JarCastingpoint-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.
pointsis an array of[x,y, x,y, ...]or[[x,y], [x,y], ...]coordinates.idsis 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.
boxcan 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
Development supported by plot.ly.