import requests
from datetime import datetime
import pytz
from typing import List, Dict, Union, Optional
import csv

# get_data 是用来获取 历史数据，也就是非实时数据的接口
# get_realtime 是用来获取 实时数据

def convert_timestamp_to_beijing_time(timestamp: Union[int, float]) -> datetime:
    # 将毫秒级时间戳转换为秒级时间戳
    timestamp_seconds = timestamp / 1000

    # 将时间戳转换为datetime对象
    utc_time = datetime.utcfromtimestamp(timestamp_seconds)

    # 设定UTC时区
    utc_timezone = pytz.timezone('UTC')

    # 转换为北京时间
    beijing_timezone = pytz.timezone('Asia/Shanghai')
    beijing_time = utc_time.replace(tzinfo=utc_timezone).astimezone(beijing_timezone)

    return beijing_time


def beijing_time_to_utc(beijing_time_str: str) -> str:
    # 定义北京时区
    beijing_timezone = pytz.timezone('Asia/Shanghai')

    # 将字符串转换为datetime对象
    beijing_time = datetime.strptime(beijing_time_str, '%Y-%m-%d %H:%M:%S')

    # 本地化时间对象
    beijing_time = beijing_timezone.localize(beijing_time)

    # 转换为UTC时间
    utc_time = beijing_time.astimezone(pytz.utc)

    # 转换为ISO 8601格式的字符串
    return utc_time.strftime('%Y-%m-%dT%H:%M:%SZ')


def get_history_data(ids: str, begin_date: str, end_date: str, downsample: Optional[str]) -> List[Dict[str, Union[str, datetime, int, float]]]:
# def get_history_data(ids: str, begin_date: str, end_date: str, downsample: Optional[str]) -> None:
    # 转换输入的北京时间为UTC时间
    begin_date_utc = beijing_time_to_utc(begin_date)
    end_date_utc = beijing_time_to_utc(end_date)

    # 数据接口的地址
    url = 'http://183.64.62.100:9057/loong/api/curves/data'
    # url = 'http://10.101.15.16:9000/loong/api/curves/data'
    # url_path = 'http://10.101.15.16:9000/loong'  # 内网

    # 设置 GET 请求的参数
    params = {
        'ids': ids,
        'beginDate': begin_date_utc,
        'endDate': end_date_utc,
        'downsample': downsample
    }

    history_data_list =[]

    try:
        # 发送 GET 请求获取数据
        response = requests.get(url, params=params)

        # 检查响应状态码，200 表示请求成功
        if response.status_code == 200:
            # 解析响应的 JSON 数据
            data = response.json()
            # 这里可以对获取到的数据进行进一步处理

            # 打印 'mpointId' 和 'mpointName'
            for item in data['items']:
                mpoint_id = str(item['mpointId'])
                mpoint_name = item['mpointName']
                # print("mpointId:", item['mpointId'])
                # print("mpointName:", item['mpointName'])

                # 打印 'dataDate' 和 'dataValue'
                for item_data in item['data']:
                    # 将时间戳转换为北京时间
                    beijing_time = convert_timestamp_to_beijing_time(item_data['dataDate'])
                    data_value = item_data['dataValue']
                    # 创建一个字典存储每条数据
                    data_dict = {
                        'time': beijing_time,
                        'device_ID': str(mpoint_id),
                        'description': mpoint_name,
                        # 'dataDate (Beijing Time)': beijing_time.strftime('%Y-%m-%d %H:%M:%S'),
                        'monitored_value': data_value  # 保留原有类型
                    }

                    history_data_list.append(data_dict)
        else:
            # 如果请求不成功，打印错误信息
            print("请求失败，状态码:", response.status_code)

    except Exception as e:
        # 捕获异常
        print("发生异常:", e)

    return history_data_list


# 使用示例
# data_list = get_history_data(ids='9572',
#                              begin_date='2025-02-08 06:00:00',
#                              end_date='2025-02-08 12:00:00',
#                              downsample='1m')
#
# # 打印数据列表
# for data in data_list:
#     print(data)

# # 定义 CSV 文件的路径
# csv_file_path = './influxdb_data_4984.csv'
# # 将数据写入 CSV 文件
# # with open(csv_file_path, mode='w', newline='') as file:
# #     writer = csv.writer(file)
# #
# #     # 写入表头
# #     writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
# #
# #     # 写入数据
# #     for data in data_list:
# #         measurement = data['mpointName']
# #         mpointId = data['mpointId']
# #         date = data['datetime'].strftime('%Y-%m-%d')
# #         dataValue = data['dataValue']
# #         datetime_str = data['datetime']
# #
# #         # 写入一行
# #         writer.writerow([measurement, mpointId, date, dataValue, datetime_str])
# #
# #
# # print(f"数据已保存到 {csv_file_path}")
#
# filtered_csv_file_path = './filtered_influxdb_data_4984.csv'
# #
# # # # 读取并筛选数据
# data_list1 = []
#
# with open(csv_file_path, mode='r') as file:
#     csv_reader = csv.DictReader(file)
#     for row in csv_reader:
#         # 将 datetime 列解析为 datetime 对象
#         datetime_value = datetime.strptime(row['datetime'], '%Y-%m-%d %H:%M:%S%z')
#
#         # 只保留时间为 15 分钟倍数的行
#         if datetime_value.minute % 15 == 0:
#             data_list1.append(row)
#
# # 将筛选后的数据写入新的 CSV 文件
# with open(filtered_csv_file_path, mode='w', newline='') as file:
#     writer = csv.writer(file)
#
#     # 写入表头
#     writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
#
#     # 写入筛选后的数据
#     for data in data_list1:
#         writer.writerow([data['measurement'], data['mpointId'], data['date'], data['dataValue'], data['datetime']])
#
# print(f"筛选后的数据已保存到 {filtered_csv_file_path}")