nPerlinNoise 0.1.3a1

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 Improving docs
  All Packages: releases
  CHANGELOG
  

  > Tested on Python 3.10, Windows 10

• Future work:

  optimization for higher dimensions and single value coordinates
  

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

• 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 haveing 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 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
• 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.

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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

Maintainer:

Amith M