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tymorrow committed Dec 7, 2023
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[![Python](https://img.shields.io/pypi/pyversions/riid)](https://badge.fury.io/py/riid)
[![PyPI](https://badge.fury.io/py/riid.svg)](https://badge.fury.io/py/riid)

Welcome to PyRIID! PyRIID is a Python package providing models and data synthesis utilities supporting machine learning-based research into radioisotope-related detection, identification, and quantification.
Welcome to PyRIID!
PyRIID is a Python package providing models and data synthesis utilities supporting
machine learning-based research into radioisotope-related detection, identification, and quantification.

## Installation

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A virtual environment is recommended.

Tests and examples are ran via Actions on many combinations of Python version and operating system.
Tests and examples are run via Actions on many combinations of Python version and operating system.
You can verify support for your platform by checking the workflow files.

### For Use

To use the latest version on PyPI (note: changes are currently slower to appear here), run:
To use the latest version on PyPI (note: changes are slower to appear here), run:

```sh
pip install riid
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Unit tests for this package can be found [here](https://github.com/sandialabs/PyRIID/blob/main/tests).

Run all unit tests with the following command:
Run all unit tests with the following:

```sh
python -m unittest tests/*.py -v
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API documentation can be found [here](https://sandialabs.github.io/PyRIID).

You can also build the docs with the following commands:
Build the docs with the following:

```sh
pip install -r pdoc/requirements.txt
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## Copyright

Full copyright details are outlined [here](https://github.com/sandialabs/PyRIID/blob/main/NOTICE.md)
Full copyright details can be found [here](https://github.com/sandialabs/PyRIID/blob/main/NOTICE.md).

## Acknowlegements
## Acknowledgements

**Thank you** to the U.S. Department of Energy, National Nuclear Security Administration,
Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) for funding that has led to version `2.x`.

Additionally, **thank you** to the following individuals who have provided invaluable subject-matter expertise:

- Paul Thelen (also an author)
- Ben Maestas
- Greg Thoreson
- Michael Enghauser
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