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    "info": {
        "author": "Ryan Helinski",
        "author_email": "rhelins@sandia.gov",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Security :: Cryptography"
        ],
        "description": "BiEntropy Randomness Metrics for Python\n=======================================\n\nThis Python package provides high-performance implementations of the functions\nand examples presented in \"BiEntropy - The Approximate Entropy of a Finite\nBinary String\" by Grenville J.  Croll, presented at ANPA 34 in 2013.\nhttps://arxiv.org/abs/1305.0954\n\nAccording to the paper, BiEntropy is \"a simple algorithm which computes the\napproximate entropy of a finite binary string of arbitrary length\" using \"a\nweighted average of the Shannon Entropies of the string and all but the last\nbinary derivative of the string.\" In other words, these metrics can be used to\nhelp assess the disorder or randomness of binary or byte strings, particularly\nthose that are too short for other randomness tests.\n\nThis module includes both a Python C extension and a pure Python module\nimplementing the BiEn and TBiEn metrics from the paper, as well as a suite of\ntests that verify their correctness. These implementations are available under\nthe submodules 'cbientropy' and 'pybientropy'.\n\nAliases of C versions of BiEn and TBiEn are included at the top level of this\nmodule for convenience.\n\n\nBasic Usage\n-----------\n\nThe `bien` and `tbien` functions support inputs of both binary (i.e., not\nunicode) strings and object types, such as those provided by the  `bitstring`\npackage, that have both a `tobytes()` method that returns a binary string and a\n`len()` method that returns the length in bits.\n\n```\nIn [1]: from bientropy import bien, tbien\n\nIn [2]: from bitstring import Bits\n\nIn [3]: bien(Bits('0b1011')), tbien(Bits('0b1011'))\nOut[3]: (0.9496956846525874, 0.9305948708049089)\n\nIn [4]: bien(Bits('0xfa1afe1')), tbien(Bits('0xfa1afe1'))\nOut[4]: (0.05957853232204588, 0.7189075024152897)\n\nIn [5]: bien(b'\\xde\\xad\\xbe\\xef'), tbien(b'\\xde\\xad\\xbe\\xef')\nOut[5]: (0.060189286721883305, 0.7898265151674035)\n\n```\n\nSee [demo.py](/bientropy/demo.py) for more examples.\n\n\nPerformance\n-----------\n\nAccording to the paper, the \"BiEntropy algorithm evaluates the order and\ndisorder of a binary string of length n in O(n^2) time using O(n) memory.\" In\nother words, the run time has quadratic growth and the memory requirement has\nlinear growth with respect to the string length.\n\nThe metrics are implemented in Python using the 'bitstring' package for\nhandling arbitrary length binary strings and in native C using the GNU Multiple\nPrecision (GMP) arithmetic library.\n\nThe following is a table of speed-ups from the Python to the C implementation\nfor various string byte lengths:\n\n| Bytes | BiEn    | TBiEn   |\n|-------|---------|---------|\n|    16 |     229 |     155 |\n|    32 |     217 |     149 |\n|    48 |     212 |     150 |\n|    64 |     221 |     161 |\n|   128 |     267 |     196 |\n|   256 |     340 |     257 |\n|   512 |     502 |     370 |\n|  1024 |     802 |     537 |\n\nFollowing is a log-log plot of the average time to compute the various\nimplementations of BiEntropy on a 2.40GHz Intel(R) Xeon(R) E5645 CPU versus the\nlength of the input in bytes.\n\n![Run Times](artwork/bientropy_times.png)\n\n\nRequirements\n------------\n\nThis package is tested with Python versions 2.7, 3.4, 3.5 and 3.6.\n\nInstallation:\n* Python http://python.org/ (>= 2.7 or >= 3.4)\n* bitstring http://pythonhosted.org/bitstring/\n* NumPy http://numpy.org/\n\nCompiling:\n* GCC http://gcc.gnu.org/ on Linux\n* MSVC 9 if using Python 2.7 on Windows\n  * https://www.microsoft.com/EN-US/DOWNLOAD/confirmation.aspx?id=44266\n* MSVC 14 if using Python 3.x on Windows\n  * http://landinghub.visualstudio.com/visual-cpp-build-tools\n* GMP http://gmplib.org/ or MPIR http://mpir.org/ on Windows\n\nFor running tests:\n* mock https://pypi.org/project/mock/ if using Python 2.7\n\nTo check which version you may already have installed, run the command:\n```\npython -c \"import pkg_resources; print('BiEntropy version: '+pkg_resources.get_distribution('bientropy').version)\"\n```\n\n\nInstall from pip\n----------------\n\nThis package includes a C extension which has to be compiled for each platform.\nPython wheels include compiled binary code and allow the extension to be\ninstalled without requiring a compiler.\n\n`pip >= 1.4` with `setuptools >= 0.8` will use a wheel if there is one\navailable for the target platform:\n```\npip install --user BiEntropy\n```\n\nOnce installed, the tests should be run with the command:\n```\npython -m bientropy.tests\n```\n\nA list of available wheel files is available at:\nhttps://pypi.org/project/BiEntropy/#files\n\n\nInstall from Source\n-------------------\n\nThe source code for the `bientropy` package can be cloned or downloaded from:\n* GitHub: https://github.com/sandialabs/bientropy\n* PyPI: https://pypi.org/project/BiEntropy\n\nThe [GMP library](http://gmplib.org/) and headers need to be installed before\ncompiling.\n\nOn Debian/Ubuntu:\n```\napt-get install libgmp-dev\n```\n\nOn RedHat:\n```\nyum install gmp-devel\n```\n\nThen, use `setup.py` to compile and install the package:\n```\npython setup.py install --user\n```\n\nOnce installed, the tests should be run with the command:\n```\npython -m bientropy.tests\n```\n\n\nCompiling on Windows\n--------------------\n\nCompiling GMP on Microsoft Windows is only supported under Cygwin, MinGW or\nDJGPP. However, this package can be compiled with MPIR, a fork of GMP, on\nWindows. The source for MPIR is available at http://mpir.org/\nThe `setup.py` script expects the header files, library files and DLL to be\navailable under `mpir/dll/x64/Release`.\n\nA compiled distribution of the MPIR library was also available at:\nhttp://www.holoborodko.com/pavel/mpfr/#download\nTo use it, download the `MPFR-MPIR-x86-x64-MSVC2010.zip` file and extract\n`mpir` from the ZIP file to this directory.\n\nOnce MPIR is ready, proceed as usual.\n```\npython setup.py install --user\n```\n\nAfter installing, the tests should be run with the command:\n```\npython -m bientropy.tests\n```\n\nSee https://github.com/cython/cython/wiki/CythonExtensionsOnWindows for more\ninformation.\n\n\nIncluded Scripts\n----------------\n\nAfter installing, a demonstration can be run with this command:\n```\npython -m bientropy.demo\n```\nThis runs [demo.py](/bientropy/demo.py), which also serves as an example for\nusing the package.\n\nThe same benchmark script used to generate the data shown in the table and plot\nabove is also included. It can be run with:\n```\npython -m bientropy.benchmark\n```\n\n\nDevelopment\n-----------\n\nTo compile with debug symbols and with extra output, use:\n```\npython setup.py build_ext --force --debug --define DEBUG\n```\n\nTo also disable compiler optimizations, use:\n```\nCFLAGS=-O0 python setup.py build_ext --force --debug --define DEBUG\n```\n\nTo debug the extension with GDB:\n```\n$ gdb python\n(gdb) run setup.py test\n```\n\nTo run the Valgrind memcheck tool to check for memory corruption and leaks:\n```\nvalgrind --xml=yes --xml-file=valgrind.xml ${python} setup.py test\n```\n\n\nAuthors\n-------\n\nThis package, consisting of the C implementations, Python implementations and\nPython bindings were written by Ryan Helinski <rhelins@sandia.gov>.\n\n\nLicense\n-------\nCopyright 2018 National Technology & Engineering Solutions of Sandia, LLC\n(NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.\nGovernment retains certain rights in this software.\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see <http://www.gnu.org/licenses/>.\n\n\n",
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        "name": "BiEntropy",
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        "summary": "High-performance implementations of BiEntropy metrics proposed by Grenville J. Croll",
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