# Compass UI Improvements Implementation Plan > **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. **Goal:** Fix three independent UI issues in the political compass: (1) update stale axis 3/4/5 descriptions in SVD_THEMES, (2) fix broken Y-axis direction arrows, (3) add voting discipline section below compass. **Architecture:** All changes are confined to `explorer.py`. No new files. No schema changes. The discipline helper reads `mp_votes` read-only via DuckDB. Tests for the discipline function live in `tests/test_political_compass.py`. **Tech Stack:** Python, Streamlit, Plotly Express/Graph Objects, DuckDB, pytest (run via `uv run pytest`) --- ## File Map | File | Change | |------|--------| | `explorer.py` | Update SVD_THEMES axes 3–5; fix Y-axis labels in two px.scatter calls and one update_layout; add `compute_party_discipline`; add discipline rendering in `build_compass_tab` | | `tests/test_political_compass.py` | Add tests for `compute_party_discipline` | --- ## Task 1: Update SVD_THEMES axes 3, 4, 5 **Files:** - Modify: `explorer.py:1156–1204` These descriptions were written for an earlier dataset. The new text reflects stable multi-year patterns (not a single year's snapshot). The `flip` booleans are unchanged. **New text for axis 3** (flip=True — currently "Sociaal-economisch links versus marktliberaal en landelijk rechts"): The positive pole consistently shows SP, PvdD, GL-PvdA on social welfare motions; the negative pole shows VVD on market-oriented motions. But PVV also appears prominently positive (anti-establishment spending motions), meaning this is not a clean left-right economic axis — it's more accurately described as *state intervention versus market liberalism*, with populist anti-establishment motions on the same side as the socialist left. **New text for axis 4** (flip=True — currently "Christelijk-sociaal centrum versus populistisch-soevereinistisch"): NSC, SGP, CU and CDA consistently dominate the positive pole; VVD and GL-PvdA the negative. FVD scores near zero across years. The axis captures *religious-conservative institutionalism vs secular liberalism*, not populism vs mainstream. **New text for axis 5** (flip=False — currently "Christelijk-conservatief en ruraal sociaal versus seculier-progressief"): CDA, CU, SGP score positive; SP, PvdD, DENK score negative. D66 tends positive (not strongly negative), and NSC tends negative. The stable pattern is *established-institutional vs protest-populist* rather than a religious/secular split. - [ ] **Step 1: Replace axis 3 entry in `explorer.py`** In `explorer.py`, replace lines 1156–1171: ```python 3: { "label": "Staatsingrijpen en publieke sector versus marktliberalisme", "explanation": ( "Deze as weerspiegelt de spanning tussen staatsingrijpen en marktliberalisme. " "Aan de positieve kant staan SP-moties die bezuinigingen op zorg en gemeentefonds " "willen terugdraaien, winstuitkeringen in de zorg verbieden en publieke controle " "over fusies eisen. Ook PVV stemt positief — niet vanuit sociaal ideaal maar vanuit " "anti-establishment populisme dat neigt naar overheidsinterventie voor de eigen achterban. " "Aan de negatieve kant staan VVD-moties over marktwerking en deregulering, en NSC- en " "BBB-moties met een agrarisch-marktgericht karakter. " "Samengevat: de as scheidt voorstanders van staatsingrijpen (links én populistisch-rechts) " "van marktliberalen en agrarisch-rechts." ), "positive_pole": "Staatsingrijpen: SP, PvdD, GL-PvdA, PVV (populistisch)", "negative_pole": "Marktliberaal en agrarisch-rechts: VVD, NSC, BBB", "flip": True, }, ``` - [ ] **Step 2: Replace axis 4 entry in `explorer.py`** Replace lines 1172–1188: ```python 4: { "label": "Christelijk-conservatief institutionalisme versus seculier-liberalisme", "explanation": ( "Deze as scheidt christelijk-conservatieve partijen die hechten aan traditionele " "instituties en religieuze waarden (NSC, SGP, CU, CDA) van seculier-liberale partijen " "die nadruk leggen op individuele rechten en internationale openheid (VVD, GL-PvdA). " "CU-moties over vaderbetrokkenheid, huwelijksrecht en internationale samenwerking staan " "aan de positieve kant; VVD-moties over marktregulering en D66-moties over internationale " "verdragen aan de negatieve kant. FVD scoort dicht bij nul — het past noch in het " "christelijk-conservatieve noch in het seculier-liberale kamp op deze as. " "Dit is geen populisme-as maar een religieus-institutionele breuklijn." ), "positive_pole": "Christelijk-conservatief institutioneel: NSC, SGP, CU, CDA", "negative_pole": "Seculier-liberaal: VVD, GL-PvdA, D66", "flip": True, }, ``` - [ ] **Step 3: Replace axis 5 entry in `explorer.py`** Replace lines 1189–1204: ```python 5: { "label": "Gevestigd-institutioneel versus protest en populistisch", "explanation": ( "Deze as scheidt gevestigde centrumpartijen die vertrouwen op bestaande instituties " "(CDA, CU, SGP, D66) van protest- en populistische partijen die dat vertrouwen " "afwijzen (SP, PvdD, DENK, NSC). CDA-moties over vrijwilligers in schuldhulp, " "maatschappelijke diensttijd en WW-hervorming staan aan de positieve kant. " "SP- en PvdD-moties over meerouderschap, abortusrecht en buitenlandse beïnvloeding " "staan aan de negatieve kant. NSC scoort negatief — ondanks zijn christelijk-conservatieve " "karakter op andere assen is het hier een protest­partij die systeemkritiek uitdraagt. " "D66 scoort licht positief, consistent met zijn institutionele en pro-EU profiel." ), "positive_pole": "Gevestigd-institutioneel: CDA, CU, SGP, D66", "negative_pole": "Protest en populistisch: SP, PvdD, DENK, NSC", "flip": False, }, ``` - [ ] **Step 4: Run tests to confirm nothing broken** ```bash uv run pytest tests/test_political_compass.py -v ``` Expected: all 3 tests PASS (these tests don't touch SVD_THEMES). - [ ] **Step 5: Commit** ```bash git add explorer.py git commit -m "fix: update SVD_THEMES axes 3-5 descriptions to reflect stable multi-year patterns" ``` --- ## Task 2: Fix Y-axis direction indicators in compass and trajectories **Files:** - Modify: `explorer.py:810–812` (partijen scatter labels) - Modify: `explorer.py:830` (kamerleden scatter labels) - Modify: `explorer.py:833–838` (compass update_layout — add helper call) - Modify: `explorer.py:921–927` (trajectories update_layout) Plotly rotates Y-axis titles 90° counter-clockwise, so `↑` and `↓` in the title string point sideways. Fix: strip arrows from the axis title; add two `fig.add_annotation` calls to place `▲ Progressief` at the top and `▼ Conservatief` at the bottom of the chart area using `xref="paper", yref="paper"`. - [ ] **Step 1: Add `_add_y_direction_annotations` helper near top of `build_compass_tab`** Add this function just before `build_compass_tab` (after line 692, before line 694): ```python def _add_y_direction_annotations(fig: go.Figure) -> None: """Add ▲ Progressief / ▼ Conservatief labels above and below the Y axis.""" common = dict( xref="paper", yref="paper", x=-0.07, showarrow=False, font=dict(size=11, color="#666666"), ) fig.add_annotation(**common, y=1.02, text="▲ Progressief", xanchor="center") fig.add_annotation(**common, y=-0.06, text="▼ Conservatief", xanchor="center") ``` - [ ] **Step 2: Update labels in the "Partijen" scatter (line 810–814)** Change: ```python labels={ "x": "Links ← → Rechts", "y": "Progressief ↑ / Conservatief ↓", "n": "Kamerleden", }, ``` To: ```python labels={ "x": "Links ← → Rechts", "y": "Progressief / Conservatief", "n": "Kamerleden", }, ``` - [ ] **Step 3: Update labels in the "Kamerleden" scatter (line 830)** Change: ```python labels={"x": "Links ← → Rechts", "y": "Progressief ↑ / Conservatief ↓"}, ``` To: ```python labels={"x": "Links ← → Rechts", "y": "Progressief / Conservatief"}, ``` - [ ] **Step 4: Call the annotation helper after `fig.update_layout` in `build_compass_tab` (after line 838)** Change: ```python fig.update_layout( height=600, legend_title_text="Partij", xaxis={"range": [-1, 1]}, yaxis={"range": [-0.6, 0.6]}, ) with col1: st.plotly_chart(fig, use_container_width=True) ``` To: ```python fig.update_layout( height=600, legend_title_text="Partij", 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) ``` - [ ] **Step 5: Fix trajectories Y-axis title (line 924) and add annotation** Change `build_trajectories_tab` update_layout block: ```python fig.update_layout( title="Partij trajectories", xaxis_title="Links ← → Rechts", yaxis_title="Progressief ↑ / Conservatief ↓", height=600, legend_title_text="Partij", ) st.plotly_chart(fig, use_container_width=True) ``` To: ```python fig.update_layout( title="Partij trajectories", xaxis_title="Links ← → Rechts", yaxis_title="Progressief / Conservatief", height=600, legend_title_text="Partij", ) _add_y_direction_annotations(fig) st.plotly_chart(fig, use_container_width=True) ``` - [ ] **Step 6: Run tests** ```bash uv run pytest tests/test_political_compass.py -v ``` Expected: all 3 tests PASS. - [ ] **Step 7: Commit** ```bash git add explorer.py git commit -m "fix: replace sideways Y-axis arrows with proper top/bottom annotations" ``` --- ## Task 3: Add voting discipline section below compass **Files:** - Modify: `explorer.py` — add `compute_party_discipline` function; add rendering block in `build_compass_tab` - Modify: `tests/test_political_compass.py` — add two tests ### 3a: Write the failing tests first - [ ] **Step 1: Add tests to `tests/test_political_compass.py`** Append at the end of `tests/test_political_compass.py`: ```python # --------------------------------------------------------------------------- # Tests for compute_party_discipline # --------------------------------------------------------------------------- def _make_mp_votes_db(): """Create an in-memory DuckDB with mp_votes fixture data. 6 motions, 2 parties (SP, VVD), each with 4 MPs. SP is perfectly disciplined (all 4 vote the same each time). VVD has 1 dissident on 2 of 6 motions → Rice index = (4+4+4+4+3+3)/6/4 ≈ 0.917. Dates span 2023-01-01 to 2023-12-31. """ import duckdb conn = duckdb.connect(":memory:") conn.execute(""" CREATE TABLE mp_votes ( id INTEGER, motion_id VARCHAR, mp_name VARCHAR, party VARCHAR, vote VARCHAR, date DATE, created_at TIMESTAMP ) """) rows = [] # motions 1-6, dates in 2023 dates = [ "2023-01-10", "2023-03-15", "2023-05-20", "2023-07-25", "2023-09-30", "2023-11-05", ] sp_mps = ["Janssen, A.", "Pietersen, B.", "Willemsen, C.", "Hendriksen, D."] vvd_mps = ["Adams, E.", "Bakker, F.", "Claassen, G.", "Dekker, H."] for i, date in enumerate(dates, start=1): m_id = f"M{i:03d}" # SP: all vote 'voor' every motion (perfectly disciplined) for mp in sp_mps: rows.append((i * 10 + 1, m_id, mp, "SP", "voor", date, "2023-01-01")) # VVD: motions 5 and 6 have one dissident (votes 'tegen' while others vote 'voor') if i <= 4: for mp in vvd_mps: rows.append((i * 10 + 2, m_id, mp, "VVD", "voor", date, "2023-01-01")) else: for mp in vvd_mps[:3]: rows.append((i * 10 + 2, m_id, mp, "VVD", "voor", date, "2023-01-01")) rows.append((i * 10 + 3, m_id, vvd_mps[3], "VVD", "tegen", date, "2023-01-01")) conn.executemany( "INSERT INTO mp_votes VALUES (?, ?, ?, ?, ?, ?, ?)", rows ) return conn def test_compute_party_discipline_basic(monkeypatch): """compute_party_discipline returns correct Rice index for fixture data.""" import duckdb as _duckdb fixture_conn = _make_mp_votes_db() monkeypatch.setattr( _duckdb, "connect", lambda path, **kw: fixture_conn ) # Import after monkeypatch so explorer can be imported without Streamlit crashing import importlib import sys # explorer imports streamlit — provide a minimal stub if not already stubbed if "streamlit" not in sys.modules: import types st_stub = types.ModuleType("streamlit") st_stub.cache_data = lambda **kw: (lambda f: f) sys.modules["streamlit"] = st_stub # We need the function directly; import the module import explorer as _explorer importlib.reload(_explorer) df = _explorer.compute_party_discipline( db_path="dummy", start_date="2023-01-01", end_date="2023-12-31", ) assert not df.empty assert set(df.columns) >= {"party", "n_motions", "discipline"} sp_row = df[df["party"] == "SP"].iloc[0] vvd_row = df[df["party"] == "VVD"].iloc[0] assert sp_row["n_motions"] == 6 assert sp_row["discipline"] == pytest.approx(1.0, abs=1e-6) assert vvd_row["n_motions"] == 6 # 4 motions fully disciplined (4/4=1.0), 2 motions with one dissident (3/4=0.75) expected_vvd = (4 * 1.0 + 2 * 0.75) / 6 assert vvd_row["discipline"] == pytest.approx(expected_vvd, abs=1e-4) # All values in [0, 1] assert (df["discipline"] >= 0).all() and (df["discipline"] <= 1).all() def test_compute_party_discipline_empty_range(monkeypatch): """Returns empty DataFrame when no motions fall in the date range.""" import duckdb as _duckdb fixture_conn = _make_mp_votes_db() monkeypatch.setattr(_duckdb, "connect", lambda path, **kw: fixture_conn) import importlib, sys if "streamlit" not in sys.modules: import types st_stub = types.ModuleType("streamlit") st_stub.cache_data = lambda **kw: (lambda f: f) sys.modules["streamlit"] = st_stub import explorer as _explorer importlib.reload(_explorer) df = _explorer.compute_party_discipline( db_path="dummy", start_date="2000-01-01", end_date="2000-12-31", ) assert df.empty ``` - [ ] **Step 2: Run the failing tests** ```bash uv run pytest tests/test_political_compass.py::test_compute_party_discipline_basic tests/test_political_compass.py::test_compute_party_discipline_empty_range -v ``` Expected: FAIL with `AttributeError: module 'explorer' has no attribute 'compute_party_discipline'` ### 3b: Implement `compute_party_discipline` - [ ] **Step 3: Add `compute_party_discipline` to `explorer.py`** Add this function after the `load_active_mps` function (find it, then place `compute_party_discipline` immediately after). The function must be a plain function (not decorated with `@st.cache_data`) so tests can monkeypatch `duckdb.connect`. ```python def compute_party_discipline( db_path: str, start_date: str, end_date: str, ) -> pd.DataFrame: """Compute per-party voting discipline (Rice index) for roll-call votes in a date range. Only individual MP vote rows are used (mp_name LIKE '%,%'). Returns a DataFrame with columns [party, n_motions, discipline] sorted by discipline ascending. Returns an empty DataFrame if fewer than 1 qualifying motion exists or on any DB error. Rice index per motion per party = fraction of party MPs voting with the party majority. The per-party score is the average Rice index across all motions in the date range. """ try: conn = duckdb.connect(db_path, read_only=True) result = conn.execute( """ WITH individual_votes AS ( -- Only individual MP rows (mp_name contains a comma, e.g. "Janssen, A.") SELECT motion_id, party, LOWER(vote) AS vote FROM mp_votes WHERE mp_name LIKE '%,%' AND date >= CAST(? AS DATE) AND date <= CAST(? AS DATE) AND vote IN ('voor', 'tegen', 'afwezig', 'onthouden') ), vote_counts AS ( -- Count each vote token per (motion, party) SELECT motion_id, party, vote, COUNT(*) AS cnt FROM individual_votes GROUP BY motion_id, party, vote ), majority_vote AS ( -- Determine the majority vote token per (motion, party) SELECT motion_id, party, FIRST(vote ORDER BY cnt DESC, vote ASC) AS maj_vote, SUM(cnt) AS total_mp_votes FROM vote_counts GROUP BY motion_id, party ), rice_per_motion AS ( -- Rice index: fraction voting with majority SELECT mv.motion_id, mv.party, SUM(CASE WHEN vc.vote = mv.maj_vote THEN vc.cnt ELSE 0 END) * 1.0 / mv.total_mp_votes AS rice FROM majority_vote mv JOIN vote_counts vc ON mv.motion_id = vc.motion_id AND mv.party = vc.party GROUP BY mv.motion_id, mv.party, mv.total_mp_votes ) SELECT party, COUNT(DISTINCT motion_id) AS n_motions, AVG(rice) AS discipline FROM rice_per_motion GROUP BY party ORDER BY discipline ASC """, [start_date, end_date], ).fetchdf() conn.close() return result except Exception as exc: logger.warning("compute_party_discipline failed: %s", exc) return pd.DataFrame(columns=["party", "n_motions", "discipline"]) ``` - [ ] **Step 4: Run the tests to confirm they pass** ```bash uv run pytest tests/test_political_compass.py::test_compute_party_discipline_basic tests/test_political_compass.py::test_compute_party_discipline_empty_range -v ``` Expected: both PASS. - [ ] **Step 5: Run all compass tests** ```bash uv run pytest tests/test_political_compass.py -v ``` Expected: all 5 tests PASS. ### 3c: Render discipline section in `build_compass_tab` - [ ] **Step 6: Add `_window_to_dates` helper just before `build_compass_tab`** Add this function just before `build_compass_tab` (around line 692, after the `_add_y_direction_annotations` helper added in Task 2): ```python def _window_to_dates(window_id: str) -> tuple[str, str]: """Return (start_date, end_date) ISO strings for a given window_id. Annual windows like '2024' → ('2024-01-01', '2024-12-31'). 'current_parliament' → ('2023-11-22', '2099-12-31') (2023 formation date, open end). Unknown formats → ('2000-01-01', '2099-12-31') (effectively all time). """ if window_id == "current_parliament": return ("2023-11-22", "2099-12-31") if re.fullmatch(r"\d{4}", window_id): return (f"{window_id}-01-01", f"{window_id}-12-31") # Quarterly e.g. '2020-Q3' → 2020-07-01 to 2020-09-30 m = re.fullmatch(r"(\d{4})-Q([1-4])", window_id) if m: year, q = int(m.group(1)), int(m.group(2)) starts = {1: "01-01", 2: "04-01", 3: "07-01", 4: "10-01"} ends = {1: "03-31", 2: "06-30", 3: "09-30", 4: "12-31"} return (f"{year}-{starts[q]}", f"{year}-{ends[q]}") return ("2000-01-01", "2099-12-31") ``` - [ ] **Step 7: Add discipline rendering after `st.plotly_chart` in `build_compass_tab`** The current end of `build_compass_tab` is (around line 840–841): ```python with col1: st.plotly_chart(fig, use_container_width=True) ``` Add the discipline section immediately after (still inside the function, but outside the `with col1:` block): ```python # --- Voting discipline section --- _MIN_MOTIONS_FOR_DISCIPLINE = 5 start_date, end_date = _window_to_dates(window_idx) disc_df = compute_party_discipline(db_path, start_date, end_date) st.subheader("Stemgedrag cohesie") if disc_df.empty or disc_df["n_motions"].max() < _MIN_MOTIONS_FOR_DISCIPLINE: st.caption( "Te weinig hoofdelijke stemmingen in dit venster voor een cohesieanalyse." ) else: # Filter to parties that appear in the compass compass_parties = set(df_pos["party"].unique()) disc_df = disc_df[disc_df["party"].isin(compass_parties)].copy() if disc_df.empty: st.caption("Geen overlappende partijen tussen kompas en stemmingsdata.") else: disc_df["discipline_pct"] = (disc_df["discipline"] * 100).round(1) disc_df["party_label"] = disc_df.apply( lambda r: f"{r['party']} ({int(r['n_motions'])} moties)", axis=1 ) bar_fig = px.bar( disc_df.sort_values("discipline"), x="discipline_pct", y="party_label", orientation="h", color="discipline_pct", color_continuous_scale="RdYlGn", range_color=[80, 100], labels={"discipline_pct": "Cohesie (%)", "party_label": "Partij"}, title="Cohesie bij hoofdelijke stemmingen", ) bar_fig.update_layout( height=max(300, len(disc_df) * 35 + 80), showlegend=False, coloraxis_showscale=False, yaxis_title="", ) st.plotly_chart(bar_fig, use_container_width=True) # Extremes table top3 = disc_df.nlargest(3, "discipline")[["party", "discipline_pct", "n_motions"]] bot3 = disc_df.nsmallest(3, "discipline")[["party", "discipline_pct", "n_motions"]] col_a, col_b = st.columns(2) with col_a: st.markdown("**Meest eensgezind**") st.dataframe( top3.rename(columns={"party": "Partij", "discipline_pct": "Cohesie (%)", "n_motions": "Moties"}), hide_index=True, use_container_width=True, ) with col_b: st.markdown("**Meest verdeeld**") st.dataframe( bot3.rename(columns={"party": "Partij", "discipline_pct": "Cohesie (%)", "n_motions": "Moties"}), hide_index=True, use_container_width=True, ) ``` - [ ] **Step 8: Run all tests** ```bash uv run pytest tests/test_political_compass.py -v ``` Expected: all 5 tests PASS. - [ ] **Step 9: Commit** ```bash git add explorer.py tests/test_political_compass.py git commit -m "feat: add voting discipline section below political compass" ``` --- ## Self-Review Checklist - [x] **Spec coverage:** Task 1 → SVD_THEMES axes 3–5. Task 2 → Y-axis arrows. Task 3 → discipline function + rendering. All three design requirements covered. - [x] **Placeholder scan:** No TBD or TODO. All code blocks are complete. - [x] **Type consistency:** `compute_party_discipline` returns `pd.DataFrame` with columns `["party", "n_motions", "discipline"]` — referenced consistently in tests and rendering code. `_window_to_dates` returns `tuple[str, str]` — used as `start_date, end_date` in rendering. - [x] **Test isolation:** Tests monkeypatch `duckdb.connect` to return in-memory DB; tests add a minimal `streamlit` stub to avoid import errors. Both patterns match existing test style in the file. - [x] **Edge case:** `_MIN_MOTIONS_FOR_DISCIPLINE = 5` guard ensures the section degrades gracefully for sparse windows. Empty DataFrame from `compute_party_discipline` is also handled.