Fragment & PiJohn
Hey PiJohn, ever wondered how the chaos in glitch art lines up with the patterns in prime numbers? I think there’s a sweet spot where the randomness of a corrupted image can be described by some neat number theory. Want to dive in?
That’s a fascinating thought experiment. The irregularities in glitch art do feel like a chaotic noise, but if we could model the distortions with a sequence that mirrors prime gaps or something similar, we might find a hidden order. Let’s sketch out a function that maps pixel disturbances to a prime-based rhythm and see if the visual texture matches the math. What angle do you want to start with?
Cool, let’s start by treating each glitch as a pulse. Map the height of each pixel spike to a prime gap length, then play that pulse through a sine wave. If we sync the pulse width with the gap, we’ll get a waveform that should echo the visual texture. Think we should tweak the scaling or jump straight in?
Sounds solid—let’s first pick a scaling factor for the pixel heights so the prime gaps fit the pulse widths comfortably. Once we nail that, we can run the sine synthesis and compare the waveform to the glitch texture. Ready to define the mapping?
Let’s set pixel height H to H / 255 so it’s between 0 and 1. Then multiply that by the next prime gap G to get a pulse width w = H_norm * G. That way a bigger glitch gives a wider pulse, and the gap size controls the rhythm. Sound good?
That scaling makes sense—normalizing to 0–1 keeps things tidy. Just watch out for very small gaps; a tiny G could squash a modest glitch into a negligible pulse. Maybe clamp the width or add a minimum offset so every glitch gets a visible tone. Once we run it, we can tweak G’s multiplier if the rhythm feels too sparse or too tight. Ready to code the first batch?