def make_dipole_projectors(info, pos, ori, bem, trans, verbose=None):
"""Make dipole projectors.
Parameters
----------
info : instance of Info
The measurement info.
pos : ndarray, shape (n_dip, 3)
The dipole positions.
ori : ndarray, shape (n_dip, 3)
The dipole orientations.
bem : instance of ConductorModel
The conductor model to use.
trans : instance of Transform
The mri-to-head transformation.
%(verbose)s
Returns
-------
projs : list of Projection
The projectors.
"""
pos = np.atleast_2d(pos).astype(float)
ori = np.atleast_2d(ori).astype(float)
if pos.shape[1] != 3 or pos.shape != ori.shape:
raise ValueError('pos and ori must be 2D, the same shape, and have '
f'last dimension 3, got {pos.shape} and {ori.shape}')
dip = Dipole(pos=pos,
ori=ori,
amplitude=np.ones(pos.shape[0]),
gof=np.ones(pos.shape[0]),
times=np.arange(pos.shape[0]))
info = pick_info(info, pick_types(info, meg=True, eeg=True, exclude=()))
fwd, _ = make_forward_dipole(dip, bem, info, trans)
assert fwd['sol']['data'].shape[1] == pos.shape[0]
projs = list()
for kind in ('meg', 'eeg'):
kwargs = dict(meg=False, eeg=False, exclude=())
kwargs.update({kind: True})
picks = pick_types(info, **kwargs)
if len(picks) > 0:
ch_names = [info['ch_names'][pick] for pick in picks]
projs.extend([
Projection(data=dict(data=p[np.newaxis, picks],
row_names=None,
nrow=1,
col_names=list(ch_names),
ncol=len(ch_names)),
kind=FIFF.FIFFV_PROJ_ITEM_DIP_FIX,
explained_var=None,
active=False,
desc=f'Dipole #{pi}')
for pi, p in enumerate(fwd['sol']['data'].T, 1)
])
return projs
def gen_forward_solution(pos, bem, info, trans, verbose=True):
# NOTE:
# Both, dipole amplitude and orientation, are set to arbitrary values
# here: They are merely required to instantiate a Dipole object, which
# we then use to conveniently retrieve a forward solution via
# `make_forward_dipole()`. This forward solution is returned in "fixed"
# orientation. We then convert it back to "free" orientation mode before
# returning the forward object.
amplitude = np.array([1]).reshape(1, ) # Arbitraty amplitude.
ori = np.array([1., 1., 1.]).reshape(1, 3) # Arbitrary orientation.
ori /= np.linalg.norm(ori)
pos = pos.reshape(1, 3)
gof = np.array([100]).reshape(1, )
dip = mne.Dipole(times=[0], pos=pos, ori=ori, amplitude=amplitude, gof=gof)
fwd, _ = mne.make_forward_dipole(dip,
bem=bem,
info=info,
trans=trans,
verbose=verbose)
fwd = mne.convert_forward_solution(fwd, force_fixed=False, verbose=verbose)
return fwd
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.)
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.)
evoked_fit_left.info.normalize_proj()
evoked_fit_right.info.normalize_proj()
cov_fit_left['projs'] = evoked_fit_left.info['projs']
cov_fit_right['projs'] = evoked_fit_right.info['projs']
# Fit the dipoles with the subset of sensors.
dip_left, _ = mne.fit_dipole(evoked_fit_left, cov_fit_left, bem)
dip_right, _ = mne.fit_dipole(evoked_fit_right, cov_fit_right, bem)
###############################################################################
# Now that we have the location and orientations of the dipoles, compute the
# full timecourses using MNE, assigning activity to both dipoles at the same
# time while preventing leakage between the two. We use a very low ``lambda``
# value to ensure both dipoles are fully used.
fwd, _ = mne.make_forward_dipole([dip_left, dip_right], bem, info)
# Apply MNE inverse
inv = make_inverse_operator(info, fwd, cov, fixed=True, depth=0)
stc_left = apply_inverse(evoked_left, inv, method='MNE', lambda2=1E-6)
stc_right = apply_inverse(evoked_right, inv, method='MNE', lambda2=1E-6)
# Plot the timecourses of the resulting source estimate
fig, axes = plt.subplots(nrows=2, sharex=True, sharey=True)
axes[0].plot(stc_left.times, stc_left.data.T)
axes[0].set_title('Left auditory stimulation')
axes[0].legend(['Dipole 1', 'Dipole 2'])
axes[1].plot(stc_right.times, stc_right.data.T)
axes[1].set_title('Right auditory stimulation')
axes[1].set_xlabel('Time (s)')
fig.supylabel('Dipole amplitude')
