In this package we implement two versions of SPOD, both available as parallel and distributed (i.e. they can run on multiple cores/nodes on large-scale HPC machines) via mpi4py:
- spod_standard: this is the batch algorithm as described in (Schmidt and Towne, 2019).
- spod_streaming: that is the streaming algorithm presented in (Schmidt and Towne, 2019).
We additionally implement the calculation of time coefficients and the reconstruction of the data, given a set of modes
To see how to use the PySPOD package, you can look at the Tutorials.
For additional information, you can also consult the PySPOD website: http://www.mathexlab.com/PySPOD/.
Current references to the PySPOD library is:
@article{rogowski2024unlocking,
title={Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package},
author={Rogowski, Marcin and Yeung, Brandon CY and Schmidt, Oliver T and Maulik, Romit and Dalcin, Lisandro and Parsani, Matteo and Mengaldo, Gianmarco},
journal={Computer Physics Communications},
pages={109246},
year={2024},
publisher={Elsevier}
}@article{mengaldo2021pyspod,
title={Pyspod: A python package for spectral proper orthogonal decomposition (spod)},
author={Mengaldo, Gianmarco and Maulik, Romit},
journal={Journal of Open Source Software},
volume={6},
number={60},
pages={2862},
year={2021}
}SPOD can be applied to wide-sense stationary data. Examples of these arise in different fields, including fluidmechanics, and weather and climate, among others.
If you want to download and install the latest version from main:
- download the library
- from the top directory of PySPOD, type
python3 setup.py installTo allow for parallel capabilities, you need to have installed an MPI distribution in your machine. Currently MPI distributions tested are Open MPI, and Mpich. Note that the library will still work in serial (no parallel capabilities), if you do not have MPI.
Please, contact me if you used PySPOD for a publication and you want it to be advertised here.
- D.J. Lusher, A. Sansica, N.D. Sandham, J. Meng, B. Siklosi, A. Hashimoto. OpenSBLI v3.0: High-fidelity multi-block transonic aerofoil CFD simulations using domain specific languages on GPUs Computer Physics Communications (2025).
- D.J. Lusher, A. Sansica, A. Hashimoto. Implicit large eddy simulations of three-dimensional turbulent transonic buffet on wide-span infinite wings Journal of Fluid Mechanics (2025).
- A. Lario, R. Maulik, G. Rozza, G. Mengaldo, Neural-Network learning of SPOD latent space, Journal of Computational Physics (2022).
PySPOD is currently developed and mantained by
- G. Mengaldo, National University of Singapore (Singapore).
Current active contributors include:
- M. Rogowski, King Abdullah University of Science and Technology (Saudi Arabia).
- L. Dalcin, King Abdullah University of Science and Technology (Saudi Arabia).
- R. Maulik, Argonne National Laboratory (US).
- A. Lario, SISSA (Italy)
Contributions improving code and documentation, as well as suggestions about new features are more than welcome!
The guidelines to contribute are as follows:
- open a new issue describing the bug you intend to fix or the feature you want to add.
- fork the project and open your own branch related to the issue you just opened, and call the branch
fix/name-of-the-issueif it is a bug fix, orfeature/name-of-the-issueif you are adding a feature. - ensure to use 4 spaces for formatting the code.
- if you add a feature, it should be accompanied by relevant tests to ensure it functions correctly, while the code continue to be developed.
- commit your changes with a self-explanatory commit message.
- push your commits and submit a pull request. Please, remember to rebase properly in order to maintain a clean, linear git history.
Contact us by email for further information or questions about PySPOD or ways on how to contribute.
See the LICENSE file for license rights and limitations (MIT).