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annotate LazyTensors/src/LazyTensors.jl @ 186:715ff09bb2ce boundary_conditions

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Rename and export types in LazyTensors follow julia conventions
author Jonatan Werpers <jonatan@werpers.com>
date Thu, 20 Jun 2019 21:31:15 +0200
parents 6945c15a6a7a
children 8964b3165097
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6945c15a6a7a Rename package LazyTensor to LazyTensors
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1 module LazyTensors
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4 """
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5 TensorMapping{T,R,D}
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6
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7 Describes a mapping of a D dimension tensor to an R dimension tensor.
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8 The action of the mapping is implemented through the method
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9
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10 apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
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11
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12 The size of range tensor should be dependent on the size of the domain tensor
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13 and the type should implement the methods
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15 range_size(::TensorMapping{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D}
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16 domain_size(::TensorMapping{T,R,D}, range_size::NTuple{R,Integer}) where {T,R,D}
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17
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18 to allow querying for one or the other.
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20 Optionally the action of the transpose may be defined through
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21 apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
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22 """
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23 abstract type TensorMapping{T,R,D} end
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24 export TensorMapping
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26 """
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27 apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
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28
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29 Return the result of the mapping for a given index.
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30 """
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31 function apply end
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32 export apply
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34 """
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35 apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {R,D,T}
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37 Return the result of the transposed mapping for a given index.
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38 """
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39 function apply_transpose end
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40 export apply_transpose
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41
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42 """
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43 Return the dimension of the range space of a given mapping
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44 """
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45 range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
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47 """
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48 Return the dimension of the domain space of a given mapping
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49 """
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50 domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D
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51
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52 export range_dim, domain_dim
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54 """
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55 range_size(M::TensorMapping, domain_size)
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56
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57 Return the resulting range size for the mapping applied to a given domain_size
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58 """
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59 function range_size end
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60
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61 """
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62 domain_size(M::TensorMapping, range_size)
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63
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64 Return the resulting domain size for the mapping applied to a given range_size
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65 """
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66 function domain_size end
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67
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68 export range_size, domain_size
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69 # TODO: Think about boundschecking!
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72 """
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73 TensorOperator{T,D}
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74
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75 A `TensorMapping{T,D,D}` where the range and domain tensor have the same number of
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76 dimensions and the same size.
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77 """
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78 abstract type TensorOperator{T,D} <: TensorMapping{T,D,D} end
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79 export TensorOperator
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80 domain_size(::TensorOperator{T,D}, range_size::NTuple{D,Integer}) where {T,D} = range_size
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81 range_size(::TensorOperator{T,D}, domain_size::NTuple{D,Integer}) where {T,D} = domain_size
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85 """
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86 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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87
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88 Struct for lazy transpose of a TensorMapping.
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89
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90 If a mapping implements the the `apply_transpose` method this allows working with
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91 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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92 the appropriate methods of `m`.
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93 """
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94 struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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95 tm::TensorMapping{T,R,D}
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96 end
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97 export LazyTensorMappingTranspose
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99 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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100 Base.adjoint(t::TensorMapping) = LazyTensorMappingTranspose(t)
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101 Base.adjoint(t::LazyTensorMappingTranspose) = t.tm
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102
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103 apply(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
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104 apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,D} = apply(tm.tm, v, I...)
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105
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106 range_size(tmt::LazyTensorMappingTranspose{T,R,D}, d_size::NTuple{R,Integer}) where {T,R,D} = domain_size(tmt.tm, domain_size)
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107 domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, range_size)
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110 """
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111 LazyTensorMappingApplication{T,R,D} <: AbstractArray{T,R}
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112
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113 Struct for lazy application of a TensorMapping. Created using `*`.
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114
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115 Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
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116 With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
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117 The actual result will be calcualted when indexing into `m*v`.
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118 """
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119 struct LazyTensorMappingApplication{T,R,D} <: AbstractArray{T,R}
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120 t::TensorMapping{T,R,D}
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121 o::AbstractArray{T,D}
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122 end
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123 export LazyTensorMappingApplication
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124
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125 Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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127 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg) where {T,R,D} = apply(ta.t, ta.o, I...)
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128 Base.size(ta::LazyTensorMappingApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
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129 # TODO: What else is needed to implement the AbstractArray interface?
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132 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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133 Base.:*(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,args)
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134 # # Should we overload some other infix binary operator?
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135 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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136 # TODO: We need to be really careful about good error messages.
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137 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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138
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139
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140
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141 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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142 # t1::TensorMapping{T,R,K}
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143 # t2::TensorMapping{T,K,D}
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144 # end
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145
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146 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
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147
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148 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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149 # range_size(tm.t1, domain_size(tm.t2, domain_size))
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150 # end
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151
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152 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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153 # domain_size(tm.t1, domain_size(tm.t2, range_size))
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154 # end
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155
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156 # function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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157 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
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158 # end
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159
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160 # function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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161 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
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162 # end
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163
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164 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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165
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166 # export →
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167
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168
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169 end # module