Delete unused lines

Author: SabineHaas

windpowerlib/power_output.py

@@ -427,52 +427,3 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
                 wake_losses_method) +
             "'constant_efficiency' or 'wind_efficiency_curve'")
     return power_curve_df
-
-
-#def summarize_power_curves(wind_turbine_fleet): # TODO different parameter (also for cluster)
-#    # TODO: with pandas or without pandas?
-#    r"""
-#    Creates a power curve for a wind turbine fleet.
-#
-#    Power curve is created by summing up all power curves.
-#
-#    Parameters
-#    ----------
-#    power_curves : list
-#        Contains power curves that are to be summarized. The form of each power
-#        curve is a list: [power_curve_wind_speeds, power_curve_values].
-#
-#    Returns
-#    -------
-#    summarized_power_curve: pd.DataFrame
-#        Summarized power curve. DataFrame has 'wind_speed' and
-#        'power' columns with wind speeds in m/s and the corresponding power
-#        curve value in W.
-#
-#    """
-#    # Initialize data frame for power curve values
-#    df = pd.DataFrame()
-#    for turbine_type_dict in wind_turbine_fleet:
-#        # Get original power curve
-#        power_curve = pd.DataFrame(
-#            turbine_type_dict['wind_turbine'].power_curve) # TODO necessary, TODO: copy problem
-#        # Add power curve scaled with the amount of turbines of the same type
-#        # to data frame after renaming columns
-#        power_curve.columns = ['wind_speed',
-#                               turbine_type_dict['wind_turbine'].object_name]
-#        df = pd.concat([df, pd.DataFrame(
-#            power_curve.set_index(['wind_speed']) *
-#            turbine_type_dict['number_of_turbines'])], axis=1)
-#    # Rename back TODO: copy()
-#    power_curve.columns = ['wind_speed', 'power']
-#    # Sum up power curves of all turbine types
-#    summarized_power_curve = pd.DataFrame(
-#        sum(df[item].interpolate(method='index') for item in list(df)))
-#    summarized_power_curve.columns = ['power'] # TODO is this a df?
-#    # Create DataFrame of the above power curve data # TODO: necessary?
-#    summarized_power_curve_df = pd.DataFrame(
-#        data=[list(summarized_power_curve.index),
-#              list(summarized_power_curve['power'].values)]).transpose()
-#    # Rename columns of DataFrame
-#    summarized_power_curve_df.columns = ['wind_speed', 'power']
-#    return summarized_power_curve_df

windpowerlib/wind_farm_modelchain.py

@@ -107,11 +107,6 @@ def wind_farm_power_curve(self, **kwargs):
         self
 
         """
-        # Create kwargs # TODO: not needed see below delete!
-        # if 'turbulence_intensity' in kwargs:
-        #     kwargs['turbulence_intensity'] = kwargs['turbulence_intensity']
-        # if 'roughness_length' in kwargs:
-        #     kwargs['roughness_length'] = kwargs['roughness_length']
         # Initialize data frame for power curve values
         df = pd.DataFrame()
         for turbine_type_dict in self.wind_farm.wind_turbine_fleet:
@@ -187,12 +182,6 @@ def wind_farm_power_curve(self, **kwargs):
                     wake_losses_method=self.wake_losses_method,
                     wind_farm_efficiency=self.wind_farm.efficiency))
         self.wind_farm.power_curve = summarized_power_curve_df
-    #        self.wind_farm.power_curve = power_output.summarized_power_curve(
-    #            self.wind_farm.wind_turbine_fleet, smoothing=self.smoothing,
-    #            density_correction=self.density_correction,
-    #            wake_losses_method=self.wake_losses_method,
-    #            block_width=self.block_width,
-    #            standard_deviation_method=self.standard_deviation_method, **kwargs)
         return self
 
     def get_modelchain_data(self, **kwargs):