pf-value-noise

N-Dimensional Value Noise Generator - A value noise generator for any number of dimensions. Similar to, but faster than Perlin noise.

Examples

// Require the module to use it.
const ValueNoise = require('pf-value-noise')
 
// Create a 3D value noise generator.
const noise3D = new ValueNoise({ dimensions: 3 })
 
// Use it to make a 100×100×100 grid of values
const resolution = 100
let data = []
for (let x = 0; x < resolution; ++x) {
  for (let y = 0; y < resolution; ++y) {
    for (let z = 0; z < resolution; ++z) {
      data.push(noise3D.get([ x / resolution, y / resolution, z / resolution ]))
    }
  }
}
 
const _ = require('lodash')
data = _(data).chunk(resolution).chunk(resolution).value()
data[5][62][17]
// 0.6594545530358533

The following example creates the above picture.

// Create the canvas
const { createCanvas } = require('canvas')
const [ width, height ] = [ 800, 200 ]
const canvas = createCanvas(width, height)
const ctx = canvas.getContext('2d', { alpha: false })
 
// Create the image data
const ValueNoise = require('pf-value-noise')
const noise3D = new ValueNoise({ dimensions: 3, seed: 'pillow' })
const resolution = 100
const imageData = ctx.createImageData(width, height)
let dataIndex = 0
for (let row = 0; row < height; ++row) {
  for (let col = 0; col < width; ++col) {
    imageData.data[dataIndex++] = noise3D.get([ row / resolution, col / resolution, 0 ]) * 256 | 0
    imageData.data[dataIndex++] = noise3D.get([ row / resolution, col / resolution, 1 ]) * 256 | 0
    imageData.data[dataIndex++] = noise3D.get([ row / resolution, col / resolution, 2 ]) * 256 | 0
    ++dataIndex
  }
}
 
// Export the image data
const fs = require('fs')
ctx.putImageData(imageData, 0, 0)
canvas.createPNGStream()
  .pipe(fs.createWriteStream('rainbow-value-noise.png'))

API

ValueNoise

({Class}): Represents a value noise generator.

const ValueNoise = require('pf-value-noise')
const noiseGenerator = new ValueNoise()

ValueNoise.constructor([options])

Arguments

1. [options] (Object): An objects of options.

Option	Type	Default	Description
seed	String	null	RNG's seed
dimensions	Number	2	Number of dimensions
min	Number	0	Minimum value returned
max	Number	1	Maximum value returned
wavelength	Number	1	Size of the first octave
octaves	Number	8	Number of octaves to sample
octaveScale	Number	1/2	Scaling for successive octaves
persistence	Number	1/2	Weight for successive octaves
interpolation	Function	cosine	Interpolation function used

Note that even with the same seed, a different order of <Perlin>.get() calls can change the overall noise function since its values are generated lazily.

wavelength sets the size of the first octave, and each successive octave will be octaveScale times the previous. The octaves are centered about the origin and added together according to their weight. The first octave has a weight of 1, and each successive octave will be persistence times the previous.

The octaves are sampled using the interpolation function with signature function(a, b, t) that returns a value between a and b according to the parameter 0 <= t <= 1. The default interpolation function used is cosine interpolation.

interpolation: function (a, b, t) {
  return (1 - Math.cos(Math.PI * t)) / 2 * (b - a) + a
}

After the octaves are sampled and added together, the values are adjusted to fall between min and max. Note that the value distribution is roughly Gaussian depending on the number of octaves.

ValueNoise.prototype.get(coordinates)

Arguments

1. coordinates (Array): The data point to get. Its length should match dimensions.

Returns

• (Number): The value at those coordinates.

const noise4D = new ValueNoise({ dimensions: 4 })
 
noise4D.get([ 1, 2, 3, 4 ])
// 0.538503118881535