• Fixes 0.6 deprecation warnings

• Drops 0.4 support

Author: dlfivefifty

REQUIRE

@@ -1,3 +1,2 @@
-julia 0.4
+julia 0.5
 ToeplitzMatrices 0.1
-Compat 0.8.4

src/ChebyshevJacobiPlan.jl

@@ -134,7 +134,7 @@ function ForwardChebyshevJacobiPlan{T}(c_jac::AbstractVector{T},α::T,β::T,M::I
     θ = N > 0 ? T[k/N for k=zero(T):N] : T[0]
 
     # Initialize sines and cosines
-    tempsin = sinpi(θ/2)
+    tempsin = sinpi.(θ./2)
     tempcos = reverse(tempsin)
     tempcosβsinα,tempmindices = zero(c_jac),zero(c_jac)
     @inbounds for i=1:N+1 tempcosβsinα[i] = tempcos[i]^(β+1/2)*tempsin[i]^(α+1/2) end
@@ -176,7 +176,7 @@ function BackwardChebyshevJacobiPlan{T}(c_cheb::AbstractVector{T},α::T,β::T,M:
     w = N > 0 ? clenshawcurtisweights(2N+1,α,β,p₁) : T[0]
 
     # Initialize sines and cosines
-    tempsin = sinpi(θ/2)
+    tempsin = sinpi.(θ./2)
     tempcos = reverse(tempsin)
     tempcosβsinα,tempmindices = zero(c_cheb2),zero(c_cheb2)
     @inbounds for i=1:2N+1 tempcosβsinα[i] = tempcos[i]^(β+1/2)*tempsin[i]^(α+1/2) end

src/ChebyshevUltrasphericalPlan.jl

@@ -131,9 +131,9 @@ function ForwardChebyshevUltrasphericalPlan{T}(c_ultra::AbstractVector{T},λ::T,
     θ = N > 0 ? T[k/N for k=zero(T):N] : T[0]
 
     # Initialize sines and cosines
-    tempsin = sinpi(θ)
+    tempsin = sinpi.(θ)
     @inbounds for i = 1:N÷2 tempsin[N+2-i] = tempsin[i] end
-    tempsin2 = sinpi(θ/2)
+    tempsin2 = sinpi.(θ./2)
     tempsinλ,tempmindices = zero(c_ultra),zero(c_ultra)
     @inbounds for i=1:N+1 tempsinλ[i] = tempsin[i]^λ end
     tempsinλm = similar(tempsinλ)
@@ -172,9 +172,9 @@ function BackwardChebyshevUltrasphericalPlan{T}(c_ultra::AbstractVector{T},λ::T
     w = N > 0 ? clenshawcurtisweights(2N+1,λ-half(λ),λ-half(λ),p₁) : T[0]
 
     # Initialize sines and cosines
-    tempsin = sinpi(θ)
+    tempsin = sinpi.(θ)
     @inbounds for i = 1:N tempsin[2N+2-i] = tempsin[i] end
-    tempsin2 = sinpi(θ/2)
+    tempsin2 = sinpi.(θ/2)
     tempsinλ,tempmindices = zero(c_cheb2),zero(c_cheb2)
     @inbounds for i=1:2N+1 tempsinλ[i] = tempsin[i]^λ end
     tempsinλm = similar(tempsinλ)

src/FastTransforms.jl

@@ -1,10 +1,10 @@
 __precompile__()
 module FastTransforms
 
-using Base, ToeplitzMatrices, Compat
+using Base, ToeplitzMatrices
 
 import Base: *
-import Compat: view
+import Base: view
 
 export cjt, icjt, jjt, plan_cjt, plan_icjt
 export leg2cheb, cheb2leg, leg2chebu, ultra2ultra, jac2jac
@@ -53,5 +53,5 @@ include("gaunt.jl")
 
 include("precompile.jl")
 _precompile_()
- 
+
 end # module

src/cjt.jl

@@ -101,7 +101,7 @@ end
 
 function *{D,T<:AbstractFloat}(p::FastTransformPlan{D,T},c::AbstractVector{Complex{T}})
     cr,ci = reim(c)
-    complex(p*cr,p*ci)
+    complex.(p*cr,p*ci)
 end
 
 function *(p::FastTransformPlan,c::AbstractMatrix)

src/fftBigFloat.jl

@@ -10,7 +10,7 @@ function Base.fft{T<:BigFloats}(x::Vector{T})
     n = length(x)
     ispow2(n) && return fft_pow2(x)
     ks = linspace(zero(real(T)),n-one(real(T)),n)
-    Wks = exp(-im*convert(T,π)*ks.^2/n)
+    Wks = exp.((-im).*convert(T,π).*ks.^2./n)
     xq,wq = x.*Wks,conj([exp(-im*convert(T,π)*n);reverse(Wks);Wks[2:end]])
     return Wks.*conv(xq,wq)[n+1:2n]
 end
@@ -90,14 +90,14 @@ end
 function fft_pow2{T<:BigFloat}(x::Vector{Complex{T}})
     y = interlace(real(x),imag(x))
     fft_pow2!(y)
-    return complex(y[1:2:end],y[2:2:end])
+    return complex.(y[1:2:end],y[2:2:end])
 end
 fft_pow2{T<:BigFloat}(x::Vector{T}) = fft_pow2(complex(x))
 
 function ifft_pow2{T<:BigFloat}(x::Vector{Complex{T}})
     y = interlace(real(x),-imag(x))
     fft_pow2!(y)
-    return complex(y[1:2:end],-y[2:2:end])/length(x)
+    return complex.(y[1:2:end],-y[2:2:end])/length(x)
 end
 
 

src/specialfunctions.jl

@@ -24,7 +24,7 @@ two{T<:Number}(::Type{T}) = convert(T,2)
 The Kronecker δ function.
 """
 δ(k::Integer,j::Integer) = k == j ? 1 : 0
-@vectorize_2arg Integer δ
+
 
 """
 Pochhammer symbol (x)_n = Γ(x+n)/Γ(x) for the rising factorial.
@@ -96,7 +96,7 @@ function stirlingseries(z::Float64)
     end
 end
 
-@vectorize_1arg Number stirlingseries
+
 
 stirlingremainder(z::Number,N::Int) = (1+zeta(N))*gamma(N)/((2π)^(N+1)*z^N)/stirlingseries(z)
 stirlingremainder{T<:Number}(z::AbstractVector{T},N::Int) = (1+zeta(N))*gamma(N)/(2π)^(N+1)./z.^N./stirlingseries(z)
@@ -152,7 +152,7 @@ function Λ(x::Float64)
         (x+1.0)*Λ(x+1.0)/(x+0.5)
     end
 end
-@vectorize_1arg Number Λ
+
 
 """
 The Lambda function Λ(z,λ₁,λ₂) = Γ(z+λ₁)/Γ(z+λ₂) for the ratio of gamma functions.

test/fftBigFloattest.jl

@@ -2,8 +2,8 @@ using FastTransforms
 using Base.Test
 
 c = collect(linspace(-big(1.0),1,16))
-@test norm(fft(c) - fft(map(Float64,c))) < 3Float64(norm(c))*eps()
-@test norm(ifft(c) - ifft(map(Float64,c))) < 3Float64(norm(c))*eps()
+@test norm(fft(c) - fft(Float64.(c))) < 3Float64(norm(c))*eps()
+@test norm(ifft(c) - ifft(Float64.(c))) < 3Float64(norm(c))*eps()
 
 c = collect(linspace(-big(1.0),1.0,201))
 @test norm(ifft(fft(c))-c) < 200norm(c)eps(BigFloat)

test/runtests.jl

@@ -8,13 +8,13 @@ n = 0:1000_000
 @time FastTransforms.Cnλ(n,λ);
 
 x = linspace(0,20,81);
-@test norm((FastTransforms.Λ(x)-FastTransforms.Λ(big(x)))./FastTransforms.Λ(big(x)),Inf) < 2eps()
+@test norm((FastTransforms.Λ.(x)-FastTransforms.Λ.(big.(x)))./FastTransforms.Λ.(big.(x)),Inf) < 2eps()
 
 x = 0:0.5:10_000
 λ₁,λ₂ = 0.125,0.875
-@test norm((FastTransforms.Λ(x,λ₁,λ₂)-FastTransforms.Λ(big(x),big(λ₁),big(λ₂)))./FastTransforms.Λ(big(x),big(λ₁),big(λ₂)),Inf) < 4eps()
+@test norm((FastTransforms.Λ.(x,λ₁,λ₂).-FastTransforms.Λ.(big.(x),big(λ₁),big(λ₂)))./FastTransforms.Λ.(big.(x),big(λ₁),big(λ₂)),Inf) < 4eps()
 λ₁,λ₂ = 1//3,2//3
-@test norm((FastTransforms.Λ(x,Float64(λ₁),Float64(λ₂))-FastTransforms.Λ(big(x),big(λ₁),big(λ₂)))./FastTransforms.Λ(big(x),big(λ₁),big(λ₂)),Inf) < 4eps()
+@test norm((FastTransforms.Λ.(x,Float64(λ₁),Float64(λ₂))-FastTransforms.Λ.(big.(x),big(λ₁),big(λ₂)))./FastTransforms.Λ.(big.(x),big(λ₁),big(λ₂)),Inf) < 4eps()
 
 n = 0:1000
 α = 0.125
@@ -31,11 +31,11 @@ N = 20
 f(x) = exp(x)
 
 x,w = FastTransforms.fejer1(N,0.,0.)
-@test norm(dot(f(x),w)-2sinh(1)) ≤ 4eps()
+@test norm(dot(f.(x),w)-2sinh(1)) ≤ 4eps()
 x,w = FastTransforms.fejer2(N,0.,0.)
-@test norm(dot(f(x),w)-2sinh(1)) ≤ 4eps()
+@test norm(dot(f.(x),w)-2sinh(1)) ≤ 4eps()
 x,w = FastTransforms.clenshawcurtis(N,0.,0.)
-@test norm(dot(f(x),w)-2sinh(1)) ≤ 4eps()
+@test norm(dot(f.(x),w)-2sinh(1)) ≤ 4eps()
 
 #=
 x = Fun(identity)
@@ -44,11 +44,11 @@ val = sum(g)
 =#
 
 x,w = FastTransforms.fejer1(N,0.25,0.35)
-@test norm(dot(f(x),w)-2.0351088204147243) ≤ 4eps()
+@test norm(dot(f.(x),w)-2.0351088204147243) ≤ 4eps()
 x,w = FastTransforms.fejer2(N,0.25,0.35)
-@test norm(dot(f(x),w)-2.0351088204147243) ≤ 4eps()
+@test norm(dot(f.(x),w)-2.0351088204147243) ≤ 4eps()
 x,w = FastTransforms.clenshawcurtis(N,0.25,0.35)
-@test norm(dot(f(x),w)-2.0351088204147243) ≤ 4eps()
+@test norm(dot(f.(x),w)-2.0351088204147243) ≤ 4eps()
 
 println("Testing the Chebyshev–Jacobi transform")
 
@@ -57,7 +57,7 @@ v = zeros(Nr)
 Na,Nb = 5,5
 V = zeros(Na,Nb)
 
-for N in round(Int,logspace(1,3,3))
+for N in round.(Int,logspace(1,3,3))
     println("")
     println("N = ",N)
     println("")
@@ -117,7 +117,7 @@ c_11 = [1.13031820798497,0.7239875720908708,0.21281687927358628,0.04004015866217
 @test norm(icjt(c_cheb,0.5,0.5)-c_11,Inf) < eps()
 
 
-c = exp(-collect(1:1000)./30)
+c = exp.(collect(1:1000)./(-30))
 
 println("Testing increment/decrement operators for α,β ≤ -0.5")
 
@@ -158,11 +158,11 @@ p1,p2 = plan_cjt(c,α,β),plan_icjt(c,α,β)
 println("Testing for complex coefficients")
 
 α,β = 0.12,0.34
-c = complex(rand(100),rand(100))
+c = complex.(rand(100),rand(100))
 
-@test cjt(c,α,β) == complex(cjt(real(c),α,β),cjt(imag(c),α,β))
-@test icjt(c,α,β) == complex(icjt(real(c),α,β),icjt(imag(c),α,β))
-@test jjt(c,α,β,α,β) == complex(jjt(real(c),α,β,α,β),jjt(imag(c),α,β,α,β))
+@test cjt(c,α,β) == complex.(cjt(real(c),α,β),cjt(imag(c),α,β))
+@test icjt(c,α,β) == complex.(icjt(real(c),α,β),icjt(imag(c),α,β))
+@test jjt(c,α,β,α,β) == complex.(jjt(real(c),α,β,α,β),jjt(imag(c),α,β,α,β))
 @test norm(jjt(c,α,β,α,β)-c,Inf) < 200eps()
 
 println("Testing for Vector{Float32}")
@@ -178,7 +178,7 @@ cL32 = cjt(c32,0.f0,0.f0)
 println("Testing for Matrix of coefficients")
 
 c = rand(100,100)
-@test maxabs(jjt(c,α,β,α,β)-c) < 10000eps()
+@test maximum(abs,jjt(c,α,β,α,β)-c) < 10000eps()
 
 println("Testing Gaunt coefficients")