Getting started#

The preferred entry point mirrors NumPy’s generator style:

from scikitplot.random import default_rng

rng = default_rng(42)
values = rng.random(4)
integers = rng.integers(0, 10, size=4)

A generator is stateful: calls advance its stream. Re-create the generator with the same seed when you need the same initial stream rather than resetting it implicitly in the middle of an algorithm.

Context-managed use#

kiss_context(seed) and generator context-manager support make lifetime explicit for code that wants a scoped stream. A context manager does not make a pseudo-random generator cryptographically secure.