First, I now have several ways to index the sequence. Up until now, I've been using a spiral pattern from the inside (center of image) outward to an edge that I've defined. I've added three new options. First, a spiral pattern from the outside in. Next, a sequential pattern starting at 0,0 and going to the max dimensions of the image. Finally, a random sequence.
Second, I'm looking at the sequences generated by the individual objects. Specifically, the 1st derivative of the sequences is most interesting and usually highly structured. Higher derivatives don't show much difference from the 1st derivative, so I'll limit my study to those.
Here's the now familiar 'Two Knight' emergent pattern with the sequence in a spiral pattern from the center outward:
Now here's the same 'Two Knight' thing but this time the sequence is a spiral pattern from the outside, inward:
Here is the same 'Two Knight' rule but now the sequence is from the upper right to the lower left, in pixel order:
(ahem, also not so interesting).
Just for completeness, here's the same rules with a random sequence:
take note of the subtle gradient. I've seen this in all the random sequences I've looked at and can't explain it.
But when I add a third Knight:
I can look at each individual object (color), and I can also see where there's a blank pixel. Using the inward spiral above, I see this pattern:
And now, let's look at the sequence derivatives. I'll use this inward spiral as the first (and maybe only) example.
I'll look at the sequence generated by the red object. To calculate the 1st derivative at each point, I simply subtract the current value from the previous value. If I plot these differences, I get this:
Here's another example. This is the 1st derivative plot of the 3rd object (marked in blue) using the linear sequence indexing: