ruarai

Working on genomics stuff right now and gene ontology (GO) is ubiquitous.

My primary complaint with GO is that it doesn't map to any statistical analyses very well

This is an area that needs work, but basically there's the table of 'edge intensity' that gets multiplied by a constant baseChance variable for every pixel.

baseChance = 8 * samples / (width * height)

samples is the desired number of samples.

Again, this needs work. I'm pretty sure there's a way I can more accurately match the number of output samples to the desired number of samples.

Edit: And no, it can't produce a response if there's no edge (a value of zero). However, there's always some level of noise. This is true of the example you've shown - there's very light noise visible.

This doesn't seem to have any benefit unfortunately. It seems like the current approach already produces the before-after edge effect.

Wow, that works pretty well. I was mistaken in thinking that either the Dictionary class or the process of sampling would sort them.

Mind if I merge that? Or you could submit a pull request. Either would be great!

Also, do you know of any resources for learning about how to optimise for gzip compression? Google is just telling me about compression for websites.

Not sure if that applies for my purposes, since I'm not actually using linear interpolation barycentric coordinates (I don't think that's possible). The barycentric coordinates supply the gradient within themselves.

I may have to read further, though. That's a lot of math.

I tried your struct-of-array idea, and that's produced an okay improvement ~1%.

Sorting them seems tough as the index of each value must match for each channel, so any sorting would have to occur beforehand. Except that is already sorted by x-y values, and my attempts otherwise have failed to produce results.