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from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
def plot(output_root: Path, format):
output_root = output_root / Path("contiguous")
csv_file1 = output_root / Path("load16.csv")
csv_file2 = output_root / Path("load16_mte.csv")
df1 = pd.read_csv(csv_file1, sep=";")
df2 = pd.read_csv(csv_file2, sep=";")
df1["kb"] = df1["len"] * 64 // 1024
df2["kb"] = df2["len"] * 64 // 1024
grouped1 = df1.groupby("kb")["duration"].agg(["mean", "std"]).reset_index()
grouped2 = df2.groupby("kb")["duration"].agg(["mean", "std"]).reset_index()
merged = pd.merge(grouped1, grouped2, on="kb", suffixes=("_1", "_2"))
_, ax1 = plt.subplots(figsize=(10, 6))
ax1.errorbar(
merged["kb"],
merged["mean_1"],
yerr=merged["std_1"],
fmt="o-",
color="#a6bddb",
capsize=5,
label="MTE disabled",
)
ax1.errorbar(
merged["kb"],
merged["mean_2"],
yerr=merged["std_2"],
fmt="s-",
color="#fc9272",
capsize=5,
label="MTE enabled",
)
merged["lower_1"] = merged["mean_1"] - merged["std_1"]
merged["lower_2"] = merged["mean_2"] - merged["std_2"]
merged["upper_1"] = merged["mean_1"] + merged["std_1"]
merged["upper_2"] = merged["mean_2"] + merged["std_2"]
ax1.set_xscale("log", base=2)
ax1.set_yscale("log", base=2)
ax1.legend(loc="upper left")
ax1.grid(True, which="both", linestyle="--", linewidth=0.5)
xticks = grouped1["kb"].to_numpy()
xticks_filtered = [x for i, x in enumerate(xticks) if i % 2 == 0]
xtick_labels = [
f"{int(x/1024)} MiB" if x >= 1024 else f"{int(x)} KiB" for x in xticks_filtered
]
plt.xticks(xticks_filtered, xtick_labels, rotation=45, ha="right")
plt.title("Lower is better ↓", color="navy")
plt.ylabel("Time (ns - logarithmic scale)")
plt.xlabel("Memory size (logarithmic scale)")
ax1.spines["top"].set_visible(False)
ax1.spines["right"].set_visible(False)
plt.tight_layout()
output = output_root / Path(f"result.{format}")
plt.savefig(output, format=format)
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