PyMPDATA v1

PyMPDATA v1: Numba-accelerated implementation of MPDATA with examples in Python, Julia and Matlab - Published in JOSS (2022)

https://github.com/open-atmos/PyMPDATA

Science Score: 59.0%

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Keywords

advection advection-diffusion atmospheric-modelling numba numerical-integration pde-solver pypi-package python research simulation

Keywords from Contributors

atmospheric-physics nvrtc particle-system physics-simulation pint thrust graph-generation ode meshes gravitational-lenses
Last synced: 6 months ago · JSON representation

Repository

Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia, Rust and Matlab

Basic Info
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  • Stars: 30
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  • Forks: 22
  • Open Issues: 45
  • Releases: 59
Topics
advection advection-diffusion atmospheric-modelling numba numerical-integration pde-solver pypi-package python research simulation
Created about 6 years ago · Last pushed 6 months ago
Metadata Files
Readme License Zenodo

README.md

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PyMPDATA

Python 3 LLVM Linux OK macOS OK Windows OK Jupyter Maintenance OpenHub
status DOI
EU Funding PL Funding License: GPL v3

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PyPI version API docs

PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA algorithm of Smolarkiewicz et al. used in geophysical fluid dynamics and beyond for numerically solving generalised convection-diffusion PDEs in 1D, 2D and 3D structured meshes with coordinate transformations.

In short, PyMPDATA numerically solves the following equation:

$$ \partial_t (G \psi) + \nabla \cdot (Gu \psi) + \mu \Delta (G \psi) = 0 $$

where scalar field $\psi$ is referred to as the advectee, vector field u is referred to as advector, and the G factor corresponds to optional coordinate transformation. The inclusion of the Fickian diffusion term is optional and is realised through modification of the advective velocity field with MPDATA handling both the advection and diffusion (for discussion see, e.g. Smolarkiewicz and Margolin 1998, sec. 3.5, par. 4).

PyMPDATA documentation is generated via pdoc.

A [PyMPDATA] and [numba-mpi] coupler sandbox called PyMPDATA-MPI depicts how numba-mpi can be used to enable distributed memory parallelism in PyMPDATA.applications, and provide a validation of the implementation and its performance.

Dependencies and installation

To install PyMPDATA, one may use: pip install PyMPDATA (or pip install git+https://github.com/open-atmos/PyMPDATA.git to get updates beyond the latest release). PyMPDATA depends on NumPy and Numba.

Running the tests shipped with the package requires additional packages that are installed if pip is invoked with: pip install PyMPDATA[tests].

Examples (Jupyter notebooks reproducing results from literature):

PyMPDATA examples are bundled with PyMPDATA and located in the examples subfolder. They constitute a separate PyMPDATA_examples Python package which is also available at PyPI. The examples have additional dependencies listed in PyMPDATA_examples package setup.py file. Running the examples requires the PyMPDATA_examples package to be installed. Since the examples package includes Jupyter notebooks (and their execution requires write access), the suggested install and launch steps are: git clone https://github.com/open-atmos/PyMPDATA-examples.git cd PyMPDATA-examples pip install -e . jupyter-notebook Alternatively, one can also install the examples package from pypi.org by using pip install PyMPDATA-examples.

Package structure and API:

The key classes constituting the PyMPDATA interface are summarised below.

Options class

The Options class groups both algorithm variant options as well as some implementation-related flags.

Arakawa-C grid layer

In PyMPDATA, the solution domain is assumed to extend from the first cell's boundary to the last cell's boundary (thus the first scalar field value is at $[\Delta x/2, \Delta y/2]$. The ScalarField and VectorField classes implement the Arakawa-C staggered grid logic.

Boundary conditions

Boundary conditions are implemented as classes defined in BoundaryCondition.

Stepper

The logic of the MPDATA iterative solver is represented in PyMPDATA by the Stepper class.

Solver

Instances of the Solver class are used to control the integration and access solution data. During instantiation, additional memory required by the solver is allocated according to the options provided.

Contributing, reporting issues, seeking support

Submitting new code to the project, please preferably use GitHub pull requests (or the PyMPDATA-examples PR site if working on examples) - it helps to keep record of code authorship, track and archive the code review workflow and allows to benefit from the continuous integration setup which automates execution of tests with the newly added code.

As of now, the copyright to the entire PyMPDATA codebase is with the Jagiellonian University (2019-2023) and AGH University of Krakow (2023 onwards) - work places of the main maintainer. Code contributions are assumed to imply transfer of copyright. Should there be a need to make an exception, please indicate it when creating a pull request or contributing code in any other way. In any case, the license of the contributed code must be compatible with GPL v3.

Developing the code, we follow The Way of Python and the KISS principle. The codebase has greatly benefited from PyCharm code inspections and Pylint code analysis (Pylint checks are part of the CI workflows).

Issues regarding any incorrect, unintuitive or undocumented bahaviour of PyMPDATA are best to be reported on the GitHub issue tracker. Feature requests are recorded in the "Ideas..." PyMPDATA wiki page.

We encourage to use the GitHub Discussions feature (rather than the issue tracker) for seeking support in understanding, using and extending PyMPDATA code.

Please use the PyMPDATA issue-tracking and dicsussion infrastructure for PyMPDATA-examples as well. We look forward to your contributions and feedback.

Credits:

Development of PyMPDATA was supported by the EU through a grant of the Foundation for Polish Science (POIR.04.04.00-00-5E1C/18) and by the Polish National Science Centre (grant no. 2020/39/D/ST10/01220)

copyright: Jagiellonian University (2019-2023) & AGH University of Krakow (2023 onwards)
licence: GPL v3

Owner

  • Name: open-atmos
  • Login: open-atmos
  • Kind: organization

GitHub Events

Total
  • Fork event: 3
  • Create event: 89
  • Commit comment event: 2
  • Release event: 21
  • Issues event: 46
  • Watch event: 6
  • Delete event: 80
  • Member event: 2
  • Issue comment event: 181
  • Push event: 154
  • Pull request review comment event: 36
  • Pull request event: 198
  • Pull request review event: 72
Last Year
  • Fork event: 3
  • Create event: 89
  • Commit comment event: 2
  • Release event: 21
  • Issues event: 46
  • Watch event: 6
  • Delete event: 80
  • Member event: 2
  • Issue comment event: 181
  • Push event: 154
  • Pull request review comment event: 36
  • Pull request event: 198
  • Pull request review event: 72

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 1,113
  • Total Committers: 18
  • Avg Commits per committer: 61.833
  • Development Distribution Score (DDS): 0.266
Past Year
  • Commits: 94
  • Committers: 9
  • Avg Commits per committer: 10.444
  • Development Distribution Score (DDS): 0.436
Top Committers
Name Email Commits
Sylwester Arabas s****s@u****l 817
Michael o****l@g****m 165
prbartman p****n@g****m 46
Paweł Magnuszewski 4****u 17
Maciej Manna m****a@g****m 15
Kacper Derlatka 5****r 11
Michael Olesik 4****6 9
Sfonxu 8****u 9
AgnieszkaZaba 5****a 8
dependabot[bot] 4****] 4
kruci-no j****b@g****m 3
Arfon Smith a****n 2
NeKo2903 9****3 2
Caastlefox 1****x 1
Konrad Pawlik 7****1 1
Sanky 7****e 1
The Codacy Badger b****r@c****m 1
Wojciech Neuman 6****n 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 78
  • Total pull requests: 330
  • Average time to close issues: about 1 year
  • Average time to close pull requests: 11 days
  • Total issue authors: 9
  • Total pull request authors: 16
  • Average comments per issue: 3.88
  • Average comments per pull request: 1.16
  • Merged pull requests: 191
  • Bot issues: 0
  • Bot pull requests: 96
Past Year
  • Issues: 36
  • Pull requests: 210
  • Average time to close issues: 15 days
  • Average time to close pull requests: 10 days
  • Issue authors: 5
  • Pull request authors: 10
  • Average comments per issue: 0.39
  • Average comments per pull request: 1.27
  • Merged pull requests: 93
  • Bot issues: 0
  • Bot pull requests: 87
Top Authors
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Pull Request Authors
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Top Labels
Issue Labels
no-issue-activity (18) no-activity (7) MPI (6) student-project (2) good first issue (1) regression (1)
Pull Request Labels
dependencies (96) python (81) no-pr-activity (2) no-activity (2) MPI (2) documentation (2)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 13,947 last-month
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 108
  • Total maintainers: 3
pypi.org: pympdata

Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia, Rust and Matlab

  • Versions: 56
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 13,812 Last month
Rankings
Downloads: 2.7%
Dependent packages count: 3.2%
Average: 10.5%
Forks count: 11.0%
Stargazers count: 13.4%
Dependent repos count: 22.1%
Maintainers (3)
Last synced: 6 months ago
pypi.org: pympdata-examples

PyMPDATA usage examples reproducing results from literature and depicting how to use PyMPDATA in Python from Jupyter notebooks

  • Versions: 52
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 135 Last month
Rankings
Dependent packages count: 7.3%
Forks count: 11.0%
Stargazers count: 13.4%
Average: 15.5%
Dependent repos count: 22.1%
Downloads: 23.6%
Maintainers (3)
Last synced: 6 months ago

Dependencies

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