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d2ef88f5e8
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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