data_analyze_platform/USER/test.py

import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.pyplot import MultipleLocator
import os
import numpy as np
from LIB.BACKEND import DBManager
import pymysql
import datetime

#数据库配置
# host='rm-uf6bt29r702r14jh4.mysql.rds.aliyuncs.com'
# port=3306
# user='u_devlid'
# password='EgL#EuQOlRqGml'
# db='db_lidian'
# mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
# cursor = mysql.cursor()

# param='vin,sn,pack_model_code,cell_battery_type'
# sql =  "select %s from %s" %(param,'vin_pack_cell_info')
# cursor.execute(sql)
# res = cursor.fetchall()
# df_vin_pack_cell= pd.DataFrame(res,columns=param.split(','))
# df_vin_pack_cell=df_vin_pack_cell.set_index('vin')

# vin='LUZAGAAA4KA003771'
# param='time_st,time_sp,sn,method,short_current'
# sql =  "select %s from %s where sn='02YPEAG029603HA4D1302525 '" %(param,'algo_mid_intershort_result')
# cursor.execute(sql)
# res = cursor.fetchall()
# df_short= pd.DataFrame(res,columns=param.split(','))
# df_short.to_excel(r'C:\Users\zldc\project\User\spf\hz-application-algo\USER\spf\100Result\safetywaring\\'+vin+'内短路结果.xlsx')

# 内短路
filepath=r'D:\01WorkSpace\项目\Alibaba\data\内短路数据_2022-01-10_2022-02-10'
files=os.listdir(filepath)
# files=[vin+'内短路结果.xlsx']
for filename in files:
    # if '内短路' in filename:
        vin=filename[:-4]
        df_short= pd.read_csv(filepath+'\\'+filename,encoding='gb18030')
        df_short['time_sp']=pd.to_datetime(df_short['time_sp'], format='%Y-%m-%d %H:%M:%S')
        cellnums=len(eval(df_short.loc[0,'short_current']))
        cellvolt_name=['单体'+str(x) for x in range(1,cellnums+1)]
        df=pd.DataFrame(columns=cellvolt_name)
        for i in range(len(df_short)):
            df.loc[i]=eval(df_short.loc[i,'short_current'])
        plt.figure(figsize=(20,8))
        for i in range(df.shape[1]):
            x=df_short['time_sp']
            y=df['单体'+str(i+1)]
            plt.plot(x, y,marker='*',markersize=15)
            plt.plot(x, [-20]*len(x),c='r')
            plt.title(filename[0:17],fontsize=25)
            plt.xlabel('时间', fontsize=20)
            plt.ylabel('内短路电流', fontsize=20)
            plt.xticks(fontsize=20,rotation=10)
            plt.yticks(fontsize=20)
            # plt.xlim(0,120)
            plt.ylim(-25,30)
            # plt.scatter( xtime1,VoltChange[volt_column[i]],marker='o')
            # plt.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
            plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
            plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
        plt.savefig(filepath+'\\'+vin+"内短路.png")
        plt.show()
        plt.close()
        

        # df_short= pd.read_excel(filepath+'\\'+filename)
        # cellnums=len(eval(df_short.loc[0,'short_current']))
        # cellvolt_name=['单体'+str(x) for x in range(1,cellnums+1)]
        # plt.figure(figsize=(16,9))
        # for i in range(len(df_short)):
        #     x=list(range(1,cellnums+1))
        #     y=eval(df_short.loc[i,'short_current'])
        #     plt.plot(x, y,label=df_short.loc[i,'time_st'],marker='*',markersize=15)
        #     plt.title(filename[0:17],fontsize=18)
        #     plt.xlabel('电芯序号', fontsize=15)
        #     plt.ylabel('内短路电流', fontsize=15)
        #     plt.xticks(fontsize=15)
        #     plt.yticks(fontsize=15)
        #     # plt.xlim(0,120)
        #     # plt.ylim(-20,20)
        #     # plt.scatter( xtime1,VoltChange[volt_column[i]],marker='o')
        #     plt.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
        #     plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
        #     plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
        # plt.savefig(filepath+'\\'+filename[0:17]+".png")
        # plt.show()
        # plt.close()


# #离群度
# filepath=r'D:\01WorkSpace\项目\Alibaba\data\原始数据_2022-01-10_2022-02-10'
# files=os.listdir(filepath)
# for filename in files:
#     # if '内短路' in filename:
#     vin=filename[:-4]
#     df_bms= pd.read_csv(filepath+'\\'+filename,encoding='gb18030')
#     df_bms=df_bms.replace('', np.nan)
#     # df_bms.sort_values(by="time" , inplace=True, ascending=True)
#     df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
#     now_time='2022-4-3 14:00:00'
#     now_time=datetime.datetime.strptime(now_time,'%Y-%m-%d %H:%M:%S')
#     start_time='2022-1-25 00:00:00'
#     end_time='2022-2-1 00:00:00'
#     start_time=datetime.datetime.strptime(start_time,'%Y-%m-%d %H:%M:%S')
#     end_time=datetime.datetime.strptime(end_time,'%Y-%m-%d %H:%M:%S')
#     df_bms=df_bms[(df_bms['time']>start_time) & (df_bms['time']<end_time)]

#     CellVoltNums = len((df_bms.iloc[0]['cell_volt']).split(','))  # 找出电芯数量出现最多的次数
#     cellvolt_name=['CellVoltage'+str(x) for x in range(1,CellVoltNums+1)]
#     df_volt=pd.DataFrame(df_bms['cell_volt'].str.split(',').tolist()).iloc[:,list(range(CellVoltNums))]
#     df_volt.columns = cellvolt_name
#     df_volt=df_volt.astype('float')
#     df_volt.index=df_bms['time']
#     # df_volt=df_volt[(df_volt>2) &(df_volt<4.5)]
#     # df_volt=df_volt['2022-1-26 00:00:00':'2022-1-29 18:00:00']

#     plt.figure(figsize=(16,9))
#     for i in cellvolt_name[50:61]:
#         x=df_volt.index
#         y=df_volt[i]
#         plt.plot(x, y,label=i)
#         plt.title(vin,fontsize=18)
#         plt.xlabel('时间', fontsize=15)
#         plt.ylabel('电压', fontsize=15)
#         plt.xticks(fontsize=15)
#         plt.yticks(fontsize=15)
#         # plt.xlim(0,120)
#         # plt.ylim(-20,20)
#         # plt.scatter( xtime1,VoltChange[volt_column[i]],marker='o')
#         plt.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
#         plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
#         plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
#     plt.savefig(filepath+'\\'+vin+"离群度.png")
#     plt.show()
#     plt.close()