
Research
/Security News
Contagious Interview Campaign Escalates With 67 Malicious npm Packages and New Malware Loader
North Korean threat actors deploy 67 malicious npm packages using the newly discovered XORIndex malware loader.
point-cluster
Advanced tools
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 })
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 ) )
})
© 2017 Dmitry Yv. MIT License
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FAQs
Fast nd point clustering.
The npm package point-cluster receives a total of 10,745 weekly downloads. As such, point-cluster popularity was classified as popular.
We found that point-cluster demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
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