Buddhabrot Chromatica
In my previous post on Buddhabrots I demonstrated that different iteration or “bail-out” levels can produce different looking Buddhabrots. Buddhabrot discoverer Melinda Green also described the method of mapping differently-iterated Buddhabrots to the different channels of an RGB image. This can result in strikingly “Nebulous” images, reminiscent of those famous images from the Hubble space telescope.
Obviously this was the next thing I had to try.
3000r, 1500g, 500b. Channels normalized individually.
This image uses 3000 iterations on the red channel, 1500 on the green channel, and 500 on the blue channel. I have normalized the channels individually which seems to favor the channel with the least iterations.
3000r, 1500g, 500b. Channels normalized together.
This images uses the same configuration of iterations per channel as the last. This seems to favor the channel with the most iterations.
It strikes me that both of these approaches are rather arbitrary. The best solution would be to combine the channels in a decent image editing program and adjust the levels of each until the result is visually appealing (These images had their channels combined at creation time in Python). Unfortunately GIMP is not the decent image editing program I’m looking for. CinePaint, however, seems perfect for this job.
1000r, 200g, 100b. Adjusted in CinePaint
“To infinity… and beyond!”
1000 iterations red, 200 green, 100 blue. Adjusted to Hubbletastic perfection. Thanks CinePaint!
The next step is probably to render some super-big color images.
Make sure that each channel rendering is “fully cooked” in that they lose their grainy quality and have stopped changing. The highest bailout channel can take a very long time. Note also that if you want to eliminate the outer circle of black, then unlike for m-set images you shouldn’t stop iterating once it is 2 units from the origin. Instead, wait until it is well outside the image bounds.
Before attempting a really big one, be completely sure that you know the exact parameters that you want. It’s also helpful to save the counter arrays so that you can continue them later in case you crash. You can even run on several different machines and add the results together before equalizing and generating images from them.