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_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')
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 test_simulate_raw_chpi():
"""Test simulation of raw data with cHPI."""
raw = read_raw_fif(raw_chpi_fname, allow_maxshield='yes')
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)
quats = read_head_pos(pos_fname)
_assert_quats(quats, quats_sim, dist_tol=0.006, angle_tol=4)
