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https://github.com/jiangyi15/tf-pwa

Partial Wave Analysis using TensorFlow.
https://github.com/jiangyi15/tf-pwa

gpu partial-wave-analysis particle-physics python tensorflow

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Partial Wave Analysis using TensorFlow.

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README 

        
# A Partial Wave Analysis program using Tensorflow



[![Documentation build status](https://readthedocs.org/projects/tf-pwa/badge/?version=latest)](https://tf-pwa.readthedocs.io)

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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

```



### conda channel (experimental)




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

```






###  pip




When using `pip`, you will need to install CUDA to use GPU (The newest

tensorflow support install with CUDA runtime directly as

`pip install tensorflow[and-cuda]`). 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]

```



You can also install from pypi.org directly without cloning the repo manually.



```bash

python3 -m pip install TFPWA

```



And also for the newest version from github



```bash

python3 -m pip install git+https://github.com/jiangyi15/tf-pwa.git

```






## 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