feat: expose global_mean in compute_2d_axes axes dict

tests/test_political_compass.py

@@ -1,6 +1,115 @@
 import numpy as np
 import types
 import sys
+import types as _types
+
+# Provide a minimal duckdb stub when the real package is not available in the test env
+try:
+    import duckdb as _duckdb
+except Exception:
+    import pandas as _pd
+
+    class FakeDuckDBConnection:
+        def __init__(self):
+            # storage for mp_votes rows: list of tuples matching _make_mp_votes_db
+            self._mp_votes = []
+
+        def execute(self, sql, params=None):
+            s = sql.strip().lower()
+            # simple create/select handling: return empty results for schema queries
+            if s.startswith("create table") or s.startswith(
+                "select distinct window_id"
+            ):
+                return _types.SimpleNamespace(fetchall=lambda: [])
+
+            # compute_party_discipline query detection
+            if (
+                "from rice_per_motion" in s
+                or "select\n                party,\n                count(distinct motion_id) as n_motions"
+                in sql
+            ):
+                # params: [start_date, end_date]
+                start_date, end_date = params or [None, None]
+                # filter rows by mp_name like '%,%' and date range and vote in ('voor','tegen')
+                rows = [r for r in self._mp_votes if ("," in (r[2] or ""))]
+                if start_date:
+                    rows = [r for r in rows if r[5] >= start_date and r[5] <= end_date]
+                rows = [r for r in rows if (r[4] in ("voor", "tegen"))]
+
+                # build counts per motion_id, party, vote
+                from collections import defaultdict
+
+                counts = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))
+                motions = set()
+                for _id, motion_id, mp_name, party, vote, date, created_at in rows:
+                    counts[motion_id][party][vote] += 1
+                    motions.add((motion_id, party))
+
+                # compute rice per (motion, party)
+                rice_vals = defaultdict(list)  # party -> list of rice per motion
+                motion_part_set = set()
+                for motion_id, party_counts in counts.items():
+                    for party, vc in party_counts.items():
+                        total = sum(vc.values())
+                        if total == 0:
+                            continue
+                        # majority vote: vote with max count, tie-breaker by vote asc
+                        maj_vote = sorted(vc.items(), key=lambda kv: (-kv[1], kv[0]))[
+                            0
+                        ][0]
+                        same = vc.get(maj_vote, 0)
+                        rice = same / float(total)
+                        rice_vals[party].append((motion_id, rice))
+                        motion_part_set.add((motion_id, party))
+
+                # aggregate per party
+                import pandas as pd
+
+                rows_out = []
+                for party, lst in rice_vals.items():
+                    n_motions = len({m for m, _ in lst})
+                    avg_rice = sum(r for _, r in lst) / n_motions if n_motions else 0.0
+                    rows_out.append(
+                        {"party": party, "n_motions": n_motions, "discipline": avg_rice}
+                    )
+
+                df = pd.DataFrame(rows_out)
+                return _types.SimpleNamespace(fetchdf=lambda: df)
+
+            # default fallback
+            return _types.SimpleNamespace(fetchall=lambda: [])
+
+        def executemany(self, sql, rows):
+            s = sql.strip().lower()
+            if s.startswith("insert into mp_votes"):
+                for r in rows:
+                    self._mp_votes.append(r)
+
+        def close(self):
+            return None
+
+    _fake_duckdb = _types.ModuleType("duckdb")
+    _fake_duckdb.connect = lambda *a, **kw: FakeDuckDBConnection()
+    sys.modules["duckdb"] = _fake_duckdb
+    _duckdb = _fake_duckdb
+
+# Provide a minimal plotly.express stub so explorer imports in tests without requiring plotly
+try:
+    import plotly.express as px  # type: ignore
+except Exception:
+    _px = types.ModuleType("plotly.express")
+    _px.scatter = lambda *a, **kw: None
+    _px.line = lambda *a, **kw: None
+    # Ensure top-level 'plotly' package exists and exposes express
+    _plotly_pkg = types.ModuleType("plotly")
+    _plotly_pkg.express = _px
+    sys.modules["plotly"] = _plotly_pkg
+    sys.modules["plotly.express"] = _px
+    px = _px
+    # stub plotly.graph_objects too
+    _go = types.ModuleType("plotly.graph_objects")
+    _go.Figure = lambda *a, **kw: None
+    sys.modules["plotly.graph_objects"] = _go
 
 import pytest
 
@@ -469,3 +578,33 @@ def test_axis_classifier_missing_csv(tmp_path, monkeypatch):
     # Must not crash and must return the original axes dict unchanged
     assert result is axes
     assert "x_label" not in result
+
+
+def test_compute_2d_axes_exposes_global_mean(monkeypatch):
+    """axes dict returned by compute_2d_axes must contain 'global_mean'."""
+    fake_traj = types.SimpleNamespace()
+    fake_traj._load_window_ids = lambda db: ["w1"]
+    aligned = {
+        "w1": {
+            "Alice": np.array([1.0, 0.0, 0.0]),
+            "Bob": np.array([-1.0, 0.5, 0.0]),
+        }
+    }
+    fake_traj._load_mp_vectors_for_window = lambda db, w: aligned.get(w, {})
+    fake_traj._procrustes_align_windows = lambda x: aligned
+    monkeypatch.setitem(sys.modules, "analysis.trajectory", fake_traj)
+    # Provide a minimal duckdb stub so importing analysis.political_axis succeeds
+    import types as _types
+
+    fake_conn = _types.SimpleNamespace(
+        execute=lambda q: _types.SimpleNamespace(fetchall=lambda: []),
+        close=lambda: None,
+    )
+    fake_duckdb = _types.SimpleNamespace(connect=lambda db_path, **kw: fake_conn)
+    monkeypatch.setitem(sys.modules, "duckdb", fake_duckdb)
+
+    from analysis.political_axis import compute_2d_axes
+
+    _, axis_def = compute_2d_axes(db_path="dummy", window_ids=["w1"], method="pca")
+    assert "global_mean" in axis_def
+    assert isinstance(axis_def["global_mean"], np.ndarray)