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Compute modal decompositions and reduced-order models, easily, efficiently, and in parallel.

Project description

Welcome to the modred library!
--------------

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 the National Science Foundation (NSF)
and the Air Force Office of Scientific Research (AFOSR).

Installation
--------------

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

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Source Distribution

modred-2.0.0.tar.gz (82.6 kB view hashes)

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