pointpats.random.poisson

pointpats.random.poisson(hull, intensity=None, size=None, rng=None)

Simulate a poisson random point process with a specified intensity.

Parameters:

hullA geometry-like object

This encodes the “space” in which to simulate the poisson pattern. All points will lie within this hull. Supported values are: - a bounding box encoded as numpy.array([xmin, ymin, xmax, ymax]) - an (N,2) array of points for which the bounding box will be computed & used - a shapely polygon/multipolygon - a scipy convex hull

intensityfloat

the number of observations per unit area in the hull to use. If provided, then size must be an integer describing the number of replications to use.

sizetuple or int

a tuple of (n_observations, n_replications), where the first number is the number of points to simulate in each replication and the second number is the number of total replications. So, (10, 4) indicates 10 points, 4 times. If an integer is provided and intensity is None, n_replications is assumed to be 1. If size is an integer and intensity is also provided, then size indicates n_replications, and the number of observations is computed from the intensity.

rngint, numpy.random.Generator, or None, optional

A source of randomness. This can be:

• A numpy.random.Generator instance (recommended)

• An int seed, used to initialize a new Generator

• None (default), which uses a new numpy.random.default_rng() instance

This interface follows Scientific Python SPEC 7, ensuring consistent and reproducible random number generation across libraries.

Returns:

numpy.ndarray

either an (n_replications, n_observations, 2) or (n_observations,2) array containing the simulated realizations.