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    // Copyright 2018 Developers of the Rand project.
    // Copyright 2013 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.

    //! The normal and derived distributions.
    #![allow(deprecated)]

    use crate::Rng;
    use crate::distributions::{ziggurat_tables, Distribution, Open01};
    use crate::distributions::utils::ziggurat;

    /// Samples floating-point numbers according to the normal distribution
    /// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to
    /// `Normal::new(0.0, 1.0)` but faster.
    ///
    /// See `Normal` for the general normal distribution.
    ///
    /// Implemented via the ZIGNOR variant[^1] of the Ziggurat method.
    ///
    /// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
    ///       Generate Normal Random Samples*](
    ///       https://www.doornik.com/research/ziggurat.pdf).
    ///       Nuffield College, Oxford
    #[deprecated(since="0.7.0", note="moved to rand_distr crate")]
    #[derive(Clone, Copy, Debug)]
    pub struct StandardNormal;

    impl Distribution<f64> for StandardNormal {
        fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
            #[inline]
            fn pdf(x: f64) -> f64 {
                (-x*x/2.0).exp()
            }
            #[inline]
            fn zero_case<R: Rng + ?Sized>(rng: &mut R, u: f64) -> f64 {
                // compute a random number in the tail by hand

                // strange initial conditions, because the loop is not
                // do-while, so the condition should be true on the first
                // run, they get overwritten anyway (0 < 1, so these are
                // good).
                let mut x = 1.0f64;
                let mut y = 0.0f64;

                while -2.0 * y < x * x {
                    let x_: f64 = rng.sample(Open01);
                    let y_: f64 = rng.sample(Open01);

                    x = x_.ln() / ziggurat_tables::ZIG_NORM_R;
                    y = y_.ln();
                }

                if u < 0.0 { x - ziggurat_tables::ZIG_NORM_R } else { ziggurat_tables::ZIG_NORM_R - x }
            }

            ziggurat(rng, true, // this is symmetric
                     &ziggurat_tables::ZIG_NORM_X,
                     &ziggurat_tables::ZIG_NORM_F,
                     pdf, zero_case)
        }
    }

    /// The normal distribution `N(mean, std_dev**2)`.
    ///
    /// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`]
    /// for more details.
    ///
    /// Note that [`StandardNormal`] is an optimised implementation for mean 0, and
    /// standard deviation 1.
    ///
    /// [`StandardNormal`]: crate::distributions::StandardNormal
    #[deprecated(since="0.7.0", note="moved to rand_distr crate")]
    #[derive(Clone, Copy, Debug)]
    pub struct Normal {
        mean: f64,
        std_dev: f64,
    }

    impl Normal {
        /// Construct a new `Normal` distribution with the given mean and
        /// standard deviation.
        ///
        /// # Panics
        ///
        /// Panics if `std_dev < 0`.
        #[inline]
        pub fn new(mean: f64, std_dev: f64) -> Normal {
            assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0");
            Normal {
                mean,
                std_dev
            }
        }
    }
    impl Distribution<f64> for Normal {
        fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
            let n = rng.sample(StandardNormal);
            self.mean + self.std_dev * n
        }
    }

    /// The log-normal distribution `ln N(mean, std_dev**2)`.
    ///
    /// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)`
    /// distributed.
    #[deprecated(since="0.7.0", note="moved to rand_distr crate")]
    #[derive(Clone, Copy, Debug)]
    pub struct LogNormal {
        norm: Normal
    }

    impl LogNormal {
        /// Construct a new `LogNormal` distribution with the given mean
        /// and standard deviation.
        ///
        /// # Panics
        ///
        /// Panics if `std_dev < 0`.
        #[inline]
        pub fn new(mean: f64, std_dev: f64) -> LogNormal {
            assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0");
            LogNormal { norm: Normal::new(mean, std_dev) }
        }
    }
    impl Distribution<f64> for LogNormal {
        fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
            self.norm.sample(rng).exp()
        }
    }

    #[cfg(test)]
    mod tests {
        use crate::distributions::Distribution;
        use super::{Normal, LogNormal};

        #[test]
        fn test_normal() {
            let norm = Normal::new(10.0, 10.0);
            let mut rng = crate::test::rng(210);
            for _ in 0..1000 {
                norm.sample(&mut rng);
            }
        }
        #[test]
        #[should_panic]
        fn test_normal_invalid_sd() {
            Normal::new(10.0, -1.0);
        }

        #[test]
        fn test_log_normal() {
            let lnorm = LogNormal::new(10.0, 10.0);
            let mut rng = crate::test::rng(211);
            for _ in 0..1000 {
                lnorm.sample(&mut rng);
            }
        }
        #[test]
        #[should_panic]
        fn test_log_normal_invalid_sd() {
            LogNormal::new(10.0, -1.0);
        }
    }

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