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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
"""
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Draw ΔS & λ_observe charts into images/.
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"""
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import argparse, os, pandas as pd, matplotlib.pyplot as plt
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parser = argparse.ArgumentParser()
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parser.add_argument("--csv", default="data/metrics.csv")
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parser.add_argument("--threshold", type=float, default=0.4)
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parser.add_argument("--outdir", default="images")
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args = parser.parse_args()
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df = pd.read_csv(args.csv)
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os.makedirs(args.outdir, exist_ok=True)
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ax = df[["ΔS_baseline", "ΔS_WFGY"]].mean().plot.bar(
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color=["#ff5722", "#4caf50"], rot=0)
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ax.set_ylabel("ΔS (lower = better)")
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ax.set_title("Average ΔS Drift")
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plt.tight_layout()
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plt.savefig(f"{args.outdir}/drift_comparison.png", dpi=300)
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plt.close()
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lambda_base = (df["ΔS_baseline"] < args.threshold).mean()
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lambda_wfgy = (df["ΔS_WFGY"] < args.threshold).mean()
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plt.bar(["Baseline", "WFGY"], [lambda_base, lambda_wfgy],
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color=["#ff5722", "#4caf50"])
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plt.ylim(0, 1)
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plt.ylabel("λ_observe pass‑rate")
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plt.title(f"λ_observe (ΔS < {args.threshold})")
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for idx, val in enumerate([lambda_base, lambda_wfgy]):
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plt.text(idx, val + 0.02, f"{val*100:.0f}%", ha="center")
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plt.tight_layout()
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plt.savefig(f"{args.outdir}/lambda_pass.png", dpi=300)
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plt.close()
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print("✓ charts saved to images/")
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