chaos-rng 0.1.4

Chaos-based random number generator using three-body dynamics

Chaos RNG

Chaos RNG is a Python library for experimenting with random number generation driven by chaotic three-body dynamics. It provides a ThreeBodyRNG API for direct use, NumPy-compatible generator utilities, and optional validation / post-processing helpers for offline analysis and integration experiments.

The project is aimed at research, exploration, and reproducible benchmarking of chaos-based RNG behavior. It is not positioned as a drop-in replacement for audited cryptographic random number generators, but it includes optional tools that help evaluate output quality and integration patterns.

This project is currently documented README-first for the 0.1.x releases.

Installation

Core package:

pip install chaos-rng

With optional crypto and test utilities:

pip install "chaos-rng[crypto,test]"

Quick Start

from chaos_rng import ThreeBodyRNG

rng = ThreeBodyRNG(seed=42)

print(rng.random(5))            # floats in [0, 1)
print(rng.randint(0, 10, 5))    # integers
print(rng.bytes(16).hex())      # raw bytes

Notes:

• First calls may be slower because numerical kernels warm up (Numba/JIT).
• Use a fixed seed for reproducible tests and experiments.

NumPy Integration

from chaos_rng.generators import create_chaos_generator

gen = create_chaos_generator(seed=42)

normal = gen.normal(size=1000)
uniform = gen.uniform(0, 10, size=100)
integers = gen.integers(0, 100, size=50)

Optional Crypto Utilities

The crypto extra provides helper utilities for secure seeding and post-processing. These tools are useful for experimentation and integration, but this project should not be treated as a drop-in replacement for a security-audited cryptographic RNG.

from chaos_rng import ThreeBodyRNG
from chaos_rng.security import CryptoPostProcessor, SecureSeed

seed = SecureSeed().generate_seed()
rng = ThreeBodyRNG(seed=seed)

raw = rng.bytes(64)
processed = CryptoPostProcessor(method="hash").process(raw)

print(len(processed))

Validation Utilities (Optional / Expensive)

Validation helpers are included under chaos_rng.utils.validation. They can be computationally expensive and are best used for offline analysis.

from chaos_rng import ThreeBodyRNG
from chaos_rng.utils.validation import EntropyValidator

rng = ThreeBodyRNG(seed=42)
bits = (rng.random(10000) * 2).astype(int)

validator = EntropyValidator()
result = validator.validate_entropy_rate(bits)
print(result)

NISTTestSuite and ContinuousValidator are also available, but they are not part of the default CI gate in 0.1.4.

Examples

Focused example scripts are available in examples/:

• examples/quick_start.py
• examples/reproducibility.py
• examples/numpy_integration.py
• examples/validation_offline.py
• examples/crypto_optional.py

Run one example from the repository root:

python3 -m pip install -e .[crypto,test]
python3 examples/quick_start.py

Benchmarking

Use the benchmark runner in benchmarks/run_benchmarks.py to collect reproducible JSON results.

Quick benchmark profile:

python3 -m pip install -e .[crypto,test]
python3 benchmarks/run_benchmarks.py --profile quick --output benchmarks/results/latest.json

Fuller benchmark profile:

python3 benchmarks/run_benchmarks.py --profile full --output benchmarks/results/full.json

Benchmark reporting guidance:

• Report cold start (time-to-first-output) separately from warm throughput.
• Include environment details (CPU, OS, Python, NumPy, Numba).
• Compare against baselines (numpy.random.default_rng, random.Random, os.urandom, secrets.token_bytes) with the same batch sizes.

Suggested README/issue table format:

Scenario	Mode	Batch	Mean Time (s)	Throughput	Notes
ThreeBodyRNG.random	cold start	1	...	n/a	includes init + JIT
ThreeBodyRNG.random (lsb)	warm	100000	...	... values/s	after warm-up
numpy.default_rng().random	warm	100000	...	... values/s	baseline
ThreeBodyRNG.bytes	warm	4096 bytes	...	... bytes/s	after warm-up
os.urandom	warm	4096 bytes	...	... bytes/s	baseline

Performance and Caveats

• This library prioritizes clarity and reproducibility over minimal dependency footprint (numpy, scipy, numba).
• Some operations are expensive for large sample sizes.
• Cryptographic helpers are optional and not a substitute for audited crypto libraries or formal security review.

Development

Common commands:

make install-dev
make lint
make test
make test-slow
make build
make check-build

Local release gate:

make release-check

Release Flow (0.1.x)

The intended release path is:

1. Tag release (v0.1.4)
2. Publish to TestPyPI
3. Smoke-test install from TestPyPI
4. Publish to PyPI

GitHub Actions workflows:

• CI (.github/workflows/ci.yml)
• Release (.github/workflows/release.yml)

License

MIT License. See LICENSE.