A very fast static spatial index for 2D points based on a flat KD-tree. Compared to RBush:
- points only — no rectangles
- static — you can't add/remove items
- indexing is 5-8 times faster
const index = new KDBush(x,y); // make an index
const ids1 = index.range(10, 10, 20, 20); // bbox search - minX, minY, maxX, maxY
const ids2 = index.within(10, 10, 5); // radius search - x, y, radius
Install
Install using NPM (npm install @julien.cousineau/kdbush
), then:
// import as a ES module
import KDBush from '@julien.cousineau/kdbush';
// or require in Node / Browserify
const KDBush = require('kdbush');
API
new KDBush(points[, getX, getY, nodeSize, arrayType])
Creates an index from the given points.
-
x
: Input array of x. -
y
: Input array of y. -
nodeSize
: Size of the KD-tree node,64
by default. Higher means faster indexing but slower search, and vise versa. -
arrayType
: Array type to use for storing coordinate values.Float64Array
by default, but if your coordinates are integer values,Int32Array
makes things a bit faster.
const x = new Float32Array(n);
const y = new Float32Array(n);
const index = new KDBush(x, y, 64, Float32Array);
index.range(minX, minY, maxX, maxY)
Finds all items within the given bounding box and returns an array of indices that refer to the items in the original x
and y
input array.
const results = index.range(10, 10, 20, 20).map(id => [x[id],y[id]]);
index.within(x, y, radius)
Finds all items within a given radius from the query point and returns an array of indices.
const results = index.within(10, 10, 5).map(id => [x[id],y[id]]);