from re import X
import pandas as pd
import numpy as np
from pandas.core.frame import DataFrame
import BatParam
import pandas as pd
# 计算充电过程
def preprocess(df):
    # 滤除前后电压存在一增一减的情况（采样异常）
    pass

# 计算SOC变化率
def cal_volt_change(dfin, volt_column):

    df = dfin.copy()
    df_volt_rolling = df[volt_column]

    df_volt_rolling_sum=df_volt_rolling.sum(1)-df_volt_rolling.max(1)
    df_volt_rolling_sum=df_volt_rolling_sum-df_volt_rolling.min(1)
    mean1 = df_volt_rolling_sum/(len(volt_column)-2)
    df_volt_rolling_norm = df_volt_rolling.sub(mean1, axis=0)#.div(std,axis=0)
    df_volt_rolling_norm = df_volt_rolling_norm.reset_index(drop=True)#和均值的距离

    return df_volt_rolling_norm

# 计算电压离群
def cal_volt_sigma(dfin, volt_column):

    df = dfin.copy()
    df_volt_rolling = df[volt_column]

    mean1=df_volt_rolling.mean(axis=1)
    std = df_volt_rolling.std(axis=1)
    std = std.replace(0,0.000001)
    df_volt_rolling = df_volt_rolling.sub(mean1, axis=0).div(std,axis=0)
    df_volt_rolling = df_volt_rolling.reset_index(drop=True)#分布

    return df_volt_rolling


# # 计算电压变化量的偏离度
# def cal_voltdiff_uniform(dfin, volt_column, window=10, step=5, window2=10, step2=5,threshold=3):

#     df = dfin.copy()
#     time_list = dfin['time'].tolist()

#     # 电压滤波
#     df_volt = df[volt_column]
#     df_volt_rolling = df_volt.rolling(window).mean()[window-1::step]  # 滑动平均值
#     time_list = time_list[window-1::step]

#     # 计算电压变化量的绝对值（# 计算前后的差值的绝对值，  时间列-1）
#     df_volt_diff = abs(df_volt_rolling.diff()[1:])
#     df_volt_diff = df_volt_diff.reset_index(drop=True)
#     time_list = time_list[1:]

#     # 压差归一化（偏离度）
#     # mean = df_volt_diff.mean(axis=1)
#     # std = df_volt_diff.std(axis=1)
#     # df_voltdiff_norm = df_volt_diff.sub(mean, axis=0).div(std,axis=0)
#     df_voltdiff_norm = df_volt_diff.copy()

#     # 压差偏离度滑动平均滤波
#     df_voltdiff_rolling = df_voltdiff_norm.rolling(window2).mean()[window2-1::step2]  # 滑动平均值
#     time_list = time_list[window2-1::step2]
#     df_voltdiff_rolling_sum=df_voltdiff_rolling.sum(1)-df_voltdiff_rolling.max(1)
#     df_voltdiff_rolling_sum=df_voltdiff_rolling_sum-df_voltdiff_rolling.min(1)
#     mean = df_voltdiff_rolling_sum/(len(volt_column)-2)
#     std = df_voltdiff_rolling.std(axis=1)
#     # mean = [np.array(sorted(x)[1:-1]).mean() for x in df_voltdiff_rolling.values]
#     # std = [np.array(sorted(x)[1:-1]).std() for x in df_voltdiff_rolling.values]
#     df_voltdiff_rolling_norm = df_voltdiff_rolling.sub(mean, axis=0)#.div(std,axis=0)
#     df_voltdiff_rolling_norm = df_voltdiff_rolling_norm.reset_index(drop=True)
#     return df_voltdiff_rolling_norm, time_list
def main(sn,df_bms,celltype):
    param=BatParam.BatParam(celltype)
    df_bms['PackCrnt']=df_bms['PackCrnt']*param.PackCrntDec
    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
    volt_column = ['CellVolt'+str(i) for i in range(1,param.CellVoltNums+1)]
    columns=['time']+volt_column
    df_bms=df_bms[(df_bms['PackSOC']>10)]
    # df_bms=df_bms[(df_bms['PackCrnt']<1)]
    # df_chrg=df_bms[(df_bms['PackCrnt']<-1)]

    #电压/SOC变化率计算
    if celltype<50:
        df_ori = df_bms[columns]
        df = df_ori.drop_duplicates(subset=['time']) # 删除时间相同的数据
        df= df.set_index('time')
        df=df[(df[volt_column]>2) & (df[volt_column]<4.5)]
        df[volt_column]=pd.DataFrame(df[volt_column],dtype=np.float)
        df=df.resample('H').mean()  #取一个小时的平均值
        df=df.dropna(how='any')
        time_list1=df.index.tolist()

        fun=lambda x: np.interp(x, param.LookTab_OCV, param.LookTab_SOC)
        df_soc=df.applymap(fun)

        VolChng = cal_volt_change(df_soc,volt_column)
    else:
        # df_bms=df_bms[(df_bms['PackCrnt']>-0.1) & (df_bms['PackCrnt']<0.1)]
        df_ori = df_bms[columns]
        df = df_ori.drop_duplicates(subset=['time']) # 删除时间相同的数据
        df= df.set_index('time')
        df=df[(df[volt_column]>3.2) & (df[volt_column]<3.4)]
        df[volt_column]=pd.DataFrame(df[volt_column],dtype=np.float)
        df=df.resample('H').mean()  #取一个小时的平均值
        df=df.dropna(how='any')
        time_list1=df.index.tolist()

        VolChng = cal_volt_change(df,volt_column)


    VolSigma = cal_volt_sigma(df,volt_column)

    OutLineVol=DataFrame(columns=['time','sn','VolOl_Uni','VolChng_Uni'])

    #静置电压变化率和离群度计算
    if len(VolChng)>5 and len(VolSigma)>5:
        VolChng['time'] = time_list1
        VolChng= VolChng.set_index('time')
        VolChng_Uni_result=VolChng.values.tolist()#改

        VolSigma['time'] = time_list1
        VolSigma= VolSigma.set_index('time')
        VolOl_Uni_result=VolSigma.values.tolist()#改

        for i in range(0,len(VolChng)):
            OutLineVol.loc[i,'VolOl_Uni']=str(list(np.around(VolOl_Uni_result[i],decimals=2)))
            OutLineVol.loc[i,'VolChng_Uni']=str(list(np.around(VolChng_Uni_result[i],decimals=2)))
        OutLineVol=OutLineVol[~OutLineVol['VolOl_Uni'].str.contains('nan')]
        OutLineVol=OutLineVol[~OutLineVol['VolChng_Uni'].str.contains('nan')]
        OutLineVol=OutLineVol.applymap((lambda x:''.join(x.split()) if type(x) is str else x))
        OutLineVol=OutLineVol.reset_index(drop=True)
        OutLineVol['time']= VolSigma.index
        OutLineVol['sn']=sn

    return(OutLineVol)