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annotate LazyTensors/src/lazy_tensor_operations.jl @ 267:634453a4e1d8 boundary_conditions

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Restructure code in LazyTensors
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Thu, 05 Dec 2019 09:28:04 +0100
parents LazyTensors/src/lazy_operations.jl@8ffd9c2e2119
children 11010bb74260
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1 """
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2 LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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3
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4 Struct for lazy application of a TensorMapping. Created using `*`.
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5
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6 Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
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7 With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
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8 The actual result will be calcualted when indexing into `m*v`.
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9 """
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10 struct LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
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11 t::TensorMapping{T,R,D}
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12 o::AbstractArray{T,D}
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13 end
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14 export LazyTensorMappingApplication
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15
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16 Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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17
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18 Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Int,R}) where {T,R,D} = apply(ta.t, ta.o, I)
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19 Base.size(ta::LazyTensorMappingApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
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20 # TODO: What else is needed to implement the AbstractArray interface?
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21
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22 # # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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23 Base.:*(a::TensorMapping{T,R,D}, b::TensorMapping{T,D,K}, args::Union{TensorMapping{T}, AbstractArray{T}}...) where {T,R,D,K} = foldr(*,(a,b,args...))
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24 # # Should we overload some other infix binary operator?
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25 # →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
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26 # TODO: We need to be really careful about good error messages.
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27 # For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
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28
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29 """
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30 LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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31
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32 Struct for lazy transpose of a TensorMapping.
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33
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34 If a mapping implements the the `apply_transpose` method this allows working with
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35 the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
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36 the appropriate methods of `m`.
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37 """
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38 struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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39 tm::TensorMapping{T,R,D}
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40 end
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41 export LazyTensorMappingTranspose
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42
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43 # # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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44 Base.adjoint(t::TensorMapping) = LazyTensorMappingTranspose(t)
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45 Base.adjoint(t::LazyTensorMappingTranspose) = t.tm
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46
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47 apply(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::NTuple{D,Int}) where {T,R,D} = apply_transpose(tm.tm, v, I)
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48 apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,D} = apply(tm.tm, v, I)
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49
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50 range_size(tmt::LazyTensorMappingTranspose{T,R,D}, d_size::NTuple{R,Integer}) where {T,R,D} = domain_size(tmt.tm, d_size)
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51 domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, r_size)
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52
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55
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56 struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
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57 A::T1
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58 B::T2
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59
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60 @inline function LazyTensorMappingBinaryOperation{Op,T,R,D}(A::T1,B::T2) where {Op,T,R,D, T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}}
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61 return new{Op,T,R,D,T1,T2}(A,B)
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62 end
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63 end
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65 apply(mb::LazyTensorMappingBinaryOperation{:+,T,R,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,D} = apply(mb.A, v, I...) + apply(mb.B,v,I...)
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66 apply(mb::LazyTensorMappingBinaryOperation{:-,T,R,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,D} = apply(mb.A, v, I...) - apply(mb.B,v,I...)
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67
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68 range_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, domain_size::NTuple{D,Integer}) where {Op,T,R,D} = range_size(mp.A, domain_size)
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69 domain_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, range_size::NTuple{R,Integer}) where {Op,T,R,D} = domain_size(mp.A, range_size)
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70
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71 Base.:+(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(A,B)
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72 Base.:-(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(A,B)
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74
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75 # TODO: Write tests and documentation for LazyTensorMappingComposition
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76 # struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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77 # t1::TensorMapping{T,R,K}
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78 # t2::TensorMapping{T,K,D}
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79 # end
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80
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81 # Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
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83 # function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
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84 # range_size(tm.t1, domain_size(tm.t2, domain_size))
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85 # end
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86
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87 # function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
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88 # domain_size(tm.t1, domain_size(tm.t2, range_size))
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89 # end
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91 # function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,K,D}
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92 # apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
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93 # end
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95 # function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{D,Int}) where {T,R,K,D}
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96 # apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
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97 # end
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98
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99 # # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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100
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101 # export →