# lib.rs - source
Source of the Rust file `/home/key/.cargo/registry/src/github.com-1ecc6299db9ec823/rand-0.8.5/src/lib.rs`.
Keywords: rust, rustlang, rust-lang

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    // Copyright 2018 Developers of the Rand project.
    // Copyright 2013-2017 The Rust Project Developers.
    //
    // Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
    // https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
    // <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
    // option. This file may not be copied, modified, or distributed
    // except according to those terms.

    //! Utilities for random number generation
    //!
    //! Rand provides utilities to generate random numbers, to convert them to
    //! useful types and distributions, and some randomness-related algorithms.
    //!
    //! # Quick Start
    //!
    //! To get you started quickly, the easiest and highest-level way to get
    //! a random value is to use [`random()`]; alternatively you can use
    //! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
    //! the [`distributions`] and [`seq`] modules provide further
    //! functionality on top of RNGs.
    //!
    //! ```
    //! use rand::prelude::*;
    //!
    //! if rand::random() { // generates a boolean
    //!     // Try printing a random unicode code point (probably a bad idea)!
    //!     println!("char: {}", rand::random::<char>());
    //! }
    //!
    //! let mut rng = rand::thread_rng();
    //! let y: f64 = rng.gen(); // generates a float between 0 and 1
    //!
    //! let mut nums: Vec<i32> = (1..100).collect();
    //! nums.shuffle(&mut rng);
    //! ```
    //!
    //! # The Book
    //!
    //! For the user guide and further documentation, please read
    //! [The Rust Rand Book](https://rust-random.github.io/book).

    #![doc(
        html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
        html_favicon_url = "https://www.rust-lang.org/favicon.ico",
        html_root_url = "https://rust-random.github.io/rand/"
    )]
    #![deny(missing_docs)]
    #![deny(missing_debug_implementations)]
    #![doc(test(attr(allow(unused_variables), deny(warnings))))]
    #![no_std]
    #![cfg_attr(feature = "simd_support", feature(stdsimd))]
    #![cfg_attr(doc_cfg, feature(doc_cfg))]
    #![allow(
        clippy::float_cmp,
        clippy::neg_cmp_op_on_partial_ord,
    )]

    #[cfg(feature = "std")] extern crate std;
    #[cfg(feature = "alloc")] extern crate alloc;

    #[allow(unused)]
    macro_rules! trace { ($($x:tt)*) => (
        #[cfg(feature = "log")] {
            log::trace!($($x)*)
        }
    ) }
    #[allow(unused)]
    macro_rules! debug { ($($x:tt)*) => (
        #[cfg(feature = "log")] {
            log::debug!($($x)*)
        }
    ) }
    #[allow(unused)]
    macro_rules! info { ($($x:tt)*) => (
        #[cfg(feature = "log")] {
            log::info!($($x)*)
        }
    ) }
    #[allow(unused)]
    macro_rules! warn { ($($x:tt)*) => (
        #[cfg(feature = "log")] {
            log::warn!($($x)*)
        }
    ) }
    #[allow(unused)]
    macro_rules! error { ($($x:tt)*) => (
        #[cfg(feature = "log")] {
            log::error!($($x)*)
        }
    ) }

    // Re-exports from rand_core
    pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng};

    // Public modules
    pub mod distributions;
    pub mod prelude;
    mod rng;
    pub mod rngs;
    pub mod seq;

    // Public exports
    #[cfg(all(feature = "std", feature = "std_rng"))]
    pub use crate::rngs::thread::thread_rng;
    pub use rng::{Fill, Rng};

    #[cfg(all(feature = "std", feature = "std_rng"))]
    use crate::distributions::{Distribution, Standard};

    /// Generates a random value using the thread-local random number generator.
    ///
    /// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for
    /// documentation of the entropy source and [`Standard`] for documentation of
    /// distributions and type-specific generation.
    ///
    /// # Provided implementations
    ///
    /// The following types have provided implementations that
    /// generate values with the following ranges and distributions:
    ///
    /// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
    ///   over all values of the type.
    /// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
    ///   code points in the range `0...0x10_FFFF`, except for the range
    ///   `0xD800...0xDFFF` (the surrogate code points). This includes
    ///   unassigned/reserved code points.
    /// * `bool`: Generates `false` or `true`, each with probability 0.5.
    /// * Floating point types (`f32` and `f64`): Uniformly distributed in the
    ///   half-open range `[0, 1)`. See notes below.
    /// * Wrapping integers (`Wrapping<T>`), besides the type identical to their
    ///   normal integer variants.
    ///
    /// Also supported is the generation of the following
    /// compound types where all component types are supported:
    ///
    /// *   Tuples (up to 12 elements): each element is generated sequentially.
    /// *   Arrays (up to 32 elements): each element is generated sequentially;
    ///     see also [`Rng::fill`] which supports arbitrary array length for integer
    ///     types and tends to be faster for `u32` and smaller types.
    /// *   `Option<T>` first generates a `bool`, and if true generates and returns
    ///     `Some(value)` where `value: T`, otherwise returning `None`.
    ///
    /// # Examples
    ///
    /// ```
    /// let x = rand::random::<u8>();
    /// println!("{}", x);
    ///
    /// let y = rand::random::<f64>();
    /// println!("{}", y);
    ///
    /// if rand::random() { // generates a boolean
    ///     println!("Better lucky than good!");
    /// }
    /// ```
    ///
    /// If you're calling `random()` in a loop, caching the generator as in the
    /// following example can increase performance.
    ///
    /// ```
    /// use rand::Rng;
    ///
    /// let mut v = vec![1, 2, 3];
    ///
    /// for x in v.iter_mut() {
    ///     *x = rand::random()
    /// }
    ///
    /// // can be made faster by caching thread_rng
    ///
    /// let mut rng = rand::thread_rng();
    ///
    /// for x in v.iter_mut() {
    ///     *x = rng.gen();
    /// }
    /// ```
    ///
    /// [`Standard`]: distributions::Standard
    #[cfg(all(feature = "std", feature = "std_rng"))]
    #[cfg_attr(doc_cfg, doc(cfg(all(feature = "std", feature = "std_rng"))))]
    #[inline]
    pub fn random<T>() -> T
    where Standard: Distribution<T> {
        thread_rng().gen()
    }

    #[cfg(test)]
    mod test {
        use super::*;

        /// Construct a deterministic RNG with the given seed
        pub fn rng(seed: u64) -> impl RngCore {
            // For tests, we want a statistically good, fast, reproducible RNG.
            // PCG32 will do fine, and will be easy to embed if we ever need to.
            const INC: u64 = 11634580027462260723;
            rand_pcg::Pcg32::new(seed, INC)
        }

        #[test]
        #[cfg(all(feature = "std", feature = "std_rng"))]
        fn test_random() {
            let _n: usize = random();
            let _f: f32 = random();
            let _o: Option<Option<i8>> = random();
            #[allow(clippy::type_complexity)]
            let _many: (
                (),
                (usize, isize, Option<(u32, (bool,))>),
                (u8, i8, u16, i16, u32, i32, u64, i64),
                (f32, (f64, (f64,))),
            ) = random();
        }
    }