telemetry: Add new metrics to smoothness benchmark.

tools/perf/metrics/discrepancy_unittest.py

+# Copyright 2013 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+import unittest
+import random
+
+from metrics import discrepancy
+
+def Relax(samples, iterations=10):
+  ''' Lloyd relaxation in 1D. Keeps the position of the first and last
+      sample.
+  '''
+  for _ in xrange(0, iterations):
+    voronoi_boundaries = []
+    for i in xrange(1, len(samples)):
+      voronoi_boundaries.append((samples[i] + samples[i-1]) * 0.5)
+
+    relaxed_samples = []
+    relaxed_samples.append(samples[0])
+    for i in xrange(1, len(samples)-1):
+      relaxed_samples.append(
+          (voronoi_boundaries[i-1] + voronoi_boundaries[i]) * 0.5)
+    relaxed_samples.append(samples[-1])
+    samples = relaxed_samples
+  return samples
+
+class DiscrepancyUnitTest(unittest.TestCase):
+  def testRandom(self):
+    ''' Generates 10 sets of 10 random samples, computes the discrepancy,
+        relaxes the samples using Llloyd's algorithm in 1D, and computes the
+        discrepancy of the relaxed samples. Discrepancy of the relaxed samples
+        must be less than or equal to the discrepancy of the original samples.
+    '''
+    random.seed(1234567)
+    for _ in xrange(0, 10):
+      samples = []
+      num_samples = 10
+      clock = 0.0
+      samples.append(clock)
+      for _ in xrange(1, num_samples):
+        clock += random.random()
+        samples.append(clock)
+      samples = discrepancy.NormalizeSamples(samples)[0]
+      d = discrepancy.Discrepancy(samples)
+
+      relaxed_samples = Relax(samples)
+      d_relaxed = discrepancy.Discrepancy(relaxed_samples)
+
+      self.assertLessEqual(d_relaxed, d)
+
+  def testAnalytic(self):
+    ''' Computes discrepancy for sample sets with known discrepancy. '''
+    interval_multiplier = 100000
+
+    samples = [1.0/8.0, 3.0/8.0, 5.0/8.0, 7.0/8.0]
+    d = discrepancy.Discrepancy(samples, interval_multiplier)
+    self.assertAlmostEquals(round(d, 2), 0.25)
+
+    samples = [0.0, 1.0/3.0, 2.0/3.0, 1.0]
+    d = discrepancy.Discrepancy(samples, interval_multiplier)
+    self.assertAlmostEquals(round(d, 2), 0.5)
+
+    samples = discrepancy.NormalizeSamples(samples)[0]
+    d = discrepancy.Discrepancy(samples, interval_multiplier)
+    self.assertAlmostEquals(round(d, 2), 0.25)
+
+    time_stamps_a = [0, 1, 2, 3, 5, 6]
+    time_stamps_b = [0, 1, 2, 3, 5, 7]
+    time_stamps_c = [0, 2, 3, 4]
+    time_stamps_d = [0, 2, 3, 4, 5]
+    d_abs_a = discrepancy.FrameDiscrepancy(time_stamps_a, True,
+                                           interval_multiplier)
+    d_abs_b = discrepancy.FrameDiscrepancy(time_stamps_b, True,
+                                           interval_multiplier)
+    d_abs_c = discrepancy.FrameDiscrepancy(time_stamps_c, True,
+                                           interval_multiplier)
+    d_abs_d = discrepancy.FrameDiscrepancy(time_stamps_d, True,
+                                           interval_multiplier)
+    d_rel_a = discrepancy.FrameDiscrepancy(time_stamps_a, False,
+                                           interval_multiplier)
+    d_rel_b = discrepancy.FrameDiscrepancy(time_stamps_b, False,
+                                           interval_multiplier)
+    d_rel_c = discrepancy.FrameDiscrepancy(time_stamps_c, False,
+                                           interval_multiplier)
+    d_rel_d = discrepancy.FrameDiscrepancy(time_stamps_d, False,
+                                           interval_multiplier)
+
+    self.assertLess(d_abs_a, d_abs_b)
+    self.assertLess(d_rel_a, d_rel_b)
+    self.assertLess(d_rel_d, d_rel_c)
+    self.assertEquals(round(d_abs_d, 2), round(d_abs_c, 2))