feat(analysis): add 2D political compass (PCA/anchor) and 2D trajectories + visualizations

- compute_2d_axes (pca, anchor) with optional L2-normalisation pre-projection
- compute_2d_trajectories: per-MP coords, step vectors, magnitudes, totals
- plot_political_compass and plot_2d_trajectories (Plotly HTML)
- tests/test_political_compass.py (synthetic unit test)

analysis/political_axis.py

@@ -133,6 +133,7 @@ def compute_2d_axes(
     window_ids: Optional[List[str]] = None,
     method: str = "pca",
     anchor_kwargs: Optional[Dict] = None,
+    normalize_vectors: bool = True,
 ) -> Tuple[Dict[str, Dict[str, Tuple[float, float]]], Dict[str, np.ndarray]]:
     """Compute 2D coordinates for MPs per window.
 
@@ -174,6 +175,10 @@ def compute_2d_axes(
     entity_index = []  # parallel list of (window_id, entity)
     for wid, d in aligned_window_vecs.items():
         for ent, v in d.items():
+            if normalize_vectors:
+                n = np.linalg.norm(v)
+                all_vecs.append(v / n if n > 1e-10 else v)
+            else:
                 all_vecs.append(v)
             entity_index.append((wid, ent))
 

analysis/trajectory.py

@@ -196,7 +196,10 @@ def compute_trajectories(
 
 
 def compute_2d_trajectories(
-    db_path: str, method: str = "pca", anchor_kwargs: Optional[Dict] = None
+    db_path: str,
+    method: str = "pca",
+    anchor_kwargs: Optional[Dict] = None,
+    normalize_vectors: bool = True,
 ) -> Dict[str, Dict]:
     """Compute 2D trajectory positions for MPs using compute_2d_axes.
 
@@ -219,7 +222,11 @@ def compute_2d_trajectories(
         return {}
 
     positions_by_window, axes = compute_2d_axes(
-        db_path, window_ids=window_ids, method=method, anchor_kwargs=anchor_kwargs
+        db_path,
+        window_ids=window_ids,
+        method=method,
+        anchor_kwargs=anchor_kwargs,
+        normalize_vectors=normalize_vectors,
     )
 
     # Build per-MP time-ordered coords