A data visualization ecosystem for Julia
Content from the webinar slides for easier browsing.
There are many options to create plots in Julia. Some of the most popular ones are:
matplotlib.pyplot,This webinar focuses on Makie.jl.
Makie consists of a core package (Makie), with the plots functionalities.
In addition to this, a backend is needed to render plots into images or vector graphics. Three backends are available:
CairoMakie: vector graphics or high-quality 2D plots. Creates, but does not display plots (you need an IDE that does or you can use ElectronDisplay.jl),GLMakie: based on OpenGL; 3D rendering and interactivity in GLFW window (no vector graphics),WGLMakie: web version of GLMakie (plots rendered in a browser instead of a window).Here are some links and resources useful to get started with the Makie ecosystem:
for 2D plotting:
for 3D plotting:
CairoMakie and WGLMakie should install without issues. Installing GLMakie however can be challenging. This page may lead you towards a solution.
A number of extensions have been built on top of Makie:
Load the package (here, we are using CairoMakie):
using CairoMakie # no need to import Makie itselfPrecompiling packages... 1939.0 ms ✓ QuartoNotebookWorkerLaTeXStringsExt (serial) 1 dependency successfully precompiled in 2 seconds Precompiling packages... 5409.2 ms ✓ QuartoNotebookWorkerMakieExt (serial) 1 dependency successfully precompiled in 5 seconds Precompiling packages... 5281.2 ms ✓ QuartoNotebookWorkerCairoMakieExt (serial) 1 dependency successfully precompiled in 6 seconds
Create a Figure (container object):
fig = Figure()
typeof(fig)FigureYou can customize a Figure:
fig2 = Figure(backgroundcolor=:grey22, size=(300, 300))
Makie uses the Colors.jl package as a dependency. You can find a list of all named colours here.
To use CSS specification (e.g. hex), you need to install Colors explicitly and use its color parsing capabilities:
using Colors
fig3 = Figure(backgroundcolor=colorant"#adc2eb")
Then, you can create an Axis:
ax = Axis(Figure()[1, 1])Makie.Axis with 0 plots:typeof(ax)AxisAxis(fig3[1, 1]) # fig3[1, 1] sets the subplot layout: fig[row, col]
fig3
Axis(fig[2, 3]) # This is what happens if we change the layout
fig
Axis(fig3[2, 3]) # We can add another axis on fig3
fig3
Axes are customizable:
fig4 = Figure()
Axis(fig4[1, 1],
xlabel="x label",
ylabel="y label",
title="Title of the plot")
fig4
Finally, you can add a plot:
fig = Figure()
ax = Axis(fig[1, 1])
x = LinRange(-10, 10, 20)
y = x
scatter!(ax, x, y) # Functions with ! transform their arguments
fig
Of course, there are many plotting functions, e.g. scatterlines!:
fig = Figure()
ax = Axis(fig[1, 1])
x = LinRange(-10, 10, 20)
y = x
scatterlines!(ax, x, y) # Functions with ! transform their arguments
fig
We can also use lines!:
fig = Figure()
ax = Axis(fig[1, 1])
x = LinRange(-10, 10, 20)
y = sin.(x) # The . means that the function is broadcast to each element of x
lines!(ax, x, y)
fig
Let’s add points to get a smoother line:
fig = Figure()
ax = Axis(fig[1, 1])
x = LinRange(-10, 10, 1000)
y = sin.(x) # The . means that the function is broadcast to each element of x
lines!(ax, x, y)
fig
Now, you don’t have to create the Figure, Axis, and plot one at a time. You can create them at the same time with, for instance lines:
x = LinRange(-10, 10, 1000)
y = sin.(x)
lines(x, y) # Note the use of lines instead of lines!
Or even more simply:
x = LinRange(-10, 10, 1000)
lines(x, sin)
This is a lot simpler, but it is important to understand the concepts of the Figure and Axis objects as you will need it to customize them:
x = LinRange(-10, 10, 1000)
y = cos.(x)
lines(x, y;
figure=(; backgroundcolor=:green),
axis=(; title="Cosinus function", xlabel="x label", ylabel="y label"))
When you create the Figure, Axis, and plot at the same time, you create a FigureAxisPlot object:
x = LinRange(-10, 10, 1000)
y = cos.(x)
obj = lines(x, y;
figure=(; backgroundcolor=:green),
axis=(; title="Cosinus function",
xlabel="x label",
ylabel="y label"));
typeof(obj)Makie.FigureAxisPlotNote the ; in the figure and axis value. This is because these are one-element NamedTuples.
The mutating functions (with !) can be used to add plots to an existing figure, but first, you need to decompose the FigureAxisPlot object:
fig, ax, plot = lines(x, sin)
lines!(ax, x, cos) # Remember that we are transforming the Axis object
fig # Now we can plot the transformed Figure
Or we can add several plots on different Axis in the same Figure:
fig, ax1, plot = lines(x, sin)
ax2 = Axis(fig[1, 2])
lines!(ax2, x, cos)
fig
using CairoMakie
using StatsBase, LinearAlgebra
using Interpolations, OnlineStats
using Distributions
CairoMakie.activate!(type = "png")
function eq_hist(matrix; nbins = 256 * 256)
h_eq = fit(Histogram, vec(matrix), nbins = nbins)
h_eq = normalize(h_eq, mode = :density)
cdf = cumsum(h_eq.weights)
cdf = cdf / cdf[end]
edg = h_eq.edges[1]
interp_linear = LinearInterpolation(edg, [cdf..., cdf[end]])
out = reshape(interp_linear(vec(matrix)), size(matrix))
return out
end
function getcounts!(h, fn; n = 100)
for _ in 1:n
vals = eigvals(fn())
x0 = real.(vals)
y0 = imag.(vals)
fit!(h, zip(x0,y0))
end
end
m(;a=10rand()-5, b=10rand()-5) = [0 0 0 a; -1 -1 1 0; b 0 0 0; -1 -1 -1 -1]
h = HeatMap(range(-3.5,3.5,length=1200), range(-3.5,3.5, length=1200))
getcounts!(h, m; n=2_000_000)
with_theme(theme_black()) do
fig = Figure(figure_padding=0,size=(600,600))
ax = Axis(fig[1,1]; aspect = DataAspect())
heatmap!(ax,-3.5..3.5, -3.5..3.5, eq_hist(h.counts); colormap = :bone_1)
hidedecorations!(ax)
hidespines!(ax)
fig
end
using GLMakie, Random
GLMakie.activate!()
Random.seed!(13)
x = -6:0.5:6
y = -6:0.5:6
z = 6exp.( -(x.^2 .+ y' .^ 2)./4)
box = Rect3(Point3f(-0.5), Vec3f(1))
n = 100
g(x) = x^(1/10)
alphas = [g(x) for x in range(0,1,length=n)]
cmap_alpha = resample_cmap(:linear_worb_100_25_c53_n256, n, alpha = alphas)
with_theme(theme_dark()) do
fig, ax, = meshscatter(x, y, z;
marker=box,
markersize = 0.5,
color = vec(z),
colormap = cmap_alpha,
colorrange = (0,6),
axis = (;
type = Axis3,
aspect = :data,
azimuth = 7.3,
elevation = 0.189,
perspectiveness = 0.5),
figure = (;
size=(1200,800)))
meshscatter!(ax, x .+ 7, y, z./2;
markersize = 0.25,
color = vec(z./2),
colormap = cmap_alpha,
colorrange = (0, 6),
ambient = Vec3f(0.85, 0.85, 0.85),
backlight = 1.5f0)
xlims!(-5.5,10)
ylims!(-5.5,5.5)
hidedecorations!(ax; grid = false)
hidespines!(ax)
fig
end
For more examples, have a look at Beautiful Makie.
CairoMakie will run without problem on the Alliance clusters. It is not designed for interactivity, so saving to file is what makes the most sense.
Example:
save("graph.png", fig)Remember however that CairoMakie is 2D only (for now).
GLMakie relies on GLFW to create windows with OpenGL. GLFW doesn’t support creating contexts without an associated window. The dependency GLFW.jl will thus not install in the clusters—even with X11 forwarding—unless you use VDI nodes, VNC, or Virtual GL.
You can setup a server with JSServe.jl as per the documentation. However, this method is intended for the creation of interactive widgets, e.g. for a website. While this is really cool, it isn’t optimized for performance. There might also be a way to create an SSH tunnel to your local browser, although there is no documentation on this.
The best solution probably is to save your plot to a file.
Conclusion about the Makie ecosystem on production clusters: