{
    "info": {
        "author": "Josh Izaac",
        "author_email": "josh@iza.ac",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Fortran",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": ".. image:: http://img.shields.io/github/release/josh146/pyctqw.png?style=flat\r\n    :target: https://github.com/josh146/pyctqw/releases/latest\r\n    \r\n.. image:: http://img.shields.io/pypi/v/pyCTQW.png?style=flat\r\n    :target: https://pypi.python.org/pypi/pyCTQW\r\n    \r\n.. image:: http://img.shields.io/pypi/dm/pyCTQW.png?style=flat\r\n    :target: https://pypi.python.org/pypi/pyCTQW\r\n\r\nFortran library and Python module to calculate continuous-time quantum walks\r\n\r\nThis is intended to provide a framework to quickly and easily work with quantum\r\nwalkers, take advantage of high performance computing, and allow easy visualisation.\r\n\r\nFor more information on how to use this package, please see the `online documentation <http://pyctqw.readthedocs.org>`_\r\n\r\nFeatures\r\n============\r\n* Fortran and Python bindings (in the form of a library and module respectively)\r\n* Supports MPI through the use of the PETSc and SLEPc high-performance sparse\r\n  matrix libraries (CUDA support planned)\r\n* Has in-built support for infinite-line hamiltonians\r\n* Import and export matrices/states in binary or text \r\n* Can import custom adjancency matrices\r\n* Supports both one and two walkers (non-interacting)\r\n* Import and export matrices/states in binary, text or matlab format\r\n* Python module supports plotting and visualisation using matplotlib and networkx\r\n* Entanglement calculations\r\n* Ability to place diagonal defects/barriers on graph vertices\r\n* MPI graph isomorphism methods, for both 2 and 3 interacting particles\r\n\r\nTODO\r\n============\r\n* CUDA support\r\n* Calculation of transmission\r\n* Add fallback modes when PETSc/SLEPc are not present\r\n\r\nDependencies\r\n============\r\n\r\nIn addition to an MPI implementation (e.g. `MPICH <http://www.mpich.org/>`_ or `Open MPI <http://www.open-mpi.org/>`_), the python ``pyCTQW.MPI`` module depends on the following components:\r\n\r\n- `Python <http://www.python.org/>`_ >= 2.7\r\n- `NumPy <http://www.numpy.org/>`_ >= 1.6.0\r\n- `PETSc <http://www.mcs.anl.gov/petsc/>`_ >= 3.4.2 \r\n- `SLEPc <http://www.grycap.upv.es/slepc/>`_ >= 3.4.1   \r\n- `petsc4py 3.4 or petsc4py-dev <https://bitbucket.org/petsc/petsc4py>`_\r\n- `mpi4py <http://mpi4py.scipy.org/>`_      (recommended, used for some plotting)\r\n- `matplotlib <http://matplotlib.org/>`_    (recommended, for node plotting and graph visualisation)\r\n- `SciPy <http://www.scipy.org/>`_          (recommended, for some I/O operations)\r\n- `NetworkX <http://networkx.github.io/>`_      (recommended, graph visualisation)\r\n\r\n\r\nInstallation using `pip`\r\n===========================\r\n\r\nAfter ensuring NumPy and petsc4py are installed (and all PETSc, SLEPc and MPI environment variables are properly set), pyCTQW can be installed using `pip`:\r\n\r\n.. code-block:: bash\r\n    \r\n    $ pip install pyCTQW\r\n\r\n\r\nInstallation from source code\r\n==============================\r\n\r\nAlternatively, the source code can be downloaded and compiled manually:\r\n\r\n1) Ensure all dependencies required above are installed\r\n\r\n2) Extract the ``pyCTQW`` folder, and ``cd`` into the extracted directory:\r\n\r\n    .. code-block:: bash\r\n        \r\n        $ tar xvzf pyctqw-1.1.0.tar.gz\r\n        $ cd pyctqw-1.1.0\r\n\r\n3) Ensure that your PETSc and SLEPc environment variables are correctly set; for example,\r\n\r\n    .. code-block:: bash\r\n\r\n        $ export PETSC_DIR=/path/to/petsc\r\n        $ export PETSC_ARCH=linux-gnu\r\n        $ export SLEPC_DIR=/path/to/slepc\r\n\r\n    If you are unsure what your PETSc or SLEPc variables should be, please refer to their documentation.\r\n\r\n    .. important::\r\n        If you plan to install ``pyCTQW.MPI`` using ``root`` to a **system** directory, the PETSc and SLEPc environment variables must be available to the root user.\r\n\r\n4) Compile the Python module ``pyCTQW.MPI`` by running\r\n\r\n    .. code-block:: bash\r\n        \r\n        $ python setup.py build\r\n\r\n5) System-wide install:\r\n\r\n    .. code-block:: bash\r\n        \r\n        $ sudo -E python setup.py install\r\n\r\n    where the command ``-E`` ensures that the environment variables set in step 3 are passed to the root.\r\n\r\n    .. note::\r\n        If you do not have root access, or the above command does not appear to work, you can install the package locally by running\r\n\r\n        .. code-block:: bash\r\n            \r\n            $ python setup.py install --user\r\n\r\n    Now, have a go running some of the examples!\r\n\r\n*Optional*: compiling ``libctqwMPI``\r\n===========================================================\r\n\r\nIn addition to an MPI implementation (e.g. `MPICH <http://www.mpich.org/>`_ or `Open MPI <http://www.open-mpi.org/>`_), the Fortran library ``libctqwMPI`` depends on the following components:\r\n- `PETSc <http://www.mcs.anl.gov/petsc/>`_ >= 3.4.2   \r\n- `SLEPc <http://www.grycap.upv.es/slepc/>`_ >= 3.4.1\r\n\r\nOnce these dependencies are installed, simply open a terminal in the root directory of ``pyCTQW-X.Y`` and run\r\n     \r\n.. code-block:: bash    \r\n    \r\n    $ make fortran [options]\r\n\r\nwhere available options include\r\n\r\n.. list-table::\r\n    :widths: 3 3 30\r\n    :header-rows: 1\r\n\r\n    * - Option\r\n      - Values\r\n      - Description\r\n\r\n    * - ``shared_lib``\r\n      - 0 (default), 1\r\n      - whether to build ``libctqwMPI`` as a shared library (``shared_lib=1``, producing ``libctqwMPI.so``) or a static library (``shared_lib=0`` (default), producing ``libctqwMPI.a``).\r\n\r\n        If built as a shared library, compiled programs will be smaller, but ``libctqwMPI.so`` will need to be added to a directory used by ``ld`` (either by setting the environment variable ``LD_LIBRARY_PATH`` or by placing ``libctqwMPI.so`` in ``/usr/local/lib`` etc).\r\n        \r\nThe fortran library (``libctqwMPI.so`` or ``libctqwMPI.a``) can be found in the ``pyCTQW-X.Y/lib`` directory, with required module files found in the ``pyCTQW-X.Y/include`` directory.\r\n\r\n\r\n*Optional:* build documentation \r\n=======================================\r\n\r\nIf `Sphinx <http://sphinx-doc.org/>`_ is installed, the documentation can be compiled by running\r\n\r\n.. code-block:: bash\r\n    \r\n    $ pip install -r docs/requirements.txt\r\n    $ make docs\r\n\r\nDocumentation\r\n===============\r\n\r\nFor more information on how to use this package, please see the `online documentation <http://pyctqw.readthedocs.org>`_\r\n\r\nAcknowledgements\r\n===========================\r\n\r\nThe graph isomorphism subroutine ``GraphISCert`` uses the external subroutine ``d_refsor``, a highly optimised Fortran sorting implementation written by Michel Olagnon and part of the `ORDERPACK 2.0 <http://www.fortran-2000.com/rank/>`_ suite of ranking and sorting algorithms for Fortran 90.",
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