Package: pyspod
PySPOD: A parallel (distributed) Python SPOD packageWhat do we implement?In this package we implement two versions of SPOD, both available asparallel and distributed(i.e. they can run on multiple cores/nodes on large-scale HPC machines) viampi4py:spod_standard: this is thebatchalgorithm as described in(Schmidt and Towne, 2019).spod_streaming: that is thestreamingalgorithm 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 $\phi$ and coefficientsa, as explained in(Chu and Schmidt, 2021)and(Nekkanti and Schmidt, 2021). The library comes with a package to emulating the reduced space, that is to forecasting the time coefficients using neural networks, as described inLario et al., 2022.To see how to use thePySPODpackage, you can look at theTutorials.For additional information, you can also consult the PySPOD website:http://www.mathexlab.com/PySPOD/.How to cite this workCurrent reference to the PySPOD library is:@article{mengaldo2021pyspod,title={Pyspod:Apythonpackageforspectralproperorthogonaldecomposition(spod)}author={Mengaldo,GianmarcoandMaulik,Romit},journal={JournalofOpenSourceSoftware},volume={6},number={60},pages={2862},year={2021}}What data can we apply SPOD to?SPOD can be applied towide-sense stationary data. Examples of these arise in different fields, includingfluidmechanics, andweatherandclimate, among others.How do I install the library?If you want to download and install the latest version frommain:download the libraryfrom the top directory of PySPOD, typepython3setup.pyinstallTo allow for parallel capabilities, you need to have installed an MPI distribution in your machine. Currently MPI distributions tested areOpen MPI, andMpich. Note that the library will still work inserial(no parallel capabilities), if youdo not have MPI.Recent works withPySPODPlease,contact meif you used PySPOD for a publication and you want it to be advertised here.A. Lario, R. Maulik, G. Rozza, G. Mengaldo,Neural-Network learning of SPOD latent spaceAuthors and contributorsPySPODis currently developed and mantained byG. 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)How to contributeContributions 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 branchfix/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 usby email for further information or questions aboutPySPODor ways on how to contribute.LicenseSee theLICENSEfile for license rights and limitations (MIT).