fix(trajectory): add diagnostics to compute_party_centroids for NaN detection

explorer_helpers.py

@@ -253,18 +253,31 @@ def compute_party_centroids(
     Returns (party_centroids, metadata)
     - party_centroids: mapping party -> list of (x,y) tuples of length len(windows).
       Entries without MPs are (np.nan, np.nan).
-    - metadata: dict with keys 'per_party_counts', 'total_windows', 'parties'
+    - metadata: dict with keys 'per_party_counts', 'total_windows', 'parties',
+      'input_windows', 'input_party_map_entries', 'windows_processed',
+      'parties_with_positions', 'parties_all_nan', 'name_mismatch_samples'
     """
+    # Initialize diagnostics metadata
+    metadata = {
+        "input_windows": len(positions_by_window) if positions_by_window else 0,
+        "input_party_map_entries": len(party_map) if party_map else 0,
+        "windows_processed": 0,
+        "parties_with_positions": set(),
+        "parties_all_nan": [],
+        "name_mismatch_samples": [],
+        "per_party_counts": {},
+        "total_windows": len(windows),
+        "parties": [],
+    }
+
     party_centroids: Dict[str, List[Tuple[float, float]]] = {}
     # collect all parties from party_map values
     parties = sorted(set(party_map.values()))
+    metadata["parties"] = parties
+
     # if no parties known, return empty dict but still metadata
     if not parties:
-        return {}, {
+        return {}, metadata
-            "per_party_counts": {},
-            "total_windows": len(windows),
-            "parties": [],
-        }
 
     # initialize lists
     for p in parties:
@@ -273,25 +286,31 @@ def compute_party_centroids(
     # for each window, compute party coords using compute_party_coords for that window
     for w in windows:
         coords, _ = compute_party_coords(positions_by_window or {}, party_map, w)
+        metadata["windows_processed"] += 1
         for p in parties:
             if p in coords:
                 # ensure numeric floats
                 party_centroids[p].append((float(coords[p][0]), float(coords[p][1])))
+                metadata["parties_with_positions"].add(p)
             else:
                 party_centroids[p].append((float(np.nan), float(np.nan)))
 
-    # metadata
+    # Convert set to list for JSON serialization
-    per_party_counts: Dict[str, int] = {}
+    metadata["parties_with_positions"] = sorted(
+        list(metadata["parties_with_positions"])
+    )
+
+    # metadata per-party counts
     for p, vals in party_centroids.items():
         count = 0
         for x, y in vals:
             if not (np.isnan(x) or np.isnan(y)):
                 count += 1
-        per_party_counts[p] = count
+        metadata["per_party_counts"][p] = count
+
+    # Check for parties with all NaN centroids
+    for party, coords in party_centroids.items():
+        if all(np.isnan(x) and np.isnan(y) for x, y in coords):
+            metadata["parties_all_nan"].append(party)
 
-    metadata = {
-        "per_party_counts": per_party_counts,
-        "total_windows": len(windows),
-        "parties": parties,
-    }
     return party_centroids, metadata