Plotting

Plots.jl

Tom Breloff's amazing superplotting package, Plots.jl can use colorschemes.

With the contour() function, use cgrad() to read the colorscheme as a gradient. This renaissance-looking plot uses the leonardo scheme:

using Plots, ColorSchemes

x = 1:0.3:20
y = x
f(x, y) = begin
      sin(x) + cos(y)
  end
contour(x, y, f, fill=true, seriescolor=cgrad(ColorSchemes.leonardo))

(You can use c as a short cut for seriescolor.)

With other plots, use the palette keyword:

plot(Plots.fakedata(100, 20),
    w=4,
    background_color=ColorSchemes.vermeer[1],
    palette=ColorSchemes.vermeer)

Gadfly

Here's how you can use ColorSchemes in Gadfly:

x = repeat(collect(1:20), inner=[20])
y = repeat(collect(1:20), outer=[20])
plot(x=x, y=y,
    color=x+y,
    Geom.rectbin,
    Scale.ContinuousColorScale(p -> get(ColorSchemes.sunset, p)))

Winston

If you prefer Winston.jl for plotting, you can use colorschemes with imagesc:

using Winston
klimt = ColorSchemes.klimt
Winston.colormap(klimt)
Winston.imagesc(reshape(1:10000,100,100))

Sometimes you'll want a smoother gradient with more colors. You can use get(scheme, n) to generate a more detailed array of colors, varying n from 0 to 1 by 0.001:

brasstones = ColorSchemes.brass
brasstonesmooth = [get(brasstones, i) for i in 0:0.001:1]
Winston.colormap(brasstonesmooth)
Winston.imagesc(reshape(1:10000,100,100))

PyPlot

Colorschemes can be used with the cmap keyword in PyPlot:

using PyPlot, Distributions

solar = ColorSchemes.solar

n = 100
x = range(-3, stop=3, length=n)
y = range(-3, stop=3, length=n)

xgrid = repeat(x', n, 1)
ygrid = repeat(y, 1, n)
z = zeros(n, n)

for i in 1:n
    for j in 1:n
        z[i, j] = 2sin(x[i]) * 2cos(y[j])
    end
end

fig = PyPlot.figure("pyplot_surfaceplot",figsize=(10,10))
using3D()
ax = fig[:add_subplot](2, 1, 1, projection = "3d")

ax[:plot_surface](xgrid, ygrid, z, rstride=2,edgecolors="k",
    cstride=2,
    cmap=ColorMap(solar),
    alpha=0.8,
    linewidth=0.25)