mirror of
https://github.com/BlackMATov/flat.hpp.git
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124 lines
4.0 KiB
Python
Executable File
124 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Script to visualize google-benchmark output"""
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from __future__ import print_function
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import argparse
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import sys
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import logging
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import pandas as pd
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import matplotlib.pyplot as plt
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logging.basicConfig(format='[%(levelname)s] %(message)s')
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METRICS = ['real_time', 'cpu_time', 'bytes_per_second', 'items_per_second']
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TRANSFORMS = {
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'': lambda x: x,
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'inverse': lambda x: 1.0 / x
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}
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def get_default_ylabel(args):
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"""Compute default ylabel for commandline args"""
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label = ''
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if args.transform == '':
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label = args.metric
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else:
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label = args.transform + '(' + args.metric + ')'
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if args.relative_to is not None:
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label += ' relative to %s' % args.relative_to
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return label
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def parse_args():
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"""Parse commandline arguments"""
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parser = argparse.ArgumentParser(
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description='Visualize google-benchmark output')
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parser.add_argument(
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'-f', metavar='FILE', type=argparse.FileType('r'), default=sys.stdin,
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dest='file', help='path to file containing the csv benchmark data')
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parser.add_argument(
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'-m', metavar='METRIC', choices=METRICS, default=METRICS[0], dest='metric',
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help='metric to plot on the y-axis, valid choices are: %s' % ', '.join(METRICS))
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parser.add_argument(
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'-t', metavar='TRANSFORM', choices=TRANSFORMS.keys(), default='',
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help='transform to apply to the chosen metric, valid choices are: %s'
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% ', '.join(list(TRANSFORMS)), dest='transform')
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parser.add_argument(
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'-r', metavar='RELATIVE_TO', type=str, default=None,
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dest='relative_to', help='plot metrics relative to this label')
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parser.add_argument(
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'--xlabel', type=str, default='input size', help='label of the x-axis')
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parser.add_argument(
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'--ylabel', type=str, help='label of the y-axis')
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parser.add_argument(
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'--title', type=str, default='', help='title of the plot')
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parser.add_argument(
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'--logx', action='store_true', help='plot x-axis on a logarithmic scale')
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parser.add_argument(
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'--logy', action='store_true', help='plot y-axis on a logarithmic scale')
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args = parser.parse_args()
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if args.ylabel is None:
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args.ylabel = get_default_ylabel(args)
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return args
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def parse_input_size(name):
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splits = name.split('/')
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if len(splits) == 1:
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return 1
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return int(splits[1])
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def read_data(args):
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"""Read and process dataframe using commandline args"""
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try:
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data = pd.read_csv(args.file, usecols=['name', args.metric])
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except ValueError:
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msg = 'Could not parse the benchmark data. Did you forget "--benchmark_format=csv"?'
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logging.error(msg)
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exit(1)
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data['label'] = data['name'].apply(lambda x: x.split('/')[0])
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data['input'] = data['name'].apply(parse_input_size)
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data[args.metric] = data[args.metric].apply(TRANSFORMS[args.transform])
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return data
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def plot_groups(label_groups, args):
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"""Display the processed data"""
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for label, group in label_groups.items():
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plt.plot(group['input'], group[args.metric], label=label, marker='.')
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if args.logx:
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plt.xscale('log')
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if args.logy:
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plt.yscale('log')
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plt.xlabel(args.xlabel)
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plt.ylabel(args.ylabel)
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plt.title(args.title)
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plt.legend()
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plt.show()
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def main():
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"""Entry point of the program"""
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args = parse_args()
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data = read_data(args)
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label_groups = {}
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for label, group in data.groupby('label'):
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label_groups[label] = group.set_index('input', drop=False)
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if args.relative_to is not None:
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try:
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baseline = label_groups[args.relative_to][args.metric].copy()
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except KeyError as key:
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msg = 'Key %s is not present in the benchmark output'
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logging.error(msg, str(key))
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exit(1)
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if args.relative_to is not None:
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for label in label_groups:
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label_groups[label][args.metric] /= baseline
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plot_groups(label_groups, args)
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if __name__ == '__main__':
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main()
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