03181031下次数据库用这个传，添加散点图

.gitignore

@@ -7,4 +7,5 @@ ipynb_checkpoints/**
 *$py.class
 *.zip
 **/.ipynb_checkpoints/
-.idea
+.idea
+**/data/

DataBase/db-wind/Arg.py

@@ -1,16 +1,16 @@
 class Arg:
     def __init__(self):
         # 数据库地址
-        self.database = "mysql+pymysql://root:mysql_T7yN3E@192.168.12.10:19306/ipfcst_j00314_20240516121839"
+        self.database = "mysql+pymysql://root:mysql_T7yN3E@192.168.12.10:19306/ipfcst_j00399_20250312125506"
         # 数据存放位置
-        self.dataloc = "../data/314"
+        self.dataloc = "../../data/399"
         # 变量存放位置
-        self.varloc = "../data/314/var"
+        self.varloc = "../../data/399/var"
         # 测风塔个数
         self.towerloc = [1]
         # 机头编号
         self.turbineloc = [i for i in range(1, 16)]
 
-        self.begin = '2023-03-01'
-        self.end = '2024-03-31'
+        self.begin = '2024-12-01'
+        self.end = '2025-02-28'
 

DataBase/db-wind/inputData.py

@@ -279,12 +279,12 @@ class DataBase(object):
         """
         self.clear_data()
         try:
-            # self.get_process_power()
-            # self.get_process_dq()
-            # self.get_process_cdq()
-            # self.get_process_NWP()
-            # self.get_process_tower()
-            self.get_process_turbine()
+            self.get_process_power()
+            self.get_process_dq()
+            self.get_process_cdq()
+            self.get_process_NWP()
+            self.get_process_tower()
+            # self.get_process_turbine()
             self.indep_process()
         except Exception as e:
             print("导出数据出错：{}".format(e.args))

DataBase/db-wind/main.py

@@ -209,14 +209,14 @@ if __name__ == "__main__":
     arg = Arg.Arg()
     db = DataBase(arg=arg)
     db.data_process()
-    input_dir = "../data/314/turbine-15"  # 输入文件夹路径
-    output_dir = "../data/314/output_clean_csv_files"  # 输出文件夹路径
+    # input_dir = "../data/314/turbine-15"  # 输入文件夹路径
+    # output_dir = "../data/314/output_clean_csv_files"  # 输出文件夹路径
     # 对机头风速连续异常值和-99进行清洗,第三个参数是连续5个值不变以后就认为异常
     # 这步会生成一个"output_clean_csv_files"文件夹，里面包含全部单机的数据，存储的机头风速只清理了-99，参数50是风机数量+1，风机参数5就是连续5个点的认为是异常值，全部剔除。
-    process_csv_files(input_dir, output_dir, 50, 5)
-    output_dir_time_Merge = "../data/314/output_filtered_csv_files"
+    # process_csv_files(input_dir, output_dir, 50, 5)
+    # output_dir_time_Merge = "../data/314/output_filtered_csv_files"
     # 这步会生成一个"output_filtered_csv_files"文件夹，在上一步的基础上，对齐了全部风机的时间，只各自保留了交集。
-    TimeMerge(output_dir,output_dir_time_Merge,50)
+    # TimeMerge(output_dir,output_dir_time_Merge,50)
     # output_complete_data = "../data_mts/complete_data"
     # 这步会生成一个"complete_data"文件夹，在上一步的基础上，填充了10个时间点之内的缺失。
     # MissingPointProcessing(output_dir_time_Merge,output_complete_data,50,10)

DataMatplot/plt.py

@@ -7,7 +7,6 @@
 
 
 import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
 import math
 
@@ -25,7 +24,6 @@ union = union.loc['2023-06']
 union.reset_index(inplace=True)
 union = union[['C_TIME', 'C_REAL_VALUE', 'C_WS_INST_HUB_HEIGHT']]
 union['C_TIME'] = pd.to_datetime(union['C_TIME'])
-# union.to_csv("./趋势对比1分钟.csv", index=False)
 
 
 union5, union_index = [], [0]  # 功率表，索引表

DataMatplot/scatter.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# @FileName  :scatter.py
+# @Time      :2025/3/18 10:04
+# @Author    :David
+# @Company: shenyang JY
+import pandas as pd
+import matplotlib.pyplot as plt
+# 设置中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+
+
+if __name__ == "__main__":
+    tower = pd.read_csv('../data/399/tower-1-process.csv')
+    power = pd.read_csv('../data/399/power.csv')
+    union = pd.merge(tower, power, on='C_TIME')
+    plt.figure(figsize=(8, 6))  # 设置画布大小
+    plt.scatter(
+        x=union['C_WS_INST_HUB_HEIGHT'],  # X轴数据
+        y=union['C_REAL_VALUE'],  # Y轴数据
+        c='blue',  # 点颜色
+        alpha=0.6,  # 透明度（0-1）
+        edgecolors='w'  # 点边缘颜色
+    )
+
+    # 添加图表元素
+    plt.title('井岗一轮毂高度风速-出力散点图')  # 标题
+    plt.xlabel('轮毂风速')  # X轴标签
+    plt.ylabel('实际功率')  # Y轴标签
+    plt.grid(True, linestyle='--', alpha=0.5)  # 显示网格线
+    plt.show()