Renam not consistent parameters and keys

Author: SabineHaas

example/basic_example.py

@@ -97,7 +97,7 @@ def initialise_wind_turbines():
     # specification of own wind turbine (Note: power coefficient values and
     # nominal power have to be in Watt)
     myTurbine = {
-        'turbine_name': 'myTurbine',
+        'object_name': 'myTurbine',
         'nominal_power': 3e6,  # in W
         'hub_height': 105,  # in m
         'rotor_diameter': 90,  # in m
@@ -113,7 +113,7 @@ def initialise_wind_turbines():
     # if you want to use the power coefficient curve change the value of
     # 'fetch_curve' to 'power_coefficient_curve'
     enerconE126 = {
-        'turbine_name': 'ENERCON E 126 7500',  # turbine name as in register
+        'object_name': 'ENERCON E 126 7500',  # turbine name as in register
         'hub_height': 135,  # in m
         'rotor_diameter': 127,  # in m
         'fetch_curve': 'power_curve'  # fetch power curve

windpowerlib/wind_turbine.py

@@ -31,8 +31,8 @@ class WindTurbine(object):
         Diameter of the rotor in m.
     power_coefficient_curve : None, pandas.DataFrame or dictionary
         Power coefficient curve of the wind turbine. DataFrame/dictionary must
-        have 'wind_speed' and 'values' columns/keys with wind speeds in m/s
-        and the corresponding power coefficients. Default: None.
+        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
         'wind_speed' and 'values' columns/keys with wind speeds in m/s and the
@@ -56,11 +56,11 @@ class WindTurbine(object):
         Diameter of the rotor in m.
     power_coefficient_curve : None, pandas.DataFrame or dictionary
         Power coefficient curve of the wind turbine. DataFrame/dictionary must
-        have 'wind_speed' and 'values' columns/keys with wind speeds in m/s
-        and the corresponding power coefficients. Default: None.
+        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
-        'wind_speed' and 'values' columns/keys with wind speeds in m/s and the
+        'wind_speed' and 'power' columns/keys with wind speeds in m/s and the
         corresponding power curve value in W. Default: None.
     nominal_power : None or float
         The nominal output of the wind turbine in W.
@@ -92,7 +92,7 @@ class WindTurbine(object):
 
     """
 
-    def __init__(self, turbine_name, hub_height, rotor_diameter=None,
+    def __init__(self, object_name, hub_height, rotor_diameter=None,
                  power_coefficient_curve=None, power_curve=None,
                  nominal_power=None, fetch_curve=None):
 
@@ -135,7 +135,7 @@ def fetch_turbine_data(self):
         ...    'object_name': 'ENERCON E 126 7500',
         ...    'fetch_curve': 'power_coefficient_curve'}
         >>> e126 = wind_turbine.WindTurbine(**enerconE126)
-        >>> print(e126.power_coefficient_curve['values'][5])
+        >>> print(e126.power_coefficient_curve['power coefficient'][5])
         0.423
         >>> print(e126.nominal_power)
         7500000
@@ -179,7 +179,11 @@ def restructure_data():
                         data = np.vstack((data, np.array(
                             [float(col), float(wpp_df[col])])))
             data = np.delete(data, 0, 0)
-            df = pd.DataFrame(data, columns=['wind_speed', 'values'])
+            if self.fetch_curve == 'power_curve':
+                df = pd.DataFrame(data, columns=['wind_speed', 'power'])
+            if self.fetch_curve == 'power_coefficient_curve':
+                df = pd.DataFrame(data, columns=['wind_speed',
+                                                 'power coefficient'])
             nominal_power = wpp_df['p_nom'].iloc[0]
             return df, nominal_power
         if self.fetch_curve == 'power_curve':