BARNI - Benchmark Algorithm for RadioNuclide Identification

BARNI is a software for radionuclide identification from gamma-ray spectra.

It uses a machine learning approach to train for a variaity of spectroscopic gamma-ray radiation detectors.

Introduction

This README file is meant as a simple overview of the BARNI repository.

Documentation

Full documentation is generated by sphinx.

Directory Structure

The following is an overview of the top directory structure folders:

• barni: Code of the BARNI python package.
• doc: The Sphinx documentation of the BARNI package.
• examples: Input and configuration files for running BARNI identification and training routines
• test: Unit tests for the code found in the barni folder.

In addition, there are various files on the top directory:

• barni_cli.py: BARNI command line interface module.
• barni_cli.spec: PyInstaller configuration file.
• pyinstall.py: PyInstaller build script.
• barni.yml: Anaconda environment file.
• nose2.cfg: Nose2 (unit test) configuration file.
• setup.py: BARNI package installation script.
• LICENSE: The liscence description.

Required Libraries

• Python 3.7+
• Numpy 1.17+
• SciKit-Learn 0.20+
• Bokeh 1.4+
• Pandas 0.25+

Contributing

Contributing to BARNI is relatively easy. Just send us a pull request. When you send your request, make develop the destination branch on the barni repository.

Your PR must pass BARNI's unit tests and documentation tests, and must be PEP 8 compliant. We enforce these guidelines with Travis CI. To run these tests locally simply use tox. BARNI uses a rough approximation of the Git Flow branching model. The develop branch contains the latest contributions, and master is always tagged and points to the latest stable release.

Authors

• Mateusz Monterial, LLNL
• Karl Nelson, LLNL

License

BARNI is released under an MIT license. For more details see the LICENSE file.

SPDX-License-Identifier: MIT

LLNL-CODE-805904