rr[:, others[1]],
marker='o',
linestyle='none',
alpha=0.1,
markeredgecolor='none',
markerfacecolor='b',
zorder=-2)
ax.plot(x_sphere + r0[others[0]],
y_sphere + r0[others[1]],
color='y',
alpha=0.25,
zorder=-1)
ax.quiver(rr[:, others[0]],
rr[:, others[1]],
amp * nn[:, others[0]],
amp * nn[:, others[1]],
angles='xy',
units='x',
color='k',
alpha=0.5)
ax.set_aspect('equal')
ax.set_xlabel(' - ' + 'xyz'[others[0]] + ' + ')
ax.set_ylabel(' - ' + 'xyz'[others[1]] + ' + ')
ax.set_xticks([])
ax.set_yticks([])
plt.setp(list(ax.spines.values()), color='none')
plt.tight_layout()
if plot_raw or plot_evoked:
with printer('Low-pass filtering simulated data'):
events = mne.find_events(raw_mv, 'STI101', verbose=False)
b, a = signal.butter(4,
40. / (raw.info['sfreq'] / 2.),
'low',
analog=False)
raw_mv.filter(None,
40.,
method='iir',
iir_params=dict(b=b, a=a),
verbose=False,
n_jobs=n_jobs)
if plot_raw:
with printer('Plotting raw data'):
raw_mv.plot(clipping='transparent', events=events, show=False)
if plot_evoked:
with printer('Plotting evoked data'):
picks = mne.pick_types(raw_mv.info, meg=True, eeg=True)
events[:, 2] = 1
evoked = mne.Epochs(raw_mv, events, {
'Simulated': 1
}, 0, duration, None, picks).average()
evoked.plot_topomap(np.unique(tmids), show=False)
print('\nTotal time: %0.1f sec' % (time.time() - t0))
sys.stdout.flush()
if any([plot_dipoles, plot_raw, plot_evoked]):
plt.show(block=True)
def test_iterable():
"""Test iterable support for simulate_raw."""
raw = read_raw_fif(raw_fname_short).load_data()
raw.pick_channels(raw.ch_names[:10] + ['STI 014'])
src = setup_volume_source_space(
pos=dict(rr=[[-0.05, 0, 0], [0.1, 0, 0]], nn=[[0, 1., 0], [0, 1., 0]]))
assert src.kind == 'discrete'
trans = None
sphere = make_sphere_model(head_radius=None, info=raw.info)
tstep = 1. / raw.info['sfreq']
rng = np.random.RandomState(0)
vertices = np.array([1])
data = rng.randn(1, 2)
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
with pytest.raises(ValueError, match='at least three time points'):
simulate_raw(raw.info, stc, trans, src, sphere, None)
data = rng.randn(1, 1000)
n_events = (len(raw.times) - 1) // 1000 + 1
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
with catch_logging() as log:
with pytest.deprecated_call():
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
None,
verbose=True)
log = log.getvalue()
assert 'Making 15 copies of STC' in log
assert_allclose(raw.times, raw_sim.times)
events = find_events(raw_sim, initial_event=True)
assert len(events) == n_events
assert_array_equal(events[:, 2], 1)
# Degenerate STCs
with pytest.raises(RuntimeError,
match=r'Iterable did not provide stc\[0\]'):
simulate_raw(raw.info, [], trans, src, sphere, None)
with pytest.raises(RuntimeError,
match=r'Iterable did not provide stc\[2\].*duration'):
with pytest.deprecated_call():
simulate_raw(raw, [stc, stc], trans, src, sphere, None)
# tuple with ndarray
event_data = np.zeros(len(stc.times), int)
event_data[0] = 3
raw_new = simulate_raw(raw.info, [(stc, event_data)] * 15,
trans,
src,
sphere,
None,
first_samp=raw.first_samp)
assert raw_new.n_times == 15000
raw_new.crop(0, raw_sim.times[-1])
_assert_iter_sim(raw_sim, raw_new, 3)
with pytest.raises(ValueError, match='event data had shape .* but need'):
simulate_raw(raw.info, [(stc, event_data[:-1])], trans, src, sphere,
None)
with pytest.raises(ValueError, match='stim_data in a stc tuple .* int'):
simulate_raw(raw.info, [(stc, event_data * 1.)], trans, src, sphere,
None)
# iterable
def stc_iter():
stim_data = np.zeros(len(stc.times), int)
stim_data[0] = 4
ii = 0
while ii < 100:
ii += 1
yield (stc, stim_data)
with pytest.deprecated_call():
raw_new = simulate_raw(raw, stc_iter(), trans, src, sphere, None)
_assert_iter_sim(raw_sim, raw_new, 4)
def stc_iter_bad():
ii = 0
while ii < 100:
ii += 1
yield (stc, 4, 3)
with pytest.raises(ValueError, match='stc, if tuple, must be length'):
simulate_raw(raw.info, stc_iter_bad(), trans, src, sphere, None)
_assert_iter_sim(raw_sim, raw_new, 4)
def stc_iter_bad():
ii = 0
while ii < 100:
ii += 1
stc_new = stc.copy()
stc_new.vertices = np.array([ii % 2])
yield stc_new
with pytest.raises(RuntimeError, match=r'Vertex mismatch for stc\[1\]'):
simulate_raw(raw.info, stc_iter_bad(), trans, src, sphere, None)
# Forward omission
vertices = np.array([0, 1])
data = rng.randn(2, 1000)
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
# XXX eventually we should support filtering based on sphere radius, too,
# by refactoring the code in source_space.py that does it!
surf = _get_ico_surface(3)
surf['rr'] *= 60 # mm
model = _surfaces_to_bem([surf], [FIFF.FIFFV_BEM_SURF_ID_BRAIN], [0.3])
bem = make_bem_solution(model)
with pytest.warns(RuntimeWarning, match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw, stc, trans, src, bem, None)
info.update(sfreq=sfreq, bads=[])
# Only use gradiometers
picks = mne.pick_types(info, meg='grad', stim=True, exclude=())
mne.pick_info(info, picks, copy=False)
# This is the raw object that will be used as a template for the simulation.
raw = mne.io.RawArray(np.zeros((info['nchan'], len(stc.times))), info)
# Define a covariance matrix for the simulated noise. In this tutorial, we use
# a simple diagonal matrix.
cov = mne.cov.make_ad_hoc_cov(info)
cov['data'] *= (20. / snr) ** 2 # Scale the noise to achieve the desired SNR
# Simulate the raw data, with a lowpass filter on the noise
raw = simulate_raw(raw, stc, trans_fname, src_fname, bem_fname, cov=cov,
random_state=rand, iir_filter=[4, -4, 0.8])
###############################################################################
# We create an :class:`mne.Epochs` object containing two trials: one with
# both noise and signal and one with just noise
t0 = raw.first_samp # First sample in the data
t1 = t0 + n_times - 1 # Sample just before the second trial
epochs = mne.Epochs(
raw,
events=np.array([[t0, 0, 1], [t1, 0, 2]]),
event_id=dict(signal=1, noise=2),
tmin=0, tmax=10,
preload=True,
)
def test_simulate_raw_sphere(raw_data, tmpdir):
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = raw_data
assert len(pick_types(raw.info, meg=False, ecg=True)) == 1
tempdir = str(tmpdir)
# head pos
head_pos_sim = _get_head_pos_sim(raw)
#
# Test raw simulation with basic parameters
#
raw.info.normalize_proj()
cov = read_cov(cov_fname)
cov['projs'] = raw.info['projs']
raw.info['bads'] = raw.ch_names[:1]
with pytest.deprecated_call(match='cov is deprecated'):
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov,
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed,
verbose=True)
with pytest.warns(RuntimeWarning, match='applying projector with'):
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)
with pytest.raises(RuntimeError, match='Maximum number of STC iterations'):
simulate_raw(raw.info, [stc] * 5,
trans_fname,
src_fname,
sphere,
cov=None,
max_iter=1)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
std = dict(grad=2e-13, mag=10e-15, eeg=0.1e-6)
with pytest.deprecated_call():
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
make_ad_hoc_cov(raw.info, std=std),
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed)
with pytest.deprecated_call():
raw_sim_2 = simulate_raw(raw,
stc,
trans_fname,
src_fname,
sphere,
cov=std,
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
sphere_norad = make_sphere_model('auto', None, raw.info)
raw_meg = raw.copy().pick_types()
with pytest.deprecated_call():
raw_sim = simulate_raw(raw_meg,
stc,
trans,
src,
sphere_norad,
cov=None,
head_pos=head_pos_sim,
blink=True,
ecg=True,
random_state=seed)
with pytest.deprecated_call():
raw_sim_2 = simulate_raw(raw_meg,
stc,
trans_fname,
src_fname,
sphere_norad,
cov=None,
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
test_outname = op.join(tempdir, 'sim_test_raw.fif')
raw_sim.save(test_outname)
raw_sim_loaded = read_raw_fif(test_outname, preload=True)
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)):
with pytest.deprecated_call():
raw_sim_3 = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
head_pos=head_pos_sim,
blink=eog,
ecg=ecg,
random_state=seed)
with pytest.deprecated_call():
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 np.mean(close) > 0.7
far = ~np.isclose(
raw_sim_3[diff_picks][0], raw_sim_4[diff_picks][0], atol=1e-20)
assert np.mean(far) > 0.99
del raw_sim_3, raw_sim_4
# make sure it works with EEG-only and MEG-only
with pytest.deprecated_call():
raw_sim_meg = simulate_raw(raw.copy().pick_types(meg=True, eeg=False),
stc,
trans,
src,
sphere,
cov=None)
raw_sim_eeg = simulate_raw(raw.copy().pick_types(meg=False, eeg=True),
stc,
trans,
src,
sphere,
cov=None)
raw_sim_meeg = simulate_raw(raw.copy().pick_types(meg=True, eeg=True),
stc,
trans,
src,
sphere,
cov=None)
for this_raw in (raw_sim_meg, raw_sim_eeg, raw_sim_meeg):
add_eog(this_raw, random_state=seed)
for this_raw in (raw_sim_meg, raw_sim_meeg):
add_ecg(this_raw, random_state=seed)
with pytest.raises(RuntimeError, match='only add ECG artifacts if MEG'):
add_ecg(raw_sim_eeg)
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 raw-as-info is supported
n_samp = len(stc.times)
raw_crop = raw.copy().crop(0., (n_samp - 1.) / raw.info['sfreq'])
assert len(raw_crop.times) == len(stc.times)
with pytest.deprecated_call():
raw_sim = simulate_raw(raw_crop, stc, trans, src, sphere, cov=None)
with catch_logging() as log:
raw_sim_2 = simulate_raw(raw_crop.info,
stc,
trans,
src,
sphere,
cov=None,
verbose=True)
log = log.getvalue()
assert '1 STC iteration provided' in log
assert len(raw_sim_2.times) == n_samp
assert_allclose(raw_sim[:, :n_samp][0],
raw_sim_2[:, :n_samp][0],
rtol=1e-5,
atol=1e-30)
del raw_sim, raw_sim_2
# check that different interpolations are similar given small movements
with pytest.deprecated_call():
raw_sim = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
head_pos=head_pos_sim,
interp='linear')
with pytest.deprecated_call():
raw_sim_hann = simulate_raw(raw,
stc,
trans,
src,
sphere,
cov=None,
head_pos=head_pos_sim,
interp='hann')
assert_allclose(raw_sim[:][0], raw_sim_hann[:][0], rtol=1e-1, atol=1e-14)
del raw_sim_hann
# check that new Generator objects can be used
if check_version('numpy', '1.17'):
random_state = np.random.default_rng(seed)
add_ecg(raw_sim, random_state=random_state)
add_eog(raw_sim, random_state=random_state)
def test_degenerate(raw_data):
"""Test degenerate conditions."""
raw, src, stc, trans, sphere = raw_data
info = raw.info
# Make impossible transform (translate up into helmet) and ensure failure
hp_err = _get_head_pos_sim(raw)
hp_err[1.][2, 3] -= 0.1 # z trans upward 10cm
with pytest.raises(RuntimeError, match='collided with inner skull'):
simulate_raw(info, stc, trans, src, sphere, cov=None,
head_pos=hp_err)
# other degenerate conditions
with pytest.raises(TypeError, match='info must be an instance of'):
simulate_raw('foo', stc, trans, src, sphere)
with pytest.raises(TypeError, match='stc must be an instance of'):
simulate_raw(info, 'foo', trans, src, sphere)
with pytest.raises(ValueError, match='stc must have at least three time'):
simulate_raw(info, stc.copy().crop(0, 0), trans, src, sphere)
with pytest.raises(TypeError, match='must be an instance of Raw or Info'):
simulate_raw(0, stc, trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
with pytest.raises(ValueError, match='same sample rate'):
simulate_raw(info, stc_bad, trans, src, sphere)
with pytest.raises(TypeError, match='Covariance matrix type'):
with pytest.deprecated_call():
simulate_raw(info, stc, trans, src, sphere, cov=0)
with pytest.raises(RuntimeError, match='cHPI information not found'):
with pytest.deprecated_call():
simulate_raw(info, stc, trans, src, sphere, chpi=True)
with pytest.raises(ValueError, match='interp must be one of'):
simulate_raw(info, stc, trans, src, sphere, interp='foo')
with pytest.raises(TypeError, match='unknown head_pos type'):
simulate_raw(info, stc, trans, src, sphere, head_pos=1.)
with pytest.raises(RuntimeError, match='All position times'):
with pytest.deprecated_call():
simulate_raw(raw, stc, trans, src, sphere, head_pos=pos_fname)
head_pos_sim_err = _get_head_pos_sim(raw)
head_pos_sim_err[-1.] = head_pos_sim_err[1.] # negative time
with pytest.raises(RuntimeError, match='All position times'):
simulate_raw(info, stc, trans, src, sphere,
head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
with pytest.raises(RuntimeError, match='Cannot fit headshape'):
with pytest.deprecated_call():
simulate_raw(raw_bad, stc, trans, src, sphere, blink=True)
with pytest.raises(RuntimeError, match='Cannot fit headshape'):
add_eog(raw_bad)
def run():
t0 = time.time()
parser = get_optparser(__file__)
parser.add_option("--raw", dest="raw_in",
help="Input raw FIF file", metavar="FILE")
parser.add_option("--pos", dest="pos", default=None,
help="Position definition text file. Can be 'constant' "
"to hold the head position fixed", metavar="FILE")
parser.add_option("--dipoles", dest="dipoles", default=None,
help="Dipole definition file", metavar="FILE")
parser.add_option("--cov", dest="cov",
help="Covariance to use for noise generation. Can be "
"'simple' to use a diagonal covariance, or 'off' to "
"omit noise",
metavar="FILE", default='simple')
parser.add_option("--duration", dest="duration", default=None,
help="Duration of each epoch (sec). If omitted, the last"
" time point in the dipole definition file plus 200 ms "
"will be used", type="float")
parser.add_option("-j", "--jobs", dest="n_jobs", help="Number of jobs to"
" run in parallel", type="int", default=1)
parser.add_option("--out", dest="raw_out",
help="Output raw filename", metavar="FILE")
parser.add_option("--plot-dipoles", dest="plot_dipoles", help="Plot "
"input dipole positions", action="store_true")
parser.add_option("--plot-raw", dest="plot_raw", help="Plot the resulting "
"raw traces", action="store_true")
parser.add_option("--plot-evoked", dest="plot_evoked", help="Plot evoked "
"data", action="store_true")
parser.add_option("-p", "--plot", dest="plot", help="Plot dipoles, raw, "
"and evoked", action="store_true")
parser.add_option("--overwrite", dest="overwrite", help="Overwrite the"
"output file if it exists", action="store_true")
options, args = parser.parse_args()
raw_in = options.raw_in
pos = options.pos
raw_out = options.raw_out
dipoles = options.dipoles
n_jobs = options.n_jobs
plot = options.plot
plot_dipoles = options.plot_dipoles or plot
plot_raw = options.plot_raw or plot
plot_evoked = options.plot_evoked or plot
overwrite = options.overwrite
duration = options.duration
cov = options.cov
# check parameters
if not (raw_out or plot_raw or plot_evoked):
raise ValueError('data must either be saved (--out) or '
'plotted (--plot-raw or --plot_evoked)')
if raw_out and op.isfile(raw_out) and not overwrite:
raise ValueError('output file exists, use --overwrite (%s)' % raw_out)
if raw_in is None or pos is None or dipoles is None:
parser.print_help()
sys.exit(1)
s = 'Simulate raw data with head movements'
print('\n%s\n%s\n%s\n' % ('-' * len(s), s, '-' * len(s)))
# setup the simulation
with printer('Reading dipole definitions'):
if not op.isfile(dipoles):
raise IOError('dipole file not found:\n%s' % dipoles)
dipoles = np.loadtxt(dipoles, skiprows=1, dtype=float)
n_dipoles = dipoles.shape[0]
if dipoles.shape[1] != 8:
raise ValueError('dipoles must have 8 columns')
rr = dipoles[:, :3] * 1e-3
nn = dipoles[:, 3:6]
t = dipoles[:, 6:8]
duration = t.max() + 0.2 if duration is None else duration
if (t[:, 0] > t[:, 1]).any():
raise ValueError('found tmin > tmax in dipole file')
if (t < 0).any():
raise ValueError('found t < 0 in dipole file')
if (t > duration).any():
raise ValueError('found t > duration in dipole file')
amp = np.sqrt(np.sum(nn * nn, axis=1)) * 1e-9
mne.surface._normalize_vectors(nn)
nn[(nn == 0).all(axis=1)] = (1, 0, 0)
src = mne.SourceSpaces([
dict(rr=rr, nn=nn, inuse=np.ones(n_dipoles, int),
coord_frame=FIFF.FIFFV_COORD_HEAD)])
for key in ['pinfo', 'nuse_tri', 'use_tris', 'patch_inds']:
src[0][key] = None
trans = {'from': FIFF.FIFFV_COORD_HEAD, 'to': FIFF.FIFFV_COORD_MRI,
'trans': np.eye(4)}
if (amp > 100e-9).any():
print('')
warnings.warn('Largest dipole amplitude %0.1f > 100 nA'
% (amp.max() * 1e9))
if pos == 'constant':
print('Holding head position constant')
pos = None
else:
with printer('Loading head positions'):
pos = mne.get_chpi_positions(pos)
with printer('Loading raw data file'):
with warnings.catch_warnings(record=True):
raw = mne.io.Raw(raw_in, preload=False, allow_maxshield=True,
verbose=False)
if cov == 'simple':
print('Using diagonal covariance for brain noise')
elif cov == 'off':
print('Omitting brain noise in the simulation')
cov = None
else:
with printer('Loading covariance file for brain noise'):
cov = mne.read_cov(cov)
with printer('Setting up spherical model'):
bem = mne.bem.make_sphere_model('auto', 'auto', raw.info,
verbose=False)
# check that our sources are reasonable
rad = bem['layers'][0]['rad']
r0 = bem['r0']
outside = np.sqrt(np.sum((rr - r0) ** 2, axis=1)) >= rad
n_outside = outside.sum()
if n_outside > 0:
print('')
raise ValueError(
'%s dipole%s outside the spherical model, are your positions '
'in mm?' % (n_outside, 's were' if n_outside != 1 else ' was'))
with printer('Constructing source estimate'):
tmids = t.mean(axis=1)
t = np.round(t * raw.info['sfreq']).astype(int)
t[:, 1] += 1 # make it inclusive
n_samp = int(np.ceil(duration * raw.info['sfreq']))
data = np.zeros((n_dipoles, n_samp))
for di, (t_, amp_) in enumerate(zip(t, amp)):
data[di, t_[0]:t_[1]] = amp_ * np.hanning(t_[1] - t_[0])
stc = mne.VolSourceEstimate(data, np.arange(n_dipoles),
0, 1. / raw.info['sfreq'])
# do the simulation
print('')
raw_mv = simulate_raw(raw, stc, trans, src, bem, cov=cov, head_pos=pos,
chpi=True, n_jobs=n_jobs, verbose=True)
print('')
if raw_out:
with printer('Saving data'):
raw_mv.save(raw_out, overwrite=overwrite)
# plot results -- must be *after* save because we low-pass filter
if plot_dipoles:
with printer('Plotting dipoles'):
fig, axs = plt.subplots(1, 3, figsize=(10, 3), facecolor='w')
fig.canvas.set_window_title('Dipoles')
meg_info = mne.pick_info(raw.info,
mne.pick_types(raw.info,
meg=True, eeg=False))
helmet_rr = [ch['coil_trans'][:3, 3].copy()
for ch in meg_info['chs']]
helmet_nn = np.zeros_like(helmet_rr)
helmet_nn[:, 2] = 1.
surf = dict(rr=helmet_rr, nn=helmet_nn,
coord_frame=FIFF.FIFFV_COORD_DEVICE)
helmet_rr = mne.surface.transform_surface_to(
surf, 'head', meg_info['dev_head_t'])['rr']
p = np.linspace(0, 2 * np.pi, 40)
x_sphere, y_sphere = rad * np.sin(p), rad * np.cos(p)
for ai, ax in enumerate(axs):
others = np.setdiff1d(np.arange(3), [ai])
ax.plot(helmet_rr[:, others[0]], helmet_rr[:, others[1]],
marker='o', linestyle='none', alpha=0.1,
markeredgecolor='none', markerfacecolor='b', zorder=-2)
ax.plot(x_sphere + r0[others[0]], y_sphere + r0[others[1]],
color='y', alpha=0.25, zorder=-1)
ax.quiver(rr[:, others[0]], rr[:, others[1]],
amp * nn[:, others[0]], amp * nn[:, others[1]],
angles='xy', units='x', color='k', alpha=0.5)
ax.set_aspect('equal')
ax.set_xlabel(' - ' + 'xyz'[others[0]] + ' + ')
ax.set_ylabel(' - ' + 'xyz'[others[1]] + ' + ')
ax.set_xticks([])
ax.set_yticks([])
plt.setp(list(ax.spines.values()), color='none')
plt.tight_layout()
if plot_raw or plot_evoked:
with printer('Low-pass filtering simulated data'):
events = mne.find_events(raw_mv, 'STI101', verbose=False)
b, a = signal.butter(4, 40. / (raw.info['sfreq'] / 2.), 'low',
analog=False)
raw_mv.filter(None, 40., method='iir', iir_params=dict(b=b, a=a),
verbose=False, n_jobs=n_jobs)
if plot_raw:
with printer('Plotting raw data'):
raw_mv.plot(clipping='transparent', events=events,
show=False)
if plot_evoked:
with printer('Plotting evoked data'):
picks = mne.pick_types(raw_mv.info, meg=True, eeg=True)
events[:, 2] = 1
evoked = mne.Epochs(raw_mv, events, {'Simulated': 1},
0, duration, None, picks).average()
evoked.plot_topomap(np.unique(tmids), show=False)
print('\nTotal time: %0.1f sec' % (time.time() - t0))
sys.stdout.flush()
if any([plot_dipoles, plot_raw, plot_evoked]):
plt.show(block=True)
# limit activation to a square pulse in time at two vertices in space
labels = [
read_labels_from_annot(subject, "aparc.a2009s", hemi, regexp="G_temp_sup-G_T_transv")[0]
for hi, hemi in enumerate(("lh", "rh"))
]
stc = stc.in_label(labels[0] + labels[1])
stc.data.fill(0)
stc.data[:, (stc.times >= pulse_tmin) & (stc.times <= pulse_tmax)] = 10e-9
# ############################################################################
# Simulate data
# Simulate data with movement
with warnings.catch_warnings(record=True):
raw = Raw(fname_raw, allow_maxshield=True)
raw_movement = simulate_raw(raw, stc, trans, src, bem, chpi=True, head_pos=fname_pos_orig, n_jobs=6, verbose=True)
# Simulate data with no movement (use initial head position)
raw_stationary = simulate_raw(raw, stc, trans, src, bem, chpi=True, n_jobs=6, verbose=True)
# Extract positions
trans_orig, rot_orig, t_orig = get_chpi_positions(fname_pos_orig)
t_orig -= raw.first_samp / raw.info["sfreq"]
trans_move, rot_move, t_move = _calculate_chpi_positions(raw_movement)
trans_stat, rot_stat, t_stat = _calculate_chpi_positions(raw_stationary)
# ############################################################################
# Let's look at the results, just translation for simplicity
axes = "XYZ"
fig = plt.figure(dpi=200)
info.update(sfreq=sfreq, bads=[])
# Only use gradiometers
picks = mne.pick_types(info, meg='grad', stim=True, exclude=())
mne.pick_info(info, picks, copy=False)
# Define a covariance matrix for the simulated noise. In this tutorial, we use
# a simple diagonal matrix.
cov = mne.cov.make_ad_hoc_cov(info)
cov['data'] *= (20. / snr) ** 2 # Scale the noise to achieve the desired SNR
# Simulate the raw data, with a lowpass filter on the noise
stcs = [(stc_signal, unit_impulse(n_samp, dtype=int) * 1),
(stc_noise, unit_impulse(n_samp, dtype=int) * 2)] # stacked in time
duration = (len(stc_signal.times) * 2) / sfreq
raw = simulate_raw(info, stcs, forward=fwd)
add_noise(raw, cov, iir_filter=[4, -4, 0.8], random_state=rand)
###############################################################################
# We create an :class:`mne.Epochs` object containing two trials: one with
# both noise and signal and one with just noise
events = mne.find_events(raw, initial_event=True)
tmax = (len(stc_signal.times) - 1) / sfreq
epochs = mne.Epochs(raw, events, event_id=dict(signal=1, noise=2),
tmin=0, tmax=tmax, baseline=None, preload=True)
assert len(epochs) == 2 # ensure that we got the two expected events
# Plot some of the channels of the simulated data that are situated above one
# of our simulated sources.
def test_simulate_raw_bem(raw_data):
"""Test simulation of raw data with BEM."""
raw, src_ss, stc, trans, sphere = raw_data
src = setup_source_space('sample', 'oct1', subjects_dir=subjects_dir)
for s in src:
s['nuse'] = 3
s['vertno'] = src[1]['vertno'][:3]
s['inuse'].fill(0)
s['inuse'][s['vertno']] = 1
# 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'])
stcs = [stc] * 15
raw_sim_sph = simulate_raw(raw.info, stcs, trans, src, sphere)
raw_sim_bem = simulate_raw(raw.info, stcs, trans, src, bem_fname)
# 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))
med_corr = np.median(np.diag(corr[:n_ch, -n_ch:]))
assert med_corr > 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, 2),
(raw_sim_bem, bem_fname, 31)):
events = find_events(use_raw, 'STI 014')
assert len(locs) == 6
evoked = Epochs(use_raw, events, 1, 0, tmax, baseline=None).average()
assert 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
med_diff = np.median(diffs)
assert med_diff < tol, '%s: %s' % (bem, med_diff)
# also test event timings with SourceSimulator
first_samp = raw.first_samp
events = find_events(raw, initial_event=True, verbose=False)
evt_times = events[:, 0]
assert len(events) == 3
labels_sim = [[], [], []] # random l+r hemisphere points
labels_sim[0] = Label([src_ss[0]['vertno'][1]], hemi='lh')
labels_sim[1] = Label([src_ss[0]['vertno'][4]], hemi='lh')
labels_sim[2] = Label([src_ss[1]['vertno'][2]], hemi='rh')
wf_sim = np.array([2, 1, 0])
for this_fs in (0, first_samp):
ss = SourceSimulator(src_ss, 1. / raw.info['sfreq'],
first_samp=this_fs)
for i in range(3):
ss.add_data(labels_sim[i], wf_sim, events[np.newaxis, i])
assert ss.n_times == evt_times[-1] + len(wf_sim) - this_fs
raw_sim = simulate_raw(raw.info, ss, src=src_ss, bem=bem_fname,
first_samp=first_samp)
data = raw_sim.get_data()
amp0 = data[:, evt_times - first_samp].max()
amp1 = data[:, evt_times + 1 - first_samp].max()
amp2 = data[:, evt_times + 2 - first_samp].max()
assert_allclose(amp0 / amp1, wf_sim[0] / wf_sim[1], rtol=1e-5)
assert amp2 == 0
assert raw_sim.n_times == ss.n_times
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)
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)
def test_simulate_raw_sphere(raw_data, tmpdir):
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = raw_data
assert len(pick_types(raw.info, meg=False, ecg=True)) == 1
tempdir = str(tmpdir)
# head pos
head_pos_sim = _get_head_pos_sim(raw)
#
# Test raw simulation with basic parameters
#
raw.info.normalize_proj()
cov = read_cov(cov_fname)
cov['projs'] = raw.info['projs']
raw.info['bads'] = raw.ch_names[:1]
sphere_norad = make_sphere_model('auto', None, raw.info)
raw_meg = raw.copy().pick_types()
raw_sim = simulate_raw(raw_meg.info, stc, trans, src, sphere_norad,
head_pos=head_pos_sim)
# Test IO on processed data
test_outname = op.join(tempdir, 'sim_test_raw.fif')
raw_sim.save(test_outname)
raw_sim_loaded = read_raw_fif(test_outname, preload=True)
assert_allclose(raw_sim_loaded[:][0], raw_sim[:][0], rtol=1e-6, atol=1e-20)
del raw_sim
# make sure it works with EEG-only and MEG-only
raw_sim_meg = simulate_raw(
raw.copy().pick_types(meg=True, eeg=False).info,
stc, trans, src, sphere)
raw_sim_eeg = simulate_raw(
raw.copy().pick_types(meg=False, eeg=True).info,
stc, trans, src, sphere)
raw_sim_meeg = simulate_raw(
raw.copy().pick_types(meg=True, eeg=True).info,
stc, trans, src, sphere)
for this_raw in (raw_sim_meg, raw_sim_eeg, raw_sim_meeg):
add_eog(this_raw, random_state=seed)
for this_raw in (raw_sim_meg, raw_sim_meeg):
add_ecg(this_raw, random_state=seed)
with pytest.raises(RuntimeError, match='only add ECG artifacts if MEG'):
add_ecg(raw_sim_eeg)
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 raw-as-info is supported
n_samp = len(stc.times)
raw_crop = raw.copy().crop(0., (n_samp - 1.) / raw.info['sfreq'])
assert len(raw_crop.times) == len(stc.times)
raw_sim = simulate_raw(raw_crop.info, stc, trans, src, sphere)
with catch_logging() as log:
raw_sim_2 = simulate_raw(raw_crop.info, stc, trans, src, sphere,
verbose=True)
log = log.getvalue()
assert '1 STC iteration provided' in log
assert len(raw_sim_2.times) == n_samp
assert_allclose(raw_sim[:, :n_samp][0],
raw_sim_2[:, :n_samp][0], rtol=1e-5, atol=1e-30)
del raw_sim, raw_sim_2
# check that different interpolations are similar given small movements
raw_sim = simulate_raw(raw.info, stc, trans, src, sphere,
head_pos=head_pos_sim, interp='linear')
raw_sim_hann = simulate_raw(raw.info, stc, trans, src, sphere,
head_pos=head_pos_sim, interp='hann')
assert_allclose(raw_sim[:][0], raw_sim_hann[:][0], rtol=1e-1, atol=1e-14)
del raw_sim_hann
# check that new Generator objects can be used
if check_version('numpy', '1.17'):
random_state = np.random.default_rng(seed)
add_ecg(raw_sim, random_state=random_state)
add_eog(raw_sim, random_state=random_state)
def test_iterable():
"""Test iterable support for simulate_raw."""
raw = read_raw_fif(raw_fname_short).load_data()
raw.pick_channels(raw.ch_names[:10] + ['STI 014'])
src = setup_volume_source_space(
pos=dict(rr=[[-0.05, 0, 0], [0.1, 0, 0]],
nn=[[0, 1., 0], [0, 1., 0]]))
assert src.kind == 'discrete'
trans = None
sphere = make_sphere_model(head_radius=None, info=raw.info)
tstep = 1. / raw.info['sfreq']
rng = np.random.RandomState(0)
vertices = np.array([1])
data = rng.randn(1, 2)
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
with pytest.raises(ValueError, match='at least three time points'):
simulate_raw(raw.info, stc, trans, src, sphere, None)
data = rng.randn(1, 1000)
n_events = (len(raw.times) - 1) // 1000 + 1
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
with catch_logging() as log:
with pytest.deprecated_call():
raw_sim = simulate_raw(raw, stc, trans, src, sphere, None,
verbose=True)
log = log.getvalue()
assert 'Making 15 copies of STC' in log
assert_allclose(raw.times, raw_sim.times)
events = find_events(raw_sim, initial_event=True)
assert len(events) == n_events
assert_array_equal(events[:, 2], 1)
# Degenerate STCs
with pytest.raises(RuntimeError,
match=r'Iterable did not provide stc\[0\]'):
simulate_raw(raw.info, [], trans, src, sphere, None)
with pytest.raises(RuntimeError,
match=r'Iterable did not provide stc\[2\].*duration'):
with pytest.deprecated_call():
simulate_raw(raw, [stc, stc], trans, src, sphere, None)
# tuple with ndarray
event_data = np.zeros(len(stc.times), int)
event_data[0] = 3
raw_new = simulate_raw(raw.info, [(stc, event_data)] * 15,
trans, src, sphere, None, first_samp=raw.first_samp)
assert raw_new.n_times == 15000
raw_new.crop(0, raw_sim.times[-1])
_assert_iter_sim(raw_sim, raw_new, 3)
with pytest.raises(ValueError, match='event data had shape .* but need'):
simulate_raw(raw.info, [(stc, event_data[:-1])], trans, src, sphere,
None)
with pytest.raises(ValueError, match='stim_data in a stc tuple .* int'):
simulate_raw(raw.info, [(stc, event_data * 1.)], trans, src, sphere,
None)
# iterable
def stc_iter():
stim_data = np.zeros(len(stc.times), int)
stim_data[0] = 4
ii = 0
while ii < 100:
ii += 1
yield (stc, stim_data)
with pytest.deprecated_call():
raw_new = simulate_raw(raw, stc_iter(), trans, src, sphere, None)
_assert_iter_sim(raw_sim, raw_new, 4)
def stc_iter_bad():
ii = 0
while ii < 100:
ii += 1
yield (stc, 4, 3)
with pytest.raises(ValueError, match='stc, if tuple, must be length'):
simulate_raw(raw.info, stc_iter_bad(), trans, src, sphere, None)
_assert_iter_sim(raw_sim, raw_new, 4)
def stc_iter_bad():
ii = 0
while ii < 100:
ii += 1
stc_new = stc.copy()
stc_new.vertices = np.array([ii % 2])
yield stc_new
with pytest.raises(RuntimeError, match=r'Vertex mismatch for stc\[1\]'):
simulate_raw(raw.info, stc_iter_bad(), trans, src, sphere, None)
# Forward omission
vertices = np.array([0, 1])
data = rng.randn(2, 1000)
stc = VolSourceEstimate(data, vertices, 0, tstep)
assert isinstance(stc.vertices, np.ndarray)
# XXX eventually we should support filtering based on sphere radius, too,
# by refactoring the code in source_space.py that does it!
surf = _get_ico_surface(3)
surf['rr'] *= 60 # mm
model = _surfaces_to_bem([surf], [FIFF.FIFFV_BEM_SURF_ID_BRAIN], [0.3])
bem = make_bem_solution(model)
with pytest.warns(RuntimeWarning,
match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw, stc, trans, src, bem, None)
def test_simulate_calculate_chpi_positions():
"""Test calculation of cHPI positions with simulated data."""
# Read info dict from raw FIF file
info = read_info(raw_fname)
# Tune the info structure
chpi_channel = u'STI201'
ncoil = len(info['hpi_results'][0]['order'])
coil_freq = 10 + np.arange(ncoil) * 5
hpi_subsystem = {
'event_channel':
chpi_channel,
'hpi_coils': [{
'event_bits':
np.array([256, 0, 256, 256], dtype=np.int32)
}, {
'event_bits':
np.array([512, 0, 512, 512], dtype=np.int32)
}, {
'event_bits':
np.array([1024, 0, 1024, 1024], dtype=np.int32)
}, {
'event_bits':
np.array([2048, 0, 2048, 2048], dtype=np.int32)
}],
'ncoil':
ncoil
}
info['hpi_subsystem'] = hpi_subsystem
for l, freq in enumerate(coil_freq):
info['hpi_meas'][0]['hpi_coils'][l]['coil_freq'] = freq
picks = pick_types(info, meg=True, stim=True, eeg=False, exclude=[])
info['sfreq'] = 100. # this will speed it up a lot
info = pick_info(info, picks)
info['chs'][info['ch_names'].index('STI 001')]['ch_name'] = 'STI201'
info._update_redundant()
info['projs'] = []
info_trans = info['dev_head_t']['trans'].copy()
dev_head_pos_ini = np.concatenate(
[rot_to_quat(info_trans[:3, :3]), info_trans[:3, 3]])
ez = np.array([0, 0, 1]) # Unit vector in z-direction of head coordinates
# Define some constants
duration = 30 # Time / s
# Quotient of head position sampling frequency
# and raw sampling frequency
head_pos_sfreq_quotient = 0.1
# Round number of head positions to the next integer
S = int(duration / (info['sfreq'] * head_pos_sfreq_quotient))
dz = 0.001 # Shift in z-direction is 0.1mm for each step
dev_head_pos = np.zeros((S, 10))
dev_head_pos[:, 0] = np.arange(S) * info['sfreq'] * head_pos_sfreq_quotient
dev_head_pos[:, 1:4] = dev_head_pos_ini[:3]
dev_head_pos[:, 4:7] = dev_head_pos_ini[3:] + \
np.outer(np.arange(S) * dz, ez)
dev_head_pos[:, 7] = 1.0
# cm/s
dev_head_pos[:, 9] = 100 * dz / (info['sfreq'] * head_pos_sfreq_quotient)
# Round number of samples to the next integer
raw_data = np.zeros((len(picks), int(duration * info['sfreq'] + 0.5)))
raw = RawArray(raw_data, info)
dip = Dipole(np.array([0.0, 0.1, 0.2]),
np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
np.array([1e-9, 1e-9, 1e-9]),
np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
np.array([1.0, 1.0, 1.0]), 'dip')
sphere = make_sphere_model('auto',
'auto',
info=info,
relative_radii=(1.0, 0.9),
sigmas=(0.33, 0.3))
fwd, stc = make_forward_dipole(dip, sphere, info)
stc.resample(info['sfreq'])
raw = simulate_raw(raw,
stc,
None,
fwd['src'],
sphere,
cov=None,
blink=False,
ecg=False,
chpi=True,
head_pos=dev_head_pos,
mindist=1.0,
interp='zero',
verbose=None)
quats = _calculate_chpi_positions(
raw,
t_step_min=raw.info['sfreq'] * head_pos_sfreq_quotient,
t_step_max=raw.info['sfreq'] * head_pos_sfreq_quotient,
t_window=1.0)
_assert_quats(quats, dev_head_pos, dist_tol=0.001, angle_tol=1.)
def test_simulate_raw_sphere():
"""Test simulation of raw data with sphere model."""
seed = 42
raw, src, stc, trans, sphere = _get_data()
assert 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])
std = dict(grad=2e-13, mag=10e-15, eeg=0.1e-6)
raw_sim = simulate_raw(raw, stc, trans, src, sphere,
make_ad_hoc_cov(raw.info, std=std),
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=std, head_pos=head_pos_sim, blink=True,
ecg=True, random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
sphere_norad = make_sphere_model('auto', None, raw.info)
raw_meg = raw.copy().pick_types()
raw_sim = simulate_raw(raw_meg, stc, trans, src, sphere_norad,
make_ad_hoc_cov(raw.info, std=None),
head_pos=head_pos_sim, blink=True, ecg=True,
random_state=seed)
raw_sim_2 = simulate_raw(raw_meg, stc, trans_fname, src_fname,
sphere_norad,
cov='simple', 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)
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 np.mean(close) > 0.7
far = ~np.isclose(raw_sim_3[diff_picks][0],
raw_sim_4[diff_picks][0], atol=1e-20)
assert 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 raw-as-info is supported
raw_sim = simulate_raw(raw, stc, trans, src, sphere, cov=None)
n_samp = int(round(raw.info['sfreq']))
for use_raw in (raw, raw.info):
raw_sim_2 = simulate_raw(use_raw, stc, trans, src, sphere, cov=None,
duration=1.)
assert len(raw_sim_2.times) == n_samp
assert_allclose(raw_sim[:, :n_samp][0],
raw_sim_2[:, :n_samp][0], rtol=1e-5, atol=1e-30)
del raw_sim, raw_sim_2
# check that different interpolations are similar given small movements
raw_sim = simulate_raw(raw, stc, trans, src, sphere, cov=None,
head_pos=head_pos_sim, interp='linear')
raw_sim_hann = simulate_raw(raw, stc, trans, src, sphere, cov=None,
head_pos=head_pos_sim, interp='hann')
assert_allclose(raw_sim[:][0], raw_sim_hann[:][0], rtol=1e-1, atol=1e-14)
del raw_sim, raw_sim_hann
# 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
pytest.raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
ecg=False, blink=False, head_pos=head_pos_sim_err)
pytest.raises(RuntimeError, simulate_raw, raw, stc, trans, src,
bem_fname, ecg=False, blink=False,
head_pos=head_pos_sim_err)
# other degenerate conditions
pytest.raises(TypeError, simulate_raw, 'foo', stc, trans, src, sphere)
pytest.raises(TypeError, simulate_raw, raw, 'foo', trans, src, sphere)
pytest.raises(ValueError, simulate_raw, raw, stc.copy().crop(0, 0),
trans, src, sphere)
with pytest.raises(ValueError, match='duration cannot be None'):
simulate_raw(raw.info, stc, trans, src, sphere)
with pytest.raises(TypeError, match='must be an instance of Raw or Info'):
simulate_raw(0, stc, trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
pytest.raises(ValueError, simulate_raw, raw, stc_bad, trans, src, sphere)
pytest.raises(TypeError, simulate_raw, raw, stc, trans, src, sphere,
cov=0) # wrong covariance type
pytest.raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
chpi=True) # no cHPI info
pytest.raises(ValueError, simulate_raw, raw, stc, trans, src, sphere,
interp='foo')
pytest.raises(TypeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=1.)
pytest.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
pytest.raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
pytest.raises(RuntimeError, simulate_raw, raw_bad, stc, trans, src, sphere,
blink=True)
pos = np.zeros((len(rots), 10))
for ii in range(len(pos)):
pos[ii] = np.concatenate([[ii], rot_to_quat(rots[ii]), center, [0] * 3])
pos[:, 0] += raw.first_samp / raw.info['sfreq'] # initial offset
# Let's activate a vertices bilateral auditory cortices
src = mne.read_source_spaces(src_fname)
labels = mne.read_labels_from_annot('sample', 'aparc.a2009s', 'both',
regexp='G_temp_sup-Plan_tempo',
subjects_dir=subjects_dir)
assert len(labels) == 2 # one left, one right
vertices = [np.intersect1d(l.vertices, s['vertno'])
for l, s in zip(labels, src)]
data = np.zeros([sum(len(v) for v in vertices), int(raw.info['sfreq'])])
activation = np.hanning(int(raw.info['sfreq'] * 0.2)) * 1e-9 # nAm
t_offset = int(np.ceil(0.2 * raw.info['sfreq'])) # 200 ms in (after baseline)
data[:, t_offset:t_offset + len(activation)] = activation
stc = mne.SourceEstimate(data, vertices, tmin=-0.2,
tstep=1. / raw.info['sfreq'])
# Simulate the movement
raw = simulate_raw(raw, stc, trans_fname, src, bem_fname,
head_pos=pos, interp='zero', n_jobs=-1)
raw_stat = simulate_raw(raw, stc, trans_fname, src, bem_fname,
head_pos=None, n_jobs=-1)
# Save the results
raw.save('simulated_movement_raw.fif', buffer_size_sec=1.)
raw_stat.save('simulated_stationary_raw.fif', buffer_size_sec=1.)
mne.chpi.write_head_quats('simulated_quats.pos', pos)
stc.save('simulated_activation')
info.update(sfreq=sfreq, bads=[])
# Only use gradiometers
picks = mne.pick_types(info, meg='grad', stim=True, exclude=())
mne.pick_info(info, picks, copy=False)
# Define a covariance matrix for the simulated noise. In this tutorial, we use
# a simple diagonal matrix.
cov = mne.cov.make_ad_hoc_cov(info)
cov['data'] *= (20. / snr)**2 # Scale the noise to achieve the desired SNR
# Simulate the raw data, with a lowpass filter on the noise
stcs = [(stc_signal, unit_impulse(n_samp, dtype=int) * 1),
(stc_noise, unit_impulse(n_samp, dtype=int) * 2)] # stacked in time
duration = (len(stc_signal.times) * 2) / sfreq
raw = simulate_raw(info, stcs, forward=fwd)
add_noise(raw, cov, iir_filter=[4, -4, 0.8], random_state=rand)
###############################################################################
# We create an :class:`mne.Epochs` object containing two trials: one with
# both noise and signal and one with just noise
events = mne.find_events(raw, initial_event=True)
tmax = (len(stc_signal.times) - 1) / sfreq
epochs = mne.Epochs(raw,
events,
event_id=dict(signal=1, noise=2),
tmin=0,
tmax=tmax,
baseline=None,
preload=True)
data = 25e-9 * np.sin(2. * np.pi * 10. * n * times)
data *= window
return data
times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
src = read_source_spaces(src_fname)
stc = simulate_sparse_stc(src, n_dipoles=n_dipoles, times=times,
data_fun=data_fun, random_state=0)
# look at our source data
fig, ax = plt.subplots(1)
ax.plot(times, 1e9 * stc.data.T)
ax.set(ylabel='Amplitude (nAm)', xlabel='Time (sec)')
mne.viz.utils.plt_show()
##############################################################################
# Simulate raw data
raw_sim = simulate_raw(raw, stc, trans_fname, src, bem_fname, cov='simple',
iir_filter=[0.2, -0.2, 0.04], ecg=True, blink=True,
n_jobs=1, verbose=True)
raw_sim.plot()
##############################################################################
# Plot evoked data
events = find_events(raw_sim) # only 1 pos, so event number == 1
epochs = Epochs(raw_sim, events, 1, -0.2, epoch_duration)
cov = compute_covariance(epochs, tmax=0., method='empirical',
verbose='error') # quick calc
evoked = epochs.average()
evoked.plot_white(cov)
def SimulateRaw(amp1=50, amp2=100, freq=1., batch=1):
"""Create simulated raw data and events of two kinds
Keyword Args:
amp1 (float): amplitude of first condition effect
amp2 (float): ampltiude of second condition effect,
null hypothesis amp1=amp2
freq (float): Frequency of simulated signal 1. for ERP 10. for alpha
batch (int): number of groups of 255 trials in each condition
Returns:
raw: simulated EEG MNE raw object with two event types
event_id: dict of the two events for input to PreProcess()
"""
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
src_fname = data_path + '/subjects/sample/bem/sample-oct-6-src.fif'
bem_fname = (data_path +
'/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif')
raw_single = mne.io.read_raw_fif(raw_fname, preload=True)
raw_single.set_eeg_reference(projection=True)
raw_single = raw_single.crop(0., 255.)
raw_single = raw_single.copy().pick_types(meg=False,
eeg=True,
eog=True,
stim=True)
#concatenate 4 raws together to make 1000 trials
raw = []
for i in range(batch):
raw.append(raw_single)
raw = concatenate_raws(raw)
epoch_duration = 1.
def data_fun(amp, freq):
"""Create function to create fake signal"""
def data_fun_inner(times):
"""Create fake signal with no noise"""
n_samp = len(times)
window = np.zeros(n_samp)
start, stop = [int(ii * float(n_samp) / 2) for ii in (0, 1)]
window[start:stop] = np.hamming(stop - start)
data = amp * 1e-9 * np.sin(2. * np.pi * freq * times)
data *= window
return data
return data_fun_inner
times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
src = read_source_spaces(src_fname)
stc_zero = simulate_sparse_stc(src,
n_dipoles=1,
times=times,
data_fun=data_fun(amp1, freq),
random_state=0)
stc_one = simulate_sparse_stc(src,
n_dipoles=1,
times=times,
data_fun=data_fun(amp2, freq),
random_state=0)
raw_sim_zero = simulate_raw(raw,
stc_zero,
trans_fname,
src,
bem_fname,
cov='simple',
blink=True,
n_jobs=1,
verbose=True)
raw_sim_one = simulate_raw(raw,
stc_one,
trans_fname,
src,
bem_fname,
cov='simple',
blink=True,
n_jobs=1,
verbose=True)
stim_pick = raw_sim_one.info['ch_names'].index('STI 014')
raw_sim_one._data[stim_pick][np.where(
raw_sim_one._data[stim_pick] == 1)] = 2
raw = concatenate_raws([raw_sim_zero, raw_sim_one])
event_id = {'CondZero': 1, 'CondOne': 2}
return raw, event_id
n_noise_dipoles,
times,
data_fun=generate_random,
random_state=config.random,
labels=labels)
###########################################################################
# Project to sensor space
###########################################################################
stc = add_stcs(stc_signal, config.noise * stc_noise)
raw = simulate_raw(
info,
stc,
trans=None,
src=None,
bem=None,
forward=fwd,
cov=None,
random_state=config.random,
duration=config.trial_length,
)
raw_list.append(raw)
print('%02d/%02d' % (i + 1, config.n_trials))
raw = mne.concatenate_raws(raw_list)
###############################################################################
# Use empty room noise as sensor noise
###############################################################################
er_raw = mne.io.read_raw_fif(fname.ernoise, preload=True)
def test_simulate_round_trip(raw_data):
"""Test simulate_raw round trip calculations."""
# Check a diagonal round-trip
raw, src, stc, trans, sphere = raw_data
raw.pick_types(meg=True, stim=True)
bem = read_bem_solution(bem_1_fname)
old_bem = bem.copy()
old_src = src.copy()
old_trans = trans.copy()
fwd = make_forward_solution(raw.info, trans, src, bem)
# no omissions
assert (sum(len(s['vertno']) for s in src) ==
sum(len(s['vertno']) for s in fwd['src']) ==
36)
# make sure things were not modified
assert (old_bem['surfs'][0]['coord_frame'] ==
bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
data = np.eye(fwd['nsource'])
raw.crop(0, (len(data) - 1) / raw.info['sfreq'])
stc = SourceEstimate(data, [s['vertno'] for s in fwd['src']],
0, 1. / raw.info['sfreq'])
for use_fwd in (None, fwd):
if use_fwd is None:
use_trans, use_src, use_bem = trans, src, bem
else:
use_trans = use_src = use_bem = None
with pytest.deprecated_call():
this_raw = simulate_raw(raw, stc, use_trans, use_src, use_bem,
cov=None, forward=use_fwd)
this_raw.pick_types(meg=True, eeg=True)
assert (old_bem['surfs'][0]['coord_frame'] ==
bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
this_fwd = convert_forward_solution(fwd, force_fixed=True)
assert_allclose(this_raw[:][0], this_fwd['sol']['data'],
atol=1e-12, rtol=1e-6)
with pytest.raises(ValueError, match='If forward is not None then'):
simulate_raw(raw.info, stc, trans, src, bem, forward=fwd)
# Not iterable
with pytest.raises(TypeError, match='SourceEstimate, tuple, or iterable'):
simulate_raw(raw.info, 0., trans, src, bem, None)
# STC with a source that `src` does not have
assert 0 not in src[0]['vertno']
vertices = [[0, fwd['src'][0]['vertno'][0]], []]
stc_bad = SourceEstimate(data[:2], vertices, 0, 1. / raw.info['sfreq'])
with pytest.warns(RuntimeWarning,
match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw.info, stc_bad, trans, src, bem)
assert 0 not in fwd['src'][0]['vertno']
with pytest.warns(RuntimeWarning,
match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw.info, stc_bad, None, None, None, forward=fwd)
# dev_head_t mismatch
fwd['info']['dev_head_t']['trans'][0, 0] = 1.
with pytest.raises(ValueError, match='dev_head_t.*does not match'):
simulate_raw(raw.info, stc, None, None, None, forward=fwd)
def test_degenerate(raw_data):
"""Test degenerate conditions."""
raw, src, stc, trans, sphere = raw_data
info = raw.info
# Make impossible transform (translate up into helmet) and ensure failure
hp_err = _get_head_pos_sim(raw)
hp_err[1.][2, 3] -= 0.1 # z trans upward 10cm
with pytest.raises(RuntimeError, match='collided with inner skull'):
simulate_raw(info, stc, trans, src, sphere, cov=None, head_pos=hp_err)
# other degenerate conditions
with pytest.raises(TypeError, match='info must be an instance of'):
simulate_raw('foo', stc, trans, src, sphere)
with pytest.raises(TypeError, match='stc must be an instance of'):
simulate_raw(info, 'foo', trans, src, sphere)
with pytest.raises(ValueError, match='stc must have at least three time'):
simulate_raw(info, stc.copy().crop(0, 0), trans, src, sphere)
with pytest.raises(TypeError, match='must be an instance of Raw or Info'):
simulate_raw(0, stc, trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
with pytest.raises(ValueError, match='same sample rate'):
simulate_raw(info, stc_bad, trans, src, sphere)
with pytest.raises(TypeError, match='Covariance matrix type'):
with pytest.deprecated_call():
simulate_raw(info, stc, trans, src, sphere, cov=0)
with pytest.raises(RuntimeError, match='cHPI information not found'):
with pytest.deprecated_call():
simulate_raw(info, stc, trans, src, sphere, chpi=True)
with pytest.raises(ValueError, match='interp must be one of'):
simulate_raw(info, stc, trans, src, sphere, interp='foo')
with pytest.raises(TypeError, match='unknown head_pos type'):
simulate_raw(info, stc, trans, src, sphere, head_pos=1.)
with pytest.raises(RuntimeError, match='All position times'):
with pytest.deprecated_call():
simulate_raw(raw, stc, trans, src, sphere, head_pos=pos_fname)
head_pos_sim_err = _get_head_pos_sim(raw)
head_pos_sim_err[-1.] = head_pos_sim_err[1.] # negative time
with pytest.raises(RuntimeError, match='All position times'):
simulate_raw(info, stc, trans, src, sphere, head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
with pytest.raises(RuntimeError, match='Cannot fit headshape'):
with pytest.deprecated_call():
simulate_raw(raw_bad, stc, trans, src, sphere, blink=True)
with pytest.raises(RuntimeError, match='Cannot fit headshape'):
add_eog(raw_bad)
def simulate_raw_data(self):
"""
Simulates raw data
"""
def expand_sim_stc_data(orig_times, t_index, data, interval):
template = np.zeros((data.shape[0], len(orig_times)))
template[:, t_index:t_index + interval] = data
return template
print("Generating simulated raw data...")
if not self.data_template:
self.data_template = self.__create_data_template()
if self.n_dipoles == 1:
time_steps = int(self.samples_per_dipole * self.spacing_t_steps *
(1 + self.empty_signal))
else:
time_steps = int(self.samples_per_dipole *
(self.n_simulations + self.spacing_t_steps) *
(1 + self.empty_signal))
data_template = self.data_template.copy()
data_template = data_template.crop(
0.,
data_template.times[time_steps]) # Crop a suitable piece of data
t_delta = np.mean(np.gradient(data_template.times))
times = np.arange(time_steps) * t_delta
sim_stc = simulate_sparse_stc(
self.src, n_dipoles=0, times=times,
data_fun=self.data_func) # Create empty time series
s = 0
lh_labels = mne.Label(self.lh_labels, hemi="lh")
rh_labels = mne.Label(self.rh_labels, hemi="rh")
lh_labels.values = np.zeros(lh_labels.values.shape)
rh_labels.values = np.zeros(rh_labels.values.shape)
# TODO: selection of hemisphere(s)
self.times_per_dipole = int(self.samples_per_dipole *
(1 + self.empty_signal))
if self.n_dipoles == 1:
print(
"Simulating 1 dipole in each vertex of the right hemisphere.")
for rh in range(0, len(rh_labels.values)):
template = sim_stc.copy()
#lh_labels2 = lh_labels
#lh_labels2.values[rh] = 1.
rh_labels2 = rh_labels
rh_labels2.values[rh] = 1.
sim_stc2 = simulate_sparse_stc(
self.src,
n_dipoles=1,
times=times[s:s + self.times_per_dipole],
data_fun=self.data_func,
labels=[rh_labels2])
# TODO: use labels for deterministically simulating all vertices
sim_stc.expand(sim_stc2.vertices), sim_stc2.expand(
sim_stc.vertices), template.expand(sim_stc2.vertices)
template.data[:, :] = expand_sim_stc_data(
times, s, sim_stc2.data, self.times_per_dipole)
sim_stc += template
s += self.times_per_dipole
else:
print("Simulating up to %s dipoles on the right hemisphere." %
self.n_dipoles)
for t in range(0, len(times), self.times_per_dipole):
template = sim_stc.copy()
dipoles = random.randint(2, self.n_dipoles)
label_indices = sorted(
random.sample(range(0, len(rh_labels)), dipoles))
chosen_label = rh_labels
chosen_labels = []
for l in label_indices:
chosen_label.values[l] = 1.
chosen_labels.append(chosen_label)
chosen_label.values[l] = 0.
# TODO there may be a bug in MNE-Python 0.14.1 where forward model dipoles are mapped to non-unique
# source space vertices, e.g. in this case rh vertices 298 and 411 both map to source space vertex
# 112071 which raises an error in source_estimate.py at line 434
try:
sim_stc2 = simulate_sparse_stc(
self.src,
n_dipoles=dipoles,
times=times[t:t + self.times_per_dipole],
data_fun=self.data_func,
labels=chosen_labels)
except ValueError:
s -= self.times_per_dipole
continue
sim_stc.expand(sim_stc2.vertices), sim_stc2.expand(
sim_stc.vertices), template.expand(sim_stc2.vertices)
template.data[:, :] = expand_sim_stc_data(
times, t, sim_stc2.data, self.times_per_dipole)
sim_stc.data[:, :] = sim_stc.data + template.data
t += 1
if s >= self.n_simulations:
break
# Remove unnecessary zeros from series
# sim_stc._data = sim_stc.data[:, ~np.all(abs(sim_stc.data) < 1e-20, axis=0)] # TODO: add some zeroes to compensate?
#sim_stc.times = sim_stc.times[:sim_stc.data.shape[1]]
#template_times = np.arange(sim_stc.data.shape[1]) * (t_delta * (1 + self.empty_signal))
#data_template = data_template.crop(0., template_times[-1])
self.sim_data = simulate_raw(data_template,
sim_stc,
self.trans_path,
self.src,
self.bem_path,
cov='simple',
iir_filter=self.iir_filter,
ecg=self.ecg,
blink=self.blink,
n_jobs=CPU_THREADS,
verbose=self.verbose,
use_cps=True)
# self.cov = self.compute_covariance(self.simulate_evokeds(self.sim_data))
self.sim_data = self.raw_preprocessing(self.sim_data)
self.sim_stc = sim_stc
self.cov = mne.compute_raw_covariance(self.sim_data,
reject=self.reject,
n_jobs=CPU_THREADS)
return self.sim_data, self.sim_stc
def test_simulate_round_trip(raw_data):
"""Test simulate_raw round trip calculations."""
# Check a diagonal round-trip
raw, src, stc, trans, sphere = raw_data
raw.pick_types(meg=True, stim=True)
bem = read_bem_solution(bem_1_fname)
old_bem = bem.copy()
old_src = src.copy()
old_trans = trans.copy()
fwd = make_forward_solution(raw.info, trans, src, bem)
# no omissions
assert (sum(len(s['vertno'])
for s in src) == sum(len(s['vertno'])
for s in fwd['src']) == 36)
# make sure things were not modified
assert (
old_bem['surfs'][0]['coord_frame'] == bem['surfs'][0]['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
data = np.eye(fwd['nsource'])
raw.crop(0, (len(data) - 1) / raw.info['sfreq'])
stc = SourceEstimate(data, [s['vertno'] for s in fwd['src']], 0,
1. / raw.info['sfreq'])
for use_fwd in (None, fwd):
if use_fwd is None:
use_trans, use_src, use_bem = trans, src, bem
else:
use_trans = use_src = use_bem = None
with pytest.deprecated_call():
this_raw = simulate_raw(raw,
stc,
use_trans,
use_src,
use_bem,
cov=None,
forward=use_fwd)
this_raw.pick_types(meg=True, eeg=True)
assert (old_bem['surfs'][0]['coord_frame'] == bem['surfs'][0]
['coord_frame'])
assert trans == old_trans
_compare_source_spaces(src, old_src)
this_fwd = convert_forward_solution(fwd, force_fixed=True)
assert_allclose(this_raw[:][0],
this_fwd['sol']['data'],
atol=1e-12,
rtol=1e-6)
with pytest.raises(ValueError, match='If forward is not None then'):
simulate_raw(raw.info, stc, trans, src, bem, forward=fwd)
# Not iterable
with pytest.raises(TypeError, match='SourceEstimate, tuple, or iterable'):
simulate_raw(raw.info, 0., trans, src, bem, None)
# STC with a source that `src` does not have
assert 0 not in src[0]['vertno']
vertices = [[0, fwd['src'][0]['vertno'][0]], []]
stc_bad = SourceEstimate(data[:2], vertices, 0, 1. / raw.info['sfreq'])
with pytest.warns(RuntimeWarning, match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw.info, stc_bad, trans, src, bem)
assert 0 not in fwd['src'][0]['vertno']
with pytest.warns(RuntimeWarning, match='1 of 2 SourceEstimate vertices'):
simulate_raw(raw.info, stc_bad, None, None, None, forward=fwd)
# dev_head_t mismatch
fwd['info']['dev_head_t']['trans'][0, 0] = 1.
with pytest.raises(ValueError, match='dev_head_t.*does not match'):
simulate_raw(raw.info, stc, None, None, None, forward=fwd)
times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
fwd = mne.read_forward_solution(fwd_fname)
src = fwd['src']
stc = simulate_sparse_stc(src, n_dipoles=n_dipoles, times=times,
data_fun=data_fun, random_state=rng)
# look at our source data
fig, ax = plt.subplots(1)
ax.plot(times, 1e9 * stc.data.T)
ax.set(ylabel='Amplitude (nAm)', xlabel='Time (sec)')
mne.viz.utils.plt_show()
##############################################################################
# Simulate raw data
raw_sim = simulate_raw(raw.info, [stc] * 10, forward=fwd, verbose=True)
cov = make_ad_hoc_cov(raw_sim.info)
add_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], random_state=rng)
add_ecg(raw_sim, random_state=rng)
add_eog(raw_sim, random_state=rng)
raw_sim.plot()
##############################################################################
# Plot evoked data
events = find_events(raw_sim) # only 1 pos, so event number == 1
epochs = Epochs(raw_sim, events, 1, tmin=-0.2, tmax=epoch_duration)
cov = compute_covariance(epochs, tmax=0., method='empirical',
verbose='error') # quick calc
evoked = epochs.average()
evoked.plot_white(cov, time_unit='s')
picks = mne.pick_types(info, meg='grad', stim=True, exclude=())
mne.pick_info(info, picks, copy=False)
# This is the raw object that will be used as a template for the simulation.
raw = mne.io.RawArray(np.zeros((info['nchan'], len(stc.times))), info)
# Define a covariance matrix for the simulated noise. In this tutorial, we use
# a simple diagonal matrix.
cov = mne.cov.make_ad_hoc_cov(info)
cov['data'] *= (20. / snr)**2 # Scale the noise to achieve the desired SNR
# Simulate the raw data, with a lowpass filter on the noise
raw = simulate_raw(raw,
stc,
trans_fname,
src_fname,
bem_fname,
cov=cov,
random_state=rand,
iir_filter=[4, -4, 0.8])
###############################################################################
# We create an :class:`mne.Epochs` object containing two trials: one with
# both noise and signal and one with just noise
t0 = raw.first_samp # First sample in the data
t1 = t0 + n_times - 1 # Sample just before the second trial
epochs = mne.Epochs(
raw,
events=np.array([[t0, 0, 1], [t1, 0, 2]]),
event_id=dict(signal=1, noise=2),
tmin=0,
def simulate_eeg(label_names,
signal_generator,
epoch_duration=3.0,
n_trials=10):
# Getting the paths to filenames of dataset
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
bem_fname = (data_path +
'/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif')
# Load real data as the template
raw = mne.io.read_raw_fif(raw_fname)
raw = raw.crop(0., n_trials * epoch_duration)
# Loading parameters for the forward solution
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
fwd = mne.read_forward_solution(fwd_fname, force_fixed=True, surf_ori=True)
fwd = mne.pick_types_forward(fwd,
meg=False,
eeg=True,
ref_meg=False,
exclude=raw.info['bads'])
# Get the labels and their centers
labels = [
mne.read_label(data_path + '/MEG/sample/labels/%s.label' % ln)
for ln in label_names
]
subjects_dir = data_path + '/subjects'
hemi_to_ind = {'lh': 0, 'rh': 1}
for i, label in enumerate(labels):
# The `center_of_mass` function needs labels to have values.
labels[i].values.fill(1.)
# Restrict the eligible vertices to be those on the surface under
# consideration and within the label.
surf_vertices = fwd['src'][hemi_to_ind[label.hemi]]['vertno']
restrict_verts = np.intersect1d(surf_vertices, label.vertices)
com = labels[i].center_of_mass(subject='sample',
subjects_dir=subjects_dir,
restrict_vertices=restrict_verts,
surf='white')
# Convert the center of vertex index from surface vertex list
# to Label's vertex list.
cent_idx = np.where(label.vertices == com)[0][0]
# Create a mask with 1 at center vertex and zeros elsewhere.
labels[i].values.fill(0.)
labels[i].values[cent_idx] = 1.
n_labels = len(label_names)
times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
signal = signal_generator(n_labels, times)
# Generate the sources in each label
dt = times[1] - times[0]
stc = simulate_stc(fwd['src'],
labels,
signal,
times[0],
dt,
value_fun=lambda x: x)
# Simulate raw data
raw_sim = simulate_raw(raw,
stc,
trans_fname,
fwd['src'],
bem_fname,
cov='simple',
iir_filter=[2, -2, 0.4],
ecg=False,
blink=False,
n_jobs=1,
verbose=True)
# Get just the EEG data and the stimulus channel
raw_eeg = raw_sim.load_data().pick_types(meg=False, eeg=True, stim=True)
return raw_eeg
def test_simulate_calculate_chpi_positions():
"""Test calculation of cHPI positions with simulated data."""
# Read info dict from raw FIF file
info = read_info(raw_fname)
# Tune the info structure
chpi_channel = u'STI201'
ncoil = len(info['hpi_results'][0]['order'])
coil_freq = 10 + np.arange(ncoil) * 5
hpi_subsystem = {'event_channel': chpi_channel,
'hpi_coils': [{'event_bits': np.array([256, 0, 256, 256],
dtype=np.int32)},
{'event_bits': np.array([512, 0, 512, 512],
dtype=np.int32)},
{'event_bits':
np.array([1024, 0, 1024, 1024],
dtype=np.int32)},
{'event_bits':
np.array([2048, 0, 2048, 2048],
dtype=np.int32)}],
'ncoil': ncoil}
info['hpi_subsystem'] = hpi_subsystem
for l, freq in enumerate(coil_freq):
info['hpi_meas'][0]['hpi_coils'][l]['coil_freq'] = freq
picks = pick_types(info, meg=True, stim=True, eeg=False, exclude=[])
info['sfreq'] = 100. # this will speed it up a lot
info = pick_info(info, picks)
info['chs'][info['ch_names'].index('STI 001')]['ch_name'] = 'STI201'
info._update_redundant()
info['projs'] = []
info_trans = info['dev_head_t']['trans'].copy()
dev_head_pos_ini = np.concatenate([rot_to_quat(info_trans[:3, :3]),
info_trans[:3, 3]])
ez = np.array([0, 0, 1]) # Unit vector in z-direction of head coordinates
# Define some constants
duration = 30 # Time / s
# Quotient of head position sampling frequency
# and raw sampling frequency
head_pos_sfreq_quotient = 0.1
# Round number of head positions to the next integer
S = int(duration / (info['sfreq'] * head_pos_sfreq_quotient))
dz = 0.001 # Shift in z-direction is 0.1mm for each step
dev_head_pos = np.zeros((S, 10))
dev_head_pos[:, 0] = np.arange(S) * info['sfreq'] * head_pos_sfreq_quotient
dev_head_pos[:, 1:4] = dev_head_pos_ini[:3]
dev_head_pos[:, 4:7] = dev_head_pos_ini[3:] + \
np.outer(np.arange(S) * dz, ez)
dev_head_pos[:, 7] = 1.0
# cm/s
dev_head_pos[:, 9] = 100 * dz / (info['sfreq'] * head_pos_sfreq_quotient)
# Round number of samples to the next integer
raw_data = np.zeros((len(picks), int(duration * info['sfreq'] + 0.5)))
raw = RawArray(raw_data, info)
dip = Dipole(np.array([0.0, 0.1, 0.2]),
np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]),
np.array([1e-9, 1e-9, 1e-9]),
np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
np.array([1.0, 1.0, 1.0]), 'dip')
sphere = make_sphere_model('auto', 'auto', info=info,
relative_radii=(1.0, 0.9), sigmas=(0.33, 0.3))
fwd, stc = make_forward_dipole(dip, sphere, info)
stc.resample(info['sfreq'])
raw = simulate_raw(raw, stc, None, fwd['src'], sphere, cov=None,
blink=False, ecg=False, chpi=True,
head_pos=dev_head_pos, mindist=1.0, interp='zero',
verbose=None, use_cps=True)
quats = _calculate_chpi_positions(
raw, t_step_min=raw.info['sfreq'] * head_pos_sfreq_quotient,
t_step_max=raw.info['sfreq'] * head_pos_sfreq_quotient, t_window=1.0)
_assert_quats(quats, dev_head_pos, dist_tol=0.001, angle_tol=1.)
len(s_dict['lab'].vertices)
# Generate a template raw object
raw_template = mne.io.RawArray(np.zeros((len(s_dict['info']['chs']),
stc_activation.shape[1])),
info=s_dict['info'])
# Get scalar for noise covariance to achieve desired SNR
noiseless_act = np.ones((1, 3)) * sim_amplitude
raw_template_noiseless = mne.io.RawArray(np.zeros((len(s_dict['info']['chs']),
noiseless_act.shape[1])),
info=s_dict['info'])
stc_noiseless = simulate_stc(subj_d[di]['inv']['src'], [subj_d[di]['lab']],
stc_data=noiseless_act, tmin=0, tstep=0.001)
eeg_noiseless = simulate_raw(raw_template_noiseless, stc_noiseless,
s_dict['inv']['mri_head_t'],
src=s_dict['inv']['src'],
bem=s_dict['bem_fname'], cov=None,
blink=True, n_jobs=n_jobs, verbose=False)
s_dict['noise_scale'] = get_noise_scale(eeg_noiseless, s_dict)
print 'Noise covariance scalar calculated: %s' % s_dict['noise_scale']
# Simulate cortical activations.
# Maintain: Noise
# Switch: Noise + [unit current / label area] in RTPJ
raw_sim = []
stc_est = []
stc_est_sph = []
for act_scale, trial_type in zip([0, 1], ['switch', 'maintain']):
# Generate simulated stc activation
stc_sim = simulate_stc(subj_d[di]['inv']['src'], [subj_d[di]['lab']],
