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joshicolajoshicola
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Merge pull request #1 from flywheel-apps/dev
First iteration with gear-toolkit...With reviews
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‎.gitignore‎

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# Pyre type checker
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.pyre/
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.DS_Store

‎Dockerfile‎

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# Inheriting from established docker image:
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FROM poldracklab/pydeface:37-2e0c2d
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# Inheriting from established docker image:
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LABEL maintainer="Flywheel <[email protected]>"
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# Flywheel gears are run as root
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# poldracklab/pydeface:37-2e0c2d leaves it in the "neuro" user
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USER root
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# For interactive docker... Not needed in gear execution
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RUN head -n 7 /neurodocker/startup.sh >> ~/.bashrc
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ENV FLYWHEEL /flywheel/v0
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WORKDIR ${FLYWHEEL}
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ENV PATH=/opt/conda/envs/neuro/bin/:$PATH
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COPY requirements.txt $FLYWHEEL/
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RUN pip install -r requirements.txt
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# Copy executable, manifest, and tools to Gear
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COPY run.py manifest.json ${FLYWHEEL}/
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COPY utils ${FLYWHEEL}/utils
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RUN chmod a+x ${FLYWHEEL}/run.py
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# Configure entrypoint
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ENTRYPOINT ["/flywheel/v0/run.py"]

‎README.md‎

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# pydeface-gear
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A flywheel gear to remove facial structure from MRI images.
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Leverages [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL),
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[Python 3](https://www.python.org/downloads/),
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[NumPy](http://www.numpy.org/),
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[NiBabel](http://nipy.org/nibabel/), and
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[Nipype](http://nipype.readthedocs.io/en/latest/)
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to coregister a provided image to a template and removes facial features by removing the facial structure in accordance with the registration template and face mask. User may provide a template and face mask.
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## Website
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The source code of pydeface can be found at [github](https://github.com/poldracklab/pydeface)
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## Usage Notes
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### inputs
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* infile (required): The NIfTI file to remove facial structure from
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* template (optional): The optional template image that will be used as the registration target instead of the default.
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* facemask (optional): The optional face mask image that will be used instead of the default.
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### parameters
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* cost: FSL-FLIRT cost function. "mutualinfo" default.
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* nocleanup: Do not cleanup temporary files. Off by default.
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* verbose: Show additional status prints. Off by default.

‎manifest.json‎

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{
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"name": "pydeface-gear",
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"label": "Pydeface Gear",
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"description": "A gear to remove facial structure from MRI images.",
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"author": "Flywheel",
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"maintainer": "Flywheel <[email protected]>",
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"license": "MIT",
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"url": "https://github.com/flywheel-apps/test-gear",
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"source": "https://github.com/flywheel-apps/pydeface-gear",
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"cite": "https://github.com/poldracklab/pydeface",
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"version": "0.1.0",
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"custom": {
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"docker-image": "flywheel/pydeface-gear:0.1.0",
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"gear-builder": {
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"category": "analysis",
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"image": "flywheel/pydeface-gear:0.1.0"
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}
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},
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"inputs": {
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"infile": {
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"base": "file",
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"description": "input nifti",
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"optional": false,
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"type": {
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"enum": [
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"nifti"
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]
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}
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},
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"template": {
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"base": "file",
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"description": "Optional template image that will be used as the registration target instead of the default.",
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"optional": true,
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"type": {
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"enum": [
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"nifti"
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]
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}
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},
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"facemask": {
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"base": "file",
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"description": "Optional face mask image that will be used instead of the default.",
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"optional": true,
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"type": {
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"enum": [
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"nifti"
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]
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}
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}
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},
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"config": {
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"cost": {
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"type": "string",
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"default": "mutualinfo",
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"description": "FSL-FLIRT cost function",
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"enum": [
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"mutualinfo",
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"corratio",
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"normcorr",
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"normmi",
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"leastsq",
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"labeldiff",
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"bbr"
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]
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},
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"nocleanup": {
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"type": "boolean",
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"description": "Do not cleanup temporary files. True by default.",
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"default": "true"
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},
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"verbose": {
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"type": "boolean",
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"description": "Show additional status prints. True by default.",
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"default": "true"
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}
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},
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"command": "/flywheel/v0/run.py",
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"environment":{
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"CONDA_SHLVL": "1",
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"LC_ALL": "C.UTF-8",
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"CONDA_EXE": "/opt/conda/bin/conda",
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"FSLWISH": "/opt/fsl/bin/fslwish",
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"FSLDIR": "/opt/fsl",
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"LANG": "en_US.UTF-8",
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"HOSTNAME": "d8012530f084",
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"FSLMACHINELIST": "",
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"CONDA_PREFIX": "/opt/conda/envs/neuro",
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"CONDA_DIR": "/opt/conda",
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"FSLTCLSH": "/opt/fsl/bin/fsltclsh",
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"FSLREMOTECALL": "",
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"_CE_M": "",
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"PWD": "/flywheel/v0",
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"HOME": "/root",
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"CONDA_PYTHON_EXE": "/opt/conda/bin/python",
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"FSLLOCKDIR": "",
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"_CE_CONDA": "",
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"FLYWHEEL": "/flywheel/v0",
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"CONDA_PROMPT_MODIFIER": "(neuro) ",
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"FSLGECUDAQ": "cuda.q",
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"ND_ENTRYPOINT": "/neurodocker/startup.sh",
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"TERM": "xterm",
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"SHLVL": "1",
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"PATH": "/opt/conda/envs/neuro/bin:/opt/conda/condabin:/opt/conda/bin:/opt/fsl/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin",
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"CONDA_DEFAULT_ENV": "neuro",
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"FSLOUTPUTTYPE": "NIFTI_GZ",
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"FSLMULTIFILEQUIT": "TRUE",
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"_": "/opt/conda/envs/neuro/bin/python"
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}
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}

‎requirements.txt‎

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flywheel-sdk==10.7.4
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flywheel-gear-toolkit==0.1.0.rc1

‎run.py‎

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#!/usr/bin/env python3
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import os
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import os.path as op
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import json
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import logging
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from gear_toolkit import gear_toolkit_context
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from utils import args
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log = logging.getLogger(__name__)
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def main(context):
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# Build and Execute Parameters
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try:
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# build the command string
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params = args.build(context)
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# Execute on those parameters.
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args.execute(context, params)
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except Exception as e:
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context.log.exception(e)
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context.log.fatal('Error executing pydeface-gear.')
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return 1
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context.log.info("pydeface-gear completed Successfully!")
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return 0
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if __name__ == '__main__':
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with gear_toolkit_context.GearToolkitContext() as gear_context:
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gear_context.init_logging()
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gear_context.log_config()
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exit_status = main(gear_context)
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log.info('exit_status is %s', exit_status)
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os.sys.exit(exit_status)

‎utils/__init__.py‎

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‎utils/args.py‎

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from collections import OrderedDict
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import os.path as op
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from pathlib import Path
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from gear_toolkit.command_line import build_command_list, exec_command
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def build(context):
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# use Ordered Dictionary to keep the order created.
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# Default in Python 3.6 onward
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params = OrderedDict()
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config = context.config
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for key in config.keys():
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params[key] = config[key]
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infile = Path(context.get_input_path('infile'))
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params['outfile'] = context.output_dir / \
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infile.name.replace('.nii.gz', '_defaced.nii.gz')
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return params
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def execute(context, params, dry_run=False):
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# Get Params
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command = ['pydeface']
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# Build command-line parameters
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command = build_command_list(command, params)
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# Extend with positional argument
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command.append(context.get_input_path('infile'))
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exec_command(command, dry_run=dry_run)

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