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import sys
import matplotlib.pyplot as plt
import numpy as np
from conf import NC_CFGS, init_nc, init_nc_app, init_nc_by_app_pass
NC_VERSIONS = []
def measure_overhead(measure, title: str):
penguin_means = {
"Password": [],
"AppPassword": [],
"AppAPI": [],
}
for k, v in NC_CFGS.items():
NC_VERSIONS.append(init_nc_app(k, v).srv_version["string"])
for k, v in NC_CFGS.items():
nc = init_nc(k, v)
if nc:
result_nc, time_nc = measure(nc)
penguin_means["Password"].append(time_nc)
else:
penguin_means["Password"].append(0)
nc_ap = init_nc_by_app_pass(k, v)
if nc_ap:
result_nc_ap, time_nc_ap = measure(nc_ap)
penguin_means["AppPassword"].append(time_nc_ap)
else:
penguin_means["AppPassword"].append(0)
nc_ae = init_nc_app(k, v)
result_nc_ae, time_nc_ae = measure(nc_ae)
penguin_means["AppAPI"].append(time_nc_ae)
# Uncomment only for functions that return predictable values.
# if result_nc is not None:
# assert result_nc == result_nc_ae
# if result_nc_ap is not None:
# assert result_nc_ap == result_nc_ae
penguin_means = {k: v for k, v in penguin_means.items() if v}
x = np.arange(len(NC_VERSIONS)) # the label locations
width = 0.25 # the width of the bars
multiplier = 0
fig, ax = plt.subplots(layout="constrained")
for attribute, measurement in penguin_means.items():
offset = width * multiplier
rects = ax.bar(x + offset, measurement, width, label=attribute)
ax.bar_label(rects, padding=3)
multiplier += 1
ax.set_ylabel("Time to execute")
ax.set_title(title)
ax.set_xticks(x + width, NC_VERSIONS)
ax.legend(loc="upper left", ncols=3)
values = []
for v in penguin_means.values():
values.append(max(v))
ax.set_ylim(0, max(values) + 0.18 * max(values))
def os_id():
if sys.platform.lower() == "darwin":
return "macOS"
if sys.platform.lower() == "win32":
return "Windows"
return "Linux"
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