# 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

`minimum_bounding_rectangle`(points) | 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

## Quadrat Based Statistics

`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