pointpats.f_test

pointpats.f_test(coordinates, support=None, distances=None, metric='euclidean', hull=None, edge_correction=None, keep_simulations=False, n_simulations=9999, n_jobs=-1)[source]

Ripley’s F function

The so-called “empty space” function, this is the cumulative density function of the distances from a random set of points to the known points in the pattern.

When the estimated statistic is larger than simulated values at a given distance, then the pattern is considered “dispersed” or “regular”

Parameters:
coordinatesgeopandas object | numpy.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.

n_jobsint (default: -1)

The number of CPU cores to use for running the Monte Carlo simulations. Simulations are independent and can be run in parallel to significantly reduce execution time.

  • If n_jobs = -1, all available CPU cores will be used.

  • If n_jobs = 1, the execution will be forced to run sequentially (serially), disabling parallel processing. This is often useful for debugging or testing purposes.

  • If n_jobs > 1, that specific number of cores will be used.

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)