1# Copyright 2013 The Chromium Authors 2# Use of this source code is governed by a BSD-style license that can be 3# found in the LICENSE file. 4 5 6import re 7import sys 8 9import json 10import logging 11import math 12 13from lib.common import perf_result_data_type 14 15 16# Mapping from result type to test output 17RESULT_TYPES = {perf_result_data_type.UNIMPORTANT: 'RESULT ', 18 perf_result_data_type.DEFAULT: '*RESULT ', 19 perf_result_data_type.INFORMATIONAL: '', 20 perf_result_data_type.UNIMPORTANT_HISTOGRAM: 'HISTOGRAM ', 21 perf_result_data_type.HISTOGRAM: '*HISTOGRAM '} 22 23 24def _EscapePerfResult(s): 25 """Escapes |s| for use in a perf result.""" 26 return re.sub('[\:|=/#&,]', '_', s) 27 28 29def FlattenList(values): 30 """Returns a simple list without sub-lists.""" 31 ret = [] 32 for entry in values: 33 if isinstance(entry, list): 34 ret.extend(FlattenList(entry)) 35 else: 36 ret.append(entry) 37 return ret 38 39 40def GeomMeanAndStdDevFromHistogram(histogram_json): 41 histogram = json.loads(histogram_json) 42 # Handle empty histograms gracefully. 43 if not 'buckets' in histogram: 44 return 0.0, 0.0 45 count = 0 46 sum_of_logs = 0 47 for bucket in histogram['buckets']: 48 if 'high' in bucket: 49 bucket['mean'] = (bucket['low'] + bucket['high']) / 2.0 50 else: 51 bucket['mean'] = bucket['low'] 52 if bucket['mean'] > 0: 53 sum_of_logs += math.log(bucket['mean']) * bucket['count'] 54 count += bucket['count'] 55 56 if count == 0: 57 return 0.0, 0.0 58 59 sum_of_squares = 0 60 geom_mean = math.exp(sum_of_logs / count) 61 for bucket in histogram['buckets']: 62 if bucket['mean'] > 0: 63 sum_of_squares += (bucket['mean'] - geom_mean) ** 2 * bucket['count'] 64 return geom_mean, math.sqrt(sum_of_squares / count) 65 66 67def _ValueToString(v): 68 # Special case for floats so we don't print using scientific notation. 69 if isinstance(v, float): 70 return '%f' % v 71 else: 72 return str(v) 73 74 75def _MeanAndStdDevFromList(values): 76 avg = None 77 sd = None 78 if len(values) > 1: 79 try: 80 value = '[%s]' % ','.join([_ValueToString(v) for v in values]) 81 avg = sum([float(v) for v in values]) / len(values) 82 sqdiffs = [(float(v) - avg) ** 2 for v in values] 83 variance = sum(sqdiffs) / (len(values) - 1) 84 sd = math.sqrt(variance) 85 except ValueError: 86 value = ', '.join(values) 87 else: 88 value = values[0] 89 return value, avg, sd 90 91 92def PrintPages(page_list): 93 """Prints list of pages to stdout in the format required by perf tests.""" 94 print('Pages: [%s]' % ','.join([_EscapePerfResult(p) for p in page_list])) 95 96 97def PrintPerfResult(measurement, trace, values, units, 98 result_type=perf_result_data_type.DEFAULT, 99 print_to_stdout=True): 100 """Prints numerical data to stdout in the format required by perf tests. 101 102 The string args may be empty but they must not contain any colons (:) or 103 equals signs (=). 104 105 Args: 106 measurement: A description of the quantity being measured, e.g. "vm_peak". 107 On the dashboard, this maps to a particular graph. Mandatory. 108 trace: A description of the particular data point, e.g. "reference". 109 On the dashboard, this maps to a particular "line" in the graph. 110 Mandatory. 111 values: A list of numeric measured values. An N-dimensional list will be 112 flattened and treated as a simple list. 113 units: A description of the units of measure, e.g. "bytes". 114 result_type: Accepts values of perf_result_data_type.ALL_TYPES. 115 print_to_stdout: If True, prints the output in stdout instead of returning 116 the output to caller. 117 118 Returns: 119 String of the formated perf result. 120 """ 121 assert perf_result_data_type.IsValidType(result_type), \ 122 'result type: %s is invalid' % result_type 123 124 trace_name = _EscapePerfResult(trace) 125 126 if (result_type == perf_result_data_type.UNIMPORTANT or 127 result_type == perf_result_data_type.DEFAULT or 128 result_type == perf_result_data_type.INFORMATIONAL): 129 assert isinstance(values, list) 130 assert '/' not in measurement 131 flattened_values = FlattenList(values) 132 assert len(flattened_values) 133 value, avg, sd = _MeanAndStdDevFromList(flattened_values) 134 output = '%s%s: %s%s%s %s' % ( 135 RESULT_TYPES[result_type], 136 _EscapePerfResult(measurement), 137 trace_name, 138 # Do not show equal sign if the trace is empty. Usually it happens when 139 # measurement is enough clear to describe the result. 140 '= ' if trace_name else '', 141 value, 142 units) 143 else: 144 assert perf_result_data_type.IsHistogram(result_type) 145 assert isinstance(values, list) 146 # The histograms can only be printed individually, there's no computation 147 # across different histograms. 148 assert len(values) == 1 149 value = values[0] 150 output = '%s%s: %s= %s %s' % ( 151 RESULT_TYPES[result_type], 152 _EscapePerfResult(measurement), 153 trace_name, 154 value, 155 units) 156 avg, sd = GeomMeanAndStdDevFromHistogram(value) 157 158 if avg: 159 output += '\nAvg %s: %f%s' % (measurement, avg, units) 160 if sd: 161 output += '\nSd %s: %f%s' % (measurement, sd, units) 162 if print_to_stdout: 163 print(output) 164 sys.stdout.flush() 165 return output 166 167 168def ReportPerfResult(chart_data, graph_title, trace_title, value, units, 169 improvement_direction='down', important=True): 170 """Outputs test results in correct format. 171 172 If chart_data is None, it outputs data in old format. If chart_data is a 173 dictionary, formats in chartjson format. If any other format defaults to 174 old format. 175 176 Args: 177 chart_data: A dictionary corresponding to perf results in the chartjson 178 format. 179 graph_title: A string containing the name of the chart to add the result 180 to. 181 trace_title: A string containing the name of the trace within the chart 182 to add the result to. 183 value: The value of the result being reported. 184 units: The units of the value being reported. 185 improvement_direction: A string denoting whether higher or lower is 186 better for the result. Either 'up' or 'down'. 187 important: A boolean denoting whether the result is important or not. 188 """ 189 if chart_data and isinstance(chart_data, dict): 190 chart_data['charts'].setdefault(graph_title, {}) 191 chart_data['charts'][graph_title][trace_title] = { 192 'type': 'scalar', 193 'value': value, 194 'units': units, 195 'improvement_direction': improvement_direction, 196 'important': important 197 } 198 else: 199 PrintPerfResult(graph_title, trace_title, [value], units) 200