# API reference¶

## Point Pattern¶

 PointPattern(points[, window, names, ...]) Planar Point Pattern Class 2-D.

## Point Processes¶

 PointProcess(window, n, samples[, asPP]) Point Process base class. PoissonPointProcess(window, n, samples[, ...]) Poisson point process including $$N$$-conditioned CSR process and $$\lambda$$-conditioned CSR process. PoissonClusterPointProcess(window, n, ...[, ...]) Poisson cluster point process (Neyman Scott).

## Centrography¶

 Find minimum bounding rectangle of a point array. hull(points) Find convex hull of a point array. mean_center(points) Find mean center of a point array. weighted_mean_center(points, weights) Find weighted mean center of a marked point pattern. manhattan_median(points) Find manhattan median of a point array. std_distance(points) Calculate standard distance of a point array. euclidean_median(points) Calculate the Euclidean median for a point pattern. ellipse(points) Calculate parameters of standard deviational ellipse for a point pattern. skyum(points) WARNING: This function is deprecated in favor of minimum_bounding_circle dtot(coord, points) Sum of Euclidean distances between event points and a selected point.

## Density¶

 plot_density(data, bandwidth[, kernel, ...]) Plot kernel density of a given point pattern

 RectangleM(pp[, count_column, count_row, ...]) Rectangle grid structure for quadrat-based method. HexagonM(pp, lh) Hexagon grid structure for quadrat-based method. QStatistic(pp[, shape, nx, ny, ...]) Quadrat analysis of point pattern.

## Distance Based Statistics¶

 f(coordinates[, support, distances, metric, ...]) Ripley's F function g(coordinates[, support, distances, metric, ...]) Ripley's G function k(coordinates[, support, distances, metric, ...]) Ripley's K function j(coordinates[, support, distances, metric, ...]) Ripely's J function l(coordinates[, support, permutations, ...]) Ripley's L function f_test(coordinates[, support, distances, ...]) Ripley's F function g_test(coordinates[, support, distances, ...]) Ripley's G function k_test(coordinates[, support, distances, ...]) Ripley's K function j_test(coordinates[, support, distances, ...]) Ripley's J function l_test(coordinates[, support, distances, ...]) Ripley's L function

## Window functions¶

 Window(parts[, holes]) Geometric container for point patterns. as_window(pysal_polygon) Convert a libpysal polygon to a Window. poly_from_bbox(bbox) to_ccf(poly)

## Random distributions¶

 random.poisson(hull[, intensity, size]) Simulate a poisson random point process with a specified intensity. random.normal(hull[, center, cov, size]) Simulate a multivariate random normal point cluster random.cluster_poisson(hull[, intensity, ...]) Simulate a cluster poisson random point process with a specified intensity & number of seeds. random.cluster_normal(hull[, cov, size, n_seeds]) Simulate a cluster poisson random point process with a specified intensity & number of seeds.

## Space-Time Interaction Tests¶

 SpaceTimeEvents(path, time_col[, ...]) Method for reformatting event data stored in a shapefile for use in calculating metrics of spatio-temporal interaction. Knox(s_coords, t_coords, delta, tau[, ...]) Global Knox statistic for space-time interactions KnoxLocal(s_coords, t_coords, delta, tau[, ...]) Local Knox statistics for space-time interactions mantel(s_coords, t_coords[, permutations, ...]) Standardized Mantel test for spatio-temporal interaction. jacquez(s_coords, t_coords, k[, permutations]) Jacquez k nearest neighbors test for spatio-temporal interaction. modified_knox(s_coords, t_coords, delta, tau) Baker's modified Knox test for spatio-temporal interaction.

## Visualization¶

 plot_density(data, bandwidth[, kernel, ...]) Plot kernel density of a given point pattern