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annotate src/SbpOperators/laplace/secondderivative.jl @ 371:241bd2512c20 feature/lazy_function

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Add a LazyFunctionArray that evaluates a function for each index.
author Jonatan Werpers <jonatan@werpers.com>
date Wed, 30 Sep 2020 19:48:17 +0200
parents e73af120ad38
children 81053b1992b6
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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7247e85dc1e8 Start separating ConstantStencilOp into multiple 1D tensor mappings, e.g. ConstantLaplaceOp. Sketch an implementation of the multi-D laplace tensor operator as a tuple of 1D laplace tensor operators.
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1 """
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2 SecondDerivative{T<:Real,N,M,K} <: TensorOperator{T,1}
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3 Implements the Laplace tensor operator `L` with constant grid spacing and coefficients
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4 in 1D dimension
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5 """
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6
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7 struct SecondDerivative{T,N,M,K} <: TensorMapping{T,1,1}
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8 h_inv::T # The grid spacing could be included in the stencil already. Preferable?
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9 innerStencil::Stencil{T,N}
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10 closureStencils::NTuple{M,Stencil{T,K}}
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11 parity::Parity
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12 size::NTuple{1,Int}
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13 end
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14 export SecondDerivative
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16 function SecondDerivative(grid::EquidistantGrid{1}, innerStencil, closureStencils)
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17 h_inv = grid.inverse_spacing[1]
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18 return SecondDerivative(h_inv, innerStencil, closureStencils, even, size(grid))
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19 end
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20
0844069ab5ff Reinclude SbpOperators and fix most of the code and tests there.
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21 LazyTensors.range_size(D2::SecondDerivative) = D2.size
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22 LazyTensors.domain_size(D2::SecondDerivative) = D2.size
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24 #TODO: The 1D tensor mappings should not have to dispatch on 1D tuples if we write LazyTensor.apply for vararg right?!?!
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25 # Currently have to index the Tuple{Index} in each method in order to call the stencil methods which is ugly.
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26 # I thought I::Vararg{Index,R} fell back to just Index for R = 1
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28 # Apply for different regions Lower/Interior/Upper or Unknown region
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29 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Lower}) where T
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30 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(I)], v, Int(I))
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31 end
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33 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Interior}) where T
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34 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.innerStencil, v, Int(I))
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35 end
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37 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Upper}) where T
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38 N = length(v) # TODO: Use domain_size here instead? N = domain_size(D2,size(v))
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39 return @inbounds D2.h_inv*D2.h_inv*Int(D2.parity)*apply_stencil_backwards(D2.closureStencils[N-Int(I)+1], v, Int(I))
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40 end
7247e85dc1e8 Start separating ConstantStencilOp into multiple 1D tensor mappings, e.g. ConstantLaplaceOp. Sketch an implementation of the multi-D laplace tensor operator as a tuple of 1D laplace tensor operators.
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42 function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, index::Index{Unknown}) where T
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43 N = length(v) # TODO: Use domain_size here instead?
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44 r = getregion(Int(index), closuresize(D2), N)
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45 I = Index(Int(index), r)
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46 return LazyTensors.apply(D2, v, I)
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47 end
7247e85dc1e8 Start separating ConstantStencilOp into multiple 1D tensor mappings, e.g. ConstantLaplaceOp. Sketch an implementation of the multi-D laplace tensor operator as a tuple of 1D laplace tensor operators.
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49 closuresize(D2::SecondDerivative{T,N,M,K}) where {T<:Real,N,M,K} = M