HS$^3$ Utilities

Python HS$^3$ Utilities (hs3) is a collection of command-line tools and utilities designed to support the High-Energy Physics Statistics Serialization Standard (HS$^3$). This package provides tools for working with HS$^3$ JSON files, including functionality for generating GraphML representations and diffing HS$^3$ files.

Features

• hs3diff: A tool to compare two HS$^3$ JSON files and generate a diff.
• hs3tographml: A tool to convert HS$^3$ JSON files into GraphML format for visualization.

Installation

You can install HS$^3$ Utilities from PyPI using pip:

pip install hs3

Alternatively, you can clone this repository and install the package locally:

git clone https://github.com/hep-statistics-serialization-standard/python-hep-statistics-serialization-standard
cd python-hep-statistics-serialization-standard
pip install .

Usage

hs3diff

hs3diff is used to compare two HS$^3$ JSON files and output the differences.

Usage Example:

hs3diff file1.json file2.json

This command will output the differences between file1.json and file2.json.

hs3tographml

hs3tographml converts an HS$^3$ JSON file into a GraphML file, which can be visualized using tools like Gephi.

Usage Example:

hs3tographml -i model.json -o model.graphml

This command will convert model.json into model.graphml.

Options

• -i, --input: Input JSON file (required).
• -o, --output: Output GraphML file (required).
• -l, --likelihood: Name of the likelihood to use (optional).

Contributing

Contributions are welcome! If you’d like to contribute to HS$^3$ Utilities, please follow these steps:

• Fork the repository.
• Create a new branch (git checkout -b feature-branch).
• Make your changes and commit them (git commit -am 'Add new feature').
• Push to the branch (git push origin feature-branch).
• Open a Pull Request.

Please ensure your code follows the project's coding standards and is well-tested.

License

This project is licensed under the BSD 3-Clause License. See the LICENSE file for details.

Authors

• Carsten Burgard - cburgard@cern.ch

Acknowledgments

Special thanks to the contributors and the HEP statistics standard community for their ongoing support and feedback.