bagel-cli

The Bagel CLI is a simple Python command line tool to automatically read and annotate a BIDS dataset so that it can be integrated into the Neurobagel graph.

Please refer to our official neurobagel documentation for more information on how to use the CLI.

Installation

Docker

Option 1 (RECOMMENDED): Pull the Docker image for the CLI from Docker Hub: docker pull neurobagel/bagelcli

Option 2: Clone the repository and build the Docker image locally:

git clone https://github.com/neurobagel/bagel-cli.git
cd bagel-cli
docker build -t bagel .

Singularity

Build a Singularity image for bagel-cli using the Docker Hub image:
singularity pull bagel.sif docker://neurobagel/bagelcli

Running the CLI

CLI commands can be accessed using the Docker/Singularity image.

NOTE: The Docker examples below assume that you are using the official Neurobagel Docker Hub image for the CLI. If you have instead locally built an image, replace neurobagel/bagelcli in commands with your built image tag.

To see the CLI options:

# Docker
docker run --rm neurobagel/bagelcli  # this is a shorthand for `docker run --rm neurobagel/bagelcli --help

# Singularity
singularity run bagel.sif

For a specific command:

# Docker
docker run --rm neurobagel/bagelcli <command-name> --help

# Singularity
singularity run bagel.sif <command-name> --help

To run the CLI on data:

1. cd into your local directory containing (1) your phenotypic .tsv file, (2) Neurobagel-annotated data dictionary, and (3) BIDS directory (if available).
2. Run a bagel-cli container and include your CLI command at the end in the following format:

# Docker
docker run --rm --volume=$PWD:$PWD -w $PWD neurobagel/bagelcli <CLI command here>

# Singularity
singularity run --no-home --bind $PWD --pwd $PWD /path/to/bagel.sif <CLI command here>

In the above command, --volume=$PWD:$PWD -w $PWD (or --bind $PWD --pwd $PWD for Singularity) mounts your current working directory (containing all inputs for the CLI) at the same path inside the container, and also sets the container's working directory to the mounted path (so it matches your location on your host machine). This allows you to pass paths to the containerized CLI which are composed the same way as on your local machine. (And both absolute paths and relative top-down paths from your working directory will work!)

Example:

If your data live in /home/data/Dataset1:

home/
└── data/
    └── Dataset1/
        ├── neurobagel/
        │   ├── Dataset1_pheno.tsv
        │   └── Dataset1_pheno.json
        └── bids/
            ├── sub-01
            ├── sub-02
            └── ...

You could run the following: (for a Singularity container, replace the first part of the Docker commands with the Singularity command from the above template)

cd /home/data/Dataset1

# 1. Construct phenotypic subject dictionaries (pheno.jsonld)
docker run --rm --volume=$PWD:$PWD -w $PWD neurobagel/bagelcli pheno \
    --pheno "neurobagel/Dataset1_pheno.tsv" \
    --dictionary "neurobagel/Dataset1_pheno.json" \
    --output "neurobagel" \
    --name "Dataset1"

# 2. Add BIDS data to pheno.jsonld generated by step 1
docker run --rm --volume=$PWD:$PWD -w $PWD neurobagel/bagelcli bids \
    --jsonld-path "neurobagel/pheno.jsonld" \
    --bids-dir "bids" \
    --output "neurobagel"

Development environment

To ensure that our Docker images are built in a predictable way, we use requirements.txt as a lock-file. That is, requirements.txt includes the entire dependency tree of our tool, with pinned versions for every dependency (see also)

Setting up a local development environment

We suggest that you create a development environment that is as close as possible to the environment we run in production.

To do so, we first need to install the dependencies from our lockfile (dev_requirements.txt):

pip install -r dev_requirements.txt

And then we install the CLI without touching the dependencies

pip install --no-deps -e .

Finally, to run the test suite we need to install the bids-examples and neurobagel_examples submodules:

git submodule init
git submodule update

Confirm that everything works well by running a test pytest .

Update python lock-file

The requirements.txt file is automatically generated from the setup.cfg constraints. To update it, we use pip-compile from the pip-tools package. Here is how you can use these tools to update the requirements.txt file.

To install:

pip install pip-tools

To update the runtime dependencies in requirements.txt, run

pip-compile -o requirements.txt --upgrade

The above command only updates the runtime dependencies. To update the developer dependencies in dev_requirements.txt, run:

pip-compile -o dev_requirements.txt --extra all