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"""Plot directional finite-difference Lyapunov perturbation robustness."""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
import matplotlib.pyplot as plt
COLORS = {
"trm_baseline_best": "#334155",
"trm_multi4_best": "#0f766e",
"trm_multi4_final": "#dc2626",
}
MARKERS = {
"trm_baseline_best": "o",
"trm_multi4_best": "s",
"trm_multi4_final": "X",
}
def read_rows(paths: list[Path]) -> list[dict[str, str]]:
rows: list[dict[str, str]] = []
for path in paths:
with path.open() as f:
rows.extend(csv.DictReader(f))
return rows
def f(row: dict[str, str], key: str) -> float:
return float(row[key])
def write_combined(path: Path, rows: list[dict[str, str]]) -> None:
keys: list[str] = []
for row in rows:
for key in row:
if key not in keys:
keys.append(key)
with path.open("w", newline="") as out:
writer = csv.DictWriter(out, fieldnames=keys)
writer.writeheader()
writer.writerows(rows)
def plot_metric_grid(rows: list[dict[str, str]], metric: str, ylabel: str, out: Path) -> None:
labels = sorted({r["label"] for r in rows})
afters = sorted({int(float(r["perturb_after"])) for r in rows})
fig, axes = plt.subplots(1, len(afters), figsize=(4.1 * len(afters), 4.25), sharey=True)
if len(afters) == 1:
axes = [axes]
for ax, after in zip(axes, afters):
for label in labels:
lr = [r for r in rows if r["label"] == label and int(float(r["perturb_after"])) == after]
lr.sort(key=lambda r: f(r, "sigma"))
ax.plot(
[f(r, "sigma") for r in lr],
[f(r, metric) for r in lr],
marker=MARKERS.get(label, "o"),
linewidth=2.0,
markersize=5,
color=COLORS.get(label),
label=label.replace("trm_", "").replace("_", " "),
)
ax.set_xscale("symlog", linthresh=1e-3)
ax.set_title(f"after {after}")
ax.set_xlabel("σ along selected direction")
ax.set_ylim(-0.02, 1.02)
ax.grid(alpha=0.23)
axes[0].set_ylabel(ylabel)
axes[-1].legend(frameon=False, fontsize=8, loc="best")
fig.suptitle(f"Finite-difference Lyapunov-direction perturbation: {ylabel}")
fig.tight_layout()
fig.savefig(out, dpi=220, bbox_inches="tight")
plt.close(fig)
def plot_sigma_slice(rows: list[dict[str, str]], sigma: float, out: Path) -> None:
labels = sorted({r["label"] for r in rows})
metrics = [
("retain_worst_on_clean_success", "Clean-success robust retention"),
("rescue_best_on_clean_fail", "Clean-fail best-sign rescue"),
]
fig, axes = plt.subplots(1, 2, figsize=(11.2, 4.6), sharex=True)
for ax, (metric, title) in zip(axes, metrics):
for label in labels:
lr = [
r
for r in rows
if r["label"] == label and abs(float(r["sigma"]) - sigma) <= max(1e-12, sigma * 1e-6)
]
lr.sort(key=lambda r: int(float(r["perturb_after"])))
ax.plot(
[int(float(r["perturb_after"])) for r in lr],
[f(r, metric) for r in lr],
marker=MARKERS.get(label, "o"),
linewidth=2.1,
markersize=6,
color=COLORS.get(label),
label=label.replace("trm_", "").replace("_", " "),
)
ax.set_title(title)
ax.set_xlabel("Perturb after ACT step")
ax.set_ylim(-0.02, 1.02)
ax.grid(alpha=0.23)
axes[0].set_ylabel("Conditional probability")
axes[1].legend(frameon=False, loc="best")
fig.suptitle(f"Lyapunov-direction conditional behavior at σ={sigma:g}")
fig.tight_layout()
fig.savefig(out, dpi=220, bbox_inches="tight")
plt.close(fig)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--summaries", nargs="+", required=True)
parser.add_argument("--out-dir", required=True)
parser.add_argument("--slice-sigma", type=float, default=0.03)
args = parser.parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
rows = read_rows([Path(p) for p in args.summaries])
write_combined(out_dir / "directional_lyap_perturb_combined.csv", rows)
plot_metric_grid(rows, "mean_sign_exact", "Mean ± sign exact", out_dir / "directional_mean_sign_exact_grid.png")
plot_metric_grid(rows, "worst_sign_exact", "Worst-sign exact", out_dir / "directional_worst_sign_exact_grid.png")
plot_metric_grid(rows, "retain_worst_on_clean_success", "Clean-success worst-sign retention", out_dir / "directional_retention_worst_grid.png")
plot_metric_grid(rows, "rescue_best_on_clean_fail", "Clean-fail best-sign rescue", out_dir / "directional_rescue_best_grid.png")
plot_metric_grid(rows, "selected_growth_mean", "Selected direction finite-time growth", out_dir / "directional_selected_growth_grid.png")
plot_sigma_slice(rows, args.slice_sigma, out_dir / f"directional_retention_rescue_sigma{args.slice_sigma:g}.png")
print(f"wrote {out_dir}")
if __name__ == "__main__":
main()
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