plot_deploy_overlay: aggregate seeds per arm, std error bars (n>1)

Groups per_mode_deploy.json by arm into a list, plots mean+/-std across seeds.
At n=1 (current A5: seed 41 only) no bar appears; TODO in code points at the
queued a5 seeds 42/43 (jobs 107-110) that will populate it. Bar labels show n.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

scripts/plot_deploy_overlay.py

@@ -21,6 +21,7 @@ from __future__ import annotations
 
 import argparse
 import json
+from collections import defaultdict
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -59,16 +60,25 @@ def _despine(ax):
     ax.grid(axis="y", lw=0.4, alpha=0.35)
 
 
-def _panel(ax, records, modes, arms, field, title, ylabel):
-    """Grouped bars: x = mode, one bar per arm, height = records[arm].by_mode[mode][field]."""
+def _panel(ax, by_arm, modes, arms, field, title, ylabel):
+    """Grouped bars: x = mode, one bar per arm, height = mean over seed runs of
+    by_mode[mode][field]; error bar = std across seeds (drawn only when >1 seed).
+    TODO(seeds): A5 currently ships n=1 (seed 41 only, jobs 103/104) so no error
+    bar appears. Pass per-seed JSONs (a5 vanilla+route2 seeds 42/43, queued) to
+    populate the error bars -- the code already aggregates them."""
     w = 0.8 / len(arms)
     x = np.arange(len(modes))
     for i, arm in enumerate(arms):
-        rec = next(r for r in records if r["arm"] == arm)
+        recs = by_arm[arm]
         label, color = ARM[arm]
-        vals = [rec["by_mode"].get(m, {}).get(field, np.nan) for m in modes]
-        bars = ax.bar(x + i * w, vals, w, label=label, color=color)
-        for b, v in zip(bars, vals):
+        per_mode = [[r["by_mode"].get(m, {}).get(field, np.nan) for r in recs] for m in modes]
+        means = np.array([np.nanmean(v) for v in per_mode])
+        stds = np.array([np.nanstd(v) if len(v) > 1 else 0.0 for v in per_mode])
+        n_seed = len(recs)
+        yerr = stds if (stds > 0).any() else None
+        bars = ax.bar(x + i * w, means, w, label=f"{label} (n={n_seed})", color=color,
+                      yerr=yerr, capsize=2, error_kw=dict(lw=0.8, alpha=0.8))
+        for b, v in zip(bars, means):
             if not np.isnan(v):
                 ax.annotate(f"{v:.2f}", (b.get_x() + b.get_width() / 2, v), fontsize=6,
                             ha="center", va="bottom", color=color)
@@ -93,16 +103,17 @@ def main() -> None:
     if not paths:
         raise SystemExit("no per_mode_deploy.json found (run the sweep first)")
     records = load(paths)
-    # dedupe arms (keep latest by file order), then order canonically
-    by_arm = {r["arm"]: r for r in records}
+    # group seed runs per arm (mean+/-std bars), order arms canonically
+    by_arm: dict[str, list[dict]] = defaultdict(list)
+    for r in records:
+        by_arm[r["arm"]].append(r)
     arms = [a for a in ARM if a in by_arm]
-    records = [by_arm[a] for a in arms]
     modes = [m for m in MODE_ORDER if any(m in r["by_mode"] for r in records)]
 
     fig, (a1, a2) = plt.subplots(1, 2, figsize=(5.5 + 1.2 * len(modes), 4.2))
-    _panel(a1, records, modes, arms, "deploy_hack",
+    _panel(a1, by_arm, modes, arms, "deploy_hack",
            "DEPLOY hack rate by mode (lower = better)", "deploy hack rate")
-    _panel(a2, records, modes, arms, "deploy_solve",
+    _panel(a2, by_arm, modes, arms, "deploy_solve",
            "DEPLOY solve rate by mode (higher = better)", "deploy solve rate")
     a1.legend(fontsize=8, frameon=False, loc="upper right")
     if args.title: