Page MenuHomec4science
Files F91217965

plot.py
No OneTemporary
Actions

File Metadata

Created
Sat, Nov 9, 02:08

plot.py
View Options

#!/usr/bin/env python
"""Script to visualize google-benchmark output"""
from __future__ import print_function
import argparse
import sys
import logging
import json
import pandas as pd
import matplotlib.pyplot as plt
import pathlib
logging.basicConfig(format="[%(levelname)s] %(message)s")
METRICS = [
"real_time",
"cpu_time",
"bytes_per_second",
"items_per_second",
"iterations",
]
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 or json 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"
)
parser.add_argument(
"--output", type=str, default="", help="File in which to save the graph"
)
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"""
extension = pathlib.Path(args.file.name).suffix
try:
if extension == ".csv":
data = pd.read_csv(args.file, usecols=["name", args.metric])
elif extension == ".json":
json_data = json.load(args.file)
data = pd.DataFrame(json_data["benchmarks"])
else:
logging.error("Unsupported file extension '{}'".format(extension))
exit(1)
except ValueError:
logging.error(
'Could not parse the benchmark data. Did you forget "--benchmark_format=[csv|json] when running the benchmark"?'
)
exit(1)
data["label"] = data["name"].apply(lambda x: x.split("/")[-2])
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"""
fig, ax = plt.subplots()
for label, group in label_groups.items():
ax.plot(
group["input"],
group[args.metric] / 1024 / 1024 / 1024,
label=label,
marker=".",
)
if args.logx:
ax.set_xscale("log", base=2)
if args.logy:
ax.set_yscale("log")
ax.set_xlabel(args.xlabel)
ax.set_ylabel(args.ylabel)
ax.set_title(args.title)
ax.legend()
# ax.vlines([32, 1024, 19712], 0, 120, color="gray")
# ax.text(16, 115, "L1")
# ax.text(512, 115, "L2")
# ax.text(19712 / 2, 115, "L3")
caches = [48, 1280, 55296]
ax.vlines(caches, 0, 120, color="gray")
for i, c in enumerate(caches):
ax.text(c / 2, 115, f"L{i}")
if args.output:
logging.info("Saving to %s" % args.output)
plt.savefig(args.output)
else:
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()

Event Timeline

c4science · Help