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/src/lib.rs

/*!
# glam

`glam` is a simple and fast linear algebra library for games and graphics.

## Features

* [`f32`](mod@f32) types
  * vectors: [`Vec2`], [`Vec3`], [`Vec3A`] and [`Vec4`]
  * square matrices: [`Mat2`], [`Mat3`], [`Mat3A`] and [`Mat4`]
  * a quaternion type: [`Quat`]
  * affine transformation types: [`Affine2`] and [`Affine3A`]
* [`f64`](mod@f64) types
  * vectors: [`DVec2`], [`DVec3`] and [`DVec4`]
  * square matrices: [`DMat2`], [`DMat3`] and [`DMat4`]
  * a quaternion type: [`DQuat`]
  * affine transformation types: [`DAffine2`] and [`DAffine3`]
* [`i8`](mod@i8) types
  * vectors: [`I8Vec2`], [`I8Vec3`] and [`I8Vec4`]
* [`u8`](mod@u8) types
  * vectors: [`U8Vec2`], [`U8Vec3`] and [`U8Vec4`]
* [`i16`](mod@i16) types
  * vectors: [`I16Vec2`], [`I16Vec3`] and [`I16Vec4`]
* [`u16`](mod@u16) types
  * vectors: [`U16Vec2`], [`U16Vec3`] and [`U16Vec4`]
* [`i32`](mod@i32) types
  * vectors: [`IVec2`], [`IVec3`] and [`IVec4`]
* [`u32`](mod@u32) types
  * vectors: [`UVec2`], [`UVec3`] and [`UVec4`]
* [`i64`](mod@i64) types
  * vectors: [`I64Vec2`], [`I64Vec3`] and [`I64Vec4`]
* [`u64`](mod@u64) types
  * vectors: [`U64Vec2`], [`U64Vec3`] and [`U64Vec4`]
* [`usize`](mod@usize) types
  * vectors: [`USizeVec2`], [`USizeVec3`] and [`USizeVec4`]
* [`bool`](mod@bool) types
  * vectors: [`BVec2`], [`BVec3`] and [`BVec4`]

## SIMD

`glam` is built with SIMD in mind. Many `f32` types use 128-bit SIMD vector types for storage
and/or implementation. The use of SIMD generally enables better performance than using primitive
numeric types such as `f32`.

Some `glam` types use SIMD for storage meaning they are 16 byte aligned, these types include
`Mat2`, `Mat3A`, `Mat4`, `Quat`, `Vec3A`, `Vec4`, `Affine2` an `Affine3A`. Types
with an `A` suffix are a SIMD alternative to a scalar type, e.g. `Vec3` uses `f32` storage and
`Vec3A` uses SIMD storage.

When SIMD is not available on the target the types will maintain 16 byte alignment and internal
padding so that object sizes and layouts will not change between architectures. There are scalar
math fallback implementations exist when SIMD is not available. It is intended to add support for
other SIMD architectures once they appear in stable Rust.

Currently only SSE2 on x86/x86_64, NEON on Aarch64, and simd128 on WASM are supported.

## Vec3A and Mat3A

`Vec3A` is a SIMD optimized version of the `Vec3` type, which due to 16 byte alignment results
in `Vec3A` containing 4 bytes of padding making it 16 bytes in size in total. `Mat3A` is composed
of three `Vec3A` columns.

| Type       | `f32` bytes | Align bytes | Size bytes | Padding |
|:-----------|------------:|------------:|-----------:|--------:|
|[`Vec3`]    |           12|            4|          12|        0|
|[`Vec3A`]   |           12|           16|          16|        4|
|[`Mat3`]    |           36|            4|          36|        0|
|[`Mat3A`]   |           36|           16|          48|       12|

Despite this wasted space the SIMD implementations tend to outperform `f32` implementations in
[**mathbench**](https://github.com/bitshifter/mathbench-rs) benchmarks.

`glam` treats [`Vec3`] as the default 3D vector type and [`Vec3A`] a special case for optimization.
When methods need to return a 3D vector they will generally return [`Vec3`].

There are [`From`] trait implementations for converting from [`Vec4`] to a [`Vec3A`] and between
[`Vec3`] and [`Vec3A`] (and vice versa).

```
use glam::{Vec3, Vec3A, Vec4};

let v4 = Vec4::new(1.0, 2.0, 3.0, 4.0);

// Convert from `Vec4` to `Vec3A`, this is a no-op if SIMD is supported.
// We use an explicit method here instead of a From impl as data is lost in the conversion.
let v3a = Vec3A::from_vec4(v4);
assert_eq!(Vec3A::new(1.0, 2.0, 3.0), v3a);

// Convert from `Vec3A` to `Vec3`.
let v3 = Vec3::from(v3a);
assert_eq!(Vec3::new(1.0, 2.0, 3.0), v3);

// Convert from `Vec3` to `Vec3A`.
let v3a = Vec3A::from(v3);
assert_eq!(Vec3A::new(1.0, 2.0, 3.0), v3a);
```

## Affine2 and Affine3A

`Affine2` and `Affine3A` are composed of a linear transform matrix and a vector translation. The
represent 2D and 3D affine transformations which are commonly used in games.

The table below shows the performance advantage of `Affine2` over `Mat3A` and `Mat3A` over `Mat3`.

| operation          | `Mat3`      | `Mat3A`    | `Affine2`  |
|--------------------|-------------|------------|------------|
| inverse            | 11.4±0.09ns | 7.1±0.09ns | 5.4±0.06ns |
| mul self           | 10.5±0.04ns | 5.2±0.05ns | 4.0±0.05ns |
| transform point2   |  2.7±0.02ns | 2.7±0.03ns | 2.8±0.04ns |
| transform vector2  |  2.6±0.01ns | 2.6±0.03ns | 2.3±0.02ns |

Performance is much closer between `Mat4` and `Affine3A` with the affine type being faster to
invert.

| operation          | `Mat4`      | `Affine3A`  |
|--------------------|-------------|-------------|
| inverse            | 15.9±0.11ns | 10.8±0.06ns |
| mul self           |  7.3±0.05ns |  7.0±0.06ns |
| transform point3   |  3.6±0.02ns |  4.3±0.04ns |
| transform point3a  |  3.0±0.02ns |  3.0±0.04ns |
| transform vector3  |  4.1±0.02ns |  3.9±0.04ns |
| transform vector3a |  2.8±0.02ns |  2.8±0.02ns |

Benchmarks were taken on an Intel Core i7-4710HQ.

## Linear algebra conventions

`glam` interprets vectors as column matrices (also known as column vectors) meaning when
transforming a vector with a matrix the matrix goes on the left.

```
use glam::{Mat3, Vec3};
let m = Mat3::IDENTITY;
let x = Vec3::X;
let v = m * x;
assert_eq!(v, x);
```

Matrices are stored in memory in column-major order.

All angles are in radians. Rust provides the `f32::to_radians()` and `f64::to_radians()` methods to
convert from degrees.

## Direct element access

Because some types may internally be implemented using SIMD types, direct access to vector elements
is supported by implementing the [`Deref`] and [`DerefMut`] traits.

```
use glam::Vec3A;
let mut v = Vec3A::new(1.0, 2.0, 3.0);
assert_eq!(3.0, v.z);
v.z += 1.0;
assert_eq!(4.0, v.z);
```

[`Deref`]: https://doc.rust-lang.org/std/ops/trait.Deref.html
[`DerefMut`]: https://doc.rust-lang.org/std/ops/trait.DerefMut.html

## glam assertions

`glam` does not enforce validity checks on method parameters at runtime. For example methods that
require normalized vectors as input such as `Quat::from_axis_angle(axis, angle)` will not check
that axis is a valid normalized vector. To help catch unintended misuse of `glam` the
`debug-glam-assert` or `glam-assert` features can be enabled to add checks ensure that inputs to
are valid.

## Vector swizzles

`glam` vector types have functions allowing elements of vectors to be reordered, this includes
creating a vector of a different size from the vectors elements.

The swizzle functions are implemented using traits to add them to each vector type. This is
primarily because there are a lot of swizzle functions which can obfuscate the other vector
functions in documentation and so on. The traits are [`Vec2Swizzles`], [`Vec3Swizzles`] and
[`Vec4Swizzles`].

Note that the [`Vec3Swizzles`] implementation for [`Vec3A`] will return a [`Vec3A`] for 3 element
swizzles, all other implementations will return [`Vec3`].

```
use glam::{swizzles::*, Vec2, Vec3, Vec3A, Vec4};

let v = Vec4::new(1.0, 2.0, 3.0, 4.0);

// Reverse elements of `v`, if SIMD is supported this will use a vector shuffle.
let wzyx = v.wzyx();
assert_eq!(Vec4::new(4.0, 3.0, 2.0, 1.0), wzyx);

// Swizzle the yzw elements of `v` into a `Vec3`
let yzw = v.yzw();
assert_eq!(Vec3::new(2.0, 3.0, 4.0), yzw);

// To swizzle a `Vec4` into a `Vec3A` swizzle the `Vec4` first then convert to
// `Vec3A`. If SIMD is supported this will use a vector shuffle. The last
// element of the shuffled `Vec4` is ignored by the `Vec3A`.
let yzw = Vec3A::from_vec4(v.yzwx());
assert_eq!(Vec3A::new(2.0, 3.0, 4.0), yzw);

// You can swizzle from a `Vec4` to a `Vec2`
let xy = v.xy();
assert_eq!(Vec2::new(1.0, 2.0), xy);

// And back again
let yyxx = xy.yyxx();
assert_eq!(Vec4::new(2.0, 2.0, 1.0, 1.0), yyxx);
```

## SIMD and scalar consistency

`glam` types implement `serde` `Serialize` and `Deserialize` traits to ensure
that they will serialize and deserialize exactly the same whether or not
SIMD support is being used.

The SIMD versions implement the `core::fmt::Debug` and `core::fmt::Display`
traits so they print the same as the scalar version.

```
use glam::Vec4;
let a = Vec4::new(1.0, 2.0, 3.0, 4.0);
assert_eq!(format!("{}", a), "[1, 2, 3, 4]");
```

## Feature gates

All `glam` dependencies are optional, however some are required for tests
and benchmarks.

* `std` - the default feature, has no dependencies.
* `arbitrary` - implementations of `Arbitrary` trait for all `glam` types.
* `approx` - traits and macros for approximate float comparisons
* `bytemuck` - for casting into slices of bytes
* `libm` - uses `libm` math functions instead of `std`
* `nostd-libm` - uses `libm` math functions if `std` is not available
* `mint` - for interoperating with other 3D math libraries
* `rand` - implementations of `Distribution` trait for all `glam` types.
* `rkyv` - implementations of `Archive`, `Serialize` and `Deserialize` for all
  `glam` types. Note that serialization is not interoperable with and without the
  `scalar-math` feature. It should work between all other builds of `glam`.
  Endian conversion is currently not supported
* `bytecheck` - to perform archive validation when using the `rkyv` feature
* `encase` - `encase` trait implementations for `glam` types.
* `serde` - implementations of `Serialize` and `Deserialize` for all `glam`
  types. Note that serialization should work between builds of `glam` with and without SIMD enabled
* `speedy` - implementations of `speedy`'s `Readable` and `Writable` for all `glam` types.
* `scalar-math` - disables SIMD support and uses native alignment for all types.
* `debug-glam-assert` - adds assertions in debug builds which check the validity of parameters
  passed to `glam` to help catch runtime errors.
* `glam-assert` - adds assertions to all builds which check the validity of parameters passed to
  `glam` to help catch runtime errors.
* `cuda` - forces `glam` types to match expected cuda alignment
* `fast-math` - By default, glam attempts to provide bit-for-bit identical
  results on all platforms. Using this feature will enable platform specific
  optimizations that may not be identical to other platforms. **Intermediate
  libraries should not use this feature and defer the decision to the final
  binary build**.
* `core-simd` - enables SIMD support via the portable simd module. This is an
  unstable feature which requires a nightly Rust toolchain and `std` support.

## Minimum Supported Rust Version (MSRV)

The minimum supported Rust version is `1.68.2`.

*/
#![doc(html_root_url = "https://docs.rs/glam/0.30.8")]
#![cfg_attr(not(feature = "std"), no_std)]
#![cfg_attr(target_arch = "spirv", feature(repr_simd))]
#![deny(
    rust_2018_compatibility,
    rust_2018_idioms,
    future_incompatible,
    nonstandard_style
)]
// clippy doesn't like `to_array(&self)`
#![allow(clippy::wrong_self_convention)]
#![cfg_attr(
    all(feature = "core-simd", not(feature = "scalar-math")),
    feature(portable_simd)
)]

#[cfg(all(
    not(feature = "std"),
    not(feature = "libm"),
    not(feature = "nostd-libm")
))]
compile_error!(
    "You must specify a math backend. Consider enabling either `std`, `libm`, or `nostd-libm`."
);

#[macro_use]
mod macros;

mod align16;
mod deref;
mod euler;
mod features;

#[cfg(all(
    target_arch = "aarch64",
    not(any(feature = "core-simd", feature = "scalar-math"))
))]
mod neon;

#[cfg(target_arch = "spirv")]
mod spirv;

#[cfg(all(
    target_feature = "sse2",
    not(any(feature = "core-simd", feature = "scalar-math"))
))]
mod sse2;

#[cfg(all(
    target_feature = "simd128",
    not(any(feature = "core-simd", feature = "scalar-math"))
))]
mod wasm32;

#[cfg(all(feature = "core-simd", not(feature = "scalar-math")))]
mod coresimd;

#[cfg(all(
    target_feature = "sse2",
    not(any(feature = "core-simd", feature = "scalar-math"))
))]
use align16::Align16;

/** `bool` vector mask types. */
pub mod bool;
pub use self::bool::*;

/** `f32` vector, quaternion and matrix types. */
pub mod f32;
pub use self::f32::*;

/** `f64` vector, quaternion and matrix types. */
pub mod f64;
pub use self::f64::*;

/** `i8` vector types. */
pub mod i8;
pub use self::i8::*;

/** `u8` vector types. */
pub mod u8;
pub use self::u8::*;

/** `i16` vector types. */
pub mod i16;
pub use self::i16::*;

/** `u16` vector types. */
pub mod u16;
pub use self::u16::*;

/** `i32` vector types. */
pub mod i32;
pub use self::i32::*;

/** `u32` vector types. */
pub mod u32;
pub use self::u32::*;

/** `i64` vector types. */
pub mod i64;
pub use self::i64::*;

/** `u64` vector types. */
pub mod u64;
pub use self::u64::*;

/** `usize` vector types. */
pub mod usize;
pub use self::usize::*;

/** Traits adding swizzle methods to all vector types. */
pub mod swizzles;
pub use self::swizzles::{Vec2Swizzles, Vec3Swizzles, Vec4Swizzles};

/** Rotation Helper */
pub use euler::EulerRot;

/** A trait for extending [`prim@f32`] and [`prim@f64`] with extra methods. */
mod float;
pub use float::FloatExt;

1	/*!
2	# glam
3
4	`glam` is a simple and fast linear algebra library for games and graphics.
5
6	## Features
7
8	* [`f32`](mod@f32) types
9	* vectors: [`Vec2`], [`Vec3`], [`Vec3A`] and [`Vec4`]
10	* square matrices: [`Mat2`], [`Mat3`], [`Mat3A`] and [`Mat4`]
11	* a quaternion type: [`Quat`]
12	* affine transformation types: [`Affine2`] and [`Affine3A`]
13	* [`f64`](mod@f64) types
14	* vectors: [`DVec2`], [`DVec3`] and [`DVec4`]
15	* square matrices: [`DMat2`], [`DMat3`] and [`DMat4`]
16	* a quaternion type: [`DQuat`]
17	* affine transformation types: [`DAffine2`] and [`DAffine3`]
18	* [`i8`](mod@i8) types
19	* vectors: [`I8Vec2`], [`I8Vec3`] and [`I8Vec4`]
20	* [`u8`](mod@u8) types
21	* vectors: [`U8Vec2`], [`U8Vec3`] and [`U8Vec4`]
22	* [`i16`](mod@i16) types
23	* vectors: [`I16Vec2`], [`I16Vec3`] and [`I16Vec4`]
24	* [`u16`](mod@u16) types
25	* vectors: [`U16Vec2`], [`U16Vec3`] and [`U16Vec4`]
26	* [`i32`](mod@i32) types
27	* vectors: [`IVec2`], [`IVec3`] and [`IVec4`]
28	* [`u32`](mod@u32) types
29	* vectors: [`UVec2`], [`UVec3`] and [`UVec4`]
30	* [`i64`](mod@i64) types
31	* vectors: [`I64Vec2`], [`I64Vec3`] and [`I64Vec4`]
32	* [`u64`](mod@u64) types
33	* vectors: [`U64Vec2`], [`U64Vec3`] and [`U64Vec4`]
34	* [`usize`](mod@usize) types
35	* vectors: [`USizeVec2`], [`USizeVec3`] and [`USizeVec4`]
36	* [`bool`](mod@bool) types
37	* vectors: [`BVec2`], [`BVec3`] and [`BVec4`]
38
39	## SIMD
40
41	`glam` is built with SIMD in mind. Many `f32` types use 128-bit SIMD vector types for storage
42	and/or implementation. The use of SIMD generally enables better performance than using primitive
43	numeric types such as `f32`.
44
45	Some `glam` types use SIMD for storage meaning they are 16 byte aligned, these types include
46	`Mat2`, `Mat3A`, `Mat4`, `Quat`, `Vec3A`, `Vec4`, `Affine2` an `Affine3A`. Types
47	with an `A` suffix are a SIMD alternative to a scalar type, e.g. `Vec3` uses `f32` storage and
48	`Vec3A` uses SIMD storage.
49
50	When SIMD is not available on the target the types will maintain 16 byte alignment and internal
51	padding so that object sizes and layouts will not change between architectures. There are scalar
52	math fallback implementations exist when SIMD is not available. It is intended to add support for
53	other SIMD architectures once they appear in stable Rust.
54
55	Currently only SSE2 on x86/x86_64, NEON on Aarch64, and simd128 on WASM are supported.
56
57	## Vec3A and Mat3A
58
59	`Vec3A` is a SIMD optimized version of the `Vec3` type, which due to 16 byte alignment results
60	in `Vec3A` containing 4 bytes of padding making it 16 bytes in size in total. `Mat3A` is composed
61	of three `Vec3A` columns.
62
63	\| Type \| `f32` bytes \| Align bytes \| Size bytes \| Padding \|
64	\|:-----------\|------------:\|------------:\|-----------:\|--------:\|
65	\|[`Vec3`] \| 12\| 4\| 12\| 0\|
66	\|[`Vec3A`] \| 12\| 16\| 16\| 4\|
67	\|[`Mat3`] \| 36\| 4\| 36\| 0\|
68	\|[`Mat3A`] \| 36\| 16\| 48\| 12\|
69
70	Despite this wasted space the SIMD implementations tend to outperform `f32` implementations in
71	[mathbench](https://github.com/bitshifter/mathbench-rs) benchmarks.
72
73	`glam` treats [`Vec3`] as the default 3D vector type and [`Vec3A`] a special case for optimization.
74	When methods need to return a 3D vector they will generally return [`Vec3`].
75
76	There are [`From`] trait implementations for converting from [`Vec4`] to a [`Vec3A`] and between
77	[`Vec3`] and [`Vec3A`] (and vice versa).
78
79	```
80	use glam::{Vec3, Vec3A, Vec4};
81
82	let v4 = Vec4::new(1.0, 2.0, 3.0, 4.0);
83
84	// Convert from `Vec4` to `Vec3A`, this is a no-op if SIMD is supported.
85	// We use an explicit method here instead of a From impl as data is lost in the conversion.
86	let v3a = Vec3A::from_vec4(v4);
87	assert_eq!(Vec3A::new(1.0, 2.0, 3.0), v3a);
88
89	// Convert from `Vec3A` to `Vec3`.
90	let v3 = Vec3::from(v3a);
91	assert_eq!(Vec3::new(1.0, 2.0, 3.0), v3);
92
93	// Convert from `Vec3` to `Vec3A`.
94	let v3a = Vec3A::from(v3);
95	assert_eq!(Vec3A::new(1.0, 2.0, 3.0), v3a);
96	```
97
98	## Affine2 and Affine3A
99
100	`Affine2` and `Affine3A` are composed of a linear transform matrix and a vector translation. The
101	represent 2D and 3D affine transformations which are commonly used in games.
102
103	The table below shows the performance advantage of `Affine2` over `Mat3A` and `Mat3A` over `Mat3`.
104
105	\| operation \| `Mat3` \| `Mat3A` \| `Affine2` \|
106	\|--------------------\|-------------\|------------\|------------\|
107	\| inverse \| 11.4±0.09ns \| 7.1±0.09ns \| 5.4±0.06ns \|
108	\| mul self \| 10.5±0.04ns \| 5.2±0.05ns \| 4.0±0.05ns \|
109	\| transform point2 \| 2.7±0.02ns \| 2.7±0.03ns \| 2.8±0.04ns \|
110	\| transform vector2 \| 2.6±0.01ns \| 2.6±0.03ns \| 2.3±0.02ns \|
111
112	Performance is much closer between `Mat4` and `Affine3A` with the affine type being faster to
113	invert.
114
115	\| operation \| `Mat4` \| `Affine3A` \|
116	\|--------------------\|-------------\|-------------\|
117	\| inverse \| 15.9±0.11ns \| 10.8±0.06ns \|
118	\| mul self \| 7.3±0.05ns \| 7.0±0.06ns \|
119	\| transform point3 \| 3.6±0.02ns \| 4.3±0.04ns \|
120	\| transform point3a \| 3.0±0.02ns \| 3.0±0.04ns \|
121	\| transform vector3 \| 4.1±0.02ns \| 3.9±0.04ns \|
122	\| transform vector3a \| 2.8±0.02ns \| 2.8±0.02ns \|
123
124	Benchmarks were taken on an Intel Core i7-4710HQ.
125
126	## Linear algebra conventions
127
128	`glam` interprets vectors as column matrices (also known as column vectors) meaning when
129	transforming a vector with a matrix the matrix goes on the left.
130
131	```
132	use glam::{Mat3, Vec3};
133	let m = Mat3::IDENTITY;
134	let x = Vec3::X;
135	let v = m * x;
136	assert_eq!(v, x);
137	```
138
139	Matrices are stored in memory in column-major order.
140
141	All angles are in radians. Rust provides the `f32::to_radians()` and `f64::to_radians()` methods to
142	convert from degrees.
143
144	## Direct element access
145
146	Because some types may internally be implemented using SIMD types, direct access to vector elements
147	is supported by implementing the [`Deref`] and [`DerefMut`] traits.
148
149	```
150	use glam::Vec3A;
151	let mut v = Vec3A::new(1.0, 2.0, 3.0);
152	assert_eq!(3.0, v.z);
153	v.z += 1.0;
154	assert_eq!(4.0, v.z);
155	```
156
157	[`Deref`]: https://doc.rust-lang.org/std/ops/trait.Deref.html
158	[`DerefMut`]: https://doc.rust-lang.org/std/ops/trait.DerefMut.html
159
160	## glam assertions
161
162	`glam` does not enforce validity checks on method parameters at runtime. For example methods that
163	require normalized vectors as input such as `Quat::from_axis_angle(axis, angle)` will not check
164	that axis is a valid normalized vector. To help catch unintended misuse of `glam` the
165	`debug-glam-assert` or `glam-assert` features can be enabled to add checks ensure that inputs to
166	are valid.
167
168	## Vector swizzles
169
170	`glam` vector types have functions allowing elements of vectors to be reordered, this includes
171	creating a vector of a different size from the vectors elements.
172
173	The swizzle functions are implemented using traits to add them to each vector type. This is
174	primarily because there are a lot of swizzle functions which can obfuscate the other vector
175	functions in documentation and so on. The traits are [`Vec2Swizzles`], [`Vec3Swizzles`] and
176	[`Vec4Swizzles`].
177
178	Note that the [`Vec3Swizzles`] implementation for [`Vec3A`] will return a [`Vec3A`] for 3 element
179	swizzles, all other implementations will return [`Vec3`].
180
181	```
182	use glam::{swizzles::*, Vec2, Vec3, Vec3A, Vec4};
183
184	let v = Vec4::new(1.0, 2.0, 3.0, 4.0);
185
186	// Reverse elements of `v`, if SIMD is supported this will use a vector shuffle.
187	let wzyx = v.wzyx();
188	assert_eq!(Vec4::new(4.0, 3.0, 2.0, 1.0), wzyx);
189
190	// Swizzle the yzw elements of `v` into a `Vec3`
191	let yzw = v.yzw();
192	assert_eq!(Vec3::new(2.0, 3.0, 4.0), yzw);
193
194	// To swizzle a `Vec4` into a `Vec3A` swizzle the `Vec4` first then convert to
195	// `Vec3A`. If SIMD is supported this will use a vector shuffle. The last
196	// element of the shuffled `Vec4` is ignored by the `Vec3A`.
197	let yzw = Vec3A::from_vec4(v.yzwx());
198	assert_eq!(Vec3A::new(2.0, 3.0, 4.0), yzw);
199
200	// You can swizzle from a `Vec4` to a `Vec2`
201	let xy = v.xy();
202	assert_eq!(Vec2::new(1.0, 2.0), xy);
203
204	// And back again
205	let yyxx = xy.yyxx();
206	assert_eq!(Vec4::new(2.0, 2.0, 1.0, 1.0), yyxx);
207	```
208
209	## SIMD and scalar consistency
210
211	`glam` types implement `serde` `Serialize` and `Deserialize` traits to ensure
212	that they will serialize and deserialize exactly the same whether or not
213	SIMD support is being used.
214
215	The SIMD versions implement the `core::fmt::Debug` and `core::fmt::Display`
216	traits so they print the same as the scalar version.
217
218	```
219	use glam::Vec4;
220	let a = Vec4::new(1.0, 2.0, 3.0, 4.0);
221	assert_eq!(format!("{}", a), "[1, 2, 3, 4]");
222	```
223
224	## Feature gates
225
226	All `glam` dependencies are optional, however some are required for tests
227	and benchmarks.
228
229	* `std` - the default feature, has no dependencies.
230	* `arbitrary` - implementations of `Arbitrary` trait for all `glam` types.
231	* `approx` - traits and macros for approximate float comparisons
232	* `bytemuck` - for casting into slices of bytes
233	* `libm` - uses `libm` math functions instead of `std`
234	* `nostd-libm` - uses `libm` math functions if `std` is not available
235	* `mint` - for interoperating with other 3D math libraries
236	* `rand` - implementations of `Distribution` trait for all `glam` types.
237	* `rkyv` - implementations of `Archive`, `Serialize` and `Deserialize` for all
238	`glam` types. Note that serialization is not interoperable with and without the
239	`scalar-math` feature. It should work between all other builds of `glam`.
240	Endian conversion is currently not supported
241	* `bytecheck` - to perform archive validation when using the `rkyv` feature
242	* `encase` - `encase` trait implementations for `glam` types.
243	* `serde` - implementations of `Serialize` and `Deserialize` for all `glam`
244	types. Note that serialization should work between builds of `glam` with and without SIMD enabled
245	* `speedy` - implementations of `speedy`'s `Readable` and `Writable` for all `glam` types.
246	* `scalar-math` - disables SIMD support and uses native alignment for all types.
247	* `debug-glam-assert` - adds assertions in debug builds which check the validity of parameters
248	passed to `glam` to help catch runtime errors.
249	* `glam-assert` - adds assertions to all builds which check the validity of parameters passed to
250	`glam` to help catch runtime errors.
251	* `cuda` - forces `glam` types to match expected cuda alignment
252	* `fast-math` - By default, glam attempts to provide bit-for-bit identical
253	results on all platforms. Using this feature will enable platform specific
254	optimizations that may not be identical to other platforms. **Intermediate
255	libraries should not use this feature and defer the decision to the final
256	binary build**.
257	* `core-simd` - enables SIMD support via the portable simd module. This is an
258	unstable feature which requires a nightly Rust toolchain and `std` support.
259
260	## Minimum Supported Rust Version (MSRV)
261
262	The minimum supported Rust version is `1.68.2`.
263
264	*/
265	#![doc(html_root_url = "https://docs.rs/glam/0.30.8")]
266	#![cfg_attr(not(feature = "std"), no_std)]
267	#![cfg_attr(target_arch = "spirv", feature(repr_simd))]
268	#![deny(
269	rust_2018_compatibility,
270	rust_2018_idioms,
271	future_incompatible,
272	nonstandard_style
273	)]
274	// clippy doesn't like `to_array(&self)`
275	#![allow(clippy::wrong_self_convention)]
276	#![cfg_attr(
277	all(feature = "core-simd", not(feature = "scalar-math")),
278	feature(portable_simd)
279	)]
280
281	#[cfg(all(
282	not(feature = "std"),	×
283	not(feature = "libm"),	×
284	not(feature = "nostd-libm")	×
285	))]
286	compile_error!(	×
287	"You must specify a math backend. Consider enabling either `std`, `libm`, or `nostd-libm`."
288	);
289
290	#[macro_use]
291	mod macros;
292
293	mod align16;
294	mod deref;
295	mod euler;
296	mod features;
297
298	#[cfg(all(
299	target_arch = "aarch64",
300	not(any(feature = "core-simd", feature = "scalar-math"))
301	))]
302	mod neon;
303
304	#[cfg(target_arch = "spirv")]
305	mod spirv;
306
307	#[cfg(all(
308	target_feature = "sse2",
309	not(any(feature = "core-simd", feature = "scalar-math"))
310	))]
311	mod sse2;
312
313	#[cfg(all(
314	target_feature = "simd128",
315	not(any(feature = "core-simd", feature = "scalar-math"))
316	))]
317	mod wasm32;
318
319	#[cfg(all(feature = "core-simd", not(feature = "scalar-math")))]
320	mod coresimd;
321
322	#[cfg(all(
323	target_feature = "sse2",
324	not(any(feature = "core-simd", feature = "scalar-math"))
325	))]
326	use align16::Align16;
327
328	/** `bool` vector mask types. */
329	pub mod bool;
330	pub use self::bool::*;
331
332	/** `f32` vector, quaternion and matrix types. */
333	pub mod f32;
334	pub use self::f32::*;
335
336	/** `f64` vector, quaternion and matrix types. */
337	pub mod f64;
338	pub use self::f64::*;
339
340	/** `i8` vector types. */
341	pub mod i8;
342	pub use self::i8::*;
343
344	/** `u8` vector types. */
345	pub mod u8;
346	pub use self::u8::*;
347
348	/** `i16` vector types. */
349	pub mod i16;
350	pub use self::i16::*;
351
352	/** `u16` vector types. */
353	pub mod u16;
354	pub use self::u16::*;
355
356	/** `i32` vector types. */
357	pub mod i32;
358	pub use self::i32::*;
359
360	/** `u32` vector types. */
361	pub mod u32;
362	pub use self::u32::*;
363
364	/** `i64` vector types. */
365	pub mod i64;
366	pub use self::i64::*;
367
368	/** `u64` vector types. */
369	pub mod u64;
370	pub use self::u64::*;
371
372	/** `usize` vector types. */
373	pub mod usize;
374	pub use self::usize::*;
375
376	/** Traits adding swizzle methods to all vector types. */
377	pub mod swizzles;
378	pub use self::swizzles::{Vec2Swizzles, Vec3Swizzles, Vec4Swizzles};
379
380	/** Rotation Helper */
381	pub use euler::EulerRot;
382
383	/** A trait for extending [`prim@f32`] and [`prim@f64`] with extra methods. */
384	mod float;
385	pub use float::FloatExt;

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