Fix RNG re-seeding per party and vectorize bootstrap loop

Move rng initialization before the party loop so each party gets a
unique segment of the random stream instead of identical sequences.
Replace Python bootstrap loop with vectorized numpy indexing.

analysis/political_axis.py

@@ -649,6 +649,7 @@ def compute_party_bootstrap_cis(
     lo_pct = alpha / 2.0
     hi_pct = 100.0 - lo_pct
 
+    rng = np.random.default_rng(seed)
     result: Dict[str, Dict] = {}
 
     for party, vectors in party_vectors.items():
@@ -670,12 +671,8 @@ def compute_party_bootstrap_cis(
             }
             continue
 
-        rng = np.random.default_rng(seed)
-        boot_centroids = np.empty((n_boot, mat.shape[1]))
-
-        for b in range(n_boot):
-            idx = rng.integers(0, n_mps, size=n_mps)
-            boot_centroids[b] = mat[idx].mean(axis=0)
+        idx = rng.integers(0, n_mps, size=(n_boot, n_mps))
+        boot_centroids = mat[idx].mean(axis=1)  # (n_boot, dim)
 
         ci_lower = np.percentile(boot_centroids, lo_pct, axis=0)
         ci_upper = np.percentile(boot_centroids, hi_pct, axis=0)