pointpats.random.cluster_poisson¶
- pointpats.random.cluster_poisson(hull, intensity=None, size=None, n_seeds=2, cluster_radius=None)[source]¶
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
- 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.
- n_seedsint
the number of sub-clusters to use.
- cluster_radiusfloat or iterable
the radius of each cluster. If a float, the same radius is used for all clusters. If an array, then there must be the same number of radii as clusters. If None, 50% of the minimum inter-point distance is used, which may fluctuate across replications.
- Returns:
- : numpy.ndarray
- either an (n_replications, n_observations, 2) or (n_observations,2) array containing
- the simulated realizations.