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(
