def searchlight(ds, **kwargs):
if __debug__:
debug.active += ["SLC"]
radius = 3
for arg in kwargs:
if (arg == 'radius'):
radius = kwargs[arg]
"""
[fclf, cvte] = setup_classifier(**kwargs)
"""
clf = LinearCSVMC(C=1, probability=1, enable_ca=['probabilities'])
cv = CrossValidation(clf, HalfPartitioner(attr='chunks'))
sl = sphere_searchlight(cv, radius, space='voxel_indices')
#sl = Searchlight(MahalanobisMeasure, queryengine, add_center_fa, results_postproc_fx, results_backend, results_fx, tmp_prefix, nblocks)
#sl = sphere_searchlight(MahalanobisMeasure(), 3, space= 'voxel_indices')
sl_map = sl(ds)
sl_map.samples *= -1
sl_map.samples += 1
nif = map2nifti(sl_map, imghdr=ds.a.imghdr)
nif.set_qform(ds.a.imgaffine)
#Results packing
d_result = dict({'map': nif, 'radius': radius})
return d_result
def searchlight(ds, **kwargs):
if __debug__:
debug.active += ["SLC"]
radius = 3
for arg in kwargs:
if (arg == 'radius'):
radius = kwargs[arg]
"""
[fclf, cvte] = setup_classifier(**kwargs)
"""
clf = LinearCSVMC(C=1, probability=1, enable_ca=['probabilities'])
cv = CrossValidation(clf, HalfPartitioner(attr='chunks'))
sl = sphere_searchlight(cv, radius, space = 'voxel_indices')
#sl = Searchlight(MahalanobisMeasure, queryengine, add_center_fa, results_postproc_fx, results_backend, results_fx, tmp_prefix, nblocks)
#sl = sphere_searchlight(MahalanobisMeasure(), 3, space= 'voxel_indices')
sl_map = sl(ds)
sl_map.samples *= -1
sl_map.samples += 1
nif = map2nifti(sl_map, imghdr=ds.a.imghdr)
nif.set_qform(ds.a.imgaffine)
#Results packing
d_result = dict({'map': nif,
'radius': radius})
return d_result
def main():
sac_attr = SampleAttributes(TSTATS_DIR + 'sac_attr.txt')
nav_attr = SampleAttributes(TSTATS_DIR + 'nav_bin_attr.txt')
# labels
xtask_run = [
# down/powerless, up/powerful
[1, 2], # down/powerless
[2, 1], # up/powerful
]
intask_run = [ # no comparison within a task
[0, 0], [0, 0]
]
labels = squareform(
np.vstack([np.hstack([intask_run] * 6 + [xtask_run] * 2)] * 6 +
[np.hstack([xtask_run] * 6 + [intask_run] * 2)] * 2))
for subj in subject_list:
subj_mask = MASK_DIR + '%s_ribbon_rsmp0_dil3mm.nii.gz' % subj
dataset = get_nav_sac_data(nav_attr, sac_attr, subj, subj_mask)
dataset = remove_invariant_features(dataset)
print(dataset.targets, dataset.chunks)
# searchlight
similarity = CustomDist(labels)
searchlight = sphere_searchlight(similarity, SEARCHLIGHT_RADIUS)
searchlight_map = searchlight(dataset)
# save files
nifti = map2nifti(data=searchlight_map, dataset=dataset)
nifti.to_filename(OUTDIR + '%s_%s_%dvox_sim.nii.gz' %
(subj, DIRCT, SEARCHLIGHT_RADIUS))
def main():
subject_list = sys.argv[1:] if len(sys.argv) > 1 else EVERYONE
print(subject_list)
attr = SampleAttributes(TSTATS_DIR + TSTATS_NAME + '_attr.txt')
for subj in subject_list:
tstats_file = TSTATS_DIR + TSTATS_NAME + '_tstats/%s_%s.nii.gz' % (
subj, TSTATS_NAME)
dataset = fmri_dataset(samples=tstats_file,
mask=MASK_DIR +
'%s_ribbon_rsmp0_dil3mm.nii.gz' % subj)
dataset.sa['chunks'] = attr.chunks
dataset.sa['targets'] = attr.targets
dataset = remove_invariant_features(dataset)
similarity = CustomDist(squareform(LABELS_NAV))
searchlight = sphere_searchlight(similarity, SEARCHLIGHT_RADIUS)
searchlight_map = searchlight(dataset)
# save files
nifti = map2nifti(data=searchlight_map, dataset=dataset)
nifti.to_filename(OUTDIR + OUTFILE % (subj, SEARCHLIGHT_RADIUS))
stimuli = []
for i in range(0, 54):
stimuli.append(ds.uniquetargets[i])
#create all possible pairs for confusion matrix
pair_list = list(itertools.combinations(range(len(stimuli)), 2))
pair_list2 = []
for x in range(0, len(pair_list)):
pair_list2.append([stimuli[pair_list[x][0]], stimuli[pair_list[x][1]]])
# enable debug output for searchlight call
#if __debug__:
mvpa2.debug.active += ["SLC"]
sl = mvpa2.sphere_searchlight(clf_wrapper,
radius=3,
space='voxel_indices',
nproc=1)
ds = ds.copy(deep=False,
sa=['targets', 'chunks'],
fa=['voxel_indices'],
a=['mapper'])
sl_map = sl(ds)
data_file1 = os.path.join(job_path, filename)
file_path_full = open(data_file1, 'w')
pickle.dump(sl_map, file_path_full)
#jobs up to 22000 now... (12 hours each?)
y[ds.targets == ds.uniquetargets[0]] = 1
# We needs to modify the chunks in order to use sklearn
ds.chunks = np.arange(len(ds.chunks))
permut_ = []
i = 3
partitioner = SKLCrossValidation(StratifiedKFold(y, n_folds=i))
cvte = CrossValidation(clf,
partitioner,
enable_ca=['stats', 'probabilities'])
sl = sphere_searchlight(cvte, radius=3, space = 'voxel_indices')
maps = []
for p_ in range(100):
print '-------- '+str(p_+1)+' of 100 ------------'
y_perm = permutation(range(len(ds.targets)))
ds.targets = ds.targets[y_perm]
sl_map = sl(ds)
sl_map.samples *= -1
sl_map.samples += 1
