# Objective
A bunch of code is used only if you care about the `Curve` trait. Put it
behind a feature so it can be ignored if wanted.
## Solution
Added a default feature `curve` to `bevy_math` which feature-gates the
`curve` module and internal integrations.
## Testing
Tested compiling with the feature enabled and disabled.
# Objective
We introduced the fancy Curve API earlier in this version. The goal of
this PR is to provide a level of integration between that API and the
existing spline constructions in `bevy_math`.
Note that this PR only covers the integration of position-sampling via
the `Curve` API. Other (substantially more complex) planned work will
introduce general facilities for handling derivatives.
## Solution
`CubicSegment`, `CubicCurve`, `RationalSegment`, and `RationalCurve` all
now implement `Curve`, using their `position` function to sample the
output.
Additionally, some documentation has been updated/corrected, and
`Serialize`/`Deserialize` derives have been added for all the curve
structs. (Note that there are some barriers to automatic registration of
`ReflectSerialize`/`ReflectSerialize` involving generics that have not
been resolved in this PR.)
---
## Migration Guide
The `RationalCurve::domain` method has been renamed to
`RationalCurve::length`. Calling `.domain()` on a `RationalCurve` now
returns its entire domain as an `Interval`.
# 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
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
Previously, our cubic spline constructors would produce
`CubicCurve`/`RationalCurve` output with no data when they themselves
didn't hold enough control points to produce a well-formed curve.
Attempting to sample the resulting empty "curves" (e.g. by calling
`CubicCurve::position`) would crash the program (😓).
The objectives of this PR are:
1. Ensure that the curve output of `bevy_math`'s spline constructions
are never invalid as data.
2. Provide a type-level guarantee that `CubicCurve` and `RationalCurve`
actually function as curves.
## Solution
This has a few pieces. Firstly, the curve generator traits
`CubicGenerator`, `CyclicCubicGenerator`, and `RationalGenerator` are
now fallible — they have associated error types, and the
curve-generation functions are allowed to fail:
```rust
/// Implement this on cubic splines that can generate a cubic curve from their spline parameters.
pub trait CubicGenerator<P: VectorSpace> {
/// An error type indicating why construction might fail.
type Error;
/// Build a [`CubicCurve`] by computing the interpolation coefficients for each curve segment.
fn to_curve(&self) -> Result<CubicCurve<P>, Self::Error>;
}
```
All existing spline constructions use this together with errors that
indicate when they didn't have the right control data and provide curves
which have at least one segment whenever they return an `Ok` variant.
Next, `CubicCurve` and `RationalCurve` have been blessed with a
guarantee that their internal array of segments (`segments`) is never
empty. In particular, this field is no longer public, so that invalid
curves cannot be built using struct instantiation syntax. To compensate
for this shortfall for users (in particular library authors who might
want to implement their own generators), there is a new method
`from_segments` on these for constructing a curve from a list of
segments, failing if the list is empty:
```rust
/// Create a new curve from a collection of segments. If the collection of segments is empty,
/// a curve cannot be built and `None` will be returned instead.
pub fn from_segments(segments: impl Into<Vec<CubicSegment<P>>>) -> Option<Self> { //... }
```
All existing methods on `CyclicCurve` and `CubicCurve` maintain the
invariant, so the direct construction of invalid values by users is
impossible.
## Testing
Run unit tests from `bevy_math::cubic_splines`. Additionally, run the
`cubic_splines` example and try to get it to crash using small numbers
of control points: it uses the fallible constructors directly, so if
invalid data is ever constructed, it is basically guaranteed to crash.
---
## Migration Guide
The `to_curve` method on Bevy's cubic splines is now fallible (returning
a `Result`), meaning that any existing calls will need to be updated by
handling the possibility of an error variant.
Similarly, any custom implementation of `CubicGenerator` or
`RationalGenerator` will need to be amended to include an `Error` type
and be made fallible itself.
Finally, the fields of `CubicCurve` and `RationalCurve` are now private,
so any direct constructions of these structs from segments will need to
be replaced with the new `CubicCurve::from_segments` and
`RationalCurve::from_segments` methods.
---
## Design
The main thing to justify here is the choice for the curve internals to
remain the same. After all, if they were able to cause crashes in the
first place, it's worth wondering why safeguards weren't put in place on
the types themselves to prevent that.
My view on this is that the problem was really that the internals of
these methods implicitly relied on the assumption that the value they
were operating on was *actually a curve*, when this wasn't actually
guaranteed. Now, it's possible to make a bunch of small changes inside
the curve struct methods to account for that, but I think that's worse
than just guaranteeing that the data is valid upstream — sampling is
about as hot a code path as we're going to get in this area, and hitting
an additional branch every time it happens just to check that the struct
contains valid data is probably a waste of resources.
Another way of phrasing this is that even if we're only interested in
solving the crashes, the curve's validity needs to be checked at some
point, and it's almost certainly better to do this once at the point of
construction than every time the curve is sampled.
In cases where the control data is supplied dynamically, users would
already have to deal with empty curve outputs basically not working.
Anecdotally, I ran into this while writing the `cubic_splines` example,
and I think the diff illustrates the improvement pretty nicely — the
code no longer has to anticipate whether the output will be good or not;
it just has to handle the `Result`.
The cost of all this, of course, is that we have to guarantee that the
new invariant is actually maintained whenever we extend the API.
However, for the most part, I don't expect users to want to do much
surgery on the internals of their curves anyway.
# Objective
Fill a gap in the functionality of our curve constructions by allowing
users to easily build cyclic curves from control data.
## Solution
Here I opted for something lightweight and discoverable. There is a new
`CyclicCubicGenerator` trait with a method `to_curve_cyclic` which uses
splines' control data to create curves that are cyclic. For now, its
signature is exactly like that of `CubicGenerator` — `to_curve_cyclic`
just yields a `CubicCurve`:
```rust
/// Implement this on cubic splines that can generate a cyclic cubic curve from their spline parameters.
///
/// This makes sense only when the control data can be interpreted cyclically.
pub trait CyclicCubicGenerator<P: VectorSpace> {
/// Build a cyclic [`CubicCurve`] by computing the interpolation coefficients for each curve segment.
fn to_curve_cyclic(&self) -> CubicCurve<P>;
}
```
This trait has been implemented for `CubicHermite`,
`CubicCardinalSpline`, `CubicBSpline`, and `LinearSpline`:
<img width="753" alt="Screenshot 2024-07-01 at 8 58 27 PM"
src="https://github.com/bevyengine/bevy/assets/2975848/69ae0802-3b78-4fb9-b73a-6f842cf3b33c">
<img width="628" alt="Screenshot 2024-07-01 at 9 00 14 PM"
src="https://github.com/bevyengine/bevy/assets/2975848/2992175a-a96c-40fc-b1a1-5206c3572cde">
<img width="606" alt="Screenshot 2024-07-01 at 8 59 36 PM"
src="https://github.com/bevyengine/bevy/assets/2975848/9e99eb3a-dbe6-42da-886c-3d3e00410d03">
<img width="603" alt="Screenshot 2024-07-01 at 8 59 01 PM"
src="https://github.com/bevyengine/bevy/assets/2975848/d037bc0c-396a-43af-ab5c-fad9a29417ef">
(Each type pictured respectively with the control points rendered as
green spheres; tangents not pictured in the case of the Hermite spline.)
These curves are all parametrized so that the output of `to_curve` and
the output of `to_curve_cyclic` are similar. For instance, in
`CubicCardinalSpline`, the first output segment is a curve segment
joining the first and second control points in each, although it is
constructed differently. In the other cases, the segments from
`to_curve` are a subset of those in `to_curve_cyclic`, with the new
segments appearing at the end.
## Testing
I rendered cyclic splines from control data and made sure they looked
reasonable. Existing tests are intact for splines where previous code
was modified. (Note that the coefficient computation for cyclic spline
segments is almost verbatim identical to that of their non-cyclic
counterparts.)
The Bezier benchmarks also look fine.
---
## Changelog
- Added `CyclicCubicGenerator` trait to `bevy_math::cubic_splines` for
creating cyclic curves from control data.
- Implemented `CyclicCubicGenerator` for `CubicHermite`,
`CubicCardinalSpline`, `CubicBSpline`, and `LinearSpline`.
- `bevy_math` now depends on `itertools`.
---
## Discussion
### Design decisions
The biggest thing here is just the approach taken in the first place:
namely, the cyclic constructions use new methods on the same old
structs. This choice was made to reduce friction and increase
discoverability but also because creating new ones just seemed
unnecessary: the underlying data would have been the same, so creating
something like "`CyclicCubicBSpline`" whose internally-held control data
is regarded as cyclic in nature doesn't really accomplish much — the end
result for the user is basically the same either way.
Similarly, I don't presently see a pressing need for `to_curve_cyclic`
to output something other than a `CubicCurve`, although changing this in
the future may be useful. See below.
A notable omission here is that `CyclicCubicGenerator` is not
implemented for `CubicBezier`. This is not a gap waiting to be filled —
`CubicBezier` just doesn't have enough data to join its start with its
end without just making up the requisite control points wholesale. In
all the cases where `CyclicCubicGenerator` has been implemented here,
the fashion in which the ends are connected is quite natural and follows
the semantics of the associated spline construction.
### Future direction
There are two main things here:
1. We should investigate whether we should do something similar for
NURBS. I just don't know that much about NURBS at the moment, so I
regarded this as out of scope for the PR.
2. We may eventually want to change the output type of
`CyclicCubicGenerator::to_curve_cyclic` to a type which reifies the
cyclic nature of the curve output. This wasn't done in this PR because
I'm unsure how much value a type-level guarantee of cyclicity actually
has, but if some useful features make sense only in the case of cyclic
curves, this might be worth pursuing.
# Objective
Fixes#13535.
## Solution
I implemented `Reflect` for close to all math types now, except for some
types that it would cause issues (like some boxed types).
## Testing
- Everything seems to still build, will await CI though.
---
## Changelog
- Made close to all math types implement `Reflect`.
# Objective
Fixes#13456
## Solution
Moved `bevy_math`'s `Reflect` impls from `bevy_reflect` to `bevy_math`.
### Quick note
I accidentally used the same commit message while resolving a merge
conflict (first time I had to resolve a conflict). Sorry about that.
# Objective
Fixes#13189
## Solution
To add the reflect impls I needed to make all the struct fields pub. I
don't think there's any harm for these types, but just a note for
review.
---------
Co-authored-by: Ben Harper <ben@tukom.org>
# 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>
# Objective
- Fixes#12570
## Solution
Previously, cardinal splines constructed by `CubicCardinalSpline` would
leave out their endpoints when constructing the cubic curve segments
connecting their points. (See the linked issue for details.)
Now, cardinal splines include the endpoints. For instance, the provided
usage example
```rust
let points = [
vec2(-1.0, -20.0),
vec2(3.0, 2.0),
vec2(5.0, 3.0),
vec2(9.0, 8.0),
];
let cardinal = CubicCardinalSpline::new(0.3, points).to_curve();
let positions: Vec<_> = cardinal.iter_positions(100).collect();
```
will actually produce a spline that connects all four of these points
instead of just the middle two "interior" points.
Internally, this is achieved by duplicating the endpoints of the vector
of control points before performing the construction of the associated
`CubicCurve`. This amounts to specifying that the tangents at the
endpoints `P_0` and `P_n` (say) should be parallel to `P_1 - P_0` and
`P_n - P_{n-1}`.
---
## Migration Guide
Any users relying on the old behavior of `CubicCardinalSpline` will have
to truncate any parametrizations they used in order to access a curve
identical to the one they had previously. This would be done by chopping
off a unit-distance segment from each end of the parametrizing interval.
For instance, if a user's existing code looks as follows
```rust
fn interpolate(t: f32) -> Vec2 {
let points = [
vec2(-1.0, -20.0),
vec2(3.0, 2.0),
vec2(5.0, 3.0),
vec2(9.0, 8.0),
];
let my_curve = CubicCardinalSpline::new(0.3, points).to_curve();
my_curve.position(t)
}
```
then in order to obtain similar behavior, `t` will need to be shifted up
by 1, since the output of `CubicCardinalSpline::to_curve` has introduced
a new segment in the interval [0,1], displacing the old segment from
[0,1] to [1,2]:
```rust
fn interpolate(t: f32) -> Vec2 {
let points = [
vec2(-1.0, -20.0),
vec2(3.0, 2.0),
vec2(5.0, 3.0),
vec2(9.0, 8.0),
];
let my_curve = CubicCardinalSpline::new(0.3, points).to_curve();
my_curve.position(t+1)
}
```
(Note that this does not provide identical output for values of `t`
outside of the interval [0,1].)
On the other hand, any user who was specifying additional endpoint
tangents simply to get the curve to pass through the right points (i.e.
not requiring exactly the same output) can simply omit the endpoints
that were being supplied only for control purposes.
---
## Discussion
### Design considerations
This is one of the two approaches outlined in #12570 — in this PR, we
are basically declaring that the docs are right and the implementation
was flawed.
One semi-interesting question is how the endpoint tangents actually
ought to be defined when we include them, and another option considered
was mirroring the control points adjacent to the endpoints instead of
duplicating them, which would have had the advantage that the expected
length of the corresponding difference should be more similar to that of
the other difference-tangents, provided that the points are equally
spaced.
In this PR, the duplication method (which produces smaller tangents) was
chosen for a couple reasons:
- It seems to be more standard
- It is exceptionally simple to implement
- I was a little concerned that the aforementioned alternative would
result in some over-extrapolation
### An annoyance
If you look at the code, you'll see I was unable to find a satisfactory
way of doing this without allocating a new vector. This doesn't seem
like a big problem given the context, but it does bother me. In
particular, if there is some easy parallel to `slice::windows` for
iterators that doesn't pull in an external dependency, I would love to
know about it.
# Objective
Improve the `bevy::math::cubic_splines` module by making it more
flexible and adding new curve types.
Closes#10220
## Solution
Added new spline types and improved existing
---
## Changelog
### Added
- `CubicNurbs` rational cubic curve generator, allows setting the knot
vector and weights associated with every point
- `LinearSpline` curve generator, allows generating a linearly
interpolated curve segment
- Ability to push additional cubic segments to `CubicCurve`
- `IntoIterator` and `Extend` implementations for `CubicCurve`
### Changed
- `Point` trait has been implemented for more types: `Quat` and `Vec4`.
- `CubicCurve::coefficients` was moved to `CubicSegment::coefficients`
because the function returns `CubicSegment`, so it seems logical to be
associated with `CubicSegment` instead. The method is still not public.
### Fixed
- `CubicBSpline::new` was referencing Cardinal spline instead of
B-Spline
---------
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
Co-authored-by: IQuick 143 <IQuick143cz@gmail.com>
Co-authored-by: Miles Silberling-Cook <nth.tensor@gmail.com>
Co-authored-by: Joona Aalto <jondolf.dev@gmail.com>
# Objective
- Implementing `Default` for
[`CubicCurve`](https://docs.rs/bevy/latest/bevy/math/cubic_splines/struct.CubicCurve.html)
does not make sense because it cannot be mutated after creation.
- Closes#11209.
- Alternative to #11211.
## Solution
- Remove `Default` from `CubicCurve`'s derive statement.
Based off of @mockersf comment
(https://github.com/bevyengine/bevy/pull/11211#issuecomment-1880088036):
> CubicCurve can't be updated once created... I would prefer to remove
the Default impl as it doesn't make sense
---
## Changelog
- Removed the `Default` implementation for `CubicCurve`.
## Migration Guide
- Remove `CubicCurve` from any structs that implement `Default`.
- Wrap `CubicCurve` in a new type and provide your own default.
```rust
#[derive(Deref)]
struct MyCubicCurve<P: Point>(pub CubicCurve<P>);
impl Default for MyCubicCurve<Vec2> {
fn default() -> Self {
let points = [[
vec2(-1.0, -20.0),
vec2(3.0, 2.0),
vec2(5.0, 3.0),
vec2(9.0, 8.0),
]];
Self(CubicBezier::new(points).to_curve())
}
}
```
# Objective
A Bezier curve is a curve defined by two or more control points. In the
simplest form, it's just a line. The (arguably) most common type of
Bezier curve is a cubic Bezier, defined by four control points. These
are often used in animation, etc. Bevy has a Bezier curve struct called
`Bezier`. However, this is technically a misnomer as it only represents
cubic Bezier curves.
## Solution
This PR changes the struct name to `CubicBezier` to more accurately
reflect the struct's usage. Since it's exposed in Bevy's prelude, it can
potentially collide with other `Bezier` implementations. While that
might instead be an argument for removing it from the prelude, there's
also something to be said for adding a more general `Bezier` into Bevy,
in which case we'd likely want to use the name `Bezier`. As a final
motivator, not only is the struct located in `cubic_spines.rs`, there
are also several other spline-related structs which follow the
`CubicXxx` naming convention where applicable. For example,
`CubicSegment` represents a cubic Bezier curve (with coefficients
pre-baked).
---
## Migration Guide
- Change all `Bezier` references to `CubicBezier`
# Objective
[Rust 1.72.0](https://blog.rust-lang.org/2023/08/24/Rust-1.72.0.html) is
now stable.
# Notes
- `let-else` formatting has arrived!
- I chose to allow `explicit_iter_loop` due to
https://github.com/rust-lang/rust-clippy/issues/11074.
We didn't hit any of the false positives that prevent compilation, but
fixing this did produce a lot of the "symbol soup" mentioned, e.g. `for
image in &mut *image_events {`.
Happy to undo this if there's consensus the other way.
---------
Co-authored-by: François <mockersf@gmail.com>
## Objective
- Provide a way to use `CubicCurve` non-iter methods
- Accept a `FnMut` over a `fn` pointer on `iter_samples`
- Improve `build_*_cubic_100_points` benchmark by -45% (this means they
are twice as fast)
### Solution
Previously, the only way to iterate over an evenly spaced set of points
on a `CubicCurve` was to use one of the `iter_*` methods.
The return value of those methods were bound by `&self` lifetime, making
them unusable in certain contexts.
Furthermore, other `CubicCurve` methods (`position`, `velocity`,
`acceleration`) required normalizing `t` over the `CubicCurve`'s
internal segment count.
There were no way to access this segment count, making those methods
pretty much unusable.
The newly added `segment_count` allows accessing the segment count.
`iter_samples` used to accept a `fn`, a function pointer. This is
surprising and contrary to the rust stdlib APIs, which accept `Fn`
traits for `Iterator` combinators.
`iter_samples` now accepts a `FnMut`.
I don't trust a bit the bevy benchmark suit, but according to it, this
doubles (-45%) the performance on the `build_pos_cubic_100_points` and
`build_accel_cubic_100_points` benchmarks.
---
## Changelog
- Added the `CubicCurve::segments` method to access the underlying
segments of a cubic curve
- Allow closures as `CubicCurve::iter_samples` `sample_function`
argument.
# Objective
Fix `CubicCurve::iter_samples` iteration count.
## Solution
If I understand the function and the docs correctly, this should iterate
over `0..=subdivisions` instead of `0..subdivisions`.
For example: Now the iteration returns 3 points at `subdivisions = 2`,
as indicated in the documentation.
# Objective
- Make cubic splines more flexible and more performant
- Remove the existing spline implementation that is generic over many degrees
- This is a potential performance footgun and adds type complexity for negligible gain.
- Add implementations of:
- Bezier splines
- Cardinal splines (inc. Catmull-Rom)
- B-Splines
- Hermite splines
https://user-images.githubusercontent.com/2632925/221780519-495d1b20-ab46-45b4-92a3-32c46da66034.mp4https://user-images.githubusercontent.com/2632925/221780524-2b154016-699f-404f-9c18-02092f589b04.mp4https://user-images.githubusercontent.com/2632925/221780525-f934f99d-9ad4-4999-bae2-75d675f5644f.mp4
## Solution
- Implements the concept that splines are curve generators (e.g. https://youtu.be/jvPPXbo87ds?t=3488) via the `CubicGenerator` trait.
- Common splines are bespoke data types that implement this trait. This gives us flexibility to add custom spline-specific methods on these types, while ultimately all generating a `CubicCurve`.
- All splines generate `CubicCurve`s, which are a chain of precomputed polynomial coefficients. This means that all splines have the same evaluation cost, as the calculations for determining position, velocity, and acceleration are all identical. In addition, `CubicCurve`s are simply a list of `CubicSegment`s, which are evaluated from t=0 to t=1. This also means cubic splines of different type can be chained together, as ultimately they all are simply a collection of `CubicSegment`s.
- Because easing is an operation on a singe segment of a Bezier curve, we can simply implement easing on `Beziers` that use the `Vec2` type for points. Higher level crates such as `bevy_ui` can wrap this in a more ergonomic interface as needed.
### Performance
Measured on a desktop i5 8600K (6-year-old CPU):
- easing: 2.7x faster (19ns)
- cubic vec2 position sample: 1.5x faster (1.8ns)
- cubic vec3 position sample: 1.5x faster (2.6ns)
- cubic vec3a position sample: 1.9x faster (1.4ns)
On a laptop i7 11800H:
- easing: 16ns
- cubic vec2 position sample: 1.6ns
- cubic vec3 position sample: 2.3ns
- cubic vec3a position sample: 1.2ns
---
## Changelog
- Added a generic cubic curve trait, and implementation for Cardinal splines (including Catmull-Rom), B-Splines, Beziers, and Hermite Splines. 2D cubic curve segments also implement easing functionality for animation.
