pet_files = []
pet_img = []
for idx, row in data.iterrows():
pet_file = glob.glob(os.path.join(BASE_DIR,
'I' + str(row.Image_ID_y), 'I*.nii'))
if len(pet_file)>0:
pet_files.append(pet_file[0])
img = nib.load(pet_file[0])
pet_img.append(img)
masker = NiftiMasker(mask_strategy='epi',
mask_args=dict(opening=8))
masker.fit(pet_files)
pet_masked = masker.transform_niimgs(pet_files, n_jobs=2)
#pet_masked = np.vstack(pet_masked)
mask = masker.mask_img_.get_data().astype(np.bool)
shape = mask.shape
connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1],
n_z=shape[2], mask=mask)
# Computing the ward for the first time, this is long...
start = time.time()
ward = WardAgglomeration(n_clusters=1000, connectivity=connectivity,
memory='nilearn_cache')
ward.fit(pet_masked[0])
print "Ward agglomeration 1000 clusters: %.2fs" % (time.time() - start)
