"""
Module 1: 风速-功率异常检测

优化点:
  - PowerCurveDetector 改为逐点输出：训练时保存 bin 级正常范围 (mean±σ)，
    predict 时将每个点映射到所属 bin，计算 z-score 作为逐点异常分数
  - ScatterDetector 增加环境温度特征（如果 ambient_temp 列存在），
    因为空气密度影响功率输出
  - 训练/预测时过滤有效发电区间 (WIND_VALID_MIN ~ WIND_VALID_MAX)
  - 分箱样本量权重过滤，样本不足的箱不参与训练

子检测器:
  A. PowerCurveDetector  - 基于分箱统计的功率曲线异常（z-score 逐点）
  B. ScatterDetector     - 基于原始散点的风速-功率异常（IsolationForest）
"""
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import joblib
from pathlib import Path

from config import (
    COL_WIND_SPD, COL_P_ACTIVE, COL_AMBIENT_TEMP,
    WIND_BIN_WIDTH, WIND_BIN_MIN, WIND_BIN_MAX,
    ISO_CONTAMINATION, ISO_RANDOM_STATE, ISO_N_ESTIMATORS,
    WIND_VALID_MIN, WIND_VALID_MAX,
)

# 分箱最小样本量，样本不足的箱不参与训练
_MIN_BIN_COUNT = 30

# 功率偏离阈值（标准差倍数），按风速区间分段
# 低风速段波动大，阈值放宽；高风速段接近额定，阈值收紧
_SIGMA_LOW  = 4.0   # < 8 m/s
_SIGMA_MID  = 3.0   # 8~16 m/s
_SIGMA_HIGH = 2.5   # > 16 m/s


def _get_sigma(wind_spd: float) -> float:
    if wind_spd < 8.0:
        return _SIGMA_LOW
    elif wind_spd <= 16.0:
        return _SIGMA_MID
    else:
        return _SIGMA_HIGH


def _bin_wind_speed(series: pd.Series) -> pd.Series:
    bins = np.arange(WIND_BIN_MIN, WIND_BIN_MAX + WIND_BIN_WIDTH, WIND_BIN_WIDTH)
    return pd.cut(series, bins=bins, labels=False)


def _filter_valid_wind(df: pd.DataFrame) -> pd.DataFrame:
    """过滤有效发电风速区间，排除切入/切出段噪声。"""
    mask = (df[COL_WIND_SPD] >= WIND_VALID_MIN) & (df[COL_WIND_SPD] <= WIND_VALID_MAX)
    return df[mask]


# ── A. 功率曲线检测器（逐点输出） ──────────────────────────────────────────────

class PowerCurveDetector:
    """
    训练: 按风速分箱统计每个 bin 的 (mean, std)，保存为正常范围。
    预测: 将每个数据点映射到所属 bin，计算功率偏离度 z-score，
          按风速区间分段 sigma 判异常（低风放宽/高风收紧）。
          输出与原始数据等长。
    """
    def __init__(self):
        self.bin_stats: pd.DataFrame = pd.DataFrame()

    def fit(self, df: pd.DataFrame) -> "PowerCurveDetector":
        d = _filter_valid_wind(df[[COL_WIND_SPD, COL_P_ACTIVE]].copy())
        d["wind_bin"] = _bin_wind_speed(d[COL_WIND_SPD])
        stats = (
            d.groupby("wind_bin")[COL_P_ACTIVE]
            .agg(mean_power="mean", std_power="std", count="count")
            .reset_index()
            .dropna()
        )
        stats = stats[stats["count"] >= _MIN_BIN_COUNT]
        # std 为 0 时用全局 std 兜底
        global_std = d[COL_P_ACTIVE].std()
        stats["std_power"] = stats["std_power"].replace(0, global_std)
        if len(stats) < 3:
            raise ValueError("功率曲线有效分箱不足")
        self.bin_stats = stats
        return self

    def predict(self, df: pd.DataFrame) -> pd.DataFrame:
        # 保留原始索引，最终结果与输入等长
        out = pd.DataFrame({"anomaly": False, "score": np.nan}, index=df.index)

        valid_mask = (
            df[COL_WIND_SPD].notna() & df[COL_P_ACTIVE].notna() &
            (df[COL_WIND_SPD] >= WIND_VALID_MIN) & (df[COL_WIND_SPD] <= WIND_VALID_MAX)
        )
        d = df.loc[valid_mask, [COL_WIND_SPD, COL_P_ACTIVE]].copy()
        if d.empty:
            return out

        d["wind_bin"] = _bin_wind_speed(d[COL_WIND_SPD])
        bin_map = self.bin_stats.set_index("wind_bin")[["mean_power", "std_power"]]
        d["mean_power"] = d["wind_bin"].map(bin_map["mean_power"])
        d["std_power"]  = d["wind_bin"].map(bin_map["std_power"])

        has_stat = d["mean_power"].notna() & d["std_power"].notna()
        d_stat = d.loc[has_stat].copy()
        z = (d_stat[COL_P_ACTIVE] - d_stat["mean_power"]) / d_stat["std_power"]
        sigma_arr = d_stat[COL_WIND_SPD].map(_get_sigma)
        out.loc[d_stat.index, "score"]   = z.values
        out.loc[d_stat.index, "anomaly"] = (z.abs() > sigma_arr).values
        return out

    def save(self, path: Path):
        joblib.dump(self, path)

    @classmethod
    def load(cls, path: Path) -> "PowerCurveDetector":
        return joblib.load(path)


# ── B. 散点检测器 ─────────────────────────────────────────────────────────────

class ScatterDetector:
    """
    对有效发电区间内的 (wind_spd, p_active) 点对做异常检测。
    增加 p_active / wind_spd^3 特征（近似 Cp），捕捉风能利用率偏离。
    可选: 如果数据中包含 ambient_temp 列，加入温度特征（空气密度影响功率）。
    """
    def __init__(self, contamination=ISO_CONTAMINATION):
        self.contamination = contamination
        self.scaler = StandardScaler()
        self.model  = IsolationForest(
            n_estimators=ISO_N_ESTIMATORS,
            contamination=contamination,
            random_state=ISO_RANDOM_STATE,
        )
        self._has_temp = False

    def _features(self, df: pd.DataFrame) -> pd.DataFrame:
        cols = [COL_WIND_SPD, COL_P_ACTIVE]
        has_temp = COL_AMBIENT_TEMP in df.columns
        if has_temp:
            cols.append(COL_AMBIENT_TEMP)
        d = _filter_valid_wind(df[cols].copy()).dropna()
        wind3 = d[COL_WIND_SPD] ** 3
        d["cp_proxy"] = d[COL_P_ACTIVE] / wind3.replace(0, np.nan)
        if has_temp:
            # 温度越低空气密度越大，同风速下功率应更高
            d["temp"] = d[COL_AMBIENT_TEMP]
        return d.dropna()

    def fit(self, df: pd.DataFrame) -> "ScatterDetector":
        feat = self._features(df)
        if feat.empty:
            raise ValueError("散点特征为空，检查风速功率数据")
        self._has_temp = COL_AMBIENT_TEMP in feat.columns
        X = self.scaler.fit_transform(feat.select_dtypes(include=[np.number]))
        self.model.fit(X)
        return self

    def predict(self, df: pd.DataFrame) -> pd.DataFrame:
        out = pd.DataFrame({"anomaly": False, "score": np.nan}, index=df.index)
        feat = self._features(df)
        if feat.empty:
            return out
        X = self.scaler.transform(feat.select_dtypes(include=[np.number]))
        out.loc[feat.index, "anomaly"] = self.model.predict(X) == -1
        out.loc[feat.index, "score"]   = self.model.score_samples(X)
        return out

    def save(self, path: Path):
        joblib.dump(self, path)

    @classmethod
    def load(cls, path: Path) -> "ScatterDetector":
        return joblib.load(path)