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

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Project description

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nPerlinNoise

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 Improving docs
    All Packages: releases
    CHANGELOG

    Tested on Python 3.10, Windows 10
  • Future work:

    optimization for higher dimensions and single value coordinates

Screenshots:


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

  • import nPerlinNoise as nPN
    
    noise = nPN.Noise(seed=69420)
    
    # get noise values at given n-dimensional coordinates by calling noise with those coords
    # coordinates can be single value, or an iterable
    # noise(..., l, m, n, ...) where l, m, n, ... are single numeric values
    # or
    # noise(...., [l1, l2, ..., lx], [m1, m2, ..., mx], [n1, n2, ..., nx], ....)
    # where .... are iterable of homogeneous-dimensions
    # the output will be of same shape of input homogeneous-dimensions
    
    noise(73)  # 0.5207113
    noise(73, 11, 7)  # 0.5700986
    noise(0, 73, 7, 11, 0, 3)  # 5222712
    
    noise([73, 49])  # [0.52071124, 0.6402224]
    noise([73, 49], [2, 2])  # [0.4563121 , 0.63378346]
    
    noise([[73], [49], [0]],
          [[2], [2], [2]],
          [[0], [1], [2]])
    # -> [[0.4563121],
    #     [0.6571784],
    #     [0.16369209]]
    
    noise([[1, 2], [2, 3]],
          [[1, 1], [1, 1]],
          [[2, 2], [2, 2]])
    # -> [[0.08666219, 0.09778494],
    #     [0.09778494, 0.14886124]]
    
    # noise(..., l, m, n, ...) has same values with trailing dimensions having zero as coordinate
    # i.e noise(..., l, m, n) = noise(..., l, m, n, 0) = noise(..., l, m, n, 0, 0) = noise(..., l, m, n, 0, 0, ...)
    noise(73)  # 0.5207113
    noise(73, 0)  # 0.5207113
    noise(73, 0, 0) # 0.5207113
    

for detailed usage see EXAMPLE

How to test the software

to see all tests see Tests

Known issues

  • No Known Bugs
  • NPerlin.findBounds is bottleneck
  • noise(a, b, c, d, e, f, ...) is slow for single value coordinates

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
  • Looking for Contributors for Documentation
  • Looking for Contributors for feature additions
  • Looking for Contributors for optimization
  • Fork the repository and issue a PR to contribute

General instructions on how to contribute CONTRIBUTING.


Open source licensing info

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

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

Maintainer:

Amith M
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