# 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
Bevy seems to want to standardize on "American English" spellings. Not
sure if this is laid out anywhere in writing, but see also #15947.
While perusing the docs for `typos`, I noticed that it has a `locale`
config option and tried it out.
## Solution
Switch to `en-us` locale in the `typos` config and run `typos -w`
## Migration Guide
The following methods or fields have been renamed from `*dependants*` to
`*dependents*`.
- `ProcessorAssetInfo::dependants`
- `ProcessorAssetInfos::add_dependant`
- `ProcessorAssetInfos::non_existent_dependants`
- `AssetInfo::dependants_waiting_on_load`
- `AssetInfo::dependants_waiting_on_recursive_dep_load`
- `AssetInfos::loader_dependants`
- `AssetInfos::remove_dependants_and_labels`
# Objective
Unlike `Capsule3d` which has the `.to_cylinder` method, `Capsule2d`
doesn't have an equivalent `.to_inner_rectangle` method and as shown by
#15191 this is surprisingly easy to get wrong
## Solution
Implemented a `Capsule2d::to_inner_rectangle` method as it is
implemented in the fixed `Capsule2d` shape sampling, and as I was adding
tests I noticed `Capsule2d` didn't implement `Measure2d` so I did this
as well.
## Changelog
### Added
- `Capsule2d::to_inner_rectangle`, `Capsule2d::area` and
`Capsule2d::perimeter`
---------
Co-authored-by: Joona Aalto <jondolf.dev@gmail.com>
Co-authored-by: James Liu <contact@jamessliu.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
`Capsule2d::sample_interior` uses the radius of the capsule for the
width of its rectangular section. It should be using two times the
radius for the full width!
I noticed this as I was getting incorrect results for angular inertia
approximated from a point cloud of points sampled on the capsule. This
hinted that something was wrong with the sampling.
## Solution
Multiply the radius by two to get the full width of the rectangular
section. With this, the sampling produces the correct result in my
tests.
# 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
Allow random sampling from the surfaces of triangle meshes.
## Solution
This has two parts.
Firstly, rendering meshes can now yield their collections of triangles
through a method `Mesh::triangles`. This has signature
```rust
pub fn triangles(&self) -> Result<Vec<Triangle3d>, MeshTrianglesError> { //... }
```
and fails in a variety of cases — the most obvious of these is that the
mesh must have either the `TriangleList` or `TriangleStrip` topology,
and the others correspond to malformed vertex or triangle-index data.
With that in hand, we have the second piece, which is
`UniformMeshSampler`, which is a `Vec3`-valued
[distribution](https://docs.rs/rand/latest/rand/distributions/trait.Distribution.html)
that samples uniformly from collections of triangles. It caches the
triangles' distribution of areas so that after its initial setup,
sampling is allocation-free. It is constructed via
`UniformMeshSampler::try_new`, which looks like this:
```rust
pub fn try_new<T: Into<Vec<Triangle3d>>>(triangles: T) -> Result<Self, ZeroAreaMeshError> { //... }
```
It fails if the collection of triangles has zero area.
The sum of these parts means that you can sample random points from a
mesh as follows:
```rust
let triangles = my_mesh.triangles().unwrap();
let mut rng = StdRng::seed_from_u64(8765309);
let distribution = UniformMeshSampler::try_new(triangles).unwrap();
// 10000 random points from the surface of my_mesh:
let sample_points: Vec<Vec3> = distribution.sample_iter(&mut rng).take(10000).collect();
```
## Testing
Tested by instantiating meshes and sampling as demonstrated above.
---
## Changelog
- Added `Mesh::triangles` method to get a collection of triangles from a
mesh.
- Added `UniformMeshSampler` to `bevy_math::sampling`. This is a
distribution which allows random sampling over collections of triangles
(such as those provided through meshes).
---
## Discussion
### Design decisions
The main thing here was making sure to have a good separation between
the parts of this in `bevy_render` and in `bevy_math`. Getting the
triangles from a mesh seems like a reasonable step after adding
`Triangle3d` to `bevy_math`, so I decided to make all of the random
sampling operate at that level, with the fallible conversion to
triangles doing most of the work.
Notably, the sampler could be called something else that reflects that
its input is a collection of triangles, but if/when we add other kinds
of meshes to `bevy_math` (e.g. half-edge meshes), the fact that
`try_new` takes an `impl Into<Vec<Triangle3d>>` means that those meshes
just need to satisfy that trait bound in order to work immediately with
this sampling functionality. In that case, the result would just be
something like this:
```rust
let dist = UniformMeshSampler::try_new(mesh).unwrap();
```
I think this highlights that most of the friction is really just from
extracting data from `Mesh`.
It's maybe worth mentioning also that "collection of triangles"
(`Vec<Triangle3d>`) sits downstream of any other kind of triangle mesh,
since the topology connecting the triangles has been effectively erased,
which makes an `Into<Vec<Triangle3d>>` trait bound seem all the more
natural to me.
---------
Co-authored-by: Alice Cecile <alice.i.cecile@gmail.com>
# Objective
- `Rotation2d` is a very long name for a commonly used type.
## Solution
- Rename it to `Rot2` to match `glam`'s naming convention (e.g. `Vec2`)
I ran a poll, and `Rot2` was the favorite of the candidate names.
This is not actually a breaking change, since `Rotation2d` has not been
shipped yet.
---------
Co-authored-by: Alice Cecile <alice.i.cecil@gmail.com>
# Objective
Fill the gap in this functionality by implementing it for `Rotation2d`.
We have this already for `Quat` in addition to the direction types.
## Solution
`bevy_math::sampling` now contains an implementation of
`Distribution<Rotation2d>` for `Standard`, along with the associated
convenience implementation `Rotation2d: FromRng`, which allows syntax
like this for creating a random rotation:
```rust
// With `FromRng`:
let rotation = Rotation2d::from_rng(rng);
// With `rand::random`:
let another_rotation: Rotation2d = random();
// With `Rng::gen`:
let yet_another_rotation: Rotation2d = rng.gen();
```
I also cleaned up the documentation a little bit, seeding the `Rng`s
instead of building them from entropy, along with adding a handful of
inline directives.
# Objective
Add random sampling for the `Annulus` primitive. This is part of ongoing
work to bring the various `bevy_math` primitives to feature parity.
## Solution
`Annulus` implements `ShapeSample`. Boundary sampling is implemented in
the obvious way, and interior sampling works exactly as in the
implementation for `Circle`, using the fact that the square of the
radius should be taken uniformly from between r^2 and R^2, where r and R
are the inner and outer radii respectively.
## Testing
I generated a bunch of random points and rendered them. Here's 1000
points on the interior of the default annulus:
<img width="1440" alt="Screenshot 2024-05-22 at 8 01 34 AM"
src="https://github.com/bevyengine/bevy/assets/2975848/19c31bb0-edba-477f-b247-2b12d854afae">
This looks kind of weird around the edges, but I verified that they're
all actually inside the annulus, so I assume it has to do with the fact
that the rendered circles have some radius.
Stolen from #12835.
# Objective
Sometimes you want to sample a whole bunch of points from a shape
instead of just one. You can write your own loop to do this, but it's
really more idiomatic to use a `rand`
[`Distribution`](https://docs.rs/rand/latest/rand/distributions/trait.Distribution.html)
with the `sample_iter` method. Distributions also support other useful
things like mapping, and they are suitable as generic items for
consumption by other APIs.
## Solution
`ShapeSample` has been given two new automatic trait methods,
`interior_dist` and `boundary_dist`. They both have similar signatures
(recall that `Output` is the output type for `ShapeSample`):
```rust
fn interior_dist(self) -> impl Distribution<Self::Output>
where Self: Sized { //... }
```
These have default implementations which are powered by wrapper structs
`InteriorOf` and `BoundaryOf` that actually implement `Distribution` —
the implementations effectively just call `ShapeSample::sample_interior`
and `ShapeSample::sample_boundary` on the contained type.
The upshot is that this allows iteration as follows:
```rust
// Get an iterator over boundary points of a rectangle:
let rectangle = Rectangle::new(1.0, 2.0);
let boundary_iter = rectangle.boundary_dist().sample_iter(rng);
// Collect a bunch of boundary points at once:
let boundary_pts: Vec<Vec2> = boundary_iter.take(1000).collect();
```
Alternatively, you can use `InteriorOf`/`BoundaryOf` explicitly to
similar effect:
```rust
let boundary_pts: Vec<Vec2> = BoundaryOf(rectangle).sample_iter(rng).take(1000).collect();
```
---
## Changelog
- Added `InteriorOf` and `BoundaryOf` distribution wrapper structs in
`bevy_math::sampling::shape_sampling`.
- Added `interior_dist` and `boundary_dist` automatic trait methods to
`ShapeSample`.
- Made `shape_sampling` module public with explanatory documentation.
---
## Discussion
### Design choices
The main point of interest here is just the choice of `impl
Distribution` instead of explicitly using `InteriorOf`/`BoundaryOf`
return types for `interior_dist` and `boundary_dist`. The reason for
this choice is that it allows future optimizations for repeated sampling
— for example, instead of just wrapping the base type,
`interior_dist`/`boundary_dist` could construct auxiliary data that is
held over between sampling operations.
# Objective
Add interior and boundary sampling for the `Tetrahedron` primitive. This
is part of ongoing work to bring the primitives to parity with each
other in terms of their capabilities.
## Solution
`Tetrahedron` implements the `ShapeSample` trait. To support this, there
is a new public method `Tetrahedron::faces` which gets the faces of a
tetrahedron as `Triangle3d`s. There are more sophisticated ideas for
getting the faces we might want to consider in the future (e.g.
adjusting according to the orientation), but this method gives the most
mathematically straightforward answer, giving the faces the orientation
induced by the tetrahedron itself.
# Objective
Augment Bevy's random sampling capabilities by providing good tools for
producing random directions and rotations.
## Solution
The `rand` crate has a natural tool for providing `Distribution`s whose
output is a type that doesn't require any additional data to sample
values — namely,
[`Standard`](https://docs.rs/rand/latest/rand/distributions/struct.Standard.html).
Here, our existing `ShapeSample` implementations have been put to good
use in providing these, resulting in patterns like the following:
```rust
// Using thread-local rng
let random_direction1: Dir3 = random();
// Using an explicit rng
let random_direction2: Dir3 = rng.gen();
// Using an explicit rng coupled explicitly with Standard
let random_directions: Vec<Dir3> = rng.sample_iter(Standard).take(5).collect();
```
Furthermore, we have introduced a trait `FromRng` which provides sugar
for `rng.gen()` that is more namespace-friendly (in this author's
opinion):
```rust
let random_direction = Dir3::from_rng(rng);
```
The types this has been implemented for are `Dir2`, `Dir3`, `Dir3A`, and
`Quat`. Notably, `Quat` uses `glam`'s implementation rather than an
in-house one, and as a result, `bevy_math`'s "rand" feature now enables
that of `glam`.
---
## Changelog
- Created `standard` submodule in `sampling` to hold implementations and
other items related to the `Standard` distribution.
- "rand" feature of `bevy_math` now enables that of `glam`.
---
## Discussion
From a quick glance at `Quat`'s distribution implementation in `glam`, I
am a bit suspicious, since it is simple and doesn't match any algorithm
that I came across in my research. I will do a little more digging as a
follow-up to this and see if it's actually uniform (maybe even using
those tools I wrote — what a thrill).
As an aside, I'd also like to say that I think
[`Distribution`](https://docs.rs/rand/latest/rand/distributions/trait.Distribution.html)
is really, really good. It integrates with distributions provided
externally (e.g. in `rand` itself and its extensions) along with doing a
good job of isolating the source of randomness, so that output can be
reliably reproduced if need be. Finally, `Distribution::sample_iter` is
quite good for ergonomically acquiring lots of random values. At one
point I found myself writing traits to describe random sampling and
essentially reinvented this one. I just think it's good, and I think
it's worth centralizing around to a significant extent.
