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:unstable, which tracks the master branch, or poldracklab/fmriprep:latest, which tracks the latest release version (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 Docker wrapper 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/

Adding dependencies

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$ VERSION=$( python get_version.py )
~/projects/fmriprep$ docker build -t fmriprep --build-arg VERSION=$VERSION .

The VERSION build argument is necessary to ensure that help text can be reliably generated. The get_version.py tool constructs the version string from the current repository state.

To work in this image, replace poldracklab/fmriprep:latest with fmriprep in any of the above commands. This image may be accessed by the Docker wrapper via the -i flag, e.g.,

$ fmriprep-docker -i fmriprep --shell

Code-Server Development Environment (Experimental)

To get the best of working with containers and having an interactive development environment, we have an experimental setup with code-server.

Note

We have a video walking through the process if you want a visual guide.

1. Build the Docker image

We will use the Dockerfile_devel file to build our development docker image:

$ cd $HOME/projects/fmriprep
$ docker build -t fmriprep_devel -f Dockerfile_devel .

2. Run the Docker image

We can start a docker container using the image we built (fmriprep_devel):

$ docker run -it -p 127.0.0.1:8445:8080 -v ${PWD}:/src/fmriprep fmriprep_devel:latest

Note

If you are using windows shell, ${PWD} may not be defined, instead use the absolute path to your fmriprep directory.

Note

If you are using Docker-Toolbox, you will need to change your virtualbox settings using these steps as a guide. (For step 6, instead of Name = rstudio; Host Port = 8787; Guest Port = 8787, have Name = code-server; Host Port = 8443; Guest Port = 8080.) Then in the docker command above, change 127.0.0.1:8445:8080 to 192.168.99.100:8445:8080.

If the container started correctly, you should see the following on your console:

INFO  Server listening on http://localhost:8080
INFO    - No authentication
INFO    - Not serving HTTPS

Now you can switch to your favorite browser and go to: 127.0.0.1:8445 (or 192.168.99.100:8445 for Docker Toolbox).

3. Copy fmriprep.egg-info into your fmriprep directory

fmriprep.egg-info makes the fmriprep package exacutable inside the docker container. Open a terminal in vscode and type the following:

$ cp -R /src/fmriprep.egg-info /src/fmriprep/

Code-Server Development Environment Features

• The editor is vscode

• There are several preconfigured debugging tests under the debugging icon in the activity bar

  • see vscode debugging python for details.

• The gitlens and python extensions are preinstalled to improve the development experience in vscode.

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>')