kdtree 0.8.1

K-dimensional tree in Rust for fast geospatial indexing and nearest neighbors lookup
Documentation 
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//! # kdtree
//!
//! K-dimensional tree for Rust (bucket point-region implementation)
//!
//! ## Usage
//!
//! ```
//! use kdtree::KdTree;
//! use kdtree::ErrorKind;
//! use kdtree::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 dimensions = 2;
//! let mut kdtree = KdTree::new(dimensions);
//!
//! kdtree.add(&a.0, a.1).unwrap();
//! kdtree.add(&b.0, b.1).unwrap();
//! kdtree.add(&c.0, c.1).unwrap();
//! kdtree.add(&d.0, d.1).unwrap();
//!
//! assert_eq!(kdtree.size(), 4);
//! assert_eq!(
//!     kdtree.nearest(&a.0, 0, &squared_euclidean).unwrap(),
//!     vec![]
//! );
//! assert_eq!(
//!     kdtree.nearest(&a.0, 1, &squared_euclidean).unwrap(),
//!     vec![(0f64, &0)]
//! );
//! assert_eq!(
//!     kdtree.nearest(&a.0, 2, &squared_euclidean).unwrap(),
//!     vec![(0f64, &0), (2f64, &1)]
//! );
//! assert_eq!(
//!     kdtree.nearest(&a.0, 3, &squared_euclidean).unwrap(),
//!     vec![(0f64, &0), (2f64, &1), (8f64, &2)]
//! );
//! assert_eq!(
//!     kdtree.nearest(&a.0, 4, &squared_euclidean).unwrap(),
//!     vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
//! );
//! assert_eq!(
//!     kdtree.nearest(&a.0, 5, &squared_euclidean).unwrap(),
//!     vec![(0f64, &0), (2f64, &1), (8f64, &2), (18f64, &3)]
//! );
//! assert_eq!(
//!     kdtree.nearest(&b.0, 4, &squared_euclidean).unwrap(),
//!     vec![(0f64, &1), (2f64, &0), (2f64, &2), (8f64, &3)]
//! );
//! assert_eq!(
//!     kdtree
//!         .nearest_within_radius(&a.0, 4, Some(5f64), &squared_euclidean)
//!         .unwrap(),
//!     vec![(0f64, &0), (2f64, &1)]
//! );
//! ```
//!
//! `nearest_within_radius` keeps the exact ordering guarantees of `nearest`; it
//! still truncates by `k` and only filters out items whose distance exceeds the
//! optional `radius`. Mutating results remains an iterator-only operation
//! (e.g. `iter_nearest_within_radius_mut`) so the tree never hands out overlapping
//! mutable borrows.
//!
//! `within` (and `within_count`) produce unordered batches that include every point
//! inside the requested radius—sort the returned vector manually if you need a
//! deterministic order. Use `bounding_box` for axis-aligned range queries when you
//! only need raw `&T` references without ordering guarantees.

#[cfg(feature = "serialize")]
#[cfg_attr(feature = "serialize", macro_use)]
extern crate serde_derive;

pub mod distance;
mod heap_element;
pub mod kdtree;
pub use crate::kdtree::ErrorKind;
pub use crate::kdtree::KdTree;