Improve inferrability (#48)

This is technically a breaking change because it returns the sizes as
tuples rather than vectors. Hence I've taken the liberty of bumping
the version number. The consensus is we can go to 1.0.0.

Project.toml

@@ -1,6 +1,6 @@
 name = "AbstractFFTs"
 uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
-version = "0.5.0"
+version = "1.0.0"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/definitions.jl

@@ -256,7 +256,7 @@ summary(p::ScaledPlan) = string(p.scale, " * ", summary(p.p))
 *(p::Plan, I::UniformScaling) = ScaledPlan(p, I.λ)
 
 # Normalization for ifft, given unscaled bfft, is 1/prod(dimensions)
-normalization(T, sz, region) = one(T) / Int(prod([sz...][[region...]]))::Int
+normalization(::Type{T}, sz, region) where T = one(T) / Int(prod([sz...][[region...]]))::Int
 normalization(X, region) = normalization(real(eltype(X)), size(X), region)
 
 plan_ifft(x::AbstractArray, region; kws...) =
@@ -316,19 +316,17 @@ transformed dimensions (of the real output array) in order to obtain the inverse
 """
 brfft
 
-function rfft_output_size(x::AbstractArray, region)
+rfft_output_size(x::AbstractArray, region) = rfft_output_size(size(x), region)
+function rfft_output_size(sz::Dims{N}, region) where {N}
     d1 = first(region)
-    osize = [size(x)...]
-    osize[d1] = osize[d1]>>1 + 1
-    return osize
+    return ntuple(d -> d == d1 ? sz[d]>>1 + 1 : sz[d], Val(N))
 end
 
-function brfft_output_size(x::AbstractArray, d::Integer, region)
+brfft_output_size(x::AbstractArray, d::Integer, region) = brfft_output_size(size(x), d, region)
+function brfft_output_size(sz::Dims{N}, d::Integer, region) where {N}
     d1 = first(region)
-    osize = [size(x)...]
-    @assert osize[d1] == d>>1 + 1
-    osize[d1] = d
-    return osize
+    @assert sz[d1] == d>>1 + 1
+    return ntuple(i -> i == d1 ? d : sz[i], Val(N))
 end
 
 plan_irfft(x::AbstractArray{Complex{T}}, d::Integer, region; kws...) where {T} =
@@ -432,7 +430,7 @@ Return the discrete Fourier transform (DFT) sample frequencies for a DFT of leng
 bin centers at every sample point. `fs` is the sampling rate of the
 input signal, which is the reciprocal of the sample spacing.
 
-Given a window of length `n` and a sampling rate `fs`, the frequencies returned are 
+Given a window of length `n` and a sampling rate `fs`, the frequencies returned are
 
 ```julia
 [0:n÷2-1; -n÷2:-1]  * fs/n   # if n is even
@@ -467,7 +465,7 @@ The returned `Frequencies` object is an `AbstractVector`
 containing the frequency bin centers at every sample point. `fs`
 is the sampling rate of the input signal, which is the reciprocal of the sample spacing.
 
-Given a window of length `n` and a sampling rate `fs`, the frequencies returned are 
+Given a window of length `n` and a sampling rate `fs`, the frequencies returned are
 
 ```julia
 [0:n÷2;]  * fs/n  # if n is even

test/runtests.jl

@@ -5,6 +5,16 @@ using AbstractFFTs: Plan
 using LinearAlgebra
 using Test
 
+@testset "rfft sizes" begin
+    A = rand(11, 10)
+    @test @inferred(AbstractFFTs.rfft_output_size(A, 1)) == (6, 10)
+    @test @inferred(AbstractFFTs.rfft_output_size(A, 2)) == (11, 6)
+    A1 = rand(6, 10); A2 = rand(11, 6)
+    @test @inferred(AbstractFFTs.brfft_output_size(A1, 11, 1)) == (11, 10)
+    @test @inferred(AbstractFFTs.brfft_output_size(A2, 10, 2)) == (11, 10)
+    @test_throws AssertionError AbstractFFTs.brfft_output_size(A1, 10, 2)
+end
+
 mutable struct TestPlan{T} <: Plan{T}
     region
     pinv::Plan{T}