Mercurial > repos > public > sbplib_julia
view src/SbpOperators/volumeops/derivatives/second_derivative_variable.jl @ 891:f72cc96a58c6 feature/variable_derivatives
Add some size tests
author | Jonatan Werpers <jonatan@werpers.com> |
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date | Tue, 08 Feb 2022 10:58:31 +0100 |
parents | 069e58fb3829 |
children | 422c9f22cf92 |
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export SecondDerivativeVariable # """ # SecondDerivativeVariable(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 `SecondDerivativeVariable` 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? # # Create 1D volume operator in along coordinate direction # op = SecondDerivativeVariable(restrict(grid, direction), inner_stencil, closure_stencils, parity) # # 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 volume operator # parts = Base.setindex(Is, op, direction) # return foldl(⊗, parts) # end """ SecondDerivativeVariable{T,N,M,K} <: TensorOperator{T,1} Implements the one-dimensional second derivative with variable coefficients. """ struct SecondDerivativeVariable{Dir,T,D,N,M,K,TArray<:AbstractArray} <: TensorMapping{T,D,D} inner_stencil::NestedStencil{T,N} closure_stencils::NTuple{M,NestedStencil{T,K}} size::NTuple{D,Int} coefficient::TArray function SecondDerivativeVariable{Dir, D}(inner_stencil::NestedStencil{T,N}, closure_stencils::NTuple{M,NestedStencil{T,K}}, size::NTuple{D,Int}, coefficient::TArray) where {Dir,T,D,N,M,K,TArray<:AbstractArray} return new{Dir,T,D,N,M,K,TArray}(inner_stencil,closure_stencils,size, coefficient) end end function SecondDerivativeVariable(grid::EquidistantGrid, coeff::AbstractArray, inner_stencil, closure_stencils, dir) return SecondDerivativeVariable{dir, dimension(grid)}(inner_stencil, Tuple(closure_stencils), size(grid), coeff) end function SecondDerivativeVariable(grid::EquidistantGrid{1}, coeff::AbstractVector, inner_stencil, closure_stencils) return SecondDerivativeVariable(grid, coeff, inner_stencil, closure_stencils, 1) end derivative_direction(::SecondDerivativeVariable{Dir}) where {Dir} = Dir closure_size(op::SecondDerivativeVariable) = length(op.closure_stencils) LazyTensors.range_size(op::SecondDerivativeVariable) = op.size LazyTensors.domain_size(op::SecondDerivativeVariable) = op.size function derivative_view(op, a, I) d = derivative_direction(op) Iview = Base.setindex(I,:,d) return @view a[Iview...] # D = domain_dim(op) # Iₗ, _, Iᵣ = split_tuple(I, Val(d-1), Val(1), Val(D-d)) # return @view a[Iₗ..., :, Iᵣ...] end function apply_lower(op::SecondDerivativeVariable, v, I...) ṽ = derivative_view(op, v, I) c̃ = derivative_view(op, op.coefficient, I) i = I[derivative_direction(op)] return @inbounds apply_stencil(op.closure_stencils[i], c̃, ṽ, i) end function apply_interior(op::SecondDerivativeVariable, v, I...) ṽ = derivative_view(op, v, I) c̃ = derivative_view(op, op.coefficient, I) i = I[derivative_direction(op)] return apply_stencil(op.inner_stencil, c̃, ṽ, i) end function apply_upper(op::SecondDerivativeVariable, v, I...) ṽ = derivative_view(op, v, I) c̃ = derivative_view(op, op.coefficient, I) i = I[derivative_direction(op)] return @inbounds apply_stencil_backwards(op.closure_stencils[op.size[1]-i+1], c̃, ṽ, i) end function LazyTensors.apply(op::SecondDerivativeVariable, v::AbstractVector, I::Vararg{Index}) if I[derivative_direction(op)] isa Index{Lower} return apply_lower(op, v, Int.(I)...) elseif I[derivative_direction(op)] isa Index{Upper} return apply_upper(op, v, Int.(I)...) else return apply_interior(op, v, Int.(I)...) end end function LazyTensors.apply(op::SecondDerivativeVariable, v::AbstractVector, I...) i = I[derivative_direction(op)] r = getregion(i, closure_size(op), op.size[1]) return LazyTensors.apply(op, v, Index(i, r)) end # TODO: Rename SecondDerivativeVariable -> VariableSecondDerivative