LinearInterpolation: Output Vector for u::AbstractMatrix

[ashutosh-b-b]

Checklist

• Appropriate tests were added
• Any code changes were done in a way that does not break public API
• All documentation related to code changes were updated
• The new code follows the
  contributor guidelines, in particular the SciML Style Guide and
  COLPRAC.
• Any new documentation only uses public API

Additional context

Add any other context about the problem here.

[ChrisRackauckas]

This only fixes matrix, not the array. Fix the array dispatch.

[ashutosh-b-b]

This only fixes matrix, not the array. Fix the array dispatch.

We intend to fix the AbstractMatrix right? The AbstractArray corresponds to when ndims(A) > 2. Right now the output looks like this:

julia> using DataInterpolations

julia> u2d = rand(5,5);

julia> t = 0:1:4
0:1:4

julia> LinearInterpolation(u2d, t)(0.1)
5-element Vector{Float64}:
 0.6412661219777369
 0.45226380542841466
 0.667059578536869
 0.3410690837298372
 0.8883286346809633

julia> u3d = rand(5,5,5);

julia> LinearInterpolation(u3d, t)(0.1)
5×5×1 Array{Float64, 3}:
[:, :, 1] =
 0.885232  0.433556   0.225437  0.683801  0.450121
 0.572518  0.0916726  0.244869  0.339845  0.472721
 0.346758  0.568071   0.823261  0.231083  0.803863
 0.627925  0.158742   0.909838  0.38132   0.846073
 0.381645  0.756691   0.31842   0.106938  0.565953

[sathvikbhagavan]

For N-D called with just a single time point, shouldn't it be (N-1)-D output?

[ashutosh-b-b]

For N-D called with just a single time point, shouldn't it be (N-1)-D output?

Right, fixed it now the output looks like this:

julia> u2d = rand(5,5);

julia> u3d = rand(5,5,5);

julia> t = 0:1:4
0:1:4

julia> LinearInterpolation(u2d, t)(0.12)
5-element Vector{Float64}:
 0.8955879753612471
 0.17218902066690886
 0.5403394528994946
 0.28106129729671586
 0.264249745025073

julia> LinearInterpolation(u3d, t)(0.12)
5×5 Matrix{Float64}:
 0.491654  0.237982  0.819793  0.93269    0.516005
 0.480924  0.733239  0.543345  0.0662969  0.67747
 0.552783  0.18947   0.362321  0.238529   0.0915166
 0.625032  0.844209  0.498186  0.171338   0.357868
 0.485161  0.653258  0.703258  0.860978   0.0744935