pointpats.g_test

pointpats.g_test(coordinates, support=None, distances=None, metric='euclidean', hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999)

Ripley’s G function

The G function is computed from the cumulative density function of the nearest neighbor distances between points in the pattern.

When the G function is below the simulated values, it 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)