run.py

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

__version__ = open(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                'version')).read()

def run(command, env={}):
    merged_env = os.environ
    merged_env.update(env)
    process = subprocess.Popen(command, stdout=subprocess.PIPE,
                               stderr=subprocess.STDOUT, shell=True,
                               env=merged_env)
    while True:
        line = process.stdout.readline()
        line = str(line, 'utf-8')[:-1]
        print(line)
        if line == '' and process.poll() != None:
            break
    if process.returncode != 0:
        raise Exception("Non zero return code: %d"%process.returncode)

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="+")
parser.add_argument('--skip_bids_validator', help='Whether or not to perform BIDS dataset validation',
                   action='store_true')
parser.add_argument(
    '-v',
    '--version',
    action='version',
    version=f'BIDS-App example version {__version__}',
)


args = parser.parse_args()

if not args.skip_bids_validator:
    run(f'bids-validator {args.bids_dir}')

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, f"sub-{subject_label}", "anat", "*_T1w.nii*"))
                        + glob(os.path.join(args.bids_dir, f"sub-{subject_label}", "ses-*", "anat", "*_T1w.nii*"))):
            out_file = os.path.split(T1_file)[-1].replace("_T1w.", "_brain.")
            cmd = f"bet {T1_file} {os.path.join(args.output_dir, out_file)}"
            print(cmd)
            run(cmd)

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, f"sub-{subject_label}*.nii*")):
            data = nibabel.load(brain_file).get_fdata()
            # 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())