sync to server

explorer.py

@@ -481,7 +481,6 @@ def load_positions(
     """
     from analysis.political_axis import compute_2d_axes
 
-    # Use only annual windows (quarterly windows are excluded by get_uniform_dim_windows).
     all_available = get_uniform_dim_windows(db_path)
 
     if not all_available:
@@ -539,6 +538,56 @@ def load_active_mps(db_path: str) -> set:
     return explorer_data.load_active_mps(db_path)
 
 
+def get_aligned_party_scores(
+    db_path: str, window: str, active_mps: set | None = None
+) -> Dict[str, np.ndarray]:
+    """Get party scores for all N components from aligned PCA positions.
+
+    For current_parliament, pass active_mps to filter to only seated MPs
+    (matching the compass behaviour). Historical windows include all MPs.
+
+    Args:
+        db_path: Path to DuckDB database
+        window: Window identifier (e.g. 'current_parliament', '2025')
+        active_mps: Set of active MP names to filter current_parliament by.
+                    Required when window is 'current_parliament' to match compass.
+    """
+    from analysis.political_axis import compute_nd_axes
+
+    annual_windows = get_uniform_dim_windows(db_path)
+    scores_by_window, _ = compute_nd_axes(
+        db_path, window_ids=annual_windows, n_components=10
+    )
+    window_scores = scores_by_window.get(window, {})
+    if not window_scores:
+        return {}
+
+    # For current_parliament, filter to active MPs (still seated) to match compass.
+    # Historical windows include all MPs active at the time — no restriction needed.
+    if window == "current_parliament" and active_mps is not None:
+        window_scores = {mp: sc for mp, sc in window_scores.items() if mp in active_mps}
+
+    # Load party map to convert MP names to parties
+    _party_map = load_party_map(db_path)
+
+    # Aggregate MP scores to party centroids per component
+    n_comps = 10
+    party_scores_agg: 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_scores_agg.setdefault(party, []).append(scores[:n_comps])
+
+    # Compute mean scores per party for each component
+    return {
+        party: np.mean(np.vstack(score_list), axis=0)
+        for party, score_list in party_scores_agg.items()
+        if score_list
+    }
+
+
 def compute_party_discipline(
     db_path: str,
     start_date: str,
@@ -2640,35 +2689,10 @@ def build_svd_components_tab(db_path: str) -> None:
     # This ensures consistency between compass and SVD components tab.
     def _get_aligned_party_scores(window: str) -> Dict[str, np.ndarray]:
         """Get party scores for all N components from aligned PCA positions."""
-        from analysis.political_axis import compute_nd_axes
-
-        annual_windows = get_uniform_dim_windows(db_path)
-        scores_by_window, _ = compute_nd_axes(
-            db_path, window_ids=annual_windows, n_components=10
+        active_mps = (
+            load_active_mps(db_path) if window == "current_parliament" else None
         )
-        window_scores = scores_by_window.get(window, {})
-        if not window_scores:
-            return {}
-
-        # Load party map to convert MP names to parties
-        _party_map = load_party_map(db_path)
-
-        # Aggregate MP scores to party centroids per component
-        n_comps = 10
-        party_scores_agg: 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_scores_agg.setdefault(party, []).append(scores[:n_comps])
-
-        # Compute mean scores per party for each component
-        return {
-            party: np.mean(np.vstack(score_list), axis=0)
-            for party, score_list in party_scores_agg.items()
-            if score_list
-        }
+        return get_aligned_party_scores(db_path, window, active_mps)
 
     # Extract 1D scores for this component using Procrustes-aligned PCA scores.
     # All 10 components use _get_aligned_party_scores (compute_nd_axes with annual-only