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.
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