added example, readme, website corrections

.gitignore

@@ -1,3 +1,10 @@
+logs/
+cache*/
+cache/
+trained/
+mathematica/
+*.egg-info/
+dist/
 saved_models/
 .ipynb_checkpoints/
 playground/*.txt

README.md

@@ -1,5 +1,7 @@
 # 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"/>
@@ -42,6 +44,8 @@ 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.

docs/index.rst

@@ -0,0 +1,96 @@
+.. physDBD documentation master file, created by
+   sphinx-quickstart on Thu Jun 17 14:20:48 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Physics-based dynamic PCA models in TensorFlow
+==============================================
+
+.. image:: figures/fig_1.png
+  :width: 800
+  :alt: Reaction model image
+
+This is the source repo. for the `physDBD Python package <https://github.com/smrfeld/phys_dbd>`_. 
+It allows the creation of physics-based machine learning models in `TensorFlow` for modeling stochastic reaction networks.
+
+Quickstart
+==========
+
+1. Install:
+
+.. code-block:: python
+
+   pip install physDBD
+    
+2. See the example notebook in the example folder of the `GitHub repo <https://github.com/smrfeld/phys_dbd>`_.
+
+3. Scan the :ref:`api_ref`.
+
+About
+=====
+
+This repo. implements a TensorFlow package for modeling stochastic reaction networks 
+with a dynamic PCA model. Please see `this <XXX>`_ 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
+============
+
+Either: use `pip`:
+
+.. code-block:: python
+
+   pip install physDBD
+
+Or alternatively, clone this `repo. from GitHub <https://github.com/smrfeld/phys_dbd>`_ and use the provided `setup.py`:
+
+.. code-block:: python
+
+   python setup.py install
+
+API Documentation
+=================
+
+See the :ref:`api_ref`.
+
+Example
+=======
+
+See the notebook in the example directory in `GitHub repo. <https://github.com/smrfeld/phys_dbd>`_
+
+Citing
+======
+
+Please cite the following paper:
+```
+XXX
+```
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+Contents
+========
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   modules.rst

docs/modules.rst

@@ -1,5 +1,7 @@
-physDBD
-=======
+.. _api_ref:
+
+API reference
+=============
 
 .. toctree::
    :maxdepth: 4