Merge branch 'main' of github.com:JuliaImageRecon/LinearOperatorCollection.jl

Author: nHackel

Project.toml

@@ -1,36 +1,36 @@
 name = "LinearOperatorCollection"
 uuid = "a4a2c56f-fead-462a-a3ab-85921a5f2575"
 authors = ["Tobias Knopp <[email protected]> and contributors"]
-version = "1.1.1"
+version = "1.1.3-DEV"
 
 [deps]
+InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 
-[weakdeps]
-Wavelets = "29a6e085-ba6d-5f35-a997-948ac2efa89a"
-FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
-NFFT = "efe261a4-0d2b-5849-be55-fc731d526b0d"
-
-[extensions]
-LinearOperatorWaveletExt = "Wavelets"
-LinearOperatorFFTWExt = "FFTW"
-LinearOperatorNFFTExt = ["NFFT", "FFTW"]
+[extras]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 julia = "1.9"
-FFTW = "1.0"
+NFFT = "0.13"
 LinearOperators = "2.3.3"
-Reexport = "1.0"
 Wavelets = "0.9, 0.10"
-NFFT = "0.13"
+Reexport = "1.0"
+FFTW = "1.0"
 
-[extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+[weakdeps]
+NFFT = "efe261a4-0d2b-5849-be55-fc731d526b0d"
+Wavelets = "29a6e085-ba6d-5f35-a997-948ac2efa89a"
+FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 
 [targets]
 test = ["Test", "FFTW", "Wavelets", "NFFT"]
+
+[extensions]
+LinearOperatorNFFTExt = ["NFFT", "FFTW"]
+LinearOperatorFFTWExt = "FFTW"
+LinearOperatorWaveletExt = "Wavelets"

src/GradientOp.jl

@@ -1,38 +1,33 @@
-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,
-                          (res,x) -> (grad!(res,x,shape,dim) ),
-                          (res,x) -> (grad_t!(res,x,shape,dim) ),
-                          nothing )
+                          (res,x) -> (grad!(res,x,shape,dim)),
+                          (res,x) -> (grad_t!(res,x,shape,dim)),
+                          (res,x) -> (grad_t!(res,x,shape,dim))
+                          )
 end
 
 # directional gradients