docs: def simple_profile(self, annual_demand, **kwargs):
          """
          Create industrial load profile
  
          Parameters
          ----------
          annual_demand : float
              Total demand.
  
          Other Parameters
          ----------------
          am : datetime.time
              beginning of workday
          pm : datetime.time
              end of workday
          week : list
              list of weekdays
          weekend : list
              list of weekend days
          profile_factors : dictionary
              dictionary with scaling factors for night and day of weekdays and
              weekend days
          """
  
          # Day(am to pm), night (pm to am), week day (week),
          # weekend day (weekend)
          am = kwargs.get('am', settime(7, 00, 0))
          pm = kwargs.get('pm', settime(23, 30, 0))
  
          week = kwargs.get('week', [1, 2, 3, 4, 5])
          weekend = kwargs.get('weekend', [0, 6, 7])
  
          default_factors = {'week': {'day': 0.8, 'night': 0.6},
                             'weekend': {'day': 0.9, 'night': 0.7}}
  
          profile_factors = kwargs.get('profile_factors', default_factors)
  
          self.dataframe['ind'] = 0
  
          self.dataframe['ind'].mask(
              self.dataframe['weekday'].between_time(am, pm).isin(week),
              profile_factors['week']['day'], True)
          self.dataframe['ind'].mask(
              self.dataframe['weekday'].between_time(pm, am).isin(week),
              profile_factors['week']['night'], True)
          self.dataframe['ind'].mask(
              self.dataframe['weekday'].between_time(am, pm).isin(weekend),
              profile_factors['weekend']['day'], True)
          self.dataframe['ind'].mask(
              self.dataframe['weekday'].between_time(pm, am).isin(weekend),
              profile_factors['weekend']['night'], True)
  
          if self.dataframe['ind'].isnull().any(axis=0):
              logging.error('NAN value found in industrial load profile')
  
          time_interval = self.dataframe.index.freq.nanos / 3.6e12
  
          return (self.dataframe['ind'] / self.dataframe['ind'].sum() *
                  annual_demand / time_interval)