{"id":19454140,"url":"https://github.com/ultralytics/sandd","last_synced_at":"2026-03-09T11:05:26.102Z","repository":{"id":103258354,"uuid":"179965489","full_name":"ultralytics/sandd","owner":"ultralytics","description":null,"archived":false,"fork":false,"pushed_at":"2025-05-11T00:13:11.000Z","size":47,"stargazers_count":45,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-02-25T03:18:41.044Z","etag":null,"topics":["data-analysis","data-science","neutrino","particle-physics"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ultralytics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":"glenn-jocher","patreon":"ultralytics","open_collective":"ultralytics"}},"created_at":"2019-04-07T12:48:38.000Z","updated_at":"2026-02-05T14:51:32.000Z","dependencies_parsed_at":"2025-01-06T21:21:41.228Z","dependency_job_id":"a5511578-7377-47da-ba5d-b4a0102c7fb3","html_url":"https://github.com/ultralytics/sandd","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ultralytics/sandd","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultralytics%2Fsandd","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultralytics%2Fsandd/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultralytics%2Fsandd/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultralytics%2Fsandd/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ultralytics","download_url":"https://codeload.github.com/ultralytics/sandd/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultralytics%2Fsandd/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30291856,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-09T02:57:19.223Z","status":"ssl_error","status_checked_at":"2026-03-09T02:56:26.373Z","response_time":61,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","neutrino","particle-physics"],"created_at":"2024-11-10T17:08:08.471Z","updated_at":"2026-03-09T11:05:26.069Z","avatar_url":"https://github.com/ultralytics.png","language":"Python","funding_links":["https://github.com/sponsors/glenn-jocher","https://patreon.com/ultralytics","https://opencollective.com/ultralytics"],"categories":[],"sub_categories":[],"readme":"\u003ca href=\"https://www.ultralytics.com/\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg\" width=\"320\" alt=\"Ultralytics logo\"\u003e\u003c/a\u003e\n\n# 🎉 Introduction\n\nWelcome 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.\n\nThis 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/).\n\n[![Ultralytics Actions](https://github.com/ultralytics/sandd/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/sandd/actions/workflows/format.yml)\n[![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord\u0026logoColor=white\u0026label=Discord\u0026color=blue)](https://discord.com/invite/ultralytics)\n[![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com\u0026logo=discourse\u0026label=Forums\u0026color=blue)](https://community.ultralytics.com/)\n[![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat\u0026logo=reddit\u0026logoColor=white\u0026label=Reddit\u0026color=blue)](https://reddit.com/r/ultralytics)\n\n# 📜 Description\n\nThe [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).\n\n# 📦 Requirements\n\nTo 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:\n\n```bash\npip3 install -U -r requirements.txt\n```\n\nKey package requirements include:\n\n- `numpy`: Fundamental package for numerical computation.\n- `scipy`: Used for scientific and technical computing tasks.\n- `torch` (version 0.4.0+): An open-source ML framework for building and training neural networks.\n- `tensorflow` (version 1.8.0+): A comprehensive ecosystem for ML, offering tools, libraries, and community resources.\n- `plotly` (optional): For creating interactive data visualizations.\n\nYou 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/).\n\n# 🚀 Running the Code\n\nSeveral scripts are available to execute the WAVE models:\n\n- **PyTorch Implementation**: Use `wave_pytorch.py` for models developed with the PyTorch framework.\n- **TensorFlow Implementation**: Run `wave_tf.py` for models based on TensorFlow.\n- **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.\n\n# ✨ Visualizations\n\nHere are some example visualizations showcasing waveforms processed by WAVE and the training progress of the models:\n\n![](https://github.com/ultralytics/wave/blob/main/data/waveforms.png \"Waveforms\") ![](https://github.com/ultralytics/wave/blob/main/data/wave.png \"Training Progress\")\n\n# 📄 Citation\n\nIf you utilize this project in your research or publications, we appreciate it if you cite our work. Please use the following citation format:\n\n```bibtex\n@misc{jocher2018wave,\n      title={WAVE: Machine Learning for Full-Waveform Time-Of-Flight Detectors},\n      author={Glenn Jocher and Kurt Nishimura and Jacob Koblanski and Victor Li},\n      year={2018},\n      eprint={1811.05875},\n      archivePrefix={arXiv},\n      primaryClass={physics.ins-det}\n}\n```\n\nYou can access the paper on [ArXiv.org](https://arxiv.org/abs/1811.05875).\n\n# 🤝 Contribute\n\nWe 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.\n\nWe also encourage you to share your experiences with Ultralytics projects by filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey). Your feedback helps us improve. A huge 🙏 thank you to all our contributors!\n\n[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)\n\n# ©️ License\n\nUltralytics provides two licensing options to accommodate different use cases:\n\n- **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.\n- **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).\n\n# 📬 Contact Us\n\nFor 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)!\n\n\u003cbr\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"3%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"3%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"3%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://youtube.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"3%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"3%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"3%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"space\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"3%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultralytics%2Fsandd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fultralytics%2Fsandd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultralytics%2Fsandd/lists"}