perlin-numpy VS PyFastNoiseLite

Have you used numpy for this sort of thing? I found https://github.com/pvigier/perlin-numpy which does the whole Perlin noise + fractal noise with numpy operations. Haven't tested it yet to see how fast it is on large matrices

/u/FMWizard I would try this https://github.com/tizilogic/PyFastNoiseLite/ with these configs. It's a C wrapper, so it should be faster than the python libraries.

Another piece of info you might find interesting is that the traditional approach of warping, where you distort each axis individually, isn't the only way to do that. The choice of noise function certainly makes more visual impact, but there is more directionally-uniform way to domain-warp too: explicitly-designed vector-output noise. Such noise chooses a random output-space direction to apply to each internal gradient ramp contribution, which results in a warp distribution that's effectively radially-symmetric. It also only takes one noise evaluation instead of two or three. I wrote this as part of my contributions to FastNoiseLite. There does exist PyFastNoiseLite as a wrapper for that, though it unfortunately doesn't currently have wrappers for the domain warping functions.