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annotate src/Grids/tensor_grid.jl @ 1689:e11b5b6940a2 feature/grids/curvilinear

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Add more tests for `normal`
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 23 Aug 2024 09:22:29 +0200
parents 51f0c5f895fb
children ff7aeda1b292
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1 # TODO: Check this file and other grids for duplicate implementation of general methods implemented for Grid
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2
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3 """
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4 TensorGrid{T,D} <: Grid{T,D}
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5
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6 A grid constructed as the tensor product of other grids.
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7
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8 Currently only supports grids with the `HasShape`-trait.
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9 """
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10 struct TensorGrid{T,D,GT<:NTuple{N,Grid} where N} <: Grid{T,D}
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11 grids::GT
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12
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13 function TensorGrid(gs...)
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14 T = mapreduce(eltype, combined_coordinate_vector_type, gs)
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15 D = sum(ndims, gs)
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16
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17 return new{T,D,typeof(gs)}(gs)
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18 end
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19 end
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20
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21 # Indexing interface
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22 function Base.getindex(g::TensorGrid, I::Vararg{Int})
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23 szs = ndims.(g.grids)
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24
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25 Is = LazyTensors.split_tuple(I, szs)
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26 ps = map((g,I)->SVector(g[I...]), g.grids, Is)
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27
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28 return vcat(ps...)
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29 end
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30
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31 function Base.eachindex(g::TensorGrid)
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32 szs = LazyTensors.concatenate_tuples(size.(g.grids)...)
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33 return CartesianIndices(szs)
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34 end
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35
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36 function Base.axes(g::TensorGrid, d)
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37 i, ld = grid_and_local_dim_index(ndims.(g.grids), d)
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38 return axes(g.grids[i], ld)
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39 end
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40
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41 # Iteration interface
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42 Base.iterate(g::TensorGrid) = iterate(Iterators.product(g.grids...)) |> _iterate_combine_coords
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43 Base.iterate(g::TensorGrid, state) = iterate(Iterators.product(g.grids...), state) |> _iterate_combine_coords
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44 _iterate_combine_coords(::Nothing) = nothing
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45 _iterate_combine_coords((next,state)) = combine_coordinates(next...), state
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46
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47 Base.IteratorSize(::Type{<:TensorGrid{<:Any, D}}) where D = Base.HasShape{D}()
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48 Base.length(g::TensorGrid) = prod(length, g.grids)
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49 Base.size(g::TensorGrid) = LazyTensors.concatenate_tuples(size.(g.grids)...)
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50 Base.size(g::TensorGrid, d) = size(g)[d]
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51
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52
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53 function min_spacing(g::TensorGrid)
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54 relevant_grids = filter(g->!isa(g,ZeroDimGrid),g.grids)
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55 d = min_spacing.(relevant_grids)
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56 return minimum(d)
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57 end
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58
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59 refine(g::TensorGrid, r::Int) = mapreduce(g->refine(g,r), TensorGrid, g.grids)
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60 coarsen(g::TensorGrid, r::Int) = mapreduce(g->coarsen(g,r), TensorGrid, g.grids)
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61
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62 """
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63 TensorGridBoundary{N, BID} <: BoundaryIdentifier
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64
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65 A boundary identifier for a tensor grid. `N` Specifies which grid in the
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66 tensor product and `BID` which boundary on that grid.
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67 """
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68 struct TensorGridBoundary{N, BID} <: BoundaryIdentifier end
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69 grid_id(::TensorGridBoundary{N, BID}) where {N, BID} = N
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70 boundary_id(::TensorGridBoundary{N, BID}) where {N, BID} = BID()
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71
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72 """
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73 boundary_identifiers(g::TensorGrid)
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74
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75 Returns a tuple containing the boundary identifiers of `g`.
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76 """
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77 function boundary_identifiers(g::TensorGrid)
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78 per_grid = map(eachindex(g.grids)) do i
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79 return map(bid -> TensorGridBoundary{i, typeof(bid)}(), boundary_identifiers(g.grids[i]))
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80 end
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81 return LazyTensors.concatenate_tuples(per_grid...)
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82 end
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83
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84 """
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85 boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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86
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87 The grid for the boundary of `g` specified by `id`.
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88 """
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89 function boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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90 local_boundary_grid = boundary_grid(g.grids[grid_id(id)], boundary_id(id))
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91 new_grids = Base.setindex(g.grids, local_boundary_grid, grid_id(id))
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92 return TensorGrid(new_grids...)
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93 end
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94
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95 function boundary_indices(g::TensorGrid, id::TensorGridBoundary)
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96 per_grid_ind = map(g.grids) do g
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97 ntuple(i->:, ndims(g))
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98 end
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99
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100 local_b_ind = boundary_indices(g.grids[grid_id(id)], boundary_id(id))
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101 b_ind = Base.setindex(per_grid_ind, local_b_ind, grid_id(id))
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102
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103 return LazyTensors.concatenate_tuples(b_ind...)
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104 end
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105
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106 function combined_coordinate_vector_type(coordinate_types...)
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107 combined_coord_length = mapreduce(_ncomponents, +, coordinate_types)
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108 combined_coord_type = mapreduce(eltype, promote_type, coordinate_types)
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109
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110 if combined_coord_length == 1
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111 return combined_coord_type
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112 else
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113 return SVector{combined_coord_length, combined_coord_type}
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114 end
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115 end
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116
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117 function combine_coordinates(coords...)
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118 return mapreduce(SVector, vcat, coords)
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119 end
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120
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121 """
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122 grid_and_local_dim_index(nds, d)
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123
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124 Given a tuple of number of dimensions `nds`, and a global dimension index `d`,
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125 calculate which grid index, and local dimension, `d` corresponds to.
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126
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127 `nds` would come from broadcasting `ndims` on the grids tuple of a
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128 `TensorGrid`. If you are interested in a dimension `d` of a tensor grid `g`
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129 ```julia
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130 gi, ldi = grid_and_local_dim_index(ndims.(g.grids), d)
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131 ```
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132 tells you which grid it belongs to (`gi`) and which index it is at within that
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133 grid (`ldi`).
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134 """
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135 function grid_and_local_dim_index(nds, d)
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136 I = findfirst(>=(d), cumsum(nds))
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137
c0208286234e Add `grid_and_local_dim_index`
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138 if I == 1
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139 return (1, d)
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140 else
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141 return (I, d-cumsum(nds)[I-1])
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142 end
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143 end