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# 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. |
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