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annotate TensorMappings.jl @ 160:d33b13d2d92b boundary_conditions

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Move things around in TensorMappings and improve the comments
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 10 May 2019 16:08:31 +0200
parents b790082032da
children ea01b5550ff6
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1 module TensorMappings
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2 # Needs a better name ImplicitTensorMappings? Get rid of "Tensor" in the name_
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3
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4 abstract type TensorMapping{T,R,D} end
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6 range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
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7 domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D
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8 # range_size(::TensorMapping{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D}
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9 # domain_size(::TensorMapping{T,R,D}, range_size::NTuple{R,Integer}) where {T,R,D}
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11 # apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T} =
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12 # apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T} =
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13 # Implementing apply_transpose and domain_size is only needed if you want to take transposes of the TensorMapping.
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14 # TODO: Think about boundschecking!
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15
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16 abstract type TensorOperator{T,D} <: TensorMapping{T,D,D} end
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17 domain_size(::TensorOperator{T,D}, range_size::NTuple{D,Integer}) where {T,D} = range_size
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18 range_size(::TensorOperator{T,D}, domain_size::NTuple{D,Integer}) where {T,D} = domain_size
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22 # Allow using the ' operator:
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23 struct TensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
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24 tm::TensorMapping{T,R,D}
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25 end
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27 Base.adjoint(t::TensorMapping) = TensorMappingTranspose(t)
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28 # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
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29 Base.adjoint(t::TensorMappingTranspose) = t.tm
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30
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31 apply(tm::TensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
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32 apply_transpose(tm::TensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,D} = apply(tm.tm, v, I...)
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33
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34 # range_size(::TensorMappingTranspose{T,R,D}, domain_size::NTuple{}) = range_size_of_transpose???
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38 struct TensorApplication{T,R,D} <: AbstractArray{T,R}
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39 t::TensorMapping{R,D}
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40 o::AbstractArray{T,D}
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41 end
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42
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43 Base.size(ta::TensorApplication) = range_size(ta.t,size(ta.o))
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44 Base.getindex(tm::TensorApplication, I::Vararg) = apply(tm.t, tm.o, I...)
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45 # TODO: What else is needed to implement the AbstractArray interface?
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46
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47 →(t::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = TensorApplication(t,o)
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48 # Should we overload some other infix binary operator?
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49 # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
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51 import Base.*
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52 *(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,args)
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53 *(t::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = TensorApplication(t,o)
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54 # We need to be really careful about good error messages.
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55 # For example what happens if you try to multiply TensorApplication with a TensorMapping(wrong order)?
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58
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59 struct TensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
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60 t1::TensorMapping{T,R,K}
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61 t2::TensorMapping{T,K,D}
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62 end
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63
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64 import Base.∘
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65 ∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = TensorMappingComposition(s,t)
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66
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67 function apply(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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68 apply(c.t1, TensorApplication(c.t2,v), I...)
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69 end
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70
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71 function apply_transpose(c::TensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::Vararg) where {T,R,K,D}
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72 apply_transpose(c.t2, TensorApplication(c.t1',v), I...)
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73 end
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74
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75 # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
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77
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78 export apply
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79 export apply_transpose
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80 export range_dim
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81 export domain_dim
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82 export range_size
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83 export →
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84
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85
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86 end #module