ChaCha Cipher-based RNG¶

class randomgen.chacha.ChaCha(seed=None, *, counter=None, key=None, rounds=20, mode='sequence')¶

Container for the ChaCha family of Counter pseudo-random number generators

Parameters:¶

seed=None¶

Random seed initializing the pseudo-random number generator. Can be an integer in [0, 2**256), an array of 4 uint64 values, a SeedSequence instance or None (the default). If seed is None, then data is read from /dev/urandom (or the Windows analog) if available. If unavailable, a hash of the time and process ID is used.

counter=None¶

Counter to use in the ChaCha state. Can be either a Python int in [0, 2**128) or a 2-element uint64 array. If not provided, the counter is initialized at 0.

key=None¶

Key to use in the ChaCha state. Unlike seed, which is run through another RNG before use, the value in key is directly set. Can be either a Python int in [0, 2**256) or a 4-element uint64 array. key and seed cannot both be used.

rounds=20¶

Number of rounds to run the ChaCha mixer. Must be an even integer. The standard number of rounds in 20. Smaller values, usually 8 or more, can be used to reduce security properties of the random stream while improving performance.

mode='sequence'¶

Deprecated parameter. Do not use.

lock¶

Lock instance that is shared so that the same bit git generator can be used in multiple Generators without corrupting the state. Code that generates values from a bit generator should hold the bit generator’s lock.

Type:¶

threading.Lock

seed_seq¶

The SeedSequence instance used to initialize the generator if mode is “sequence” or is seed is a SeedSequence.

Type:¶

{None, SeedSequence}

Notes

ChaCha is a 64-bit PRNG that uses a counter-based design based on the ChaCha cipher [1]. Instances using different values of the key produce distinct sequences. ChaCha has a period of \(2^{128}\) and supports arbitrary advancing and jumping the sequence in increments of \(2^{64}\). These features allow multiple non-overlapping sequences to be generated.

ChaCha provides a capsule containing function pointers that produce doubles, and unsigned 32 and 64- bit integers. These are not directly consumable in Python and must be consumed by a Generator or similar object that supports low-level access.

See AESCounter a related counter-based PRNG.

State and Seeding

The ChaCha state vector consists of a 16-element array of uint32 that capture buffered draws from the distribution, an 8-element array of uint32s holding the seed, and an 2-element array of uint64 that holds the counter ([low, high]). The elements of the seed are the value provided by the user (or from the entropy pool). The final value rounds contains the number of rounds used. Typical values are 8, 12, or 20 (for high security).

ChaCha is seeded using either a single 256-bit unsigned integer or a vector of 4 64-bit unsigned integers. In either case, the seed is used as an input for a second random number generator, SplitMix64, and the output of this PRNG function is used as the initial state. Using a single 64-bit value for the seed can only initialize a small range of the possible initial state values.

Parallel Features

ChaCha can be used in parallel applications by calling the jump method to advances the state as-if \(2^{64}\) random numbers have been generated. Alternatively, advance can be used to advance the counter for any positive step in [0, 2**128). When using jump, all generators should be initialized with the same seed to ensure that the segments come from the same sequence.

>>> from numpy.random import Generator
>>> from randomgen import ChaCha
>>> rg = [Generator(ChaCha(1234)) for _ in range(10)]
# Advance each ChaCha instances by i jumps
>>> for i in range(10):
...     rg[i].bit_generator.jump(i)

Alternatively, ChaCha can be used in parallel applications by using a sequence of distinct keys where each instance uses different key.

>>> key = 2**93 + 2**65 + 2**33 + 2**17 + 2**9
>>> rg = [Generator(ChaCha(key=key+i)) for i in range(10)]

Compatibility Guarantee

ChaCha makes a guarantee that a fixed seed and will always produce the same random integer stream.

Examples

>>> from numpy.random import Generator
>>> from randomgen import ChaCha
>>> rg = Generator(ChaCha(1234, rounds=8))
>>> rg.standard_normal()
0.123  # random

References

Seeding and State¶

Extending¶

Testing¶