--- a/DiffOps/src/laplace.jl	Wed Jun 26 21:22:36 2019 +0200
+++ b/DiffOps/src/laplace.jl	Thu Jun 27 09:43:44 2019 +0200
@@ -1,40 +1,39 @@
-"""
-    NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
-
-Implements the boundary operator `d` as a TensorMapping
-"""
-struct NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
-	op::D2{T,N,M,K}
-	grid::EquidistantGrid
-	bId::CartesianBoundary
+struct Laplace{Dim,T<:Real,N,M,K} <: DiffOpCartesian{Dim}
+    grid::EquidistantGrid{Dim,T}
+    a::T
+    op::D2{Float64,N,M,K}
+    # e::BoundaryValue
+    # d::NormalDerivative
 end
-export NormalDerivative
 
-# TODO: This is obviouly strange. Is domain_size just discarded? Is there a way to avoid storing grid in BoundaryValue?
-# Can we give special treatment to TensorMappings that go to a higher dim?
-LazyTensors.range_size(e::NormalDerivative{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid)
-LazyTensors.domain_size(e::NormalDerivative{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],)
+function apply(L::Laplace{Dim}, v::AbstractArray{T,Dim} where T, I::CartesianIndex{Dim}) where Dim
+    error("not implemented")
+end
 
-# Not correct abstraction level
-# TODO: Not type stable D:<
-function LazyTensors.apply(d::NormalDerivative, v::AbstractArray, I::NTuple{2,Int})
-	i = I[dim(d.bId)]
-	j = I[3-dim(d.bId)]
-	N_i = size(d.grid)[dim(d.bId)]
-
-	if region(d.bId) == Lower
-		# Note, closures are indexed by offset. Fix this D:<
-		return d.grid.inverse_spacing[dim(d.bId)]*d.op.dClosure[i-1]*v[j]
-	elseif region(d.bId) == Upper
-		return -d.grid.inverse_spacing[dim(d.bId)]*d.op.dClosure[N_i-i]*v[j]
-	end
+# u = L*v
+function apply(L::Laplace{1}, v::AbstractVector, i::Int)
+    uᵢ = L.a * SbpOperators.apply(L.op, L.grid.spacing[1], v, i)
+    return uᵢ
 end
 
-function LazyTensors.apply_transpose(d::NormalDerivative, v::AbstractArray, I::NTuple{1,Int})
-    u = selectdim(v,3-dim(d.bId),I[1])
-    return apply_d(d.op, d.grid.inverse_spacing[dim(d.bId)], u, region(d.bId))
+@inline function apply(L::Laplace{2}, v::AbstractArray{T,2} where T, I::Tuple{Index{R1}, Index{R2}}) where {R1, R2}
+    # 2nd x-derivative
+    @inbounds vx = view(v, :, Int(I[2]))
+    @inbounds uᵢ = L.a*SbpOperators.apply(L.op, L.grid.inverse_spacing[1], vx , I[1])
+    # 2nd y-derivative
+    @inbounds vy = view(v, Int(I[1]), :)
+    @inbounds uᵢ += L.a*SbpOperators.apply(L.op, L.grid.inverse_spacing[2], vy, I[2])
+    # NOTE: the package qualifier 'SbpOperators' can problably be removed once all "applying" objects use LazyTensors
+    return uᵢ
 end
 
+# Slow but maybe convenient?
+function apply(L::Laplace{2}, v::AbstractArray{T,2} where T, i::CartesianIndex{2})
+    I = Index{Unknown}.(Tuple(i))
+    apply(L, v, I)
+end
+
+
 
 """
     BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1}
@@ -73,40 +72,43 @@
 
 
 
-struct Laplace{Dim,T<:Real,N,M,K} <: DiffOpCartesian{Dim}
-    grid::EquidistantGrid{Dim,T}
-    a::T
-    op::D2{Float64,N,M,K}
-    # e::BoundaryValue
-    # d::NormalDerivative
+"""
+    NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
+
+Implements the boundary operator `d` as a TensorMapping
+"""
+struct NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
+	op::D2{T,N,M,K}
+	grid::EquidistantGrid
+	bId::CartesianBoundary
 end
+export NormalDerivative
 
-function apply(L::Laplace{Dim}, v::AbstractArray{T,Dim} where T, I::CartesianIndex{Dim}) where Dim
-    error("not implemented")
+# TODO: This is obviouly strange. Is domain_size just discarded? Is there a way to avoid storing grid in BoundaryValue?
+# Can we give special treatment to TensorMappings that go to a higher dim?
+LazyTensors.range_size(e::NormalDerivative{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid)
+LazyTensors.domain_size(e::NormalDerivative{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],)
+
+# Not correct abstraction level
+# TODO: Not type stable D:<
+function LazyTensors.apply(d::NormalDerivative, v::AbstractArray, I::NTuple{2,Int})
+	i = I[dim(d.bId)]
+	j = I[3-dim(d.bId)]
+	N_i = size(d.grid)[dim(d.bId)]
+
+	if region(d.bId) == Lower
+		# Note, closures are indexed by offset. Fix this D:<
+		return d.grid.inverse_spacing[dim(d.bId)]*d.op.dClosure[i-1]*v[j]
+	elseif region(d.bId) == Upper
+		return -d.grid.inverse_spacing[dim(d.bId)]*d.op.dClosure[N_i-i]*v[j]
+	end
 end
 
-# u = L*v
-function apply(L::Laplace{1}, v::AbstractVector, i::Int)
-    uᵢ = L.a * SbpOperators.apply(L.op, L.grid.spacing[1], v, i)
-    return uᵢ
+function LazyTensors.apply_transpose(d::NormalDerivative, v::AbstractArray, I::NTuple{1,Int})
+    u = selectdim(v,3-dim(d.bId),I[1])
+    return apply_d(d.op, d.grid.inverse_spacing[dim(d.bId)], u, region(d.bId))
 end
 
-@inline function apply(L::Laplace{2}, v::AbstractArray{T,2} where T, I::Tuple{Index{R1}, Index{R2}}) where {R1, R2}
-    # 2nd x-derivative
-    @inbounds vx = view(v, :, Int(I[2]))
-    @inbounds uᵢ = L.a*SbpOperators.apply(L.op, L.grid.inverse_spacing[1], vx , I[1])
-    # 2nd y-derivative
-    @inbounds vy = view(v, Int(I[1]), :)
-    @inbounds uᵢ += L.a*SbpOperators.apply(L.op, L.grid.inverse_spacing[2], vy, I[2])
-    # NOTE: the package qualifier 'SbpOperators' can problably be removed once all "applying" objects use LazyTensors
-    return uᵢ
-end
-
-# Slow but maybe convenient?
-function apply(L::Laplace{2}, v::AbstractArray{T,2} where T, i::CartesianIndex{2})
-    I = Index{Unknown}.(Tuple(i))
-    apply(L, v, I)
-end
 
 
 struct Neumann{Bid<:BoundaryIdentifier} <: BoundaryCondition end