pointpats.random.normal

pointpats.random.normal(hull, center=None, cov=None, size=None)

Simulate a multivariate random normal point cluster

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

centeriterable of shape (2, )

A point where the simulations will be centered.

covfloat or a numpy array of shape (2,2)

either the standard deviation of an independent and identically distributed normal distribution, or a 2 by 2 covariance matrix expressing the covariance of the x and y for the distribution. Default is half of the width or height of the hull’s bounding box, whichever is larger.

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, n_replications is assumed to be 1.

Returns:

: numpy.ndarray

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

the simulated realizations.