ENH: Add closed generator to randint

Add closed option to randint to simplify some cases

doc/source/about.rst

@@ -8,7 +8,7 @@ needed for scientific computing with Python. This package contains:
 - sophisticated :ref:`(broadcasting) functions <ufuncs>`
 - basic :ref:`linear algebra functions <routines.linalg>`
 - basic :ref:`Fourier transforms <routines.fft>`
-- sophisticated :ref:`random number capabilities <routines.random>`
+- sophisticated :ref:`random number capabilities <numpyrandom>`
 - tools for integrating Fortran code
 - tools for integrating C/C++ code
 

doc/source/conf.py

@@ -364,7 +364,3 @@ def linkcode_resolve(domain, info):
         return "https://github.com/numpy/numpy/blob/v%s/numpy/%s%s" % (
            numpy.__version__, fn, linespec)
 
-doctest_global_setup = '''
-import numpy as np
-from numpy.random import randomgen
-'''

doc/source/reference/random/index.rst

@@ -1,14 +1,10 @@
+.. _numpyrandom:
+
 .. currentmodule:: numpy.random
 
 numpy.random
 ============
 
-<<<<<<< HEAD
-A `~RandomGenerator` can
-be initialized with a number of different Random Number Generators (RNG)s, and
-exposes many different probability distributions.
-
-=======
 Numpy's random number routines produce psuedo random numbers using
 combinations of a `BitGenerator` to create sequences and a `Generator`
 to use those sequences to sample from different statistical distributions:
@@ -30,37 +26,23 @@ available, but limited to a single BitGenerator.
 For convenience and backward compatibility, a single `RandomState`
 instance's methods are imported into the numpy.random namespace, see
 :ref:`legacy` for the complete list.
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 
 Quick Start
 -----------
 
-<<<<<<< HEAD
-By default, `RandomGenerator` uses normals provided by
-`xoroshiro128.Xoroshiro128` which will be faster than the legacy methods in
-`mtrand.RandomState`
-=======
 By default, `Generator` uses normals provided by `xoroshiro128.Xoroshiro128`
 which will be faster than the legacy methods in `RandomState`
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 
 .. code-block:: python
 
   # Uses the old numpy.random.RandomState
   from numpy import random
   random.standard_normal()
 
-<<<<<<< HEAD
-`RandomGenerator` can be used as a direct replacement for
-`~RandomState`, although the random values are generated by
-`~xoroshiro128.Xoroshiro128`. The `RandomGenerator` holds an instance of a RNG.
-It is accessable as ``gen.brng``.
-=======
 `Generator` can be used as a direct replacement for `RandomState`,
 although the random values are generated by `~xoroshiro128.Xoroshiro128`. The
 `Generator` holds an instance of a BitGenerator. It is accessable as
 ``gen.bit_generator``.
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 
 .. code-block:: python
 
@@ -85,49 +67,23 @@ generator`.
 Introduction
 ------------
 RandomGen takes a different approach to producing random numbers from the
-<<<<<<< HEAD
-:class:`numpy.random.RandomState` object.  Random number generation is
-separated into two components, a basic RNG and a random generator.
-=======
 `RandomState` object.  Random number generation is separated into two
 components, a bit generator and a random generator.
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 
 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.
 
-<<<<<<< HEAD
-The `random generator <~RandomGenerator>` takes the
-basic RNG-provided functions and transforms them into more useful
-=======
 The `random generator <Generator>` takes the
 bit generator-provided stream and transforms them into more useful
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 distributions, e.g., simulated normal random values. This structure allows
 alternative basic RNGs to be used without code duplication.
 
-<<<<<<< HEAD
-The ``RandomGenerator`` is the user-facing object
-that is nearly identical to :class:`~.RandomState`. The canonical
-method to initialize a generator passes a basic RNG -- `~mt19937.MT19937`, the
-underlying RNG in NumPy  -- as the sole argument. Note that the basic RNG must
-be instantized.
-
-.. code-block:: python
-
-  from numpy.random import RandomGenerator, MT19937
-  rg = RandomGenerator(MT19937())
-  rg.random_sample()
-
-Seed information is directly passed to the basic RNG.
-=======
 The `Generator` is the user-facing object that is nearly identical to
 `RandomState`. The canonical method to initialize a generator passes a
 `~mt19937.MT19937` bit generator, the underlying bit generator in Python -- as
 the sole argument. Note that the bit generator must be instantiated.
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
 
 .. code-block:: python
 
@@ -147,13 +103,8 @@ What's New or Different
 .. warning::
 
   The Box-Muller method used to produce NumPy's normals is no longer available
-<<<<<<< HEAD
-  in `~.RandomGenerator`.  It is not possible to reproduce the exact random
-  values using ``RandomGenerator`` for the normal distribution or any other
-=======
   in `Generator`.  It is not possible to reproduce the exact random
   values using Generator for the normal distribution or any other
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
   distribution that relies on the normal such as the `numpy.random.gamma` or
   `numpy.random.standard_t`. If you require bitwise backward compatible
   streams, use `RandomState`.
@@ -169,17 +120,6 @@ What's New or Different
 * `~entropy.random_entropy` provides access to the system
   source of randomness that is used in cryptographic applications (e.g.,
   ``/dev/urandom`` on Unix).
-<<<<<<< HEAD
-* All basic random generators functions can produce doubles, uint64s and
-  uint32s via CTypes (`~xoroshiro128.Xoroshiro128.ctypes`)
-  and CFFI (:meth:`~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
-  :ref:`Cython <randomgen_cython>`.
-* Support for Lemire’s method [Lemire]_ of generating uniform integers on an
-  arbitrary interval by setting ``use_masked=True`` in
-  `~RandomGenerator.randint`.
-=======
 * All BitGenerators can produce doubles, uint64s and uint32s via CTypes
   (`~xoroshiro128.Xoroshiro128.ctypes`) and CFFI
   (:meth:`~xoroshiro128.Xoroshiro128.cffi`). This allows the bit generators to
@@ -195,8 +135,6 @@ What's New or Different
   random numbers, which replaces `random_sample`, `sample`, and `ranf`. This
   is consistent with Python's `random.random`.
 
->>>>>>> d7650d9f2... DOC: use more BitGenerator instead of other termonology
-
 See :ref:`new-or-different` for a complete list of improvements and
 differences.
 

doc/source/reference/routines.rst

@@ -41,7 +41,6 @@ indentation.
    routines.other
    routines.padding
    routines.polynomials
-   routines.random
    random/index
    routines.set
    routines.sort

numpy/random/bounded_integers.pxd.in

@@ -22,5 +22,5 @@ py:
 inttypes = ('uint64','uint32','uint16','uint8','bool','int64','int32','int16','int8')
 }}
 {{for inttype in inttypes}}
-cdef object _rand_{{inttype}}(object low, object high, object size, bint use_masked, brng_t *state, object lock)
+cdef object _rand_{{inttype}}(object low, object high, object size, bint use_masked, bint closed, brng_t *state, object lock)
 {{endfor}}