✔ Interpolations.jl find neighbor points with weightedindex · helpdesk (published)

I have some interpolation itp from which I want to find the neighboring points of the grid. I checked the docs and found in the developer documentation section WeightedIndex, which is probably close to what I'm looking for. However, I don't understand the following behavior:

julia> A = reshape(1:27, 3, 3, 3)
3×3×3 reshape(::UnitRange{Int64}, 3, 3, 3) with eltype Int64:
[:, :, 1] =
 1  4  7
 2  5  8
 3  6  9

[:, :, 2] =
 10  13  16
 11  14  17
 12  15  18

[:, :, 3] =
 19  22  25
 20  23  26
 21  24  27

julia> A = reshape(1:27, 3, 3, 3);

julia> itp = LinearInterpolation((-1:1:1,-1:1:1,-1:1:1),A);

julia> x = (1.0,0.0,0.75);

julia> itp(x...)
21.75

julia> wis = Interpolations.weightedindexes((Interpolations.value_weights,), Interpolations.itpinfo(itp)..., x)
(Interpolations.WeightedAdjIndex{2, Float64}(1, (1.0, 0.0)), Interpolations.WeightedAdjIndex{2, Float64}(0, (1.0, 0.0)), Interpolations.WeightedAdjIndex{2, Float64}(0, (0.25, 0.75)))

julia> itp.itp.itp[wis...]
1.0e-323

julia> A[wis...]
-4.25

According to the docs the evaluation of the itp as well as itp.itp.itp[wis...] should yield the same result, shouldn't it? Is the scaling the problem? Is there in general a better way to detect all contributing points for the position of interest x and their corresponding weights?

Stream: helpdesk (published)

Topic: ✔ Interpolations.jl find neighbor points with weightedindex

Max Köhler (Apr 12 2022 at 08:20):

Max Köhler (Apr 12 2022 at 08:41):

Notification Bot (Apr 12 2022 at 08:48):