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, thisContrastStr,
filterLen, filterOrd,
paramEst, chunklen, alphas=np.logspace(0, 3, 20), debug=False, write=False, roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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])
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub], contrasts) # adding features
# we can model out motion and just not use those betas.
# Ridge
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
# instead of binarizing each one, make them parametric
desX, rds = lmvpa.make_designmat(rds, events, time_attr='time_coords', condition_attr=thisContrast,
design_kwargs={'hrf_model': 'canonical', 'drift_model': 'blank'},
regr_attrs=None)
# want to collapse ap and cr, but have anim separate
desX['motion'] = make_dmtx(rds.sa['time_coords'].value, paradigm=None, add_regs=mc_params[sub], drift_model='blank')
des = lmvpa.make_parammat(desX, hrf='canonical', zscore=True)
# set chunklen and nchunks
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
nchunks = int(len(thisDS)*paramEst / chunklen)
nboots=50
covarmat = None
mus = None
lwts, lalphas, lres, lceil = bsr.bootstrap_ridge(rds[lidx], ldes, chunklen=chunklen, nchunks=nchunks,
cov0=covarmat, mu0=mus, part_attr='chunks', mode='test',
alphas=alphas, single_alpha=True, normalpha=False,
nboots=nboots, corrmin=.2, singcutoff=1e-10, joined=None,
plot=debug, use_corr=True)
pwts, palphas, pres, pceil = bsr.bootstrap_ridge(rds[pidx], pdes, chunklen=chunklen, nchunks=nchunks,
part_attr='chunks', mode='test',
alphas=alphas, single_alpha=True, normalpha=False,
nboots=nboots, corrmin=.2, singcutoff=1e-10, joined=None,
plot=debug, use_corr=True)
print 'language ' + str(np.mean(lres))
# pictures within
print 'pictures: ' + str(np.mean(pres))
# need to change outstring
if write:
from mvpa2.base import dataset
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_corrs.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_weights.nii.gz'))
map2nifti(thisDS, dataset.vstack([lalphas, palphas])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_alphas.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_ceiling.nii.gz'))
del lres, pres, lwts, pwts, lalphas, palphas, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cwts, calphas, cres, cceil = bsr.bootstrap_ridge(crossSet, des, chunklen=chunklen, nchunks=nchunks,
part_attr='chunks', mode='test',
alphas=alphas, single_alpha=True, normalpha=False,
nboots=nboots, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
print 'cross: ' + str(np.mean(cres))
if write:
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_ridge_corr.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_ridge_corr.nii.gz'))
map2nifti(thisDS, cwts[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_ridge_weights.nii.gz'))
map2nifti(thisDS, cwts[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_ridge_weights.nii.gz'))
map2nifti(thisDS, calphas[calphas.chunks==1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_ridge_alphas.nii.gz'))
map2nifti(thisDS, calphas[calphas.chunks==2]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_ridge_alphas.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_ridge_ceiling.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_ridge_ceiling.nii.gz'))
del cres, cwts, calphas, cceil
def runsub(sub, thisContrast, thisContrastStr, testContrast,
filterLen, filterOrd, write=False, debug=False,
alphas=1, roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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
# get timing data from timing files
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub], contrasts) # adding features
# we can model out motion and just not use those betas.
# Ridge
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
desX, rds = lmvpa.make_designmat(rds, events, time_attr='time_coords', condition_attr=thisContrast,
design_kwargs={'hrf_model': 'canonical', 'drift_model': 'blank'},
regr_attrs=None)
# 'add_regs': mc_params[sub]
desX['motion'] = make_dmtx(rds.sa['time_coords'].value, paradigm=None, add_regs=mc_params[sub], drift_model='blank')
des = lmvpa.make_parammat(desX, hrf='canonical', zscore=True)
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
covarmat = None
mus = None
lwts, _, lres, lceil = bsr.bootstrap_ridge(ds=rds[lidx], des=ldes, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[0]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
print 'language ' + str(np.mean(lres))
pwts, _, pres, pceil = bsr.bootstrap_ridge(ds=rds[pidx], des=pdes, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[1]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
# pictures within
print 'pictures: ' + str(np.mean(pres))
if write:
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_corrs.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_wts.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_ceiling.nii.gz'))
for t in testContrast:
tstr = '+'.join(t)
lcorr = lmvpa.testmodel(wts=lwts, des=ldes, ds=rds[lidx], tc=cp.copy(t), use_corr=True)
pcorr = lmvpa.testmodel(wts=pwts, des=pdes, ds=rds[pidx], tc=cp.copy(t), use_corr=True)
if write:
map2nifti(thisDS, dataset.vstack([lcorr, pcorr])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_test_corrs.nii.gz'))
del lres, pres, lwts, pwts, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
# cwts, cres, cceil = bsr.ridge(rds[pidx], pdes, mu0=mus, cov0=covarmat,
# part_attr='chunks', mode='test', alphas=alphas[0], single_alpha=True,
# normalpha=False, corrmin=.2, singcutoff=1e-10, joined=None,
# use_corr=True)
cwts, _, cres, cceil = bsr.bootstrap_ridge(ds=crossSet, des=des, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[2]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
for t in testContrast:
tstr = '+'.join(t)
ccorr = lmvpa.testmodel(wts=cwts, des=des, ds=crossSet, tc=cp.copy(t), use_corr=True)
if write:
map2nifti(thisDS, ccorr[0]) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_test_corr.nii.gz'))
map2nifti(thisDS, ccorr[1]) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_test_corr.nii.gz'))
print 'cross: ' + str(np.mean(cres))
if write:
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_corr.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_corr.nii.gz'))
map2nifti(thisDS, cwts[cwts.chunks==1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_wts.nii.gz'))
map2nifti(thisDS, cwts[cwts.chunks==2]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha' + str(alphas[2]) + '_wts.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_ceiling.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_ceiling.nii.gz'))
del cres, cwts, cceil
def runsub(sub, thisContrast, thisContrastStr, testContrast,
filterLen, filterOrd, write=False, debug=False,
alphas=1, roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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
# get timing data from timing files
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub], contrasts) # adding features
# we can model out motion and just not use those betas.
# Ridge
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
desX, rds = lmvpa.make_designmat(rds, events, time_attr='time_coords', condition_attr=thisContrast,
design_kwargs={'hrf_model': 'canonical', 'drift_model': 'blank'},
regr_attrs=None)
# 'add_regs': mc_params[sub]
desX['motion'] = make_dmtx(rds.sa['time_coords'].value, paradigm=None, add_regs=mc_params[sub], drift_model='blank')
des = lmvpa.make_parammat(desX, hrf='canonical', zscore=True)
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
covarmat = None
mus = None
lwts, _, lres, lceil = bsr.bootstrap_ridge(ds=rds[lidx], des=ldes, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[0]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
print 'language ' + str(np.mean(lres))
pwts, _, pres, pceil = bsr.bootstrap_ridge(ds=rds[pidx], des=pdes, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[1]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
# pictures within
print 'pictures: ' + str(np.mean(pres))
if write:
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_corrs.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_wts.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_ceiling.nii.gz'))
for t in testContrast:
tstr = '+'.join(t)
lcorr = lmvpa.testmodel(wts=lwts, des=ldes, ds=rds[lidx], tc=cp.copy(t), use_corr=True)
pcorr = lmvpa.testmodel(wts=pwts, des=pdes, ds=rds[pidx], tc=cp.copy(t), use_corr=True)
if write:
map2nifti(thisDS, dataset.vstack([lcorr, pcorr])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_ridge_la_' + str(alphas[0]) + '_pa_' + str(alphas[1]) + '_test_corrs.nii.gz'))
del lres, pres, lwts, pwts, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
# cwts, cres, cceil = bsr.ridge(rds[pidx], pdes, mu0=mus, cov0=covarmat,
# part_attr='chunks', mode='test', alphas=alphas[0], single_alpha=True,
# normalpha=False, corrmin=.2, singcutoff=1e-10, joined=None,
# use_corr=True)
cwts, _, cres, cceil = bsr.bootstrap_ridge(ds=crossSet, des=des, chunklen=1, nchunks=1,
cov0=None, mu0=None, part_attr='chunks', mode='test',
alphas=[alphas[2]], single_alpha=True, normalpha=False,
nboots=1, corrmin=.2, singcutoff=1e-10, joined=None,
use_corr=True)
for t in testContrast:
tstr = '+'.join(t)
ccorr = lmvpa.testmodel(wts=cwts, des=des, ds=crossSet, tc=cp.copy(t), use_corr=True)
if write:
map2nifti(thisDS, ccorr[0]) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_test_corr.nii.gz'))
map2nifti(thisDS, ccorr[1]) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + tstr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_test_corr.nii.gz'))
print 'cross: ' + str(np.mean(cres))
if write:
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_corr.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_corr.nii.gz'))
map2nifti(thisDS, cwts[cwts.chunks==1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_wts.nii.gz'))
map2nifti(thisDS, cwts[cwts.chunks==2]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha' + str(alphas[2]) + '_wts.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_P2L_ridge_alpha_' + str(alphas[2]) + '_ceiling.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_L2P_ridge_alpha_' + str(alphas[2]) + '_ceiling.nii.gz'))
del cres, cwts, cceil
def runsub(sub, thisContrast, filterLen, filterOrd, thisContrastStr, roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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], contrasts) # adding features
# we can model out motion and just not use those betas.
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
# instead of binarizing each one, make them parametric
desX, rds = lmvpa.make_designmat(rds, events, time_attr='time_coords', condition_attr=thisContrast,
design_kwargs={'hrf_model': 'canonical', 'drift_model': 'blank'},
regr_attrs=None)
# want to collapse ap and cr, but have anim separate
des = lmvpa.make_parammat(desX)
# set chunklen and nchunks
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
lwts, lres, lceil = bsr.bootstrap_linear(rds[lidx], ldes, part_attr='chunks', mode='test')
pwts, pres, pceil = bsr.bootstrap_linear(rds[pidx], pdes, part_attr='chunks', mode='test')
# now I have betas per chunk. could just correlate the betas, or correlate the predictions for corresponding runs
print 'language ' + str(np.mean(lres))
# pictures within
print 'pictures: ' + str(np.mean(pres))
from mvpa2.base import dataset
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_corr.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_betas.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_ceiling.nii.gz'))
del lres, pres, lwts, pwts, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cwts, cres, cceil = bsr.bootstrap_linear(crossSet, des, part_attr='chunks', mode='test')
print 'cross: ' + str(np.mean(cres))
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_univar.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_univar.nii.gz'))
map2nifti(thisDS, cwts[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_betas.nii.gz'))
map2nifti(thisDS, cwts[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_betas.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_P2L_betas.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr + '_L2P_betas.nii.gz'))
# 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')
# 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]
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,
filterLen,
filterOrd,
thisContrastStr,
roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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],
contrasts) # adding features
# we can model out motion and just not use those betas.
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
# instead of binarizing each one, make them parametric
desX, rds = lmvpa.make_designmat(rds,
events,
time_attr='time_coords',
condition_attr=thisContrast,
design_kwargs={
'hrf_model': 'canonical',
'drift_model': 'blank'
},
regr_attrs=None)
# want to collapse ap and cr, but have anim separate
des = lmvpa.make_parammat(desX)
# set chunklen and nchunks
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(
thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(
thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
lwts, lres, lceil = bsr.bootstrap_linear(rds[lidx],
ldes,
part_attr='chunks',
mode='test')
pwts, pres, pceil = bsr.bootstrap_linear(rds[pidx],
pdes,
part_attr='chunks',
mode='test')
# now I have betas per chunk. could just correlate the betas, or correlate the predictions for corresponding runs
print 'language ' + str(np.mean(lres))
# pictures within
print 'pictures: ' + str(np.mean(pres))
from mvpa2.base import dataset
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_corr.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_betas.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_univar_ceiling.nii.gz'))
del lres, pres, lwts, pwts, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cwts, cres, cceil = bsr.bootstrap_linear(crossSet,
des,
part_attr='chunks',
mode='test')
print 'cross: ' + str(np.mean(cres))
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_P2L_univar.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_L2P_univar.nii.gz'))
map2nifti(thisDS, cwts[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_P2L_betas.nii.gz'))
map2nifti(thisDS, cwts[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_L2P_betas.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_P2L_betas.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding',
sub + '_' + roi + '_' + thisContrastStr + '_L2P_betas.nii.gz'))
# 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')
# 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']
def runsub(sub,
thisContrast,
thisContrastStr,
filterLen,
filterOrd,
paramEst,
chunklen,
alphas=np.logspace(0, 3, 20),
debug=False,
write=False,
roi='grayMatter'):
thisSub = {sub: subList[sub]}
mc_params = lmvpa.loadmotionparams(paths, thisSub)
beta_events = lmvpa.loadevents(paths, thisSub)
dsdict = lmvpa.loadsubdata(paths, thisSub, m=roi, c='trial_type')
thisDS = dsdict[sub]
# 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')
# 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])
rds, events = lmvpa.amendtimings(thisDS.copy(), beta_events[sub],
contrasts) # adding features
# we can model out motion and just not use those betas.
# Ridge
if isinstance(thisContrast, basestring):
thisContrast = [thisContrast]
# instead of binarizing each one, make them parametric
desX, rds = lmvpa.make_designmat(rds,
events,
time_attr='time_coords',
condition_attr=thisContrast,
design_kwargs={
'hrf_model': 'canonical',
'drift_model': 'blank'
},
regr_attrs=None)
# want to collapse ap and cr, but have anim separate
desX['motion'] = make_dmtx(rds.sa['time_coords'].value,
paradigm=None,
add_regs=mc_params[sub],
drift_model='blank')
des = lmvpa.make_parammat(desX, hrf='canonical', zscore=True)
# set chunklen and nchunks
# split by language and pictures
lidx = thisDS.chunks < thisDS.sa['chunks'].unique[len(
thisDS.sa['chunks'].unique) / 2]
pidx = thisDS.chunks >= thisDS.sa['chunks'].unique[len(
thisDS.sa['chunks'].unique) / 2]
ldes = cp.copy(des)
pdes = cp.copy(des)
ldes.matrix = ldes.matrix[lidx]
pdes.matrix = pdes.matrix[pidx]
nchunks = int(len(thisDS) * paramEst / chunklen)
nboots = 50
covarmat = None
mus = None
lwts, lalphas, lres, lceil = bsr.bootstrap_ridge(rds[lidx],
ldes,
chunklen=chunklen,
nchunks=nchunks,
cov0=covarmat,
mu0=mus,
part_attr='chunks',
mode='test',
alphas=alphas,
single_alpha=True,
normalpha=False,
nboots=nboots,
corrmin=.2,
singcutoff=1e-10,
joined=None,
plot=debug,
use_corr=True)
pwts, palphas, pres, pceil = bsr.bootstrap_ridge(rds[pidx],
pdes,
chunklen=chunklen,
nchunks=nchunks,
part_attr='chunks',
mode='test',
alphas=alphas,
single_alpha=True,
normalpha=False,
nboots=nboots,
corrmin=.2,
singcutoff=1e-10,
joined=None,
plot=debug,
use_corr=True)
print 'language ' + str(np.mean(lres))
# pictures within
print 'pictures: ' + str(np.mean(pres))
# need to change outstring
if write:
from mvpa2.base import dataset
map2nifti(thisDS, dataset.vstack([lres, pres])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_corrs.nii.gz'))
map2nifti(thisDS, dataset.vstack([lwts, pwts])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_weights.nii.gz'))
map2nifti(thisDS, dataset.vstack([lalphas, palphas])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_alphas.nii.gz'))
map2nifti(thisDS, dataset.vstack([lceil, pceil])) \
.to_filename(os.path.join(paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' + thisContrastStr +
'_ridge_ceiling.nii.gz'))
del lres, pres, lwts, pwts, lalphas, palphas, lceil, pceil
crossSet = thisDS.copy()
crossSet.chunks[lidx] = 1
crossSet.chunks[pidx] = 2
cwts, calphas, cres, cceil = bsr.bootstrap_ridge(crossSet,
des,
chunklen=chunklen,
nchunks=nchunks,
part_attr='chunks',
mode='test',
alphas=alphas,
single_alpha=True,
normalpha=False,
nboots=nboots,
corrmin=.2,
singcutoff=1e-10,
joined=None,
use_corr=True)
print 'cross: ' + str(np.mean(cres))
if write:
map2nifti(thisDS, cres[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_P2L_ridge_corr.nii.gz'))
map2nifti(thisDS, cres[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_L2P_ridge_corr.nii.gz'))
map2nifti(thisDS, cwts[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_P2L_ridge_weights.nii.gz'))
map2nifti(thisDS, cwts[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_L2P_ridge_weights.nii.gz'))
map2nifti(thisDS, calphas[calphas.chunks == 1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_P2L_ridge_alphas.nii.gz'))
map2nifti(thisDS, calphas[calphas.chunks == 2]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_L2P_ridge_alphas.nii.gz'))
map2nifti(thisDS, cceil[0]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_P2L_ridge_ceiling.nii.gz'))
map2nifti(thisDS, cceil[1]).to_filename(
os.path.join(
paths[0], 'Maps', 'Encoding', sub + '_' + roi + '_' +
thisContrastStr + '_L2P_ridge_ceiling.nii.gz'))
del cres, cwts, calphas, cceil