docstrings and PEP8

Author: SabineHaas

windpowerlib/modelchain.py

@@ -318,7 +318,7 @@ def wind_speed_hub(self, weather_df):
 
     def calculate_power_output(self, wind_speed_hub, density_hub):
         r"""
-        Calculates the power output of the wind turbine. # todo power plant output????
+        Calculates the power output of the wind power plant.
 
         The method specified by the parameter `power_output_model` is used.
 

windpowerlib/power_curves.py

@@ -126,7 +126,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
         raise ValueError("{} is no valid `standard_deviation_method`. Valid "
                          + "options are 'turbulence_intensity', or "
                          + "'Staffell_Pfenninger'".format(
-            standard_deviation_method))
+                                 standard_deviation_method))
     # Initialize list for power curve values
     smoothed_power_curve_values = []
     # Append wind speeds to `power_curve_wind_speeds`
@@ -225,7 +225,8 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
                     wake_losses_model, wind_farm_efficiency))
         df = pd.concat([power_curve_df.set_index('wind_speed'),
                         wind_farm_efficiency.set_index('wind_speed')], axis=1)
-        # Add column with reduced power (nan values of efficiency are interpolated)
+        # Add column with reduced power (nan values of efficiency are
+        # interpolated)
         df['reduced_power'] = df['power'] * df['efficiency'].interpolate(
             method='index')
         reduced_power = df['reduced_power'].dropna()

windpowerlib/wake_losses.py

@@ -136,7 +136,7 @@ def get_wind_efficiency_curve(curve_name='all'):
         # Raise error if wind efficiency curve specified in 'curve_name' does
         # not exist
         if curve_name not in list(wind_efficiency_curves):
-            msg = ("Efficiency curve <{0}> does not exist. Must be one of the" 
+            msg = ("Efficiency curve <{0}> does not exist. Must be one of the"
                    "following: {1}.")
             raise ValueError(msg.format(curve_name, *possible_curve_names))
 

windpowerlib/wind_turbine.py

@@ -65,7 +65,7 @@ class WindTurbine(object):
         Diameter of the rotor in m. Default: None.
     power_coefficient_curve : None, pandas.DataFrame or dictionary
         Power coefficient curve of the wind turbine. DataFrame/dictionary must
-        have 'wind_speed' and 'power coefficient' columns/keys with wind speeds
+        have 'wind_speed' and 'power_coefficient' columns/keys with wind speeds
         in m/s and the corresponding power coefficients. Default: None.
     power_curve : None, pandas.DataFrame or dictionary
         Power curve of the wind turbine. DataFrame/dictionary must have
@@ -82,11 +82,12 @@ class WindTurbine(object):
     Notes
     ------
     Your wind turbine object should have a power coefficient or power curve.
-    You can set the `fetch_curve` parameter and the `data_source` parameter
-    want to automatically fetch a curve from a data set provided in the Open
-    Energy Database (oedb) or want to read a csv file that you provide.
+    You can set the `fetch_curve` parameter and the `data_source` parameter if
+    you want to automatically fetch a curve from a data set provided in the
+    Open Energy Database (oedb) or want to read a csv file that you provide.
     See `example_power_curves.csv' and `example_power_coefficient_curves.csv`
-    in example/data for the required form of such a csv file.
+    in example/data for the required form of such a csv file (more columns can
+    be added).
 
     Examples
     --------
@@ -127,11 +128,10 @@ def fetch_turbine_data(self, fetch_curve, data_source):
 
         Method fetches nominal power as well as power coefficient curve or
         power curve from a data set provided in the Open Energy Database
-        (oedb). You can also use this function to import your own power
-        (coefficient) curves from a file. For that the wind speeds in m/s have
-        to be in the first row and the corresponding power coefficient curve
-        values or power curve values in W in a row where the first column
-        contains the turbine name.
+        (oedb). You can also import your own power (coefficient) curves from a
+        file. For that the wind speeds in m/s have to be in the first row and
+        the corresponding power coefficient curve values or power curve values
+        in W in a row where the first column contains the turbine name.
         See `example_power_curves.csv' and
         `example_power_coefficient_curves.csv` in example/data for the required
         form of a csv file (more columns can be added). See
@@ -350,7 +350,10 @@ def get_turbine_types(print_out=True):
     ----------
     print_out : boolean
         Directly prints a tabular containing the turbine types in column
-        'turbine_type'. Default: True.
+        'turbine_type', the manufacturer in column 'manufacturer' and
+        information about whether a power (coefficient) curve exists (True) or
+        not (False) in columns 'has_power_curve' and 'has_cp_curve'.
+        Default: True.
 
     Examples
     --------
@@ -377,7 +380,7 @@ def get_turbine_types(print_out=True):
     p_curves_df = df.iloc[df.loc[df['has_power_curve'] == True].index][
         ['manufacturer', 'turbine_type', 'has_power_curve']]
     curves_df = pd.merge(p_curves_df, cp_curves_df, how='outer',
-                        sort=True).fillna(False)
+                         sort=True).fillna(False)
     if print_out:
         pd.set_option('display.max_rows', len(curves_df))
         print(curves_df)