https://github.com/ultralytics/sandd
https://github.com/ultralytics/sandd
data-analysis data-science neutrino particle-physics
Last synced: 9 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/ultralytics/sandd
- Owner: ultralytics
- License: agpl-3.0
- Created: 2019-04-07T12:48:38.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2025-03-28T01:39:34.000Z (10 months ago)
- Last Synced: 2025-03-28T02:34:51.021Z (10 months ago)
- Topics: data-analysis, data-science, neutrino, particle-physics
- Language: Python
- Size: 38.1 KB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# đ Introduction
Welcome to the Ultralytics WAVE repository! This directory contains innovative code developed by Ultralytics for **WA**veform **V**ector **E**xploitation, focusing on particle physics detector readout and reconstruction. Our work leverages cutting-edge [Machine Learning (ML)](https://www.ultralytics.com/glossary/machine-learning-ml) and [Deep Neural Networks (DNNs)](https://www.ultralytics.com/glossary/deep-learning-dl) to enhance data analysis.
This software is available for use and redistribution under the **AGPL-3.0 license**. For a comprehensive overview of our projects and solutions, please visit [Ultralytics](https://www.ultralytics.com/).
[](https://github.com/ultralytics/sandd/actions/workflows/format.yml)
[](https://discord.com/invite/ultralytics)
[](https://community.ultralytics.com/)
[](https://reddit.com/r/ultralytics)
# đ Description
The [Ultralytics WAVE repository](https://github.com/ultralytics/wave) offers a novel approach to particle physics detector readout and reconstruction through **WA**veform **V**ector **E**xploitation. By utilizing advanced ML and DNN techniques, WAVE aims to improve the precision and efficiency of interpreting complex waveform data from Time-Of-Flight detectors, contributing to advancements in [AI research](https://www.ultralytics.com/blog/the-role-of-deep-research-models-in-ai-advancements).
# đĻ Requirements
To get started with WAVE, you'll need [Python](https://www.python.org/) 3.7 or newer. The necessary libraries can be easily installed using `pip` and the provided `requirements.txt` file:
```bash
pip3 install -U -r requirements.txt
```
Key package requirements include:
- `numpy`: Fundamental package for numerical computation.
- `scipy`: Used for scientific and technical computing tasks.
- `torch` (version 0.4.0+): An open-source ML framework for building and training neural networks.
- `tensorflow` (version 1.8.0+): A comprehensive ecosystem for ML, offering tools, libraries, and community resources.
- `plotly` (optional): For creating interactive data visualizations.
You can find more information about these tools on their respective websites: [NumPy](https://numpy.org/), [SciPy](https://scipy.org/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and [Plotly](https://plotly.com/python/).
# đ Running the Code
Several scripts are available to execute the WAVE models:
- **PyTorch Implementation**: Use `wave_pytorch.py` for models developed with the PyTorch framework.
- **TensorFlow Implementation**: Run `wave_tf.py` for models based on TensorFlow.
- **PyTorch on Google Cloud Platform**: Deploy `wave_pytorch_gcp.py` for running PyTorch models within the [Google Cloud Platform (GCP)](https://cloud.google.com/) environment.
# ⨠Visualizations
Here are some example visualizations showcasing waveforms processed by WAVE and the training progress of the models:
 
# đ Citation
If you utilize this project in your research or publications, we appreciate it if you cite our work. Please use the following citation format:
```bibtex
@misc{jocher2018wave,
title={WAVE: Machine Learning for Full-Waveform Time-Of-Flight Detectors},
author={Glenn Jocher and Kurt Nishimura and Jacob Koblanski and Victor Li},
year={2018},
eprint={1811.05875},
archivePrefix={arXiv},
primaryClass={physics.ins-det}
}
```
You can access the paper on [ArXiv.org](https://arxiv.org/abs/1811.05875).
# đ¤ Contribute
We actively welcome contributions from the open-source community! Whether it's fixing bugs, adding new features, or improving documentation, your help is valuable. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for more details on how to get started.
We also encourage you to share your experiences with Ultralytics projects by filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). Your feedback helps us improve. A huge đ thank you to all our contributors!
[](https://github.com/ultralytics/ultralytics/graphs/contributors)
# Šī¸ License
Ultralytics provides two licensing options to accommodate different use cases:
- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-v3) open-source license is ideal for students, researchers, and enthusiasts who wish to collaborate and share knowledge openly. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details.
- **Enterprise License**: Designed for commercial applications, this license permits the integration of Ultralytics software and AI models into commercial products and services without the open-source requirements of AGPL-3.0. If your project requires an Enterprise License, please contact us through [Ultralytics Licensing](https://www.ultralytics.com/license).
# đŦ Contact Us
For bug reports, feature requests, and contributions, please visit [GitHub Issues](https://github.com/ultralytics/sandd/issues). For broader questions and discussions about WAVE or other Ultralytics projects, join our vibrant community on [Discord](https://discord.com/invite/ultralytics)!
