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, 371 deletions

diff --git a/rand/src/distributions/gamma.rs b/rand/src/distributions/gamma.rs
deleted file mode 100644
index b5a97f5..0000000
--- a/rand/src/distributions/gamma.rs
+++ /dev/null
@@ -1,371 +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 Gamma and derived distributions.
-#![allow(deprecated)]
-
-use self::GammaRepr::*;
-use self::ChiSquaredRepr::*;
-
-use crate::Rng;
-use crate::distributions::normal::StandardNormal;
-use crate::distributions::{Distribution, Exp, Open01};
-
-/// The Gamma distribution `Gamma(shape, scale)` distribution.
-///
-/// The density function of this distribution is
-///
-/// ```text
-/// f(x) =  x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)
-/// ```
-///
-/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the
-/// scale and both `k` and `θ` are strictly positive.
-///
-/// The algorithm used is that described by Marsaglia & Tsang 2000[^1],
-/// falling back to directly sampling from an Exponential for `shape
-/// == 1`, and using the boosting technique described in that paper for
-/// `shape < 1`.
-///
-/// [^1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for
-///       Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3
-///       (September 2000), 363-372.
-///       DOI:[10.1145/358407.358414](https://doi.acm.org/10.1145/358407.358414)
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct Gamma {
-    repr: GammaRepr,
-}
-
-#[derive(Clone, Copy, Debug)]
-enum GammaRepr {
-    Large(GammaLargeShape),
-    One(Exp),
-    Small(GammaSmallShape)
-}
-
-// These two helpers could be made public, but saving the
-// match-on-Gamma-enum branch from using them directly (e.g. if one
-// knows that the shape is always > 1) doesn't appear to be much
-// faster.
-
-/// Gamma distribution where the shape parameter is less than 1.
-///
-/// Note, samples from this require a compulsory floating-point `pow`
-/// call, which makes it significantly slower than sampling from a
-/// gamma distribution where the shape parameter is greater than or
-/// equal to 1.
-///
-/// See `Gamma` for sampling from a Gamma distribution with general
-/// shape parameters.
-#[derive(Clone, Copy, Debug)]
-struct GammaSmallShape {
-    inv_shape: f64,
-    large_shape: GammaLargeShape
-}
-
-/// Gamma distribution where the shape parameter is larger than 1.
-///
-/// See `Gamma` for sampling from a Gamma distribution with general
-/// shape parameters.
-#[derive(Clone, Copy, Debug)]
-struct GammaLargeShape {
-    scale: f64,
-    c: f64,
-    d: f64
-}
-
-impl Gamma {
-    /// Construct an object representing the `Gamma(shape, scale)`
-    /// distribution.
-    ///
-    /// Panics if `shape <= 0` or `scale <= 0`.
-    #[inline]
-    pub fn new(shape: f64, scale: f64) -> Gamma {
-        assert!(shape > 0.0, "Gamma::new called with shape <= 0");
-        assert!(scale > 0.0, "Gamma::new called with scale <= 0");
-
-        let repr = if shape == 1.0 {
-            One(Exp::new(1.0 / scale))
-        } else if shape < 1.0 {
-            Small(GammaSmallShape::new_raw(shape, scale))
-        } else {
-            Large(GammaLargeShape::new_raw(shape, scale))
-        };
-        Gamma { repr }
-    }
-}
-
-impl GammaSmallShape {
-    fn new_raw(shape: f64, scale: f64) -> GammaSmallShape {
-        GammaSmallShape {
-            inv_shape: 1. / shape,
-            large_shape: GammaLargeShape::new_raw(shape + 1.0, scale)
-        }
-    }
-}
-
-impl GammaLargeShape {
-    fn new_raw(shape: f64, scale: f64) -> GammaLargeShape {
-        let d = shape - 1. / 3.;
-        GammaLargeShape {
-            scale,
-            c: 1. / (9. * d).sqrt(),
-            d
-        }
-    }
-}
-
-impl Distribution<f64> for Gamma {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        match self.repr {
-            Small(ref g) => g.sample(rng),
-            One(ref g) => g.sample(rng),
-            Large(ref g) => g.sample(rng),
-        }
-    }
-}
-impl Distribution<f64> for GammaSmallShape {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let u: f64 = rng.sample(Open01);
-
-        self.large_shape.sample(rng) * u.powf(self.inv_shape)
-    }
-}
-impl Distribution<f64> for GammaLargeShape {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        loop {
-            let x = rng.sample(StandardNormal);
-            let v_cbrt = 1.0 + self.c * x;
-            if v_cbrt <= 0.0 { // a^3 <= 0 iff a <= 0
-                continue
-            }
-
-            let v = v_cbrt * v_cbrt * v_cbrt;
-            let u: f64 = rng.sample(Open01);
-
-            let x_sqr = x * x;
-            if u < 1.0 - 0.0331 * x_sqr * x_sqr ||
-                u.ln() < 0.5 * x_sqr + self.d * (1.0 - v + v.ln()) {
-                return self.d * v * self.scale
-            }
-        }
-    }
-}
-
-/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of
-/// freedom.
-///
-/// For `k > 0` integral, this distribution is the sum of the squares
-/// of `k` independent standard normal random variables. For other
-/// `k`, this uses the equivalent characterisation
-/// `χ²(k) = Gamma(k/2, 2)`.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct ChiSquared {
-    repr: ChiSquaredRepr,
-}
-
-#[derive(Clone, Copy, Debug)]
-enum ChiSquaredRepr {
-    // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1,
-    // e.g. when alpha = 1/2 as it would be for this case, so special-
-    // casing and using the definition of N(0,1)^2 is faster.
-    DoFExactlyOne,
-    DoFAnythingElse(Gamma),
-}
-
-impl ChiSquared {
-    /// Create a new chi-squared distribution with degrees-of-freedom
-    /// `k`. Panics if `k < 0`.
-    pub fn new(k: f64) -> ChiSquared {
-        let repr = if k == 1.0 {
-            DoFExactlyOne
-        } else {
-            assert!(k > 0.0, "ChiSquared::new called with `k` < 0");
-            DoFAnythingElse(Gamma::new(0.5 * k, 2.0))
-        };
-        ChiSquared { repr }
-    }
-}
-impl Distribution<f64> for ChiSquared {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        match self.repr {
-            DoFExactlyOne => {
-                // k == 1 => N(0,1)^2
-                let norm = rng.sample(StandardNormal);
-                norm * norm
-            }
-            DoFAnythingElse(ref g) => g.sample(rng)
-        }
-    }
-}
-
-/// The Fisher F distribution `F(m, n)`.
-///
-/// This distribution is equivalent to the ratio of two normalised
-/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) /
-/// (χ²(n)/n)`.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct FisherF {
-    numer: ChiSquared,
-    denom: ChiSquared,
-    // denom_dof / numer_dof so that this can just be a straight
-    // multiplication, rather than a division.
-    dof_ratio: f64,
-}
-
-impl FisherF {
-    /// Create a new `FisherF` distribution, with the given
-    /// parameter. Panics if either `m` or `n` are not positive.
-    pub fn new(m: f64, n: f64) -> FisherF {
-        assert!(m > 0.0, "FisherF::new called with `m < 0`");
-        assert!(n > 0.0, "FisherF::new called with `n < 0`");
-
-        FisherF {
-            numer: ChiSquared::new(m),
-            denom: ChiSquared::new(n),
-            dof_ratio: n / m
-        }
-    }
-}
-impl Distribution<f64> for FisherF {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        self.numer.sample(rng) / self.denom.sample(rng) * self.dof_ratio
-    }
-}
-
-/// The Student t distribution, `t(nu)`, where `nu` is the degrees of
-/// freedom.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct StudentT {
-    chi: ChiSquared,
-    dof: f64
-}
-
-impl StudentT {
-    /// Create a new Student t distribution with `n` degrees of
-    /// freedom. Panics if `n <= 0`.
-    pub fn new(n: f64) -> StudentT {
-        assert!(n > 0.0, "StudentT::new called with `n <= 0`");
-        StudentT {
-            chi: ChiSquared::new(n),
-            dof: n
-        }
-    }
-}
-impl Distribution<f64> for StudentT {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let norm = rng.sample(StandardNormal);
-        norm * (self.dof / self.chi.sample(rng)).sqrt()
-    }
-}
-
-/// The Beta distribution with shape parameters `alpha` and `beta`.
-#[deprecated(since="0.7.0", note="moved to rand_distr crate")]
-#[derive(Clone, Copy, Debug)]
-pub struct Beta {
-    gamma_a: Gamma,
-    gamma_b: Gamma,
-}
-
-impl Beta {
-    /// Construct an object representing the `Beta(alpha, beta)`
-    /// distribution.
-    ///
-    /// Panics if `shape <= 0` or `scale <= 0`.
-    pub fn new(alpha: f64, beta: f64) -> Beta {
-        assert!((alpha > 0.) & (beta > 0.));
-        Beta {
-            gamma_a: Gamma::new(alpha, 1.),
-            gamma_b: Gamma::new(beta, 1.),
-        }
-    }
-}
-
-impl Distribution<f64> for Beta {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
-        let x = self.gamma_a.sample(rng);
-        let y = self.gamma_b.sample(rng);
-        x / (x + y)
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use crate::distributions::Distribution;
-    use super::{Beta, ChiSquared, StudentT, FisherF};
-
-    const N: u32 = 100;
-
-    #[test]
-    fn test_chi_squared_one() {
-        let chi = ChiSquared::new(1.0);
-        let mut rng = crate::test::rng(201);
-        for _ in 0..N {
-            chi.sample(&mut rng);
-        }
-    }
-    #[test]
-    fn test_chi_squared_small() {
-        let chi = ChiSquared::new(0.5);
-        let mut rng = crate::test::rng(202);
-        for _ in 0..N {
-            chi.sample(&mut rng);
-        }
-    }
-    #[test]
-    fn test_chi_squared_large() {
-        let chi = ChiSquared::new(30.0);
-        let mut rng = crate::test::rng(203);
-        for _ in 0..N {
-            chi.sample(&mut rng);
-        }
-    }
-    #[test]
-    #[should_panic]
-    fn test_chi_squared_invalid_dof() {
-        ChiSquared::new(-1.0);
-    }
-
-    #[test]
-    fn test_f() {
-        let f = FisherF::new(2.0, 32.0);
-        let mut rng = crate::test::rng(204);
-        for _ in 0..N {
-            f.sample(&mut rng);
-        }
-    }
-
-    #[test]
-    fn test_t() {
-        let t = StudentT::new(11.0);
-        let mut rng = crate::test::rng(205);
-        for _ in 0..N {
-            t.sample(&mut rng);
-        }
-    }
-
-    #[test]
-    fn test_beta() {
-        let beta = Beta::new(1.0, 2.0);
-        let mut rng = crate::test::rng(201);
-        for _ in 0..N {
-            beta.sample(&mut rng);
-        }
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_beta_invalid_dof() {
-        Beta::new(0., 0.);
-    }
-}