self_example/phm_rotate/PHMWarehouse/dataSouce/test.py

111 lines
2.6 KiB
Python
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

# -*- encoding:utf-8 -*-
'''
@Author : dingjiawen
@Date : 2023/5/16 21:51
@Usage :
@Desc :测试
'''
import numpy as np
import pandas as pd
from datetime import datetime
import json
import os
####TODO 测试CMS数据读取
root = r"E:\data\cmsDemo/"
now_time = datetime.now()
def loadData():
file_name = os.listdir(root)
dataList=[]
for file in file_name:
if file.startswith("华能三塘湖"):
read_name = os.path.join(root + file)
# print(read_name)
data = pd.read_csv(read_name, encoding='utf-8',header=None)
total_data = data.values[0]
name = file.split("_")
data = {
"windfarm": name[0],
"wt_no": 5,
"realtime": file.split(".")[0].split("_")[-1],
"location": name[2]+"_"+name[3],
"g": 3,
"RPM": 14.41,
"freq": name[4],
"x": [],
"time": now_time.strftime("%Y%m%d%H%M%S")
}
dataList.append((data,total_data))
return dataList
pass
dataList = loadData()
print(dataList)
cms_data_path = r"E:\data\cmsDemo\华能三塘湖项目一期_D3-29_Shaft2_径向_25600_加速度g_14.41RPM_20180618003450.csv"
cms_data_df = pd.read_csv(cms_data_path, encoding='gbk', header=None)
x = list(cms_data_df)
send_data = list(cms_data_df.values[0])
print(send_data)
print(type(send_data))
data = {
"windfarm": "华能三塘湖(一期)",
"wt_no": 5,
"realtime": "20180618003450",
"location": "shaft_径向",
"g": 3,
"RPM": 14.41,
"freq": 25600,
"x": send_data[:3],
"time": now_time.strftime("%Y%m%d%H%M%S")
}
print(data)
# print(type(data))
data_send = json.dumps(data).encode("gbk")
print(data_send)
####TODO 测试燃机数据读取
# now_time = datetime.now()
# data_path = r"E:\data\Historian(1)\historian_data_demo\2023_01_02.npy"
# total_data = np.load(data_path)
# print(total_data)
# print(len(total_data))
#
# # # 获得行值
# rows = np.unique(total_data[:, 2])
# rows = np.insert(rows, 0, "Time")
# # 排序操作,升序排序,按照第一列进行排序,
# data_sorted = total_data[total_data[:, 2].argsort()]
# # 获得列值
# columns = np.unique(data_sorted[:, 0])
# # 获得集合,在此基础上进行添加
# values = rows[:, np.newaxis]
# print("==================")
# print(data_sorted)
# single_data = total_data[1, :]
# print(single_data)
# data = {
# "tagName": single_data[0],
# "value": single_data[1],
# "realtime": single_data[2],
# "time": now_time.strftime("%Y%m%d%H%M%S")
# }
# print(data)