Change order of parameters in p_curve() for consistency

Author: SabineHaas

windpowerlib/modelchain.py

@@ -313,8 +313,8 @@ def turbine_power_output(self, v_wind, rho_hub):
                                 " are missing.")
             if self.density_corr is False:
                 logging.debug('Calculating power output using power curve.')
-                output = power_output.p_curve(self.wind_turbine.p_values,
-                                              v_wind)
+                output = power_output.p_curve(v_wind,
+                                              self.wind_turbine.p_values)
             elif self.density_corr is True:
                 logging.debug('Calculating power output using density ' +
                               'corrected power curve.')

windpowerlib/power_output.py

@@ -116,7 +116,7 @@ def cp_curve_density_corr(v_wind, rho_hub, d_rotor, cp_values):
     return p_curve_density_corr(v_wind, rho_hub, p_values)
 
 
-def p_curve(p_values, v_wind):
+def p_curve(v_wind, p_values):
     r"""
     Calculates the turbine power output using a power curve.
 
@@ -126,12 +126,12 @@ def p_curve(p_values, v_wind):
 
     Parameters
     ----------
+    v_wind : pandas.Series or numpy.array
+        Wind speed at hub height in m/s.
     p_values : pandas.DataFrame
         Power curve of the wind turbine.
         Indices are the wind speeds of the power curve in m/s, the
         corresponding power values in W are in the column 'p'.
-    v_wind : pandas.Series or numpy.array
-        Wind speed at hub height in m/s.
 
     Returns
     -------