`PointDiscretizedBoxMap`

A generalization of MonteCarloBoxMap and GridBoxMap can be defined as follows:

we provide a "global" set of test points within the unit cube $[-1,1]^d$.
For each box Box(c,r), we rescale the global test points to lie within the box by calculating c .+ r .* p for each global test point p.

GAIO.PointDiscretizedBoxMap — Function

BoxMap(:pointdiscretized, map, domain, points) -> SampledBoxMap

Construct a SampledBoxMap that uses the iterator points as test points. points must be an array or iterator of test points within the unit cube [-1,1]^N.

source

BoxMap(:pointdiscretized, :simd, map, domain, points) -> CPUSampledBoxMap

Construct a CPUSampledBoxMap that uses the iterator points as test points. points must have eltype SVector{N, SIMD.Vec{S,T}} and be within the unit cube [-1,1]^N.

source

BoxMap(:pointdiscretized, :gpu, map, domain::Box{N}, points) -> GPUSampledBoxMap

Construct a GPUSampledBoxMap that uses the Vector points as test points. points must be a VECTOR of test points within the unit cube [-1,1]^N.

Requires a CUDA-capable gpu.

source

Example

julia> using StaticArrays
       # create a map that tests the vertices of a box
julia> global_test_points = SVector{2,Float64}[
           (1,  1),
           (1, -1),
           (-1, 1),
           (-1, -1)
       ]4-element Vector{SVector{2, Float64}}:
 [1.0, 1.0]
 [1.0, -1.0]
 [-1.0, 1.0]
 [-1.0, -1.0]
julia> F = BoxMap(:pointdiscretized, f, domain, global_test_points)SampledBoxMap with 4 sample points
julia> p = plot!(
           p, F(B),
           color=RGBA(1.,0.,0.,0.5),
           lab="Images of test points along the vertices"
       )Plot{Plots.GRBackend() n=4}

Point Discretized BoxMap