Auto merge of rust-lang#39156 - GuillaumeGomez:debug_librand, r=alexcrichton

Add missing Debug implementation for librand structs

Part of rust-lang#31869.

Author: bors

src/librand/chacha.rs

@@ -10,6 +10,7 @@
 
 //! The ChaCha random number generator.
 
+use core::fmt;
 use {Rand, Rng, SeedableRng};
 
 const KEY_WORDS: usize = 8; // 8 words for the 256-bit key
@@ -32,6 +33,16 @@ pub struct ChaChaRng {
     index: usize, // Index into state
 }
 
+impl fmt::Debug for ChaChaRng {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("ChaChaRng")
+         .field("buffer", &self.buffer.iter())
+         .field("state", &self.state.iter())
+         .field("index", &self.index)
+         .finish()
+    }
+}
+
 static EMPTY: ChaChaRng = ChaChaRng {
     buffer: [0; STATE_WORDS],
     state: [0; STATE_WORDS],

src/librand/distributions/exponential.rs

@@ -10,6 +10,8 @@
 
 //! The exponential distribution.
 
+use core::fmt;
+
 #[cfg(not(test))] // only necessary for no_std
 use FloatMath;
 
@@ -55,6 +57,14 @@ impl Rand for Exp1 {
     }
 }
 
+impl fmt::Debug for Exp1 {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_tuple("Exp1")
+         .field(&self.0)
+         .finish()
+    }
+}
+
 /// The exponential distribution `Exp(lambda)`.
 ///
 /// This distribution has density function: `f(x) = lambda *
@@ -79,13 +89,22 @@ impl Sample<f64> for Exp {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for Exp {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         let Exp1(n) = rng.gen::<Exp1>();
         n * self.lambda_inverse
     }
 }
 
+impl fmt::Debug for Exp {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("Exp")
+         .field("lambda_inverse", &self.lambda_inverse)
+         .finish()
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use distributions::{IndependentSample, Sample};

src/librand/distributions/gamma.rs

@@ -10,6 +10,8 @@
 
 //! The Gamma and derived distributions.
 
+use core::fmt;
+
 use self::GammaRepr::*;
 use self::ChiSquaredRepr::*;
 
@@ -44,6 +46,19 @@ pub struct Gamma {
     repr: GammaRepr,
 }
 
+impl fmt::Debug for Gamma {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("Gamma")
+         .field("repr",
+                &match self.repr {
+                    GammaRepr::Large(_) => "Large",
+                    GammaRepr::One(_) => "Exp",
+                    GammaRepr::Small(_) => "Small"
+                })
+          .finish()
+    }
+}
+
 enum GammaRepr {
     Large(GammaLargeShape),
     One(Exp),
@@ -182,6 +197,18 @@ pub struct ChiSquared {
     repr: ChiSquaredRepr,
 }
 
+impl fmt::Debug for ChiSquared {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("ChiSquared")
+         .field("repr",
+                &match self.repr {
+                    ChiSquaredRepr::DoFExactlyOne => "DoFExactlyOne",
+                    ChiSquaredRepr::DoFAnythingElse(_) => "DoFAnythingElse",
+                })
+         .finish()
+    }
+}
+
 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-
@@ -203,11 +230,13 @@ impl ChiSquared {
         ChiSquared { repr: repr }
     }
 }
+
 impl Sample<f64> for ChiSquared {
     fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for ChiSquared {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         match self.repr {
@@ -248,17 +277,29 @@ impl FisherF {
         }
     }
 }
+
 impl Sample<f64> for FisherF {
     fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for FisherF {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         self.numer.ind_sample(rng) / self.denom.ind_sample(rng) * self.dof_ratio
     }
 }
 
+impl fmt::Debug for FisherF {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("FisherF")
+         .field("numer", &self.numer)
+         .field("denom", &self.denom)
+         .field("dof_ratio", &self.dof_ratio)
+         .finish()
+    }
+}
+
 /// The Student t distribution, `t(nu)`, where `nu` is the degrees of
 /// freedom.
 pub struct StudentT {
@@ -277,18 +318,29 @@ impl StudentT {
         }
     }
 }
+
 impl Sample<f64> for StudentT {
     fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for StudentT {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         let StandardNormal(norm) = rng.gen::<StandardNormal>();
         norm * (self.dof / self.chi.ind_sample(rng)).sqrt()
     }
 }
 
+impl fmt::Debug for StudentT {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("StudentT")
+         .field("chi", &self.chi)
+         .field("dof", &self.dof)
+         .finish()
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use distributions::{IndependentSample, Sample};

src/librand/distributions/mod.rs

@@ -17,6 +17,8 @@
 //! internally. The `IndependentSample` trait is for generating values
 //! that do not need to record state.
 
+use core::fmt;
+
 #[cfg(not(test))] // only necessary for no_std
 use core::num::Float;
 
@@ -78,6 +80,12 @@ impl<Sup: Rand> IndependentSample<Sup> for RandSample<Sup> {
     }
 }
 
+impl<Sup> fmt::Debug for RandSample<Sup> {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.pad("RandSample { .. }")
+    }
+}
+
 /// A value with a particular weight for use with `WeightedChoice`.
 pub struct Weighted<T> {
     /// The numerical weight of this item
@@ -86,6 +94,15 @@ pub struct Weighted<T> {
     pub item: T,
 }
 
+impl<T: fmt::Debug> fmt::Debug for Weighted<T> {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("Weighted")
+         .field("weight", &self.weight)
+         .field("item", &self.item)
+         .finish()
+    }
+}
+
 /// A distribution that selects from a finite collection of weighted items.
 ///
 /// Each item has an associated weight that influences how likely it
@@ -189,6 +206,15 @@ impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> {
     }
 }
 
+impl<'a, T: fmt::Debug> fmt::Debug for WeightedChoice<'a, T> {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("WeightedChoice")
+         .field("items", &self.items)
+         .field("weight_range", &self.weight_range)
+         .finish()
+    }
+}
+
 mod ziggurat_tables;
 
 /// Sample a random number using the Ziggurat method (specifically the

src/librand/distributions/normal.rs

@@ -10,6 +10,8 @@
 
 //! The normal and derived distributions.
 
+use core::fmt;
+
 #[cfg(not(test))] // only necessary for no_std
 use FloatMath;
 
@@ -73,6 +75,14 @@ impl Rand for StandardNormal {
     }
 }
 
+impl fmt::Debug for StandardNormal {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_tuple("StandardNormal")
+         .field(&self.0)
+         .finish()
+    }
+}
+
 /// The normal distribution `N(mean, std_dev**2)`.
 ///
 /// This uses the ZIGNOR variant of the Ziggurat method, see
@@ -98,18 +108,29 @@ impl Normal {
         }
     }
 }
+
 impl Sample<f64> for Normal {
     fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for Normal {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         let StandardNormal(n) = rng.gen::<StandardNormal>();
         self.mean + self.std_dev * n
     }
 }
 
+impl fmt::Debug for Normal {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("Normal")
+         .field("mean", &self.mean)
+         .field("std_dev", &self.std_dev)
+         .finish()
+    }
+}
+
 
 /// The log-normal distribution `ln N(mean, std_dev**2)`.
 ///
@@ -132,17 +153,27 @@ impl LogNormal {
         LogNormal { norm: Normal::new(mean, std_dev) }
     }
 }
+
 impl Sample<f64> for LogNormal {
     fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
         self.ind_sample(rng)
     }
 }
+
 impl IndependentSample<f64> for LogNormal {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
         self.norm.ind_sample(rng).exp()
     }
 }
 
+impl fmt::Debug for LogNormal {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("LogNormal")
+         .field("norm", &self.norm)
+         .finish()
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use distributions::{IndependentSample, Sample};

src/librand/distributions/range.rs

@@ -12,6 +12,7 @@
 
 // this is surprisingly complicated to be both generic & correct
 
+use core::fmt;
 use core::marker::Sized;
 use Rng;
 use distributions::{IndependentSample, Sample};
@@ -50,12 +51,23 @@ impl<Sup: SampleRange> Sample<Sup> for Range<Sup> {
         self.ind_sample(rng)
     }
 }
+
 impl<Sup: SampleRange> IndependentSample<Sup> for Range<Sup> {
     fn ind_sample<R: Rng>(&self, rng: &mut R) -> Sup {
         SampleRange::sample_range(self, rng)
     }
 }
 
+impl<X: fmt::Debug> fmt::Debug for Range<X> {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("Range")
+         .field("low", &self.low)
+         .field("range", &self.range)
+         .field("accept_zone", &self.accept_zone)
+         .finish()
+    }
+}
+
 /// The helper trait for types that have a sensible way to sample
 /// uniformly between two values. This should not be used directly,
 /// and is only to facilitate `Range`.