{"id":24343246,"url":"https://github.com/jiangyi15/tf-pwa","last_synced_at":"2025-09-28T03:32:52.467Z","repository":{"id":37705291,"uuid":"295283408","full_name":"jiangyi15/tf-pwa","owner":"jiangyi15","description":"Partial Wave Analysis using TensorFlow.","archived":false,"fork":false,"pushed_at":"2024-04-13T13:06:03.000Z","size":2619,"stargazers_count":25,"open_issues_count":5,"forks_count":12,"subscribers_count":5,"default_branch":"dev","last_synced_at":"2024-04-14T01:16:50.575Z","etag":null,"topics":["gpu","partial-wave-analysis","particle-physics","python","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jiangyi15.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","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}},"created_at":"2020-09-14T02:32:30.000Z","updated_at":"2024-04-24T10:59:40.511Z","dependencies_parsed_at":"2023-10-03T15:04:57.731Z","dependency_job_id":"5f9f4238-0924-4aff-8526-2f23211764df","html_url":"https://github.com/jiangyi15/tf-pwa","commit_stats":{"total_commits":1171,"total_committers":8,"mean_commits":146.375,"dds":"0.19982920580700259","last_synced_commit":"3351ad08b5cd707b811a4de0abed072c3ff69b88"},"previous_names":[],"tags_count":12,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiangyi15%2Ftf-pwa","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiangyi15%2Ftf-pwa/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiangyi15%2Ftf-pwa/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiangyi15%2Ftf-pwa/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jiangyi15","download_url":"https://codeload.github.com/jiangyi15/tf-pwa/tar.gz/refs/heads/dev","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":234482741,"owners_count":18840325,"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":["gpu","partial-wave-analysis","particle-physics","python","tensorflow"],"created_at":"2025-01-18T08:20:12.136Z","updated_at":"2025-09-28T03:32:42.453Z","avatar_url":"https://github.com/jiangyi15.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# A Partial Wave Analysis program using Tensorflow\n\n[![Documentation build status](https://readthedocs.org/projects/tf-pwa/badge/?version=latest)](https://tf-pwa.readthedocs.io)\n[![CI status](https://github.com/jiangyi15/tf-pwa/workflows/CI/badge.svg)](https://github.com/jiangyi15/tf-pwa/actions?query=branch%3Adev+workflow%3ACI)\n[![Test coverage](https://codecov.io/gh/jiangyi15/tf-pwa/branch/dev/graph/badge.svg)](https://codecov.io/gh/jiangyi15/tf-pwa)\n[![conda cloud](https://anaconda.org/jiangyi15/tf-pwa/badges/version.svg)](https://anaconda.org/jiangyi15/tf-pwa)\n[![pypi](https://img.shields.io/pypi/v/TFPWA)](https://pypi.org/project/TFPWA/)\n[![license](https://anaconda.org/jiangyi15/tf-pwa/badges/license.svg)](https://choosealicense.com/licenses/mit/)\n\u003cbr\u003e\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jiangyi15/tf-pwa/HEAD)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen)](https://github.com/pre-commit/pre-commit)\n[![Prettier](https://camo.githubusercontent.com/687a8ae8d15f9409617d2cc5a30292a884f6813a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64655f7374796c652d70726574746965722d6666363962342e7376673f7374796c653d666c61742d737175617265)](https://prettier.io/)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat\u0026labelColor=ef8336)](https://pycqa.github.io/isort/)\n\nThis is a package and application for partial wave analysis (PWA) using\nTensorFlow. By using simple configuration file (and some scripts), PWA can be\ndone fast and automatically.\n\n## Install\n\nYou can go to\n[http://tf-pwa.readthedocs.io/install](http://tf-pwa.readthedocs.io/en/latest/install.html)\nfor more informations. Get the packages using\n\n```\ngit clone https://github.com/jiangyi15/tf-pwa.git\n```\n\nThe dependencies can be installed by `conda` or `pip`.\n\n### conda (recommended)\n\nWhen using conda, you don't need to install CUDA for TensorFlow specially.\n\n1. Get miniconda for python3 from\n   [miniconda3](https://docs.conda.io/en/latest/miniconda.html) and install it.\n\n2. Install requirements, we recommed Ampere card users to install with\n   `tensorflow_2_6_requirements.txt` (see this\n   [technical FAQ](https://tf-pwa.readthedocs.io/en/latest/tensorflow_version.html)).\n\nYou can install a tensorflow gpu version in anaconda as\n\n```\nconda install tensorflow[build=\"gpu*\"]=2.8\n```\n\nand then install the rest dependences\n\n```\nconda install --file requirements-min.txt\n```\n\n**Or**\n\nYou can install a newer version in conda-forge as\n\n```\nconda install --file tensorflow_2_6_requirements.txt -c conda-forge\n```\n\n3. The following command can be used to set environment variables of Python.\n   (Use `--no-deps` to make sure that no PyPI package will be installed. Using\n   `-e`, so it can be updated by `git pull` directly.)\n\n```\npython -m pip install -e . --no-deps\n```\n\n4. (option) There are some option packages, such as `uproot` for reading root\n   file. It can be installed as\n\n```\nconda install uproot -c conda-forge\n```\n\n\u003cdetails\u003e\u003csummary\u003e\n### conda channel (experimental)\n\u003c/summary\u003e\u003cp\u003e\n\nA pre-built conda package (Linux only) is also provided, just run following\ncommand to install it.\n\n```\nconda config --add channels jiangyi15\nconda install tf-pwa\n```\n\n\u003c/p\u003e\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003e\n###  pip\n\u003c/summary\u003e\u003cp\u003e\n\nWhen using `pip`, you will need to install CUDA to use GPU (The newest\ntensorflow support install with CUDA runtime directly as\n`pip install tensorflow[and-cuda]`). Just run the following command :\n\n```bash\npython3 -m pip install -e .\n```\n\nTo contribute to the project, please also install additional developer tools\nwith:\n\n```bash\npython3 -m pip install -e .[dev]\n```\n\nYou can also install from pypi.org directly without cloning the repo manually.\n\n```bash\npython3 -m pip install TFPWA\n```\n\nAnd also for the newest version from github\n\n```bash\npython3 -m pip install git+https://github.com/jiangyi15/tf-pwa.git\n```\n\n\u003c/p\u003e\u003c/details\u003e\n\n## Scripts\n\n### fit.py\n\nsimple fit scripts, decay structure is described in `config.yml`, here `[]`\nmeans options.\n\n```\npython fit.py [--config config.yml]  [--init_params init_params.json]\n```\n\nfit parameters will save in final_params.json, figure can be found in\n`figure/`.\n\n### state_cache.sh\n\nscript for cache state, using the latest \\*\\_params.json file as parameters and\ncache newer files in `path` (the default is `trash/`).\n\n```\n./state_cache.sh [path]\n```\n\n## Documents\n\nSee [tf-pwa.rtfd.io](http://tf-pwa.readthedocs.io) for more information.\n\nAutodoc using sphinx-doc, need sphinx-doc\n\n```\npython setup.py build_sphinx\n```\n\nThen, the documents can be found in build/sphinx/index.html.\n\nDocuments can also build with `Makefile` in `docs` as\n\n```\ncd docs \u0026\u0026 make html\n```\n\nThen, the documents can be found in docs/\\_build/html.\n\n## Other resources\n\n[面向 BESIII 用户的 TF-PWA 使用手册](https://note.ihep.ac.cn/s/xAr0zQXf8)\n\n[HADRON 2023](https://agenda.infn.it/event/33110/contributions/198135/)\n\n[PWA 12](https://indico.cern.ch/event/885396/timetable/#52-reach-on-the-partial-wave-a)\n\n## Dependencies\n\ntensorflow or tensorflow-gpu \u003e= 2.0.0\n\ncudatoolkit : CUDA library for GPU acceleration\n\nsympy : symbolic expression\n\nPyYAML : config.yml file\n\nmatplotlib : plot\n\nscipy : fit\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiangyi15%2Ftf-pwa","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjiangyi15%2Ftf-pwa","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiangyi15%2Ftf-pwa/lists"}