By: DSB

Re-posted from: https://medium.com/coffee-in-a-klein-bottle/visualizing-data-with-julia-using-makie-7685d7850f06?source=rss-8bd6ec95ab58------2

Plotting in Julia with Makie

A Brief Tutorial on Makie.jl

When starting learning Julia, one might get lost in the many different packages available to do data visualization. Right out of the cuff, there is Plots, Gadfly, VegaLite … and there is Makie.

Makie is fairly new (~2018), yet, it’s very versatile, actively developed and quickly growing in number of users. This article is a quick introduction to Makie, yet, by the end of it, you will be able to do a plethora of different plots.

The Future of Plotting in Julia

When I started coding in Julia, Makie was not one of the contenders for “best” plotting libraries. As time passed, I started to here more and more about it around the community. For some reason, people were saying that:

“Makie is the future” — People in the Julia Community

I never fully understood why that was the case, and every time I tried to learn it, I’d be turned off by the verbose syntax, and, frankly, ugly examples. It was only when I bumped into Beautiful Makie that I decided to put aside my prejudices and get on with the times.

Hence, if you are starting to code in Julia, and is wondering which plotting package you should invest your time to learn, I say to you that Makie is the way to go, since I guess “Makie is the future”.

Starting with Makie… Pick your backend

The versatility in Makie can make it a bit unwelcoming for those that “just want to do a damn scatter plot”. First of all, there is Makie.jl, CairoMakie.jl, GLMakie.jl WGLMakie.jl 😰. Which one should you use?

Well, here is the deal. Makie.jl is the main plotting package, but you have to choose a backend to which you will display your plots. The choice depends on your objectives. So yes, besides Makie.jl, you will need to install one of the backends. Here is a small description to help you chose:

• CairoMakie.jl: It’s the easiest to use of all three, and it’s the ideal choice if you just want to produce static plots (not interactive);
• GLMakie.jl: Uses OpenGL to display the plots, hence, you need to have OpenGL installed. Once you do a plot and run the display(myplot) , it’ll open an interactive window with your plot. If you want to do interactive 3D plots, then this is the backend for you;
• WGLMakie.jl: It’s the hardest one to work with. Still, if you want to create interactive visualizations in the web, this is your choice.

In this tutorial, we’ll use CairoMakie.jl.

Your first plot

After picking our backend, we can now start plotting! I’ll go out on a limb and say that Makie is very similar to Matplotlib. It does not work with any fancy “Grammar of Graphics” (but if you like this sort of stuff, take a look at the AlgebraOfGraphics.jl, which implements an “Algebra of Graphics” on Makie).

Thus, there are a bunch of ready to use functions for some of the most common plots.

using CairoMakie #Yeah, no need to import Makie
scatter(rand(10,2))

Easy breezy… Yet, if you are plotting this in a Jupyter Notebook, you might be slightly ticked off by two things. First, the image is just too large. And second, it’s kind of low quality. What is going on?

By default, CairoMakie uses raster format for images, and the default size is a bit large. If you are like me and prefer your plots to be in svg and a bit smaller, then no worries! Just do the following:

using CairoMakie
CairoMakie.activate!(type = "svg")
scatter(rand(10,2),figure=(;resolution=(300,300)))

In the code above, the CairoMakie.activate!() is a command that tells Makie which backend you are using. You can import more than one backend at a time, and switch between them using this activation commands. Also, the CairoMakie backend has the option to do svg plots (to my knowledge, this is not possible for the other backends). Hence, with this small line of code, all our plots will now be displayed in high quality.

Next, we defined a “resolution” to our figure. In my opinion, this is a bit of an unfortunate name, because the resolution is actually the size of our image. Yet, as we’ll see further on, the attribute resolution actually belongs to our figure, and not to the actual scatter plot. For this reason we pass the whole figure = (; resolution=(300,300)) (if you are new to Julia, the ; is just a way of separating attributes that have names, from unnamed ones, i.e. args and kwags).

Congrats! You now know the bare minimum of Makie to do a whole bunch of different plots! Just go to the Makie’s website and see how to use all the different ready-to-use plotting functions! In order to be self contained, here is a small cheat sheet from the great book Julia Data Science.

Of course, we still haven’t talked about a bunch of important things, like titles, subplots, legends, axes limits, etc. Just keep on reading…

Figure, Axis and Plot

Commands like scatter produce a “FigureAxisPlot” object, which contains a figure, a set of axes and the actual plot. Each of these objects has different attributes and are fundamental in order to customize your visualization. By doing:

fig, ax, plt = scatter(rand(10,2))

We save each of these objects in a different variable, and can more easily modify them. In this example, the function scatter is actually creating all three objects, and not only the plot. We could instead create each of these objects individually. Here is how we do it:

fig = Figure(resolution=(600, 400)) 
ax = Axis(fig[1, 1], xlabel = "x label", ylabel = "y label",
    title = "Title")
lines!(ax, 1:0.1:10, x->sin(x))

Let’s explain the code above. First, we created the empty figure and stored it in fig . Next, we created an “Axis”. But, we need to tell to which figure this object belongs, and this is where the fig[1,1] comes in. But, what is this “[1,1]”?

Every figure in Makie comes with a grid layout underneath, which enable us to easily create subplots in the same figure. Hence, the fig[1,1] means “Axis belongs to fig row 1 and column 1”. Since our figure only has one element, then our axis will occupy the whole thing. Still confused? Don’t worry, once we do subplots you’ll understand why this is so useful.

The rest of the arguments in “Axis” are easy to understand. We are just defining the names in each axis and then the title.

Finally, we add the plot using lines! . The exclamation is a standard in Julia that means that a function is actually modifying an object. In our case, the lines!(ax, 1:0.1:10, x->sin(x)) is appending a line plot to the ax axis.

It’s clear now how we can, for example, add more line plots. By running the same lines! , this will append more plots to our ax axis. In this case, let’s also add a legend to our plot.

fig = Figure(resolution=(600, 400)) 
ax = Axis(fig[1, 1], xlabel = "x label", ylabel = "y label",
    title = "Title")
lines!(ax, 1:0.1:10, x->sin(x), label="sin")
stairs!(ax, 1:0.1:10, x->cos(x), label="cos", color=:black)
axislegend(ax)

#*Tip*: if you are using Jupyter and want to display your
#       visualization, you can do display(fig) or just write fig in
#       the end of the cell.

Ok, our plots are starting to look good. Let me end this section talking about subplots. As I said, this is where the whole “fig[1,1]” comes into play. If instead of doing two plots in the same axis we wanted to create two parallel plots in the same figure, here is how we would do this.

fig = Figure(resolution=(600, 300)) 
ax1 = Axis(fig[1, 1], xlabel = "x label", ylabel = "y label",
    title = "Title1")
ax2 = Axis(fig[1, 2], xlabel = "x label", ylabel = "y label",
    title = "Title2")

lines!(ax1, 1:0.1:10, x->sin(x), label="sin")
stairs!(ax1, 1:0.1:10, x->cos(x), label="cos", color=:black)
density!(ax2, randn(100))

axislegend(ax)
save("figure.png", fig)

This time, in the same figure, we created two axis, but the first one is in the first row and first column, while the second one is in the second column. We then just append the plot to the respective axis. Lastly, we save the figure in “png” format.

Final Words

That’s it for this tutorial. Of course, there is much more the talk about, as we have only scratched the surface. Makie has some awesome capabilities in terms of animations, and much more attributes/objects to play with in order to create truly astonishing visualizations. If you want to learn more, take a look at Makie’s documentation, it’s very nice. And also, the Julia Data Science book has a chapter only on Makie.

References

This article draws heavily on the Julia Data Science book and Makie’s own documentation.

Storopoli, Huijzer and Alonso (2021). Julia Data Science. https://juliadatascience.io. ISBN: 9798489859165.

Danisch & Krumbiegel, (2021). Makie.jl: Flexible high-performance data visualization for Julia. Journal of Open Source Software, 6(65), 3349, https://doi.org/10.21105/joss.03349

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