kiddo 5.0.3

A high-performance, flexible, ergonomic k-d tree library. Ideal for geo- and astro- nearest-neighbour and k-nearest-neighbor queries
Documentation 
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#[doc(hidden)]
#[macro_export]
macro_rules! generate_nearest_n_within_unsorted {
    ($comments:tt) => {
        doc_comment! {
            concat!$comments,

            #[inline]
            pub fn nearest_n_within<D>(&self, query: &[A; K], dist: A, max_items: std::num::NonZero<usize>, sorted: bool) -> Vec<NearestNeighbour<A, T>>
            where
                D: DistanceMetric<A, K>,
            {
                if sorted || max_items < std::num::NonZero::new(usize::MAX).unwrap() {
                    if max_items <= std::num::NonZero::new(MAX_VEC_RESULT_SIZE).unwrap() {
                        self.nearest_n_within_stub::<D, SortedVec<NearestNeighbour<A, T>>>(query, dist, max_items.get(), sorted)
                    } else {
                        self.nearest_n_within_stub::<D, BinaryHeap<NearestNeighbour<A, T>>>(query, dist, max_items.get(), sorted)
                    }
                } else {
                    self.nearest_n_within_stub::<D, Vec<NearestNeighbour<A,T>>>(query, dist, 0, sorted)
                }
            }

            fn nearest_n_within_stub<D: DistanceMetric<A, K>, H: ResultCollection<A, T>>(
                &self, query: &[A; K], dist: A, res_capacity: usize, sorted: bool
            ) -> Vec<NearestNeighbour<A, T>> {
                let mut matching_items = H::new_with_capacity(res_capacity);
                let mut off = [A::zero(); K];

                unsafe {
                    self.nearest_n_within_unsorted_recurse::<D, H>(
                        query,
                        dist,
                        self.root_index,
                        0,
                        &mut matching_items,
                        &mut off,
                        A::zero(),
                    );
                }

                if sorted {
                    matching_items.into_sorted_vec()
                } else {
                    matching_items.into_vec()
                }
            }

            #[allow(clippy::too_many_arguments)]
            unsafe fn nearest_n_within_unsorted_recurse<D, R: ResultCollection<A, T>>(
                &self,
                query: &[A; K],
                radius: A,
                curr_node_idx: IDX,
                split_dim: usize,
                matching_items: &mut R,
                off: &mut [A; K],
                rd: A,
            ) where
                D: DistanceMetric<A, K>,
            {
                if is_stem_index(curr_node_idx) {
                    let node = self.stems.get_unchecked(curr_node_idx.az::<usize>());

                    let mut rd = rd;
                    let old_off = off[split_dim];
                    let new_off = query[split_dim].saturating_dist(node.split_val);

                    let [closer_node_idx, further_node_idx] =
                        if *query.get_unchecked(split_dim) < node.split_val {
                            [node.left, node.right]
                        } else {
                            [node.right, node.left]
                        };
                    let next_split_dim = (split_dim + 1).rem(K);

                    self.nearest_n_within_unsorted_recurse::<D, R>(
                        query,
                        radius,
                        closer_node_idx,
                        next_split_dim,
                        matching_items,
                        off,
                        rd,
                    );

                    rd = Axis::rd_update(rd, D::dist1(new_off, old_off));

                    if rd <= radius {
                        off[split_dim] = new_off;
                        self.nearest_n_within_unsorted_recurse::<D, R>(
                            query,
                            radius,
                            further_node_idx,
                            next_split_dim,
                            matching_items,
                            off,
                            rd,
                        );
                        off[split_dim] = old_off;
                    }
                } else {
                    let leaf_node = self
                        .leaves
                        .get_unchecked((curr_node_idx - IDX::leaf_offset()).az::<usize>());

                    leaf_node
                        .content_points
                        .iter()
                        .enumerate()
                        .take(leaf_node.size.az::<usize>())
                        .for_each(|(idx, entry)| {
                            let distance = D::dist(query, entry);

                            if distance < radius {
                                matching_items.add(NearestNeighbour {
                                    distance,
                                    item: *leaf_node.content_items.get_unchecked(idx.az::<usize>()),
                                })
                            }
                        });
                }
            }
        }
    };
}