Update nitrokey crate to 0.4.0

This change finally updates the version of the nitrokey crate that we
consume to 0.4.0. Along with that we update rand_core, one of its
dependencies, to 0.5.1. Further more we add cfg-if in version 0.1.10 and
getrandom in version 0.1.13, both of which are now new (non-development)
dependencies.

Import subrepo nitrokey/:nitrokey at e81057037e9b4f370b64c0a030a725bc6bdfb870
Import subrepo cfg-if/:cfg-if at 4484a6faf816ff8058088ad857b0c6bb2f4b02b2
Import subrepo getrandom/:getrandom at d661aa7e1b8cc80b47dabe3d2135b3b47d2858af
Import subrepo rand/:rand at d877ed528248b52d947e0484364a4e1ae59ca502

1 files changed, 219 insertions, 0 deletions

diff --git a/rand/rand_distr/src/normal.rs b/rand/rand_distr/src/normal.rs
new file mode 100644
index 0000000..882754f
--- /dev/null
+++ b/rand/rand_distr/src/normal.rs
@@ -0,0 +1,219 @@
+// 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.
+
+use rand::Rng;
+use crate::{ziggurat_tables, Distribution, Open01};
+use crate::utils::{ziggurat, Float};
+
+/// 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
+///
+/// # Example
+/// ```
+/// use rand::prelude::*;
+/// use rand_distr::StandardNormal;
+///
+/// let val: f64 = thread_rng().sample(StandardNormal);
+/// println!("{}", val);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct StandardNormal;
+
+impl Distribution<f32> for StandardNormal {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f32 {
+        // TODO: use optimal 32-bit implementation
+        let x: f64 = self.sample(rng);
+        x as f32
+    }
+}
+
+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.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{Normal, Distribution};
+///
+/// // mean 2, standard deviation 3
+/// let normal = Normal::new(2.0, 3.0).unwrap();
+/// let v = normal.sample(&mut rand::thread_rng());
+/// println!("{} is from a N(2, 9) distribution", v)
+/// ```
+///
+/// [`StandardNormal`]: crate::StandardNormal
+#[derive(Clone, Copy, Debug)]
+pub struct Normal<N> {
+    mean: N,
+    std_dev: N,
+}
+
+/// Error type returned from `Normal::new` and `LogNormal::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Error {
+    /// `std_dev < 0` or `nan`.
+    StdDevTooSmall,
+}
+
+impl<N: Float> Normal<N>
+where StandardNormal: Distribution<N>
+{
+    /// Construct a new `Normal` distribution with the given mean and
+    /// standard deviation.
+    #[inline]
+    pub fn new(mean: N, std_dev: N) -> Result<Normal<N>, Error> {
+        if !(std_dev >= N::from(0.0)) {
+            return Err(Error::StdDevTooSmall);
+        }
+        Ok(Normal {
+            mean,
+            std_dev
+        })
+    }
+}
+
+impl<N: Float> Distribution<N> for Normal<N>
+where StandardNormal: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        let n: 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.
+///
+/// # Example
+///
+/// ```
+/// use rand_distr::{LogNormal, Distribution};
+///
+/// // mean 2, standard deviation 3
+/// let log_normal = LogNormal::new(2.0, 3.0).unwrap();
+/// let v = log_normal.sample(&mut rand::thread_rng());
+/// println!("{} is from an ln N(2, 9) distribution", v)
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct LogNormal<N> {
+    norm: Normal<N>
+}
+
+impl<N: Float> LogNormal<N>
+where StandardNormal: Distribution<N>
+{
+    /// Construct a new `LogNormal` distribution with the given mean
+    /// and standard deviation of the logarithm of the distribution.
+    #[inline]
+    pub fn new(mean: N, std_dev: N) -> Result<LogNormal<N>, Error> {
+        if !(std_dev >= N::from(0.0)) {
+            return Err(Error::StdDevTooSmall);
+        }
+        Ok(LogNormal { norm: Normal::new(mean, std_dev).unwrap() })
+    }
+}
+
+impl<N: Float> Distribution<N> for LogNormal<N>
+where StandardNormal: Distribution<N>
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
+        self.norm.sample(rng).exp()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::Distribution;
+    use super::{Normal, LogNormal};
+
+    #[test]
+    fn test_normal() {
+        let norm = Normal::new(10.0, 10.0).unwrap();
+        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).unwrap();
+    }
+
+
+    #[test]
+    fn test_log_normal() {
+        let lnorm = LogNormal::new(10.0, 10.0).unwrap();
+        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).unwrap();
+    }
+}