Convert results.Add to results.AddValue for files in web_perf/

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 fa19d7da8a25d8254d31bb16c7ad7e95b03faeab..9a49ed0243bb264d052465d647eb3eb23de98aff 100644
--- a/tools/telemetry/telemetry/web_perf/metrics/smoothness.py
+++ b/tools/telemetry/telemetry/web_perf/metrics/smoothness.py
@@ -6,6 +6,8 @@ from telemetry.perf_tests_helper import FlattenList
 from telemetry.web_perf.metrics import timeline_based_metric
 from telemetry.web_perf.metrics import rendering_stats
 from telemetry.util import statistics
+from telemetry.value import scalar
+from telemetry.value import list_of_scalar_values
 
 
 class SmoothnessMetric(timeline_based_metric.TimelineBasedMetric):
@@ -25,36 +27,47 @@ class SmoothnessMetric(timeline_based_metric.TimelineBasedMetric):
         input_event_latency)
       input_event_latency_discrepancy = statistics.DurationsDiscrepancy(
         input_event_latency)
-      results.Add('mean_input_event_latency', 'ms',
-                  round(mean_input_event_latency, 3))
-      results.Add('input_event_latency_discrepancy', 'ms',
-                  round(input_event_latency_discrepancy, 4))
+      results.AddValue(scalar.ScalarValue(
+          results.current_page, 'mean_input_event_latency', 'ms',
+          round(mean_input_event_latency, 3)))
+      results.AddValue(scalar.ScalarValue(
+          results.current_page, 'input_event_latency_discrepancy', 'ms',
+          round(input_event_latency_discrepancy, 4)))
 
     # List of queueing durations
     frame_queueing_durations = FlattenList(stats.frame_queueing_durations)
     if frame_queueing_durations:
-      results.Add('queueing_durations', 'ms', frame_queueing_durations)
+      results.AddValue(list_of_scalar_values.ListOfScalarValues(
+          results.current_page, 'queueing_durations', 'ms',
+          frame_queueing_durations))
 
     # List of raw frame times.
     frame_times = FlattenList(stats.frame_times)
-    results.Add('frame_times', 'ms', frame_times)
+    results.AddValue(list_of_scalar_values.ListOfScalarValues(
+        results.current_page, 'frame_times', 'ms', frame_times))
 
     # Arithmetic mean of frame times.
     mean_frame_time = statistics.ArithmeticMean(frame_times)
-    results.Add('mean_frame_time', 'ms', round(mean_frame_time, 3))
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, 'mean_frame_time', 'ms',
+        round(mean_frame_time, 3)))
 
     # Absolute discrepancy of frame time stamps.
     frame_discrepancy = statistics.TimestampsDiscrepancy(
       stats.frame_timestamps)
-    results.Add('jank', 'ms', round(frame_discrepancy, 4))
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, 'jank', 'ms', round(frame_discrepancy, 4)))
 
     # 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.Add('mostly_smooth', 'score', 1.0 if percentile_95 < 19.0 else 0.0)
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, 'mostly_smooth', 'score',
+        1.0 if percentile_95 < 19.0 else 0.0))
 
     # Mean percentage of pixels approximated (missing tiles, low resolution
     # tiles, non-ideal resolution tiles)
-    results.Add('mean_pixels_approximated', 'percent',
-                round(statistics.ArithmeticMean(
-                    FlattenList(stats.approximated_pixel_percentages)), 3))
+    results.AddValue(scalar.ScalarValue(
+        results.current_page, 'mean_pixels_approximated', 'percent',
+        round(statistics.ArithmeticMean(
+            FlattenList(stats.approximated_pixel_percentages)), 3)))