Update timeline to use results.AddValue(...)

tools/perf/metrics/timeline.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 collections
 
 from telemetry.web_perf.metrics import timeline_based_metric
+from telemetry.value import scalar
 
 
 class LoadTimesTimelineMetric(timeline_based_metric.TimelineBasedMetric):
   def __init__(self):
     super(LoadTimesTimelineMetric, self).__init__()
     self.report_main_thread_only = True
 
   def AddResults(self, model, renderer_thread, interaction_records, results):
     assert model
     assert len(interaction_records) == 1, (
@@ skipping 20 matching lines @@
 
       for event_name, event_group in events_by_name.iteritems():
         times = [event.self_time for event in event_group]
         total = sum(times)
         biggest_jank = max(times)
 
         # Results objects cannot contain the '.' character, so remove that here.
         sanitized_event_name = event_name.replace('.', '_')
 
         full_name = thread_name + '|' + sanitized_event_name
-        results.Add(full_name, 'ms', total)
-        results.Add(full_name + '_max', 'ms', biggest_jank)
-        results.Add(full_name + '_avg', 'ms', total / len(times))
+        results.AddValue(scalar.ScalarValue(
+            results.current_page, full_name, 'ms', total))
+        results.AddValue(scalar.ScalarValue(
+            results.current_page, full_name + '_max', 'ms', biggest_jank))
+        results.AddValue(scalar.ScalarValue(
+            results.current_page, full_name + '_avg', 'ms', total / len(times)))
 
     for counter_name, counter in renderer_process.counters.iteritems():
       total = sum(counter.totals)
 
       # Results objects cannot contain the '.' character, so remove that here.
       sanitized_counter_name = counter_name.replace('.', '_')
 
-      results.Add(sanitized_counter_name, 'count', total)
-      results.Add(sanitized_counter_name + '_avg', 'count',
-                  total / float(len(counter.totals)))
+      results.AddValue(scalar.ScalarValue(
+          results.current_page, sanitized_counter_name, 'count', total))
+      results.AddValue(scalar.ScalarValue(
+          results.current_page, sanitized_counter_name + '_avg', 'count',
+          total / float(len(counter.totals))))
 
 # We want to generate a consistant picture of our thread usage, despite
 # having several process configurations (in-proc-gpu/single-proc).
 # Since we can't isolate renderer threads in single-process mode, we
 # always sum renderer-process threads' times. We also sum all io-threads
 # for simplicity.
 TimelineThreadCategories =  {
   "Chrome_InProcGpuThread": "GPU",
   "CrGpuMain"             : "GPU",
   "AsyncTransferThread"   : "GPU_transfer",
@@ skipping 101 matching lines @@
           slices_in_actions.append(event)
           break
     return slices_in_actions
 
   def AppendThreadSlices(self, thread):
     self.all_slices.extend(self.SlicesInActions(thread.all_slices))
     self.toplevel_slices.extend(self.SlicesInActions(thread.toplevel_slices))
 
   def AddResults(self, num_frames, results):
     cpu_per_frame = (float(self.cpu_time) / num_frames) if num_frames else 0
-    results.Add(ThreadCpuTimeResultName(self.name), 'ms', cpu_per_frame)
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, ThreadCpuTimeResultName(self.name),
+        'ms', cpu_per_frame))
 
   def AddDetailedResults(self, num_frames, results):
     slices_by_category = collections.defaultdict(list)
     for s in self.all_slices:
       slices_by_category[s.category].append(s)
     all_self_times = []
     for category, slices_in_category in slices_by_category.iteritems():
       self_time = sum([x.self_time for x in slices_in_category])
       all_self_times.append(self_time)
       self_time_result = (float(self_time) / num_frames) if num_frames else 0
-      results.Add(ThreadDetailResultName(self.name, category),
-                  'ms', self_time_result)
+      results.AddValue(scalar.ScalarValue(
+          results.current_page, ThreadDetailResultName(self.name, category),
+          'ms', self_time_result))
     all_measured_time = sum(all_self_times)
     all_action_time = \
         sum([record_range.bounds for record_range in self.record_ranges])
     idle_time = max(0, all_action_time - all_measured_time)
     idle_time_result = (float(idle_time) / num_frames) if num_frames else 0
-    results.Add(ThreadDetailResultName(self.name, "idle"),
-                'ms', idle_time_result)
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, ThreadDetailResultName(self.name, "idle"),
+        'ms', idle_time_result))
 
 
 class ThreadTimesTimelineMetric(timeline_based_metric.TimelineBasedMetric):
   def __init__(self):
     super(ThreadTimesTimelineMetric, self).__init__()
     # Minimal traces, for minimum noise in CPU-time measurements.
     self.results_to_report = AllThreads
     self.details_to_report = NoThreads
 
   def CountSlices(self, slices, substring):
@@ skipping 29 matching lines @@
     num_frames = self.CountSlices(frame_slices, FrameTraceName)
 
     # Report the desired results and details.
     for thread_results in thread_category_results.values():
       if thread_results.name in self.results_to_report:
         thread_results.AddResults(num_frames, results)
       # TOOD(nduca): When generic results objects are done, this special case
       # can be replaced with a generic UI feature.
       if thread_results.name in self.details_to_report:
         thread_results.AddDetailedResults(num_frames, results)