# pointpats.l_test¶

pointpats.l_test(coordinates, support=None, distances=None, metric='euclidean', hull=None, edge_correction=None, linearized=False, keep_simulations=False, n_simulations=9999)[source]

Ripley’s L function

This is a scaled and shifted version of the K function that accounts for the K function’s increasing expected value as distances increase. This means that the L function, for a completely random pattern, should be close to zero at all distance values in the support.

When the L function is negative, this suggests dispersion.

Parameters:
coordinatesnumpy.ndarray, (n,2)

input coordinates to function

supporttuple of length 1, 2, or 3, int, or numpy.ndarray

tuple, encoding (stop,), (start, stop), or (start, stop, num) int, encoding number of equally-spaced intervals numpy.ndarray, used directly within numpy.histogram

distances: numpy.ndarray, (n, p) or (p,)

distances from every point in a random point set of size p to some point in coordinates

metric: str or callable

distance metric to use when building search tree

hull: bounding box, scipy.spatial.ConvexHull, shapely.geometry.Polygon

the hull used to construct a random sample pattern, if distances is None

edge_correction: bool or str

whether or not to conduct edge correction. Not yet implemented.

keep_simulations: bool

whether or not to keep the simulation envelopes. If so, will be returned as the result’s simulations attribute

n_simulations: int

how many simulations to conduct, assuming that the reference pattern has complete spatial randomness.

Returns:
a named tuple with properties
• support, the exact distance values used to evalute the statistic
• statistic, the values of the statistic at each distance
• pvalue, the percent of simulations that were as extreme as the observed value
• simulations, the distribution of simulated statistics (shaped (n_simulations, n_support_points))

or None if keep_simulations=False (which is the default)