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# SPDX-License-Identifier: BSD-2-Clause
# Copyright (C) 2016, 2024 embedded brains GmbH & Co. KG
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import json
import math
import re
import statistics
import matplotlib.pyplot as plt # type: ignore
from matplotlib import ticker # type: ignore
def _normed_coefficient_of_variation(counter: list[int]) -> float:
return (statistics.stdev(counter) / statistics.mean(counter)) / math.sqrt(
len(counter))
def _plot(data: dict) -> None:
_, axes = plt.subplots()
axes.set_title("SMP Lock Fairness")
axes.set_xlabel("Active Workers")
axes.set_ylabel("Normed Coefficient of Variation")
axes.set_yscale("symlog", linthresh=1e-6)
x = list(range(2, len(data[0]["results"]) + 1))
axes.xaxis.set_major_locator(ticker.FixedLocator(x))
for samples in data:
if samples["lock-object"] != "global":
continue
if samples["section-type"] != "local counter":
continue
y = [
_normed_coefficient_of_variation(results["counter"])
for results in samples["results"][1:]
]
axes.plot(x, y, label=samples["lock-type"], marker="o")
axes.legend(loc="best")
plt.savefig("smplock01fair.png")
plt.savefig("smplock01fair.pdf")
plt.close()
_JSON_DATA = re.compile(
r"\*\*\* BEGIN OF JSON DATA \*\*\*(.*)"
r"\*\*\* END OF JSON DATA \*\*\*", re.DOTALL)
with open("smplock01.scn", "r", encoding="utf-8") as src:
match = _JSON_DATA.search(src.read())
data = json.loads(match.group(1))
_plot(data)
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