173 lines
5.8 KiB
Python
173 lines
5.8 KiB
Python
import requests
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from datetime import datetime
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import pytz
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from typing import List, Dict, Union, Optional
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import csv
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# get_data 是用来获取 历史数据，也就是非实时数据的接口
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# get_realtime 是用来获取 实时数据
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def convert_timestamp_to_beijing_time(timestamp: Union[int, float]) -> datetime:
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# 将毫秒级时间戳转换为秒级时间戳
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timestamp_seconds = timestamp / 1000
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# 将时间戳转换为datetime对象
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utc_time = datetime.fromtimestamp(timestamp_seconds)
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# 设定UTC时区
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utc_timezone = pytz.timezone("UTC")
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# 转换为北京时间
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beijing_timezone = pytz.timezone("Asia/Shanghai")
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beijing_time = utc_time.replace(tzinfo=utc_timezone).astimezone(beijing_timezone)
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return beijing_time
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def beijing_time_to_utc(beijing_time_str: str) -> str:
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# 定义北京时区
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beijing_timezone = pytz.timezone("Asia/Shanghai")
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# 将字符串转换为datetime对象
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beijing_time = datetime.strptime(beijing_time_str, "%Y-%m-%d %H:%M:%S")
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# 本地化时间对象
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beijing_time = beijing_timezone.localize(beijing_time)
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# 转换为UTC时间
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utc_time = beijing_time.astimezone(pytz.utc)
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# 转换为ISO 8601格式的字符串
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return utc_time.strftime("%Y-%m-%dT%H:%M:%SZ")
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def get_history_data(
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ids: str, begin_date: str, end_date: str, downsample: Optional[str]
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) -> List[Dict[str, Union[str, datetime, int, float]]]:
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# def get_history_data(ids: str, begin_date: str, end_date: str, downsample: Optional[str]) -> None:
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# 转换输入的北京时间为UTC时间
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begin_date_utc = beijing_time_to_utc(begin_date)
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end_date_utc = beijing_time_to_utc(end_date)
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# 数据接口的地址
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url = "http://183.64.62.100:9057/loong/api/curves/data"
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# url = 'http://10.101.15.16:9000/loong/api/curves/data'
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# url_path = 'http://10.101.15.16:9000/loong' # 内网
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# 设置 GET 请求的参数
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params = {
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"ids": ids,
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"beginDate": begin_date_utc,
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"endDate": end_date_utc,
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"downsample": downsample,
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}
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history_data_list = []
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try:
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# 发送 GET 请求获取数据
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response = requests.get(url, params=params)
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# 检查响应状态码，200 表示请求成功
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if response.status_code == 200:
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# 解析响应的 JSON 数据
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data = response.json()
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# 这里可以对获取到的数据进行进一步处理
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# 打印 'mpointId' 和 'mpointName'
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for item in data["items"]:
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mpoint_id = str(item["mpointId"])
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mpoint_name = item["mpointName"]
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# print("mpointId:", item['mpointId'])
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# print("mpointName:", item['mpointName'])
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# 打印 'dataDate' 和 'dataValue'
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for item_data in item["data"]:
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# 将时间戳转换为北京时间
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beijing_time = convert_timestamp_to_beijing_time(
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item_data["dataDate"]
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)
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data_value = item_data["dataValue"]
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# 创建一个字典存储每条数据
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data_dict = {
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"time": beijing_time,
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"device_ID": str(mpoint_id),
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"description": mpoint_name,
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# 'dataDate (Beijing Time)': beijing_time.strftime('%Y-%m-%d %H:%M:%S'),
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"monitored_value": data_value, # 保留原有类型
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}
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history_data_list.append(data_dict)
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else:
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# 如果请求不成功，打印错误信息
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print("请求失败，状态码:", response.status_code)
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except Exception as e:
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# 捕获异常
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print("发生异常:", e)
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return history_data_list
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# 使用示例
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# data_list = get_history_data(ids='9572',
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# begin_date='2025-02-08 06:00:00',
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# end_date='2025-02-08 12:00:00',
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# downsample='1m')
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#
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# # 打印数据列表
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# for data in data_list:
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# print(data)
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# # 定义 CSV 文件的路径
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# csv_file_path = './influxdb_data_4984.csv'
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# # 将数据写入 CSV 文件
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# # with open(csv_file_path, mode='w', newline='') as file:
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# # writer = csv.writer(file)
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# #
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# # # 写入表头
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# # writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
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# #
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# # # 写入数据
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# # for data in data_list:
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# # measurement = data['mpointName']
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# # mpointId = data['mpointId']
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# # date = data['datetime'].strftime('%Y-%m-%d')
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# # dataValue = data['dataValue']
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# # datetime_str = data['datetime']
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# #
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# # # 写入一行
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# # writer.writerow([measurement, mpointId, date, dataValue, datetime_str])
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# #
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# #
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# # print(f"数据已保存到 {csv_file_path}")
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#
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# filtered_csv_file_path = './filtered_influxdb_data_4984.csv'
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# #
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# # # # 读取并筛选数据
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# data_list1 = []
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#
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# with open(csv_file_path, mode='r') as file:
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# csv_reader = csv.DictReader(file)
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# for row in csv_reader:
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# # 将 datetime 列解析为 datetime 对象
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# datetime_value = datetime.strptime(row['datetime'], '%Y-%m-%d %H:%M:%S%z')
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#
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# # 只保留时间为 15 分钟倍数的行
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# if datetime_value.minute % 15 == 0:
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# data_list1.append(row)
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#
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# # 将筛选后的数据写入新的 CSV 文件
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# with open(filtered_csv_file_path, mode='w', newline='') as file:
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# writer = csv.writer(file)
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#
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# # 写入表头
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# writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
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#
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# # 写入筛选后的数据
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# for data in data_list1:
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# writer.writerow([data['measurement'], data['mpointId'], data['date'], data['dataValue'], data['datetime']])
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#
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# print(f"筛选后的数据已保存到 {filtered_csv_file_path}")
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