Add key_idle_power_cases for ensuring idle activity on Android

tools/perf/metrics/timeline.py

Index: tools/perf/metrics/timeline.py
diff --git a/tools/perf/metrics/timeline.py b/tools/perf/metrics/timeline.py
index 1a1ca25e219edde5f445920118c05da1725b3db6..bdfcbcf99d678b40008a2d4564a8bc2f086629c3 100644
--- a/tools/perf/metrics/timeline.py
+++ b/tools/perf/metrics/timeline.py
@@ -127,23 +127,13 @@ def ThreadCategoryName(thread_name):
     thread_category = TimelineThreadCategories[thread_name]
   return thread_category
 
-def ThreadCpuTimeResultName(thread_category):
-  # This isn't a good name, but I don't want to change it and lose continuity.
-  return "thread_" + thread_category + "_cpu_time_per_frame"
-
-def ThreadTasksResultName(thread_category):
-  return "tasks_per_frame_" + thread_category
-
-def ThreadMeanFrameTimeResultName(thread_category):
-  return "mean_frame_time_" + thread_category
-
 def ThreadDetailResultName(thread_category, detail):
   detail_sanitized = detail.replace('.','_')
   return "thread_" + thread_category + "|" + detail_sanitized
 
 
 class ResultsForThread(object):
-  def __init__(self, model, record_ranges, name):
+  def __init__(self, model, record_ranges, name, measure_per_frame):
     self.model = model
     self.toplevel_slices = []
     self.all_slices = []
@@ -151,6 +141,7 @@ class ResultsForThread(object):
     self.record_ranges = record_ranges
     self.all_action_time = \
         sum([record_range.bounds for record_range in self.record_ranges])
+    self.measure_per_frame = measure_per_frame
 
   @property
   def clock_time(self):
@@ -191,25 +182,28 @@ class ResultsForThread(object):
   # Currently we report cpu-time per frame, tasks per frame, and possibly
   # the mean frame (if there is a trace specified to find it).
   def AddResults(self, num_frames, results):
-    cpu_per_frame = Rate(self.cpu_time, num_frames)
-    tasks_per_frame = Rate(len(self.toplevel_slices), num_frames)
+    num_intervals = num_frames if self.measure_per_frame else 1
+    cpu_per_interval = Rate(self.cpu_time, num_intervals)
+    tasks_per_interval = Rate(len(self.toplevel_slices), num_intervals)
     results.AddValue(scalar.ScalarValue(
-        results.current_page, ThreadCpuTimeResultName(self.name),
-        'ms', cpu_per_frame))
+        results.current_page,
+        self.ThreadCpuTimeResultName(self.name), 'ms', cpu_per_interval))
     results.AddValue(scalar.ScalarValue(
-        results.current_page, ThreadTasksResultName(self.name),
-        'tasks', tasks_per_frame))
+        results.current_page, self.ThreadTasksResultName(self.name),
+        'tasks', tasks_per_interval))
     # Report mean frame time if this is the thread we are using for normalizing
     # other results. We could report other frame rates (eg. renderer_main) but
     # this might get confusing.
     if self.name == FrameTraceThreadName:
-      num_frames = self.CountTracesWithName(FrameTraceName)
-      mean_frame_time = Rate(self.all_action_time, num_frames)
+      num_intervals = self.CountTracesWithName(FrameTraceName) \
+          if self.measure_per_frame else 1
+      frame_time = Rate(self.all_action_time, num_intervals)
       results.AddValue(scalar.ScalarValue(
-          results.current_page, ThreadMeanFrameTimeResultName(self.name),
-          'ms', mean_frame_time))
+          results.current_page, self.ThreadFrameTimeResultName(self.name),
+          'ms', frame_time))
 
   def AddDetailedResults(self, num_frames, results):
+    num_intervals = num_frames if self.measure_per_frame else 1
     slices_by_category = collections.defaultdict(list)
     for s in self.all_slices:
       slices_by_category[s.category].append(s)
@@ -217,13 +211,15 @@ class ResultsForThread(object):
     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
+      self_time_result = \
+          (float(self_time) / num_intervals) if num_intervals else 0
       results.AddValue(scalar.ScalarValue(
           results.current_page, ThreadDetailResultName(self.name, category),
           'ms', self_time_result))
     all_measured_time = sum(all_self_times)
     idle_time = max(0, self.all_action_time - all_measured_time)
-    idle_time_result = (float(idle_time) / num_frames) if num_frames else 0
+    idle_time_result = \
+        (float(idle_time) / num_intervals) if num_intervals else 0
     results.AddValue(scalar.ScalarValue(
         results.current_page, ThreadDetailResultName(self.name, "idle"),
         'ms', idle_time_result))
@@ -235,19 +231,38 @@ class ResultsForThread(object):
         count += 1
     return count
 
+  def ThreadCpuTimeResultName(self, thread_category):
+    # This isn't a good name, but I don't want to change it and lose continuity.
+    if self.measure_per_frame:
+      return "thread_" + thread_category + "_cpu_time_per_frame"
+    return "thread_" + thread_category + "_cpu_time_total"
+
+  def ThreadTasksResultName(self, thread_category):
+    if self.measure_per_frame:
+      return "tasks_per_frame_" + thread_category
+    return "tasks_" + thread_category
+
+  def ThreadFrameTimeResultName(self, thread_category):
+    if self.measure_per_frame:
+      return "mean_frame_time_" + thread_category
+    return "total_frame_time_" + thread_category
+
+
 class ThreadTimesTimelineMetric(timeline_based_metric.TimelineBasedMetric):
-  def __init__(self):
+  def __init__(self, measure_per_frame=True):
     super(ThreadTimesTimelineMetric, self).__init__()
     # Minimal traces, for minimum noise in CPU-time measurements.
     self.results_to_report = AllThreads
     self.details_to_report = NoThreads
+    self.measure_per_frame = measure_per_frame
 
   def AddResults(self, model, _, interaction_records, results):
     # Set up each thread category for consistant results.
     thread_category_results = {}
     for name in TimelineThreadCategories.values():
       thread_category_results[name] = ResultsForThread(
-        model, [r.GetBounds() for r in interaction_records], name)
+        model, [r.GetBounds() for r in interaction_records], name,
+        self.measure_per_frame)
 
     # Group the slices by their thread category.
     for thread in model.GetAllThreads():