import pandas as pd
import os
from datetime import datetime, timedelta
import numpy as np

from functools import reduce

# =========================
# 全局数据路径（统一入口）
# =========================
DATA_PATH = "../datasets/high_freq_cip"

# =========================
# 全局数据格式
# =========================
UNIT_MAP = {
    1: "RO1",
    2: "RO2",
    3: "RO3",
    4: "RO4"
}

# =========================
# 方法1：基于控制逻辑（强规则）
# =========================

def read_raw_signal(file_path, col_name):
    """
    读取单变量CSV（列名就是变量名）

    输入：
        file_path: CSV路径
        col_name: 重命名后的标准变量名（model / qxb1 等）
    """
    df = pd.read_csv(file_path)
    df['time'] = pd.to_datetime(df['time'])

    # 找到除 time 之外的唯一数据列
    value_cols = [c for c in df.columns if c != 'time']

    if len(value_cols) != 1:
        raise ValueError(f"❌ 文件列结构异常（应只有1个数据列）: {file_path} | 实际列: {value_cols}")

    value_col = value_cols[0]

    # 重命名为统一字段（非常关键）
    df = df[['time', value_col]].rename(columns={value_col: col_name})

    return df

def read_method1_signals(unit_id):
    """
    读取方法1所需的所有控制逻辑信号

    包括：
        - model_word（运行模式）
        - CIP泵状态（QXB1/2）
        - 阀门反馈（JSF1/2）

    注意：
        - 每个信号是独立CSV（非连续记录）
        - 可能存在缺失文件（用exists保护）
    """
    base_path = DATA_PATH

    files = {
        "model": f"{base_path}/C_M_RO{unit_id}_DB_model_word_raw.csv",
        "qxb1": f"{base_path}/C_M_CIP_QXB1_run_raw.csv",
        "qxb2": f"{base_path}/C_M_CIP_QXB2_run_raw.csv",
        "jsf1": f"{base_path}/C_M_RO{unit_id}_CIP_JSF1_open_feedback_raw.csv",
        "jsf2": f"{base_path}/C_M_RO{unit_id}_CIP_JSF2_open_feedback_raw.csv",
    }

    dfs = []
    for key, path in files.items():
        if os.path.exists(path):
            dfs.append(read_raw_signal(path, key))

    return dfs


def merge_signals(dfs):
    """
    多信号时间对齐 + 前向填充

    原理：
        - outer join 合并时间轴
        - ffill 将“状态保持”转为连续时间序列

    关键假设：
        控制信号在未更新时保持不变（工业数据常见）
    """
    df_merged = reduce(
        lambda left, right: pd.merge(left, right, on='time', how='outer'),
        dfs
    )
    df_merged = df_merged.sort_values('time')
    df_merged = df_merged.ffill()
    return df_merged


def get_method1_dates(unit_id):
    """
    方法1核心：
    基于控制逻辑判断某天是否发生CIP

    判定条件（逐时刻）：
        model == 0
        AND (QXB1==1 OR QXB2==1)
        AND (JSF1==1 OR JSF2==1)

    日级别判定：
        当天只要出现一次 → 即判定为CIP日

    输出：
        set(date)
    """
    print(f"方法1处理 {unit_id} 号机组...")

    dfs = read_method1_signals(unit_id)
    if not dfs:
        return set()

    df = merge_signals(dfs)

    df['cip_flag_m1'] = (
            (df['model'] == 0) &
            ((df['qxb1'] == 1) | (df['qxb2'] == 1)) &
            ((df['jsf1'] == 1) | (df['jsf2'] == 1))
    )

    df['date'] = df['time'].dt.date

    # 任意时刻满足 → 当天为CIP
    daily_flag = df.groupby('date')['cip_flag_m1'].any()

    return set(daily_flag[daily_flag].index)


# =========================
# 方法2：基于运行行为（弱规则）
# =========================

def read_control_word_data(unit_id):
    """
    读取控制字（高频离散）

    用于检测“异常运行段”
    """
    file_path = f'{DATA_PATH}/C_M_RO{unit_id}_DB_control_word_raw.csv'
    df = pd.read_csv(file_path)
    df['time'] = pd.to_datetime(df['time'])
    df = df.sort_values('time')
    df['date'] = df['time'].dt.date
    return df


def read_pressure_data():
    """
    读取1分钟级压力数据

    用于识别低压运行（CIP特征）
    """
    file_path = f'{DATA_PATH}/cip_sensors_1min_merged.csv'
    df = pd.read_csv(file_path)
    df['time'] = pd.to_datetime(df['time'])
    df = df.sort_values('time')
    df['date'] = df['time'].dt.date
    return df


def find_control_word_events(df, unit_id):
    """
    检测“控制字异常段”

    判定逻辑：
        - 控制字 != 24（非正常运行）
        - 持续时间 > 3h

    输出：
        result_days：存在异常的日期
        control_events_by_date：详细时间段
    """
    result_days = []
    control_events_by_date = {}

    dates = sorted(df['date'].unique())
    prev_day_last_control_word = None

    for i, current_date in enumerate(dates):

        day_data = df[df['date'] == current_date].copy()
        day_data = day_data.sort_values('time').reset_index(drop=True)

        day_events = []

        # ===== 处理跨天初始段 =====
        first_record_time = day_data.iloc[0]['time']
        first_record_control_word = day_data.iloc[0][f'C.M.RO{unit_id}_DB@control_word']

        if i == 0:
            first_segment_control_word = 0
        else:
            first_segment_control_word = prev_day_last_control_word

        day_start = datetime.combine(current_date, datetime.min.time())
        first_segment_duration = (first_record_time - day_start).total_seconds() / 60

        if (first_segment_duration > 180 and
                first_segment_control_word != 24.0 and
                first_segment_control_word is not None):
            day_events.append({
                'start_time': day_start,
                'end_time': first_record_time,
                'duration_minutes': first_segment_duration,
                'control_word': first_segment_control_word,
                'is_initial_segment': True
            })

        # ===== 中间段 =====
        current_control_word = first_record_control_word
        current_start_time = first_record_time

        for j in range(1, len(day_data)):
            current_time = day_data.iloc[j]['time']
            current_control = day_data.iloc[j][f'C.M.RO{unit_id}_DB@control_word']

            if current_control != current_control_word:
                duration = (current_time - current_start_time).total_seconds() / 60

                if duration > 180 and current_control_word != 24.0:
                    day_events.append({
                        'start_time': current_start_time,
                        'end_time': current_time,
                        'duration_minutes': duration,
                        'control_word': current_control_word,
                        'is_initial_segment': False
                    })

                current_control_word = current_control
                current_start_time = current_time

        # ===== 结束段 =====
        last_record_time = day_data.iloc[-1]['time']
        last_control_word = day_data.iloc[-1][f'C.M.RO{unit_id}_DB@control_word']

        day_end = datetime.combine(current_date, datetime.max.time()).replace(hour=23, minute=59, second=59)
        last_segment_duration = (day_end - last_record_time).total_seconds() / 60

        if (last_segment_duration > 180 and last_control_word != 24.0):
            day_events.append({
                'start_time': last_record_time,
                'end_time': day_end,
                'duration_minutes': last_segment_duration,
                'control_word': last_control_word,
                'is_initial_segment': False
            })

        prev_day_last_control_word = last_control_word

        if day_events:
            result_days.append(current_date)
            control_events_by_date[current_date] = day_events

    return result_days, control_events_by_date


def find_pressure_events(pressure_df, target_dates, unit_id):
    """
    检测低压事件（CIP特征）

    判定：
        - 压力在 [0.05, 0.5]
        - 连续 >=20min
        - 允许5分钟断点

    输出：
        每天的低压事件
    """
    pressure_events = {}

    for date in target_dates:

        day_data = pressure_df[pressure_df['date'] == date].copy()
        if len(day_data) == 0:
            continue

        day_data = day_data.sort_values('time')
        day_data['pressure_valid'] = day_data[f'C.M.RO{unit_id}_PT_JS@out'].between(0.05, 0.5)

        events = []
        current_streak = 0
        gap_count = 0
        event_start = None
        event_end = None

        for i, row in day_data.iterrows():
            if row['pressure_valid']:
                if current_streak == 0:
                    event_start = row['time']
                current_streak += 1
                gap_count = 0
                event_end = row['time']
            else:
                if current_streak > 0:
                    gap_count += 1
                    if gap_count > 5:
                        duration = (event_end - event_start).total_seconds() / 60 + 1
                        if duration >= 20:
                            events.append({
                                'start_time': event_start,
                                'end_time': event_end,
                                'duration_minutes': duration,
                                'avg_pressure': day_data.loc[
                                    (day_data['time'] >= event_start) &
                                    (day_data['time'] <= event_end) &
                                    day_data['pressure_valid']
                                    ][f'C.M.RO{unit_id}_PT_JS@out'].mean()
                            })
                        current_streak = 0
                        gap_count = 0

        # 处理最后一段
        if current_streak > 0:
            duration = (event_end - event_start).total_seconds() / 60 + 1
            if duration >= 20:
                events.append({
                    'start_time': event_start,
                    'end_time': event_end,
                    'duration_minutes': duration,
                    'avg_pressure': day_data.loc[
                        (day_data['time'] >= event_start) &
                        (day_data['time'] <= event_end) &
                        day_data['pressure_valid']
                        ][f'C.M.RO{unit_id}_PT_JS@out'].mean()
                })

        if events:
            pressure_events[date] = events

    return pressure_events


def match_events(control_events, pressure_events):
    """
    控制异常 × 低压异常 → 时间重叠

    只有同时满足 → 才认为是“CIP行为”
    """
    matched_results = []

    for control_event in control_events:
        for pressure_event in pressure_events:

            overlap_start = max(control_event['start_time'], pressure_event['start_time'])
            overlap_end = min(control_event['end_time'], pressure_event['end_time'])

            if overlap_start < overlap_end:
                overlap_duration = (overlap_end - overlap_start).total_seconds() / 60

                if overlap_duration > 0:
                    matched_results.append({
                        'overlap_start': overlap_start,
                        'overlap_end': overlap_end,
                        'overlap_duration_minutes': overlap_duration
                    })

    return matched_results


def get_method2_dates(unit_id):
    """
    方法2最终输出：
        返回满足“控制异常 + 压力异常重叠”的日期集合
    """
    print(f"方法2处理 {unit_id} 号机组...")

    control_df = read_control_word_data(unit_id)
    target_dates, control_events_by_date = find_control_word_events(control_df, unit_id)

    pressure_df = read_pressure_data()
    pressure_events_by_date = find_pressure_events(pressure_df, target_dates, unit_id)

    both_exist_dates = [d for d in target_dates if d in pressure_events_by_date]

    matched_dates = set()

    for date in both_exist_dates:
        control_events = control_events_by_date.get(date, [])
        pressure_events = pressure_events_by_date.get(date, [])

        matched_events = match_events(control_events, pressure_events)

        if matched_events:
            matched_dates.add(date)

    return matched_dates


# =========================
# 融合层
# =========================

def label_dates(method1_dates, method2_dates, unit_id):
    """
    融合标注规则：
        0 → 两种方法都检测到（高置信）
        1 → 仅方法1
        2 → 仅方法2
    """
    all_dates = sorted(method1_dates | method2_dates)

    results = []

    for d in all_dates:
        if d in method1_dates and d in method2_dates:
            label = 0
        elif d in method1_dates:
            label = 1
        else:
            label = 2

        results.append({
            "date": d,
            "label": label,
            "unit_id": unit_id
        })

    return pd.DataFrame(results)


def analyze_unit(unit_id):
    """
    单机组完整流程：
        方法1 → 日期集合
        方法2 → 日期集合
        融合 → 标签
    """
    unit_name = UNIT_MAP[unit_id]
    print(f"\n处理 {unit_name} 号机组...")

    method1_dates = get_method1_dates(unit_id)
    method2_dates = get_method2_dates(unit_id)

    label_df = label_dates(method1_dates, method2_dates, unit_name)

    return label_df


def main():

    all_results = []

    for unit_id in range(1, 5):

        df = analyze_unit(unit_id)
        all_results.append(df)

    final_df = pd.concat(all_results, ignore_index=True)

    final_df["unit_id"] = final_df["unit_id"].astype(str)

    final_df = final_df.sort_values(["unit_id", "date"])

    save_path = "../use_data/cip_day_labels_all_units.csv"
    final_df.to_csv(save_path, index=False)

    print(f"\n按天CIP标签已保存到 {save_path}")


if __name__ == "__main__":
    main()