[trace_processing] Remove obsolete trace-cmp.py script
This script is unmaintained and obsolete. Its modern replacement can be
found at
https://fuchsia.googlesource.com/fuchsia/+/659e209abb74cd98a264bd8e7847266aecb7ec01/sdk/testing/sl4f/client/bin/process_trace.dart.
Change-Id: Ia9d95d0e6a3addaac2582112c0d68b5cef7d6856
Reviewed-on: https://fuchsia-review.googlesource.com/c/topaz/+/372256
Reviewed-by: Eric Jia <ejia@google.com>
Reviewed-by: Andrew Wilson <anwilson@google.com>
Commit-Queue: Nathan Rogers <nathanrogers@google.com>
diff --git a/tools/uiperf/trace-cmp.py b/tools/uiperf/trace-cmp.py
deleted file mode 100755
index 755e6e0..0000000
--- a/tools/uiperf/trace-cmp.py
+++ /dev/null
@@ -1,256 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2018 The Fuchsia 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 argparse
-import collections
-import json
-import sys
-
-try:
- import matplotlib.patches
- import matplotlib.pyplot as plt
- import numpy as np
- import pandas as pd
- import scipy as sp
- import scipy.stats
- import seaborn as sns
- plt.style.use('ggplot')
-except ImportError as e:
- print("""\
-Looks like you didn't have one of the following libraries installed:
- * matplotlib
- * numpy
- * pandas
- * scipy
- * seaborn
-Try installing them with:
- $ sudo apt install python3-matplotlib python3-numpy python3-pandas python3-scipy python3-seaborn""")
- raise e
-
-parser = argparse.ArgumentParser(
- description="""\
-Compare the distribution of event durations between two (groups of) trace files.
-
-Example usage:
- ./topaz/tools/uiperf/trace-cmp.py \\
- --event_name 'vsync callback' \\
- --thread_names 'assistant_card_image_grid.ui' \\
- --before trace-2018-10-09T21:00:25.json trace-2018-10-09T21:08:14.json \\
- --after trace-2018-10-09T21:12:14.json trace-2018-10-09T22:03:04.json
-""",
- formatter_class=argparse.RawDescriptionHelpFormatter)
-
-parser.add_argument(
- '--event_name',
- default='vsync callback',
- nargs='?',
- help=
- 'What event name to look for in each trace. By default, this is '
- '\'vsync callback\', which is the total per frame workload on the Flutter '
- 'UI thread.'
-)
-parser.add_argument(
- '--thread_names',
- default=[],
- nargs='*',
- help=
- 'What thread names to search for |event_name| in. Example: "dashboard.ui".'
-)
-parser.add_argument(
- '--before',
- default=[],
- nargs='+',
- help=
- 'A list of before (a build without your changes) json trace files. Example: "trace-2018-10-09T22:18:26.json".'
-)
-parser.add_argument(
- '--after',
- default=[],
- nargs='+',
- help=
- 'A list of after (a build with your changes) json trace files. Example: "trace-2018-10-09T22:25:01.json".'
-)
-# TODO: Add cdf as a graph option.
-valid_graph_args = {'none', 'density'}
-parser.add_argument(
- '--graph',
- default='none',
- nargs='?',
- help='Optionally display a density plot of the distribution of event durations. Must be one of {}'.format(valid_graph_args)
-)
-
-args = parser.parse_args()
-
-target_event_name = args.event_name
-target_thread_names = set(args.thread_names)
-before_filenames = args.before
-after_filenames = args.after
-
-if len(before_filenames) == 0:
- print('List of before tracefiles must be non-empty.')
- parser.print_usage()
- sys.exit(1)
-if len(after_filenames) == 0:
- parser.print_usage()
- print('List of after tracefiles must be non-empty.')
- sys.exit(1)
-
-if args.graph not in valid_graph_args:
- parser.print_usage()
- print('Invalid --graph argument, must be one of {}'.format(valid_graph_args))
- sys.exit(1)
-
-# {
-# pthread:0x1234: {'Before': [e0, e1, ...], 'After': [e0, e1, ...]},
-# pthread:0x1235: {'Before': [e0, e1, ...], 'After': [e0, e1, ...]},
-# }
-thread_name_to_groups = collections.defaultdict(
- lambda: collections.defaultdict(list))
-
-for group, filenames in [('Before', before_filenames), ('After',
- after_filenames)]:
- for filename in filenames:
- with open(filename) as f:
- root_object = json.load(f)
-
- system_trace_events = root_object['systemTraceEvents']
- trace_events = root_object['traceEvents']
-
- # A mapping of thread ids to thread names.
- tid_to_name = {}
- for e in system_trace_events['events']:
- if e['ph'] == 't':
- tid_to_name[e['tid']] = e['name']
-
- # Group (events sorted by ts) by tid.
- by_tid = collections.defaultdict(list)
- for e in trace_events:
- by_tid[e['tid']].append(e)
- for k, v in by_tid.items():
- by_tid[k] = sorted(v, key=lambda e: e['ts'])
-
- for tid, es in by_tid.items():
- thread_name = tid_to_name[tid]
- if len(
- target_thread_names) > 0 and thread_name not in target_thread_names:
- continue
-
- events = [e for e in es if e['name'] == target_event_name]
- event_durations = []
- begin_stack = []
- # If the first event's phase is end, then remove it.
- if len(events) > 0 and events[0]['ph'] == 'E':
- events.pop(0)
- for e in events:
- if e['ph'] == 'B':
- begin_stack.append(e)
- elif e['ph'] == 'E':
- popped = begin_stack.pop()
- event_durations.append(e['ts'] - popped['ts'])
- else:
- assert False
- assert len(begin_stack) == 0
-
- if len(event_durations) == 0:
- continue
-
- thread_name_to_groups[thread_name][group].append(event_durations)
-
-for thread_name, groups in thread_name_to_groups.items():
- if ('Before' in groups) ^ ('After' in groups):
- print('Found thread name "{}" only in {} trace'.format(
- thread_name, ['Before', 'After']['After' in groups]))
- df = pd.DataFrame(columns=[
- 'Group',
- 'Count',
- # https://en.wikipedia.org/wiki/Sample_maximum_and_minimum
- 'Minimum',
- # https://en.wikipedia.org/wiki/Percentile
- '25th Percentile',
- # https://en.wikipedia.org/wiki/Median
- 'Median',
- # https://en.wikipedia.org/wiki/Sample_mean_and_covariance
- 'Mean',
- '75th Percentile',
- '90th Percentile',
- '95th Percentile',
- '99th Percentile',
- 'Maximum',
- # https://en.wikipedia.org/wiki/Standard_deviation#Sample_standard_deviation
- 'Standard Deviation',
- # https://en.wikipedia.org/wiki/Median_absolute_deviation
- 'Median Absolute Deviation',
- # https://en.wikipedia.org/wiki/Outlier#Tukey's_fences
- 'Tukey Outlier Count',
- ])
- yss = []
- for key in ['Before', 'After']:
- group = groups[key]
- ys = []
- for item in group:
- ys += item
- ys = np.array(ys)
- yss.append(ys)
- color = {
- 'Before': 'blue',
- 'After': 'orange',
- }[key]
- sns.distplot(ys, rug=True, color=color, label=key, bins=20)
-
- row = {}
- row['Group'] = key
- row['Count'] = ys.shape[0]
- row['Minimum'] = ys.min()
- row['25th Percentile'] = np.percentile(ys, 25)
- row['Median'] = np.percentile(ys, 50)
- row['Mean'] = ys.mean()
- row['75th Percentile'] = np.percentile(ys, 75)
- row['90th Percentile'] = np.percentile(ys, 90)
- row['95th Percentile'] = np.percentile(ys, 95)
- row['99th Percentile'] = np.percentile(ys, 99)
- row['Maximum'] = ys.max()
- row['Standard Deviation'] = ys.std()
- median = np.median(ys)
- row['Median Absolute Deviation'] = np.median(
- [np.abs(y - median) for y in ys])
- q1 = np.percentile(ys, 25)
- q3 = np.percentile(ys, 75)
- iqr = q3 - q1
- l = q1 - 1.5 * iqr
- u = q3 + 1.5 * iqr
- outliers = [y for y in ys if not (l <= y <= u)]
- row['Tukey Outlier Count'] = len(outliers)
-
- df = df.append(row, ignore_index=True)
- print('Results for {}:'.format(thread_name))
- print('Units: Microseconds')
- print(df.to_string(index=False, float_format='%.2f', justify='center'))
-
- # https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test
- u_statistic, p_value = sp.stats.mannwhitneyu(
- yss[0], yss[1], alternative='two-sided')
- cutoff = 0.05
- report_result = 'DIFFERED' if p_value < cutoff else 'DID NOT DIFFER'
- print('Mann–Whitney U test:')
- print(
- 'The distributions of the two groups {} significantly (Mann-Whitney U={:.2f}, P={:.2f}, cutoff={:.2f}).'
- .format(report_result, u_statistic, p_value, cutoff))
-
- if args.graph == 'density':
- plt.title('{}: Before/After {} Durations'.format(thread_name,
- target_event_name))
- plt.xlabel('Microseconds')
- plt.ylabel('Density')
- before_patch = matplotlib.patches.Patch(color='blue', label='Before')
- after_patch = matplotlib.patches.Patch(color='orange', label='After')
- plt.legend(handles=[before_patch, after_patch])
- plt.show()
-
- print('')
- print('===')
- print('')
