Find efficiently in k-dimensional data

API

• new Octree/new Quadtree(array<{[key]}>, <{key?: string = 'coords', transform?: function = x => x, depth?: number = 4}>)
• add(array<{[key]}>): Add an array of items
• closest(value): Search for the closest item by key value
• remove(value): Remove an item from the tree by key value

import colorNames from 'color-names';
import { Octree } from 'ktree'; // colors are 3-dimensional, so use Octree

// simple hex-to-rgb (assuming no short formats, else see https://unpkg.com/color-tf/hexToRgb.js)
const hexToRgb = s => [s.slice(-6, -4), s.slice(-4, -2), s.slice(-2)].map(x => parseInt(x, 16));

const colors = Object.entries(colorNames).map(([hex, name]) => ({ name, hex }));

// Octree constructor needs an array of {[key], ...} objects, where key is configurable
// We'll use 'hex' here, and add a 'transform' property to map those 'hex' values to 3D coordinates
const tree = new Octree(colors, { key: 'hex', transform: hexToRgb });
console.log(tree.closest('5544df')); // { name: 'Majorelle Blue', hex: '#6050dc', d2: 273 }
tree.remove('#6050dc');
console.log(tree.closest('5544df')); // { name: 'Iris', hex: '#5a4fcf', d2: 402 }