Add nitrokey as a dependency to nitrocli

The nitrokey crate provides a simple interface to the Nitrokey Storage
and the Nitrokey Pro based on the libnitrokey library developed by
Nitrokey UG.  The low-level bindings to this library are available in
the nitrokey-sys crate.

This patch adds version v0.2.1 of the nitrokey crate as a dependency
for nitrocli.  It includes the indirect dependencies nitrokey-sys
(version 3.4.1) and rand (version 0.4.3).

Import subrepo nitrokey/:nitrokey at 2eccc96ceec2282b868891befe9cda7f941fbe7b
Import subrepo nitrokey-sys/:nitrokey-sys at f1a11ebf72610fb9cf80ac7f9f147b4ba1a5336f
Import subrepo rand/:rand at d7d5da49daf7ceb3e5940072940d495cced3a1b3

1 files changed, 409 insertions, 0 deletions

diff --git a/rand/src/distributions/mod.rs b/rand/src/distributions/mod.rs
new file mode 100644
index 0000000..5de8efb
--- /dev/null
+++ b/rand/src/distributions/mod.rs
@@ -0,0 +1,409 @@
+// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// http://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! Sampling from random distributions.
+//!
+//! This is a generalization of `Rand` to allow parameters to control the
+//! exact properties of the generated values, e.g. the mean and standard
+//! deviation of a normal distribution. The `Sample` trait is the most
+//! general, and allows for generating values that change some state
+//! internally. The `IndependentSample` trait is for generating values
+//! that do not need to record state.
+
+use core::marker;
+
+use {Rng, Rand};
+
+pub use self::range::Range;
+#[cfg(feature="std")]
+pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT};
+#[cfg(feature="std")]
+pub use self::normal::{Normal, LogNormal};
+#[cfg(feature="std")]
+pub use self::exponential::Exp;
+
+pub mod range;
+#[cfg(feature="std")]
+pub mod gamma;
+#[cfg(feature="std")]
+pub mod normal;
+#[cfg(feature="std")]
+pub mod exponential;
+
+#[cfg(feature="std")]
+mod ziggurat_tables;
+
+/// Types that can be used to create a random instance of `Support`.
+pub trait Sample<Support> {
+    /// Generate a random value of `Support`, using `rng` as the
+    /// source of randomness.
+    fn sample<R: Rng>(&mut self, rng: &mut R) -> Support;
+}
+
+/// `Sample`s that do not require keeping track of state.
+///
+/// Since no state is recorded, each sample is (statistically)
+/// independent of all others, assuming the `Rng` used has this
+/// property.
+// FIXME maybe having this separate is overkill (the only reason is to
+// take &self rather than &mut self)? or maybe this should be the
+// trait called `Sample` and the other should be `DependentSample`.
+pub trait IndependentSample<Support>: Sample<Support> {
+    /// Generate a random value.
+    fn ind_sample<R: Rng>(&self, &mut R) -> Support;
+}
+
+/// A wrapper for generating types that implement `Rand` via the
+/// `Sample` & `IndependentSample` traits.
+#[derive(Debug)]
+pub struct RandSample<Sup> {
+    _marker: marker::PhantomData<fn() -> Sup>,
+}
+
+impl<Sup> Copy for RandSample<Sup> {}
+impl<Sup> Clone for RandSample<Sup> {
+    fn clone(&self) -> Self { *self }
+}
+
+impl<Sup: Rand> Sample<Sup> for RandSample<Sup> {
+    fn sample<R: Rng>(&mut self, rng: &mut R) -> Sup { self.ind_sample(rng) }
+}
+
+impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> {
+    fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup {
+        rng.gen()
+    }
+}
+
+impl<Sup> RandSample<Sup> {
+    pub fn new() -> RandSample<Sup> {
+        RandSample { _marker: marker::PhantomData }
+    }
+}
+
+/// A value with a particular weight for use with `WeightedChoice`.
+#[derive(Copy, Clone, Debug)]
+pub struct Weighted<T> {
+    /// The numerical weight of this item
+    pub weight: u32,
+    /// The actual item which is being weighted
+    pub item: T,
+}
+
+/// A distribution that selects from a finite collection of weighted items.
+///
+/// Each item has an associated weight that influences how likely it
+/// is to be chosen: higher weight is more likely.
+///
+/// The `Clone` restriction is a limitation of the `Sample` and
+/// `IndependentSample` traits. Note that `&T` is (cheaply) `Clone` for
+/// all `T`, as is `u32`, so one can store references or indices into
+/// another vector.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Weighted, WeightedChoice, IndependentSample};
+///
+/// let mut items = vec!(Weighted { weight: 2, item: 'a' },
+///                      Weighted { weight: 4, item: 'b' },
+///                      Weighted { weight: 1, item: 'c' });
+/// let wc = WeightedChoice::new(&mut items);
+/// let mut rng = rand::thread_rng();
+/// for _ in 0..16 {
+///      // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
+///      println!("{}", wc.ind_sample(&mut rng));
+/// }
+/// ```
+#[derive(Debug)]
+pub struct WeightedChoice<'a, T:'a> {
+    items: &'a mut [Weighted<T>],
+    weight_range: Range<u32>
+}
+
+impl<'a, T: Clone> WeightedChoice<'a, T> {
+    /// Create a new `WeightedChoice`.
+    ///
+    /// Panics if:
+    ///
+    /// - `items` is empty
+    /// - the total weight is 0
+    /// - the total weight is larger than a `u32` can contain.
+    pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> {
+        // strictly speaking, this is subsumed by the total weight == 0 case
+        assert!(!items.is_empty(), "WeightedChoice::new called with no items");
+
+        let mut running_total: u32 = 0;
+
+        // we convert the list from individual weights to cumulative
+        // weights so we can binary search. This *could* drop elements
+        // with weight == 0 as an optimisation.
+        for item in items.iter_mut() {
+            running_total = match running_total.checked_add(item.weight) {
+                Some(n) => n,
+                None => panic!("WeightedChoice::new called with a total weight \
+                               larger than a u32 can contain")
+            };
+
+            item.weight = running_total;
+        }
+        assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0");
+
+        WeightedChoice {
+            items: items,
+            // we're likely to be generating numbers in this range
+            // relatively often, so might as well cache it
+            weight_range: Range::new(0, running_total)
+        }
+    }
+}
+
+impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> {
+    fn sample<R: Rng>(&mut self, rng: &mut R) -> T { self.ind_sample(rng) }
+}
+
+impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> {
+    fn ind_sample<R: Rng>(&self, rng: &mut R) -> T {
+        // we want to find the first element that has cumulative
+        // weight > sample_weight, which we do by binary since the
+        // cumulative weights of self.items are sorted.
+
+        // choose a weight in [0, total_weight)
+        let sample_weight = self.weight_range.ind_sample(rng);
+
+        // short circuit when it's the first item
+        if sample_weight < self.items[0].weight {
+            return self.items[0].item.clone();
+        }
+
+        let mut idx = 0;
+        let mut modifier = self.items.len();
+
+        // now we know that every possibility has an element to the
+        // left, so we can just search for the last element that has
+        // cumulative weight <= sample_weight, then the next one will
+        // be "it". (Note that this greatest element will never be the
+        // last element of the vector, since sample_weight is chosen
+        // in [0, total_weight) and the cumulative weight of the last
+        // one is exactly the total weight.)
+        while modifier > 1 {
+            let i = idx + modifier / 2;
+            if self.items[i].weight <= sample_weight {
+                // we're small, so look to the right, but allow this
+                // exact element still.
+                idx = i;
+                // we need the `/ 2` to round up otherwise we'll drop
+                // the trailing elements when `modifier` is odd.
+                modifier += 1;
+            } else {
+                // otherwise we're too big, so go left. (i.e. do
+                // nothing)
+            }
+            modifier /= 2;
+        }
+        return self.items[idx + 1].item.clone();
+    }
+}
+
+/// Sample a random number using the Ziggurat method (specifically the
+/// ZIGNOR variant from Doornik 2005). Most of the arguments are
+/// directly from the paper:
+///
+/// * `rng`: source of randomness
+/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0.
+/// * `X`: the $x_i$ abscissae.
+/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$)
+/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
+/// * `pdf`: the probability density function
+/// * `zero_case`: manual sampling from the tail when we chose the
+///    bottom box (i.e. i == 0)
+
+// the perf improvement (25-50%) is definitely worth the extra code
+// size from force-inlining.
+#[cfg(feature="std")]
+#[inline(always)]
+fn ziggurat<R: Rng, P, Z>(
+            rng: &mut R,
+            symmetric: bool,
+            x_tab: ziggurat_tables::ZigTable,
+            f_tab: ziggurat_tables::ZigTable,
+            mut pdf: P,
+            mut zero_case: Z)
+            -> f64 where P: FnMut(f64) -> f64, Z: FnMut(&mut R, f64) -> f64 {
+    const SCALE: f64 = (1u64 << 53) as f64;
+    loop {
+        // reimplement the f64 generation as an optimisation suggested
+        // by the Doornik paper: we have a lot of precision-space
+        // (i.e. there are 11 bits of the 64 of a u64 to use after
+        // creating a f64), so we might as well reuse some to save
+        // generating a whole extra random number. (Seems to be 15%
+        // faster.)
+        //
+        // This unfortunately misses out on the benefits of direct
+        // floating point generation if an RNG like dSMFT is
+        // used. (That is, such RNGs create floats directly, highly
+        // efficiently and overload next_f32/f64, so by not calling it
+        // this may be slower than it would be otherwise.)
+        // FIXME: investigate/optimise for the above.
+        let bits: u64 = rng.gen();
+        let i = (bits & 0xff) as usize;
+        let f = (bits >> 11) as f64 / SCALE;
+
+        // u is either U(-1, 1) or U(0, 1) depending on if this is a
+        // symmetric distribution or not.
+        let u = if symmetric {2.0 * f - 1.0} else {f};
+        let x = u * x_tab[i];
+
+        let test_x = if symmetric { x.abs() } else {x};
+
+        // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i])
+        if test_x < x_tab[i + 1] {
+            return x;
+        }
+        if i == 0 {
+            return zero_case(rng, u);
+        }
+        // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
+        if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) {
+            return x;
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+
+    use {Rng, Rand};
+    use super::{RandSample, WeightedChoice, Weighted, Sample, IndependentSample};
+
+    #[derive(PartialEq, Debug)]
+    struct ConstRand(usize);
+    impl Rand for ConstRand {
+        fn rand<R: Rng>(_: &mut R) -> ConstRand {
+            ConstRand(0)
+        }
+    }
+
+    // 0, 1, 2, 3, ...
+    struct CountingRng { i: u32 }
+    impl Rng for CountingRng {
+        fn next_u32(&mut self) -> u32 {
+            self.i += 1;
+            self.i - 1
+        }
+        fn next_u64(&mut self) -> u64 {
+            self.next_u32() as u64
+        }
+    }
+
+    #[test]
+    fn test_rand_sample() {
+        let mut rand_sample = RandSample::<ConstRand>::new();
+
+        assert_eq!(rand_sample.sample(&mut ::test::rng()), ConstRand(0));
+        assert_eq!(rand_sample.ind_sample(&mut ::test::rng()), ConstRand(0));
+    }
+    #[test]
+    fn test_weighted_choice() {
+        // this makes assumptions about the internal implementation of
+        // WeightedChoice, specifically: it doesn't reorder the items,
+        // it doesn't do weird things to the RNG (so 0 maps to 0, 1 to
+        // 1, internally; modulo a modulo operation).
+
+        macro_rules! t {
+            ($items:expr, $expected:expr) => {{
+                let mut items = $items;
+                let wc = WeightedChoice::new(&mut items);
+                let expected = $expected;
+
+                let mut rng = CountingRng { i: 0 };
+
+                for &val in expected.iter() {
+                    assert_eq!(wc.ind_sample(&mut rng), val)
+                }
+            }}
+        }
+
+        t!(vec!(Weighted { weight: 1, item: 10}), [10]);
+
+        // skip some
+        t!(vec!(Weighted { weight: 0, item: 20},
+                Weighted { weight: 2, item: 21},
+                Weighted { weight: 0, item: 22},
+                Weighted { weight: 1, item: 23}),
+           [21,21, 23]);
+
+        // different weights
+        t!(vec!(Weighted { weight: 4, item: 30},
+                Weighted { weight: 3, item: 31}),
+           [30,30,30,30, 31,31,31]);
+
+        // check that we're binary searching
+        // correctly with some vectors of odd
+        // length.
+        t!(vec!(Weighted { weight: 1, item: 40},
+                Weighted { weight: 1, item: 41},
+                Weighted { weight: 1, item: 42},
+                Weighted { weight: 1, item: 43},
+                Weighted { weight: 1, item: 44}),
+           [40, 41, 42, 43, 44]);
+        t!(vec!(Weighted { weight: 1, item: 50},
+                Weighted { weight: 1, item: 51},
+                Weighted { weight: 1, item: 52},
+                Weighted { weight: 1, item: 53},
+                Weighted { weight: 1, item: 54},
+                Weighted { weight: 1, item: 55},
+                Weighted { weight: 1, item: 56}),
+           [50, 51, 52, 53, 54, 55, 56]);
+    }
+
+    #[test]
+    fn test_weighted_clone_initialization() {
+        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+        let clone = initial.clone();
+        assert_eq!(initial.weight, clone.weight);
+        assert_eq!(initial.item, clone.item);
+    }
+
+    #[test] #[should_panic]
+    fn test_weighted_clone_change_weight() {
+        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+        let mut clone = initial.clone();
+        clone.weight = 5;
+        assert_eq!(initial.weight, clone.weight);
+    }
+
+    #[test] #[should_panic]
+    fn test_weighted_clone_change_item() {
+        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
+        let mut clone = initial.clone();
+        clone.item = 5;
+        assert_eq!(initial.item, clone.item);
+
+    }
+
+    #[test] #[should_panic]
+    fn test_weighted_choice_no_items() {
+        WeightedChoice::<isize>::new(&mut []);
+    }
+    #[test] #[should_panic]
+    fn test_weighted_choice_zero_weight() {
+        WeightedChoice::new(&mut [Weighted { weight: 0, item: 0},
+                                  Weighted { weight: 0, item: 1}]);
+    }
+    #[test] #[should_panic]
+    fn test_weighted_choice_weight_overflows() {
+        let x = ::std::u32::MAX / 2; // x + x + 2 is the overflow
+        WeightedChoice::new(&mut [Weighted { weight: x, item: 0 },
+                                  Weighted { weight: 1, item: 1 },
+                                  Weighted { weight: x, item: 2 },
+                                  Weighted { weight: 1, item: 3 }]);
+    }
+}