--- a/src/DiffOps/DiffOps.jl	Fri Jan 14 09:01:12 2022 +0100
+++ b/src/DiffOps/DiffOps.jl	Fri Jan 14 09:19:07 2022 +0100
@@ -93,6 +93,7 @@
 
 
 """
+    BoundaryCondition
 A BoundaryCondition should implement the method
     sat(::DiffOp, v::AbstractArray, data::AbstractArray, ...)
 """

--- a/src/LazyTensors/lazy_array.jl	Fri Jan 14 09:01:12 2022 +0100
+++ b/src/LazyTensors/lazy_array.jl	Fri Jan 14 09:19:07 2022 +0100
@@ -42,10 +42,10 @@
 
 """
     LazyElementwiseOperation{T,D,Op} <: LazyArray{T,D}
-Struct allowing for lazy evaluation of elementwise operations on AbstractArrays.
+Struct allowing for lazy evaluation of elementwise operations on `AbstractArray`s.
 
-A LazyElementwiseOperation contains two arrays together with an operation.
-The operations are carried out when the LazyElementwiseOperation is indexed.
+A `LazyElementwiseOperation` contains two arrays together with an operation.
+The operations are carried out when the `LazyElementwiseOperation` is indexed.
 """
 struct LazyElementwiseOperation{T,D,Op,T1<:AbstractArray{T,D},T2<:AbstractArray{T,D}} <: LazyArray{T,D}
     a::T1

--- a/src/LazyTensors/lazy_tensor_operations.jl	Fri Jan 14 09:01:12 2022 +0100
+++ b/src/LazyTensors/lazy_tensor_operations.jl	Fri Jan 14 09:19:07 2022 +0100
@@ -76,7 +76,7 @@
 """
     TensorMappingComposition{T,R,K,D}
 
-Lazily compose two TensorMappings, so that they can be handled as a single TensorMapping.
+Lazily compose two `TensorMapping`s, so that they can be handled as a single `TensorMapping`.
 """
 struct TensorMappingComposition{T,R,K,D, TM1<:TensorMapping{T,R,K}, TM2<:TensorMapping{T,K,D}} <: TensorMapping{T,R,D}
     t1::TM1
@@ -165,8 +165,8 @@
 apply_transpose(tmi::IdentityMapping{T,D}, v::AbstractArray{T,D}, I::Vararg{Any,D}) where {T,D} = v[I...]
 
 """
-Base.:∘(tm, tmi)
-Base.:∘(tmi, tm)
+    Base.:∘(tm, tmi)
+    Base.:∘(tmi, tm)
 
 Composes a `Tensormapping` `tm` with an `IdentityMapping` `tmi`, by returning `tm`
 """
@@ -316,7 +316,7 @@
     slice_tuple(t, Val(l), Val(u))
 
 Get a slice of a tuple in a type stable way.
-Equivalent to t[l:u] but type stable.
+Equivalent to `t[l:u]` but type stable.
 """
 function slice_tuple(t,::Val{L},::Val{U}) where {L,U}
     return ntuple(i->t[i+L-1], U-L+1)
@@ -327,7 +327,7 @@
 
 Split the tuple `t` into two parts. the first part is `M` long.
 E.g
-```
+```julia
 split_tuple((1,2,3,4),Val(3)) -> (1,2,3), (4,)
 ```
 """
@@ -357,17 +357,19 @@
 flatten_tuple(t::Tuple) = ((flatten_tuple.(t)...)...,) # simplify?
 flatten_tuple(ts::Vararg) = flatten_tuple(ts)
 
-"""
-   LazyOuterProduct(tms...)
+@doc raw"""
+    LazyOuterProduct(tms...)
 
 Creates a `TensorMappingComposition` for the outerproduct of `tms...`.
 This is done by separating the outer product into regular products of outer products involving only identity mappings and one non-identity mapping.
 
 First let
 ```math
-A = A_{I,J}
-B = B_{M,N}
-C = C_{P,Q}
+\begin{aligned}
+A &= A_{I,J} \\
+B &= B_{M,N} \\
+C &= C_{P,Q} \\
+\end{aligned}
 ```
 
 where ``I``, ``M``, ``P`` are  multi-indexes for the ranges of ``A``, ``B``, ``C``, and ``J``, ``N``, ``Q`` are multi-indexes of the domains.
@@ -385,10 +387,12 @@
 ```math
 A⊗B⊗C = (A⊗I_{|M|}⊗I_{|P|})(I_{|J|}⊗B⊗I_{|P|})(I_{|J|}⊗I_{|N|}⊗C)
 ```
-where |.| of a multi-index is a vector of sizes for each dimension. ``I_v`` denotes the identity tensor of size ``v[i]`` in each direction
+where ``|⋅|`` of a multi-index is a vector of sizes for each dimension. ``I_v`` denotes the identity tensor of size ``v[i]`` in each direction
 To apply ``A⊗B⊗C`` we evaluate
 
+```math
 (A⊗B⊗C)v = [(A⊗I_{|M|}⊗I_{|P|})  [(I_{|J|}⊗B⊗I_{|P|}) [(I_{|J|}⊗I_{|N|}⊗C)v]]]
+```
 """
 function LazyOuterProduct end
 export LazyOuterProduct

--- a/src/LazyTensors/tensor_mapping.jl	Fri Jan 14 09:01:12 2022 +0100
+++ b/src/LazyTensors/tensor_mapping.jl	Fri Jan 14 09:19:07 2022 +0100
@@ -1,21 +1,24 @@
 """
     TensorMapping{T,R,D}
 
-Describes a mapping of a D dimension tensor to an R dimension tensor.
+Describes a mapping of a `D` dimension tensor to an `R` dimension tensor.
 The action of the mapping is implemented through the method
-
+```julia
     apply(t::TensorMapping{T,R,D}, v::AbstractArray{T,D}, I::Vararg) where {R,D,T}
+```
 
 The size of the range and domain that the operator works with should be returned by
 the functions
-
+```julia
     range_size(::TensorMapping)
     domain_size(::TensorMapping)
-
+```
 to allow querying for one or the other.
 
 Optionally the action of the transpose may be defined through
+```julia
     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
@@ -37,11 +40,13 @@
 export apply_transpose
 
 """
+    range_dim(::TensorMapping)
 Return the dimension of the range space of a given mapping
 """
 range_dim(::TensorMapping{T,R,D}) where {T,R,D} = R
 
 """
+    domain_dim(::TensorMapping)
 Return the dimension of the domain space of a given mapping
 """
 domain_dim(::TensorMapping{T,R,D}) where {T,R,D} = D

--- a/src/StaticDicts/StaticDicts.jl	Fri Jan 14 09:01:12 2022 +0100
+++ b/src/StaticDicts/StaticDicts.jl	Fri Jan 14 09:19:07 2022 +0100
@@ -10,7 +10,7 @@
 
 The immutable nature means that `StaticDict` can be compared with `===`, in
 constrast to regular `Dict` or `ImmutableDict` which can not. (See
-https://github.com/JuliaLang/julia/issues/4648 for details) One important
+<https://github.com/JuliaLang/julia/issues/4648> for details) One important
 aspect of this is that `StaticDict` can be used in a struct while still
 allowing the struct to be comared using the default implementation of `==` for
 structs.