birth: Diffusing Echoes in Monochrome
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index.html
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158
index.html
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<!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>Neurameba Art</title>
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<style>
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body {
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margin: 0;
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padding: 0;
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overflow: hidden;
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background: #0a0a0a;
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display: flex;
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justify-content: center;
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align-items: center;
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height: 100vh;
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font-family: monospace;
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}
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canvas {
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display: block;
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}
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#info {
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position: absolute;
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bottom: 20px;
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color: #666;
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font-size: 11px;
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text-align: center;
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width: 100%;
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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="info">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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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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// Reaction-diffusion parameters
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const params = {
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feed: 0.055,
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kill: 0.062,
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color_offset: 0.1,
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diffusion_rate_a: 1.0,
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diffusion_rate_b: 0.5,
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time_step: 1.0,
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grid_size: 4,
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decay: 0.99
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};
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const cols = Math.floor(canvas.width / params.grid_size);
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const rows = Math.floor(canvas.height / params.grid_size);
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const grid = Array(cols).fill().map(() => Array(rows).fill(0));
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const next = Array(cols).fill().map(() => Array(rows).fill(0));
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// Initialize with random sparse patterns
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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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grid[i][j] = Math.random() * 0.1;
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}
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}
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// Palette: dryness/monochrome with subtle playfulness
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const palette = [
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'#f0f0f0',
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'#d0d0d0',
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'#b0b0b0',
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'#909090',
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'#707070',
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'#505050',
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'#303030',
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'#101010'
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];
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function update() {
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// Reaction-diffusion step
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for (let x = 1; x < cols - 1; x++) {
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for (let y = 1; y < rows - 1; y++) {
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const a = grid[x][y];
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const b = grid[x][y];
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const laplace_a = (grid[x-1][y] + grid[x+1][y] + grid[x][y-1] + grid[x][y+1] - 4*a) * params.diffusion_rate_a;
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const laplace_b = (grid[x-1][y] + grid[x+1][y] + grid[x][y-1] + grid[x][y+1] - 4*b) * params.diffusion_rate_b;
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const reaction = a * b * b;
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next[x][y] = a + params.time_step * (laplace_a - reaction + params.feed * (1 - a));
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next[x][y] = b + params.time_step * (laplace_b + reaction - (params.kill + params.feed) * b);
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// Apply decay
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next[x][y] *= params.decay;
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}
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}
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// Swap grids
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[grid, next].forEach((g, i) => {
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if (i === 0) {
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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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grid[x][y] = next[x][y];
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}
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}
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}
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});
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// Draw
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ctx.clearRect(0, 0, canvas.width, canvas.height);
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const imageData = ctx.createImageData(canvas.width, canvas.height);
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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 val = grid[x][y];
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if (val > 0.1) {
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const idx = (x * params.grid_size + y * params.grid_size * canvas.width) * 4;
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const c = palette[Math.floor(val * (palette.length - 1))];
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const hex = parseInt(c.replace('#', ''), 16);
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const r = (hex >> 16) & 0xff;
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const g = (hex >> 8) & 0xff;
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const b = hex & 0xff;
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for (let dx = 0; dx < params.grid_size; dx++) {
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for (let dy = 0; dy < params.grid_size; dy++) {
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const px = x * params.grid_size + dx;
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const py = y * params.grid_size + dy;
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if (px < canvas.width && py < canvas.height) {
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const pixelIdx = (px + py * canvas.width) * 4;
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data[pixelIdx] = r;
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data[pixelIdx + 1] = g;
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data[pixelIdx + 2] = b;
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data[pixelIdx + 3] = 255 * (val > 0.2 ? 1 : val / 0.2);
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}
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}
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}
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}
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}
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}
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ctx.putImageData(imageData, 0, 0);
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}
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function animate() {
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update();
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requestAnimationFrame(animate);
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}
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animate();
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</script>
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</body>
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</html>
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