pure-prng 2.8.2

Generate professional pseudo-random number package in python.

Pure_PRNG

Generate professional pseudo-random number package in python.

Only the pseudo-random number algorithm with good statistical properties is implemented.

There are "methods" that specify the period of a multi-precision pseudo-random sequence.

Realized pseudo-random number generation algorithm

Quadratic Congruential Generator

Cubic Congruential Generator

Inversive Congruential Generator

PCG64_XSL_RR; PCG64_DXSM

LCG64_32_ext

LCG128Mix_XSL_RR; LCG128Mix_DXSM; LCG128Mix_MURMUR3

XSM64

EFIIX64

Ran64

PhiloxCounter

ThreeFryCounter

AESCounter

ChaChaCounter

SPECKCounter

Please inform me if any PRNG fails to pass the new statistical test.

Installation

Installation can be done through pip. You must have python version >= 3.8

pip install pure-prng

Usage

The statement to import the package:

from pure_prng_package import pure_prng

Example:

>>> seed = 170141183460469231731687303715884105727
>>> period = 115792089237316195423570985008687907853269984665640564039457584007913129639747

>>> prng_instance = pure_prng(seed)
>>> source_random_number = prng_instance.source_random_number()
>>> next(source_random_number)
65852230656997158461166665751696465914198450243194923777324019418213544382100

>>> prng_instance = pure_prng(seed, new_prng_period = 2 ** 512)
>>> source_random_number = prng_instance.source_random_number()
>>> next(source_random_number)
8375486648769878807557228126183349922765245383564825377649864304632902242469125910865615742661048315918259479944116325466004411700005484642554244082978452

>>> prng_instance = pure_prng(seed)
>>> rand_bits = prng_instance.rand_bits(512)
>>> next(rand_bits)
mpz(6144768950704661248519702670268583753928668607451020183407159490385670202458730311510261255705698403097105657582435836672179668357656056427608305574891156)
>>> rand_bits = prng_instance.rand_bits(512, period)
>>> next(rand_bits)
mpz(2954964798889411590155032615694646383408546750268072607273800792672971321854983100133610686738061114434885994588970398525439724215184541467422573311905001)

>>> prng_instance = pure_prng(seed)
>>> rand_float = prng_instance.rand_float(100)
>>> next(rand_float)
mpfr('0.56576176351048513846261940831522',100)

>>> prng_instance = pure_prng(seed)
>>> rand_int = prng_instance.rand_int(100, 1)
>>> next(rand_int)
mpz(21)

>>> prng_instance = pure_prng(seed)
>>> get_randint_set = prng_instance.get_randint_set(100, 1, 6)
>>> next(get_randint_set)
{mpz(34), mpz(99), mpz(37), mpz(45), mpz(19), mpz(21)}

>>> prng_instance = pure_prng(seed)
>>> rand_with_period = prng_instance.rand_with_period(period)
>>> next(rand_with_period)
mpz(65852230656997158461166665751696465914198450243194923777324019418213544381986)
>>> rand_with_period = prng_instance.rand_with_period(period, 'raw_binary_number')
>>> next(rand_with_period)
mpz(53067260390396280968027884646874354062063398901623645439544105836818444733296)

Future work

The following algorithm is intended to be implemented:

NLFSR

Cellular automate period pseudo random

GPU Philox