🎉 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.

📜 Description

The Ultralytics WAVE repository offers leading-edge WAveform Vector Exploitation 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:

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:

📄 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 to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our 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 open-source license encourages collaborative learning and knowledge sharing. Please refer to the 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.

📬 Contact Us

For bug reports, feature requests, and contributions, head to GitHub Issues. For questions and discussions about this project and other Ultralytics endeavors, join us on Discord!

ultralytics / sandd