more deprecation fixes

src/Plots.jl

@@ -6,7 +6,7 @@ using Reexport
 
 import StaticArrays
 using StaticArrays.FixedSizeArrays
-using Dates, Printf, Statistics, Base64
+using Dates, Printf, Statistics, Base64, LinearAlgebra
 
 @reexport using RecipesBase
 import RecipesBase: plot, plot!, animate

src/backends/gr.jl

@@ -317,7 +317,7 @@ function normalize_zvals(zv::AVec, clims::NTuple{2, <:Real})
     if vmin == vmax
         zeros(length(zv))
     else
-        clamp.((zv - vmin) ./ (vmax - vmin), 0, 1)
+        clamp.((zv .- vmin) ./ (vmax .- vmin), 0, 1)
     end
 end
 

src/examples.jl

@@ -52,7 +52,7 @@ the `z` argument to turn on series gradients.
     [:(begin
 y = rand(100)
 plot(0:10:100,rand(11,4),lab="lines",w=3,palette=:grays,fill=0, α=0.6)
-scatter!(y, zcolor=abs.(y-.5), m=(:heat,0.8,stroke(1,:green)), ms=10*abs.(y-0.5)+4,
+scatter!(y, zcolor=abs.(y.-0.5), m=(:heat,0.8,stroke(1,:green)), ms=10*abs.(y.-0.5).+4,
          lab="grad")
     end)]
 ),
@@ -69,7 +69,7 @@ the preprocessing step. You can also use shorthand functions: `title!`, `xaxis!`
 y = rand(20,3)
 plot(y, xaxis=("XLABEL",(-5,30),0:2:20,:flip), background_color = RGB(0.2,0.2,0.2),
      leg=false)
-hline!(mean(y,1)+rand(1,3), line=(4,:dash,0.6,[:lightgreen :green :darkgreen]))
+hline!(mean(y, dims = 1)+rand(1,3), line=(4,:dash,0.6,[:lightgreen :green :darkgreen]))
 vline!([5,10])
 title!("TITLE")
 yaxis!("YLABEL", :log10)
@@ -145,7 +145,7 @@ styles = filter(s -> s in Plots.supported_styles(),
                 [:solid, :dash, :dot, :dashdot, :dashdotdot])
 styles = reshape(styles, 1, length(styles)) # Julia 0.6 unfortunately gives an error when transposing symbol vectors
 n = length(styles)
-y = cumsum(randn(20,n),1)
+y = cumsum(randn(20,n), dims = 1)
 plot(y, line = (5, styles), label = map(string,styles), legendtitle = "linestyle")
              end)]
 ),
@@ -202,7 +202,7 @@ plot(Plots.fakedata(100,10), layout=4, palette=[:grays :blues :heat :lightrainbo
 PlotExample("",
     "",
     [:(begin
-        srand(111)
+        Random.srand(111)
         plot!(Plots.fakedata(100,10))
     end)]
 ),
@@ -215,7 +215,7 @@ subsequently create a :path series with the appropriate line segments.
 """,
     [:(begin
 n=20
-hgt=rand(n)+1
+hgt=rand(n).+1
 bot=randn(n)
 openpct=rand(n)
 closepct=rand(n)
@@ -254,7 +254,7 @@ verts = [(-1.0,1.0),(-1.28,0.6),(-0.2,-1.4),(0.2,-1.4),(1.28,0.6),(1.0,1.0),
          (-1.0,1.0),(-0.2,-0.6),(0.0,-0.2),(-0.4,0.6),(1.28,0.6),(0.2,-1.4),
          (-0.2,-1.4),(0.6,0.2),(-0.2,0.2),(0.0,-0.2),(0.2,0.2),(-0.2,-0.6)]
 x = 0.1:0.2:0.9
-y = 0.7rand(5)+0.15
+y = 0.7rand(5).+0.15
 plot(x, y, line = (3,:dash,:lightblue), marker = (Shape(verts),30,RGBA(0,0,0,0.2)),
      bg=:pink, fg=:darkblue, xlim = (0,1), ylim=(0,1), leg=false)
     end)]
@@ -439,7 +439,7 @@ each line segment or marker in the plot.
 # make and display one plot
 function test_examples(pkgname::Symbol, idx::Int; debug = false, disp = true)
   Plots._debugMode.on = debug
-  info("Testing plot: $pkgname:$idx:$(_examples[idx].header)")
+  @info("Testing plot: $pkgname:$idx:$(_examples[idx].header)")
   backend(pkgname)
   backend()
   map(eval, _examples[idx].exprs)

src/pipeline.jl

@@ -11,11 +11,11 @@ function _expand_seriestype_array(d::KW, args)
     sts = get(d, :seriestype, :path)
     if typeof(sts) <: AbstractArray
         delete!(d, :seriestype)
-        rd = Vector{RecipeData}(size(sts, 1))
+        rd = Vector{RecipeData}(undef, size(sts, 1))
         for r in 1:size(sts, 1)
             dc = copy(d)
             dc[:seriestype] = sts[r:r,:]
-            rd[i] = RecipeData(dc, args)
+            rd[r] = RecipeData(dc, args)
         end
         rd
     else
@@ -153,7 +153,7 @@ function _add_smooth_kw(kw_list::Vector{KW}, kw::KW)
         x, y = kw[:x], kw[:y]
         β, α = convert(Matrix{Float64}, [x ones(length(x))]) \ convert(Vector{Float64}, y)
         sx = [ignorenan_minimum(x), ignorenan_maximum(x)]
-        sy = β * sx + α
+        sy = β .* sx .+ α
         push!(kw_list, merge(copy(kw), KW(
             :seriestype => :path,
             :x => sx,

src/plot.jl

@@ -14,7 +14,7 @@ function current()
     if isplotnull()
         error("No current plot/subplot")
     end
-    get(CURRENT_PLOT.nullableplot)
+    CURRENT_PLOT.nullableplot
 end
 current(plot::AbstractPlot) = (CURRENT_PLOT.nullableplot = plot)
 
@@ -65,7 +65,7 @@ function plot(plt1::Plot, plts_tail::Plot...; kw...)
 
     # build our plot vector from the args
     n = length(plts_tail) + 1
-    plts = Array{Plot}(n)
+    plts = Array{Plot}(undef, n)
     plts[1] = plt1
     for (i,plt) in enumerate(plts_tail)
         plts[i+1] = plt

src/recipes.jl

@@ -586,7 +586,7 @@ function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::S
     _iqr(v) = (q = quantile(v, 0.75) - quantile(v, 0.25); q > 0 ? q : oftype(q, 1))
     _span(v) = ignorenan_maximum(v) - ignorenan_minimum(v)
 
-    n_samples = length(linearindices(first(vs)))
+    n_samples = length(LinearIndices(first(vs)))
 
     # The nd estimator is the key to most automatic binning methods, and is modified for twodimensional histograms to include correlation
     nd = n_samples^(1/(2+N))

src/utils.jl

@@ -382,7 +382,7 @@ end
 function convert_to_polar(x, y, r_extrema = calc_r_extrema(x, y))
     rmin, rmax = r_extrema
     theta, r = filter_radial_data(x, y, r_extrema)
-    r = (r - rmin) / (rmax - rmin)
+    r = (r .- rmin) ./ (rmax .- rmin)
     x = r.*cos.(theta)
     y = r.*sin.(theta)
     x, y