# Objective
- bevy_math fails to publish because of the self dev-dependency
- it's used to enable the `approx` feature in tests
## Solution
- Don't specify a version in the dev-dependency. dependencies without a
version are ignored by cargo when publishing
- Gate all the tests that depend on the `approx` feature so that it
doesn't fail to compile when not enabled
- Also gate an import that wasn't used without `bevy_reflect`
## Testing
- with at least cargo 1.84: `cargo package -p bevy_math`
- `cd target/package/bevy_math_* && cargo test`
# Objective
The docs of `EaseFunction` don't visualize the different functions,
requiring you to check out the Bevy repo and running the
`easing_function` example.
## Solution
- Add tool to generate suitable svg graphs. This only needs to be re-run
when adding new ease functions.
- works with all themes
- also add missing easing functions to example.
---
## Showcase
---------
Co-authored-by: François Mockers <mockersf@gmail.com>
# Objective
- We kind of missed out on implementing the `Ease` trait for some
objects like `Isometry2D` and `Isometry3D` even though it makes sense
and isn't that hard
- Fixes#17539
## Testing
- wrote some minimal tests
- ~~noticed that quat easing isn't working as expected yet~~ I just
confused degrees and radians once again 🙈
# Objective
Stumbled upon a `from <-> form` transposition while reviewing a PR,
thought it was interesting, and went down a bit of a rabbit hole.
## Solution
Fix em
# Background
In `no_std` compatible crates, there is often an `std` feature which
will allow access to the standard library. Currently, with the `std`
feature _enabled_, the
[`std::prelude`](https://doc.rust-lang.org/std/prelude/index.html) is
implicitly imported in all modules. With the feature _disabled_, instead
the [`core::prelude`](https://doc.rust-lang.org/core/prelude/index.html)
is implicitly imported. This creates a subtle and pervasive issue where
`alloc` items _may_ be implicitly included (if `std` is enabled), or
must be explicitly included (if `std` is not enabled).
# Objective
- Make the implicit imports for `no_std` crates consistent regardless of
what features are/not enabled.
## Solution
- Replace the `cfg_attr` "double negative" `no_std` attribute with
conditional compilation to _include_ `std` as an external crate.
```rust
// Before
#![cfg_attr(not(feature = "std"), no_std)]
// After
#![no_std]
#[cfg(feature = "std")]
extern crate std;
```
- Fix imports that are currently broken but are only now visible with
the above fix.
## Testing
- CI
## Notes
I had previously used the "double negative" version of `no_std` based on
general consensus that it was "cleaner" within the Rust embedded
community. However, this implicit prelude issue likely was considered
when forming this consensus. I believe the reason why is the items most
affected by this issue are provided by the `alloc` crate, which is
rarely used within embedded but extensively used within Bevy.
# Objective
We want to deny the following lints:
* `clippy::allow_attributes` - Because there's no reason to
`#[allow(...)]` an attribute if it wouldn't lint against anything; you
should always use `#[expect(...)]`
* `clippy::allow_attributes_without_reason` - Because documenting the
reason for allowing/expecting a lint is always good
## Solution
Set the `clippy::allow_attributes` and
`clippy::allow_attributes_without_reason` lints to `deny`, and bring
`bevy_math` in line with the new restrictions.
No code changes have been made - except if a lint that was previously
`allow(...)`'d could be removed via small code changes. For example,
`unused_variables` can be handled by adding a `_` to the beginning of a
field's name.
## Testing
I ran `cargo clippy`, and received no errors.
---------
Co-authored-by: IQuick 143 <IQuick143cz@gmail.com>
# Objective
The way `Curve` presently achieves dyn-compatibility involves shoving
`Self: Sized` bounds on a bunch of methods to forbid them from appearing
in vtables. (This is called *explicit non-dispatchability*.) The `Curve`
trait probably also just has way too many methods on its own.
In the past, using extension traits instead to achieve similar
functionality has been discussed. The upshot is that this would allow
the "core" of the curve trait, on which all the automatic methods rely,
to live in a very simple dyn-compatible trait, while other functionality
is implemented by extensions. For instance, `dyn Curve<T>` cannot use
the `Sized` methods, but `Box<dyn Curve<T>>` is `Sized`, hence would
automatically implement the extension trait, containing the methods
which are currently non-dispatchable.
Other motivations for this include modularity and code organization: the
`Curve` trait itself has grown quite large with the addition of numerous
adaptors, and refactoring it to demonstrate the separation of
functionality that is already present makes a lot of sense. Furthermore,
resampling behavior in particular is dependent on special traits that
may be mimicked or analogized in user-space, and creating extension
traits to achieve similar behavior in user-space is something we ought
to encourage by example.
## Solution
`Curve` now contains only `domain` and the `sample` methods.
`CurveExt` has been created, and it contains all adaptors, along with
the other sampling convenience methods (`samples`, `sample_iter`, etc.).
It is implemented for all `C` where `C: Curve<T> + Sized`.
`CurveResampleExt` has been created, and it contains all resampling
methods. It is implemented for all `C` where `C: Curve<T> + ?Sized`.
## Testing
It compiles and `cargo doc` succeeds.
---
## Future work
- Consider writing extension traits for resampling curves in related
domains (e.g. resampling for `Curve<T>` where `T: Animatable` into an
`AnimatableKeyframeCurve`).
- `CurveExt` might be further broken down to separate the adaptor and
sampling methods.
---
## Migration Guide
`Curve` has been refactored so that much of its functionality is now in
extension traits. Adaptors such as `map`, `reparametrize`, `reverse`,
and so on now require importing `CurveExt`, while the resampling methods
`resample_*` require importing `CurveResampleExt`. Both of these new
traits are exported through `bevy::math::curve` and through
`bevy::math::prelude`.
# Objective
Make it so that users can ease between tuples of easeable values.
## Solution
Use `variadics_please`'s `all_tuples_enumerated` macro to generate code
that creates these trait implementations. For two elements, the result
looks like this:
```rust
impl<T0: Ease, T1: Ease> Ease for (T0, T1) {
fn interpolating_curve_unbounded(start: Self, end: Self) -> impl Curve<Self> {
let curve_tuple = (
<T0 as Ease>::interpolating_curve_unbounded(start.0, end.0),
<T1 as Ease>::interpolating_curve_unbounded(start.1, end.1),
);
FunctionCurve::new(Interval::EVERYWHERE, move |t| {
(
curve_tuple.0.sample_unchecked(t),
curve_tuple.1.sample_unchecked(t),
)
})
}
}
```
## Testing
It compiles, and I futzed about with some manual examples, which seem to
work as expected.
---
## Showcase
Easing curves now support easing tuples of values that can themselves be
eased. For example:
```rust
// Easing between two `(Vec3, Quat)` values:
let easing_curve = EasingCurve::new(
(vec3(0.0, 0.0, 0.0), Quat::from_rotation_z(-FRAC_PI_2)),
(vec3(1.0, 1.0, 1.0), Quat::from_rotation_z(FRAC_PI_2)),
EaseFunction::ExponentialInOut
);
```
# Objective
Almost all of the `*InOut` easing functions are not actually smooth
(`SineInOut` is the one exception).
Because they're defined piecewise, they jump from accelerating upwards
to accelerating downwards, causing infinite jerk at t=½.
## Solution
This PR adds the well-known
[smoothstep](https://registry.khronos.org/OpenGL-Refpages/gl4/html/smoothstep.xhtml),
as well as its higher-degree version
[smootherstep](https://en.wikipedia.org/wiki/Smoothstep#Variations), as
easing functions.
Mathematically, these are the classic [Hermite
interpolation](https://en.wikipedia.org/wiki/Hermite_interpolation)
results:
- for smoothstep, the cubic with velocity zero at both ends
- for smootherstep, the quintic with velocity zero *and acceleration
zero* at both ends
And because they're simple polynomials, there's no branching and thus
they don't have the acceleration jump in the middle.
I also added some more information and cross-linking to the
documentation for these and some of the other easing functions, to help
clarify why one might want to use these over other existing ones. In
particular, I suspect that if people are willing to pay for a quintic
they might prefer `SmootherStep` to `QuinticInOut`.
For consistency with how everything else has triples, I added
`Smooth(er)Step{In,Out}` as well, in case people want to run the `In`
and `Out` versions separately for some reason. Qualitatively they're not
hugely different from `Quadratic{In,Out}` or `Cubic{In,Out}`, though, so
could be removed if you'd rather. They're low cost to keep, though, and
convenient for testing.
## Testing
These are simple polynomials, so their coefficients can be read directly
from the Horner's method implementation and compared to the reference
materials. The tests from #16910 were updated to also test these 6 new
easing functions, ensuring basic behaviour, plus one was updated to
better check that the InOut versions of things match their rescaled In
and Out versions.
Even small changes like
```diff
- (((2.5 + (-1.875 + 0.375*t) * t) * t) * t) * t
+ (((2.5 + (-1.85 + 0.375*t) * t) * t) * t) * t
```
are caught by multiple tests this way.
If you want to confirm them visually, here are the 6 new ones graphed:
<https://www.desmos.com/calculator/2d3ofujhry>
---
## Migration Guide
This version of bevy marks `EaseFunction` as `#[non_exhaustive]` to that
future changes to add more easing functions will be non-breaking. If you
were exhaustively matching that enum -- which you probably weren't --
you'll need to add a catch-all (`_ =>`) arm to cover unknown easing
functions.
And add a bunch of tests to show that all the monotonic easing functions
have roughly the expected shape.
# Objective
The `EaseFunction::Exponential*` variants aren't actually smooth as
currently implemented, because they jump by about 1‰ at the
start/end/both.
- Fixes#16676
- Subsumes #16675
## Solution
This PR slightly tweaks the shifting and scaling of all three variants
to ensure they hit (0, 0) and (1, 1) exactly while gradually
transitioning between them.
Graph demonstration of the new easing function definitions:
<https://www.desmos.com/calculator/qoc5raus2z>
(Yes, they look completely identical to the previous ones at that scale.
[Here's a zoomed-in
comparison](https://www.desmos.com/calculator/ken6nk89of) between the
old and the new if you prefer.)
The approach taken was to keep the core 2¹⁰ᵗ shape, but to [ask
WolframAlpha](https://www.wolframalpha.com/input?i=solve+over+the+reals%3A+pow%282%2C+10-A%29+-+pow%282%2C+-A%29%3D+1)
what scaling factor to use such that f(1)-f(0)=1, then shift the curve
down so that goes from zero to one instead of ¹/₁₀₂₃ to ¹⁰²⁴/₁₀₂₃.
## Testing
I've included in this PR a bunch of general tests for all monotonic
easing functions to ensure they hit (0, 0) to (1, 1), that the InOut
functions hit (½, ½), and that they have the expected convexity.
You can also see by inspection that the difference is small. The change
for `exponential_in` is from `exp2(10 * t - 10)` to `exp2(10 * t -
9.99859…) - 0.0009775171…`.
The problem for `exponential_in(0)` is also simple to see without a
calculator: 2⁻¹⁰ is obviously not zero, but with the new definition
`exp2(-LOG2_1023) - FRAC_1_1023` => `1/(exp2(LOG2_1023)) - FRAC_1_1023`
=> `FRAC_1_1023 - FRAC_1_1023` => `0`.
---
## Migration Guide
This release of bevy slightly tweaked the definitions of
`EaseFunction::ExponentialIn`, `EaseFunction::ExponentialOut`, and
`EaseFunction::ExponentialInOut`. The previous definitions had small
discontinuities, while the new ones are slightly rescaled to be
continuous. For the output values that changed, that change was less
than 0.001, so visually you might not even notice the difference.
However, if you depended on them for determinism, you'll need to define
your own curves with the previous definitions.
---------
Co-authored-by: IQuick 143 <IQuick143cz@gmail.com>
# 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>
# Objective
- Remove `derive_more`'s error derivation and replace it with
`thiserror`
## Solution
- Added `derive_more`'s `error` feature to `deny.toml` to prevent it
sneaking back in.
- Reverted to `thiserror` error derivation
## Notes
Merge conflicts were too numerous to revert the individual changes, so
this reversion was done manually. Please scrutinise carefully during
review.
# 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>
# Objective
We currently use special "floating" constructors for `EasingCurve`,
`FunctionCurve`, and `ConstantCurve` (ex: `easing_curve`). This erases
the type being created (and in general "what is happening"
structurally), for very minimal ergonomics improvements. With rare
exceptions, we prefer normal `X::new()` constructors over floating `x()`
constructors in Bevy. I don't think this use case merits special casing
here.
## Solution
Add `EasingCurve::new()`, use normal constructors everywhere, and remove
the floating constructors.
I think this should land in 0.15 in the interest of not breaking people
later.
# Objective
Improve the average user's ability to understand what the heck is going
on with the Curve API.
## Solution
I wrote some docs. I doubt these are perfect; I'm probably far too close
to this for that to be the case. :)
# Objective
The previous `PhantomData` instances were written somewhat lazily, so
they were just things like `PhantomData<T>` for curves with an output
type of `T`. This looks innocuous, but it unnecessarily constrains
`Send/Sync` inference based on `T`. See
[here](https://doc.rust-lang.org/nomicon/phantom-data.html#table-of-phantomdata-patterns).
## Solution
Switch to `PhantomData` of the form `PhantomData<fn() -> T>` for most of
these adaptors. Since they only have a functional relationship to `T`
(i.e. it shows up in the return type of trait methods), this is more
accurate.
## Testing
Tested by compiling Bevy.
Co-authored-by: François Mockers <mockersf@gmail.com>
# Objective
- `interpolation` crates provides all the curves functions, but some of
them were wrong
- We have a partial solution where some functions comes from the
external crate, some from bevy_math
## Solution
- Move them all to bevy_math
- Remove the dependency on `interpolation`
## Testing
Playing the `easing_functions` example
# 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.
# Objective
Allow curve adaptors to be reliably `Reflect` even if the curves they
hold are not `FromReflect`. This allows them, for example, to be used in
`bevy_animation`. I previously addressed this with the functional
adaptors, but I forgot to address this in the case of fields that hold
other curves and not arbitrary functions.
## Solution
Do the following on every curve adaptor that holds another curve:
```rust
// old:
#[derive(Reflect)]
```
```rust
// new:
#[derive(Reflect, FromReflect)]
#[reflect(from_reflect = false)]
```
This looks inane, but it's necessary because the default
`#[derive(Reflect)]` macro places `FromReflect` bounds on everything. To
avoid this, we opt out of deriving `FromReflect` with that macro by
adding `#[reflect(from_reflect = false)]`, then separately derive
`FromReflect`. (Of course, the latter still has the `FromReflect`
bounds, which is fine.)
# Objective
- Followup for #14788
- Support most usual ease function
## Solution
- Use the crate
[`interpolation`](https://docs.rs/interpolation/0.3.0/interpolation/trait.Ease.html)
which has them all
- it's already used by bevy_easings, bevy_tweening, be_tween,
bevy_tweening_captured, bevy_enoki, kayak_ui in the Bevy ecosystem for
various easing/tweening/interpolation
# Objective
Currently, sample-interpolated curves (such as those used by the glTF
loader for animations) do unnecessary extra work when `sample_clamped`
is called, since their implementations of `sample_unchecked` are already
clamped. Eliminating this redundant sampling is a small, easy
performance win which doesn't compromise on the animation system's
internal usage of `sample_clamped`, which guarantees that it never
samples curves out-of-bounds.
## Solution
For sample-interpolated curves, define `sample_clamped` in the way
`sample_unchecked` is currently defined, and then redirect
`sample_unchecked` to `sample_clamped`. This is arguably a more
idiomatic way of using the `cores` as well, which is nice.
## Testing
Ran `many_foxes` to make sure I didn't break anything.
# Objective
Citing @mweatherley
> There is a lot of shortfall for simple cases— e.g., we should have
library functions for making a curve connecting two points, eased
versions of that, and so on.
## Solution
This PR implements
- a simple `Easing` trait which is implemented for all `impl Curve<f32>`
types. We can't really guarantee that these curves have unit interval
domain, which some people would probably expect, but it is documented
that this isn't the case for these types and we redirect to
`EasingCurve` which is used for that purpose
- an `EasingCurve` struct, which is used to interpolate between two
values `start` and `end` using a `impl Easing` curve where the curve
will be guaranteed to be reparametrized
- a `LinearCurve` which linearly interpolates between two values `start`
and `end`
- a `CubicBezierCurve` which interpolates between `start` and `end`
values using a `CubicSegment`
- a `StepCurve` which interpolates between `start` and `end` with an
step-function with `n` steps
- an `ElasticCurve` which interpolates between `start` and `end` with
spring like behavior where the elasticity of the spring is configurable
- some `FunctionCurve` easing curves for different popular functions
including: `quadratic_ease_in`, `quadratic_ease_out`, `smoothstep`,
`identity`
## Testing
- there are a few new tests for all of these in the main module
---------
Co-authored-by: eckz <567737+eckz@users.noreply.github.com>
Co-authored-by: Miles Silberling-Cook <NthTensor@users.noreply.github.com>
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
Co-authored-by: Matty <weatherleymatthew@gmail.com>
# Objective
This PR extends and reworks the material from #15282 by allowing
arbitrary curves to be used by the animation system to animate arbitrary
properties. The goals of this work are to:
- Allow far greater flexibility in how animations are allowed to be
defined in order to be used with `bevy_animation`.
- Delegate responsibility over keyframe interpolation to `bevy_math` and
the `Curve` libraries and reduce reliance on keyframes in animation
definitions generally.
- Move away from allowing the glTF spec to completely define animations
on a mechanical level.
## Solution
### Overview
At a high level, curves have been incorporated into the animation system
using the `AnimationCurve` trait (closely related to what was
`Keyframes`). From the top down:
1. In `animate_targets`, animations are driven by `VariableCurve`, which
is now a thin wrapper around a `Box<dyn AnimationCurve>`.
2. `AnimationCurve` is something built out of a `Curve`, and it tells
the animation system how to use the curve's output to actually mutate
component properties. The trait looks like this:
```rust
/// A low-level trait that provides control over how curves are actually applied to entities
/// by the animation system.
///
/// Typically, this will not need to be implemented manually, since it is automatically
/// implemented by [`AnimatableCurve`] and other curves used by the animation system
/// (e.g. those that animate parts of transforms or morph weights). However, this can be
/// implemented manually when `AnimatableCurve` is not sufficiently expressive.
///
/// In many respects, this behaves like a type-erased form of [`Curve`], where the output
/// type of the curve is remembered only in the components that are mutated in the
/// implementation of [`apply`].
///
/// [`apply`]: AnimationCurve::apply
pub trait AnimationCurve: Reflect + Debug + Send + Sync {
/// Returns a boxed clone of this value.
fn clone_value(&self) -> Box<dyn AnimationCurve>;
/// The range of times for which this animation is defined.
fn domain(&self) -> Interval;
/// Write the value of sampling this curve at time `t` into `transform` or `entity`,
/// as appropriate, interpolating between the existing value and the sampled value
/// using the given `weight`.
fn apply<'a>(
&self,
t: f32,
transform: Option<Mut<'a, Transform>>,
entity: EntityMutExcept<'a, (Transform, AnimationPlayer, Handle<AnimationGraph>)>,
weight: f32,
) -> Result<(), AnimationEvaluationError>;
}
```
3. The conversion process from a `Curve` to an `AnimationCurve` involves
using wrappers which communicate the intent to animate a particular
property. For example, here is `TranslationCurve`, which wraps a
`Curve<Vec3>` and uses it to animate `Transform::translation`:
```rust
/// This type allows a curve valued in `Vec3` to become an [`AnimationCurve`] that animates
/// the translation component of a transform.
pub struct TranslationCurve<C>(pub C);
```
### Animatable Properties
The `AnimatableProperty` trait survives in the transition, and it can be
used to allow curves to animate arbitrary component properties. The
updated documentation for `AnimatableProperty` explains this process:
<details>
<summary>Expand AnimatableProperty example</summary
An `AnimatableProperty` is a value on a component that Bevy can animate.
You can implement this trait on a unit struct in order to support
animating
custom components other than transforms and morph weights. Use that type
in
conjunction with `AnimatableCurve` (and perhaps
`AnimatableKeyframeCurve`
to define the animation itself). For example, in order to animate font
size of a
text section from 24 pt. to 80 pt., you might use:
```rust
#[derive(Reflect)]
struct FontSizeProperty;
impl AnimatableProperty for FontSizeProperty {
type Component = Text;
type Property = f32;
fn get_mut(component: &mut Self::Component) -> Option<&mut Self::Property> {
Some(&mut component.sections.get_mut(0)?.style.font_size)
}
}
```
You can then create an `AnimationClip` to animate this property like so:
```rust
let mut animation_clip = AnimationClip::default();
animation_clip.add_curve_to_target(
animation_target_id,
AnimatableKeyframeCurve::new(
[
(0.0, 24.0),
(1.0, 80.0),
]
)
.map(AnimatableCurve::<FontSizeProperty, _>::from_curve)
.expect("Failed to create font size curve")
);
```
Here, the use of `AnimatableKeyframeCurve` creates a curve out of the
given keyframe time-value
pairs, using the `Animatable` implementation of `f32` to interpolate
between them. The
invocation of `AnimatableCurve::from_curve` with `FontSizeProperty`
indicates that the `f32`
output from that curve is to be used to animate the font size of a
`Text` component (as
configured above).
</details>
### glTF Loading
glTF animations are now loaded into `Curve` types of various kinds,
depending on what is being animated and what interpolation mode is being
used. Those types get wrapped into and converted into `Box<dyn
AnimationCurve>` and shoved inside of a `VariableCurve` just like
everybody else.
### Morph Weights
There is an `IterableCurve` abstraction which allows sampling these from
a contiguous buffer without allocating. Its only reason for existing is
that Rust disallows you from naming function types, otherwise we would
just use `Curve` with an iterator output type. (The iterator involves
`Map`, and the name of the function type would have to be able to be
named, but it is not.)
A `WeightsCurve` adaptor turns an `IterableCurve` into an
`AnimationCurve`, so it behaves like everything else in that regard.
## Testing
Tested by running existing animation examples. Interpolation logic has
had additional tests added within the `Curve` API to replace the tests
in `bevy_animation`. Some kinds of out-of-bounds errors have become
impossible.
Performance testing on `many_foxes` (`animate_targets`) suggests that
performance is very similar to the existing implementation. Here are a
couple trace histograms across different runs (yellow is this branch,
red is main).
<img width="669" alt="Screenshot 2024-09-27 at 9 41 50 AM"
src="https://github.com/user-attachments/assets/5ba4e9ac-3aea-452e-aaf8-1492acc2d7fc">
<img width="673" alt="Screenshot 2024-09-27 at 9 45 18 AM"
src="https://github.com/user-attachments/assets/8982538b-04cf-46b5-97b2-164c6bc8162e">
---
## Migration Guide
Most user code that does not directly deal with `AnimationClip` and
`VariableCurve` will not need to be changed. On the other hand,
`VariableCurve` has been completely overhauled. If you were previously
defining animation curves in code using keyframes, you will need to
migrate that code to use curve constructors instead. For example, a
rotation animation defined using keyframes and added to an animation
clip like this:
```rust
animation_clip.add_curve_to_target(
animation_target_id,
VariableCurve {
keyframe_timestamps: vec![0.0, 1.0, 2.0, 3.0, 4.0],
keyframes: Keyframes::Rotation(vec![
Quat::IDENTITY,
Quat::from_axis_angle(Vec3::Y, PI / 2.),
Quat::from_axis_angle(Vec3::Y, PI / 2. * 2.),
Quat::from_axis_angle(Vec3::Y, PI / 2. * 3.),
Quat::IDENTITY,
]),
interpolation: Interpolation::Linear,
},
);
```
would now be added like this:
```rust
animation_clip.add_curve_to_target(
animation_target_id,
AnimatableKeyframeCurve::new([0.0, 1.0, 2.0, 3.0, 4.0].into_iter().zip([
Quat::IDENTITY,
Quat::from_axis_angle(Vec3::Y, PI / 2.),
Quat::from_axis_angle(Vec3::Y, PI / 2. * 2.),
Quat::from_axis_angle(Vec3::Y, PI / 2. * 3.),
Quat::IDENTITY,
]))
.map(RotationCurve)
.expect("Failed to build rotation curve"),
);
```
Note that the interface of `AnimationClip::add_curve_to_target` has also
changed (as this example shows, if subtly), and now takes its curve
input as an `impl AnimationCurve`. If you need to add a `VariableCurve`
directly, a new method `add_variable_curve_to_target` accommodates that
(and serves as a one-to-one migration in this regard).
### For reviewers
The diff is pretty big, and the structure of some of the changes might
not be super-obvious:
- `keyframes.rs` became `animation_curves.rs`, and `AnimationCurve` is
based heavily on `Keyframes`, with the adaptors also largely following
suite.
- The Curve API adaptor structs were moved from `bevy_math::curve::mod`
into their own module `adaptors`. There are no functional changes to how
these adaptors work; this is just to make room for the specialized
reflection implementations since `mod.rs` was getting kind of cramped.
- The new module `gltf_curves` holds the additional curve constructions
that are needed by the glTF loader. Note that the loader uses a mix of
these and off-the-shelf `bevy_math` curve stuff.
- `animatable.rs` no longer holds logic related to keyframe
interpolation, which is now delegated to the existing abstractions in
`bevy_math::curve::cores`.
---------
Co-authored-by: Gino Valente <49806985+MrGVSV@users.noreply.github.com>
Co-authored-by: aecsocket <43144841+aecsocket@users.noreply.github.com>
(Note: #15434 implements something very similar to this for functional
curve adaptors, which is why they aren't present in this PR.)
# Objective
Previously, there was basically no chance that the
explicitly-interpolating sample curve structs from the `Curve` API would
actually be `Reflect`. The reason for this is functional programming:
the structs contain an explicit interpolation `I: Fn(&T, &T, f32) -> T`
which, under typical circumstances, will never be `Reflect`, which
prevents the derive from realistically succeeding. In fact, they won't
be a lot of other things either, notably including both`Debug` and
`TypePath`, which are also required for reflection to succeed.
The goal of this PR is to weaken the implementations of reflection
traits for these structs so that they can implement `Reflect` under
reasonable circumstances. (Notably, they will still not be
`FromReflect`, which is unavoidable.)
## Solution
The function fields are marked as `#[reflect(ignore)]`, and the derive
macro for `Reflect` has `FromReflect` disabled. (This is not fully
optimal, but we don't presently have any kind of "read-only" attribute
for these fields.) Additionally, these structs receive custom `Debug`
and `TypePath` implementations that display the function's (unstable!)
type name instead of its value or type path (respectively). In the case
of `TypePath`, this is a bit janky, but the instability of `type_name`
won't generally present an issue for generics, which would have to be
registered manually in the type registry anyway, which is impossible
because the function type parameters cannot be named.
(And in general, the "blessed" route for such cases would generally
involve manually monomorphizing the function parameter away, which also
allows access to `FromReflect` etc. through very ordinary use of the
derive macro.)
## Testing
Tests in the new `bevy_math::curve::sample_curves` module guarantee that
these are actually `Reflect` under reasonable circumstances.
---
## Future changes
If and when function item types become `Default`, these types will need
to receive custom `FromReflect` implementations that exploit it. Such a
custom implementation would also be desirable if users start doing
things like wrapping function items in `Default`/`FromReflect` wrappers
that still implement a `Fn` trait.
Additionally, if function types become nameable in user-space, the
stance on `Debug`/`TypePath` may bear reexamination, since partial
monomorphization through wrappers would make implementing reflect traits
for function types potentially more viable.
---------
Co-authored-by: Gino Valente <49806985+MrGVSV@users.noreply.github.com>
# Objective
This implements another item on the way to complete the `Curves`
implementation initiative
Citing @mweatherley
> Curve adaptors for making a curve repeat or ping-pong would be useful.
This adds three widely applicable adaptors:
- `ReverseCurve` "plays" the curve backwards
- `RepeatCurve` to repeat the curve for `n` times where `n` in `[0,inf)`
- `ForeverCurve` which extends the curves domain to `EVERYWHERE`
- `PingPongCurve` (name wip (?)) to chain the curve with it's reverse.
This would be achievable with `ReverseCurve` and `ChainCurve`, but it
would require the use of `by_ref` which can be restrictive in some
scenarios where you'd rather just consume the curve. Users can still
create the same effect by combination of the former two, but since this
will be most likely a very typical adaptor we should also provide it on
the library level. (Why it's typical: you can create a single period of
common waves with it pretty easily, think square wave (= pingpong +
step), triangle wave ( = pingpong + linear), etc.)
- `ContinuationCurve` which chains two curves but also makes sure that
the samples of the second curve are translated so that
`sample(first.end) == sample(second.start)`
## Solution
Implement the adaptors above. (More suggestions are welcome!)
## Testing
- [x] add simple tests. One per adaptor
---------
Co-authored-by: eckz <567737+eckz@users.noreply.github.com>
Co-authored-by: Matty <2975848+mweatherley@users.noreply.github.com>
Co-authored-by: IQuick 143 <IQuick143cz@gmail.com>
Co-authored-by: Matty <weatherleymatthew@gmail.com>
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
# 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>
# Objective
- `Curve<T>` was meant to be object safe, but one of the latest commits
made it not object safe.
- When trying to use `Curve<T>` as `&dyn Curve<T>` this compile error is
raised:
```
error[E0038]: the trait `curve::Curve` cannot be made into an object
--> crates/bevy_math/src/curve/mod.rs:1025:20
note: for a trait to be "object safe" it needs to allow building a vtable to allow the call to be resolvable dynamically; for more information visit <https://doc.rust-lang.org/reference/items/traits.html#object-safety>
--> crates/bevy_math/src/curve/mod.rs:60:8
|
23 | pub trait Curve<T> {
| ----- this trait cannot be made into an object...
...
60 | fn sample_iter(&self, iter: impl IntoIterator<Item = f32>) -> impl Iterator<Item = Option<T>> {
| ^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ...because method `sample_iter` references an `impl Trait` type in its return type
| |
| ...because method `sample_iter` has generic type parameters
...
```
## Solution
- Making `Curve<T>` object safe again by adding `Self: Sized` to newly
added methods.
## Testing
- Added new test that ensures the `Curve<T>` trait can be made into an
objet.
# Objective
This is a value that is and will be used as a domain of curves pretty
often. By adding it as a dedicated constant we can get rid of some
`unwraps` and function calls.
## Solution
added `Interval::UNIT`
## Testing
I replaced all occurrences of `interval(0.0, 1.0).unwrap()` with the new
`Interval::UNIT` constant in tests and doc tests.
# Objective
Citing @mweatherley
> As mentioned before, a multi-sampling function in the API which takes
an iterator is probably something we want (e.g. `sample_iter(iter: impl
IntoIterator<Item = f32>) -> impl IntoIterator<Item = T> { //... }`, but
there are some design choices to be made on the details (e.g. does this
filter out points that aren't in the domain? does it do sorting? etc.)
## Solution
I think the most flexible solution for end users is to expose all the
`sample_...` functions with an `iter` equivalent, so we'll have
- `sample_iter`
- `sample_iter_unchecked`
- `sample_iter_clamped`
Answering some questions from the original idea:
> does this filter out points that aren't in the domain?
With the methods the user has the choice to just sample or if they want
to filter out invalid types us `sample_iter` and then apply `filter_map`
to the iterator returned themselves.
> does it do sorting?
I think it's the same thing. If the user wants it, they need to do it
themselves by either collecting and sorting a `Vec` or using
`itertools`. I think there is a legit use case for "please sample me
this collection of points that are unordered" and we would destroy it if
we take away to much agency from users by sorting for them
## Testing
- Added a test which covers all three methods
# Objective
Finish what we started in #14630. The Curve RFC is
[here](https://github.com/bevyengine/rfcs/blob/main/rfcs/80-curve-trait.md).
## Solution
This contains the rest of the library from my branch. The main things
added here are:
- Bulk sampling / resampling methods on `Curve` itself
- Data structures supporting the above
- The `cores` submodule that those data structures use to encapsulate
sample interpolation
The weirdest thing in here is probably `ChunkedUnevenCore` in `cores`,
which is not used by anything in the Curve library itself but which is
required for efficient storage of glTF animation curves. (See #13105.)
We can move it into a different PR if we want to; I don't have strong
feelings either way.
## Testing
New tests related to resampling are included. As I write this, I realize
we could use some tests in `cores` itself, so I will add some on this
branch before too long.
---------
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
Co-authored-by: Robert Walter <26892280+RobWalt@users.noreply.github.com>
# 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>
# Objective
This PR implements part of the [Curve
RFC](https://github.com/bevyengine/rfcs/blob/main/rfcs/80-curve-trait.md).
See that document for motivation, objectives, etc.
## Solution
For purposes of reviewability, this PR excludes the entire part of the
RFC related to taking multiple samples, resampling, and interpolation
generally. (This means the entire `cores` submodule is also excluded.)
On the other hand, the entire `Interval` type and all of the functional
`Curve` adaptors are included.
## Testing
Test modules are included and can be run locally (but they are also
included in CI).
---------
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
