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Installation

Requirements

  • Python ≥ 3.10 (3.12+ recommended)
  • Runtime dependencies: pandas (≥2.0), numpy (≥1.24), pyyaml (≥6.0), pydantic (≥2.0), plotly (≥5.0), jinja2 (≥3.0)

The package is named resxr and installs a resxr command-line entry point.

Clone the repository and create the environment from the provided spec file.

git clone https://github.com/ResXR/resxr-python-pipeline.git
cd resxr-python-pipeline
conda env create -f environment.yml
conda activate resxr_env

Creates the resxr_env environment and installs the package (non-editable).

git clone https://github.com/ResXR/resxr-python-pipeline.git
cd resxr-python-pipeline
conda env create -f environment-dev.yml
conda activate resxr_dev

Creates the resxr_dev environment, installs the package in editable mode with the [dev] extra, and adds pytest and ruff.

Option 2 — uv

git clone https://github.com/ResXR/resxr-python-pipeline.git
cd resxr-python-pipeline
uv sync --all-extras

This is the workflow used in CI. It creates a virtual environment from uv.lock with all dependencies, including dev extras.

Verify the install

resxr --version

This prints ResXR <version>. You can also run the package as a module:

python -m resxr --version

Running the tests

The project ships an extensive pytest suite (BIDS writers, readers, splitter, validation checks, config, and a full end-to-end integration test).

uv run pytest

pytest

Linting

The codebase uses Ruff for linting and formatting (line length 100). CI runs ruff check . and ruff format --check on every pull request, alongside the test suite, on Python 3.10.