Re-add precompute flag and update keyword args delimter

Author: nHackel

ext/LinearOperatorNFFTExt/NFFTOp.jl

@@ -6,13 +6,16 @@ generates a `NFFTOpImpl` which evaluates the MRI Fourier signal encoding operato
 
 # Arguments:
 * `shape::NTuple{D,Int64}`  - size of image to encode/reconstruct
-* `tr`                      - Either a `Trajectory` object, or a `ND x Nsamples` matrix for an ND-dimenensional (e.g. 2D or 3D) NFFT with `Nsamples` k-space samples
-* (`nodes=nothing`)         - Array containg the trajectory nodes (redundant)
-* (`kargs`)                 - additional keyword arguments for the NFFT plan
+* `nodes=nothing`         - Array containg the trajectory nodes
+* `toeplitz=false`        - 
+* `oversamplingFactor=1.25`
+* `kernelSize=3`
+* `precompute = AbstractNFFTs.TENSOR` Precompute flag for the NFFT backend
+* (`kargs`)                 - additional keyword arguments for the NFFT plan, 
 """
 function LinearOperatorCollection.NFFTOp(::Type{T};
     shape::Tuple, nodes::AbstractMatrix{U}, toeplitz=false, oversamplingFactor=1.25, 
-   kernelSize=3, kargs...) where {U <: Number, T <: Number}
+   kernelSize=3, precompute = AbstractNFFTs.TENSOR, kargs...) where {U <: Number, T <: Number}
   return NFFTOpImpl(shape, nodes; toeplitz, oversamplingFactor, kernelSize, kargs... )
 end
 
@@ -38,10 +41,10 @@ end
 
 LinearOperators.storage_type(op::NFFTOpImpl) = typeof(op.Mv5)
 
-function NFFTOpImpl(shape::Tuple, tr::AbstractMatrix{T}; toeplitz=false, oversamplingFactor=1.25, kernelSize=3, S = Vector{Complex{T}}, kargs...) where {T}
+function NFFTOpImpl(shape::Tuple, tr::AbstractMatrix{T}; toeplitz, oversamplingFactor, kernelSize, S = Vector{Complex{T}}, kargs...) where {T}
 
   baseArrayType = Base.typename(S).wrapper # https://github.com/JuliaLang/julia/issues/35543
-  plan = plan_nfft(baseArrayType, tr, shape, m=kernelSize, σ=oversamplingFactor,
+  plan = plan_nfft(baseArrayType, tr, shape; m=kernelSize, σ=oversamplingFactor,
 		                          fftflags=FFTW.ESTIMATE, blocking=true, kargs...)
 
   return NFFTOpImpl{eltype(S), S, typeof(plan)}(size(tr,2), prod(shape), false, false
@@ -143,7 +146,7 @@ function NFFTToeplitzNormalOp(nfft::NFFTOp{T}, W=nothing; kwargs...) where {T}
   shape_os = 2 .* shape
   baseArrayType = Base.typename(typeof(tmpVec)).wrapper # https://github.com/JuliaLang/julia/issues/35543
   p = plan_nfft(baseArrayType, nfft.plan.k, shape_os; m = nfft.plan.params.m, σ = nfft.plan.params.σ,
-		precompute=NFFT.POLYNOMIAL, fftflags=FFTW.ESTIMATE, blocking=true)
+		precompute=AbstractNFFTs.POLYNOMIAL, fftflags=FFTW.ESTIMATE, blocking=true)
   tmpOnes = similar(tmpVec, size(nfft.plan.k, 2))
   tmpOnes .= one(T)