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      1 #!/usr/bin/env python
      2 
      3 import argparse
      4 import sys
      5 
      6 have_scipy = True
      7 try:
      8     import scipy.stats
      9 except:
     10     have_scipy = False
     11 
     12 SIGNIFICANCE_THRESHOLD = 0.0001
     13 
     14 parser = argparse.ArgumentParser(
     15     formatter_class=argparse.RawDescriptionHelpFormatter,
     16     description='Compare performance of two runs from nanobench.')
     17 parser.add_argument('--use_means', action='store_true', default=False,
     18                     help='Use means to calculate performance ratios.')
     19 parser.add_argument('baseline', help='Baseline file.')
     20 parser.add_argument('experiment', help='Experiment file.')
     21 args = parser.parse_args()
     22 
     23 a,b = {},{}
     24 for (path, d) in [(args.baseline, a), (args.experiment, b)]:
     25     for line in open(path):
     26         try:
     27             tokens = line.split()
     28             if tokens[0] != "Samples:":
     29                 continue
     30             samples  = tokens[1:-1]
     31             label    = tokens[-1]
     32             d[label] = map(float, samples)
     33         except:
     34             pass
     35 
     36 common = set(a.keys()).intersection(b.keys())
     37 
     38 def mean(xs):
     39     return sum(xs) / len(xs)
     40 
     41 ps = []
     42 for key in common:
     43     p, asem, bsem = 0, 0, 0
     44     m = mean if args.use_means else min
     45     am, bm = m(a[key]), m(b[key])
     46     if have_scipy:
     47         _, p = scipy.stats.mannwhitneyu(a[key], b[key])
     48         asem, bsem = scipy.stats.sem(a[key]), scipy.stats.sem(b[key])
     49     ps.append((bm/am, p, key, am, bm, asem, bsem))
     50 ps.sort(reverse=True)
     51 
     52 def humanize(ns):
     53     for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]:
     54         if ns > threshold:
     55             return "%.3g%s" % (ns/threshold, suffix)
     56 
     57 maxlen = max(map(len, common))
     58 
     59 # We print only signficant changes in benchmark timing distribution.
     60 bonferroni = SIGNIFICANCE_THRESHOLD / len(ps)  # Adjust for the fact we've run multiple tests.
     61 for ratio, p, key, am, bm, asem, bsem in ps:
     62     if p < bonferroni:
     63         str_ratio = ('%.2gx' if ratio < 1 else '%.3gx') % ratio
     64         if args.use_means:
     65             print '%*s\t%6s(%6s) -> %6s(%6s)\t%s' % (maxlen, key, humanize(am), humanize(asem),
     66                                                      humanize(bm), humanize(bsem), str_ratio)
     67         else:
     68             print '%*s\t%6s -> %6s\t%s' % (maxlen, key, humanize(am), humanize(bm), str_ratio)
     69