Make bin/c and bin/compare work on Windows.

bin/compare

 #!/usr/bin/env python
 
 import argparse
-import numpy
 import sys
-from scipy.stats import mannwhitneyu
-from scipy.stats import sem
+
+have_scipy = True
+try:
+    import scipy.stats
+except:
+    have_scipy = False
 
 SIGNIFICANCE_THRESHOLD = 0.0001
 
 parser = argparse.ArgumentParser(
     formatter_class=argparse.RawDescriptionHelpFormatter,
     description='Compare performance of two runs from nanobench.')
 parser.add_argument('--use_means', action='store_true', default=False,
                     help='Use means to calculate performance ratios.')
 parser.add_argument('baseline', help='Baseline file.')
 parser.add_argument('experiment', help='Experiment file.')
 args = parser.parse_args()
 
 a,b = {},{}
 for (path, d) in [(args.baseline, a), (args.experiment, b)]:
     for line in open(path):
         try:
             tokens = line.split()
             if tokens[0] != "Samples:":
                 continue
             samples  = tokens[1:-1]
             label    = tokens[-1]
             d[label] = map(float, samples)
         except:
             pass
 
 common = set(a.keys()).intersection(b.keys())
 
+def mean(xs):
+    return sum(xs) / len(xs)
+
 ps = []
 for key in common:
-    _, p = mannwhitneyu(a[key], b[key])    # Non-parametric t-test.  Doesn't assume normal dist.
-    if args.use_means:
-        am, bm = numpy.mean(a[key]), numpy.mean(b[key])
-        asem, bsem = sem(a[key]), sem(b[key])
-    else:
-        am, bm = min(a[key]), min(b[key])
-        asem, bsem = 0, 0
+    p, asem, bsem = 0, 0, 0
+    m = mean if args.use_means else min
+    am, bm = m(a[key]), m(b[key])
+    if have_scipy:
+        _, p = scipy.stats.mannwhitneyu(a[key], b[key])
+        asem, bsem = scipy.stats.sem(a[key]), sem(b[key])
     ps.append((bm/am, p, key, am, bm, asem, bsem))
 ps.sort(reverse=True)
 
 def humanize(ns):
     for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]:
         if ns > threshold:
             return "%.3g%s" % (ns/threshold, suffix)
 
 maxlen = max(map(len, common))
 
 # We print only signficant changes in benchmark timing distribution.
 bonferroni = SIGNIFICANCE_THRESHOLD / len(ps)  # Adjust for the fact we've run multiple tests.
 for ratio, p, key, am, bm, asem, bsem in ps:
     if p < bonferroni:
         str_ratio = ('%.2gx' if ratio < 1 else '%.3gx') % ratio
         if args.use_means:
             print '%*s\t%6s(%6s) -> %6s(%6s)\t%s' % (maxlen, key, humanize(am), humanize(asem),
                                                      humanize(bm), humanize(bsem), str_ratio)
         else:
             print '%*s\t%6s -> %6s\t%s' % (maxlen, key, humanize(am), humanize(bm), str_ratio)