Julia is a hip language for computational types; there for it has cut-throat gangs of competing hip graphing technologies.
The julia wiki includes worked examples using various engines.
A curious feature of many julia toolkits is that they produce SVG graphics per default, which easily become gigantic for even medium-large data sets and start flogging your CPU/RAM real hard. This is ok for printing, or for small data. For exploratory data analysis I disable SVG or “interactive” JS stuff in favour of some rasterised format like PNG.
Pkg.add("Gadfly") Pkg.add("Cairo") Pkg.add("Fontconfig")
Now to force PNG output:
draw( PNG(150, 150), plot(data, x=:gamma, y=:p, Geom.point, Geom.line) )
Another weird characteristic is that Gadfly seemed slow on initial startup; This is survivable. But even after startup iut does seem slow on some basic stuff. If I histogram a million data points it takes 30 seconds for each plot of that histogram. It saps the utility of an elegant graph grammar if it’s not responsive to your adjustments. I wonder if this can be improved?
Gadfly is based on a clever declarative vector graphics system called Compose.jl, which might be independently useful.
Plots.jl wraps many other plotting packages as backends, although notably not Gadfly. The author explains this is because Gadfly does not support 3d or interactivity, although since I want neither of those things in general, especially for publication, this is a contraindication for the compatibility of our mutual priorities. I have had enough grief trying to persuade various mathematics department that this whole “internet” thing is anything other than a PDF download engine; I don’t need to compromise my print output for animation, of all the useless fripperies. We tell you, this “multimedia” thing is a fad, and the punters will be going back to quill and vellum soon enough.
Anyway, one can totally shoehorn
Plots into print-quality CMYK plots as well as
web stuff, so disregard my grumping.
The Plots documentation is not fantastic, since it’s notionally simply wrapping some other plot libraries, and should defer to them. Except of course they all have their own terminology and APIs and what-have-you, so the whole system is confusing. You can more-or-less work it out by perusing the attributes documentation, checking specific backend examples, e.g. GR.
Plots has a rich extensions ecosystem. PlotRecipes and StatPlots use the “Recipes” system defined in RecipesBase) to provide a macro(?)-based data-specific plotting tools. For example, StatPlots causes Plots to have sensible DataFrame support.
Table-like data structures, … are supported thanks to the macro @df which allows passing columns as symbols.
using StatPlots using DataFrames, IndexedTables gr(size=(400,300)) df = DataFrame(a = 1:10, b = 10 .* rand(10), c = 10 .* rand(10)) @df df plot(:a, [:b :c], colour = [:red :blue])
Now, some backends.
GR is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs. Applications range from publication quality 2D graphs to the representation of complex 3D scenes. …
GR is essentially based on an implementation of a Graphical Kernel System (GKS) and OpenGL. … The GR framework is especially suitable for real-time environments.
Anyway, here is one important tip: if you aren’t rendering graphs for publication output, but for say exploratory data analysis, switch to PNG from SVG because SVG is very large for images with lots of details.
ENV["GKS_ENCODING"] = "UTF-8" using Plots: gr, default gr() default(fmt=:png)
using LaTeXStrings, Plots plot(rand(20), lab=L"a = \beta = c", title=L"before\;f(\theta)\;and\;after")
If you want to suppress latex rendering, ensure that your label string does not both start and end with
$. I do this
by padding with a trailing space.
If anything is flaky on first execution, you might need to check the GR installation instructions which includes such steps (on Ubuntu) as:
apt install libxt6 libxrender1 libxext6 libgl1-mesa-glx libqt5widgets5
The observant will notice that this requires root access on the machine. I’m sure there must be a workaround but I can’t be arsed discovering it right now.
Also, every time the version of
GR.jl increments there is a loooong
recompilation process, which seems to be single-threaded and takes many minutes
on my fancy 8-core machine. So be aware that it is not fast in every sense.
For all its smoothness and speed when it is up and running, GR Plots are not IMO all that beautiful and it is not clear how to make them beautiful, since beauty is hidden down at the toolkit layer. There is some deep metaphor here.
InspectDR does interaction-focusses plots that lean towards signal processing, simulation and time series analysis. Which is my jam.
Makie is an OpenGL-backed
visualisation library so without investigating further I would presume
it does great on screen-quality 3d possibly at the expense of
print-quality 2d. Haven’t tried it, since learning the
Gadfly.jl APIs has filled up my brain, but the
gallery is pretty.
Edited highlights of its justification for existing:
Makieis a high level plotting interface for
GLVisualize, with a focus on interactivity and speed.
It can also be seen as a prototype for a new design of
Plots.jl, since it will implement a very similar interface and incorporate a lot of the ideas.
A fresh start instead of the already available
Plots.jlwas needed for the following reasons:
Plots.jlwas written to create static plots without any interaction. This is deeply reflected in the internal design and makes it hard to integrate the high performance interaction possibilities from
Plots.jlhas many high level plotting packages as a backend \[and\] there is no straight interface a backend needs to implement, \[…\]- which lead to a lot of duplicated work for the lower level backends and a lot of inconsistent behavior since the code isn’t shared between backends.
- The attributes a plot/series contains and where the default creation happens is opaque and not well documented. Sometimes it’s the task of the backend to create defaults for missing attributes, sometimes
Plots.jlcreates the defaults. A missing attribute is signalled in too many different ways (e.g.
"") which then needs to be checked and filled in by the backend. \[…\]
- …There should be a finite set of "atomic" drawing operations (which can’t be decomposed further) which a backend needs to implement and the rest should be implemented via recipes using those atomic operations.
- Backend loading is done in
Plots.jlvia evaling the backend code. This has … negative consequences:
- Backend code can’t be precompiled leading to longer load times
- Backend dependencies are not in the
That sounds promising for the future, eh?
It’s unclear to me how either of these work with print graphics, and they are both lagging behind the latest version of the underlying Vega library, so I’m wary of them.
Winston.jl has a brutalist simple approach but seems to go.