Documentation site: https://beykyle.github.io/jitr/

just-in-time R-Matrix (jitr)

A nuclear reaction toolkit, production ready for calibration and uncertainty-quantification, featuring:

• fast calculable $\mathcal{R}$-matrix solver for parametric reaction models
• built in uncertainty-quantified optical potentials
• built in nuclear data
• plenty of examples demonstrating the propagation of uncertainties into reaction observables and model calibration

Give your nuclear reaction UQ workflow a caffeine-kick with jitr!

Documentation

The documentation site is available at https://beykyle.github.io/jitr/.

To build the docs locally:

uv run --group docs sphinx-build -W -b html docs docs/_build/html

Installation and Development

For users

Install the latest released version of jitr from PyPI:

pip install jitr

Then use it in Python:

import jitr

To check the installed version:

python -c "import jitr; print(jitr.__version__)"

If you use uv, you can add jitr to a uv-managed project with:

uv add jitr

Or install it into the current environment with:

uv pip install jitr

For developers

This repository uses uv for Python packaging, dependency management, virtual environments, and locking.

Clone the repository

git clone https://github.com/beykyle/jitr.git
cd jitr

Create and sync the development environment

Install all development dependencies:

uv sync --all-groups

This creates a local .venv/ environment and installs the package in editable mode along with the development and example dependencies defined in pyproject.toml.

You can run commands through uv:

uv run python
uv run pytest

Or activate the environment manually:

source .venv/bin/activate

Testing

Run the unit tests

uv run pytest

Run the notebook tests

The example notebooks are tested with pytest and nbval:

uv run --group examples pytest --nbval-lax examples/notebooks/

Tutorials

Tutorials live in examples/notebooks/. Currently, the best way to run the notebooks is by cloning the repo and running them with the uv-managed environment as the kernel. This way, you can be sure that all the dependencies are correct and that the notebooks will run as expected.

There are some additional requirements to run the examples. Once you've cloned the repo, from the main directory, run:

uv sync --group examples

Then, register the project environment as a Jupyter kernel:

uv run python -m ipykernel install --user --name jitr --display-name "Python (jitr)"

and start a Jupyter-lab server:

uv run --with jupyter jupyter lab

In JupyterLab, select the Python (jitr) kernel. Notebooks using this kernel will run against the uv-managed .venv/ environment.

Then, you can run the notebooks.

BAND

This package is part of the BAND Framework

citations

Please consider citing both this package and the BAND Framework if you use this code in your research. The BibTeX entries are:

@software{Beyer_JITR_2024,
author = {Beyer, Kyle},
license = {BSD-3-Clause},
month = oct,
title = {{JITR}},
url = {https://github.com/beykyle/jitr},
version = {1.3.0},
year = {2024}
}

@techreport{bandframework,
    title       = {{BANDFramework: An} Open-Source Framework for {Bayesian} Analysis of Nuclear Dynamics},
    author      = {Kyle Beyer and Landon Buskirk and Manuel Catacora Rios and Moses Y-H. Chan and Tyler H. Chang and Troy Dasher 
    and Richard James DeBoer and Christian Drischler and Richard J. Furnstahl and Pablo Giuliani and
    Kyle Godbey and Kevin Ingles and Sunil Jaiswal and An Le and Dananjaya Liyanage and Filomena M. Nunes
    and Daniel Odell and David O'Gara and Jared O'Neal and Daniel R. Phillips and Matthew Plumlee
    and Matthew T. Pratola and Scott Pratt and Oleh Savchuk and Alexandra C. Semposki and \"Ozge S\"urer and 
    Stefan M. Wild and John C. Yannotty},
    institution = {},
    number      = {Version 0.5.0},
    year        = {2025},
    url         = {https://github.com/bandframework/bandframework}
}