I have always wondered about procedurally generated content, so I wanted a quick project to toy arround and implement a noise function. In this proyect I programmed the Perlin Noise Generator, and a State Model (to force a coherent string structure), with probability associated transitions, like a Markov Chain.
First, we generate the State model transitions, in which each letter is a state, with transition to the next letter and itself. The random variable used for the transitions is the Comulative Distribution Function of a segment of the Perlin Noise. If I just used the Perlin noise, the string may had come out completely unrecognizable, so the State Model is used for basic formatting.
The Perlin noise in N-Dimensions algortihm, consists in interpolating values between a N+1, unit distanced, random gradient vectors, in order to generate a smooth surface between the gradients. But the implmentation that Ken Perlin did, presents a more confusing implementation, with perfomance in mind. Taking into consideration the moment the paper was presented, the processing power and memmory where limited, so he implemented a pseudo-random, low-cost, hashed random gradient.
This is implemented in the calculate1D method in PerlinNoise.py.
I also included a more straight-forward implementation of the algorithm, in the calculate1DNew method, in PerlinNoise.py.
Both methods have been comented with a lot of detail, in order to provide some help undestanding both.
- Tweepy (for tweeting)
- Numpy
- Random
- Math
- Matplotlib (for testing the PerlinNoise.py)
In order to lauch the Bot, just execute
python BruhBot.py
If you want to test the PerlinNoise.py, use
python PerlinNoise.py
- Understanding Perlin Noise Very good read, to gather some intuition and first hand knowledge on 3D Perlin Noise, with code samples.
- Improved Noise Implementation Implementation of the improved Noise generator, by Kim Perlin, in with I based this code sample.
- The original Perlin Noise Paper
Bruh