##修订电池的状态码，处理对象为清洗后并合并清洗后合并gps的数据
import pandas as pd
import numpy as np



def stand_status(df_merge,s_order_delta=600,lon_delta=2):
    ##构建状态的flag
    ##构建连续的flag标记为flag_block
    df_merge=df_merge.sort_values(["sn","time"],ascending = [True, True])
    df_merge["pack_crnt2"]=df_merge["pack_crnt"].copy()
    df_merge["pack_crnt2"].fillna(8888,inplace=True)
    df_merge['stand_block']=(df_merge["pack_crnt2"].shift(1) != 8888).astype(int).cumsum()
    ##首先统计在每个flag区间上的开始结束时间
    df_merge_time_b=df_merge[["time","sn","stand_block"]].groupby(["sn","stand_block"]).first()
    df_merge_time_e=df_merge[["time","sn","stand_block"]].groupby(["sn","stand_block"]).last()
    df_merge_longitude_min=df_merge[["longitude","sn","stand_block"]].groupby(["sn","stand_block"]).min()
    df_merge_longitude_max=df_merge[["longitude","sn","stand_block"]].groupby(["sn","stand_block"]).max()
    frames=[df_merge_time_b,df_merge_time_e,df_merge_longitude_min,df_merge_longitude_max]
        
    df_merge_choice = pd.concat(frames, axis=1, join='inner')
    df_merge_choice=df_merge_choice.reset_index()
        
    df_merge_choice.columns=["sn","stand_block","time_first","time_last","lon_min","lon_max"]
        
    df_merge_choice["order_delta"]=pd.to_timedelta(pd.to_datetime(df_merge_choice["time_last"])-pd.to_datetime(df_merge_choice["time_first"])).dt.total_seconds()
    df_merge_choice["order_delta_h"]=round(df_merge_choice["order_delta"]/3600,2)
    df_merge_choice["lon_delta"]=df_merge_choice["lon_max"] - df_merge_choice["lon_min"]         
    
    df_merge_choice_result1=df_merge_choice[(df_merge_choice["order_delta"]>=s_order_delta)&(df_merge_choice["lon_delta"]<=lon_delta)]  

    df_merge21=df_merge[ (df_merge["sn"].isin(df_merge_choice_result1["sn"]))&(df_merge["stand_block"].isin(df_merge_choice_result1["stand_block"])) ]
    
    df_merge=df_merge[~df_merge.index.isin(df_merge21.index)]
    
    df_merge=df_merge.drop(['pack_crnt2','stand_block'], axis=1, inplace=False)

    return df_merge