BENCH: convert bencmarks to asv format

remove files that were part of the origal repo
rework randomgen docs to integrate with numpy and fix some links
remove convenience functions, require explicit call to gen.brng
move code out of numpy.random.randomgen into numpy.random

4 files changed, 76 insertions, 79 deletions

diff --git a/doc/source/reference/randomgen/extending.rst b/doc/source/reference/randomgen/extending.rst
index c9d987b59..64f2ddcc1 100644
--- a/doc/source/reference/randomgen/extending.rst
+++ b/doc/source/reference/randomgen/extending.rst
@@ -59,6 +59,8 @@ directly in Numba after compiling the file distributions.c into a DLL or so.
 An example showing the use of a more complicated distribution is in the
 examples folder.
 
+.. _randomgen_cython:
+
 Cython
 ======
 
diff --git a/doc/source/reference/randomgen/generator.rst b/doc/source/reference/randomgen/generator.rst
index d59efd68c..54325f4d3 100644
--- a/doc/source/reference/randomgen/generator.rst
+++ b/doc/source/reference/randomgen/generator.rst
@@ -1,29 +1,26 @@
+.. currentmodule:: numpy.random.randomgen
+
 Random Generator
 ----------------
-The :class:`~randomgen.generator.RandomGenerator` provides access to 
-a wide range of distributions, and served as a replacement for 
-:class:`~numpy.random.RandomState`.  The main difference between 
-the two is that :class:`~randomgen.generator.RandomGenerator` relies
-on an additional basic RNG to manage state and generate the random 
-bits which are then transformed into random values from useful 
-distributions. The default basic RNG used by 
-:class:`~randomgen.generator.RandomGenerator` is 
-:class:`~randomgen.xoroshiro128.Xoroshiro128`.  The basic RNG can be 
-changed by passing an instantized basic RNG to 
-:class:`~randomgen.generator.RandomGenerator`. 
+The :class:`~RandomGenerator` provides access to
+a wide range of distributions, and served as a replacement for
+:class:`~numpy.random.RandomState`.  The main difference between
+the two is that ``RandomGenerator`` relies on an additional basic RNG to
+manage state and generate the random bits, which are then transformed into
+random values from useful distributions. The default basic RNG used by
+``RandomGenerator`` is :class:`~xoroshiro128.Xoroshiro128`.  The basic RNG can be
+changed by passing an instantized basic RNG to ``RandomGenerator``.
 
-.. currentmodule:: numpy.random.randomgen.generator
 
 .. autoclass:: RandomGenerator
 	:exclude-members:
 
-Seed and State Manipulation
-===========================
+Accessing the RNG
+=================
 .. autosummary::
    :toctree: generated/
 
-   ~RandomGenerator.seed
-   ~RandomGenerator.state
+   ~RandomGenerator.brng
 
 Simple random data
 ==================
diff --git a/doc/source/reference/randomgen/index.rst b/doc/source/reference/randomgen/index.rst
index 67d0441a2..a8b6f1c9b 100644
--- a/doc/source/reference/randomgen/index.rst
+++ b/doc/source/reference/randomgen/index.rst
@@ -1,17 +1,21 @@
-Randomgen.RandomGen
-===================
-This package contains replacements for the NumPy
-:class:`~numpy.random.RandomState` object that allows the core random number
-generator be be changed.
-
 .. current_module numpy.random.randomgen
 
+numpy.random.randomgen
+======================
+
+This package modernizes the legacy
+`~numpy.random.RandomState` object and allows changing the core random
+number generator (RNG). The `~numpy.random.randomgen.RandomGenerator` can
+be initialized with a number of different RNGs, and exposes many different
+probability distributions.
+
+
 Quick Start
 -----------
 
-Like :mod:`numpy.random`, RandomGen can be used at the module level. 
-This uses the default :class:`~randomgen.generator.RandomGenerator` which 
-uses normals provided by :class:`~randomgen.xoroshiro128.Xoroshiro128`.
+By default, `generator.RandomGenerator` uses normals provided by
+`xoroshiro128.Xoroshiro128` which will be faster than the legacy methods in
+`numpy.random`
 
 .. code-block:: python
 
@@ -19,12 +23,11 @@ uses normals provided by :class:`~randomgen.xoroshiro128.Xoroshiro128`.
   import randomgen.generator as random
   random.standard_normal()
 
-:class:`~randomgen.generator.RandomGenerator` can also be used as a 
-replacement for :class:`~numpy.random.RandomState`, although the random
-values are generated by :class:`~randomgen.xoroshiro128.Xoroshiro128`. It 
-also isn't possible to directly seed a 
-:class:`~randomgen.generator.RandomGenerator`.
-
+`numpy.random.randomgen.RandomGenerator` can also be used as a replacement for
+`~numpy.random.RandomState`, although the random values are generated by
+`~numpyrandom.randomgen.xoroshiro128.Xoroshiro128`. Since ``randomgen``
+separates the `~numpy.random.randomgen.RandomGenerator` from the RNG, it is not
+possible to directly seed the generator.
 
 .. code-block:: python
 
@@ -34,9 +37,9 @@ also isn't possible to directly seed a
   rg.standard_normal()
 
 
-Seeds can be passed to any of the basic RNGs. Here :class:`~randomgen.mt19937.MT19937`
-is used and the :class:`~randomgen.generator.RandomGenerator` is accessed via 
-the property :attr:`~randomgen.mt19937.MT19937.generator`.
+Seeds can be passed to any of the basic RNGs. Here `numpy.random.randomgen.
+mt19937.MT19937` is used and the `~numpy.random.randomgen.RandomGenerator` is
+accessed via the attribute `~numpy.random.randomgen.mt19937.MT19937.generator`.
 
 .. code-block:: python
 
@@ -48,24 +51,23 @@ the property :attr:`~randomgen.mt19937.MT19937.generator`.
 Introduction
 ------------
 RandomGen takes a different approach to producing random numbers from the
-:class:`numpy.random.RandomState` object used in NumPy.  Random number
-generation is separated into two components, a basic RNG and a random
-generator.
+:class:`numpy.random.RandomState` object.  Random number generation is
+separated into two components, a basic RNG and a random generator.
 
-The basic RNG has a limited set of responsibilities -- it manages the
+The basic RNG has a limited set of responsibilities. It manages the
 underlying RNG state and provides functions to produce random doubles and
 random unsigned 32- and 64-bit values. The basic random generator also handles
 all seeding since this varies when using alternative basic RNGs.
 
-The random generator (:class:`~randomgen.generator.RandomGenerator`) takes the
+The `random generator <~numpy.random.randomgen.RandomGenerator>` takes the
 basic RNG-provided functions and transforms them into more useful
 distributions, e.g., simulated normal random values. This structure allows
 alternative basic RNGs to be used without code duplication.
 
-The :class:`~randomgen.generator.RandomGenerator` is the user-facing object
+The `~numpy.random.randomgen.RandomGenerator` is the user-facing object
 that is nearly identical to :class:`~numpy.random.RandomState`. The canonical
-method to initialize a generator passes a basic RNG --
-:class:`~randomgen.mt19937.MT19937`, the underlying RNG in NumPy  -- as the
+method to initialize a generator passes a basic RNG -- `~numpy.random.
+randomgen.mt19937.MT19937`, the underlying RNG in NumPy  -- as the
 sole argument. Note that the basic RNG must be instantized.
 
 .. code-block:: python
@@ -81,9 +83,9 @@ Seed information is directly passed to the basic RNG.
   rg = RandomGenerator(MT19937(12345))
   rg.random_sample()
 
-A shorthand method is also available which uses the
-:meth:`~randomgen.mt19937.MT19937.generator` property from a basic RNG to
-access an embedded random generator.
+A shorthand method is also available which uses the `~numpy.random.randomgen.
+mt19937.MT19937.generator` property from a basic RNG to access an embedded
+random generator.
 
 .. code-block:: python
 
@@ -95,13 +97,14 @@ What's New or Different
 .. warning::
 
   The Box-Muller method used to produce NumPy's normals is no longer available
-  in :class:`~randomgen.generator.RandomGenerator`.  It is not possible to 
-  reproduce the random values using :class:`~randomgen.generator.RandomGenerator` 
-  for the normal distribution or any other distribution that relies on the
-  normal such as the gamma or student's t. If you require backward compatibility, a 
-  legacy generator, :class:`~randomgen.legacy.LegacyGenerator`, has been created
-  which can fully reproduce the sequence produced by NumPy.
-  
+  in `~numpy.random.randomgen.RandomGenerator`.  It is not possible to
+  reproduce the random values using `~numpy.random.randomgen.RandomGenerator`
+  for the normal distribution or any other distribution that
+  relies on the normal such as the gamma or student's t. If you require
+  backward compatibility, a legacy generator, `~numpy.random.randomgen.legacy.
+  LegacyGenerator`, has been created which can fully reproduce the sequence
+  produced by NumPy.
+
 * The normal, exponential and gamma generators use 256-step Ziggurat
   methods which are 2-10 times faster than NumPy's Box-Muller or inverse CDF
   implementations.
@@ -110,20 +113,18 @@ What's New or Different
   select distributions
 * Optional ``out`` argument that allows existing arrays to be filled for
   select distributions
-* Simulate from the complex normal distribution
-  (:meth:`~randomgen.generator.RandomGenerator.complex_normal`)
-* :func:`~randomgen.entropy.random_entropy` provides access to the system
+* `~numpy.random.randomgen.entropy.random_entropy` provides access to the system
   source of randomness that is used in cryptographic applications (e.g.,
   ``/dev/urandom`` on Unix).
-* All basic random generators functions to produce doubles, uint64s and
-  uint32s via CTypes (:meth:`~randomgen.xoroshiro128.Xoroshiro128.ctypes`)
-  and CFFI (:meth:`~randomgen.xoroshiro128.Xoroshiro128.cffi`).  This allows
-  these basic RNGs to be used in numba.
+* All basic random generators functions can produce doubles, uint64s and
+  uint32s via CTypes (`~numpy.random.randomgen.xoroshiro128.Xoroshiro128.ctypes`)
+  and CFFI (:meth:`~numpy.random.randomgen.xoroshiro128.Xoroshiro128.cffi`).
+  This allows these basic RNGs to be used in numba.
 * The basic random number generators can be used in downstream projects via
-  Cython.
+  :ref:`Cython <randomgen_cython>`.
 * Support for Lemire’s method [Lemire]_ of generating uniform integers on an
   arbitrary interval by setting ``use_masked=True`` in
-  (:meth:`~randomgen.generator.RandomGenerator.randint`).
+  `~umpy.random.randomgen.generator.RandomGenerator.randint`.
 
 
 See :ref:`new-or-different` for a complete list of improvements and
@@ -144,9 +145,9 @@ The main innovation is the inclusion of a number of alternative pseudo-random nu
 generators, 'in addition' to the standard PRNG in NumPy.  The included PRNGs are:
 
 * MT19937 - The standard NumPy generator.  Produces identical results to NumPy
-  using the same seed/state. Adds a jump function that advances the generator
-  as-if 2**128 draws have been made (:meth:`~randomgen.mt19937.MT19937.jump`).
-  See `NumPy's documentation`_.
+  using the same seed/state. Adds a
+  `~numpy.random.randomgen.mt19937.MT19937.jump` function that advances the
+  generator as-if ``2**128`` draws have been made.  See `numpy.random`.
 * dSFMT - SSE2 enabled versions of the MT19937 generator.  Theoretically
   the same, but with a different state and so it is not possible to produce a
   sequence identical to MT19937. Supports ``jump`` and so can
@@ -154,31 +155,30 @@ generators, 'in addition' to the standard PRNG in NumPy.  The included PRNGs are
 * XoroShiro128+ - Improved version of XorShift128+ with better performance
   and statistical quality. Like the XorShift generators, it can be jumped
   to produce multiple streams in parallel applications. See
-  :meth:`~randomgen.xoroshiro128.Xoroshiro128.jump` for details.
+  `~numpy.random.randomgen.xoroshiro128.Xoroshiro128.jump` for details.
   More information about this PRNG is available at the
   `xorshift, xoroshiro and xoshiro authors' page`_.
 * XorShift1024*φ - Fast fast generator based on the XSadd
   generator. Supports ``jump`` and so can be used in
   parallel applications. See the documentation for
-  :meth:`~randomgen.xorshift1024.Xorshift1024.jump` for details. More information
-  about these PRNGs is available at the
+  `~numpy.random.randomgen.xorshift1024.Xorshift1024.jump` for details. More
+  information about these PRNGs is available at the
   `xorshift, xoroshiro and xoshiro authors' page`_.
 * Xorshiro256** and Xorshiro512** - The most recently introduced XOR,
   shift, and rotate generator. Supports ``jump`` and so can be used in
   parallel applications. See the documentation for
-  :meth:`~randomgen.xoshiro256starstar.Xoshirt256StarStar.jump` for details. More
-  information about these PRNGs is available at the
+  `~numpy.random.randomgen.xoshiro256starstar.Xoshirt256StarStar.jump` for
+  details. More information about these PRNGs is available at the
   `xorshift, xoroshiro and xoshiro authors' page`_.
 * PCG-64 - Fast generator that support many parallel streams and
   can be advanced by an arbitrary amount. See the documentation for
-  :meth:`~randomgen.pcg64.PCG64.advance`. PCG-64 has a period of
+  `~numpy.random.randomgen.pcg64.PCG64.advance`. PCG-64 has a period of
   :math:`2^{128}`. See the `PCG author's page`_ for more details about
   this class of PRNG.
 * ThreeFry and Philox - counter-based generators capable of being advanced an
   arbitrary number of steps or generating independent streams. See the
   `Random123`_ page for more details about this class of PRNG.
 
-.. _`NumPy's documentation`: https://docs.scipy.org/doc/numpy/reference/routines.random.html
 .. _`dSFMT authors' page`: http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/
 .. _`xorshift, xoroshiro and xoshiro authors' page`:  http://xoroshiro.di.unimi.it/
 .. _`PCG author's page`: http://www.pcg-random.org/
@@ -215,14 +215,12 @@ New Features
 
 Changes
 ~~~~~~~
+
+This package was developed independently of NumPy and was integrated in version
+1.17.0. The original repo is at https://github.com/bashtage/randomgen.
+
 .. toctree::
    :maxdepth: 2
 
    Change Log <change-log>
 
-Indices and tables
-~~~~~~~~~~~~~~~~~~
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/source/reference/randomgen/new-or-different.rst b/doc/source/reference/randomgen/new-or-different.rst
index 0a27b9aa1..089efd6fb 100644
--- a/doc/source/reference/randomgen/new-or-different.rst
+++ b/doc/source/reference/randomgen/new-or-different.rst
@@ -64,9 +64,9 @@ What's New or Different
 
 .. ipython:: python
 
-  rg.seed(0)
+  rg.brng.seed(0)
   rg.random_sample(3, dtype='d')
-  rg.seed(0)
+  rg.brng.seed(0)
   rg.random_sample(3, dtype='f')
 
 * Optional ``out`` argument that allows existing arrays to be filled for