Fix conv for empty arrays (#628)

Author: wheeheee

Project.toml

@@ -1,6 +1,6 @@
 name = "DSP"
 uuid = "717857b8-e6f2-59f4-9121-6e50c889abd2"
-version = "0.8.2"
+version = "0.8.3"
 
 [deps]
 Bessels = "0e736298-9ec6-45e8-9647-e4fc86a2fe38"

src/dspbase.jl

@@ -717,24 +717,24 @@ function conv!(
         return (first(au)+first(av) : last(au)+last(av)) .- offset
     end)
 
-    if algorithm===:auto
+    if algorithm === :auto
         algorithm = T <: FFTTypes ? :fast : :direct
     end
-    if algorithm===:fast
+    if algorithm === :fast
         if length(u) * length(v) < 2^16 # TODO: better heuristic
             algorithm = :direct
         else
             algorithm = :fft
         end
     end
-    if algorithm===:direct
+    if algorithm === :direct || any(isempty, (u, v)) # ensure correct handling of empty arrays
         return _conv_td!(out, output_indices, u, v)
     else
         if output_indices != CartesianIndices(out)
             fill!(out, zero(eltype(out)))
         end
         os_nffts = length(u) >= length(v) ? map(optimalfftfiltlength, size(v), size(u)) : map(optimalfftfiltlength, size(u), size(v))
-        if algorithm===:fft
+        if algorithm === :fft
             if any(os_nffts .< size(output_indices))
                 algorithm = :fft_overlapsave
             else

test/dsp.jl

@@ -1,8 +1,8 @@
 # This file was formerly a part of Julia. License is MIT: https://julialang.org/license
 # TODO: parameterize conv tests
 using Test, OffsetArrays
-using DSP: filt, filt!, deconv, conv, xcorr,
-           optimalfftfiltlength, unsafe_conv_kern_os!, _conv_kern_fft!
+using DSP: filt, filt!, deconv, conv, conv!, xcorr,
+           optimalfftfiltlength, unsafe_conv_kern_os!, _conv_kern_fft!,
            nextfastfft
 
 
@@ -38,6 +38,16 @@ end
 
     @test optimalfftfiltlength(1, 3) == 1 # Should be at least the first input
 
+    # check that empty arrays are handled correctly (issue #168)
+    for N in (1, 2, 3), algorithm in (:direct, :fft, :fft_simple, :fft_overlapsave)
+        A = rand(ntuple(_ -> 5, N)...)
+        B = Array{Float64}(undef, ntuple(_ -> 0, N))
+        out = zeros(ntuple(_ -> 4, N))
+        @test conv(A, B; algorithm) == out
+        @test conv(B, A; algorithm) == out
+        @test conv(B, B; algorithm) == B
+    end
+
     @testset "conv-1D" begin
         # Convolution
         a = [1, 2, 1, 2]