run.py

#!/usr/bin/env python3
import argparse
import os
import subprocess
import nibabel
import numpy
from glob import glob

parser = argparse.ArgumentParser(description='Example BIDS App entrypoint script.')
parser.add_argument('bids_dir', help='The directory with the input dataset '
					'formatted according to the BIDS standard.')
parser.add_argument('output_dir', help='The directory where the output files '
					'should be stored. If you are running group level analysis '
					'this folder should be prepopulated with the results of the'
					'participant level analysis.')
parser.add_argument('analysis_level', help='Level of the analysis that will be performed. '
					'Multiple participant level analyses can be run independently '
					'(in parallel) using the same output_dir.',
					choices=['participant', 'group'])
parser.add_argument('--participant_label', help='The label(s) of the participant(s) that should be analyzed. The label '
				   'corresponds to sub-<participant_label> from the BIDS spec '
				   '(so it does not include "sub-"). If this parameter is not '
				   'provided all subjects should be analyzed. Multiple '
				   'participants can be specified with a space separated list.',
				   nargs="+")

args = parser.parse_args()

subjects_to_analyze = []
# only for a subset of subjects
if args.participant_label:
	subjects_to_analyze = args.participant_label
# for all subjects
else:
	subject_dirs = glob(os.path.join(args.bids_dir, "sub-*"))
	subjects_to_analyze = [subject_dir.split("-")[-1] for subject_dir in subject_dirs]

# running participant level
if args.analysis_level == "participant":

	# find all T1s and skullstrip them
	for subject_label in subjects_to_analyze:
		for T1_file in glob(os.path.join(args.bids_dir, "sub-%s"%subject_label,
										 "anat", "*_T1w.nii*")) + glob(os.path.join(args.bids_dir,"sub-%s"%subject_label,"ses-*","anat", "*_T1w.nii*")):
			out_file = os.path.split(T1_file)[-1].replace("_T1w.", "_brain.")
			cmd = "bet %s %s"%(T1_file, os.path.join(args.output_dir, out_file))
			print(cmd)
			subprocess.run(cmd, shell=True, check=True)

# running group level
elif args.analysis_level == "group":
	brain_sizes = []
	for subject_label in subjects_to_analyze:
		for brain_file in glob(os.path.join(args.output_dir, "sub-%s*.nii*"%subject_label)):
			data = nibabel.load(brain_file).get_data()
			# calcualte average mask size in voxels
			brain_sizes.append((data != 0).sum())

	with open(os.path.join(args.output_dir, "avg_brain_size.txt"), 'w') as fp:
		fp.write("Average brain size is %g voxels"%numpy.array(brain_sizes).mean())