pointpats.Jenv

class pointpats.Jenv(pp, n=100, intervals=10, dmin=0.0, dmax=None, d=None, pct=0.05, realizations=None)[source]

Simulation envelope for J function.

Parameters
ppPointPattern

Point Pattern instance.

nint

Number of empty space points (random points).

intervalsint

The length of distance domain sequence. Default is 10.

dminfloat

The minimum of the distance domain.

dmaxfloat

Upper limit of distance range. If dmax is None, dmax will be set to maximum nearest neighbor distance.

dsequence

The distance domain sequence. If d is specified, intervals, dmin and dmax are ignored.

pctfloat

1-alpha, alpha is the significance level. Default is 0.05, which means 95% confidence level for the envelopes.

realizations: :class:`.PointProcess`

Point process instance with more than 1 realizations.

Examples

>>> import libpysal as ps
>>> from pointpats import Jenv, PoissonPointProcess, Window
>>> from libpysal.cg import shapely_ext
>>> va = ps.io.open(ps.examples.get_path("vautm17n.shp"))
>>> polys = [shp for shp in va]
>>> state = shapely_ext.cascaded_union(polys)
>>> pp = PoissonPointProcess(Window(state.parts), 100, 1, asPP=True).realizations[0]
>>> csrs = PoissonPointProcess(pp.window, 100, 100, asPP=True)
>>> jenv = Jenv(pp, realizations=csrs)
>>> jenv.plot()

(Source code)

Attributes
namestring

Name of the function. (“G”, “F”, “J”, “K” or “L”)

observedarray

A 2-dimensional numpy array of 2 columns. The first column is the distance domain sequence for the observed point pattern. The second column is J function for the observed point pattern.

lowarray

A 1-dimensional numpy array. Lower bound of the simulation envelope.

higharray

A 1-dimensional numpy array. Higher bound of the simulation envelope.

meanarray

A 1-dimensional numpy array. Mean values of the simulation envelope.

Methods

calc

mapper

plot

__init__(self, pp, n=100, intervals=10, dmin=0.0, dmax=None, d=None, pct=0.05, realizations=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, pp[, n, intervals, dmin, …])

Initialize self.

calc(self, \*args, \*\*kwargs)

mapper(self, realizations)

plot(self)