Skip to main content

Shenfun -- Automated Spectral-Galerkin framework

Project description

https://api.codacy.com/project/badge/Grade/dc9c6e8e33c34382b76d38916852b36b https://travis-ci.org/spectralDNS/shenfun.svg?branch=master https://circleci.com/gh/spectralDNS/shenfun.svg?style=svg https://codecov.io/gh/spectralDNS/shenfun/branch/master/graph/badge.svg https://anaconda.org/conda-forge/shenfun/badges/platforms.svg

Try it in a jupyter hub using Binder

binder

Description

Shenfun is a high performance computing platform for solving partial differential equations (PDEs) by the spectral Galerkin method. The user interface to shenfun is very similar to FEniCS, but applications are limited to multidimensional tensor product grids. The code is parallelized with MPI through the mpi4py-fft package.

Shenfun enables fast development of efficient and accurate PDE solvers (spectral order and accuracy), in the comfortable high-level Python language. The spectral accuracy is ensured by using high-order global orthogonal basis functions (Fourier, Legendre, Chebyshev, Laguerre and Hermite), as opposed to finite element codes that are using low-order local basis functions. Efficiency is ensured through vectorization (Numpy), parallelization (mpi4py) and by moving critical routines to Cython or Numba. Shenfun has been used to run turbulence simulations (Direct Numerical Simulations) on thousands of processors on high-performance supercomputers, see the spectralDNS repository.

The demo folder contains several examples for the Poisson, Helmholtz and Biharmonic equations. For extended documentation and installation instructions see ReadTheDocs or this paper. Note that, since the publication of that paper, shenfun has been further developed with the possibility to use two non-periodic directions (see biharmonic demo). Furthermore, equations may be solved coupled and implicit (see MixedPoisson.py).

Installation

Shenfun can be installed using either pip or conda, see installation chapter on readthedocs.

Dependencies

Contact

For comments, issues, bug-reports and requests, please use the issue tracker of the current repository, or see How to contribute? at readthedocs. Otherwise the principal author can be reached at:

Mikael Mortensen
mikaem at math.uio.no
http://folk.uio.no/mikaem/
Department of Mathematics
University of Oslo
Norway

Project details


Download files

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

Source Distribution

shenfun-2.0.6.tar.gz (111.5 kB view details)

Uploaded Source

File details

Details for the file shenfun-2.0.6.tar.gz.

File metadata

  • Download URL: shenfun-2.0.6.tar.gz
  • Upload date:
  • Size: 111.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.2

File hashes

Hashes for shenfun-2.0.6.tar.gz
Algorithm Hash digest
SHA256 482310bb80409de0393c77a1e525cdfdf31ed21eb36cdb5bf850a854f8755d85
MD5 e49ba6301da2ffdebd476e9e2df85ac2
BLAKE2b-256 8e9dfc07a4fc34001355705266a056491bfe7d799492511535c4867a5d730fe3

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