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https://github.com/ultralytics/sandd

data-analysis data-science neutrino particle-physics

Last synced: 5 days ago
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Host: GitHub
URL: https://github.com/ultralytics/sandd
Owner: ultralytics
License: agpl-3.0
Created: 2019-04-07T12:48:38.000Z (over 5 years ago)
Default Branch: main
Last Pushed: 2024-08-14T18:50:55.000Z (3 months ago)
Last Synced: 2024-08-14T19:46:37.470Z (3 months ago)
Topics: data-analysis, data-science, neutrino, particle-physics
Language: Python
Size: 55.7 KB
Stars: 2
Watchers: 3
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# 🎉 Introduction

This directory is part of the innovative work developed by Ultralytics and **is available for use and redistribution under the AGPL-3.0 license**. For an insightful overview of our diverse projects, we invite you to visit [Ultralytics](https://www.ultralytics.com/).

[![Ultralytics Actions](https://github.com/ultralytics/sandd/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/sandd/actions/workflows/format.yml)

# 📜 Description

The [Ultralytics WAVE repository](https://github.com/ultralytics/wave) offers leading-edge **WA**veform **V**ector **E**xploitation code. This novel approach to particle physics detector readout and reconstruction leverages Machine Learning and Deep Neural Networks to enhance data analysis and interpretation.

# 📦 Requirements

To dive into WAVE, ensure you have Python 3.7 or newer. Necessary libraries can be installed via `pip` using the provided `requirements.txt` with the following command:

```bash
pip3 install -U -r requirements.txt
```

The essential packages required are:

- `numpy`: For numerical computing.
- `scipy`: For scientific and technical computing.
- `torch` (version 0.4.0 or higher): For constructing and training neural networks.
- `tensorflow` (version 1.8.0 or higher): Provides a comprehensive, flexible ecosystem of tools, libraries, and community resources.
- `plotly`: Optional for creating interactive plots.

# 🚀 Running

To execute WAVE models, you have several scripts at your disposal:

- **PyTorch Implementation**: Utilize `wave_pytorch.py` for models based on the PyTorch framework.
- **TensorFlow Implementation**: Call upon `wave_tf.py` for TensorFlow-based models.
- **PyTorch on Google Cloud Platform**: Deploy `wave_pytorch_gcp.py` within the Google Cloud Platform ecosystem.

# Visualizations

Below are example visualizations of waveforms and training processes:

![](https://github.com/ultralytics/wave/blob/main/data/waveforms.png "Waveforms") ![](https://github.com/ultralytics/wave/blob/main/data/wave.png "Training Progress")

# 📄 Citation

If you find this project useful in your research or wish to reference it, please consider citing our publication:

```
Jocher, G., Nishimura, K., Koblanski, J. and Li, V. (2018). WAVE: Machine Learning for Full-Waveform Time-Of-Flight Detectors. ArXiv.org. Available at: https://arxiv.org/abs/1811.05875.
```

# 🤝 Contribute

We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!

# ©️ License

Ultralytics is excited to offer two different licensing options to meet your needs:

- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/license) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).

# 📬 Contact Us

For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/velocity/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://discord.com/invite/ultralytics)!