{"id":19863547,"url":"https://github.com/sandialabs/pyriid","last_synced_at":"2025-05-02T04:31:08.459Z","repository":{"id":57461965,"uuid":"381080801","full_name":"sandialabs/PyRIID","owner":"sandialabs","description":"ML-based radioisotope identification and estimation from gamma spectra in Python.","archived":false,"fork":false,"pushed_at":"2025-03-13T00:07:36.000Z","size":871,"stargazers_count":17,"open_issues_count":1,"forks_count":4,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-06T22:38:47.589Z","etag":null,"topics":["gamma-spectra","machine-learning","neural-networks","python","radiation","radiation-sensor","radioisotope-identification","scr-2618","snl-applications","snl-data-analysis","snl-science-libs","snl-visualization","tensorflow2"],"latest_commit_sha":null,"homepage":"https://sandialabs.github.io/PyRIID/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sandialabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":"CODE_OF_CONDUCT.md","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}},"created_at":"2021-06-28T15:34:09.000Z","updated_at":"2025-03-13T00:06:58.000Z","dependencies_parsed_at":"2022-08-28T02:00:20.284Z","dependency_job_id":"55aaa7e9-08bc-4dc6-84ba-0eed71eed457","html_url":"https://github.com/sandialabs/PyRIID","commit_stats":{"total_commits":140,"total_committers":6,"mean_commits":"23.333333333333332","dds":0.1785714285714286,"last_synced_commit":"1d9542155a4b48488456d5205432b2689896af8e"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FPyRIID","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FPyRIID/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FPyRIID/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sandialabs%2FPyRIID/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sandialabs","download_url":"https://codeload.github.com/sandialabs/PyRIID/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251986784,"owners_count":21675951,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["gamma-spectra","machine-learning","neural-networks","python","radiation","radiation-sensor","radioisotope-identification","scr-2618","snl-applications","snl-data-analysis","snl-science-libs","snl-visualization","tensorflow2"],"created_at":"2024-11-12T15:15:06.925Z","updated_at":"2025-05-02T04:31:08.453Z","avatar_url":"https://github.com/sandialabs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/1079118/124811147-623bd280-df1f-11eb-9f3a-a4a5e6ec5f94.png\" alt=\"PyRIID\"\u003e\n\u003c/p\u003e\n\n![Python Version from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fsandialabs%2FPyRIID%2Frefs%2Fheads%2Fmain%2Fpyproject.toml)\n![PyPI](https://badge.fury.io/py/riid.svg)\n\nPyRIID is a Python package providing modeling and data synthesis utilities for machine learning-based research and development of radioisotope-related detection, identification, and quantification.\n\n## Installation\n\nRequirements:\n\n- Python version: 3.10 to 3.12\n  - Note: we recommended the highest Python version you can manage as anecdotally, we have noticed that everything just tends to get faster.\n- Operating systems: Windows, Mac, or Ubuntu\n\nTests and examples are run via Actions on many combinations of Python version and operating system.\nYou can verify support for your platform by checking the workflow files.\n\n### For Use\n\nTo use the latest version on PyPI, run:\n\n```sh\npip install riid\n```\n\nNote that changes are slower to appear on PyPI, so for the latest features, run:**\n\n```sh\npip install git+https://github.com/sandialabs/pyriid.git@main\n```\n\n### For Development\n\nIf you are developing PyRIID, clone this repository and run:\n\n```sh\npip install -e \".[dev]\"\n```\n\nIf you encounter Pylance issues, try:\n\n```sh\npip install -e \".[dev]\" --config-settings editable_mode=compat\n```\n\n## Examples\n\nExamples for how to use this package can be found [here](https://github.com/sandialabs/PyRIID/blob/main/examples).\n\n## Tests\n\nUnit tests for this package can be found [here](https://github.com/sandialabs/PyRIID/blob/main/tests).\n\nRun all unit tests with the following:\n\n```sh\npython -m unittest tests/*.py -v\n```\n\nYou can also run one of the `run_tests.*` scripts, whichever is appropriate for your platform.\n\n## Docs\n\nAPI documentation can be found [here](https://sandialabs.github.io/PyRIID).\n\nDocs can be built locally with the following:\n\n```sh\npip install -r pdoc/requirements.txt\npdoc riid -o docs/ --html --template-dir pdoc\n```\n\n## Contributing\n\nPull requests are welcome.\nFor major changes, please open an issue first to discuss what you would like to change.\n\nPlease make sure to update tests as appropriate and adhere to our [code of conduct](https://github.com/sandialabs/PyRIID/blob/main/CODE_OF_CONDUCT.md).\n\n## Contacts\n\nMaintainers and authors can be found [here](https://github.com/sandialabs/PyRIID/blob/main/pyproject.toml).\n\n## Copyright\n\nFull copyright details can be found [here](https://github.com/sandialabs/PyRIID/blob/main/NOTICE.md).\n\n## Acknowledgements\n\n**Thank you** to the U.S. Department of Energy, National Nuclear Security Administration,\nOffice of Defense Nuclear Nonproliferation Research and Development (DNN R\u0026D) for funding that has led to versions `2.0` and `2.1`.\n\nAdditionally, **thank you** to the following individuals who have provided invaluable subject-matter expertise:\n\n- Paul Thelen (also an author)\n- Ben Maestas\n- Greg Thoreson\n- Michael Enghauser\n- Elliott Leonard\n\n## Citing\n\nWhen citing PyRIID, please reference the U.S. Department of Energy Office of Science and Technology Information (OSTI) record here:\n[10.11578/dc.20221017.2](https://doi.org/10.11578/dc.20221017.2)\n\n## Related Reports, Publications, and Projects\n\n1. Alan Van Omen, *\"A Semi-Supervised Model for Multi-Label Radioisotope Classification and Out-of-Distribution Detection.\"* Diss. 2023. doi: [10.7302/7200](https://dx.doi.org/10.7302/7200).\n2. Tyler Morrow, *\"Questionnaire for Radioisotope Identification and Estimation from Gamma Spectra using PyRIID v2.\"* United States: N. p., 2023. Web. doi: [10.2172/2229893](https://doi.org/10.2172/2229893).\n3. Aaron Fjeldsted, Tyler Morrow, and Douglas Wolfe, *\"Identifying Signal-to-Noise Ratios Representative of Gamma Detector Response in Realistic Scenarios,\"* 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), Vancouver, BC, Canada, 2023. doi: [10.1109/NSSMICRTSD49126.2023.10337860](https://doi.org/10.1109/NSSMICRTSD49126.2023.10337860).\n4. Alan Van Omen and Tyler Morrow, *\"A Semi-supervised Learning Method to Produce Explainable Radioisotope Proportion Estimates for NaI-based Synthetic and Measured Gamma Spectra.\"* United States: N. p., 2024. Web. doi: [10.2172/2335904](https://doi.org/10.2172/2335904).\n    - [Code, data, and best model](https://zenodo.org/doi/10.5281/zenodo.10223445)\n5. Alan Van Omen and Tyler Morrow, *\"Controlling Radioisotope Proportions When Randomly Sampling from Dirichlet Distributions in PyRIID.\"* United States: N. p., 2024. Web. doi: [10.2172/2335905](https://doi.org/10.2172/2335905).\n6. Alan Van Omen, Tyler Morrow, et al., *\"Multilabel Proportion Prediction and Out-of-distribution Detection on Gamma Spectra of Short-lived Fission Products.\"* Annals of Nuclear Energy 208 (2024): 110777. doi: [10.1016/j.anucene.2024.110777](https://doi.org/10.1016/j.anucene.2024.110777).\n    - [Code, data, and best models](https://zenodo.org/doi/10.5281/zenodo.12796964)\n7. Aaron Fjeldsted, Tyler Morrow, et al., *\"A Novel Methodology for Gamma-Ray Spectra Dataset Procurement over Varying Standoff Distances and Source Activities,\"* Nuclear Instruments and Methods in Physics Research Section A (2024): 169681. doi: [10.1016/j.nima.2024.169681](https://doi.org/10.1016/j.nima.2024.169681).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fpyriid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fpyriid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fpyriid/lists"}