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 a grant from the the National Science Foundation.
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 belson17 at gmail.com 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://packages.python.org/modred