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        "author": "Matt Shannon",
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        "description": "bandmat\n=======\n\n|Linux build status| |Windows build status|\n\n.. |Linux build status| image:: https://travis-ci.org/MattShannon/bandmat.svg?branch=master\n   :alt: Linux build status\n   :target: https://travis-ci.org/MattShannon/bandmat\n.. |Windows build status| image:: https://ci.appveyor.com/api/projects/status/cy8ouawd9bnus0ai/branch/master?svg=true\n   :alt: Windows build status\n   :target: https://ci.appveyor.com/project/MattShannon/bandmat/branch/master\n\nThis package provides a simple banded matrix library for python.\nIt supports banded matrix-vector and matrix-matrix multiplication, converting\nbetween full and banded matrix representations, and certain linear algebra\noperations on banded matrices.\nIt builds on the excellent numpy and scipy packages, which have limited support\nfor banded matrix operations.\n\nOverview\n--------\n\nA banded matrix is a matrix where only the diagonal, a number of superdiagonals\nand a number of subdiagonals are non-zero.\nThe well-known BLAS interface and LAPACK library for linear algebra define\nseveral banded matrix operations, and some of these, such as banded Cholesky\ndecomposition, are wrapped in the excellent python package scipy, specifically\nin scipy.linalg.\nThe bandmat package re-uses the banded matrix representation used by BLAS,\nLAPACK and scipy.linalg, wrapping it in a lightweight class for ease of use.\nSee the docstring for the BandMat class for full details of the representation\nused.\n\nThe bandmat package provides:\n\n- a lightweight class wrapping the LAPACK-style banded matrix representation.\n  This class keeps track of things like bandwidths to allow a more direct\n  coding style when working with banded matrices.\n- some basic banded matrix operations not present in scipy.\n  For example, banded matrix-vector multiplication is defined by BLAS but not\n  wrapped by scipy, and banded matrix-matrix multiplication is not defined in\n  BLAS or in scipy.\n  The bandmat package contains C implementations of these operations written in\n  cython.\n- helper functions for converting between full and banded matrix\n  representations.\n- certain linear algebra operations on banded matrices, including computing the\n  band of the inverse of a banded matrix.\n\nOnly square banded matrices are supported by this package.\n\nLicense\n-------\n\nPlease see the file ``License`` for details of the license and warranty for\nbandmat.\n\nInstallation\n------------\n\nFor most purposes the simplest way to install bandmat is to use pip.\nFor example in Debian and Ubuntu::\n\n    # (for python3, use \"python3-numpy python3-scipy\" instead)\n    sudo apt-get install python-numpy python-scipy\n    sudo pip install bandmat\n\nThe first command installs numpy and scipy from the system repository, since\ninstalling numpy and scipy using pip is generally not recommended.\nThe second command installs the latest released version of\n`bandmat on PyPI <https://pypi.python.org/pypi/bandmat>`_, together with any\ncurrently uninstalled python packages required by bandmat.\n\nbandmat can also be installed in a virtualenv::\n\n    # (for python3, use \"python3-numpy python3-scipy\" instead)\n    sudo apt-get install python-numpy python-scipy\n    virtualenv --system-site-packages env\n    env/bin/pip install bandmat\n\nThe latest development version of bandmat is available from a github repository\n(see below).\n\nTo check that bandmat is installed correctly you can run the test suite::\n\n    python -m unittest discover bandmat\n\nExamples\n--------\n\nSee the package docstring (run ``import bandmat as bm; help(bm)`` in the python\ninterpreter) for some examples of usage.\nThe python script ``example.py`` also contains some simple examples of usage.\nTo run it::\n\n    python example.py\n\nDevelopment\n-----------\n\nThe source code is hosted in the\n`bandmat github repository <https://github.com/MattShannon/bandmat>`_.\nTo obtain the latest source code using git::\n\n    git clone git://github.com/MattShannon/bandmat.git\n\nDevelopment is in fact done using `darcs <http://darcs.net/>`_, with the darcs\nrepository converted to a git repository using\n`darcs-to-git <https://github.com/purcell/darcs-to-git>`_.\n\nTo install any currently uninstalled python packages required by bandmat::\n\n    # (for python3, use \"cython3 python3-numpy python3-scipy\" instead)\n    sudo apt-get install cython python-numpy python-scipy\n    sudo pip install -r requirements.txt\n\nTo compile the cython part of bandmat in the current directory::\n\n    python setup.py build_ext --inplace\n\nThis command must be run after every modification to the source ``.pyx`` files.\n\nA note on ``setup.py``\n----------------------\n\nThe included ``setup.py`` file operates in one of two modes depending on\nwhether or not the file ``dev`` is present in the project root directory.\nIn development mode (``dev`` present, as for the github repository), the\n``build_ext`` command uses cython to compile cython modules from their ``.pyx``\nsource, and the ``sdist`` command is modified to first use cython to compile\ncython modules from their ``.pyx`` source to ``.c`` files.\nIn distribution mode (``dev`` absent, as for source distributions such as the\ncode on PyPI), the ``build_ext`` command uses a C compiler to directly compile\ncython modules from the corresponding ``.c`` files.\nThis approach ensures that source distributions can be installed on systems\nwithout cython or with an incompatible version of cython, while ensuring that\ndistributed ``.c`` files are always up-to-date and that the source ``.pyx``\nfiles are used instead of ``.c`` files during development.\n\nThe author would welcome any suggestions for more elegant ways to achieve a\nsimilar effect to the approach described above!\n\nBugs\n----\n\nPlease use the\n`issue tracker <https://github.com/MattShannon/bandmat/issues>`_ to submit bug\nreports.\n\nContact\n-------\n\nThe author of bandmat is `Matt Shannon <mailto:matt.shannon@cantab.net>`_.",
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