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, 145 deletions

diff --git a/rand/rand_distr/src/exponential.rs b/rand/rand_distr/src/exponential.rs
deleted file mode 100644
index 8322489..0000000
--- a/rand/rand_distr/src/exponential.rs
+++ /dev/null
@@ -1,145 +0,0 @@
-// 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 exponential distribution.
-
-use rand::Rng;
-use crate::{ziggurat_tables, Distribution};
-use crate::utils::{ziggurat, Float};
-
-/// Samples floating-point numbers according to the exponential distribution,
-/// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or
-/// sampling with `-rng.gen::<f64>().ln()`, but faster.
-///
-/// See `Exp` for the general exponential distribution.
-///
-/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. The exact
-/// description in the paper was adjusted to use tables for the exponential
-/// distribution rather than normal.
-///
-/// [^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::Exp1;
-///
-/// let val: f64 = thread_rng().sample(Exp1);
-/// println!("{}", val);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Exp1;
-
-impl Distribution<f32> for Exp1 {
-    #[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
-    }
-}
-
-// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
-impl Distribution<f64> for Exp1 {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        #[inline]
-        fn pdf(x: f64) -> f64 {
-            (-x).exp()
-        }
-        #[inline]
-        fn zero_case<R: Rng + ?Sized>(rng: &mut R, _u: f64) -> f64 {
-            ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln()
-        }
-
-        ziggurat(rng, false,
-                 &ziggurat_tables::ZIG_EXP_X,
-                 &ziggurat_tables::ZIG_EXP_F,
-                 pdf, zero_case)
-    }
-}
-
-/// The exponential distribution `Exp(lambda)`.
-///
-/// This distribution has density function: `f(x) = lambda * exp(-lambda * x)`
-/// for `x > 0`.
-/// 
-/// Note that [`Exp1`](crate::Exp1) is an optimised implementation for `lambda = 1`.
-///
-/// # Example
-///
-/// ```
-/// use rand_distr::{Exp, Distribution};
-///
-/// let exp = Exp::new(2.0).unwrap();
-/// let v = exp.sample(&mut rand::thread_rng());
-/// println!("{} is from a Exp(2) distribution", v);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Exp<N> {
-    /// `lambda` stored as `1/lambda`, since this is what we scale by.
-    lambda_inverse: N
-}
-
-/// Error type returned from `Exp::new`.
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum Error {
-    /// `lambda <= 0` or `nan`.
-    LambdaTooSmall,
-}
-
-impl<N: Float> Exp<N>
-where Exp1: Distribution<N>
-{
-    /// Construct a new `Exp` with the given shape parameter
-    /// `lambda`.
-    #[inline]
-    pub fn new(lambda: N) -> Result<Exp<N>, Error> {
-        if !(lambda > N::from(0.0)) {
-            return Err(Error::LambdaTooSmall);
-        }
-        Ok(Exp { lambda_inverse: N::from(1.0) / lambda })
-    }
-}
-
-impl<N: Float> Distribution<N> for Exp<N>
-where Exp1: Distribution<N>
-{
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
-        rng.sample(Exp1) * self.lambda_inverse
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use crate::Distribution;
-    use super::Exp;
-
-    #[test]
-    fn test_exp() {
-        let exp = Exp::new(10.0).unwrap();
-        let mut rng = crate::test::rng(221);
-        for _ in 0..1000 {
-            assert!(exp.sample(&mut rng) >= 0.0);
-        }
-    }
-    #[test]
-    #[should_panic]
-    fn test_exp_invalid_lambda_zero() {
-        Exp::new(0.0).unwrap();
-    }
-    #[test]
-    #[should_panic]
-    fn test_exp_invalid_lambda_neg() {
-        Exp::new(-10.0).unwrap();
-    }
-}