Delete tools/telemetry.

tools/telemetry/telemetry/web_perf/metrics/gpu_timeline.py

Index: tools/telemetry/telemetry/web_perf/metrics/gpu_timeline.py
diff --git a/tools/telemetry/telemetry/web_perf/metrics/gpu_timeline.py b/tools/telemetry/telemetry/web_perf/metrics/gpu_timeline.py
deleted file mode 100644
index ff362be883035420d46cef985f240b14205fe9bf..0000000000000000000000000000000000000000
--- a/tools/telemetry/telemetry/web_perf/metrics/gpu_timeline.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright 2015 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
-import math
-import sys
-
-from telemetry.timeline import model as model_module
-from telemetry.value import improvement_direction
-from telemetry.value import list_of_scalar_values
-from telemetry.value import scalar
-from telemetry.web_perf.metrics import timeline_based_metric
-
-TOPLEVEL_GL_CATEGORY = 'gpu_toplevel'
-TOPLEVEL_SERVICE_CATEGORY = 'disabled-by-default-gpu.service'
-TOPLEVEL_DEVICE_CATEGORY = 'disabled-by-default-gpu.device'
-
-SERVICE_FRAME_END_MARKER = (TOPLEVEL_SERVICE_CATEGORY, 'SwapBuffer')
-DEVICE_FRAME_END_MARKER = (TOPLEVEL_DEVICE_CATEGORY, 'SwapBuffer')
-
-TRACKED_GL_CONTEXT_NAME = {'RenderCompositor': 'render_compositor',
-                           'BrowserCompositor': 'browser_compositor',
-                           'Compositor': 'browser_compositor'}
-
-
-def _CalculateFrameTimes(events_per_frame, event_data_func):
-  """Given a list of events per frame and a function to extract event time data,
-     returns a list of frame times."""
-  times_per_frame = []
-  for event_list in events_per_frame:
-    event_times = [event_data_func(event) for event in event_list]
-    times_per_frame.append(sum(event_times))
-  return times_per_frame
-
-
-def _CPUFrameTimes(events_per_frame):
-  """Given a list of events per frame, returns a list of CPU frame times."""
-  # CPU event frames are calculated using the event thread duration.
-  # Some platforms do not support thread_duration, convert those to 0.
-  return _CalculateFrameTimes(events_per_frame,
-                              lambda event: event.thread_duration or 0)
-
-
-def _GPUFrameTimes(events_per_frame):
-  """Given a list of events per frame, returns a list of GPU frame times."""
-  # GPU event frames are asynchronous slices which use the event duration.
-  return _CalculateFrameTimes(events_per_frame,
-                              lambda event: event.duration)
-
-
-def TimelineName(name, source_type, value_type):
-  """Constructs the standard name given in the timeline.
-
-  Args:
-    name: The name of the timeline, for example "total", or "render_compositor".
-    source_type: One of "cpu", "gpu" or None. None is only used for total times.
-    value_type: the type of value. For example "mean", "stddev"...etc.
-  """
-  if source_type:
-    return '%s_%s_%s_time' % (name, value_type, source_type)
-  else:
-    return '%s_%s_time' % (name, value_type)
-
-
-class GPUTimelineMetric(timeline_based_metric.TimelineBasedMetric):
-  """Computes GPU based metrics."""
-
-  def __init__(self):
-    super(GPUTimelineMetric, self).__init__()
-
-  def AddResults(self, model, _, interaction_records, results):
-    self.VerifyNonOverlappedRecords(interaction_records)
-    service_times = self._CalculateGPUTimelineData(model)
-    for value_item, durations in service_times.iteritems():
-      count = len(durations)
-      avg = 0.0
-      stddev = 0.0
-      maximum = 0.0
-      if count:
-        avg = sum(durations) / count
-        stddev = math.sqrt(sum((d - avg) ** 2 for d in durations) / count)
-        maximum = max(durations)
-
-      name, src = value_item
-
-      if src:
-        frame_times_name = '%s_%s_frame_times' % (name, src)
-      else:
-        frame_times_name = '%s_frame_times' % (name)
-
-      if durations:
-        results.AddValue(list_of_scalar_values.ListOfScalarValues(
-            results.current_page, frame_times_name, 'ms', durations,
-            tir_label=interaction_records[0].label,
-            improvement_direction=improvement_direction.DOWN))
-
-      results.AddValue(scalar.ScalarValue(
-          results.current_page, TimelineName(name, src, 'max'), 'ms', maximum,
-          tir_label=interaction_records[0].label,
-          improvement_direction=improvement_direction.DOWN))
-      results.AddValue(scalar.ScalarValue(
-          results.current_page, TimelineName(name, src, 'mean'), 'ms', avg,
-          tir_label=interaction_records[0].label,
-          improvement_direction=improvement_direction.DOWN))
-      results.AddValue(scalar.ScalarValue(
-          results.current_page, TimelineName(name, src, 'stddev'), 'ms', stddev,
-          tir_label=interaction_records[0].label,
-          improvement_direction=improvement_direction.DOWN))
-
-  def _CalculateGPUTimelineData(self, model):
-    """Uses the model and calculates the times for various values for each
-       frame. The return value will be a dictionary of the following format:
-         {
-           (EVENT_NAME1, SRC1_TYPE): [FRAME0_TIME, FRAME1_TIME...etc.],
-           (EVENT_NAME2, SRC2_TYPE): [FRAME0_TIME, FRAME1_TIME...etc.],
-         }
-
-       Events:
-         swap - The time in milliseconds between each swap marker.
-         total - The amount of time spent in the renderer thread.
-         TRACKED_NAMES: Using the TRACKED_GL_CONTEXT_NAME dict, we
-                        include the traces per frame for the
-                        tracked name.
-       Source Types:
-         None - This will only be valid for the "swap" event.
-         cpu - For an event, the "cpu" source type signifies time spent on the
-               gpu thread using the CPU. This uses the "gpu.service" markers.
-         gpu - For an event, the "gpu" source type signifies time spent on the
-               gpu thread using the GPU. This uses the "gpu.device" markers.
-    """
-    all_service_events = []
-    current_service_frame_end = sys.maxint
-    current_service_events = []
-
-    all_device_events = []
-    current_device_frame_end = sys.maxint
-    current_device_events = []
-
-    tracked_events = {}
-    tracked_events.update(
-        dict([((value, 'cpu'), [])
-              for value in TRACKED_GL_CONTEXT_NAME.itervalues()]))
-    tracked_events.update(
-        dict([((value, 'gpu'), [])
-              for value in TRACKED_GL_CONTEXT_NAME.itervalues()]))
-
-    # These will track traces within the current frame.
-    current_tracked_service_events = collections.defaultdict(list)
-    current_tracked_device_events = collections.defaultdict(list)
-
-    event_iter = model.IterAllEvents(
-        event_type_predicate=model_module.IsSliceOrAsyncSlice)
-    for event in event_iter:
-      # Look for frame end markers
-      if (event.category, event.name) == SERVICE_FRAME_END_MARKER:
-        current_service_frame_end = event.end
-      elif (event.category, event.name) == DEVICE_FRAME_END_MARKER:
-        current_device_frame_end = event.end
-
-      # Track all other toplevel gl category markers
-      elif event.args.get('gl_category', None) == TOPLEVEL_GL_CATEGORY:
-        base_name = event.name
-        dash_index = base_name.rfind('-')
-        if dash_index != -1:
-          base_name = base_name[:dash_index]
-        tracked_name = TRACKED_GL_CONTEXT_NAME.get(base_name, None)
-
-        if event.category == TOPLEVEL_SERVICE_CATEGORY:
-          # Check if frame has ended.
-          if event.start >= current_service_frame_end:
-            if current_service_events:
-              all_service_events.append(current_service_events)
-              for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-                tracked_events[(value, 'cpu')].append(
-                    current_tracked_service_events[value])
-            current_service_events = []
-            current_service_frame_end = sys.maxint
-            current_tracked_service_events.clear()
-
-          current_service_events.append(event)
-          if tracked_name:
-            current_tracked_service_events[tracked_name].append(event)
-
-        elif event.category == TOPLEVEL_DEVICE_CATEGORY:
-          # Check if frame has ended.
-          if event.start >= current_device_frame_end:
-            if current_device_events:
-              all_device_events.append(current_device_events)
-              for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-                tracked_events[(value, 'gpu')].append(
-                    current_tracked_device_events[value])
-            current_device_events = []
-            current_device_frame_end = sys.maxint
-            current_tracked_device_events.clear()
-
-          current_device_events.append(event)
-          if tracked_name:
-            current_tracked_device_events[tracked_name].append(event)
-
-    # Append Data for Last Frame.
-    if current_service_events:
-      all_service_events.append(current_service_events)
-      for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-        tracked_events[(value, 'cpu')].append(
-            current_tracked_service_events[value])
-    if current_device_events:
-      all_device_events.append(current_device_events)
-      for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-        tracked_events[(value, 'gpu')].append(
-            current_tracked_device_events[value])
-
-    # Calculate Mean Frame Time for the CPU side.
-    frame_times = []
-    if all_service_events:
-      prev_frame_end = all_service_events[0][0].start
-      for event_list in all_service_events:
-        last_service_event_in_frame = event_list[-1]
-        frame_times.append(last_service_event_in_frame.end - prev_frame_end)
-        prev_frame_end = last_service_event_in_frame.end
-
-    # Create the timeline data dictionary for service side traces.
-    total_frame_value = ('swap', None)
-    cpu_frame_value = ('total', 'cpu')
-    gpu_frame_value = ('total', 'gpu')
-    timeline_data = {}
-    timeline_data[total_frame_value] = frame_times
-    timeline_data[cpu_frame_value] = _CPUFrameTimes(all_service_events)
-    for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-      cpu_value = (value, 'cpu')
-      timeline_data[cpu_value] = _CPUFrameTimes(tracked_events[cpu_value])
-
-    # Add in GPU side traces if it was supported (IE. device traces exist).
-    if all_device_events:
-      timeline_data[gpu_frame_value] = _GPUFrameTimes(all_device_events)
-      for value in TRACKED_GL_CONTEXT_NAME.itervalues():
-        gpu_value = (value, 'gpu')
-        tracked_gpu_event = tracked_events[gpu_value]
-        timeline_data[gpu_value] = _GPUFrameTimes(tracked_gpu_event)
-
-    return timeline_data