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changeset 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 9ad447176ba1
children f67ce2eb6019
files LazyTensors/src/LazyTensors.jl LazyTensors/src/lazy_array.jl LazyTensors/src/lazy_operations.jl LazyTensors/src/lazy_tensor_operations.jl
diffstat 4 files changed, 266 insertions(+), 270 deletions(-) [+]
line wrap: on
line diff
--- a/LazyTensors/src/LazyTensors.jl	Thu Dec 05 09:27:31 2019 +0100
+++ b/LazyTensors/src/LazyTensors.jl	Thu Dec 05 09:28:04 2019 +0100
@@ -1,6 +1,7 @@
 module LazyTensors
 
 include("tensor_mapping.jl")
-include("lazy_operations.jl")
+include("lazy_array.jl")
+include("lazy_tensor_operations.jl")
 
 end # module
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LazyTensors/src/lazy_array.jl	Thu Dec 05 09:28:04 2019 +0100
@@ -0,0 +1,163 @@
+"""
+    LazyArray{T,D} <: AbstractArray{T,D}
+
+Array which is calcualted lazily when indexing.
+
+A subtype of `LazyArray` will use lazy version of `+`, `-`, `*`, `/`.
+"""
+abstract type LazyArray{T,D} <: AbstractArray{T,D} end
+export LazyArray
+
+"""
+    LazyElementwiseOperation{T,D,Op,T1,T2} <: LazyArray{T,D}
+Struct allowing for lazy evaluation of elementwise operations on AbstractArrays.
+
+A LazyElementwiseOperation contains two datatypes T1, and T2, together with an operation,
+where at least one of T1 and T2 is an AbstractArray, and one may be a Real.
+The operations are carried out when the LazyElementwiseOperation is indexed.
+"""
+struct LazyElementwiseOperation{T,D,Op,T1,T2} <: LazyArray{T,D}
+    a::T1
+    b::T2
+
+    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
+        @boundscheck if size(a) != size(b)
+            throw(DimensionMismatch("dimensions must match"))
+        end
+        return new{T,D,Op,T1,T2}(a,b)
+    end
+
+    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:AbstractArray{T,D},T2<:Real}
+        return new{T,D,Op,T1,T2}(a,b)
+    end
+
+    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:Real,T2<:AbstractArray{T,D}}
+        return new{T,D,Op,T1,T2}(a,b)
+    end
+end
+# TODO: Move Op to be the first parameter? Compare to Binary operations
+
+Base.size(v::LazyElementwiseOperation) = size(v.a)
+
+# TODO: Make sure boundschecking is done properly and that the lenght of the vectors are equal
+# NOTE: Boundschecking in getindex functions now assumes that the size of the
+# vectors in the LazyElementwiseOperation are the same size. If we remove the
+# size assertion in the constructor we might have to handle
+# boundschecking differently.
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] + leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] - leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] * leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] / leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] + leo.b
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] - leo.b
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] * leo.b
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
+    @boundscheck if !checkbounds(Bool,leo.a,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a[I...] / leo.b
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.b,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a + leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.b,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a - leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.b,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a * leo.b[I...]
+end
+
+Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
+    @boundscheck if !checkbounds(Bool,leo.b,I...)
+        throw(BoundsError([leo],I...))
+    end
+    return leo.a / leo.b[I...]
+end
+
+# Define lazy operations for AbstractArrays. Operations constructs a LazyElementwiseOperation which
+# can later be indexed into. Lazy operations are denoted by the usual operator followed by a tilde
+Base.@propagate_inbounds +̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
+Base.@propagate_inbounds -̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
+Base.@propagate_inbounds *̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
+Base.@propagate_inbounds /̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
+
+Base.@propagate_inbounds +̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
+Base.@propagate_inbounds -̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
+Base.@propagate_inbounds *̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
+Base.@propagate_inbounds /̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
+
+Base.@propagate_inbounds +̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
+Base.@propagate_inbounds -̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
+Base.@propagate_inbounds *̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
+Base.@propagate_inbounds /̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
+
+
+
+# NOTE: Är det knas att vi har till exempel * istället för .* ??
+# Oklart om det ens går att lösa..
+Base.@propagate_inbounds Base.:+(a::LazyArray{T,D}, b::LazyArray{T,D}) where {T,D} = a +̃ b
+Base.@propagate_inbounds Base.:+(a::LazyArray{T,D}, b::AbstractArray{T,D}) where {T,D} = a +̃ b
+Base.@propagate_inbounds Base.:+(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = a +̃ b
+
+Base.@propagate_inbounds Base.:-(a::LazyArray{T,D}, b::LazyArray{T,D}) where {T,D} = a -̃ b
+Base.@propagate_inbounds Base.:-(a::LazyArray{T,D}, b::AbstractArray{T,D}) where {T,D} = a -̃ b
+Base.@propagate_inbounds Base.:-(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = a -̃ b
+
+# Element wise operation for `*` and `\` are not overloaded due to conflicts with the behavior
+# of regular `*` and `/` for AbstractArrays. Use tilde versions instead.
+
+export +̃, -̃, *̃, /̃
--- a/LazyTensors/src/lazy_operations.jl	Thu Dec 05 09:27:31 2019 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,269 +0,0 @@
-"""
-    LazyArray{T,D} <: AbstractArray{T,D}
-
-Array which is calcualted lazily when indexing.
-
-A subtype of `LazyArray` will use lazy version of `+`, `-`, `*`, `/`.
-"""
-abstract type LazyArray{T,D} <: AbstractArray{T,D} end
-export LazyArray
-
-
-
-"""
-    LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
-
-Struct for lazy application of a TensorMapping. Created using `*`.
-
-Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
-With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
-The actual result will be calcualted when indexing into `m*v`.
-"""
-struct LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
-    t::TensorMapping{T,R,D}
-    o::AbstractArray{T,D}
-end
-export LazyTensorMappingApplication
-
-Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
-
-Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Int,R}) where {T,R,D} = apply(ta.t, ta.o, I)
-Base.size(ta::LazyTensorMappingApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
-# TODO: What else is needed to implement the AbstractArray interface?
-
-# # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
-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...))
-# # Should we overload some other infix binary operator?
-# →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
-# TODO: We need to be really careful about good error messages.
-# For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
-
-"""
-    LazyElementwiseOperation{T,D,Op,T1,T2} <: LazyArray{T,D}
-Struct allowing for lazy evaluation of elementwise operations on AbstractArrays.
-
-A LazyElementwiseOperation contains two datatypes T1, and T2, together with an operation,
-where at least one of T1 and T2 is an AbstractArray, and one may be a Real.
-The operations are carried out when the LazyElementwiseOperation is indexed.
-"""
-struct LazyElementwiseOperation{T,D,Op,T1,T2} <: LazyArray{T,D}
-    a::T1
-    b::T2
-
-    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
-        @boundscheck if size(a) != size(b)
-            throw(DimensionMismatch("dimensions must match"))
-        end
-        return new{T,D,Op,T1,T2}(a,b)
-    end
-
-    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:AbstractArray{T,D},T2<:Real}
-        return new{T,D,Op,T1,T2}(a,b)
-    end
-
-    @inline function LazyElementwiseOperation{T,D,Op}(a::T1,b::T2) where {T,D,Op,T1<:Real,T2<:AbstractArray{T,D}}
-        return new{T,D,Op,T1,T2}(a,b)
-    end
-end
-# TODO: Move Op to be the first parameter? Compare to Binary operations
-
-Base.size(v::LazyElementwiseOperation) = size(v.a)
-
-# TODO: Make sure boundschecking is done properly and that the lenght of the vectors are equal
-# NOTE: Boundschecking in getindex functions now assumes that the size of the
-# vectors in the LazyElementwiseOperation are the same size. If we remove the
-# size assertion in the constructor we might have to handle
-# boundschecking differently.
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] + leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] - leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] * leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] / leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] + leo.b
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] - leo.b
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] * leo.b
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:AbstractArray{T,D},T2<:Real}
-    @boundscheck if !checkbounds(Bool,leo.a,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a[I...] / leo.b
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:+,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.b,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a + leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:-,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.b,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a - leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:*,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.b,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a * leo.b[I...]
-end
-
-Base.@propagate_inbounds @inline function Base.getindex(leo::LazyElementwiseOperation{T,D,:/,T1,T2}, I::Vararg{Int,D}) where {T,D,T1<:Real,T2<:AbstractArray{T,D}}
-    @boundscheck if !checkbounds(Bool,leo.b,I...)
-        throw(BoundsError([leo],I...))
-    end
-    return leo.a / leo.b[I...]
-end
-
-# Define lazy operations for AbstractArrays. Operations constructs a LazyElementwiseOperation which
-# can later be indexed into. Lazy operations are denoted by the usual operator followed by a tilde
-Base.@propagate_inbounds +̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
-Base.@propagate_inbounds -̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
-Base.@propagate_inbounds *̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
-Base.@propagate_inbounds /̃(a::AbstractArray{T,D}, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
-
-Base.@propagate_inbounds +̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
-Base.@propagate_inbounds -̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
-Base.@propagate_inbounds *̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
-Base.@propagate_inbounds /̃(a::AbstractArray{T,D}, b::Real) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
-
-Base.@propagate_inbounds +̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:+}(a,b)
-Base.@propagate_inbounds -̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:-}(a,b)
-Base.@propagate_inbounds *̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:*}(a,b)
-Base.@propagate_inbounds /̃(a::Real, b::AbstractArray{T,D}) where {T,D} = LazyElementwiseOperation{T,D,:/}(a,b)
-
-
-
-# NOTE: Är det knas att vi har till exempel * istället för .* ??
-# Oklart om det ens går att lösa..
-Base.@propagate_inbounds Base.:+(a::LazyArray{T,D}, b::LazyArray{T,D}) where {T,D} = a +̃ b
-Base.@propagate_inbounds Base.:+(a::LazyArray{T,D}, b::AbstractArray{T,D}) where {T,D} = a +̃ b
-Base.@propagate_inbounds Base.:+(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = a +̃ b
-
-Base.@propagate_inbounds Base.:-(a::LazyArray{T,D}, b::LazyArray{T,D}) where {T,D} = a -̃ b
-Base.@propagate_inbounds Base.:-(a::LazyArray{T,D}, b::AbstractArray{T,D}) where {T,D} = a -̃ b
-Base.@propagate_inbounds Base.:-(a::AbstractArray{T,D}, b::LazyArray{T,D}) where {T,D} = a -̃ b
-
-# Element wise operation for `*` and `\` are not overloaded due to conflicts with the behavior
-# of regular `*` and `/` for AbstractArrays. Use tilde versions instead.
-
-export +̃, -̃, *̃, /̃
-
-
-
-"""
-    LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
-
-Struct for lazy transpose of a TensorMapping.
-
-If a mapping implements the the `apply_transpose` method this allows working with
-the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
-the appropriate methods of `m`.
-"""
-struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
-    tm::TensorMapping{T,R,D}
-end
-export LazyTensorMappingTranspose
-
-# # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
-Base.adjoint(t::TensorMapping) = LazyTensorMappingTranspose(t)
-Base.adjoint(t::LazyTensorMappingTranspose) = t.tm
-
-apply(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::NTuple{D,Int}) where {T,R,D} = apply_transpose(tm.tm, v, I)
-apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,D} = apply(tm.tm, v, I)
-
-range_size(tmt::LazyTensorMappingTranspose{T,R,D}, d_size::NTuple{R,Integer}) where {T,R,D} = domain_size(tmt.tm, d_size)
-domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, r_size)
-
-
-
-
-struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
-    A::T1
-    B::T2
-
-    @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}}
-        return new{Op,T,R,D,T1,T2}(A,B)
-    end
-end
-
-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...)
-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...)
-
-range_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, domain_size::NTuple{D,Integer}) where {Op,T,R,D} = range_size(mp.A, domain_size)
-domain_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, range_size::NTuple{R,Integer}) where {Op,T,R,D} = domain_size(mp.A, range_size)
-
-Base.:+(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(A,B)
-Base.:-(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(A,B)
-
-
-# TODO: Write tests and documentation for LazyTensorMappingComposition
-# struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
-#     t1::TensorMapping{T,R,K}
-#     t2::TensorMapping{T,K,D}
-# end
-
-# Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
-
-# function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
-#     range_size(tm.t1, domain_size(tm.t2, domain_size))
-# end
-
-# function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
-#     domain_size(tm.t1, domain_size(tm.t2, range_size))
-# end
-
-# function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,K,D}
-#     apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
-# end
-
-# function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{D,Int}) where {T,R,K,D}
-#     apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
-# end
-
-# # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
-
-# export →
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LazyTensors/src/lazy_tensor_operations.jl	Thu Dec 05 09:28:04 2019 +0100
@@ -0,0 +1,101 @@
+"""
+    LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
+
+Struct for lazy application of a TensorMapping. Created using `*`.
+
+Allows the result of a `TensorMapping` applied to a vector to be treated as an `AbstractArray`.
+With a mapping `m` and a vector `v` the LazyTensorMappingApplication object can be created by `m*v`.
+The actual result will be calcualted when indexing into `m*v`.
+"""
+struct LazyTensorMappingApplication{T,R,D} <: LazyArray{T,R}
+    t::TensorMapping{T,R,D}
+    o::AbstractArray{T,D}
+end
+export LazyTensorMappingApplication
+
+Base.:*(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
+
+Base.getindex(ta::LazyTensorMappingApplication{T,R,D}, I::Vararg{Int,R}) where {T,R,D} = apply(ta.t, ta.o, I)
+Base.size(ta::LazyTensorMappingApplication{T,R,D}) where {T,R,D} = range_size(ta.t,size(ta.o))
+# TODO: What else is needed to implement the AbstractArray interface?
+
+# # We need the associativity to be a→b→c = a→(b→c), which is the case for '→'
+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...))
+# # Should we overload some other infix binary operator?
+# →(tm::TensorMapping{T,R,D}, o::AbstractArray{T,D}) where {T,R,D} = LazyTensorMappingApplication(tm,o)
+# TODO: We need to be really careful about good error messages.
+# For example what happens if you try to multiply LazyTensorMappingApplication with a TensorMapping(wrong order)?
+
+"""
+    LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
+
+Struct for lazy transpose of a TensorMapping.
+
+If a mapping implements the the `apply_transpose` method this allows working with
+the transpose of mapping `m` by using `m'`. `m'` will work as a regular TensorMapping lazily calling
+the appropriate methods of `m`.
+"""
+struct LazyTensorMappingTranspose{T,R,D} <: TensorMapping{T,D,R}
+    tm::TensorMapping{T,R,D}
+end
+export LazyTensorMappingTranspose
+
+# # TBD: Should this be implemented on a type by type basis or through a trait to provide earlier errors?
+Base.adjoint(t::TensorMapping) = LazyTensorMappingTranspose(t)
+Base.adjoint(t::LazyTensorMappingTranspose) = t.tm
+
+apply(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,R}, I::NTuple{D,Int}) where {T,R,D} = apply_transpose(tm.tm, v, I)
+apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,D} = apply(tm.tm, v, I)
+
+range_size(tmt::LazyTensorMappingTranspose{T,R,D}, d_size::NTuple{R,Integer}) where {T,R,D} = domain_size(tmt.tm, d_size)
+domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, r_size)
+
+
+
+
+struct LazyTensorMappingBinaryOperation{Op,T,R,D,T1<:TensorMapping{T,R,D},T2<:TensorMapping{T,R,D}} <: TensorMapping{T,D,R}
+    A::T1
+    B::T2
+
+    @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}}
+        return new{Op,T,R,D,T1,T2}(A,B)
+    end
+end
+
+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...)
+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...)
+
+range_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, domain_size::NTuple{D,Integer}) where {Op,T,R,D} = range_size(mp.A, domain_size)
+domain_size(mp::LazyTensorMappingBinaryOperation{Op,T,R,D}, range_size::NTuple{R,Integer}) where {Op,T,R,D} = domain_size(mp.A, range_size)
+
+Base.:+(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:+,T,R,D}(A,B)
+Base.:-(A::TensorMapping{T,R,D}, B::TensorMapping{T,R,D}) where {T,R,D} = LazyTensorMappingBinaryOperation{:-,T,R,D}(A,B)
+
+
+# TODO: Write tests and documentation for LazyTensorMappingComposition
+# struct LazyTensorMappingComposition{T,R,K,D} <: TensorMapping{T,R,D}
+#     t1::TensorMapping{T,R,K}
+#     t2::TensorMapping{T,K,D}
+# end
+
+# Base.:∘(s::TensorMapping{T,R,K}, t::TensorMapping{T,K,D}) where {T,R,K,D} = LazyTensorMappingComposition(s,t)
+
+# function range_size(tm::LazyTensorMappingComposition{T,R,K,D}, domain_size::NTuple{D,Integer}) where {T,R,K,D}
+#     range_size(tm.t1, domain_size(tm.t2, domain_size))
+# end
+
+# function domain_size(tm::LazyTensorMappingComposition{T,R,K,D}, range_size::NTuple{R,Integer}) where {T,R,K,D}
+#     domain_size(tm.t1, domain_size(tm.t2, range_size))
+# end
+
+# function apply(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{R,Int}) where {T,R,K,D}
+#     apply(c.t1, LazyTensorMappingApplication(c.t2,v), I...)
+# end
+
+# function apply_transpose(c::LazyTensorMappingComposition{T,R,K,D}, v::AbstractArray{T,D}, I::NTuple{D,Int}) where {T,R,K,D}
+#     apply_transpose(c.t2, LazyTensorMappingApplication(c.t1',v), I...)
+# end
+
+# # Have i gone too crazy with the type parameters? Maybe they aren't all needed?
+
+# export →