kiddo

K-dimensional tree library (bucket point-region implementation). A fork of kdtree. Refactored to use const generics, with some performance improvements and extra features. Thanks and kudos to mrhooray for the original kdtree library on which kiddo is based.

Ideal for neareast-neighbour stype queries on astronomical and geospatial datasets.

Installation

Add kiddo to Cargo.toml

[dependencies]
kiddo = "0.2.1"

Usage

use kiddo::KdTree;
use kiddo::ErrorKind;
use kiddo::distance::squared_euclidean;

let a: ([f64; 2], usize) = ([0f64, 0f64], 0);
let b: ([f64; 2], usize) = ([1f64, 1f64], 1);
let c: ([f64; 2], usize) = ([2f64, 2f64], 2);
let d: ([f64; 2], usize) = ([3f64, 3f64], 3);

let mut kdtree = KdTree::new();

kdtree.add(&a.0, a.1)?;
kdtree.add(&b.0, b.1)?;
kdtree.add(&c.0, c.1)?;
kdtree.add(&d.0, d.1)?;

assert_eq!(kdtree.size(), 4);
assert_eq!(
    kdtree.nearest(&a.0, 0, &squared_euclidean)?,
    vec![]
);
assert_eq!(
    kdtree.nearest(&a.0, 1, &squared_euclidean)?,
    vec![(0f64, &0)]
);
assert_eq!(
    kdtree.nearest(&a.0, 2, &squared_euclidean)?,
    vec![(0f64, &0), (2f64, &1)]
);
assert_eq!(
    kdtree.nearest(&a.0, 3, &squared_euclidean)?,
    vec![(0f64, &0), (2f64, &1), (8f64, &2)]
);
assert_eq!(
    kdtree.nearest(&a.0, 4, &squared_euclidean)?,
    vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
    kdtree.nearest(&a.0, 5, &squared_euclidean)?,
    vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
);
assert_eq!(
    kdtree.nearest(&b.0, 4, &squared_euclidean)?,
    vec![(0f64, &1), (2f64, &0), (2f64, &2), (8f64, &3)]
);