--- a/src/SbpOperators/laplace/secondderivative.jl	Thu Nov 26 17:53:40 2020 +0100
+++ b/src/SbpOperators/laplace/secondderivative.jl	Sun Nov 29 21:18:45 2020 +0100
@@ -20,29 +20,24 @@
 LazyTensors.range_size(D2::SecondDerivative) = D2.size
 LazyTensors.domain_size(D2::SecondDerivative) = D2.size
 
-#TODO: The 1D tensor mappings should not have to dispatch on 1D tuples if we write LazyTensor.apply for vararg right?!?!
-#      Currently have to index the Tuple{Index} in each method in order to call the stencil methods which is ugly.
-#      I thought I::Vararg{Index,R} fell back to just Index for R = 1
+# Apply for different regions Lower/Interior/Upper or Unknown region
+function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, i::Index{Lower}) where T
+    return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(i)], v, Int(i))
+end
 
-# Apply for different regions Lower/Interior/Upper or Unknown region
-function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Lower}) where T
-    return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(I)], v, Int(I))
+function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, i::Index{Interior}) where T
+    return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.innerStencil, v, Int(i))
 end
 
-function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Interior}) where T
-    return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.innerStencil, v, Int(I))
+function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, i::Index{Upper}) where T
+    N = length(v) # TODO: Use domain_size here instead? N = domain_size(D2,size(v))
+    return @inbounds D2.h_inv*D2.h_inv*apply_stencil_backwards(D2.closureStencils[N-Int(i)+1], v, Int(i))
 end
 
-function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Upper}) where T
-    N = length(v) # TODO: Use domain_size here instead? N = domain_size(D2,size(v))
-    return @inbounds D2.h_inv*D2.h_inv*apply_stencil_backwards(D2.closureStencils[N-Int(I)+1], v, Int(I))
-end
-
-function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, index::Index{Unknown}) where T
+function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, i) where T
     N = length(v)  # TODO: Use domain_size here instead?
-    r = getregion(Int(index), closuresize(D2), N)
-    I = Index(Int(index), r)
-    return LazyTensors.apply(D2, v, I)
+    r = getregion(i, closuresize(D2), N)
+    return LazyTensors.apply(D2, v, Index(i, r))
 end
 
 closuresize(D2::SecondDerivative{T,N,M,K}) where {T<:Real,N,M,K} = M