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

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-2.0.1.tar.gz (107.1 kB view details)

Uploaded Source

File details

Details for the file shenfun-2.0.1.tar.gz.

File metadata

  • Download URL: shenfun-2.0.1.tar.gz
  • Upload date:
  • Size: 107.1 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/3.7.1

File hashes

Hashes for shenfun-2.0.1.tar.gz
Algorithm Hash digest
SHA256 dffbf541693ca338bd6bba033efd8e09a7b4ec6c1be60791b831b6cf2152dd26
MD5 5c45b52b5eb1a90433d47d78561c18d4
BLAKE2b-256 aecf43d5154a65b4c7bb17980219b750bfc161fe2d3ddf7c017c57c17733287b

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