Skip to main content

Framework for studying fluid dynamics.

Project description

Latest version Supported Python versions Documentation status Code coverage Heptapod CI Github Actions

FluidDyn project is an ecosystem of packages for research and teaching in fluid dynamics. The Python package fluiddyn contains:

  • basic utilities to manage: File I/O for some esoteric formats, publication quality figures, job submission on clusters, MPI

  • powerful classes to handle: parameters, arrays, series of files

  • simplified interfaces to calculate: FFT, spherical harmonics

and much more. It is used as a library in the other specialized packages of the FluidDyn project (in particular in fluidfft, fluidsim, fluidlab and fluidimage).

Documentation: Read the Docs, Heptapod Pages

Installation

The simplest way to install fluiddyn is by using pip:

pip install fluiddyn

Requirements

Minimum

Python (>=3.9), numpy matplotlib h5py psutil

Full functionality

h5py h5netcdf pillow imageio mpi4py scipy pyfftw (requires FFTW library), SHTns

Optional

OpenCV with Python bindings, scikit-image

Note: Detailed instructions to install the above dependencies using Anaconda / Miniconda or in a specific operating system such as Ubuntu, macOS etc. can be found here.

Tests

With an editable installation, you can run the tests with:

pytest

Metapaper and citation

If you use any of the FluidDyn packages to produce scientific articles, please cite our metapaper presenting the FluidDyn project and the fluiddyn package:

@article{fluiddyn,
doi = {10.5334/jors.237},
year = {2019},
publisher = {Ubiquity Press,  Ltd.},
volume = {7},
author = {Pierre Augier and Ashwin Vishnu Mohanan and Cyrille Bonamy},
title = {{FluidDyn}: A Python Open-Source Framework for Research and Teaching in Fluid Dynamics
    by Simulations,  Experiments and Data Processing},
journal = {Journal of Open Research Software}
}

History

The FluidDyn project started in 2015 as the evolution of two packages previously developed by Pierre Augier (CNRS researcher at LEGI, Grenoble): solveq2d (a numerical code to solve fluid equations in a periodic two-dimensional space with a pseudo-spectral method, developed at KTH, Stockholm) and fluidlab (a toolkit to do experiments, developed in the G. K. Batchelor Fluid Dynamics Laboratory at DAMTP, University of Cambridge).

Keywords and ambitions: fluid dynamics research with Python (>= 3.6), modular, object-oriented, collaborative, tested and documented, free and open-source software.

License

FluidDyn is distributed under the CeCILL-B License, a BSD compatible french license.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fluiddyn-0.7.0.tar.gz (123.1 kB view details)

Uploaded Source

Built Distribution

fluiddyn-0.7.0-py3-none-any.whl (161.6 kB view details)

Uploaded Python 3

File details

Details for the file fluiddyn-0.7.0.tar.gz.

File metadata

  • Download URL: fluiddyn-0.7.0.tar.gz
  • Upload date:
  • Size: 123.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: pdm/2.20.1 CPython/3.10.15 Linux/5.10.0-23-amd64

File hashes

Hashes for fluiddyn-0.7.0.tar.gz
Algorithm Hash digest
SHA256 144cebddfd9bf9866bf955ce98ae5cc8c0edb2147a2ff186a16edf5d57d4ef99
MD5 af5a1afb09be680d9eccf2458a2f877f
BLAKE2b-256 2aa910112bd26d649514383cac67148f0453215dcf5e7fd8f42168d34888a13c

See more details on using hashes here.

File details

Details for the file fluiddyn-0.7.0-py3-none-any.whl.

File metadata

  • Download URL: fluiddyn-0.7.0-py3-none-any.whl
  • Upload date:
  • Size: 161.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: pdm/2.20.1 CPython/3.10.15 Linux/5.10.0-23-amd64

File hashes

Hashes for fluiddyn-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 33447e1145d8b904b1854c34eef3a56ad314e30afce64323d2891901180fbb0d
MD5 edd584de24b2dd158a024ec41e658cfc
BLAKE2b-256 689d4fbbb00ae8ac2407ba4e98abcbca7e933d7a8f2e5e7eac4954955fce70bd

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page