# A Partial Wave Analysis program using Tensorflow
[](https://tf-pwa.readthedocs.io)
[](https://github.com/jiangyi15/tf-pwa/actions?query=branch%3Adev+workflow%3ACI)
[](https://codecov.io/gh/jiangyi15/tf-pwa)
[](https://anaconda.org/jiangyi15/tf-pwa)
[](https://pypi.org/project/TFPWA/)
[](https://choosealicense.com/licenses/mit/)
<br>
[](https://mybinder.org/v2/gh/jiangyi15/tf-pwa/HEAD)
[](https://github.com/pre-commit/pre-commit)
[](https://prettier.io/)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
This is a package and application for partial wave analysis (PWA) using
TensorFlow. By using simple configuration file (and some scripts), PWA can be
done fast and automatically.
## Install
You can go to
[http://tf-pwa.readthedocs.io/install](http://tf-pwa.readthedocs.io/en/latest/install.html)
for more informations. Get the packages using
```
git clone https://github.com/jiangyi15/tf-pwa.git
```
The dependencies can be installed by `conda` or `pip`.
### conda (recommended)
When using conda, you don't need to install CUDA for TensorFlow specially.
1. Get miniconda for python3 from
[miniconda3](https://docs.conda.io/en/latest/miniconda.html) and install it.
2. Install requirements, we recommed Ampere card users to install with
`tensorflow_2_6_requirements.txt` (see this
[technical FAQ](https://tf-pwa.readthedocs.io/en/latest/tensorflow_version.html)).
You can install a tensorflow gpu version in anaconda as
```
conda install tensorflow[build="gpu*"]=2.8
```
and then install the rest dependences
```
conda install --file requirements-min.txt
```
**Or**
You can install a newer version in conda-forge as
```
conda install --file tensorflow_2_6_requirements.txt -c conda-forge
```
3. The following command can be used to set environment variables of Python.
(Use `--no-deps` to make sure that no PyPI package will be installed. Using
`-e`, so it can be updated by `git pull` directly.)
```
python -m pip install -e . --no-deps
```
4. (option) There are some option packages, such as `uproot` for reading root
file. It can be installed as
```
conda install uproot -c conda-forge
```
<details><summary>
### conda channel (experimental)
</summary><p>
A pre-built conda package (Linux only) is also provided, just run following
command to install it.
```
conda config --add channels jiangyi15
conda install tf-pwa
```
</p></details>
<details><summary>
### pip
</summary><p>
When using `pip`, you will need to install CUDA to use GPU. Just run the
following command :
```bash
python3 -m pip install -e .
```
To contribute to the project, please also install additional developer tools
with:
```bash
python3 -m pip install -e .[dev]
```
</p></details>
## Scripts
### fit.py
simple fit scripts, decay structure is described in `config.yml`, here `[]`
means options.
```
python fit.py [--config config.yml] [--init_params init_params.json]
```
fit parameters will save in final_params.json, figure can be found in
`figure/`.
### state_cache.sh
script for cache state, using the latest \*\_params.json file as parameters and
cache newer files in `path` (the default is `trash/`).
```
./state_cache.sh [path]
```
## Documents
See [tf-pwa.rtfd.io](http://tf-pwa.readthedocs.io) for more information.
Autodoc using sphinx-doc, need sphinx-doc
```
python setup.py build_sphinx
```
Then, the documents can be found in build/sphinx/index.html.
Documents can also build with `Makefile` in `docs` as
```
cd docs && make html
```
Then, the documents can be found in docs/\_build/html.
## Other resources
[面向 BESIII 用户的 TF-PWA 使用手册](https://note.ihep.ac.cn/s/xAr0zQXf8)
[HADRON 2023](https://agenda.infn.it/event/33110/contributions/198135/)
[PWA 12](https://indico.cern.ch/event/885396/timetable/#52-reach-on-the-partial-wave-a)
## Dependencies
tensorflow or tensorflow-gpu >= 2.0.0
cudatoolkit : CUDA library for GPU acceleration
sympy : symbolic expression
PyYAML : config.yml file
matplotlib : plot
scipy : fit
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"description": "# A Partial Wave Analysis program using Tensorflow\n\n[](https://tf-pwa.readthedocs.io)\n[](https://github.com/jiangyi15/tf-pwa/actions?query=branch%3Adev+workflow%3ACI)\n[](https://codecov.io/gh/jiangyi15/tf-pwa)\n[](https://anaconda.org/jiangyi15/tf-pwa)\n[](https://pypi.org/project/TFPWA/)\n[](https://choosealicense.com/licenses/mit/)\n<br>\n[](https://mybinder.org/v2/gh/jiangyi15/tf-pwa/HEAD)\n[](https://github.com/pre-commit/pre-commit)\n[](https://prettier.io/)\n[](https://github.com/psf/black)\n[](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<details><summary>\n### conda channel (experimental)\n</summary><p>\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</p></details>\n\n<details><summary>\n### pip\n</summary><p>\nWhen using `pip`, you will need to install CUDA to use GPU. Just run the\nfollowing 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\n</p></details>\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 && make html\n```\n\nThen, the documents can be found in docs/\\_build/html.\n\n## Other resources\n\n[\u9762\u5411 BESIII \u7528\u6237\u7684 TF-PWA \u4f7f\u7528\u624b\u518c](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 >= 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",
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