Change output to Series or array depending on wind speed

Author: birgits

tests/test_power_output.py

@@ -181,26 +181,38 @@ def test_power_curve_density_correction(self):
 
         # Test wind_speed as pd.Series with density and power_curve as
         # pd.Series and np.array
-        power_output_exp = [0.0, 461.00290572240806, 0.0]
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        power_output_exp = pd.Series(data=[0.0, 461.00290572, 0.0],
+                                     name='feedin_wind_turbine')
+        assert_series_equal(power_curve_density_correction(**parameters),
+                            power_output_exp)
         parameters['density'] = np.array(parameters['density'])
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        assert_series_equal(power_curve_density_correction(**parameters),
+                            power_output_exp)
         parameters['power_curve_values'] = np.array(
             parameters['power_curve_values'])
         parameters['power_curve_wind_speeds'] = np.array(
             parameters['power_curve_wind_speeds'])
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        assert_series_equal(power_curve_density_correction(**parameters),
+                            power_output_exp)
 
         # Test wind_speed as np.array with density and power_curve as np.array
         # and pd.Series
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        parameters['wind_speed'] = np.array(parameters['wind_speed'])
+        power_output_exp = np.array([0.0, 461.00290572, 0.0])
+        assert_allclose(power_curve_density_correction(**parameters),
+                        power_output_exp)
+        assert isinstance(power_curve(**parameters), np.ndarray)
         parameters['density'] = pd.Series(data=parameters['density'])
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        assert_allclose(power_curve_density_correction(**parameters),
+                        power_output_exp)
+        assert isinstance(power_curve(**parameters), np.ndarray)
         parameters['power_curve_wind_speeds'] = pd.Series(
             data=parameters['power_curve_wind_speeds'])
         parameters['power_curve_values'] = pd.Series(
             data=parameters['power_curve_values'])
-        assert power_curve_density_correction(**parameters) == power_output_exp
+        assert_allclose(power_curve_density_correction(**parameters),
+                        power_output_exp)
+        assert isinstance(power_curve(**parameters), np.ndarray)
 
         # Raise TypeError due to density is None
         with pytest.raises(TypeError):

windpowerlib/power_output.py

@@ -78,24 +78,24 @@ def power_coefficient_curve(wind_speed, power_coefficient_curve_wind_speeds,
         power_output = (1 / 8 * density * rotor_diameter ** 2 * np.pi *
                         np.power(wind_speed, 3) *
                         power_coefficient_time_series)
+        # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
+        if isinstance(wind_speed, pd.Series):
+            power_output = pd.Series(data=power_output, index=wind_speed.index,
+                                     name='feedin_wind_turbine')
+        else:
+            power_output = np.array(power_output)
     elif density_correction is True:
         power_curve_values = (1 / 8 * 1.225 * rotor_diameter ** 2 * np.pi *
-                    np.power(power_coefficient_curve_wind_speeds, 3) *
-                    power_coefficient_curve_values)
+                              np.power(power_coefficient_curve_wind_speeds,
+                                       3) *
+                              power_coefficient_curve_values)
         power_output = power_curve_density_correction(
             wind_speed, power_coefficient_curve_wind_speeds,
             power_curve_values, density)
     else:
         raise TypeError("'{0}' is an invalid type. ".format(type(
                         density_correction)) + "`density_correction` must " +
                         "be Boolean (True or False).")
-
-    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
-    if isinstance(wind_speed, pd.Series):
-        power_output = pd.Series(data=power_output, index=wind_speed.index,
-                                 name='feedin_wind_turbine')
-    else:
-        power_output = np.array(power_output)
     return power_output
 
 
@@ -142,19 +142,19 @@ def power_curve(wind_speed, power_curve_wind_speeds, power_curve_values,
     if density_correction is False:
         power_output = np.interp(wind_speed, power_curve_wind_speeds,
                                  power_curve_values, left=0, right=0)
+        # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
+        if isinstance(wind_speed, pd.Series):
+            power_output = pd.Series(data=power_output, index=wind_speed.index,
+                                     name='feedin_wind_turbine')
+        else:
+            power_output = np.array(power_output)
     elif density_correction is True:
         power_output = power_curve_density_correction(
             wind_speed, power_curve_wind_speeds, power_curve_values, density)
     else:
         raise TypeError("'{0}' is an invalid type. ".format(type(
                         density_correction)) + "`density_correction` must " +
                         "be Boolean (True or False).")
-    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
-    if isinstance(wind_speed, pd.Series):
-        power_output = pd.Series(data=power_output, index=wind_speed.index,
-                                 name='feedin_wind_turbine')
-    else:
-        power_output = np.array(power_output)
     return power_output
 
 
@@ -228,9 +228,15 @@ def power_curve_density_correction(wind_speed, power_curve_wind_speeds,
         raise TypeError("`density` is None. For the calculation with a " +
                         "density corrected power curve density at hub " +
                         "height is needed.")
-    return [(np.interp(wind_speed[i],
-                       power_curve_wind_speeds * (1.225 / density[i])**(
-                           np.interp(power_curve_wind_speeds, [7.5, 12.5],
-                                     [1/3, 2/3])),
-                       power_curve_values, left=0, right=0))
-            for i in range(len(wind_speed))]
+    power_output = [(np.interp(
+        wind_speed[i], power_curve_wind_speeds * (1.225 / density[i]) ** (
+            np.interp(power_curve_wind_speeds, [7.5, 12.5], [1/3, 2/3])),
+        power_curve_values, left=0, right=0)) for i in range(len(wind_speed))]
+
+    # Power_output as pd.Series if wind_speed is pd.Series (else: np.array)
+    if isinstance(wind_speed, pd.Series):
+        power_output = pd.Series(data=power_output, index=wind_speed.index,
+                                 name='feedin_wind_turbine')
+    else:
+        power_output = np.array(power_output)
+    return power_output