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import sys
import json
import statistics
import collections
import numpy as np
import matplotlib.pyplot as plt
def read_bench():
with open("benchmark.json", "r") as file:
bench = json.loads(file.read())
return bench
def plot_bench_ops(b):
r = np.arange(b["start"], b["end"], b["step"])
nlogn = r * np.log(r)
n_square = r ** 2
means = []
for runs in b["data"]:
means.append(statistics.mean(
[runs[i]["ops_num"] for i in range(len(runs))]
))
fig, ax = plt.subplots()
ax.set(xlabel="stack length", ylabel="op number", title="push_swap benchmark")
ax.grid()
ax.plot(r, means)
ax.plot(r, nlogn, label=r"$n \log(n)$")
ax.plot(r, n_square, label=r"$n^2$")
ax.plot(r, r, label=r"$n$")
ax.set_ylim([0, max(means)])
ax.set_xlim([0, max(r)])
plt.legend()
plt.show()
def plot_bench_distribution(b):
counter = collections.Counter()
for runs in b["data"]:
for run in runs:
counter.update(run["ops_counter"])
fig, ax = plt.subplots()
ax.set(xlabel="op", ylabel="time used", title="push_swap benchmark ops distribution")
ax.bar(counter.keys(), counter.values())
plt.show()
if __name__ == "__main__":
b = read_bench()
if len(sys.argv) == 2 and sys.argv[1] == "--dist":
plot_bench_distribution(b)
else:
plot_bench_ops(b)
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