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"686a369c0f4eadcbf56de7a343b052e872414fed", "registry": "npmjs" }, "kdbush-2.0.1.tgz": { "url": "https://registry.npmjs.org/kdbush/-/kdbush-2.0.1.tgz", "sha": "90c6128e3001ac68c550d7c9e2f222c0269666f1", "registry": "npmjs" }, "kdbush-3.0.0.tgz": { "url": "https://registry.npmjs.org/kdbush/-/kdbush-3.0.0.tgz", "sha": "f8484794d47004cc2d85ed3a79353dbe0abc2bf0", "registry": "npmjs" } }, "_attachments": { "kdbush-1.0.1.tgz": { "shasum": "3cbd03e9dead9c0f6f66ccdb96450e5cecc640e0" } }, "_rev": "2-0b6ffad723ac1573", "_id": "kdbush", "readme": "## KDBush [![Build Status](https://travis-ci.org/mourner/kdbush.svg?branch=master)](https://travis-ci.org/mourner/kdbush) [![Simply Awesome](https://img.shields.io/badge/simply-awesome-brightgreen.svg)](https://github.com/mourner/projects)\n\nA very fast static spatial index for 2D points based on a flat KD-tree.\nCompared to [RBush](https://github.com/mourner/rbush):\n\n- points only — no rectangles\n- static — you can't add/remove items\n- indexing is 5-8 times faster\n\n```js\nconst index = new KDBush(points); // make an index\nconst ids1 = index.range(10, 10, 20, 20); // bbox search - minX, minY, maxX, maxY\nconst ids2 = index.within(10, 10, 5); // radius search - x, y, radius\n```\n\n## Install\n\nInstall using NPM (`npm install kdbush`) or Yarn (`yarn add kdbush`), then:\n\n```js\n// import as a ES module\nimport KDBush from 'kdbush';\n\n// or require in Node / Browserify\nconst KDBush = require('kdbush');\n```\n\nOr use a browser build directly:\n\n```html\n\n```\n\n## API\n\n#### new KDBush(points[, getX, getY, nodeSize, arrayType])\n\nCreates an index from the given points.\n\n- `points`: Input array of points.\n- `getX`, `getY`: Functions to get `x` and `y` from an input point. By default, it assumes `[x, y]` format.\n- `nodeSize`: Size of the KD-tree node, `64` by default. Higher means faster indexing but slower search, and vise versa.\n- `arrayType`: Array type to use for storing coordinate values. `Float64Array` by default, but if your coordinates are integer values, `Int32Array` makes things a bit faster.\n\n```js\nconst index = kdbush(points, p => p.x, p => p.y, 64, Int32Array);\n```\n\n#### index.range(minX, minY, maxX, maxY)\n\nFinds all items within the given bounding box and returns an array of indices that refer to the items in the original `points` input array.\n\n```js\nconst results = index.range(10, 10, 20, 20).map(id => points[id]);\n```\n\n#### index.within(x, y, radius)\n\nFinds all items within a given radius from the query point and returns an array of indices.\n\n```js\nconst results = index.within(10, 10, 5).map(id => points[id]);\n```" }