--- a/LazyTensors/src/LazyTensors.jl	Thu Jun 20 22:22:43 2019 +0200
+++ b/LazyTensors/src/LazyTensors.jl	Thu Jun 20 22:48:07 2019 +0200
@@ -1,169 +1,6 @@
 module LazyTensors
 
-
-"""
-    TensorMapping{T,R,D}
-
-Describes a mapping of a D dimension tensor to an R dimension tensor.
-The action of the mapping is implemented through the method
-
-    apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
-
-The size of range tensor should be dependent on the size of the domain tensor
-and the type should implement the methods
-
-    range_size(::TensorMapping{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D}
-    domain_size(::TensorMapping{T,R,D}, range_size::NTuple{R,Integer}) where {T,R,D}
-
-to allow querying for one or the other.
-
-Optionally the action of the transpose may be defined through
-    apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
-"""
-abstract type TensorMapping{T,R,D} end
-export TensorMapping
-
-"""
-    apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
-
-Return the result of the mapping for a given index.
-"""
-function apply end
-export apply
-
-"""
-    apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {R,D,T}
-
-Return the result of the transposed mapping for a given index.
-"""
-function apply_transpose end
-export apply_transpose
-
-"""
-Return the dimension of the range space of a given mapping
-"""
-range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
-
-"""
-Return the dimension of the domain space of a given mapping
-"""
-domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D
-
-export range_dim, domain_dim
-
-"""
-    range_size(M::TensorMapping, domain_size)
-
-Return the resulting range size for the mapping applied to a given domain_size
-"""
-function range_size end
-
-"""
-    domain_size(M::TensorMapping, range_size)
-
-Return the resulting domain size for the mapping applied to a given range_size
-"""
-function domain_size end
-
-export range_size, domain_size
-# TODO: Think about boundschecking!
-
-
-"""
-    TensorOperator{T,D}
-
-A `TensorMapping{T,D,D}` where the range and domain tensor have the same number of
-dimensions and the same size.
-"""
-abstract type TensorOperator{T,D} <: TensorMapping{T,D,D} end
-export TensorOperator
-domain_size(::TensorOperator{T,D}, range_size::NTuple{D,Integer}) where {T,D} = range_size
-range_size(::TensorOperator{T,D}, domain_size::NTuple{D,Integer}) where {T,D} = domain_size
-
-
-
-"""
-    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::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
-apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) 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, domain_size)
-domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, range_size)
-
-
-"""
-    LazyTensorMappingApplication{T,R,D} <: AbstractArray{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} <: AbstractArray{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) 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.:*(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,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)?
-
-
-
-# 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::Vararg) 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::Vararg) 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 →
-
+include("tensor_mapping.jl")
+include("lazy_operations.jl")
 
 end # module

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LazyTensors/src/lazy_operations.jl	Thu Jun 20 22:48:07 2019 +0200
@@ -0,0 +1,84 @@
+"""
+    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::Vararg) where {T,R,D} = apply_transpose(tm.tm, v, I...)
+apply_transpose(tm::LazyTensorMappingTranspose{T,R,D}, v::AbstractArray{T,D}, I::Vararg) 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, domain_size)
+domain_size(tmt::LazyTensorMappingTranspose{T,R,D}, r_size::NTuple{D,Integer}) where {T,R,D} = range_size(tmt.tm, range_size)
+
+
+
+"""
+    LazyTensorMappingApplication{T,R,D} <: AbstractArray{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} <: AbstractArray{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) 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.:*(args::Union{TensorMapping{T}, AbstractArray{T}}...) where T = foldr(*,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)?
+
+
+
+# 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::Vararg) 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::Vararg) 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/tensor_mapping.jl	Thu Jun 20 22:48:07 2019 +0200
@@ -0,0 +1,78 @@
+"""
+    TensorMapping{T,R,D}
+
+Describes a mapping of a D dimension tensor to an R dimension tensor.
+The action of the mapping is implemented through the method
+
+    apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
+
+The size of range tensor should be dependent on the size of the domain tensor
+and the type should implement the methods
+
+    range_size(::TensorMapping{T,R,D}, domain_size::NTuple{D,Integer}) where {T,R,D}
+    domain_size(::TensorMapping{T,R,D}, range_size::NTuple{R,Integer}) where {T,R,D}
+
+to allow querying for one or the other.
+
+Optionally the action of the transpose may be defined through
+    apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
+"""
+abstract type TensorMapping{T,R,D} end
+export TensorMapping
+
+"""
+    apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
+
+Return the result of the mapping for a given index.
+"""
+function apply end
+export apply
+
+"""
+    apply_transpose(t::TensorMapping{T,R,D}, v::AbstractArray{T,R}, I::Vararg) where {R,D,T}
+
+Return the result of the transposed mapping for a given index.
+"""
+function apply_transpose end
+export apply_transpose
+
+"""
+Return the dimension of the range space of a given mapping
+"""
+range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
+
+"""
+Return the dimension of the domain space of a given mapping
+"""
+domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D
+
+export range_dim, domain_dim
+
+"""
+    range_size(M::TensorMapping, domain_size)
+
+Return the resulting range size for the mapping applied to a given domain_size
+"""
+function range_size end
+
+"""
+    domain_size(M::TensorMapping, range_size)
+
+Return the resulting domain size for the mapping applied to a given range_size
+"""
+function domain_size end
+
+export range_size, domain_size
+# TODO: Think about boundschecking!
+
+
+"""
+    TensorOperator{T,D}
+
+A `TensorMapping{T,D,D}` where the range and domain tensor have the same number of
+dimensions and the same size.
+"""
+abstract type TensorOperator{T,D} <: TensorMapping{T,D,D} end
+export TensorOperator
+domain_size(::TensorOperator{T,D}, range_size::NTuple{D,Integer}) where {T,D} = range_size
+range_size(::TensorOperator{T,D}, domain_size::NTuple{D,Integer}) where {T,D} = domain_size