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annotate src/Grids/tensor_grid.jl @ 1871:b1eb33e62d1e refactor/grids/iterable_boundary_indices

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Remove unneded type paramter name
author Jonatan Werpers <jonatan@werpers.com>
date Mon, 27 Jan 2025 09:17:54 +0100
parents 516eaabf1169
children
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1 """
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2 TensorGrid{T,D} <: Grid{T,D}
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3
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4 A grid constructed as the tensor product of other grids.
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5
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6 Currently only supports grids with the `HasShape`-trait.
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7 """
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8 struct TensorGrid{T,D,GT<:NTuple{N,Grid} where N} <: Grid{T,D}
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9 grids::GT
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10
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11 function TensorGrid(gs...)
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12 T = mapreduce(eltype, combined_coordinate_vector_type, gs)
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13 D = sum(ndims, gs)
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14
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15 return new{T,D,typeof(gs)}(gs)
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16 end
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17 end
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18
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19 # Indexing interface
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20 function Base.getindex(g::TensorGrid, I::Vararg{Int})
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21 szs = ndims.(g.grids)
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22
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23 Is = LazyTensors.split_tuple(I, szs)
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24 ps = map((g,I)->SVector(g[I...]), g.grids, Is)
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25
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26 return vcat(ps...)
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27 end
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28
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29 function Base.eachindex(g::TensorGrid)
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30 szs = LazyTensors.concatenate_tuples(size.(g.grids)...)
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31 return CartesianIndices(szs)
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32 end
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33
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34 function Base.axes(g::TensorGrid, d)
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35 i, ld = grid_and_local_dim_index(ndims.(g.grids), d)
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36 return axes(g.grids[i], ld)
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37 end
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38
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39 # Iteration interface
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40 Base.iterate(g::TensorGrid) = iterate(Iterators.product(g.grids...)) |> _iterate_combine_coords
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41 Base.iterate(g::TensorGrid, state) = iterate(Iterators.product(g.grids...), state) |> _iterate_combine_coords
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42 _iterate_combine_coords(::Nothing) = nothing
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43 _iterate_combine_coords((next,state)) = combine_coordinates(next...), state
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44
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45 Base.IteratorSize(::Type{<:TensorGrid{<:Any, D}}) where D = Base.HasShape{D}()
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46 Base.length(g::TensorGrid) = prod(length, g.grids)
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47 Base.size(g::TensorGrid) = LazyTensors.concatenate_tuples(size.(g.grids)...)
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48 Base.size(g::TensorGrid, d) = size(g)[d]
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49
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50 function spacing(g::TensorGrid)
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51 relevant_grids = filter(g->!isa(g,ZeroDimGrid),g.grids)
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52 return spacing.(relevant_grids)
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53 end
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54
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55 function min_spacing(g::TensorGrid)
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56 relevant_grids = filter(g->!isa(g,ZeroDimGrid),g.grids)
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57 d = min_spacing.(relevant_grids)
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58 return minimum(d)
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59 end
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60
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61 refine(g::TensorGrid, r::Int) = mapreduce(g->refine(g,r), TensorGrid, g.grids)
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62 coarsen(g::TensorGrid, r::Int) = mapreduce(g->coarsen(g,r), TensorGrid, g.grids)
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63
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64 """
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65 TensorGridBoundary{N, BID} <: BoundaryIdentifier
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66
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67 A boundary identifier for a tensor grid. `N` Specifies which grid in the
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68 tensor product and `BID` which boundary on that grid.
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69 """
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70 struct TensorGridBoundary{N, BID} <: BoundaryIdentifier end
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71 grid_id(::TensorGridBoundary{N, BID}) where {N, BID} = N
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72 boundary_id(::TensorGridBoundary{N, BID}) where {N, BID} = BID()
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73
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74 """
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75 boundary_identifiers(g::TensorGrid)
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76
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77 Returns a tuple containing the boundary identifiers of `g`.
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78 """
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79 function boundary_identifiers(g::TensorGrid)
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80 per_grid = map(eachindex(g.grids)) do i
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81 return map(bid -> TensorGridBoundary{i, typeof(bid)}(), boundary_identifiers(g.grids[i]))
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82 end
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83 return LazyTensors.concatenate_tuples(per_grid...)
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84 end
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85
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86 """
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87 boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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88
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89 The grid for the boundary of `g` specified by `id`.
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90 """
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91 function boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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92 local_boundary_grid = boundary_grid(g.grids[grid_id(id)], boundary_id(id))
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93 new_grids = Base.setindex(g.grids, local_boundary_grid, grid_id(id))
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94 return TensorGrid(new_grids...)
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95 end
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96
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97
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98 function boundary_indices(g::TensorGrid{<:Any, 1}, id::TensorGridBoundary)
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99 return boundary_indices(g.grids[grid_id(id)], boundary_id(id))
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100 end
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101 function boundary_indices(g::TensorGrid, id::TensorGridBoundary)
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102 local_b_ind = boundary_indices(g.grids[grid_id(id)], boundary_id(id))
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103
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104 b_ind = Base.setindex(map(eachindex, g.grids), local_b_ind, grid_id(id))
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105
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106 return view(eachindex(g), b_ind...)
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107 end
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108
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109 function combined_coordinate_vector_type(coordinate_types...)
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110 combined_coord_length = mapreduce(_ncomponents, +, coordinate_types)
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111 combined_coord_type = mapreduce(eltype, promote_type, coordinate_types)
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112
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113 if combined_coord_length == 1
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114 return combined_coord_type
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115 else
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116 return SVector{combined_coord_length, combined_coord_type}
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117 end
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118 end
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119
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120 function combine_coordinates(coords...)
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121 return mapreduce(SVector, vcat, coords)
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122 end
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123
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124 """
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125 grid_and_local_dim_index(nds, d)
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126
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127 Given a tuple of number of dimensions `nds`, and a global dimension index `d`,
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128 calculate which grid index, and local dimension, `d` corresponds to.
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129
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130 `nds` would come from broadcasting `ndims` on the grids tuple of a
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131 `TensorGrid`. If you are interested in a dimension `d` of a tensor grid `g`
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132 ```julia
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133 gi, ldi = grid_and_local_dim_index(ndims.(g.grids), d)
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134 ```
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135 tells you which grid it belongs to (`gi`) and which index it is at within that
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136 grid (`ldi`).
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137 """
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138 function grid_and_local_dim_index(nds, d)
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139 I = findfirst(>=(d), cumsum(nds))
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140
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141 if I == 1
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142 return (1, d)
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143 else
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144 return (I, d-cumsum(nds)[I-1])
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145 end
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146 end