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, 125 deletions

diff --git a/rand/rand_distr/src/triangular.rs b/rand/rand_distr/src/triangular.rs
deleted file mode 100644
index dd0bbfb..0000000
--- a/rand/rand_distr/src/triangular.rs
+++ /dev/null
@@ -1,125 +0,0 @@
-// Copyright 2018 Developers of the Rand project.
-//
-// 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 triangular distribution.
-
-use rand::Rng;
-use crate::{Distribution, Standard};
-use crate::utils::Float;
-
-/// The triangular distribution.
-/// 
-/// A continuous probability distribution parameterised by a range, and a mode
-/// (most likely value) within that range.
-///
-/// The probability density function is triangular. For a similar distribution
-/// with a smooth PDF, see the [`Pert`] distribution.
-///
-/// # Example
-///
-/// ```rust
-/// use rand_distr::{Triangular, Distribution};
-///
-/// let d = Triangular::new(0., 5., 2.5).unwrap();
-/// let v = d.sample(&mut rand::thread_rng());
-/// println!("{} is from a triangular distribution", v);
-/// ```
-///
-/// [`Pert`]: crate::Pert
-#[derive(Clone, Copy, Debug)]
-pub struct Triangular<N> {
-    min: N,
-    max: N,
-    mode: N,
-}
-
-/// Error type returned from [`Triangular::new`].
-#[derive(Clone, Copy, Debug, PartialEq, Eq)]
-pub enum TriangularError {
-    /// `max < min` or `min` or `max` is NaN.
-    RangeTooSmall,
-    /// `mode < min` or `mode > max` or `mode` is NaN.
-    ModeRange,
-}
-
-impl<N: Float> Triangular<N>
-where Standard: Distribution<N>
-{
-    /// Set up the Triangular distribution with defined `min`, `max` and `mode`.
-    #[inline]
-    pub fn new(min: N, max: N, mode: N) -> Result<Triangular<N>, TriangularError> {
-        if !(max >= min) {
-            return Err(TriangularError::RangeTooSmall);
-        }
-        if !(mode >= min && max >= mode) {
-            return Err(TriangularError::ModeRange);
-        }
-        Ok(Triangular { min, max, mode })
-    }
-}
-
-impl<N: Float> Distribution<N> for Triangular<N>
-where Standard: Distribution<N>
-{
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> N {
-        let f: N = rng.sample(Standard);
-        let diff_mode_min = self.mode - self.min;
-        let range = self.max - self.min;
-        let f_range = f * range;
-        if f_range < diff_mode_min {
-            self.min + (f_range * diff_mode_min).sqrt()
-        } else {
-            self.max - ((range - f_range) * (self.max - self.mode)).sqrt()
-        }
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use std::f64;
-    use rand::{Rng, rngs::mock};
-    use super::*;
-
-    #[test]
-    fn test_triangular() {
-        let mut half_rng = mock::StepRng::new(0x8000_0000_0000_0000, 0);
-        assert_eq!(half_rng.gen::<f64>(), 0.5);
-        for &(min, max, mode, median) in &[
-            (-1., 1., 0., 0.),
-            (1., 2., 1., 2. - 0.5f64.sqrt()),
-            (5., 25., 25., 5. + 200f64.sqrt()),
-            (1e-5, 1e5, 1e-3, 1e5 - 4999999949.5f64.sqrt()),
-            (0., 1., 0.9, 0.45f64.sqrt()),
-            (-4., -0.5, -2., -4.0 + 3.5f64.sqrt()),
-        ] {
-            println!("{} {} {} {}", min, max, mode, median);
-            let distr = Triangular::new(min, max, mode).unwrap();
-            // Test correct value at median:
-            assert_eq!(distr.sample(&mut half_rng), median);
-        }
-
-        for &(min, max, mode) in &[
-            (-1., 1., 2.),
-            (-1., 1., -2.),
-            (2., 1., 1.),
-        ] {
-            assert!(Triangular::new(min, max, mode).is_err());
-        }
-    }
-    
-    #[test]
-    fn value_stability() {
-        let rng = crate::test::rng(860);
-        let distr = Triangular::new(2., 10., 3.).unwrap();
-        let seq = distr.sample_iter(rng).take(5).collect::<Vec<f64>>();
-        println!("seq: {:?}", seq);
-        let expected = vec![5.74373257511361, 7.890059162791258,
-                4.7256280652553455, 2.9474808121184077, 3.058301946314053];
-        assert!(seq == expected);
-    }
-}