Merge pull request #5 from JuliaImageRecon/Fix-adjoint-gradient-bug

GradOp Cleanup + docstring

Author: JakobAsslaender

src/GradientOp.jl

@@ -1,32 +1,26 @@
-function LinearOperatorCollection.GradientOp(::Type{T};
-  shape::Tuple, dims=nothing) where T <: Number
-  if dims == nothing
-    return GradientOpImpl(T, shape)
-  else
-    return GradientOpImpl(T, shape, dims)
-  end
-end
-
 """
-    GradOp(T::Type, shape::NTuple{N,Int64})
+  GradientOp(T::Type; shape::Tuple, dims=1:length(shape))
 
-Nd gradient operator for an array of size `shape`
-"""
-function GradientOpImpl(T::Type, shape)
-  shape = typeof(shape) <: Number ? (shape,) : shape # convert Number to Tuple
-  return vcat([GradientOpImpl(T, shape, i) for i ∈ eachindex(shape)]...)
-end
+directional gradient operator along the dimensions `dims` for an array of size `shape`.
 
-"""
-    GradOp(T::Type, shape::NTuple{N,Int64}, dims)
+# Required Argument
+  * `T`                        - type of elements, .e.g. `Float64` for `ComplexF32`
+
+# Required Keyword argument
+  * `shape::NTuple{N,Int}`     - shape of the array (e.g., image)
 
-directional gradient operator along the dimensions `dims`
-for an array of size `shape`
+# Optional Keyword argument
+  * `dims`                     - dimension(s) along which the gradient is applied; default is `1:length(shape)`
 """
-function GradientOpImpl(T::Type, shape::NTuple{N,Int64}, dims) where N
+function GradientOp(::Type{T}; shape::NTuple{N,Int}, dims=1:length(shape)) where {T <: Number, N}
+  return GradientOpImpl(T, shape, dims)
+end
+
+function GradientOpImpl(T::Type, shape::NTuple{N,Int}, dims) where N
   return vcat([GradientOpImpl(T, shape, dim) for dim ∈ dims]...)
 end
-function GradientOpImpl(T::Type, shape::NTuple{N,Int64}, dim::Integer) where N
+
+function GradientOpImpl(T::Type, shape::NTuple{N,Int}, dim::Int) where N
   nrow = div( (shape[dim]-1)*prod(shape), shape[dim] )
   ncol = prod(shape)
   return LinearOperator{T}(nrow, ncol, false, false,