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//! Broccoli is a broad-phase collision detection library.
//! The base data structure is a hybrid between a [KD Tree](https://en.wikipedia.org/wiki/K-d_tree) and [Sweep and Prune](https://en.wikipedia.org/wiki/Sweep_and_prune).
//!
//! Checkout the github [examples](https://github.com/tiby312/broccoli-project/tree/master/broccoli/examples).
#![doc(
html_logo_url = "https://raw.githubusercontent.com/tiby312/broccoli-project/master/assets/logo.png",
html_favicon_url = "https://raw.githubusercontent.com/tiby312/broccoli-project/master/assets/logo.png"
)]
#![forbid(unsafe_code)]
#[cfg(doctest)]
mod test_readme {
macro_rules! external_doc_test {
($x:expr) => {
#[doc = $x]
extern "C" {}
};
}
external_doc_test!(include_str!("../../README.md"));
}
#[macro_use]
extern crate alloc;
pub use axgeom;
pub mod build;
pub mod node;
pub mod aabb;
use aabb::pin::AabbPin;
use aabb::pin::AabbPinIter;
use aabb::pin::*;
use aabb::*;
use axgeom::*;
use build::*;
use node::*;
#[cfg(test)]
mod tests;
pub mod assert;
pub mod queries;
use assert::Assert;
use assert::Naive;
pub use axgeom::rect;
///Shorthand constructor of [`BBox`]
#[inline(always)]
#[must_use]
pub fn bbox<N, T>(rect: axgeom::Rect<N>, inner: T) -> BBox<N, T> {
BBox::new(rect, inner)
}
///Shorthand constructor of [`BBoxMut`]
#[inline(always)]
#[must_use]
pub fn bbox_mut<N, T>(rect: axgeom::Rect<N>, inner: &mut T) -> BBoxMut<N, T> {
BBoxMut::new(rect, inner)
}
///
/// Used to de-couple tree information from
/// the underlying lifetimed slice of elements
/// to be combined again later on.
///
/// See [`Tree::get_tree_data()`] and [`Tree::from_tree_data()`]
///
#[derive(Clone)]
pub struct TreeData<N: Num> {
nodes: Vec<NodeData<N>>,
}
///
/// Convenience function to call unpack_inner on any number of arguments.
///
#[macro_export]
macro_rules! unpack {
( $( $x:ident ),* ) => {
$(
let mut $x = $x.unpack_inner();
)*
};
}
///
/// Use a macro to save a step build the Cache and calling build on it.
///
#[macro_export]
macro_rules! from_cached_key {
( $x:ident ,$y:expr,$z:expr) => {
let mut $x = $crate::Cached::new_by_cached_key($y, $z);
let mut $x = $x.build();
};
}
///
/// Automatically create semi-direct bbox layouts
///
pub struct Cached<'a, N, T> {
rects: Vec<BBoxMut<'a, N, T>>,
}
impl<'a, N: Num, T> Cached<'a, N, T> {
///
/// Finish building the tree
///
pub fn build<'b>(&'b mut self) -> Tree<'b, BBoxMut<'a, N, T>> {
Tree::new(&mut self.rects)
}
///
/// Caches the bboxes one time and sorts them.
///
pub fn new_by_cached_key(
a: &'a mut [T],
mut key: impl FnMut(&T) -> Rect<N>,
) -> Cached<'a, N, T> {
let rects = a.iter_mut().map(|a| BBoxMut::new(key(a), a)).collect();
Cached { rects }
}
}
///
/// A broccoli Tree.
///
pub struct Tree<'a, T: Aabb> {
nodes: Box<[Node<'a, T, T::Num>]>,
}
impl<'a, T: Aabb + 'a> Tree<'a, T> {
///
/// User responsibility to provide a distribution that is a
/// valid broccoli tree.
///
pub fn from_nodes(nodes: Vec<Node<'a, T, T::Num>>) -> Self {
Tree {
nodes: nodes.into_boxed_slice(),
}
}
///
/// Return the underlying data.
///
pub fn into_nodes(self) -> Vec<Node<'a, T, T::Num>> {
self.nodes.into_vec()
}
///
/// Store tree data such as the number of
/// elements per node, as well as the bounding
/// range for each node.
///
pub fn get_tree_data(&self) -> TreeData<T::Num> {
let nodes = self.nodes.iter().map(|x| x.as_data()).collect();
TreeData { nodes }
}
///
/// Create a Tree using stored treedata and the original
/// list of elements in the same order.
///
/// Use this function if you want to store a constructed tree
/// outside of lifetimes.
///
/// It is the user responsibility to feed this function the same
/// distribution of aabbs in the same order as the distribution that
/// was used in the original tree from which [`Tree::get_tree_data()`] was called.
/// Not doing so will make an invalid tree with no error notification.
///
/// Consider calling [`assert::assert_tree_invariants()`] after tree construction
/// if you don't know if it was the same distribution which will atleast tell
/// you if the distribution makes a valid tree.
///
pub fn from_tree_data(bots: &'a mut [T], data: &TreeData<T::Num>) -> Self {
let mut last = Some(bots);
let nodes = data
.nodes
.iter()
.map(|x| {
let (range, rest) = last.take().unwrap().split_at_mut(x.range);
last = Some(rest);
Node {
range: AabbPin::from_mut(range),
cont: x.cont,
div: x.div,
min_elem: x.min_elem,
//num_elem: x.num_elem,
}
})
.collect();
assert!(last.unwrap().is_empty());
Tree { nodes }
}
///
/// Create a new tree with the default tree height heuristic
///
pub fn new(bots: &'a mut [T]) -> Self
where
T: ManySwap,
{
let (mut e, v) = TreeEmbryo::new(bots);
e.recurse(v, &mut DefaultSorter);
e.finish()
}
#[inline(always)]
pub fn vistr_mut(&mut self) -> VistrMutPin<Node<'a, T, T::Num>> {
let tree = compt::dfs_order::CompleteTreeMut::from_preorder_mut(&mut self.nodes).unwrap();
VistrMutPin::new(tree.vistr_mut())
}
#[inline(always)]
pub fn vistr(&self) -> Vistr<Node<'a, T, T::Num>> {
let tree = compt::dfs_order::CompleteTree::from_preorder(&self.nodes).unwrap();
tree.vistr()
}
#[must_use]
#[inline(always)]
pub fn num_levels(&self) -> usize {
compt::dfs_order::CompleteTree::from_preorder(&self.nodes)
.unwrap()
.get_height()
}
#[must_use]
#[inline(always)]
pub fn num_nodes(&self) -> usize {
self.nodes.len()
}
#[must_use]
#[inline(always)]
pub fn get_nodes(&self) -> &[Node<'a, T, T::Num>] {
&self.nodes
}
#[must_use]
#[inline(always)]
pub fn get_nodes_mut(&mut self) -> AabbPin<&mut [Node<'a, T, T::Num>]> {
AabbPin::from_mut(&mut self.nodes)
}
}
///
/// Tools to determine the best tree height for the given number of elements.
///
pub mod num_level {
#[cfg(test)]
mod test {
use super::*;
#[test]
fn test_num_nodes() {
assert_eq!(num_nodes(1), 01);
assert_eq!(num_nodes(2), 03);
assert_eq!(num_nodes(3), 07);
assert_eq!(num_nodes(4), 15);
}
}
///
/// The number of nodes for a tree with the given height.
///
pub const fn num_nodes(num_levels: usize) -> usize {
assert!(num_levels >= 1);
2usize.rotate_left((num_levels - 1) as u32) - 1
}
///The default number of elements per node
///
///If we had a node per bot, the tree would have too many levels. Too much time would be spent recursing.
///If we had too many bots per node, you would lose the properties of a tree, and end up with plain sweep and prune.
///Theory would tell you to just make a node per bot, but there is
///a sweet spot inbetween determined by the real-word properties of your computer.
///we want each node to have space for around num_per_node bots.
///there are 2^h nodes.
///2^h*200>=num_bots. Solve for h s.t. h is an integer.
///Make this number too small, and the tree will have too many levels,
///and too much time will be spent recursing.
///Make this number too high, and you will lose the properties of a tree,
///and you will end up with just sweep and prune.
///This number was chosen empirically from running the Tree_alg_data project,
///on two different machines.
pub const DEFAULT_NUMBER_ELEM_PER_NODE: usize = 80;
///
/// Use the default heuristic for tree height.
///
#[must_use]
pub fn default(num_elements: usize) -> usize {
with_num_elem_in_leaf(num_elements, DEFAULT_NUMBER_ELEM_PER_NODE)
}
///Specify a custom default number of elements per leaf
#[must_use]
pub const fn with_num_elem_in_leaf(num_elements: usize, num_elem_leaf: usize) -> usize {
#[must_use]
const fn log_2(x: u64) -> u64 {
const fn num_bits<T>() -> usize {
core::mem::size_of::<T>() * 8
}
num_bits::<u64>() as u64 - x.leading_zeros() as u64 - 1
}
//we want each node to have space for around 300 bots.
//there are 2^h nodes.
//2^h*200>=num_bots. Solve for h s.t. h is an integer.
if num_elements <= num_elem_leaf {
1
} else {
let (num_bots, num_per_node) = (num_elements as u64, num_elem_leaf as u64);
let a = num_bots / num_per_node;
let a = log_2(a);
let k = (((a / 2) * 2) + 1) as usize;
assert!(k % 2 == 1);
assert!(k >= 1);
k
}
}
}