Canvas.setpenopacity(.15);
(function(global){
var module = global.noise = {};
function Grad(x, y, z) {
this.x = x; this.y = y; this.z = z;
}
Grad.prototype.dot2 = function(x, y) {
return this.x*x + this.y*y;
};
Grad.prototype.dot3 = function(x, y, z) {
return this.x*x + this.y*y + this.z*z;
};
var grad3 = [new Grad(1,1,0),new Grad(-1,1,0),new Grad(1,-1,0),new Grad(-1,-1,0),
new Grad(1,0,1),new Grad(-1,0,1),new Grad(1,0,-1),new Grad(-1,0,-1),
new Grad(0,1,1),new Grad(0,-1,1),new Grad(0,1,-1),new Grad(0,-1,-1)];
var p = [151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180];
// To remove the need for index wrapping, double the permutation table length
var perm = new Array(512);
var gradP = new Array(512);
// This isn't a very good seeding function, but it works ok. It supports 2^16
// different seed values. Write something better if you need more seeds.
module.seed = function(seed) {
if(seed > 0 && seed < 1) {
// Scale the seed out
seed *= 65536;
}
seed = Math.floor(seed);
if(seed < 256) {
seed |= seed << 8;
}
for(var i = 0; i < 256; i++) {
var v;
if (i & 1) {
v = p[i] ^ (seed & 255);
} else {
v = p[i] ^ ((seed>>8) & 255);
}
perm[i] = perm[i + 256] = v;
gradP[i] = gradP[i + 256] = grad3[v % 12];
}
};
// Initialize the noise generator with a random seed
// ************************************************
// ***** THIS IS THE MODIFIED LINE *****
// ************************************************
module.seed(Math.random());
// ************************************************
// Skewing and unskewing factors for 2, 3, and 4 dimensions
var F2 = 0.5*(Math.sqrt(3)-1);
var G2 = (3-Math.sqrt(3))/6;
var F3 = 1/3;
var G3 = 1/6;
// 2D simplex noise
module.simplex2 = function(xin, yin) {
var n0, n1, n2; // Noise contributions from the three corners
// Skew the input space to determine which simplex cell we're in
var s = (xin+yin)*F2; // Hairy factor for 2D
var i = Math.floor(xin+s);
var j = Math.floor(yin+s);
var t = (i+j)*G2;
var x0 = xin-i+t; // The x,y distances from the cell origin, unskewed.
var y0 = yin-j+t;
// For the 2D case, the simplex shape is an equilateral triangle.
// Determine which simplex we are in.
var i1, j1; // Offsets for second (middle) corner of simplex in (i,j) coords
if(x0>y0) { // lower triangle, XY order: (0,0)->(1,0)->(1,1)
i1=1; j1=0;
} else { // upper triangle, YX order: (0,0)->(0,1)->(1,1)
i1=0; j1=1;
}
// A step of (1,0) in (i,j) means a step of (1-c,-c) in (x,y), and
// a step of (0,1) in (i,j) means a step of (-c,1-c) in (x,y), where
// c = (3-sqrt(3))/6
var x1 = x0 - i1 + G2; // Offsets for middle corner in (x,y) unskewed coords
var y1 = y0 - j1 + G2;
var x2 = x0 - 1 + 2 * G2; // Offsets for last corner in (x,y) unskewed coords
var y2 = y0 - 1 + 2 * G2;
// Work out the hashed gradient indices of the three simplex corners
i &= 255;
j &= 255;
var gi0 = gradP[i+perm[j]];
var gi1 = gradP[i+i1+perm[j+j1]];
var gi2 = gradP[i+1+perm[j+1]];
// Calculate the contribution from the three corners
var t0 = 0.5 - x0*x0-y0*y0;
if(t0<0) {
n0 = 0;
} else {
t0 *= t0;
n0 = t0 * t0 * gi0.dot2(x0, y0); // (x,y) of grad3 used for 2D gradient
}
var t1 = 0.5 - x1*x1-y1*y1;
if(t1<0) {
n1 = 0;
} else {
t1 *= t1;
n1 = t1 * t1 * gi1.dot2(x1, y1);
}
var t2 = 0.5 - x2*x2-y2*y2;
if(t2<0) {
n2 = 0;
} else {
t2 *= t2;
n2 = t2 * t2 * gi2.dot2(x2, y2);
}
// Add contributions from each corner to get the final noise value.
// The result is scaled to return values in the interval [-1,1].
return 70 * (n0 + n1 + n2);
};
// 3D simplex noise (included for completeness, though not used in drawing)
module.simplex3 = function(xin, yin, zin) {
var n0, n1, n2, n3; // Noise contributions from the four corners
// Skew the input space to determine which simplex cell we're in
var s = (xin+yin+zin)*F3; // Hairy factor for 3D
var i = Math.floor(xin+s);
var j = Math.floor(yin+s);
var k = Math.floor(zin+s);
var t = (i+j+k)*G3;
var x0 = xin-i+t; // The x,y,z distances from the cell origin, unskewed.
var y0 = yin-j+t;
var z0 = zin-k+t;
// For the 3D case, the simplex shape is a slightly irregular tetrahedron.
// Determine which simplex we are in.
var i1, j1, k1; // Offsets for second corner of simplex in (i,j,k) coords
var i2, j2, k2; // Offsets for third corner of simplex in (i,j,k) coords
if(x0 >= y0) {
if(y0 >= z0) { i1=1; j1=0; k1=0; i2=1; j2=1; k2=0; }
else if(x0 >= z0) { i1=1; j1=0; k1=0; i2=1; j2=0; k2=1; }
else { i1=0; j1=0; k1=1; i2=1; j2=0; k2=1; }
} else {
if(y0 < z0) { i1=0; j1=0; k1=1; i2=0; j2=1; k2=1; }
else if(x0 < z0) { i1=0; j1=1; k1=0; i2=0; j2=1; k2=1; }
else { i1=0; j1=1; k1=0; i2=1; j2=1; k2=0; }
}
// A step of (1,0,0) in (i,j,k) means a step of (1-c,-c,-c) in (x,y,z),
// a step of (0,1,0) in (i,j,k) means a step of (-c,1-c,-c) in (x,y,z), and
// a step of (0,0,1) in (i,j,k) means a step of (-c,-c,1-c) in (x,y,z), where
// c = 1/6.
var x1 = x0 - i1 + G3; // Offsets for second corner
var y1 = y0 - j1 + G3;
var z1 = z0 - k1 + G3;
var x2 = x0 - i2 + 2 * G3; // Offsets for third corner
var y2 = y0 - j2 + 2 * G3;
var z2 = z0 - k2 + 2 * G3;
var x3 = x0 - 1 + 3 * G3; // Offsets for fourth corner
var y3 = y0 - 1 + 3 * G3;
var z3 = z0 - 1 + 3 * G3;
// Work out the hashed gradient indices of the four simplex corners
i &= 255;
j &= 255;
k &= 255;
var gi0 = gradP[i+ perm[j+ perm[k ]]];
var gi1 = gradP[i+i1+perm[j+j1+perm[k+k1]]];
var gi2 = gradP[i+i2+perm[j+j2+perm[k+k2]]];
var gi3 = gradP[i+ 1+perm[j+ 1+perm[k+ 1]]];
// Calculate the contribution from the four corners
var t0 = 0.6 - x0*x0 - y0*y0 - z0*z0;
if(t0<0) {
n0 = 0;
} else {
t0 *= t0;
n0 = t0 * t0 * gi0.dot3(x0, y0, z0);
}
var t1 = 0.6 - x1*x1 - y1*y1 - z1*z1;
if(t1<0) {
n1 = 0;
} else {
t1 *= t1;
n1 = t1 * t1 * gi1.dot3(x1, y1, z1);
}
var t2 = 0.6 - x2*x2 - y2*y2 - z2*z2;
if(t2<0) {
n2 = 0;
} else {
t2 *= t2;
n2 = t2 * t2 * gi2.dot3(x2, y2, z2);
}
var t3 = 0.6 - x3*x3 - y3*y3 - z3*z3;
if(t3<0) {
n3 = 0;
} else {
t3 *= t3;
n3 = t3 * t3 * gi3.dot3(x3, y3, z3);
}
// Add contributions from each corner to get the final noise value.
// The result is scaled to return values in the interval [-1,1].
return 32 * (n0 + n1 + n2 + n3);
};
// ##### Perlin noise stuff (Included for completeness) #####
function fade(t) {
return t*t*t*(t*(t*6-15)+10);
}
function lerp(a, b, t) {
return (1-t)*a + t*b;
}
// 2D Perlin Noise
module.perlin2 = function(x, y) {
// Find unit grid cell containing point
var X = Math.floor(x), Y = Math.floor(y);
// Get relative xy coordinates of point within that cell
x = x - X; y = y - Y;
// Wrap the integer cells at 255 (smaller integer period can be introduced here)
X = X & 255; Y = Y & 255;
// Calculate noise contributions from each of the four corners
var n00 = gradP[X+perm[Y]].dot2(x, y);
var n01 = gradP[X+perm[Y+1]].dot2(x, y-1);
var n10 = gradP[X+1+perm[Y]].dot2(x-1, y);
var n11 = gradP[X+1+perm[Y+1]].dot2(x-1, y-1);
// Compute the fade curve value for x
var u = fade(x);
// Interpolate the four results
return lerp(
lerp(n00, n10, u),
lerp(n01, n11, u),
fade(y));
};
// 3D Perlin Noise
module.perlin3 = function(x, y, z) {
// Find unit grid cell containing point
var X = Math.floor(x), Y = Math.floor(y), Z = Math.floor(z);
// Get relative xyz coordinates of point within that cell
x = x - X; y = y - Y; z = z - Z;
// Wrap the integer cells at 255 (smaller integer period can be introduced here)
X = X & 255; Y = Y & 255; Z = Z & 255;
// Calculate noise contributions from each of the eight corners
var n000 = gradP[X+ perm[Y+ perm[Z ]]].dot3(x, y, z);
var n001 = gradP[X+ perm[Y+ perm[Z+1]]].dot3(x, y, z-1);
var n010 = gradP[X+ perm[Y+1+perm[Z ]]].dot3(x, y-1, z);
var n011 = gradP[X+ perm[Y+1+perm[Z+1]]].dot3(x, y-1, z-1);
var n100 = gradP[X+1+perm[Y+ perm[Z ]]].dot3(x-1, y, z);
var n101 = gradP[X+1+perm[Y+ perm[Z+1]]].dot3(x-1, y, z-1);
var n110 = gradP[X+1+perm[Y+1+perm[Z ]]].dot3(x-1, y-1, z);
var n111 = gradP[X+1+perm[Y+1+perm[Z+1]]].dot3(x-1, y-1, z-1);
// Compute the fade curve value for x, y, z
var u = fade(x);
var v = fade(y);
var w = fade(z);
// Interpolate
return lerp(
lerp(
lerp(n000, n100, u),
lerp(n001, n101, u), w),
lerp(
lerp(n010, n110, u),
lerp(n011, n111, u), w),
v);
};
})(this);
// Fractional Brownian Motion using Simplex Noise
function FBM(x, y)
{
// Initial frequency and amplitude (you might need noise.simplex2, not just simplex2)
var total = 0;
var amplitude = 1;
var frequency = freq; // Use the global freq defined later
var maxAmplitude = 0; // Used for normalizing result to [-1, 1] or [0, 1]
for(var i = 0; i < 5; i++) { // Number of octaves (layers of noise)
total += noise.simplex2(x * frequency, y * frequency) * amplitude;
maxAmplitude += amplitude;
amplitude *= 0.5; // Persistence (how much each octave contributes)
frequency *= 2; // Lacunarity (how much frequency increases each octave)
}
// Normalize the result if needed, depends on how you use it.
// This normalization maps it roughly to [-1, 1]
// return total / maxAmplitude;
// The original code didn't normalize explicitly, scale might be handled by 'amp' later
return total;
}
// Calculate the gradient of the FBM field at a point (x, y)
// This tells us the direction of "steepest ascent" of the noise value
function gradient (x, y)
{
// Small offset for finite difference calculation
var eps = 0.01; // Smaller epsilon might give more accurate gradient
// Calculate FBM at slightly offset points
var v0 = FBM(x - eps, y); // FBM slightly to the left
var v1 = FBM(x + eps, y); // FBM slightly to the right
var v2 = FBM(x, y - eps); // FBM slightly below
var v3 = FBM(x, y + eps); // FBM slightly above
// Approximate partial derivatives using central difference
// dFBM/dx ≈ (FBM(x+eps, y) - FBM(x-eps, y)) / (2 * eps)
// dFBM/dy ≈ (FBM(x, y+eps) - FBM(x, y-eps)) / (2 * eps)
// Note: Original code used (v0-v1)/eps and (v2-v3)/eps.
// (v0-v1) = FBM(x-eps, y) - FBM(x+eps, y) which is -(v1-v0)
// (v2-v3) = FBM(x, y-eps) - FBM(x, y+eps) which is -(v3-v2)
// This gives the negative gradient (direction of steepest descent), which might be intended for flow lines.
// Let's stick to the original calculation's *effect* even if the formula differs slightly
// Using (v1 - v0) / (2*eps) for dx and (v3 - v2) / (2*eps) for dy is standard central difference.
// The original code effectively computed: [-(v1-v0)/eps, -(v3-v2)/eps]
// Let's use a slightly adjusted eps for the original formula:
var gradX = (v1 - v0) / (2*eps); // Approx dFBM/dx
var gradY = (v3 - v2) / (2*eps); // Approx dFBM/dy
// The original code returned [(v0 - v1) / eps, (v2 - v3) / eps]
// which is [-2 * gradX, -2 * gradY] approximately.
// So the turtles move opposite to the gradient, scaled by 2/eps factor (absorbed into 'amp').
// Let's return the standard gradient and adjust 'amp' or direction if needed.
// Or, match the original output structure:
return [ (v0 - v1) / eps, (v2 - v3) / eps ]; // Matches original output
// return [gradX, gradY]; // Standard gradient output
}
// --- Simulation Parameters ---
const dim = 128; // Grid dimension (dim x dim turtles)
const freq = 0.0015; // Base frequency for the noise field
const amp = 0.1 / freq; // Movement amplitude/step size (adjust as needed)
const startSize = 200; // Range for random starting positions (centered at 0,0)
const length = 50; // Number of steps each turtle takes
// --- Turtle Initialization ---
const turtles = [];
for(var y = 0; y < dim; y++){
turtles[y] = [];
for(var x = 0; x < dim; x++){
var t = new Turtle();
t.penup();
// Random start position within the startSize box
t.goto((Math.random() - 0.5) * startSize, (Math.random() - 0.5) * startSize);
t.pendown();
// Optional: Initial random turn based on local noise - might not be needed
// t.right((FBM(t.x(), t.y()) ) * 360);
turtles[y][x] = t;
}
}
// --- Animation Loop Function ---
function walk(i) {
for(var y = 0; y < dim; y++){
for(var x = 0; x < dim; x++){
const turtle = turtles[y][x];
// Get the gradient (direction) from the noise field at the turtle's current position
var grad = gradient(turtle.x(), turtle.y());
// Move the turtle along the gradient (or opposite to it, based on gradient calculation)
// The amplitude 'amp' scales the step size.
turtle.goto(turtle.x() + grad[0] * amp, turtle.y() + grad[1] * amp);
}
}
// Continue simulation as long as step count 'i' is less than 'length'
return i < length;
}
