py-pde
py-pde: A Python package for solving partial differential equations - Published in JOSS (2020)
Science Score: 95.0%
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Found 6 DOI reference(s) in README and JOSS metadata -
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Links to: joss.theoj.org -
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3 of 9 committers (33.3%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Repository
Python package for solving partial differential equations using finite differences.
Basic Info
- Host: GitHub
- Owner: zwicker-group
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://py-pde.readthedocs.io
- Size: 10.7 MB
Statistics
- Stars: 445
- Watchers: 11
- Forks: 61
- Open Issues: 5
- Releases: 115
Topics
Metadata Files
README.md
py-pde
py-pde is a Python package for solving partial differential equations (PDEs).
The package provides classes for grids on which scalar and tensor fields can be
defined. The associated differential operators are computed using a
numba-compiled implementation of finite differences. This allows defining,
inspecting, and solving typical PDEs that appear for instance in the study of
dynamical systems in physics. The focus of the package lies on easy usage to
explore the behavior of PDEs. However, core computations can be compiled
transparently using numba for speed.
Installation
py-pde is available on pypi, so you should be able to install it through pip:
bash
pip install py-pde
In order to have all features of the package available, you might want to install the following optional packages:
bash
pip install h5py pandas mpi4py numba-mpi
Moreover, ffmpeg needs to be installed for creating movies.
As an alternative, you can install py-pde through conda
using the conda-forge channel:
bash
conda install -c conda-forge py-pde
Installation with conda includes all dependencies of py-pde.
Usage
A simple example showing the evolution of the diffusion equation in 2d:
```python import pde
grid = pde.UnitGrid([64, 64]) # generate grid state = pde.ScalarField.random_uniform(grid) # generate initial condition
eq = pde.DiffusionPDE(diffusivity=0.1) # define the pde result = eq.solve(state, t_range=10) # solve the pde result.plot() # plot the resulting field ```
PDEs can also be specified by simply writing expressions of the evolution rate.
For instance, the
Cahn-Hilliard equation
can be implemented as
python
eq = pde.PDE({'c': 'laplace(c**3 - c - laplace(c))'})
which can be used in place of the DiffusionPDE in the example above.
More information
- Tutorial notebooks in the tutorials folder
- Examples gallery with an overview of the capabilities of the package
- The Discussions on GitHub
- Full documentation on readthedocs or as a single PDF file.
- The paper published in the Journal of Open Source Software
- Practical tutorial on pattern formation in biological cells
Owner
- Name: Zwicker Group
- Login: zwicker-group
- Kind: organization
- Location: Göttingen, Germany
- Website: www.zwickergroup.org
- Twitter: ZwickerGroup
- Repositories: 4
- Profile: https://github.com/zwicker-group
Max Planck Research Group "Theory of Biological Physics"
JOSS Publication
py-pde: A Python package for solving partial differential equations
Authors
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Editor
Juanjo Bazán
Tags
partial differential equation dynamical systems finite-difference just-in-time compilationGitHub Events
Total
- Create event: 65
- Issues event: 24
- Release event: 5
- Watch event: 38
- Delete event: 55
- Issue comment event: 21
- Push event: 99
- Pull request review event: 2
- Pull request review comment event: 2
- Pull request event: 113
- Fork event: 8
Last Year
- Create event: 65
- Issues event: 25
- Release event: 5
- Watch event: 38
- Delete event: 55
- Issue comment event: 21
- Push event: 100
- Pull request review event: 2
- Pull request review comment event: 2
- Pull request event: 113
- Fork event: 8
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| David Zwicker | d****r@d****e | 1,197 |
| Juanjo Bazán | j****n@g****m | 3 |
| evidalh | e****l@d****e | 3 |
| Daniel S. Katz | d****z@i****g | 2 |
| noah-ziethen | 5****n | 1 |
| lmenou | m****9@g****m | 1 |
| Johannes Krausser | j****r@p****e | 1 |
| Estefania Vidal | t****l@g****m | 1 |
| Diego Volpatto | d****o@e****o | 1 |
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 70
- Total pull requests: 395
- Average time to close issues: 17 days
- Average time to close pull requests: about 13 hours
- Total issue authors: 37
- Total pull request authors: 1
- Average comments per issue: 1.39
- Average comments per pull request: 0.19
- Merged pull requests: 381
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 19
- Pull requests: 128
- Average time to close issues: 13 days
- Average time to close pull requests: about 8 hours
- Issue authors: 6
- Pull request authors: 1
- Average comments per issue: 0.47
- Average comments per pull request: 0.02
- Merged pull requests: 120
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- david-zwicker (31)
- yohad (3)
- Benj-Ward (2)
- parsarahimi (1)
- ReHoss (1)
- icarosadero (1)
- qingpowuwu (1)
- kallegrens (1)
- ChengXiaoSi (1)
- hxwe (1)
- vnikoofard (1)
- OliverPaulin (1)
- tranqui (1)
- risinggard (1)
- martin-phys (1)
Pull Request Authors
- david-zwicker (395)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 5,137 last-month
- Total docker downloads: 115
-
Total dependent packages: 4
(may contain duplicates) -
Total dependent repositories: 8
(may contain duplicates) - Total versions: 154
- Total maintainers: 1
pypi.org: py-pde
Python package for solving partial differential equations
- Documentation: https://py-pde.readthedocs.io/
- License: MIT
-
Latest release: 0.45.0
published 7 months ago
Rankings
Maintainers (1)
conda-forge.org: py-pde
py-pde is a Python package for solving partial differential equations (PDEs). The package provides classes for grids on which scalar and tensor fields can be defined. The associated differential operators are computed using a numba-compiled implementation of finite differences. This allows defining, inspecting, and solving typical PDEs that appear for instance in the study of dynamical systems in physics. The focus of the package lies on easy usage to explore the behavior of PDEs. However, core computations can be compiled transparently using numba for speed.
- Homepage: https://github.com/zwicker-group/py-pde
- License: MIT
-
Latest release: 0.23.0
published about 3 years ago
Rankings
Dependencies
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