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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 select_transformers(self):
 """ Selects appropriate transformers for the HV-MV substation. The transformers are chosen according to max. of load case and feedin-case considering load factors. The HV-MV transformer with the next higher available nominal apparent power is chosen. If one trafo is not sufficient, multiple trafos are used. Additionally, in a second step an redundant trafo is installed with max. capacity of the selected trafos of the first step according to general planning principles for MV distribution grids (n-1). Parameters transformers : dict Contains technical information of p hv/mv transformers **kwargs : dict Should contain a value behind the key 'peak_load' Notes ----- Parametrization of transformers bases on [#]_. Potential hv-mv-transformers are chosen according to [#]_. References .. [#] Deutsche Energie-Agentur GmbH (dena), "dena-Verteilnetzstudie. Ausbau- und Innovationsbedarf der Stromverteilnetze in Deutschland bis 2030.", 2012 .. [#] X. Tao, "Automatisierte Grundsatzplanung von Mittelspannungsnetzen", Dissertation, 2006 """


        # get power factor for loads and generators
        cos_phi_load = cfg_ding0.get('assumptions', 'cos_phi_load')
        cos_phi_feedin = cfg_ding0.get('assumptions', 'cos_phi_gen')

        # get trafo load factors
        load_factor_mv_trans_lc_normal = float(cfg_ding0.get('assumptions',
                                                             'load_factor_mv_trans_lc_normal'))
        load_factor_mv_trans_fc_normal = float(cfg_ding0.get('assumptions',
                                                             'load_factor_mv_trans_fc_normal'))

        # get equipment parameters of MV transformers
        trafo_parameters = self.grid.network.static_data['MV_trafos']

        # get peak load and peak generation
        cum_peak_load = self.peak_load / cos_phi_load
        cum_peak_generation = self.peak_generation(mode='MVLV') / cos_phi_feedin

        # check if load or generation is greater respecting corresponding load factor
        if (cum_peak_load / load_factor_mv_trans_lc_normal) > \
           (cum_peak_generation / load_factor_mv_trans_fc_normal):
            # use peak load and load factor from load case
            load_factor_mv_trans = load_factor_mv_trans_lc_normal
            residual_apparent_power = cum_peak_load
        else:
            # use peak generation and load factor for feedin case
            load_factor_mv_trans = load_factor_mv_trans_fc_normal
            residual_apparent_power = cum_peak_generation

        # determine number and size of required transformers

        # get max. trafo
        transformer_max = trafo_parameters.iloc[trafo_parameters['S_nom'].idxmax()]

        while residual_apparent_power > 0:
            if residual_apparent_power > load_factor_mv_trans * transformer_max['S_nom']:
                transformer = transformer_max
            else:
                # choose trafo
                transformer = trafo_parameters.iloc[
                    trafo_parameters[trafo_parameters['S_nom'] * load_factor_mv_trans >
                                     residual_apparent_power]['S_nom'].idxmin()]

            # add transformer on determined size with according parameters
            self.add_transformer(TransformerDing0(**{'grid': self.grid,
                                                     'v_level': self.grid.v_level,
                                                     's_max_longterm': transformer['S_nom']}))
            # calc residual load
            residual_apparent_power -= (load_factor_mv_trans *
                                        transformer['S_nom'])

        # if no transformer was selected (no load in grid district), use smallest one
        if len(self._transformers) == 0:
            transformer = trafo_parameters.iloc[trafo_parameters['S_nom'].idxmin()]

            self.add_transformer(
                TransformerDing0(grid=self.grid,
                                 v_level=self.grid.v_level,
                                 s_max_longterm=transformer['S_nom']))

        # add redundant transformer of the size of the largest transformer
        s_max_max = max((o.s_max_a for o in self._transformers))
        self.add_transformer(TransformerDing0(**{'grid': self.grid,
                                                 'v_level': self.grid.v_level,
                                                 's_max_longterm': s_max_max}))