fixed type conversion pyright issues

Author: networmix

metrics/aggregate.py

@@ -36,11 +36,13 @@ def summarize_across_seeds(series_by_seed: Dict[int, Any], label: str) -> dict:
         med = df.median(axis=1, numeric_only=True)
         q25 = df.quantile(0.25, axis=1, numeric_only=True)
         q75 = df.quantile(0.75, axis=1, numeric_only=True)
+        # Convert Series to dict[int, float] using explicit index iteration
+        # (avoids type issues with to_dict() returning dict[Hashable, Any])
         return {
             "type": "series",
-            "median": {int(k): float(v) for k, v in med.to_dict().items()},
-            "q25": {int(k): float(v) for k, v in q25.to_dict().items()},
-            "q75": {int(k): float(v) for k, v in q75.to_dict().items()},
+            "median": {i: float(med.iloc[i]) for i in range(len(med))},
+            "q25": {i: float(q25.iloc[i]) for i in range(len(q25))},
+            "q75": {i: float(q75.iloc[i]) for i in range(len(q75))},
         }
     else:
         arr = np.array(

metrics/summary.py

@@ -1356,13 +1356,14 @@ def _holm_adjust_1d(p_series: pd.Series) -> pd.Series:
         p = p_adj_map.get(base, df.get(f"{base}__p"))
         vals: List[str] = []
         for i in range(df.shape[0]):
-            m = mean.iat[i] if mean is not None else float("nan")
-            nval = int(n.iat[i]) if n is not None and pd.notna(n.iat[i]) else 0
-            pval = p.iat[i] if p is not None else float("nan")
-            if not math.isfinite(float(m)):
+            m_val = float(mean.iloc[i]) if mean is not None else float("nan")
+            n_raw = n.iloc[i] if n is not None else None
+            nval = int(n_raw) if n_raw is not None and pd.notna(n_raw) else 0
+            pval = float(p.iloc[i]) if p is not None else float("nan")
+            if not math.isfinite(m_val):
                 vals.append("–")
             else:
-                vals.append(f"{m:.3f} (n={nval}, adj_p={pval:.3f})")
+                vals.append(f"{m_val:.3f} (n={nval}, adj_p={pval:.3f})")
         out_df[base] = vals
     print_pretty_table(
         out_df,