Here's my version of a design taken from the Jared Tarbell talk that I referenced earlier.
The recipe is simple:
* Fully connect points that are equispaced around the circumference of a circle
* Repeat for circles of various radii
This exercise posed an interesting challenge. Full details in my repository here, but the short version is that I thought I could take a clever approach using Processing's scale transform and—well—it didn't pan out.
So it was pretty obvious from the symmetry of the pattern that I would want to do my math in polar coordinates and then convert to cartesian at the last moment. And rather than recalculating the points around the edge of the circle whenever I change the radius of the circle, I can leave the points' r and theta (and their equivalent x and y) untouched and use Processing's scale() transform to change the underlying basis vectors and hence the points' position on the screen. Neat! No recalculations!
Unfortunately, scale() scales everything. It's not just that all the basis vectors are scaled, the actual screen representation of a pixel is scaled too. So if I call scale(8) all of a sudden any line (say) that used to be one pixel wide is now 8 pixels wide. And although I can downscale the resulting line widths, I cannot do so below the new representation of 1 pixel. For example, the minimum value that I can pass to strokeWeight() is 1. This is 1 transformed pixel, not 1 actual pixel. So when I use a large value in scale(), my lines suddenly start to get thick and they stay thick. Here's a detail from an early iteration of the design:
The solution is not to use scale(): instead simply recalculate the points
I have been tinkering around with the code in Pearson's "Generative Art" and came up with this and the image above.
The author's intention was to show how complex patterns could spontaneously emerge from the interaction of multiple components that individually exhibit simple behavior. In this case, a couple of dozen invisible disks track across the display; whenever the disks intersect they cause a circle to suddenly appear at the point of intersection. So a little like the bubble chamber that I'd used in physics lab at university. You don't observe the particle directly, you observe the evidence of its passage through a medium. (My code is here if you want to play around with it.)
But what the images really remind me of are the belemnites that Cynthia and I had tried (and failed) to find in the fossil beds of Dorset and Yorkshire.
Belemnites are the fossilized remains of little squid-like creatures. Most of the times you find individuals—a small one looks a little like a shark's tooth—but sometimes you find a mass of them in a single slab, a "belemnite battlefield".
How do these battlefields occur? I found a paper by Doyle and MacDonald from 1993 in which the authors identify five pathways: post-spawning mortality, catastrophic mass mortality, predation concentration, stratigraphical condensation, and resedimentation. I don't know which occurred in the slab shown above but hey, I now know more potential pathways than I did before I read that paper.
And that's why art is fun. One moment you are messing around with RGB codes to get just the right sandy color for the background of a picture, the next you are researching natural phenomena that until an hour ago you never knew even had a name.
I had speculated in an earlier post that I could get better (pseudo-)random colors if I pulled from color mixing models like Pantone or perceptual color models like NCS or Munsell. I'd love to, but it turns out that they are all copyright. That's a dead end then.
However my instinct that HSB is probably a better model from which to draw than RGB is supported elsewhere. There's an old post on Martin Ankeri's blog that has an interesting discussion on the issue, and he comes out firmly on the HSB side.
So let's think about how I want to vary hue, saturation, and brightness:
Hue: I don't really care about the underlying color so I will just pull from a uniform distribution between 0 and 1. (By the way, I'm assuming HSB are all on a 0-1 scale).
Saturation: I know I'm going to want some sort of lumpiness but I'm not sure exactly how much and where. Maybe I want a lot of the colors bleached out. Or really saturated. This suggests that I need should pull randomly from a beta distribution and then play around with the distribution's parameters to change where the lump/lumps occur.
Brightness: ditto for brightness. I want it non-uniform but tweakable. Another beta distribution.
I'm now using the python version of Processing instead of Java. It may have some issue around importing third-party libraries but this last week of using the Java version has just reminded me how much I don't like that damn language.
The translation of the Java code to Python was simple; the inclusion of the beta distributions is straightforward too as they are already available in Python's random module.
If you want to play with the code it is in my repository here. If you want to mess around with the paramaters in a beta distributions and see the impact on the shape of the distribution, there's a handly online tool here.
I show a couple of examples below. Personally I think they are an improvement on the earlier examples. (And yes, I've gone full Richter. No gutters here.)
Obviously, the colors are different every time I run the code. But it's the overall feel of how these colors work together that interests me. And this overall feel is—again, as one would expect—pretty sensitive with respect to the parameters you set in the beta-distributions for saturation and brightness.
Here was my first attempt at a simple grid of colored squares. And yes, it is more than a little influenced by Richter's 4900 Colours.
That is odd. I am using the RGB color model—typical in computer graphics—in which each element has 256 levels. I randomly select one of the 256 levels for each of the three dimensions, so I have a space of 2563 possible colors from which to draw. That's a big number (16,777,216 to be precise).
So why do so many of those 25 squares look so similar? Who knew there were so many minty greens?
Maybe it is a function of my color model. Let's try Hue-Saturation-Brightness instead.
I guess it is more visually appealing but I seem to have an awful lot of almost-blacks now. In short: just as bad.
I simplistically thought "OK, so I might be making some false comparisons here. Who says that an HSB model needs to have 256 levels on each of its components? And is a unit difference equally perceptible irrespective of level, i.e. is the color (0, 0, 0) as distinguishable from (0, 0, 1) as the color (255, 255, 255) is from (255, 255, 254)? Perhaps I just need to choose a different number of levels and I'll get 'better' random colors".
Not so much. A quick Google search reveals a huge rabbit hole down which I could disappear. Even simple questions like "How many colors can we perceive?" give answers ranging from 100,000 to 10,000,000. And there are a bunch of perceptual colour models that I had never heard of before. NCS? Munsell? I never got past Goethe. Maybe I should just find a Pantone color list and draw randomly off that.
This might take a while.
By the way, if you want a copy of the Processing script that produced these images, you can find it here.
It is usually Richter's big smeary abstracts that grab my attention (like this one); I've only recently come across his series 4900 Colours and 1024 Farben. Unfortunately I have only encountered these online (this site has a great analysis of why I should seek one out in person); I think that is why I indulged myself in the response of the philistine: "Huh, I bet I could do that".
Long story short: even creating a small panel with smooth, well-executed, regular patches of color is beyond my abilities (good enough for my living room wall, though). A more immediate and positive effect is that this project got me into generative art.
How so? Well, before committing acrylic to board I wanted to get an idea of what the end product would look like. So why not prototype it? Sure, I could write a python script to knock something like this out, but I don't need to reinvent the wheel when tools like Processing and NodeBox already exist.
My first attempts with NodeBox were a bust. I liked the idea of playing with a GUI that allows you to build a network of nodes and edges that represent series of actions and the flow of their results, but in practice it was just too clunky and I was too impatient. "Why does this node have so many input connections? Why can't I right-click this edge? How the heck do I reseed the random number generator?".
So Processing it was. Processing comes in two flavors: Java and Python. I have been programming Python for years and I really wanted to like that version. But it is just not as mature and complete as the original Java. I guess it is time for me to brush up my Java.
These are early days in my exploration of generative art. I think I am going to kick off with a Richter simulator. Seems simple, right? I shall post images and code when I can.