import argparse
import json
import matplotlib.pyplot as plt
import numpy as np
def moving_average(times, num_runs):
times_padded = np.pad(
times, (num_runs // 2, num_runs - 1 - num_runs // 2), mode="edge"
)
kernel = np.ones(num_runs) / num_runs
return np.convolve(times_padded, kernel, mode="valid")
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("file", help="JSON file with benchmark results")
parser.add_argument("--title", help="Plot Title")
parser.add_argument("-o", "--output", help="Save image to the given filename.")
parser.add_argument(
"-w",
"--moving-average-width",
type=int,
metavar="num_runs",
help="Width of the moving-average window (default: N/5)",
)
parser.add_argument(
"--no-moving-average",
action="store_true",
help="Do not show moving average curve",
)
args = parser.parse_args()
with open(args.file) as f:
results = json.load(f)["results"]
for result in results:
label = result["command"]
times = result["times"]
num = len(times)
nums = range(num)
plt.scatter(x=nums, y=times, marker=".")
plt.ylim([0, None])
plt.xlim([-1, num])
if not args.no_moving_average:
moving_average_width = (
num // 5 if args.moving_average_width is None else args.moving_average_width
)
average = moving_average(times, moving_average_width)
plt.plot(nums, average, "-")
if args.title:
plt.title(args.title)
legend = []
for result in results:
legend.append(result["command"])
if not args.no_moving_average:
legend.append("moving average")
plt.legend(legend)
plt.ylabel("Time [s]")
if args.output:
plt.savefig(args.output)
else:
plt.show()