# RandomGen
This package contains additional bit generators for NumPy's
`Generator` and an `ExtendedGenerator` exposing methods not in `Generator`.
**Continuous Integration**
[](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=2&branchName=main)
[](https://cirrus-ci.com/github/bashtage/randomgen)
**Coverage**
[](https://codecov.io/gh/bashtage/randomgen)
**Latest Release**
[](https://pypi.org/project/randomgen/)
[](https://anaconda.org/conda-forge/randomgen)
**License**
[](https://opensource.org/licenses/NCSA)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://zenodo.org/badge/latestdoi/122181085)
This is a library and generic interface for alternative random
generators in Python and NumPy.
## New Features
The the [development documentation](https://bashtage.github.io/randomgen/change-log.html) for the latest features,
or the [stable documentation](https://bashtage.github.io/randomgen/devel/change-log.html) for the latest released features.
# WARNINGS
## Changes in v1.24
``Generator`` and ``RandomState`` were **removed** in 1.23.0.
## Changes from 1.18 to 1.19
``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 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 from 1.16 to 1.18
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 `DeprecationWarning`s 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 `BigGenerator`s).
## Future Plans
* 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](https://github.com/numpy/numpy/blob/main/numpy/random/mtrand/),
the NumPy rng
* [dSFMT](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/) a
SSE2-aware version of the MT19937 generator that is especially fast at
generating doubles
* [xoroshiro128+](https://prng.di.unimi.it/),
[xorshift1024*φ](https://prng.di.unimi.it/),
[xoshiro256**](https://prng.di.unimi.it/),
and [xoshiro512**](https://prng.di.unimi.it/)
* [PCG64](https://www.pcg-random.org/)
* ThreeFry and Philox from [Random123](https://www.deshawresearch.com/resources_random123.html)
* 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 3.7, 3.8, 3.9, 3.10
* Linux (ARM/ARM64), Python 3.8
* OSX 64-bit, Python 3.9
* Windows 32/64 bit, Python 3.7, 3.8, 3.9, 3.10
* FreeBSD 64-bit
## Version
The package version matches the latest version of NumPy when the package
is released.
## Documentation
Documentation for the latest release is available on
[my GitHub pages](https://bashtage.github.io/randomgen/). Documentation for
the latest commit (unreleased) is available under
[devel](https://bashtage.github.io/randomgen/devel/).
## Requirements
Building requires:
* Python (3.6, 3.7, 3.8, 3.9, 3.10)
* NumPy (1.17+)
* Cython (0.29+)
* tempita (0.5+), if not provided by Cython
Testing requires pytest (6+).
**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
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.
## Installing
Either install from PyPi using
```bash
python -m pip install randomgen
```
or, if you want the latest version,
```bash
python -m pip install git+https://github.com/bashtage/randomgen.git
```
or from a cloned repo,
```bash
python -m pip install .
```
If you use conda, you can install using conda forge
```bash
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:
```bash
export RANDOMGEN_NO_SSE2=1
python -m pip install .
```
### 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.
Raw data
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"description": "# RandomGen\r\n\r\nThis package contains additional bit generators for NumPy's\r\n`Generator` and an `ExtendedGenerator` exposing methods not in `Generator`.\r\n\r\n\r\n**Continuous Integration**\r\n\r\n[](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=2&branchName=main)\r\n[](https://cirrus-ci.com/github/bashtage/randomgen)\r\n\r\n**Coverage**\r\n\r\n[](https://codecov.io/gh/bashtage/randomgen)\r\n\r\n**Latest Release**\r\n\r\n[](https://pypi.org/project/randomgen/)\r\n[](https://anaconda.org/conda-forge/randomgen)\r\n\r\n**License**\r\n\r\n[](https://opensource.org/licenses/NCSA)\r\n[](https://opensource.org/licenses/BSD-3-Clause)\r\n[](https://zenodo.org/badge/latestdoi/122181085)\r\n\r\nThis is a library and generic interface for alternative random\r\ngenerators in Python and NumPy.\r\n\r\n## New Features\r\n\r\nThe the [development documentation](https://bashtage.github.io/randomgen/change-log.html) for the latest features,\r\nor the [stable documentation](https://bashtage.github.io/randomgen/devel/change-log.html) for the latest released features.\r\n\r\n\r\n# WARNINGS\r\n\r\n## Changes in v1.24\r\n\r\n``Generator`` and ``RandomState`` were **removed** in 1.23.0.\r\n\r\n## Changes from 1.18 to 1.19\r\n\r\n``Generator`` and ``RandomState`` have been officially deprecated in 1.19, and will\r\nwarn with a ``FutureWarning`` about their removal. They will also receive virtually\r\nno maintenance. It is now time to move to NumPy's ``np.random.Generator`` which has\r\nfeatures not in ``randomstate.Generator`` and is maintained more actively.\r\n\r\nA few distributions that are not present in ``np.random.Generator`` have been moved\r\nto ``randomstate.ExtendedGenerator``:\r\n\r\n* `multivariate_normal`: which supports broadcasting\r\n* `uintegers`: fast 32 and 64-bit uniform integers\r\n* `complex_normal`: scalar complex normals\r\n\r\nThere are no plans to remove any of the bit generators, e.g., ``AESCounter``,\r\n``ThreeFry``, or ``PCG64``. \r\n\r\n### Changes from 1.16 to 1.18\r\nThere are many changes between v1.16.x and v1.18.x. These reflect API\r\ndecision taken in conjunction with NumPy in preparation of the core\r\nof `randomgen` being used as the preferred random number generator in\r\nNumPy. These all issue `DeprecationWarning`s except for `BasicRNG.generator`\r\nwhich raises `NotImplementedError`. The C-API has also changed to reflect\r\nthe preferred naming the underlying Pseudo-RNGs, which are now known as\r\nbit generators (or `BigGenerator`s).\r\n\r\n## Future Plans\r\n\r\n* Add some distributions that are not supported in NumPy. _Ongoing_\r\n* Add any interesting bit generators I come across. _Recent additions include the DXSM and CM-DXSM variants of PCG64 and the LXM generator._\r\n\r\n## Included Pseudo Random Number Generators\r\n\r\nThis module includes a number of alternative random\r\nnumber generators in addition to the MT19937 that is included in NumPy.\r\nThe RNGs include:\r\n\r\n* Cryptographic cipher-based random number generator based on AES, ChaCha20, HC128 and Speck128.\r\n* [MT19937](https://github.com/numpy/numpy/blob/main/numpy/random/mtrand/),\r\n the NumPy rng\r\n* [dSFMT](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/) a\r\n SSE2-aware version of the MT19937 generator that is especially fast at\r\n generating doubles\r\n* [xoroshiro128+](https://prng.di.unimi.it/),\r\n [xorshift1024*\u03c6](https://prng.di.unimi.it/),\r\n [xoshiro256**](https://prng.di.unimi.it/),\r\n and [xoshiro512**](https://prng.di.unimi.it/)\r\n* [PCG64](https://www.pcg-random.org/)\r\n* ThreeFry and Philox from [Random123](https://www.deshawresearch.com/resources_random123.html)\r\n* Other cryptographic-based generators: `AESCounter`, `SPECK128`, `ChaCha`, and `HC128`.\r\n* Hardware (non-reproducible) random number generator on AMD64 using `RDRAND`.\r\n* Chaotic PRNGS: Small-Fast Chaotic (`SFC64`) and Jenkin's Small-Fast (`JSF`).\r\n\r\n \r\n## Status\r\n\r\n* Builds and passes all tests on:\r\n * Linux 32/64 bit, Python 3.7, 3.8, 3.9, 3.10\r\n * Linux (ARM/ARM64), Python 3.8\r\n * OSX 64-bit, Python 3.9\r\n * Windows 32/64 bit, Python 3.7, 3.8, 3.9, 3.10\r\n * FreeBSD 64-bit\r\n\r\n## Version\r\n\r\nThe package version matches the latest version of NumPy when the package\r\nis released.\r\n\r\n## Documentation\r\n\r\nDocumentation for the latest release is available on\r\n[my GitHub pages](https://bashtage.github.io/randomgen/). 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