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comparison src/SbpOperators/volumeops/volume_operator.jl @ 1022:bbbc31953367 refactor/sbpoperators/inflation

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Introduce an inflate function in lazy tensors and use it in volume_operator and boundary_operator
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 18 Mar 2022 16:57:00 +0100
parents 469ed954b493
children 52f07c77299d
comparison
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988:83046af6143a 1022:bbbc31953367
10 y-direction is `I⊗op⊗I`. 10 y-direction is `I⊗op⊗I`.
11 """ 11 """
12 function volume_operator(grid::EquidistantGrid, inner_stencil, closure_stencils, parity, direction) 12 function volume_operator(grid::EquidistantGrid, inner_stencil, closure_stencils, parity, direction)
13 #TODO: Check that direction <= Dim? 13 #TODO: Check that direction <= Dim?
14 14
15 # Create 1D volume operator in along coordinate direction
16 op = VolumeOperator(restrict(grid, direction), inner_stencil, closure_stencils, parity) 15 op = VolumeOperator(restrict(grid, direction), inner_stencil, closure_stencils, parity)
17 # Create 1D IdentityMappings for each coordinate direction 16 return LazyTensors.inflate(op, size(grid), direction)
18 one_d_grids = restrict.(Ref(grid), Tuple(1:dimension(grid)))
19 Is = IdentityMapping{eltype(grid)}.(size.(one_d_grids))
20 # Formulate the correct outer product sequence of the identity mappings and
21 # the volume operator
22 parts = Base.setindex(Is, op, direction)
23 return foldl(⊗, parts)
24 end 17 end
18 # TBD: Should the inflation happen here or should we remove this method and do it at the caller instead?
25 19
26 """ 20 """
27 VolumeOperator{T,N,M,K} <: TensorOperator{T,1} 21 VolumeOperator{T,N,M,K} <: TensorOperator{T,1}
28 Implements a one-dimensional constant coefficients volume operator 22 Implements a one-dimensional constant coefficients volume operator
29 """ 23 """
57 51
58 function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i) 52 function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i)
59 r = getregion(i, closure_size(op), op.size[1]) 53 r = getregion(i, closure_size(op), op.size[1])
60 return LazyTensors.apply(op, v, Index(i, r)) 54 return LazyTensors.apply(op, v, Index(i, r))
61 end 55 end
56 # TODO: Move this to LazyTensors when we have the region communication down.