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comparison SbpOperators/src/InverseQuadrature.jl @ 322:777063b6f049

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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 bd09d67ebb22
children 9cc5d1498b2d
comparison
equal deleted inserted replaced
315:7a7d9daa9eb7 322:777063b6f049
10 Hi::NTuple{Dim,InverseDiagonalNorm{T,N,M}} 10 Hi::NTuple{Dim,InverseDiagonalNorm{T,N,M}}
11 end 11 end
12 12
13 LazyTensors.domain_size(Qi::InverseQuadrature{Dim}, range_size::NTuple{Dim,Integer}) where Dim = range_size 13 LazyTensors.domain_size(Qi::InverseQuadrature{Dim}, range_size::NTuple{Dim,Integer}) where Dim = range_size
14 14
15 function LazyTensors.apply(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::NTuple{Dim,Index}) where {T,Dim} 15 function LazyTensors.apply(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {T,Dim}
16 error("not implemented") 16 error("not implemented")
17 end 17 end
18 18
19 LazyTensors.apply_transpose(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where {Dim,T} = LazyTensors.apply(Q,v,I) 19 LazyTensors.apply_transpose(Qi::InverseQuadrature{Dim,T}, v::AbstractArray{T,Dim}, I::Vararg{Index,Dim}) where {Dim,T} = LazyTensors.apply(Q,v,I)
20 20
21 @inline function LazyTensors.apply(Qi::InverseQuadrature{1,T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T 21 @inline function LazyTensors.apply(Qi::InverseQuadrature{1,T}, v::AbstractVector{T}, I::Index) where T
22 @inbounds q = apply(Qi.Hi[1], v , I[1]) 22 @inbounds q = apply(Qi.Hi[1], v , I)
23 return q 23 return q
24 end 24 end
25 25
26 @inline function LazyTensors.apply(Qi::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T 26 @inline function LazyTensors.apply(Qi::InverseQuadrature{2,T}, v::AbstractArray{T,2}, I::Index, J::Index) where T
27 # InverseQuadrature in x direction 27 # InverseQuadrature in x direction
28 @inbounds vx = view(v, :, Int(I[2])) 28 @inbounds vx = view(v, :, Int(J))
29 @inbounds qx_inv = apply(Qi.Hi[1], vx , I[1]) 29 @inbounds qx_inv = apply(Qi.Hi[1], vx , I)
30 # InverseQuadrature in y-direction 30 # InverseQuadrature in y-direction
31 @inbounds vy = view(v, Int(I[1]), :) 31 @inbounds vy = view(v, Int(I), :)
32 @inbounds qy_inv = apply(Qi.Hi[2], vy, I[2]) 32 @inbounds qy_inv = apply(Qi.Hi[2], vy, J)
33 return qx_inv*qy_inv 33 return qx_inv*qy_inv
34 end 34 end
35 35
36 """ 36 """
37 InverseQuadrature{Dim,T<:Real,N,M,K} <: TensorMapping{T,Dim,Dim} 37 InverseQuadrature{Dim,T<:Real,N,M,K} <: TensorMapping{T,Dim,Dim}
43 h_inv::T # The reciprocl grid spacing could be included in the stencil already. Preferable? 43 h_inv::T # The reciprocl grid spacing could be included in the stencil already. Preferable?
44 closure::NTuple{M,T} 44 closure::NTuple{M,T}
45 #TODO: Write a nice constructor 45 #TODO: Write a nice constructor
46 end 46 end
47 47
48 @inline function LazyTensors.apply(Hi::InverseDiagonalNorm{T}, v::AbstractVector{T}, I::NTuple{1,Index}) where T 48 @inline function LazyTensors.apply(Hi::InverseDiagonalNorm{T}, v::AbstractVector{T}, I:Index) where T
49 return @inbounds apply(Hi, v, I[1]) 49 return @inbounds apply(Hi, v, I)
50 end 50 end
51 51
52 LazyTensors.apply_transpose(Hi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::NTuple{2,Index}) where T = LazyTensors.apply(Hi,v,I) 52 LazyTensors.apply_transpose(Hi::InverseQuadrature{Dim,T}, v::AbstractArray{T,2}, I::Index) where T = LazyTensors.apply(Hi,v,I)
53 53
54 @inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, i::Index{Lower}) where T 54 @inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, I::Index{Lower}) where T
55 return @inbounds Hi.h_inv*Hi.closure[Int(i)]*v[Int(i)] 55 return @inbounds Hi.h_inv*Hi.closure[Int(i)]*v[Int(I)]
56 end 56 end
57 @inline LazyTensors.apply(Hi::InverseDiagonalNorm,v::AbstractVector{T}, i::Index{Upper}) where T 57 @inline LazyTensors.apply(Hi::InverseDiagonalNorm,v::AbstractVector{T}, I::Index{Upper}) where T
58 N = length(v); 58 N = length(v);
59 return @inbounds Hi.h_inv*Hi.closure[N-Int(i)+1]v[Int(i)] 59 return @inbounds Hi.h_inv*Hi.closure[N-Int(I)+1]v[Int(I)]
60 end 60 end
61 61
62 @inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, i::Index{Interior}) where T 62 @inline LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, I::Index{Interior}) where T
63 return @inbounds Hi.h_inv*v[Int(i)] 63 return @inbounds Hi.h_inv*v[Int(I)]
64 end 64 end
65 65
66 function LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, index::Index{Unknown}) where T 66 function LazyTensors.apply(Hi::InverseDiagonalNorm, v::AbstractVector{T}, index::Index{Unknown}) where T
67 N = length(v); 67 N = length(v);
68 r = getregion(Int(index), closuresize(Hi), N) 68 r = getregion(Int(index), closuresize(Hi), N)