Porting relevant legacy conversion code from performance_lp to src side

tools/perf/generate_legacy_perf_dashboard_json.py

+#!/usr/bin/env python
+# Copyright 2016 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.
+
+""" Generates legacy perf dashboard json from non-telemetry based perf tests.
+Taken from chromium/build/scripts/slave/performance_log_processory.py
+(https://goo.gl/03SQRk)
+"""
+
+import collections
+import json
+import math
+import logging
+import re
+
+
+class LegacyResultsProcessor(object):
+  """Class for any log processor expecting standard data to be graphed.
+
+  The log will be parsed looking for any lines of the forms:
+    <*>RESULT <graph_name>: <trace_name>= <value> <units>
+  or
+    <*>RESULT <graph_name>: <trace_name>= [<value>,value,value,...] <units>
+  or
+    <*>RESULT <graph_name>: <trace_name>= {<mean>, <std deviation>} <units>
+
+  For example,
+    *RESULT vm_final_browser: OneTab= 8488 kb
+    RESULT startup: ref= [167.00,148.00,146.00,142.00] ms
+    RESULT TabCapturePerformance_foo: Capture= {30.7, 1.45} ms
+
+  The leading * is optional; it indicates that the data from that line should
+  be considered "important", which may mean for example that it's graphed by
+  default.
+
+  If multiple values are given in [], their mean and (sample) standard
+  deviation will be written; if only one value is given, that will be written.
+  A trailing comma is permitted in the list of values.
+
+  NOTE: All lines except for RESULT lines are ignored, including the Avg and
+  Stddev lines output by Telemetry!
+
+  Any of the <fields> except <value> may be empty, in which case the
+  not-terribly-useful defaults will be used. The <graph_name> and <trace_name>
+  should not contain any spaces, colons (:) nor equals-signs (=). Furthermore,
+  the <trace_name> will be used on the waterfall display, so it should be kept
+  short. If the trace_name ends with '_ref', it will be interpreted as a
+  reference value, and shown alongside the corresponding main value on the
+  waterfall.
+
+  Semantic note: The terms graph and chart are used interchangeably here.
+  """
+
+  RESULTS_REGEX = re.compile(r'(?P<IMPORTANT>\*)?RESULT '
+                             r'(?P<GRAPH>[^:]*): (?P<TRACE>[^=]*)= '
+                             r'(?P<VALUE>[\{\[]?[-\d\., ]+[\}\]]?)('
+                             r' ?(?P<UNITS>.+))?')
+  # TODO(eyaich): Determine if this format is still used by any perf tests
+  HISTOGRAM_REGEX = re.compile(r'(?P<IMPORTANT>\*)?HISTOGRAM '
+                               r'(?P<GRAPH>[^:]*): (?P<TRACE>[^=]*)= '
+                               r'(?P<VALUE_JSON>{.*})(?P<UNITS>.+)?')
+
+  def __init__(self):
+    # A dict of Graph objects, by name.
+    self._graphs = {}
+    # A dict mapping output file names to lists of lines in a file.
+    self._output = {}
+    self._percentiles = [.1, .25, .5, .75, .90, .95, .99]
+
+  class Trace(object):
+    """Encapsulates data for one trace. Here, this means one point."""
+
+    def __init__(self):
+      self.important = False
+      self.value = 0.0
+      self.stddev = 0.0
+
+    def __str__(self):
+      result = _FormatHumanReadable(self.value)
+      if self.stddev:
+        result += '+/-%s' % _FormatHumanReadable(self.stddev)
+      return result
+
+  class Graph(object):
+    """Encapsulates a set of points that should appear on the same graph."""
+
+    def __init__(self):
+      self.units = None
+      self.traces = {}
+
+    def IsImportant(self):
+      """A graph is considered important if any of its traces is important."""
+      for trace in self.traces.itervalues():
+        if trace.important:
+          return True
+      return False
+
+    def BuildTracesDict(self):
+      """Returns a dictionary mapping trace names to [value, stddev]."""
+      traces_dict = {}
+      for name, trace in self.traces.items():
+        traces_dict[name] = [str(trace.value), str(trace.stddev)]
+      return traces_dict
+
+
+  def GenerateJsonResults(self, filename):
+    # Iterate through the file and process each output line
+    with open(filename) as f:
+      for line in f.readlines():
+        self.ProcessLine(line)
+    # After all results have been seen, generate the graph json data
+    return self.GenerateGraphJson()
+
+
+  def _PrependLog(self, filename, data):
+    """Prepends some data to an output file."""
+    self._output[filename] = data + self._output.get(filename, [])
+
+
+  def ProcessLine(self, line):
+    """Processes one result line, and updates the state accordingly."""
+    results_match = self.RESULTS_REGEX.search(line)
+    histogram_match = self.HISTOGRAM_REGEX.search(line)
+    if results_match:
+      self._ProcessResultLine(results_match)
+    elif histogram_match:
+      raise Exception("Error: Histogram results parsing not supported yet")
+
+
+  def _ProcessResultLine(self, line_match):
+    """Processes a line that matches the standard RESULT line format.
+
+    Args:
+      line_match: A MatchObject as returned by re.search.
+    """
+    match_dict = line_match.groupdict()
+    graph_name = match_dict['GRAPH'].strip()
+    trace_name = match_dict['TRACE'].strip()
+
+    graph = self._graphs.get(graph_name, self.Graph())
+    graph.units = match_dict['UNITS'] or ''
+    trace = graph.traces.get(trace_name, self.Trace())
+    trace.value = match_dict['VALUE']
+    trace.important = match_dict['IMPORTANT'] or False
+
+    # Compute the mean and standard deviation for a list or a histogram,
+    # or the numerical value of a scalar value.
+    if trace.value.startswith('['):
+      try:
+        value_list = [float(x) for x in trace.value.strip('[],').split(',')]
+      except ValueError:
+        # Report, but ignore, corrupted data lines. (Lines that are so badly
+        # broken that they don't even match the RESULTS_REGEX won't be
+        # detected.)
+        logging.warning("Bad test output: '%s'" % trace.value.strip())
+        return
+      trace.value, trace.stddev, filedata = self._CalculateStatistics(
+          value_list, trace_name)
+      assert filedata is not None
+      for filename in filedata:
+        self._PrependLog(filename, filedata[filename])
+    elif trace.value.startswith('{'):
+      stripped = trace.value.strip('{},')
+      try:
+        trace.value, trace.stddev = [float(x) for x in stripped.split(',')]
+      except ValueError:
+        logging.warning("Bad test output: '%s'" % trace.value.strip())
+        return
+    else:
+      try:
+        trace.value = float(trace.value)
+      except ValueError:
+        logging.warning("Bad test output: '%s'" % trace.value.strip())
+        return
+
+    graph.traces[trace_name] = trace
+    self._graphs[graph_name] = graph
+
+
+  def GenerateGraphJson(self):
+    """Writes graph json for each graph seen.
+    """
+    charts = {}
+    for graph_name, graph in self._graphs.iteritems():
+      graph_dict = collections.OrderedDict([
+        ('traces', graph.BuildTracesDict()),
+        ('units', str(graph.units)),
+      ])
+
+      # Include a sorted list of important trace names if there are any.
+      important = [t for t in graph.traces.keys() if graph.traces[t].important]
+      if important:
+        graph_dict['important'] = sorted(important)
+
+      charts[graph_name] = graph_dict
+    return json.dumps(charts)
+
+
+  # _CalculateStatistics needs to be a member function.
+  # pylint: disable=R0201
+  # Unused argument value_list.
+  # pylint: disable=W0613
+  def _CalculateStatistics(self, value_list, trace_name):
+    """Returns a tuple with some statistics based on the given value list.
+
+    This method may be overridden by subclasses wanting a different standard
+    deviation calcuation (or some other sort of error value entirely).
+
+    Args:
+      value_list: the list of values to use in the calculation
+      trace_name: the trace that produced the data (not used in the base
+          implementation, but subclasses may use it)
+
+    Returns:
+      A 3-tuple - mean, standard deviation, and a dict which is either
+          empty or contains information about some file contents.
+    """
+    n = len(value_list)
+    if n == 0:
+      return 0.0, 0.0, {}
+    mean = float(sum(value_list)) / n
+    variance = sum([(element - mean)**2 for element in value_list]) / n
+    stddev = math.sqrt(variance)
+
+    return mean, stddev, {}
+
+
+def _FormatHumanReadable(number):
+  """Formats a float into three significant figures, using metric suffixes.
+
+  Only m, k, and M prefixes (for 1/1000, 1000, and 1,000,000) are used.
+  Examples:
+    0.0387    => 38.7m
+    1.1234    => 1.12
+    10866     => 10.8k
+    682851200 => 683M
+  """
+  metric_prefixes = {-3: 'm', 0: '', 3: 'k', 6: 'M'}
+  scientific = '%.2e' % float(number)     # 6.83e+005
+  e_idx = scientific.find('e')            # 4, or 5 if negative
+  digits = float(scientific[:e_idx])      # 6.83
+  exponent = int(scientific[e_idx + 1:])  # int('+005') = 5
+  while exponent % 3:
+    digits *= 10
+    exponent -= 1
+  while exponent > 6:
+    digits *= 10
+    exponent -= 1
+  while exponent < -3:
+    digits /= 10
+    exponent += 1
+  if digits >= 100:
+    # Don't append a meaningless '.0' to an integer number.
+    digits = int(digits)
+  # Exponent is now divisible by 3, between -3 and 6 inclusive: (-3, 0, 3, 6).
+  return '%s%s' % (digits, metric_prefixes[exponent])