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Flatbush\n\nA really fast **static spatial index** for 2D points and rectangles in JavaScript.\n\nAn efficient implementation of the [packed Hilbert R-tree](https://en.wikipedia.org/wiki/Hilbert_R-tree#Packed_Hilbert_R-trees) algorithm. Enables fast spatial queries on a very large number of objects (e.g. millions), which is very useful in maps, data visualizations and computational geometry algorithms. Similar to [RBush](https://github.com/mourner/rbush), with the following key differences:\n\n- **Static**: you can't add/remove items after initial indexing.\n- **Faster** indexing and search, with much lower **memory** footprint.\n- Index is stored as a single **array buffer** (to [transfer](https://developer.mozilla.org/en-US/docs/Glossary/Transferable_objects) between threads or save as a compact binary file).\n\nSupports geographic locations with the [geoflatbush](https://github.com/mourner/geoflatbush) extension. See also: [KDBush](https://github.com/mourner/kdbush), a similar library for points.\n\n[![Build Status](https://github.com/mourner/flatbush/actions/workflows/node.yml/badge.svg)](https://github.com/mourner/flatbush/actions) [![minzipped size](https://img.shields.io/bundlephobia/minzip/flatbush)](https://esm.run/flatbush) [![Simply Awesome](https://img.shields.io/badge/simply-awesome-brightgreen.svg)](https://github.com/mourner/projects)\n\n## Usage\n\n```js\n// initialize Flatbush for 1000 items\nconst index = new Flatbush(1000);\n\n// fill it with 1000 rectangles\nfor (const p of items) {\n    index.add(p.minX, p.minY, p.maxX, p.maxY);\n}\n\n// perform the indexing\nindex.finish();\n\n// make a bounding box query\nconst found = index.search(minX, minY, maxX, maxY).map((i) => items[i]);\n\n// make a k-nearest-neighbors query\nconst neighborIds = index.neighbors(x, y, 5);\n\n// instantly transfer the index from a worker to the main thread\npostMessage(index.data, [index.data]);\n\n// reconstruct the index from a raw array buffer\nconst index = Flatbush.from(e.data);\n\n```\n\n## Install\n\nInstall with NPM: `npm install flatbush`, then import as a module:\n\n```js\nimport Flatbush from 'flatbush';\n```\n\nOr use as a module directly in the browser with [jsDelivr](https://www.jsdelivr.com/esm):\n\n```html\n<script type=\"module\">\n    import Flatbush from 'https://cdn.jsdelivr.net/npm/flatbush/+esm';\n</script>\n```\n\nAlternatively, there's a browser bundle with a `Flatbush` global variable:\n\n```html\n<script src=\"https://cdn.jsdelivr.net/npm/flatbush\"></script>\n```\n\n## API\n\n#### `new Flatbush(numItems[, nodeSize, ArrayType, ArrayBufferType])`\n\nCreates a Flatbush index that will hold a given number of items (`numItems`). Additionally accepts:\n\n- `nodeSize`: size of the tree node (`16` by default); experiment with different values for best performance (increasing this value makes indexing faster and queries slower, and vise versa).\n- `ArrayType`: the array type used for coordinates storage (`Float64Array` by default);\nother types may be faster in certain cases (e.g. `Int32Array` when your data is integer).\n- `ArrayBufferType`: the array buffer type used to store data (`ArrayBuffer` by default);\nyou may prefer `SharedArrayBuffer` if you want to share the index between threads (multiple `Worker`, `SharedWorker` or `ServiceWorker`).\n\n#### `index.add(minX, minY[, maxX, maxY])`\n\nAdds a given rectangle to the index. Returns a zero-based, incremental number that represents the newly added rectangle.\nIf not provided, `maxX` and `maxY` default to `minX` and `minY` (essentially adding a point).\n\n#### `index.finish()`\n\nPerforms indexing of the added rectangles.\nTheir number must match the one provided when creating a `Flatbush` object.\n\n#### `index.search(minX, minY, maxX, maxY[, filterFn])`\n\nReturns an array of indices of items intersecting or touching a given bounding box. Item indices refer to the value returned by [`index.add()`](#indexaddminx-miny-maxx-maxy).\n\n```js\nconst ids = index.search(10, 10, 20, 20);\n```\n\nIf given a `filterFn`, calls it on every found item (passing the item's index & bounding box coordinates)\nand only includes it if the function returned a truthy value.\n\n```js\nconst ids = index.search(10, 10, 20, 20, (i) => items[i].foo === 'bar');\n```\n\nAlternatively, instead of using the array of indices returned by `search`, you can handle the results in the function:\n\n```js\nindex.search(10, 10, 20, 20, (i, x0, y0, x1, y1) => {\n    console.log(`Item found: ${items[i]}, bbox: ${x0} ${y0} ${x1} ${y1}`);\n})\n```\n\n#### `index.neighbors(x, y[, maxResults, maxDistance, filterFn])`\n\nReturns an array of item indices in order of distance from the given `x, y`\n(known as K nearest neighbors, or KNN). Item indices refer to the value returned by [`index.add()`](#indexaddminx-miny-maxx-maxy).\n\n```js\nconst ids = index.neighbors(10, 10, 5); // returns 5 ids\n```\n\n`maxResults` and `maxDistance` are `Infinity` by default.\n\nIf given a `filterFn`, calls it on items that potentially belong to the results (passing the item's index)\nand only includes an item if the function returned a truthy value.\nUnlike `search`, it shouldn't be used for handling results.\n\n#### `Flatbush.from(data[, byteOffset])`\n\nRecreates a Flatbush index from raw `ArrayBuffer` or `SharedArrayBuffer` data\n(that's exposed as `index.data` on a previously indexed Flatbush instance).\nVery useful for transferring or sharing indices between threads or storing them in a file.\n\n### Properties\n\n- `data`: array buffer that holds the index.\n- `minX`, `minY`, `maxX`, `maxY`: bounding box of the data.\n- `numItems`: number of stored items.\n- `nodeSize`: number of items in a node tree.\n- `ArrayType`: array type used for internal coordinates storage.\n- `IndexArrayType`: array type used for internal item indices storage.\n\n## Performance\n\nRunning `node bench.js` with Node v14:\n\nbench | flatbush | rbush\n--- | --- | ---\nindex 1,000,000 rectangles | 273ms | 1143ms\n1000 searches 10% | 575ms | 781ms\n1000 searches 1% | 63ms | 155ms\n1000 searches 0.01% | 6ms | 17ms\n1000 searches of 100 neighbors | 24ms | 43ms\n1 search of 1,000,000 neighbors | 133ms | 280ms\n100,000 searches of 1 neighbor | 710ms | 1170ms\n\n## Ports\n\n- [chusitoo/flatbush](https://github.com/chusitoo/flatbush) (C++ port)\n- [jbuckmccready/static_aabb2d_index](https://github.com/jbuckmccready/static_aabb2d_index) (Rust port)\n- [jbuckmccready/Flatbush](https://github.com/jbuckmccready/Flatbush) (C# port)\n- [bmharper/flatbush-python](https://github.com/bmharper/flatbush-python) (Python port)\n- [FlatGeobuf](https://github.com/flatgeobuf/flatgeobuf) (a geospatial format inspired by Flatbush)\n- [IMQS/flatbush](https://github.com/IMQS/flatbush) (C++ port, no longer maintained)\n- [msfstef/flatbush-dart](https://github.com/msfstef/flatbush-dart) (Dart port)\n- [kylebarron/geo-index](https://github.com/kylebarron/geo-index) (Rust port and Python bindings, with ABI compatibility to this library)\n- [kylebarron/literate-flatbush](https://github.com/kylebarron/literate-flatbush) ([\"literate\"](https://en.wikipedia.org/wiki/Literate_programming) JS port that documents the internal algorithm)","users":{}}