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comparison src/SbpOperators/laplace/secondderivative.jl @ 543:1a53eb83ed24 refactor/tensor_index_coupling

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Remove some unecessary Index types
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 27 Nov 2020 12:02:47 +0100
parents 011ca1639153
children 9330338d6ab5
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542:011ca1639153 543:1a53eb83ed24
17 return SecondDerivative(h_inv, innerStencil, closureStencils, size(grid)) 17 return SecondDerivative(h_inv, innerStencil, closureStencils, size(grid))
18 end 18 end
19 19
20 LazyTensors.range_size(D2::SecondDerivative) = D2.size 20 LazyTensors.range_size(D2::SecondDerivative) = D2.size
21 LazyTensors.domain_size(D2::SecondDerivative) = D2.size 21 LazyTensors.domain_size(D2::SecondDerivative) = D2.size
22
23 #TODO: The 1D tensor mappings should not have to dispatch on 1D tuples if we write LazyTensor.apply for vararg right?!?!
24 # Currently have to index the Tuple{Index} in each method in order to call the stencil methods which is ugly.
25 # I thought I::Vararg{Index,R} fell back to just Index for R = 1
26 22
27 # Apply for different regions Lower/Interior/Upper or Unknown region 23 # Apply for different regions Lower/Interior/Upper or Unknown region
28 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Lower}) where T 24 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Lower}) where T
29 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(I)], v, Int(I)) 25 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(I)], v, Int(I))
30 end 26 end