This package contains additional bit generators for NumPy's
Generator and an ExtendedGenerator exposing methods not in Generator.
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This is a library and generic interface for alternative random generators in Python and NumPy.
The the development documentation for the latest features, or the stable documentation for the latest released features.
Generator and RandomState were removed in 1.23.0.
Generator and RandomState have been officially deprecated in 1.19, 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 broadcastinguintegers: fast 32 and 64-bit uniform integerscomplex_normal: scalar complex normalsThere are no plans to remove any of the bit generators, e.g., AESCounter,
ThreeFry, or PCG64.
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).
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**
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).
The package version matches the latest version of NumPy when the package is released.
Documentation for the latest release is available on my GitHub pages. Documentation for the latest commit (unreleased) is available under devel.
Building requires:
Testing requires pytest (7+).
Note: it might work with other versions but only tested with these versions.
All development has been on 64-bit Linux, and it is regularly tested on Azure (Linux-AMD64, Window, and OSX) and Cirrus (FreeBSD and Linux-ARM).
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.
Either install from PyPi using
python -m pip install randomgen
or, if you want the latest version,
python -m pip install git+https://github.com/bashtage/randomgen.git
or from a cloned repo,
python -m pip install .
If you use conda, you can install using conda forge
conda install -c conda-forge randomgen
dSFTM makes use of SSE2 by default. If you have a very old computer
or are building on non-x86, you can install using:
export RANDOMGEN_NO_SSE2=1
python -m pip install .
Either use a binary installer, or if building from scratch, use Python 3.6/3.7 with Visual Studio 2015 Build Toolx.
Dual: BSD 3-Clause and NCSA, plus sub licenses for components.
FAQs
Random generator supporting multiple PRNGs
We found that randomgen demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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