pyMOR is a software library for building model order reduction applications with the Python programming language. Its main focus lies on the application of reduced basis methods to parameterized partial differential equations. All algorithms in pyMOR are formulated in terms of abstract interfaces for seamless integration with external high-dimensional PDE solvers. Moreover, pure Python implementations of finite element and finite volume discretizations using the NumPy/SciPy scientific computing stack are provided for getting started quickly.
Copyright 2013-2016 pyMOR developers and contributors. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The following files contain source code originating from other open source software projects:
- docs/source/pymordocstring.py (sphinxcontrib-napoleon)
- src/pymor/la/genericsolvers.py (SciPy)
See these files for more information.
Packages for Ubuntu are available via our PPA:
sudo apt-add-repository ppa:pymor/stable sudo apt-get update sudo apt-get install python-pymor
Daily snapshots are available via the pymor/daily PPA.
Demo applications and documentation are packaged separately:
sudo apt-get install python-pymor-demos sudo apt-get install python-pymor-doc
The latter makes a pymor-demo script available, which can be used to run all installed demos.
Installation via pip
pyMOR can also easily be installed via the pip command:
pip install [–user] pymor
This will install the latest release of pyMOR on your system. If you pass the optional –user argument, pyMOR will only be installed for your local user, not requiring administrator privileges. To install the latest development version of pyMOR, execute
pip install [–user] git+https://github.com/pymor/pymor
which will require that the git version control system is installed on your system.
From time to time, the master branch of pyMOR undergoes major changes and things might break (this is usually announced on our mailing list), so you might prefer to install pyMOR from the current release branch:
pip install [–user] git+https://firstname.lastname@example.org
Release branches will always stay stable and will only receive bugfix commits after the corresponding release has been made.
Note that pyMOR depends on Cython, as well as the NumPy and SciPy packages. On all major Linux distributions, these packages can be easily installed via the distribution’s package manager. For Debian-based systems (e.g. Ubuntu), the following command should work:
sudo apt-get install cython python-pip python-numpy python-scipy
When not available on your system, pip will automatically build and install these dependencies. This, however, will in turn require a full C/C++ compiler toolchain and header files for several libraries (BLAS, etc.).
After installation of pyMOR, further optional packages will be suggested if not already installed. Some of these (PySide, matplotlib, pyopengl, mpi4py) are again most easily installed via your package manager. For Debian-based systems, try:
sudo apt-get install python-pyside python-matplotlib python-opengl python-mpi4py
Again, all these dependencies can also be installed directly via pip.
Warning: Ubuntu 16.04 currently ships broken mpi4py packages which will cause pyMOR to fail at import time. Fixed packages can be found in the pyMOR PPA.
Documentation is available online at Read the Docs or offline in the python-pymor-doc package.
To build the documentation yourself, execute
inside the root directory of the pyMOR source tree. This will generate HTML documentation in docs/_build/html.
External PDE solvers
pyMOR has been designed with easy integration of external PDE solvers in mind.
A basic approach is to use the solver only to generate high-dimensional system matrices which are then read by pyMOR from disk (pymor.discretizers.disk). Another possibility is to steer the solver via an appropriate network protocol.
Whenever possible, we recommend to recompile the solver as a Python extension module which gives pyMOR direct access to the solver without any communication overhead. A basic example using pybindgen can be found in src/pymordemos/minimal_cpp_demo. A more elaborate nonlinear example using Boost.Python can be found here. Moreover, we provide bindings for the following solver libraries:
MPI-compatible wrapper classes for dolfin linear algebra data structures are shipped with pyMOR (pymor.vectorarrays.fenics, pymor.operators.fenics). For an example see pymordemos.thermalbock, pymordemos.thermalblock_simple.
Python bindings and pyMOR wrapper classes can be found here.
dune-pymor automatically wraps dune-hdd discretizations for use with pyMOR.
Do not hesitate to contact us if you need help with the integration of your PDE solver.
Setting up an Environment for pyMOR Development
First make sure that all dependencies are installed. This can be easily achieved by first installing pyMOR with its dependencies as described above. Then uninstall the pyMOR package itself, e.g.
sudo apt-get uninstall python-pymor
pip uninstall pyMOR
Then, clone the pyMOR git repository using
git clone https://github.com/pymor/pymor $PYMOR_SOURCE_DIR cd $PYMOR_SOURCE_DIR
and, optionally, switch to the branch you are interested in, e.g.
git checkout 0.4.x
Then, add pyMOR to the search path of your Python interpreter, either by setting PYTHONPATH
or by using a .pth file:
echo “$PYMOR_SOURCE_DIR/src” > $PYTHON_ROOT/lib/python2.7/site-packages/pymor.pth
Here, PYTHON_ROOT is either ‘/usr’, ‘$HOME/.local’ or the root of your virtual environment. Finally, build the Cython extension modules as described in the next section.
Cython extension modules
pyMOR uses Cython extension modules to speed up numerical algorithms which cannot be efficiently expressed using NumPy idioms. The source files of these modules (files with extension .pyx) have to be processed by Cython into a .c-file which then must be compiled into a shared object (.so file). The whole build process is handled automatically by setup.py.
If you want to develop Cython extensions modules for pyMOR yourself, you should add your module to the ext_modules list defined in the _setup method of setup.py. Calling
python setup.py build_ext –inplace
will then build the extension module and place it into your pyMOR source tree.
pyMOR uses pytest for unit testing. To run the test suite, simply execute make test in the base directory of the pyMOR repository. This will also create a test coverage report which can be found in the htmlcov directory. Alternatively, you can run make full-test which will also enable pyflakes and pep8 checks.
All tests are contained within the src/pymortests directory and can be run individually by executing py.test src/pymortests/the_module.py.
Should you have any questions regarding pyMOR or wish to contribute, do not hesitate to contact us via our development mailing list: