Remove vendored dependencies

While it appears that by now we actually can get successful builds
without Cargo insisting on Internet access by virtue of using the
--frozen flag, maintaining vendored dependencies is somewhat of a pain
point. This state will also get worse with upcoming changes that replace
argparse in favor of structopt and pull in a slew of new dependencies by
doing so. Then there is also the repository structure aspect, which is
non-standard due to the way we vendor dependencies and a potential
source of confusion.
In order to fix these problems, this change removes all the vendored
dependencies we have.

Delete subrepo argparse/:argparse
Delete subrepo base32/:base32
Delete subrepo cc/:cc
Delete subrepo cfg-if/:cfg-if
Delete subrepo getrandom/:getrandom
Delete subrepo lazy-static/:lazy-static
Delete subrepo libc/:libc
Delete subrepo nitrokey-sys/:nitrokey-sys
Delete subrepo nitrokey/:nitrokey
Delete subrepo rand/:rand

1 files changed, 0 insertions, 233 deletions

diff --git a/rand/rand_distr/src/poisson.rs b/rand/rand_distr/src/poisson.rs
deleted file mode 100644
index 4f4a0b7..0000000
--- a/rand/rand_distr/src/poisson.rs
+++ /dev/null
@@ -1,233 +0,0 @@
-// Copyright 2018 Developers of the Rand project.
-// Copyright 2016-2017 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.
-
-//! The Poisson distribution.
-
-use rand::Rng;
-use crate::{Distribution, Cauchy, Standard};
-use crate::utils::Float;
-
-/// The Poisson distribution `Poisson(lambda)`.
-///
-/// This distribution has a density function:
-/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
-///
-/// # Example
-///
-/// ```
-/// use rand_distr::{Poisson, Distribution};
-///
-/// let poi = Poisson::new(2.0).unwrap();
-/// let v: u64 = poi.sample(&mut rand::thread_rng());
-/// println!("{} is from a Poisson(2) distribution", v);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Poisson<N> {
-    lambda: N,
-    // precalculated values
-    exp_lambda: N,
-    log_lambda: N,
-    sqrt_2lambda: N,
-    magic_val: N,
-}
-
-/// Error type returned from `Poisson::new`.
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum Error {
-    /// `lambda <= 0` or `nan`.
-    ShapeTooSmall,
-}
-
-impl<N: Float> Poisson<N>
-where Standard: Distribution<N>
-{
-    /// Construct a new `Poisson` with the given shape parameter
-    /// `lambda`.
-    pub fn new(lambda: N) -> Result<Poisson<N>, Error> {
-        if !(lambda > N::from(0.0)) {
-            return Err(Error::ShapeTooSmall);
-        }
-        let log_lambda = lambda.ln();
-        Ok(Poisson {
-            lambda,
-            exp_lambda: (-lambda).exp(),
-            log_lambda,
-            sqrt_2lambda: (N::from(2.0) * lambda).sqrt(),
-            magic_val: lambda * log_lambda - (N::from(1.0) + lambda).log_gamma(),
-        })
-    }
-}
-
-impl<N: Float> Distribution<N> for Poisson<N>
-where Standard: Distribution<N>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
-        // using the algorithm from Numerical Recipes in C
-
-        // for low expected values use the Knuth method
-        if self.lambda < N::from(12.0) {
-            let mut result = N::from(0.);
-            let mut p = N::from(1.0);
-            while p > self.exp_lambda {
-                p *= rng.gen::<N>();
-                result += N::from(1.);
-            }
-            result - N::from(1.)
-        }
-        // high expected values - rejection method
-        else {
-            // we use the Cauchy distribution as the comparison distribution
-            // f(x) ~ 1/(1+x^2)
-            let cauchy = Cauchy::new(N::from(0.0), N::from(1.0)).unwrap();
-            let mut result;
-
-            loop {
-                let mut comp_dev;
-
-                loop {
-                    // draw from the Cauchy distribution
-                    comp_dev = rng.sample(cauchy);
-                    // shift the peak of the comparison ditribution
-                    result = self.sqrt_2lambda * comp_dev + self.lambda;
-                    // repeat the drawing until we are in the range of possible values
-                    if result >= N::from(0.0) {
-                        break;
-                    }
-                }
-                // now the result is a random variable greater than 0 with Cauchy distribution
-                // the result should be an integer value
-                result = result.floor();
-
-                // this is the ratio of the Poisson distribution to the comparison distribution
-                // the magic value scales the distribution function to a range of approximately 0-1
-                // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1
-                // this doesn't change the resulting distribution, only increases the rate of failed drawings
-                let check = N::from(0.9) * (N::from(1.0) + comp_dev * comp_dev)
-                    * (result * self.log_lambda - (N::from(1.0) + result).log_gamma() - self.magic_val).exp();
-
-                // check with uniform random value - if below the threshold, we are within the target distribution
-                if rng.gen::<N>() <= check {
-                    break;
-                }
-            }
-            result
-        }
-    }
-}
-
-impl<N: Float> Distribution<u64> for Poisson<N>
-where Standard: Distribution<N>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
-        let result: N = self.sample(rng);
-        result.to_u64().unwrap()
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use crate::Distribution;
-    use super::Poisson;
-
-    #[test]
-    fn test_poisson_10() {
-        let poisson = Poisson::new(10.0).unwrap();
-        let mut rng = crate::test::rng(123);
-        let mut sum_u64 = 0;
-        let mut sum_f64 = 0.;
-        for _ in 0..1000 {
-            let s_u64: u64 = poisson.sample(&mut rng);
-            let s_f64: f64 = poisson.sample(&mut rng);
-            sum_u64 += s_u64;
-            sum_f64 += s_f64;
-        }
-        let avg_u64 = (sum_u64 as f64) / 1000.0;
-        let avg_f64 = sum_f64 / 1000.0;
-        println!("Poisson averages: {} (u64)  {} (f64)", avg_u64, avg_f64);
-        for &avg in &[avg_u64, avg_f64] {
-            assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
-        }
-    }
-
-    #[test]
-    fn test_poisson_15() {
-        // Take the 'high expected values' path
-        let poisson = Poisson::new(15.0).unwrap();
-        let mut rng = crate::test::rng(123);
-        let mut sum_u64 = 0;
-        let mut sum_f64 = 0.;
-        for _ in 0..1000 {
-            let s_u64: u64 = poisson.sample(&mut rng);
-            let s_f64: f64 = poisson.sample(&mut rng);
-            sum_u64 += s_u64;
-            sum_f64 += s_f64;
-        }
-        let avg_u64 = (sum_u64 as f64) / 1000.0;
-        let avg_f64 = sum_f64 / 1000.0;
-        println!("Poisson average: {} (u64)  {} (f64)", avg_u64, avg_f64);
-        for &avg in &[avg_u64, avg_f64] {
-            assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
-        }
-    }
-
-    #[test]
-    fn test_poisson_10_f32() {
-        let poisson = Poisson::new(10.0f32).unwrap();
-        let mut rng = crate::test::rng(123);
-        let mut sum_u64 = 0;
-        let mut sum_f32 = 0.;
-        for _ in 0..1000 {
-            let s_u64: u64 = poisson.sample(&mut rng);
-            let s_f32: f32 = poisson.sample(&mut rng);
-            sum_u64 += s_u64;
-            sum_f32 += s_f32;
-        }
-        let avg_u64 = (sum_u64 as f32) / 1000.0;
-        let avg_f32 = sum_f32 / 1000.0;
-        println!("Poisson averages: {} (u64)  {} (f32)", avg_u64, avg_f32);
-        for &avg in &[avg_u64, avg_f32] {
-            assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
-        }
-    }
-
-    #[test]
-    fn test_poisson_15_f32() {
-        // Take the 'high expected values' path
-        let poisson = Poisson::new(15.0f32).unwrap();
-        let mut rng = crate::test::rng(123);
-        let mut sum_u64 = 0;
-        let mut sum_f32 = 0.;
-        for _ in 0..1000 {
-            let s_u64: u64 = poisson.sample(&mut rng);
-            let s_f32: f32 = poisson.sample(&mut rng);
-            sum_u64 += s_u64;
-            sum_f32 += s_f32;
-        }
-        let avg_u64 = (sum_u64 as f32) / 1000.0;
-        let avg_f32 = sum_f32 / 1000.0;
-        println!("Poisson average: {} (u64)  {} (f32)", avg_u64, avg_f32);
-        for &avg in &[avg_u64, avg_f32] {
-            assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
-        }
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_poisson_invalid_lambda_zero() {
-        Poisson::new(0.0).unwrap();
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_poisson_invalid_lambda_neg() {
-        Poisson::new(-10.0).unwrap();
-    }
-}