"""
    volume_operator(grid, inner_stencil, closure_stencils, parity, direction)

Creates a volume operator on a `Dim`-dimensional grid acting along the
specified coordinate `direction`. The action of the operator is determined by
the stencils `inner_stencil` and `closure_stencils`. When `Dim=1`, the
corresponding `VolumeOperator` tensor mapping is returned. When `Dim>1`, the
returned operator is the appropriate outer product of a one-dimensional
operators and `IdentityMapping`s, e.g for `Dim=3` the volume operator in the
y-direction is `I⊗op⊗I`.
"""
function volume_operator(grid::EquidistantGrid, inner_stencil, closure_stencils, parity, direction)
    #TODO: Check that direction <= Dim?

    op = VolumeOperator(restrict(grid, direction), inner_stencil, closure_stencils, parity)
    return LazyTensors.inflate(op, size(grid), direction)
end
# TBD: Should the inflation happen here or should we remove this method and do it at the caller instead?

"""
    VolumeOperator{T,N,M,K} <: TensorOperator{T,1}
Implements a one-dimensional constant coefficients volume operator
"""
struct VolumeOperator{T,N,M,K} <: TensorMapping{T,1,1}
    inner_stencil::Stencil{T,N}
    closure_stencils::NTuple{M,Stencil{T,K}}
    size::NTuple{1,Int}
    parity::Parity
end

function VolumeOperator(grid::EquidistantGrid{1}, inner_stencil, closure_stencils, parity)
    return VolumeOperator(inner_stencil, Tuple(closure_stencils), size(grid), parity)
end

closure_size(::VolumeOperator{T,N,M}) where {T,N,M} = M

LazyTensors.range_size(op::VolumeOperator) = op.size
LazyTensors.domain_size(op::VolumeOperator) = op.size

function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Lower})
    return @inbounds apply_stencil(op.closure_stencils[Int(i)], v, Int(i))
end

function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Interior})
    return apply_stencil(op.inner_stencil, v, Int(i))
end

function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Upper})
    return @inbounds Int(op.parity)*apply_stencil_backwards(op.closure_stencils[op.size[1]-Int(i)+1], v, Int(i))
end

function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i)
    r = getregion(i, closure_size(op), op.size[1])
    return LazyTensors.apply(op, v, Index(i, r))
end
# TODO: Move this to LazyTensors when we have the region communication down.