Tag Archives: Julia

Drawing Vector Graphics with Julia can be Awesome

By: DSB

Re-posted from: https://medium.com/coffee-in-a-klein-bottle/drawing-vector-graphics-with-julia-can-be-awesome-298169609032?source=rss-8bd6ec95ab58------2

A quick tutorial on Luxor.jl, the go-to package for static vector graphics

Image from Luxor’s documentation.

We are all very used to plotting packages, yet, from time to time, we want to create visualizations that require more freedom. When that happens, it might be better to go straight to vector graphics. This is an area where people tend to be less experienced, and it’s not always clear what package to use.

Vector Graphics Packages

When it comes to vector graphics, you’ve probably heard the name Cairo, since it is perhaps on of the most famous 2D graphics libraries, and it’s commonly used by plotting packages as a backend. Thus, a first option for drawing your vector graphics is using Cairo.jl, which is Julia’s api for the Cairo library. Yet, it might not be a very pleasant experience, as Cairo can be fairly complicated for newcomers.

Here is where Luxor.jl comes in.

Luxor is a Julia package for drawing simple static vector graphics. It provides basic drawing functions and utilities for working with shapes, polygons, clipping masks, PNG and SVG images, turtle graphics, and simple animations.

The focus of Luxor is on simplicity and ease of use: it should be easier to use than plain Cairo.jl, with shorter names, fewer underscores, default contexts, and simplified functions. — Luxor’s documentation.

I could actually stop here and tell you to go read the docs, cause this package is insanely well documented. But since you are already here, I might as well take some time to quickly spill out some of the basics.

Quick Introduction to Luxor.jl

Perhaps the first thing to note about Luxor is that it’s procedural and static, which means that you code a series of drawing commands until you say it’s finished, which will in turn produce either a PNG, SVG, PDF or EPS based on what you defined at the start.

If you are used to dynamic programming (which you probably are, since you are using Julia), then this static way of drawing with Luxor will be a bit strange at first. Yet, there is a flip side. Luxor is fast! What do I mean? Just try it and you’ll see that not only the precompilation time is minimal, but every redrawing is also quite fast (compared specially to plotting packages such as VegaLite.jl and Plotly.jl).

Let’s do our first drawing in Luxor.

using Luxor
Drawing(500, 500, "my-drawing.svg")
origin()
setcolor("red")
circle(Point(0, 0), 100, :fill)
finish()

The code above encapsulates very well the workflow in Luxor. The very first line is just importing the package. In the second line, we create a drawing with size of 500 by 500 pixels, and once we finish the drawing, Luxor will create a file called “my-drawing.svg” in the folder we are working at. All good, let’s proceed to the third line of the code.

You might think that the orgin() line is “starting” the drawing, but what it’s actually doing in altering the origin of the coordinate system to the center of the image… What do I mean? Well, in Luxor the default origin is in the top left of the figure, and the y-axis actually points down. Thus, the origin() function is responsible to altering the location of origin coordinate. And if we call this function without arguments, it places the origin at the very center of the drawing (you might be a bit confused, unless you’ve already used other vector drawing packages, in which case, this convention might actually make sense to you).

Moving on, the next line is setcolor("red") and it sets the current color to red. The important thing to note here is that this is similar to choosing the current color of the drawing pen. Hence, every drawing that we do after calling setcolor("red") will be red, until we change the color of the pen.

Let’s finally draw something in our canvas. And the command circle(Point(0,0),100,:fill) does exactly that. This command is drawing a circle with center at point (0,0) , which is the center of our figure, since we used the command origin() . The next argument sets the radius, which in our case is 100 pixels. Finally, the :fill argument indicates that we must paint the inside of the circle. Again, this is all very common if you have worked with vector drawings before.

We conclude our drawing with the finish() command, which then saves the drawing file. Now, if you want to see the drawing right after finishing it, you can just use the function preview() , which will show the most current finished drawing.

Another Simple Example

Let’s change a bit of our first drawing.

Drawing(200, 200, "my-drawing.png")
background(0.9, 0.2, 0.2, .5)
setcolor("blue")
setline(10)
setdash("dash")
circle(Point(0, 0), 100, :stroke)
finish()

This is very similar to our initial drawing, but we’ve added some new functions and removed the origin() . Note that now our circle is actually centered in the top left corner of our figure, which can now be seen because of the background() function. These numbers inside the background function is just the color specification in hsla.

Another difference is that we used :stroke instead of :fill in the circle function. Thus, instead of filling the circle, it just draws the border. The setline() function alters the thickness of the stroke, and the setdash() changes the line style. There are many more variations in line styles, which you can check in the function docstring.

Luxor for the Mathematically Inclined

All I showed up until now is very basic, and perhaps not very useful, so perhaps this next example will grab your interest, that is, if you have a more mathematical inclination.

Let’s use Luxor to draw some diagrams, which occur in a discipline called Category Theory 🥸.

using MathTeXEngine
Drawing(200, 200, "my-drawing.png")
# Drawing the borders of our drawing. Note that the `O` is a
# shortcut to Point(0,0).
rect(O,200,200,:stroke)
# Let's change the origin to the center of the drawing
origin()
# We now draw the our diagram
setline(10)
arrow(Point(-40,0),Point(40,0))
# Let's define the font size and write some text
fontsize(15)
text(L"f",Point(0,20), halign = :center)
circle(Point(-50,0),5,:fill)
text(L"ℕ",Point(-50,20),halign = :center)
circle(Point(50,0),5,:fill)
text(L"ℚ",Point(50,20),halign = :center)
#### From here, we are drawing the looping arrows ####
loopx = 30
loopy = 40
adjx = 6
adjy = -2
arrow(
Point(-50,0) + Point(-adjx,adjy),
Point(-50,0) + Point(-loopx,-loopy),
Point(-50,0)+ Point(loopx,-loopy),
Point(-50,0)+Point(adjx,adjy)
)
arrow(
Point(50,0) + Point(-adjx,adjy),
Point(50,0) + Point(-loopx,-loopy),
Point(50,0) + Point(loopx,-loopy),
Point(50,0) + Point(adjx,adjy)
)
finish()

Pretty neat, no? You might be a bit overwhelmed by the code above, but just read it through and you will see it’s quite straightforward. Here are some comments on things that might be obscure.

First, you might have noticed that we can write LaTeX in Luxor (I know, it’s awesome)! Just writeL”\sum^n_{i=10}”, i.e. place “L” before the string. The MathTeX is a package that is required in order to render the LaTeX string without actually needing to use a LaTeX version installed in your computer.

The final part is the code is more complicated, but the only thing going on there is that one can pass control points in order to draw curves. Hence, instead of passing only the start and finish of the arrow, I’m actually passing four points in order to make the arrow loop, and I’m also altering the start and finish position so that they do not touch the circle. The loopx and the other defined variables are just some auxiliary variables that I used in order to figure out the control points to give me a nice looking curve.

Final Words and a Hot Tip

There are many other things I could say about Luxor, since the package is quite extensive. Fortunately, as I’ve already said, the docs are very good. Not only it has many examples, but it also has many explanations about the many functionalities.

Yet, the fact that the docs are so comprehensive also makes a bit harder to find some specific things one might be looking for. Hence, here are some hot tips that might come in handy.

If you are working in a notebook environment such as Jupyter, you might wish to store the drawings in variables, instead of saving it to files right away. Also, you might wish to do small drawings, and then place them inside another drawing… Is this possible? YES! Here is how to do both.

d1 = Drawing(200,200,:svg)
origin()
circle(O+Point(30,0),10,:fill)
finish()
d2 = Drawing(200,200,:svg)
origin()
setcolor("blue")
circle(O,10,:fill)
finish()
d = Drawing(200,200,:svg)
placeimage(d1)
placeimage(d2)
finish()

I recommend always working with svg if your drawing is not too heavy, since you’ll always have crystal clear quality. With the code above, you can just run d in a cell in order to visualize the drawing stored in the variable.

Finally, you might wish to see the actual svg specification. Here is a possible solution.

dsvg = String(copy(d.bufferdata))

This will give you the svg string, which you can just save into a mydrawing.svg file.

That’s all. Now go read the docs!


Drawing Vector Graphics with Julia can be Awesome was originally published in Coffee in a Klein Bottle on Medium, where people are continuing the conversation by highlighting and responding to this story.

Composing Modeling and Simulation with Julia (2021 Modelica Conference)

By: Christopher Rackauckas

Re-posted from: http://www.stochasticlifestyle.com/composing-modeling-and-simulation-with-julia-2021-modelica-conference/

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN). The foundation of this environment, ModelingToolkit.jl, is an acausal modeling language which can compose the trained surrogates as components within its staged compilation process. As a complementary factor we present the JuliaSim model library, a standard library with differential-algebraic equations and pre-trained surrogates, which can be composed using the modeling system for design, optimization, and control. We demonstrate the effectiveness of the surrogate-accelerated modeling and simulation approach on HVAC dynamics by showing that the CTESN surrogates accurately capture the dynamics of a HVAC cycle at less than 4\% error while accelerating its simulation by 340x. We illustrate the use of surrogate acceleration in the design process via global optimization of simulation parameters using the embedded surrogate, yielding a speedup of two orders of magnitude to find the optimum. We showcase the surrogate deployed in a co-simulation loop, as a drop-in replacement for one of the coupled FMUs, allowing engineers to effectively explore the design space of a coupled system. Together this demonstrates a workflow for automating the integration of machine learning techniques into traditional modeling and simulation processes.

Author(s): Chris Rackauckas, Ranjan Anantharaman, Alan Edelman, Shashi Gowda, Maja Gwozdz, Anand Jain, Chris Laughman, Yingbo Ma, Francesco Martinuzzi, Avik Pal, Utkarsh Rajput, Elliot Saba, Viral Shah

© 2021 Chris Rackauckas, Ranjan Anantharaman, Alan Edelman, Shashi Gowda, Maja Gwozdz, Anand Jain, Chris Laughman, Yingbo Ma, Francesco Martinuzzi, Avik Pal, Utkarsh Rajput, Elliot Saba, Viral Shah

The post Composing Modeling and Simulation with Julia (2021 Modelica Conference) appeared first on Stochastic Lifestyle.