pointpats.random.cluster_normal

pointpats.random.cluster_normal(hull, cov=None, size=None, n_seeds=2)

Simulate a cluster poisson random point process with a specified intensity & number of seeds. A cluster poisson process is a poisson process where the center of each “cluster” is itself distributed according to a spatial poisson process.

Parameters:

hullA geometry-like object

This encodes the “space” in which to simulate the normal pattern. All points will lie within this hull. Supported values are: - a bounding box encoded in a numpy array 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 convexh hull

covfloat, int, or numpy.ndarray of shape (2,2)

The covariance structure for clusters. By default, this is the squared average distance between cluster seeds.

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.

n_seedsint

the number of sub-clusters to use.

Returns:

: numpy.ndarray

either an (n_replications, n_observations, 2) or (n_observations,2) array containing

the simulated realizations.