Linrange scaling (#294)

src/scaling/scaling.jl

@@ -92,9 +92,9 @@ coordlookup(r::UnitRange, x) = x - r.start + oneunit(eltype(r))
 # coordlookup(i::Bool, r::AbstractRange, x) = i ? coordlookup(r, x) : convert(typeof(coordlookup(r,x)), x)
 coordlookup(r::StepRange, x) = (x - r.start) / r.step + oneunit(eltype(r))
 
-coordlookup(r::StepRangeLen, x) = (x - first(r)) / step(r) + oneunit(eltype(r))
-boundstep(r::StepRangeLen) = 0.5*step(r)
-rescale_gradient(r::StepRangeLen, g) = g / step(r)
+coordlookup(r::AbstractRange, x) = (x - first(r)) / step(r) + oneunit(eltype(r))
+boundstep(r::AbstractRange) = 0.5*step(r)
+rescale_gradient(r::AbstractRange, g) = g / step(r)
 
 basetype(::Type{ScaledInterpolation{T,N,ITPT,IT,RT}}) where {T,N,ITPT,IT,RT} = ITPT
 basetype(sitp::ScaledInterpolation) = basetype(typeof(sitp))

test/scaling/scaling.jl

@@ -11,12 +11,25 @@ using Test, LinearAlgebra, StaticArrays
     @test typeof(sitp) <: Interpolations.ScaledInterpolation
     @test parent(sitp) === itp
 
-    for (x,y) in zip(-3:.05:1.5, 1:.1:10)
+    for (x,y) in zip(-3:.05:1.5, 1:.1:10,)
         @test sitp(x) ≈ y
     end
 
     @test_throws ArgumentError scale(itp, reverse(-3:.5:1.5))
 
+    # Model linear interpolation of y = -3 + .5x by interpolating y=x
+    # and then scaling to the new x range, this time using a LinRange,
+    # which should give the same values as with the StepRangeLen used previously
+
+    itp = interpolate(1:1.0:10, BSpline(Linear()))
+
+    sitp = @inferred(scale(itp, LinRange(-3,1.5,10)))
+    @test typeof(sitp) <: Interpolations.ScaledInterpolation
+
+    for (x,y) in zip(-3:.05:1.5, 1:.1:10,)
+        @test sitp(x) ≈ y
+    end
+
     # Verify that it works in >1D, with different types of ranges
 
     gauss(phi, mu, sigma) = exp(-(phi-mu)^2 / (2sigma)^2)
@@ -58,6 +71,20 @@ using Test, LinearAlgebra, StaticArrays
                 cos(x) * cos(y) -sin(x) * sin(y)] ≈ h atol=0.03
     end
 
+    # Test Hessians of scaled grids with LinRange
+    xs = LinRange(-pi, pi, 62)
+    ys = LinRange(-pi, pi, 32)
+    zs = sin.(xs) .* sin.(ys')
+    itp = interpolate(zs, BSpline(Cubic(Line(OnGrid()))))
+    sitp = @inferred scale(itp, xs, ys)
+
+    for x in xs[2:end-1], y in ys[2:end-1]
+        h = @inferred(Interpolations.hessian(sitp, x, y))
+        @test issymmetric(h)
+        @test [-sin(x) * sin(y) cos(x) * cos(y)
+                cos(x) * cos(y) -sin(x) * sin(y)] ≈ h atol=0.03
+    end
+
     # Verify that return types are reasonable
     @inferred(sitp2(-3.4, 1.2))
     @inferred(sitp2(-3, 1))