FMI-compliant Model Estimation in Python

Description

modestpy facilitates parameter estimation in models compliant with Functional Mock-up Interface.

Features:

combination of global and local search methods (genetic algorithm, pattern search, truncated Newton method, L-BFGS-B, sequential least squares),
suitable also for non-continuous and non-differentiable models,
scalable to multiple cores (genetic algorithm from modestga),
Python 3.

Installation with pip (recommended)

It is now possible install ModestPy with a single command:

pip install modestpy

Alternatively:

pip install https://github.com/sdu-cfei/modest-py/archive/master.zip

Installation with conda

Conda is installation is less frequently tested, but should work:

conda config --add channels conda-forge
conda install modestpy

Test your installation

The unit tests will work only if you installed modestpy with conda or cloned the project from GitHub. To run tests:

>>> from modestpy.test import run
>>> run.tests()

cd <project_directory>
python ./bin/test.py

Usage

Users are supposed to call only the high level API included in modestpy.Estimation. The API is fully discussed in the docs. You can also check out this simple example. The basic usage is as follows:

from modestpy import Estimation

if __name__ == "__main__":
    session = Estimation(workdir, fmu_path, inp, known, est, ideal)
    estimates = session.estimate()
    err, res = session.validate()

More control is possible via optional arguments, as discussed in the documentation.

The if __name__ == "__main__": wrapper is needed on Windows, because modestpy relies on multiprocessing. You can find more explanation on why this is needed here.

modestpy automatically saves results in the working directory including csv files with estimates and some useful plots, e.g.:

Error evolution in combined GA+PS estimation (dots represent switch from GA to PS):
Visualization of GA evolution:
Scatter matrix plot for interdependencies between parameters:

Cite

To cite ModestPy, please use:

K. Arendt, M. Jradi, M. Wetter, C.T. Veje, ModestPy: An Open-Source Python Tool for Parameter Estimation in Functional Mock-up Units, Proceedings of the American Modelica Conference 2018, Cambridge, MA, USA, October 9-10, 2018.

The preprint version of the conference paper presenting ModestPy is available here. The paper was based on v.0.0.8.

License

This code is licensed under BSD 2-clause license. See LICENSE file in the project root for license terms.

shjiang/modest-py