Metadata-Version: 1.1
Name: randomgen
Version: 1.16.6
Summary: Random generator supporting multiple PRNGs
Home-page: https://github.com/bashtage/randomgen
Author: Kevin Sheppard
Author-email: kevin.k.sheppard@gmail.com
License: NCSA
Description: RandomGen
        =========
        
        Random Number Generator using settable Basic RNG interface for future
        NumPy RandomState evolution.
        
        Continuous Integration
        ~~~~~~~~~~~~~~~~~~~~~~
        
        |Travis Build Status| |Appveyor Build Status| |Build Status|
        
        Coverage
        ~~~~~~~~
        
        |Coverage Status| |codecov|
        
        Latest Release
        ~~~~~~~~~~~~~~
        
        |PyPI version| |Anacnoda Cloud|
        
        License
        ~~~~~~~
        
        |NCSA License| |BSD License| |DOI|
        
        This is a library and generic interface for alternative random
        generators in Python and NumPy.
        
        Python 2.7 Support
        ------------------
        
        v1.16 is the final major version that supports Python 2.7. Any bugs in
        v1.16 will be patched until the end of 2019. All future releases are
        Python 3, with an initial minimum version of 3.5.
        
        Compatibility Warning
        ---------------------
        
        ``RandomGenerator`` does not support Box-Muller normal variates and so
        it not 100% compatible with NumPy (or randomstate). Box-Muller normals
        are slow to generate and all functions which previously relied on
        Box-Muller normals now use the faster Ziggurat implementation. If you
        require backward compatibility, a legacy generator, ``RandomState``, has
        been created which can fully reproduce the sequence produced by NumPy.
        
        Features
        --------
        
        -  Replacement for NumPy’s RandomState
        
           .. code:: python
        
              from randomgen import RandomGenerator, MT19937
              rnd = RandomGenerator(MT19937())
              x = rnd.standard_normal(100)
              y = rnd.random_sample(100)
              z = rnd.randn(10,10)
        
        -  Default random generator is a fast generator called Xoroshiro128plus
        -  Support for random number generators that support independent streams
           and jumping ahead so that sub-streams can be generated
        -  Faster random number generation, especially for normal, standard
           exponential and standard gamma using the Ziggurat method
        
           .. code:: python
        
              from randomgen import RandomGenerator
              # Default basic PRNG is Xoroshiro128
              rnd = RandomGenerator()
              w = rnd.standard_normal(10000)
              x = rnd.standard_exponential(10000)
              y = rnd.standard_gamma(5.5, 10000)
        
        -  Support for 32-bit floating randoms for core generators. Currently
           supported:
        
           -  Uniforms (``random_sample``)
           -  Exponentials (``standard_exponential``, both Inverse CDF and
              Ziggurat)
           -  Normals (``standard_normal``)
           -  Standard Gammas (via ``standard_gamma``)
        
           **WARNING**: The 32-bit generators are **experimental** and subject
           to change.
        
           **Note**: There are *no* plans to extend the alternative precision
           generation to all distributions.
        
        -  Support for filling existing arrays using ``out`` keyword argument.
           Currently supported in (both 32- and 64-bit outputs)
        
           -  Uniforms (``random_sample``)
           -  Exponentials (``standard_exponential``)
           -  Normals (``standard_normal``)
           -  Standard Gammas (via ``standard_gamma``)
        
        -  Support for Lemire’s method of generating uniform integers on an
           arbitrary interval by setting ``use_masked=True``.
        
        Included Pseudo Random Number Generators
        ----------------------------------------
        
        This module includes a number of alternative random number generators in
        addition to the MT19937 that is included in NumPy. The RNGs include:
        
        -  `MT19937 <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/>`__,
           the NumPy rng
        -  `dSFMT <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/>`__ a
           SSE2-aware version of the MT19937 generator that is especially fast
           at generating doubles
        -  `xoroshiro128+ <http://xoroshiro.di.unimi.it/>`__,
           `xorshift1024*φ <http://xorshift.di.unimi.it/>`__,
           `xoshiro256*\* <http://xorshift.di.unimi.it/>`__, and
           `xoshiro512*\* <http://xorshift.di.unimi.it/>`__
        -  `PCG64 <http://www.pcg-random.org/>`__
        -  ThreeFry and Philox from
           `Random123 <https://www.deshawresearch.com/resources_random123.html>`__
        
        Differences from ``numpy.random.RandomState``
        ---------------------------------------------
        
        New Features
        ~~~~~~~~~~~~
        
        -  ``standard_normal``, ``normal``, ``randn`` and
           ``multivariate_normal`` all use the much faster (100%+) Ziggurat
           method.
        -  ``standard_gamma`` and ``gamma`` both use the much faster Ziggurat
           method.
        -  ``standard_exponential`` ``exponential`` both support an additional
           ``method`` keyword argument which can be ``inv`` or ``zig`` where
           ``inv`` corresponds to the current method using the inverse CDF and
           ``zig`` uses the much faster (100%+) Ziggurat method.
        -  Core random number generators can produce either single precision
           (``np.float32``) or double precision (``np.float64``, the default)
           using the optional keyword argument ``dtype``
        -  Core random number generators can fill existing arrays using the
           ``out`` keyword argument
        -  Standardizes integer-values random values as int64 for all platforms.
        -  ``randint`` supports generating using rejection sampling on masked
           values (the default) or Lemire’s method. Lemire’s method can be much
           faster when the required interval length is much smaller than the
           closes power of 2.
        
        New Functions
        ~~~~~~~~~~~~~
        
        -  ``random_entropy`` - Read from the system entropy provider, which is
           commonly used in cryptographic applications
        -  ``random_raw`` - Direct access to the values produced by the
           underlying PRNG. The range of the values returned depends on the
           specifics of the PRNG implementation.
        -  ``random_uintegers`` - unsigned integers, either 32-
           (``[0, 2**32-1]``) or 64-bit (``[0, 2**64-1]``)
        -  ``jump`` - Jumps RNGs that support it. ``jump`` moves the state a
           great distance. *Only available if supported by the RNG.*
        -  ``advance`` - Advanced the RNG ‘as-if’ a number of draws were made,
           without actually drawing the numbers. *Only available if supported by
           the RNG.*
        
        Status
        ------
        
        -  Builds and passes all tests on:
        
           -  Linux 32/64 bit, Python 2.7, 3.5, 3.6, 3.7
           -  PC-BSD (FreeBSD) 64-bit, Python 2.7
           -  OSX 64-bit, Python 2.7, 3.5, 3.6, 3.7
           -  Windows 32/64 bit, Python 2.7, 3.5 and 3.6, 3.7
        
        Version
        -------
        
        The package version matches the latest version of NumPy where
        ``RandomState(MT19937())`` passes all NumPy test.
        
        Documentation
        -------------
        
        Documentation for the latest release is available on `my GitHub
        pages <http://bashtage.github.io/randomgen/>`__. Documentation for the
        latest commit (unreleased) is available under
        `devel <http://bashtage.github.io/randomgen/devel/>`__.
        
        Plans
        -----
        
        This module is essentially complete. There are a few rough edges that
        need to be smoothed.
        
        -  Creation of additional streams from where supported (i.e. a
           ``next_stream()`` method)
        
        Requirements
        ------------
        
        Building requires:
        
        -  Python (2.7, 3.5, 3.6, 3.7)
        -  NumPy (1.13, 1.14, 1.15, 1.16)
        -  Cython (0.26+)
        -  tempita (0.5+), if not provided by Cython
        
        Testing requires pytest (4.0+).
        
        **Note:** it might work with other versions but only tested with these
        versions.
        
        Development and Testing
        -----------------------
        
        All development has been on 64-bit Linux, and it is regularly tested on
        Travis-CI (Linux/OSX) and Appveyor (Windows). The library is
        occasionally tested on Linux 32-bit and Free BSD 11.1.
        
        Basic tests are in place for all RNGs. The MT19937 is tested against
        NumPy’s implementation for identical results. It also passes NumPy’s
        test suite where still relevant.
        
        Installing
        ----------
        
        Either install from PyPi using
        
        .. code:: bash
        
           pip install randomgen
        
        or, if you want the latest version,
        
        .. code:: bash
        
           pip install git+https://github.com/bashtage/randomgen.git
        
        or from a cloned repo,
        
        .. code:: bash
        
           python setup.py install
        
        SSE2
        ~~~~
        
        ``dSFTM`` makes use of SSE2 by default. If you have a very old computer
        or are building on non-x86, you can install using:
        
        .. code:: bash
        
           python setup.py install --no-sse2
        
        Windows
        ~~~~~~~
        
        Either use a binary installer, or if building from scratch, use Python
        3.6 with Visual Studio 2015/2017 Community Edition. It can also be build
        using Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7.
        
        Using
        -----
        
        The separate generators are importable from ``randomgen``
        
        .. code:: python
        
           from randomgen import RandomGenerator, ThreeFry, PCG64, MT19937
           rg = RandomGenerator(ThreeFry())
           rg.random_sample(100)
        
           rg = RandomGenerator(PCG64())
           rg.random_sample(100)
        
           # Identical to NumPy
           rg = RandomGenerator(MT19937())
           rg.random_sample(100)
        
        .. _license-1:
        
        License
        -------
        
        Standard NCSA, plus sub licenses for components.
        
        Performance
        -----------
        
        Performance is promising, and even the mt19937 seems to be faster than
        NumPy’s mt19937.
        
        ::
        
           Speed-up relative to NumPy (Uniform Doubles)
           ************************************************************
           DSFMT                 184.9%
           MT19937                17.3%
           PCG32                  83.3%
           PCG64                 108.3%
           Philox                 -4.9%
           ThreeFry              -12.0%
           ThreeFry32            -63.9%
           Xoroshiro128          159.5%
           Xorshift1024          150.4%
           Xoshiro256StarStar    145.7%
           Xoshiro512StarStar    113.1%
        
           Speed-up relative to NumPy (64-bit unsigned integers)
           ************************************************************
           DSFMT                  17.4%
           MT19937                 7.8%
           PCG32                  60.3%
           PCG64                  73.5%
           Philox                -25.5%
           ThreeFry              -30.5%
           ThreeFry32            -67.8%
           Xoroshiro128          124.0%
           Xorshift1024          109.4%
           Xoshiro256StarStar    100.3%
           Xoshiro512StarStar     63.5%
        
           Speed-up relative to NumPy (Standard normals)
           ************************************************************
           DSFMT                 183.0%
           MT19937               169.0%
           PCG32                 240.7%
           PCG64                 231.6%
           Philox                131.3%
           ThreeFry              118.3%
           ThreeFry32             21.6%
           Xoroshiro128          332.1%
           Xorshift1024          232.4%
           Xoshiro256StarStar    306.6%
           Xoshiro512StarStar    274.6%
        
        .. |Travis Build Status| image:: https://travis-ci.org/bashtage/randomgen.svg?branch=master
           :target: https://travis-ci.org/bashtage/randomgen
        .. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/odc5c4ukhru5xicl/branch/master?svg=true
           :target: https://ci.appveyor.com/project/bashtage/randomgen/branch/master
        .. |Build Status| image:: https://cloud.drone.io/api/badges/bashtage/randomgen/status.svg
           :target: https://cloud.drone.io/bashtage/randomgen
        .. |Coverage Status| image:: https://coveralls.io/repos/github/bashtage/randomgen/badge.svg
           :target: https://coveralls.io/github/bashtage/randomgen
        .. |codecov| image:: https://codecov.io/gh/bashtage/randomgen/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/bashtage/randomgen
        .. |PyPI version| image:: https://badge.fury.io/py/randomgen.svg
           :target: https://pypi.org/project/randomgen/
        .. |Anacnoda Cloud| image:: https://anaconda.org/bashtage/randomgen/badges/version.svg
           :target: https://anaconda.org/bashtage/randomgen
        .. |NCSA License| image:: https://img.shields.io/badge/License-NCSA-blue.svg
           :target: https://opensource.org/licenses/NCSA
        .. |BSD License| image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
           :target: https://opensource.org/licenses/BSD-3-Clause
        .. |DOI| image:: https://zenodo.org/badge/122181085.svg
           :target: https://zenodo.org/badge/latestdoi/122181085
        
Keywords: pseudo random numbers,PRNG,RNG,RandomState,random,random numbers,parallel random numbers,PCG,XorShift,dSFMT,MT19937,Random123,ThreeFry,Philox
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Artistic Software
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Security :: Cryptography
