# 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)

  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:
    coordinates numpy.ndarray, (n,2)

      input coordinates to function

    support tuple 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)