[analyzer] [testing] Compute data on path length, compute percentiles

[NoQ]

Looks good!

utils/analyzer/CmpRuns.py

def compareResults(A, B, opts):
     if opts.relative_log_path_histogram or opts.relative_path_histogram or \
             opts.absolute_path_histogram:
         from matplotlib import pyplot
         pyplot.hist(path_difference_data, bins=100)
         pyplot.show()
 
     return res
 
+def computePercentile(l, percentile):
+    """
+    Return computed percentile.
+    """
+    return sorted(l)[int(round(percentile * len(l) + 0.5)) - 1]
+
 def deriveStats(results):
     # Assume all keys are the same in each statistics bucket.
     combined_data = defaultdict(list)
+
+    # Collect data on paths length.
+    for report in results.reports:
+        for diagnostic in report.diagnostics:
+            combined_data['PathsLength'].append(diagnostic.getPathLength())
+
     for stat in results.stats:
         for key, value in stat.iteritems():
             combined_data[key].append(value)
     combined_stats = {}
     for key, values in combined_data.iteritems():
         combined_stats[str(key)] = {
             "max": max(values),
             "min": min(values),
             "mean": sum(values) / len(values),
+            "90th %tile": computePercentile(values, 0.9),
+            "95th %tile": computePercentile(values, 0.95),
             "median": sorted(values)[len(values) / 2],
             "total": sum(values)
         }
     return combined_stats
 
 
 def compareStats(resultsA, resultsB):
     statsA = deriveStats(resultsA)