Neurobagel CLI

The Neurobagel CLI is a Python command-line tool to automatically parse and describe subject phenotypic and imaging attributes in an annotated dataset for integration into the Neurobagel graph.

For information on how to use the CLI, please refer to the official Neurobagel documentation.

Installation

The Neurobagel CLI is available as a Python package that can be installed from PyPI using:

pip install bagel

(If you prefer to run the CLI using Docker or Singularity containers instead, please refer to the Neurobagel documentation.)

Development environment

Setting up a local development environment

To work on the CLI, we suggest that you create a development environment that is as close as possible to the environment we run in production.

1. Clone the repository

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

2. Install the CLI and all development dependencies in editable mode:

   pip install -e ".[dev]"

3. Install the bids-examples and neurobagel_examples submodules needed to run the test suite:

   git submodule init
   git submodule update

Confirm that everything works well by running the tests: pytest .

Setting up code formatting and linting (recommended)

pre-commit is configured in the development environment for this repository, and can be set up to automatically run a number of code linters and formatters on any commit you make according to the consistent code style set for this project.

Run the following from the repository root to install the configured pre-commit "hooks" for your local clone of the repo:

pre-commit install

pre-commit will now run automatically whenever you run git commit.

Updating dependencies

If new runtime or development dependencies are needed, add them to pyproject.toml using minimal version constraints.

Regenerating the Neurobagel vocabulary file

Terms in the Neurobagel namespace (nb prefix) and their class relationships are serialized to a file called nb_vocab.ttl, which is automatically uploaded to new Neurobagel graph deployments. This vocabulary is used by Neurobagel APIs to fetch available attributes and attribute instances from a graph store.

When the Neurobagel graph data model is updated (e.g., if new classes or subclasses are created), this file should be regenerated by running:

python helper_scripts/generate_nb_vocab_file.py

This will create a file called nb_vocab.ttl in the current working directory.