data_analyze_platform/USER/LZX/01算法开发/02析锂检测/liplated/liplated_range.py

from ctypes import Structure
import pandas as pd
import numpy as np
import datetime
import time, datetime
import matplotlib.pyplot as plt
from pylab import*
import math
import os
import log

path = r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\02析锂分析\各项目析锂情况_211130\增加波峰高度与峰谷比计算结果'

mylog=log.Mylog('log_diag.txt','error')
mylog.logcfg()

def get_file():                   #创建一个空列表
    files =os.listdir(path)
    files.sort() #排序
    file_list= []
    for file in files:
        if not  os.path.isdir(path +file):  #判断该文件是否是一个文件夹       
            f_name = str(file)        
#             print(f_name)
            tr = '\\'   #多增加一个斜杠
            filename = path + tr + f_name        
            file_list.append(filename)  
    return file_list 

file_list = get_file()
for file in file_list:
    df_lipltd_level = pd.DataFrame(columns=['sn','total_num','total_amount','liplt_num','liplated_amount','liplated_level','warnning_level'])
    data = pd.read_csv(file,encoding='GB18030')
    #----------------------------------------筛选充电后静置数据------------------------------------------------------------
    # MGMCLN_name = list(filter(lambda x: 'MGMCLN' in x, data['sn']))
    # df_MGMCLN7255 = data.loc[data['sn'].isin(MGMCLN_name)]
    df_MGMCLN7255_del = data.drop_duplicates(subset = ['sn','time'], keep = 'first', inplace = False)#去除重复值
    MGMCLN_list = np.unique(df_MGMCLN7255_del['sn'], return_index = False, return_inverse = False, return_counts = False)#
    temperature = [15, 20, 22, 21, 17, 6, 4, -0.5]#5~11月平均气温
    temperatuer = list(map(lambda x: x + 273.15, temperature))
    for item in MGMCLN_list:
        df_liamount = pd.DataFrame()
        df_amount_temp = pd.DataFrame()
        df_num = pd.DataFrame()
        df_Num_temp = pd.DataFrame()
        df_data_temp = df_MGMCLN7255_del.loc[df_MGMCLN7255_del['sn'] == item]
        df_data_temp.reset_index(drop = True,inplace=True)
        df_month_temp = pd.to_datetime(df_data_temp['time'])
        df_month = df_month_temp.dt.month
        df_month = list(map(lambda x: x - 5, df_month))#月份对应的温度位置
        for i in range(0,len(df_data_temp)):#len(df_data_temp)
            df_Num_temp = pd.DataFrame(eval(df_data_temp.iloc[i]['liplated']))#统计析锂次数
            df_num = pd.concat([df_num,df_Num_temp], axis = 1)#统计总次数
            df_lipltamount_temp = eval(df_data_temp.iloc[i]['liplated_amount'])#解析原数据列表
            df_lipltamount_corrt = 4e-17*math.exp(0.1289*temperatuer[df_month[i]])#析锂量的温度系数
            df_amount_temp = pd.DataFrame(list(map(lambda x: x*df_lipltamount_corrt, df_lipltamount_temp)))
            df_liamount = pd.concat([df_liamount,df_amount_temp], axis = 1)#按列拼接各个该sn不同时刻的析锂量，每一行为一个电芯
        df_sn_liamount = np.sum(df_liamount, axis = 1)#对该sn中每个电芯的析锂量求和
        df_total_amount = np.sum(df_sn_liamount)#所有的析锂量
        df_sn_num = np.sum(df_num, axis = 1)#对该sn中每个电芯析锂次数的求和
        df_total_num = np.sum(df_sn_num)#所有的析锂次数
        df_level_temp = df_sn_liamount.tolist()[:]
        df_temp_level = np.array(df_level_temp)
        df_temp_level[df_temp_level < 30] = 0
        df_temp_level[(df_temp_level >= 30) & (df_temp_level < 60)] = 1
        df_temp_level[(df_temp_level >= 60) & (df_temp_level < 90)] = 2
        df_temp_level[(df_temp_level >= 90) & (df_temp_level < 120)] = 3
        df_temp_level[(df_temp_level >= 120) & (df_temp_level < 180)] = 4
        df_temp_level[(df_temp_level >= 180) & (df_temp_level <240)] = 5
        df_temp_level[df_temp_level >= 240] = 6
        df_level = np.sum(df_temp_level)#电池包析锂量评级
        df_liplt_num = df_sn_num.tolist()
        df_liamount_list = df_sn_liamount.tolist()
        df_liplated_level = df_temp_level.tolist()
        df_warnning_level = df_level.tolist()
        df_lipltd_confir_level = pd.DataFrame({"sn":[item],"total_num":[df_total_num],"total_amount":[df_total_amount],"liplt_num":[str(df_liplt_num)], "liplated_amount":[str(df_liamount_list)], "liplated_level":[str(df_liplated_level)],"warnning_level":[str(df_warnning_level)]})
        df_lipltd_level = df_lipltd_level.append(df_lipltd_confir_level)
        df_lipltd_level = df_lipltd_level.reset_index(drop = True)
        df_lipltd_level.sort_values(by = ['total_amount'], ascending=False,inplace=True)#对故障信息按照时间进行排序
    temp = file.split('\\')
    sn_name = temp[-1].split('.')[0]
    title = sn_name
    df_lipltd_level.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\02析锂分析\各项目析锂情况_211130\增加波峰高度与峰谷比计算结果\\'+ sn_name + '析锂排序.csv',index=False,encoding='GB18030')
        
        
        # df_li_amount = df_data_temp['liplated_amount']



    # #--------------------------------------------------------确认是否析锂----------------------------------------------------------------------------
    # # df_lipltd_data = pd.DataFrame(columns=['sn','date','liplated'])
    # for item in range(0,k):
    #     lipltd_confirm = []
    #     lipltd_amount = []
    #     df_check_liplated_temp = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == item].reset_index(drop = True)
    #     df_lipltd_volt_temp = df_check_liplated_temp[cellvolt_list]
    #     df_lipltd_volt_len = len(df_lipltd_volt_temp)
    #     df_data_temp_add = df_lipltd_volt_temp.iloc[df_lipltd_volt_len-4:df_lipltd_volt_len-1]
    #     df_lipltd_volt_temp_add = df_lipltd_volt_temp.append(df_data_temp_add)
    #     df_lipltd_volt_temp_dif = np.diff(df_lipltd_volt_temp_add,axis=0)#电压一次微分，计算dv/dt
    #     df_lipltd_volt_temp_dif = pd.DataFrame(df_lipltd_volt_temp_dif)
    #     df_lipltd_volt_temp_dif.columns = cellvolt_list
    #     df_lipltd_volt_temp_difdif = np.diff(df_lipltd_volt_temp_dif,axis=0)#电压二次微分，判断升降
    #     df_lipltd_volt_temp_difdif = pd.DataFrame(df_lipltd_volt_temp_difdif)
    #     df_lipltd_volt_temp_difdif.columns = cellvolt_list
    #     df_lipltd_volt_temp_difdif_temp = df_lipltd_volt_temp_difdif
    #     df_lipltd_volt_temp_difdif_temp[df_lipltd_volt_temp_difdif_temp >= 0] = 1
    #     df_lipltd_volt_temp_difdif_temp[df_lipltd_volt_temp_difdif_temp < 0] = -1
    #     df_lipltd_volt_temp_difdifdif = np.diff(df_lipltd_volt_temp_difdif_temp,axis=0)#三次微分，利用-2，2判断波分和波谷
    #     df_lipltd_volt_difdifdif = pd.DataFrame(df_lipltd_volt_temp_difdifdif)
    #     df_lipltd_volt_difdifdif.columns = cellvolt_list
    #     df_lipltd_volt_difdifdif['chrgr_rest'] = k
    #     df_lipltd_volt_difdifdif['时间戳'] = list(df_check_liplated_temp['时间戳'])
    #     df_lipltd_volt_difdifdif = df_lipltd_volt_difdifdif.reset_index(drop = True)
    #     df_lipltd_data_temp = df_lipltd_volt_difdifdif.loc[df_lipltd_volt_difdifdif['时间戳'] < (df_check_liplated_temp['时间戳'][0] + datetime.timedelta(minutes=90))]
    #     for cell_name in cellvolt_list:#对每个电芯判断
    #         df_check_plated_data = df_lipltd_data_temp[cell_name]
    #         peak_pos = np.where(df_check_plated_data == -2)
    #         bot_pos = np.where(df_check_plated_data == 2)
    #         if len(peak_pos[0]) & len(bot_pos[0]):
    #             if (peak_pos[0][0] > bot_pos[0][0]) & (df_lipltd_volt_temp_dif[cell_name][peak_pos[0][0] + 1] < 0):
    #                 lipltd_confirm.append(1)#1为析锂，0为非析锂
    #                 lipltd_amount.append((df_check_liplated_temp['时间戳'][bot_pos[0][0] + 2] - df_check_liplated_temp['时间戳'][0])/pd.Timedelta(1, 'min'))
    #             else:
    #                 lipltd_confirm.append(0)
    #                 lipltd_amount.append(0)
    #         else:
    #             lipltd_confirm.append(0)
    #             lipltd_amount.append(0)
    #     if any(lipltd_confirm) & (max(lipltd_amount) > 5):
    #         df_lipltd_confir_temp = pd.DataFrame({"sn":['sn'], "time":[df_check_liplated_temp['时间戳'][0]], "liplated":[str(lipltd_confirm)], "liplated_amount":[str(lipltd_amount)]})
    #         df_lipltd_data = df_lipltd_data.append(df_lipltd_confir_temp)
    #         df_lipltd_data = df_lipltd_data.reset_index(drop = True)
    #         df_lipltd_data.sort_values(by = ['time'], axis = 0, ascending=True,inplace=True)#对故障信息按照时间进行排序
    #         df_lipltd_data.to_csv(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\析锂检测.csv',index=False,encoding='GB18030')
    
    # # for i in range(1,k):
    # #     # fig = plt.figure(figsize=(20,10))
    # #     df_fig = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == i]
    # #     df_fig = df_fig.reset_index(drop=True)
    # #     length = len(df_fig)
    # #     for volt_num in cellvolt_name[5:6]:
    # #         # plt.scatter([x for x in range(0, length)], df_fig[volt_num][0:length], label=volt_num)
    # #         plt.plot(df_fig['时间戳'], df_fig[volt_num],linewidth = 2, linestyle = '-', marker = 's',label=volt_num)
    # #     plt.rcParams['font.family']=['SimHei']
    # #     plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
    # #     plt.legend()
    # #     temp = file.split('\\')
    # #     sn_name = temp[9].split('.')[0]
    # #     title = sn_name + '-' + df_fig['时间戳'][0].strftime('%y-%m-%d')#df_fig['device_id'][0] + '-' + 
    # #     print(type(title))
    # #     plt.xlabel('时间', fontsize=14)
    # #     plt.ylabel('电压的时间微分(mv/min)', fontsize=14)
    # #     plt.xlim(df_fig['时间戳'][0]-datetime.timedelta(minutes=5),df_fig['时间戳'][0] + datetime.timedelta(hours=3))
    # #     # plt.ylim(-2.5,0.5)
    # #     plt.title(title)
    # #     plt.savefig(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\\'+title+'.png', dpi=300)
    # #     # plt.show()
    # fig = plt.figure(figsize=(20,10))
    # for i in range(3,4):#0,k
    #     # fig = plt.figure(figsize=(20,10))
    #     df_fig = df_rest_volt_diffdt.loc[df_rest_volt_diffdt['chrgr_rest'] == i]
    #     df_fig = df_fig.reset_index(drop=True)
    #     length = len(df_fig)
    #     temp = file.split('\\')
    #     sn_name = temp[9].split('.')[0]
    #     title = sn_name + '-' + df_fig['时间戳'][0].strftime('%y-%m-%d')#df_fig['device_id'][0] + '-' + 
    #     plt_data_len = len(df_fig)
    #     # for volt_num in cellvolt_name[5:6]:
    #         # plt.scatter([x for x in range(0, length)], df_fig[volt_num][0:length], label=volt_num)
    #     # plt.plot([x for x in range(0, plt_data_len)], df_fig[cellvolt_name[5]],linewidth = 2, linestyle = '-', marker = 's',label = title)
    #     # plt.plot([x for x in range(0, plt_data_len)], df_fig[cellvolt_name[2]],linewidth = 2, linestyle = '-', marker = 's',label = title)
    #     plt.plot(df_fig['时间戳'], df_fig[cellvolt_name[5]],linewidth = 2, linestyle = '-', marker = 's',label = cellvolt_name[5])
    #     mpl.rcParams['font.sans-serif']=['SimHei']
    #     mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号
    #     plt.legend()
    #     plt.xlabel('采样点', fontsize=14)
    #     plt.ylabel('电压的时间微分(mv/min)', fontsize=14)
    #     plt.xlim(df_fig['时间戳'][0]-datetime.timedelta(minutes=5),df_fig['时间戳'][0] + datetime.timedelta(hours=5))
    #     # plt.xlim(0, 60)
    #         # plt.ylim(-2.5,0.5)
    #     plt.title('6#电芯不同时间电压微分曲线')
    #     plt.savefig(r'D:\Work\Code_write\data_analyze_platform\USER\lzx\01算法开发\02析锂检测\01下载数据\MGMCLN750N215N049\\'+'6#电芯析锂'+'.png', dpi=300)
    # # plt.show()