def runsub(sub, thisContrast, r, dstype='raw', roi='grayMatter', filterLen=49, filterOrd=3, write=False):
if dstype == 'raw':
outdir='PyMVPA'
print "working with raw data"
thisSub = {sub: subList[sub]}
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi)
thisDS = dsdict[sub]
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
# savitsky golay filtering
sg.sg_filter(thisDS, filterLen, filterOrd)
# gallant group zscores before regression.
# zscore w.r.t. rest trials
# zscore(thisDS, param_est=('targets', ['rest']), chunks_attr='chunks')
# zscore entire set. if done chunk-wise, there is no double-dipping (since we leave a chunk out at a time).
zscore(thisDS, chunks_attr='chunks')
print "beta extraction"
## BETA EXTRACTION ##
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub])
evds = er.fit_event_hrf_model(rds, events, time_attr='time_coords',
condition_attr=('trial_type', 'chunks'),
design_kwargs={'add_regs': mc_params[sub], 'hrf_model': 'canonical'},
return_model=True)
fds = lmvpa.replacetargets(evds, contrasts, thisContrast)
fds = fds[fds.targets != '0']
else:
outdir=os.path.join('LSS', dstype)
print "loading betas"
fds = lmvpa.loadsubbetas(paths, sub, btype=dstype, m=roi)
fds.sa['targets'] = fds.sa[thisContrast]
zscore(fds, chunks_attr='chunks')
fds = lmvpa.sortds(fds)
print "searchlights"
## initialize classifier
clf = svm.LinearNuSVMC()
cv = CrossValidation(clf, NFoldPartitioner())
from mvpa2.measures.searchlight import sphere_searchlight
cvSL = sphere_searchlight(cv, radius=r)
# now I have betas per chunk. could just correlate the betas, or correlate the predictions for corresponding runs
lidx = fds.chunks < fds.sa['chunks'].unique[len(fds.sa['chunks'].unique)/2]
pidx = fds.chunks >= fds.sa['chunks'].unique[len(fds.sa['chunks'].unique) / 2]
lres = sl.run_cv_sl(cvSL, fds[lidx].copy(deep=False))
pres = sl.run_cv_sl(cvSL, fds[pidx].copy(deep=False))
if write:
from mvpa2.base import dataset
map2nifti(fds, dataset.vstack([lres, pres])).\
to_filename(os.path.join(
paths[0], 'Maps', outdir,
sub + '_' + roi + '_' + thisContrast + '_cvsl.nii.gz'))
del lres, pres, cvSL
cvSL = sphere_searchlight(cv, radius=r)
crossSet = fds.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cres = sl.run_cv_sl(cvSL, crossSet.copy(deep=False))
if write:
map2nifti(fds, cres[0]).to_filename(
os.path.join(paths[0], 'Maps', outdir,
sub + '_' + roi + '_' + (thisContrast) + '_P2L.nii.gz'))
map2nifti(fds, cres[1]).to_filename(
os.path.join(paths[0], 'Maps', outdir,
sub + '_' + roi + '_' + (thisContrast) + '_L2P.nii.gz'))
def runsub(sub, thisContrast, r, dstype="raw", roi="grayMatter", filterLen=49, filterOrd=3, write=False):
if dstype == "raw":
outdir = "PyMVPA"
print "working with raw data"
thisSub = {sub: subList[sub]}
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi)
thisDS = dsdict[sub]
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
# savitsky golay filtering
sg.sg_filter(thisDS, filterLen, filterOrd)
# gallant group zscores before regression.
# zscore w.r.t. rest trials
# zscore(thisDS, param_est=('targets', ['rest']), chunks_attr='chunks')
# zscore entire set. if done chunk-wise, there is no double-dipping (since we leave a chunk out at a time).
zscore(thisDS, chunks_attr="chunks")
print "beta extraction"
## BETA EXTRACTION ##
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub])
evds = er.fit_event_hrf_model(
rds,
events,
time_attr="time_coords",
condition_attr=("trial_type", "chunks"),
design_kwargs={"add_regs": mc_params[sub], "hrf_model": "canonical"},
return_model=True,
)
fds = lmvpa.replacetargets(evds, contrasts, thisContrast)
fds = fds[fds.targets != "0"]
else:
outdir = os.path.join("LSS", dstype)
print "loading betas"
fds = lmvpa.loadsubbetas(paths, sub, btype=dstype, m=roi)
fds.sa["targets"] = fds.sa[thisContrast]
zscore(fds, chunks_attr="chunks")
fds = lmvpa.sortds(fds)
print "searchlights"
## initialize classifier
clf = svm.LinearNuSVMC()
cv = CrossValidation(clf, NFoldPartitioner())
from mvpa2.measures.searchlight import sphere_searchlight
cvSL = sphere_searchlight(cv, radius=r)
# now I have betas per chunk. could just correlate the betas, or correlate the predictions for corresponding runs
lidx = fds.chunks < fds.sa["chunks"].unique[len(fds.sa["chunks"].unique) / 2]
pidx = fds.chunks >= fds.sa["chunks"].unique[len(fds.sa["chunks"].unique) / 2]
lres = sl.run_cv_sl(cvSL, fds[lidx].copy(deep=False))
pres = sl.run_cv_sl(cvSL, fds[pidx].copy(deep=False))
if write:
from mvpa2.base import dataset
map2nifti(fds, dataset.vstack([lres, pres])).to_filename(
os.path.join(paths[0], "Maps", outdir, sub + "_" + roi + "_" + thisContrast + "_cvsl.nii.gz")
)
del lres, pres, cvSL
cvSL = sphere_searchlight(cv, radius=r)
crossSet = fds.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cres = sl.run_cv_sl(cvSL, crossSet.copy(deep=False))
if write:
map2nifti(fds, cres[0]).to_filename(
os.path.join(paths[0], "Maps", outdir, sub + "_" + roi + "_" + (thisContrast) + "_P2L.nii.gz")
)
map2nifti(fds, cres[1]).to_filename(
os.path.join(paths[0], "Maps", outdir, sub + "_" + roi + "_" + (thisContrast) + "_L2P.nii.gz")
)
# kay method: leave out a model run, use it to fit an HRF for each voxel
# huth method: essentially use FIR
# mumford method: deconvolution with canonical HRF
# refit events and regress...
# get timing data from timing files
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub])
#OLS beta extraction
betas = 'LSA'
evds = er.fit_event_hrf_model(rds, events, time_attr='time_coords',
condition_attr=('trial_type', 'chunks'),
design_kwargs={'add_regs': mc_params[sub], 'hrf_model': 'canonical'},
return_model=True)
fds = lmvpa.replacetargets(evds, contrasts, thisContrast)
fds = fds[fds.targets != '0']
fds = fds[fds.targets != 'rest']
rsaresults.append(dsm(fds))
lidx = fds.chunks < fds.sa['chunks'].unique[len(fds.sa['chunks'].unique)/2]
pidx = fds.chunks >= fds.sa['chunks'].unique[len(fds.sa['chunks'].unique) / 2]
lfds = fds[lidx]
pfds = fds[pidx]
lres = cv(lfds)
lresults.append(lres)
print "language: " + str(np.mean(lres.samples))
cv.untrain()
pres = cv(pfds)
# get timing data from timing files
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub])
#OLS beta extraction
betas = 'LSA'
evds = er.fit_event_hrf_model(rds,
events,
time_attr='time_coords',
condition_attr=('trial_type', 'chunks'),
design_kwargs={
'add_regs': mc_params[sub],
'hrf_model': 'canonical'
},
return_model=True)
fds = lmvpa.replacetargets(evds, contrasts, thisContrast)
fds = fds[fds.targets != '0']
fds = fds[fds.targets != 'rest']
rsaresults.append(dsm(fds))
lidx = fds.chunks < fds.sa['chunks'].unique[len(fds.sa['chunks'].unique) /
2]
pidx = fds.chunks >= fds.sa['chunks'].unique[len(fds.sa['chunks'].unique) /
2]
lfds = fds[lidx]
pfds = fds[pidx]
lres = cv(lfds)
lresults.append(lres)
print "language: " + str(np.mean(lres.samples))
