RandomGen

This package contains additional bit generators for NumPy's Generator and an ExtendedGenerator exposing methods not in Generator.

Continuous Integration

Coverage

Latest Release

License

This is a library and generic interface for alternative random generators in Python and NumPy.

New Features

The the development documentation for the latest features, or the stable documentation for the latest released features.

WARNINGS

Changes in v1.19

Generator and RandomState have been officially deprecated, and will warn with a FutureWarning about their removal. They will also receive virtually no maintenance. It is now time to move to NumPy's np.random.Generator which has features not in randomstate.Generator and is maintained more actively.

A few distributions that are not present in np.random.Generator have been moved to randomstate.ExtendedGenerator:

multivariate_normal: which supports broadcasting
uintegers: fast 32 and 64-bit uniform integers
complex_normal: scalar complex normals

There are no plans to remove any of the bit generators, e.g., AESCounter, ThreeFry, or PCG64.

Changes in v1.18

There are many changes between v1.16.x and v1.18.x. These reflect API decision taken in conjunction with NumPy in preparation of the core of randomgen being used as the preferred random number generator in NumPy. These all issue DeprecationWarnings except for BasicRNG.generator which raises NotImplementedError. The C-API has also changed to reflect the preferred naming the underlying Pseudo-RNGs, which are now known as bit generators (or BigGenerators).

Future Plans

A substantial portion of randomgen has been merged into NumPy. Revamping NumPy's random number generation was always the goal of this project (and its predecessor NextGen NumPy RandomState), and so it has succeeded.

While I have no immediate plans to remove anything, after a 1.19 release I will:

Remove Generator and RandomState. These duplicate NumPy and will diverge over time. The versions in NumPy are authoritative. Deprecated
Preserve novel methods of Generator in a new class, ExtendedGenerator. Done
Add some distributions that are not supported in NumPy. Ongoing
Add any interesting bit generators I come across. Recent additions include the DXSM and CM-DXSM variants of PCG64 and the LXM generator.

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:

Cryptographic cipher-based random number generator based on AES, ChaCha20, HC128 and Speck128.
MT19937, the NumPy rng
dSFMT a SSE2-aware version of the MT19937 generator that is especially fast at generating doubles
xoroshiro128+, xorshift1024*φ, xoshiro256**, and xoshiro512**
PCG64
ThreeFry and Philox from Random123
Other cryptographic-based generators: AESCounter, SPECK128, ChaCha, and HC128.
Hardware (non-reproducible) random number generator on AMD64 using RDRAND.
Chaotic PRNGS: Small-Fast Chaotic (SFC64) and Jenkin's Small-Fast (JSF).

Status

Builds and passes all tests on:
- Linux 32/64 bit, Python 2.7, 3.5, 3.6, 3.7
- Linux (ARM/ARM64), Python 3.7
- OSX 64-bit, Python 2.7, 3.5, 3.6, 3.7
- Windows 32/64 bit, Python 2.7, 3.5, 3.6, 3.7
- FreeBSD 64-bit

Version

The package version matches the latest version of NumPy where Generator(MT19937()) passes all NumPy test.

Documentation

Documentation for the latest release is available on my GitHub pages. Documentation for the latest commit (unreleased) is available under devel.

Requirements

Building requires:

Python (3.6, 3.7, 3.8)
NumPy (1.14, 1.15, 1.16, 1.17, 1.18, 1.19)
Cython (0.29+)
tempita (0.5+), if not provided by Cython

Testing requires pytest (5.0.1+).

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-AMD64, Linux-PPC-LE, Linus-S390X, and OSX), Appveyor (Windows 32/64), Cirrus (FreeBSD) and Drone.io (ARM/ARM64 Linux).

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

pip install randomgen

or, if you want the latest version,

pip install git+https://github.com/bashtage/randomgen.git

or from a cloned repo,

python setup.py install

If you use conda, you can install using conda forge

conda install -c conda-forge randomgen

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:

python setup.py install --no-sse2

Windows

Either use a binary installer, or if building from scratch, use Python 3.6/3.7 with Visual Studio 2015 Build Toolx.

License

Dual: BSD 3-Clause and NCSA, plus sub licenses for components.

rurban/randomgen