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use rayon::prelude::*;
use crate::attraction::{apply_attraction, apply_nodewise_attraction};
use crate::barnes_hut::BarnesHutTree;
use crate::data::{FA2Data, NeighborhoodIndex};
use crate::forces::{apply_forces, apply_nodewise_forces};
use crate::gravity::{apply_gravity, apply_nodewise_gravity};
use crate::repulsion::{apply_nodewise_repulsion, apply_pairwise_repulsion};
use crate::settings::{FA2Settings, RepulsionMode};
use crate::traits::Float;
enum RepulsionIndex<F: Float> {
None,
BarnesHut(BarnesHutTree<F>),
}
impl<F: Float> RepulsionIndex<F> {
fn new(mode: &RepulsionMode<F>, order: usize) -> Self {
match mode {
RepulsionMode::Pairwise => Self::None,
RepulsionMode::BarnesHut { .. } => Self::BarnesHut(BarnesHutTree::with_capacity(order)),
}
}
}
/// The struct responsible for actually running the iterations of the FA2
/// algorithm.
///
/// It is generic over float precision.
///
/// It must be built from a [`FA2Settings`], using some [`FA2Data`].
pub struct FA2Layout<F: Float> {
settings: FA2Settings<F>,
data: FA2Data<F>,
neighborhood_index: Option<NeighborhoodIndex<F>>,
repulsion_index: RepulsionIndex<F>,
}
impl<F: Float> FA2Layout<F> {
pub(crate) fn new(settings: FA2Settings<F>, data: FA2Data<F>) -> Self {
let repulsion_index = RepulsionIndex::new(&settings.repulsion_mode, data.order());
let neighborhood_index = settings.parallel.then(|| NeighborhoodIndex::from(&data));
Self {
settings,
data,
neighborhood_index,
repulsion_index,
}
}
/// Run a single iteration, or "epoch", of the FA2 algorithm.
pub fn epoch(&mut self) -> F {
self.data.reset();
if let RepulsionIndex::BarnesHut(tree) = &mut self.repulsion_index {
let extent = self.data.positions_extent().unwrap();
tree.reset_with_extent(extent);
tree.rebuild(&self.data.xs, &self.data.ys, &self.data.ms);
}
// Parallel path
if self.settings.parallel {
let neighborhood_index = self.neighborhood_index.as_ref().unwrap();
self.data
.delta_xs
.par_iter_mut()
.zip(self.data.delta_ys.par_iter_mut())
.enumerate()
.for_each(|(n, (out_x, out_y))| {
match &self.repulsion_index {
RepulsionIndex::None => {
apply_nodewise_repulsion(
&self.settings,
self.data.xs[n],
self.data.ys[n],
self.data.ms[n],
&self.data.xs,
&self.data.ys,
&self.data.ms,
out_x,
out_y,
);
}
RepulsionIndex::BarnesHut(tree) => {
tree.apply_nodewise_repulsion(
&self.settings,
n,
&self.data.xs,
&self.data.ys,
&self.data.ms,
out_x,
out_y,
);
}
};
apply_nodewise_gravity(
&self.settings,
self.data.xs[n],
self.data.ys[n],
self.data.ms[n],
out_x,
out_y,
);
apply_nodewise_attraction(
&self.settings,
neighborhood_index,
n,
&self.data.xs,
&self.data.ys,
out_x,
out_y,
);
});
self.data
.xs
.par_iter_mut()
.zip(self.data.ys.par_iter_mut())
.zip(self.data.convergences.par_iter_mut())
.enumerate()
.map(|(n, ((x, y), c))| {
apply_nodewise_forces(
&self.settings,
x,
y,
self.data.ms[n],
self.data.delta_xs[n],
self.data.delta_ys[n],
self.data.old_delta_xs[n],
self.data.old_delta_ys[n],
c,
)
})
.sum()
}
// Sequential path
else {
match &mut self.repulsion_index {
RepulsionIndex::None => {
apply_pairwise_repulsion(
&self.settings,
&self.data.xs,
&self.data.ys,
&self.data.ms,
&mut self.data.delta_xs,
&mut self.data.delta_ys,
);
}
RepulsionIndex::BarnesHut(tree) => {
for n in 0..self.data.xs.len() {
tree.apply_nodewise_repulsion(
&self.settings,
n,
&self.data.xs,
&self.data.ys,
&self.data.ms,
&mut self.data.delta_xs[n],
&mut self.data.delta_ys[n],
);
}
}
};
apply_gravity(
&self.settings,
&self.data.xs,
&self.data.ys,
&self.data.ms,
&mut self.data.delta_xs,
&mut self.data.delta_ys,
);
apply_attraction(
&self.settings,
&self.data.xs,
&self.data.ys,
&self.data.edges,
&mut self.data.delta_xs,
&mut self.data.delta_ys,
);
apply_forces(
&self.settings,
&mut self.data.xs,
&mut self.data.ys,
&self.data.ms,
&self.data.delta_xs,
&self.data.delta_ys,
&self.data.old_delta_xs,
&self.data.old_delta_ys,
&mut self.data.convergences,
)
}
}
/// Return a reference to the wrapped [`FA2Data`] instance.
pub fn data(&self) -> &FA2Data<F> {
&self.data
}
/// Unwrap inner [`FA2Data`] instance, disposing of this [`FA2Layout`] runner.
pub fn into_data(self) -> FA2Data<F> {
self.data
}
/// Run given number of iterations of the FA2 algorithm.
pub fn run(&mut self, iterations: usize) {
for _ in 0..iterations {
self.epoch();
}
}
}