extra: Put the nail in the coffin, delete libextra

This commit shreds all remnants of libextra from the compiler and standard
distribution. Two modules, c_vec/tempfile, were moved into libstd after some
cleanup, and the other modules were moved to separate crates as seen fit.

Closes #8784
Closes #12413
Closes #12576

1 files changed, 1056 insertions, 0 deletions

diff --git a/src/libtest/stats.rs b/src/libtest/stats.rs
new file mode 100644
index 00000000000..b3fd06bd6ad
--- /dev/null
+++ b/src/libtest/stats.rs
@@ -0,0 +1,1056 @@
+// Copyright 2012 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.
+
+#[allow(missing_doc)];
+
+use std::hash::Hash;
+use std::io;
+use std::mem;
+use std::num;
+use collections::hashmap;
+
+// NB: this can probably be rewritten in terms of num::Num
+// to be less f64-specific.
+
+fn f64_cmp(x: f64, y: f64) -> Ordering {
+    // arbitrarily decide that NaNs are larger than everything.
+    if y.is_nan() {
+        Less
+    } else if x.is_nan() {
+        Greater
+    } else if x < y {
+        Less
+    } else if x == y {
+        Equal
+    } else {
+        Greater
+    }
+}
+
+fn f64_sort(v: &mut [f64]) {
+    v.sort_by(|x: &f64, y: &f64| f64_cmp(*x, *y));
+}
+
+/// Trait that provides simple descriptive statistics on a univariate set of numeric samples.
+pub trait Stats {
+
+    /// Sum of the samples.
+    ///
+    /// Note: this method sacrifices performance at the altar of accuracy
+    /// Depends on IEEE-754 arithmetic guarantees. See proof of correctness at:
+    /// ["Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates"]
+    /// (http://www.cs.cmu.edu/~quake-papers/robust-arithmetic.ps)
+    /// *Discrete & Computational Geometry 18*, 3 (Oct 1997), 305-363, Shewchuk J.R.
+    fn sum(self) -> f64;
+
+    /// Minimum value of the samples.
+    fn min(self) -> f64;
+
+    /// Maximum value of the samples.
+    fn max(self) -> f64;
+
+    /// Arithmetic mean (average) of the samples: sum divided by sample-count.
+    ///
+    /// See: https://en.wikipedia.org/wiki/Arithmetic_mean
+    fn mean(self) -> f64;
+
+    /// Median of the samples: value separating the lower half of the samples from the higher half.
+    /// Equal to `self.percentile(50.0)`.
+    ///
+    /// See: https://en.wikipedia.org/wiki/Median
+    fn median(self) -> f64;
+
+    /// Variance of the samples: bias-corrected mean of the squares of the differences of each
+    /// sample from the sample mean. Note that this calculates the _sample variance_ rather than the
+    /// population variance, which is assumed to be unknown. It therefore corrects the `(n-1)/n`
+    /// bias that would appear if we calculated a population variance, by dividing by `(n-1)` rather
+    /// than `n`.
+    ///
+    /// See: https://en.wikipedia.org/wiki/Variance
+    fn var(self) -> f64;
+
+    /// Standard deviation: the square root of the sample variance.
+    ///
+    /// Note: this is not a robust statistic for non-normal distributions. Prefer the
+    /// `median_abs_dev` for unknown distributions.
+    ///
+    /// See: https://en.wikipedia.org/wiki/Standard_deviation
+    fn std_dev(self) -> f64;
+
+    /// Standard deviation as a percent of the mean value. See `std_dev` and `mean`.
+    ///
+    /// Note: this is not a robust statistic for non-normal distributions. Prefer the
+    /// `median_abs_dev_pct` for unknown distributions.
+    fn std_dev_pct(self) -> f64;
+
+    /// Scaled median of the absolute deviations of each sample from the sample median. This is a
+    /// robust (distribution-agnostic) estimator of sample variability. Use this in preference to
+    /// `std_dev` if you cannot assume your sample is normally distributed. Note that this is scaled
+    /// by the constant `1.4826` to allow its use as a consistent estimator for the standard
+    /// deviation.
+    ///
+    /// See: http://en.wikipedia.org/wiki/Median_absolute_deviation
+    fn median_abs_dev(self) -> f64;
+
+    /// Median absolute deviation as a percent of the median. See `median_abs_dev` and `median`.
+    fn median_abs_dev_pct(self) -> f64;
+
+    /// Percentile: the value below which `pct` percent of the values in `self` fall. For example,
+    /// percentile(95.0) will return the value `v` such that 95% of the samples `s` in `self`
+    /// satisfy `s <= v`.
+    ///
+    /// Calculated by linear interpolation between closest ranks.
+    ///
+    /// See: http://en.wikipedia.org/wiki/Percentile
+    fn percentile(self, pct: f64) -> f64;
+
+    /// Quartiles of the sample: three values that divide the sample into four equal groups, each
+    /// with 1/4 of the data. The middle value is the median. See `median` and `percentile`. This
+    /// function may calculate the 3 quartiles more efficiently than 3 calls to `percentile`, but
+    /// is otherwise equivalent.
+    ///
+    /// See also: https://en.wikipedia.org/wiki/Quartile
+    fn quartiles(self) -> (f64,f64,f64);
+
+    /// Inter-quartile range: the difference between the 25th percentile (1st quartile) and the 75th
+    /// percentile (3rd quartile). See `quartiles`.
+    ///
+    /// See also: https://en.wikipedia.org/wiki/Interquartile_range
+    fn iqr(self) -> f64;
+}
+
+/// Extracted collection of all the summary statistics of a sample set.
+#[deriving(Clone, Eq)]
+#[allow(missing_doc)]
+pub struct Summary {
+    sum: f64,
+    min: f64,
+    max: f64,
+    mean: f64,
+    median: f64,
+    var: f64,
+    std_dev: f64,
+    std_dev_pct: f64,
+    median_abs_dev: f64,
+    median_abs_dev_pct: f64,
+    quartiles: (f64,f64,f64),
+    iqr: f64,
+}
+
+impl Summary {
+
+    /// Construct a new summary of a sample set.
+    pub fn new(samples: &[f64]) -> Summary {
+        Summary {
+            sum: samples.sum(),
+            min: samples.min(),
+            max: samples.max(),
+            mean: samples.mean(),
+            median: samples.median(),
+            var: samples.var(),
+            std_dev: samples.std_dev(),
+            std_dev_pct: samples.std_dev_pct(),
+            median_abs_dev: samples.median_abs_dev(),
+            median_abs_dev_pct: samples.median_abs_dev_pct(),
+            quartiles: samples.quartiles(),
+            iqr: samples.iqr()
+        }
+    }
+}
+
+impl<'a> Stats for &'a [f64] {
+
+    // FIXME #11059 handle NaN, inf and overflow
+    fn sum(self) -> f64 {
+        let mut partials : ~[f64] = ~[];
+
+        for &mut x in self.iter() {
+            let mut j = 0;
+            // This inner loop applies `hi`/`lo` summation to each
+            // partial so that the list of partial sums remains exact.
+            for i in range(0, partials.len()) {
+                let mut y = partials[i];
+                if num::abs(x) < num::abs(y) {
+                    mem::swap(&mut x, &mut y);
+                }
+                // Rounded `x+y` is stored in `hi` with round-off stored in
+                // `lo`. Together `hi+lo` are exactly equal to `x+y`.
+                let hi = x + y;
+                let lo = y - (hi - x);
+                if lo != 0f64 {
+                    partials[j] = lo;
+                    j += 1;
+                }
+                x = hi;
+            }
+            if j >= partials.len() {
+                partials.push(x);
+            } else {
+                partials[j] = x;
+                partials.truncate(j+1);
+            }
+        }
+        partials.iter().fold(0.0, |p, q| p + *q)
+    }
+
+    fn min(self) -> f64 {
+        assert!(self.len() != 0);
+        self.iter().fold(self[0], |p, q| p.min(*q))
+    }
+
+    fn max(self) -> f64 {
+        assert!(self.len() != 0);
+        self.iter().fold(self[0], |p, q| p.max(*q))
+    }
+
+    fn mean(self) -> f64 {
+        assert!(self.len() != 0);
+        self.sum() / (self.len() as f64)
+    }
+
+    fn median(self) -> f64 {
+        self.percentile(50.0)
+    }
+
+    fn var(self) -> f64 {
+        if self.len() < 2 {
+            0.0
+        } else {
+            let mean = self.mean();
+            let mut v = 0.0;
+            for s in self.iter() {
+                let x = *s - mean;
+                v += x*x;
+            }
+            // NB: this is _supposed to be_ len-1, not len. If you
+            // change it back to len, you will be calculating a
+            // population variance, not a sample variance.
+            v/((self.len()-1) as f64)
+        }
+    }
+
+    fn std_dev(self) -> f64 {
+        self.var().sqrt()
+    }
+
+    fn std_dev_pct(self) -> f64 {
+        (self.std_dev() / self.mean()) * 100.0
+    }
+
+    fn median_abs_dev(self) -> f64 {
+        let med = self.median();
+        let abs_devs = self.map(|&v| num::abs(med - v));
+        // This constant is derived by smarter statistics brains than me, but it is
+        // consistent with how R and other packages treat the MAD.
+        abs_devs.median() * 1.4826
+    }
+
+    fn median_abs_dev_pct(self) -> f64 {
+        (self.median_abs_dev() / self.median()) * 100.0
+    }
+
+    fn percentile(self, pct: f64) -> f64 {
+        let mut tmp = self.to_owned();
+        f64_sort(tmp);
+        percentile_of_sorted(tmp, pct)
+    }
+
+    fn quartiles(self) -> (f64,f64,f64) {
+        let mut tmp = self.to_owned();
+        f64_sort(tmp);
+        let a = percentile_of_sorted(tmp, 25.0);
+        let b = percentile_of_sorted(tmp, 50.0);
+        let c = percentile_of_sorted(tmp, 75.0);
+        (a,b,c)
+    }
+
+    fn iqr(self) -> f64 {
+        let (a,_,c) = self.quartiles();
+        c - a
+    }
+}
+
+
+// Helper function: extract a value representing the `pct` percentile of a sorted sample-set, using
+// linear interpolation. If samples are not sorted, return nonsensical value.
+fn percentile_of_sorted(sorted_samples: &[f64],
+                             pct: f64) -> f64 {
+    assert!(sorted_samples.len() != 0);
+    if sorted_samples.len() == 1 {
+        return sorted_samples[0];
+    }
+    assert!(0.0 <= pct);
+    assert!(pct <= 100.0);
+    if pct == 100.0 {
+        return sorted_samples[sorted_samples.len() - 1];
+    }
+    let rank = (pct / 100.0) * ((sorted_samples.len() - 1) as f64);
+    let lrank = rank.floor();
+    let d = rank - lrank;
+    let n = lrank as uint;
+    let lo = sorted_samples[n];
+    let hi = sorted_samples[n+1];
+    lo + (hi - lo) * d
+}
+
+
+/// Winsorize a set of samples, replacing values above the `100-pct` percentile and below the `pct`
+/// percentile with those percentiles themselves. This is a way of minimizing the effect of
+/// outliers, at the cost of biasing the sample. It differs from trimming in that it does not
+/// change the number of samples, just changes the values of those that are outliers.
+///
+/// See: http://en.wikipedia.org/wiki/Winsorising
+pub fn winsorize(samples: &mut [f64], pct: f64) {
+    let mut tmp = samples.to_owned();
+    f64_sort(tmp);
+    let lo = percentile_of_sorted(tmp, pct);
+    let hi = percentile_of_sorted(tmp, 100.0-pct);
+    for samp in samples.mut_iter() {
+        if *samp > hi {
+            *samp = hi
+        } else if *samp < lo {
+            *samp = lo
+        }
+    }
+}
+
+/// Render writes the min, max and quartiles of the provided `Summary` to the provided `Writer`.
+pub fn write_5_number_summary(w: &mut io::Writer,
+                              s: &Summary) -> io::IoResult<()> {
+    let (q1,q2,q3) = s.quartiles;
+    write!(w, "(min={}, q1={}, med={}, q3={}, max={})",
+                     s.min,
+                     q1,
+                     q2,
+                     q3,
+                     s.max)
+}
+
+/// Render a boxplot to the provided writer. The boxplot shows the min, max and quartiles of the
+/// provided `Summary` (thus includes the mean) and is scaled to display within the range of the
+/// nearest multiple-of-a-power-of-ten above and below the min and max of possible values, and
+/// target `width_hint` characters of display (though it will be wider if necessary).
+///
+/// As an example, the summary with 5-number-summary `(min=15, q1=17, med=20, q3=24, max=31)` might
+/// display as:
+///
+/// ~~~~ignore
+///   10 |        [--****#******----------]          | 40
+/// ~~~~
+
+pub fn write_boxplot(w: &mut io::Writer, s: &Summary,
+                     width_hint: uint) -> io::IoResult<()> {
+
+    let (q1,q2,q3) = s.quartiles;
+
+    // the .abs() handles the case where numbers are negative
+    let lomag = (10.0_f64).powf(&(s.min.abs().log10().floor()));
+    let himag = (10.0_f64).powf(&(s.max.abs().log10().floor()));
+
+    // need to consider when the limit is zero
+    let lo = if lomag == 0.0 {
+        0.0
+    } else {
+        (s.min / lomag).floor() * lomag
+    };
+
+    let hi = if himag == 0.0 {
+        0.0
+    } else {
+        (s.max / himag).ceil() * himag
+    };
+
+    let range = hi - lo;
+
+    let lostr = lo.to_str();
+    let histr = hi.to_str();
+
+    let overhead_width = lostr.len() + histr.len() + 4;
+    let range_width = width_hint - overhead_width;;
+    let char_step = range / (range_width as f64);
+
+    try!(write!(w, "{} |", lostr));
+
+    let mut c = 0;
+    let mut v = lo;
+
+    while c < range_width && v < s.min {
+        try!(write!(w, " "));
+        v += char_step;
+        c += 1;
+    }
+    try!(write!(w, "["));
+    c += 1;
+    while c < range_width && v < q1 {
+        try!(write!(w, "-"));
+        v += char_step;
+        c += 1;
+    }
+    while c < range_width && v < q2 {
+        try!(write!(w, "*"));
+        v += char_step;
+        c += 1;
+    }
+    try!(write!(w, r"\#"));
+    c += 1;
+    while c < range_width && v < q3 {
+        try!(write!(w, "*"));
+        v += char_step;
+        c += 1;
+    }
+    while c < range_width && v < s.max {
+        try!(write!(w, "-"));
+        v += char_step;
+        c += 1;
+    }
+    try!(write!(w, "]"));
+    while c < range_width {
+        try!(write!(w, " "));
+        v += char_step;
+        c += 1;
+    }
+
+    try!(write!(w, "| {}", histr));
+    Ok(())
+}
+
+/// Returns a HashMap with the number of occurrences of every element in the
+/// sequence that the iterator exposes.
+pub fn freq_count<T: Iterator<U>, U: Eq+Hash>(mut iter: T) -> hashmap::HashMap<U, uint> {
+    let mut map: hashmap::HashMap<U,uint> = hashmap::HashMap::new();
+    for elem in iter {
+        map.insert_or_update_with(elem, 1, |_, count| *count += 1);
+    }
+    map
+}
+
+// Test vectors generated from R, using the script src/etc/stat-test-vectors.r.
+
+#[cfg(test)]
+mod tests {
+    use stats::Stats;
+    use stats::Summary;
+    use stats::write_5_number_summary;
+    use stats::write_boxplot;
+    use std::io;
+    use std::str;
+    use std::f64;
+
+    macro_rules! assert_approx_eq(
+        ($a:expr, $b:expr) => ({
+            let (a, b) = (&$a, &$b);
+            assert!((*a - *b).abs() < 1.0e-6,
+                    "{} is not approximately equal to {}", *a, *b);
+        })
+    )
+
+    fn check(samples: &[f64], summ: &Summary) {
+
+        let summ2 = Summary::new(samples);
+
+        let mut w = io::stdout();
+        let w = &mut w as &mut io::Writer;
+        (write!(w, "\n")).unwrap();
+        write_5_number_summary(w, &summ2).unwrap();
+        (write!(w, "\n")).unwrap();
+        write_boxplot(w, &summ2, 50).unwrap();
+        (write!(w, "\n")).unwrap();
+
+        assert_eq!(summ.sum, summ2.sum);
+        assert_eq!(summ.min, summ2.min);
+        assert_eq!(summ.max, summ2.max);
+        assert_eq!(summ.mean, summ2.mean);
+        assert_eq!(summ.median, summ2.median);
+
+        // We needed a few more digits to get exact equality on these
+        // but they're within float epsilon, which is 1.0e-6.
+        assert_approx_eq!(summ.var, summ2.var);
+        assert_approx_eq!(summ.std_dev, summ2.std_dev);
+        assert_approx_eq!(summ.std_dev_pct, summ2.std_dev_pct);
+        assert_approx_eq!(summ.median_abs_dev, summ2.median_abs_dev);
+        assert_approx_eq!(summ.median_abs_dev_pct, summ2.median_abs_dev_pct);
+
+        assert_eq!(summ.quartiles, summ2.quartiles);
+        assert_eq!(summ.iqr, summ2.iqr);
+    }
+
+    #[test]
+    fn test_min_max_nan() {
+        let xs = &[1.0, 2.0, f64::NAN, 3.0, 4.0];
+        let summary = Summary::new(xs);
+        assert_eq!(summary.min, 1.0);
+        assert_eq!(summary.max, 4.0);
+    }
+
+    #[test]
+    fn test_norm2() {
+        let val = &[
+            958.0000000000,
+            924.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 1882.0000000000,
+            min: 924.0000000000,
+            max: 958.0000000000,
+            mean: 941.0000000000,
+            median: 941.0000000000,
+            var: 578.0000000000,
+            std_dev: 24.0416305603,
+            std_dev_pct: 2.5549022912,
+            median_abs_dev: 25.2042000000,
+            median_abs_dev_pct: 2.6784484591,
+            quartiles: (932.5000000000,941.0000000000,949.5000000000),
+            iqr: 17.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_norm10narrow() {
+        let val = &[
+            966.0000000000,
+            985.0000000000,
+            1110.0000000000,
+            848.0000000000,
+            821.0000000000,
+            975.0000000000,
+            962.0000000000,
+            1157.0000000000,
+            1217.0000000000,
+            955.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 9996.0000000000,
+            min: 821.0000000000,
+            max: 1217.0000000000,
+            mean: 999.6000000000,
+            median: 970.5000000000,
+            var: 16050.7111111111,
+            std_dev: 126.6914010938,
+            std_dev_pct: 12.6742097933,
+            median_abs_dev: 102.2994000000,
+            median_abs_dev_pct: 10.5408964451,
+            quartiles: (956.7500000000,970.5000000000,1078.7500000000),
+            iqr: 122.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_norm10medium() {
+        let val = &[
+            954.0000000000,
+            1064.0000000000,
+            855.0000000000,
+            1000.0000000000,
+            743.0000000000,
+            1084.0000000000,
+            704.0000000000,
+            1023.0000000000,
+            357.0000000000,
+            869.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 8653.0000000000,
+            min: 357.0000000000,
+            max: 1084.0000000000,
+            mean: 865.3000000000,
+            median: 911.5000000000,
+            var: 48628.4555555556,
+            std_dev: 220.5186059170,
+            std_dev_pct: 25.4846418487,
+            median_abs_dev: 195.7032000000,
+            median_abs_dev_pct: 21.4704552935,
+            quartiles: (771.0000000000,911.5000000000,1017.2500000000),
+            iqr: 246.2500000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_norm10wide() {
+        let val = &[
+            505.0000000000,
+            497.0000000000,
+            1591.0000000000,
+            887.0000000000,
+            1026.0000000000,
+            136.0000000000,
+            1580.0000000000,
+            940.0000000000,
+            754.0000000000,
+            1433.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 9349.0000000000,
+            min: 136.0000000000,
+            max: 1591.0000000000,
+            mean: 934.9000000000,
+            median: 913.5000000000,
+            var: 239208.9888888889,
+            std_dev: 489.0899599142,
+            std_dev_pct: 52.3146817750,
+            median_abs_dev: 611.5725000000,
+            median_abs_dev_pct: 66.9482758621,
+            quartiles: (567.2500000000,913.5000000000,1331.2500000000),
+            iqr: 764.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_norm25verynarrow() {
+        let val = &[
+            991.0000000000,
+            1018.0000000000,
+            998.0000000000,
+            1013.0000000000,
+            974.0000000000,
+            1007.0000000000,
+            1014.0000000000,
+            999.0000000000,
+            1011.0000000000,
+            978.0000000000,
+            985.0000000000,
+            999.0000000000,
+            983.0000000000,
+            982.0000000000,
+            1015.0000000000,
+            1002.0000000000,
+            977.0000000000,
+            948.0000000000,
+            1040.0000000000,
+            974.0000000000,
+            996.0000000000,
+            989.0000000000,
+            1015.0000000000,
+            994.0000000000,
+            1024.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 24926.0000000000,
+            min: 948.0000000000,
+            max: 1040.0000000000,
+            mean: 997.0400000000,
+            median: 998.0000000000,
+            var: 393.2066666667,
+            std_dev: 19.8294393937,
+            std_dev_pct: 1.9888308788,
+            median_abs_dev: 22.2390000000,
+            median_abs_dev_pct: 2.2283567134,
+            quartiles: (983.0000000000,998.0000000000,1013.0000000000),
+            iqr: 30.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_exp10a() {
+        let val = &[
+            23.0000000000,
+            11.0000000000,
+            2.0000000000,
+            57.0000000000,
+            4.0000000000,
+            12.0000000000,
+            5.0000000000,
+            29.0000000000,
+            3.0000000000,
+            21.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 167.0000000000,
+            min: 2.0000000000,
+            max: 57.0000000000,
+            mean: 16.7000000000,
+            median: 11.5000000000,
+            var: 287.7888888889,
+            std_dev: 16.9643416875,
+            std_dev_pct: 101.5828843560,
+            median_abs_dev: 13.3434000000,
+            median_abs_dev_pct: 116.0295652174,
+            quartiles: (4.2500000000,11.5000000000,22.5000000000),
+            iqr: 18.2500000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_exp10b() {
+        let val = &[
+            24.0000000000,
+            17.0000000000,
+            6.0000000000,
+            38.0000000000,
+            25.0000000000,
+            7.0000000000,
+            51.0000000000,
+            2.0000000000,
+            61.0000000000,
+            32.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 263.0000000000,
+            min: 2.0000000000,
+            max: 61.0000000000,
+            mean: 26.3000000000,
+            median: 24.5000000000,
+            var: 383.5666666667,
+            std_dev: 19.5848580967,
+            std_dev_pct: 74.4671410520,
+            median_abs_dev: 22.9803000000,
+            median_abs_dev_pct: 93.7971428571,
+            quartiles: (9.5000000000,24.5000000000,36.5000000000),
+            iqr: 27.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_exp10c() {
+        let val = &[
+            71.0000000000,
+            2.0000000000,
+            32.0000000000,
+            1.0000000000,
+            6.0000000000,
+            28.0000000000,
+            13.0000000000,
+            37.0000000000,
+            16.0000000000,
+            36.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 242.0000000000,
+            min: 1.0000000000,
+            max: 71.0000000000,
+            mean: 24.2000000000,
+            median: 22.0000000000,
+            var: 458.1777777778,
+            std_dev: 21.4050876611,
+            std_dev_pct: 88.4507754589,
+            median_abs_dev: 21.4977000000,
+            median_abs_dev_pct: 97.7168181818,
+            quartiles: (7.7500000000,22.0000000000,35.0000000000),
+            iqr: 27.2500000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_exp25() {
+        let val = &[
+            3.0000000000,
+            24.0000000000,
+            1.0000000000,
+            19.0000000000,
+            7.0000000000,
+            5.0000000000,
+            30.0000000000,
+            39.0000000000,
+            31.0000000000,
+            13.0000000000,
+            25.0000000000,
+            48.0000000000,
+            1.0000000000,
+            6.0000000000,
+            42.0000000000,
+            63.0000000000,
+            2.0000000000,
+            12.0000000000,
+            108.0000000000,
+            26.0000000000,
+            1.0000000000,
+            7.0000000000,
+            44.0000000000,
+            25.0000000000,
+            11.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 593.0000000000,
+            min: 1.0000000000,
+            max: 108.0000000000,
+            mean: 23.7200000000,
+            median: 19.0000000000,
+            var: 601.0433333333,
+            std_dev: 24.5161851301,
+            std_dev_pct: 103.3565983562,
+            median_abs_dev: 19.2738000000,
+            median_abs_dev_pct: 101.4410526316,
+            quartiles: (6.0000000000,19.0000000000,31.0000000000),
+            iqr: 25.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_binom25() {
+        let val = &[
+            18.0000000000,
+            17.0000000000,
+            27.0000000000,
+            15.0000000000,
+            21.0000000000,
+            25.0000000000,
+            17.0000000000,
+            24.0000000000,
+            25.0000000000,
+            24.0000000000,
+            26.0000000000,
+            26.0000000000,
+            23.0000000000,
+            15.0000000000,
+            23.0000000000,
+            17.0000000000,
+            18.0000000000,
+            18.0000000000,
+            21.0000000000,
+            16.0000000000,
+            15.0000000000,
+            31.0000000000,
+            20.0000000000,
+            17.0000000000,
+            15.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 514.0000000000,
+            min: 15.0000000000,
+            max: 31.0000000000,
+            mean: 20.5600000000,
+            median: 20.0000000000,
+            var: 20.8400000000,
+            std_dev: 4.5650848842,
+            std_dev_pct: 22.2037202539,
+            median_abs_dev: 5.9304000000,
+            median_abs_dev_pct: 29.6520000000,
+            quartiles: (17.0000000000,20.0000000000,24.0000000000),
+            iqr: 7.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_pois25lambda30() {
+        let val = &[
+            27.0000000000,
+            33.0000000000,
+            34.0000000000,
+            34.0000000000,
+            24.0000000000,
+            39.0000000000,
+            28.0000000000,
+            27.0000000000,
+            31.0000000000,
+            28.0000000000,
+            38.0000000000,
+            21.0000000000,
+            33.0000000000,
+            36.0000000000,
+            29.0000000000,
+            37.0000000000,
+            32.0000000000,
+            34.0000000000,
+            31.0000000000,
+            39.0000000000,
+            25.0000000000,
+            31.0000000000,
+            32.0000000000,
+            40.0000000000,
+            24.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 787.0000000000,
+            min: 21.0000000000,
+            max: 40.0000000000,
+            mean: 31.4800000000,
+            median: 32.0000000000,
+            var: 26.5933333333,
+            std_dev: 5.1568724372,
+            std_dev_pct: 16.3814245145,
+            median_abs_dev: 5.9304000000,
+            median_abs_dev_pct: 18.5325000000,
+            quartiles: (28.0000000000,32.0000000000,34.0000000000),
+            iqr: 6.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_pois25lambda40() {
+        let val = &[
+            42.0000000000,
+            50.0000000000,
+            42.0000000000,
+            46.0000000000,
+            34.0000000000,
+            45.0000000000,
+            34.0000000000,
+            49.0000000000,
+            39.0000000000,
+            28.0000000000,
+            40.0000000000,
+            35.0000000000,
+            37.0000000000,
+            39.0000000000,
+            46.0000000000,
+            44.0000000000,
+            32.0000000000,
+            45.0000000000,
+            42.0000000000,
+            37.0000000000,
+            48.0000000000,
+            42.0000000000,
+            33.0000000000,
+            42.0000000000,
+            48.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 1019.0000000000,
+            min: 28.0000000000,
+            max: 50.0000000000,
+            mean: 40.7600000000,
+            median: 42.0000000000,
+            var: 34.4400000000,
+            std_dev: 5.8685603004,
+            std_dev_pct: 14.3978417577,
+            median_abs_dev: 5.9304000000,
+            median_abs_dev_pct: 14.1200000000,
+            quartiles: (37.0000000000,42.0000000000,45.0000000000),
+            iqr: 8.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_pois25lambda50() {
+        let val = &[
+            45.0000000000,
+            43.0000000000,
+            44.0000000000,
+            61.0000000000,
+            51.0000000000,
+            53.0000000000,
+            59.0000000000,
+            52.0000000000,
+            49.0000000000,
+            51.0000000000,
+            51.0000000000,
+            50.0000000000,
+            49.0000000000,
+            56.0000000000,
+            42.0000000000,
+            52.0000000000,
+            51.0000000000,
+            43.0000000000,
+            48.0000000000,
+            48.0000000000,
+            50.0000000000,
+            42.0000000000,
+            43.0000000000,
+            42.0000000000,
+            60.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 1235.0000000000,
+            min: 42.0000000000,
+            max: 61.0000000000,
+            mean: 49.4000000000,
+            median: 50.0000000000,
+            var: 31.6666666667,
+            std_dev: 5.6273143387,
+            std_dev_pct: 11.3913245723,
+            median_abs_dev: 4.4478000000,
+            median_abs_dev_pct: 8.8956000000,
+            quartiles: (44.0000000000,50.0000000000,52.0000000000),
+            iqr: 8.0000000000,
+        };
+        check(val, summ);
+    }
+    #[test]
+    fn test_unif25() {
+        let val = &[
+            99.0000000000,
+            55.0000000000,
+            92.0000000000,
+            79.0000000000,
+            14.0000000000,
+            2.0000000000,
+            33.0000000000,
+            49.0000000000,
+            3.0000000000,
+            32.0000000000,
+            84.0000000000,
+            59.0000000000,
+            22.0000000000,
+            86.0000000000,
+            76.0000000000,
+            31.0000000000,
+            29.0000000000,
+            11.0000000000,
+            41.0000000000,
+            53.0000000000,
+            45.0000000000,
+            44.0000000000,
+            98.0000000000,
+            98.0000000000,
+            7.0000000000,
+        ];
+        let summ = &Summary {
+            sum: 1242.0000000000,
+            min: 2.0000000000,
+            max: 99.0000000000,
+            mean: 49.6800000000,
+            median: 45.0000000000,
+            var: 1015.6433333333,
+            std_dev: 31.8691595957,
+            std_dev_pct: 64.1488719719,
+            median_abs_dev: 45.9606000000,
+            median_abs_dev_pct: 102.1346666667,
+            quartiles: (29.0000000000,45.0000000000,79.0000000000),
+            iqr: 50.0000000000,
+        };
+        check(val, summ);
+    }
+
+    #[test]
+    fn test_boxplot_nonpositive() {
+        fn t(s: &Summary, expected: ~str) {
+            use std::io::MemWriter;
+            let mut m = MemWriter::new();
+            write_boxplot(&mut m as &mut io::Writer, s, 30).unwrap();
+            let out = str::from_utf8_owned(m.unwrap()).unwrap();
+            assert_eq!(out, expected);
+        }
+
+        t(&Summary::new([-2.0, -1.0]), ~"-2 |[------******#*****---]| -1");
+        t(&Summary::new([0.0, 2.0]), ~"0 |[-------*****#*******---]| 2");
+        t(&Summary::new([-2.0, 0.0]), ~"-2 |[------******#******---]| 0");
+
+    }
+    #[test]
+    fn test_sum_f64s() {
+        assert_eq!([0.5, 3.2321, 1.5678].sum(), 5.2999);
+    }
+    #[test]
+    fn test_sum_f64_between_ints_that_sum_to_0() {
+        assert_eq!([1e30, 1.2, -1e30].sum(), 1.2);
+    }
+}
+
+#[cfg(test)]
+mod bench {
+    use BenchHarness;
+    use std::vec;
+    use stats::Stats;
+
+    #[bench]
+    pub fn sum_three_items(bh: &mut BenchHarness) {
+        bh.iter(|| {
+            [1e20, 1.5, -1e20].sum();
+        })
+    }
+    #[bench]
+    pub fn sum_many_f64(bh: &mut BenchHarness) {
+        let nums = [-1e30, 1e60, 1e30, 1.0, -1e60];
+        let v = vec::from_fn(500, |i| nums[i%5]);
+
+        bh.iter(|| {
+            v.sum();
+        })
+    }
+}