Distinguishing between audio and video stats in WebRTC telemetry.

tools/perf/metrics/webrtc_stats.py

 # Copyright 2014 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 json
+import logging
+import re
 
 from metrics import Metric
 from telemetry.core import camel_case
 from telemetry.value import list_of_scalar_values
 
 INTERESTING_METRICS = {
     'packetsReceived': {
         'units': 'packets',
         'description': 'Packets received by the peer connection',
     },
     'packetsSent': {
         'units': 'packets',
         'description': 'Packets sent by the peer connection',
     },
     'googDecodeMs': {
         'units': 'ms',
         'description': 'Time spent decoding.',
     },
     'googMaxDecodeMs': {
         'units': 'ms',
         'description': 'Maximum time spent decoding one frame.',
     },
     # TODO(phoglund): Add much more interesting metrics.
 }
 
+
+def GetReportKind(report):
+  if 'audioInputLevel' in report or 'audioOutputLevel' in report:
+    return 'audio'
+  if 'googFrameRateSent' in report or 'googFrameRateReceived' in report:
+    return 'video'
+
+  logging.error('Did not recognize report batch: %s.', report.keys())
+  return 'unknown'
+
+
+def DistinguishAudioAndVideo(report, stat_name):
+  return GetReportKind(report) + '_' + stat_name
+
+
+def StripAudioVideoDistinction(stat_name):
+  return re.sub('^(audio|video)_', '', stat_name)
+
+
 def SortStatsIntoTimeSeries(report_batches):
   time_series = {}
   for report_batch in report_batches:
     for report in report_batch:
       for stat_name, value in report.iteritems():
         if stat_name not in INTERESTING_METRICS:
           continue
-        time_series.setdefault(stat_name, []).append(float(value))
+        full_stat_name = DistinguishAudioAndVideo(report, stat_name)
+        time_series.setdefault(full_stat_name, []).append(float(value))
 
   return time_series
 
 
 class WebRtcStatisticsMetric(Metric):
   """Makes it possible to measure stats from peer connections."""
 
   def __init__(self):
     super(WebRtcStatisticsMetric, self).__init__()
     self._all_reports = None
@@ skipping 10 matching lines @@
     if not self._all_reports:
       return
 
     reports = json.loads(self._all_reports)
     for i, report in enumerate(reports):
       time_series = SortStatsIntoTimeSeries(report)
 
       for stat_name, values in time_series.iteritems():
         stat_name_underscored = camel_case.ToUnderscore(stat_name)
         trace_name = 'peer_connection_%d_%s' % (i, stat_name_underscored)
+        general_name = StripAudioVideoDistinction(stat_name)
         results.AddValue(list_of_scalar_values.ListOfScalarValues(
             results.current_page, trace_name,
-            INTERESTING_METRICS[stat_name]['units'], values,
-            description=INTERESTING_METRICS[stat_name]['description'],
+            INTERESTING_METRICS[general_name]['units'], values,
+            description=INTERESTING_METRICS[general_name]['description'],
             important=False))