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
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.
