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Refactor all types in their own modules.

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This commit is contained in:
Dimitri Sabadie 2019-04-19 13:39:37 +02:00
parent 935565ca22
commit 99068fb2d0
7 changed files with 394 additions and 369 deletions
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79
src/interpolate.rs Normal file
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#[cfg(feature = "std")] use std::ops::{Div, Mul};
#[cfg(not(feature = "std"))] use core::ops::{Div, Mul};
use num_traits::Float;
/// Keys that can be interpolated in between. Implementing this trait is required to perform
/// sampling on splines.
///
/// `T` is the variable used to sample with. Typical implementations use `f32` or `f64`, but you’re
/// free to use the ones you like.
pub trait Interpolate<T>: Sized + Copy {
/// Linear interpolation.
fn lerp(a: Self, b: Self, t: T) -> Self;
/// Cubic hermite interpolation.
///
/// Default to `Self::lerp`.
fn cubic_hermite(_: (Self, T), a: (Self, T), b: (Self, T), _: (Self, T), t: T) -> Self {
Self::lerp(a.0, b.0, t)
}
}
// Default implementation of Interpolate::cubic_hermite.
//
// `V` is the value being interpolated. `T` is the sampling value (also sometimes called time).
pub(crate) fn cubic_hermite_def<V, T>(x: (V, T), a: (V, T), b: (V, T), y: (V, T), t: T) -> V
where V: Float + Mul<T, Output = V> + Div<T, Output = V>,
T: Float {
// some stupid generic constants, because Rust doesn’t have polymorphic literals…
let two_t = T::one() + T::one(); // lolololol
let three_t = two_t + T::one(); // megalol
// sampler stuff
let t2 = t * t;
let t3 = t2 * t;
let two_t3 = t3 * two_t;
let three_t2 = t2 * three_t;
// tangents
let m0 = (b.0 - x.0) / (b.1 - x.1);
let m1 = (y.0 - a.0) / (y.1 - a.1);
a.0 * (two_t3 - three_t2 + T::one()) + m0 * (t3 - t2 * two_t + t) + b.0 * (three_t2 - two_t3) + m1 * (t3 - t2)
}
macro_rules! impl_interpolate_simple {
($t:ty) => {
impl Interpolate<$t> for $t {
fn lerp(a: Self, b: Self, t: $t) -> Self {
a * (1. - t) + b * t
}
fn cubic_hermite(x: (Self, $t), a: (Self, $t), b: (Self, $t), y: (Self, $t), t: $t) -> Self {
cubic_hermite_def(x, a, b, y, t)
}
}
}
}
impl_interpolate_simple!(f32);
impl_interpolate_simple!(f64);
macro_rules! impl_interpolate_via {
($t:ty, $v:ty) => {
impl Interpolate<$t> for $v {
fn lerp(a: Self, b: Self, t: $t) -> Self {
a * (1. - t as $v) + b * t as $v
}
fn cubic_hermite((x, xt): (Self, $t), (a, at): (Self, $t), (b, bt): (Self, $t), (y, yt): (Self, $t), t: $t) -> Self {
cubic_hermite_def((x, xt as $v), (a, at as $v), (b, bt as $v), (y, yt as $v), t as $v)
}
}
}
}
impl_interpolate_via!(f32, f64);
impl_interpolate_via!(f64, f32);

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#[cfg(feature = "serialization")] use serde_derive::{Deserialize, Serialize};
/// Interpolation mode.
#[derive(Copy, Clone, Debug)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
#[cfg_attr(feature = "serialization", serde(rename_all = "snake_case"))]
pub enum Interpolation<T> {
/// Hold a [`Key`] until the interpolator value passes the normalized step threshold, in which
/// case the next key is used.
///
/// > Note: if you set the threshold to `0.5`, the first key will be used until half the time
/// > between the two keys; the second key will be in used afterwards. If you set it to `1.0`, the
/// > first key will be kept until the next key. Set it to `0.` and the first key will never be
/// > used.
Step(T),
/// Linear interpolation between a key and the next one.
Linear,
/// Cosine interpolation between a key and the next one.
Cosine,
/// Catmull-Rom interpolation, performing a cubic Hermite interpolation using four keys.
CatmullRom
}
impl<T> Default for Interpolation<T> {
/// `Interpolation::Linear` is the default.
fn default() -> Self {
Interpolation::Linear
}
}

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use crate::{Key, Spline};
/// Iterator over spline keys.
///
/// This iterator type assures you to iterate over sorted keys.
pub struct Iter<'a, T, V> where T: 'a, V: 'a {
anim_param: &'a Spline<T, V>,
i: usize
}
impl<'a, T, V> Iterator for Iter<'a, T, V> {
type Item = &'a Key<T, V>;
fn next(&mut self) -> Option<Self::Item> {
let r = self.anim_param.0.get(self.i);
if let Some(_) = r {
self.i += 1;
}
r
}
}
impl<'a, T, V> IntoIterator for &'a Spline<T, V> {
type Item = &'a Key<T, V>;
type IntoIter = Iter<'a, T, V>;
fn into_iter(self) -> Self::IntoIter {
Iter {
anim_param: self,
i: 0
}
}
}

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#[cfg(feature = "serialization")] use serde_derive::{Deserialize, Serialize};
use crate::interpolation::Interpolation;
/// A spline control point.
///
/// This type associates a value at a given interpolation parameter value. It also contains an
/// interpolation hint used to determine how to interpolate values on the segment defined by this
/// key and the next one – if existing.
#[derive(Copy, Clone, Debug)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
#[cfg_attr(feature = "serialization", serde(rename_all = "snake_case"))]
pub struct Key<T, V> {
/// Interpolation parameter at which the [`Key`] should be reached.
pub t: T,
/// Held value.
pub value: V,
/// Interpolation mode.
pub interpolation: Interpolation<T>
}
impl<T, V> Key<T, V> {
/// Create a new key.
pub fn new(t: T, value: V, interpolation: Interpolation<T>) -> Self {
Key { t, value, interpolation }
}
}

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#![cfg_attr(not(feature = "std"), feature(alloc))] #![cfg_attr(not(feature = "std"), feature(alloc))]
#![cfg_attr(not(feature = "std"), feature(core_intrinsics))] #![cfg_attr(not(feature = "std"), feature(core_intrinsics))]
#[cfg(feature = "impl-nalgebra")] use nalgebra as na; pub mod interpolate;
pub mod interpolation;
pub mod iter;
pub mod key;
#[cfg(feature = "impl-nalgebra")] mod nalgebra;
pub mod spline;
#[cfg(feature = "std")] use std::cmp::Ordering; pub use crate::interpolate::Interpolate;
#[cfg(feature = "std")] use std::ops::{Div, Mul}; pub use crate::interpolation::Interpolation;
pub use crate::key::Key;
#[cfg(feature = "serialization")] use serde_derive::{Deserialize, Serialize}; pub use crate::spline::Spline;
#[cfg(not(feature = "std"))] use alloc::vec::Vec;
#[cfg(not(feature = "std"))] use core::cmp::Ordering;
#[cfg(not(feature = "std"))] use core::ops::{Add, Div, Mul, Sub};
use num_traits::{Float, FloatConst};
/// A spline control point.
///
/// This type associates a value at a given interpolation parameter value. It also contains an
/// interpolation hint used to determine how to interpolate values on the segment defined by this
/// key and the next one – if existing.
#[derive(Copy, Clone, Debug)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
#[cfg_attr(feature = "serialization", serde(rename_all = "snake_case"))]
pub struct Key<T, V> {
/// Interpolation parameter at which the [`Key`] should be reached.
pub t: T,
/// Held value.
pub value: V,
/// Interpolation mode.
pub interpolation: Interpolation<T>
}
impl<T, V> Key<T, V> {
/// Create a new key.
pub fn new(t: T, value: V, interpolation: Interpolation<T>) -> Self {
Key { t, value, interpolation }
}
}
/// Interpolation mode.
#[derive(Copy, Clone, Debug)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
#[cfg_attr(feature = "serialization", serde(rename_all = "snake_case"))]
pub enum Interpolation<T> {
/// Hold a [`Key`] until the interpolator value passes the normalized step threshold, in which
/// case the next key is used.
///
/// > Note: if you set the threshold to `0.5`, the first key will be used until half the time
/// > between the two keys; the second key will be in used afterwards. If you set it to `1.0`, the
/// > first key will be kept until the next key. Set it to `0.` and the first key will never be
/// > used.
Step(T),
/// Linear interpolation between a key and the next one.
Linear,
/// Cosine interpolation between a key and the next one.
Cosine,
/// Catmull-Rom interpolation, performing a cubic Hermite interpolation using four keys.
CatmullRom
}
impl<T> Default for Interpolation<T> {
/// `Interpolation::Linear` is the default.
fn default() -> Self {
Interpolation::Linear
}
}
/// Spline curve used to provide interpolation between control points (keys).
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
pub struct Spline<T, V>(Vec<Key<T, V>>);
impl<T, V> Spline<T, V> {
/// Create a new spline out of keys. The keys don’t have to be sorted even though it’s recommended
/// to provide ascending sorted ones (for performance purposes).
pub fn from_vec(mut keys: Vec<Key<T, V>>) -> Self where T: PartialOrd {
keys.sort_by(|k0, k1| k0.t.partial_cmp(&k1.t).unwrap_or(Ordering::Less));
Spline(keys)
}
/// Create a new spline by consuming an `Iterater<Item = Key<T>>`. They keys don’t have to be
/// sorted.
///
/// # Note on iterators
///
/// It’s valid to use any iterator that implements `Iterator<Item = Key<T>>`. However, you should
/// use `Spline::from_vec` if you are passing a `Vec<_>`. This will remove dynamic allocations.
pub fn from_iter<I>(iter: I) -> Self where I: Iterator<Item = Key<T, V>>, T: PartialOrd {
Self::from_vec(iter.collect())
}
/// Retrieve the keys of a spline.
pub fn keys(&self) -> &[Key<T, V>] {
&self.0
}
/// Sample a spline at a given time.
///
/// The current implementation, based on immutability, cannot perform in constant time. This means
/// that sampling’s processing complexity is currently *O(log n)*. It’s possible to achieve *O(1)*
/// performance by using a slightly different spline type. If you are interested by this feature,
/// an implementation for a dedicated type is foreseen yet not started yet.
///
/// # Return
///
/// `None` if you try to sample a value at a time that has no key associated with. That can also
/// happen if you try to sample between two keys with a specific interpolation mode that makes the
/// sampling impossible. For instance, `Interpolate::CatmullRom` requires *four* keys. If you’re
/// near the beginning of the spline or its end, ensure you have enough keys around to make the
/// sampling.
pub fn sample(&self, t: T) -> Option<V> where T: Float + FloatConst, V: Interpolate<T> {
let keys = &self.0;
let i = search_lower_cp(keys, t)?;
let cp0 = &keys[i];
match cp0.interpolation {
Interpolation::Step(threshold) => {
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
Some(if nt < threshold { cp0.value } else { cp1.value })
}
Interpolation::Linear => {
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
Some(Interpolate::lerp(cp0.value, cp1.value, nt))
}
Interpolation::Cosine => {
let two_t = T::one() + T::one();
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
let cos_nt = (T::one() - (nt * T::PI()).cos()) / two_t;
Some(Interpolate::lerp(cp0.value, cp1.value, cos_nt))
}
Interpolation::CatmullRom => {
// We need at least four points for Catmull Rom; ensure we have them, otherwise, return
// None.
if i == 0 || i >= keys.len() - 2 {
None
} else {
let cp1 = &keys[i+1];
let cpm0 = &keys[i-1];
let cpm1 = &keys[i+2];
let nt = normalize_time(t, cp0, cp1);
Some(Interpolate::cubic_hermite((cpm0.value, cpm0.t), (cp0.value, cp0.t), (cp1.value, cp1.t), (cpm1.value, cpm1.t), nt))
}
}
}
}
/// Sample a spline at a given time with clamping.
///
/// # Return
///
/// If you sample before the first key or after the last one, return the first key or the last
/// one, respectively. Otherwise, behave the same way as `Spline::sample`.
///
/// # Error
///
/// This function returns `None` if you have no key.
pub fn clamped_sample(&self, t: T) -> Option<V> where T: Float + FloatConst, V: Interpolate<T> {
if self.0.is_empty() {
return None;
}
self.sample(t).or_else(move || {
let first = self.0.first().unwrap();
if t <= first.t {
Some(first.value)
} else {
let last = self.0.last().unwrap();
if t >= last.t {
Some(last.value)
} else {
None
}
}
})
}
}
/// Iterator over spline keys.
///
/// This iterator type assures you to iterate over sorted keys.
pub struct Iter<'a, T, V> where T: 'a, V: 'a {
anim_param: &'a Spline<T, V>,
i: usize
}
impl<'a, T, V> Iterator for Iter<'a, T, V> {
type Item = &'a Key<T, V>;
fn next(&mut self) -> Option<Self::Item> {
let r = self.anim_param.0.get(self.i);
if let Some(_) = r {
self.i += 1;
}
r
}
}
impl<'a, T, V> IntoIterator for &'a Spline<T, V> {
type Item = &'a Key<T, V>;
type IntoIter = Iter<'a, T, V>;
fn into_iter(self) -> Self::IntoIter {
Iter {
anim_param: self,
i: 0
}
}
}
/// Keys that can be interpolated in between. Implementing this trait is required to perform
/// sampling on splines.
///
/// `T` is the variable used to sample with. Typical implementations use `f32` or `f64`, but you’re
/// free to use the ones you like.
pub trait Interpolate<T>: Sized + Copy {
/// Linear interpolation.
fn lerp(a: Self, b: Self, t: T) -> Self;
/// Cubic hermite interpolation.
///
/// Default to `Self::lerp`.
fn cubic_hermite(_: (Self, T), a: (Self, T), b: (Self, T), _: (Self, T), t: T) -> Self {
Self::lerp(a.0, b.0, t)
}
}
macro_rules! impl_interpolate_simple {
($t:ty) => {
impl Interpolate<$t> for $t {
fn lerp(a: Self, b: Self, t: $t) -> Self {
a * (1. - t) + b * t
}
fn cubic_hermite(x: (Self, $t), a: (Self, $t), b: (Self, $t), y: (Self, $t), t: $t) -> Self {
cubic_hermite_def(x, a, b, y, t)
}
}
}
}
impl_interpolate_simple!(f32);
impl_interpolate_simple!(f64);
macro_rules! impl_interpolate_via {
($t:ty, $v:ty) => {
impl Interpolate<$t> for $v {
fn lerp(a: Self, b: Self, t: $t) -> Self {
a * (1. - t as $v) + b * t as $v
}
fn cubic_hermite((x, xt): (Self, $t), (a, at): (Self, $t), (b, bt): (Self, $t), (y, yt): (Self, $t), t: $t) -> Self {
cubic_hermite_def((x, xt as $v), (a, at as $v), (b, bt as $v), (y, yt as $v), t as $v)
}
}
}
}
impl_interpolate_via!(f32, f64);
impl_interpolate_via!(f64, f32);
macro_rules! impl_interpolate_na_vector {
($($t:tt)*) => {
#[cfg(feature = "impl-nalgebra")]
impl<T, V> Interpolate<T> for $($t)*<V> where T: Float, V: na::Scalar + Interpolate<T> {
fn lerp(a: Self, b: Self, t: T) -> Self {
na::Vector::zip_map(&a, &b, |c1, c2| Interpolate::lerp(c1, c2, t))
}
}
}
}
impl_interpolate_na_vector!(na::Vector1);
impl_interpolate_na_vector!(na::Vector2);
impl_interpolate_na_vector!(na::Vector3);
impl_interpolate_na_vector!(na::Vector4);
impl_interpolate_na_vector!(na::Vector5);
impl_interpolate_na_vector!(na::Vector6);
#[cfg(feature = "impl-nalgebra")]
impl<T, N, D> Interpolate<T> for na::Point<N, D>
where D: na::DimName,
na::DefaultAllocator: na::allocator::Allocator<N, D>,
<na::DefaultAllocator as na::allocator::Allocator<N, D>>::Buffer: Copy,
N: na::Scalar + Interpolate<T>,
T: Float {
fn lerp(a: Self, b: Self, t: T) -> Self {
// The 'coords' of a point is just a vector, so we can interpolate component-wise
// over these vectors.
let coords = na::Vector::zip_map(&a.coords, &b.coords, |c1, c2| Interpolate::lerp(c1, c2, t));
na::Point::from(coords)
}
}
// Default implementation of Interpolate::cubic_hermite.
//
// `V` is the value being interpolated. `T` is the sampling value (also sometimes called time).
pub(crate) fn cubic_hermite_def<V, T>(x: (V, T), a: (V, T), b: (V, T), y: (V, T), t: T) -> V
where V: Float + Mul<T, Output = V> + Div<T, Output = V>,
T: Float {
// some stupid generic constants, because Rust doesn’t have polymorphic literals…
let two_t = T::one() + T::one(); // lolololol
let three_t = two_t + T::one(); // megalol
// sampler stuff
let t2 = t * t;
let t3 = t2 * t;
let two_t3 = t3 * two_t;
let three_t2 = t2 * three_t;
// tangents
let m0 = (b.0 - x.0) / (b.1 - x.1);
let m1 = (y.0 - a.0) / (y.1 - a.1);
a.0 * (two_t3 - three_t2 + T::one()) + m0 * (t3 - t2 * two_t + t) + b.0 * (three_t2 - two_t3) + m1 * (t3 - t2)
}
// Normalize a time ([0;1]) given two control points.
#[inline(always)]
pub(crate) fn normalize_time<T, V>(
t: T,
cp: &Key<T, V>,
cp1: &Key<T, V>
) -> T where T: Float {
assert!(cp1.t != cp.t, "overlapping keys");
(t - cp.t) / (cp1.t - cp.t)
}
// Find the lower control point corresponding to a given time.
fn search_lower_cp<T, V>(cps: &[Key<T, V>], t: T) -> Option<usize> where T: PartialOrd {
let mut i = 0;
let len = cps.len();
if len < 2 {
return None;
}
loop {
let cp = &cps[i];
let cp1 = &cps[i+1];
if t >= cp1.t {
if i >= len - 2 {
return None;
}
i += 1;
} else if t < cp.t {
if i == 0 {
return None;
}
i -= 1;
} else {
break; // found
}
}
Some(i)
}

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use crate::Interpolate;
use nalgebra as na;
use num_traits::Float;
macro_rules! impl_interpolate_na_vector {
($($t:tt)*) => {
impl<T, V> Interpolate<T> for $($t)*<V> where T: Float, V: na::Scalar + Interpolate<T> {
fn lerp(a: Self, b: Self, t: T) -> Self {
na::Vector::zip_map(&a, &b, |c1, c2| Interpolate::lerp(c1, c2, t))
}
}
}
}
impl_interpolate_na_vector!(na::Vector1);
impl_interpolate_na_vector!(na::Vector2);
impl_interpolate_na_vector!(na::Vector3);
impl_interpolate_na_vector!(na::Vector4);
impl_interpolate_na_vector!(na::Vector5);
impl_interpolate_na_vector!(na::Vector6);
impl<T, N, D> Interpolate<T> for na::Point<N, D>
where D: na::DimName,
na::DefaultAllocator: na::allocator::Allocator<N, D>,
<na::DefaultAllocator as na::allocator::Allocator<N, D>>::Buffer: Copy,
N: na::Scalar + Interpolate<T>,
T: Float {
fn lerp(a: Self, b: Self, t: T) -> Self {
// The 'coords' of a point is just a vector, so we can interpolate component-wise
// over these vectors.
let coords = na::Vector::zip_map(&a.coords, &b.coords, |c1, c2| Interpolate::lerp(c1, c2, t));
na::Point::from(coords)
}
}

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#[cfg(feature = "serialization")] use serde_derive::{Deserialize, Serialize};
#[cfg(feature = "std")] use std::cmp::Ordering;
#[cfg(not(feature = "std"))] use core::cmp::Ordering;
#[cfg(not(feature = "std"))] use alloc::vec::Vec;
use crate::interpolate::Interpolate;
use crate::interpolation::Interpolation;
use crate::key::Key;
use num_traits::{Float, FloatConst};
/// Spline curve used to provide interpolation between control points (keys).
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serialization", derive(Deserialize, Serialize))]
pub struct Spline<T, V>(pub(crate) Vec<Key<T, V>>);
impl<T, V> Spline<T, V> {
/// Create a new spline out of keys. The keys don’t have to be sorted even though it’s recommended
/// to provide ascending sorted ones (for performance purposes).
pub fn from_vec(mut keys: Vec<Key<T, V>>) -> Self where T: PartialOrd {
keys.sort_by(|k0, k1| k0.t.partial_cmp(&k1.t).unwrap_or(Ordering::Less));
Spline(keys)
}
/// Create a new spline by consuming an `Iterater<Item = Key<T>>`. They keys don’t have to be
/// sorted.
///
/// # Note on iterators
///
/// It’s valid to use any iterator that implements `Iterator<Item = Key<T>>`. However, you should
/// use `Spline::from_vec` if you are passing a `Vec<_>`. This will remove dynamic allocations.
pub fn from_iter<I>(iter: I) -> Self where I: Iterator<Item = Key<T, V>>, T: PartialOrd {
Self::from_vec(iter.collect())
}
/// Retrieve the keys of a spline.
pub fn keys(&self) -> &[Key<T, V>] {
&self.0
}
/// Sample a spline at a given time.
///
/// The current implementation, based on immutability, cannot perform in constant time. This means
/// that sampling’s processing complexity is currently *O(log n)*. It’s possible to achieve *O(1)*
/// performance by using a slightly different spline type. If you are interested by this feature,
/// an implementation for a dedicated type is foreseen yet not started yet.
///
/// # Return
///
/// `None` if you try to sample a value at a time that has no key associated with. That can also
/// happen if you try to sample between two keys with a specific interpolation mode that makes the
/// sampling impossible. For instance, `Interpolate::CatmullRom` requires *four* keys. If you’re
/// near the beginning of the spline or its end, ensure you have enough keys around to make the
/// sampling.
pub fn sample(&self, t: T) -> Option<V> where T: Float + FloatConst, V: Interpolate<T> {
let keys = &self.0;
let i = search_lower_cp(keys, t)?;
let cp0 = &keys[i];
match cp0.interpolation {
Interpolation::Step(threshold) => {
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
Some(if nt < threshold { cp0.value } else { cp1.value })
}
Interpolation::Linear => {
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
Some(Interpolate::lerp(cp0.value, cp1.value, nt))
}
Interpolation::Cosine => {
let two_t = T::one() + T::one();
let cp1 = &keys[i+1];
let nt = normalize_time(t, cp0, cp1);
let cos_nt = (T::one() - (nt * T::PI()).cos()) / two_t;
Some(Interpolate::lerp(cp0.value, cp1.value, cos_nt))
}
Interpolation::CatmullRom => {
// We need at least four points for Catmull Rom; ensure we have them, otherwise, return
// None.
if i == 0 || i >= keys.len() - 2 {
None
} else {
let cp1 = &keys[i+1];
let cpm0 = &keys[i-1];
let cpm1 = &keys[i+2];
let nt = normalize_time(t, cp0, cp1);
Some(Interpolate::cubic_hermite((cpm0.value, cpm0.t), (cp0.value, cp0.t), (cp1.value, cp1.t), (cpm1.value, cpm1.t), nt))
}
}
}
}
/// Sample a spline at a given time with clamping.
///
/// # Return
///
/// If you sample before the first key or after the last one, return the first key or the last
/// one, respectively. Otherwise, behave the same way as `Spline::sample`.
///
/// # Error
///
/// This function returns `None` if you have no key.
pub fn clamped_sample(&self, t: T) -> Option<V> where T: Float + FloatConst, V: Interpolate<T> {
if self.0.is_empty() {
return None;
}
self.sample(t).or_else(move || {
let first = self.0.first().unwrap();
if t <= first.t {
Some(first.value)
} else {
let last = self.0.last().unwrap();
if t >= last.t {
Some(last.value)
} else {
None
}
}
})
}
}
// Normalize a time ([0;1]) given two control points.
#[inline(always)]
pub(crate) fn normalize_time<T, V>(
t: T,
cp: &Key<T, V>,
cp1: &Key<T, V>
) -> T where T: Float {
assert!(cp1.t != cp.t, "overlapping keys");
(t - cp.t) / (cp1.t - cp.t)
}
// Find the lower control point corresponding to a given time.
fn search_lower_cp<T, V>(cps: &[Key<T, V>], t: T) -> Option<usize> where T: PartialOrd {
let mut i = 0;
let len = cps.len();
if len < 2 {
return None;
}
loop {
let cp = &cps[i];
let cp1 = &cps[i+1];
if t >= cp1.t {
if i >= len - 2 {
return None;
}
i += 1;
} else if t < cp.t {
if i == 0 {
return None;
}
i -= 1;
} else {
break; // found
}
}
Some(i)
}