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annotate SbpOperators/src/laplace/secondderivative.jl @ 320:d705b397aa33

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Clean up Notes.md and TODO.md
author Jonatan Werpers <jonatan@werpers.com>
date Thu, 10 Sep 2020 11:03:22 +0200
parents f2d6ec89dfc5
children 9cc5d1498b2d
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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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7 struct SecondDerivative{T,N,M,K} <: TensorOperator{T,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 #TODO: Write a nice constructor
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13 end
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14 export SecondDerivative
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16 LazyTensors.domain_size(D2::SecondDerivative, range_size::NTuple{1,Integer}) = range_size
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17
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18 #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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19 # Currently have to index the Tuple{Index} in each method in order to call the stencil methods which is ugly.
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20 # I thought I::Vararg{Index,R} fell back to just Index for R = 1
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22 # Apply for different regions Lower/Interior/Upper or Unknown region
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23 @inline function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Lower}) where T
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24 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.closureStencils[Int(I)], v, Int(I))
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25 end
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27 @inline function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Interior}) where T
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28 return @inbounds D2.h_inv*D2.h_inv*apply_stencil(D2.innerStencil, v, Int(I))
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29 end
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31 @inline function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, I::Index{Upper}) where T
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32 N = length(v) # TODO: Use domain_size here instead? N = domain_size(D2,size(v))
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33 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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34 end
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36 @inline function LazyTensors.apply(D2::SecondDerivative{T}, v::AbstractVector{T}, index::Index{Unknown}) where T
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37 N = length(v) # TODO: Use domain_size here instead?
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38 r = getregion(Int(index), closuresize(D2), N)
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39 I = Index(Int(index), r)
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40 return LazyTensors.apply(D2, v, I)
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41 end
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44 @inline function LazyTensors.apply_transpose(D2::SecondDerivative, v::AbstractVector, I::Index)
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45 return LazyTensors.apply(D2, v, I)
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46 end
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49 function closuresize(D2::SecondDerivative{T,N,M,K}) where {T<:Real,N,M,K}
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50 return M
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51 end