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https://github.com/sdu-cfei/modest-py

FMI-compliant Model Estimation in Python
https://github.com/sdu-cfei/modest-py

fmi fmu genetic-algorithm optimization parameter-estimation pattern-search

Last synced: 3 months ago
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FMI-compliant Model Estimation in Python

Host: GitHub
URL: https://github.com/sdu-cfei/modest-py
Owner: sdu-cfei
License: bsd-2-clause
Created: 2017-06-16T01:07:15.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2022-06-22T19:24:29.000Z (over 2 years ago)
Last Synced: 2024-07-07T23:23:06.646Z (4 months ago)
Topics: fmi, fmu, genetic-algorithm, optimization, parameter-estimation, pattern-search
Language: Python
Homepage:
Size: 9.05 MB
Stars: 47
Watchers: 10
Forks: 11
Open Issues: 3
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES.txt
- License: LICENSE

Awesome Lists containing this project

awesome-fmi - modestpy - Python package for parameter estimation in FMUs. [BSD] (FMI 2 / Libraries)

README

FMI-compliant Model Estimation in Python
========================================

.. figure:: /docs/img/modest-logo.png
:alt: modestpy

.. figure:: https://github.com/sdu-cfei/modest-py/actions/workflows/python-package.yml/badge.svg?branch=master
:alt: unit_test_status
:target: https://github.com/sdu-cfei/modest-py/actions/workflows/python-package.yml

.. figure:: https://img.shields.io/pypi/dm/modestpy.svg
:alt: downloads
:target: https://pypistats.org/packages/modestpy

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.

**Due to time constraints, ModestPy is no longer actively developed. The last system known to work well was Ubuntu 18.04.**
Unit tests in GitHub Actions are run on Ubuntu 18.04 and Python 3.6/3.7.
It does not mean it will not work on other systems, but it is not guaranteed.
Use Docker (as described below) if you want to run ModestPy on a tested platform.

Installation with pip (recommended)
-----------------------------------

It is now possible to 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

Docker
------------

**Due to time constraints, Modestpy is no longer actively developed.
The last system known to work well was Ubuntu 18.04.**
If you encounter any issues with running ModestPy on your system (e.g. some libs missing), try using Docker.

I prepared a ``Dockerfile`` and some initial ``make`` commands:

- ``make build`` - build an image with ModestPy, based on Ubuntu 18.04 (tag = ``modestpy``)
- ``make run`` - run the container (name = ``modestpy_container``)
- ``make test`` - run unit tests in the running container and print output to terminal
- ``make bash`` - run Bash in the running container

Most likely you will like to modify ``Dockerfile`` and ``Makefile`` to your needs, e.g. by adding bind volumes with your FMUs.

Test your installation
----------------------

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

.. code:: python

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

cd
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:

.. code:: python

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.:

1) Error evolution in combined GA+PS estimation (dots represent switch
from GA to PS): |Error-evolution|

2) Visualization of GA evolution: |GA-evolution|

3) Scatter matrix plot for interdependencies between parameters:
|Intedependencies|

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.