"""
状态打标模块

功能:
  1. get_model_statistics()  - 按机型统计额定功率、基准桨距角等自适应阈值
  2. label_dataframe()       - 对 DataFrame 打标：运行 / 限功率 / 停机 / 传感器异常-xxx异常
  3. DETECTOR_SENSOR_COLS    - 每个检测器所依赖的测点，用于 detect.py 判断传感器异常

状态优先级（高→低）: 传感器异常 > 停机 > 限功率 > 运行

传感器异常列命名规则:
  d_val_power   功率值越界
  d_val_wind    风速值越界
  d_val_pitch   变桨值越界
  d_val_spd     转速值越界
  d_val_torque  扭矩值越界
  d_logic_wind_pwr    风速-功率逻辑悖论
  d_logic_torque_pwr  转速-扭矩逻辑悖论
"""
import pandas as pd
import numpy as np
from typing import Dict, Optional

from config import (
    STATUS_POWER_UPPER_RATIO, STATUS_POWER_LOWER_RATIO,
    STATUS_SHUTDOWN_RATIO, STATUS_CURTAIL_LOW_RATIO,
    STATUS_CURTAIL_HIGH_RATIO, STATUS_CURTAIL_PITCH_OFFSET,
    STATUS_WIND_MAX, STATUS_WIND_MIN,
    STATUS_PITCH_MAX, STATUS_PITCH_MIN,
    STATUS_SPD_UPPER_RATIO, STATUS_TORQUE_UPPER_RATIO, STATUS_TORQUE_LOWER_ABS,
    STATUS_LOGIC_WIND_MIN, STATUS_LOGIC_POWER_MIN,
    STATUS_MODEL_SPECIAL_RULES,
)

# ── 每个检测器依赖的传感器异常列 ──────────────────────────────────────────────
# detect.py 用此映射判断：传感器异常数据中，该检测器所需测点是否有异常
# 若有 → 直接输出传感器异常标签；若无 → 正常做异常检测
DETECTOR_SENSOR_COLS: Dict[str, list] = {
    "wind_power_curve":  ["d_val_wind", "d_val_power", "d_logic_wind_pwr"],
    "wind_power_scatter":["d_val_wind", "d_val_power", "d_logic_wind_pwr"],
    "yaw_static":        [],   # yaw_ang 无对应传感器异常列，不直接跳过
    "yaw_twist":         [],
    "pitch_regulation":  ["d_val_pitch"],
    "pitch_coord":       ["d_val_pitch", "d_val_spd", "d_val_power"],
    "pitch_min":         ["d_val_pitch"],
    "ctrl_power_quality": ["d_val_power"],
    "ctrl_op_state":      ["d_val_power", "d_val_spd", "d_val_pitch"],
}

# 传感器异常列 → 可读标签（用于输出结果）
SENSOR_COL_LABEL: Dict[str, str] = {
    "d_val_power":          "功率值异常",
    "d_val_wind":           "风速值异常",
    "d_val_pitch":          "变桨值异常",
    "d_val_spd":            "转速值异常",
    "d_val_torque":         "扭矩值异常",
    "d_logic_wind_pwr":     "风速功率逻辑异常",
    "d_logic_torque_pwr":   "转速扭矩逻辑异常",
}


def get_model_statistics(df: pd.DataFrame) -> dict:
    """计算机型自适应统计阈值（需传入机型全量数据）。"""
    stats: dict = {}
    stats["p_max_observed"] = df["p_active"].quantile(0.995)
    stats["torque_limit"] = (
        df["actual_torque"].quantile(0.999) if "actual_torque" in df.columns else None
    )
    stats["spd_limit"] = (
        df["gen_spd"].quantile(0.999) if "gen_spd" in df.columns else None
    )

    partial_mask = (
        (df["p_active"] > stats["p_max_observed"] * 0.2) &
        (df["p_active"] < stats["p_max_observed"] * 0.6)
    )
    if partial_mask.any() and "pitch_ang_act_1" in df.columns:
        stats["baseline_pitch"] = df.loc[partial_mask, "pitch_ang_act_1"].median()
    else:
        stats["baseline_pitch"] = 0.0

    print(
        f"  [自适应统计] 额定: {stats['p_max_observed']:.1f}kW"
        f" | 基准桨距: {stats['baseline_pitch']:.2f}°"
    )
    return stats


def label_dataframe(df_input: pd.DataFrame, stats: dict, model_name: str) -> pd.DataFrame:
    """
    对 DataFrame 打标，返回含以下新列的 DataFrame:
      - d_val_*  / d_logic_*  : 各传感器异常布尔列
      - sensor_anomaly_tags   : 逗号拼接的传感器异常标签字符串（无异常为空串）
      - status                : 运行 / 限功率 / 停机 / 传感器异常-xxx异常
    """
    df = df_input.copy()
    P_MAX      = stats["p_max_observed"]
    PITCH_BASE = stats["baseline_pitch"]

    # ── 1. 传感器异常列 ────────────────────────────────────────────────────────
    df["d_val_power"] = (
        (df["p_active"] > P_MAX * STATUS_POWER_UPPER_RATIO) |
        (df["p_active"] < P_MAX * STATUS_POWER_LOWER_RATIO)
    )

    if "wind_spd" in df.columns:
        df["d_val_wind"] = (df["wind_spd"] > STATUS_WIND_MAX) | (df["wind_spd"] < STATUS_WIND_MIN)
    else:
        df["d_val_wind"] = False

    pitch_cols = [c for c in df.columns if "pitch_ang_act" in c]
    df["d_val_pitch"] = False
    for col in pitch_cols:
        df["d_val_pitch"] |= (df[col] > STATUS_PITCH_MAX) | (df[col] < STATUS_PITCH_MIN)

    df["d_val_spd"] = False
    if stats["spd_limit"] and "gen_spd" in df.columns:
        df["d_val_spd"] = (
            (df["gen_spd"] > stats["spd_limit"] * STATUS_SPD_UPPER_RATIO) |
            (df["gen_spd"] < -200)
        )

    df["d_val_torque"] = False
    if stats["torque_limit"] and "actual_torque" in df.columns:
        df["d_val_torque"] = (
            (df["actual_torque"] > stats["torque_limit"] * STATUS_TORQUE_UPPER_RATIO) |
            (df["actual_torque"] < STATUS_TORQUE_LOWER_ABS)
        )

    # ── 2. 逻辑悖论列 ──────────────────────────────────────────────────────────
    if "wind_spd" in df.columns:
        df["d_logic_wind_pwr"] = (
            (df["wind_spd"] < STATUS_LOGIC_WIND_MIN) &
            (df["p_active"] > STATUS_LOGIC_POWER_MIN)
        )
        for wind_thresh, pwr_thresh in STATUS_MODEL_SPECIAL_RULES.get(model_name, []):
            df["d_logic_wind_pwr"] |= (
                (df["wind_spd"] < wind_thresh) & (df["p_active"] > pwr_thresh)
            )
    else:
        df["d_logic_wind_pwr"] = False

    df["d_logic_torque_pwr"] = False
    if stats["torque_limit"] and "actual_torque" in df.columns:
        df["d_logic_torque_pwr"] = (
            (df["p_active"] > P_MAX * 0.1) &
            (df["actual_torque"].abs() < stats["torque_limit"] * 0.01)
        )

    # ── 3. 汇总传感器异常标签 ──────────────────────────────────────────────────
    all_sensor_cols = list(SENSOR_COL_LABEL.keys())
    existing = [c for c in all_sensor_cols if c in df.columns]

    # 向量化拼接：逐列生成标签，再按行合并（避免 apply axis=1）
    if existing:
        tag_matrix = np.where(
            df[existing].values,
            np.array([SENSOR_COL_LABEL[c] for c in existing]),
            "",
        )
        df["sensor_anomaly_tags"] = pd.Series(
            [",".join(t for t in row if t) for row in tag_matrix],
            index=df.index,
        )
    else:
        df["sensor_anomaly_tags"] = ""

    # ── 4. 业务状态打标 ────────────────────────────────────────────────────────
    any_sensor_anomaly = df[[c for c in existing if c in df.columns]].any(axis=1)

    df["status"] = "运行"

    shutdown_thresh = max(10.0, P_MAX * STATUS_SHUTDOWN_RATIO)
    mask_shutdown = (~df["d_val_power"]) & (df["p_active"] <= shutdown_thresh)
    df.loc[mask_shutdown, "status"] = "停机"

    if "pitch_ang_act_1" in df.columns:
        mask_curtail = (
            (df["status"] != "停机") &
            (~df["d_val_power"]) &
            (~df["d_val_pitch"]) &
            (df["p_active"] > P_MAX * STATUS_CURTAIL_LOW_RATIO) &
            (df["p_active"] < P_MAX * STATUS_CURTAIL_HIGH_RATIO) &
            (df["pitch_ang_act_1"] > (PITCH_BASE + STATUS_CURTAIL_PITCH_OFFSET))
        )
        df.loc[mask_curtail, "status"] = "限功率"

    # 传感器异常覆盖（最高优先级）
    mask_sensor = any_sensor_anomaly
    df.loc[mask_sensor, "status"] = (
        "传感器异常-" + df.loc[mask_sensor, "sensor_anomaly_tags"]
    )

    return df