from dataclasses import dataclass, field
from datetime import datetime, timezone
from typing import Optional


@dataclass
class AnalysisResult:
    """vessel_analysis_results 테이블 28컬럼 매핑."""

    mmsi: str
    timestamp: datetime

    # 분류 결과
    vessel_type: str = 'UNKNOWN'
    confidence: float = 0.0
    fishing_pct: float = 0.0
    cluster_id: int = -1
    season: str = 'UNKNOWN'

    # ALGO 01: 위치
    zone: str = 'EEZ_OR_BEYOND'
    dist_to_baseline_nm: float = 999.0

    # ALGO 02: 활동 상태
    activity_state: str = 'UNKNOWN'
    ucaf_score: float = 0.0
    ucft_score: float = 0.0

    # ALGO 03: 다크 베셀
    is_dark: bool = False
    gap_duration_min: int = 0

    # ALGO 04: GPS 스푸핑
    spoofing_score: float = 0.0
    bd09_offset_m: float = 0.0
    speed_jump_count: int = 0

    # ALGO 05+06: 선단
    cluster_size: int = 0
    is_leader: bool = False
    fleet_role: str = 'NOISE'

    # ALGO 07: 위험도
    risk_score: int = 0
    risk_level: str = 'LOW'

    # 특징 벡터
    features: dict = field(default_factory=dict)

    # 메타
    analyzed_at: Optional[datetime] = None

    def __post_init__(self):
        if self.analyzed_at is None:
            self.analyzed_at = datetime.now(timezone.utc)

    def to_db_tuple(self) -> tuple:
        import json

        def _f(v: object) -> float:
            """numpy float → Python float 변환."""
            return float(v) if v is not None else 0.0

        def _i(v: object) -> int:
            """numpy int → Python int 변환."""
            return int(v) if v is not None else 0

        # features dict 내부 numpy 값도 변환
        safe_features = {k: float(v) for k, v in self.features.items()} if self.features else {}

        return (
            str(self.mmsi),
            self.timestamp,
            str(self.vessel_type),
            _f(self.confidence),
            _f(self.fishing_pct),
            _i(self.cluster_id),
            str(self.season),
            str(self.zone),
            _f(self.dist_to_baseline_nm),
            str(self.activity_state),
            _f(self.ucaf_score),
            _f(self.ucft_score),
            bool(self.is_dark),
            _i(self.gap_duration_min),
            _f(self.spoofing_score),
            _f(self.bd09_offset_m),
            _i(self.speed_jump_count),
            _i(self.cluster_size),
            bool(self.is_leader),
            str(self.fleet_role),
            _i(self.risk_score),
            str(self.risk_level),
            json.dumps(safe_features),
            self.analyzed_at,
        )
