from __future__ import annotations

import json
import math
import sqlite3
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Tuple

# Import Wikidata service for remote lookups
try:
    from services.wikidata_service import (
        search_events_by_geo_time as wikidata_search,
        get_event_detail as wikidata_get_detail,
        search_events_by_name as wikidata_search_by_name,
    )
    WIKIDATA_AVAILABLE = True
except ImportError:
    WIKIDATA_AVAILABLE = False
    print("[history_service] Wikidata service not available, using curated data only")

ROOT_DIR = Path(__file__).resolve().parent.parent
DATA_DIR = ROOT_DIR / "data"
DATA_DIR.mkdir(parents=True, exist_ok=True)
DB_PATH = DATA_DIR / "meridian_history.db"

# Wikidata settings
ENABLE_WIKIDATA_FALLBACK = True
WIKIDATA_CONFIDENCE_THRESHOLD = 0.5

EVENT_SCHEMA_VERSION = 2
EVENT_EXTRA_COLUMNS: Dict[str, str] = {
    "slug": "TEXT",
    "summary": "TEXT",
    "narrative": "TEXT",
    "start_year": "INTEGER",
    "end_year": "INTEGER",
    "month": "INTEGER",
    "day": "INTEGER",
    "themes": "TEXT",
    "actors": "TEXT",
    "artifacts": "TEXT",
    "visual_motifs": "TEXT",
    "facets": "TEXT",
    "sources": "TEXT",
    "time_range": "TEXT",
    "geo_anchor": "TEXT",
    "confidence": "REAL",
    "relationships": "TEXT",
}


def _get_connection() -> sqlite3.Connection:
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    return conn


def _serialize(value: object) -> str:
    return json.dumps(value, ensure_ascii=False)


def _deserialize(value: Optional[str], default):
    if value is None or value == "":
        return default
    try:
        return json.loads(value)
    except json.JSONDecodeError:
        return default


def ensure_schema() -> None:
    conn = _get_connection()
    cursor = conn.cursor()

    cursor.execute(
        """
        CREATE TABLE IF NOT EXISTS events (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT UNIQUE,
            year INTEGER,
            lat REAL,
            lon REAL
        )
        """
    )

    cursor.execute(
        """
        CREATE TABLE IF NOT EXISTS schema_meta (
            key TEXT PRIMARY KEY,
            value TEXT
        )
        """
    )

    # Add new columns if missing
    cursor.execute("PRAGMA table_info(events)")
    existing_columns = {row["name"] for row in cursor.fetchall()}
    for column, column_type in EVENT_EXTRA_COLUMNS.items():
        if column not in existing_columns:
            cursor.execute(f"ALTER TABLE events ADD COLUMN {column} {column_type}")

    # Update schema version
    cursor.execute(
        """
        INSERT INTO schema_meta(key, value)
        VALUES('event_schema_version', ?)
        ON CONFLICT(key) DO UPDATE SET value=excluded.value
        """,
        (str(EVENT_SCHEMA_VERSION),),
    )

    cursor.execute("CREATE INDEX IF NOT EXISTS idx_events_year ON events(year)")
    cursor.execute("CREATE INDEX IF NOT EXISTS idx_events_coordinates ON events(lat, lon)")

    conn.commit()
    conn.close()


def seed_curated_events(force_refresh: bool = False) -> None:
    conn = _get_connection()
    cursor = conn.cursor()

    if force_refresh:
        cursor.execute("DELETE FROM events")

    for event in CURATED_EVENTS:
        cursor.execute(
            """
            INSERT OR IGNORE INTO events (
                name, slug, year, start_year, end_year, month, day,
                lat, lon, summary, narrative, themes, actors, artifacts,
                visual_motifs, facets, sources, time_range, geo_anchor,
                confidence, relationships
            ) VALUES (
                :name, :slug, :year, :start_year, :end_year, :month, :day,
                :lat, :lon, :summary, :narrative, :themes, :actors, :artifacts,
                :visual_motifs, :facets, :sources, :time_range, :geo_anchor,
                :confidence, :relationships
            )
            """,
            {
                "name": event["name"],
                "slug": event.get("slug") or event["name"].lower().replace(" ", "_"),
                "year": event.get("year"),
                "start_year": event.get("start_year", event.get("year")),
                "end_year": event.get("end_year", event.get("year")),
                "month": event.get("month"),
                "day": event.get("day"),
                "lat": event.get("lat"),
                "lon": event.get("lon"),
                "summary": event.get("summary"),
                "narrative": event.get("narrative"),
                "themes": _serialize(event.get("themes", [])),
                "actors": _serialize(event.get("actors", [])),
                "artifacts": _serialize(event.get("artifacts", [])),
                "visual_motifs": _serialize(event.get("visual_motifs", [])),
                "facets": _serialize(event.get("facets", {})),
                "sources": _serialize(event.get("sources", [])),
                "time_range": _serialize(event.get("time_range", {})),
                "geo_anchor": _serialize(event.get("geo_anchor", {})),
                "confidence": event.get("confidence", 0.85),
                "relationships": _serialize(event.get("relationships", {})),
            },
        )

    conn.commit()
    conn.close()


def initialize_history(force_refresh: bool = False) -> None:
    ensure_schema()
    seed_curated_events(force_refresh=force_refresh)


def load_events_from_db() -> List[dict]:
    conn = _get_connection()
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM events")
    rows = cursor.fetchall()
    conn.close()

    events = []
    for row in rows:
        event = dict(row)
        event["themes"] = _deserialize(event.get("themes"), [])
        event["actors"] = _deserialize(event.get("actors"), [])
        event["artifacts"] = _deserialize(event.get("artifacts"), [])
        event["visual_motifs"] = _deserialize(event.get("visual_motifs"), [])
        event["facets"] = _deserialize(event.get("facets"), {})
        event["sources"] = _deserialize(event.get("sources"), [])
        event["time_range"] = _deserialize(event.get("time_range"), {})
        event["geo_anchor"] = _deserialize(event.get("geo_anchor"), {})
        event["relationships"] = _deserialize(event.get("relationships"), {})
        events.append(event)

    return events


def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
    radius = 6371.0
    phi1, phi2 = math.radians(lat1), math.radians(lat2)
    delta_phi = math.radians(lat2 - lat1)
    delta_lambda = math.radians(lon2 - lon1)

    a = (
        math.sin(delta_phi / 2) ** 2
        + math.cos(phi1) * math.cos(phi2) * math.sin(delta_lambda / 2) ** 2
    )
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(max(0.0, 1 - a)))
    return radius * c


def _compute_match_score(
    event: dict,
    lat: float,
    lon: float,
    year: int,
    year_weight: float = 1.0,
) -> Tuple[float, float, float, float]:
    """
    Compute match score for an event based on distance and year.
    
    Args:
        event: Event dictionary
        lat: Query latitude
        lon: Query longitude
        year: Query year
        year_weight: Weight for year matching (0.0-2.0)
            - 0.0 = distance only
            - 1.0 = balanced (default)
            - 2.0 = strongly prefer year matches
    
    Returns:
        Tuple of (distance_km, year_delta, confidence, match_score)
    """
    event_year = event.get("year") or year
    event_lat = event.get("lat") or lat
    event_lon = event.get("lon") or lon
    
    distance = haversine_distance(lat, lon, event_lat, event_lon)
    year_delta = abs(event_year - year)
    base_confidence = event.get("confidence", 0.8)
    
    # Year-weighted scoring:
    # - Exact year match (delta=0): massive bonus
    # - Within 5 years: strong bonus
    # - Within 10 years: moderate bonus
    # - Beyond 10 years: penalty increases
    
    if year_delta == 0:
        year_score = -50 * year_weight  # Big bonus for exact year
    elif year_delta <= 2:
        year_score = -30 * year_weight  # Strong bonus for ±2 years
    elif year_delta <= 5:
        year_score = -15 * year_weight  # Good bonus for ±5 years
    elif year_delta <= 10:
        year_score = 0  # Neutral for ±10 years
    else:
        year_score = year_delta * 3 * year_weight  # Penalty for distant years
    
    # Distance scoring (normalized):
    # - Within 50km: strong bonus
    # - Within 200km: moderate bonus
    # - Beyond 500km: penalty
    if distance < 50:
        distance_score = -20 * (1 - year_weight * 0.3)  # Bonus, reduced if year-weighted
    elif distance < 200:
        distance_score = distance * 0.1
    else:
        distance_score = distance * 0.2 * (1 - year_weight * 0.3)  # Reduced penalty if year-weighted
    
    # Combined score (lower is better)
    match_score = distance_score + year_score
    
    # Confidence calculation
    confidence = base_confidence
    if year_delta == 0:
        confidence += 0.15
    elif year_delta <= 5:
        confidence += 0.08
    
    if distance < 100:
        confidence += 0.1
    elif distance < 300:
        confidence += 0.05
    
    confidence = max(0.0, min(0.99, confidence))
    
    return distance, year_delta, confidence, match_score


def get_events_by_coordinates(
    lat: float,
    lon: float,
    year: int,
    radius_km: float = 250.0,
    limit: int = 5,
    include_wikidata: bool = True,
    year_weight: float = 1.5,
) -> List[dict]:
    """
    Get historical events near coordinates and year.
    
    First searches curated local database, then optionally queries Wikidata
    for additional results if enabled and local results are insufficient.
    
    Args:
        lat: Latitude
        lon: Longitude
        year: Target year (negative for BCE)
        radius_km: Search radius in kilometers
        limit: Maximum number of results
        include_wikidata: Whether to include Wikidata results
        year_weight: How much to prioritize year matches (0.0-2.0)
            - 0.0 = distance only (ignore year)
            - 1.0 = balanced
            - 1.5 = prefer year matches (default)
            - 2.0 = strongly prefer year matches
    
    Returns:
        List of event dictionaries sorted by relevance
    """
    # Step 1: Search curated local database
    events = load_events_from_db()
    matches: List[dict] = []

    # Use larger radius when year-weighted to find more year matches
    effective_radius = radius_km * (1 + year_weight * 0.5) if year_weight > 1.0 else radius_km

    for event in events:
        distance, year_delta, confidence, match_score = _compute_match_score(
            event, lat, lon, year, year_weight=year_weight
        )
        
        # Include if within radius OR if year matches closely
        if distance > effective_radius and year_delta > 10:
            continue
        
        # Always include exact year matches regardless of distance
        if year_delta > 15 and distance > radius_km:
            continue

        match = dict(event)
        match["distance_km"] = round(distance, 2)
        match["year_delta"] = year_delta
        match["match_confidence"] = round(confidence, 3)
        match["match_score"] = match_score
        match["source"] = "curated"
        matches.append(match)

    matches.sort(key=lambda item: item["match_score"])
    curated_results = matches[:limit]
    
    # Step 2: If enabled and we have few/no curated results, query Wikidata
    if (
        include_wikidata
        and ENABLE_WIKIDATA_FALLBACK
        and WIKIDATA_AVAILABLE
        and len(curated_results) < limit
    ):
        try:
            print(f"[history_service] Querying Wikidata for additional events...")
            wikidata_results = wikidata_search(
                lat=lat,
                lon=lon,
                year=year,
                radius_km=radius_km,
                limit=limit * 2,  # Get extra to filter
            )
            
            # Merge Wikidata results, avoiding duplicates by name
            curated_names = {m.get("name", "").lower() for m in curated_results}
            
            for wd_event in wikidata_results:
                # Skip if we already have this event from curated data
                event_name = wd_event.get("name", "").lower()
                if event_name in curated_names:
                    continue
                
                # Skip low-confidence results
                confidence = wd_event.get("match_confidence", wd_event.get("confidence", 0))
                if confidence < WIKIDATA_CONFIDENCE_THRESHOLD:
                    continue
                
                # Add source marker and compute year-weighted score
                wd_event["source"] = "wikidata"
                wd_year_delta = wd_event.get("year_delta", 99)
                wd_distance = wd_event.get("distance_km", 999)
                
                # Year-weighted scoring for Wikidata results
                if wd_year_delta == 0:
                    year_score = -50 * year_weight
                elif wd_year_delta <= 2:
                    year_score = -30 * year_weight
                elif wd_year_delta <= 5:
                    year_score = -15 * year_weight
                elif wd_year_delta <= 10:
                    year_score = 0
                else:
                    year_score = wd_year_delta * 3 * year_weight
                
                distance_score = wd_distance * 0.1 * (1 - year_weight * 0.3)
                wd_event["match_score"] = distance_score + year_score - confidence * 10
                
                curated_results.append(wd_event)
                curated_names.add(event_name)
                
                if len(curated_results) >= limit:
                    break
            
            # Re-sort combined results
            curated_results.sort(key=lambda item: item.get("match_score", 999))
            
        except Exception as e:
            print(f"[history_service] Wikidata lookup failed: {e}")
    
    return curated_results[:limit]


def search_events_globally(
    lat: float,
    lon: float,
    year: int,
    radius_km: float = 500.0,
    limit: int = 10,
    year_weight: float = 1.5,
) -> List[dict]:
    """
    Search for historical events with broader radius, always including Wikidata.
    
    This is useful for finding events when the user doesn't have precise coordinates.
    Uses year-weighted scoring by default to prioritize temporal matches.
    """
    return get_events_by_coordinates(
        lat=lat,
        lon=lon,
        year=year,
        radius_km=radius_km,
        limit=limit,
        include_wikidata=True,
        year_weight=year_weight,
    )


def get_event_by_slug(slug: str) -> Optional[dict]:
    conn = _get_connection()
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM events WHERE slug = ?", (slug,))
    row = cursor.fetchone()
    conn.close()

    if not row:
        return None

    event = dict(row)
    event["themes"] = _deserialize(event.get("themes"), [])
    event["actors"] = _deserialize(event.get("actors"), [])
    event["artifacts"] = _deserialize(event.get("artifacts"), [])
    event["visual_motifs"] = _deserialize(event.get("visual_motifs"), [])
    event["facets"] = _deserialize(event.get("facets"), {})
    event["sources"] = _deserialize(event.get("sources"), [])
    event["time_range"] = _deserialize(event.get("time_range"), {})
    event["geo_anchor"] = _deserialize(event.get("geo_anchor"), {})
    event["relationships"] = _deserialize(event.get("relationships"), {})
    return event


def get_event_by_name(name: str, include_wikidata: bool = True) -> Optional[dict]:
    """
    Get event by name, checking curated data first, then Wikidata.
    """
    # Try curated data first
    event = get_event_by_slug(name.lower().replace(" ", "_"))
    if event:
        event["source"] = "curated"
        return event
    
    # Try Wikidata if enabled
    if include_wikidata and ENABLE_WIKIDATA_FALLBACK and WIKIDATA_AVAILABLE:
        try:
            results = wikidata_search_by_name(name, limit=1)
            if results:
                results[0]["source"] = "wikidata"
                return results[0]
        except Exception as e:
            print(f"[history_service] Wikidata name search failed: {e}")
    
    return None


def get_event_by_qid(qid: str) -> Optional[dict]:
    """
    Get detailed event information from Wikidata by QID.
    """
    if not WIKIDATA_AVAILABLE:
        return None
    
    try:
        return wikidata_get_detail(qid)
    except Exception as e:
        print(f"[history_service] Wikidata QID lookup failed: {e}")
        return None


def get_artifacts_for_year(year: int, limit: int = 4) -> List[dict]:
    matches: List[dict] = []
    for artifact in CURATED_ARTIFACTS:
        era_start, era_end = artifact.get("era", [None, None])
        if era_start is None or era_end is None:
            matches.append(artifact)
            continue
        if era_start <= year <= era_end:
            matches.append(artifact)
    if not matches:
        matches = CURATED_ARTIFACTS[:]
    return matches[:limit]


def summarize_event(event: dict) -> str:
    summary = event.get("summary") or event.get("narrative") or event.get("name")
    return summary


def ensure_iterable(value: Optional[Iterable[str]]) -> List[str]:
    if value is None:
        return []
    return list(value)


CURATED_EVENTS: List[dict] = [
    {
        "name": "Fall of the Berlin Wall",
        "slug": "fall_of_the_berlin_wall",
        "year": 1989,
        "start_year": 1989,
        "end_year": 1989,
        "month": 11,
        "day": 9,
        "lat": 52.5163,
        "lon": 13.3777,
        "summary": "East and West Berliners gather at the Brandenburg Gate as border checkpoints open and the concrete wall begins to fall.",
        "narrative": (
            "A sea of Berliners clamber atop graffiti-streaked concrete slabs, cheering as border guards lift the barriers. "
            "People pass champagne bottles, wield sledgehammers, and chip away fragments while floodlights and television crews illuminate the night."
        ),
        "themes": ["political", "reunification", "cold war"],
        "actors": ["East German civilians", "West Berlin residents", "border guards", "international journalists"],
        "artifacts": ["Graffiti-covered concrete", "Champagne bottles", "Trabant cars", "Floodlights", "Metal barricades"],
        "visual_motifs": ["floodlit night sky", "cold autumn breath", "television cameras", "crowded concrete wall"],
        "facets": {"era": "late 20th century", "region": "western_europe", "type": "political upheaval"},
        "sources": [{"label": "Wikipedia", "url": "https://en.wikipedia.org/wiki/Fall_of_the_Berlin_Wall"}],
        "time_range": {"start": "1989-11-09T18:00:00", "end": "1989-11-10T02:00:00"},
        "geo_anchor": {"lat": 52.5163, "lon": 13.3777, "radius_km": 4},
        "confidence": 0.96,
        "relationships": {"consequences": ["German reunification 1990"]},
    },
    {
        "name": "D-Day Landing at Omaha Beach",
        "slug": "d_day_landing_at_omaha_beach",
        "year": 1944,
        "start_year": 1944,
        "end_year": 1944,
        "month": 6,
        "day": 6,
        "lat": 49.4144,
        "lon": -0.8322,
        "summary": "Allied assault troops storm Omaha Beach under heavy German fire at dawn during Operation Overlord.",
        "narrative": (
            "Pre-dawn haze lifts as landing craft ramps crash open and American soldiers sprint through waist-high surf toward fortified bluffs. "
            "Machine-gun tracers stitch the air, artillery craters erupt in wet sand, and medics tend to the wounded beside hedgehog obstacles."
        ),
        "themes": ["military", "WWII", "allied victory"],
        "actors": ["US 1st Infantry Division", "US 29th Infantry Division", "German Atlantic Wall defenders", "Combat medics"],
        "artifacts": ["Higgins landing craft", "Browning machine guns", "M1 helmets", "Beach obstacles", "Signal flares"],
        "visual_motifs": ["morning fog", "breaking waves", "artillery smoke", "olive drab uniforms"],
        "facets": {"era": "mid 20th century", "region": "western_europe", "type": "amphibious assault"},
        "sources": [{"label": "National WWII Museum", "url": "https://www.nationalww2museum.org"}],
        "time_range": {"start": "1944-06-06T05:30:00", "end": "1944-06-06T10:00:00"},
        "geo_anchor": {"lat": 49.4144, "lon": -0.8322, "radius_km": 12},
        "confidence": 0.94,
        "relationships": {"parallel": ["Sword Beach landings", "Utah Beach landings"]},
    },
    {
        "name": "Signing of the Declaration of Independence",
        "slug": "signing_of_the_declaration_of_independence",
        "year": 1776,
        "start_year": 1776,
        "end_year": 1776,
        "month": 7,
        "day": 4,
        "lat": 39.9489,
        "lon": -75.1500,
        "summary": "Delegates of the Continental Congress sign the Declaration inside Independence Hall, Philadelphia.",
        "narrative": (
            "Sunlight streams through tall sash windows onto polished wood floors as delegates in powdered wigs lean over parchment. "
            "Quill pens scratch, wax seals glisten, and brass bellows stir a warm July breeze through the Assembly Room."
        ),
        "themes": ["political", "founding documents", "revolution"],
        "actors": ["Thomas Jefferson", "John Hancock", "Continental Congress delegates"],
        "artifacts": ["Quill pens", "Parchment scrolls", "Wax seals", "Mahogany desks"],
        "visual_motifs": ["golden afternoon light", "colonial interior", "powder wigs", "rich green drapery"],
        "facets": {"era": "late 18th century", "region": "north_america", "type": "political charter"},
        "sources": [{"label": "US National Archives", "url": "https://www.archives.gov/founding-docs/declaration"}],
        "time_range": {"start": "1776-07-04T10:00:00", "end": "1776-07-04T15:00:00"},
        "geo_anchor": {"lat": 39.9489, "lon": -75.1500, "radius_km": 1},
        "confidence": 0.9,
        "relationships": {"causes": ["Continental Congress debates"], "consequences": ["American Revolutionary War escalation"]},
    },
    {
        "name": "Battle of Waterloo",
        "slug": "battle_of_waterloo",
        "year": 1815,
        "start_year": 1815,
        "end_year": 1815,
        "month": 6,
        "day": 18,
        "lat": 50.6794,
        "lon": 4.4125,
        "summary": "Coalition forces defeat Napoleon Bonaparte near Waterloo, ending the Hundred Days campaign.",
        "narrative": (
            "Under rain-darkened skies, British squares brace against French cavalry charges across muddy Belgian fields. "
            "Cannon smoke drifts low, cuirassiers clash with bayonet lines, and signal flags ripple above the La Haye Sainte farmhouse."
        ),
        "themes": ["military", "napoleonic wars"],
        "actors": ["British infantry", "Dutch-Belgian troops", "French Imperial Guard", "Prussian reinforcements"],
        "artifacts": ["Cuirass armor", "Sabers", "Field cannon", "Signal flags"],
        "visual_motifs": ["storm clouds", "muddy terrain", "cavalry charge", "gunpowder smoke"],
        "facets": {"era": "early 19th century", "region": "western_europe", "type": "decisive battle"},
        "sources": [{"label": "Waterloo Battlefield", "url": "https://www.waterloo1815.be"}],
        "time_range": {"start": "1815-06-18T11:30:00", "end": "1815-06-18T20:30:00"},
        "geo_anchor": {"lat": 50.6794, "lon": 4.4125, "radius_km": 8},
        "confidence": 0.88,
        "relationships": {"consequences": ["Exile of Napoleon to Saint Helena"]},
    },
    {
        "name": "Hiroshima Atomic Bombing",
        "slug": "hiroshima_atomic_bombing",
        "year": 1945,
        "start_year": 1945,
        "end_year": 1945,
        "month": 8,
        "day": 6,
        "lat": 34.3853,
        "lon": 132.4553,
        "summary": "The United States detonates an atomic bomb over Hiroshima, Japan, causing widespread destruction.",
        "narrative": (
            "Moments after the blinding flash, a mushroom cloud towers above shattered city blocks. "
            "Wooden houses ignite, survivors stagger through debris-clogged streets, and the iconic Genbaku Dome stands amid the devastation."
        ),
        "themes": ["military", "WWII", "nuclear warfare"],
        "actors": ["Civilians", "First responders", "US bomber crew (distant)"],
        "artifacts": ["Genbaku Dome", "Debris-laden streets", "Shattered windows", "Charred telegraph poles"],
        "visual_motifs": ["mushroom cloud", "ashen fallout", "burning skyline", "silhouetted survivors"],
        "facets": {"era": "mid 20th century", "region": "east_asia", "type": "aerial bombardment"},
        "sources": [{"label": "Hiroshima Peace Memorial Museum", "url": "https://hpmmuseum.jp/?lang=en"}],
        "time_range": {"start": "1945-08-06T08:15:00", "end": "1945-08-06T12:00:00"},
        "geo_anchor": {"lat": 34.3853, "lon": 132.4553, "radius_km": 15},
        "confidence": 0.87,
        "relationships": {"consequences": ["Surrender of Japan 1945"]},
    },
    {
        "name": "Tiananmen Square Protests",
        "slug": "tiananmen_square_protests",
        "year": 1989,
        "start_year": 1989,
        "end_year": 1989,
        "month": 6,
        "day": 4,
        "lat": 39.9042,
        "lon": 116.4074,
        "summary": "Chinese citizens hold pro-democracy demonstrations in Beijing's Tiananmen Square before military suppression.",
        "narrative": (
            "In early dawn haze, students link arms facing a line of armored vehicles. "
            "The Goddess of Democracy statue rises above banners, bicycle couriers weave through tents, and the Gate of Heavenly Peace looms in the background."
        ),
        "themes": ["political", "protest", "democracy"],
        "actors": ["Student demonstrators", "People's Liberation Army soldiers", "Beijing residents"],
        "artifacts": ["Goddess of Democracy statue", "Banners and loudspeakers", "Tents", "Armored personnel carriers"],
        "visual_motifs": ["morning haze", "stone square", "red flags", "human chain"],
        "facets": {"era": "late 20th century", "region": "east_asia", "type": "protest movement"},
        "sources": [{"label": "BBC Timeline", "url": "https://www.bbc.com/news/world-asia-china-12661772"}],
        "time_range": {"start": "1989-06-03T22:00:00", "end": "1989-06-04T07:00:00"},
        "geo_anchor": {"lat": 39.9042, "lon": 116.4074, "radius_km": 6},
        "confidence": 0.88,
        "relationships": {"parallel": ["1989 global protest movements"]},
    },
    {
        "name": "Apollo 11 Moon Launch",
        "slug": "apollo_11_moon_launch",
        "year": 1969,
        "start_year": 1969,
        "end_year": 1969,
        "month": 7,
        "day": 16,
        "lat": 28.5729,
        "lon": -80.6490,
        "summary": "NASA launches Apollo 11 from Kennedy Space Center, beginning the first crewed mission to land on the Moon.",
        "narrative": (
            "Spectators line the Causeway as the Saturn V rockets skyward, engines roaring and painting the morning sky orange. "
            "Camera crews pan across mission control staff, astronauts in white suits wave before boarding, and the vehicle assembly building looms nearby."
        ),
        "themes": ["space exploration", "science", "Cold War"],
        "actors": ["Neil Armstrong", "Buzz Aldrin", "Michael Collins", "Mission control engineers"],
        "artifacts": ["Saturn V rocket", "Launch gantry", "Mission patches", "Telemetry consoles"],
        "visual_motifs": ["plume of fire", "sunrise glow", "American flags", "NASA vehicles"],
        "facets": {"era": "late 20th century", "region": "north_america", "type": "space mission"},
        "sources": [{"label": "NASA History", "url": "https://www.nasa.gov/specials/apollo50th/"}],
        "time_range": {"start": "1969-07-16T09:32:00", "end": "1969-07-16T10:00:00"},
        "geo_anchor": {"lat": 28.5729, "lon": -80.6490, "radius_km": 10},
        "confidence": 0.89,
        "relationships": {"consequences": ["Apollo 11 moon landing"]},
    },
    {
        "name": "Wright Brothers First Flight",
        "slug": "wright_brothers_first_flight",
        "year": 1903,
        "start_year": 1903,
        "end_year": 1903,
        "month": 12,
        "day": 17,
        "lat": 36.0177,
        "lon": -75.6694,
        "summary": "Orville and Wilbur Wright achieve the first powered, sustained flight at Kitty Hawk, North Carolina.",
        "narrative": (
            "On windswept dunes, Orville lies prone on the Flyer as Wilbur steadies a wingtip. "
            "A small crowd of lifesavers braces the launch rail, camera ready, as the biplane lifts into the cold December air for twelve seconds."
        ),
        "themes": ["aviation", "innovation"],
        "actors": ["Orville Wright", "Wilbur Wright", "Kill Devil Hills lifesavers"],
        "artifacts": ["Wright Flyer", "Launch rail", "Oil-stained overalls", "Box camera"],
        "visual_motifs": ["wind-scoured dunes", "frosty breath", "canvas wings", "wooden spars"],
        "facets": {"era": "early 20th century", "region": "north_america", "type": "technological milestone"},
        "sources": [{"label": "Smithsonian Air & Space", "url": "https://airandspace.si.edu"}],
        "time_range": {"start": "1903-12-17T10:35:00", "end": "1903-12-17T10:47:00"},
        "geo_anchor": {"lat": 36.0177, "lon": -75.6694, "radius_km": 3},
        "confidence": 0.86,
        "relationships": {"consequences": ["Development of powered flight"]},
    },
    {
        "name": "Grito de Dolores",
        "slug": "grito_de_dolores",
        "year": 1810,
        "start_year": 1810,
        "end_year": 1810,
        "month": 9,
        "day": 16,
        "lat": 21.1561,
        "lon": -100.9326,
        "summary": "Father Miguel Hidalgo y Costilla calls for Mexican independence with the famous Grito de Dolores.",
        "narrative": (
            "Before dawn, church bells ring out as Father Hidalgo addresses villagers in the plaza, torchlight illuminating insurgent banners. "
            "Peasants clutch farming tools turned weapons while women distribute ammunition from woven baskets."
        ),
        "themes": ["revolution", "latin america"],
        "actors": ["Father Miguel Hidalgo", "Town villagers", "Criollo supporters"],
        "artifacts": ["Church bell rope", "Guadalupe banner", "Torches", "Improvised spears"],
        "visual_motifs": ["torchlit plaza", "colonial church facade", "Mexican flag colors", "dawn sky"],
        "facets": {"era": "early 19th century", "region": "central_america", "type": "independence movement"},
        "sources": [{"label": "Mexican History", "url": "https://www.gob.mx"}],
        "time_range": {"start": "1810-09-16T05:00:00", "end": "1810-09-16T07:00:00"},
        "geo_anchor": {"lat": 21.1561, "lon": -100.9326, "radius_km": 5},
        "confidence": 0.82,
        "relationships": {"consequences": ["Mexican War of Independence"]},
    },
    {
        "name": "Storming of the Bastille",
        "slug": "storming_of_the_bastille",
        "year": 1789,
        "start_year": 1789,
        "end_year": 1789,
        "month": 7,
        "day": 14,
        "lat": 48.8530,
        "lon": 2.3692,
        "summary": "Parisian revolutionaries seize the Bastille fortress, igniting the French Revolution.",
        "narrative": (
            "Parisians wielding pikes and muskets swarm the Bastille's stone courtyard as smoke billows from cannon fire. "
            "National Guardsmen drag royal cannons into position while prisoners emerge to cheering crowds waving tricolor cockades."
        ),
        "themes": ["revolution", "political upheaval"],
        "actors": ["Parisian crowds", "National Guardsmen", "Royal soldiers"],
        "artifacts": ["Tricolor cockades", "Iron portcullis", "Cannons", "Stone battlements"],
        "visual_motifs": ["smoke-filled courtyard", "stormy summer sky", "stone fortress", "crowd surge"],
        "facets": {"era": "late 18th century", "region": "western_europe", "type": "revolutionary uprising"},
        "sources": [{"label": "French Archives", "url": "https://www.archives-nationales.culture.gouv.fr"}],
        "time_range": {"start": "1789-07-14T09:00:00", "end": "1789-07-14T17:00:00"},
        "geo_anchor": {"lat": 48.8530, "lon": 2.3692, "radius_km": 3},
        "confidence": 0.84,
        "relationships": {"consequences": ["Declaration of the Rights of Man"]},
    },
    {
        "name": "Assassination of Julius Caesar",
        "slug": "assassination_of_julius_caesar",
        "year": -44,
        "start_year": -44,
        "end_year": -44,
        "month": 3,
        "day": 15,
        "lat": 41.8933,
        "lon": 12.4729,
        "summary": "Julius Caesar is stabbed by Roman senators inside the Theatre of Pompey during the Ides of March.",
        "narrative": (
            "Late morning sunlight filters through the marble portico as Caesar takes his seat. "
            "Senators in scarlet-trimmed togas encircle him; daggers flash, and the dictator staggers toward the statue of Pompey "
            "beneath frescoed arches and hanging laurel wreaths."
        ),
        "themes": ["political", "assassination", "ancient rome"],
        "actors": ["Julius Caesar", "Marcus Junius Brutus", "Gaius Cassius Longinus", "Roman senators"],
        "artifacts": ["Marble curule chair", "Bronze daggers", "Laurel wreaths", "Blood-stained togas"],
        "visual_motifs": ["marble columns", "sunbeam through smoke", "collapsing laurel crown"],
        "facets": {"era": "classical antiquity", "region": "western_europe", "type": "political assassination"},
        "sources": [{"label": "Ancient Rome", "url": "https://en.wikipedia.org/wiki/Assassination_of_Julius_Caesar"}],
        "time_range": {"start": "-0044-03-15T11:00:00", "end": "-0044-03-15T12:00:00"},
        "geo_anchor": {"lat": 41.8933, "lon": 12.4729, "radius_km": 2},
        "confidence": 0.9,
        "relationships": {"consequences": ["Liberators' civil war"]},
    },
]


CURATED_ARTIFACTS: List[dict] = [
    {"title": "Graffiti fragment of the Berlin Wall", "culture": "German", "period": "Cold War", "era": (1961, 1990)},
    {"title": "Allied M1 Helmet", "culture": "American", "period": "World War II", "era": (1941, 1945)},
    {"title": "Continental Congress inkwell", "culture": "American", "period": "Revolutionary", "era": (1765, 1783)},
    {"title": "French cuirassier armor", "culture": "French", "period": "Napoleonic", "era": (1800, 1815)},
    {"title": "Goddess of Democracy maquette", "culture": "Chinese", "period": "Late 20th century", "era": (1980, 1990)},
    {"title": "Saturn V mission patch", "culture": "American", "period": "Space Age", "era": (1960, 1975)},
    {"title": "Wright Flyer blueprint", "culture": "American", "period": "Early Aviation", "era": (1899, 1905)},
    {"title": "Bastille prison key", "culture": "French", "period": "Revolutionary", "era": (1789, 1799)},
]

ERA_VISUAL_VOCABULARY: Dict[Tuple[int, int], dict] = {
    (-5000, 1700): {
        "architecture": "stone structures, timber framing, open marketplaces",
        "clothing": "homespun fabrics, cloaks, leather sandals",
        "technology": "handcrafted tools, smoke from hearth fires, animal-drawn transport",
        "transport": "horses, carts, foot traffic",
        "mood": "earthy textures, smoke and torchlight",
    },
    (1700, 1850): {
        "architecture": "Georgian and neoclassical facades, stone avenues, colonial interiors",
        "clothing": "powdered wigs, waistcoats, breeches, corseted gowns",
        "technology": "printing presses, quill ink, carronade cannons",
        "transport": "horse-drawn carriages, sailing ships, infantry columns",
        "mood": "oil-painted lighting, warm candle glow and shadow",
    },
    (1850, 1918): {
        "architecture": "industrial brick mills, iron train stations, Victorian terraces",
        "clothing": "bowler hats, uniforms with brass buttons, layered dresses",
        "technology": "steam locomotives, telegraph poles, gas lanterns",
        "transport": "steam trains, horse omnibuses, early bicycles",
        "mood": "coal smoke haze, sepia-toned atmosphere",
    },
    (1918, 1950): {
        "architecture": "art deco facades, reinforced bunkers, concrete civic plazas",
        "clothing": "military uniforms, flapper dresses, utilitarian workwear",
        "technology": "radio towers, field telephones, propeller aircraft",
        "transport": "steel warships, troop trucks, streetcars",
        "mood": "black-and-white newsreel grit, halation from searchlights",
    },
    (1950, 1990): {
        "architecture": "mid-century modern lines, brutalist government blocks, neon signage",
        "clothing": "denim jackets, tailored suits, Cold War uniforms",
        "technology": "cathode-ray cameras, satellite dishes, analog broadcast vans",
        "transport": "boxy sedans, subway trains, patrol jeeps",
        "mood": "sodium-vapor glow, vivid chromatic contrasts",
    },
    (1990, 2030): {
        "architecture": "glass high-rises, LED billboards, postmodern cultural centers",
        "clothing": "synthetic fabrics, streetwear, modern uniforms",
        "technology": "smart devices, digital screens, drones",
        "transport": "light rail, electric cars, bicycles with LED lights",
        "mood": "clean highlights, cinematic depth of field, vibrant color grading",
    },
}

REGIONAL_CONTEXT: Dict[str, dict] = {
    "western_europe": {
        "architecture": "historic stone plazas, cathedrals, tram-lined boulevards",
        "climate": "temperate weather with layered clouds and soft rain",
    },
    "eastern_europe": {
        "architecture": "Soviet-era apartment blocks, neoclassical government buildings",
        "climate": "continental climate with sharp seasonal contrast",
    },
    "north_america": {
        "architecture": "brick row houses, colonial meeting halls, steel skyscrapers",
        "climate": "varied weather, from humid summers to snowy winters",
    },
    "east_asia": {
        "architecture": "pagoda rooftops, dense urban districts, neon signage",
        "climate": "humid subtropical seasons with monsoon rains",
    },
    "central_america": {
        "architecture": "stucco plazas, colonial churches, cobblestone streets",
        "climate": "warm highland mornings with misty horizons",
    },
    "western_asia": {
        "architecture": "stone citadels, market arcades, desert courtyards",
        "climate": "arid sunlight, dust carried on dry winds",
    },
}


def get_era_vocabulary(year: int) -> dict:
    for (start, end), vocab in ERA_VISUAL_VOCABULARY.items():
        if start <= year < end:
            return vocab
    # Default to modern vocabulary
    return ERA_VISUAL_VOCABULARY[(1950, 1990)]


def get_region_context(region_key: Optional[str]) -> dict:
    if not region_key:
        return {}
    return REGIONAL_CONTEXT.get(region_key.lower(), {})


def format_event_digest(event: dict) -> dict:
    return {
        "name": event.get("name"),
        "slug": event.get("slug"),
        "year": event.get("year"),
        "start_year": event.get("start_year"),
        "end_year": event.get("end_year"),
        "month": event.get("month"),
        "day": event.get("day"),
        "lat": event.get("lat"),
        "lon": event.get("lon"),
        "summary": event.get("summary"),
        "themes": ensure_iterable(event.get("themes")),
        "facets": event.get("facets", {}),
        "distance_km": event.get("distance_km"),
        "year_delta": event.get("year_delta"),
        "match_confidence": event.get("match_confidence"),
        "sources": ensure_iterable(event.get("sources")),
    }


def build_event_context(event: dict) -> dict:
    return {
        "event": format_event_digest(event),
        "narrative": event.get("narrative"),
        "actors": ensure_iterable(event.get("actors")),
        "artifacts": ensure_iterable(event.get("artifacts")),
        "visual_motifs": ensure_iterable(event.get("visual_motifs")),
        "relationships": event.get("relationships", {}),
        "time_range": event.get("time_range"),
        "geo_anchor": event.get("geo_anchor"),
        "confidence": event.get("match_confidence", event.get("confidence")),
    }


def get_events_response(
    lat: float,
    lon: float,
    year: int,
    radius_km: float = 250.0,
    limit: int = 5,
) -> dict:
    matches = get_events_by_coordinates(lat, lon, year, radius_km=radius_km, limit=limit)
    return {
        "query": {"lat": lat, "lon": lon, "year": year, "radius_km": radius_km, "limit": limit},
        "count": len(matches),
        "events": [format_event_digest(event) for event in matches],
    }