def apply_solver(evoked, forward, noise_cov, loose=0.2, depth=0.8, K=2000):
all_ch_names = evoked.ch_names
# put the forward solution in fixed orientation if it's not already
if loose is None and not is_fixed_orient(forward):
forward = deepcopy(forward)
forward = mne.convert_forward_solution(forward, force_fixed=True)
gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
forward,
evoked.info,
noise_cov,
pca=False,
depth=depth,
loose=loose,
weights=None,
weights_min=None)
n_locations = gain.shape[1]
sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
M = evoked.data[sel]
# Whiten data
M = np.dot(whitener, M)
n_orient = 1 if is_fixed_orient(forward) else 3
# The value of lambda for which the solution will be all zero
lambda_max = norm_l2inf(np.dot(gain.T, M), n_orient)
lambda_ref = 0.1 * lambda_max
out = mm_mixed_norm_bayes(M,
gain,
lambda_ref,
n_orient=n_orient,
K=K,
return_lpp=True)
(Xs, active_sets), _, _, _, _ = out
solution_support = np.zeros((K, n_locations))
stcs, obj_fun = [], []
for k in range(K):
X = np.zeros((n_locations, Xs[k].shape[1]))
X[active_sets[k]] = Xs[k]
block_norms_new = compute_block_norms(X, n_orient)
block_norms_new = (block_norms_new > 0.05 * block_norms_new.max())
solution_support[k, :] = block_norms_new
stc = _make_sparse_stc(Xs[k],
active_sets[k],
forward,
tmin=0.,
tstep=1. / evoked.info['sfreq'])
stcs.append(stc)
obj_fun.append(
energy_l2half_reg(M, gain, stc.data, active_sets[k], lambda_ref,
n_orient))
return solution_support, stcs, obj_fun
def apply_solver_isdr(solver,
evoked,
forward,
noise_cov,
loose=0.2,
depth=0.8):
# Import the necessary private functions
from mne.inverse_sparse.mxne_inverse import \
(_prepare_gain, _check_loose_forward, is_fixed_orient,
_reapply_source_weighting, _make_sparse_stc)
all_ch_names = evoked.ch_names
loose, forward = _check_loose_forward(loose, forward)
# put the forward solution in fixed orientation if it's not already
if loose == 0. and not is_fixed_orient(forward):
forward = mne.convert_forward_solution(forward,
surf_ori=True,
force_fixed=True,
copy=True,
use_cps=True)
# Handle depth weighting and whitening (here is no weights)
gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
forward,
evoked.info,
noise_cov,
pca=False,
depth=depth,
loose=loose,
weights=None,
weights_min=None)
print source_weighting
# Select channels of interest
sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
M = evoked.data[sel]
# Whiten data
M = np.dot(whitener, M).astype(np.double)
active_set = np.zeros(gain.shape[1], dtype=bool)
gain = gain[:, :].astype(np.double)
n_orient = 1 if is_fixed_orient(forward) else 3
SC = np.eye(np.shape(gain)[1]).astype(np.int32)
GA = gain.copy().astype(np.double)
X, n_active_set, coef = solver_isdr(M, gain, GA, SC)
active_set[n_active_set] = True
#X = _reapply_source_weighting(X, source_weighting, active_set, n_orient)
#Z = np.zeros((np.shape(gain)[1], np.shape(M)[1]))
#Z[np.array(n_active_set), :] = X[:np.shape(M)[1], :len(n_active_set)].T
#stc = _make_sparse_stc(Z, active_set, forward, tmin=evoked.times[0], tstep=1. / evoked.info['sfreq'])
return X #stc
def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):
"""Call a custom solver on evoked data.
This function does all the necessary computation:
- to select the channels in the forward given the available ones in
the data
- to take into account the noise covariance and do the spatial whitening
- to apply loose orientation constraint as MNE solvers
- to apply a weigthing of the columns of the forward operator as in the
weighted Minimum Norm formulation in order to limit the problem
of depth bias.
Parameters
----------
solver : callable
The solver takes 3 parameters: data M, gain matrix G, number of
dipoles orientations per location (1 or 3). A solver shall return
2 variables: X which contains the time series of the active dipoles
and an active set which is a boolean mask to specify what dipoles are
present in X.
evoked : instance of mne.Evoked
The evoked data
forward : instance of Forward
The forward solution.
noise_cov : instance of Covariance
The noise covariance.
loose : float in [0, 1] | 'auto'
Value that weights the source variances of the dipole components
that are parallel (tangential) to the cortical surface. If loose
is 0 then the solution is computed with fixed orientation.
If loose is 1, it corresponds to free orientations.
The default value ('auto') is set to 0.2 for surface-oriented source
space and set to 1.0 for volumic or discrete source space.
depth : None | float in [0, 1]
Depth weighting coefficients. If None, no depth weighting is performed.
Returns
-------
stc : instance of SourceEstimate
The source estimates.
"""
# Import the necessary private functions
from mne.inverse_sparse.mxne_inverse import \
(_prepare_gain, is_fixed_orient,
_reapply_source_weighting, _make_sparse_stc)
all_ch_names = evoked.ch_names
# Handle depth weighting and whitening (here is no weights)
forward, gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
forward, evoked.info, noise_cov, pca=False, depth=depth,
loose=loose, weights=None, weights_min=None, rank=None)
# Select channels of interest
sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
M = evoked.data[sel]
# Whiten data
M = np.dot(whitener, M)
n_orient = 1 if is_fixed_orient(forward) else 3
X, active_set = solver(M, gain, n_orient)
X = _reapply_source_weighting(X, source_weighting, active_set)
stc = _make_sparse_stc(X, active_set, forward, tmin=evoked.times[0],
tstep=1. / evoked.info['sfreq'])
return stc
def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):
"""Function to call a custom solver on evoked data
This function does all the necessary computation:
- to select the channels in the forward given the available ones in
the data
- to take into account the noise covariance and do the spatial whitening
- to apply loose orientation constraint as MNE solvers
- to apply a weigthing of the columns of the forward operator as in the
weighted Minimum Norm formulation in order to limit the problem
of depth bias.
Parameters
----------
solver : callable
The solver takes 3 parameters: data M, gain matrix G, number of
dipoles orientations per location (1 or 3). A solver shall return
2 variables: X which contains the time series of the active dipoles
and an active set which is a boolean mask to specify what dipoles are
present in X.
evoked : instance of mne.Evoked
The evoked data
forward : instance of Forward
The forward solution.
noise_cov : instance of Covariance
The noise covariance.
loose : None | float in [0, 1]
Value that weights the source variances of the dipole components
defining the tangent space of the cortical surfaces. Requires surface-
based, free orientation forward solutions.
depth : None | float in [0, 1]
Depth weighting coefficients. If None, no depth weighting is performed.
Returns
-------
stc : instance of SourceEstimate
The source estimates.
"""
# Import the necessary private functions
from mne.inverse_sparse.mxne_inverse import \
(_prepare_gain, _to_fixed_ori, is_fixed_orient,
_reapply_source_weighting, _make_sparse_stc)
all_ch_names = evoked.ch_names
# put the forward solution in fixed orientation if it's not already
if loose is None and not is_fixed_orient(forward):
forward = forward.copy()
_to_fixed_ori(forward)
# Handle depth weighting and whitening (here is no weights)
gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
forward, evoked.info, noise_cov, pca=False, depth=depth,
loose=loose, weights=None, weights_min=None)
# Select channels of interest
sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
M = evoked.data[sel]
# Whiten data
M = np.dot(whitener, M)
n_orient = 1 if is_fixed_orient(forward) else 3
X, active_set = solver(M, gain, n_orient)
X = _reapply_source_weighting(X, source_weighting, active_set, n_orient)
stc = _make_sparse_stc(X, active_set, forward, tmin=evoked.times[0],
tstep=1. / evoked.info['sfreq'])
return stc