Revert "fix: use annual-only windows for SVD to restore EVR (~20% PC1)"

This reverts commit ffd8b191ef.

analysis/political_axis.py

@@ -14,7 +14,6 @@ Both modes return a dict mapping mp_name → scalar score for the given window.
 
 import json
 import logging
-import re
 from typing import Dict, List, Optional, Tuple
 
 import numpy as np
@@ -191,23 +190,17 @@ def compute_2d_axes(
 
     aligned_window_vecs = _trajectory._procrustes_align_windows(raw_window_vecs)
 
-    # Stack all vectors across windows into a single matrix for PCA if needed.
-    # pca_vecs / pca_index: annual windows only (e.g. "2024") — used for SVD axis derivation.
-    # all_vecs / entity_index: every window — used for projection onto the derived axes.
-    pca_vecs = []
+    # Stack all vectors across windows into a single matrix for PCA if needed
     all_vecs = []
     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)
-                vec = v / n if n > 1e-10 else v
+                all_vecs.append(v / n if n > 1e-10 else v)
             else:
-                vec = v
-            all_vecs.append(vec)
+                all_vecs.append(v)
             entity_index.append((wid, ent))
-            if re.match(r"^\d{4}$", wid):
-                pca_vecs.append(vec)
 
     if len(all_vecs) == 0:
         _logger.info("No vectors loaded for windows %s", window_ids)
@@ -215,19 +208,9 @@ def compute_2d_axes(
 
     M = np.vstack(all_vecs)
 
-    # If no annual windows found, fall back to all windows for SVD.
-    if len(pca_vecs) == 0:
-        _logger.warning(
-            "No annual windows found; falling back to all %d windows for SVD axis derivation",
-            len(aligned_window_vecs),
-        )
-        M_pca = M
-    else:
-        M_pca = np.vstack(pca_vecs)
-
     if method == "pca":
-        # centre using annual-only mean so SVD axes are not diluted by quarterly windows
-        Mc = M_pca - M_pca.mean(axis=0)
+        # centre globally
+        Mc = M - M.mean(axis=0)
         try:
             U, s, Vt = np.linalg.svd(Mc, full_matrices=False)
         except np.linalg.LinAlgError:
@@ -375,8 +358,8 @@ def compute_2d_axes(
                 evr1 * 100,
             )
 
-        # project per-window vectors (centre by annual-window global mean, consistent with SVD axes)
-        global_mean = M_pca.mean(axis=0)
+        # project per-window vectors (centre by global mean)
+        global_mean = M.mean(axis=0)
         axes["global_mean"] = global_mean
         positions_by_window: Dict[str, Dict[str, Tuple[float, float]]] = {
             wid: {} for wid in window_ids