pySDC 3.0

A Python implementation of spectral deferred correction methods and the likes

The pySDC project is a Python implementation of the spectral deferred correction (SDC) approach and its flavors, esp. the multilevel extension MLSDC and PFASST. It is intended for rapid prototyping and educational purposes. New ideas like e.g. sweepers or predictors can be tested and first toy problems can be easily implemented.

Features

• Variants of SDC: explicit, implicit, IMEX, multi-implicit, Verlet, multi-level, diagonal, multi-step

• Variants of PFASST: virtual parallel or MPI-based parallel, classical of multigrid perspective

• 7 tutorials: from setting up a first collocation problem to SDC, PFASST and advanced topics

• Projects: many documented projects with defined and tested outcomes

• Many different examples, collocation types, data types already implemented

• Works with PETSc through petsc4py and FEniCS

• Continuous integration via Travis-CI

• Fully compatible with 3.6 (or higher)

Getting started

The code is hosted on GitHub, see https://github.com/Parallel-in-Time/pySDC, and PyPI, see https://pypi.python.org/pypi/pySDC. Either use pip install pySDC to get the latest stable release including the core dependencies or check out the code on Github. Note that using pip install pySDC or python setup.py install will only install the core dependencies, omitting mpi4py and petsc4py (see below). All package requirements are listed in the files requirements.txt .

To check your installation, run

nosetests -v pySDC/tests

You may need to update your PYTHONPATH by running

export PYTHONPATH=$PYTHONPATH:../../..

in particular if you want to run any of the playgrounds, projects or tutorials. All import statements there assume that the pySDC’s base directory is part of PYTHONPATH.

Note: When installing both mpi4py and petsc4py, make sure they use the same MPI installation (e.g. MPICH3). You can achieve this e.g. by using the Anaconda distribution of Python and then run

conda install -c conda-forge mpich petsc4py mpi4py

Most of the code is tested automatically using Travis-CI, so a working version of the installation process can always be found in the install-block of the .travis.yml file.

For more details on pySDC, check out http://www.parallel-in-time.org/pySDC.