A Python implementation of spectral deferred correction methods and the likes
Project description
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: classic (libpfasst-style) and multigrid, virtual parallel or MPI-based parallel
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
Continuous integration via Travis-CI
Fully compatible with Python 2.7 and 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. All package requirements are listed in the files requirements.txt (for the core dependencies) and requirements-optional.txt for the more advanced features.
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 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.
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