import psycopg2

import PGDB_Read
import requests
import json
from writeDataToRealtimeData import upsert_realtime_data


def calc_water_quality():
    nm_values = (
        'lfgawqyq_plc3_Tag17',
        'lfgawqyq_plc3_Tag22',
        'lfgawqyq_plc3_Tag20',
        'lfgawqyq_plc10_Tag45',
        'lfgawqyq_plc10_Tag43',
        'lfgawqyq_plc10_Tag52',
        'lfgawqyq_plc10_Tag50',
        'lfgawqyq_plc10_Tag51',
        'lfgawqyq_plc10_Tag53',
    )

    # 连接到数据库
    with open('db_params.json', 'r') as f:
        db_params = json.load(f)
        db_params = db_params['test']

    # 建立连接
    conn = psycopg2.connect(**db_params)
    cursor = conn.cursor()
    json_data = PGDB_Read.query_database(cursor, nm_values)
    print(json_data)

    # 目标 URL
    url = 'http://localhost:8087/water_quality'

    # 发送 POST 请求
    response = requests.post(url, data=json_data, headers={'Content-Type': 'application/json'})

    # 打印响应内容
    result = response.json()

    write_data_list = [
        'lfgawqyq_plc11_Tag23',
        'lfgawqyq_plc11_Tag124',
        'lfgawqyq_plc11_Tag25',
        'lfgawqyq_plc11_Tag122',
        'lfgawqyq_xn_cscod',
        'lfgawqyq_xn_cstn',
    ]

    # 使用字典推导式将标签和值对应起来
    data_mapping = {tag: value for tag, value in zip(write_data_list, result)}

    # 打印结果字典
    upsert_realtime_data(cursor, data_mapping)
    # 关闭游标和连接
    cursor.close()
    conn.close()
    return json.dumps(data_mapping)