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annotate src/Grids/tensor_grid.jl @ 1572:157c43966b06 feature/grids/manifolds

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Add some tests and implement parameterspace for ConcreteChart
author Jonatan Werpers <jonatan@werpers.com>
date Thu, 25 Apr 2024 14:13:41 +0200
parents d9d9ab18cdfc
children b02917bcd7d5
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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 refine(g::TensorGrid, r::Int) = mapreduce(g->refine(g,r), TensorGrid, g.grids)
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54 coarsen(g::TensorGrid, r::Int) = mapreduce(g->coarsen(g,r), TensorGrid, g.grids)
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55
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56 """
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57 TensorGridBoundary{N, BID} <: BoundaryIdentifier
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58
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59 A boundary identifier for a tensor grid. `N` Specifies which grid in the
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60 tensor product and `BID` which boundary on that grid.
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61 """
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62 struct TensorGridBoundary{N, BID} <: BoundaryIdentifier end
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63 grid_id(::TensorGridBoundary{N, BID}) where {N, BID} = N
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64 boundary_id(::TensorGridBoundary{N, BID}) where {N, BID} = BID()
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65
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66 """
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67 boundary_identifiers(g::TensorGrid)
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68
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69 Returns a tuple containing the boundary identifiers of `g`.
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70 """
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71 function boundary_identifiers(g::TensorGrid)
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72 per_grid = map(eachindex(g.grids)) do i
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73 return map(bid -> TensorGridBoundary{i, typeof(bid)}(), boundary_identifiers(g.grids[i]))
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74 end
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75 return LazyTensors.concatenate_tuples(per_grid...)
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76 end
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77
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78 """
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79 boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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80
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81 The grid for the boundary of `g` specified by `id`.
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82 """
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83 function boundary_grid(g::TensorGrid, id::TensorGridBoundary)
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84 local_boundary_grid = boundary_grid(g.grids[grid_id(id)], boundary_id(id))
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85 new_grids = Base.setindex(g.grids, local_boundary_grid, grid_id(id))
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86 return TensorGrid(new_grids...)
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87 end
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88
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89 function boundary_indices(g::TensorGrid, id::TensorGridBoundary)
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90 per_grid_ind = map(g.grids) do g
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91 ntuple(i->:, ndims(g))
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92 end
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93
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94 local_b_ind = boundary_indices(g.grids[grid_id(id)], boundary_id(id))
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95 b_ind = Base.setindex(per_grid_ind, local_b_ind, grid_id(id))
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96
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97 return LazyTensors.concatenate_tuples(b_ind...)
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98 end
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99
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100 function combined_coordinate_vector_type(coordinate_types...)
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101 combined_coord_length = mapreduce(_ncomponents, +, coordinate_types)
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102 combined_coord_type = mapreduce(eltype, promote_type, coordinate_types)
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103
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104 if combined_coord_length == 1
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105 return combined_coord_type
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106 else
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107 return SVector{combined_coord_length, combined_coord_type}
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108 end
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109 end
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110
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111 function combine_coordinates(coords...)
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112 return mapreduce(SVector, vcat, coords)
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113 end
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114
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115 """
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116 grid_and_local_dim_index(nds, d)
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117
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118 Given a tuple of number of dimensions `nds`, and a global dimension index `d`,
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119 calculate which grid index, and local dimension, `d` corresponds to.
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120
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121 `nds` would come from broadcasting `ndims` on the grids tuple of a
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122 `TensorGrid`. If you are interested in a dimension `d` of a tensor grid `g`
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123 ```julia
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124 gi, ldi = grid_and_local_dim_index(ndims.(g.grids), d)
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125 ```
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126 tells you which grid it belongs to (`gi`) and which index it is at within that
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127 grid (`ldi`).
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128 """
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129 function grid_and_local_dim_index(nds, d)
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130 I = findfirst(>=(d), cumsum(nds))
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131
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132 if I == 1
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133 return (1, d)
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Jonatan Werpers <jonatan@werpers.com>
parents: 1349
diff changeset
134 else
c0208286234e Add `grid_and_local_dim_index`
Jonatan Werpers <jonatan@werpers.com>
parents: 1349
diff changeset
135 return (I, d-cumsum(nds)[I-1])
c0208286234e Add `grid_and_local_dim_index`
Jonatan Werpers <jonatan@werpers.com>
parents: 1349
diff changeset
136 end
c0208286234e Add `grid_and_local_dim_index`
Jonatan Werpers <jonatan@werpers.com>
parents: 1349
diff changeset
137 end