rusty-tree 0.0.1

//! Data structures and functions to create regular and adaptive Octrees.

use ndarray::{Array1, ArrayView1, ArrayView2, Axis};
use rusty_kernel_tools::RealType;
use std::collections::{HashMap, HashSet};
use std::fmt;
use std::time::Duration;

pub mod adaptive_octree;
pub mod regular_octree;

pub use adaptive_octree::*;
pub use regular_octree::*;

/// The type of the octree.
pub enum OctreeType {
    /// Regular octree.
    Regular,
    /// Use for balanced adaptive octrees.
    BalancedAdaptive,
    /// Use for unbalanced adaptive octrees.
    UnbalancedAdaptive,
}

/// The basic Octree data structure
pub struct Octree<'a, T: RealType> {
    /// A (3, N) array of N particles.
    pub particles: ArrayView2<'a, T>,

    /// The maximum level in the tree.
    pub max_level: usize,

    /// The origin of the bounding box for the particles.
    pub origin: [f64; 3],

    /// The diameter across each dimension of the bounding box.
    pub diameter: [f64; 3],

    /// The non-empty keys for each level of the tree.
    pub level_keys: HashMap<usize, HashSet<usize>>,

    /// The keys associated with the particles.
    pub particle_keys: Array1<usize>,

    /// The set of near-field keys for each non-empty key.
    pub near_field: HashMap<usize, HashSet<usize>>,

    /// The set of keys in the interaction list for each non-empty key.
    pub interaction_list: HashMap<usize, HashSet<usize>>,

    /// The index set of particles associated with each leaf key.
    pub leaf_key_to_particles: HashMap<usize, HashSet<usize>>,

    /// The set of all non-empty keys in the tree.
    pub all_keys: HashSet<usize>,

    /// The type of the Octree.
    pub octree_type: OctreeType,

    /// Statistics for the tree.
    pub statistics: Statistics,
}

/// A structure that stores various statistics for a tree.
pub struct Statistics {
    pub number_of_particles: usize,
    pub max_level: usize,
    pub number_of_leafs: usize,
    pub number_of_keys: usize,
    pub creation_time: Duration,
    pub minimum_number_of_particles_in_leaf: usize,
    pub maximum_number_of_particles_in_leaf: usize,
    pub average_number_of_particles_in_leaf: f64,
}

impl std::fmt::Display for Statistics {
    /// Create an output string for tree statistics.
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(
            f,
            "\n\nOctree Statistics\n\
                   ==============================\n\
                   Number of Particles: {}\n\
                   Maximum level: {}\n\
                   Number of leaf keys: {}\n\
                   Number of keys in tree: {}\n\
                   Creation time [s]: {}\n\
                   Minimum number of particles in leaf node: {}\n\
                   Maximum number of particles in leaf node: {}\n\
                   Average number of particles in leaf node: {:.2}\n\
                   ==============================\n\n
                   ",
            self.number_of_particles,
            self.max_level,
            self.number_of_leafs,
            self.number_of_keys,
            (self.creation_time.as_millis() as f64) / 1000.0,
            self.minimum_number_of_particles_in_leaf,
            self.maximum_number_of_particles_in_leaf,
            self.average_number_of_particles_in_leaf
        )
    }
}

/// Given the set of all keys, compute the interaction list for each key.
///
/// Returns a map from keys to the corresponding interaction list, represented by
/// a set of keys.
pub fn compute_interaction_list_map(all_keys: &HashSet<usize>) -> HashMap<usize, HashSet<usize>> {
    use crate::morton::compute_interaction_list;
    use rayon::prelude::*;

    let mut interaction_list_map = HashMap::<usize, HashSet<usize>>::new();

    for &key in all_keys {
        interaction_list_map.insert(key, HashSet::<usize>::new());
    }

    interaction_list_map
        .par_iter_mut()
        .for_each(|(&key, hash_set)| {
            let current_interaction_list = compute_interaction_list(key);
            hash_set.extend(&current_interaction_list);
        });

    interaction_list_map
}

/// Given the set of all keys, compute the near field for each key.
///
/// Returns a map from keys to the corresponding near field, represented by
/// a set of keys.
pub fn compute_near_field_map(all_keys: &HashSet<usize>) -> HashMap<usize, HashSet<usize>> {
    use crate::morton::compute_near_field;
    use rayon::prelude::*;

    let mut near_field_map = HashMap::<usize, HashSet<usize>>::new();

    for &key in all_keys {
        near_field_map.insert(key, HashSet::<usize>::new());
    }

    near_field_map.par_iter_mut().for_each(|(&key, hash_set)| {
        let current_near_field = compute_near_field(key);
        hash_set.extend(&current_near_field);
    });

    near_field_map
}

/// Compute the leaf map.
///
/// Returns a map from leaf keys to associated particle indices.
pub fn compute_leaf_map(particle_keys: ArrayView1<usize>) -> HashMap<usize, HashSet<usize>> {
    use itertools::Itertools;

    let mut leaf_key_to_particles = HashMap::<usize, HashSet<usize>>::new();
    for &key in particle_keys.iter().unique() {
        leaf_key_to_particles.insert(key, HashSet::<usize>::new());
    }

    for (index, key) in particle_keys.iter().enumerate() {
        leaf_key_to_particles.get_mut(key).unwrap().insert(index);
    }

    leaf_key_to_particles
}

/// Given an array of keys. Return the level information of the tree.
///
/// The function returns a 3-tuple `(max_level, all_keys, level_keys)`.
/// `max_level` us a `usize` that contains the maximum level of the keys.
/// The set `all_keys` contains all keys from the tree by completing the tree
/// from the leaf onwards to the top and storing all parent keys along the way.
/// The map `level_keys` is a map from a given level to the set of all keys contained
/// in the level.
pub fn compute_level_information(
    particle_keys: ArrayView1<usize>,
) -> (usize, HashSet<usize>, HashMap<usize, HashSet<usize>>) {
    use crate::morton::{find_level, find_parent};
    use std::iter::FromIterator;

    let max_level = particle_keys
        .iter()
        .map(|&item| find_level(item))
        .max()
        .unwrap();

    let nlevels = max_level + 1;
    let leaf_keys: HashSet<usize> = particle_keys.iter().copied().collect();
    let mut level_keys = HashMap::<usize, HashSet<usize>>::new();

    // Distribute the keys among different sets for each level
    for current_level in 0..nlevels {
        level_keys.insert(
            current_level,
            HashSet::from_iter(
                leaf_keys
                    .iter()
                    .filter(|&&key| find_level(key) == current_level)
                    .copied(),
            ),
        );
    }

    // Now fill up the sets with all the various parent keys.
    for current_level in (1..nlevels).rev() {
        let parent_index = current_level - 1;
        let current_set: HashSet<usize> = level_keys
            .get(&current_level)
            .unwrap()
            .iter()
            .copied()
            .collect();
        let parent_set = level_keys.get_mut(&parent_index).unwrap();
        parent_set.extend(current_set.iter().map(|&key| find_parent(key)));
    }

    let mut all_keys = HashSet::<usize>::new();

    for (_, current_keys) in level_keys.iter() {
        all_keys.extend(current_keys.iter());
    }

    (max_level, all_keys, level_keys)
}

/// Create a regular octree from an array of particles.
///
/// Returns the set of all non-empty keys associated with a regular octree created
/// from an array of particles.
/// # Arguments
/// * `particles` - A (3, N) array of particles
/// * `max_level` - The deepest level of the tree.
/// * `origin` - The origin of the bounding box.
/// * `diameter` - The diameter of the bounding box in each dimension.
pub fn compute_complete_regular_tree<T: RealType>(
    particles: ArrayView2<T>,
    max_level: usize,
    origin: &[f64; 3],
    diameter: &[f64; 3],
) -> HashSet<usize> {
    use super::morton::encode_points;

    let keys = encode_points(particles, max_level, origin, diameter);
    let (_, all_keys, _) = compute_level_information(keys.view());

    all_keys
}

/// Export an octree to vtk
pub fn export_to_vtk<T: RealType>(tree: &Octree<T>, filename: &str) {
    use super::morton::{serialize_box_from_key};
    use std::iter::FromIterator;
    use vtkio::model::*;
    use std::path::PathBuf;

    let filename = filename.to_owned();

    // We use a vtk voxel type, which has
    // 8 points per cell, i.e. 24 float numbers
    // per cell.
    const POINTS_PER_CELL: usize = 8;

    let num_particles = tree.particles.len_of(Axis(1));

    let all_keys = &tree.all_keys;
    let num_keys = all_keys.len();

    let num_floats = 3 * POINTS_PER_CELL * num_keys;

    let mut cell_points = Vec::<f64>::with_capacity(num_floats);

    for &key in all_keys {
        let serialized = serialize_box_from_key(key, &tree.origin, &tree.diameter);
        cell_points.extend(serialized);
    }

    for index in 0..num_particles {
        cell_points.push(tree.particles[[0, index]].to_f64().unwrap());
        cell_points.push(tree.particles[[1, index]].to_f64().unwrap());
        cell_points.push(tree.particles[[2, index]].to_f64().unwrap());
    }

    let num_points = 8 * (num_keys as u64) + (num_particles as u64);

    let connectivity = Vec::<u64>::from_iter(0..num_points);
    let mut offsets = Vec::<u64>::from_iter((0..(num_keys as u64)).map(|item| 8 * item + 8));
    offsets.push(num_points);

    let mut types = vec![CellType::Voxel; num_keys];
    types.push(CellType::PolyVertex);

    let mut cell_data = Vec::<i32>::with_capacity(num_points as usize);

    for _ in 0..num_keys {
        cell_data.push(0);
    }
    cell_data.push(1);


    let model = Vtk {
        version: Version { major: 1, minor: 0 },
        title: String::new(),
        byte_order: ByteOrder::BigEndian,
        file_path: Some(PathBuf::from(&filename)),
        data: DataSet::inline(UnstructuredGridPiece {
            points: IOBuffer::F64(cell_points),
            cells: Cells {
                cell_verts: VertexNumbers::XML {
                    connectivity: connectivity,
                    offsets: offsets,
                },
                types: types,
            },
            data: Attributes {
                point: vec![],
                cell: vec![Attribute::DataArray(DataArrayBase {
                    name: String::from("colors"),
                    elem: ElementType::Scalars {
                        num_comp: 1,
                        lookup_table: None,
                    },
                    data: IOBuffer::I32(cell_data),
                })],
            },
        }),
    };


    model.export(filename).unwrap();
}