## Load files. Get number of parcels.
fileSourceIdentities = glob.glob(subjectPath + sourceIdPattern)[0]
fileForwardOperator = glob.glob(subjectPath + forwardPattern)[0]
fileInverseOperator = glob.glob(subjectPath + inversePattern)[0]
fileSourceFidelities = glob.glob(subjectPath + sourceFidPattern)[0]
identities = np.load(
fileSourceIdentities
) # Source length vector. Expected ids for parcels are 0 to n-1, where n is number of parcels, and -1 for sources that do not belong to any parcel.
forward = np.matrix(np.load(fileForwardOperator)) # sensors x sources
inverse = np.matrix(np.load(fileInverseOperator)) # sources x sensors
sourceFids = np.load(fileSourceFidelities) # sources
weights = source_fid_to_weights(sourceFids,
exponent=exponent,
normalize=normalize,
inverse=inverse,
identities=identities,
flips=flips)
inverse_w = np.einsum('ij,i->ij', inverse, weights)
n_parcels = get_n_parcels(identities)
""" Do network estimation. Get cross-patch complex PLV values from paired data"""
parcelSeriesPairs, pairs = make_series_paired(n_parcels, n_samples, seed=1)
_, cp_PLVPW = fidelity_estimation(forward,
inverse_w,
identities,
parcel_series=parcelSeriesPairs)
_, cp_PLVPO = fidelity_estimation(forward,
inverse,
identities,
parcel_series=parcelSeriesPairs)
print('Simulation resolution: ' + str(resolution1))
for i2, resolution2 in enumerate(resolutions):
print('\t Modeling resolution: ' + str(resolution2))
for isub, subject in enumerate(subjects):
""" Make simulated parcel series at resolution1. Measure plv at
resolution2. Get fidelity with original and weighted inv ops. """
simulatedParcelSeries = make_series(n_parcels,
n_samples,
n_cut_samples=40,
widths=range(5, 6))
simulatedSourceSeries = simulatedParcelSeries[idArray[isub][i1]]
for iexp, exponent in enumerate(exponents):
newWeights = source_fid_to_weights(
sourceFidArray[isub][i2],
exponent=exponent,
normalize=True,
inverse=invOps[isub],
identities=idArray[isub][i2])
weightedInvOp = np.einsum('ij,i->ij', invOps[isub], newWeights)
modeledSourceSeriesW = np.dot(
weightedInvOp, np.dot(forwards[isub],
simulatedSourceSeries))
indicesS = tuple([iexp, isub, i1, i2,
range(int(resolution1))
]) # indicesS = simulation resolution
indicesM = tuple([iexp, isub, i1, i2,
range(int(resolution2))
]) # indicesM = modeling resolution
plvArray_meth1[indicesM] = np.abs(