--- a/src/SbpOperators/boundaryops/boundary_operator.jl	Mon Feb 21 10:33:58 2022 +0100
+++ b/src/SbpOperators/boundaryops/boundary_operator.jl	Fri Feb 03 22:14:47 2023 +0100
@@ -1,48 +1,17 @@
 """
-    boundary_operator(grid,closure_stencil,boundary)
-
-Creates a boundary operator on a `Dim`-dimensional grid for the
-specified `boundary`. The action of the operator is determined by `closure_stencil`.
-
-When `Dim=1`, the corresponding `BoundaryOperator` tensor mapping is returned.
-When `Dim>1`, the `BoundaryOperator` `op` is inflated by the outer product
-of `IdentityMappings` in orthogonal coordinate directions, e.g for `Dim=3`,
-the boundary restriction operator in the y-direction direction is `Ix⊗op⊗Iz`.
-"""
-function boundary_operator(grid::EquidistantGrid, closure_stencil, boundary::CartesianBoundary)
-    #TODO:Check that dim(boundary) <= Dim?
+    BoundaryOperator{T,R,N} <: LazyTensor{T,0,1}
 
-    # Create 1D boundary operator
-    r = region(boundary)
-    d = dim(boundary)
-    op = BoundaryOperator(restrict(grid, d), closure_stencil, r)
-
-    # Create 1D IdentityMappings for each coordinate direction
-    one_d_grids = restrict.(Ref(grid), Tuple(1:dimension(grid)))
-    Is = IdentityMapping{eltype(grid)}.(size.(one_d_grids))
-
-    # Formulate the correct outer product sequence of the identity mappings and
-    # the boundary operator
-    parts = Base.setindex(Is, op, d)
-    return foldl(⊗, parts)
-end
-
-"""
-    BoundaryOperator{T,R,N} <: TensorMapping{T,0,1}
-
-Implements the boundary operator `op` for 1D as a `TensorMapping`
+Implements the boundary operator `op` for 1D as a `LazyTensor`
 
 `op` is the restriction of a grid function to the boundary using some closure `Stencil{T,N}`.
 The boundary to restrict to is determined by `R`.
 `op'` is the prolongation of a zero dimensional array to the whole grid using the same closure stencil.
 """
-struct BoundaryOperator{T,R<:Region,N} <: TensorMapping{T,0,1}
+struct BoundaryOperator{T,R<:Region,N} <: LazyTensor{T,0,1}
     stencil::Stencil{T,N}
     size::Int
 end
 
-BoundaryOperator{R}(stencil::Stencil{T,N}, size::Int) where {T,R,N} = BoundaryOperator{T,R,N}(stencil, size)
-
 """
     BoundaryOperator(grid::EquidistantGrid{1}, closure_stencil, region)
 
@@ -55,6 +24,7 @@
 
 """
     closure_size(::BoundaryOperator)
+
 The size of the closure stencil.
 """
 closure_size(::BoundaryOperator{T,R,N}) where {T,R,N} = N
@@ -62,27 +32,27 @@
 LazyTensors.range_size(op::BoundaryOperator) = ()
 LazyTensors.domain_size(op::BoundaryOperator) = (op.size,)
 
-function LazyTensors.apply(op::BoundaryOperator{T,Lower}, v::AbstractVector{T}) where T
+function LazyTensors.apply(op::BoundaryOperator{<:Any,Lower}, v::AbstractVector)
     apply_stencil(op.stencil,v,1)
 end
 
-function LazyTensors.apply(op::BoundaryOperator{T,Upper}, v::AbstractVector{T}) where T
+function LazyTensors.apply(op::BoundaryOperator{<:Any,Upper}, v::AbstractVector)
     apply_stencil_backwards(op.stencil,v,op.size)
 end
 
-function LazyTensors.apply_transpose(op::BoundaryOperator{T,Lower}, v::AbstractArray{T,0}, i::Index{Lower}) where T
+function LazyTensors.apply_transpose(op::BoundaryOperator{<:Any,Lower}, v::AbstractArray{<:Any,0}, i::Index{Lower})
     return op.stencil[Int(i)-1]*v[]
 end
 
-function LazyTensors.apply_transpose(op::BoundaryOperator{T,Upper}, v::AbstractArray{T,0}, i::Index{Upper}) where T
+function LazyTensors.apply_transpose(op::BoundaryOperator{<:Any,Upper}, v::AbstractArray{<:Any,0}, i::Index{Upper})
     return op.stencil[op.size[1] - Int(i)]*v[]
 end
 
 # Catch all combinations of Lower, Upper and Interior not caught by the two previous methods.
-function LazyTensors.apply_transpose(op::BoundaryOperator{T}, v::AbstractArray{T,0}, i::Index) where T
-    return zero(T)
+function LazyTensors.apply_transpose(op::BoundaryOperator, v::AbstractArray{<:Any,0}, i::Index)
+    return zero(eltype(v))
 end
 
-function LazyTensors.apply_transpose(op::BoundaryOperator{T}, v::AbstractArray{T,0}, i) where T
+function LazyTensors.apply_transpose(op::BoundaryOperator, v::AbstractArray{<:Any,0}, i)
     return LazyTensors.apply_transpose_with_region(op, v, closure_size(op), op.size[1], i)
 end