Mercurial > repos > public > sbplib_julia
changeset 322:777063b6f049
Dispatch applys on vararg Index instead of tuples
author | Vidar Stiernström <vidar.stiernstrom@it.uu.se> |
---|---|
date | Wed, 09 Sep 2020 21:42:55 +0200 |
parents | 7a7d9daa9eb7 |
children | b2ddc5e4d41a |
files | SbpOperators/src/InverseQuadrature.jl SbpOperators/src/Quadrature.jl |
diffstat | 2 files changed, 36 insertions(+), 36 deletions(-) [+] |
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--- a/SbpOperators/src/InverseQuadrature.jl Wed Sep 09 21:23:19 2020 +0200 +++ b/SbpOperators/src/InverseQuadrature.jl Wed Sep 09 21:42:55 2020 +0200 @@ -12,24 +12,24 @@ LazyTensors.domain_size(Qi::InverseQuadrature{Dim}, range_size::NTuple{Dim,Integer}) where Dim = range_size -function LazyTensors.apply(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::NTuple{Dim,Index}) where {T,Dim} +function LazyTensors.apply(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {T,Dim} error("not implemented") end -LazyTensors.apply_transpose(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where {Dim,T} = LazyTensors.apply(Q,v,I) +LazyTensors.apply_transpose(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {Dim,T} = LazyTensors.apply(Q,v,I) -@inline function LazyTensors.apply(Qi::InverseQuadrature{1,T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T - @inbounds q = apply(Qi.Hi[1], v , I[1]) +@inline function LazyTensors.apply(Qi::InverseQuadrature{1,T}, v::AbstractVector{T}, I::Index) where T + @inbounds q = apply(Qi.Hi[1], v , I) return q end -@inline function LazyTensors.apply(Qi::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T +@inline function LazyTensors.apply(Qi::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::Index, J::Index) where T # InverseQuadrature in x direction - @inbounds vx = view(v, :, Int(I[2])) - @inbounds qx_inv = apply(Qi.Hi[1], vx , I[1]) + @inbounds vx = view(v, :, Int(J)) + @inbounds qx_inv = apply(Qi.Hi[1], vx , I) # InverseQuadrature in y-direction - @inbounds vy = view(v, Int(I[1]), :) - @inbounds qy_inv = apply(Qi.Hi[2], vy, I[2]) + @inbounds vy = view(v, Int(I), :) + @inbounds qy_inv = apply(Qi.Hi[2], vy, J) return qx_inv*qy_inv end @@ -45,22 +45,22 @@ #TODO: Write a nice constructor end -@inline function LazyTensors.apply(Hi::InverseDiagonalNorm{T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T - return @inbounds apply(Hi, v, I[1]) +@inline function LazyTensors.apply(Hi::InverseDiagonalNorm{T}, v::AbstractVector{T}, I:Index) where T + return @inbounds apply(Hi, v, I) end -LazyTensors.apply_transpose(Hi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T = LazyTensors.apply(Hi,v,I) +LazyTensors.apply_transpose(Hi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::Index) where T = LazyTensors.apply(Hi,v,I) -@inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, i::Index{Lower}) where T - return @inbounds Hi.h_inv*Hi.closure[Int(i)]*v[Int(i)] +@inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, I::Index{Lower}) where T + return @inbounds Hi.h_inv*Hi.closure[Int(i)]*v[Int(I)] end -@inline LazyTensors.apply(Hi::InverseDiagonalNorm,v::AbstractVector{T}, i::Index{Upper}) where T +@inline LazyTensors.apply(Hi::InverseDiagonalNorm,v::AbstractVector{T}, I::Index{Upper}) where T N = length(v); - return @inbounds Hi.h_inv*Hi.closure[N-Int(i)+1]v[Int(i)] + return @inbounds Hi.h_inv*Hi.closure[N-Int(I)+1]v[Int(I)] end -@inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, i::Index{Interior}) where T - return @inbounds Hi.h_inv*v[Int(i)] +@inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, I::Index{Interior}) where T + return @inbounds Hi.h_inv*v[Int(I)] end function LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, index::Index{Unknown}) where T
--- a/SbpOperators/src/Quadrature.jl Wed Sep 09 21:23:19 2020 +0200 +++ b/SbpOperators/src/Quadrature.jl Wed Sep 09 21:42:55 2020 +0200 @@ -13,24 +13,24 @@ LazyTensors.domain_size(Q::Quadrature{Dim}, range_size::NTuple{Dim,Integer}) where Dim = range_size -function LazyTensors.apply(Q::Quadrature{Dim,T}, v::AbstractArray{T,Dim}, I::NTuple{Dim,Index}) where {T,Dim} +function LazyTensors.apply(Q::Quadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {T,Dim} error("not implemented") end -LazyTensors.apply_transpose(Q::Quadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where {Dim,T} = LazyTensors.apply(Q,v,I) +LazyTensors.apply_transpose(Q::Quadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {Dim,T} = LazyTensors.apply(Q,v,I) -@inline function LazyTensors.apply(Q::Quadrature{1,T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T - @inbounds q = apply(Q.H[1], v , I[1]) +@inline function LazyTensors.apply(Q::Quadrature{1,T}, v::AbstractVector{T}, I::Index) where T + @inbounds q = apply(Q.H[1], v , I) return q end -@inline function LazyTensors.apply(Q::Quadrature{2,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T +@inline function LazyTensors.apply(Q::Quadrature{2,T}, v::AbstractArray{T,2}, I::Index, J::Index) where T # Quadrature in x direction - @inbounds vx = view(v, :, Int(I[2])) - @inbounds qx = apply(Q.H[1], vx , I[1]) + @inbounds vx = view(v, :, Int(J)) + @inbounds qx = apply(Q.H[1], vx , I) # Quadrature in y-direction - @inbounds vy = view(v, Int(I[1]), :) - @inbounds qy = apply(Q.H[2], vy, I[2]) + @inbounds vy = view(v, Int(I), :) + @inbounds qy = apply(Q.H[2], vy, J) return qx*qy end @@ -46,22 +46,22 @@ #TODO: Write a nice constructor end -@inline function LazyTensors.apply(H::DiagonalNorm{T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T - return @inbounds apply(H, v, I[1]) +@inline function LazyTensors.apply(H::DiagonalNorm{T}, v::AbstractVector{T}, I::Index) where T + return @inbounds apply(H, v, I) end -LazyTensors.apply_transpose(H::Quadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T = LazyTensors.apply(H,v,I) +LazyTensors.apply_transpose(H::Quadrature{Dim,T}, v::AbstractArray{T,2}, I::Index) where T = LazyTensors.apply(H,v,I) -@inline LazyTensors.apply(H::DiagonalNorm, v::AbstractVector{T}, i::Index{Lower}) where T - return @inbounds H.h*H.closure[Int(i)]*v[Int(i)] +@inline LazyTensors.apply(H::DiagonalNorm, v::AbstractVector{T}, I::Index{Lower}) where T + return @inbounds H.h*H.closure[Int(I)]*v[Int(I)] end -@inline LazyTensors.apply(H::DiagonalNorm,v::AbstractVector{T}, i::Index{Upper}) where T +@inline LazyTensors.apply(H::DiagonalNorm,v::AbstractVector{T}, I::Index{Upper}) where T N = length(v); - return @inbounds H.h*H.closure[N-Int(i)+1]v[Int(i)] + return @inbounds H.h*H.closure[N-Int(I)+1]v[Int(I)] end -@inline LazyTensors.apply(H::DiagonalNorm, v::AbstractVector{T}, i::Index{Interior}) where T - return @inbounds H.h*v[Int(i)] +@inline LazyTensors.apply(H::DiagonalNorm, v::AbstractVector{T}, I::Index{Interior}) where T + return @inbounds H.h*v[Int(I)] end function LazyTensors.apply(H::DiagonalNorm, v::AbstractVector{T}, index::Index{Unknown}) where T