Merge pull request #34 from wind-python/rename_power_cp_curve_column

Rename columns of power an cp curves to value

Author: SabineHaas

doc/whatsnew/v0-1-0.txt

@@ -43,7 +43,7 @@ API changes
 
  * renamed attribute `turbine_name` of WindTurbine class to `name` to match with `name` attribute of WindFarm and WindTurbineCluster class
  * renamed `basic_example` to `modelchain_example`
- * renamed column 'values' of power curves to 'power' and column 'values' of power coefficient curves to 'power coefficient'
+ * renamed column 'values' of power (coefficient) curves to 'value' to prevent errors using df.value(s)
  * renamed `run_basic_example()` to `run_example()` in `modelchain_example`
  * renamed parameter `wind_turbine` of `ModelChain` object to `power_plant`
  * removed parameter `density_correction` from `power_plant.power_coefficient()`

example/modelchain_example.ipynb

@@ -237,7 +237,7 @@
     "    'hub_height': 105,  # in m\n",
     "    'rotor_diameter': 90,  # in m\n",
     "    'power_curve': pd.DataFrame(\n",
-    "            data={'power': [p * 1000 for p in [\n",
+    "            data={'value': [p * 1000 for p in [\n",
     "                      0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],  # in W\n",
     "                  'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]})  # in m/s\n",
     "    }  \n",
@@ -525,20 +525,20 @@
     "if plt:\n",
     "    if e126.power_coefficient_curve is not None:\n",
     "        e126.power_coefficient_curve.plot(\n",
-    "            x='wind_speed', y='power coefficient', style='*',\n",
+    "            x='wind_speed', y='value', style='*',\n",
     "            title='Enercon E126 power coefficient curve')\n",
     "        plt.show()\n",
     "    if e126.power_curve is not None:\n",
-    "        e126.power_curve.plot(x='wind_speed', y='power', style='*',\n",
+    "        e126.power_curve.plot(x='wind_speed', y='value', style='*',\n",
     "                              title='Enercon E126 power curve')\n",
     "        plt.show()\n",
     "    if my_turbine.power_coefficient_curve is not None:\n",
     "        my_turbine.power_coefficient_curve.plot(\n",
-    "            x='wind_speed', y='power coefficient', style='*',\n",
+    "            x='wind_speed', y='value', style='*',\n",
     "            title='myTurbine power coefficient curve')\n",
     "        plt.show()\n",
     "    if my_turbine.power_curve is not None:\n",
-    "        my_turbine.power_curve.plot(x='wind_speed', y='power', style='*',\n",
+    "        my_turbine.power_curve.plot(x='wind_speed', y='value', style='*',\n",
     "                                    title='myTurbine power curve')\n",
     "        plt.show()"
    ]

example/modelchain_example.py

@@ -111,7 +111,7 @@ def initialize_wind_turbines():
         'hub_height': 105,  # in m
         'rotor_diameter': 90,  # in m
         'power_curve': pd.DataFrame(
-            data={'power': [p * 1000 for p in [
+            data={'value': [p * 1000 for p in [
                       0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]],  # in W
                   'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]})  # in m/s
     }
@@ -246,7 +246,7 @@ def plot_or_print(my_turbine, e126, dummy_turbine):
                 title='Enercon E126 power coefficient curve')
             plt.show()
         if e126.power_curve is not None:
-            e126.power_curve.plot(x='wind_speed', y='power', style='*',
+            e126.power_curve.plot(x='wind_speed', y='value', style='*',
                                   title='Enercon E126 power curve')
             plt.show()
         if my_turbine.power_coefficient_curve is not None:
@@ -255,7 +255,7 @@ def plot_or_print(my_turbine, e126, dummy_turbine):
                 title='myTurbine power coefficient curve')
             plt.show()
         if my_turbine.power_curve is not None:
-            my_turbine.power_curve.plot(x='wind_speed', y='power', style='*',
+            my_turbine.power_curve.plot(x='wind_speed', y='value', style='*',
                                         title='myTurbine power curve')
             plt.show()
     else:

tests/test_power_curves.py

@@ -18,7 +18,7 @@ def setup_class(self):
     def test_smooth_power_curve(self):
         test_curve = wt.WindTurbine(**self.test_turbine).power_curve
         parameters = {'power_curve_wind_speeds': test_curve['wind_speed'],
-                      'power_curve_values': test_curve['power'],
+                      'power_curve_values': test_curve['value'],
                       'standard_deviation_method': 'turbulence_intensity'}
 
         # Raise ValueError - `turbulence_intensity` missing
@@ -32,7 +32,7 @@ def test_smooth_power_curve(self):
                                           name='wind_speed')
         power_values_exp = pd.Series([
             1141906.9806766496, 1577536.8085282773, 1975480.993355767,
-            2314059.4022704284, 2590216.6802602503], name='power')
+            2314059.4022704284, 2590216.6802602503], name='value')
         smoothed_curve_exp = pd.DataFrame(data=pd.concat([
             wind_speed_values_exp, power_values_exp], axis=1))
         smoothed_curve_exp.index = np.arange(5, 10, 1)
@@ -43,7 +43,7 @@ def test_smooth_power_curve(self):
         parameters['standard_deviation_method'] = 'Staffell_Pfenninger'
         power_values_exp = pd.Series([
             929405.1348918702, 1395532.5468724659, 1904826.6851982325,
-            2402659.118305521, 2844527.1732449625], name='power')
+            2402659.118305521, 2844527.1732449625], name='value')
         smoothed_curve_exp = pd.DataFrame(
             data=pd.concat([wind_speed_values_exp, power_values_exp], axis=1))
         smoothed_curve_exp.index = np.arange(5, 10, 1)
@@ -58,13 +58,13 @@ def test_smooth_power_curve(self):
     def test_wake_losses_to_power_curve(self):
         test_curve = wt.WindTurbine(**self.test_turbine).power_curve
         parameters = {'power_curve_wind_speeds': test_curve['wind_speed'],
-                      'power_curve_values': test_curve['power'],
+                      'power_curve_values': test_curve['value'],
                       'wind_farm_efficiency': 0.9,
                       'wake_losses_model': 'constant_efficiency'}
 
         # Test constant efficiency
         power_curve_exp = test_curve.copy(deep=True)
-        power_curve_exp['power'] = power_curve_exp['power'].values * 0.9
+        power_curve_exp['value'] = power_curve_exp['value'].values * 0.9
         assert_frame_equal(wake_losses_to_power_curve(**parameters),
                            power_curve_exp)
 
@@ -85,8 +85,8 @@ def test_wake_losses_to_power_curve(self):
         parameters['wind_farm_efficiency'].columns = ['wind_speed',
                                                       'efficiency']
         power_curve_exp = test_curve.copy(deep=True)
-        power_curve_exp['power'] = (
-            power_curve_exp['power'].values * parameters[
+        power_curve_exp['value'] = (
+            power_curve_exp['value'].values * parameters[
                 'wind_farm_efficiency']['efficiency'])
         assert_frame_equal(wake_losses_to_power_curve(**parameters),
                            power_curve_exp)

windpowerlib/modelchain.py

@@ -344,7 +344,7 @@ def calculate_power_output(self, wind_speed_hub, density_hub):
             return (power_output.power_curve(
                         wind_speed_hub,
                         self.power_plant.power_curve['wind_speed'],
-                        self.power_plant.power_curve['power'],
+                        self.power_plant.power_curve['value'],
                         density_hub, self.density_correction))
         elif self.power_output_model == 'power_coefficient_curve':
             if self.power_plant.power_coefficient_curve is None:
@@ -358,7 +358,7 @@ def calculate_power_output(self, wind_speed_hub, density_hub):
                         self.power_plant.power_coefficient_curve[
                             'wind_speed'],
                         self.power_plant.power_coefficient_curve[
-                            'power_coefficient'],
+                            'value'],
                         self.power_plant.rotor_diameter, density_hub))
         else:
             raise ValueError("'{0}' is an invalid value. ".format(

windpowerlib/power_curves.py

@@ -54,7 +54,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
     Returns
     -------
     smoothed_power_curve_df : pd.DataFrame
-        Smoothed power curve. DataFrame has 'wind_speed' and 'power' columns
+        Smoothed power curve. DataFrame has 'wind_speed' and 'value' columns
         with wind speeds in m/s and the corresponding power curve value in W.
 
     Notes
@@ -168,7 +168,7 @@ def smooth_power_curve(power_curve_wind_speeds, power_curve_values,
         data=[list(power_curve_wind_speeds.values),
               smoothed_power_curve_values]).transpose()
     # Rename columns of the data frame
-    smoothed_power_curve_df.columns = ['wind_speed', 'power']
+    smoothed_power_curve_df.columns = ['wind_speed', 'value']
     return smoothed_power_curve_df
 
 
@@ -200,7 +200,7 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
     -------
     power_curve_df : pd.DataFrame
         Power curve with power values reduced by a wind farm efficiency.
-        DataFrame has 'wind_speed' and 'power' columns with wind speeds in m/s
+        DataFrame has 'wind_speed' and 'value' columns with wind speeds in m/s
         and the corresponding power curve value in W.
 
     """
@@ -209,13 +209,13 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
         data=[list(power_curve_wind_speeds),
               list(power_curve_values)]).transpose()
     # Rename columns of DataFrame
-    power_curve_df.columns = ['wind_speed', 'power']
+    power_curve_df.columns = ['wind_speed', 'value']
     if wake_losses_model == 'constant_efficiency':
         if not isinstance(wind_farm_efficiency, float):
             raise TypeError("'wind_farm_efficiency' must be float if " +
                             "`wake_losses_model´ is '{}' but is {}".format(
                                 wake_losses_model, wind_farm_efficiency))
-        power_curve_df['power'] = power_curve_values * wind_farm_efficiency
+        power_curve_df['value'] = power_curve_values * wind_farm_efficiency
     elif wake_losses_model == 'power_efficiency_curve':
         if (not isinstance(wind_farm_efficiency, dict) and
                 not isinstance(wind_farm_efficiency, pd.DataFrame)):
@@ -227,12 +227,12 @@ def wake_losses_to_power_curve(power_curve_wind_speeds, power_curve_values,
                         wind_farm_efficiency.set_index('wind_speed')], axis=1)
         # Add column with reduced power (nan values of efficiency are
         # interpolated)
-        df['reduced_power'] = df['power'] * df['efficiency'].interpolate(
+        df['reduced_power'] = df['value'] * df['efficiency'].interpolate(
             method='index')
         reduced_power = df['reduced_power'].dropna()
         power_curve_df = pd.DataFrame([reduced_power.index,
                                        reduced_power.values]).transpose()
-        power_curve_df.columns = ['wind_speed', 'power']
+        power_curve_df.columns = ['wind_speed', 'value']
     else:
         raise ValueError(
             "`wake_losses_model` is {} but should be ".format(

windpowerlib/wind_farm.py

@@ -254,7 +254,7 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
             # Editions to the power curves before the summation
             if smoothing and smoothing_order == 'turbine_power_curves':
                 power_curve = power_curves.smooth_power_curve(
-                    power_curve['wind_speed'], power_curve['power'],
+                    power_curve['wind_speed'], power_curve['value'],
                     standard_deviation_method=standard_deviation_method,
                     block_width=block_width, **kwargs)
             else:
@@ -263,12 +263,12 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
                 if power_curve.iloc[0]['wind_speed'] != 0.0:
                     power_curve = pd.concat(
                         [pd.DataFrame(data={
-                            'power': [0.0], 'wind_speed': [0.0]}),
+                            'value': [0.0], 'wind_speed': [0.0]}),
                          power_curve])
-                if power_curve.iloc[-1]['power'] != 0.0:
+                if power_curve.iloc[-1]['value'] != 0.0:
                     power_curve = pd.concat(
                         [power_curve, pd.DataFrame(data={
-                            'power': [0.0], 'wind_speed': [
+                            'value': [0.0], 'wind_speed': [
                                 power_curve['wind_speed'].loc[
                                     power_curve.index[-1]] + 0.5]})])
             # Add power curves of all turbine types to data frame
@@ -279,21 +279,21 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
         # Aggregate all power curves
         wind_farm_power_curve = pd.DataFrame(
             df.interpolate(method='index').sum(axis=1))
-        wind_farm_power_curve.columns = ['power']
+        wind_farm_power_curve.columns = ['value']
         wind_farm_power_curve.reset_index('wind_speed', inplace=True)
         # Editions to the power curve after the summation
         if smoothing and smoothing_order == 'wind_farm_power_curves':
             wind_farm_power_curve = power_curves.smooth_power_curve(
                 wind_farm_power_curve['wind_speed'],
-                wind_farm_power_curve['power'],
+                wind_farm_power_curve['value'],
                 standard_deviation_method=standard_deviation_method,
                 block_width=block_width, **kwargs)
         if (wake_losses_model == 'constant_efficiency' or
                 wake_losses_model == 'power_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,
+                    wind_farm_power_curve['value'].values,
                     wake_losses_model=wake_losses_model,
                     wind_farm_efficiency=self.efficiency))
         self.power_curve = wind_farm_power_curve

windpowerlib/wind_turbine.py

@@ -30,11 +30,11 @@ 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 'value' 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 'power' columns/keys with wind speeds in m/s and the
+        'wind_speed' and 'value' 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. Default: None.
@@ -65,11 +65,11 @@ 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 'value' 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 'power' columns/keys with wind speeds in m/s and the
+        'wind_speed' and 'value' 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. Default: None.
@@ -164,7 +164,7 @@ def fetch_turbine_data(self, fetch_curve, data_source):
         ...    'fetch_curve': 'power_coefficient_curve',
         ...    'data_source': 'oedb'}
         >>> e126 = wind_turbine.WindTurbine(**enerconE126)
-        >>> print(e126.power_coefficient_curve['power_coefficient'][5])
+        >>> print(e126.power_coefficient_curve['value'][5])
         0.44
         >>> print(e126.nominal_power)
         4200000.0
@@ -177,13 +177,8 @@ def fetch_turbine_data(self, fetch_curve, data_source):
             curve_df, nominal_power = get_turbine_data_from_file(
                 turbine_type=self.name, file_=data_source)
         if fetch_curve == 'power_curve':
-            curve_df.columns = ['wind_speed', 'power']
-            if data_source == 'oedb':
-                # power values in W
-                curve_df['power'] = curve_df['power'] * 1000
             self.power_curve = curve_df
         elif fetch_curve == 'power_coefficient_curve':
-            curve_df.columns = ['wind_speed', 'power_coefficient']
             self.power_coefficient_curve = curve_df
         else:
             raise ValueError("'{0}' is an invalid value. ".format(
@@ -233,7 +228,7 @@ def get_turbine_data_from_file(turbine_type, file_):
     ...    'fetch_curve': 'power_curve',
     ...    'data_source': source}
     >>> e_t_1 = wind_turbine.WindTurbine(**example_turbine)
-    >>> print(e_t_1.power_curve['power'][7])
+    >>> print(e_t_1.power_curve['value'][7])
     18000.0
     >>> print(e_t_1.nominal_power)
     150000
@@ -263,7 +258,8 @@ def isfloat(x):
     cols = [_ for _ in wpp_df.columns if isfloat(_)]
     curve_data = wpp_df[cols].dropna(axis=1)
     df = curve_data.transpose().reset_index()
-    df['index'] = df['index'].apply(lambda x: float(x))
+    df.columns = ['wind_speed', 'value']
+    df['wind_speed'] = df['wind_speed'].apply(lambda x: float(x))
     nominal_power = wpp_df['p_nom'].iloc[0]
     return df, nominal_power
 
@@ -307,6 +303,10 @@ def get_turbine_data_from_oedb(turbine_type, fetch_curve):
                        "possible wind turbine types.")
     nominal_power = turbine_data.loc[turbine_type][
                         'installed_capacity_kw'] * 1000
+    df.columns = ['wind_speed', 'value']
+    if fetch_curve == 'power_curve':
+        # power in W
+        df['value'] = df['value'] * 1000
     return df, nominal_power
 
 

windpowerlib/wind_turbine_cluster.py

@@ -181,12 +181,12 @@ def assign_power_curve(self, wake_losses_model='power_efficiency_curve',
                 turbulence_intensity=turbulence_intensity, **kwargs)
         # Create data frame from power curves of all wind farms
         df = pd.concat([farm.power_curve.set_index(['wind_speed']).rename(
-            columns={'power': farm.name}) for
+            columns={'value': farm.name}) for
             farm in self.wind_farms], axis=1)
         # Sum up power curves
         cluster_power_curve = pd.DataFrame(
             df.interpolate(method='index').sum(axis=1))
-        cluster_power_curve.columns = ['power']
+        cluster_power_curve.columns = ['value']
         # Return wind speed (index) to a column of the data frame
         cluster_power_curve.reset_index('wind_speed', inplace=True)
         self.power_curve = cluster_power_curve