Perlin Noise is a kind of algorithmic random value generator that, unlike pure random methods where each value is unrelated to the previous or next, generates values in a way that whenever you look at one particular value, you know the adjecent values are not radically far of.
I highly recommend this series by Daniel Shiffman of The Coding Train on Perlin Noise for some high energy, high silliness but super informative information on Perlin Noise.
In fact - and without surprise - it was mostly the Coding Train that nudged me to get off my ass and start these experiments (done and redone by thousands of people throughout the internet).
In this particular exercise I’m mapping Perlin Noise onto a line, traveling forward along the x-axis of the Perlin generated noise.
The code is shown bellow, it is literally the code that generates whatever image you’re seeing and I’ll try to work out more interesting experiments, so come back now and then.