This library provides a static k-d tree data structure for performing efficient orthogonal range queries, nearest neighbor searches, and k-nearest neighbor queries on multidimensional point data. It's designed for JavaScript environments including Node.js and browsers, offering significantly faster performance than alternative implementations while consuming minimal memory. The project is particularly useful for developers working with spatial data in dimensions where k-d trees remain practical, typically up to medium dimensions where the number of points isn't too large.
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Support the dependencies of mikolalysenko/static-kdtree
Bit twiddling hacks for JavaScript
Index calculations for inorder layout of balanced binary trees
Packs an array-of-arrays into a single ndarray
Pooled memory for ndarrays
Linear time selection algorithm for ndarrays
Reuse typed arrays
Common operations for ndarray arrays
Multidimensional Arrays
Higher-order functions and common patterns for asynchronous code
Initialize an array of arrays to a constant
tap-producing test harness for node and browsers
A simple-yet-powerful KD-tree library for NodeJS, with support for lightning-fast k-Nearest Neighbour queries. Supports normalization, weights, key and filter parameters
Basic libkdtree binding to node
Generates an array of consecutive integers starting at 0
Converts an ndarray into an array-of-arrays
local development server that aims to make using browserify fast and fun
browser-side require() the node way
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