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GernotMaier merged 3 commits into from
Dec 22, 2025
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Readme #12

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GernotMaier Dec 22, 2025
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GernotMaier Dec 22, 2025
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GernotMaier Dec 22, 2025
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6 changes: 6 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -7,6 +7,12 @@ This changelog is generated using [Towncrier](https://towncrier.readthedocs.io/)

<!-- towncrier release notes start -->

## [v0.1.1](https://github.com/Eventdisplay/Eventdisplay-ML/releases/tag/v0.1.1) - 2025-12-22

### Maintenance

- Add PyPI project. ([#12](https://github.com/Eventdisplay/Eventdisplay-ML/pull/12))


## [v0.1.0](https://github.com/Eventdisplay/Eventdisplay-ML/releases/tag/v0.1.0) - 2025-12-22

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2 changes: 1 addition & 1 deletion CITATION.cff
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Expand Up @@ -5,7 +5,7 @@ message: If you use this software, please cite it using these metadata.
title: "Eventdisplay-ML: a machine learning toolkit for Eventdisplay"
# doi: 10.5281/zenodo.6827832
version: 0.1.0
date-released: 2025-12-20
date-released: 2025-12-25
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Copilot AI Dec 22, 2025

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There is a version mismatch between the CITATION.cff file (which shows version 0.1.0 with release date 2025-12-25) and the CHANGELOG.md file (which shows version 0.1.1 released on 2025-12-22). The version number in CITATION.cff should be updated to match the latest release version 0.1.1, and the dates should be consistent across files.

Copilot uses AI. Check for mistakes.
keywords:
- "gamma-ray astronomy"
- "astronomy software"
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4 changes: 4 additions & 0 deletions README.md
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@@ -1,5 +1,9 @@
# Machine learning for Eventdisplay

[![LICENSE](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://github.com/Eventdisplay/Eventdisplay-ML/blob/main/LICENSE)
[![release](https://img.shields.io/github/v/release/eventdisplay/eventdisplay-ml)](https://github.com/Eventdisplay/Eventdisplay-ML/releases)
[![pypi](https://badge.fury.io/py/eventdisplay-ml.svg)](https://badge.fury.io/py/eventdisplay-ml)

Toolkit to interface and run machine learning methods together with the Eventdisplay software package for gamma-ray astronomy data analysis.

Provides examples on how to use e.g., scikit-learn or XGBoost regression trees to estimate event direction, energies, and gamma/hadron separators.
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