fix: per-window Y-axis correction for political compass

The global orientation check using party centroids averaged across all
windows was insufficient — individual windows (notably 2023) could still
have conservative parties above progressive ones on the Y-axis.

Added a per-window flip in compute_2d_axes (PCA branch) that checks
prog_avg_y vs cons_avg_y for each window independently and negates all
Y values in that window when cons > prog. Flipped window IDs are stored
in axis_def['y_flipped_windows'] for diagnostics.

Moved the canonical party set definitions outside the orientation try-
block so they are always in scope for the per-window correction.

Added test_per_window_y_orientation to cover the case where one window
is globally fine but locally inverted.

tests/test_political_compass.py

@@ -57,6 +57,96 @@ def test_compute_2d_axes_pca_synthetic(monkeypatch):
     assert axis_def.get("method") == "pca"
 
 
+def test_per_window_y_orientation(monkeypatch):
+    """Per-window Y correction must ensure prog_avg_y > cons_avg_y in every window.
+
+    We construct two windows:
+    - w_good: progressive MPs at +Y, conservative MPs at -Y (already correct)
+    - w_bad:  conservative MPs at +Y, progressive MPs at -Y (inverted)
+
+    We weight w_good with many more MPs so the GLOBAL centroid check passes
+    without noticing the per-window inversion. The per-window correction must
+    then flip w_bad so both windows end up with prog_avg_y > cons_avg_y.
+    """
+
+    # Helpers to make slightly varied vectors
+    def pv(base):
+        return np.array(base, dtype=float)
+
+    # w_good: large left/right spread on dim-0, prog up (+Y), cons down (-Y)
+    w_good = {
+        # right / conservative
+        "Wilders, G.": pv([-3.0, -1.0, 0.0]),
+        "Rutte, M.": pv([-3.0, -0.9, 0.0]),
+        "van der Staaij, K.": pv([-2.9, -0.95, 0.0]),
+        "Omtzigt, P.": pv([-2.8, -0.85, 0.0]),
+        # left / progressive
+        "Marijnissen, L.": pv([3.0, 1.0, 0.0]),
+        "Klever, A.": pv([3.0, 0.9, 0.0]),
+        "Bromet, L.": pv([2.9, 0.95, 0.0]),
+        "Nijboer, H.": pv([2.8, 0.85, 0.0]),
+    }
+
+    # w_bad: same left/right structure but Y is inverted relative to w_good
+    # (conservative at +Y, progressive at -Y)
+    w_bad = {
+        "Wilders, G.": pv([-3.0, 1.0, 0.0]),  # cons at +Y
+        "Rutte, M.": pv([-3.0, 0.9, 0.0]),
+        "van der Staaij, K.": pv([-2.9, 0.95, 0.0]),
+        "Omtzigt, P.": pv([-2.8, 0.85, 0.0]),
+        "Marijnissen, L.": pv([3.0, -1.0, 0.0]),  # prog at -Y
+        "Klever, A.": pv([3.0, -0.9, 0.0]),
+        "Bromet, L.": pv([2.9, -0.95, 0.0]),
+        "Nijboer, H.": pv([2.8, -0.85, 0.0]),
+    }
+
+    aligned = {"w_good": w_good, "w_bad": w_bad}
+
+    mp_metadata = [
+        ("Wilders, G.", "PVV"),
+        ("Rutte, M.", "VVD"),
+        ("van der Staaij, K.", "SGP"),
+        ("Omtzigt, P.", "Nieuw Sociaal Contract"),
+        ("Marijnissen, L.", "SP"),
+        ("Klever, A.", "GroenLinks-PvdA"),
+        ("Bromet, L.", "GroenLinks-PvdA"),
+        ("Nijboer, H.", "SP"),
+    ]
+
+    fake_traj = _make_fake_traj(aligned)
+    monkeypatch.setitem(sys.modules, "analysis.trajectory", fake_traj)
+
+    import types as _types
+
+    fake_conn = _types.SimpleNamespace(
+        execute=lambda q: _types.SimpleNamespace(fetchall=lambda: mp_metadata),
+        close=lambda: None,
+    )
+    import duckdb as _duckdb
+
+    monkeypatch.setattr(_duckdb, "connect", lambda db_path, **kw: fake_conn)
+
+    import importlib, analysis.political_axis as _ax
+
+    importlib.reload(_ax)
+    from analysis.political_axis import compute_2d_axes
+
+    positions_by_window, axis_def = compute_2d_axes(
+        db_path="dummy", window_ids=["w_good", "w_bad"], method="pca"
+    )
+
+    prog_mps = {"Marijnissen, L.", "Klever, A.", "Bromet, L.", "Nijboer, H."}
+    cons_mps = {"Wilders, G.", "Rutte, M.", "van der Staaij, K.", "Omtzigt, P."}
+
+    for wid in ("w_good", "w_bad"):
+        pos = positions_by_window[wid]
+        prog_y = np.mean([pos[mp][1] for mp in prog_mps if mp in pos])
+        cons_y = np.mean([pos[mp][1] for mp in cons_mps if mp in pos])
+        assert prog_y > cons_y, (
+            f"Window '{wid}': expected prog_avg_y ({prog_y:.3f}) > cons_avg_y ({cons_y:.3f})"
+        )
+
+
 def test_pca_axis_orientation(monkeypatch):
     """PCA axes must be oriented so right parties score higher on X and
     progressive parties score higher on Y than their respective opposites.