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deepfield.js

v1.0.0

Published

Monte Carlo sampling library inspired by HUDF

Readme

🌌 deepfield.js

A high-performance, zero-dependency Monte Carlo sampling library for 1D, 2D, and 3D geometries with uniform distribution guarantees.

deepfield.js Banner

Why Deep Field? Just as the Hubble telescope revealed thousands of galaxies in a tiny, seemingly empty patch of sky, deepfield.js reveals complex volumes through the accumulation of millions of discrete points.


🚀 Performance

deepfield.js is optimized for real-time graphics and procedural generation.

  • Fast: Benchmarked at ~2.4M samples/sec for complex CSG Composite Shapes (Union/Difference) on standard mobile hardware (Thinkpad T14).
  • Memory Efficient: Designed to fill Float32Array buffers for direct WebGL/REGL integration.
  • Zero Dependencies: Pure mathematical implementation.

✨ Features

  • 1D Paths: Uniform Arc-Length sampling for Lines, Arcs, and Bezier curves (no "speed-up" on curves).
  • 2D Shapes: Circles, Ellipses, Rectangles, Triangles, and Convex Polygons.
  • 3D Volumes: Spheres, Ellipsoids, Boxes, Cones, and Cylinders.
  • CSG (Boolean) Operations: Create complex geometry using Union, Intersection, and Difference with uniform density across overlaps.

🛠 Quick Start

import { Sphere3D, Cylinder3D, CompositeShape } from 'deepfield.js';

// 1. Create a complex shape (A Sphere with a Hole)
const base = new Sphere3D({ x: 0, y: 0, z: 5 }, 5);
const hole = new Cylinder3D({ x: 0, y: 0, z: 0 }, 2, 10);

const hollowSphere = new CompositeShape('difference', [base, hole]);

// 2. Generate 1 million points for a WebGL Buffer
const iterations = 1000000;
const buffer = new Float32Array(iterations * 3);

for (let i = 0; i < iterations; i++) {
  const p = hollowSphere.sample();
  buffer[i * 3]     = p.x;
  buffer[i * 3 + 1] = p.y;
  buffer[i * 3 + 2] = p.z;
}

🧪 Mathematical Rigor

We use the "Gold Standard" for uniform distribution:

  • 2D: Square-root distribution for radial sampling to prevent center-clumping.

  • 3D: Cube-root distribution for volumes and acos mapping for spherical coordinates to prevent polar-clumping.

  • CSG: Rejection sampling for Unions to ensure overlapping regions don't become "brighter" or denser than the rest of the shape.

🤝 Contributing

We are currently building out Deterministic Sampling (sample(t)) for path animation. Check out Issue #1 to help out!

📜 License

MIT © Tsogt Baigalmaa