def test_chpi_adjust():
"""Test cHPI logging and adjustment"""
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes')
with catch_logging() as log:
_get_hpi_info(raw.info, adjust=True, verbose='debug')
# Ran MaxFilter (with -list, -v, -movecomp, etc.), and got:
msg = ['HPIFIT: 5 coils digitized in order 5 1 4 3 2',
'HPIFIT: 3 coils accepted: 1 2 4',
'Hpi coil moments (3 5):',
'2.08542e-15 -1.52486e-15 -1.53484e-15',
'2.14516e-15 2.09608e-15 7.30303e-16',
'-3.2318e-16 -4.25666e-16 2.69997e-15',
'5.21717e-16 1.28406e-15 1.95335e-15',
'1.21199e-15 -1.25801e-19 1.18321e-15',
'HPIFIT errors: 0.3, 0.3, 5.3, 0.4, 3.2 mm.',
'HPI consistency of isotrak and hpifit is OK.',
'HP fitting limits: err = 5.0 mm, gval = 0.980.',
'Using 5 HPI coils: 83 143 203 263 323 Hz', # actually came earlier
]
log = log.getvalue().splitlines()
assert_true(set(log) == set(msg), '\n' + '\n'.join(set(msg) - set(log)))
# Then took the raw file, did this:
raw.info['dig'][5]['r'][2] += 1.
# And checked the result in MaxFilter, which changed the logging as:
msg = msg[:8] + [
'HPIFIT errors: 0.3, 0.3, 5.3, 999.7, 3.2 mm.',
'Note: HPI coil 3 isotrak is adjusted by 5.3 mm!',
'Note: HPI coil 5 isotrak is adjusted by 3.2 mm!'] + msg[-2:]
with catch_logging() as log:
_get_hpi_info(raw.info, adjust=True, verbose='debug')
log = log.getvalue().splitlines()
assert_true(set(log) == set(msg), '\n' + '\n'.join(set(msg) - set(log)))
def test_chpi_adjust():
"""Test cHPI logging and adjustment."""
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes')
with catch_logging() as log:
_get_hpi_info(raw.info, adjust=True, verbose='debug')
# Ran MaxFilter (with -list, -v, -movecomp, etc.), and got:
msg = ['HPIFIT: 5 coils digitized in order 5 1 4 3 2',
'HPIFIT: 3 coils accepted: 1 2 4',
'Hpi coil moments (3 5):',
'2.08542e-15 -1.52486e-15 -1.53484e-15',
'2.14516e-15 2.09608e-15 7.30303e-16',
'-3.2318e-16 -4.25666e-16 2.69997e-15',
'5.21717e-16 1.28406e-15 1.95335e-15',
'1.21199e-15 -1.25801e-19 1.18321e-15',
'HPIFIT errors: 0.3, 0.3, 5.3, 0.4, 3.2 mm.',
'HPI consistency of isotrak and hpifit is OK.',
'HP fitting limits: err = 5.0 mm, gval = 0.980.',
'Using 5 HPI coils: 83 143 203 263 323 Hz', # actually came earlier
]
log = log.getvalue().splitlines()
assert_true(set(log) == set(msg), '\n' + '\n'.join(set(msg) - set(log)))
# Then took the raw file, did this:
raw.info['dig'][5]['r'][2] += 1.
# And checked the result in MaxFilter, which changed the logging as:
msg = msg[:8] + [
'HPIFIT errors: 0.3, 0.3, 5.3, 999.7, 3.2 mm.',
'Note: HPI coil 3 isotrak is adjusted by 5.3 mm!',
'Note: HPI coil 5 isotrak is adjusted by 3.2 mm!'] + msg[-2:]
with catch_logging() as log:
_get_hpi_info(raw.info, adjust=True, verbose='debug')
log = log.getvalue().splitlines()
assert_true(set(log) == set(msg), '\n' + '\n'.join(set(msg) - set(log)))
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."""
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"""
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_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',
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 plot_chpi_snr_raw(raw,
win_length,
n_harmonics=None,
show=True,
verbose=True):
"""Compute and plot cHPI SNR from raw data
Parameters
----------
win_length : float
Length of window to use for SNR estimates (seconds). A longer window
will naturally include more low frequency power, resulting in lower
SNR.
n_harmonics : int or None
Number of line frequency harmonics to include in the model. If None,
use all harmonics up to the MEG analog lowpass corner.
show : bool
Show figure if True.
Returns
-------
fig : instance of matplotlib.figure.Figure
cHPI SNR as function of time, residual variance.
Notes
-----
A general linear model including cHPI and line frequencies is fit into
each data window. The cHPI power obtained from the model is then divided
by the residual variance (variance of signal unexplained by the model) to
obtain the SNR.
The SNR may decrease either due to decrease of cHPI amplitudes (e.g.
head moving away from the helmet), or due to increase in the residual
variance. In case of broadband interference that overlaps with the cHPI
frequencies, the resulting decreased SNR accurately reflects the true
situation. However, increased narrowband interference outside the cHPI
and line frequencies would also cause an increase in the residual variance,
even though it wouldn't necessarily affect estimation of the cHPI
amplitudes. Thus, this method is intended for a rough overview of cHPI
signal quality. A more accurate picture of cHPI quality (at an increased
computational cost) can be obtained by examining the goodness-of-fit of
the cHPI coil fits.
"""
import matplotlib.pyplot as plt
from mne.chpi import _get_hpi_info
# plotting parameters
legend_fontsize = 6
title_fontsize = 10
tick_fontsize = 10
label_fontsize = 10
# get some info from fiff
sfreq = raw.info['sfreq']
linefreq = raw.info['line_freq']
if n_harmonics is not None:
linefreqs = (np.arange(n_harmonics + 1) + 1) * linefreq
else:
linefreqs = np.arange(linefreq, raw.info['lowpass'], linefreq)
buflen = int(win_length * sfreq)
if buflen <= 0:
raise ValueError('Window length should be >0')
cfreqs = _get_hpi_info(raw.info, verbose=False)[0]
if verbose:
print('Nominal cHPI frequencies: %s Hz' % cfreqs)
print('Sampling frequency: %s Hz' % sfreq)
print('Using line freqs: %s Hz' % linefreqs)
print('Using buffers of %s samples = %s seconds\n' %
(buflen, buflen / sfreq))
pick_meg = pick_types(raw.info, meg=True, exclude=[])
pick_mag = pick_types(raw.info, meg='mag', exclude=[])
pick_grad = pick_types(raw.info, meg='grad', exclude=[])
nchan = len(pick_meg)
# grad and mag indices into an array that already has meg channels only
pick_mag_ = np.in1d(pick_meg, pick_mag).nonzero()[0]
pick_grad_ = np.in1d(pick_meg, pick_grad).nonzero()[0]
# create general linear model for the data
t = np.arange(buflen) / float(sfreq)
model = np.empty((len(t), 2 + 2 * (len(linefreqs) + len(cfreqs))))
model[:, 0] = t
model[:, 1] = np.ones(t.shape)
# add sine and cosine term for each freq
allfreqs = np.concatenate([linefreqs, cfreqs])
model[:, 2::2] = np.cos(2 * np.pi * t[:, np.newaxis] * allfreqs)
model[:, 3::2] = np.sin(2 * np.pi * t[:, np.newaxis] * allfreqs)
inv_model = linalg.pinv(model)
# drop last buffer to avoid overrun
bufs = np.arange(0, raw.n_times, buflen)[:-1]
tvec = bufs / sfreq
snr_avg_grad = np.zeros([len(cfreqs), len(bufs)])
hpi_pow_grad = np.zeros([len(cfreqs), len(bufs)])
snr_avg_mag = np.zeros([len(cfreqs), len(bufs)])
resid_vars = np.zeros([nchan, len(bufs)])
for ind, buf0 in enumerate(bufs):
if verbose:
print('Buffer %s/%s' % (ind + 1, len(bufs)))
megbuf = raw[pick_meg, buf0:buf0 + buflen][0].T
coeffs = np.dot(inv_model, megbuf)
coeffs_hpi = coeffs[2 + 2 * len(linefreqs):]
resid_vars[:, ind] = np.var(megbuf - np.dot(model, coeffs), 0)
# get total power by combining sine and cosine terms
# sinusoidal of amplitude A has power of A**2/2
hpi_pow = (coeffs_hpi[0::2, :]**2 + coeffs_hpi[1::2, :]**2) / 2
hpi_pow_grad[:, ind] = hpi_pow[:, pick_grad_].mean(1)
# divide average HPI power by average variance
snr_avg_grad[:, ind] = hpi_pow_grad[:, ind] / \
resid_vars[pick_grad_, ind].mean()
snr_avg_mag[:, ind] = hpi_pow[:, pick_mag_].mean(1) / \
resid_vars[pick_mag_, ind].mean()
cfreqs_legend = ['%s Hz' % fre for fre in cfreqs]
fig, axs = plt.subplots(4, 1, sharex=True)
# SNR plots for gradiometers and magnetometers
ax = axs[0]
lines1 = ax.plot(tvec, 10 * np.log10(snr_avg_grad.T))
lines1_med = ax.plot(tvec,
10 * np.log10(np.median(snr_avg_grad, axis=0)),
lw=2,
ls=':',
color='k')
ax.set_xlim([tvec.min(), tvec.max()])
ax.set(ylabel='SNR (dB)')
ax.yaxis.label.set_fontsize(label_fontsize)
ax.set_title('Mean cHPI power / mean residual variance, gradiometers',
fontsize=title_fontsize)
ax.tick_params(axis='both', which='major', labelsize=tick_fontsize)
ax = axs[1]
lines2 = ax.plot(tvec, 10 * np.log10(snr_avg_mag.T))
lines2_med = ax.plot(tvec,
10 * np.log10(np.median(snr_avg_mag, axis=0)),
lw=2,
ls=':',
color='k')
ax.set_xlim([tvec.min(), tvec.max()])
ax.set(ylabel='SNR (dB)')
ax.yaxis.label.set_fontsize(label_fontsize)
ax.set_title('Mean cHPI power / mean residual variance, magnetometers',
fontsize=title_fontsize)
ax.tick_params(axis='both', which='major', labelsize=tick_fontsize)
ax = axs[2]
lines3 = ax.plot(tvec, hpi_pow_grad.T)
lines3_med = ax.plot(tvec,
np.median(hpi_pow_grad, axis=0),
lw=2,
ls=':',
color='k')
ax.set_xlim([tvec.min(), tvec.max()])
ax.set(ylabel='Power (T/m)$^2$')
ax.yaxis.label.set_fontsize(label_fontsize)
ax.set_title('Mean cHPI power, gradiometers', fontsize=title_fontsize)
ax.tick_params(axis='both', which='major', labelsize=tick_fontsize)
# residual (unexplained) variance as function of time
ax = axs[3]
cls = plt.get_cmap('plasma')(np.linspace(0., 0.7, len(pick_meg)))
ax.set_prop_cycle(color=cls)
ax.semilogy(tvec, resid_vars[pick_grad_, :].T, alpha=.4)
ax.set_xlim([tvec.min(), tvec.max()])
ax.set(ylabel='Var. (T/m)$^2$', xlabel='Time (s)')
ax.xaxis.label.set_fontsize(label_fontsize)
ax.yaxis.label.set_fontsize(label_fontsize)
ax.set_title('Residual (unexplained) variance, all gradiometer channels',
fontsize=title_fontsize)
ax.tick_params(axis='both', which='major', labelsize=tick_fontsize)
tight_layout(pad=.5, w_pad=.1, h_pad=.2) # from mne.viz
# tight_layout will screw these up
ax = axs[0]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# order curve legends according to mean of data
sind = np.argsort(snr_avg_grad.mean(axis=1))[::-1]
handles = [lines1[i] for i in sind]
handles.append(lines1_med[0])
labels = [cfreqs_legend[i] for i in sind]
labels.append('Median')
leg_kwargs = dict(
prop={'size': legend_fontsize},
bbox_to_anchor=(
1.02,
0.5,
),
loc='center left',
borderpad=1,
handlelength=1,
)
ax.legend(handles, labels, **leg_kwargs)
ax = axs[1]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
sind = np.argsort(snr_avg_mag.mean(axis=1))[::-1]
handles = [lines2[i] for i in sind]
handles.append(lines2_med[0])
labels = [cfreqs_legend[i] for i in sind]
labels.append('Median')
ax.legend(handles, labels, **leg_kwargs)
ax = axs[2]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
sind = np.argsort(hpi_pow_grad.mean(axis=1))[::-1]
handles = [lines3[i] for i in sind]
handles.append(lines3_med[0])
labels = [cfreqs_legend[i] for i in sind]
labels.append('Median')
ax.legend(handles, labels, **leg_kwargs)
ax = axs[3]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
if show:
plt.show()
return fig