This is an easy-to-use and parallelized library for finding modal decompositions and reduced-order models.
Parallel implementations of the proper orthogonal decomposition (POD), balanced POD (BPOD), dynamic mode decomposition (DMD), and Petrov-Galerkin projection are provided, as well as serial implementations of the Observer Kalman filter Identification method (OKID) and the Eigensystem Realization Algorithm (ERA). modred is applicable to a wide range of problems and nearly any type of data.
For smaller and simpler datasets, there is a Matlab-like interface. For larger and more complicated datasets, you can provide modred classes with functions to interact with your data.
This work was supported by grants from the National Science Foundation (NSF) and the Air Force Office of Scientific Research (AFOSR).
To install:
[sudo] pip install modred
or, download the source code and run:
[sudo] python setup.py install
To check the installation, you can run the unit tests (parallel requires mpi4py):
python -c 'import modred.tests; modred.tests.run()' mpiexec -n 3 python -c 'import modred.tests; modred.tests.run()'
Please report failures and installation problems to [email protected] with the following information:
- Copy of the entire output of the tests or installation
- Python version (
python -V) - Numpy version (
python -c 'import numpy; print numpy.__version__') - Your operating system
The documentation is available at http://modred.readthedocs.io or can be built from source by navigating to the install directory and calling:
sphinx-build doc doc/build
Then simply open index.html in a web browser. (Note that Sphinx 1.4 or higher is required.)