Add option to test with FFTW backend

.github/workflows/CI.yml

@@ -22,6 +22,12 @@ jobs:
           - windows-latest
         arch:
           - x64
+        group:
+          - TestPlans
+          - FFTW 
+        exclude:
+          - version: '1.0'
+            group: FFTW
     steps:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
@@ -40,7 +46,10 @@ jobs:
             ${{ runner.os }}-
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
+        env:
+          GROUP: ${{ matrix.group }}
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v1
         with:
           file: lcov.info
+          flag-name: group-${{ matrix.group }} # unique name for coverage report of each group

Project.toml

@@ -12,9 +12,10 @@ julia = "^1.0"
 
 [extras]
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
+FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [targets]
-test = ["ChainRulesTestUtils", "Random", "Test", "Unitful"]
+test = ["ChainRulesTestUtils", "FFTW", "Random", "Test", "Unitful"]

test/TestPlans.jl

@@ -0,0 +1,235 @@
+module TestPlans
+
+    using AbstractFFTs
+    using AbstractFFTs: Plan
+
+    mutable struct TestPlan{T,N} <: Plan{T}
+        region
+        sz::NTuple{N,Int}
+        pinv::Plan{T}
+        function TestPlan{T}(region, sz::NTuple{N,Int}) where {T,N}
+            return new{T,N}(region, sz)
+        end
+    end
+
+    mutable struct InverseTestPlan{T,N} <: Plan{T}
+        region
+        sz::NTuple{N,Int}
+        pinv::Plan{T}
+        function InverseTestPlan{T}(region, sz::NTuple{N,Int}) where {T,N}
+            return new{T,N}(region, sz)
+        end
+    end
+
+    Base.size(p::TestPlan) = p.sz
+    Base.ndims(::TestPlan{T,N}) where {T,N} = N
+    Base.size(p::InverseTestPlan) = p.sz
+    Base.ndims(::InverseTestPlan{T,N}) where {T,N} = N
+
+    function AbstractFFTs.plan_fft(x::AbstractArray{T}, region; kwargs...) where {T}
+        return TestPlan{T}(region, size(x))
+    end
+    function AbstractFFTs.plan_bfft(x::AbstractArray{T}, region; kwargs...) where {T}
+        return InverseTestPlan{T}(region, size(x))
+    end
+
+    function AbstractFFTs.plan_inv(p::TestPlan{T}) where {T}
+        unscaled_pinv = InverseTestPlan{T}(p.region, p.sz)
+        N = AbstractFFTs.normalization(T, p.sz, p.region)
+        unscaled_pinv.pinv = AbstractFFTs.ScaledPlan(p, N)
+        pinv = AbstractFFTs.ScaledPlan(unscaled_pinv, N)
+        return pinv
+    end
+    function AbstractFFTs.plan_inv(pinv::InverseTestPlan{T}) where {T}
+        unscaled_p = TestPlan{T}(pinv.region, pinv.sz)
+        N = AbstractFFTs.normalization(T, pinv.sz, pinv.region)
+        unscaled_p.pinv = AbstractFFTs.ScaledPlan(pinv, N)
+        p = AbstractFFTs.ScaledPlan(unscaled_p, N)
+        return p
+    end
+
+    # Just a helper function since forward and backward are nearly identical
+    # The function does not check if the size of `y` and `x` are compatible, this
+    # is done in the function where `dft!` is called since the check differs for FFTs
+    # with complex and real-valued signals
+    function dft!(
+        y::AbstractArray{<:Complex,N},
+        x::AbstractArray{<:Union{Complex,Real},N},
+        dims,
+        sign::Int
+    ) where {N}
+        # check that dimensions that are transformed are unique
+        allunique(dims) || error("dimensions have to be unique")
+
+        T = eltype(y)
+        # we use `size(x, d)` since for real-valued signals
+        # `size(y, first(dims)) = size(x, first(dims)) ÷ 2 + 1`
+        cs = map(d -> T(sign * 2π / size(x, d)), dims)
+        fill!(y, zero(T))
+        for yidx in CartesianIndices(y)
+            # set of indices of `x` on which `y[yidx]` depends
+            xindices = CartesianIndices(
+                ntuple(i -> i in dims ? axes(x, i) : yidx[i]:yidx[i], Val(N))
+            )
+            for xidx in xindices
+                y[yidx] += x[xidx] * cis(sum(c * (yidx[d] - 1) * (xidx[d] - 1) for (c, d) in zip(cs, dims)))
+            end
+        end
+        return y
+    end
+
+    function mul!(
+        y::AbstractArray{<:Complex,N}, p::TestPlan, x::AbstractArray{<:Union{Complex,Real},N}
+    ) where {N}
+        size(y) == size(p) == size(x) || throw(DimensionMismatch())
+        dft!(y, x, p.region, -1)
+    end
+    function mul!(
+        y::AbstractArray{<:Complex,N}, p::InverseTestPlan, x::AbstractArray{<:Union{Complex,Real},N}
+    ) where {N}
+        size(y) == size(p) == size(x) || throw(DimensionMismatch())
+        dft!(y, x, p.region, 1)
+    end
+
+    Base.:*(p::TestPlan, x::AbstractArray) = mul!(similar(x, complex(float(eltype(x)))), p, x)
+    Base.:*(p::InverseTestPlan, x::AbstractArray) = mul!(similar(x, complex(float(eltype(x)))), p, x)
+
+    mutable struct TestRPlan{T,N} <: Plan{T}
+        region
+        sz::NTuple{N,Int}
+        pinv::Plan{T}
+        TestRPlan{T}(region, sz::NTuple{N,Int}) where {T,N} = new{T,N}(region, sz)
+    end
+
+    mutable struct InverseTestRPlan{T,N} <: Plan{T}
+        d::Int
+        region
+        sz::NTuple{N,Int}
+        pinv::Plan{T}
+        function InverseTestRPlan{T}(d::Int, region, sz::NTuple{N,Int}) where {T,N}
+            sz[first(region)::Int] == d ÷ 2 + 1 || error("incompatible dimensions")
+            return new{T,N}(d, region, sz)
+        end
+    end
+
+    function AbstractFFTs.plan_rfft(x::AbstractArray{T}, region; kwargs...) where {T}
+        return TestRPlan{T}(region, size(x))
+    end
+    function AbstractFFTs.plan_brfft(x::AbstractArray{T}, d, region; kwargs...) where {T}
+        return InverseTestRPlan{T}(d, region, size(x))
+    end
+    function AbstractFFTs.plan_inv(p::TestRPlan{T,N}) where {T,N}
+        firstdim = first(p.region)::Int
+        d = p.sz[firstdim]
+        sz = ntuple(i -> i == firstdim ? d ÷ 2 + 1 : p.sz[i], Val(N))
+        _N = AbstractFFTs.normalization(T, p.sz, p.region)
+
+        unscaled_pinv = InverseTestRPlan{T}(d, p.region, sz)
+        unscaled_pinv.pinv = AbstractFFTs.ScaledPlan(p, _N)
+        pinv = AbstractFFTs.ScaledPlan(unscaled_pinv, _N)
+        return pinv
+    end
+
+    function AbstractFFTs.plan_inv(pinv::InverseTestRPlan{T,N}) where {T,N}
+        firstdim = first(pinv.region)::Int
+        sz = ntuple(i -> i == firstdim ? pinv.d : pinv.sz[i], Val(N))
+        _N = AbstractFFTs.normalization(T, sz, pinv.region)
+
+        unscaled_p = TestRPlan{T}(pinv.region, sz)
+        unscaled_p.pinv = AbstractFFTs.ScaledPlan(pinv, _N)
+        p = AbstractFFTs.ScaledPlan(unscaled_p, _N)
+        return p
+    end
+
+    Base.size(p::TestRPlan) = p.sz
+    Base.ndims(::TestRPlan{T,N}) where {T,N} = N
+    Base.size(p::InverseTestRPlan) = p.sz
+    Base.ndims(::InverseTestRPlan{T,N}) where {T,N} = N
+
+    function real_invdft!(
+        y::AbstractArray{<:Real,N},
+        x::AbstractArray{<:Union{Complex,Real},N},
+        dims,
+    ) where {N}
+        # check that dimensions that are transformed are unique
+        allunique(dims) || error("dimensions have to be unique")
+
+        firstdim = first(dims)
+        size_x_firstdim = size(x, firstdim)
+        iseven_firstdim = iseven(size(y, firstdim))
+        # we do not check that the input corresponds to a real-valued signal
+        # (i.e., that the first and, if `iseven_firstdim`, the last value in dimension
+        # `haldim` of `x` are real values) due to numerical inaccuracies
+        # instead we just use the real part of these entries
+
+        T = eltype(y)
+        # we use `size(y, d)` since `size(x, first(dims)) = size(y, first(dims)) ÷ 2 + 1`
+        cs = map(d -> T(2π / size(y, d)), dims)
+        fill!(y, zero(T))
+        for yidx in CartesianIndices(y)
+            # set of indices of `x` on which `y[yidx]` depends
+            xindices = CartesianIndices(
+                ntuple(i -> i in dims ? axes(x, i) : yidx[i]:yidx[i], Val(N))
+            )
+            for xidx in xindices
+                coeffimag, coeffreal = sincos(
+                    sum(c * (yidx[d] - 1) * (xidx[d] - 1) for (c, d) in zip(cs, dims))
+                )
+
+                # the first and, if `iseven_firstdim`, the last term of the DFT are scaled
+                # with 1 instead of 2 and only the real part is used (see note above)
+                xidx_firstdim = xidx[firstdim]
+                if xidx_firstdim == 1 || (iseven_firstdim && xidx_firstdim == size_x_firstdim)
+                    y[yidx] += coeffreal * real(x[xidx])
+                else
+                    xreal, ximag = reim(x[xidx])
+                    y[yidx] += 2 * (coeffreal * xreal - coeffimag * ximag)
+                end
+            end
+        end
+
+        return y
+    end
+
+    to_real!(x::AbstractArray) = map!(real, x, x)
+
+    function Base.:*(p::TestRPlan, x::AbstractArray)
+        size(p) == size(x) || error("array and plan are not consistent")
+
+        # create output array
+        firstdim = first(p.region)::Int
+        d = size(x, firstdim)
+        firstdim_size = d ÷ 2 + 1
+        T = complex(float(eltype(x)))
+        sz = ntuple(i -> i == firstdim ? firstdim_size : size(x, i), Val(ndims(x)))
+        y = similar(x, T, sz)
+
+        # compute DFT
+        dft!(y, x, p.region, -1)
+
+        # we clean the output a bit to make sure that we return real values
+        # whenever the output is mathematically guaranteed to be a real number
+        to_real!(selectdim(y, firstdim, 1))
+        if iseven(d)
+            to_real!(selectdim(y, firstdim, firstdim_size))
+        end
+
+        return y
+    end
+
+    function Base.:*(p::InverseTestRPlan, x::AbstractArray)
+        size(p) == size(x) || error("array and plan are not consistent")
+
+        # create output array
+        firstdim = first(p.region)::Int
+        d = p.d
+        sz = ntuple(i -> i == firstdim ? d : size(x, i), Val(ndims(x)))
+        y = similar(x, real(float(eltype(x))), sz)
+
+        # compute DFT
+        real_invdft!(y, x, p.region)
+
+        return y
+    end
+
+end