INPUT_PATH,
smoothing_fwhm=None,
logger=logger)
original_masker = global_masker_50
new_masker = global_masker_95
original_masker.set_params(detrend=False, standardize=False)
new_masker.set_params(detrend=False, standardize=False)
params = global_masker_95.get_params()
new_img = new_img_like(
global_masker_95.mask_img,
scipy.ndimage.binary_dilation(global_masker_95.mask_img.get_data()))
dilated_global_masker_95 = NiftiMasker()
dilated_global_masker_95.set_params(**params)
dilated_global_masker_95.mask_img = new_img
dilated_global_masker_95.fit()
atlas_maps, labels = reporting.load_atlas()
subject_names_list = [
utils.get_subject_name(sub_id)
for sub_id in utils.possible_subjects_id(language)
]
subject_ids_list = utils.possible_subjects_id(language)
def dilate_img(img):
if type(img) == str:
img = nib.load(img)
data = img.get_data()
new_data = dilate_data(data)
