202 lines
No EOL
6 KiB
HTML
202 lines
No EOL
6 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Neural Diffusion</title>
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<style>
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body {
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margin: 0;
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overflow: hidden;
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background: #0a0a0a;
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color: #ddd;
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font-family: 'Courier New', monospace;
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display: flex;
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flex-direction: column;
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height: 100vh;
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}
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canvas {
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display: block;
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width: 100%;
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height: 100%;
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}
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#attribution {
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position: absolute;
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bottom: 10px;
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right: 10px;
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font-size: 10px;
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opacity: 0.5;
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pointer-events: none;
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}
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</style>
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</head>
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<body>
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<canvas id="canvas"></canvas>
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<div id="attribution">neurameba · motd.social</div>
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<script>
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const canvas = document.getElementById('canvas');
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const ctx = canvas.getContext('2d');
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// Set canvas to full window size
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function resizeCanvas() {
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canvas.width = window.innerWidth;
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canvas.height = window.innerHeight;
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}
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window.addEventListener('resize', resizeCanvas);
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resizeCanvas();
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// Parameters aligned with the brief
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const params = {
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motion: 0.5,
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density: 0.5,
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complexity: 0.5,
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connectedness: 0.5,
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lifespan: 0.5,
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pulse: { avg: 1.03, min: 0.80, max: 1.20 }
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};
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// Reaction-diffusion simulation with Turing patterns
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const cols = Math.floor(canvas.width / 2);
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const rows = Math.floor(canvas.height / 2);
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const grid = create2DArray(cols, rows);
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const next = create2DArray(cols, rows);
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// Adjusted for low motion, high density feel
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const F = params.complexity * 0.05 + 0.051; // Feed rate
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const K = params.connectedness * 0.06 + 0.06; // Kill rate
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const DA = params.complexity * 0.2 + 0.1;
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const DB = params.connectedness * 0.1 + 0.05;
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// Add some noise
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for (let i = 0; i < cols; i++) {
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for (let j = 0; j < rows; j++) {
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if (Math.random() < params.density * 0.8) {
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grid[i][j] = {
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a: 1,
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b: 0
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};
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} else {
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grid[i][j] = {
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a: 0,
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b: 0
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};
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}
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}
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}
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// Drop a few perturbations
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for (let k = 0; k < cols * rows * 0.001; k++) {
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const i = Math.floor(Math.random() * cols);
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const j = Math.floor(Math.random() * rows);
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grid[i][j].b = 1;
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}
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function create2DArray(w, h) {
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const arr = new Array(w);
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for (let i = 0; i < w; i++) {
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arr[i] = new Array(h);
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}
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return arr;
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}
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function laplacianA(x, y) {
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let sum = 0;
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for (let dx = -1; dx <= 1; dx++) {
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for (let dy = -1; dy <= 1; dy++) {
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const xx = (x + dx + cols) % cols;
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const yy = (y + dy + rows) % rows;
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sum += grid[xx][yy].a;
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}
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}
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return (sum / 9) - grid[x][y].a;
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}
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function laplacianB(x, y) {
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let sum = 0;
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for (let dx = -1; dx <= 1; dx++) {
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for (let dy = -1; dy <= 1; dy++) {
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const xx = (x + dx + cols) % cols;
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const yy = (y + dy + rows) % rows;
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sum += grid[xx][yy].b;
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}
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}
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return (sum / 9) - grid[x][y].b;
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}
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// Pulse modulation
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let time = 0;
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function update() {
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time += 0.01;
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// Apply pulse to parameters
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const pulseFactor = params.pulse.avg + Math.sin(time * 0.3) * 0.2;
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for (let x = 0; x < cols; x++) {
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for (let y = 0; y < rows; y++) {
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const a = grid[x][y].a;
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const b = grid[x][y].b;
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const laplacianAVal = laplacianA(x, y);
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const laplacianBVal = laplacianB(x, y);
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// Gray-Scott model
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next[x][y].a = a + (
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DA * laplacianAVal -
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a * b * b +
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F * (1 - a)
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) * pulseFactor;
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next[x][y].b = b + (
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DB * laplacianBVal +
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a * b * b -
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(F + K) * b
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) * pulseFactor;
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}
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}
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// Swap buffers
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const temp = grid;
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grid = next;
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next = temp;
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draw();
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requestAnimationFrame(update);
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}
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function draw() {
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ctx.fillStyle = '#0a0a0a';
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ctx.fillRect(0, 0, canvas.width, canvas.height);
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const imageData = ctx.createImageData(cols, rows);
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const data = imageData.data;
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for (let x = 0; x < cols; x++) {
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for (let y = 0; y < rows; y++) {
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const idx = (y * cols + x) * 4;
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const a = grid[x][y].a;
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const b = grid[x][y].b;
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// Monochrome with dryness
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const value = Math.min(1, Math.max(0, (b - a) * 2));
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const gray = Math.floor(value * 255);
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data[idx] = gray;
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data[idx + 1] = gray;
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data[idx + 2] = gray;
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data[idx + 3] = 255;
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}
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}
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ctx.putImageData(imageData, 0, 0);
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// Apply slight blur for texture
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ctx.filter = 'blur(0.5px)';
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ctx.drawImage(canvas, 0, 0, cols, rows, 0, 0, canvas.width, canvas.height);
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ctx.filter = 'none';
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}
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update();
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</script>
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</body>
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</html> |