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GradOp Cleanup + docstring #5

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Dec 8, 2023
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GradOp Cleanup + docstring #5

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38 changes: 16 additions & 22 deletions src/GradientOp.jl
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Original file line number Diff line number Diff line change
@@ -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,
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