{
    "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 :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Adaptive Technologies",
            "Topic :: Artistic Software",
            "Topic :: Office/Business :: Financial",
            "Topic :: Scientific/Engineering",
            "Topic :: Security :: Cryptography"
        ],
        "description": "randomstate\r\n===========\r\n\r\n|Travis Build Status| |Appveyor Build Status| |PyPI version|\r\n\r\nFuture development in `randomgen <https://github.com/bashtage/randomgen>`__\r\n===========================================================================\r\n\r\n**End-of-life notification**\r\n\r\nThis library was designed to bring alternative generators to the NumPy\r\ninfrastructure. It as been successful in advancing the conversation for\r\na future implementation of a new random number API in NumPy which will\r\nallow new algorithms and/or generators. The next step in this process is\r\nto separate the basic (or core RNG) from the functions that transform\r\nrandom bits into useful random numbers. This has been implemented in a\r\nsuccessor project **randomgen** available on\r\n`GitHub <https://github.com/bashtage/randomgen>`__ or\r\n`PyPi <https://pypi.org/project/randomstate/>`__.\r\n\r\n`randomgen <https://github.com/bashtage/randomgen>`__ has a slightly\r\ndifferent API, so please see the `randomgen\r\ndocumentation <https://bashtage.github.io/randomgen>`__.\r\n\r\n--------------\r\n\r\nIntroduction\r\n------------\r\n\r\nThis is a library and generic interface for alternative random\r\ngenerators in Python and NumPy.\r\n\r\nFeatures\r\n--------\r\n\r\n-  Immediate drop in replacement for NumPy's RandomState\r\n\r\n.. code:: python\r\n\r\n    # import numpy.random as rnd\r\n    import randomstate as rnd\r\n    x = rnd.standard_normal(100)\r\n    y = rnd.random_sample(100)\r\n    z = rnd.randn(10,10)\r\n\r\n-  Default random generator is identical to NumPy's RandomState (i.e.,\r\n   same seed, same random numbers).\r\n-  Support for random number generators that support independent streams\r\n   and jumping ahead so that sub-streams can be generated\r\n-  Faster random number generation, especially for normal, standard\r\n   exponential and standard gamma using the Ziggurat method\r\n\r\n.. code:: python\r\n\r\n    import randomstate as rnd\r\n    w = rnd.standard_normal(10000, method='zig')\r\n    x = rnd.standard_exponential(10000, method='zig')\r\n    y = rnd.standard_gamma(5.5, 10000, method='zig')\r\n\r\n-  Support for 32-bit floating randoms for core generators. Currently\r\n   supported:\r\n\r\n   -  Uniforms (``random_sample``)\r\n   -  Exponentials (``standard_exponential``, both Inverse CDF and\r\n      Ziggurat)\r\n   -  Normals (``standard_normal``, both Box-Muller and Ziggurat)\r\n   -  Standard Gammas (via ``standard_gamma``, both Inverse CDF and\r\n      Ziggurat)\r\n\r\n**WARNING**: The 32-bit generators are **experimental** and subject to\r\nchange.\r\n\r\n**Note**: There are *no* plans to extend the alternative precision\r\ngeneration to all random number types.\r\n\r\n-  Support for filling existing arrays using ``out`` keyword argument.\r\n   Currently supported in (both 32- and 64-bit outputs)\r\n\r\n   -  Uniforms (``random_sample``)\r\n   -  Exponentials (``standard_exponential``)\r\n   -  Normals (``standard_normal``)\r\n   -  Standard Gammas (via ``standard_gamma``)\r\n\r\nIncluded Pseudo Random Number Generators\r\n----------------------------------------\r\n\r\nThis modules includes a number of alternative random number generators\r\nin addition to the MT19937 that is included in NumPy. The RNGs include:\r\n\r\n-  `MT19937 <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/>`__,\r\n   the NumPy rng\r\n-  `dSFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>`__ a\r\n   SSE2-aware version of the MT19937 generator that is especially fast\r\n   at generating doubles\r\n-  `SFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>`__ a\r\n   SSE2-aware version of the MT19937 generator that is optimized for\r\n   integer values\r\n-  `xorshift128+ <http://xorshift.di.unimi.it/>`__,\r\n   `xoroshiro128+ <http://xoroshiro.di.unimi.it/>`__ and\r\n   `xorshift1024\\* <http://xorshift.di.unimi.it/>`__\r\n-  `PCG32 <http://www.pcg-random.org/>`__ and\r\n   `PCG64 <http:w//www.pcg-random.org/>`__\r\n-  `MRG32K3A <http://simul.iro.umontreal.ca/rng>`__\r\n-  A multiplicative lagged fibonacci generator (LFG(63, 1279, 861, \\*))\r\n\r\nDifferences from ``numpy.random.RandomState``\r\n---------------------------------------------\r\n\r\nNew Features\r\n~~~~~~~~~~~~\r\n\r\n-  ``standard_normal``, ``normal``, ``randn`` and\r\n   ``multivariate_normal`` all support an additional ``method`` keyword\r\n   argument which can be ``bm`` or ``zig`` where ``bm`` corresponds to\r\n   the current method using the Box-Muller transformation and ``zig``\r\n   uses the much faster (100%+) Ziggurat method.\r\n-  ``standard_exponential`` and ``standard_gamma`` both support an\r\n   additional ``method`` keyword argument which can be ``inv`` or\r\n   ``zig`` where ``inv`` corresponds to the current method using the\r\n   inverse CDF and ``zig`` uses the much faster (100%+) Ziggurat method.\r\n-  Core random number generators can produce either single precision\r\n   (``np.float32``) or double precision (``np.float64``, the default)\r\n   using an the optional keyword argument ``dtype``\r\n-  Core random number generators can fill existing arrays using the\r\n   ``out`` keyword argument\r\n\r\nNew Functions\r\n~~~~~~~~~~~~~\r\n\r\n-  ``random_entropy`` - Read from the system entropy provider, which is\r\n   commonly used in cryptographic applications\r\n-  ``random_raw`` - Direct access to the values produced by the\r\n   underlying PRNG. The range of the values returned depends on the\r\n   specifics of the PRNG implementation.\r\n-  ``random_uintegers`` - unsigned integers, either 32-\r\n   (``[0, 2**32-1]``) or 64-bit (``[0, 2**64-1]``)\r\n-  ``jump`` - Jumps RNGs that support it. ``jump`` moves the state a\r\n   great distance. *Only available if supported by the RNG.*\r\n-  ``advance`` - Advanced the core RNG 'as-if' a number of draws were\r\n   made, without actually drawing the numbers. *Only available if\r\n   supported by the RNG.*\r\n\r\nStatus\r\n------\r\n\r\n-  Complete drop-in replacement for ``numpy.random.RandomState``. The\r\n   ``mt19937`` generator is identical to ``numpy.random.RandomState``,\r\n   and will produce an identical sequence of random numbers for a given\r\n   seed.\r\n-  Builds and passes all tests on:\r\n-  Linux 32/64 bit, Python 2.7, 3.4, 3.5, 3.6 (probably works on 2.6 and\r\n   3.3)\r\n-  PC-BSD (FreeBSD) 64-bit, Python 2.7\r\n-  OSX 64-bit, Python 2.7\r\n-  Windows 32/64 bit (only tested on Python 2.7, 3.5 and 3.6, but should\r\n   work on 3.3/3.4)\r\n\r\nVersion\r\n-------\r\n\r\nThe version matched the latest version of NumPy where\r\n``randomstate.prng.mt19937`` passes all NumPy test.\r\n\r\nDocumentation\r\n-------------\r\n\r\nAn occasionally updated build of the documentation is available on `my\r\ngithub pages <http://bashtage.github.io/ng-numpy-randomstate/>`__.\r\n\r\nPlans\r\n-----\r\n\r\nThis module is essentially complete. There are a few rough edges that\r\nneed to be smoothed.\r\n\r\n-  Stream support for MLFG\r\n-  Creation of additional streams from a RandomState where supported\r\n   (i.e. a ``next_stream()`` method)\r\n\r\nRequirements\r\n------------\r\n\r\nBuilding requires:\r\n\r\n-  Python (2.7, 3.4, 3.5, 3.6)\r\n-  NumPy (1.9, 1.10, 1.11, 1.12)\r\n-  Cython (0.22, **not** 0.23, 0.24, 0.25)\r\n-  tempita (0.5+), if not provided by Cython\r\n\r\nTesting requires pytest (3.0+).\r\n\r\n**Note:** it might work with other versions but only tested with these\r\nversions.\r\n\r\nDevelopment and Testing\r\n-----------------------\r\n\r\n| All development has been on 64-bit Linux, and it is regularly tested\r\n  on Travis-CI. The library is occasionally tested on Linux 32-bit,\r\n| OSX 10.10, PC-BSD 10.2 (should also work on Free BSD) and Windows\r\n  (Python 2.7/3.5, both 32 and 64-bit).\r\n\r\nBasic tests are in place for all RNGs. The MT19937 is tested against\r\nNumPy's implementation for identical results. It also passes NumPy's\r\ntest suite.\r\n\r\nInstalling\r\n----------\r\n\r\n.. code:: bash\r\n\r\n    python setup.py install\r\n\r\nSSE2\r\n~~~~\r\n\r\n``dSFTM`` makes use of SSE2 by default. If you have a very old computer\r\nor are building on non-x86, you can install using:\r\n\r\n.. code:: bash\r\n\r\n    python setup.py install --no-sse2\r\n\r\nWindows\r\n~~~~~~~\r\n\r\nEither use a binary installer, or if building from scratch, use Python\r\n3.5 with Visual Studio 2015 Community Edition. It can also be build\r\nusing Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7,\r\nalthough some modifications may be needed to ``distutils`` to find the\r\ncompiler.\r\n\r\nUsing\r\n-----\r\n\r\nThe separate generators are importable from ``randomstate.prng``.\r\n\r\n.. code:: python\r\n\r\n    import randomstate\r\n    rs = randomstate.prng.xorshift128.RandomState()\r\n    rs.random_sample(100)\r\n\r\n    rs = randomstate.prng.pcg64.RandomState()\r\n    rs.random_sample(100)\r\n\r\n    # Identical to NumPy\r\n    rs = randomstate.prng.mt19937.RandomState()\r\n    rs.random_sample(100)\r\n\r\nLike NumPy, ``randomstate`` also exposes a single instance of the\r\n``mt19937`` generator directly at the module level so that commands like\r\n\r\n.. code:: python\r\n\r\n    import randomstate\r\n    randomstate.standard_normal()\r\n    randomstate.exponential(1.0, 1.0, size=10)\r\n\r\nwill work.\r\n\r\nLicense\r\n-------\r\n\r\nStandard NCSA, plus sub licenses for components.\r\n\r\nPerformance\r\n-----------\r\n\r\nPerformance is promising, and even the mt19937 seems to be faster than\r\nNumPy's mt19937.\r\n\r\n::\r\n\r\n    Speed-up relative to NumPy (Uniform Doubles)\r\n    ************************************************************\r\n    randomstate.prng-dsfmt-random_sample               313.5%\r\n    randomstate.prng-mlfg_1279_861-random_sample       459.4%\r\n    randomstate.prng-mrg32k3a-random_sample            -57.6%\r\n    randomstate.prng-mt19937-random_sample              72.5%\r\n    randomstate.prng-pcg32-random_sample               232.8%\r\n    randomstate.prng-pcg64-random_sample               330.6%\r\n    randomstate.prng-xoroshiro128plus-random_sample    609.9%\r\n    randomstate.prng-xorshift1024-random_sample        348.8%\r\n    randomstate.prng-xorshift128-random_sample         489.7%\r\n\r\n    Speed-up relative to NumPy (Normals using Box-Muller)\r\n    ************************************************************\r\n    randomstate.prng-dsfmt-standard_normal                26.8%\r\n    randomstate.prng-mlfg_1279_861-standard_normal        30.9%\r\n    randomstate.prng-mrg32k3a-standard_normal            -14.8%\r\n    randomstate.prng-mt19937-standard_normal              17.7%\r\n    randomstate.prng-pcg32-standard_normal                24.5%\r\n    randomstate.prng-pcg64-standard_normal                26.2%\r\n    randomstate.prng-xoroshiro128plus-standard_normal     31.4%\r\n    randomstate.prng-xorshift1024-standard_normal         27.4%\r\n    randomstate.prng-xorshift128-standard_normal          30.3%\r\n\r\n    Speed-up relative to NumPy (Normals using Ziggurat)\r\n    ************************************************************\r\n    randomstate.prng-dsfmt-standard_normal               491.7%\r\n    randomstate.prng-mlfg_1279_861-standard_normal       439.6%\r\n    randomstate.prng-mrg32k3a-standard_normal            101.2%\r\n    randomstate.prng-mt19937-standard_normal             354.4%\r\n    randomstate.prng-pcg32-standard_normal               531.0%\r\n    randomstate.prng-pcg64-standard_normal               517.9%\r\n    randomstate.prng-xoroshiro128plus-standard_normal    674.0%\r\n    randomstate.prng-xorshift1024-standard_normal        486.7%\r\n    randomstate.prng-xorshift128-standard_normal         617.0%\r\n\r\n.. |Travis Build Status| image:: https://travis-ci.org/bashtage/ng-numpy-randomstate.svg?branch=master\r\n   :target: https://travis-ci.org/bashtage/ng-numpy-randomstate\r\n.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/odc5c4ukhru5xicl/branch/master?svg=true\r\n   :target: https://ci.appveyor.com/project/bashtage/ng-numpy-randomstate/branch/master\r\n.. |PyPI version| image:: https://badge.fury.io/py/randomstate.svg\r\n   :target: https://badge.fury.io/py/randomstate\r\n\r\n\r\n",
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