Mercurial > repos > public > sbplib_julia
view src/SbpOperators/volumeops/volume_operator.jl @ 1354:150313ed2cae
Merge refactor/grids (missed delete of a note)
Changes from previous merge:
* `EquidistantGrid` is now only a 1D thing.
* Higher dimensions are supported through `TensorGrid`.
* The old behavior of `EquidistantGrid` has been moved to the function `equidistant_grid`.
* Grids embedded in higher dimensions are now supported through tensor products with `ZeroDimGrid`s.
* Vector valued grid functions are now supported and the default element type is `SVector`.
* Grids are now expected to support Julia's indexing and iteration interface.
* `eval_on` can be called with both `f(x,y,...)` and `f(x̄)`.
author | Jonatan Werpers <jonatan@werpers.com> |
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date | Sat, 20 May 2023 14:19:20 +0200 |
parents | e94ddef5e72f |
children | 4684c7f1c4cb |
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""" VolumeOperator{T,N,M,K} <: LazyTensor{T,1,1} A one-dimensional constant coefficients stencil operator. """ struct VolumeOperator{T,N,M,K} <: LazyTensor{T,1,1} inner_stencil::Stencil{T,N} closure_stencils::NTuple{M,Stencil{T,K}} size::NTuple{1,Int} parity::Parity end function VolumeOperator(grid::EquidistantGrid, inner_stencil, closure_stencils, parity) return VolumeOperator(inner_stencil, Tuple(closure_stencils), size(grid), parity) end closure_size(::VolumeOperator{T,N,M}) where {T,N,M} = M LazyTensors.range_size(op::VolumeOperator) = op.size LazyTensors.domain_size(op::VolumeOperator) = op.size function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Lower}) return @inbounds apply_stencil(op.closure_stencils[Int(i)], v, Int(i)) end function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Interior}) return apply_stencil(op.inner_stencil, v, Int(i)) end function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i::Index{Upper}) return @inbounds Int(op.parity)*apply_stencil_backwards(op.closure_stencils[op.size[1]-Int(i)+1], v, Int(i)) end function LazyTensors.apply(op::VolumeOperator, v::AbstractVector, i) r = getregion(i, closure_size(op), op.size[1]) return LazyTensors.apply(op, v, Index(i, r)) end # TODO: Move this to LazyTensors when we have the region communication down.