Use aligned PCA scores for time trajectory view

- Add _get_aligned_trajectory_scores() helper for multi-window aligned scores
- Update trajectory call to use compute_nd_axes instead of raw SVD scores
- Simplify _render_svd_time_trajectory by removing per-window flip computation

explorer.py

@@ -626,6 +626,50 @@ def _load_mp_vectors_by_window(db_path: str, window: str) -> Dict[str, np.ndarra
     return explorer_data.load_mp_vectors_by_window(db_path, window)
 
 
+def _get_aligned_trajectory_scores(
+    db_path: str, windows: List[str], n_components: int = 10
+) -> Dict[str, Dict[str, List[float]]]:
+    """Get aligned PCA scores for all windows as {window: {party: [scores per component]}}.
+
+    Uses compute_nd_axes to get PCA-projected, flip-corrected scores across all windows,
+    ensuring consistency with the single-window SVD components view.
+    """
+    from analysis.political_axis import compute_nd_axes
+
+    # Get aligned scores for all windows via PCA
+    scores_by_window, _ = compute_nd_axes(db_path, n_components=n_components)
+    if not scores_by_window:
+        return {}
+
+    # Load party map to convert MP names to parties
+    party_map = load_party_map(db_path)
+
+    # Aggregate MP scores to party centroids per window
+    result: Dict[str, Dict[str, List[float]]] = {}
+    for window in windows:
+        window_scores = scores_by_window.get(window, {})
+        if not window_scores:
+            continue
+
+        # Aggregate MP scores to party averages
+        party_vecs: Dict[str, List[np.ndarray]] = {}
+        for mp_name, scores in window_scores.items():
+            party = party_map.get(
+                mp_name, party_map.get(mp_name.split("(")[0].strip(), None)
+            )
+            if party:
+                party_vecs.setdefault(party, []).append(scores[:n_components])
+
+        # Compute mean scores per party
+        result[window] = {
+            party: np.mean(np.vstack(score_list), axis=0).tolist()
+            for party, score_list in party_vecs.items()
+            if score_list
+        }
+
+    return result
+
+
 @st.cache_data(show_spinner="SVD scores met Procrustes-uitlijning laden…")
 def load_party_scores_all_windows_aligned(
     db_path: str, windows: List[str]
@@ -1117,10 +1161,9 @@ def _render_svd_time_trajectory(
 
     idx = comp_sel - 1  # Convert to 0-indexed
 
-    # Import flip computation for per-window alignment
-    from analysis.svd_labels import compute_flip_direction
-
     # Build data structure: {party: [(window, score), ...]}
+    # Scores are already aligned and flip-corrected via compute_nd_axes,
+    # so no per-window flip computation needed.
     party_trajectories: Dict[str, List[Tuple[str, float]]] = {}
 
     # Sort windows: current_parliament first, then chronological
@@ -1134,26 +1177,13 @@ def _render_svd_time_trajectory(
     )
     sorted_windows.extend(other_windows)
 
-    # Compute per-window flip to align all windows consistently
-    # Each window's SVD has arbitrary sign, so we compute flip per window
-    window_flips = {}
-    for window in sorted_windows:
-        scores_by_party = party_scores_by_window.get(window, {})
-        # Compute flip for this specific window
-        window_flips[window] = compute_flip_direction(comp_sel, scores_by_party)
-
     for window in sorted_windows:
         scores_by_party = party_scores_by_window.get(window, {})
-        # Get the flip for this specific window
-        window_flip = window_flips.get(window, False)
         for party in selected_parties:
             scores = scores_by_party.get(party, [])
             if scores and len(scores) > idx:
                 try:
                     score = float(scores[idx])
-                    # Apply per-window flip to align orientation
-                    if window_flip:
-                        score = -score
                     party_trajectories.setdefault(party, []).append((window, score))
                 except (ValueError, TypeError):
                     continue
@@ -2693,9 +2723,8 @@ def build_svd_components_tab(db_path: str) -> None:
         has_current = "current_parliament" in available_windows
         all_windows = year_windows + (["current_parliament"] if has_current else [])
 
-        # TODO: For full consistency, this should also use aligned PCA scores for all windows.
-        # Currently uses raw SVD scores for trajectory - single-window view uses aligned scores.
-        party_scores_by_window = load_party_scores_all_windows(db_path, all_windows)
+        # Use aligned PCA scores for all windows (consistent with single-window view)
+        party_scores_by_window = _get_aligned_trajectory_scores(db_path, all_windows)
 
         _render_svd_time_trajectory(
             party_scores_by_window,