Rename ThinnedSampling -> LewisShedler

Author: juliohm

src/PointPatterns.jl

@@ -22,6 +22,9 @@ export
   UnionProcess,
   ishomogeneous,
 
+  # algorithms
+  LewisShedler,
+
   # thinning methods
   AbstractThinning,
   RandomThinning,

src/processes.jl

@@ -55,12 +55,12 @@ function default_sampling_algorithm end
 # --------------------
 
 """
-    ThinnedSampling(λmax)
+    LewisShedler(λmax)
 
 Generate sample using Lewis-Shedler algorithm (1979) with
 maximum real value `λmax` of the intensity function.
 """
-struct ThinnedSampling{T<:Real}
+struct LewisShedler{T<:Real}
   λmax::T
 end
 

src/processes/poisson.jl

@@ -34,7 +34,7 @@ ishomogeneous(p::PoissonProcess{<:AbstractVector}) = false
 
 default_sampling_algorithm(::PoissonProcess, ::Any) = DiscretizedSampling()
 
-default_sampling_algorithm(p::PoissonProcess{<:Function}, g) = ThinnedSampling(default_lambda_max(p, g))
+default_sampling_algorithm(p::PoissonProcess{<:Function}, g) = LewisShedler(default_lambda_max(p, g))
 
 function default_lambda_max(p::PoissonProcess{<:Function}, g)
   points = sample(g, HomogeneousSampling(10000))
@@ -67,7 +67,7 @@ end
 # INHOMOGENEOUS CASE
 #--------------------
 
-function rand_single(rng::Random.AbstractRNG, p::PoissonProcess{<:Function}, g, algo::ThinnedSampling)
+function rand_single(rng::Random.AbstractRNG, p::PoissonProcess{<:Function}, g, algo::LewisShedler)
   # simulate a homogeneous process
   pp = rand_single(rng, PoissonProcess(algo.λmax), g, DiscretizedSampling())
 

src/thinning.jl

@@ -26,4 +26,5 @@ thin(::PointSet, ::AbstractThinning)
 #-----------------
 # IMPLEMENTATIONS
 #-----------------
+
 include("thinning/random.jl")

test/processes.jl

@@ -46,13 +46,12 @@
     # inhomogeneous with intensity function
     p = PoissonProcess(λ)
     for g in [seg, tri, quad, box, ball, poly, grid, mesh]
-      # default thinnedsampling
       pp = rand(p, g)
       @test all(∈(g), pp)
-      # custom thinnedsampling using λmax
-      pp = rand(p, g, algo=PointPatterns.ThinnedSampling(λ(Point2(12.0, 12.0))))
+
+      pp = rand(p, g, algo=LewisShedler(λ(Point2(12.0, 12.0))))
       @test all(∈(g), pp)
-      # discretizedsampling
+
       g = discretize(g)
       pp = rand(p, g, algo=PointPatterns.DiscretizedSampling())
       @test all(∈(g), g)