ar = pool.apply_async(load_spm, args=(mat, ), kwds=dict(label=study, subject=-5, inputs=inputs, study=study)) docs.append(ar) pool.close() pool.join() docs = [doc.get() for doc in docs] return fix_docs(docs, contrast_names) if __name__ == '__main__': # sanitize command-line if len(sys.argv) > 1: output_dir = sys.argv[1] docs = get_docs(inputs=True) execute_glms(docs, output_dir, definitions, dataset_id="cauvet2009muslang", # do_preproc=False, # smoothed=5., )
# label = subject_id = os.path.split(subject_dir)[1].lower() # mapping[label] = infos.get( # subject_id, {'subject_id': subject_id}) # return mapping if __name__ == '__main__': # sanitize command-line if len(sys.argv) > 1: output_dir = sys.argv[1] docs = get_docs(inputs=True) execute_glms(docs, output_dir, definitions, dataset_id="vagharchakian2012temporal", ) # need to resample... # import nibabel as nb # import numpy as np # from nisl import resampling # target_affine = np.array([[-3., 0., 0., 78.], # [0., 3., 0., -111.], # [0., 0., 3., -51.], # [0., 0., 0., 1., ]]) # target_shape = (53, 63, 46) # for niimg in glob.glob(os.path.join(