A banded matrix library for python.
This package provides a simple banded matrix library for python. It supports banded matrix-vector and matrix-matrix multiplication, converting between full and banded matrix representations, and certain linear algebra operations on banded matrices. It builds on the excellent numpy and scipy packages, which have limited support for banded matrix operations.
A banded matrix is a matrix where only the diagonal, a number of superdiagonals and a number of subdiagonals are non-zero. The well-known BLAS interface and LAPACK library for linear algebra define several banded matrix operations, and some of these, such as banded Cholesky decomposition, are wrapped in the excellent python package scipy, specifically in scipy.linalg. The bandmat package re-uses the banded matrix representation used by BLAS, LAPACK and scipy.linalg, wrapping it in a lightweight class for ease of use. See the docstring for the BandMat class for full details of the representation used.
The bandmat package provides:
- a lightweight class wrapping the LAPACK-style banded matrix representation. This class keeps track of things like bandwidths to allow a more direct coding style when working with banded matrices.
- some basic banded matrix operations not present in scipy. For example, banded matrix-vector multiplication is defined by BLAS but not wrapped by scipy, and banded matrix-matrix multiplication is not defined in BLAS or in scipy. The bandmat package contains C implementations of these operations written in cython.
- helper functions for converting between full and banded matrix representations.
- certain linear algebra operations on banded matrices, including computing the band of the inverse of a banded matrix.
Only square banded matrices are supported by this package.
Please see the file License for details of the license and warranty for bandmat.
For most purposes the simplest way to install bandmat is to use pip. For example in Debian and Ubuntu:
sudo apt-get install python-numpy python-scipy sudo pip install bandmat
The first command installs numpy and scipy from the system repository, since installing numpy and scipy using pip is generally not recommended. The second command installs the latest released version of bandmat on PyPI, together with any currently uninstalled python packages required by bandmat.
bandmat can also be installed in a virtualenv:
sudo apt-get install python-numpy python-scipy virtualenv --system-site-packages env env/bin/pip install bandmat
The latest development version of bandmat is available from a github repository (see below).
To check that bandmat is installed correctly you can run the test suite:
python -m unittest discover bandmat
See the package docstring (run import bandmat as bm; help(bm) in the python interpreter) for some examples of usage. The python script example.py also contains some simple examples of usage. To run it:
The source code is hosted in the bandmat github repository. To obtain the latest source code using git:
git clone git://github.com/MattShannon/bandmat.git
To install any currently uninstalled python packages required by bandmat:
sudo apt-get install cython python-numpy python-scipy sudo pip install -r requirements.txt
To compile the cython part of bandmat in the current directory:
python setup.py build_ext --inplace
This command must be run after every modification to the source .pyx files.
A note on setup.py
The included setup.py file operates in one of two modes depending on whether or not the file dev is present in the project root directory. In development mode (dev present, as for the github repository), the build_ext command uses cython to compile cython modules from their .pyx source, and the sdist command is modified to first use cython to compile cython modules from their .pyx source to .c files. In distribution mode (dev absent, as for source distributions such as the code on PyPI), the build_ext command uses a C compiler to directly compile cython modules from the corresponding .c files. This approach ensures that source distributions can be installed on systems without cython or with an incompatible version of cython, while ensuring that distributed .c files are always up-to-date and that the source .pyx files are used instead of .c files during development.
The author would welcome any suggestions for more elegant ways to achieve a similar effect to the approach described above!
Please use the issue tracker to submit bug reports.
The author of bandmat is Matt Shannon.