Initialize

using Plots
unicodeplots()
Plots.UnicodePlotsBackend()

Lines

A simple line plot of the columns.

plot(Plots.fakedata(50, 5), w = 3)
            ┌────────────────────────────────────────┐
    8.53978 ⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡆⠀⠀⠀⠀⠀ y1
            ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢆⡀⢠⡄⠀⠀⠀⠀⠀⠀⠀⡜⢸⠀⠀⠀⠀⠀ y2
            ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠈⠉⠃⡇⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀ y3
            ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡴⡀⢠⠃⠀⠀⠀⠀ y4
            ⡇⠀⢸⠲⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠃⠀⠀⠀⠈⠹⣀⠤⠑⠃⠀⠀⠀⠀⡠⠃ y5
            ⡇⠀⡎⠓⠃⠓⠢⡀⠀⠀⡞⡄⠀⠀⠀⠀⠀⢀⠇⠀⠀⠀⠀⠀⠀⡾⡀⠀⠀⣀⣀⡠⠛⡄⠀⠀
            ⢠⡃⢣⡀⠀⠀⢀⣸⠀⠀⠀⠀⠀⢀⢀⠇⢣⣿⢴⣁⠀⠀
            ⢷⣺⢲⣳⢚⢶⣻⠛⣞⡾⡶⣶⡗⠳⢺⡷⣷⡻
            ⣠⠃⡜⣾⡹⡍⡾⡧⡉⡕⠙⠋⠀⠀⠀⠀⠁⠘⠢
            ⡇⠀⠀⠀⠀⠀⠀⠈⠺⠀⠀⠀⡇⢀⠤⢴⠀⠀⠸⠃⢣⠈⠁⠀⠀⠀⠀⠀⠀⢀⣄⡇⠀⠀⠀⠀⠀
            ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠎⢠⣠⠃⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⢠⢣⡎⠀⠀⠀⠀⠀⠀⠀
            ⡇⠀⠀⠀⢸⡀⣠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢇⠀⠀⠀⠀⡇⠈⠀⠀⠀⠀⠀⠀⠀⠀
            ⡇⠀⠀⠀⠀⠈⠈⡆⠀⠀⠀⠀⠀⡀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢄⡰⠼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⡇⠀⠀⠀⠀⠀⠀⠸⢄⣀⢄⢠⣀⠏⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -8.90197 ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠎⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘-0.47⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀51.47

Parametric plots

Plot function pair (x(u), y(u)).

plot(sin, (x->begin
            sin(2x)
        end), 0, 2π, line = 4, leg = false, fill = (0, :orange))
            ┌────────────────────────────────────────┐
    1.05997 ⠀⠀⠀⠀⢠⠴⠒⠢⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠔⠒⠢⣀⠀⠀⠀⠀
            ⠀⠀⠀⡜⠁⠀⠀⠀⠀⠀⠑⢄⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀⠀⠈⢢⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⡠⠚⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢦⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⡔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⡄⠀⠀⠀⡇⠀⠀⢠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢤⠀⠀⡇⠀⡔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡀⣇⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⡵⣯⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⡏⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠞⠀⠀⡇⠀⠱⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠃⠀⠀⠀⡇⠀⠀⠑⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀⠀⡇⠀⠀⠀⠀⠱⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠋⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠘⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠣⡀⠀⠀⠀⠀⢀⠴⠃⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠑⢄⠀⠀⠀⠀⠀⢀⡜⠁⠀⠀
   -1.05997 ⠀⠀⠀⠀⠉⠢⠤⠔⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠒⠢⠤⠖⠃⠀⠀⠀⠀
            └────────────────────────────────────────┘-1.05997⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.05997

Colors

Access predefined palettes (or build your own with the colorscheme method). Line/marker colors are auto-generated from the plot's palette, unless overridden. Set the z argument to turn on series gradients.

y = rand(100)
plot(0:10:100, rand(11, 4), lab = "lines", w = 3, palette = cgrad(:grays), fill = 0, α = 0.6)
scatter!(y, zcolor = abs.(y .- 0.5), m = (:heat, 0.8, Plots.stroke(1, :green)), ms = 10 * abs.(y .- 0.5) .+ 4, lab = "grad")
              ┌────────────────────────────────────────┐
        1.029 ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ lines
              ⠀⡇⠀⠀⢀⡀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⡰⠁⠉⠉⠉⢣⚬⚬ lines
              ⠀⡇⠀⡼⣇⠀⠀⚬⚬⠒⠦⠤⣀⣀⠀⠀⠀⠀⠀⠀⡸⠑⢄⠀⠀⠀⡰⠁⠀⠀⠀⠈⢆ lines
              ⡇⢿⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⢠⡆⠀⠀⠀⠀⠑⢤⡰⠁⠀⠀⠀⠀⠀ lines
              ⠀⡇⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⚬⚬⡸⢣⠀⠀⠀⣸⡃⠀⠀⠀⠀⠀⠀⠀⠀ grad
              ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⢆⡇⠸⡀⠀⠀⠀⠀⠀⠀⠀
              ⠀⡇⠀⠀⠀⡔⠊⠀⠀⢰⠁⚬⚬⠸⣆⠀⠀⠀⠀⠀⢀⠇⢠⠃
              ⠀⡇⢀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⡇⡄⢀⠉⡆⠀⠀⢰⠃⠀⠀⡠⠤⡜⢣⢀⠇⠀⠀
              ⠀⡇⢀⠎⢀⠇⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀
              ⡗⚬⠀⠀⠀⠀⠀⡼⣼⠘⡄⢰⠀⠀⠀⠀⠀
              ⠀⠀⠈⢆⚬⢲⠥⢄⡀⠀⠀⚬⚬⠈⣶⠁⠀⠀
              ⠀⠀⠀⠀⠀⠈⢆⠇⠀⠀⢀⢼⠃⣘⡔⢤⠃⠀⠀⠀⠀⢣⡎⠸⡀
              ⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠺⣎⠔⠁⠘⣄⠀⠀⠀⠀⠀⠀⠀⠀
   -0.0299709 ⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⚬⚬⠤⠤⠤⠤
              └────────────────────────────────────────┘-3⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀103

Arguments

Plot multiple series with different numbers of points. Mix arguments that apply to all series (marker/markersize) with arguments unique to each series (colors). Special arguments line, marker, and fill will automatically figure out what arguments to set (for example, we are setting the linestyle, linewidth, and color arguments with line.) Note that we pass a matrix of colors, and this applies the colors to each series.

ys = Vector[rand(10), rand(20)]
plot(ys, color = [:black :orange], line = (:dot, 4), marker = ([:hex :d], 12, 0.8, Plots.stroke(3, :gray)))
             ┌────────────────────────────────────────┐
    0.985934 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⢇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⡆⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⡀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡸⢣⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠃⢱⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⡠⠢⡀⢀⠇⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀⠀⠀⠈⠢⡀
             ⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⡎⢸⠀⠀⠈⠉⠉⡆⠀⠀⢰⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⡇⢸⠀⠀⠀⠀⠀⠀⠀⡸⢱⢀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⢠⠃⢰⠁⠘⡄⠀⠀⢠⠃⡇⠘⡄⠀⠀⠀⠀⠀⠀⡇⠈⡆⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠈⡆⢰⠁⢸⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⢰⠁⠀⠀⠀⠸⣀⡀⡆⢸⠀⠀⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⢰⠁⠀⠀⠀⠀⠀⠀⠈⠁⢇⡇⠀⠀⠀⠀⠀⠀⠀⠘⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⢣⡇⠀⠀⠀⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.0230138 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             └────────────────────────────────────────┘0.43⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20.57

Build plot in pieces

Start with a base plot...

plot(rand(100) / 3, reg = true, fill = (0, :green))
               ┌────────────────────────────────────────┐
      0.343164 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢀⡀⠀⠀⠀⠀⣇⢸⡀⠀⠀⠀⠀⠀⠀⢠⣿⢠⡇⠀⠀⠀⠀
               ⠀⠀⠀⠀⠀⠀⠀⢰⣿⢸⡇⠀⠀⠀⠀⡿⣾⡇⢠⢸⡇⠀⠀⢸⣿⢸⢳⢀⢀⠀⠀
               ⠀⠀⠀⢸⡆⢸⠀⠀⠀⠀⢸⣿⢸⡇⠀⠀⠀⠀⡇⣿⡇⢸⣿⡇⢰⣿⢻⢸⢸⢸⢸⠀⠀
               ⠀⠀⢸⢸⢿⢸⠀⠀⠀⠀⣿⣿⢸⡇⠀⠀⢀⠇⣿⡇⣿⡄⢸⣿⣇⢸⣿⢸⢸⢸⣿⢸⠀⠀
               ⡀⢸⣿⢸⣼⠀⠀⠀⠀⣿⡇⣿⡇⢀⣿⢰⢸⣿⡇⣠⢻⡇⢸⣿⣿⢸⣿⢸⢸⢸⣿⣸⠀⠀
               ⣿⢀⡇⢸⡿⢸⣿⠀⠀⣿⡇⣿⡇⢸⣿⢸⡜⣿⢱⣿⢸⡇⢸⣿⣿⣀⣸⣿⣸⡸⢼⣿⡧⡧
               ⢸⢸⡇⢸⡇⢸⣿⣆⣀⣇⣀⣿⣇⣿⡧⢼⠤⣿⡞⡗⠒⡟⢺⡏⢹⣿⢹⢹⣿⢸⢸⣿⢸⡇⠸⣿⡇⡇
               ⢺⢺⡏⢹⡏⢹⡟⢇⣿⡀⣿⡇⣿⡇⢸⡏⡇⡇⠀⠀⠸⡇⢸⢻⢸⢸⣿⣸⢸⣿⢸⡇⢸⡇⡇
               ⢸⠀⣿⢧⢸⡇⢸⡇⢸⡏⡇⣿⣿⡇⢸⡄⡇⠃⠃⠀⠀⠀⠀⠸⢸⢸⢸⣿⡟⣼⣿⠘⡇⢸⡇⢸
               ⢸⠀⠙⡇⢸⡇⢸⡇⡇⡏⣿⢱⡜⣷⠁⠀⠀⠀⠀⠀⠀⠀⢸⢸⢸⣿⡇⣿⡇⢸⡇⢸
               ⢸⠀⠀⠀⠀⠀⡇⢸⣾⠁⢸⡇⣿⢸⡇⣿⠀⠀⠀⠀⠀⠀⠀⠀⠘⡇⢸⡟⡇⣿⡇
               ⢸⠀⠀⠀⠀⠀⠁⢸⣿⢸⠇⣿⢸⠇⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢸⠃⠇⣿⡇⠀⠀⠀
               ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⡟⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠿⠃⠀⠀⠀⠀⠀
   -0.00999507 ⢼⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠧⠬⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤
               └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97

and add to it later.

scatter!(rand(100), markersize = 6, c = :orange)
              ┌────────────────────────────────────────┐
     0.990056 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
              ⢸⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬
              ⢸⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀
              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀
              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⢀⢰⣄⢠⠀⠀⠀⢰⣿⠀⠀⠀⠀⡟⣾⡇⣼⡇⚬⚬⢿⢸⢳⢠⢠⠀⠀
              ⚬⚬⣿⡏⣾⡇⣿⢰⠁⣿⢇⣤⢻⣇⣸⣿⣀⣸⣿⣸⣸⣸⣿⣼⣀
              ⠚⣾⣗⢺⡗⢺⡟⢳⡷⡎⠏⣿⡏⢹⡉⡟⚬⚬⠙⠏⢹⢻⢸⣿⡼⡇⠈⢻⡇⢇
              ⢸⠀⠉⠃⠈⢸⣴⣿⢸⡎⠀⠀⠀⠀⠀⠀⠀⠘⡎⢸⡟⡇⣿⡇⠸⠃⢸
   -0.0288366 ⢼⠤⠤⠤⠤⠤⠤⠤⠤⠤⠯⠼⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤
              └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97

Histogram2D

histogram2d(randn(10000), randn(10000), nbins = 20)
      ┌────────────────────────────────────────┐
    4                                         
                                              
                                              
                                              
                        ░░░░░░░░░             
                     ░░░░▒▒▒▒▒▒▒░░░░          
                    ░░▒▒▒▓▓▓▓▓▓▓▓░▒░          
                   ░░░▒▒▒▓▓██▓▓▓▓▒▒░░░        
                    ░░░▒▒▓▓▓▓▓▓▓▒░░░░         
                    ░░░░▒▒▒▓▒▒▒▒▒░░░          
                      ░ ░░░░░░░░░░            
                          ░  ░                
                                              
                                              
   -4                                         
      └────────────────────────────────────────┘
       -5                                     4

Line types

linetypes = [:path :steppre :steppost :sticks :scatter]
n = length(linetypes)
x = Vector[sort(rand(20)) for i = 1:n]
y = rand(20, n)
plot(x, y, line = (linetypes, 3), lab = map(string, linetypes), ms = 15)
              ┌────────────────────────────────────────┐
      1.02922 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⡆⠀⠀⠀⠀⡤⢤⠀⠀⠀⠀⠀⠀⠀ path
              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⢾⠀⠀⠀⠀⠀⠀⠀⡇⠸⠤⢤⣀⡀⠀⠀⠀ steppre
              ⡏⠉⠉⠉⠉⠉⠉⡇⠀⠀⠀⠀⠀⠀⠀⠀⡎⢸⠀⠀⠀⠉⢹⠀⠀⠀⠀⠀ steppost
              ⢸⠀⠀⠀⠀⠀⢠⠤⠀⠀⠀⠀⠀⣀⡇⠀⠀⢸⢸ sticks
              ⢸⠀⣀⣸⠀⠀⠀⠀⢰⠁⠙⣿⡇⢸⢸ scatter
              ⢸⠀⠀⠀⠀⠀⣿⡇⢸⢀⢸⢸
              ⢸⠀⠉⢹⠀⠀⣿⡇⢸⢸
              ⢸⠀⢸⠉⣿⣇⢰⠁⢸⢸⢸⢸
              ⢸⠀⢸⣀⠀⠀⣿⡇⣿⡇⡇⡸⢸⢸
              ⢸⠀⠀⠀⠀⣀⣀⣀⣿⣇⡏⠁⡇⡇⢸⣸
              ⠤⠤⠔⠀⠀⠀⣿⡇⠀⠀⣷⠁⢸⣿
              ⡇⢸⠑⠤⡠⣀⡀⠀⠀⠀⡇⢸⠀⠀⠀⠀⢸⣿
              ⡇⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢸⠀⠀⠀⠀⠀⠀⠀
              ⢸⠀⡇⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⡇⢸⠀⠀⠀⠀⠀⢸⣿⠀⠀
   -0.0299772 ⢼⠤⠧⠼⠤⠤⠭⠭⠥⠤⠤⠤⠤⠼⠼⠧⠼⠤⠤⠤⠤⠤⠿⠼⠿⠿
              └────────────────────────────────────────┘-0.0178475⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.02931

Line styles

styles = filter((s->begin
                s in Plots.supported_styles()
            end), [:solid, :dash, :dot, :dashdot, :dashdotdot])
styles = reshape(styles, 1, length(styles))
n = length(styles)
y = cumsum(randn(20, n), dims = 1)
plot(y, line = (5, styles), label = map(string, styles), legendtitle = "linestyle")
             ┌────────────────────────────────────────┐
     7.72668 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠄ solid
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠢⢄⠔⠊⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⢇⠀⠀⠀⠀⠀⠀⠀⠀⢰⠊⠉⠉⠑⠒⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠈⠢⢄⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠉⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⢠⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⡎⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⢀⠇⠀⠀⠀⠀⠱⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -0.347978 ⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒
             └────────────────────────────────────────┘0.43⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20.57

Marker types

markers = filter((m->begin
                m in Plots.supported_markers()
            end), Plots._shape_keys)
markers = permutedims(markers)
n = length(markers)
x = (range(0, stop = 10, length = n + 2))[2:end - 1]
y = repeat(reshape(reverse(x), 1, :), n, 1)
scatter(x, y, m = markers, markersize = 8, lab = map(string, markers), bg = :linen, xlim = (0, 10), ylim = (0, 10))
      ┌────────────────────────────────────────┐
   10 ⠀⠀ circle
      ⠀⠀ rect
      ⠀⠀ star5
      ⠀⠀ diamond
      ⠀⠀ hexagon
      ⠀⠀ cross
      ⠀⠀ xcross
      ⠀⠀ utriangle
      ⠀⠀ dtriangle
      ⠀⠀ rtriangle
      ⠀⠀ ltriangle
      ⠀⠀ pentagon
      ⠀⠀ star4
      ⠀⠀+++++++++++++++++++ star6
    0 ⠀⠀ star8
      └────────────────────────────────────────┘0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10

Bar

x is the midpoint of the bar. (todo: allow passing of edges instead of midpoints)

bar(randn(99))
            ┌────────────────────────────────────────┐
    2.67374 ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⡇⣿⡇⠀⠀⠀⠀⠀⠀⢸⣿⠀⠀⠀⠀⠀⠀
            ⠀⢸⠀⠀⠀⠀⡇⣿⢸⡇⣿⡇⠀⠀⚬⚬⢸⣿⣿⢠⠀⠀⠀
            ⠀⢸⠀⠀⡇⣿⢸⣿⡇⣿⚬⚬⚬⚬⚬⚬⚬⡇⡇⣿⢸⣿⣿⡇⣿⣿⠀⠀⠀
            ⠤⢼⢿⣤⡿⣿⣼⣿⣤⣤⣼⢿⣤⣧⣿⣼⡿⣧⡿⣾⣿⣿⣼⣧⣧⣿⣼⢿⣿⣧⣿⣿⣤⠤⠤
            ⠀⢸⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⢸⣿⣿⚬⚬⠀⠀
            ⠀⢸⠀⣿⡇⢸⣿⣿⣿⣿⚬⚬⢸⡇⠀⠀⣿⢸⣿⡇⠀⠀⢸⣿⣿⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⡇⠀⠀⚬⚬⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹⡇⠀⠀⢸⡇⣿⠀⠀⠀⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -2.65754 ⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘-5.50584⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀105.506

Histogram

histogram(randn(1000), bins = :scott, weights = repeat(1:5, outer = 200))
          ┌────────────────────────────────────────┐
   632.42 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠒⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠤⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠤⠀⠀⠀⠀⠀⠀⠤⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -18.42 ⠤⠤⠾⠭⠧⠤⠼⠤⠤⠧⠤⠼⠤⠤⠧⠤⠤⡧⠤⠼⠤⠤⠧⠤⠼⠤⠤⠧⠤⠼⠤⠶⠤⠤
          └────────────────────────────────────────┘-3.9326⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀3.9326

Adding to subplots

Note here the automatic grid layout, as well as the order in which new series are added to the plots.

plot(Plots.fakedata(100, 10), layout = 4, palette = cgrad.([:grays :blues :heat :lightrainbow]), bg_inside = [:orange :pink :darkblue :black])
            ┌────────────────────────────────────────┐                ┌────────────────────────────────────────┐
     5.4665 ⢰⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1     4.68156 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢱⣴⠀⠀⠀⠀⢰⣴⣦ y5             ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y6
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣷⡇⠈⡇⣠⢠⡸⠇⢹ y9             ⢸⠀⣠⡤⠲⡀⡰⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y10
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⡇⠀⠀⠧⠏⠃⣇⠇                ⡏⠁⠁⢣⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⢻⠀⠀⠀⢠⠃
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⢠⡇⢰⣼⣇⠀⠀⠀⠀⠀⠀⠀⡎⡖⡜⠙⢸⡇⠀⠀⠀⢸⠑⠀⠀⠀⠀                ⠀⠀⠀⢣⡶⠀⠀⢰⡆⢠⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⣄⡄⠀⠀
            ⣀⣀⡄⠀⠀⣸⣷⢸⡿⠀⠀⣀⣿⣿⡇⢱⡀⣠⠛⠀⠀⠀⠀                ⠤⠤⠤⠤⠤⡧⢵⡤⢴⣯⡾⣮⢷⠤⠤⠤⠤⠤⠤⢤⢴⡤⠤⠤⡤⢼⢼⣷⠿⡼⢧⢤⢼⠤⠤
            ⢹⡿⢏⣏⣹⡏⢹⢫⠉⠉⠉⣿⣍⠏⡿⠉⠉⠉⠉⠉⠉                ⠀⠀⣦⣄⠇⠛⡿⡿⣿⣿⣿⡸⣷⣷⣿⠀⠀⠀⢸⢿⡇⢠⢣⡸⢸⠟
            ⠈⣾⢾⡝⠘⠇⠉⠀⠀⢰⠁⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀                ⡟⡇⡛⠀⠀⠀⡿⠟⠸⡀⠀⠀⢱⡇⣼⠈⠀⠀⠘⡇⣧⣿
            ⢸⠀⠘⢇⢿⣿⠀⠀⠀⣾⣾⢹⢦⠇⣤⡆⠀⠀⠀⠀⠀⠀⠀⠀                ⠟⡇⠀⠀⠀⣸⠁⠀⠀⠀⠁⡇⠀⠀⠀⢸⠃⣷
            ⢸⠀⢸⡏⠀⠀⠀⢸⢸⠀⠀⠀⠀⡟⢱⣄⡜⠀⠀⠀⠀⠘⢄⢀⡀                ⢸⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠈⡆⣿⢿⠀⠀
            ⢸⠀⠘⡄⠘⠁⠘⡄⢀⡸⠈⡆⡇⠀⠀⠀⠀⠀⡜⠸⠃⠈⡆⣧⠀⠀⠀⠈⠎                ⢸⠀⠸⡜⠀⠀⢰⢹⠀⠀⠀⠀⠀⠀⡞⡄⡄⢨⡇
            ⢸⠀⠀⢣⢠⠃⠀⠀⠀⠀⠀⣷⣴⠙⠇⢣⠃⠀⠀⠀⠀⠀⡘⡿⠀⠀⠀⠀⠈⢹⢠⡇⠀⠀                ⢸⠀⠀⠀⠀⠀⢧⠃⠀⠀⠀⠀⠀⠀⠘⡄⡸⡁⣴⠁⡇⠁⡇⣿⡎⡇⡶⠁
            ⢸⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⣿⠀⠀⠀⠀⠀⠀⣾⣧⣠                ⢸⠀⠀⠀⠀⠀⠀⢳⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣀⠇⠀⠀⠀⢸⣇⣷⠁⠃⠓⠁⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⣷⠹⠁⠙⡼⠁                ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⣆⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -7.77204 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀       -7.65193 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠘⠋⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘                └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97⠀                ⠀-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97┌────────────────────────────────────────┐                ┌────────────────────────────────────────┐
    11.0341 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y3     7.72935 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y4
            ⢸⠀⠀⠀⠀⠀⠀⠀⣄⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y7             ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y8
            ⢸⠀⠀⠀⠀⠀⠀⢀⠏⠸⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡏⣿⢳⢸⠉⣿⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⢠⠛⠀⠀⠻⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠈⢸⢿⡞⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠁⢷⢰⡀⣀⣤⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⠀⣆⢀⠀⠀⠀⠀⡠⣴⠁⠀⠀⠀⠀⠀⡎⣷⠉⠻⡠⡄⣿⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠈⡞⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⡆                ⢸⠀⠀⠀⠀⠀⠀⠀⡟⠚⣦⠻⣄⠀⠀⠀⡇⢿⠀⠀⠀⠀⠀⠀⠈⠱⠇⢻⠀⠀⠃⢣⡿⣸⡀
            ⢸⠀⠀⡿⡀⠀⠀⠀⠀⠀⠀⠸⠦⠞⢴⣿⡜⣄⡆⢰⣸⣤⡄⠀⠀⢸⡆⠀⠀⠀⠀⠀⠀⢰⡜⠣                ⠀⠀⠀⠀⠀⠸⡀⢀⢿⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⠁⠉⡿
            ⢸⠀⠀⡇⡇⠀⠀⢸⡆⡀⢸⣦⡆⢀⣸⠋⢱⡎⠁⠙⡇⢰⠏⠀⠀⡀⡿⡀⡎⠃⠀⠀                ⣸⣷⣀⣀⣀⣀⣳⣠⣀⣀⣀⣀⣀⣀⣀⣀⣸⣀⣀⣀⣀⣀⣀⣀⣰⣠⣆⣸
            ⢸⠀⣰⢳⠀⠀⡜⠘⢻⠋⠙⠳⡞⠹⠈⡆⠀⠀⠘⣄⣟⡁⢧⢠⠃                ⡿⣳⠀⠀⠀⡖⣿⣮⡾⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡀⡎⢿⠸⡇
            ⣾⠯⡿⡯⡤⡧⠤⠤⠤⠤⠤⠤⠤⠤⢵⣤⣦⢤⣷⠼⡤⡧⡧⢼⡧                ⢸⠀⠀⠹⣋⣷⡟⡇⡇⣾⡇⠈⠁⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⢣⡇⠀⠀⠀⠀
            ⠀⠀⢧⠃⡇⣷⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠁⠈⡾⠈⡇⣷⠁⠸⢾⠀⠀⢸⠁                ⢸⠀⠀⠀⠀⠀⡇⢸⢧⠟⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⢠⠃⠘⠇⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠳⠃⠸⠁⠀⠀⠀⢀⡎⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⡇⢸⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⣾⠀⠀⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⡎⠃⠀⠀                ⢸⠀⠀⠀⠀⠀⠀⠙⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣄⠇⡏⠀⠀⠀⠀⠀⠀⠀
   -4.61579 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠁⠇⠀⠀⠀       -5.49555 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘                └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97⠀                ⠀-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97
using Random
Random.seed!(111)
plot!(Plots.fakedata(100, 10))
            ┌────────────────────────────────────────┐                 ┌────────────────────────────────────────┐
     5.4665 ⢰⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1      4.68156 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢱⣴⠀⠀⠀⠀⢰⣴⣦ y5              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y6
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣷⡇⠈⡇⣠⢠⡸⠇⢹ y9              ⢸⠀⣠⡤⠲⡀⡰⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y10
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⡇⠀⠀⠧⠏⠃⣇⠇ y11             ⡏⠁⠁⢣⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⢻⠀⠀⠀⢠⠃ y12
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⢠⡇⢰⣼⣇⠀⠀⠀⠀⠀⠀⠀⡎⡖⡜⠙⢸⡇⠀⠀⠀⢸⠑⠀⠀⠀⠀ y15             ⠀⠀⠀⢣⡶⠀⠀⢰⡆⢠⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⣄⡄⠀⠀ y16
            ⣾⡻⣀⣀⡄⠀⠀⣸⣷⢸⡿⠀⠀⣀⣿⣿⣦⡇⢱⡀⣠⠛⠀⠀⠀⠀ y19             ⠤⠤⠤⠤⠤⡧⢵⡤⢴⣯⡾⣮⢷⠤⠤⠤⠤⠤⠤⢤⢴⡤⠤⠤⡤⢼⢼⣷⠿⡼⢧⢤⢼⠤⠤ y20
            ⢹⡿⢏⣏⡍⣹⡏⢹⣻⢹⢫⠉⠉⠉⣿⣍⠏⡿⠉⠉⠉⠉⠉⠉                 ⠀⠀⣦⣄⠇⠛⡿⡿⣿⣿⣿⣸⡸⣷⣷⣿⠀⠀⠀⢸⢿⡇⢠⢣⡸⢸⠟
            ⠈⣾⣷⢁⢾⡝⣷⡏⠘⠇⠉⢿⢠⣿⣯⡟⢷⣧⠀⠀⢰⠁⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀                 ⡟⡇⡛⠀⠀⠀⡿⠟⠸⡀⠀⠀⢱⡇⣼⠈⠀⠀⠘⡇⣧⣿
            ⢸⠀⠘⢇⢸⠞⢿⣿⠀⠀⠀⣾⣾⢹⠹⢦⠇⣤⡆⠀⠀⠀⠀⠀⠀⠀⠀                 ⠟⡇⠀⠀⠀⣸⠁⠀⠀⠀⠁⡇⠀⠀⠀⢸⠃⣷
            ⢸⠀⢸⡏⠀⠀⠀⢸⢸⠀⠀⠀⠀⡟⢱⣄⡜⠀⠀⠀⠀⠘⢄⢀⡀                 ⢸⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠈⡆⣿⢿⠀⠀
            ⢸⠀⠘⡄⠘⠁⠘⡄⢀⡸⠈⡆⡇⠀⠀⠀⠀⠀⡜⠸⠃⠈⡆⣧⠀⠀⠀⠈⠎                 ⢸⠀⠸⡜⠀⠀⢰⢹⠀⠀⠀⠀⠀⠀⡞⡄⡄⢨⡇
            ⢸⠀⠀⢣⢠⠃⠀⠀⠀⠀⠀⣷⣴⠙⠇⢣⠃⠀⠀⠀⠀⠀⡇⡘⡿⠀⠀⠀⠀⠈⢹⢠⡇⠀⠀                 ⢸⠀⠀⠀⠀⠀⢧⠃⠀⠀⠀⠀⠀⠀⠘⡄⡸⡁⣴⠁⡇⠁⡇⣿⡎⡇⡶⠁
            ⢸⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⣿⡇⠀⠀⠀⠀⠀⠀⣾⣧⣠                 ⢸⠀⠀⠀⠀⠀⠀⢳⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣀⠇⠀⠀⠀⢸⣇⣷⠁⠃⠓⠁⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⣷⠹⠁⠙⡼⠁                 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⣆⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -7.77204 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀        -7.65193 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠘⠋⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘                 └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97⠀                 ⠀-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97┌────────────────────────────────────────┐                 ┌────────────────────────────────────────┐
    11.0341 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y3      7.72935 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y4
            ⢸⠀⠀⠀⠀⠀⠀⠀⣄⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y7              ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⡀⣦⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y8
            ⢸⠀⠀⠀⠀⠀⠀⢀⠏⠸⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y13             ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡏⣿⢳⢸⠉⣿⢿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y14
            ⢸⠀⠀⠀⠀⠀⢠⠛⠀⠀⠻⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y17             ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠈⢸⢿⡞⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y18
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠁⢷⢰⡀⣀⣤⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⢸⠀⠀⠀⠀⠀⠀⠀⣆⢀⠀⠀⠀⠀⡠⣴⠁⠀⠀⠀⠀⠀⡎⣷⠉⠻⡠⡄⣿⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠈⡞⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⡆                 ⢸⠀⠀⠀⠀⠀⠀⠀⡟⠚⣦⠻⣄⠀⠀⠀⡇⢿⠀⠀⠀⠀⠀⠀⠈⠱⠇⢻⠀⠀⠃⢣⡿⣸⡀
            ⢸⠀⠀⡿⡀⠀⠀⠀⠀⠀⠀⠸⠦⠞⢴⣿⡜⣄⡆⢰⣸⣤⡄⠀⠀⢸⡆⠀⠀⠀⠀⠀⠀⢰⡜⠣                 ⠀⠀⠀⠀⠀⠸⡀⢀⢿⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⠁⠉⡿
            ⢸⠀⠀⡇⡇⠀⠀⢸⡆⡀⢸⣦⡆⢀⣸⠋⢱⡎⠁⠙⡇⢰⠏⡟⢆⠀⠀⡀⡿⡀⡎⠃⠀⠀                 ⣸⣷⣀⣀⣀⣀⣀⣳⣠⣀⣀⣀⣀⣀⣀⣀⣀⣸⣀⣀⣀⣀⣀⣀⣀⣰⣠⣆⣸
            ⢸⠀⣰⢳⢣⢸⣿⠀⠀⡜⠘⢻⠋⠙⠳⡞⠹⠈⡆⠀⠀⠘⣄⣟⡁⢧⢠⠃                 ⡿⣳⠀⠀⠀⡖⣿⣮⡾⡏⢾⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡀⡎⢿⠸⡇
            ⣾⠯⡿⡯⢿⡤⡧⠤⠤⠤⠤⠤⠤⠤⠤⢵⣤⢷⣦⢤⣷⠼⡤⠬⡾⡧⡧⢼⡧                 ⢸⠀⠀⠹⣋⣷⡟⡇⡇⣾⡇⠈⠁⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⢣⡇⠀⠀⠀⠀
            ⠀⠀⢧⠃⡇⣷⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠁⠈⡾⠈⡇⣷⠛⠁⠸⢾⠀⠀⢸⠁                 ⢸⠀⠀⠀⠀⠀⡇⢸⢧⠟⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⢠⠃⠘⠇⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠳⠃⠸⠁⠀⠀⠀⢀⡎⠀⠀                 ⢸⠀⠀⠀⠀⠀⠀⡇⢸⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⣾⠀⠀⠀⠀⠀⠀⠀
            ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⡎⠃⠀⠀                 ⢸⠀⠀⠀⠀⠀⠀⠙⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣄⠇⡏⠀⠀⠀⠀⠀⠀⠀
   -4.61579 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠁⠇⠀⠀⠀        -5.49555 ⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘                 └────────────────────────────────────────┘-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97⠀                 ⠀-1.97⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀102.97

Open/High/Low/Close

Create an OHLC chart. Pass in a list of (open,high,low,close) tuples as your y argument. This uses recipes to first convert the tuples to OHLC objects, and subsequently create a :path series with the appropriate line segments.

n = 20
hgt = rand(n) .+ 1
bot = randn(n)
openpct = rand(n)
closepct = rand(n)
y = OHLC[(openpct[i] * hgt[i] + bot[i], bot[i] + hgt[i], bot[i], closepct[i] * hgt[i] + bot[i]) for i = 1:n]
ohlc(y)
            ┌────────────────────────────────────────┐
    4.41487 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠐⢺⠄⠀⠀⠀⠀
            ⠀⠀⠀⢸⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠁⢹⡀⢸⠄⠀⠀⢰⠂⢀⠀⠀⠀⠀⠀⠀⠀
            ⢸⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠄⠀⠀⠀⠀
            ⠀⠀⠐⢺⠀⠀⠀⡆⠐⡧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠈⡗
            ⠀⠀⠀⢸⠁⠀⠀⡇⠐⠇⡄⢸⠀⠀⠀⠀⠀⠠⡇⠀⠀⠀
            ⠀⠀⠀⡆⠐⣇⠀⠀⠈⡏⡇⠈⡗⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠉⠉⠉⠉⠉⠉⠉⠉⡏⠭⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉
            ⠀⠀⠀⠀⠀⠀⠠⡗⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -1.00886 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘0.112⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20.888

Annotations

The annotations keyword is used for text annotations in data-coordinates. Pass in a tuple (x, y, text), a vector of annotations, each of which is a tuple of x, y and text. You can position annotations using relative coordinates with the syntax ((px, py), text), where for example px=.25 positions the annotation at 25% of the subplot's axis width. text may be a simple String, or a PlotText object, which can be built with the method text(string, attrs...). This wraps font and color attributes and allows you to set text styling. text may also be a tuple (string, attrs...) of arguments which are passed to Plots.text.

annotate!(ann) is shorthand for plot!(; annotation=ann).

Series annotations are used for annotating individual data points. They require only the annotation; x/y values are computed. Series annotations require either plain Strings or PlotText objects.

y = rand(10)
plot(y, annotations = (3, y[3], Plots.text("this is #3", :left)), leg = false)
annotate!([(5, y[5], ("this is #5", 16, :red, :center)), (10, y[10], ("this is #10", :right, 20, "courier"))])
scatter!(range(2, stop = 8, length = 6), rand(6), marker = (50, 0.2, :orange), series_annotations = ["series", "annotations", "map", "to", "series", Plots.text("data", :green)])
              ┌────────────────────────────────────────┐
     0.992961 ⠀⠀series⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀to⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀annotatiomap⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠣⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠱⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠘⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠘⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀⠀⠀⠣⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠔⠊⠁⠀⠀⠀⠀⠀⠀⠑⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀this is #5⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠸⡀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀this is #10
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⢰⠁
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠈this is #3⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠑⠢⢤⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⡜⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⠁⠀⠀⠀⠀
              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.00340574 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀series⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              └────────────────────────────────────────┘0.73⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10.27

Pie

x = ["Nerds", "Hackers", "Scientists"]
y = [0.4, 0.35, 0.25]
pie(x, y, title = "The Julia Community", l = 0.5)
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀The Julia Community⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ┌────────────────────────────────────────┐
    1.05981 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠤⠤⠔⠒⠒⡗⠒⠲⠤⠤⢄⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ Nerds
            ⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠒⠉⠉⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠈⠑⠒⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀ Hackers
            ⠀⠀⠀⠀⠀⣀⠴⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠢⡀⠀⠀⠀⠀⠀ Scientists
            ⠀⠀⠀⢀⠔⠣⢄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠀
            ⠀⠀⡠⠃⠀⠀⠀⠈⠑⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⡄⠀⠀
            ⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠉⠒⠤⡀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡆
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠑⠢⢄⡀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠚⠓⡗⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⢺
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠇
            ⠀⠀⠸⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⠃⠀⠀
            ⠀⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀
            ⠀⠀⠀⠀⠀⠈⠢⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠁⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠉⠒⠤⢄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⠤⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀
   -1.05999 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠑⠒⠒⠦⠤⠤⡧⠤⠔⠒⠒⠊⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘-1.06⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.06

DataFrames

Plot using DataFrame column symbols.

using StatsPlots
import RDatasets
iris = RDatasets.dataset("datasets", "iris")
@df iris scatter(:SepalLength, :SepalWidth, group = :Species, title = "My awesome plot", xlabel = "Length", ylabel = "Width", marker = (0.5, [:cross :hex :star7], 12), bg = RGB(0.2, 0.2, 0.2))
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀My awesome plot⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ┌────────────────────────────────────────┐
   4.472 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ setosa
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ versicolor
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ virginica
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   Width ⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀
         ⚬⚬⚬⚬⚬⠀⠀⠀⚬⚬⠀⠀⠀⚬⚬⚬⚬⠀⠀⠀⚬⚬⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   1.928 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         └────────────────────────────────────────┘4.192⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Length⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀8.008

Heatmap, categorical axes, and aspect_ratio

xs = [string("x", i) for i = 1:10]
ys = [string("y", i) for i = 1:4]
z = float((1:4) * reshape(1:10, 1, :))
heatmap(xs, ys, z, aspect_ratio = 1)
     ┌───────────┐
   4 ▄▄
     
   0 ▄▄▄▄▄▄▄▄▄▄
     └───────────┘
      0        10

Boxplot and Violin series recipes

using StatsPlots
import RDatasets
singers = RDatasets.dataset("lattice", "singer")
@df singers violin(:VoicePart, :Height, line = 0, fill = (0.2, :blue))
@df singers boxplot!(:VoicePart, :Height, line = (2, :black), fill = (0.3, :orange))
         ┌────────────────────────────────────────┐
   76.48 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠐⣶⡒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
         ⡴⠒⡖⢲⠐⣶⢲⠀⠀⠀⠀⠀⠀⢀⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
         ⡏⠉⠉⢹⡇⢠⢿⢸⠀⠀⠀⡼⡄⠀⠀⢸⢸⣇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
         ⣇⣀⣀⣸⣇⣼⣸⣀⣇⢈⢿⣏⠀⠀⡎⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
         ⠀⠀⢸⣿⣇⣀⣀⣸⢀⣜⣸⣘⣄⢀⣇⣸⣈⣇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⢧⠤⡤⣼⢿⡄⠀⠀⢸⢸⠃⠀⠀⢻⢸⡇⠀⠀⢤⣤⡄⠀⠀⢼⣽⠄⠀⠀⢤⡤⠄⠀⠀⠀⠀⠀⠀
         ⡇⡇⠸⡧⢤⠤⡼⢸⠤⢤⠤⢼⢸⠇⠀⠀⢸⡇⡇⠀⠀⢸⣿⠀⠀⠀⢸⣇⠀⠀⠀⠀⠀⠀⠀
         ⠘⡆⡇⡇⢸⢸⢠⠇⢧⢸⢠⠇⢸⠤⠤⠤⣿⢸⡇⡇⢀⡇⡇⣇⠀⠀⡎⣿⡀⠠⣤⡤⠀⠀
         ⠀⠀⡇⣿⠁⠀⠀⣿⡼⠀⠀⠘⣾⡞⠀⠀⠀⣽⣖⡟⠓⢳⡆⣸⣀⣇⣸⡄⢰⠁⡇⣇⢠⢿⠸⡄
         ⠀⠀⠓⠛⠂⠐⠛⠓⠀⠀⠐⠛⠓⢸⡒⢲⠒⡟⣿⠒⠒⠚⡇⡇⠀⠀⠀⡇⡞⠒⠓⢺⡆⡏⢸
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣇⢸⢰⠁⣷⠀⠀⠀⡇⣿⠉⠉⢹⡇⡇⠀⠀⠈⣿⡏⠉⠉⣹
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠉⠙⣏⣏⣹⠁⣿⣀⣀⣸⡇⡏⠉⠉⢹⣿⢸⢀⣿
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢸⣇⣀⣀⣸⡏⢹⣹⢹⠉
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⡇⡏⡇⡼⠀⠀⣿⢸⠀⠀
   59.52 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠷⠷⠇⠘⠦⠧⠇⠠⠿⠧⠀⠀
         └────────────────────────────────────────┘0.366⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀8.634

Spy

For a matrix mat with unique nonzeros spy(mat) returns a colorless plot. If mat has various different nonzero values, a colorbar is added. The colorbar can be disabled with legend = nothing.

using SparseArrays
a = spdiagm(0 => ones(50), 1 => ones(49), -1 => ones(49), 10 => ones(40), -10 => ones(40))
b = spdiagm(0 => 1:50, 1 => 1:49, -1 => 1:49, 10 => 1:40, -10 => 1:40)
plot(spy(a), spy(b), title = ["Unique nonzeros" "Different nonzeros"])
      ⠀⠀⠀⠀⠀⠀Unique nonzeros⠀⠀⠀⠀⠀⠀           ⠀⠀⠀⠀⠀Different nonzeros⠀⠀⠀⠀
      ┌─────────────────────────┐           ┌─────────────────────────┐
    1 ⢶⣄⠀⠀⠀⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ > 0     1 ⢶⣄⠀⠀⠀⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ > 0
      ⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ < 0       ⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ < 0
      ⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀           ⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
      ⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀           ⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
      ⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀           ⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
      ⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀           ⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀
      ⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀           ⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀
      ⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀           ⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀⠀⠀
      ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀           ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀⠀⠀
      ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀           ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀⠈⠢⡀
      ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀           ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄⠀⠀
      ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄           ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⠀⠀⠙⢷⣄
   50 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⠀⠀⠀⠙⠷        50 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⠀⠀⠀⠙⠷
      └─────────────────────────┘           └─────────────────────────┘1⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀50⠀           ⠀1⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀50

Lines and markers with varying colors

You can use the line_z and marker_z properties to associate a color with each line segment or marker in the plot.

t = range(0, stop = 1, length = 100)
θ = (6π) .* t
x = t .* cos.(θ)
y = t .* sin.(θ)
p1 = plot(x, y, line_z = t, linewidth = 3, legend = false)
p2 = scatter(x, y, marker_z = (+), color = :bluesreds, legend = false)
plot(p1, p2)
             ┌────────────────────────────────────────┐               ┌────────────────────────────────────────┐
    0.799905 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⡠⠤⠤⠒⠒⠒⡗⠢⠤⠤⢄⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀      0.799905 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⡠⠔⠊⠉⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠢⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⢀⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠉⠢⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⡠⠃⠀⠀⠀⠀⠀⠀⠀⡠⠤⠒⠒⠊⠉⡗⠒⠢⢤⡀⠀⠀⠀⠀⠀⠀⠘⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠉⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣠⣊⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣘⣄⣀⣀⣀⣀⣀               ⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀
             ⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⢸⡀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣇⣀⣀⡠⠤⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡔⠁⠀⠀⠀⠀⠀⢀⡎⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠘⢄⠀⠀⠀⠀⠀⠀⠘⠢⣀⡀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⡠⠔⠉⠀⠀⠀⠀⠀⠀⡠⠃⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠈⠢⡀⠀⠀⠀⠀⠀⠀⠀⠈⠓⠢⠤⠤⣧⠤⠤⠤⠤⠒⠊⠉⠀⠀⠀⠀⠀⠀⠀⢀⠔⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠈⠢⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠁⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠑⠒⠤⣀⡀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⢀⡠⠤⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀               ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -0.968191 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠑⠒⠒⠢⡧⠤⠔⠒⠒⠊⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀     -0.968191 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             └────────────────────────────────────────┘               └────────────────────────────────────────┘-0.889625⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.05504⠀               ⠀-0.889625⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.05504

Portfolio Composition maps

see: http://stackoverflow.com/a/37732384/5075246

using Random
Random.seed!(111)
tickers = ["IBM", "Google", "Apple", "Intel"]
N = 10
D = length(tickers)
weights = rand(N, D)
weights ./= sum(weights, dims = 2)
returns = sort!((1:N) + D * randn(N))
portfoliocomposition(weights, returns, labels = permutedims(tickers))
            ┌────────────────────────────────────────┐
    13.8894 ⡗⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⣲⠖⠒⠒⠒⠒⠒⠒⠒⠒⠒⢒⡖⠒⠒⠒⠒⠒⠒⠒⠲⡖⠒⠒⠒⠒⢲ IBM
            ⠀⠀⠀⠀⠀⠀⠀⢀⡠⠞⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ Google
            ⠀⠀⠀⠀⠀⢀⡴⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡴⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀ Apple
            ⠀⠀⠀⣠⠔⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ Intel
            ⣠⠞⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡴⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡆⠀⠀
            ⡇⠈⠉⠓⠲⢤⣀⡀⠀⠀⠀⠀⠀⠀⣠⠔⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⡠⠤⠒⠒⠉⠁⠀⠀
            ⠀⠀⠀⠀⠀⠀⠉⠙⠒⠒⣒⡲⠾⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⢛⡻⠖⠒⠒⠒⣒⡶⠂⠀⠀
            ⠀⠀⠀⠀⣀⣤⣔⠒⠋⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⠤⠚⠁⢀⣀⣠⠔⠊⠁⠀⠀⠀⠀
            ⡇⣠⠤⠤⠤⠤⠤⠬⠭⠭⠭⠭⠵⠶⠶⠶⠶⠆⢴⡒⠊⠹⠷⠶⠾⠭⠭⠭⠤⠤⡀⠀⠀⠀⠀⠀⠀
            ⡇⠈⠑⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠙⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⣆⠀⠀⠀⠀⠀
            ⠒⠒⠒⠒⠒⠚⠒⠲⠶⣒⡒⠒⠒⠒⠒⢒⡲⠞⠒⠒⠒⠒⠒⠒⣒⣒⡶⠶⠶⠒⠚⠒⠒⠒⠒⠒
            ⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -4.27205 ⠀⠀⠀⠀⠠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘-0.03⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀1.03

Histogram2D (complex values)

n = 10000
x = exp.(0.1 * randn(n) .+ randn(n) .* im)
histogram2d(x, nbins = (20, 40), show_empty_bins = true, normed = true, aspect_ratio = 1)
         ┌────────────────────────────────────────┐
       2                                         
                                                 
                                                 
                           ░░░░░░░               
                              ░░░▒▒▒▒░           
                                   ░▓▓▒░         
                                    ░▒█▒░        
   Im(x)                             ▒█▓░        
                                    ░▓█▒░        
                                   ░▓▓▒░         
                               ░░▒▒▒▒░           
                            ░░░░░░░              
                                                 
                                                 
      -2                                         
         └────────────────────────────────────────┘
          -2               Re(x)                 2

Unconnected lines using missing or NaN

Missing values and non-finite values, including NaN, are not plotted. Instead, lines are separated into segments at these values.

(x, y) = ([1, 2, 2, 1, 1], [1, 2, 1, 2, 1])
plot(plot([rand(5); NaN; rand(5); NaN; rand(5)]), plot([1, missing, 2, 3], marker = true), plot([x; NaN; x .+ 2], [y; NaN; y .+ 1], arrow = 2), plot([1, 2 + 3im, Inf, 4im, 3, -Inf * im, 0, 3 + 3im], marker = true), legend = false)
             ┌────────────────────────────────────────┐          ┌────────────────────────────────────────┐
     1.00875 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀     3.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⡜⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀
             ⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠔⠁⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⢀⢷⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡜⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠒⠤⣀⠀⠀⠀⠀⠀⢠⠃          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⡇⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⡇⢸⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⠀⠀⠀⠀⢀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⢱⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⢠⠋⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             ⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣷⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢱⢀⠎⠀⠀⠀⠀          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.0795842 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠎⠀⠀⠀⠀⠀     0.94 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
             └────────────────────────────────────────┘          └────────────────────────────────────────┘0.52⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀17.48⠀          ⠀0.91⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀4.09┌────────────────────────────────────────┐                ┌────────────────────────────────────────┐
   3.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀           4.12 ⡧⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠑⢄⠀⠀⠀⠀⠀⠀⠀⢠⠊⢸                ⠀⡇⠀⠑⠤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡀⠀⠀⠀⢀⠔⠁                ⠀⡇⠀⠀⠀⠈⠒⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⣀⠔⠁⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠉⠢⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⢢⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⢀⠤⠒⠉
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠑⢄⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠒⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠔⠊⠁⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢀⠜⠀⠀⠀⠀⠀⠀⠀⠑⢄⢸                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠢⣀⠀⠀⠀⢀⠎⠀⠀⠀⠀⣀⠤⠒⠁⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠗⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠺          Im(x) ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠤⣀⠎⢀⡠⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⡇⠑⡄⠀⠀⠀⠀⠀⠀⠀⡠⠊⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣋⠶⢎⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠈⠢⡀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⡴⠉⠀⠀⠀⠉⠢⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠈⠢⣀⠔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠊⠁⡔⠁⠀⠀⠀⠀⠀⠀⠀⠑⠤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⡔⠑⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠒⠉⠀⠀⢀⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠒⢄⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⡠⠊⠀⠀⠀⠑⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⠀⠀⠀⠀⡠⠔⠊⠁⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠢⣀⠀⠀⠀⠀⠀
        ⡇⡠⠊⠀⠀⠀⠀⠀⠀⠀⠣⡀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⡇⠀⢀⠤⠒⠉⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠤⡀⠀⠀
   0.94 ⠗⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠺⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀          -0.12 ⡷⠮⠥⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠧⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠬⠶
        └────────────────────────────────────────┘                └────────────────────────────────────────┘0.91⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀4.09⠀                ⠀-0.09⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Re(x)⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀3.09

Lens

A lens lets you easily magnify a region of a plot. x and y coordinates refer to the to be magnified region and the via the inset keyword the subplot index and the bounding box (in relative coordinates) of the inset plot with the magnified plot can be specified. Additional attributes count for the inset plot.

begin
    plot([(0, 0), (0, 0.9), (1, 0.9), (2, 1), (3, 0.9), (80, 0)], legend = :outertopright)
    plot!([(0, 0), (0, 0.9), (2, 0.9), (3, 1), (4, 0.9), (80, 0)])
    plot!([(0, 0), (0, 0.9), (3, 0.9), (4, 1), (5, 0.9), (80, 0)])
    plot!([(0, 0), (0, 0.9), (4, 0.9), (5, 1), (6, 0.9), (80, 0)])
    lens!([1, 6], [0.9, 1.1], inset = (1, bbox(0.5, 0.0, 0.4, 0.4)))
end
          ┌────────────────────────────────────────┐
    1.133 ⣷⠒⢲⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
          ⣼⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
          ⣿⣿⣿⠈⠉⠉⠑⠒⠒⠤⠤⠤⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y3
          ⠳⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠉⠒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y4
          ⠀⠀⠀⠀⠳⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y5
          ⠀⠀⠀⠀⠀⠀⠀⠳⢦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y5
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⢦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠓⢦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠓⠦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠦⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠦⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠢⣄⠀⠀⠀⠀⠀⠀
          ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠲⣄⠀⠀
   -0.033 ⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠽⠶
          └────────────────────────────────────────┘-2.4⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀82.4

Array Types

Plots supports different Array types that follow the AbstractArray interface, like StaticArrays and OffsetArrays.

begin
    using StaticArrays, OffsetArrays
    sv = SVector{10}(rand(10))
    ov = OffsetVector(rand(10), -2)
    plot([sv, ov], label = ["StaticArray" "OffsetArray"])
    plot!(3ov, ribbon = ov, label = "OffsetArray ribbon")
end
              ┌────────────────────────────────────────┐
      4.11004 ⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ StaticArray
              ⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ OffsetArray
              ⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ OffsetArray ribbon
              ⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠑⡄⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠘⡄⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⢢⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠈⢾⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⠱⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⢸⢆⠀⠀⠀⠀⠀⠀⠀⠀⢀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢆⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⢸⠈⢆⠀⠀⠀⠀⠀⠀⠀⡜⠈⠑⠒⠔⠒⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⢸⠀⠈⢆⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠀⠀⠀⠀⢸⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⠑⠢⢄⣸⠀⠀⠀⠘⡄⠀⠀⢰⠁⠀⠀⠀⠀⣀⣀⡀⠀⠀⢀⡠⠤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠄
              ⠀⠀⠀⠀⢸⠒⢄⡀⠘⡄⡔⠒⠤⡴⠥⠬⠭⢖⠀⠀⠀⡲⠶⠖⠲⠒⠒⠤⠔⠊⠀⠀⠀
              ⠀⠀⠀⠀⢸⠀⠀⠘⠢⣄⠘⡼⡠⠊⢀⠎⠀⠀⠀⠀⠀⠀⠣⡀⢠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -0.0809806 ⠤⠤⠤⠤⢼⠤⠤⠤⠤⠵⠮⠤⠤⠤⠧⠤⠤⠤⠤⠤⠤⠤⠤⠼⠴⠥⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤
              └────────────────────────────────────────┘-1.33⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10.33

Setting defaults and font arguments

begin
    using Plots
    default(titlefont = (20, "times"), legendfontsize = 18, guidefont = (18, :darkgreen), tickfont = (12, :orange), guide = "x", framestyle = :zerolines, yminorgrid = true)
    plot([sin, cos], -2π, 2π, label = ["sin(θ)" "cos(θ)"], title = "Trigonometric Functions", xlabel = "θ", linewidth = 2, legend = :outertopleft)
end
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Trigonometric Functions⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ┌────────────────────────────────────────┐
    1.06 ⠲⡀⢀⠖⢦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡔⡧⡀⢠⠖⢦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠔ sin(θ)
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⢀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠏⠀⠀ cos(θ)
         ⠀⠀⠀⠀⠀⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠘⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⢠⠃⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⠀⠀⠀⣷⠁⠸⡀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
       x ⠤⠤⠤⠼⡤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⢤⡧⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⢤⠧⠤⠤⠤
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⠀⠀⢰⠁⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃
         ⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠸⡀⢠⠃⠀⠀⠀⠀⡇⠀⠀⠀⠀⠘⡄⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⢸⡃⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠘⡆⠀⠀⠀⢸⡁⠀⠀⠀⠀⠀⠀
         ⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⢀⠇⠀⠀⢰⠁⠀⠀⡇⠀⠀⠀⠀⠀⠀⠸⡀⢀⠇⠀⠀⢰⠁⠀⠀⠀
   -1.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠢⠎⠀⠀⠳⠴⠃⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠑⠴⠁⠀⠀⠳⠴⠃⠀⠀⠀⠀
         └────────────────────────────────────────┘-6.66018⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀θ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀6.66018

Linked axes

begin
    x = -5:0.1:5
    plot(plot(x, (x->begin
                    x ^ 2
                end)), plot(x, (x->begin
                    sin(x)
                end)), layout = 2, link = :y)
end
            ┌────────────────────────────────────────┐                ┌────────────────────────────────────────┐
      25.78 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1       25.78 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
            ⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡰⠁⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠘⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢢⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⡔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⡄⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠲⣀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⣀⠖⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠑⠢⣀⣀⠀⡇⣀⣀⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                ⠔⠒⠦⠤⠤⣄⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣇⣀⠤⠤⠤⠒⠢⠤⠤⢄⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀
   -1.77992 ⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉       -1.77992 ⠉⠉⠉⠉⠉⠉⠉⠉⠉⠙⠛⠛⠭⠭⠭⠭⠛⠛⠋⠉⡏⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠙⠛⠫⠭⠭⠍
            └────────────────────────────────────────┘                └────────────────────────────────────────┘-5.3⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀5.3⠀                ⠀-5.3⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀5.3

Tuples and Points as data

using GeometryBasics
using Distributions
d = MvNormal([1.0 0.75; 0.75 2.0])
plot([(1, 2), (3, 2), (2, 1), (2, 3)])
scatter!(Point2.(eachcol(rand(d, 1000))), alpha = 0.25)
            ┌────────────────────────────────────────┐
    4.38931 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y1
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⚬⚬⠀⠀⠀⚬⚬⚬⠀⠀⠀⠀⠀⠀ y2
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⚬⚬
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠤⠤⢤⡤⠄
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀
            ⠤⠤⠤⠤⠤⠤⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠤⠤⠤⠤⠤⠤⠤⠄
            ⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⚬⚬⚬⚬⚬⚬⚬⚬⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -5.51885 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘-3.04231⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀3.30099

Vectors of markershapes and segments

using Base.Iterators: cycle, take
yv = ones(9)
ys = [1; 1; NaN; ones(6)]
y = 5 .- [yv 2ys 3yv 4ys]
plt_color_rows = plot(y, seriestype = [:path :path :scatter :scatter], markershape = collect(take(cycle((:utriangle, :rect)), 9)), markersize = 8, color = collect(take(cycle((:red, :black)), 9)))
plt_z_cols = plot(y, markershape = [:utriangle :x :circle :square], markersize = [5 10 10 5], marker_z = [5 4 3 2], line_z = [1 3 3 1], linewidth = [1 10 5 1])
plot(plt_color_rows, plt_z_cols)
        ┌────────────────────────────────────────┐            ┌────────────────────────────────────────┐
   4.09 ⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒ y1    4.09 ⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒⠒ y1
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y2
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y3         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y3
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y4         ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ y4
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠉⠉⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉            ⠉⠉⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.91 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀       0.91 ⠤⠤⠤⠤⠤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤
        └────────────────────────────────────────┘            └────────────────────────────────────────┘0.76⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀9.24⠀            ⠀0.76⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀9.24

Step Types

A comparison of the various step-like seriestypes

x = 1:5
y = [1, 2, 3, 2, 1]
default(shape = :circle)
plot(plot(x, y, markershape = :circle, seriestype = :steppre, label = "steppre"), plot(x, y, markershape = :circle, seriestype = :stepmid, label = "stepmid"), plot(x, y, markershape = :circle, seriestype = :steppost, label = "steppost"), layout = (3, 1))
        ┌────────────────────────────────────────┐
   3.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠒⠒⠒⠒⠒⠒⠒⠒⠒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ steppre
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⡤⠤⠤⠤⠤⠤⠤⠤⠤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠧⠤⠤⠤⠤⠤⠤⠤⠤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.94 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠧⠤⠤⠤⠤⠤⠤⠤⠤
        └────────────────────────────────────────┘0.88⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀5.12┌────────────────────────────────────────┐
   3.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡖⠒⠒⠒⠒⠒⠒⠒⢲⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ stepmid
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⢠⠤⠤⠤⠤⠤⠤⠤⠤⠇⠀⠀⠀⠀⠀⠀⠀⠀⠸⠤⠤⠤⠤⠤⠤⠤⠤⡄⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.94 ⠤⠤⠤⠼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠧⠤⠤⠤
        └────────────────────────────────────────┘0.88⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀5.12┌────────────────────────────────────────┐
   3.06 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠒⠒⠒⠒⠒⠒⠒⠒⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ steppost
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠤⠤⠤⠤⠤⠤⠤⠤⠤⠇⠀⠀⠀⠀⠀⠀⠀⠀⠤⠤⠤⠤⠤⠤⠤⠤⢤
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   0.94 ⠤⠤⠤⠤⠤⠤⠤⠤⠼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
        └────────────────────────────────────────┘0.88⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀5.12

Guide positions and alignment

plot(rand(10, 4), layout = 4, xguide = "x guide", yguide = "y guide", xguidefonthalign = [:left :right :right :left], yguidefontvalign = [:top :bottom :bottom :top], xguideposition = :top, yguideposition = [:right :left :right :left], ymirror = [false true true false], xmirror = [false false true true], legend = false, seriestype = [:bar :scatter :path :stepmid])
              ┌────────────────────────────────────────┐              ┌────────────────────────────────────────┐
     0.941434 ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀     0.993976 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢠⠤⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠉⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⢠⠤⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠉⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⣧⠤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
      y guide ⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⣿⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀      y guide ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⣀⣀⡀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⡇⠀⠀⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⡇⠀⠀⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⡇⠀⠀⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⡇⠀⠀⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              ⡇⠀⠀⠀⠀⠀⢸⡇⠀⠀⡇⡇⠀⠀⠀⠀⢸⡏⠉⣿⠀⠀⢸⢸⠀⠀⢸⡏⠉⣿⠀⠀⠀⠀              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -0.0274204 ⡧⠤⠧⠤⠤⠿⠤⠤⠼⠧⠤⠤⠧⠧⠤⠤⠿⠤⠤⠼⠧⠤⠤⠿⠤⠤⠼⠼⠤⠤⠼⠧⠤⠤⠿⠤⠤⠼⠤⠤      0.14336 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
              └────────────────────────────────────────┘              └────────────────────────────────────────┘-0.00564⠀⠀⠀⠀⠀⠀⠀⠀x guide⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀11.0056⠀              ⠀0.73⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀x guide⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10.27┌────────────────────────────────────────┐                 ┌────────────────────────────────────────┐
    1.01207 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀        0.927328 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠒⠒⠒⢲⠀⠀⠀
            ⠀⠀⠀⠀⠀⡏⠉⠉⠉⢹⠀⠀⠀⠀⠀⠀⠀⡸⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡤⠤⠤⠤⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠇⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀⠀⠀                 ⠀⠀⠀⡖⠒⠒⠒⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠸⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀                 ⠒⠒⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠱⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠒⠒
    y guide ⠀⠀⢀⠇⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⡎⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀         y guide ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠉⠉⠉⢹⠀⠀⠀⠀⡤⠤⠤⠤⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⡄⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠒⠒⠒⠒⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠣⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠉⠉⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠢⡀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀                 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
   -0.01797 ⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠼⠼⠤⠤⠤⠤⠤⠤⠤⠤⠤       0.0337605 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠧⠤⠤⠤⠼⠀⠀⠀⠀⠀⠀⠀
            └────────────────────────────────────────┘                 └────────────────────────────────────────┘0.73⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀x guide⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10.27⠀                 ⠀0.73⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀x guide⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀10.27
  • Supported arguments: annotations, background_color, background_color_subplot, bins, bottom_margin, color_palette, discrete_values, foreground_color, foreground_color_subplot, group, guide, html_output_format, label, layout, left_margin, legend, lims, linealpha, linecolor, linestyle, link, margin, markershape, primary, projection, right_margin, scale, series_annotations, seriesalpha, seriescolor, seriestype, show, show_empty_bins, size, smooth, subplot, subplot_index, title, top_margin, x, xdiscrete_values, xerror, xguide, xlims, xlink, xscale, y, ydiscrete_values, yerror, yguide, ylims, ylink, yscale, z, zdiscrete_values, zerror, zguide, zlims, zlink, zscale
  • Supported values for linetype: :heatmap, :histogram2d, :path, :scatter, :shape, :spy, :straightline
  • Supported values for linestyle: :auto, :solid
  • Supported values for marker: :+, :auto, :circle, :cross, :diamond, :dtriangle, :hexagon, :hline, :ltriangle, :none, :pentagon, :pixel, :rect, :rtriangle, :star4, :star5, :star6, :star8, :utriangle, :vline, :x, :xcross

(Automatically generated: 2021-10-13T15:33:15.994)