PySPOD
PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD) - Published in JOSS (2021)
Science Score: 59.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 14 DOI reference(s) in README -
✓Academic publication links
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✓Committers with academic emails
2 of 10 committers (20.0%) from academic institutions -
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.2%) to scientific vocabulary
Keywords
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Scientific Fields
Repository
A Python package for spectral proper orthogonal decomposition (SPOD).
Basic Info
- Host: GitHub
- Owner: MathEXLab
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://mathexlab.github.io/PySPOD/
- Size: 152 MB
Statistics
- Stars: 109
- Watchers: 5
- Forks: 35
- Open Issues: 10
- Releases: 6
Topics
Metadata Files
README.md
PySPOD: A parallel (distributed) Python SPOD package
What do we implement?
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 $\phi$ and coefficients a, 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 in Lario et al., 2022.
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/.
How to cite this work
Current references to the PySPOD library is:
bash
@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}
}
bash
@article{lario2022neural,
title={Neural-network learning of SPOD latent dynamics},
author={Lario, Andrea and Maulik, Romit and Schmidt, Oliver T and Rozza, Gianluigi and Mengaldo, Gianmarco},
journal={Journal of Computational Physics},
volume={468},
pages={111475},
year={2022},
publisher={Elsevier}
}
bash
@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}
}
What data can we apply SPOD to?
SPOD can be applied to wide-sense stationary data. Examples of these arise in different fields, including fluidmechanics, and weather and climate, among others.
How do I install the library?
If you want to download and install the latest version from main:
- download the library
- from the top directory of PySPOD, type
bash
python3 setup.py install
To 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.
Recent works with PySPOD
Please, contact me if 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 space
Authors and contributors
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)
How to contribute
Contributions improving code and documentation, as well as suggestions about new features are more than welcome!
The guidelines to contribute are as follows:
1. open a new issue describing the bug you intend to fix or the feature you want to add.
2. fork the project and open your own branch related to the issue you just opened, and call the branch fix/name-of-the-issue if it is a bug fix, or feature/name-of-the-issue if you are adding a feature.
3. ensure to use 4 spaces for formatting the code.
4. if you add a feature, it should be accompanied by relevant tests to ensure it functions correctly, while the code continue to be developed.
5. commit your changes with a self-explanatory commit message.
6. 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.
License
See the LICENSE file for license rights and limitations (MIT).
Owner
- Name: MathEXLab
- Login: MathEXLab
- Kind: organization
- Location: Singapore
- Repositories: 2
- Profile: https://github.com/MathEXLab
Mathematical Engineering and Computational Laboratory
GitHub Events
Total
- Issues event: 1
- Watch event: 8
- Delete event: 3
- Issue comment event: 8
- Push event: 1
- Pull request review event: 2
- Pull request event: 6
- Fork event: 2
- Create event: 3
Last Year
- Issues event: 1
- Watch event: 8
- Delete event: 3
- Issue comment event: 8
- Push event: 1
- Pull request review event: 2
- Pull request event: 6
- Fork event: 2
- Create event: 3
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| mengaldo | g****o@g****m | 410 |
| Marcin Rogowski | m****i@k****a | 115 |
| Lisandro Dalcin | d****l@g****m | 12 |
| andrealario | a****o@g****m | 7 |
| dependabot[bot] | 4****] | 6 |
| Romit Maulik | r****k@a****v | 5 |
| Kyle Niemeyer | k****r@f****m | 2 |
| Gianmarco Mengaldo | g****n@G****l | 2 |
| Alessandro Luongo | 2****a | 1 |
| Andrea Lario | a****o@s****t | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 25
- Total pull requests: 32
- Average time to close issues: 3 months
- Average time to close pull requests: 19 days
- Total issue authors: 9
- Total pull request authors: 11
- Average comments per issue: 2.96
- Average comments per pull request: 0.78
- Merged pull requests: 29
- Bot issues: 0
- Bot pull requests: 6
Past Year
- Issues: 4
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: about 2 hours
- Issue authors: 3
- Pull request authors: 3
- Average comments per issue: 3.0
- Average comments per pull request: 1.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- jdmoorman (9)
- mengaldo (7)
- FrankFrank9 (3)
- MemorXuxu (1)
- BinglinW (1)
- bva99 (1)
- Joao-L-S-Almeida (1)
- Lemonade0007 (1)
- nish-ant (1)
Pull Request Authors
- mrogowski (10)
- dependabot[bot] (10)
- mengaldo (7)
- dalcinl (4)
- Romit-Maulik (3)
- Doublonmousse (2)
- hweifluids (2)
- andrealario (2)
- cyclerate (1)
- kyleniemeyer (1)
- Scinawa (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 64 last-month
- Total dependent packages: 1
- Total dependent repositories: 1
- Total versions: 8
- Total maintainers: 2
pypi.org: pyspod
Python Spectral Proper Orthogonal Decomposition
- Homepage: https://github.com/MathEXLab/PySPOD
- Documentation: https://pyspod.readthedocs.io/
- License: MIT
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Latest release: 2.0.0
published over 2 years ago
Rankings
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v3 composite
- mpi4py/setup-mpi v1 composite
- actions/checkout v4 composite
- actions/download-artifact v3 composite
- actions/setup-python v4 composite
- actions/upload-artifact v3 composite
- pypa/gh-action-pypi-publish release/v1 composite