0f153ffb44
14 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Matty Weatherley
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ee9bea1ba9
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Use variadics_please to implement StableInterpolate on tuples. (#16931)
# Objective Now that `variadics_please` has a 1.1 release, we can re-implement the original solution. ## Solution Copy-paste the code from the [original PR](https://github.com/bevyengine/bevy/pull/15931) branch :) |
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Matty Weatherley
|
c60dcea231
|
Derivative access patterns for curves (#16503)
# Objective
- For curves that also include derivatives, make accessing derivative
information via the `Curve` API ergonomic: that is, provide access to a
curve that also samples derivative information.
- Implement this functionality for cubic spline curves provided by
`bevy_math`.
Ultimately, this is to serve the purpose of doing more geometric
operations on curves, like reparametrization by arclength and the
construction of moving frames.
## Solution
This has several parts, some of which may seem redundant. However, care
has been put into this to satisfy the following constraints:
- Accessing a `Curve` that samples derivative information should be not
just possible but easy and non-error-prone. For example, given a
differentiable `Curve<Vec2>`, one should be able to access something
like a `Curve<(Vec2, Vec2)>` ergonomically, and not just sample the
derivatives piecemeal from point to point.
- Derivative access should not step on the toes of ordinary curve usage.
In particular, in the above scenario, we want to avoid simply making the
same curve both a `Curve<Vec2>` and a `Curve<(Vec2, Vec2)>` because this
requires manual disambiguation when the API is used.
- Derivative access must work gracefully in both owned and borrowed
contexts.
### `HasTangent`
We introduce a trait `HasTangent` that provides an associated `Tangent`
type for types that have tangent spaces:
```rust
pub trait HasTangent {
/// The tangent type.
type Tangent: VectorSpace;
}
```
(Mathematically speaking, it would be more precise to say that these are
types that represent spaces which are canonically
[parallelized](https://en.wikipedia.org/wiki/Parallelizable_manifold). )
The idea here is that a point moving through a `HasTangent` type may
have a derivative valued in the associated `Tangent` type at each time
in its journey. We reify this with a `WithDerivative<T>` type that uses
`HasTangent` to include derivative information:
```rust
pub struct WithDerivative<T>
where
T: HasTangent,
{
/// The underlying value.
pub value: T,
/// The derivative at `value`.
pub derivative: T::Tangent,
}
```
And we can play the same game with second derivatives as well, since
every `VectorSpace` type is `HasTangent` where `Tangent` is itself (we
may want to be more restrictive with this in practice, but this holds
mathematically).
```rust
pub struct WithTwoDerivatives<T>
where
T: HasTangent,
{
/// The underlying value.
pub value: T,
/// The derivative at `value`.
pub derivative: T::Tangent,
/// The second derivative at `value`.
pub second_derivative: <T::Tangent as HasTangent>::Tangent,
}
```
In this PR, `HasTangent` is only implemented for `VectorSpace` types,
but it would be valuable to have this implementation for types like
`Rot2` and `Quat` as well. We could also do it for the isometry types
and, potentially, transforms as well. (This is in decreasing order of
value in my opinion.)
### `CurveWithDerivative`
This is a trait for a `Curve<T>` which allows the construction of a
`Curve<WithDerivative<T>>` when derivative information is known
intrinsically. It looks like this:
```rust
/// Trait for curves that have a well-defined notion of derivative, allowing for
/// derivatives to be extracted along with values.
pub trait CurveWithDerivative<T>
where
T: HasTangent,
{
/// This curve, but with its first derivative included in sampling.
fn with_derivative(self) -> impl Curve<WithDerivative<T>>;
}
```
The idea here is to provide patterns like this:
```rust
let value_and_derivative = my_curve.with_derivative().sample_clamped(t);
```
One of the main points here is that `Curve<WithDerivative<T>>` is useful
as an output because it can be used durably. For example, in a dynamic
context, something that needs curves with derivatives can store
something like a `Box<dyn Curve<WithDerivative<T>>>`. Note that
`CurveWithDerivative` is not dyn-compatible.
### `SampleDerivative`
Many curves "know" how to sample their derivatives instrinsically, but
implementing `CurveWithDerivative` as given would be onerous or require
an annoying amount of boilerplate. There are also hurdles to overcome
that involve references to curves: for the `Curve` API, the expectation
is that curve transformations like `with_derivative` take things by
value, with the contract that they can still be used by reference
through deref-magic by including `by_ref` in a method chain.
These problems are solved simultaneously by a trait `SampleDerivative`
which, when implemented, automatically derives `CurveWithDerivative` for
a type and all types that dereference to it. It just looks like this:
```rust
pub trait SampleDerivative<T>: Curve<T>
where
T: HasTangent,
{
fn sample_with_derivative_unchecked(&self, t: f32) -> WithDerivative<T>;
// ... other sampling variants as default methods
}
```
The point is that the output of `with_derivative` is a
`Curve<WithDerivative<T>>` that uses the `SampleDerivative`
implementation. On a `SampleDerivative` type, you can also just call
`my_curve.sample_with_derivative(t)` instead of something like
`my_curve.by_ref().with_derivative().sample(t)`, which is more verbose
and less accessible.
In practice, `CurveWithDerivative<T>` is actually a "sealed" extension
trait of `SampleDerivative<T>`.
## Adaptors
`SampleDerivative` has automatic implementations on all curve adaptors
except for `FunctionCurve`, `MapCurve`, and `ReparamCurve` (because we
do not have a notion of differentiable Rust functions).
For example, `CurveReparamCurve` (the reparametrization of a curve by
another curve) can compute derivatives using the chain rule in the case
both its constituents have them.
## Testing
Tests for derivatives on the curve adaptors are included.
---
## Showcase
This development allows derivative information to be included with and
extracted from curves using the `Curve` API.
```rust
let points = [
vec2(-1.0, -20.0),
vec2(3.0, 2.0),
vec2(5.0, 3.0),
vec2(9.0, 8.0),
];
// A cubic spline curve that goes through `points`.
let curve = CubicCardinalSpline::new(0.3, points).to_curve().unwrap();
// Calling `with_derivative` causes derivative output to be included in the output of the curve API.
let curve_with_derivative = curve.with_derivative();
// A `Curve<f32>` that outputs the speed of the original.
let speed_curve = curve_with_derivative.map(|x| x.derivative.norm());
```
---
## Questions
- ~~Maybe we should seal `WithDerivative` or make it require
`SampleDerivative` (i.e. make it unimplementable except through
`SampleDerivative`).~~ I decided this is a good idea.
- ~~Unclear whether `VectorSpace: HasTangent` blanket implementation is
really appropriate. For colors, for example, I'm not sure that the
derivative values can really be interpreted as a color. In any case, it
should still remain the case that `VectorSpace` types are `HasTangent`
and that `HasTangent::Tangent: HasTangent`.~~ I think this is fine.
- Infinity bikeshed on names of traits and things.
## Future
- Faster implementations of `SampleDerivative` for cubic spline curves.
- Improve ergonomics for accessing only derivatives (and other kinds of
transformations on derivative curves).
- Implement `HasTangent` for:
- `Rot2`/`Quat`
- `Isometry` types
- `Transform`, maybe
- Implement derivatives for easing curves.
- Marker traits for continuous/differentiable curves. (It's actually
unclear to me how much value this has in practice, but we have discussed
it in the past.)
---------
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
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Zachary Harrold
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a8b9c945c7
|
Add no_std Support to bevy_math (#15810)
# Objective - Contributes to #15460 ## Solution - Added two new features, `std` (default) and `alloc`, gating `std` and `alloc` behind them respectively. - Added missing `f32` functions to `std_ops` as required. These `f32` methods have been added to the `clippy.toml` deny list to aid in `no_std` development. ## Testing - CI - `cargo clippy -p bevy_math --no-default-features --features libm --target "x86_64-unknown-none"` - `cargo test -p bevy_math --no-default-features --features libm` - `cargo test -p bevy_math --no-default-features --features "libm, alloc"` - `cargo test -p bevy_math --no-default-features --features "libm, alloc, std"` - `cargo test -p bevy_math --no-default-features --features "std"` ## Notes The following items require the `alloc` feature to be enabled: - `CubicBSpline` - `CubicBezier` - `CubicCardinalSpline` - `CubicCurve` - `CubicGenerator` - `CubicHermite` - `CubicNurbs` - `CyclicCubicGenerator` - `RationalCurve` - `RationalGenerator` - `BoxedPolygon` - `BoxedPolyline2d` - `BoxedPolyline3d` - `SampleCurve` - `SampleAutoCurve` - `UnevenSampleCurve` - `UnevenSampleAutoCurve` - `EvenCore` - `UnevenCore` - `ChunkedUnevenCore` This requirement could be relaxed in certain cases, but I had erred on the side of gating rather than modifying. Since `no_std` is a new set of platforms we are adding support to, and the `alloc` feature is enabled by default, this is not a breaking change. --------- Co-authored-by: Benjamin Brienen <benjamin.brienen@outlook.com> Co-authored-by: Matty <2975848+mweatherley@users.noreply.github.com> Co-authored-by: Joona Aalto <jondolf.dev@gmail.com> |
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Martín Maita
|
a44b668b90
|
Bump crate-ci/typos from 1.26.8 to 1.27.0 (#16236)
# Objective - Closes #16224 ## Solution - Bumps `crate-ci/typos@v1.26.8` to `crate-ci/typos@v1.27.0`. ## Testing - CI checks should pass. --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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Tau Gärtli
|
ed351294ec
|
Use #[doc(fake_variadic)] on StableInterpolate (#15933)
This is a follow-up to #15931 that adds `#[doc(fake_variadic)]` for improved docs output :) |
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Matty
|
a09104b62c
|
Infer StableInterpolate on tuples (#15931)
# Objective
Make `StableInterpolate` "just work" on tuples whose parts are each
`StableInterpolate` types. These types arise notably through
`Curve::zip` (or just through explicit mapping of a similar form). It
would otherwise be kind of frustrating to stumble upon such a thing and
then realize that, e.g., automatic resampling just doesn't work, even
though there is a very "obvious" way to do it.
## Solution
Infer `StableInterpolate` on tuples of up to size 11. I can make that
number bigger, if desired. Unfortunately, I don't think that our
standard "fake variadics" tools actually work for this; the anonymous
field accessors of tuples are `:tt` for purposes of macro expansion,
which means that you can't simplify away the identifiers by doing
something clever like using recursion (which would work if they were
`:expr`). Maybe someone who knows some incredibly dark magic could chime
in with a better solution.
The expanded impls look like this:
```rust
impl<
T0: StableInterpolate,
T1: StableInterpolate,
T2: StableInterpolate,
T3: StableInterpolate,
T4: StableInterpolate,
> StableInterpolate for (T0, T1, T2, T3, T4)
{
fn interpolate_stable(&self, other: &Self, t: f32) -> Self {
(
<T0 as StableInterpolate>::interpolate_stable(&self.0, &other.0, t),
<T1 as StableInterpolate>::interpolate_stable(&self.1, &other.1, t),
<T2 as StableInterpolate>::interpolate_stable(&self.2, &other.2, t),
<T3 as StableInterpolate>::interpolate_stable(&self.3, &other.3, t),
<T4 as StableInterpolate>::interpolate_stable(&self.4, &other.4, t),
)
}
}
```
## Testing
Expanded macros; it compiles.
## Future
Make a version of the fake variadics workflow that supports this kind of
thing.
|
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Matty
|
e563f86a1d
|
Simplified easing curves (#15711)
# Objective
Simplify the API surrounding easing curves. Broaden the base of types
that support easing.
## Solution
There is now a single library function, `easing_curve`, which constructs
a unit-parametrized easing curve between two values based on an
`EaseFunction`:
```rust
/// Given a `start` and `end` value, create a curve parametrized over [the unit interval]
/// that connects them, using the given [ease function] to determine the form of the
/// curve in between.
///
/// [the unit interval]: Interval::UNIT
/// [ease function]: EaseFunction
pub fn easing_curve<T: Ease>(start: T, end: T, ease_fn: EaseFunction) -> EasingCurve<T> { //... }
```
As this shows, the type of the output curve is generic only in `T`. In
particular, as long as `T` is `Reflect` (and `FromReflect` etc. — i.e.,
a standard "well-behaved" reflectable type), `EasingCurve<T>` is also
`Reflect`, and there is no special field handling nonsense. Therefore,
`EasingCurve` is the kind of thing that would be able to be easily
changed in an editor. This is made possible by storing the actual
`EaseFunction` on `EasingCurve<T>` instead of indirecting through some
kind of function type (which generally leads to issues with reflection).
The types that can be eased are those that implement a trait `Ease`:
```rust
/// A type whose values can be eased between.
///
/// This requires the construction of an interpolation curve that actually extends
/// beyond the curve segment that connects two values, because an easing curve may
/// extrapolate before the starting value and after the ending value. This is
/// especially common in easing functions that mimic elastic or springlike behavior.
pub trait Ease: Sized {
/// Given `start` and `end` values, produce a curve with [unlimited domain]
/// that:
/// - takes a value equivalent to `start` at `t = 0`
/// - takes a value equivalent to `end` at `t = 1`
/// - has constant speed everywhere, including outside of `[0, 1]`
///
/// [unlimited domain]: Interval::EVERYWHERE
fn interpolating_curve_unbounded(start: &Self, end: &Self) -> impl Curve<Self>;
}
```
(I know, I know, yet *another* interpolation trait. See 'Future
direction'.)
The other existing easing functions from the previous version of this
module have also become new members of `EaseFunction`: `Linear`,
`Steps`, and `Elastic` (which maybe needs a different name). The latter
two are parametrized.
## Testing
Tested using the `easing_functions` example. I also axed the
`cubic_curve` example which was of questionable value and replaced it
with `eased_motion`, which uses this API in the context of animation:
https://github.com/user-attachments/assets/3c802992-6b9b-4b56-aeb1-a47501c29ce2
---
## Future direction
Morally speaking, `Ease` is incredibly similar to `StableInterpolate`.
Probably, we should just merge `StableInterpolate` into `Ease`, and then
make `SmoothNudge` an automatic extension trait of `Ease`. The reason I
didn't do that is that `StableInterpolate` is not implemented for
`VectorSpace` because of concerns about the `Color` types, and I wanted
to avoid controversy. I think that may be a good idea though.
As Alice mentioned before, we should also probably get rid of the
`interpolation` dependency.
The parametrized `Elastic` variant probably also needs some additional
work (e.g. renaming, in/out/in-out variants, etc.) if we want to keep
it.
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Zachary Harrold
|
d70595b667
|
Add core and alloc over std Lints (#15281)
# Objective - Fixes #6370 - Closes #6581 ## Solution - Added the following lints to the workspace: - `std_instead_of_core` - `std_instead_of_alloc` - `alloc_instead_of_core` - Used `cargo +nightly fmt` with [item level use formatting](https://rust-lang.github.io/rustfmt/?version=v1.6.0&search=#Item%5C%3A) to split all `use` statements into single items. - Used `cargo clippy --workspace --all-targets --all-features --fix --allow-dirty` to _attempt_ to resolve the new linting issues, and intervened where the lint was unable to resolve the issue automatically (usually due to needing an `extern crate alloc;` statement in a crate root). - Manually removed certain uses of `std` where negative feature gating prevented `--all-features` from finding the offending uses. - Used `cargo +nightly fmt` with [crate level use formatting](https://rust-lang.github.io/rustfmt/?version=v1.6.0&search=#Crate%5C%3A) to re-merge all `use` statements matching Bevy's previous styling. - Manually fixed cases where the `fmt` tool could not re-merge `use` statements due to conditional compilation attributes. ## Testing - Ran CI locally ## Migration Guide The MSRV is now 1.81. Please update to this version or higher. ## Notes - This is a _massive_ change to try and push through, which is why I've outlined the semi-automatic steps I used to create this PR, in case this fails and someone else tries again in the future. - Making this change has no impact on user code, but does mean Bevy contributors will be warned to use `core` and `alloc` instead of `std` where possible. - This lint is a critical first step towards investigating `no_std` options for Bevy. --------- Co-authored-by: François Mockers <francois.mockers@vleue.com> |
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Clar Fon
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efda7f3f9c
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Simpler lint fixes: makes ci lints work but disables a lint for now (#15376)
Takes the first two commits from #15375 and adds suggestions from this comment: https://github.com/bevyengine/bevy/pull/15375#issuecomment-2366968300 See #15375 for more reasoning/motivation. ## Rebasing (rerunning) ```rust git switch simpler-lint-fixes git reset --hard main cargo fmt --all -- --unstable-features --config normalize_comments=true,imports_granularity=Crate cargo fmt --all git add --update git commit --message "rustfmt" cargo clippy --workspace --all-targets --all-features --fix cargo fmt --all -- --unstable-features --config normalize_comments=true,imports_granularity=Crate cargo fmt --all git add --update git commit --message "clippy" git cherry-pick e6c0b94f6795222310fb812fa5c4512661fc7887 ``` |
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Matty
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61a1530c56
|
Make bevy_math's libm feature use libm for all f32methods with unspecified precision (#14693)
# Objective Closes #14474 Previously, the `libm` feature of bevy_math would just pass the same feature flag down to glam. However, bevy_math itself had many uses of floating-point arithmetic with unspecified precision. For example, `f32::sin_cos` and `f32::powi` have unspecified precision, which means that the exact details of their output are not guaranteed to be stable across different systems and/or versions of Rust. This means that users of bevy_math could observe slightly different behavior on different systems if these methods were used. The goal of this PR is to make it so that the `libm` feature flag actually guarantees some degree of determinacy within bevy_math itself by switching to the libm versions of these functions when the `libm` feature is enabled. ## Solution bevy_math now has an internal module `bevy_math::ops`, which re-exports either the standard versions of the operations or the libm versions depending on whether the `libm` feature is enabled. For example, `ops::sin` compiles to `f32::sin` without the `libm` feature and to `libm::sinf` with it. This approach has a small shortfall, which is that `f32::powi` (integer powers of floating point numbers) does not have an equivalent in `libm`. On the other hand, this method is only used for squaring and cubing numbers in bevy_math. Accordingly, this deficit is covered by the introduction of a trait `ops::FloatPow`: ```rust pub(crate) trait FloatPow { fn squared(self) -> Self; fn cubed(self) -> Self; } ``` Next, each current usage of the unspecified-precision methods has been replaced by its equivalent in `ops`, so that when `libm` is enabled, the libm version is used instead. The exception, of course, is that `.powi(2)`/`.powi(3)` have been replaced with `.squared()`/`.cubed()`. Finally, the usage of the plain `f32` methods with unspecified precision is now linted out of bevy_math (and hence disallowed in CI). For example, using `f32::sin` within bevy_math produces a warning that tells the user to use the `ops::sin` version instead. ## Testing Ran existing tests. It would be nice to check some benchmarks on NURBS things once #14677 merges. I'm happy to wait until then if the rest of this PR is fine. --- ## Discussion In the future, it might make sense to actually expose `bevy_math::ops` as public if any downstream Bevy crates want to provide similar determinacy guarantees. For now, it's all just `pub(crate)`. This PR also only covers `f32`. If we find ourselves using `f64` internally in parts of bevy_math for better robustness, we could extend the module and lints to cover the `f64` versions easily enough. I don't know how feasible it is, but it would also be nice if we could standardize the bevy_math tests with the `libm` feature in CI, since their success is currently platform-dependent (e.g. 8 of them fail on my machine when run locally). --------- Co-authored-by: IQuick 143 <IQuick143cz@gmail.com> |
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Matty
|
9af2ef740b
|
Make bevy_math::common_traits public (#14245)
# Objective Fixes #14243 ## Solution `bevy_math::common_traits` is now a public module. |
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Matty
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a569b35c18
|
Stable interpolation and smooth following (#13741)
# Objective Partially address #13408 Rework of #13613 Unify the very nice forms of interpolation specifically present in `bevy_math` under a shared trait upon which further behavior can be based. The ideas in this PR were prompted by [Lerp smoothing is broken by Freya Holmer](https://www.youtube.com/watch?v=LSNQuFEDOyQ). ## Solution There is a new trait `StableInterpolate` in `bevy_math::common_traits` which enshrines a quite-specific notion of interpolation with a lot of guarantees: ```rust /// A type with a natural interpolation that provides strong subdivision guarantees. /// /// Although the only required method is `interpolate_stable`, many things are expected of it: /// /// 1. The notion of interpolation should follow naturally from the semantics of the type, so /// that inferring the interpolation mode from the type alone is sensible. /// /// 2. The interpolation recovers something equivalent to the starting value at `t = 0.0` /// and likewise with the ending value at `t = 1.0`. /// /// 3. Importantly, the interpolation must be *subdivision-stable*: for any interpolation curve /// between two (unnamed) values and any parameter-value pairs `(t0, p)` and `(t1, q)`, the /// interpolation curve between `p` and `q` must be the *linear* reparametrization of the original /// interpolation curve restricted to the interval `[t0, t1]`. /// /// The last of these conditions is very strong and indicates something like constant speed. It /// is called "subdivision stability" because it guarantees that breaking up the interpolation /// into segments and joining them back together has no effect. /// /// Here is a diagram depicting it: /// ```text /// top curve = u.interpolate_stable(v, t) /// /// t0 => p t1 => q /// |-------------|---------|-------------| /// 0 => u / \ 1 => v /// / \ /// / \ /// / linear \ /// / reparametrization \ /// / t = t0 * (1 - s) + t1 * s \ /// / \ /// |-------------------------------------| /// 0 => p 1 => q /// /// bottom curve = p.interpolate_stable(q, s) /// ``` /// /// Note that some common forms of interpolation do not satisfy this criterion. For example, /// [`Quat::lerp`] and [`Rot2::nlerp`] are not subdivision-stable. /// /// Furthermore, this is not to be used as a general trait for abstract interpolation. /// Consumers rely on the strong guarantees in order for behavior based on this trait to be /// well-behaved. /// /// [`Quat::lerp`]: crate::Quat::lerp /// [`Rot2::nlerp`]: crate::Rot2::nlerp pub trait StableInterpolate: Clone { /// Interpolate between this value and the `other` given value using the parameter `t`. /// Note that the parameter `t` is not necessarily clamped to lie between `0` and `1`. /// When `t = 0.0`, `self` is recovered, while `other` is recovered at `t = 1.0`, /// with intermediate values lying between the two. fn interpolate_stable(&self, other: &Self, t: f32) -> Self; } ``` This trait has a blanket implementation over `NormedVectorSpace`, where `lerp` is used, along with implementations for `Rot2`, `Quat`, and the direction types using variants of `slerp`. Other areas may choose to implement this trait in order to hook into its functionality, but the stringent requirements must actually be met. This trait bears no direct relationship with `bevy_animation`'s `Animatable` trait, although they may choose to use `interpolate_stable` in their trait implementations if they wish, as both traits involve type-inferred interpolations of the same kind. `StableInterpolate` is not a supertrait of `Animatable` for a couple reasons: 1. Notions of interpolation in animation are generally going to be much more general than those allowed under these constraints. 2. Laying out these generalized interpolation notions is the domain of `bevy_animation` rather than of `bevy_math`. (Consider also that inferring interpolation from types is not universally desirable.) Similarly, this is not implemented on `bevy_color`'s color types, although their current mixing behavior does meet the conditions of the trait. As an aside, the subdivision-stability condition is of interest specifically for the [Curve RFC](https://github.com/bevyengine/rfcs/pull/80), where it also ensures a kind of stability for subsampling. Importantly, this trait ensures that the "smooth following" behavior defined in this PR behaves predictably: ```rust /// Smoothly nudge this value towards the `target` at a given decay rate. The `decay_rate` /// parameter controls how fast the distance between `self` and `target` decays relative to /// the units of `delta`; the intended usage is for `decay_rate` to generally remain fixed, /// while `delta` is something like `delta_time` from an updating system. This produces a /// smooth following of the target that is independent of framerate. /// /// More specifically, when this is called repeatedly, the result is that the distance between /// `self` and a fixed `target` attenuates exponentially, with the rate of this exponential /// decay given by `decay_rate`. /// /// For example, at `decay_rate = 0.0`, this has no effect. /// At `decay_rate = f32::INFINITY`, `self` immediately snaps to `target`. /// In general, higher rates mean that `self` moves more quickly towards `target`. /// /// # Example /// ``` /// # use bevy_math::{Vec3, StableInterpolate}; /// # let delta_time: f32 = 1.0 / 60.0; /// let mut object_position: Vec3 = Vec3::ZERO; /// let target_position: Vec3 = Vec3::new(2.0, 3.0, 5.0); /// // Decay rate of ln(10) => after 1 second, remaining distance is 1/10th /// let decay_rate = f32::ln(10.0); /// // Calling this repeatedly will move `object_position` towards `target_position`: /// object_position.smooth_nudge(&target_position, decay_rate, delta_time); /// ``` fn smooth_nudge(&mut self, target: &Self, decay_rate: f32, delta: f32) { self.interpolate_stable_assign(target, 1.0 - f32::exp(-decay_rate * delta)); } ``` As the documentation indicates, the intention is for this to be called in game update systems, and `delta` would be something like `Time::delta_seconds` in Bevy, allowing positions, orientations, and so on to smoothly follow a target. A new example, `smooth_follow`, demonstrates a basic implementation of this, with a sphere smoothly following a sharply moving target: https://github.com/bevyengine/bevy/assets/2975848/7124b28b-6361-47e3-acf7-d1578ebd0347 ## Testing Tested by running the example with various parameters. |
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Verte
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97f0555cb0
|
Remove VectorSpace impl on Quat (#12796)
- Fixes #[12762](https://github.com/bevyengine/bevy/issues/12762). ## Migration Guide - `Quat` no longer implements `VectorSpace` as unit quaternions don't actually form proper vector spaces. If you're absolutely certain that what you're doing is correct, convert the `Quat` into a `Vec4` and perform the operations before converting back. |
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Matty
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f924b4d9ef
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Move Point out of cubic splines module and expand it (#12747)
# Objective
Previously, the `Point` trait, which abstracts all of the operations of
a real vector space, was sitting in the submodule of `bevy_math` for
cubic splines. However, the trait has broader applications than merely
cubic splines, and we should use it when possible to avoid code
duplication when performing vector operations.
## Solution
`Point` has been moved into a new submodule in `bevy_math` named
`common_traits`. Furthermore, it has been renamed to `VectorSpace`,
which is more descriptive, and an additional trait `NormedVectorSpace`
has been introduced to expand the API to cover situations involving
geometry in addition to algebra. Additionally, `VectorSpace` itself now
requires a `ZERO` constant and `Neg`. It also supports a `lerp` function
as an automatic trait method.
Here is what that looks like:
```rust
/// A type that supports the mathematical operations of a real vector space, irrespective of dimension.
/// In particular, this means that the implementing type supports:
/// - Scalar multiplication and division on the right by elements of `f32`
/// - Negation
/// - Addition and subtraction
/// - Zero
///
/// Within the limitations of floating point arithmetic, all the following are required to hold:
/// - (Associativity of addition) For all `u, v, w: Self`, `(u + v) + w == u + (v + w)`.
/// - (Commutativity of addition) For all `u, v: Self`, `u + v == v + u`.
/// - (Additive identity) For all `v: Self`, `v + Self::ZERO == v`.
/// - (Additive inverse) For all `v: Self`, `v - v == v + (-v) == Self::ZERO`.
/// - (Compatibility of multiplication) For all `a, b: f32`, `v: Self`, `v * (a * b) == (v * a) * b`.
/// - (Multiplicative identity) For all `v: Self`, `v * 1.0 == v`.
/// - (Distributivity for vector addition) For all `a: f32`, `u, v: Self`, `(u + v) * a == u * a + v * a`.
/// - (Distributivity for scalar addition) For all `a, b: f32`, `v: Self`, `v * (a + b) == v * a + v * b`.
///
/// Note that, because implementing types use floating point arithmetic, they are not required to actually
/// implement `PartialEq` or `Eq`.
pub trait VectorSpace:
Mul<f32, Output = Self>
+ Div<f32, Output = Self>
+ Add<Self, Output = Self>
+ Sub<Self, Output = Self>
+ Neg
+ Default
+ Debug
+ Clone
+ Copy
{
/// The zero vector, which is the identity of addition for the vector space type.
const ZERO: Self;
/// Perform vector space linear interpolation between this element and another, based
/// on the parameter `t`. When `t` is `0`, `self` is recovered. When `t` is `1`, `rhs`
/// is recovered.
///
/// Note that the value of `t` is not clamped by this function, so interpolating outside
/// of the interval `[0,1]` is allowed.
#[inline]
fn lerp(&self, rhs: Self, t: f32) -> Self {
*self * (1. - t) + rhs * t
}
}
```
```rust
/// A type that supports the operations of a normed vector space; i.e. a norm operation in addition
/// to those of [`VectorSpace`]. Specifically, the implementor must guarantee that the following
/// relationships hold, within the limitations of floating point arithmetic:
/// - (Nonnegativity) For all `v: Self`, `v.norm() >= 0.0`.
/// - (Positive definiteness) For all `v: Self`, `v.norm() == 0.0` implies `v == Self::ZERO`.
/// - (Absolute homogeneity) For all `c: f32`, `v: Self`, `(v * c).norm() == v.norm() * c.abs()`.
/// - (Triangle inequality) For all `v, w: Self`, `(v + w).norm() <= v.norm() + w.norm()`.
///
/// Note that, because implementing types use floating point arithmetic, they are not required to actually
/// implement `PartialEq` or `Eq`.
pub trait NormedVectorSpace: VectorSpace {
/// The size of this element. The return value should always be nonnegative.
fn norm(self) -> f32;
/// The squared norm of this element. Computing this is often faster than computing
/// [`NormedVectorSpace::norm`].
#[inline]
fn norm_squared(self) -> f32 {
self.norm() * self.norm()
}
/// The distance between this element and another, as determined by the norm.
#[inline]
fn distance(self, rhs: Self) -> f32 {
(rhs - self).norm()
}
/// The squared distance between this element and another, as determined by the norm. Note that
/// this is often faster to compute in practice than [`NormedVectorSpace::distance`].
#[inline]
fn distance_squared(self, rhs: Self) -> f32 {
(rhs - self).norm_squared()
}
}
```
Furthermore, this PR also demonstrates the use of the
`NormedVectorSpace` combined API to implement `ShapeSample` for
`Triangle2d` and `Triangle3d` simultaneously. Such deduplication is one
of the drivers for developing these APIs.
---
## Changelog
- `Point` from `cubic_splines` becomes `VectorSpace`, exported as
`bevy::math::VectorSpace`.
- `VectorSpace` requires `Neg` and `VectorSpace::ZERO` in addition to
its existing prerequisites.
- Introduced public traits `bevy::math::NormedVectorSpace` for generic
geometry tasks involving vectors.
- Implemented `ShapeSample` for `Triangle2d` and `Triangle3d`.
## Migration Guide
Since `Point` no longer exists, any projects using it must switch to
`bevy::math::VectorSpace`. Additionally, third-party implementations of
this trait now require the `Neg` trait; the constant `VectorSpace::ZERO`
must be provided as well.
---
## Discussion
### Design considerations
Originally, the `NormedVectorSpace::norm` method was part of a separate
trait `Normed`. However, I think that was probably too broad and, more
importantly, the semantics of having it in `NormedVectorSpace` are much
clearer.
As it currently stands, the API exposed here is pretty minimal, and
there is definitely a lot more that we could do, but there are more
questions to answer along the way. As a silly example, we could
implement `NormedVectorSpace::length` as an alias for
`NormedVectorSpace::norm`, but this overlaps with methods in all of the
glam types, so we would want to make sure that the implementations are
effectively identical (for what it's worth, I think they are already).
### Future directions
One example of something that could belong in the `NormedVectorSpace`
API is normalization. Actually, such a thing previously existed on this
branch before I decided to shelve it because of concerns with namespace
collision. It looked like this:
```rust
/// This element, but normalized to norm 1 if possible. Returns an error when the reciprocal of
/// the element's norm is not finite.
#[inline]
#[must_use]
fn normalize(&self) -> Result<Self, NonNormalizableError> {
let reciprocal = 1.0 / self.norm();
if reciprocal.is_finite() {
Ok(*self * reciprocal)
} else {
Err(NonNormalizableError { reciprocal })
}
}
/// An error indicating that an element of a [`NormedVectorSpace`] was non-normalizable due to having
/// non-finite norm-reciprocal.
#[derive(Debug, Error)]
#[error("Element with norm reciprocal {reciprocal} cannot be normalized")]
pub struct NonNormalizableError {
reciprocal: f32
}
```
With this kind of thing in hand, it might be worth considering
eventually making the passage from vectors to directions fully generic
by employing a wrapper type. (Of course, for our concrete types, we
would leave the existing names in place as aliases.) That is, something
like:
```rust
pub struct NormOne<T>
where T: NormedVectorSpace { //... }
```
Utterly separately, the reason that I implemented `ShapeSample` for
`Triangle2d`/`Triangle3d` was to prototype uniform sampling of abstract
meshes, so that's also a future direction.
---------
Co-authored-by: Zachary Harrold <zac@harrold.com.au>
|