Fix a bunch of deprecations on Julia 0.7 (#177)

* `Range` -> `AbstractRange` (requires Compat 0.32 on Julia 0.6)
* use `where` syntax for parametric functions
* `2.^` -> `2 .^`
* `+` -> `.+` (where necessary)

REQUIRE

@@ -1,4 +1,5 @@
 julia 0.6-rc1
+Compat 0.32
 Polynomials 0.1.0
 Reexport
 SpecialFunctions

src/Filters/filt.jl

@@ -422,7 +422,7 @@ filt(h::AbstractArray, x::AbstractArray) =
 # fftfilt and filt
 #
 
-const FFT_LENGTHS = 2.^(1:28)
+const FFT_LENGTHS = 2 .^ (1:28)
 # FFT times computed on a Core i7-3930K @4.4GHz
 # The real time doesn't matter, just the relative difference
 const FFT_TIMES = [6.36383e-7, 6.3779e-7 , 6.52212e-7, 6.65282e-7, 7.12794e-7, 7.63172e-7,

src/lpc.jl

@@ -26,7 +26,7 @@ function implements the mathematics published in [1].
 (DAFX 2003 article, Lagrange et al)
 http://www.sylvain-marchand.info/Publications/dafx03.pdf
 """
-function lpc{T <: Number}(x::AbstractVector{T}, p::Int, ::LPCBurg)
+function lpc(x::AbstractVector{T}, p::Int, ::LPCBurg) where T <: Number
     ef = x                      # forward error
     eb = x                      # backwards error
     a = [1; zeros(T, p)]        # prediction coefficients
@@ -62,7 +62,7 @@ function implements the mathematics described in [1].
 [1] - The Wiener (RMS) Error Criterion in Filter Design and Prediction
 (Studies in Applied Mathematics 1947 article, N. Levison)
 """
-function lpc{T <: Number}(x::AbstractVector{T}, p::Int, ::LPCLevinson)
+function lpc(x::AbstractVector{<:Number}, p::Int, ::LPCLevinson)
     R_xx = xcorr(x,x)[length(x):end]
     a = zeros(p,p)
     prediction_err = zeros(1,p)

src/periodograms.jl

@@ -5,6 +5,7 @@
 module Periodograms
 using ..DSP: @importffts
 using ..Util, ..Windows
+using Compat: AbstractRange
 export arraysplit, nextfastfft, periodogram, welch_pgram, mt_pgram,
        spectrogram, power, freq, stft
 @importffts
@@ -178,11 +179,11 @@ end
 
 ## PERIODOGRAMS
 abstract type TFR{T} end
-struct Periodogram{T,F<:Union{Frequencies,Range}} <: TFR{T}
+struct Periodogram{T,F<:Union{Frequencies,AbstractRange}} <: TFR{T}
     power::Vector{T}
     freq::F
 end
-struct Periodogram2{T,F1<:Union{Frequencies,Range},F2<:Union{Frequencies,Range}} <: TFR{T}
+struct Periodogram2{T,F1<:Union{Frequencies,AbstractRange},F2<:Union{Frequencies,AbstractRange}} <: TFR{T}
     power::Matrix{T}
     freq1::F1
     freq2::F2
@@ -192,13 +193,13 @@ freq(p::TFR) = p.freq
 freq(p::Periodogram2) = (p.freq1, p.freq2)
 fftshift(p::Periodogram{T,F}) where {T,F<:Frequencies} =
     Periodogram(p.freq.nreal == p.freq.n ? p.power : fftshift(p.power), fftshift(p.freq))
-fftshift(p::Periodogram{T,F}) where {T,F<:Range} = p
+fftshift(p::Periodogram{T,F}) where {T,F<:AbstractRange} = p
 # 2-d
 fftshift(p::Periodogram2{T,F1,F2}) where {T,F1<:Frequencies,F2<:Frequencies} =
     Periodogram2(p.freq1.nreal == p.freq1.n ? fftshift(p.power,2) : fftshift(p.power), fftshift(p.freq1), fftshift(p.freq2))
-fftshift(p::Periodogram2{T,F1,F2}) where {T,F1<:Range,F2<:Frequencies} =
+fftshift(p::Periodogram2{T,F1,F2}) where {T,F1<:AbstractRange,F2<:Frequencies} =
     Periodogram2(fftshift(p.power,2), p.freq1, fftshift(p.freq2))
-fftshift(p::Periodogram2{T,F1,F2}) where {T,F1<:Range,F2<:Range} = p
+fftshift(p::Periodogram2{T,F1,F2}) where {T,F1<:AbstractRange,F2<:AbstractRange} = p
 
 # Compute the periodogram of a signal S, defined as 1/N*X[s(n)]^2, where X is the
 # DTFT of the signal S.
@@ -340,14 +341,14 @@ end
     const FloatRange{T} = StepRangeLen{T,Base.TwicePrecision{T},Base.TwicePrecision{T}}
 end
 
-struct Spectrogram{T,F<:Union{Frequencies,Range}} <: TFR{T}
+struct Spectrogram{T,F<:Union{Frequencies,AbstractRange}} <: TFR{T}
     power::Matrix{T}
     freq::F
     time::FloatRange{Float64}
 end
 fftshift(p::Spectrogram{T,F}) where {T,F<:Frequencies} =
     Spectrogram(p.freq.nreal == p.freq.n ? p.power : fftshift(p.power, 1), fftshift(p.freq), p.time)
-fftshift(p::Spectrogram{T,F}) where {T,F<:Range} = p
+fftshift(p::Spectrogram{T,F}) where {T,F<:AbstractRange} = p
 Base.time(p::Spectrogram) = p.time
 
 function spectrogram(s::AbstractVector{T}, n::Int=length(s)>>3, noverlap::Int=n>>1;
@@ -357,7 +358,7 @@ function spectrogram(s::AbstractVector{T}, n::Int=length(s)>>3, noverlap::Int=n>
 
     out = stft(s, n, noverlap, PSDOnly(); onesided=onesided, nfft=nfft, fs=fs, window=window)
     Spectrogram(out, onesided ? rfftfreq(nfft, fs) : fftfreq(nfft, fs),
-                ((0:size(out,2)-1)*(n-noverlap)+n/2)/fs)
+                (n/2 : n-noverlap : (size(out,2)-1)*(n-noverlap)+n/2) / fs)
 
 end
 

test/FilterTestHelpers.jl

@@ -108,7 +108,7 @@ end
 
 # Show the poles and zeros in each biquad
 # This is not currently used for testing, but is useful for debugging
-function sosfilter_poles_zeros{T}(sos::SecondOrderSections{T})
+function sosfilter_poles_zeros(sos::SecondOrderSections)
     z = fill(complex(nan(T)), 2, length(sos.biquads))
     p = fill(complex(nan(T)), 2, length(sos.biquads))
     for (i, biquad) in enumerate(sos.biquads)

test/filt.jl

@@ -224,7 +224,7 @@ f = DSP.digitalfilter(DSP.Bandpass(0.1, 0.3), DSP.Butterworth(2))
 # fftfilt/filt
 #
 
-for xlen in 2.^(7:18).-1, blen in 2.^(1:6).-1
+for xlen in 2 .^ (7:18) .- 1, blen in 2 .^ (1:6) .- 1
     b = randn(blen)
     for x in (rand(xlen), rand(xlen, 2))
         filtres = filt(b, [1.0], x)