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, 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'))
# thisContrast = ['anim', 'verb']
# thisContrast = ['anim', 'ap', 'cr']
# thisContrast = ['anim']
roi = 'grayMatter'
filterLen = 49
filterOrd = 3
chunklen = 10 # this reflects the length of a complete trial
paramEst = .2 #this much data to be held out for ridge regression parameter estimation
paths, subList, contrasts, maskList = lmvpa.initpaths(plat)
if debug:
subList = {'LMVPA005': subList['LMVPA005']}
# ds_all = lmvpa.loadsubdata(paths, subList, m=roi, c='trial_type')
# motion parameters for all subjects
mc_params = lmvpa.loadmotionparams(paths, subList)
# events for beta extraction
# add everything as a sample attribute
beta_events = lmvpa.loadevents(paths, subList)
# import BootstrapRidgeMapper as bsr
import numpy as np
import os
import matplotlib.pyplot as plt
import sklearn.decomposition as dcmp
alphas = np.logspace(0, 3, 20)
words = [
'gravy', 'trainer', 'candle', 'bus', 'tree', 'goalie', 'singer', 'woman',
'touched', 'stretched', 'lit', 'hit', 'crushed', 'kicked', 'kissed',
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
debug=True
paths, subList, contrasts, maskList = lmvpa.initpaths(plat)
thisContrast = 'verb'
chance = .125
roi = 'grayMatter'
nVox = 1000 # number of voxels to select
nComp = 10 # number of SVD components
filterLen = 49 #for SG filter
filterOrd = 3 # for sg filter
if debug:
subList = {'LMVPA003': subList['LMVPA003']}
# this would load all data at once. not memory efficient
# ds_all = lmvpa.loadsubdata(paths, subList, m=roi, c='trial_type')
# motion parameters for all subjects
mc_params = lmvpa.loadmotionparams(paths, subList)
# events for beta extraction
# add everything as a sample attribute
beta_events = lmvpa.loadevents(paths, subList)
from mvpa2.datasets.mri import map2nifti
from mvpa2.mappers.zscore import zscore
import SavGolFilter as sg
import mvpa2.datasets.eventrelated as er
from mvpa2.mappers.svd import SVDMapper
from mvpa2.datasets.base import StaticFeatureSelection, ChainMapper
from mvpa2.featsel.helpers import FixedNElementTailSelector
import mvpa2.featsel as fs
from mvpa2.clfs import svm
from mvpa2.clfs.warehouse import OneWayAnova, LDA
from mvpa2.clfs.meta import FeatureSelectionClassifier, MappedClassifier
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'))
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
