Ooooh, this is getting interesting now. Â This shows a grid as the spacing changes from wide to small spacing.
Experimental generative images
These are all simple images where I’m testing out a very simple set of rules. I’m using a modified lorentzian function to generate the colors near each line. The position and intensity of each line is determined by a tree like algorithm. I got a lot of different effects from playing with the colors and the height of each line. Like many things in this learning process, I never thought some of these minor tweaks would have such a large impact on the resulting image. The parameters that I did expect to change things just ended up making things either too muddy, dense or too sparse. I’ve still got to give it some try with some real colors, but I’ve gotten sidetracked yet again!
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Circled out
Circular Cellular Automata
These are the same cellular automata I’ve been working with. This time I’ve just mapped them into a circular grid, which seemed to make sense as I’m using circular boundary conditions.
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Infinite Flow
Bouncing circles
More of the stereographic circle series.  In this one, the circles are defined by iterating over a circle in 3 dimensional space.  That circle is bouncing up and down in the vertical direction.The changing shape and disappearance come from the circle leaving the interior of the sphere and returning bit by bit.
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Connected components in cellular automata
This is a basic rendering just connecting cells if they are in the same state. It turned out kinda neat. I started with a ball and stick model, but the balls just added visual noise. I think I’ll try rotating a shaded ball next, but the transitions require comparing two models instead of just blending the two images. Â
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More animated Circles
This page is just playing around with linear paths through the circle space. Â I’ve tried some more complicated shapes but they quickly get too complicated.
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A Random Walk Through Some Cellular Automatons
I wanted to investigate how closely various cellular automatons that differed by only one term were to each other, so I whipped up a demo that does a random walk through the parameter space of 1-d 2 bit automatons using a neighborhood of 5 pixels. Â This is the first one that popped up.
The next one is a automaton that differed by one parameter and is either off by one either above or below the example above.
As you can see the result is much more regular and I would have considered it completely different, not a close neighbor of the previous rendering. Â Another step:
This is more interesting, but is another close neighbor of previous two.
I decided to start over and in a little more controlled environment, I came up with these two.
And one of it’s neighbors.
As you can see, this part of the space has more closely related images. Â It appears to be close to what I would have expected. Â The more “binary” you get with the 2 value spaces and smaller neighborhoods the less related the images are, and the larger the pixel and neighborhood spaces the more “continuous” the behavior becomes. Â That doesn’t mean that the hard boundaries go away, and I’m sure many measures of the resulting spaces have a self similar structure, which would be interesting to investigate in and of itself.
My next plans involve, showing single pixel deviations in the initial state, and then building graphs of all the positions of the automaton spaces. Â I’d like to compare how the graphs change as you make the automaton space larger. Â I still have more predator prey stuff on the back burner too.
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Cellular automaton #1
Well, it’s time to switch gears again for a little bit. Â I finally threw some simple code together for a 1d cellular automaton, and the patterns are fascinating. Â I’ve got a few ideas to work through with these. Â I’m not sure if there is anything worthwhile in this vein, but I see how fascinating they are now.