Mercurial > repos > public > sbplib_julia
diff SbpOperators/src/laplace/laplace.jl @ 302:6fa2ba769ae3
Create 1D tensor mapping for inverse diagonal norm, and make the multi-dimensional inverse quadrature use those. Move InverseQudrature from laplace.jl into InverseQuadrature.jl
| author | Vidar Stiernström <vidar.stiernstrom@it.uu.se> |
|---|---|
| date | Tue, 23 Jun 2020 18:56:59 +0200 |
| parents | b00eea62c78e |
| children | c1fcc35e19cb |
line wrap: on
line diff
--- a/SbpOperators/src/laplace/laplace.jl Tue Jun 23 18:53:20 2020 +0200 +++ b/SbpOperators/src/laplace/laplace.jl Tue Jun 23 18:56:59 2020 +0200 @@ -45,31 +45,6 @@ boundary_quadrature(L::Laplace, bId::CartesianBoundary) = BoundaryQuadrature(L.op, L.grid, bId) export quadrature - -""" - InverseQuadrature{Dim,T<:Real,N,M,K} <: TensorMapping{T,Dim,Dim} - -Implements the inverse quadrature operator `inv(H)` of Dim dimension as a TensorMapping -""" -struct InverseQuadrature{Dim,T<:Real,N,M,K} <: TensorOperator{T,Dim} - op::D2{T,N,M,K} - grid::EquidistantGrid{Dim,T} -end -export InverseQuadrature - -LazyTensors.domain_size(H_inv::InverseQuadrature{Dim}, range_size::NTuple{Dim,Integer}) where Dim = range_size - -@inline function LazyTensors.apply(H_inv::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T - N = size(H_inv.grid) - # Inverse quadrature in x direction - @inbounds q_inv = apply_inverse_quadrature(H_inv.op, inverse_spacing(H_inv.grid)[1], v[I] , I[1], N[1]) - # Inverse quadrature in y-direction - @inbounds q_inv = apply_inverse_quadrature(H_inv.op, inverse_spacing(H_inv.grid)[2], q_inv, I[2], N[2]) - return q_inv -end - -LazyTensors.apply_transpose(H_inv::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T = LazyTensors.apply(H_inv,v,I) - """ BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1}