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 grants from 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
--------------
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).
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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