July 28, 2023: For Version 5.3 a lot of pull requests got merged, thanks to @brownbaerchen, @tlunet, @lisawim, @ikrom96git for all the contributions. Besides the usual bugfixing and polishing,
pySDCnow comes with linear multistep methods, classical Runge Kutta methods, DAE sweepers, and more/improved projects. We have second-order SDC on board, the brand-new switch estimator, a testing ground for compression with libpressio, and more.
March 24, 2023: Version 5.2 is out and improves the code in multiple directions. The problem class can now define their parameters explicitly with the constructor, and not with a dictionary.
paramsis now a property of the problem class, that returns the problem parameters into a dictionary, and parameters are also automatically stored as problem attribute using the
_makeAttributeAndRegistermethod, allowing to define readonly paramters (warning: not backward compatible). The resilience project now comes with the quench problem, resulting a collaboration between @brownbaerchen and @eschnaubelt during the TIME-X Apps Hackathon at USI Lugano. Many of the other changes under the hood prepare
pySDCfor Version 6.
January 23, 2023: Version 5.1 brings a lot of changes to the documentation, both on Github and on the website. We revived the codecov connection and started the OpenSFF Best Practices guide. The hook classes and the way they are handled has changed (without breaking the API, hooray), the battery model got quite an update and adaptivity plays a more prominent role now. Thanks to @brownbaerchen, @tlunet, @lisawim!
October 7, 2022: Version 5 comes with many changes, both visible and invisible ones. Some of those break the existing API, but if you are using tests, you should be fine. Major changes include:
New convergence controllers: Checking whether a step has converged can be tricky, so we made separate modules out of these checks. This makes features like adaptivity easier to implement. Also, the controllers have been streamlined a bit to make them more readable/digestible. Thanks to @brownbaerchen!
Adaptivity and error estimators: SDC now comes with adaptivity and error estimation, leveraging the new convergence controllers out of the box. Thanks to @brownbaerchen!
New collocation classes: We completely rewrote the way collocation nodes and weights are computed. It is now faster, more reliable, shorter, better. But: this breaks the API, since the old collocation classes do not exist anymore. The projects, tutorials, tests and most of the playgrounds have been adapted, so have a look over there to see what to change. Thanks to @tlunet!
New projects: Resilience and energy grid simulations are ready to play with and are waiting for more ideas! We used this effort to condense and clean up the problem classes a bit, reducing the number of files and classes with only marginal differences significantly. This could potentially break your code, too, if you rely on any of those affected ones. Thanks to @brownbaerchen and @lisawim!
Better testing: The CI pipeline got a complete overhaul (again), now enabling simultaneous tests, faster/earlier linting, benchmarking (at least, in principal), separate environments and so on. The code is tested under Ubuntu and MacOS.
December 13, 2021: Version 4.2 brings compatibility with Python 3.9, including some code cleanup. The CI test suite seems to run faster now, since sorting out the dependencies is faster. Tested mamba, which for now makes the CI pipeline much faster. Also, the CI workflow can now run locally using act. We introduced markers for soem of the tests in preparation of distributed tests on different platforms. And finally, a LaTeX installation is no longer needed use plotting (but recommended).
August 11, 2021: Version 4.1 has some more changes under the hood, most of them with no significant impact to users. The CI pipeline has been completely rewritten, porting the code to Github Actions (away from Travis CI), to flake8 and to pytest (away from nose). One thing that may have an impact on users is that following the changes made in Version 4.0, the PETSc data structures are now much easier, removing a lot of unnecessary boilerplate code.
May 4, 2021: Long time, no see, but this major release 4.0 marks some improvements under the hood:
Rewrote ``mesh`` and ``particle`` data type: Creation of new arrays for each operation is now avoided by directly subclassing Numpy's
ndarray. Somewhat faster, definitively better, less code, future-proof, but also breaking the API. If you use
pySDCfor your project, make sure you adapt to the new data types (or don't upgrade).
Faster quadrature: Thanks to tlunet the computation of the weights is now faster and (even) more reliable. No breaking of any API here…
Bugfixing and version pushes: The code should run without (many) complaints with Python 3.6, 3.7 and potentially above. Also, the plotting routines have been adapted to work with recent versions of
This is not much (yet) and if it were not for the API changes, this would have been a minor release.
August 30, 2019: Version 3.1 adds many more examples like the nonlinear Schrödinger equation, more on Gray-Scott and in particular Allen-Cahn. Those are many implemented using the parallel FFT library mpi4pi-fft, which can now be used with
pySDC. There are now 8 tutorials, where step 7 shows the usage of three external libraries with
pySDC: mpi4py, FEniCS and petsc4py. The MPI controller has been improved after performaning a detailed performance analysis using Score-P and Extrae. Finally: first steps towards error/iteration estimators are taken, too.
February 14, 2019: Released version 3 of
pySDC. This release is accompanied by the ACM TOMS paper “pySDC – Prototyping spectral deferred corrections”. It release contains some breaking changes to the API. In detail:
Dropped Python 2 support: Starting with this version,
pySDCrelies on Python 3. Various incompabilities led to inconsistent treatment of dependencies, so that parts of the code had to use Python 2 while other relied on Python 3 or could do both. We follow A pledge to migrate to Python 3 with this decision, as most prominent dependencies of
Unified controllers: Instead of providing (and maintaining) four different controllers, this release only has one for emulated and one for MPI-based time-parallelization (
controller_MPI). This should avoid further confusion and makes the code easier to maintain. Both controllers use the multigrid perspective for the algorithm (first exchange data, than compute updates), but the classical way of determining when to stop locally (each time-step is stopped when ready, if the previous one is ready, too). The complete multigrid behavior can be restored using a flag. All included projects and tutorials have been adapted to this.
No more data types in the front-ends: The redundant use of data type specifications in the description dictionaries has been removed. Data types are now declared within each problem class (more precisely, in the header of the
__init__-method to allow inhertiance). All included projects and tutorials have been adapted to this.
Renewed FEniCS support: This release revives the deprecated FEniCS support, now requiring at least FEniCS 2018.1. The integration is tested using Travis-CI.
More consistent handling of local initial conditions: The treatment of
fhas been fixed and made consistent throughout the code.
As usual, many bugs have been discovered and fixed.
May 23, 3018: Version 2.4 adds support for petsc4py! You can now use PETSc data types (
pySDCships with DMDA for distributed structured grids) and parallel solvers right from your examples and problem classes. There is also a new tutorial (7.C) showing this in a bit more detail, including communicator splitting for parallelization in space and time. Warning: in order to get this to work you need to install petsc4py and mpi4py first! Make sure both use MPICH3 bindings. Downloading
pySDCfrom PyPI does not include these packages.
February 8, 2018: Ever got annoyed at
pySDC's incredibly slow setup phase when multiple time-steps are used? Version 2.3 changes this by copying the data structure of the first step to all other steps using the dill Package. Setup times could be reduced by 90% and more for certain problems. We also increase the speed for certain calculations, in particular for the Penning trap example.
November 7, 2017: Version 2.2 contains matrix-based versions of PFASST within the project
matrixPFASST. This involved quite a few changes in more or less unexpected places, e.g. in the multigrid controller and the transfer base class. The impact of these changes on other projects should be negligible, though.
October 25, 2017: For the 6th Workshop on Parallel-in-Time Integration
pySDChas been updated to version 2.1. It is now available on PyPI - the Python Package Index, see https://pypi.python.org/pypi/pySDC and can be installed simply by using
pip install pySDC. Naturally, this release contains a lot of bugfixes and minor improvements. Most notably, the file structure has been changed again to meet the standards for Python packaging (at least a bit).
November 24, 2016: Released version 2 of
pySDC. This release contains major changes to the code and its structure:
Complete redesign of code structure: The
pySDConly contains the core modules and classes, while
implementationscontains the actual implementations necessary to run something. This now includes separate files for all collocation classes, as well as a collection of problems, transfer classes and so on. Most examples have been ported to either
Introduction of tutorials: We added a tutorial (see below) to explain many of pySDC's features in a step-by-step fashion. We start with a simple spatial discretization and collocation formulations and move step by step to SDC, MLSDC and PFASST. All tutorials are accompanied by tests.
New all-inclusive controllers: Instead of having two PFASST controllers which could also do SDC and MLSDC (and more), we now have four generic controllers which can do all these methods, depending on the input. They are split into two by two class:
NonMPIfor real or virtual parallelisim as well as
multigridfor the standard and multigrid-like implementation of PFASST and the likes. Initialization has been simplified a lot, too.
Collocation-based coarsening As the standard PFASST libraries libpfasst and PFASST++
pySDCnow offers collocation-based coarsening, i.e. the number of collocation nodes can be reduced during coarsening. Also, time-step coarsening is in preparation, but not implemented yet.
Testing and documentation The core, implementations and plugin packages and their subpackages are fully documented using sphinx-apidoc, see below. This documentation as well as this website are generated automatically using Travis-CI. Most of the code is supported by tests, mainly realized by using the tutorial as the test routines with clearly defined results. Also, projects are accompanied by tests.
Further, minor changes:
Switched to more stable barycentric interpolation for the quadrature weights
New collocation class:
EquidistantSpline_Rightfor spline-based quadrature
Collocation tests are realized by generators and not by classes
Multi-step SDC (aka single-level PFASST) now works as expected
Reworked many of the internal structures for consistency and simplicity