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        "author": "John Kirkham",
        "author_email": "kirkhamj@janelia.hhmi.org",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: C++",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries"
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        "description": "|Travis Build Status| |License| |PyPI Release| |conda-forge Release| |Gitter|\n\n--------------\n\nRank Filter\n===========\n\nMotivation\n----------\n\nThis package is designed to provide an efficient linear rank order\nfilter written in C++ with Python bindings. It can take single or double\nprecision floats as input. It was needed as the equivalent percentile\nfilter in SciPy was found to be too slow and unnecessarily general.\nThere was no equivalent in VIGRA.\n\nRequirements\n------------\n\nIn order to build this package, the following requirements are needed.\n\n1. Python (2.7.x or 3.5.x)\n2. Boost (1.56.0 or later)\n3. NumPy (1.7.0 or later)\n4. Cython (0.23.0 or later)\n5. Setuptools (18.0 or later)\n\nInstallation\n------------\n\nThe easiest way to install is to install our ``conda`` package.\nAlternatively, one can install from ``pip``, but this will require a\nC++ compiler and a recent version of ``setuptools``.\n\nBuilding\n--------\n\nThere are several ways to build the package.\n\n1. Standard Python build and install.\n2. Conda recipe build and install.\n3. CMake build and install.\n\nThe vanilla install in any of these forms should be basically equivalent.\n\nGetting Started\n~~~~~~~~~~~~~~~\n\nTo start simply clone the repo and change directory to the repo.\n\n::\n\n    git clone https://github.com/nanshe-org/rank_filter\n    cd rank_filter\n\nUsing Python\n~~~~~~~~~~~~\n\nTo build/install with Python directly, simply run the following command.\n\n::\n\n    python setup.py install\n\nUsing Conda\n~~~~~~~~~~~~\n\nTo build/install with Conda, simply run the following command.\n\n::\n\n    conda build rank_filter.recipe\n    conda install --use-local rank_filter.\n\nUsing CMake\n~~~~~~~~~~~\n\nIn order to find Boost includes and libraries, the directory Boost was installed\nto must be set as ``BOOST_ROOT``.\n\n::\n\n    cmake -DBOOST_ROOT=<path-to-Boost-root> .\n\n\nAlso the CMake installer will also pick these variables up if they are set in\nthe environment and not provided.\n\n::\n\n    export BOOST_ROOT=<path-to-Boost-root>\n    cmake .\n\nAdditionally, the preferred python interpreter can be set by using the\n``PYTHON_EXECUTABLE`` variable.\n\nChecking\n********\n\nBefore building the Python bindings it is worth checking if the C++ code\npasses its own test suite. This can be done using ``make`` with the\ncommand below. It is not required to run this stage, but it will be run\nevery time when building. These test are no guarantee that the Python\nmodule will work. All they verify is that the C++ code works.\n\n::\n\n    make check\n\nBuilding\n********\n\nBuilding is done easily using ``make``. This will create a shared object\nin the slib directory, which can be imported by Python as a module. As\nmentioned in the Checking section, the C++ tests will be run first. If\nthey fail, the Python module will not be built. They do not guarantee\nthat the Python module will work. Instead the testing stage can be used\nto validate the module.\n\n::\n\n    make\n\nTesting\n*******\n\nOnce the Python module is built, it is worth testing whether it works.\nThis can be done with ``make`` using the command below. Unlike the C++\ntests, these are Python tests that use nose to run the tests. The tests\nare the Python analogues of the ones used in C++ tests. They not only\nverify that basic command run, but that they pass with correct results\nonly.\n\n::\n\n    make test\n\nInstalling\n**********\n\nAfter building and testing, it is time to install. Using ``make``, the\ncommand below will install the module in the identified Python's\nsite-package folder allowing for importing this module using that\nPython.\n\n::\n\n    make install\n\nCleaning\n********\n\nThere are a few additional options regarding cleaning. It is possible to\nclean all build intermediates (including CMake generated files) leaving\nonly the final build products. This is done by calling as below.\n\n::\n\n    make distclean\n\nIf it is desirable to eliminate the build products as well as all\nintermediates, then the call below can be used.\n\n::\n\n    make reset\n\n\n.. |Travis Build Status| image:: https://travis-ci.org/nanshe-org/rank_filter.svg?branch=master\n   :target: https://travis-ci.org/nanshe-org/rank_filter\n\n.. |License| image:: https://img.shields.io/badge/license-BSD%203--Clause-blue.svg\n   :target: https://raw.githubusercontent.com/nanshe-org/rank_filter/master/LICENSE.txt\n\n.. |PyPI Release| image:: https://img.shields.io/pypi/v/rank_filter.svg\n   :target: https://pypi.python.org/pypi/rank_filter\n\n.. |conda-forge Release| image:: https://img.shields.io/conda/vn/conda-forge/rank_filter.svg\n   :target: https://anaconda.org/conda-forge/rank_filter\n\n.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg\n   :alt: Join the chat at https://gitter.im/nanshe-org/rank_filter\n   :target: https://gitter.im/nanshe-org/rank_filter?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge",
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