"""预测式异常模块：FORECAST + 区间外点判定。"""

import logging
from typing import TypedDict

logger = logging.getLogger(__name__)


class ForecastPoint(TypedDict):
    ts: int
    value: float
    _flow: float
    _fhigh: float
    is_anomaly: bool


def run_forecast(
    conn,
    stable: str,
    algo: str = "holtwinters",
    rows: int = 10,
    conf: int = 95,
) -> list[tuple[int, float, float]]:
    """执行 FORECAST 查询。

    Args:
        conn: TDConnection 实例
        stable: supertable 名称
        algo: 预测算法，默认 holtwinters
        rows: 预测行数
        conf: 置信度百分比

    Returns:
        [(frowts_ms, flow, fhigh), ...]

    Raises:
        RuntimeError: SQL 执行失败
    """
    sql = (
        f"SELECT _FROWTS, _FLOW, _FHIGH "
        f"FROM (FORECAST(value, \"algo={algo},rows={rows},conf={conf}\") "
        f"FROM ds_{stable})"
    )
    try:
        raw = conn.execute(sql)
    except Exception as e:
        raise RuntimeError(f"FORECAST 执行失败: {e}") from e

    results = []
    for row in raw:
        frowts = int(row[0]) if row[0] is not None else 0
        flow = float(row[1]) if row[1] is not None else 0.0
        fhigh = float(row[2]) if row[2] is not None else 0.0
        results.append((frowts, flow, fhigh))
    return results


def forecast_anomaly(
    conn, stable: str, algo: str = "holtwinters", rows: int = 10, conf: int = 95
) -> list[ForecastPoint]:
    """FORECAST 预测并判定区间外异常。

    步骤：
    1. 执行 FORECAST，获取 [(_flow, _fhigh)] 区间
    2. 查询预测区间对应的原始数据
    3. 比较判定：value < _flow or value > _fhigh → is_anomaly

    Args:
        conn: TDConnection 实例
        stable: supertable 名称
        algo: 预测算法
        rows: 预测行数
        conf: 置信度百分比

    Returns:
        ForecastPoint 列表（含 is_anomaly 标记）
    """
    forecasts = run_forecast(conn, stable, algo, rows, conf)
    if not forecasts:
        return []

    frowtses = [f[0] for f in forecasts]
    start_ts = min(frowtses)
    end_ts = max(frowtses)

    sql = f"SELECT ts, value FROM s_{stable} WHERE ts >= {start_ts} AND ts <= {end_ts}"
    raw = conn.execute(sql)

    fmap = {f[0]: (f[1], f[2]) for f in forecasts}
    results: list[ForecastPoint] = []
    for row in raw:
        ts = int(row[0])
        value = float(row[1])
        if ts in fmap:
            flow, fhigh = fmap[ts]
            is_anom = value < flow or value > fhigh
        else:
            flow, fhigh = 0.0, 0.0
            is_anom = False
        results.append(ForecastPoint(
            ts=ts, value=value, _flow=flow, _fhigh=fhigh, is_anomaly=is_anom
        ))

    logger.info(
        "forecast_anomaly: %d points, %d anomalies",
        len(results), sum(1 for r in results if r["is_anomaly"]),
    )
    return results