deploy to server

analysis/political_axis.py

@@ -174,6 +174,20 @@ def compute_2d_axes(
     for wid in window_ids:
         raw_window_vecs[wid] = _trajectory._load_mp_vectors_for_window(db_path, wid)
 
+    # Pad all vectors to the maximum dimension across windows before Procrustes.
+    # Some windows (e.g. 2017 with only 30 motions) have lower-rank SVD output
+    # (dim=29 instead of 50). Padding with zeros lets Procrustes treat all vectors
+    # as the same dimension, preserving the alignment chain.
+    if raw_window_vecs:
+        max_dim = max(v.shape[0] for d in raw_window_vecs.values() for v in d.values())
+        padded: Dict[str, Dict[str, np.ndarray]] = {}
+        for wid, d in raw_window_vecs.items():
+            padded[wid] = {
+                e: np.pad(v, (0, max_dim - v.shape[0])) if v.shape[0] < max_dim else v
+                for e, v in d.items()
+            }
+        raw_window_vecs = padded
+
     aligned_window_vecs = _trajectory._procrustes_align_windows(raw_window_vecs)
 
     # Stack all vectors across windows into a single matrix for PCA if needed
@@ -246,10 +260,27 @@ def compute_2d_axes(
         # Ensure consistent left/right and progressive/conservative orientation
         # by checking canonical party centroids and flipping axis signs if needed.
         try:
-            right_parties = {"PVV", "VVD", "FVD", "BBB", "JA21"}
-            left_parties = {"SP", "PvdA", "GroenLinks", "GroenLinks-PvdA", "DENK"}
-            cons_parties = {"PVV", "VVD", "FVD", "CDA", "SGP", "BBB", "JA21"}
+            right_parties = {
+                "PVV",
+                "VVD",
+                "FVD",
+                "BBB",
+                "JA21",
+                "Nieuw Sociaal Contract",
+            }
+            left_parties = {"SP", "PvdA", "GL", "GroenLinks", "GroenLinks-PvdA", "DENK"}
+            cons_parties = {
+                "PVV",
+                "VVD",
+                "FVD",
+                "CDA",
+                "SGP",
+                "BBB",
+                "JA21",
+                "Nieuw Sociaal Contract",
+            }
             prog_parties = {
+                "GL",
                 "GroenLinks",
                 "PvdA",
                 "PvdD",
@@ -263,9 +294,12 @@ def compute_2d_axes(
 
             def _centroid_for_party_set(party_set):
                 vecs = []
+                # Party-level centroid vectors (entity_id == party name directly).
                 for p in party_set:
                     if p in ent_to_vec:
                         vecs.append(ent_to_vec[p])
+                # MP-level vectors: mp_metadata stores mp_name in the same
+                # "Lastname, Initials" format as entity_id in svd_vectors.
                 try:
                     conn = duckdb.connect(db_path)
                     rows = conn.execute(
@@ -295,7 +329,7 @@ def compute_2d_axes(
                     )
                     axes["x_axis"] = -axes["x_axis"]
 
-            # Y-axis: progressive vs conservative — prefer positive = conservative
+            # Y-axis: progressive vs conservative — positive Y = progressive
             prog_cent = _centroid_for_party_set(prog_parties)
             cons_cent = _centroid_for_party_set(cons_parties)
             if prog_cent is not None and cons_cent is not None:
@@ -378,7 +412,11 @@ def compute_2d_axes(
         cons_centroid = np.mean(np.vstack(cons_vecs), axis=0)
 
         lr = right_centroid - left_centroid
-        pc = cons_centroid - prog_centroid
+        # Construct progressive-conservative axis so that positive Y corresponds
+        # to *progressive* positions (consistent with PCA branch where we flip
+        # the sign to make progressive > conservative). Use prog - cons so a
+        # positive dot product means closer to the progressive centroid.
+        pc = prog_centroid - cons_centroid
 
         # Gram-Schmidt: make pc orthogonal to lr
         lr_norm = np.linalg.norm(lr)

analysis/trajectory.py

@@ -56,7 +56,28 @@ def _procrustes_align_windows(
 
     for wid in window_ids[1:]:
         cur = window_vecs[wid]
-        common = [e for e in cur if e in prev_aligned]
+        # Only consider common entities whose vectors share the same dimensionality
+        common = [
+            e
+            for e in cur
+            if e in prev_aligned and cur[e].shape == prev_aligned[e].shape
+        ]
+
+        # If there are common entities but their vector dimensions differ between
+        # the current and previously aligned window, skip Procrustes alignment
+        # for this window rather than raising an exception in orthogonal_procrustes.
+        if any(
+            e
+            for e in cur
+            if e in prev_aligned and cur[e].shape != prev_aligned[e].shape
+        ):
+            _logger.debug(
+                "Procrustes skipped for %s: vector dimensionality mismatch between windows",
+                wid,
+            )
+            result[wid] = cur
+            prev_aligned = cur
+            continue
 
         if len(common) < min_overlap:
             _logger.debug(