Processes
Point processes can be simulated with the function rand
on different geometries:
Base.rand
— Methodrand([rng], p, g, n=1; [algo])
Generate n
realizations of spatial point process p
inside geometry g
. Optionally specify sampling algorithm algo
and random number generator rng
.
For example, a Poisson process with given intensity in a rectangular region:
p = PoissonProcess(0.1)
b = Box((0.,0.), (100.,100.))
s = rand(p, b, 2)
plot(plot(s[1]), plot(s[2]))
or the superposition of two Binomial processes:
p₁ = BinomialProcess(50)
p₂ = BinomialProcess(50)
p = p₁ ∪ p₂ # 100 points
s = rand(p, b, 2)
plot(plot(s[1]), plot(s[2]))
The homogeneity property of a point process can be checked with the ishomogeneous
function:
PointPatterns.ishomogeneous
— Functionishomogeneous(p)
Tells whether or not the spatial point process p
is homogeneous.
Below is the list of currently implemented point processes.
BinomialProcess
PointPatterns.BinomialProcess
— TypeBinomialProcess(n)
A Binomial point process with n
points.
PoissonProcess
PointPatterns.PoissonProcess
— TypePoissonProcess(λ)
A Poisson process with intensity λ
.
UnionProcess
PointPatterns.UnionProcess
— TypeUnionProcess(p₁, p₂)
Union (or superposition) of spatial point processes p₁
and p₂
.