pyMOR is a software library for building model order reduction applications with the Python programming language. All algorithms in pyMOR are formulated in terms of abstract interfaces, allowing generic implementations to work with different backends, from NumPy/SciPy to external partial differential equation solver packages.
pyMOR is licensed under BSD-2-clause. See LICENSE.txt.
If you use pyMOR for academic work, please consider citing our publication:
R. Milk, S. Rave, F. Schindler
pyMOR - Generic Algorithms and Interfaces for Model Order Reduction
SIAM J. Sci. Comput., 38(5), pp. S194--S216, 2016
pyMOR can easily be installed using Python package managers like pip. We recommend installation of pyMOR into a virtual environment to avoid dependency conflicts.
For an installation with minimal dependencies, run
pip install pymor
Since most included demo scripts require Qt bindings such as pyside6
to function,
we recommend install pyMOR with the gui
extra:
pip install 'pymor[gui]'
The following installs the latest release of pyMOR on your system with most optional dependencies:
pip install 'pymor[full]'
To obtain an environment with the exact same package versions used in our Linux continuous integration tests, you can use the requirements-ci-current.txt, file from the pyMOR repository
pip install -r requirements-ci-current.txt
pip install pymor
If you are using a stable release, you should download the file from the corresponding release branch of the repository.
There are some optional packages not included with pymor[full]
because they need additional setup on your system:
mpi4py: support of MPI distributed models and parallelization of greedy algorithms (requires MPI development headers and a C compiler):
pip install mpi4py
Slycot: dense matrix equation solvers for system-theoretic methods and H-infinity norm calculation (requires OpenBLAS headers and a Fortran compiler):
pip install slycot
Note that building Slycot might fail for the following reasons:
numpy>=2
. However,
scikit-builds's FindF2PY.cmake
will select any globally installed f2py3 executable
to generate the Fortran wrapper code.
On most systems, an older NumPy version is installed, whose
f2py will generate incorrect wrapper code for numpy>=2
.
To mitigate this issue, install numpy>=2
into your venv
and link f2py3
to f2py
its /bin
directory.pip
install pyMOR
into an existing conda environment.If you are on Linux and don't want to build Slycot yourself, you can try our experimental manylinux wheels for Slycot.
pyMOR is packaged in conda-forge and can be installed by running
conda install -c conda-forge pymor
This will install pyMOR with its core dependencies into the current active conda environment. To replicate an environment with most optional dependencies, which is also used in our continuous integration tests, you can use the conda-linux-64.lock, conda-osx-64.lock, conda-win-64.lock lock files from the pyMOR repository:
conda create -n pymorenv --file ./conda-{linux,osx,win}-64.lock
conda activate pymorenv
conda install pymor
Documentation is available online. We recommend starting with getting started, tutorials, and technical overview.
To build the documentation locally, run the following from inside the root directory of the pyMOR source tree:
make docs
This will generate HTML documentation in docs/_build/html
.
pyMOR has been designed with easy integration of external PDE solvers in mind.
We provide bindings for the following solver libraries:
MPI-compatible wrapper classes for dolfin linear algebra data structures are
shipped with pyMOR (pymor.bindings.fenics
).
For an example see pymordemos.thermalblock
, pymordemos.thermalblock_simple
.
It is tested using FEniCS version 2019.1.0.
Python bindings and pyMOR wrapper classes can be found here.
Wrapper classes for the NGSolve finite element library are shipped with pyMOR
(pymor.bindings.ngsolve
).
For an example see pymordemos.thermalblock_simple
.
It is tested using NGSolve version v6.2.2104.
A simple example for direct integration of pyMOR with a a custom solver
can be found in pymordemos.minimal_cpp_demo
.
An alternative approach is to import system matrices from file and use
scipy.sparse
-based solvers.
Please see the Developer Documentation.
Should you have any questions regarding pyMOR or wish to contribute, do not hesitate to send us an email at
main.developers@pymor.org