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Yv","email":"df.creative@gmail.com"},"license":"MIT","homepage":"https://github.com/dy/point-cluster#readme","keywords":["snap-points-2d","supercluster","quadtree","quad-tree","kdtree","kd-tree","ann-tree","point","scatter","point2d","2d","cluster","clustering","geospatial","markers","round","data","vis","gl-vis"],"repository":{"type":"git","url":"git://github.com/dy/point-cluster.git"},"description":"Fast nd point clustering.","maintainers":[{"name":"dfcreative","email":"df.creative@gmail.com"},{"name":"dy","email":"df.creative@gmail.com"}],"readme":"# point-cluster [![Build Status](https://travis-ci.org/dy/point-cluster.svg?branch=master)](https://travis-ci.org/dy/point-cluster) [![experimental](https://img.shields.io/badge/stability-experimental-yellow.svg)](http://github.com/badges/stability-badges)\n\nPoint clustering for 2D spatial indexing. Incorporates optimized quad-tree data structure.\n\n<!--\n* [ ] quad-tree, kd-tree, ann-tree and other tree types.\n* [x] splatting by zoom layers.\n* [x] point selection/hover by range.\n* [ ] point radius and weight.\n* [ ] reverse z-index order mode to keep visible points in reclustering.\n* [ ] appending/removing points.\n* [x] no visually noticeable clustering artifacts.\n* [x] high performance (faster than [snap-points-2d](https://github.com/gl-vis/snap-points-2d)).\n* [x] no memory overuse.\n\n[DEMO](https://github.com/dy/point-cluster)\n-->\n\n\n```js\nconst cluster = require('point-cluster')\n\nlet ids = cluster(points)\n\n// get point ids in the indicated range\nlet selectedIds = ids.range([10, 10, 20, 20])\n\n// get levels of details: list of ids subranges for rendering purposes\nlet lod = ids.range([10, 10, 20, 20], { lod: true })\n```\n\n## API\n\n### `ids = cluster(points, options?)`\n\nCreate index for the set of 2d `points` based on `options`.\n\n* `points` is an array of `[x,y, x,y, ...]` or `[[x,y], [x,y], ...]` coordinates.\n* `ids` is _Uint32Array_ with point ids sorted by zoom levels, suitable for WebGL buffer, subranging or alike.\n* `options`\n\nOption | Default | Description\n---|---|---\n`bounds` | `'auto'` | Data range, if different from `points` bounds, eg. in case of subdata.\n`depth` | `256` | Max number of levels. Points below the indicated level are grouped into single level.\n`output` | `'array'` | Output data array or data format. For available formats see [dtype](https://npmjs.org/package/dtype).\n<!-- `node` | `1` | Min size of node, ie. tree traversal is stopped once the node contains less than the indicated number of points. -->\n<!-- `sort` | `'z'` | Sort values within levels by `x`-, `y`-coordinate, `z`-curve or `r` - point radius. `z` is the fastest for init, `x` or `y` are faster for `lod` and `r` is the most data-relevant. -->\n<!-- `pick` | `'first'` | `'first'`, `'last'` or a function, returning point id for the level. -->\n\n---\n\n\n### `result = ids.range(box?, options?)`\n\nGet point ids from the indicated range.\n\n* `box` can be any rectangle object, eg. `[l, t, r, b]`, see [parse-rect](https://github.com/dy/parse-rect).\n* `options`\n\nOption | Default | Description\n---|---|---\n`lod` | `false` | Makes result a list of level details instead of ids, useful for obtaining subranges to render.\n`px` | `0` | Min pixel size in data dimension (number or `[width, height]` couple) to search for, to ignore lower levels.\n`level` | `null` | Max level to limit search.\n\n```js\nlet levels = ids.range([0,0, 100, 100], { lod: true, d: dataRange / canvas.width })\n\nlevels.forEach([from, to] => {\n\t// offset and count point to range in `ids` array\n\trender( ids.subarray( from, to ) )\n})\n```\n\n\n### Related\n\n* [snap-points-2d](https://github.com/gl-vis/snap-points-2d) − grouping points by pixels.\n* [kdgrass](https://github.com/dy/kdgrass) − minimal kd-tree implementation.\n* [regl-scatter2d](https://github.com/dfreative/regl-scatter2d) − highly performant scatter2d plot.\n\n\n## License\n\n© 2017 Dmitry Yv. MIT License\n\nDevelopment supported by [plot.ly](https://github.com/plotly/).\n","readmeFilename":"README.md"}