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comparison DiffOps/src/laplace.jl @ 258:3ea8c60ccef3 boundary_conditions

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Fix a few typos in documentation and add a few TODOs
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 28 Jun 2019 14:11:57 +0200
parents e960b877e07e
children 5571d2c5bf0f
comparison
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257:d4cd4882ee9f 258:3ea8c60ccef3
37 37
38 38
39 """ 39 """
40 BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1} 40 BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1}
41 41
42 Implements the boundary operator `e` as a TensorMapping 42 Implements the boundary operator `e` as a TensorMapping
43 """ 43 """
44 struct BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1} 44 struct BoundaryValue{T,N,M,K} <: TensorMapping{T,2,1}
45 op::D2{T,N,M,K} 45 op::D2{T,N,M,K}
46 grid::EquidistantGrid{2} 46 grid::EquidistantGrid{2}
47 bId::CartesianBoundary 47 bId::CartesianBoundary
51 # TODO: This is obviouly strange. Is domain_size just discarded? Is there a way to avoid storing grid in BoundaryValue? 51 # TODO: This is obviouly strange. Is domain_size just discarded? Is there a way to avoid storing grid in BoundaryValue?
52 # Can we give special treatment to TensorMappings that go to a higher dim? 52 # Can we give special treatment to TensorMappings that go to a higher dim?
53 LazyTensors.range_size(e::BoundaryValue{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid) 53 LazyTensors.range_size(e::BoundaryValue{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid)
54 LazyTensors.domain_size(e::BoundaryValue{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],) 54 LazyTensors.domain_size(e::BoundaryValue{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],)
55 55
56 # TODO: Make this independent of dimension
56 function LazyTensors.apply(e::BoundaryValue, v::AbstractArray, I::NTuple{2,Int}) 57 function LazyTensors.apply(e::BoundaryValue, v::AbstractArray, I::NTuple{2,Int})
57 i = I[dim(e.bId)] 58 i = I[dim(e.bId)]
58 j = I[3-dim(e.bId)] 59 j = I[3-dim(e.bId)]
59 N_i = size(e.grid)[dim(e.bId)] 60 N_i = size(e.grid)[dim(e.bId)]
60 return apply_e(e.op, v[j], N_i, i, region(e.bId)) 61 return apply_e(e.op, v[j], N_i, i, region(e.bId))
68 69
69 70
70 """ 71 """
71 NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1} 72 NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
72 73
73 Implements the boundary operator `d` as a TensorMapping 74 Implements the boundary operator `d` as a TensorMapping
74 """ 75 """
75 struct NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1} 76 struct NormalDerivative{T,N,M,K} <: TensorMapping{T,2,1}
76 op::D2{T,N,M,K} 77 op::D2{T,N,M,K}
77 grid::EquidistantGrid{2} 78 grid::EquidistantGrid{2}
78 bId::CartesianBoundary 79 bId::CartesianBoundary
83 # Can we give special treatment to TensorMappings that go to a higher dim? 84 # Can we give special treatment to TensorMappings that go to a higher dim?
84 LazyTensors.range_size(e::NormalDerivative{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid) 85 LazyTensors.range_size(e::NormalDerivative{T}, domain_size::NTuple{1,Integer}) where T = size(e.grid)
85 LazyTensors.domain_size(e::NormalDerivative{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],) 86 LazyTensors.domain_size(e::NormalDerivative{T}, range_size::NTuple{2,Integer}) where T = (range_size[3-dim(e.bId)],)
86 87
87 # TODO: Not type stable D:< 88 # TODO: Not type stable D:<
89 # TODO: Make this independent of dimension
88 function LazyTensors.apply(d::NormalDerivative, v::AbstractArray, I::NTuple{2,Int}) 90 function LazyTensors.apply(d::NormalDerivative, v::AbstractArray, I::NTuple{2,Int})
89 i = I[dim(d.bId)] 91 i = I[dim(d.bId)]
90 j = I[3-dim(d.bId)] 92 j = I[3-dim(d.bId)]
91 N_i = size(d.grid)[dim(d.bId)] 93 N_i = size(d.grid)[dim(d.bId)]
92 h_inv = d.grid.inverse_spacing[dim(d.bId)] 94 h_inv = d.grid.inverse_spacing[dim(d.bId)]