Metadata-Version: 2.4
Name: DSGRN
Version: 1.9.3
Summary: DSGRN (Dynamic Signatures Generated by Regulatory Networks)
Author-Email: Marcio Gameiro <marciogameiro@gmail.com>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Project-URL: Repository, https://github.com/marciogameiro/DSGRN
Requires-Python: >=3.10
Requires-Dist: graphviz>=0.20
Requires-Dist: matplotlib>=3.6.0
Requires-Dist: numpy>=1.23.0
Requires-Dist: networkx>=3.2.1
Requires-Dist: pychomp2>=1.0.4
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Provides-Extra: signatures
Requires-Dist: progressbar2>=4.4.0; extra == "signatures"
Requires-Dist: mpi4py>=4.0.0; extra == "signatures"
Description-Content-Type: text/markdown

# DSGRN
Dynamic Signatures Generated by Regulatory Networks

[![Build Status](https://travis-ci.org/shaunharker/DSGRN.svg?branch=master)](https://travis-ci.org/shaunharker/DSGRN) [![Coverage Status](https://coveralls.io/repos/github/shaunharker/DSGRN/badge.svg?branch=master)](https://coveralls.io/github/shaunharker/DSGRN?branch=master)[![DOI](https://zenodo.org/badge/35697682.svg)](https://zenodo.org/badge/latestdoi/35697682)

[Documentation](http://dsgrn.readthedocs.io/en/latest/)

## Overview 

This project uses computational dynamics to produce
dynamical surveys of switching models of regulatory 
networks. The resulting data is stored into a database
which can then be queried for further research.


## Installation

To get the latest tagged version from the PyPI repository:

```bash
pip install DSGRN
```

To uninstall:

```bash
pip uninstall DSGRN
```

## Installing from source

Assuming you have a C++ compiler and the Python 3 dependencies installed on your system, you can install from source with the command:

	pip install --force-reinstall --no-deps --no-cache-dir git+https://github.com/marciogameiro/DSGRN.git

Alternatively, you can clone the GitHub repository and install with:

	git clone https://github.com/marciogameiro/DSGRN.git
	cd DSGRN
	./install.sh

## Usage

To check if DSGRN is installed do

```python,test
import DSGRN

network = DSGRN.Network("X1 : (~X1)(X2)\n X2 : (X1)(~X2)")
DSGRN.DrawGraph(network)
```

This should plot the network

<img src="network.png" width="120">

See the [GettingStarted.ipynb](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/GettingStarted.ipynb) jupyter notebook in the [Tutorials](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/) folder for the basic usage of DSGRN.

## Examples and Documentation

See [Tutorials](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/) folder for examples.

Also see the [documentation](https://shaunharker.github.io/DSGRN/).
