plot.py

#!/usr/bin/env python
"""Script to visualize google-benchmark output"""
from __future__ import print_function
import argparse
import sys
import logging
import pandas as pd
import matplotlib.pyplot as plt

logging.basicConfig(format="[%(levelname)s] %(message)s")

METRICS = ["real_time", "cpu_time", "bytes_per_second", "items_per_second"]
TRANSFORMS = {"": lambda x: x, "inverse": lambda x: 1.0 / x}


def get_default_ylabel(args):
    """Compute default ylabel for commandline args"""
    label = ""
    if args.transform == "":
        label = args.metric
    else:
        label = args.transform + "(" + args.metric + ")"
    if args.relative_to is not None:
        label += " relative to %s" % args.relative_to
    return label


def parse_args():
    """Parse commandline arguments"""
    parser = argparse.ArgumentParser(description="Visualize google-benchmark output")
    parser.add_argument(
        "-f",
        metavar="FILE",
        type=argparse.FileType("r"),
        default=sys.stdin,
        dest="file",
        help="path to file containing the csv benchmark data",
    )
    parser.add_argument(
        "-m",
        metavar="METRIC",
        choices=METRICS,
        default=METRICS[0],
        dest="metric",
        help="metric to plot on the y-axis, valid choices are: %s" % ", ".join(METRICS),
    )
    parser.add_argument(
        "-t",
        metavar="TRANSFORM",
        choices=TRANSFORMS.keys(),
        default="",
        help="transform to apply to the chosen metric, valid choices are: %s"
        % ", ".join(list(TRANSFORMS)),
        dest="transform",
    )
    parser.add_argument(
        "-r",
        metavar="RELATIVE_TO",
        type=str,
        default=None,
        dest="relative_to",
        help="plot metrics relative to this label",
    )
    parser.add_argument(
        "--xlabel", type=str, default="input size", help="label of the x-axis"
    )
    parser.add_argument("--ylabel", type=str, help="label of the y-axis")
    parser.add_argument("--title", type=str, default="", help="title of the plot")
    parser.add_argument(
        "--logx", action="store_true", help="plot x-axis on a logarithmic scale"
    )
    parser.add_argument(
        "--logy", action="store_true", help="plot y-axis on a logarithmic scale"
    )

    args = parser.parse_args()
    if args.ylabel is None:
        args.ylabel = get_default_ylabel(args)
    return args


def parse_input_size(name):
    splits = name.split("/")
    if len(splits) == 1:
        return 1
    return int(splits[1])


def read_data(args):
    """Read and process dataframe using commandline args"""
    try:
        data = pd.read_csv(args.file, usecols=["name", args.metric])
    except ValueError:
        msg = 'Could not parse the benchmark data. Did you forget "--benchmark_format=csv"?'
        logging.error(msg)
        exit(1)
    data["label"] = data["name"].apply(lambda x: x.split("/")[0])
    data["input"] = data["name"].apply(parse_input_size)
    data[args.metric] = data[args.metric].apply(TRANSFORMS[args.transform])
    return data


def plot_groups(label_groups, args):
    """Display the processed data"""
    for label, group in label_groups.items():
        plt.plot(group["input"], group[args.metric], label=label, marker=".")
    if args.logx:
        plt.xscale("log")
    if args.logy:
        plt.yscale("log")
    plt.xlabel(args.xlabel)
    plt.ylabel(args.ylabel)
    plt.title(args.title)
    plt.legend()
    plt.show()


def main():
    """Entry point of the program"""
    args = parse_args()
    data = read_data(args)
    label_groups = {}
    for label, group in data.groupby("label"):
        label_groups[label] = group.set_index("input", drop=False)
    if args.relative_to is not None:
        try:
            baseline = label_groups[args.relative_to][args.metric].copy()
        except KeyError as key:
            msg = "Key %s is not present in the benchmark output"
            logging.error(msg, str(key))
            exit(1)

    if args.relative_to is not None:
        for label in label_groups:
            label_groups[label][args.metric] /= baseline
    plot_groups(label_groups, args)


if __name__ == "__main__":
    main()