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Pass to plan_fft all optional keywords of NFFTPlan #25

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Apr 20, 2017
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Pass to plan_fft all optional keywords of NFFTPlan #25

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52 changes: 30 additions & 22 deletions src/NFFT.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -52,15 +52,18 @@ end

@inline dim{D,DIM}(::NFFTPlan{D,DIM}) = DIM

@doc """
"""
NFFTPlan(x, N, ...) -> plan
Compute `D` dimensional NFFT plan for sampling locations `x` (a vector or a `D`-by-`M` matrix) that can be applied on arrays of size `N` (a tuple of length `D`).
The optional arguments control the accuracy.
"""->
It takes as optional keywords all the keywords supported by `plan_fft` function (like
`flags` and `timelimit`). See documentation of `plan_fft` for reference.
"""
function NFFTPlan{D,T}(x::AbstractMatrix{T}, N::NTuple{D,Int}, m=4, sigma=2.0,
window=:kaiser_bessel, K=2000)
window=:kaiser_bessel, K=2000; kwargs...)
if !isa(x, Matrix)
x = collect(x)
end
Expand All @@ -75,8 +78,8 @@ function NFFTPlan{D,T}(x::AbstractMatrix{T}, N::NTuple{D,Int}, m=4, sigma=2.0,

M = size(x,2)

FP = plan_fft!(tmpVec)
BP = plan_bfft!(tmpVec)
FP = plan_fft!(tmpVec; kwargs...)
BP = plan_bfft!(tmpVec; kwargs...)

# Create lookup table
win, win_hat = getWindow(window)
Expand All @@ -102,20 +105,24 @@ function NFFTPlan{D,T}(x::AbstractMatrix{T}, N::NTuple{D,Int}, m=4, sigma=2.0,
NFFTPlan{D,0,T}(N, M, x, m, sigma, n, K, windowLUT, windowHatInvLUT, FP, BP, tmpVec )
end

function NFFTPlan(x::AbstractVector, N::Integer, m=4, sigma=2.0, window=:kaiser_bessel, K=2000)
NFFTPlan(reshape(x,1,length(x)), (N,), m, sigma, window, K)
function NFFTPlan(x::AbstractVector, N::Integer, m=4, sigma=2.0, window=:kaiser_bessel,
K=2000; kwargs...)
NFFTPlan(reshape(x,1,length(x)), (N,), m, sigma, window, K; kwargs...)
end


# Directional NFFT
@doc """
"""
NFFTPlan(x, d, N, ...) -> plan
Compute *directional* NFFT plan:
A 1D plan that is applied along dimension `d` of a `D` dimensional array of size `N` with sampling locations `x` (a vector).
"""->
It takes as optional keywords all the keywords supported by `plan_fft` function (like
`flags` and `timelimit`). See documentation of `plan_fft` for reference.
"""
function NFFTPlan{D,T}(x::AbstractVector{T}, dim::Integer, N::NTuple{D,Int64}, m=4,
sigma=2.0, window=:kaiser_bessel, K=2000)
sigma=2.0, window=:kaiser_bessel, K=2000; kwargs...)
n = ntuple(d->round(Int, sigma*N[d]), D)

sz = [N...]
Expand All @@ -124,8 +131,8 @@ function NFFTPlan{D,T}(x::AbstractVector{T}, dim::Integer, N::NTuple{D,Int64}, m

M = length(x)

FP = plan_fft!(tmpVec, dim)
BP = plan_bfft!(tmpVec, dim)
FP = plan_fft!(tmpVec, dim; kwargs...)
BP = plan_bfft!(tmpVec, dim; kwargs...)

# Create lookup table
win, win_hat = getWindow(window)
Expand All @@ -147,12 +154,13 @@ function NFFTPlan{D,T}(x::AbstractVector{T}, dim::Integer, N::NTuple{D,Int64}, m
NFFTPlan{D,dim,T}(N, M, reshape(x,1,M), m, sigma, n, K, windowLUT, windowHatInvLUT, FP, BP, tmpVec)
end

function NFFTPlan{D,T}(x::Matrix{T}, dim::Integer, N::NTuple{D,Int}, m=4, sigma=2.0, window=:kaiser_bessel, K=2000)
function NFFTPlan{D,T}(x::Matrix{T}, dim::Integer, N::NTuple{D,Int}, m=4, sigma=2.0,
window=:kaiser_bessel, K=2000; kwargs...)
if size(x,1) != 1 && size(x,2) != 1
throw(DimensionMismatch())
end

NFFTPlan(vec(x), dim, N, m, sigma, window, K)
NFFTPlan(vec(x), dim, N, m, sigma, window, K; kwargs...)
end


Expand Down Expand Up @@ -182,13 +190,13 @@ end

### nfft functions ###

@doc """
"""
nfft!(p, f, fHat) -> fHat
Calculate the NFFT of `f` with plan `p` and store the result in `fHat`.
Both `f` and `fHat` must be complex arrays.
"""->
"""
function nfft!{T}(p::NFFTPlan, f::AbstractArray{T}, fHat::StridedArray{T})
consistencyCheck(p, f, fHat)

Expand All @@ -203,7 +211,7 @@ function nfft!{T}(p::NFFTPlan, f::AbstractArray{T}, fHat::StridedArray{T})
return fHat
end

@doc """
"""
nfft(p, f) -> fHat
For a **non**-directional `D` dimensional plan `p` this calculates the NFFT of a `D` dimensional array `f` of size `N`.
Expand All @@ -213,7 +221,7 @@ For a **non**-directional `D` dimensional plan `p` this calculates the NFFT of a
For a **directional** `D` dimensional plan `p` both `f` and `fHat` are `D`
dimensional arrays, and the dimension specified in the plan creation is
affected.
"""->
"""
function nfft{D,T}(p::NFFTPlan{D,0}, f::AbstractArray{T,D})
fHat = zeros(T, p.M)
nfft!(p, f, fHat)
Expand All @@ -234,13 +242,13 @@ function nfft{D,DIM,T}(p::NFFTPlan{D,DIM}, f::AbstractArray{T,D})
end


@doc """
"""
nfft_adjoint!(p, fHat, f) -> f
Calculate the adjoint NFFT of `fHat` and store the result in `f`.
Both `f` and `fHat` must be complex arrays.
"""->
"""
function nfft_adjoint!(p::NFFTPlan, fHat::AbstractArray, f::StridedArray)
consistencyCheck(p, f, fHat)

Expand All @@ -254,7 +262,7 @@ function nfft_adjoint!(p::NFFTPlan, fHat::AbstractArray, f::StridedArray)
return f
end

@doc """
"""
nfft_adjoint(p, f) -> fHat
For a **non**-directional `D` dimensional plan `p` this calculates the adjoint NFFT of a length `M` vector `fHat`
Expand All @@ -264,7 +272,7 @@ For a **non**-directional `D` dimensional plan `p` this calculates the adjoint N
For a **directional** `D` dimensional plan `p` both `f` and `fHat` are `D`
dimensional arrays, and the dimension specified in the plan creation is
affected.
"""->
"""
function nfft_adjoint{D,DIM,T}(p::NFFTPlan{D,DIM}, fHat::AbstractArray{T})
f = Array{T}(p.N)
nfft_adjoint!(p, fHat, f)
Expand Down
4 changes: 2 additions & 2 deletions test/test.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ sigma = 2.0

M = prod(N)
x = rand(D,M) - 0.5
p = NFFTPlan(x, N, m, sigma, window)
p = NFFTPlan(x, N, m, sigma, window, flags = FFTW.ESTIMATE)

fHat = rand(M) + rand(M)*im
f = ndft_adjoint(p, fHat)
Expand All @@ -34,7 +34,7 @@ end
@testset "Abstract sampling points" begin
M, N = rand(100:200, 2)
x = linspace(-0.4, 0.4, M)
p = NFFTPlan(x, N)
p = NFFTPlan(x, N, flags = FFTW.MEASURE)
end

@testset "Directional NFFT $D dim" for D in 2:3 begin
Expand Down