# 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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