from cpr.main import main
from pathlib import Path
main("/data/in",
"/data/out",
"group",
participant_label=subjects,
mr_baseline_sessions_file="/data/info/mr_baseline_sessions.tsv",
stages=["learn"],
learning_out_subdir="20200304_full",
clinical_feature_file=Path("/data/features/clinical_features_X_clinsessionn.pkl"),
target_file=Path("/data/features/slopes_y.pkl"),
fs_license_file="/data/misc/license.txt", test_run=False,
n_splits=1000,
model_name="basic+fmripca100",
model_type="basic+fmripca100",
modalities=[
# 'clinical',
# 'structGlobScort',
# 'structural',
# 'fullcon',
# #
# 'clinical+structGlobScort',
# 'clinical+structural',
'clinical+fullcon',
# #
# 'clinical+structGlobScort+fullcon',
],
verbose=True,
n_jobs_outer=None)
"-t",
"--test-run",
dest="test_run",
action="store_true",
default=False,
help="Test run uses sloppy fmriprep preprocessing for speed")
return parser
if __name__ == "__main__":
opts = get_parser().parse_args()
add_args = {}
if opts.modalities:
add_args["modalities"] = opts.modalities
main(bids_dir=opts.bids_dir,
output_dir=opts.output_dir,
analysis_level=opts.analysis_level,
stages=opts.stages,
participant_label=opts.participant_label,
session_label=opts.session_label,
mr_baseline_sessions_file=opts.mr_baseline_sessions_file,
clinical_feature_file=opts.clinical_feature_file,
target_file=opts.target_file,
fs_license_file=opts.fs_license_file,
test_run=opts.test_run,
n_cpus=opts.n_cpus,
verbose=opts.verbose,
**add_args)