Plots.jl
Contents
Plots.jl¶
Install¶
Plots.jl is not installed by default when Julia is installed. To ensure it’s installed, either run
using Pkg; Pkg.add("Plots")
from the Julia REPL or enter the package manager with ] and then run
add Plots
Basic Example¶
The easiest way to learn Plots.jl is with illustrative examples.
using Plots
plot([0, 1, 2, 3])
Changing the plot style¶
There are many options for changing the plot style. You have ultimate control over the entire look and feel.
The primary way to change the look of the plot is by specifying attributes. A full list of plot attribues is available here.
plot([0, 1, 2, 3], xlabel="Some Numbers", grid=false)
Adding multiple lines¶
t = 0:0.2:5
plot(t, t, color=:blue, label="linear", xlab="t")
plot!(t, t .^ 2, color="black", label="quadratic")
plot!(t, t .^ 3, color="red", label="cubic")
Themes¶
There are many built in plot themes. A full list is available here.
theme(:dark)
t = 0:0.2:5
plot(t, t, color=:blue, label="linear", xlab="t")
plot!(t, t .^ 2, color="black", label="quadratic")
plot!(t, t .^ 3, color="red", label="cubic")
You can add attributes to the theme function and those will become the default settings for all plots that follow.
theme(:dark, grid=:false)
t = 0:0.2:5
plot(t, t, color=:blue, label="linear", xlab="t")
plot!(t, t .^ 2, color="black", label="quadratic")
plot!(t, t .^ 3, color="red", label="cubic")
Subplots / layouts¶
using CSV
using Tables
data = CSV.File("data/200wells.csv", header=false, skipto=2) |> Tables.matrix
permeability = data[:, 6]
porosity = data[:, 5];
theme(:default, label=nothing)
p1 = scatter(porosity, permeability, xlabel="porosity", ylabel="permeability")
p2 = histogram(porosity, title="Porosity")
p3 = histogram(permeability, title="Permeability")
l = @layout [
a{0.6w} [b{0.5h}
c{0.5h} ]
]
plot(p1, p2, p3, layout=l)
Contour plots¶
The following is an example of a filled contour plot in Plots.jl using the command. This figure shows the depth of a petroleum reservoir.
Contour plots must have data that is defined on a rectangular grid in the \((x, y)\) plane. In the example below, the file nechelik.npy has already been organized in this way. Scattered data must be interpolated onto a rectangular grid. Any data that has the format of a floating point NaN will be shown as white space in the contour plot.
using NPZ
data = npzread("data/nechelik.npy")
X = data[1, :, :]
Y = data[2, :, :]
Z = data[3, :, :];
contourf(X[1, :], Y[:, 1], Z, linewidth=0, grid=false)
Surface plots¶
The following example is a surface plot; however, the default backend GR does not interpolate the surface well. It’s better to use a different backend.
plot(X, Y, Z, st=:surface, display_option=Plots.GR.OPTION_SHADED_MESH)
Plotly backend¶
Not only does the Plotly backend interpolate the surface better, it also allows for interactive plots. More information and a full list of backends is available here.
plotlyjs()
plot(X, Y, Z, st=:surface)
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