# Contributing to FMRIPREP¶

This document explains how to prepare a new development environment and update an existing environment, as necessary.

Development in Docker is encouraged, for the sake of consistency and portability. By default, work should be built off of poldracklab/fmriprep:latest (see the installation guide for the basic procedure for running).

It will be assumed the developer has a working repository in $HOME/projects/fmriprep, and examples are also given for niworkflows and nipype. ## Patching working repositories¶ In order to test new code without rebuilding the Docker image, it is possible to mount working repositories as source directories within the container. The fmriprep-docker script simplifies this for the most common repositories: -f PATH, --patch-fmriprep PATH working fmriprep repository (default: None) -n PATH, --patch-niworkflows PATH working niworkflows repository (default: None) -p PATH, --patch-nipype PATH working nipype repository (default: None)  For instance, if your repositories are contained in $HOME/projects:

$fmriprep-docker -f$HOME/projects/fmriprep/fmriprep \
-n $HOME/projects/niworkflows/niworkflows \ -p$HOME/projects/nipype/nipype \
-i poldracklab/fmriprep:latest \
$HOME/fullds005$HOME/dockerout participant


Note the -i flag allows you to specify an image.

When invoking docker directly, the mount options must be specified with the -v flag:

-v $HOME/projects/fmriprep/fmriprep:/usr/local/miniconda/lib/python3.7/site-packages/fmriprep:ro -v$HOME/projects/niworkflows/niworkflows:/usr/local/miniconda/lib/python3.7/site-packages/niworkflows:ro
-v $HOME/projects/nipype/nipype:/usr/local/miniconda/lib/python3.7/site-packages/nipype:ro  For example, $ docker run --rm -v $HOME/fullds005:/data:ro -v$HOME/dockerout:/out \
-v $HOME/projects/fmriprep/fmriprep:/usr/local/miniconda/lib/python3.7/site-packages/fmriprep:ro \ poldracklab/fmriprep:latest /data /out/out participant \ -w /out/work/  In order to work directly in the container, pass the --shell flag to fmriprep-docker: $ fmriprep-docker --shell $HOME/fullds005$HOME/dockerout participant


This is the equivalent of using --entrypoint=bash and omitting the fmriprep arguments in a docker command:

$docker run --rm -v$HOME/fullds005:/data:ro -v $HOME/dockerout:/out \ -v$HOME/projects/fmriprep/fmriprep:/usr/local/miniconda/lib/python3.7/site-packages/fmriprep:ro --entrypoint=bash \
poldracklab/fmriprep:latest


Patching containers can be achieved in Singularity analogous to docker using the --bind (-B) option:

$singularity run \ -B$HOME/projects/fmriprep/fmriprep:/usr/local/miniconda/lib/python3.7/site-packages/fmriprep \
fmriprep.img \
/scratch/dataset /scratch/out participant -w /out/work/


Or you can patch Singularity containers using the PYTHONPATH variable:

$PYTHONPATH="$HOME/projects/fmriprep" singularity run fmriprep.img \
/scratch/dataset /scratch/out participant -w /out/work/


New dependencies to be inserted into the Docker image will either be Python or non-Python dependencies. Python dependencies may be added in three places, depending on whether the package is large or non-release versions are required. The image must be rebuilt after any dependency changes.

Python dependencies should generally be included in the REQUIRES list in fmriprep/__about__.py. If the latest version in PyPI is sufficient, then no further action is required.

For large Python dependencies where there will be a benefit to pre-compiled binaries, conda packages may also be added to the conda install line in the Dockerfile.

Finally, if a specific version of a repository needs to be pinned, edit the requirements.txt file. See the current file for examples.

Non-Python dependencies must also be installed in the Dockerfile, via a RUN command. For example, installing an apt package may be done as follows:

RUN apt-get update && \
apt-get install -y <PACKAGE>


## Rebuilding Docker image¶

If it is necessary to rebuild the Docker image, a local image named fmriprep may be built from within the working fmriprep repository, located in ~/projects/fmriprep:

~/projects/fmriprep$docker build -t fmriprep .  To work in this image, replace poldracklab/fmriprep:latest with fmriprep in any of the above commands. This image may be accessed by the fmriprep-docker wrapper via the -i flag, e.g., $ fmriprep-docker -i fmriprep --shell


## Adding new features to the citation boilerplate¶

The citation boilerplate is built by adding two dunder attributes of workflow objects: __desc__ and __postdesc__. Once the full fMRIPrep workflow is built, starting from the outer workflow and visiting all sub-workflows in topological order, all defined __desc__ are appended to the citation boilerplate before descending into sub-workflows. Once all the sub-workflows of a given workflow have been visited, then the __postdesc__ attribute is appended and the execution pops out to higher level workflows. The dunder attributes are written in Markdown language, and may contain references. To add a reference, just add a new Bibtex entry to the references database (/fmriprep/data/boilerplate.bib). You can then use the Bibtex handle within the Markdown text. For example, if the Bibtex handle is myreference, a citation will be generated in Markdown language with @myreference. To generate citations with parenthesis and/or additional content, brackets should be used: e.g., [see @myreference] will produce a citation like (see Doe J. et al 2018).

An example of how this works is shown here:

workflow = Workflow(name=name)
workflow.__desc__ = """\
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
mcflirt [FSL {fsl_ver}, @mcflirt].
""".format(fsl_ver=fsl.Info().version() or '<ver>')