Merge branch 'API_changes' into dev

SabineHaas · SabineHaas · commit 92dc64a522ec · 2017-06-15T11:48:44.000+02:00
diff --git a/windpowerlib/modelchain.py b/windpowerlib/modelchain.py
@@ -260,7 +260,7 @@ def v_wind_hub(self, weather, data_height):
             v_wind = wind_speed.v_wind_hellman(
                 weather['v_wind'], data_height['v_wind'],
                 self.wind_turbine.hub_height,
-                self.hellman_exp, weather['z0'])
+                weather['z0'], self.hellman_exp)
         else:
             raise ValueError("'{0}' is an invalid value.".format(
                              self.wind_model) + "`wind_model` " +
@@ -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.')
diff --git a/windpowerlib/power_output.py b/windpowerlib/power_output.py
@@ -34,7 +34,7 @@ def cp_curve(v_wind, rho_hub, d_rotor, cp_values):
 
     Returns
     -------
-    pandas.Series
+    pandas.Series or numpy.array
         Electrical power output of the wind turbine in W.
 
     Notes
@@ -60,17 +60,18 @@ def cp_curve(v_wind, rho_hub, d_rotor, cp_values):
 
     """
     # cp time series
-    cp_series = np.interp(v_wind, cp_values.index, cp_values.cp,
-                          left=0, right=0)
+    cp_time_series = np.interp(v_wind, cp_values.index, cp_values.cp,
+                               left=0, right=0)
+    # Convert rho_hub to np.array if v_wind is np.array
+    if (isinstance(v_wind, np.ndarray) and isinstance(rho_hub, pd.Series)):
+        rho_hub = np.array(rho_hub)
     power_output = (1 / 8 * rho_hub * d_rotor ** 2 * np.pi
-                    * np.power(v_wind, 3) * cp_series)
-    # Set index for time series
-    try:
-        series_index = v_wind.index
-    except AttributeError:
-        series_index = range(1, len(power_output)+1)
-    return pd.Series(data=power_output, index=series_index,
-                     name='feedin_wind_turbine')
+                    * np.power(v_wind, 3) * cp_time_series)
+    # Power_output as pd.Series if v_wind is pd.Series
+    if isinstance(v_wind, pd.Series):
+        power_output = pd.Series(data=power_output, index=v_wind.index,
+                                 name='feedin_wind_turbine')
+    return power_output
 
 
 def cp_curve_density_corr(v_wind, rho_hub, d_rotor, cp_values):
@@ -87,16 +88,16 @@ def cp_curve_density_corr(v_wind, rho_hub, d_rotor, cp_values):
         Wind speed at hub height in m/s.
     rho_hub : pandas.Series or numpy.array
         Density of air at hub height in kg/m³.
+    d_rotor : float
+        Diameter of the rotor in m.
     cp_values : pandas.DataFrame
         Power coefficient curve of the wind turbine.
         Indices are the wind speeds of the power coefficient curve in m/s, the
         corresponding power coefficient values are in the column 'cp'.
-    d_rotor : float
-        Diameter of the rotor in m.
 
     Returns
     -------
-    pandas.Series
+    pandas.Series or numpy.array
         Electrical power of the wind turbine in W.
 
     Notes
@@ -116,7 +117,7 @@ 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.
 
@@ -126,16 +127,16 @@ 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
     -------
-    pandas.Series
+    pandas.Series or numpy.array
         Electrical power output of the wind turbine in W.
 
     Notes
@@ -146,13 +147,11 @@ def p_curve(p_values, v_wind):
     """
     power_output = np.interp(v_wind, p_values.index, p_values.p,
                              left=0, right=0)
-    # Set index for time series
-    try:
-        series_index = v_wind.index
-    except AttributeError:
-        series_index = range(1, len(power_output)+1)
-    return pd.Series(data=power_output, index=series_index,
-                     name='feedin_wind_turbine')
+    # Power_output as pd.Series if v_wind is pd.Series
+    if isinstance(v_wind, pd.Series):
+        power_output = pd.Series(data=power_output, index=v_wind.index,
+                                 name='feedin_wind_turbine')
+    return power_output
 
 
 def p_curve_density_corr(v_wind, rho_hub, p_values):
@@ -176,7 +175,7 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
 
     Returns
     -------
-    pandas.Series
+    pandas.Series or numpy.array
         Electrical power output of the wind turbine in W.
 
     Notes
@@ -219,17 +218,19 @@ def p_curve_density_corr(v_wind, rho_hub, p_values):
             at Reiner Lemoine Institute, 2014, p. 13
 
     """
+    # Convert rho_hub to np.array if v_wind is np.array
+    if (isinstance(v_wind, np.ndarray) and isinstance(rho_hub, pd.Series)):
+        rho_hub = np.array(rho_hub)
     power_output = [(np.interp(v_wind[i],
                                p_values.index * (1.225 / rho_hub[i])**(
                                    np.interp(p_values.index,
                                              [7.5, 12.5], [1/3, 2/3])),
                                p_values.p, left=0, right=0))
                     for i in range(len(v_wind))]
-
-    # Set index for time series
-    try:
-        series_index = v_wind.index
-    except AttributeError:
-        series_index = range(1, len(power_output)+1)
-    return pd.Series(data=power_output, index=series_index,
-                     name='feedin_wind_turbine')
+    # Power_output as pd.Series if v_wind is pd.Series
+    if isinstance(v_wind, pd.Series):
+        power_output = pd.Series(data=power_output, index=v_wind.index,
+                                 name='feedin_wind_turbine')
+    else:
+        power_output = np.array(power_output)
+    return power_output
diff --git a/windpowerlib/wind_speed.py b/windpowerlib/wind_speed.py
@@ -8,6 +8,7 @@
 __license__ = "GPLv3"
 
 import numpy as np
+import pandas as pd
 
 
 def logarithmic_wind_profile(v_wind, v_wind_height, hub_height, z_0,
@@ -78,12 +79,15 @@ def logarithmic_wind_profile(v_wind, v_wind_height, hub_height, z_0,
         raise ValueError("To take an obstacle height of {0} m".format(
                          obstacle_height) + " into consideration, wind" +
                          " speed data of a greater height is needed.")
+    # Return np.array if v_wind is np.array
+    if (isinstance(v_wind, np.ndarray) and isinstance(z_0, pd.Series)):
+        z_0 = np.array(z_0)
     return (v_wind * np.log((hub_height - 0.7 * obstacle_height) / z_0) /
             np.log((v_wind_height - 0.7 * obstacle_height) / z_0))
 
 
-def v_wind_hellman(v_wind, v_wind_height, hub_height, hellman_exp=None,
-                   z_0=None):
+def v_wind_hellman(v_wind, v_wind_height, hub_height, z_0=None,
+                   hellman_exp=None):
     r"""
     Calculates the wind speed at hub height using the hellman equation.
 
@@ -99,14 +103,14 @@ def v_wind_hellman(v_wind, v_wind_height, hub_height, hellman_exp=None,
         Height for which the parameter `v_wind` applies.
     hub_height : float
         Hub height of wind turbine.
+    z_0 : pandas.Series or numpy.array or float
+        Roughness length. Default: None. If given and `hellman_exp` is None:
+        `hellman_exp` = 1 / ln(h_hub/z_0), otherwise `hellman_exp` = 1/7.
     hellman_exp : float
         The Hellman exponent, which combines the increase in wind speed due to
         stability of atmospheric conditions and surface roughness into one
         constant. Default: None. If None and roughness length is given
         `hellman_exp` = 1 / ln(h_hub/z_0), otherwise `hellman_exp` = 1/7.
-    z_0 : pandas.Series or numpy.array or float
-        Roughness length. Default: None. If given
-        `hellman_exp` = 1 / ln(h_hub/z_0), otherwise `hellman_exp` = 1/7.
 
     Returns
     -------
@@ -149,6 +153,9 @@ def v_wind_hellman(v_wind, v_wind_height, hub_height, hellman_exp=None,
     """
     if hellman_exp is None:
         if z_0 is not None:
+            # Return np.array if v_wind is np.array
+            if (isinstance(v_wind, np.ndarray) and isinstance(z_0, pd.Series)):
+                z_0 = np.array(z_0)
             hellman_exp = 1 / np.log(hub_height / z_0)
         else:
             hellman_exp = 1/7
