{
    "info": {
        "author": "Kevin Sheppard",
        "author_email": "kevin.k.sheppard@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Operating System :: Unix",
            "Programming Language :: C",
            "Programming Language :: Cython",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Adaptive Technologies",
            "Topic :: Artistic Software",
            "Topic :: Office/Business :: Financial",
            "Topic :: Scientific/Engineering",
            "Topic :: Security :: Cryptography"
        ],
        "description": "RandomGen\n=========\n\nRandom Number Generator using settable Basic RNG interface for future\nNumPy RandomState evolution.\n\nContinuous Integration\n~~~~~~~~~~~~~~~~~~~~~~\n\n|Travis Build Status| |Appveyor Build Status| |Build Status|\n\nCoverage\n~~~~~~~~\n\n|Coverage Status| |codecov|\n\nLatest Release\n~~~~~~~~~~~~~~\n\n|PyPI version| |Anacnoda Cloud|\n\nLicense\n~~~~~~~\n\n|NCSA License| |BSD License| |DOI|\n\nThis is a library and generic interface for alternative random\ngenerators in Python and NumPy.\n\nPython 2.7 Support\n------------------\n\nv1.16 is the final major version that supports Python 2.7. Any bugs in\nv1.16 will be patched until the end of 2019. All future releases are\nPython 3, with an initial minimum version of 3.5.\n\nCompatibility Warning\n---------------------\n\n``RandomGenerator`` does not support Box-Muller normal variates and so\nit not 100% compatible with NumPy (or randomstate). Box-Muller normals\nare slow to generate and all functions which previously relied on\nBox-Muller normals now use the faster Ziggurat implementation. If you\nrequire backward compatibility, a legacy generator, ``RandomState``, has\nbeen created which can fully reproduce the sequence produced by NumPy.\n\nFeatures\n--------\n\n-  Replacement for NumPy\u2019s RandomState\n\n   .. code:: python\n\n      from randomgen import RandomGenerator, MT19937\n      rnd = RandomGenerator(MT19937())\n      x = rnd.standard_normal(100)\n      y = rnd.random_sample(100)\n      z = rnd.randn(10,10)\n\n-  Default random generator is a fast generator called Xoroshiro128plus\n-  Support for random number generators that support independent streams\n   and jumping ahead so that sub-streams can be generated\n-  Faster random number generation, especially for normal, standard\n   exponential and standard gamma using the Ziggurat method\n\n   .. code:: python\n\n      from randomgen import RandomGenerator\n      # Default basic PRNG is Xoroshiro128\n      rnd = RandomGenerator()\n      w = rnd.standard_normal(10000)\n      x = rnd.standard_exponential(10000)\n      y = rnd.standard_gamma(5.5, 10000)\n\n-  Support for 32-bit floating randoms for core generators. Currently\n   supported:\n\n   -  Uniforms (``random_sample``)\n   -  Exponentials (``standard_exponential``, both Inverse CDF and\n      Ziggurat)\n   -  Normals (``standard_normal``)\n   -  Standard Gammas (via ``standard_gamma``)\n\n   **WARNING**: The 32-bit generators are **experimental** and subject\n   to change.\n\n   **Note**: There are *no* plans to extend the alternative precision\n   generation to all distributions.\n\n-  Support for filling existing arrays using ``out`` keyword argument.\n   Currently supported in (both 32- and 64-bit outputs)\n\n   -  Uniforms (``random_sample``)\n   -  Exponentials (``standard_exponential``)\n   -  Normals (``standard_normal``)\n   -  Standard Gammas (via ``standard_gamma``)\n\n-  Support for Lemire\u2019s method of generating uniform integers on an\n   arbitrary interval by setting ``use_masked=True``.\n\nIncluded Pseudo Random Number Generators\n----------------------------------------\n\nThis module includes a number of alternative random number generators in\naddition to the MT19937 that is included in NumPy. The RNGs include:\n\n-  `MT19937 <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/>`__,\n   the NumPy rng\n-  `dSFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>`__ a\n   SSE2-aware version of the MT19937 generator that is especially fast\n   at generating doubles\n-  `xoroshiro128+ <http://xoroshiro.di.unimi.it/>`__,\n   `xorshift1024*\u03c6 <http://xorshift.di.unimi.it/>`__,\n   `xoshiro256*\\* <http://xorshift.di.unimi.it/>`__, and\n   `xoshiro512*\\* <http://xorshift.di.unimi.it/>`__\n-  `PCG64 <http://www.pcg-random.org/>`__\n-  ThreeFry and Philox from\n   `Random123 <https://www.deshawresearch.com/resources_random123.html>`__\n\nDifferences from ``numpy.random.RandomState``\n---------------------------------------------\n\nNew Features\n~~~~~~~~~~~~\n\n-  ``standard_normal``, ``normal``, ``randn`` and\n   ``multivariate_normal`` all use the much faster (100%+) Ziggurat\n   method.\n-  ``standard_gamma`` and ``gamma`` both use the much faster Ziggurat\n   method.\n-  ``standard_exponential`` ``exponential`` both support an additional\n   ``method`` keyword argument which can be ``inv`` or ``zig`` where\n   ``inv`` corresponds to the current method using the inverse CDF and\n   ``zig`` uses the much faster (100%+) Ziggurat method.\n-  Core random number generators can produce either single precision\n   (``np.float32``) or double precision (``np.float64``, the default)\n   using the optional keyword argument ``dtype``\n-  Core random number generators can fill existing arrays using the\n   ``out`` keyword argument\n-  Standardizes integer-values random values as int64 for all platforms.\n-  ``randint`` supports generating using rejection sampling on masked\n   values (the default) or Lemire\u2019s method. Lemire\u2019s method can be much\n   faster when the required interval length is much smaller than the\n   closes power of 2.\n\nNew Functions\n~~~~~~~~~~~~~\n\n-  ``random_entropy`` - Read from the system entropy provider, which is\n   commonly used in cryptographic applications\n-  ``random_raw`` - Direct access to the values produced by the\n   underlying PRNG. The range of the values returned depends on the\n   specifics of the PRNG implementation.\n-  ``random_uintegers`` - unsigned integers, either 32-\n   (``[0, 2**32-1]``) or 64-bit (``[0, 2**64-1]``)\n-  ``jump`` - Jumps RNGs that support it. ``jump`` moves the state a\n   great distance. *Only available if supported by the RNG.*\n-  ``advance`` - Advanced the RNG \u2018as-if\u2019 a number of draws were made,\n   without actually drawing the numbers. *Only available if supported by\n   the RNG.*\n\nStatus\n------\n\n-  Builds and passes all tests on:\n\n   -  Linux 32/64 bit, Python 2.7, 3.5, 3.6, 3.7\n   -  PC-BSD (FreeBSD) 64-bit, Python 2.7\n   -  OSX 64-bit, Python 2.7, 3.5, 3.6, 3.7\n   -  Windows 32/64 bit, Python 2.7, 3.5 and 3.6, 3.7\n\nVersion\n-------\n\nThe package version matches the latest version of NumPy where\n``RandomState(MT19937())`` passes all NumPy test.\n\nDocumentation\n-------------\n\nDocumentation for the latest release is available on `my GitHub\npages <http://bashtage.github.io/randomgen/>`__. Documentation for the\nlatest commit (unreleased) is available under\n`devel <http://bashtage.github.io/randomgen/devel/>`__.\n\nPlans\n-----\n\nThis module is essentially complete. There are a few rough edges that\nneed to be smoothed.\n\n-  Creation of additional streams from where supported (i.e.\u00a0a\n   ``next_stream()`` method)\n\nRequirements\n------------\n\nBuilding requires:\n\n-  Python (2.7, 3.5, 3.6, 3.7)\n-  NumPy (1.13, 1.14, 1.15, 1.16)\n-  Cython (0.26+)\n-  tempita (0.5+), if not provided by Cython\n\nTesting requires pytest (4.0+).\n\n**Note:** it might work with other versions but only tested with these\nversions.\n\nDevelopment and Testing\n-----------------------\n\nAll development has been on 64-bit Linux, and it is regularly tested on\nTravis-CI (Linux/OSX) and Appveyor (Windows). The library is\noccasionally tested on Linux 32-bit and Free BSD 11.1.\n\nBasic tests are in place for all RNGs. The MT19937 is tested against\nNumPy\u2019s implementation for identical results. It also passes NumPy\u2019s\ntest suite where still relevant.\n\nInstalling\n----------\n\nEither install from PyPi using\n\n.. code:: bash\n\n   pip install randomgen\n\nor, if you want the latest version,\n\n.. code:: bash\n\n   pip install git+https://github.com/bashtage/randomgen.git\n\nor from a cloned repo,\n\n.. code:: bash\n\n   python setup.py install\n\nSSE2\n~~~~\n\n``dSFTM`` makes use of SSE2 by default. If you have a very old computer\nor are building on non-x86, you can install using:\n\n.. code:: bash\n\n   python setup.py install --no-sse2\n\nWindows\n~~~~~~~\n\nEither use a binary installer, or if building from scratch, use Python\n3.6 with Visual Studio 2015/2017 Community Edition. It can also be build\nusing Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7.\n\nUsing\n-----\n\nThe separate generators are importable from ``randomgen``\n\n.. code:: python\n\n   from randomgen import RandomGenerator, ThreeFry, PCG64, MT19937\n   rg = RandomGenerator(ThreeFry())\n   rg.random_sample(100)\n\n   rg = RandomGenerator(PCG64())\n   rg.random_sample(100)\n\n   # Identical to NumPy\n   rg = RandomGenerator(MT19937())\n   rg.random_sample(100)\n\n.. _license-1:\n\nLicense\n-------\n\nStandard NCSA, plus sub licenses for components.\n\nPerformance\n-----------\n\nPerformance is promising, and even the mt19937 seems to be faster than\nNumPy\u2019s mt19937.\n\n::\n\n   Speed-up relative to NumPy (Uniform Doubles)\n   ************************************************************\n   DSFMT                 184.9%\n   MT19937                17.3%\n   PCG32                  83.3%\n   PCG64                 108.3%\n   Philox                 -4.9%\n   ThreeFry              -12.0%\n   ThreeFry32            -63.9%\n   Xoroshiro128          159.5%\n   Xorshift1024          150.4%\n   Xoshiro256StarStar    145.7%\n   Xoshiro512StarStar    113.1%\n\n   Speed-up relative to NumPy (64-bit unsigned integers)\n   ************************************************************\n   DSFMT                  17.4%\n   MT19937                 7.8%\n   PCG32                  60.3%\n   PCG64                  73.5%\n   Philox                -25.5%\n   ThreeFry              -30.5%\n   ThreeFry32            -67.8%\n   Xoroshiro128          124.0%\n   Xorshift1024          109.4%\n   Xoshiro256StarStar    100.3%\n   Xoshiro512StarStar     63.5%\n\n   Speed-up relative to NumPy (Standard normals)\n   ************************************************************\n   DSFMT                 183.0%\n   MT19937               169.0%\n   PCG32                 240.7%\n   PCG64                 231.6%\n   Philox                131.3%\n   ThreeFry              118.3%\n   ThreeFry32             21.6%\n   Xoroshiro128          332.1%\n   Xorshift1024          232.4%\n   Xoshiro256StarStar    306.6%\n   Xoshiro512StarStar    274.6%\n\n.. |Travis Build Status| image:: https://travis-ci.org/bashtage/randomgen.svg?branch=master\n   :target: https://travis-ci.org/bashtage/randomgen\n.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/odc5c4ukhru5xicl/branch/master?svg=true\n   :target: https://ci.appveyor.com/project/bashtage/randomgen/branch/master\n.. |Build Status| image:: https://cloud.drone.io/api/badges/bashtage/randomgen/status.svg\n   :target: https://cloud.drone.io/bashtage/randomgen\n.. |Coverage Status| image:: https://coveralls.io/repos/github/bashtage/randomgen/badge.svg\n   :target: https://coveralls.io/github/bashtage/randomgen\n.. |codecov| image:: https://codecov.io/gh/bashtage/randomgen/branch/master/graph/badge.svg\n   :target: https://codecov.io/gh/bashtage/randomgen\n.. |PyPI version| image:: https://badge.fury.io/py/randomgen.svg\n   :target: https://pypi.org/project/randomgen/\n.. |Anacnoda Cloud| image:: https://anaconda.org/bashtage/randomgen/badges/version.svg\n   :target: https://anaconda.org/bashtage/randomgen\n.. |NCSA License| image:: https://img.shields.io/badge/License-NCSA-blue.svg\n   :target: https://opensource.org/licenses/NCSA\n.. |BSD License| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\n   :target: https://opensource.org/licenses/BSD-3-Clause\n.. |DOI| image:: https://zenodo.org/badge/122181085.svg\n   :target: https://zenodo.org/badge/latestdoi/122181085",
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