Merge pull request #304 from fredrikekre/fe/07

fixes for julia 0.7

Author: timholy

.travis.yml

@@ -3,7 +3,7 @@ os:
     - osx
     - linux
 julia:
-    - 0.6
+    - 0.7
     - nightly
 notifications:
     email: false

README.md

@@ -182,13 +182,13 @@ A seed color or array of seed colors may be provided to `distinguishable_colors`
 ```julia
 distinguishable_colors{T<:Color}(n::Integer, seed::AbstractVector{T};
     transform::Function = identity,
-    lchoices::AbstractVector = linspace(0, 100, 15),
-    cchoices::AbstractVector = linspace(0, 100, 15),
-    hchoices::AbstractVector = linspace(0, 340, 20)
+    lchoices::AbstractVector = range(0, stop=100, length=15),
+    cchoices::AbstractVector = range(0, stop=100, length=15),
+    hchoices::AbstractVector = range(0, stop=340, length=20)
 )
 ```
 
-By default, `distinguishable_colors` chooses maximally distinguishable colors from the outer product of lightness, chroma and hue values specified by `lchoices = linspace(0, 100, 15)`, `cchoices = linspace(0, 100, 15)`, and `hchoices = linspace(0, 340, 20)`. The set of colors that `distinguishable_colors` chooses from may be specified by passing different choices as keyword arguments.
+By default, `distinguishable_colors` chooses maximally distinguishable colors from the outer product of lightness, chroma and hue values specified by `lchoices = range(0, stop=100, length=15)`, `cchoices = range(0, stop=100, length=15)`, and `hchoices = range(0, stop=340, length=20)`. The set of colors that `distinguishable_colors` chooses from may be specified by passing different choices as keyword arguments.
 
 Distinguishability is maximized with respect to the CIEDE2000 color difference formula (see `colordiff`). If a `transform` function is specified, color difference is instead maximized between colors `a` and `b` according to
 `colordiff(transform(a), transform(b))`.
@@ -198,18 +198,18 @@ Color arrays generated by `distinguishable_colors` are particularly useful for i
 ```
 vars = 1:10
 cols = distinguishable_colors(length(vars)+1,[RGB(1,1,1)])[2:end]
-pcols = map(col->(red(col),green(col),blue(col)),cols) 
+pcols = map(col->(red(col),green(col),blue(col)),cols)
 for i = 1:length(vars)
     plot(1:10,rand(10),c = pcols[i])
 end
 ```
 
-#### `linspace`
+#### `range`
 
-`linspace(c1::Color, c2::Color, n=100)`
+`range(start::Color; stop::Color, length=100)`
 
-Generates `n` colors in a linearly interpolated ramp from `c1` to
-`c2`, inclusive, returning an `Array` of colors
+Generates `n` colors in a linearly interpolated ramp from `start` to
+`stop`, inclusive, returning an `Array` of colors
 
 #### `weighted_color_mean`
 

REQUIRE

@@ -1,5 +1,4 @@
-julia 0.6
-ColorTypes 0.6.3
-FixedPointNumbers 0.3.0
+julia 0.7-alpha
+ColorTypes 0.7.0
+FixedPointNumbers 0.5.0
 Reexport
-Compat 0.32

src/Colors.jl

@@ -2,10 +2,8 @@ __precompile__()
 
 module Colors
 
-using FixedPointNumbers, ColorTypes, Reexport, Compat
+using FixedPointNumbers, ColorTypes, Reexport, Printf
 @reexport using ColorTypes
-# deprecated exports
-export U8, U16
 
 AbstractGray{T} = Color{T,1}
 using ColorTypes: TransparentGray
@@ -15,7 +13,7 @@ Color3{T} = Color{T,3}
 Transparent4{C<:Color3,T} = TransparentColor{C,T,4}
 
 import Base: ==, +, -, *, /
-import Base: convert, eltype, hex, isless, linspace, show, typemin, typemax
+import Base: convert, eltype, hex, isless, range, show, typemin, typemax
 
 # Additional exports, not exported by ColorTypes
 export weighted_color_mean,
@@ -26,6 +24,12 @@ export weighted_color_mean,
        MSC, sequential_palette, diverging_palette, colormap,
        colormatch, CIE1931_CMF, CIE1964_CMF, CIE1931J_CMF, CIE1931JV_CMF
 
+# atan(x,y) was introduced after 0.7.0-alpha
+@static if VERSION < v"0.7.0-alpha.44"
+    atan(x, y) = atan2(x, y)
+    atan(x) = Base.atan(x)
+end
+
 # Early utilities
 include("utilities.jl")
 

src/algorithms.jl

@@ -149,10 +149,6 @@ protanopic(c::Color)   = protanopic(c, 1.0)
 deuteranopic(c::Color) = deuteranopic(c, 1.0)
 tritanopic(c::Color)   = tritanopic(c, 1.0)
 
-Compat.@dep_vectorize_1arg Color protanopic
-Compat.@dep_vectorize_1arg Color deuteranopic
-Compat.@dep_vectorize_1arg Color tritanopic
-
 # MSC - Most Saturated Colorant for given hue h
 # ---------------------
 

src/colormaps.jl

@@ -23,41 +23,41 @@ Args:
 Keyword arguments:
 
 - `transform`: Transform applied to colors before measuring distance. Default is the identity; other choices include `deuteranopic` to simulate color-blindness.
-- `lchoices`: Possible lightness values (default `linspace(0,100,15)`)
-- `cchoices`: Possible chroma values (default `linspace(0,100,15)`)
-- `hchoices`: Possible hue values (default `linspace(0,340,20)`)
+- `lchoices`: Possible lightness values (default `range(0,stop=100,start=15)`)
+- `cchoices`: Possible chroma values (default `range(0,stop=100,start=15)`)
+- `hchoices`: Possible hue values (default `range(0,stop=340,start=20)`)
 
 Returns:
   A `Vector` of colors of length `n`, of the type specified in `seed`.
 """
 function distinguishable_colors(n::Integer,
                   seed::AbstractVector{T};
                   transform::Function = identity,
-                  lchoices::AbstractVector = linspace(0, 100, 15),
-                  cchoices::AbstractVector = linspace(0, 100, 15),
-                  hchoices::AbstractVector = linspace(0, 340, 20)) where T<:Color
+                  lchoices::AbstractVector = range(0, stop=100, length=15),
+                  cchoices::AbstractVector = range(0, stop=100, length=15),
+                  hchoices::AbstractVector = range(0, stop=340, length=20)) where T<:Color
     if n <= length(seed)
         return seed[1:n]
     end
 
     # Candidate colors
     N = length(lchoices)*length(cchoices)*length(hchoices)
-    candidate = Vector{Lab{Float64}}(N)
+    candidate = Vector{Lab{Float64}}(undef, N)
     j = 0
     for h in hchoices, c in cchoices, l in lchoices
         rgb = convert(RGB, LCHab(l, c, h))
         candidate[j+=1] = convert(LCHab, rgb)
     end
 
     # Transformed colors
-    candidate_t = Vector{Lab{Float64}}(N)
+    candidate_t = Vector{Lab{Float64}}(undef, N)
     for i = 1:N
         candidate_t[i] = transform(candidate[i])
     end
 
     # Start with the seed colors
-    colors = Vector{T}(n)
-    copy!(colors, seed)
+    colors = Vector{T}(undef, n)
+    copyto!(colors, seed)
 
     # Minimum distances of the current color to each previously selected color.
     ds = fill(Inf, N)
@@ -69,7 +69,7 @@ function distinguishable_colors(n::Integer,
     end
 
     for i in length(seed)+1:n
-        j = indmax(ds)
+        j = argmax(ds)
         colors[i] = candidate[j]
         tc = candidate_t[j]
         for k = 1:N
@@ -83,7 +83,7 @@ end
 
 
 distinguishable_colors(n::Integer, seed::Color; kwargs...) = distinguishable_colors(n, [seed]; kwargs...)
-distinguishable_colors(n::Integer; kwargs...) = distinguishable_colors(n, Vector{RGB{N0f8}}(0); kwargs...)
+distinguishable_colors(n::Integer; kwargs...) = distinguishable_colors(n, Vector{RGB{N0f8}}(); kwargs...)
 
 @deprecate distinguishable_colors(n::Integer,
                                 transform::Function,
@@ -159,7 +159,7 @@ function sequential_palette(h,
     if logscale
         absc = logspace(-2.,0.,N)
     else
-        absc = linspace(0.,1.,N)
+        absc = range(0.,stop=1.,length=N)
     end
 
     for t in absc

src/conversions.jl

@@ -354,8 +354,8 @@ cnvt(::Type{XYZ{T}}, c::LCHuv) where {T} = cnvt(XYZ{T}, convert(Luv{T}, c))
 function cnvt(::Type{XYZ{T}}, c::DIN99d) where T
 
     # Go back to C-h space
-    # FIXME: Clean this up (why is there no atan2d?)
-    h = rad2deg(atan2(c.b,c.a)) + 50
+    # FIXME: Clean this up (why is there no atand?)
+    h = rad2deg(atan(c.b,c.a)) + 50
     while h > 360; h -= 360; end
     while h < 0;   h += 360; end
 
@@ -492,7 +492,7 @@ function cnvt(::Type{Lab{T}}, c::DIN99o) where T
     co = sqrt(c.a^2 + c.b^2)
 
     # hue angle h99o
-    h = atan2(c.b, c.a)
+    h = atan(c.b, c.a)
 
     # revert rotation by 26°
     ho= rad2deg(h)-26
@@ -559,7 +559,7 @@ cnvt(::Type{Luv{T}}, c::Color3) where {T} = cnvt(Luv{T}, convert(XYZ{T}, c))
 # -------------------
 
 function cnvt(::Type{LCHuv{T}}, c::Luv) where T
-    h = rad2deg(atan2(c.v, c.u))
+    h = rad2deg(atan(c.v, c.u))
     while h > 360; h -= 360; end
     while h < 0;   h += 360; end
     LCHuv{T}(c.l, sqrt(c.u^2 + c.v^2), h)
@@ -573,7 +573,7 @@ cnvt(::Type{LCHuv{T}}, c::Color3) where {T} = cnvt(LCHuv{T}, convert(Luv{T}, c))
 # -------------------
 
 function cnvt(::Type{LCHab{T}}, c::Lab) where T
-    h = rad2deg(atan2(c.b, c.a))
+    h = rad2deg(atan(c.b, c.a))
     while h > 360; h -= 360; end
     while h < 0;   h += 360; end
     LCHab{T}(c.l, sqrt(c.a^2 + c.b^2), h)
@@ -656,8 +656,8 @@ function cnvt(::Type{DIN99d{T}}, c::XYZ{T}) where T
 
     # Calculate hue/chroma
     C = 22.5*log(1+0.06*G)
-    # FIXME: Clean this up (why is there no atan2d?)
-    h = rad2deg(atan2(f,ee)) + 50
+    # FIXME: Clean this up (why is there no atand?)
+    h = rad2deg(atan(f,ee)) + 50
     while h > 360; h -= 360; end
     while h < 0;   h += 360; end
 
@@ -692,7 +692,7 @@ function cnvt(::Type{DIN99o{T}}, c::Lab) where T
 
     # Temporary value for chroma
     go = sqrt(eo^2 + fo^2)
-    ho = atan2(fo,eo)
+    ho = atan(fo,eo)
     # rotation of the color space by 26°
     h  = rad2deg(ho) + 26
 

src/parse.jl

@@ -15,25 +15,25 @@ const col_pat_hsla = r"hsla\((\d+%?),(\d+%?),(\d+%?),(\d+(?:\.\d*)?%?)\)"
 # Parse a number used in the "rgb()" or "hsl()" color.
 function parse_rgb(num::AbstractString)
     if num[end] == '%'
-        return clamp(parse(Int, num[1:end-1], 10) / 100, 0, 1)
+        return clamp(parse(Int, num[1:end-1], base=10) / 100, 0, 1)
     else
-        return clamp(parse(Int, num, 10) / 255, 0, 1)
+        return clamp(parse(Int, num, base=10) / 255, 0, 1)
     end
 end
 
 function parse_hsl_hue(num::AbstractString)
     if num[end] == '%'
         error("hue cannot end in %")
     else
-        return parse(Int, num, 10)
+        return parse(Int, num, base=10)
     end
 end
 
 function parse_hsl_sl(num::AbstractString)
     if num[end] != '%'
         error("saturation and lightness must end in %")
     else
-        return parse(Int, num[1:end-1], 10) / 100
+        return parse(Int, num[1:end-1], base=10) / 100
     end
 end
 
@@ -48,19 +48,19 @@ end
 
 
 function _parse_colorant(desc::AbstractString)
-    desc_ = replace(desc, " ", "")
+    desc_ = replace(desc, " " => "")
     mat = match(col_pat_hex2, desc_)
     if mat != nothing
-        return RGB{N0f8}(parse(Int, mat.captures[2], 16) / 255,
-                       parse(Int, mat.captures[3], 16) / 255,
-                       parse(Int, mat.captures[4], 16) / 255)
+        return RGB{N0f8}(parse(Int, mat.captures[2], base=16) / 255,
+                       parse(Int, mat.captures[3], base=16) / 255,
+                       parse(Int, mat.captures[4], base=16) / 255)
     end
 
     mat = match(col_pat_hex1, desc_)
     if mat != nothing
-        return RGB{N0f8}(parse(Int, mat.captures[2], 16) / 15,
-                       parse(Int, mat.captures[3], 16) / 15,
-                       parse(Int, mat.captures[4], 16) / 15)
+        return RGB{N0f8}(parse(Int, mat.captures[2], base=16) / 15,
+                       parse(Int, mat.captures[3], base=16) / 15,
+                       parse(Int, mat.captures[4], base=16) / 15)
     end
 
     mat = match(col_pat_rgb, desc_)

src/utilities.jl

@@ -35,7 +35,7 @@ hex(c::Colorant) = hex(convert(ARGB, c))
 """
     weighted_color_mean(w1, c1, c2)
 
-Returns the color `w1*c1 + (1-w1)*c2` that is the weighted mean of `c1` and 
+Returns the color `w1*c1 + (1-w1)*c2` that is the weighted mean of `c1` and
 `c2`, where `c1` has a weight 0 ≤ `w1` ≤ 1.
 """
 function weighted_color_mean(w1::Real, c1::Colorant, c2::Colorant)
@@ -49,15 +49,18 @@ function weighted_color_mean(w1::Real, c1::Gray{Bool}, c2::Gray{Bool})
 end
 
 """
-    linspace(c1::Color, c2::Color, n=100)
+    range(start::Color; stop::Color, length=100)
 
-Generates `n`>2 colors in a linearly interpolated ramp from `c1` to`c2`,
+Generates `n`>2 colors in a linearly interpolated ramp from `start` to`stop`,
 inclusive, returning an `Array` of colors.
 """
-function linspace(c1::T, c2::T, n::Integer=100) where T<:Colorant
-    return T[weighted_color_mean(w1, c1, c2) for w1 in linspace(1.0,0.0,n)]
+function range(start::T; stop::T, length::Integer=100) where T<:Colorant
+    return T[weighted_color_mean(w1, start, stop) for w1 in range(1.0,stop=0.0,length=length)]
 end
 
+import Base: linspace
+Base.@deprecate linspace(start::Colorant, stop::Colorant, n::Integer=100) range(start, stop=stop, length=n)
+
 #Double quadratic Bezier curve
 function Bezier(t::T, p0::T, p2::T, q0::T, q1::T, q2::T) where T<:Real
     B(t,a,b,c)=a*(1.0-t)^2.0+2.0b*(1.0-t)*t+c*t^2.0

test/algorithms.jl

@@ -1,15 +1,15 @@
-using Base.Test, Colors
+using Test, Colors
 
-@test isconcrete(eltype(colormap("Grays")))
+@test isconcretetype(eltype(colormap("Grays")))
 
 @test_throws ArgumentError colormap("Grays", N=10)
 
 col = distinguishable_colors(10)
-@test isconcrete(eltype(col))
+@test isconcretetype(eltype(col))
 mindiff = Inf
 for i = 1:10
     for j = i+1:10
-        mindiff = min(mindiff, colordiff(col[i], col[j]))
+        global mindiff = min(mindiff, colordiff(col[i], col[j]))
     end
 end
 @test mindiff > 8