point-cluster

Point clustering for 2D spatial indexing. Incorporates optimized quad-tree data structure.

const cluster = require('point-cluster')

let ids = cluster(points)

// get point ids in the indicated range
let selectedIds = ids.range([10, 10, 20, 20])

// get levels of details: list of ids subranges for rendering purposes
let lod = ids.range([10, 10, 20, 20], { lod: true })

API

ids = cluster(points, options?)

Create index for the set of 2d points based on options.

• points is an array of [x,y, x,y, ...] or [[x,y], [x,y], ...] coordinates.
• ids is Uint32Array with point ids sorted by zoom levels, suitable for WebGL buffer, subranging or alike.
• options

Option | Default | Description ---|---|--- bounds | 'auto' | Data range, if different from points bounds, eg. in case of subdata. depth | 256 | Max number of levels. Points below the indicated level are grouped into single level. output | 'array' | Output data array or data format. For available formats see dtype.

result = ids.range(box?, options?)

Get point ids from the indicated range.

• box can be any rectangle object, eg. [l, t, r, b], see parse-rect.
• options

Option | Default | Description ---|---|--- lod | false | Makes result a list of level details instead of ids, useful for obtaining subranges to render. px | 0 | Min pixel size in data dimension (number or [width, height] couple) to search for, to ignore lower levels. level | null | Max level to limit search.

let levels = ids.range([0,0, 100, 100], { lod: true, d: dataRange / canvas.width })

levels.forEach([from, to] => {
	// offset and count point to range in `ids` array
	render( ids.subarray( from, to ) )
})

Related

• snap-points-2d − grouping points by pixels.
• kdgrass − minimal kd-tree implementation.
• regl-scatter2d − highly performant scatter2d plot.

License

© 2017 Dmitry Yv. MIT License

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