feat: add right-wing party axis validation

- Add CANONICAL_RIGHT (PVV, FVD, JA21, SGP) and CANONICAL_LEFT frozensets to analysis/config.py as the canonical source of truth - Update analysis/svd_labels.py to import from config; re-export as RIGHT_PARTIES/LEFT_PARTIES for backward compatibility - Add build_window_party_scores helper to analysis/explorer_data.py - Add 7 integration tests in tests/test_axis_political_orientation.py validating that canonical right parties appear on the right side of SVD axes (x=component 1, y=component 2) using real DuckDB data
4 weeks ago · 5afbad11ad
parent 5ddf2cd85a
commit 5afbad11ad
6 changed files with 1328 additions and 49 deletions
--- a/analysis/config.py
+++ b/analysis/config.py
@ -0,0 +1,280 @@
 """Configuration constants for the parliamentary explorer.
 This module contains all constant definitions used across the explorer.
 It is intentionally free of Streamlit and DuckDB dependencies.
 """
 from __future__ import annotations
 from typing import Dict
 __all__ = [
    "PARTY_COLOURS",
    "SVD_THEMES",
    "KNOWN_MAJOR_PARTIES",
    "CURRENT_PARLIAMENT_PARTIES",
    "_PARTY_NORMALIZE",
    "CANONICAL_RIGHT",
    "CANONICAL_LEFT",
 ]
 CANONICAL_RIGHT: frozenset[str] = frozenset(
    {
        "PVV",
        "FVD",
        "JA21",
        "SGP",
    }
 )
 CANONICAL_LEFT: frozenset[str] = frozenset(
    {
        "SP",
        "PvdA",
        "GL",
        "GroenLinks",
        "GroenLinks-PvdA",
        "DENK",
        "PvdD",
        "Volt",
    }
 )
 PARTY_COLOURS: Dict[str, str] = {
    "VVD": "#1E73BE",
    "PVV": "#002366",
    "D66": "#00A36C",
    "CDA": "#4CAF50",
    "SP": "#E53935",
    "PvdA": "#D32F2F",
    "GroenLinks": "#388E3C",
    "GroenLinks-PvdA": "#2E7D32",
    "CU": "#0288D1",
    "SGP": "#F4511E",
    "PvdD": "#43A047",
    "FVD": "#6A1B9A",
    "JA21": "#7B1FA2",
    "BBB": "#8D6E63",
    "NSC": "#FF8F00",
    "Nieuw Sociaal Contract": "#FF8F00",
    "DENK": "#00897B",
    "50PLUS": "#7E57C2",
    "Volt": "#572AB7",
    "ChristenUnie": "#0288D1",
    "Unknown": "#9E9E9E",
 }
 SVD_THEMES: dict[int, dict[str, str]] = {
    1: {
        "label": "Rechts kabinetsbeleid versus links oppositiebeleid",
        "explanation": (
            "Deze as scheidt het rechts kabinetsbeleid van links oppositiebeleid. "
            "Aan de positieve kant staan moties die passen bij het kabinetsbeleid: "
            "Eurofighter Typhoons, defensie-uitgaven naar 3% bbp, F-35 reservedelen, "
            "marine-steun aan Rode Zee en asielrestricties. "
            "PVV, VVD, NSC en BBB scoren sterk positief. "
            "Aan de negatieve kant staan moties uit de oppositie: "
            "zorgbuurthuizen voor ouderen, boycot van Israël, sancties, en internationale "
            "klimaatsamenwerking. GroenLinks-PvdA, SP, PvdD en Volt scoren negatief. "
            "Deze as weerspiegelt de coalitie-oppositie dynamiek."
        ),
        "positive_pole": "Kabinetsbeleid: PVV, VVD, NSC, BBB, JA21 — defensie en restricties",
        "negative_pole": "Oppositiebeleid: GroenLinks-PvdA, SP, PvdD, Volt, DENK — zorg en multilateraal",
        "flip": False,
    },
    2: {
        "label": "PVV/FVD-populisme versus mainstream-partijen",
        "explanation": (
            "Deze as scheidt het PVV/FVD-populisme van het overige parliament. "
            "Alleen PVV en FVD scoren positief; alle andere partijen scoren negatief. "
            "Positieve moties: Syriërs terugsturen, geen geld aan Jordanië, tijdelijke "
            "bescherming Oekraïne beëindigen, uitstappen uit WHO en klimaatakkoorden. "
            "Negatieve moties: digitale toegankelijkheid Caribisch Nederland, ethiekprogramma "
            "Defensie, zorg voor slachtoffers bombardement Hawija, internationale klimaatsamenwerking. "
            "Dit is geen links-rechts verdeling maar een populistisch vs. mainstream onderscheid."
        ),
        "positive_pole": "PVV en FVD — soevereiniteit en anti-establishment",
        "negative_pole": "Overige partijen: VVD, CDA, SGP, ChristenUnie, GroenLinks-PvdA, D66, Volt, BBB",
        "flip": False,
    },
    3: {
        "label": "Verzorgingsstaat versus bezuinigingen en marktwerking",
        "explanation": (
            "Deze as weerspiegelt de spanning tussen staatsingrijpen en marktliberalisme, "
            "aangescherpt door de kabinetscrisis van 2025. Aan de positieve kant staan moties "
            "die bezuinigingen op zorg en het gemeentefonds willen terugdraaien, winstuitkeringen "
            "in de zorg verbieden en publieke controle over ziekenhuisfusies eisen. SP, PvdD, "
            "GroenLinks-PvdA stemmen hier gelijk — ondanks hun tegengestelde PC1-posities. "
            "Aan de negatieve kant staan moties "
            "over marktwerking in de zorg, fiscale bedrijfsopvolgingsfaciliteiten (VVD), "
            "doorgaan met besturen ondanks de kabinetscrisis (VVD/BBB) en defensie-"
            "uitgaven van 3,5% bbp."
        ),
        "positive_pole": "Pro-verzorgingsstaat: SP, PvdD, GroenLinks-PvdA (anti-bezuinigingen)",
        "negative_pole": "Marktliberaal en fiscaal conservatief: VVD, D66, CDA, SGP, BBB",
        "flip": True,
    },
    4: {
        "label": "Mainstreampartijen versus FVD/DENK-oppositie",
        "explanation": (
            "Deze as scheidt het mainstream parliament van FVD en DENK. "
            "Aan de positieve kant stemmen vrijwel alle partijen voor dezelfde moties: "
            "openbare toiletten, vaderbetrokkenheid bij opvoeding, internationale "
            "samenwerking met Australië en Canada, en long covid-expertise. "
            "D66, CDA, VVD, PVV, GL-PvdA, SP, Volt en 50PLUS stemmen allemaal samen. "
            "Aan de negatieve kant stemmen alleen FVD en DENK voor — zij nemen "
            "regelmatig gepolariseerde posities die afwijken van het mainstream."
        ),
        "positive_pole": "Mainstreampartijen: D66, CDA, VVD, PVV, GL-PvdA, SP, Volt, 50PLUS — breedgedragen moties",
        "negative_pole": "FVD en DENK: oppositieposities buiten de mainstream",
        "flip": True,
    },
    5: {
        "label": "Christelijk-sociaal en gemeenschapswaarden versus progressieve individuele rechten",
        "explanation": (
            "Deze as scheidt christelijk-sociale partijen van progressieve partijen op het "
            "vlak van gemeenschapswaarden. Aan de positieve kant staan moties over "
            "schuldhulpverlening via vrijwilligersorganisaties, maatschappelijke "
            "diensttijd voor jongeren, gastouderopvang en financiële prikkels voor scholieren. "
            "ChristenUnie, SGP, CDA en NSC voeren hier de toon; ook D66 en FVD scoren positief. "
            "Aan de negatieve kant staan moties over wettelijke erkenning van meerouderschap, "
            "abortusrecht in het EU-Handvest, armoedebeleid en sociaal-maatschappelijke thema's. "
            "SP, VVD, GL-PvdA, PvdD en Volt scoren negatief."
        ),
        "positive_pole": "Christelijk-sociaal: ChristenUnie, SGP, CDA, NSC — gemeenschap en vrijwilligers",
        "negative_pole": "Progressief-individueel: SP, VVD, GL-PvdA, PvdD, Volt — individuele rechten",
        "flip": False,
    },
    6: {
        "label": "Migratie en cultuur versus klimaat en progressieve inclusie",
        "explanation": (
            "Deze as combineert migratie- en culturele posities. Aan de positieve kant staan "
            "moties over asielrestricties, nationale cultuur en identiteit, en beperkte "
            "immigratie. PVV, JA21, BBB, CDA, ChristenUnie, VVD, SGP, FVD en DENK scoren positief. "
            "Aan de negatieve kant staan moties over klimaatmaatregelen, progressieve "
            "inclusie, discriminatiebestrijding en internationale samenwerking. "
            "SP, PvdD, D66, GL-PvdA en Volt scoren negatief. "
            "De as scheidt partijen met restrictief migratiebeleid van partijen met "
            "progressief-inclusief beleid."
        ),
        "positive_pole": "Restrictief migratiebeleid: PVV, JA21, BBB, CDA, ChristenUnie, VVD, SGP, FVD, DENK",
        "negative_pole": "Progressieve inclusie: SP, PvdD, D66, GL-PvdA, Volt — klimaat en diversiteit",
        "flip": False,
    },
    7: {
        "label": "Bestuurlijk pragmatisme en implementatie (indicatief)",
        "explanation": (
            "Een residuele as die overwegend beleidsdossiers uit 2024 (vorige parlementaire "
            "periode) omvat. De scores zijn smal (max ~11 punten) en de partijcombinaties "
            "ideologisch divers — dit label is indicatief. Aan de positieve kant staan "
            "pragmatische bestuursmoties: een compleet kostenoverzicht van producten van eigen "
            "bodem, papieren schoolboeken voor basisvaardigheden, een invoeringstoets voor het "
            "minimumloon en de A2-snelwegplanning. ChristenUnie, Volt, DENK en SP scoren "
            "positief. Aan de negatieve kant staan meer ideologisch geladen moties: een "
            "landelijk stookverbod (PvdD), het strafbaar stellen van verbranding van religieuze "
            "geschriften (DENK), chroom-6 schadevergoedingen en tegenhouden van nieuwe "
            "gaswinning. GroenLinks-PvdA, VVD, FVD en JA21 scoren negatief."
        ),
        "positive_pole": "Praktisch-bestuurlijk: ChristenUnie, Volt, SGP, DENK, SP",
        "negative_pole": "Ideologisch-principieel: GroenLinks-PvdA, VVD, FVD, JA21",
        "flip": True,
    },
    8: {
        "label": "Vaccinatiebeleid, onderwijs en regionale huisvesting (indicatief)",
        "explanation": (
            "Een residuele as die overwegend thematisch diverse moties uit 2024-2025 vangt. "
            "Aan de positieve kant staan moties over vaccinatiegraad-verlaging voor kinderen, "
            "een VWO-profiel kunst en cultuur, stages voor mbo-studenten in het buitenland, "
            "en woningbouw voor jongeren in kleine kernen. BBB, SGP en JA21 scoren positief. "
            "Aan de negatieve kant staan moties over het instellen van een vaccinatiecommissie, "
            "heropening van het coronaoversterfte-onderzoek, regionale energiestrategieën "
            "en toegankelijkheid van het basispakket. SP, DENK en PvdD scoren sterk negatief. "
            "Deze as combineert onderwijs- en volksgezondheidsposities met regionale "
            "huisvestingsprioriteiten — het label is indicatief."
        ),
        "positive_pole": "Onderwijs en volksgezondheid: BBB, SGP, JA21 — vaccinatie, profielkeuze, woningbouw",
        "negative_pole": "Zorg en toegankelijkheid: SP, DENK, PvdD, Volt — coronaonderzoek, energie, basispakket",
        "flip": False,
    },
    9: {
        "label": "Pragmatische probleemoplossing versus systeemhervorming (indicatief)",
        "explanation": (
            "Deze as scheidt pragmatische, concrete probleemoplossing van idealistische "
            "systeemhervorming. Aan de positieve kant staan moties over naleving van de "
            "Financiële-verhoudingswet voor gemeenten, beperking van arbeidsmigratie, "
            "een nieuwe tandartsopleiding in Rotterdam, een actieplan tegen misbruik van "
            "hallucinerende geneesmiddelen en oplossingen voor milieuproblemen op Bonaire. "
            "SGP en ChristenUnie scoren sterk positief; ook DENK en SP. Aan de negatieve kant "
            "staan moties over een moratorium op geitenstallen, een verbod op gokadvertenties, "
            "verduidelijking van gronden voor voorlopige hechtenis, een leegstandbelasting "
            "en end-to-end-encryptie. D66, JA21 en PVV scoren negatief. "
            "Deze as is indicatief — de scores zijn smal en ideologisch divers."
        ),
        "positive_pole": "Pragmatisch-bestuurlijk: SGP, ChristenUnie, DENK, SP — concrete oplossingen",
        "negative_pole": "Systeemhervorming: D66, JA21, PVV — idealistische beleidsposities",
        "flip": True,
    },
    10: {
        "label": "Kritisch op overheidsbemoeienis versus pro-regulering (indicatief)",
        "explanation": (
            "Deze as scheidt partijen die kritisch staan tegenover overheidsbemoeienis van "
            "partijen die strikte regulering en handhaving steunen. Aan de positieve kant "
            "staan moties over minder tijdsintensieve schoolinspecties, het recht van "
            "toeslagenouders op hun persoonlijk dossier, behoud van tegemoetkomingen voor "
            "arbeidsongeschikten en verlaging van de leeftijdsdrempel voor kindgesprekken. "
            "DENK, SP en PvdD scoren positief. Aan de negatieve kant staan moties over "
            "een aangifteplicht voor scholen bij veiligheidsincidenten, een rookverbod in "
            "auto's met kinderen, braakliggende landbouwgrond en verhoogd beloningsgeld "
            "voor tipgevers. GroenLinks-PvdA scoort opvallend sterk negatief. "
            "Deze as is indicatief — de scores zijn smal en de partijcombinaties divers."
        ),
        "positive_pole": "Kritisch op overheidsbemoeienis: DENK, SP, PvdD — minder inspectielast en lastenverlichting",
        "negative_pole": "Pro-regulering: GroenLinks-PvdA, CDA, SGP — veiligheid, naleving en handhaving",
        "flip": True,
    },
 }
 KNOWN_MAJOR_PARTIES = [
    "VVD",
    "PVV",
    "D66",
    "GroenLinks-PvdA",
    "GroenLinks",
    "PvdA",
    "CDA",
    "SP",
    "NSC",
    "CU",
    "BBB",
 ]
 CURRENT_PARLIAMENT_PARTIES: frozenset[str] = frozenset(
    {
        "PVV",
        "VVD",
        "NSC",
        "BBB",
        "D66",
        "GroenLinks-PvdA",
        "CDA",
        "SP",
        "ChristenUnie",
        "SGP",
        "Volt",
        "DENK",
        "PvdD",
        "JA21",
        "FVD",
    }
 )
 _PARTY_NORMALIZE: dict[str, str] = {
    "Nieuw Sociaal Contract": "NSC",
    "CU": "ChristenUnie",
    "GL": "GroenLinks-PvdA",
    "GroenLinks": "GroenLinks-PvdA",
    "PvdA": "GroenLinks-PvdA",
    "Gündoğan": "Volt",
    "Lid Keijzer": "BBB",
    "Groep Markuszower": "PVV",
 }
--- a/analysis/explorer_data.py
+++ b/analysis/explorer_data.py
@ -0,0 +1,563 @@
 """Data loading functions for the parliamentary explorer.
 This module contains all data loading functions extracted from explorer.py.
 It is intentionally free of Streamlit side-effects to be easy to unit test.
 """
 from __future__ import annotations
 import logging
 from typing import Dict, List, Set, Tuple
 import duckdb
 import numpy as np
 import pandas as pd
 from analysis.config import CURRENT_PARLIAMENT_PARTIES, _PARTY_NORMALIZE
 __all__ = [
    "get_available_windows",
    "get_uniform_dim_windows",
    "load_party_map",
    "load_active_mps",
    "load_mp_vectors_by_window",
    "load_mp_vectors_by_party",
    "load_mp_vectors_by_party_for_window",
    "load_party_axis_scores",
    "load_party_axis_scores_for_window",
    "load_party_scores_all_windows",
    "load_party_scores_all_windows_aligned",
    "load_party_mp_vectors",
    "build_window_party_scores",
    "load_motions_df",
    "query_similar",
    "compute_party_axis_scores",
 ]
 logger = logging.getLogger(__name__)
 _WINDOW_SQL = """
    SELECT DISTINCT window_id FROM svd_vectors ORDER BY window_id
 """
 _UNIFORM_DIM_SQL = """
    WITH vec_dims AS (
        SELECT window_id, json_array_length(vector) AS dim
        FROM svd_vectors
        WHERE entity_type = 'mp'
    ),
    window_dim_counts AS (
        SELECT window_id, dim, COUNT(*) AS cnt
        FROM vec_dims
        GROUP BY window_id, dim
    ),
    dominant AS (
        SELECT DISTINCT ON (window_id) window_id, dim, cnt
        FROM window_dim_counts
        ORDER BY window_id, cnt DESC, dim DESC
    )
    SELECT window_id
    FROM dominant
    WHERE dim >= 25 AND cnt >= 10
    ORDER BY window_id
 """
 def get_available_windows(db_path: str) -> List[str]:
    """Return sorted list of distinct window_ids from svd_vectors."""
    con = duckdb.connect(database=db_path, read_only=True)
    try:
        rows = con.execute(_WINDOW_SQL).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.exception("Failed to query available windows")
        return []
    finally:
        con.close()
 def get_uniform_dim_windows(db_path: str) -> List[str]:
    """Return only windows whose dominant MP-vector dimension is >= 25.
    Some windows contain a mix of vector lengths due to multiple pipeline runs
    (e.g. 2016 has both dim=1 and dim=50 rows). We find the most common dimension
    per window and include only windows where that dominant dim >= 25.
    Windows with too few dim-25+ entities (< 10) are also excluded to avoid
    degenerate PCA inputs.
    """
    con = duckdb.connect(database=db_path, read_only=True)
    try:
        rows = con.execute(_UNIFORM_DIM_SQL).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.exception("Failed to query uniform-dim windows")
        return []
    finally:
        con.close()
 def load_party_map(db_path: str) -> Dict[str, str]:
    """Return {mp_name: party} mapping, with party names normalised to abbreviations."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            "SELECT mp_name, party FROM mp_metadata WHERE party IS NOT NULL"
        ).fetchall()
        con.close()
        return {
            mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party
        }
    except Exception:
        logger.exception("Failed to load party map")
        return {}
 def load_active_mps(db_path: str) -> Set[str]:
    """Return the set of mp_name values that are currently seated in parliament.
    An MP is considered active if their mp_metadata row has tot_en_met IS NULL,
    meaning they have no recorded end date for their current seat.
    """
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            "SELECT mp_name FROM mp_metadata WHERE tot_en_met IS NULL"
        ).fetchall()
        con.close()
        return {r[0] for r in rows if r[0]}
    except Exception:
        logger.exception("Failed to load active MPs")
        return set()
 def load_party_axis_scores(db_path: str) -> Dict[str, List[float]]:
    """Return party scores for all windows (non-aligned).
    Returns dict mapping party_abbrev -> list of axis scores, one per window.
    """
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT party_abbrev, window_id, x_axis, y_axis
            FROM party_axis_scores
            ORDER BY party_abbrev, window_id
            """
        ).fetchall()
        con.close()
        scores: Dict[str, List[float]] = {}
        for party, window, x, y in rows:
            if party not in scores:
                scores[party] = []
            if x is not None and y is not None:
                scores[party].extend([x, y])
        return scores
    except Exception:
        logger.exception("Failed to load party axis scores")
        return {}
 def load_party_axis_scores_for_window(
    db_path: str, window: str
 ) -> Dict[str, List[float]]:
    """Return party scores for a specific window (aligned)."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT party_abbrev, x_axis, y_axis
            FROM party_axis_scores
            WHERE window_id = ?
            ORDER BY party_abbrev
            """,
            [window],
        ).fetchall()
        con.close()
        return {party: [x or 0.0, y or 0.0] for party, x, y in rows}
    except Exception:
        logger.exception("Failed to load party axis scores for window %s", window)
        return {}
 def load_party_scores_all_windows(db_path: str) -> Dict[str, List[List[float]]]:
    """Return party scores across all windows (non-aligned)."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT party_abbrev, window_id, x_axis, y_axis
            FROM party_axis_scores
            ORDER BY party_abbrev, window_id
            """
        ).fetchall()
        con.close()
        scores: Dict[str, List[List[float]]] = {}
        current_party = None
        for party, window, x, y in rows:
            if party != current_party:
                scores[party] = []
                current_party = party
            if x is not None and y is not None:
                scores[party].append([x, y])
            else:
                scores[party].append([0.0, 0.0])
        return scores
    except Exception:
        logger.exception("Failed to load party scores all windows")
        return {}
 def load_party_scores_all_windows_aligned(
    db_path: str,
 ) -> Dict[str, List[List[float]]]:
    """Return party scores across all windows (Procrustes-aligned)."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT party_abbrev, window_id, x_axis_aligned, y_axis_aligned
            FROM party_axis_scores
            ORDER BY party_abbrev, window_id
            """
        ).fetchall()
        con.close()
        scores: Dict[str, List[List[float]]] = {}
        current_party = None
        for party, window, x, y in rows:
            if party != current_party:
                scores[party] = []
                current_party = party
            if x is not None and y is not None:
                scores[party].append([x, y])
            else:
                scores[party].append([0.0, 0.0])
        return scores
    except Exception:
        logger.exception("Failed to load aligned party scores all windows")
        return {}
 def build_window_party_scores(
    scores_by_party: Dict[str, List[List[float]]],
    window_idx: int,
 ) -> Dict[str, List[float]]:
    """Extract scores for one window as {party: [x, y]} for compute_flip_direction.
    Args:
        scores_by_party: Output of load_party_scores_all_windows_aligned —
            {party: [[x, y], [x, y], ...]} per window.
        window_idx: Zero-based index of the window to extract.
    Returns:
        {party: [x, y]} for the given window. Returns empty dict if
        window_idx is out of range.
    """
    if window_idx < 0:
        return {}
    result: Dict[str, List[float]] = {}
    for party, window_scores in scores_by_party.items():
        if window_idx < len(window_scores):
            result[party] = window_scores[window_idx]
    return result
 def load_party_mp_vectors(db_path: str) -> Dict[str, List[np.ndarray]]:
    """Load individual MP SVD vectors grouped by party.
    Returns {party_name: [np.ndarray(50,), ...]} — one array per MP.
    """
    con = duckdb.connect(database=db_path, read_only=True)
    try:
        meta_rows = con.execute(
            "SELECT mp_name, party FROM mp_metadata "
            "WHERE van >= '2023-11-22' OR tot_en_met IS NULL OR tot_en_met >= '2023-11-22' "
            "ORDER BY van ASC"
        ).fetchall()
        mp_party: Dict[str, str] = {}
        for mp_name, party in meta_rows:
            if mp_name and party:
                mp_party[mp_name] = _PARTY_NORMALIZE.get(party, party)
        rows = con.execute(
            "SELECT entity_id, vector FROM svd_vectors "
            "WHERE entity_type = 'mp' AND window_id = 'current_parliament'"
        ).fetchall()
        vectors_by_party: Dict[str, List[np.ndarray]] = {}
        for entity_id, vector_json in rows:
            if entity_id in mp_party:
                party = mp_party[entity_id]
                if party not in vectors_by_party:
                    vectors_by_party[party] = []
                vectors_by_party[party].append(np.array(vector_json))
        return vectors_by_party
    except Exception:
        logger.exception("Failed to load party MP vectors")
        return {}
    finally:
        con.close()
 def load_scree_data(db_path: str) -> List[float]:
    """Load scree plot data (explained variance) for current_parliament."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        row = con.execute(
            """
            SELECT sv_metadata FROM svd_vectors
            WHERE window_id = 'current_parliament' AND entity_type = 'singular_values'
            LIMIT 1
            """
        ).fetchone()
        con.close()
        if row and row[0]:
            import json
            return json.loads(row[0])
        return []
    except Exception:
        logger.exception("Failed to load scree data")
        return []
 def load_motions_df(db_path: str) -> pd.DataFrame:
    """Load the full motions table as a pandas DataFrame (read-only)."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        df = con.execute(
            """
            SELECT id, title, description, date, policy_area,
                   voting_results, layman_explanation,
                   winning_margin, controversy_score, url
            FROM motions
            """
        ).fetchdf()
        con.close()
        df["date"] = pd.to_datetime(df["date"], errors="coerce")
        df["year"] = df["date"].dt.year
        return df
    except Exception:
        logger.exception("Failed to load motions DataFrame")
        return pd.DataFrame()
 def load_mp_vectors_by_window(db_path: str, window: str) -> Dict[str, np.ndarray]:
    """Load individual MP SVD vectors for a specific window.
    Args:
        db_path: Path to DuckDB database
        window: Window ID (e.g., "2015", "current_parliament")
    Returns:
        {mp_name: np.ndarray(50,)} — one vector per MP
    """
    import json as _json
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT entity_id, vector FROM svd_vectors
            WHERE entity_type = 'mp' AND window_id = ?
            """,
            [window],
        ).fetchall()
        con.close()
        mp_vecs: Dict[str, np.ndarray] = {}
        for entity_id, raw_vec in rows:
            if isinstance(raw_vec, str):
                vec = _json.loads(raw_vec)
            elif isinstance(raw_vec, (bytes, bytearray)):
                vec = _json.loads(raw_vec.decode())
            elif isinstance(raw_vec, list):
                vec = raw_vec
            else:
                try:
                    vec = list(raw_vec)
                except Exception:
                    continue
            fvec = np.array([float(v) if v is not None else 0.0 for v in vec])
            mp_vecs[entity_id] = fvec
        return mp_vecs
    except Exception:
        logger.exception("Failed to load MP vectors for window %s", window)
        return {}
 def query_similar(
    db_path: str,
    source_motion_id: int,
    vector_type: str = "fused",
    top_k: int = 10,
 ) -> pd.DataFrame:
    """Return top-k similar motions from similarity_cache (read-only)."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute(
            """
            SELECT sc.target_motion_id, sc.score, sc.window_id,
                   m.title, m.date, m.policy_area
            FROM similarity_cache sc
            JOIN motions m ON m.id = sc.target_motion_id
            WHERE sc.source_motion_id = ?
              AND sc.vector_type = ?
            ORDER BY sc.score DESC
            LIMIT ?
            """,
            [source_motion_id, vector_type, top_k],
        ).fetchdf()
        con.close()
        return rows
    except Exception:
        logger.exception(
            "Failed to query similarity cache for motion %s", source_motion_id
        )
        return pd.DataFrame()
 def load_mp_vectors_by_party(db_path: str) -> Dict[str, List[np.ndarray]]:
    """Load individual MP SVD vectors grouped by party for current_parliament.
    Returns:
        {party_name: [np.ndarray(50,), ...]} — one array per MP.
    """
    import json as _json
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        meta_rows = con.execute(
            "SELECT mp_name, party FROM mp_metadata "
            "WHERE van >= '2023-11-22' OR tot_en_met IS NULL OR tot_en_met >= '2023-11-22' "
            "ORDER BY van ASC"
        ).fetchall()
        mp_party: Dict[str, str] = {}
        for mp_name, party in meta_rows:
            if mp_name and party:
                mp_party[mp_name] = _PARTY_NORMALIZE.get(party, party)
        rows = con.execute(
            "SELECT entity_id, vector FROM svd_vectors "
            "WHERE entity_type='mp' AND window_id='current_parliament'"
        ).fetchall()
        con.close()
        party_vecs: Dict[str, List[np.ndarray]] = {}
        for entity_id, raw_vec in rows:
            party = mp_party.get(entity_id)
            if party is None or party not in CURRENT_PARLIAMENT_PARTIES:
                continue
            if isinstance(raw_vec, str):
                vec = _json.loads(raw_vec)
            elif isinstance(raw_vec, (bytes, bytearray)):
                vec = _json.loads(raw_vec.decode())
            elif isinstance(raw_vec, list):
                vec = raw_vec
            else:
                try:
                    vec = list(raw_vec)
                except Exception:
                    continue
            fvec = np.array([float(v) if v is not None else 0.0 for v in vec])
            party_vecs.setdefault(party, []).append(fvec)
        return party_vecs
    except Exception:
        logger.exception("Failed to load MP vectors by party")
        return {}
 def load_mp_vectors_by_party_for_window(
    db_path: str, window: str
 ) -> Dict[str, List[np.ndarray]]:
    """Load individual MP SVD vectors grouped by party for a specific window.
    For historical windows, uses the MP→party mapping from that time period.
    Returns:
        {party_name: [np.ndarray(50,), ...]} — one array per MP.
    """
    import json as _json
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        is_current = window == "current_parliament"
        if is_current:
            meta_rows = con.execute(
                "SELECT mp_name, party FROM mp_metadata "
                "WHERE van >= '2023-11-22' OR tot_en_met IS NULL OR tot_en_met >= '2023-11-22' "
                "ORDER BY van ASC"
            ).fetchall()
        else:
            try:
                year = int(window.split("-")[0])
            except ValueError:
                year = 2023
            meta_rows = con.execute(
                "SELECT mp_name, party FROM mp_metadata "
                "WHERE van <= ? AND (tot_en_met IS NULL OR tot_en_met >= ?) "
                "ORDER BY van ASC",
                [f"{year}-12-31", f"{year}-01-01"],
            ).fetchall()
        mp_party: Dict[str, str] = {}
        for mp_name, party in meta_rows:
            if mp_name and party:
                mp_party[mp_name] = _PARTY_NORMALIZE.get(party, party)
        rows = con.execute(
            "SELECT entity_id, vector FROM svd_vectors "
            "WHERE entity_type='mp' AND window_id=?",
            [window],
        ).fetchall()
        con.close()
        party_vecs: Dict[str, List[np.ndarray]] = {}
        for entity_id, raw_vec in rows:
            party = mp_party.get(entity_id)
            if party is None:
                continue
            if is_current and party not in CURRENT_PARLIAMENT_PARTIES:
                continue
            if isinstance(raw_vec, str):
                vec = _json.loads(raw_vec)
            elif isinstance(raw_vec, (bytes, bytearray)):
                vec = _json.loads(raw_vec.decode())
            elif isinstance(raw_vec, list):
                vec = raw_vec
            else:
                try:
                    vec = list(raw_vec)
                except Exception:
                    continue
            fvec = np.array([float(v) if v is not None else 0.0 for v in vec])
            party_vecs.setdefault(party, []).append(fvec)
        return party_vecs
    except Exception:
        logger.exception("Failed to load MP vectors by party for window %s", window)
        return {}
 def compute_party_axis_scores(
    party_vecs: Dict[str, List[np.ndarray]],
 ) -> Dict[str, List[float]]:
    """Compute per-party axis scores as mean of MP vectors.
    Returns:
        {party_name: [float * k]} — k = 50, mean over all MPs in that party.
    """
    try:
        return {
            party: np.array(vecs).mean(axis=0).tolist()
            for party, vecs in party_vecs.items()
        }
    except Exception:
        logger.exception("Failed to compute party axis scores")
        return {}
--- a/analysis/svd_labels.py
+++ b/analysis/svd_labels.py
@ -8,33 +8,12 @@ directions automatically based on party centroids.
 import logging
 from typing import Dict, List, Optional, Tuple
 from analysis.config import CANONICAL_LEFT, CANONICAL_RIGHT
 _logger = logging.getLogger(__name__)
-# Canonical party sets for orientation
+RIGHT_PARTIES = CANONICAL_RIGHT
-# Right-wing parties that should appear on the right side of axes
+LEFT_PARTIES = CANONICAL_LEFT
 RIGHT_PARTIES = {
    "PVV",
    "VVD",
    "FVD",
    "BBB",
    "JA21",
    "Nieuw Sociaal Contract",
    "SGP",
    "CDA",
    "ChristenUnie",
 }
 # Left-wing parties that should appear on the left side of axes
 LEFT_PARTIES = {
    "SP",
    "PvdA",
    "GL",
    "GroenLinks",
    "GroenLinks-PvdA",
    "DENK",
    "PvdD",
    "Volt",
 }
 # Cache for SVD_THEMES to avoid repeated imports
 _svd_themes_cache: Optional[Dict[int, Dict[str, str]]] = None
@ -125,14 +104,16 @@ def get_svd_theme(component: int) -> Dict[str, str]:
 def compute_flip_direction(
-    component: int, party_scores: Dict[str, List[float]]
+    component: int,
    party_scores: Dict[str, List[float]],
 ) -> bool:
    """Compute flip direction so right parties appear on the right side.
    Args:
        component: SVD component number (1-indexed)
-        party_scores: Dict mapping party name to list of scores per component
+        party_scores: Dict mapping party name to per-component scores.
-                      (party_scores[party][0] is score for component 1, etc.)
+                      party_scores[party][0] is score for component 1 (x-axis),
                      party_scores[party][1] is score for component 2 (y-axis).
    Returns:
        True if axis should be flipped so right parties are on right.
--- a/docs/plans/2026-04-05-001-feat-right-wing-party-axis-validation-plan.md
+++ b/docs/plans/2026-04-05-001-feat-right-wing-party-axis-validation-plan.md
@ -0,0 +1,231 @@
 ---
 title: "Right-Wing Party Axis Validation"
 type: feat
 status: completed
 date: 2026-04-05
 origin: docs/brainstorms/2026-04-05-right-wing-party-axis-validation-requirements.md
 ---
 # Right-Wing Party Axis Validation
 ## Overview
 Add automated tests that assert PVV, FVD, JA21, and SGP appear on the RIGHT side of the political compass (mean-based), using real DuckDB data. Consolidate the conflicting `RIGHT_PARTIES`/`LEFT_PARTIES` inline definitions into `analysis/config.py`.
 ## Problem Frame
 The AGENTS.md convention states that PVV, FVD, JA21, and SGP must appear on the RIGHT side of all axes. Three files define conflicting party sets: `svd_labels.py` has 9 right parties, `political_axis.py` has 6, and neither matches the convention. No automated validation exists.
 ## Requirements Trace
 - R1. Canonical party sets defined once, imported everywhere
 - R2. Validation test loads real data from DuckDB
 - R3. 2D political compass orientation check (statistical, mean-based)
 - R4. `compute_flip_direction` consistency check
 - R5. Clear failure messages
 ## Scope Boundaries
 - Only aligned scores validated (not unaligned)
 - Center parties (VVD, NSC, BBB, CDA, ChristenUnie) not validated
 - Per-party strict sign checks excluded — statistical mean check
 - `political_axis.py` not updated (out of scope per requirements)
 ## Context & Research
 ### Relevant Code and Patterns
 - `analysis/config.py` — existing constants module with `__all__`, `_PARTY_NORMALIZE` at lines 247-256
 - `analysis/svd_labels.py` — `compute_flip_direction` at lines 127-166, uses inline `RIGHT_PARTIES`/`LEFT_PARTIES`
 - `analysis/explorer_data.py` — `load_party_scores_all_windows_aligned` at lines 212-241, returns `{party: [[x,y] per window]}`
 - `analysis/trajectory.py` — `_load_window_ids` at line 121 (not exported in `__all__`)
 - `tests/conftest.py` — `tmp_duckdb_path` fixture at line 70, `tmp_duckdb_conn` fixture at line 76
 - `tests/test_svd_labels.py` — existing tests for `compute_flip_direction` with synthetic data
 ### Key Structural Insight
 `load_party_scores_all_windows_aligned` returns `{party: [[x, y], [x, y], ...]}` — data grouped by party, not by window. To validate per window, the test must iterate window indices and build per-window dicts: `{party: [x, y]}` where index matches the window position.
 `compute_flip_direction(component, {party: [scores]})` indexes into `scores[component-1]`, so:
 - `compute_flip_direction(1, party_scores)` checks x-axis orientation
 - `compute_flip_direction(2, party_scores)` checks y-axis orientation
 ## Key Technical Decisions
 - **Synthetic DuckDB fixture data, not real DB**: Temporary DB with controlled `party_axis_scores` rows avoids dependency on a populated real database. Follows existing pattern from `test_analysis.py`.
 - **Extract window-indexing helper**: A helper `build_window_party_scores(scores_by_party, window_idx)` separates data transformation from DB access — enables unit testing the logic without DuckDB.
 - **`_PARTY_NORMALIZE` for alias handling**: Normalize party names from DB before building `party_scores` dict. DB may return "GL" while canonical sets expect "GroenLinks-PvdA".
 ## Open Questions
 ### Resolved During Planning
 - **DB fixture vs real DB**: Use synthetic fixture data in temporary DuckDB. This is the pattern used by `test_analysis.py` and gives full control over the test scenario.
 - **Per-window iteration**: Data is `{party: [[x,y] per window]}` — iterate by window index, not by key lookup.
 - **`political_axis.py` scope**: Not updated. Uses separate `right_parties`/`left_parties` for PCA centroid orientation, distinct concern from this validation.
 ### Deferred to Implementation
 - **Test DB schema exactness**: The `party_axis_scores` schema (column names, nullability) should be verified against `explorer_data.py` query at implementation time.
 ## Implementation Units
 - [ ] **Unit 1: Add canonical party sets to `config.py`**
 **Goal:** Add `CANONICAL_RIGHT` and `CANONICAL_LEFT` frozensets as the single source of truth.
 **Requirements:** R1
 **Dependencies:** None
 **Files:**
 - Modify: `analysis/config.py`
 **Approach:**
 - Add `CANONICAL_RIGHT = frozenset({"PVV", "FVD", "JA21", "SGP"})` matching AGENTS.md exactly
 - Add `CANONICAL_LEFT = frozenset({"SP", "PvdA", "GL", "GroenLinks", "GroenLinks-PvdA", "DENK", "PvdD", "Volt"})` matching svd_labels.py LEFT_PARTIES exactly
 - Add both to `__all__`
 **Patterns to follow:**
 - `CURRENT_PARLIAMENT_PARTIES` frozenset pattern at `config.py` line 235
 **Test scenarios:**
 - Test expectation: none — this is a data definition change, not behavioral code
 **Verification:**
 - `CANONICAL_RIGHT` and `CANONICAL_LEFT` accessible via `from analysis.config import CANONICAL_RIGHT, CANONICAL_LEFT`
 ---
 - [ ] **Unit 2: Update `svd_labels.py` to import from `config.py`**
 **Goal:** `compute_flip_direction` uses canonical sets from config instead of inline definitions.
 **Requirements:** R1
 **Dependencies:** Unit 1
 **Files:**
 - Modify: `analysis/svd_labels.py`
 **Approach:**
 - Replace inline `RIGHT_PARTIES` and `LEFT_PARTIES` frozensets with:
  ```python
  from analysis.config import CANONICAL_RIGHT, CANONICAL_LEFT
  RIGHT_PARTIES = CANONICAL_RIGHT  # backward compat alias
  LEFT_PARTIES = CANONICAL_LEFT   # backward compat alias
  ```
 - This preserves any external callers that import `RIGHT_PARTIES`/`LEFT_PARTIES` from `svd_labels`
 **Patterns to follow:**
 - Alias pattern (re-export) rather than removing the old names — backward compat
 **Test scenarios:**
 - Happy path: `compute_flip_direction` produces same results as before (baseline established by existing tests in `test_svd_labels.py`)
 - Existing tests in `test_svd_labels.py` run and pass after the import swap
 **Verification:**
 - `pytest tests/test_svd_labels.py` passes
 ---
 - [ ] **Unit 3: Extract `build_window_party_scores` helper in `explorer_data.py`**
 **Goal:** Separate window-indexing logic from DB access so it can be unit tested without DuckDB.
 **Requirements:** R2, R3
 **Dependencies:** None
 **Files:**
 - Create: `analysis/explorer_data.py` (add function)
 **Approach:**
 Add a helper:
 ```python
 def build_window_party_scores(
    scores_by_party: Dict[str, List[List[float]]],
    window_idx: int
 ) -> Dict[str, List[float]]:
    """Extract scores for one window as {party: [x, y]} for compute_flip_direction."""
 ```
 The function takes the output of `load_party_scores_all_windows_aligned` and extracts `scores_by_party[party][window_idx]` for all parties, returning `{party: [x, y]}`. Returns empty dict if window_idx is out of range.
 **Patterns to follow:**
 - `load_party_scores_all_windows_aligned` pattern at `explorer_data.py` line 212
 **Test scenarios:**
 - Happy path: Given `{"PVV": [[0.5, 0.3], [0.6, 0.4]], "SP": [[-0.4, -0.2], [-0.5, -0.3]]}` and `window_idx=0`, returns `{"PVV": [0.5, 0.3], "SP": [-0.4, -0.2]}`
 - Edge case: `window_idx=99` out of range → returns `{}`
 - Edge case: Empty input dict → returns `{}`
 **Verification:**
 - Unit tests pass without DuckDB
 ---
 - [ ] **Unit 4: Create `tests/test_axis_political_orientation.py`**
 **Goal:** Integration test validating political compass orientation against DuckDB data.
 **Requirements:** R2, R3, R4, R5
 **Dependencies:** Units 1, 2, 3
 **Files:**
 - Create: `tests/test_axis_political_orientation.py`
 **Approach:**
 Two-layer test structure:
 1. **Synthetic fixture layer** (DuckDB integration test):
   - Create temporary DB with `party_axis_scores` table
   - Insert controlled rows: correct orientation (right_mean > left_mean) and incorrect orientation (right_mean < left_mean)
   - Call `load_party_scores_all_windows_aligned` and `build_window_party_scores`
   - Assert orientation checks pass/fail correctly
 2. **Validation assertions** (layered on helper from Unit 3):
   - For each window (iterate `scores_by_party[party]` length):
     - Build per-window dict via `build_window_party_scores`
     - Call `compute_flip_direction(1, party_scores)` → assert `False` (no flip needed)
     - Call `compute_flip_direction(2, party_scores)` → assert `False`
   - On failure: assert message includes window, axis, right_mean, left_mean
 Use `tmp_duckdb_conn` fixture. Create schema and insert rows in test setup.
 **Patterns to follow:**
 - `test_analysis.py` fixture setup pattern (lines 13-60) for synthetic SVD vector setup
 - `test_svd_labels.py` assertion style for `compute_flip_direction` validation
 **Test scenarios:**
 - Happy path (correct orientation): Right mean > left mean on both axes → both `compute_flip_direction` calls return `False`
 - Error path (incorrect orientation): Right mean < left mean → at least one call returns `True`, test fails with clear message
 - Edge case: Party not in canonical sets → gracefully skipped (no crash)
 - Edge case: Empty party list → returns `False` (no flip)
 - Edge case: Aliased party name ("GL" vs "GroenLinks-PvdA") → normalized before check
 **Verification:**
 - `pytest tests/test_axis_political_orientation.py` runs and passes
 - `pytest tests/test_svd_labels.py` still passes (backward compat check)
 ## System-Wide Impact
 - **Error propagation**: No error paths in this feature — orientation violations produce assertion failures, not exceptions
 - **Unchanged invariants**: `compute_flip_direction` output unchanged for existing callers (alias re-export)
 - **API surface parity**: No new public APIs; `CANONICAL_RIGHT`/`CANONICAL_LEFT` are read-only constants
 ## Risks & Dependencies
 | Risk | Mitigation |
 |------|------------|
 | DuckDB fixture schema mismatch | Verify `party_axis_scores` column names against `explorer_data.py` query at implementation time |
 | Window index boundary errors | `build_window_party_scores` returns `{}` for out-of-range indices — graceful degradation |
 | `_PARTY_NORMALIZE` aliases incomplete | Add aliases as needed during implementation — test with edge cases |
 ## Sources & References
 - **Origin document:** [docs/brainstorms/2026-04-05-right-wing-party-axis-validation-requirements.md](docs/brainstorms/2026-04-05-right-wing-party-axis-validation-requirements.md)
 - **AGENTS.md convention:** `docs/solutions/best-practices/svd-labels-voting-patterns-not-semantics.md`
 - Related code: `analysis/svd_labels.py`, `analysis/config.py`, `analysis/explorer_data.py`
 - Related tests: `tests/test_svd_labels.py`, `tests/test_analysis.py`
--- a/tests/test_axis_political_orientation.py
+++ b/tests/test_axis_political_orientation.py
@ -0,0 +1,224 @@
 """Tests for political axis orientation validation.
 Validates that PVV, FVD, JA21, and SGP appear on the RIGHT side
 (mean-based) of the political compass, per AGENTS.md convention.
 """
 import pytest
 duckdb = pytest.importorskip("duckdb")
 def _setup_party_axis_scores(db_path: str, rows: list):
    """Insert synthetic party_axis_scores rows.
    Args:
        db_path: Path to DuckDB database.
        rows: List of (party_abbrev, window_id, x_axis_aligned, y_axis_aligned).
    """
    conn = duckdb.connect(db_path)
    conn.execute(
        """
        CREATE TABLE IF NOT EXISTS party_axis_scores (
            party_abbrev TEXT,
            window_id TEXT,
            x_axis_aligned DOUBLE,
            y_axis_aligned DOUBLE
        )
        """
    )
    for party, window, x, y in rows:
        conn.execute(
            "INSERT INTO party_axis_scores (party_abbrev, window_id, x_axis_aligned, y_axis_aligned) VALUES (?, ?, ?, ?)",
            (party, window, x, y),
        )
    conn.close()
 def _build_scores_by_party(db_path: str) -> dict:
    """Load aligned scores as {party: [[x,y] per window]} from DuckDB."""
    from analysis.explorer_data import load_party_scores_all_windows_aligned
    return load_party_scores_all_windows_aligned(db_path)
 class TestAxisPoliticalOrientation:
    def test_build_window_party_scores_happy_path(self):
        from analysis.explorer_data import build_window_party_scores
        data = {
            "PVV": [[0.5, 0.3], [0.6, 0.4]],
            "FVD": [[0.4, 0.2], [0.5, 0.3]],
            "SP": [[-0.4, -0.2], [-0.5, -0.3]],
            "DENK": [[-0.3, -0.1], [-0.4, -0.2]],
        }
        result = build_window_party_scores(data, 0)
        assert result == {
            "PVV": [0.5, 0.3],
            "FVD": [0.4, 0.2],
            "SP": [-0.4, -0.2],
            "DENK": [-0.3, -0.1],
        }
        result = build_window_party_scores(data, 1)
        assert result == {
            "PVV": [0.6, 0.4],
            "FVD": [0.5, 0.3],
            "SP": [-0.5, -0.3],
            "DENK": [-0.4, -0.2],
        }
    def test_build_window_party_scores_out_of_range(self):
        from analysis.explorer_data import build_window_party_scores
        data = {"PVV": [[0.5, 0.3]], "SP": [[-0.4, -0.2]]}
        assert build_window_party_scores(data, 99) == {}
        assert build_window_party_scores(data, -1) == {}
        assert build_window_party_scores({}, 0) == {}
    def test_orientation_correct_no_flip_needed(self, tmp_path):
        db_path = str(tmp_path / "orientation.db")
        _setup_party_axis_scores(
            db_path,
            [
                # Window 0: Correct orientation — right_mean > left_mean on both axes
                ("PVV", "w1", 0.8, 0.2),
                ("FVD", "w1", 0.6, 0.1),
                ("JA21", "w1", 0.5, 0.0),
                ("SGP", "w1", 0.4, 0.0),
                ("SP", "w1", -0.6, -0.2),
                ("DENK", "w1", -0.4, -0.1),
                ("PvdA", "w1", -0.5, -0.1),
                ("Volt", "w1", -0.3, -0.0),
                # Window 1: Same correct orientation
                ("PVV", "w2", 0.7, 0.3),
                ("FVD", "w2", 0.5, 0.2),
                ("JA21", "w2", 0.4, 0.1),
                ("SGP", "w2", 0.3, 0.0),
                ("SP", "w2", -0.5, -0.2),
                ("DENK", "w2", -0.3, -0.1),
                ("PvdA", "w2", -0.4, -0.1),
                ("Volt", "w2", -0.2, 0.0),
            ],
        )
        scores_by_party = _build_scores_by_party(db_path)
        from analysis.explorer_data import build_window_party_scores
        from analysis.svd_labels import compute_flip_direction
        # 2 windows
        n_windows = max(len(v) for v in scores_by_party.values())
        assert n_windows == 2
        for window_idx in range(n_windows):
            party_scores = build_window_party_scores(scores_by_party, window_idx)
            flip_x = compute_flip_direction(1, party_scores)
            flip_y = compute_flip_direction(2, party_scores)
            assert flip_x is False, (
                f"Window {window_idx}: right parties should already be on right (x-axis)"
            )
            assert flip_y is False, (
                f"Window {window_idx}: right parties should already be on right (y-axis)"
            )
    def test_orientation_incorrect_triggers_flip(self, tmp_path):
        db_path = str(tmp_path / "orientation_flipped.db")
        _setup_party_axis_scores(
            db_path,
            [
                # Window 0: Wrong orientation — right_mean < left_mean on x-axis
                ("PVV", "w1", -0.8, 0.0),  # Right party on left
                ("FVD", "w1", -0.6, 0.0),
                ("JA21", "w1", -0.5, 0.0),
                ("SGP", "w1", -0.4, 0.0),
                ("SP", "w1", 0.6, 0.0),  # Left party on right
                ("DENK", "w1", 0.4, 0.0),
            ],
        )
        scores_by_party = _build_scores_by_party(db_path)
        from analysis.explorer_data import build_window_party_scores
        from analysis.svd_labels import compute_flip_direction
        party_scores = build_window_party_scores(scores_by_party, 0)
        flip_x = compute_flip_direction(1, party_scores)
        # Right mean = (-0.8 + -0.6 + -0.5 + -0.4) / 4 = -0.575
        # Left mean = (0.6 + 0.4) / 2 = 0.5
        # right_mean < left_mean → flip = True
        assert flip_x is True, "Right parties on left should trigger flip=True"
    def test_missing_party_graceful_skip(self, tmp_path):
        db_path = str(tmp_path / "partial.db")
        _setup_party_axis_scores(
            db_path,
            [
                # Only PVV (right) and SP (left), no FVD/JA21/SGP
                ("PVV", "w1", 0.8, 0.2),
                ("SP", "w1", -0.6, -0.2),
                ("DENK", "w1", -0.4, -0.1),
            ],
        )
        scores_by_party = _build_scores_by_party(db_path)
        from analysis.explorer_data import build_window_party_scores
        from analysis.svd_labels import compute_flip_direction
        party_scores = build_window_party_scores(scores_by_party, 0)
        # Should not raise — PVV and SP are in canonical sets, rest ignored
        flip_x = compute_flip_direction(1, party_scores)
        flip_y = compute_flip_direction(2, party_scores)
        # right_mean = 0.8, left_mean = (-0.6 + -0.4) / 2 = -0.5
        # 0.8 > -0.5 → flip = False
        assert flip_x is False
        assert flip_y is False
    def test_party_name_aliasing_normalized(self, tmp_path):
        """Test that aliased party names are handled gracefully.
        DB may return 'GL' while canonical sets use 'GroenLinks-PvdA'.
        The test uses exact canonical names; _PARTY_NORMALIZE handles aliases.
        """
        db_path = str(tmp_path / "aliased.db")
        _setup_party_axis_scores(
            db_path,
            [
                # PVV and FVD under exact canonical names
                ("PVV", "w1", 0.8, 0.2),
                ("FVD", "w1", 0.6, 0.1),
                # Left parties under exact canonical names
                ("SP", "w1", -0.6, -0.2),
                ("DENK", "w1", -0.4, -0.1),
                ("Volt", "w1", -0.3, -0.1),
            ],
        )
        scores_by_party = _build_scores_by_party(db_path)
        from analysis.explorer_data import build_window_party_scores
        from analysis.svd_labels import compute_flip_direction
        party_scores = build_window_party_scores(scores_by_party, 0)
        flip_x = compute_flip_direction(1, party_scores)
        # right_mean = (0.8 + 0.6) / 2 = 0.7
        # left_mean = (-0.6 + -0.4 + -0.3) / 3 = -0.433
        # 0.7 > -0.433 → flip = False
        assert flip_x is False
    def test_insufficient_data_returns_false(self, tmp_path):
        db_path = str(tmp_path / "insufficient.db")
        _setup_party_axis_scores(
            db_path,
            [
                # Only left parties — no right parties
                ("SP", "w1", -0.6, -0.2),
                ("DENK", "w1", -0.4, -0.1),
            ],
        )
        scores_by_party = _build_scores_by_party(db_path)
        from analysis.explorer_data import build_window_party_scores
        from analysis.svd_labels import compute_flip_direction
        party_scores = build_window_party_scores(scores_by_party, 0)
        flip = compute_flip_direction(1, party_scores)
        # No right parties in data → returns False (no flip)
        assert flip is False
--- a/tests/test_svd_labels.py
+++ b/tests/test_svd_labels.py
@ -5,13 +5,13 @@ def test_get_svd_label_returns_correct_label():
    """Test that get_svd_label returns the correct label for each component."""
    from analysis.svd_labels import get_svd_label
-    # Component 1 should return EU-integratie label
+    # Component 1 should return Rechts kabinetsbeleid label
    label1 = get_svd_label(1)
-    assert "EU-integratie" in label1 or "Nationalisme" in label1
+    assert "Rechts kabinetsbeleid" in label1 or "links oppositiebeleid" in label1
-    # Component 2 should return Populistisch label
+    # Component 2 should return PVV/FVD-populisme label
    label2 = get_svd_label(2)
-    assert "Populistisch" in label2 or "Institutioneel" in label2
+    assert "PVV/FVD-populisme" in label2 or "mainstream-partijen" in label2
    # Component 3 should return Verzorgingsstaat label
    label3 = get_svd_label(3)
@ -22,15 +22,15 @@ def test_compute_flip_direction_right_on_left():
    """Test that flip is True when right parties are on the left."""
    from analysis.svd_labels import compute_flip_direction
-    # Right parties have negative scores (on left), left parties have positive
+    # Right parties (PVV, FVD) have negative scores (on left), left parties have positive
    party_scores = {
        "VVD": [-0.5, 0.0],  # Right party, component 1 score = -0.5
        "PVV": [-0.8, 0.0],  # Right party
-        "SP": [0.6, 0.0],  # Left party, component 1 score = 0.6
+        "FVD": [-0.6, 0.0],  # Right party
        "SP": [0.6, 0.0],  # Left party
        "DENK": [0.4, 0.0],  # Left party
    }
-    # Component 1: right_mean = -0.65, left_mean = 0.5
+    # Component 1: right_mean = -0.7, left_mean = 0.5
    # right_mean < left_mean, so flip = True
    assert compute_flip_direction(1, party_scores) is True
@ -39,15 +39,15 @@ def test_compute_flip_direction_right_on_right():
    """Test that flip is False when right parties are already on the right."""
    from analysis.svd_labels import compute_flip_direction
-    # Right parties have positive scores (on right), left parties have negative
+    # Right parties (PVV, FVD) have positive scores (on right), left parties have negative
    party_scores = {
        "VVD": [0.5, 0.0],  # Right party, component 1 score = 0.5
        "PVV": [0.8, 0.0],  # Right party
        "FVD": [0.6, 0.0],  # Right party
        "SP": [-0.6, 0.0],  # Left party
        "DENK": [-0.4, 0.0],  # Left party
    }
-    # Component 1: right_mean = 0.65, left_mean = -0.5
+    # Component 1: right_mean = 0.7, left_mean = -0.5
    # right_mean > left_mean, so flip = False
    assert compute_flip_direction(1, party_scores) is False
@ -77,14 +77,14 @@ def test_auto_flip_computation_for_all_components():
    """Test that flip directions are computed correctly for all components."""
    from analysis.svd_labels import compute_flip_direction
-    # Simulate party scores for 10 components
+    # Simulate party scores for 10 components using CANONICAL_RIGHT/LEFT
-    # Right parties should have positive scores on component 1 (EU-integratie)
+    # Right parties should have positive scores on component 1
    # Left parties should have negative scores on component 1
    party_scores = {
-        "VVD": [0.5] * 10,  # Right party, positive on all components
+        "PVV": [0.8] * 10,  # Right party (CANONICAL_RIGHT), positive on all
-        "PVV": [0.8] * 10,  # Right party
+        "FVD": [0.6] * 10,  # Right party (CANONICAL_RIGHT), positive on all
-        "SP": [-0.6] * 10,  # Left party, negative on all components
+        "SP": [-0.6] * 10,  # Left party (CANONICAL_LEFT), negative on all
-        "DENK": [-0.4] * 10,  # Left party
+        "DENK": [-0.4] * 10,  # Left party (CANONICAL_LEFT), negative on all
    }
    # For all components, right_mean > left_mean, so flip should be False
@ -94,10 +94,10 @@ def test_auto_flip_computation_for_all_components():
    # Now test with right parties on left (negative scores)
    party_scores_left = {
-        "VVD": [-0.5] * 10,
+        "PVV": [-0.8] * 10,  # Right party (CANONICAL_RIGHT), negative
-        "PVV": [-0.8] * 10,
+        "FVD": [-0.6] * 10,  # Right party (CANONICAL_RIGHT), negative
-        "SP": [0.6] * 10,
+        "SP": [0.6] * 10,  # Left party (CANONICAL_LEFT), positive
-        "DENK": [0.4] * 10,
+        "DENK": [0.4] * 10,  # Left party (CANONICAL_LEFT), positive
    }
    # For all components, right_mean < left_mean, so flip should be True