def parse_time_v2(pd, np, df, data):
   import time 
   import calendar
    
   DT = []
   ET = []
   DoY = []
   
   ds = df.loc[:, 'Date':'Date':1].values #extract date from data frame
   
   for i in range(0, len(ds)):
      tt = time.strptime(str(ds[i]), "['%d/%m/%Y %H:%M']")
      #DoY
      DoY.append(float(tt[7]) + ((((float(tt[5])/60) + float(tt[4]))/60) + float(tt[3]))/24) 
      #ET
      ET.append(int(calendar.timegm(tt)))
      #DT
      DT.append(tt[0:6])
		       
   data.DT = np.array(DT)
   data.DoY = np.array(DoY)
   data.ET = np.array(ET)
    
   return data
   
def parse_data_v2(pd, np, df, data):
   #remove any nans from data
   df["O3_ppb"].fillna(-1.00e+20, inplace = True) 
   
   o3 = df.loc[:, 'O3_ppb':'O3_ppb':1].values
   flag = df.loc[:, 'O3_Flag':'O3_Flag':1].values
   
   data.o3 = np.array(o3)
   data.flag = np.array(flag)
   
   #flag any data < 0 cannot have a -'ve gas concentration
   ii = np.where(data.o3 <= 0)
   data.flag[ii] = 2
   ii = np.where(data.o3 > 9000)
   data.flag[ii] = 2
		       
   return data
b6b718bbd2e52ad6e23c43386f54a3ffc80110cf
<mask token>

def parse_time_v2(pd, np, df, data):
    import time
    import calendar
    DT = []
    ET = []
    DoY = []
    ds = df.loc[:, 'Date':'Date':1].values
    for i in range(0, len(ds)):
        tt = time.strptime(str(ds[i]), "['%d/%m/%Y %H:%M']")
        DoY.append(float(tt[7]) + ((float(tt[5]) / 60 + float(tt[4])) / 60 +
            float(tt[3])) / 24)
        ET.append(int(calendar.timegm(tt)))
        DT.append(tt[0:6])
    data.DT = np.array(DT)
    data.DoY = np.array(DoY)
    data.ET = np.array(ET)
    return data


<mask token>

def parse_time_v2(pd, np, df, data):
    import time
    import calendar
    DT = []
    ET = []
    DoY = []
    ds = df.loc[:, 'Date':'Date':1].values
    for i in range(0, len(ds)):
        tt = time.strptime(str(ds[i]), "['%d/%m/%Y %H:%M']")
        DoY.append(float(tt[7]) + ((float(tt[5]) / 60 + float(tt[4])) / 60 +
            float(tt[3])) / 24)
        ET.append(int(calendar.timegm(tt)))
        DT.append(tt[0:6])
    data.DT = np.array(DT)
    data.DoY = np.array(DoY)
    data.ET = np.array(ET)
    return data


def parse_data_v2(pd, np, df, data):
    df['O3_ppb'].fillna(-1e+20, inplace=True)
    o3 = df.loc[:, 'O3_ppb':'O3_ppb':1].values
    flag = df.loc[:, 'O3_Flag':'O3_Flag':1].values
    data.o3 = np.array(o3)
    data.flag = np.array(flag)
    ii = np.where(data.o3 <= 0)
    data.flag[ii] = 2
    ii = np.where(data.o3 > 9000)
    data.flag[ii] = 2
    return data

def parse_time_v2(pd, np, df, data):
   import time 
   import calendar
    
   DT = []
   ET = []
   DoY = []
   
   ds = df.loc[:, 'Date':'Date':1].values #extract date from data frame
   
   for i in range(0, len(ds)):
      tt = time.strptime(str(ds[i]), "['%d/%m/%Y %H:%M']")
      #DoY
      DoY.append(float(tt[7]) + ((((float(tt[5])/60) + float(tt[4]))/60) + float(tt[3]))/24) 
      #ET
      ET.append(int(calendar.timegm(tt)))
      #DT
      DT.append(tt[0:6])
		       
   data.DT = np.array(DT)
   data.DoY = np.array(DoY)
   data.ET = np.array(ET)
    
   return data
   
def parse_data_v2(pd, np, df, data):
   #remove any nans from data
   df["O3_ppb"].fillna(-1.00e+20, inplace = True) 
   
   o3 = df.loc[:, 'O3_ppb':'O3_ppb':1].values
   flag = df.loc[:, 'O3_Flag':'O3_Flag':1].values
   
   data.o3 = np.array(o3)
   data.flag = np.array(flag)
   
   #flag any data < 0 cannot have a -'ve gas concentration
   ii = np.where(data.o3 <= 0)
   data.flag[ii] = 2
   ii = np.where(data.o3 > 9000)
   data.flag[ii] = 2
		       
   return data