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,
@@ -1416,7 +1465,16 @@ def build_compass_tab(db_path: str, window_size: str) -> None:
     active_mps = load_active_mps(db_path)
 
     # Sort windows: year windows first (ascending), current_parliament last.
-    year_windows = sorted(w for w in positions_by_window if w != "current_parliament")
+    # Exclude the current calendar year — it is already fully covered by current_parliament
+    # and showing both creates confusion (2026 ⊂ current_parliament).
+    import datetime as _dt
+
+    _current_year = str(_dt.date.today().year)
+    year_windows = sorted(
+        w
+        for w in positions_by_window
+        if w != "current_parliament" and w != _current_year
+    )
     has_current = "current_parliament" in positions_by_window
     windows = year_windows + (["current_parliament"] if has_current else [])
 
@@ -1576,22 +1634,14 @@ def build_compass_tab(db_path: str, window_size: str) -> None:
         xaxis={"range": [-1, 1]},
         yaxis={"range": [-0.6, 0.6]},
     )
-    _add_y_direction_annotations(fig)
-
     with col1:
         st.plotly_chart(fig, use_container_width=True)
         _x_interp = axis_def.get("x_interpretation", {}).get(window_idx, "")
-        _y_interp = axis_def.get("y_interpretation", {}).get(window_idx, "")
         if (
             _x_interp
             and axis_def.get("x_quality", {}).get(window_idx, 1.0) < _THRESHOLD
         ):
             st.caption(_x_interp)
-        if (
-            _y_interp
-            and axis_def.get("y_quality", {}).get(window_idx, 1.0) < _THRESHOLD
-        ):
-            st.caption(_y_interp)
 
         # Voting discipline analysis
         st.markdown("---")
@@ -2568,9 +2618,14 @@ def build_svd_components_tab(db_path: str) -> None:
     # Default party scores already loaded earlier for sidebar controls.
     # ALL components 1-10 use raw (non-aligned) SVD vectors.
     # The compass uses Procrustes-aligned PCA — separate visualization.
-    # Get available windows from svd_vectors
+    # Get available windows from svd_vectors; exclude current year (covered by current_parliament)
+    import datetime as _dt
+
+    _current_year = str(_dt.date.today().year)
     available_windows = get_uniform_dim_windows(db_path)
-    year_windows = sorted(w for w in available_windows if w != "current_parliament")
+    year_windows = sorted(
+        w for w in available_windows if w != "current_parliament" and w != _current_year
+    )
     has_current = "current_parliament" in available_windows
     svd_windows = year_windows + (["current_parliament"] if has_current else [])
 
@@ -2634,37 +2689,15 @@ 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 {}
+        return get_aligned_party_scores(db_path, window, 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
-        }
-
-    # Extract 1D scores for this component using aligned PCA scores
+    # 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
+    # windows). This is mathematically identical to the compass x/y positions for
+    # components 1 and 2, and consistently uses the same aligned data for 3-10.
     party_1d_coords: dict = {}
     aligned_all_scores = _get_aligned_party_scores(svd_window)
     for party, all_scores in aligned_all_scores.items():
@@ -2718,7 +2751,11 @@ def build_svd_components_tab(db_path: str) -> None:
     if view_mode == "Tijdtraject" and selected_parties_for_trajectory:
         # Load party scores for all windows and render time trajectory
         available_windows = get_uniform_dim_windows(db_path)
-        year_windows = sorted(w for w in available_windows if w != "current_parliament")
+        year_windows = sorted(
+            w
+            for w in available_windows
+            if w != "current_parliament" and w != _current_year
+        )
         has_current = "current_parliament" in available_windows
         all_windows = year_windows + (["current_parliament"] if has_current else [])