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, 151 deletions

diff --git a/rand/src/distributions/poisson.rs b/rand/src/distributions/poisson.rs
deleted file mode 100644
index 9fd6e99..0000000
--- a/rand/src/distributions/poisson.rs
+++ /dev/null
@@ -1,151 +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.
-#![allow(deprecated)]
-
-use crate::Rng;
-use crate::distributions::{Distribution, Cauchy};
-use crate::distributions::utils::log_gamma;
-
-/// The Poisson distribution `Poisson(lambda)`.
-///
-/// This distribution has a density function:
-/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct Poisson {
-    lambda: f64,
-    // precalculated values
-    exp_lambda: f64,
-    log_lambda: f64,
-    sqrt_2lambda: f64,
-    magic_val: f64,
-}
-
-impl Poisson {
-    /// Construct a new `Poisson` with the given shape parameter
-    /// `lambda`. Panics if `lambda <= 0`.
-    pub fn new(lambda: f64) -> Poisson {
-        assert!(lambda > 0.0, "Poisson::new called with lambda <= 0");
-        let log_lambda = lambda.ln();
-        Poisson {
-            lambda,
-            exp_lambda: (-lambda).exp(),
-            log_lambda,
-            sqrt_2lambda: (2.0 * lambda).sqrt(),
-            magic_val: lambda * log_lambda - log_gamma(1.0 + lambda),
-        }
-    }
-}
-
-impl Distribution<u64> for Poisson {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
-        // using the algorithm from Numerical Recipes in C
-
-        // for low expected values use the Knuth method
-        if self.lambda < 12.0 {
-            let mut result = 0;
-            let mut p = 1.0;
-            while p > self.exp_lambda {
-                p *= rng.gen::<f64>();
-                result += 1;
-            }
-            result - 1
-        }
-        // high expected values - rejection method
-        else {
-            let mut int_result: u64;
-
-            // we use the Cauchy distribution as the comparison distribution
-            // f(x) ~ 1/(1+x^2)
-            let cauchy = Cauchy::new(0.0, 1.0);
-
-            loop {
-                let mut result;
-                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 >= 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();
-                int_result = result as u64;
-
-                // 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 = 0.9 * (1.0 + comp_dev * comp_dev)
-                    * (result * self.log_lambda - log_gamma(1.0 + result) - self.magic_val).exp();
-
-                // check with uniform random value - if below the threshold, we are within the target distribution
-                if rng.gen::<f64>() <= check {
-                    break;
-                }
-            }
-            int_result
-        }
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use crate::distributions::Distribution;
-    use super::Poisson;
-
-    #[test]
-    #[cfg(not(miri))] // Miri is too slow
-    fn test_poisson_10() {
-        let poisson = Poisson::new(10.0);
-        let mut rng = crate::test::rng(123);
-        let mut sum = 0;
-        for _ in 0..1000 {
-            sum += poisson.sample(&mut rng);
-        }
-        let avg = (sum as f64) / 1000.0;
-        println!("Poisson average: {}", avg);
-        assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
-    }
-
-    #[test]
-    #[cfg(not(miri))] // Miri doesn't support transcendental functions
-    fn test_poisson_15() {
-        // Take the 'high expected values' path
-        let poisson = Poisson::new(15.0);
-        let mut rng = crate::test::rng(123);
-        let mut sum = 0;
-        for _ in 0..1000 {
-            sum += poisson.sample(&mut rng);
-        }
-        let avg = (sum as f64) / 1000.0;
-        println!("Poisson average: {}", avg);
-        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);
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_poisson_invalid_lambda_neg() {
-        Poisson::new(-10.0);
-    }
-}