Page MenuHomec4science

README.md
No OneTemporary

File Metadata

Created
Fri, Mar 29, 16:23

README.md

# RROMPy -- Rational Reduced Order Modeling in Python
=====================================================
Module for the solution and rational model order reduction of parametric PDE-based problem. Coded in Python 3.
## Prerequisites
**RROMPy** requires
* **numpy** and **scipy**;
* **fenics** and **mshr**;
* **matplotlib**;
* **scikit-learn**;
* and other standard Python3 modules (**os**, **typing**, **time**, **datetime**, **abc**, **pickle**, **traceback**, **itertools**, ...).
Testing requires
* **pytest**.
### Fenics
Most of the high fidelity problem engines already provided rely on [FEniCS](http://fenicsproject.org/). If you do not have FEniCS installed, you may want to create an [Anaconda3/Miniconda3](http://anaconda.org/) environment using the command
```
conda create -n fenicsenv -c conda-forge pytest pytest-runner scipy matplotlib scikit-learn fenics=2019.1.0=py38_9 mshr=2019.1.0=py38hf9f41d3_3
```
This will create an environment where FEniCS (and all other required modules) can be used. In order to use FEniCS, the environment must be activated through
```
conda activate fenicsenv
```
See the [Anaconda documentation](http://docs.conda.io/) for more information.
### Fenics and mshr versions
More recent versions of FEniCS and mshr may be preferred, but one should be careful of [inconsistent dependencies](http://fenicsproject.discourse.group/t/anaconda-installation-of-fenics-and-mshr/2062/5). If the following code snippet runs successfully, then your environment *should* have been created correctly:
```
from mshr import *
```
## Installing
Clone the repository
```
git clone http://c4science.ch/source/RROMPy.git
```
enter the main folder and install the package by typing
```
python setup.py install
```
The installation can be tested with
```
python setup.py test
```
## License
This project is licensed under the GNU GENERAL PUBLIC LICENSE license - see the !!LICENSE!! file for details.
## Acknowledgments
Part of the funding that made this module possible has been provided by the Swiss National Science Foundation through the FNS Research Project 182236.

Event Timeline