bevy/crates/bevy_math/Cargo.toml
TheBigCheese 948ea3137a
Uniform point sampling methods for some primitive shapes. (#12484)
# Objective
Give easy methods for uniform point sampling in a variety of primitive
shapes (particularly useful for circles and spheres) because in a lot of
cases its quite easy to get wrong (non-uniform).

## Solution
Added the `ShapeSample` trait to `bevy_math` and implemented it for
`Circle`, `Sphere`, `Rectangle`, `Cuboid`, `Cylinder`, `Capsule2d` and
`Capsule3d`. There are a few other shapes it would be reasonable to
implement for like `Triangle`, `Ellipse` and `Torus` but I'm not
immediately sure how these would be implemented (other than rejection
which could be the best method, and could be more performant than some
of the solutions in this pr I'm not sure). This exposes the
`sample_volume` and `sample_surface` methods to get both a random point
from its interior or its surface. EDIT: Renamed `sample_volume` to
`sample_interior` and `sample_surface` to `sample_boundary`

This brings in `rand` as a default optional dependency (without default
features), and the methods take `&mut impl Rng` which allows them to use
any random source implementing `RngCore`.

---

## Changelog
### Added
Added the methods `sample_interior` and `sample_boundary` to a variety
of primitive shapes providing easy uniform point sampling.
2024-03-17 14:48:16 +00:00

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1.6 KiB
TOML

[package]
name = "bevy_math"
version = "0.14.0-dev"
edition = "2021"
description = "Provides math functionality for Bevy Engine"
homepage = "https://bevyengine.org"
repository = "https://github.com/bevyengine/bevy"
license = "MIT OR Apache-2.0"
keywords = ["bevy"]
[dependencies]
glam = { version = "0.25", features = ["bytemuck"] }
thiserror = "1.0"
serde = { version = "1", features = ["derive"], optional = true }
libm = { version = "0.2", optional = true }
approx = { version = "0.5", optional = true }
rand = { version = "0.8", features = [
"alloc",
], default-features = false, optional = true }
[dev-dependencies]
approx = "0.5"
# Supply rngs for examples and tests
rand = "0.8"
rand_chacha = "0.3"
[features]
default = ["rand"]
serialize = ["dep:serde", "glam/serde"]
# Enable approx for glam types to approximate floating point equality comparisons and assertions
approx = ["dep:approx", "glam/approx"]
# Enable interoperation of glam types with mint-compatible libraries
mint = ["glam/mint"]
# Enable libm mathematical functions for glam types to ensure consistent outputs
# across platforms at the cost of losing hardware-level optimization using intrinsics
libm = ["dep:libm", "glam/libm"]
# Enable assertions to check the validity of parameters passed to glam
glam_assert = ["glam/glam-assert"]
# Enable assertions in debug builds to check the validity of parameters passed to glam
debug_glam_assert = ["glam/debug-glam-assert"]
# Enable the rand dependency for shape_sampling
rand = ["dep:rand"]
[lints]
workspace = true
[package.metadata.docs.rs]
all-features = true