class P5Noise { // adapted from https://github.com/processing/p5.js/blob/v1.4.0/src/math/noise.js#L36 constructor() { this.PERLIN_YWRAPB = 4; this.PERLIN_YWRAP = 1 << this.PERLIN_YWRAPB; this.PERLIN_ZWRAPB = 8; this.PERLIN_ZWRAP = 1 << this.PERLIN_ZWRAPB; this.PERLIN_SIZE = 4095; this.perlin_octaves = params.perlinOctaves; // default to medium smooth this.perlin_amp_falloff = 0.5; // 50% reduction/octave this.scaled_cosine = (i) => 0.5 * (1.0 - Math.cos(i * Math.PI)); this.perlin; // will be initialized lazily by noise() or noiseSeed() } noise(x, y = 0, z = 0) { if (this.perlin == null) { this.perlin = new Array(this.PERLIN_SIZE + 1); for (let i = 0; i < this.PERLIN_SIZE + 1; i++) { this.perlin[i] = Math.random(); } } if (x < 0) { x = -x; } if (y < 0) { y = -y; } if (z < 0) { z = -z; } let xi = Math.floor(x), yi = Math.floor(y), zi = Math.floor(z); let xf = x - xi; let yf = y - yi; let zf = z - zi; let rxf, ryf; let r = 0; let ampl = 0.5; let n1, n2, n3; for (let o = 0; o < this.perlin_octaves; o++) { let of = xi + (yi << this.PERLIN_YWRAPB) + (zi << this.PERLIN_ZWRAPB); rxf = this.scaled_cosine(xf); ryf = this.scaled_cosine(yf); n1 = this.perlin[of & this.PERLIN_SIZE]; n1 += rxf * (this.perlin[(of + 1) & this.PERLIN_SIZE] - n1); n2 = this.perlin[(of + this.PERLIN_YWRAP) & this.PERLIN_SIZE]; n2 += rxf * (this.perlin[(of + this.PERLIN_YWRAP + 1) & this.PERLIN_SIZE] - n2); n1 += ryf * (n2 - n1); of += this.PERLIN_ZWRAP; n2 = this.perlin[of & this.PERLIN_SIZE]; n2 += rxf * (this.perlin[(of + 1) & this.PERLIN_SIZE] - n2); n3 = this.perlin[(of + this.PERLIN_YWRAP) & this.PERLIN_SIZE]; n3 += rxf * (this.perlin[(of + this.PERLIN_YWRAP + 1) & this.PERLIN_SIZE] - n3); n2 += ryf * (n3 - n2); n1 += this.scaled_cosine(zf) * (n2 - n1); r += n1 * ampl; ampl *= this.perlin_amp_falloff; xi <<= 1; xf *= 2; yi <<= 1; yf *= 2; zi <<= 1; zf *= 2; if (xf >= 1.0) { xi++; xf--; } if (yf >= 1.0) { yi++; yf--; } if (zf >= 1.0) { zi++; zf--; } } return r; } noiseDetail(lod, falloff) { if (lod > 0) { this.perlin_octaves = lod; } if (falloff > 0) { this.perlin_amp_falloff = falloff; } } noiseSeed(seed) { // Linear Congruential Generator // Variant of a Lehman Generator const lcg = (() => { // Set to values from http://en.wikipedia.org/wiki/Numerical_Recipes // m is basically chosen to be large (as it is the max period) // and for its relationships to a and c const m = 4294967296; // a - 1 should be divisible by m's prime factors const a = 1664525; // c and m should be co-prime const c = 1013904223; let seed, z; return { setSeed(val) { // pick a random seed if val is undefined or null // the >>> 0 casts the seed to an unsigned 32-bit integer z = seed = (val == null ? Math.random() * m : val) >>> 0; }, getSeed() { return seed; }, rand() { // define the recurrence relationship z = (a * z + c) % m; // return a float in [0, 1) // if z = m then z / m = 0 therefore (z % m) / m < 1 always return z / m; } }; })(); lcg.setSeed(seed); this.perlin = new Array(this.PERLIN_SIZE + 1); for (let i = 0; i < this.PERLIN_SIZE + 1; i++) { this.perlin[i] = lcg.rand(); } } }