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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 assign_power_curve(self, wake_losses_model='power_efficiency_curve', smoothing=False, block_width=0.5, standard_deviation_method='turbulence_intensity', smoothing_order='wind_farm_power_curves', turbulence_intensity=None, **kwargs):
 r""" Calculates the power curve of a wind turbine cluster. The turbine cluster power curve is calculated by aggregating the wind farm power curves of wind farms within the turbine cluster. Depending on the parameters the power curves are smoothed (before or after the aggregation) and/or a wind farm efficiency is applied before the aggregation. After the calculations the power curve is assigned to the attribute `power_curve`. Parameters wake_losses_model : string Defines the method for taking wake losses within the farm into consideration. Options: 'power_efficiency_curve', 'constant_efficiency' or None. Default: 'power_efficiency_curve'. smoothing : boolean If True the power curves will be smoothed before or after the aggregation of power curves depending on `smoothing_order`. Default: False. block_width : float Width between the wind speeds in the sum of the equation in :py:func:`~.power_curves.smooth_power_curve`. Default: 0.5. standard_deviation_method : string Method for calculating the standard deviation for the Gauss distribution. Options: 'turbulence_intensity', 'Staffell_Pfenninger'. Default: 'turbulence_intensity'. smoothing_order : string Defines when the smoothing takes place if `smoothing` is True. Options: 'turbine_power_curves' (to the single turbine power curves), 'wind_farm_power_curves'. Default: 'wind_farm_power_curves'. turbulence_intensity : float Turbulence intensity at hub height of the wind farm or wind turbine cluster for power curve smoothing with 'turbulence_intensity' method. Can be calculated from `roughness_length` instead. Default: None. Other Parameters roughness_length : float, optional. Roughness length. If `standard_deviation_method` is 'turbulence_intensity' and `turbulence_intensity` is not given the turbulence intensity is calculated via the roughness length. Returns ------- self """

        # Assign wind farm power curves to wind farms of wind turbine cluster
        for farm in self.wind_farms:
            # Assign hub heights (needed for power curve and later for
            # hub height of turbine cluster)
            farm.mean_hub_height()
            # Assign wind farm power curve
            farm.assign_power_curve(
                wake_losses_model=wake_losses_model,
                smoothing=smoothing, block_width=block_width,
                standard_deviation_method=standard_deviation_method,
                smoothing_order=smoothing_order,
                turbulence_intensity=turbulence_intensity, **kwargs)
        # Create data frame from power curves of all wind farms
        df = pd.concat([farm.power_curve.set_index(['wind_speed']).rename(
            columns={'value': farm.name}) for
            farm in self.wind_farms], axis=1)
        # Sum up power curves
        cluster_power_curve = pd.DataFrame(
            df.interpolate(method='index').sum(axis=1))
        cluster_power_curve.columns = ['value']
        # Return wind speed (index) to a column of the data frame
        cluster_power_curve.reset_index('wind_speed', inplace=True)
        self.power_curve = cluster_power_curve
        return self