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//! Embedded math library featuring fast, safe floating point approximations
//! for common arithmetic operations, 2D and 3D vector types, and statistical
//! analysis.
//!
//! ## Floating point approximations
//!
//! `micromath` supports approximating many arithmetic operations on `f32`
//! using bitwise operations, providing great performance and small code size
//! at the cost of precision. For use cases like graphics and signal
//! processing, these approximations are often sufficient and the performance
//! gains worth the lost precision.
//!
//! These approximations are provided by the [micromath::F32Ext] trait which is
//! impl'd for `f32`, providing a drop-in `std`-compatible (sans lost precision) API.
//!
//! ```
//! use micromath::F32Ext;
//!
//! let n = 2.0.sqrt();
//! assert_eq!(n, 1.5); // close enough
//! ```
//!
//! ### Unused import warnings when linking `std`
//!
//! Since the `F32Ext` trait provides methods which are already defined in
//! `std`, in cases where your crate links `std` the `F32Ext` versions of
//! the same methods will not be used, in which case you will get an unused
//! import warning for `F32Ext`.
//!
//! If you encounter this, add an `#[allow(unused_imports)]` above the import.
//!
//! ```
//! #[allow(unused_imports)]
//! use micromath::F32Ext;
//! ```
//!
//! ## Vector types
//!
//! See the [`vector` module] for more information on vector types.
//!
//! The following vector types are available, all of which have `pub x` and
//! `pub y` (and on 3D vectors, `pub z`) members:
//!
//! | Rust  | 2D      | 3D      |
//! |-------|---------|---------|
//! | `i8`  | `I8x2`  | `I8x3`  |
//! | `i16` | `I16x2` | `I16x3` |
//! | `i32` | `I32x2` | `I32x3` |
//! | `u8`  | `U8x2`  | `U8x3`  |
//! | `u16` | `U16x2` | `U16x3` |
//! | `u32` | `U32x2` | `U32x3` |
//! | `f32` | `F32x2` | `F32x3` |
//!
//! ## Statistical analysis
//!
//! See the [`statistics` module] for more information on statistical analysis
//! traits and functionality.
//!
//! The following traits are available and impl'd for slices and iterators of
//! `f32` (and can be impl'd for other types):
//!
//! - [Mean] - compute arithmetic mean with the `mean()` method
//! - [StdDev] - compute standard deviation with the `stddev()` method
//! - [Trim] - cull outliers from a sample slice with the `trim()` method.
//! - [Variance] - compute variance with the `variance() method
//!
//! ## Quaternions
//!
//! See the [`quaternion` module] for more information.
//!
//! [micromath::F32Ext]: https://docs.rs/micromath/latest/micromath/trait.F32Ext.html
//! [`vector` module]: https://docs.rs/micromath/latest/micromath/vector/index.html
//! [`statistics` module]: https://docs.rs/micromath/latest/micromath/statistics/index.html
//! [`quaternion` module]: https://docs.rs/micromath/latest/micromath/quaternion/index.html
//! [Mean]: https://docs.rs/micromath/latest/micromath/statistics/trait.Mean.html
//! [StdDev]: https://docs.rs/micromath/latest/micromath/statistics/trait.StdDev.html
//! [Trim]: https://docs.rs/micromath/latest/micromath/statistics/trim/trait.Trim.html
//! [Variance]: https://docs.rs/micromath/latest/micromath/statistics/trait.Variance.html

#![no_std]
#![deny(
    warnings,
    missing_docs,
    trivial_casts,
    trivial_numeric_casts,
    unused_import_braces,
    unused_qualifications
)]
#![forbid(unsafe_code)]
#![doc(
    html_logo_url = "https://raw.githubusercontent.com/NeoBirth/micromath/develop/img/micromath-sq.png",
    html_root_url = "https://docs.rs/micromath/0.4.1"
)]

mod f32ext;
#[cfg(feature = "quaternion")]
pub mod quaternion;
#[cfg(feature = "statistics")]
pub mod statistics;
#[cfg(feature = "vector")]
pub mod vector;

pub use crate::f32ext::F32Ext;
#[cfg(feature = "quaternion")]
pub use crate::quaternion::Quaternion;
#[cfg(feature = "vector")]
pub use crate::vector::{Vector, VectorExt};
pub use generic_array;