LinearInterpolation: Fix scalar indexing and generalize for ndims(u) > 2

src/interpolation_methods.jl

@@ -33,11 +33,12 @@ function _interpolate(A::LinearInterpolation{<:AbstractVector}, t::Number, igues
     val
 end
 
-function _interpolate(A::LinearInterpolation{<:AbstractMatrix}, t::Number, iguess)
+function _interpolate(A::LinearInterpolation{<:AbstractArray}, t::Number, iguess)
     idx = get_idx(A, t, iguess)
     Δt = t - A.t[idx]
     slope = get_parameters(A, idx)
-    return A.u[:, idx] + slope * Δt
+    ax = axes(A.u)[1:(end - 1)]
+    return A.u[ax..., idx] + slope * Δt
 end
 
 # Quadratic Interpolation

src/interpolation_utils.jl

@@ -94,6 +94,21 @@ function munge_data(U::StridedMatrix, t::AbstractVector)
     return U, t
 end
 
+function munge_data(U::AbstractArray{T, N}, t) where {T, N}
+    TU = Base.nonmissingtype(eltype(U))
+    Tt = Base.nonmissingtype(eltype(t))
+    @assert length(t) == size(U, ndims(U))
+    ax = axes(U)[1:(end - 1)]
+    non_missing_indices = collect(
+        i for i in 1:length(t)
+    if !any(ismissing, U[ax..., i]) && !ismissing(t[i])
+    )
+    U = cat([TU.(U[ax..., i]) for i in non_missing_indices]...; dims = ndims(U))
+    t = Tt.([t[i] for i in non_missing_indices])
+
+    return U, t
+end
+
 seems_linear(assume_linear_t::Bool, _) = assume_linear_t
 seems_linear(assume_linear_t::Number, t) = looks_linear(t; threshold = assume_linear_t)
 

src/parameter_caches.jl

@@ -18,9 +18,11 @@ function safe_diff(b, a::T) where {T}
     b == a ? zero(T) : b - a
 end
 
-function linear_interpolation_parameters(u::AbstractArray{T}, t, idx) where {T}
-    Δu = if u isa AbstractMatrix
-        [safe_diff(u[j, idx + 1], u[j, idx]) for j in 1:size(u)[1]]
+function linear_interpolation_parameters(u::AbstractArray{T, N}, t, idx) where {T, N}
+    Δu = if N > 1
+        ax = axes(u)
+        safe_diff.(
+            u[ax[1:(end - 1)]..., (idx + 1):(idx + 1)], u[ax[1:(end - 1)]..., idx:idx])
     else
         safe_diff(u[idx + 1], u[idx])
     end

test/interpolation_tests.jl

@@ -43,11 +43,11 @@ end
         A = LinearInterpolation(u, t; extrapolate = true)
 
         for (_t, _u) in zip(t, eachcol(u))
-            @test A(_t) == _u
+            @test A(_t) == reshape(_u, :, 1)
         end
-        @test A(0) == [0.0, 0.0]
-        @test A(5.5) == [11.0, 16.5]
-        @test A(11) == [22, 33]
+        @test A(0) == [0.0; 0.0;;]
+        @test A(5.5) == [11.0; 16.5;;]
+        @test A(11) == [22; 33;;]
 
         x = 1:10
         y = 2:4