Rename parameter for consistency with other parameters

Author: SabineHaas

windpowerlib/power_curves.py

@@ -115,7 +115,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
 
 
 def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
-                               wake_losses_method='power_efficiency_curve',
+                               wake_losses_model='power_efficiency_curve',
                                wind_farm_efficiency=None):
     r"""
     Applies wake losses depending on the method to a power curve.
@@ -128,7 +128,7 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
     power_curve_values : pandas.Series or numpy.array
         Power curve values corresponding to wind speeds in
         `power_curve_wind_speeds`.
-    wake_losses_method : String
+    wake_losses_model : String
         Defines the method for talking wake losses within the farm into
         consideration. Options: 'power_efficiency_curve',
         'constant_efficiency'. Default: 'power_efficiency_curve'.
@@ -157,19 +157,19 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
               list(power_curve_values)]).transpose()
     # Rename columns of DataFrame
     power_curve_df.columns = ['wind_speed', 'power']
-    if wake_losses_method == 'constant_efficiency':
+    if wake_losses_model == 'constant_efficiency':
         if not isinstance(wind_farm_efficiency, float):
             raise TypeError("'wind_farm_efficiency' must be float if " +
-                            "`wake_losses_method´ is '{}'".format(
-                                wake_losses_method))
+                            "`wake_losses_model´ is '{}'".format(
+                                wake_losses_model))
         power_curve_df['power'] = power_curve_values * wind_farm_efficiency
-    elif wake_losses_method == 'power_efficiency_curve':
+    elif wake_losses_model == 'power_efficiency_curve':
         if (not isinstance(wind_farm_efficiency, dict) and
                 not isinstance(wind_farm_efficiency, pd.DataFrame)):
             raise TypeError(
                 "'wind_farm_efficiency' must be a dictionary or " +
-                "pd.DataFrame if `wake_losses_method´ is '{}'".format(
-                    wake_losses_method))
+                "pd.DataFrame if `wake_losses_model´ is '{}'".format(
+                    wake_losses_model))
         df = pd.concat([power_curve_df.set_index('wind_speed'),
                         wind_farm_efficiency.set_index('wind_speed')], axis=1)
         # Add by efficiency reduced power column (nan values of efficiency
@@ -182,7 +182,7 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
         power_curve_df.columns = ['wind_speed', 'power']
     else:
         raise ValueError(
-            "`wake_losses_method` is {} but should be ".format(
-                wake_losses_method) +
+            "`wake_losses_model` is {} but should be ".format(
+                wake_losses_model) +
             "'constant_efficiency' or 'power_efficiency_curve'")
     return power_curve_df

windpowerlib/turbine_cluster_modelchain.py

@@ -21,7 +21,7 @@ class TurbineClusterModelChain(ModelChain):
         A :class:`~.wind_farm.WindFarm` object representing the wind farm or
         a :class:`~.wind_turbine_cluster.WindTurbineCluster` object
         representing the wind turbine cluster.
-    wake_losses_method : String
+    wake_losses_model : String
         Defines the method for talking wake losses within the farm into
         consideration. Options: 'power_efficiency_curve',
         'constant_efficiency', 'dena_mean', 'knorr_mean', 'dena_extreme1',
@@ -79,7 +79,7 @@ class TurbineClusterModelChain(ModelChain):
         A :class:`~.wind_farm.WindFarm` object representing the wind farm or
         a :class:`~.wind_turbine_cluster.WindTurbineCluster` object
         representing the wind turbine cluster.
-    wake_losses_method : String
+    wake_losses_model : String
         Defines the method for talking wake losses within the farm into
         consideration. Options: 'power_efficiency_curve',
         'constant_efficiency', 'dena_mean', 'knorr_mean', 'dena_extreme1',
@@ -131,14 +131,14 @@ class TurbineClusterModelChain(ModelChain):
         one constant.
 
     """
-    def __init__(self, power_plant, wake_losses_method='dena_mean',
+    def __init__(self, power_plant, wake_losses_model='dena_mean',
                  smoothing=True, block_width=0.5,
                  standard_deviation_method='turbulence_intensity',
                  smoothing_order='wind_farm_power_curves', **kwargs):
         super(TurbineClusterModelChain, self).__init__(power_plant, **kwargs)
 
         self.power_plant = power_plant
-        self.wake_losses_method = wake_losses_method
+        self.wake_losses_model = wake_losses_model
         self.smoothing = smoothing
         self.block_width = block_width
         self.standard_deviation_method = standard_deviation_method
@@ -166,8 +166,6 @@ def run_model(self, weather_df):
             measured at a height of 10 m). See below for an example on how to
             create the weather_df DataFrame.
 
-# TODO rename method to model (wake_losses_method)
-
         Returns
         -------
         self
@@ -198,14 +196,14 @@ def run_model(self, weather_df):
             'turbulence_intensity' in
             weather_df.columns.get_level_values(0) else None)
         # Assign power curve
-        if (self.wake_losses_method == 'power_efficiency_curve' or
-                self.wake_losses_method == 'constant_efficiency' or
-                self.wake_losses_method is None):
-            wake_losses_method_to_power_curve = self.wake_losses_method
+        if (self.wake_losses_model == 'power_efficiency_curve' or
+                self.wake_losses_model == 'constant_efficiency' or
+                self.wake_losses_model is None):
+            wake_losses_model_to_power_curve = self.wake_losses_model
         else:
-            wake_losses_method_to_power_curve = None
+            wake_losses_model_to_power_curve = None
         self.power_plant.assign_power_curve(
-            wake_losses_method=wake_losses_method_to_power_curve,
+            wake_losses_model=wake_losses_model_to_power_curve,
             smoothing=self.smoothing, block_width=self.block_width,
             standard_deviation_method=self.standard_deviation_method,
             smoothing_order=self.smoothing_order,
@@ -219,13 +217,13 @@ def run_model(self, weather_df):
         density_hub = (None if (self.power_output_model == 'power_curve' and
                                 self.density_correction is False)
                        else self.density_hub(weather_df))
-        if (self.wake_losses_method != 'power_efficiency_curve' and
-                self.wake_losses_method != 'constant_efficiency' and
-                self.wake_losses_method is not None):
+        if (self.wake_losses_model != 'power_efficiency_curve' and
+                self.wake_losses_model != 'constant_efficiency' and
+                self.wake_losses_model is not None):
             # Reduce wind speed with wind efficiency curve
             wind_speed_hub = wake_losses.reduce_wind_speed(
                 wind_speed_hub,
-                wind_efficiency_curve_name=self.wake_losses_method)
+                wind_efficiency_curve_name=self.wake_losses_model)
         self.power_output = self.turbine_power_output(wind_speed_hub,
                                                       density_hub)
         return self

windpowerlib/wind_farm.py

@@ -124,7 +124,7 @@ def get_installed_power(self):
             wind_dict['number_of_turbines']
             for wind_dict in self.wind_turbine_fleet)
 
-    def assign_power_curve(self, wake_losses_method='power_efficiency_curve',
+    def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
                            smoothing=True, block_width=0.5,
                            standard_deviation_method='turbulence_intensity',
                            smoothing_order='wind_farm_power_curves',
@@ -140,7 +140,7 @@ def assign_power_curve(self, wake_losses_method='power_efficiency_curve',
 
         Parameters
         ----------
-        wake_losses_method : String
+        wake_losses_model : String
             Defines the method for talking wake losses within the farm into
             consideration. Options: 'power_efficiency_curve',
             'constant_efficiency' or None. Default: 'power_efficiency_curve'.
@@ -202,12 +202,12 @@ def assign_power_curve(self, wake_losses_method='power_efficiency_curve',
                             "'turbulence_intensity' as " +
                             "`standard_deviation_method` if " +
                             "`turbulence_intensity` is not given")
-            if wake_losses_method is not None:
+            if wake_losses_model is not None:
                 if self.efficiency is None:
                     raise KeyError(
                         "wind_farm_efficiency is needed if " +
-                        "`wake_losses_method´ is '{0}', but ".format(
-                            wake_losses_method) +
+                        "`wake_losses_model´ is '{0}', but ".format(
+                            wake_losses_model) +
                         " `wind_farm_efficiency` of {0} is {1}.".format(
                             self.name, self.efficiency))
             # Get original power curve
@@ -238,13 +238,13 @@ def assign_power_curve(self, wake_losses_method='power_efficiency_curve',
                 wind_farm_power_curve['power'],
                 standard_deviation_method=standard_deviation_method,
                 block_width=block_width, **kwargs)
-        if (wake_losses_method == 'constant_efficiency' or
-                wake_losses_method == 'wind_efficiency_curve'):
+        if (wake_losses_model == 'constant_efficiency' or
+                wake_losses_model == 'wind_efficiency_curve'):
             wind_farm_power_curve = (
                 power_curves.wake_losses_to_power_curve(
                     wind_farm_power_curve['wind_speed'].values,
                     wind_farm_power_curve['power'].values,
-                    wake_losses_method=wake_losses_method,
+                    wake_losses_model=wake_losses_model,
                     wind_farm_efficiency=self.efficiency))
         self.power_curve = wind_farm_power_curve
         return self

windpowerlib/wind_turbine_cluster.py

@@ -101,7 +101,7 @@ def get_installed_power(self):
         """
         return sum(wind_farm.installed_power for wind_farm in self.wind_farms)
 
-    def assign_power_curve(self, wake_losses_method='wind_efficiency_curve',
+    def assign_power_curve(self, wake_losses_model='wind_efficiency_curve',
                            smoothing=True, block_width=0.5,
                            standard_deviation_method='turbulence_intensity',
                            smoothing_order='wind_farm_power_curves',
@@ -117,7 +117,7 @@ def assign_power_curve(self, wake_losses_method='wind_efficiency_curve',
 
         Parameters
         ----------
-        wake_losses_method : String
+        wake_losses_model : String
             Defines the method for talking wake losses within the farm into
             consideration. Options: 'wind_efficiency_curve',
             'constant_efficiency' or None. Default: 'wind_efficiency_curve'.
@@ -158,7 +158,7 @@ def assign_power_curve(self, wake_losses_method='wind_efficiency_curve',
             farm.mean_hub_height()
             # Assign wind farm power curve
             farm.power_curve = farm.assign_power_curve(
-                wake_losses_method=wake_losses_method,
+                wake_losses_model=wake_losses_model,
                 smoothing=smoothing, block_width=block_width,
                 standard_deviation_method=standard_deviation_method,
                 smoothing_order=smoothing_order,