Metadata-Version: 1.0
Name: physDBD
Version: 0.1
Summary: Physics-based modeling of reaction networks with TensorFlow
Home-page: https://github.com/smrfeld/phys_dbd
Author: Oliver K. Ernst
Author-email: oernst@ucsd.edu
License: UNKNOWN
Description: # Physics-based dynamic PCA for modeling stochastic reaction networks with TensorFlow
        
        [![docs](https://github.com/smrfeld/phys_dbd/actions/workflows/docs.yml/badge.svg)](https://github.com/smrfeld/phys_dbd/actions/workflows/docs.yml)
        
        This is the source repo. for the `physDBD` Python package. It allows the creation of physics-based machine learning models in `TensorFlow` for modeling stochastic reaction networks.
        
        <img src="readme_figures/fig_1.png" alt="drawing" width="800"/>
        
        ## Quickstart
        
        1. Install:
            ```
            pip install physDBD
            ```
        2. See the [example notebook](example/).
        
        3. Read the [documentation](...).
        
        ## About
        
        This repo. implements a TensorFlow package for modeling stochastic reaction networks with a dynamic PCA model. Please see [this] paper for technical details:
        ```
        XXX
        ```
        The original implementation in the paper is written in Mathematica and can be found [here](https://github.com/smrfeld/physics-based-ml-reaction-networks). The Python package developed here translates these methods to `TensorFlow`.
        
        The only current supported probability distribution is the Gaussian distribution defined by PCA; more general Gaussian distributions are a work in progress.
        
        ## Requirements
        
        * `TensorFlow 2.5.0` or later. *Note: later versions not tested.*
        * `Python 3.7.4` or later.
        
        ## Installation
        
        Use `pip`:
        ```
        pip install physDBD
        ```
        Alternatively, clone this repo. and use the provided `setup.py`:
        ```
        python setup.py install
        ```
        
        ## Documentation
        
        See the dedicatdd [documentation page](https://smrfeld.github.io/phys_dbd).
        
        ## Example
        
        See the notebook in the [example](example/) directory.
        
        ## Tests
        
        Tests are run using `pytest` and are located in [tests](tests/).
        
        ## Citing
        
        Please cite the following paper:
        ```
        XXX
        ```
Platform: UNKNOWN
