def functions_and_metrics(resmat, src, functions, metrics, subject, stctext):
"""
Iterate over resolution functions and metrics,
save metric distributions to STC.
Parameters:
resmat: array
resolution matrix
src: instace of SourceSpace
The source space to use for metrics (e.g. from forward solution).
functions: str 'psf' | 'ctf'
The resolution function for which to compute metrics.
metrics: list of str
The resolution metrics to compute.
subject: str
The subject name (e.g. 'Sub01').
stctext: str
Text for STC filename, function and metric will be pre-pended.
Returns:
nothing
"""
# After resolution matrix, compute resolution metrics
for function in functions:
for metric in metrics:
# compute resolution metrics
resmet = resolution_metrics(resmat,
invop['src'],
function=function,
metric=metric)
# prepend psf/ctf and resolution metric
filetext = '%s_%s_%s' % (function, metric, stctext)
fname_stc = C.fname_STC(C, C.resolution_subdir, subject, filetext)
# save STC with resolution metrics
print('Writing resolution metrics to %s.' % fname_stc)
resmet.save(fname_stc)
# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr**2
# %%
# MNE
# ---
# Compute resolution matrices, peak localisation error (PLE) for point spread
# functions (PSFs), spatial deviation (SD) for PSFs:
rm_mne = make_inverse_resolution_matrix(forward,
inverse_operator,
method='MNE',
lambda2=lambda2)
ple_mne_psf = resolution_metrics(rm_mne,
inverse_operator['src'],
function='psf',
metric='peak_err')
sd_mne_psf = resolution_metrics(rm_mne,
inverse_operator['src'],
function='psf',
metric='sd_ext')
del rm_mne
# %%
# dSPM
# ----
# Do the same for dSPM:
rm_dspm = make_inverse_resolution_matrix(forward,
inverse_operator,
method='dSPM',