Merge 6b910e1 into 433d83b

README.md

@@ -31,7 +31,7 @@ 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
+`Generator` 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
@@ -43,8 +43,8 @@ which can fully reproduce the sequence produced by NumPy.
 * Replacement for NumPy's RandomState
 
   ```python
-  from randomgen import RandomGenerator, MT19937
-  rnd = RandomGenerator(MT19937())
+  from randomgen import Generator, MT19937
+  rnd = Generator(MT19937())
   x = rnd.standard_normal(100)
   y = rnd.random(100)
   z = rnd.randn(10,10)
@@ -57,9 +57,9 @@ which can fully reproduce the sequence produced by NumPy.
   exponential and standard gamma using the Ziggurat method
 
   ```python
-  from randomgen import RandomGenerator
+  from randomgen import Generator
   # Default basic PRNG is Xoroshiro128
-  rnd = RandomGenerator()
+  rnd = Generator()
   w = rnd.standard_normal(10000)
   x = rnd.standard_exponential(10000)
   y = rnd.standard_gamma(5.5, 10000)
@@ -237,15 +237,15 @@ Python 2.7.
 The separate generators are importable from `randomgen`
 
 ```python
-from randomgen import RandomGenerator, ThreeFry, PCG64, MT19937
-rg = RandomGenerator(ThreeFry())
+from randomgen import Generator, ThreeFry, PCG64, MT19937
+rg = Generator(ThreeFry())
 rg.random(100)
 
-rg = RandomGenerator(PCG64())
+rg = Generator(PCG64())
 rg.random(100)
 
 # Identical to NumPy
-rg = RandomGenerator(MT19937())
+rg = Generator(MT19937())
 rg.random(100)
 ```
 

README.rst

@@ -37,12 +37,12 @@ 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.
+``Generator`` 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
 --------
@@ -51,8 +51,8 @@ Features
 
    .. code:: python
 
-      from randomgen import RandomGenerator, MT19937
-      rnd = RandomGenerator(MT19937())
+      from randomgen import Generator, MT19937
+      rnd = Generator(MT19937())
       x = rnd.standard_normal(100)
       y = rnd.random(100)
       z = rnd.randn(10,10)
@@ -65,9 +65,9 @@ Features
 
    .. code:: python
 
-      from randomgen import RandomGenerator
+      from randomgen import Generator
       # Default basic PRNG is Xoroshiro128
-      rnd = RandomGenerator()
+      rnd = Generator()
       w = rnd.standard_normal(10000)
       x = rnd.standard_exponential(10000)
       y = rnd.standard_gamma(5.5, 10000)
@@ -263,15 +263,15 @@ The separate generators are importable from ``randomgen``
 
 .. code:: python
 
-   from randomgen import RandomGenerator, ThreeFry, PCG64, MT19937
-   rg = RandomGenerator(ThreeFry())
+   from randomgen import Generator, ThreeFry, PCG64, MT19937
+   rg = Generator(ThreeFry())
    rg.random(100)
 
-   rg = RandomGenerator(PCG64())
+   rg = Generator(PCG64())
    rg.random(100)
 
    # Identical to NumPy
-   rg = RandomGenerator(MT19937())
+   rg = Generator(MT19937())
    rg.random(100)
 
 .. _license-1:

benchmark.py

@@ -15,8 +15,8 @@
     rg = numpy.random.RandomState()
     rg.random_sample()
 else:
-    from randomgen import RandomGenerator, {brng}
-    rg = RandomGenerator({brng}())
+    from randomgen import Generator, {brng}
+    rg = Generator({brng}())
     rg.random()
 '''
 

doc/source/brng/index.rst

@@ -1,7 +1,7 @@
 Basic Random Number Generators
 ------------------------------
 
-The random values produced by :class:`~randomgen.generator.RandomGenerator` 
+The random values produced by :class:`~randomgen.generator.Generator`
 are produced by a basic RNG.  These basic RNGs do not directly provide
 random numbers and only contains methods used for seeding, getting or
 setting the state, jumping or advancing the state, and for accessing 

doc/source/change-log.rst

@@ -14,26 +14,26 @@ v1.16.5
   :func:`~randomgen.mtrand.RandomState.exponential`, and
   :func:`~randomgen.mtrand.RandomState.logistic` that could result in ``nan``
   values in rare circumstances (about 1 in :math:`10^{53}` draws).
-- Added keyword ``closed`` to :func:`~randomgen.generator.RandomGenerator.randint`
+- Added keyword ``closed`` to :func:`~randomgen.generator.Generator.randint`
   which changes sampling from the half-open interval ``[low, high)`` to the closed
   interval ``[low, high]``.
 - Fixed a bug in :func:`~randomgen.mtrand.RandomState.random_integers` that
   could lead to valid values being treated as invalid.
 
 v1.16.4
 =======
-- Add a fast path for broadcasting :func:`~randomgen.generator.RandomGenerator.randint`
+- Add a fast path for broadcasting :func:`~randomgen.generator.Generator.randint`
   when using ``uint64`` or ``int64``.
 - Refactor PCG64 so that it does not rely on Cython conditional compilation.
-- Add :func:`~randomgen.generator.RandomGenerator.brng` to access the basic RNG.
-- Allow multidimensional arrays in :func:`~randomgen.generator.RandomGenerator.choice`.
-- Speed-up :func:`~randomgen.generator.RandomGenerator.choice` when not replacing.
+- Add :func:`~randomgen.generator.Generator.brng` to access the basic RNG.
+- Allow multidimensional arrays in :func:`~randomgen.generator.Generator.choice`.
+- Speed-up :func:`~randomgen.generator.Generator.choice` when not replacing.
   The gains can be very large (1000x or more) when the input array is large but
   the sample size is small.
-- Add parameter checks in :func:`~randomgen.generator.RandomGenerator.multinomial`.
-- Fix an edge-case bug in :func:`~randomgen.generator.RandomGenerator.zipf`.
-- Allow 0 for sample in :func:`~randomgen.generator.RandomGenerator.hypergeometric`.
-- Add broadcasting to :func:`~randomgen.generator.RandomGenerator.multinomial` (see
+- Add parameter checks in :func:`~randomgen.generator.Generator.multinomial`.
+- Fix an edge-case bug in :func:`~randomgen.generator.Generator.zipf`.
+- Allow 0 for sample in :func:`~randomgen.generator.Generator.hypergeometric`.
+- Add broadcasting to :func:`~randomgen.generator.Generator.multinomial` (see
   `NumPy issue 9710 <https://github.com/numpy/numpy/pull/9710>`_)
 
 v1.16.3
@@ -48,7 +48,7 @@ v1.16.2
 
     - ``random_raw``, which have been moved to the individual basic RNGs
     - ``random_uintegers``, which can be replaced with
-      :func:`~randomgen.generator.RandomGenerator.randint`.
+      :func:`~randomgen.generator.Generator.randint`.
 
 - Added :class:`~randomgen.mtrand.RandomState` as a clone of NumPy's
   RandomState.

doc/source/extending.rst

@@ -2,7 +2,7 @@ Extending
 ---------
 The basic RNGs have been designed to be extendable using standard tools for
 high-performance Python -- numba and Cython.
-The :class:`randomgen.generator.RandomGenerator` object can also be used with
+The :class:`randomgen.generator.Generator` object can also be used with
 user-provided basic RNGs as long as these export a small set of required
 functions.
 
@@ -135,7 +135,7 @@ examples folder.
 
 New Basic RNGs
 ==============
-:class:`~randomgen.generator.RandomGenerator` can be used with other
+:class:`~randomgen.generator.Generator` can be used with other
 user-provided basic RNGs.  The simplest way to write a new basic RNG is to
 examine the pyx file of one of the existing basic RNGs. The key structure
 that must be provided is the ``capsule`` which contains a ``PyCapsule`` to a
@@ -156,7 +156,7 @@ used by the basic RNG.  The next three are function pointers which return the
 next 64- and 32-bit unsigned integers, the next random double and the next
 raw value.  This final function is used for testing and so can be set to
 the next 64-bit unsigned integer function if not needed. Functions inside
-:class:`~randomgen.generator.RandomGenerator` use this structure as in
+:class:`~randomgen.generator.Generator` use this structure as in
 
 .. code-block:: c
 

doc/source/generator.rst

@@ -1,89 +1,88 @@
 Random Generator
 ----------------
-The :class:`~randomgen.generator.RandomGenerator` provides access to 
+The :class:`~randomgen.generator.Generator` 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
+the two is that :class:`~randomgen.generator.Generator` 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.generator.Generator` is
 :class:`~randomgen.xoroshiro128.Xoroshiro128`.  The basic RNG can be 
 changed by passing an instantized basic RNG to 
-:class:`~randomgen.generator.RandomGenerator`. 
+:class:`~randomgen.generator.Generator`.
 
 .. currentmodule:: randomgen.generator
 
 .. autoclass::
-   RandomGenerator
+   Generator
 
 Seed and State Manipulation
 ===========================
 .. autosummary::
    :toctree: generated/
 
-   ~RandomGenerator.seed
-   ~RandomGenerator.state
+   ~Generator.seed
+   ~Generator.state
 
 Simple random data
 ==================
 .. autosummary::
    :toctree: generated/
 
-   ~RandomGenerator.rand
-   ~RandomGenerator.randn
-   ~RandomGenerator.integers
-   ~RandomGenerator.random_integers
-   ~RandomGenerator.random
-   ~RandomGenerator.choice
-   ~RandomGenerator.bytes
+   ~Generator.rand
+   ~Generator.randn
+   ~Generator.integers
+   ~Generator.random
+   ~Generator.choice
+   ~Generator.bytes
 
 Permutations
 ============
 .. autosummary::
    :toctree: generated/
 
-   ~RandomGenerator.shuffle
-   ~RandomGenerator.permutation
+   ~Generator.shuffle
+   ~Generator.permutation
 
 Distributions
 =============
 .. autosummary::
    :toctree: generated/
 
-   ~RandomGenerator.beta
-   ~RandomGenerator.binomial
-   ~RandomGenerator.chisquare
-   ~RandomGenerator.complex_normal
-   ~RandomGenerator.dirichlet
-   ~RandomGenerator.exponential
-   ~RandomGenerator.f
-   ~RandomGenerator.gamma
-   ~RandomGenerator.geometric
-   ~RandomGenerator.gumbel
-   ~RandomGenerator.hypergeometric
-   ~RandomGenerator.laplace
-   ~RandomGenerator.logistic
-   ~RandomGenerator.lognormal
-   ~RandomGenerator.logseries
-   ~RandomGenerator.multinomial
-   ~RandomGenerator.multivariate_normal
-   ~RandomGenerator.negative_binomial
-   ~RandomGenerator.noncentral_chisquare
-   ~RandomGenerator.noncentral_f
-   ~RandomGenerator.normal
-   ~RandomGenerator.pareto
-   ~RandomGenerator.poisson
-   ~RandomGenerator.power
-   ~RandomGenerator.rayleigh
-   ~RandomGenerator.standard_cauchy
-   ~RandomGenerator.standard_exponential
-   ~RandomGenerator.standard_gamma
-   ~RandomGenerator.standard_normal
-   ~RandomGenerator.standard_t
-   ~RandomGenerator.triangular
-   ~RandomGenerator.uniform
-   ~RandomGenerator.vonmises
-   ~RandomGenerator.wald
-   ~RandomGenerator.weibull
-   ~RandomGenerator.zipf
+   ~Generator.beta
+   ~Generator.binomial
+   ~Generator.chisquare
+   ~Generator.complex_normal
+   ~Generator.dirichlet
+   ~Generator.exponential
+   ~Generator.f
+   ~Generator.gamma
+   ~Generator.geometric
+   ~Generator.gumbel
+   ~Generator.hypergeometric
+   ~Generator.laplace
+   ~Generator.logistic
+   ~Generator.lognormal
+   ~Generator.logseries
+   ~Generator.multinomial
+   ~Generator.multivariate_normal
+   ~Generator.negative_binomial
+   ~Generator.noncentral_chisquare
+   ~Generator.noncentral_f
+   ~Generator.normal
+   ~Generator.pareto
+   ~Generator.poisson
+   ~Generator.power
+   ~Generator.rayleigh
+   ~Generator.standard_cauchy
+   ~Generator.standard_exponential
+   ~Generator.standard_gamma
+   ~Generator.standard_normal
+   ~Generator.standard_t
+   ~Generator.triangular
+   ~Generator.uniform
+   ~Generator.vonmises
+   ~Generator.wald
+   ~Generator.weibull
+   ~Generator.zipf