This annotated implementation of Ken Perlin's k-dimensional noise is meant to serve as an easy-to-understand companion guide to a more in-depth treatment of the algorithm. I wrote this code and the accompanying comments mostly for myself in order to better understand how a gradient noise algorithm like Perlin noise works. What better way to attempt to understand a topic than to implement it and explain it?
Only two packages are required numpy and Pillow. They can be installed via
make install
imagemagick was also used to create the animations.
Examples can be generated by running
make examples
