Correction in raised error

Author: SabineHaas

windpowerlib/turbine_cluster_modelchain.py

@@ -91,113 +91,11 @@ def __init__(self, wind_object, wake_losses_method='wind_efficiency_curve',
 
         if (isinstance(self.wind_object, wind_farm.WindFarm) and
                 self.smoothing_order == 'cluster_power_curve'):
-            raise ValueError("`smoothing_order` can only be " +
+            raise ValueError("`smoothing_order` can only be done to" +
                              "'cluster_power_curve' if you calculate a " +
                              "cluster but `wind_object` is an object of the " +
                              "class WindFarm.")
 
-    def wind_farm_power_curve(self, wind_farm, **kwargs):
-        r"""
-        Caluclates the power curve of a wind farm.
-
-        Depending on the parameters of the class the power curves are smoothed
-        and/or density corrected before or after the summation and/or a wind
-        farm efficiency is applied after the summation. TODO: check entry
-
-        Parameters
-        ----------
-        wind_farm : WindFarm
-            A :class:`~.wind_farm.WindFarm` object representing the wind farm.
-
-        Other Parameters
-        ----------------
-        roughness_length : Float, optional.
-            Roughness length.
-        turbulence_intensity : Float, optional.
-            Turbulence intensity.
-
-        Returns
-        -------
-        summarized_power_curve_df : pd.DataFrame
-            Calculated power curve of the wind farm.
-
-        """
-        # Initialize data frame for power curve values
-        df = pd.DataFrame()
-        for turbine_type_dict in wind_farm.wind_turbine_fleet:
-            # Check if all needed parameters are available
-            if self.smoothing:
-                if (self.standard_deviation_method ==
-                        'turbulence_intensity' and
-                        'turbulence_intensity' not in kwargs):
-                    if 'roughness_length' in kwargs:
-                        # Calculate turbulence intensity and write to kwargs
-                        turbulence_intensity = (
-                            tools.estimate_turbulence_intensity(
-                                turbine_type_dict['wind_turbine'].hub_height,
-                                kwargs['roughness_length']))
-                        kwargs['turbulence_intensity'] = turbulence_intensity
-                    else:
-                        raise ValueError(
-                            "`roughness_length` must be defined for using " +
-                            "'turbulence_intensity' as " +
-                            "`standard_deviation_method` if " +
-                            "`turbulence_intensity` is not given")
-            if self.wake_losses_method is not None:
-                if wind_farm.efficiency is None:
-                    raise KeyError(
-                        "wind_farm_efficiency is needed if " +
-                        "`wake_losses_method´ is '{0}', but ".format(
-                            self.wake_losses_method) +
-                        " `wind_farm_efficiency` of {0} is {1}.".format(
-                            self.wind_object.object_name,
-                            self.wind_object.efficiency))
-            # Get original power curve
-            power_curve = pd.DataFrame(
-                turbine_type_dict['wind_turbine'].power_curve)
-            # Editions to power curve before the summation
-            if (self.smoothing and
-                    self.smoothing_order == 'turbine_power_curves'):
-                power_curve = power_curves.smooth_power_curve(
-                    power_curve['wind_speed'], power_curve['power'],
-                    standard_deviation_method=self.standard_deviation_method,
-                    **kwargs)
-            # Add power curves of all turbines of 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(  # TODO: merge without renaming
-                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']
-        # Return wind speed (index) to a column of the data frame
-        summarized_power_curve_df = pd.DataFrame(
-            data=[list(summarized_power_curve.index),
-                  list(summarized_power_curve['power'].values)]).transpose()
-        summarized_power_curve_df.columns = ['wind_speed', 'power']
-        # Editions to power curve after the summation
-        if (self.smoothing and
-                self.smoothing_order == 'wind_farm_power_curves'):
-            summarized_power_curve_df = power_curves.smooth_power_curve(
-                summarized_power_curve_df['wind_speed'],
-                summarized_power_curve_df['power'],
-                standard_deviation_method=self.standard_deviation_method,
-                **kwargs)
-        if (self.wake_losses_method == 'constant_efficiency' or
-                self.wake_losses_method == 'wind_efficiency_curve'):
-            summarized_power_curve_df = (
-                power_curves.wake_losses_to_power_curve(
-                    summarized_power_curve_df['wind_speed'].values,
-                    summarized_power_curve_df['power'].values,
-                    wake_losses_method=self.wake_losses_method,
-                    wind_farm_efficiency=self.wind_object.efficiency))
-        return summarized_power_curve_df
-
     def turbine_cluster_power_curve(self, **kwargs):
         r"""
         Caluclates the power curve of a wind turbine cluster.