Merge pull request #55 from bashtage/add-tempita-integers

REF: Switch common integer code to tempita

Author: bashtage

.travis.yml

@@ -2,9 +2,16 @@
 # versions.  Allows substantial flexability for choosing versions of
 # required packages and is simpler to use to test up-to-date scientific Python
 # stack
-sudo: required
+group: edge
 dist: trusty
-language: generic
+sudo: required
+language: python
+
+services:
+    - docker
+
+before_install:
+    - docker pull ubuntu:16.04
 
 matrix:
   fast_finish: true
@@ -38,6 +45,7 @@ before_install:
 install:
   - conda create -n randomstate-test ${PKGS} nose pip setuptools --quiet
   - source activate randomstate-test
+  - pip install tempita
   - python setup.py install
   - export BUILD_DIR=$PWD
   - cd $HOME

README.md

@@ -100,9 +100,12 @@ need to be smoothed.
 ## Requirements
 Building requires:
 
+  * Python (2.7, 3.4, 3.5)
   * Numpy (1.9, 1.10, 1.11)
   * Cython (0.22, 0.23, 0.24)
-  * Python (2.7, 3.4, 3.5)
+  * tempita (0.5+), if not provided by Cython
+ 
+Testing requires nose (1.3+).
 
 **Note:** it might work with other versions but only tested with these 
 versions. 

README.rst

@@ -22,7 +22,7 @@ Features
    same seed, same random numbers).
 -  Support for random number generators that support independent streams
    and jumping ahead so that substreams can be generated
--  Faster random number generations, especially for Normals using the
+-  Faster random number generation, especially for Normals using the
    Ziggurat method
 
 .. code:: python
@@ -58,8 +58,8 @@ New Features
 -  ``standard_normal``, ``normal``, ``randn`` and
    ``multivariate_normal`` all support an additional ``method`` keyword
    argument which can be ``bm`` or ``zig`` where ``bm`` corresponds to
-   the current method and ``zig`` uses the much faster (100%+) ziggurat
-   method.
+   the current method using the Box-Muller transformation and ``zig``
+   uses the much faster (100%+) ziggurat method.
 
 New Functions
 ~~~~~~~~~~~~~
@@ -68,9 +68,8 @@ New Functions
    commonly used in cryptographic applications
 -  ``random_uintegers`` - unsigned integers ``[0, 2**64-1]``
 -  ``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.
+   underlying PRNG. The range of the values returned depends on the
+   specifics of the PRNG implementation.
 -  ``jump`` - Jumps RNGs that support it. ``jump`` moves the state a
    great distance. *Only available if supported by the RNG.*
 -  ``advance`` - Advanced the core RNG 'as-if' a number of draws were
@@ -85,7 +84,7 @@ Status
    and will produce an identical sequence of random numbers for a given
    seed.
 -  Builds and passes all tests on:
--  Linux 32/64 bit, Python 2.6, 2.7, 3.3, 3.4, 3.5
+-  Linux 32/64 bit, Python 2.7, 3.4, 3.5 (should work on 2.6 and 3.3)
 -  PC-BSD (FreeBSD) 64-bit, Python 2.7
 -  OSX 64-bit, Python 2.7
 -  Windows 32/64 bit (only tested on Python 2.7 and 3.5, but should work
@@ -118,9 +117,12 @@ Requirements
 
 Building requires:
 
+-  Python (2.7, 3.4, 3.5)
 -  Numpy (1.9, 1.10, 1.11)
 -  Cython (0.22, 0.23, 0.24)
--  Python (2.6, 2.7, 3.3, 3.4, 3.5)
+-  tempita (0.5+), if not provided by Cython
+
+Testing requires nose (1.3+).
 
 **Note:** it might work with other versions but only tested with these
 versions.
@@ -154,10 +156,10 @@ or are building on non-x86, you can install using:
 Windows
 ~~~~~~~
 
-Either use a binary installer or if building from scratch using Python
-3.5 and the free Visual Studio 2015 Community Edition. It can also be
-build using Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7,
-although some modifications are needed to ``distutils`` to find the
+Either use a binary installer, or if building from scratch, use Python
+3.5 with Visual Studio 2015 Community Edition. It can also be build
+using Microsoft Visual C++ Compiler for Python 2.7 and Python 2.7,
+although some modifications may be needed to ``distutils`` to find the
 compiler.
 
 Using
@@ -202,29 +204,41 @@ NumPy's mt19937.
 
 ::
 
-    Speed-up relative to NumPy (Box-Muller)
+    Speed-up relative to NumPy (Uniform Doubles)
+    ************************************************************
+    randomstate.prng-dsfmt-random_sample               313.5%
+    randomstate.prng-mlfg_1279_861-random_sample       459.4%
+    randomstate.prng-mrg32k3a-random_sample            -57.6%
+    randomstate.prng-mt19937-random_sample              72.5%
+    randomstate.prng-pcg32-random_sample               232.8%
+    randomstate.prng-pcg64-random_sample               330.6%
+    randomstate.prng-xoroshiro128plus-random_sample    609.9%
+    randomstate.prng-xorshift1024-random_sample        348.8%
+    randomstate.prng-xorshift128-random_sample         489.7%
+
+    Speed-up relative to NumPy (Normals using Box-Muller)
     ************************************************************
-    randomstate.prng-dsfmt-standard_normal                30.2%
-    randomstate.prng-mlfg_1279_861-standard_normal        24.7%
-    randomstate.prng-mrg32k3a-standard_normal            -17.8%
-    randomstate.prng-mt19937-standard_normal              11.2%
-    randomstate.prng-pcg32-standard_normal                22.0%
-    randomstate.prng-pcg64-standard_normal                21.8%
-    randomstate.prng-xoroshiro128plus-standard_normal     26.5%
-    randomstate.prng-xorshift1024-standard_normal         20.2%
-    randomstate.prng-xorshift128-standard_normal          23.5%
-
-    Speed-up relative to NumPy (Ziggurat)
+    randomstate.prng-dsfmt-standard_normal                26.8%
+    randomstate.prng-mlfg_1279_861-standard_normal        30.9%
+    randomstate.prng-mrg32k3a-standard_normal            -14.8%
+    randomstate.prng-mt19937-standard_normal              17.7%
+    randomstate.prng-pcg32-standard_normal                24.5%
+    randomstate.prng-pcg64-standard_normal                26.2%
+    randomstate.prng-xoroshiro128plus-standard_normal     31.4%
+    randomstate.prng-xorshift1024-standard_normal         27.4%
+    randomstate.prng-xorshift128-standard_normal          30.3%
+
+    Speed-up relative to NumPy (Normals using Ziggurat)
     ************************************************************
-    randomstate.prng-dsfmt-standard_normal               494.2%
-    randomstate.prng-mlfg_1279_861-standard_normal       464.2%
-    randomstate.prng-mrg32k3a-standard_normal            103.8%
-    randomstate.prng-mt19937-standard_normal             362.6%
-    randomstate.prng-pcg32-standard_normal               539.6%
-    randomstate.prng-pcg64-standard_normal               407.7%
-    randomstate.prng-xoroshiro128plus-standard_normal    722.8%
-    randomstate.prng-xorshift1024-standard_normal        506.1%
-    randomstate.prng-xorshift128-standard_normal         686.3%
+    randomstate.prng-dsfmt-standard_normal               491.7%
+    randomstate.prng-mlfg_1279_861-standard_normal       439.6%
+    randomstate.prng-mrg32k3a-standard_normal            101.2%
+    randomstate.prng-mt19937-standard_normal             354.4%
+    randomstate.prng-pcg32-standard_normal               531.0%
+    randomstate.prng-pcg64-standard_normal               517.9%
+    randomstate.prng-xoroshiro128plus-standard_normal    674.0%
+    randomstate.prng-xorshift1024-standard_normal        486.7%
+    randomstate.prng-xorshift128-standard_normal         617.0%
 
 .. |Travis Build Status| image:: https://travis-ci.org/bashtage/ng-numpy-randomstate.svg?branch=master
    :target: https://travis-ci.org/bashtage/ng-numpy-randomstate