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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)