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, 120 deletions

diff --git a/rand/rand_distr/src/cauchy.rs b/rand/rand_distr/src/cauchy.rs
deleted file mode 100644
index 6b0e7c6..0000000
--- a/rand/rand_distr/src/cauchy.rs
+++ /dev/null
@@ -1,120 +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 Cauchy distribution.
-
-use rand::Rng;
-use crate::{Distribution, Standard};
-use crate::utils::Float;
-
-/// The Cauchy distribution `Cauchy(median, scale)`.
-///
-/// This distribution has a density function:
-/// `f(x) = 1 / (pi * scale * (1 + ((x - median) / scale)^2))`
-///
-/// # Example
-///
-/// ```
-/// use rand_distr::{Cauchy, Distribution};
-///
-/// let cau = Cauchy::new(2.0, 5.0).unwrap();
-/// let v = cau.sample(&mut rand::thread_rng());
-/// println!("{} is from a Cauchy(2, 5) distribution", v);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Cauchy<N> {
-    median: N,
-    scale: N,
-}
-
-/// Error type returned from `Cauchy::new`.
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum Error {
-    /// `scale <= 0` or `nan`.
-    ScaleTooSmall,
-}
-
-impl<N: Float> Cauchy<N>
-where Standard: Distribution<N>
-{
-    /// Construct a new `Cauchy` with the given shape parameters
-    /// `median` the peak location and `scale` the scale factor.
-    pub fn new(median: N, scale: N) -> Result<Cauchy<N>, Error> {
-        if !(scale > N::from(0.0)) {
-            return Err(Error::ScaleTooSmall);
-        }
-        Ok(Cauchy {
-            median,
-            scale
-        })
-    }
-}
-
-impl<N: Float> Distribution<N> for Cauchy<N>
-where Standard: Distribution<N>
-{
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
-        // sample from [0, 1)
-        let x = Standard.sample(rng);
-        // get standard cauchy random number
-        // note that π/2 is not exactly representable, even if x=0.5 the result is finite
-        let comp_dev = (N::pi() * x).tan();
-        // shift and scale according to parameters
-        self.median + self.scale * comp_dev
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use crate::Distribution;
-    use super::Cauchy;
-
-    fn median(mut numbers: &mut [f64]) -> f64 {
-        sort(&mut numbers);
-        let mid = numbers.len() / 2;
-        numbers[mid]
-    }
-
-    fn sort(numbers: &mut [f64]) {
-        numbers.sort_by(|a, b| a.partial_cmp(b).unwrap());
-    }
-
-    #[test]
-    fn test_cauchy_averages() {
-        // NOTE: given that the variance and mean are undefined,
-        // this test does not have any rigorous statistical meaning.
-        let cauchy = Cauchy::new(10.0, 5.0).unwrap();
-        let mut rng = crate::test::rng(123);
-        let mut numbers: [f64; 1000] = [0.0; 1000];
-        let mut sum = 0.0;
-        for i in 0..1000 {
-            numbers[i] = cauchy.sample(&mut rng);
-            sum += numbers[i];
-        }
-        let median = median(&mut numbers);
-        println!("Cauchy median: {}", median);
-        assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough
-        let mean = sum / 1000.0;
-        println!("Cauchy mean: {}", mean);
-        // for a Cauchy distribution the mean should not converge
-        assert!((mean - 10.0).abs() > 0.4); // not 100% certain, but probable enough
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_cauchy_invalid_scale_zero() {
-        Cauchy::new(0.0, 0.0).unwrap();
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_cauchy_invalid_scale_neg() {
-        Cauchy::new(0.0, -10.0).unwrap();
-    }
-}