Utilities to set up and analyze Modelica simulation experiments
- Auto-generate simulation scripts,
- Browse data,
- Perform custom calculations, and
- Produce various plots and diagrams.
The figures are generated via matplotlib, which offers a rich set of plotting routines. ModelicaRes includes convenient functions to automatically pre-format and label some figures, like xy plots, Bode and Nyquist plots, and Sankey diagrams. ModelicaRes can be scripted or run from a Python interpreter with math and matrix functions from NumPy.
The easiest way to install this package is to use pip:
pip install modelicares
On Linux, it may be necessary to have root privileges:
sudo pip install modelicares
python setup.py install
Or, on Linux:
sudo python setup.py install
The matplotlibrc file in the base folder has some recommended revisions to matplotlib’s defaults. To use it, move or copy it to the working directory or matplotlib’s configuration directory. See http://matplotlib.org/users/customizing.html for details.
The main author is Kevin Davies. Improvements, bug fixes, and suggestions have been provided by Arnout Aertgeerts, Kevin Bandy, Thomas Beutlich, Martin Sjölund, Mike Tiller, and Michael Wetter.
Third-party code has been included from:
License terms and development
ModelicaRes is published under a BSD license (see LICENSE.txt). Please share any modifications you make (preferably on a Github fork from https://github.com/kdavies4/ModelicaRes) in order to help others. If you find a bug, please report it. If you have suggestions, please share them.
The following Python projects are related:
- awesim: helps run simulation experiments and organize results
- BuildingsPy: supports unit testing
- DyMat: exports Modelica simulation data to comma-separated values (CSV), Gnuplot, MATLAB®, and Network Common Data Form (netCDF)
- PyFMI: tools to work with models through the Functional Mock-Up Interface (FMI) standard
- PySimulator: elaborate GUI; supports FMI