from ctypes import Structure
from re import M
import pandas as pd
import numpy as np
import datetime
import time, datetime
import matplotlib.pyplot as plt
from pylab import*
from scipy.signal import savgol_filter
from sklearn.linear_model import LinearRegression
import os
import log

    
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 

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

fig = plt.figure(figsize=(20,15))
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\析锂前后五电池的soh'
file_list = get_file()
a1 = []
b1 = []
for file in file_list:
    df_time = []
    data = pd.read_csv(file,encoding='GB18030')
        # fig = plt.figure(figsize=(20,10))
    df_fig = data
    df_fig = df_fig.reset_index(drop=True)
    length = len(df_fig)
    df_soh_temp = df_fig['soh']
    df_soh = savgol_filter(df_soh_temp, 11, 3)
    df_hms_temp = pd.to_datetime(df_fig['time_sp'])#转换时间格式
    # df_hms = df_hms_temp.dt.time#仅保留时分秒
    df_hms_fig = df_hms_temp.apply(lambda x:x.strftime('%Y-%m-%d')) 
    temp = file.split('\\')
    sn_name = temp[-1].split('.')[0]
    title = sn_name
    time_reshape = np.array(df_hms_temp).reshape(-1,1)
    soh_reshape = df_soh.reshape(-1,1)
    delta_time = (np.diff(df_hms_temp)/pd.Timedelta(1, 'days'))#计算时间差
    df_time_len = len(delta_time) 
    for i in range(df_time_len):
        df_time.append(sum(delta_time[0:i]))
    soh_reshape = np.delete(soh_reshape, [-1])
    # b1.append(lineModel.intercept_[0])#对应sn
        #新建一个线性回归模型，并把数据放进去对模型进行训练
    lineModel = LinearRegression()
    lineModel.fit(np.array(df_time).reshape(-1,1), soh_reshape)
    # a1.append(lineModel.coef_[0][0])#拟合系数
    fit_soh = lineModel.predict(np.array(df_time).reshape(-1,1))
    fit_soh = 100*fit_soh/fit_soh[0]
        # plt.scatter([x for x in range(0, length)], df_fig[volt_num][0:length], label=volt_num)
    plt.plot(df_time, fit_soh,linewidth = 2, linestyle = '-', marker = 's',label=title)
    mpl.rcParams['font.sans-serif']=['KaiTi']
    mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号
    plt.legend(fontsize=20)
    plt.xlabel('时间(天)', fontsize=26)
    plt.ylabel('soh随时间演化', fontsize=26)
    plt.xticks(range(1,len(df_time),5),rotation=45, fontsize=26)
    plt.yticks(fontsize=26)
    # plt.xlim((df_hms_temp[0]-datetime.timedelta(minutes=5)).dt.time,(df_hms_temp[0] + datetime.timedelta(hours=1.5)).dt.time)
    # plt.ylim(-2.5,0.5)
    plt.title('析锂量前5与后5的电池包soh演化', fontsize=28)
    # plt.show()
plt.savefig(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\soh图片\\'+'析锂量前5与后5电池包的soh.png', dpi=300)