# 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.
