nPerlinNoise 0.1.3a0

A robust open source implementation of Perlin Noise Algorithm for N-Dimensions

N Perlin Noise

A robust open source implementation of Perlin Noise Algorithm for N-Dimensions in Python.

• A powerful and fast API for n-dimensional noise.
• Easy hyper-parameters selection of octaves, lacunarity and persistence as well as complex and customizable hyper-parameters for n-dimension frequency, waveLength, warp(interpolation) and range.
• Includes various helpful tools for noise generation and for procedural generation tasks such as customizable Gradient, Color Gradients, Warp classes.
• Implements custom PRNG generator for n-dimension and can be easily tuned.

Details:

• Technology stack:

  Status: v0.1.3-alpha Ready for public PyPI release
  All Packages: releases
  PyPI: v0.1.3a0
  CHANGELOG
  

Screenshots:

• raw
  
• wood
  
• hot nebula
  
• island
  
• land
  
• marble fractal
  
• patch
  
• color patch
  
• ply1
  
• ply2
  
• stripes
  
• warp
  

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Dependencies

• Python>=3.10.0

for production dependencies see Requirements
for development dependencies see Dev-Requirements

Installation

for detailed instruction on installation see INSTALLATION.

Usage

for detailed usage see EXAMPLE

How to test the software

• To test overalls run main
• To test Logical consistency run testLogic
• To test Profile Benchmarking run testProfile
• To test Visuals run testVisuals
• To test Colors run testCol

to see all tests see Tests

Known issues

• No Known Bugs
• NPerlin.findBounds is bottleneck

Getting help

• Check main.py for detailed usage
• Check docs for all documentations
• Check Usage Section

If you have questions, concerns, bug reports, etc, please file an issue in this repository's Issue Tracker or open a discussion in this repository's Discussion section.

Getting involved

• Looking for Contributors for WebApps
• Fork the repository and issue a PR to contribute

General instructions on how to contribute CONTRIBUTING.

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Open source licensing info

1. TERMS
2. LICENSE
3. CFPB Source Code Policy

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Credits and references

1. Inspired from The Coding Train -> perlin noise
2. hash function by xxhash inspired the rand3 algo and ultimately helped for O(1) time complexity n-dimensional random generator NPrng
3. StackOverflow for helping on various occasions throughout the development