Delete trace-info.json.

tools/telemetry/telemetry/web_perf/metrics/smoothness.py

Index: tools/telemetry/telemetry/web_perf/metrics/smoothness.py
diff --git a/tools/telemetry/telemetry/web_perf/metrics/smoothness.py b/tools/telemetry/telemetry/web_perf/metrics/smoothness.py
index 9a49ed0243bb264d052465d647eb3eb23de98aff..6bde6d6bb714ed3c88022d525879b3148bb709fd 100644
--- a/tools/telemetry/telemetry/web_perf/metrics/smoothness.py
+++ b/tools/telemetry/telemetry/web_perf/metrics/smoothness.py
@@ -34,7 +34,7 @@ class SmoothnessMetric(timeline_based_metric.TimelineBasedMetric):
           results.current_page, 'input_event_latency_discrepancy', 'ms',
           round(input_event_latency_discrepancy, 4)))
 
-    # List of queueing durations
+    # List of queueing durations.
     frame_queueing_durations = FlattenList(stats.frame_queueing_durations)
     if frame_queueing_durations:
       results.AddValue(list_of_scalar_values.ListOfScalarValues(
@@ -44,30 +44,45 @@ class SmoothnessMetric(timeline_based_metric.TimelineBasedMetric):
     # List of raw frame times.
     frame_times = FlattenList(stats.frame_times)
     results.AddValue(list_of_scalar_values.ListOfScalarValues(
-        results.current_page, 'frame_times', 'ms', frame_times))
+        results.current_page, 'frame_times', 'ms', frame_times,
+        description='List of raw frame times, helpful to understand the other '
+                    'metrics.'))
 
     # Arithmetic mean of frame times.
     mean_frame_time = statistics.ArithmeticMean(frame_times)
     results.AddValue(scalar.ScalarValue(
         results.current_page, 'mean_frame_time', 'ms',
-        round(mean_frame_time, 3)))
+        round(mean_frame_time, 3),
+        description='Arithmetic mean of frame times.'))
 
     # Absolute discrepancy of frame time stamps.
     frame_discrepancy = statistics.TimestampsDiscrepancy(
       stats.frame_timestamps)
     results.AddValue(scalar.ScalarValue(
-        results.current_page, 'jank', 'ms', round(frame_discrepancy, 4)))
+        results.current_page, 'jank', 'ms', round(frame_discrepancy, 4),
+        description='Absolute discrepancy of frame time stamps, where '
+                    'discrepancy is a measure of irregularity. It quantifies '
+                    'the worst jank. For a single pause, discrepancy '
+                    'corresponds to the length of this pause in milliseconds. '
+                    'Consecutive pauses increase the discrepancy. This metric '
+                    'is important because even if the mean and 95th '
+                    'percentile are good, one long pause in the middle of an '
+                    'interaction is still bad.'))
 
     # Are we hitting 60 fps for 95 percent of all frames?
     # We use 19ms as a somewhat looser threshold, instead of 1000.0/60.0.
     percentile_95 = statistics.Percentile(frame_times, 95.0)
     results.AddValue(scalar.ScalarValue(
         results.current_page, 'mostly_smooth', 'score',
-        1.0 if percentile_95 < 19.0 else 0.0))
+        1.0 if percentile_95 < 19.0 else 0.0,
+        description='Were 95 percent of the frames hitting 60 fps?'
+                    'boolean value (1/0).'))
 
     # Mean percentage of pixels approximated (missing tiles, low resolution
-    # tiles, non-ideal resolution tiles)
+    # tiles, non-ideal resolution tiles).
     results.AddValue(scalar.ScalarValue(
         results.current_page, 'mean_pixels_approximated', 'percent',
         round(statistics.ArithmeticMean(
-            FlattenList(stats.approximated_pixel_percentages)), 3)))
+            FlattenList(stats.approximated_pixel_percentages)), 3),
+        description='Percentage of pixels that were approximated '
+                    '(checkerboarding, low-resolution tiles, etc.).'))