SiSyPHE
SiSyPHE: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency - Published in JOSS (2021)
Science Score: 95.0%
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Found 10 DOI reference(s) in README and JOSS metadata -
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Simulation of Systems of interacting mean-field Particles with High Efficiency
Basic Info
- Host: GitHub
- Owner: antoinediez
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://sisyphe.readthedocs.io
- Size: 57.2 MB
Statistics
- Stars: 16
- Watchers: 1
- Forks: 2
- Open Issues: 1
- Releases: 1
Topics
Metadata Files
README.md
Simulation of Systems of interacting mean-field Particles with High Efficiency
Please visit the website for a full documentation.
The SiSyPHE library builds on recent advances in hardware and software for the efficient simulation of large scale interacting particle systems, both on the GPU and on the CPU. The implementation is based on recent libraries originally developed for machine learning purposes to significantly accelerate tensor (array) computations, namely the PyTorch package and the KeOps library. The versatile object-oriented Python interface is well suited to the comparison of new and classical many-particle models, enabling ambitious numerical experiments and leading to novel conjectures. The SiSyPHE library speeds up both traditional Python and low-level implementations by one to three orders of magnitude for systems with up to several millions of particles.
Citation
If you use SiSyPHE in a research paper, please cite the JOSS publication :
@article{Diez2021,
doi = {10.21105/joss.03653},
url = {https://doi.org/10.21105/joss.03653},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {65},
pages = {3653},
author = {Antoine Diez},
title = {`SiSyPHE`: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency},
journal = {Journal of Open Source Software}
}
Diez, A., (2021). SiSyPHE: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency. Journal of Open Source Software, 6(65), 3653, https://doi.org/10.21105/joss.03653
Installation
Requirements
- Python 3 with packages NumPy and SciPy
- PyTorch : version>= 1.5
- PyKeops : version>= 1.5
Using pip
In a terminal, type:
pip install sisyphe
On Google Colab
The easiest way to get a working version of SiSyPHE is to use the free virtual machines provided by Google Colab.
On a new Colab notebook, navigate to Edit→Notebook Settings and select GPU from the Hardware Accelerator drop-down.
Install PyKeops with the Colab specifications first by typing
!pip install pykeops[colab]
- Install SiSyPHE by typing
!pip install sisyphe
Testing the installation
In a Python terminal, type
python
import sisyphe
sisyphe.test_sisyphe()
Contributing
Contributions to make SiSyPHE grow are warmly welcome! Examples of possible (and ongoing) developments include the following.
The implementation of new models.
The implementation of more complex boundary conditions and of models on non-flat manifolds.
An improved visualization method (currently only basic visualization functions relying on Matplotlib are implemented).
Contributions can be made by opening an issue on the GitHub repository, via a pull request or by contacting directly the author.
Author
- Antoine Diez, Imperial College London
Acknowledgments
The development of this library would not have been possible without the help of Jean Feydy, his constant support and precious advice. This project was initiated by Pierre Degond and has grown out of many discussions with him.
Owner
- Name: Antoine Diez
- Login: antoinediez
- Kind: user
- Company: Kyoto University
- Website: https://antoinediez.gitlab.io/
- Repositories: 4
- Profile: https://github.com/antoinediez
Researcher at the Kyoto University Institute for the Advanced Study of Human Biology (ASHBi)
JOSS Publication
SiSyPHE: A Python package for the Simulation of Systems of interacting mean-field Particles with High Efficiency
Authors
Department of Mathematics, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
Editor
Pierre de Buyl
Tags
GPU particles mean-field self-organization swarmingGitHub Events
Total
Last Year
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Antoine Diez | a****z@f****r | 102 |
| Daniel S. Katz | d****z@i****g | 1 |
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 4
- Total pull requests: 1
- Average time to close issues: 8 days
- Average time to close pull requests: 9 minutes
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 3.5
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- junghans (4)
Pull Request Authors
- danielskatz (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 18 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 9
- Total maintainers: 1
pypi.org: sisyphe
Simulation of Systems of interacting mean-field Particles with High Efficiency
- Homepage: https://sisyphe.readthedocs.io
- Documentation: https://sisyphe.readthedocs.io/
- License: MIT License
-
Latest release: 1.2.1
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- matplotlib >=3.4.2
- pykeops >=1.5
- scipy >=1.6.3
- sphinx-gallery >=0.9.0
- sphinxcontrib-bibtex >=2.3.0
- sphinxcontrib-httpdomain >=1.7.0
- torch >=1.8.1
- matplotlib *
- numpy *
- pykeops *
- scipy *
- torch *
- actions/checkout v2 composite
- actions/setup-python v2 composite