import numpy as np
import math
import matplotlib.pyplot as plt
import pandas as pd
import pymssql
from random import choice
import json
import time
import os




class MSSQL:
    # 類的建構函式，初始化資料庫連線ip或者域名，以及使用者名稱，密碼，要連線的資料庫名稱
    def __init__(self,host,user,pwd,db): 
        self.host=host
        self.user=user
        self.pwd=pwd
        self.db=db
    
    # 得到資料庫連線資訊函式，返回: conn.cursor()
    def __GetConnect(self):
        self.conn=pymssql.connect(host=self.host,
                                  user=self.user,
                                  password=self.pwd,
                                  database=self.db,
                                  charset='utf8')
        cur=self.conn.cursor()  #將資料庫連線資訊，賦值給cur。
        if not cur:
            raise(NameError,"連線資料庫失敗")
        else:
            return cur

        
    #執行查詢語句,返回的是一個包含tuple的list，list的元素是記錄行，tuple的元素是每行記錄的欄位
    def ExecQuery(self,sql):  
        cur = self.__GetConnect() #獲得資料庫連線資訊
        cur.execute(sql)          #執行Sql語句
        resList = cur.fetchall()  #獲得所有的查詢結果
        self.conn.close()         #查詢完畢後必須關閉連線
        return resList            #返回查詢結果
    
    
    #執行Sql語句函式，無返回結果的，方向修改的
    def ExecNonQuery(self,sql):
        cur = self.__GetConnect()
        cur.execute(sql)
        self.conn.commit()
        self.conn.close()





# new一個物件
# mssql = MSSQL('192.168.2.51', 'sa', 'Sql123456', 'AEHMS20201216')
# mssql = MSSQL('.', 'sa', 'sa', 'AEHMS20201216')
mssql = None








def randomSplit(total):# 隨機分組資料
    
    foo = [60,80,150,200,300,600]
    count = 0
    arr= []
    bl = True
    
    while (bl):
        num = choice(foo)# 隨機取值
        if count + num >= total:# 最後隨機取的那個數 超出當前範圍 break
            break
        arr.append(num)
        count+=num

    if total != count:# 追加最後一個元素
        arr.append(total-count)

    print(count)
    print(arr)

    
    seg = []# 值如：[(0,50),(50,200)]
    
    print('--------------')
    curCount=0
    for num in arr:
        start = curCount
        end = curCount + num
        seg.append((start, end))
        print(start, end)
        curCount=end

    print(seg)
    return seg








def createData(pointNum, avgValue): # 生成周期性資料

    long=pointNum # 400個步長，x軸的總長度
    base=avgValue # 均值
    ybase = np.zeros((1,long))[0] + base # 所有資料
    

    period_multiply = 0.1 # 越大，幅值越大，調整波峰
    period_frequency = 500 # 越大，週期越大
    
    all_period_multiply = [0.1, 0,2]# 預設多個幅值 [0.1, 0,2, 0.3, 0.4, 0.5]
    all_period_frequency = [50, 150, 200, 300, 400, 600, 800, 1000, 1300]# 預設多個週期值

    
    seg = randomSplit(pointNum)# 原始： seg = [(0, pointNum)]

    for (i,j) in seg: # 一組一組資料的遍歷
        print(i, j)
        
        period_multiply = choice(all_period_multiply)# 隨機取值
        period_frequency = choice(all_period_frequency)# 隨機取值
        
        n = j-i # n=40,40 50
        x = np.arange(n)
        season1 = 0.2 * np.array([math.sin(i*0.2/period_frequency*math.pi) for i in x])
        season2 = 0.5 * np.array([math.sin(i*0.5/period_frequency*math.pi) for i in x])
        noise = np.random.normal(0, 0.2, len(x))
        y = season1 + season2 + noise # 可以疊加多尺度週期和噪聲
        # y = season1+season2
        
        for idx in range(i, j): # 遍歷具體的點
            # print(idx, period_multiply)
            value1 = ybase[idx] + y[idx-i] * period_multiply
            value2 = round(value1, 3) # 保留三位小數
            ybase[idx] = value2
     

    # plt.figure(figsize=(15, 3.5))
    # plt.plot(ybase)
    # plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=2.0)
    # plt.show()
    
    return ybase


# # 測試
# points = createData(200, 1.2)
# print(points)






def getIdsByCode(code): # 獲取 ids by code
    sql = "SELECT ID FROM tb_SensorRecord WHERE Code='{}' AND GetTime>='2021-01-01' AND GetTime<='2021-07-01' ORDER BY ID ASC".format(code)
    results = mssql.ExecQuery(sql)
    arr_ids = []
    for row in results:
        arr_ids.append(row[0])        
    return arr_ids


# # 測試
# ids = getIdsByCode('080906')
# print(len(ids))



# # 測試 執行sql語句
# mssql.ExecNonQuery("UPDATE tb_SensorRecord SET NewValue1='1' WHERE ID='4178839'")
# print('ok')






def getSensor(): # 獲取所有的感測器，從Excel中讀取感測器
    arr_sensors = []
    df = pd.read_excel('1.xlsx', dtype={'編碼': np.str_}) # dtype指定列的資料型別
    for index, row in df.iterrows():
        arr_sensors.append({"code":row["編碼"], "avgValue":row["均值"]})
    return arr_sensors


# # 測試
# sensors = getSensor()
# print(sensors)








# 主邏輯

startTime = time.perf_counter()
curCode = ''

try:
    dbConfig = {
        'host' : '',
        'user' : '',
        'pwd' : '',
        'db' : ''
    }
    with open('dbConfig.json', 'r') as f:# 從檔案中讀取配置資訊
        dbConfig = eval(json.load(f))
    print(dbConfig)

    mssql = MSSQL(dbConfig["host"], dbConfig["user"], dbConfig["pwd"], dbConfig["db"])

    sensors = getSensor()
    for item in sensors: # 遍歷感測器
        print(item)
        code = item["code"]
        avgValue = item["avgValue"]
        curCode = code
        
        ids = getIdsByCode(code)
        points = createData(len(ids), avgValue)
        
        sql = ""
        for index, value in enumerate(ids):
            print(index, value, points[index])            
            sql += "UPDATE tb_SensorRecord SET NewValue1='{0}' WHERE ID='{1}';".format(points[index], value)            
            if (index % 8000 == 0):# 間隔寫入到資料庫
                print("正在寫入到資料庫1")                
                mssql.ExecNonQuery(sql)
                sql = ""
            
        if sql.strip() != '':
            print("正在寫入到資料庫2")
            mssql.ExecNonQuery(sql)
            sql = ""
      
    print('處理完成') 


except Exception as e:
    print('Error:', e)
    with open('err.txt', 'w') as f:
        json.dump(str(e)+"，當前感測器："+curCode, f)


finally:
    endTime = time.perf_counter()
    print("The function run time is : %.03f seconds" %(endTime-startTime))
    os.system('pause')# 避免執行完後自動關閉控制檯


 

需要的外部檔案

json配置檔案，用於放資料庫連結

"{\"host\": \".\", \"user\": \"sa\", \"pwd\": \"sa\", \"db\": \"AEHMS20210629\"}"

Excel檔案，用於指定需要修改的感測器和均值

修改後的資料預覽