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.

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.

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.

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.

Below are example visualizations of waveforms and training processes:

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.

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!

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.

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!