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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:

1. Copy of the entire output of the tests or installation
2. Python version (python -V)
3. Numpy version (python -c 'import numpy; print numpy.__version__')
4. Your operating system

The documentation is available at: http://packages.python.org/modred