FluidDyn: a Python open-source framework for research and teaching in fluid dynamics

TL;DR

FluidDyn is an open-source Python framework that integrates tools for fluid dynamics research and education, supporting experiments, simulations, and data analysis with a focus on modularity, efficiency, and community practices.

Contribution

It introduces a comprehensive, modular Python framework for fluid dynamics that combines experimental, simulation, and data processing tools, promoting open science and code reuse.

Findings

Provides a set of Python packages for fluid dynamics research and teaching.

Demonstrates effective use of open-source practices in scientific computing.

Ensures high performance through C++, Cython, and Pythran integration.

Abstract

FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers, Mohanan et al. (2018a,b). With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community. The Mercurial repositories are available in Bitbucket (https://bitbucket.org/fluiddyn/). All codes are documented using Sphinx and Read the Docs, and tested with continuous integration run on Bitbucket, Pipelines and Travis. To improve the reuse potential, the codes are as modular as possible, leveraging the simple object-oriented programming model of Python. All codes are also written to be highly efficient, using C++, Cython and Pythran to speedup the performance of critical functions.