#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
测试部位温度记录表生成脚本（带负载状态筛选）

- 忽略传入的 experimentProcess，自行构造固定结构的数据
- 从 InfluxDB 查询每个测试部位在各时间点的瞬时温度值
- 添加 load_status = 1 的筛选条件，确保只在真正采集数据时获取温度
- 输出格式与应用中的 scriptTable 占位符兼容
- 默认把 {scriptTable1} 放在"测试部位"所在的单元格

环境变量：
    TABLE_TOKEN         目标占位符，默认 scriptTable1
    TABLE_START_ROW     写入起始行偏移，默认 0
    TABLE_START_COL     写入起始列偏移，默认 0
    TABLE_TIME_SLOTS    逗号分隔的时间刻度，默认 "0.5h,1h,1.5h,2h,2.5h,3h,3.5h"
    TABLE_MOTOR_SPEED   电机转速标签，默认 "980RPM"
    EXPERIMENT_START     实验开始时间（ISO 8601 格式，如 2024-01-01T10:00:00Z）
    EXPERIMENT_END       实验结束时间（ISO 8601 格式）
    INFLUX_URL           InfluxDB URL
    INFLUX_ORG           InfluxDB 组织
    INFLUX_TOKEN         InfluxDB 令牌
    INFLUX_BUCKET        InfluxDB 桶名，默认 PCM
    INFLUX_MEASUREMENT   InfluxDB 测量名，默认 PCM_Measurement
"""

from __future__ import annotations

import json
import logging
import os
import sys
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional


LOGGER = logging.getLogger(__name__)


def _mask_secret(value: Optional[str]) -> str:
    """掩码敏感信息"""
    if not value:
        return "<empty>"
    if len(value) <= 8:
        return "*" * len(value)
    return value[:4] + "*" * (len(value) - 8) + value[-4:]


def _setup_logging() -> None:
    """设置日志"""
    log_level_str = os.environ.get("TABLE_LOG_LEVEL", "DEBUG").upper()
    log_level = getattr(logging, log_level_str, logging.DEBUG)
    
    # 配置根日志记录器
    logging.basicConfig(
        level=log_level,
        format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
        handlers=[
            logging.StreamHandler(sys.stderr)
        ]
    )
    
    # 如果指定了日志文件，添加文件处理器
    log_file = os.environ.get("TABLE_LOG_FILE", "").strip()
    if log_file:
        try:
            file_handler = logging.FileHandler(log_file, encoding='utf-8')
            file_handler.setLevel(log_level)
            file_handler.setFormatter(logging.Formatter(
                '%(asctime)s [%(levelname)s] %(name)s: %(message)s'
            ))
            logging.getLogger().addHandler(file_handler)
            LOGGER.info("日志文件已配置: %s", log_file)
        except Exception as e:
            LOGGER.warning("配置日志文件失败: %s", e)


def _get_influx_config() -> Dict[str, str]:
    """获取InfluxDB配置"""
    config = {
        'url': os.environ.get("INFLUX_URL", "").strip(),
        'org': os.environ.get("INFLUX_ORG", "").strip(),
        'token': os.environ.get("INFLUX_TOKEN", "").strip(),
        'bucket': os.environ.get("INFLUX_BUCKET", "PCM").strip(),
        'measurement': os.environ.get("INFLUX_MEASUREMENT", "PCM_Measurement").strip(),
    }
    
    LOGGER.debug(
        "InfluxDB配置: url=%s org=%s token=%s bucket=%s measurement=%s",
        config['url'] or "<empty>",
        config['org'] or "<empty>",
        _mask_secret(config['token']),
        config['bucket'],
        config['measurement'],
    )
    
    return config


def _parse_experiment_times() -> tuple[Optional[datetime], Optional[datetime]]:
    """解析实验时间"""
    start_str = os.environ.get("EXPERIMENT_START", "").strip()
    end_str = os.environ.get("EXPERIMENT_END", "").strip()
    
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    
    if start_str:
        try:
            for fmt in ["%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S%z"]:
                try:
                    start_time = datetime.strptime(start_str, fmt)
                    if start_time.tzinfo is not None:
                        # 转换为本地时间并去除时区信息
                        start_time = start_time.astimezone(tz=None).replace(tzinfo=None)
                    break
                except ValueError:
                    continue
        except Exception as e:
            print(f"Warning: Failed to parse EXPERIMENT_START '{start_str}': {e}", file=sys.stderr)
    
    if end_str:
        try:
            for fmt in ["%Y-%m-%dT%H:%M:%SZ", "%Y-%m-%dT%H:%M:%S%z"]:
                try:
                    end_time = datetime.strptime(end_str, fmt)
                    if end_time.tzinfo is not None:
                        # 转换为本地时间并去除时区信息
                        end_time = end_time.astimezone(tz=None).replace(tzinfo=None)
                    break
                except ValueError:
                    continue
        except Exception as e:
            print(f"Warning: Failed to parse EXPERIMENT_END '{end_str}': {e}", file=sys.stderr)
    
    return start_time, end_time


def _parse_time_slot(slot_str: str) -> float:
    """解析时间槽字符串为小时数"""
    if not slot_str:
        return 0.0
    
    slot_str = slot_str.strip().lower()
    
    if slot_str.endswith('h'):
        try:
            return float(slot_str[:-1])
        except ValueError:
            pass
    
    try:
        return float(slot_str)
    except ValueError:
        pass
    
    return 0.0


def _time_slots() -> List[str]:
    raw = os.environ.get("TABLE_TIME_SLOTS", "").strip()
    if not raw:
        # 根据图片，时间刻度是：0.5h, 1h, 1.5h, 2h, 2.5h, 3h, 3.5h（7列）
        return ["0.5h", "1h", "1.5h", "2h", "2.5h", "3h", "3.5h"]
    slots = [slot.strip() for slot in raw.split(",")]
    return [slot for slot in slots if slot]


def _default_sections() -> List[Dict[str, Any]]:
    # name -> rows underneath（entries）
    # 每个 entry 对应一个测试部位，需要映射到 InfluxDB 的 field 或 tag
    return [
        {"name": "主轴承", "entries": [
            {"label": "#1", "field": "主轴承#1", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#1"},
            {"label": "#2", "field": "主轴承#2", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#2"},
            {"label": "#3", "field": "主轴承#3", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#3"},
            {"label": "#4", "field": "主轴承#4", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#4"},
            {"label": "#5", "field": "主轴承#5", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#5"},
            {"label": "#6", "field": "主轴承#6", "filters": {"data_type": "LSDAQ"}, "result_key": "主轴承#6"},
        ]},
        {"name": "十字头", "entries": [
            {"label": "#1", "field": "十字头#1", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#1"},
            {"label": "#2", "field": "十字头#2", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#2"},
            {"label": "#3", "field": "十字头#3", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#3"},
            {"label": "#4", "field": "十字头#4", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#4"},
            {"label": "#5", "field": "十字头#5", "filters": {"data_type": "LSDAQ"}, "result_key": "十字头#5"},
        ]},
        {"name": "减速箱小轴承", "entries": [
            {"label": "#1（输入法兰端）", "field": "减速箱小轴承1", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱小轴承#1"},
            {"label": "#2", "field": "减速箱小轴承#2", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱小轴承#2"},
        ]},
        {"name": "减速箱大轴承", "entries": [
            {"label": "#3（大端盖端）", "field": "减速箱大轴承#3", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱大轴承#3"},
            {"label": "#4", "field": "减速箱大轴承#4", "filters": {"data_type": "LSDAQ"}, "result_key": "减速箱大轴承#4"},
        ]},
        {"name": "润滑油温", "entries": [
            {"label": "", "field": "mean", "filters": {"data_type": "润滑油温"}, "result_key": "润滑油温"},
        ]},
        {"name": "润滑油压", "entries": [
            {"label": "(Psi)", "field": "mean", "filters": {"data_type": "润滑油压"}, "result_key": "润滑油压"},
        ]},
    ]

def _query_load_status_timeline(
    start_time: datetime,
    end_time: datetime,
    influx_url: str,
    influx_org: str,
    influx_token: str,
    influx_bucket: str,
    influx_measurement: str,
) -> List[Dict[str, Any]]:
    """查询整个实验期间的load_status时间线数据"""
    try:
        from influxdb_client import InfluxDBClient
        import pandas as pd
        import warnings
        from influxdb_client.client.warnings import MissingPivotFunction
    except ImportError:
        LOGGER.warning("InfluxDB client not available, skip load_status timeline query")
        return []

    try:
        client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
        query_api = client.query_api()

        start_rfc = start_time.strftime('%Y-%m-%dT%H:%M:%SZ')
        end_rfc = end_time.strftime('%Y-%m-%dT%H:%M:%SZ')

        # 查询load_status字段的所有数据点（在Breaker数据类型中）
        flux = f'''
from(bucket: "{influx_bucket}")
  |> range(start: {start_rfc}, stop: {end_rfc})
  |> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
  |> filter(fn: (r) => r["data_type"] == "Breaker")
  |> filter(fn: (r) => r["_field"] == "load_status")
  |> sort(columns: ["_time"])
  |> yield(name: "load_status_timeline")
'''.strip()

        LOGGER.debug("Load status timeline query:\n%s", flux)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore", MissingPivotFunction)
            frames = query_api.query_data_frame(flux)
        
        if isinstance(frames, list):
            df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
        else:
            df = frames

        if df.empty or '_value' not in df.columns or '_time' not in df.columns:
            LOGGER.warning("No load_status timeline data found")
            return []

        # 转换为时间线数据，确保时区一致性
        timeline = []
        for _, row in df.iterrows():
            time_obj = pd.to_datetime(row['_time'])
            # 转换为本地时间，去除时区信息，与start_time/end_time保持一致
            if hasattr(time_obj, 'tz') and time_obj.tz is not None:
                # 对于pandas Timestamp，先转换为本地时区再转为Python datetime
                time_obj = time_obj.tz_convert(None).to_pydatetime()
            elif hasattr(time_obj, 'to_pydatetime'):
                # 转换为Python datetime对象
                time_obj = time_obj.to_pydatetime()
            
            # 确保没有时区信息
            if hasattr(time_obj, 'tzinfo') and time_obj.tzinfo is not None:
                time_obj = time_obj.replace(tzinfo=None)
                
            timeline.append({
                'time': time_obj,
                'load_status': float(row['_value'])
            })

        LOGGER.info("Load status timeline: %d data points from %s to %s", 
                   len(timeline), start_time, end_time)
        
        # 调试：检查时间对象类型
        if timeline:
            first_time = timeline[0]['time']
            LOGGER.debug("Timeline first time: %s (type: %s, tzinfo: %s)", 
                        first_time, type(first_time), getattr(first_time, 'tzinfo', None))
        LOGGER.debug("start_time: %s (type: %s, tzinfo: %s)", 
                    start_time, type(start_time), getattr(start_time, 'tzinfo', None))
        LOGGER.debug("end_time: %s (type: %s, tzinfo: %s)", 
                    end_time, type(end_time), getattr(end_time, 'tzinfo', None))
        
        return timeline

    except Exception as e:
        LOGGER.error("Error querying load_status timeline: %s", e)
        return []
    finally:
        try:
            client.close()
        except Exception:
            pass


def _calculate_effective_time_points(
    start_time: datetime,
    end_time: datetime,
    time_slots: List[str],
    influx_config: Dict[str, str]
) -> Dict[str, Optional[datetime]]:
    """计算基于有效运行时间累计的真实时间点"""
    
    # 1. 获取load_status时间线
    timeline = _query_load_status_timeline(
        start_time, end_time,
        influx_config['url'], influx_config['org'], influx_config['token'],
        influx_config['bucket'], influx_config['measurement']
    )
    
    if not timeline:
        LOGGER.warning("No load_status timeline data, fallback to original time calculation")
        # 回退到原始时间计算
        result = {}
        for slot_str in time_slots:
            slot_hours = _parse_time_slot(slot_str)
            result[slot_str] = start_time + timedelta(hours=slot_hours)
        return result
    
    # 2. 计算有效运行时间段
    effective_periods = []
    current_period_start = None
    
    for i, point in enumerate(timeline):
        if point['load_status'] == 1.0:
            if current_period_start is None:
                current_period_start = point['time']
        else:  # load_status != 1.0
            if current_period_start is not None:
                effective_periods.append({
                    'start': current_period_start,
                    'end': point['time'],
                    'duration_hours': (point['time'] - current_period_start).total_seconds() / 3600.0
                })
                current_period_start = None
    
    # 处理最后一个周期（如果实验结束时仍在运行）
    if current_period_start is not None:
        effective_periods.append({
            'start': current_period_start,
            'end': end_time,
            'duration_hours': (end_time - current_period_start).total_seconds() / 3600.0
        })
    
    total_effective_hours = sum(period['duration_hours'] for period in effective_periods)
    LOGGER.info("Effective running periods: %d periods, total %.3f hours", 
               len(effective_periods), total_effective_hours)
    
    for period in effective_periods:
        LOGGER.debug("Effective period: %s → %s (%.3f hours)",
                    period['start'].strftime('%H:%M:%S'),
                    period['end'].strftime('%H:%M:%S'),
                    period['duration_hours'])
    
    # 3. 计算每个时间槽对应的真实时间点
    effective_time_points = {}
    
    for slot_str in time_slots:
        target_effective_hours = _parse_time_slot(slot_str)
        
        if target_effective_hours <= 0:
            effective_time_points[slot_str] = None
            continue
        
        if target_effective_hours > total_effective_hours:
            LOGGER.warning("Target effective time %.3fh exceeds total effective time %.3fh for slot %s",
                          target_effective_hours, total_effective_hours, slot_str)
            effective_time_points[slot_str] = None
            continue
        
        # 在有效时间段中查找累计运行target_effective_hours小时的时间点
        cumulative_hours = 0.0
        target_time_point = None
        
        for period in effective_periods:
            period_duration = period['duration_hours']
            
            if cumulative_hours + period_duration >= target_effective_hours:
                # 目标时间点在这个周期内
                remaining_hours = target_effective_hours - cumulative_hours
                target_time_point = period['start'] + timedelta(hours=remaining_hours)
                break
            else:
                cumulative_hours += period_duration
        
        effective_time_points[slot_str] = target_time_point
        
        if target_time_point:
            LOGGER.info("Slot %s: effective %.3fh → actual time %s",
                       slot_str, target_effective_hours, target_time_point.strftime('%H:%M:%S'))
        else:
            LOGGER.warning("Could not calculate effective time point for slot %s", slot_str)
    
    return effective_time_points


def _query_influxdb_range_with_load_status(
    field_name: str,
    start_time: datetime,
    end_time: datetime,
    influx_url: str,
    influx_org: str,
    influx_token: str,
    influx_bucket: str,
    influx_measurement: str,
    filters: Optional[Dict[str, str]] = None,
) -> Optional[float]:
    """查询 InfluxDB 获取指定字段在时间范围内的平均值（仅当 load_status = 1 时）"""
    try:
        from influxdb_client import InfluxDBClient
        import pandas as pd
        import warnings
        from influxdb_client.client.warnings import MissingPivotFunction
    except ImportError:
        LOGGER.warning("InfluxDB client not available, skip query for field=%s", field_name)
        return None

    try:
        client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
        query_api = client.query_api()

        start_rfc = start_time.strftime('%Y-%m-%dT%H:%M:%SZ')
        end_rfc = end_time.strftime('%Y-%m-%dT%H:%M:%SZ')

        # 构建过滤条件
        tag_filters = ""
        if filters:
            for key, value in filters.items():
                tag_filters += f'\n  |> filter(fn: (r) => r["{key}"] == "{value}")'

        # 对于环境温度，取全部非0数据的均值；其他字段仍需load_status=1筛选
        if field_name == "环境温度":
            flux = f'''
from(bucket: "{influx_bucket}")
  |> range(start: {start_rfc}, stop: {end_rfc})
  |> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
  |> filter(fn: (r) => r["_field"] == "{field_name}")
  |> filter(fn: (r) => r["_value"] != 0.0){tag_filters}
  |> mean()
  |> yield(name: "mean_non_zero")
'''.strip()
        else:
            flux = f'''
from(bucket: "{influx_bucket}")
  |> range(start: {start_rfc}, stop: {end_rfc})
  |> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
  |> filter(fn: (r) => r["_field"] == "{field_name}"){tag_filters}
  |> mean()
  |> yield(name: "mean_temperature_data")
'''.strip()

        LOGGER.debug("Flux查询语句 (range):\n%s", flux)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore", MissingPivotFunction)
            frames = query_api.query_data_frame(flux)
        
        if isinstance(frames, list):
            df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
        else:
            df = frames

        if df.empty or '_value' not in df.columns:
            if field_name == "环境温度":
                LOGGER.debug("No valid range data found for field=%s (non-zero data)", field_name)
            else:
                LOGGER.debug("No valid range data found for field=%s", field_name)
            return None
            
        mean_value = df['_value'].iloc[0]
        if pd.isna(mean_value):
            LOGGER.debug("Mean value is NaN for field=%s", field_name)
            return None

        value = float(mean_value)
        if field_name == "环境温度":
            LOGGER.debug("Field=%s range_mean_value=%.3f (non-zero data)", field_name, value)
        else:
            LOGGER.debug("Field=%s range_mean_value=%.3f", field_name, value)
        return value
    except Exception as e:
        LOGGER.error("Error querying InfluxDB range for field=%s: %s", field_name, e)
        return None
    finally:
        try:
            client.close()
        except Exception:
            pass


def _query_influxdb_with_load_status(
    field_name: str,
    target_time: datetime,
    influx_url: str,
    influx_org: str,
    influx_token: str,
    influx_bucket: str,
    influx_measurement: str,
    filters: Optional[Dict[str, str]] = None,
) -> Optional[float]:
    """查询 InfluxDB 获取指定字段在指定时间点的瞬时值（仅当 load_status = 1 时）"""
    try:
        from influxdb_client import InfluxDBClient
        import pandas as pd
        import warnings
        from influxdb_client.client.warnings import MissingPivotFunction
    except ImportError:
        LOGGER.warning("InfluxDB client not available, skip query for field=%s", field_name)
        return None

    try:
        client = InfluxDBClient(url=influx_url, org=influx_org, token=influx_token)
        query_api = client.query_api()

        LOGGER.debug(
            "Querying field=%s measurement=%s target_time=%s filters=%s (with load_status=1)",
            field_name,
            influx_measurement,
            target_time.strftime('%Y-%m-%dT%H:%M:%SZ'),
            filters or {},
        )

        # 查询逻辑：查询目标时间点附近的数据，但只要 load_status = 1 的数据
        # 使用一个时间窗口来查找最接近的有效数据点
        window_minutes = 10  # 前后10分钟的窗口
        
        query_start = target_time - timedelta(minutes=window_minutes)
        query_end = target_time + timedelta(minutes=window_minutes)
        
        query_start_rfc = query_start.strftime('%Y-%m-%dT%H:%M:%SZ')
        query_end_rfc = query_end.strftime('%Y-%m-%dT%H:%M:%SZ')

        # 构建过滤条件
        tag_filters = ""
        if filters:
            for key, value in filters.items():
                tag_filters += f'\n  |> filter(fn: (r) => r["{key}"] == "{value}")'

        # 查询温度数据（不需要load_status筛选，因为已经基于有效时间点查询）
        flux = f'''
from(bucket: "{influx_bucket}")
  |> range(start: {query_start_rfc}, stop: {query_end_rfc})
  |> filter(fn: (r) => r["_measurement"] == "{influx_measurement}")
  |> filter(fn: (r) => r["_field"] == "{field_name}"){tag_filters}
  |> sort(columns: ["_time"])
  |> last()
  |> yield(name: "instantaneous_at_effective_time")
'''.strip()

        LOGGER.debug("Flux查询语句:\n%s", flux)

        with warnings.catch_warnings():
            warnings.simplefilter("ignore", MissingPivotFunction)
            frames = query_api.query_data_frame(flux)
        
        if isinstance(frames, list):
            df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
        else:
            df = frames

        # 获取瞬时值（最近的一个有效数据点）
        if df.empty or '_value' not in df.columns:
            LOGGER.debug("No valid data found for field=%s at effective time point", field_name)
            return None
            
        # 取第一行的值（因为查询已经排序并取了last()）
        instant_value = df['_value'].iloc[0]
        if pd.isna(instant_value):
            LOGGER.debug("Instantaneous value is NaN for field=%s", field_name)
            return None

        value = float(instant_value)
        
        # 如果有时间信息，记录实际的数据时间点
        if '_time' in df.columns:
            actual_time = df['_time'].iloc[0]
            LOGGER.debug("Field=%s instantaneous_value=%.3f actual_time=%s (at effective time)", 
                        field_name, value, actual_time)
        else:
            LOGGER.debug("Field=%s instantaneous_value=%.3f (at effective time)", field_name, value)
            
        return value
    except Exception as e:
        LOGGER.error("Error querying InfluxDB for field=%s: %s", field_name, e)
        return None
    finally:
        try:
            client.close()
        except Exception:
            pass


def _load_temperature_data_with_load_status(
    time_slots: List[str],
    sections: List[Dict[str, Any]],
    start_time: Optional[datetime],
    end_time: Optional[datetime],
) -> Dict[str, Dict[str, float]]:
    """从 InfluxDB 查询所有测试部位在各时间点的瞬时温度值（仅当 load_status = 1 时）"""
    if not start_time or not end_time:
        LOGGER.info("Skip data query: missing start/end (%s, %s)", start_time, end_time)
        return {}
    
    influx_config = _get_influx_config()
    
    if not all([influx_config['url'], influx_config['org'], influx_config['token'], 
                influx_config['bucket'], influx_config['measurement']]):
        LOGGER.warning(
            "Skip data query: missing Influx config url=%s bucket=%s measurement=%s",
            influx_config['url'] or "<empty>",
            influx_config['bucket'] or "<empty>",
            influx_config['measurement'] or "<empty>",
        )
        return {}
    
    # 计算总时长（小时）
    total_duration = (end_time - start_time).total_seconds() / 3600.0
    LOGGER.info(
        "Fetch instantaneous temperature data (load_status=1) window=%s→%s total_hours=%.3f time_points=%s",
        start_time.isoformat(),
        end_time.isoformat(),
        total_duration,
        ",".join(time_slots),
    )
    
    # 收集所有需要查询的字段
    query_targets: List[tuple[str, Dict[str, Any]]] = []
    for section in sections:
        entries = section.get("entries") or []
        for entry in entries:
            if isinstance(entry, dict):
                field_name = entry.get("field", "")
                if field_name:
                    query_targets.append((field_name, entry))

    if not query_targets:
        return {}
    
    # 计算基于有效运行时间累计的真实时间点
    LOGGER.info("=== 开始计算有效时间点 ===")
    effective_time_points = _calculate_effective_time_points(
        start_time, end_time, time_slots, influx_config
    )
    
    # 为每个有效时间点查询温度数据
    temperature_data: Dict[str, Dict[str, float]] = {}
    
    for idx, slot_str in enumerate(time_slots):
        target_time_point = effective_time_points.get(slot_str)
        
        if target_time_point is None:
            LOGGER.warning("No effective time point calculated for slot %s, skipping", slot_str)
            continue
        
        LOGGER.debug("Processing slot %s at effective time point %s", 
                    slot_str, target_time_point.strftime('%Y-%m-%d %H:%M:%S'))
        
        for field_name, entry in query_targets:
            result_key = entry.get("result_key") or field_name
            if not result_key:
                result_key = field_name
            entry_filters = entry.get("filters") if isinstance(entry, dict) else None
            if result_key not in temperature_data:
                temperature_data[result_key] = {}

            # 使用索引作为key，因为可能有重复的时间刻度
            slot_key = f"{idx}_{slot_str}"  # 使用索引+时间刻度作为唯一key

            # 查询瞬时值（在有效时间点）
            value = _query_influxdb_with_load_status(
                field_name,
                target_time_point,
                influx_config['url'],
                influx_config['org'],
                influx_config['token'],
                influx_config['bucket'],
                influx_config['measurement'],
                filters=entry_filters if entry_filters else None,
            )

            if value is not None:
                temperature_data[result_key][slot_key] = value
                LOGGER.debug(
                    "Slot=%s field=%s value=%.3f at effective_time=%s",
                    slot_key,
                    result_key,
                    value,
                    target_time_point.strftime('%H:%M:%S')
                )
            else:
                LOGGER.debug(
                    "Slot=%s field=%s no_data at effective_time=%s",
                    slot_key,
                    result_key,
                    target_time_point.strftime('%H:%M:%S')
                )

    return temperature_data


def _build_cells_with_load_status(
    time_slots: List[str],
    sections: List[Dict[str, Any]],
    motor_speed: str,
    start_time: Optional[datetime],
    end_time: Optional[datetime],
    temperature_data: Dict[str, Dict[str, float]],
    use_defaults: bool = False,
) -> List[Dict[str, Any]]:
    """构建单元格数据（基于 load_status = 1 的有效数据）- 与原始脚本结构完全一致"""
    cells: List[Dict[str, Any]] = []

    def add_cell(row: int, col: int, value: str = "", rowspan: int = 1, colspan: int = 1) -> None:
        payload: Dict[str, Any] = {"row": row, "col": col, "value": value}
        if rowspan > 1:
            payload["rowspan"] = rowspan
        if colspan > 1:
            payload["colspan"] = colspan
        cells.append(payload)

    # 模板左侧标题列已经去除，这里仅生成纯数据区，从 (0,0) 开始填入数值。
    # current_row 对应模板中的实际数据行索引。
    current_row = 0
    for section in sections:
        entries = section.get("entries") or []
        if not entries:
            continue
        # 每个测试部位子项对应模板中的一行
        for entry in entries:
            # 支持新格式（带 field 映射）和旧格式（纯字符串）
            if isinstance(entry, dict):
                field_name = entry.get("field", "")
                entry_filters = entry.get("filters")
                entry_key = entry.get("result_key") or field_name
            else:
                field_name = ""
                entry_filters = None
                entry_key = ""

            # 仅输出数值列：列索引直接对应时间段
            # 强制填充所有列，优先使用查询数据，否则使用默认值
            if field_name:
                target_key = entry_key or field_name

                # 遍历所有时间段列，确保每一列都有数据
                for col_idx, slot in enumerate(time_slots):
                    value = None

                    # 优先使用查询到的数据
                    if temperature_data:
                        slot_data = temperature_data.get(target_key, {})
                        if slot_data:
                            slot_key = f"{col_idx}_{slot}"
                            value = slot_data.get(slot_key)

                    if value is None and use_defaults:
                        # 使用基础默认值 + 时间段偏移（每个时间段增加0.1度）
                        default_base_value = 25.0  # 简化的默认值
                        time_offset = col_idx * 0.1
                        value = default_base_value + time_offset

                    if value is None:
                        value_str = ""
                    else:
                        # 格式化为字符串（保留1位小数）
                        value_str = f"{value:.1f}"

                    add_cell(current_row, col_idx, value_str)
            else:
                # 如果没有字段名，填充空字符串
                for col_idx in range(len(time_slots)):
                    add_cell(current_row, col_idx, "")
            current_row += 1

    return cells


def build_temperature_table_with_load_status(_: Dict[str, Any]) -> Dict[str, Any]:
    """构建温度表格数据（仅使用 load_status = 1 的有效数据）"""
    _setup_logging()
    
    token = os.environ.get("TABLE_TOKEN", "scriptTable1")
    row_offset = int(os.environ.get("TABLE_START_ROW", "0") or 0)
    col_offset = int(os.environ.get("TABLE_START_COL", "0") or 0)
    motor_speed = os.environ.get("TABLE_MOTOR_SPEED", "980RPM")
    
    # 解析实验时间范围
    start_time, end_time = _parse_experiment_times()
    
    time_slots = _time_slots()
    sections = _default_sections()
    
    # 查询温度数据（仅当 load_status = 1 时）
    temperature_data = _load_temperature_data_with_load_status(time_slots, sections, start_time, end_time)
    
    # 始终禁止默认数据，保证查询不到值时保持空白
    use_defaults = False
    
    cells = _build_cells_with_load_status(
        time_slots, 
        sections, 
        motor_speed, 
        start_time, 
        end_time, 
        temperature_data,
        use_defaults=use_defaults
    )
    
    # 应用行偏移
    for cell in cells:
        cell["row"] += 4
    
    # 添加实验时间信息（与原始脚本完全一致的逻辑）
    start_time_row = 1
    start_time_value_col = 1
    end_time_value_col = 3
    
    # 获取原始时间字符串进行处理（与原始脚本保持一致）
    start_str = os.environ.get("EXPERIMENT_START", "").strip()
    if start_str and start_time:
        try:
            # 使用与原始脚本相同的时间处理逻辑
            utc_aware_dt = datetime.strptime(start_str, "%Y-%m-%dT%H:%M:%S%z")
            local_dt1 = utc_aware_dt.astimezone(tz=None)
            local_dt2 = utc_aware_dt.astimezone(tz=None) + timedelta(hours=3.5)
            start_time_value = local_dt1.strftime("%Y-%m-%d %H:%M:%S")
            end_time_value = local_dt2.strftime("%Y-%m-%d %H:%M:%S")
            cells.append({"row": start_time_row, "col": start_time_value_col, "value": start_time_value})
            cells.append({"row": start_time_row, "col": end_time_value_col, "value": end_time_value})
        except Exception as e:
            LOGGER.warning("Failed to process experiment time strings: %s", e)
    
    # 查询环境温度（与原始脚本完全一致的逻辑）
    influx_url = os.environ.get("INFLUX_URL", "").strip()
    influx_org = os.environ.get("INFLUX_ORG", "").strip()
    influx_token = os.environ.get("INFLUX_TOKEN", "").strip()
    influx_bucket = os.environ.get("INFLUX_BUCKET", "PCM").strip()
    influx_measurement = os.environ.get("INFLUX_MEASUREMENT", "PCM_Measurement").strip()
    
    if start_time and end_time:
        # 对于环境温度，使用时间范围查询（与原始脚本逻辑一致）
        value = _query_influxdb_range_with_load_status(
            "环境温度",
            start_time,
            end_time,
            influx_url,
            influx_org,
            influx_token,
            influx_bucket,
            influx_measurement,
            filters={"data_type": "LSDAQ"},
        )
        # 确保value不是None，避免Word COM操作异常（与原始脚本一致）
        if value is not None:
            cells.append({"row": 0, "col": 1, "value": f"{value:.1f}"})
        else:
            cells.append({"row": 0, "col": 1, "value": ""})
    
    LOGGER.info(
        "Temperature table built with load_status=1 filter: token=%s cells=%d time_slots=%s",
        token,
        len(cells),
        ",".join(time_slots),
    )
    
    return {
        "token": token,
        "startRow": row_offset,
        "startCol": col_offset,
        "cells": cells,
    }


def _load_payload() -> Dict[str, Any]:
    """从标准输入或环境变量加载payload数据"""
    try:
        # 尝试从标准输入读取JSON
        try:
            import select
            if select.select([sys.stdin], [], [], 0.0)[0]:
                payload_str = sys.stdin.read().strip()
                if payload_str:
                    return json.loads(payload_str)
        except ImportError:
            # Windows上select可能不可用，尝试直接读取
            import msvcrt
            if msvcrt.kbhit():
                payload_str = sys.stdin.read().strip()
                if payload_str:
                    return json.loads(payload_str)
    except Exception:
        pass
    
    # 如果没有标准输入，返回空字典
    return {}


def _log_environment_variables() -> None:
    """记录相关环境变量"""
    env_vars = [
        "TABLE_TOKEN", "TABLE_START_ROW", "TABLE_START_COL", "TABLE_TIME_SLOTS", "TABLE_MOTOR_SPEED",
        "EXPERIMENT_START", "EXPERIMENT_END",
        "INFLUX_URL", "INFLUX_ORG", "INFLUX_TOKEN", "INFLUX_BUCKET", "INFLUX_MEASUREMENT"
    ]
    
    for var in env_vars:
        value = os.environ.get(var, "")
        if "TOKEN" in var and value:
            value = _mask_secret(value)
        LOGGER.debug("ENV %s=%s", var, value or "<empty>")


def main() -> int:
    try:
        try:
            if not logging.getLogger().handlers:
                log_level_name = os.environ.get("TABLE_LOG_LEVEL", "DEBUG").strip() or "DEBUG"
                log_level = getattr(logging, log_level_name.upper(), logging.DEBUG)
                log_file_raw = os.environ.get("TABLE_LOG_FILE", "test.log").strip() or "test.log"
                log_file = os.path.abspath(log_file_raw)

                logging.basicConfig(
                    level=log_level,
                    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
                    handlers=[
                        logging.FileHandler(log_file, encoding="utf-8"),
                        logging.StreamHandler(sys.stderr),
                    ],
                )
                LOGGER.info("Logging initialized -> file=%s level=%s", log_file, logging.getLevelName(log_level))
                _log_environment_variables()
            sys.stdout.reconfigure(encoding="utf-8")  # type: ignore[attr-defined]
        except Exception:
            pass
        
        payload = _load_payload()
        table_spec = build_temperature_table_with_load_status(payload)
        result = {"tables": [table_spec]}
        print(json.dumps(result, ensure_ascii=False))
        return 0
    except Exception as exc:
        print(f"error: {exc}", file=sys.stderr)
        return 1


if __name__ == "__main__":
    sys.exit(main())