1] #used to be a loop but now runs on cluster in parallel
chunk_num = [job_table[job_num][2]]
filename = sub + '_fset' + str(fset_num) + '_chunk' + str(chunk_num[0])
print sub
print np.array(job_table).shape
behav_file = 'sub' + sub + '_attr.txt'
bold_fname = os.path.join(cwd1, sub, 'betas_sub' + sub +
'.nii.gz') #full functional timeseries (beta series)
attr_fname = os.path.join(cwd1, 'all_attr',
behav_file) #codes stimuli number and run number
attr = mvpa2.SampleAttributes(attr_fname) #loads attributes into pymvpa
ds = mvpa2.fmri_dataset(bold_fname, targets=attr.targets, chunks=attr.chunks)
ds = mvpa2.remove_nonfinite_features(ds)
ds = mvpa2.remove_invariant_features(ds)
#this basically breaks up the brain into 100 different areas (to parallelize the searchlight)
try:
ds = ds[:, fset_num * 1000:(fset_num * 1000) + 1000]
except:
ds = ds[:, fset_num * 1000:]
stimuli = []
for i in range(0, 54):
stimuli.append(ds.uniquetargets[i])
)
dataset = pickle.load(gzip.open(trimmedCache, 'rb'))
else:
if os.path.isfile(preprocessedCache) and False:
print 'loading cached preprocessed dataset', preprocessedCache, datetime.datetime.now(
)
dataset = pickle.load(gzip.open(preprocessedCache, 'rb', 5))
else:
# if not, generate directly, and then cache
print 'loading and creating dataset', datetime.datetime.now()
# chunksTargets_boldDelay="chunksTargets_boldDelay4-4.txt" #Modified
chunksTargets_boldDelay = "chunksTargets_boldDelay{0}-{1}-direction.txt".format(
boldDelay, stimulusWidth)
volAttribrutes = M.SampleAttributes(
os.path.join(sessionPath, 'behavioural',
chunksTargets_boldDelay)) # default is 3.txt.
# print volAttribrutes.targets
# print len(volAttribrutes.targets)
# print volAttribrutes.chunks
# print len(volAttribrutes.chunks)
dataset = M.fmri_dataset(
samples=os.path.join(sessionPath,
'analyze/functional/functional4D.nii'),
targets=volAttribrutes.
targets, # I think this was "labels" in versions 0.4.*
chunks=volAttribrutes.chunks,
mask=os.path.join(sessionPath,
'analyze/structural/lc2ms_deskulled.hdr'))
# DATASET ATTRIBUTES (see AttrDataset)
