Adapt function docstrings to changed names in modelchain

Author: Birgit Schachler

windpowerlib/density.py

@@ -13,8 +13,8 @@ def barometric(pressure, pressure_height, hub_height, temperature_hub_height):
     Calculates the density of air at hub height using the barometric height
     equation.
 
-    This function is carried out when the parameter `rho_model` of an instance
-    of the :class:`~.modelchain.ModelChain` class is 'barometric'.
+    This function is carried out when the parameter `density_model` of an
+    instance of the :class:`~.modelchain.ModelChain` class is 'barometric'.
 
     Parameters
     ----------
@@ -70,8 +70,8 @@ def ideal_gas(pressure, pressure_height, hub_height, temperature_hub_height):
     r"""
     Calculates the density of air at hub height using the ideal gas equation.
 
-    This function is carried out when the parameter `rho_model` of an instance
-    of the :class:`~.modelchain.ModelChain` class is 'ideal_gas'.
+    This function is carried out when the parameter `density_model` of an
+    instance of the :class:`~.modelchain.ModelChain` class is 'ideal_gas'.
 
     Parameters
     ----------

windpowerlib/power_output.py

@@ -11,15 +11,16 @@
 import pandas as pd
 
 
-def power_coefficient_curve(wind_speed, cp_values,
-                            rotor_diameter, density, density_corr=False):
+def power_coefficient_curve(wind_speed, cp_values, rotor_diameter, density,
+                            density_correction=False):
     r"""
     Calculates the turbine power output using a power coefficient curve.
 
     This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class is 'cp_values'. If
-    the parameter `density_corr` is True the density corrected power curve
-    (:py:func:`~._p_curve_density_corr`) is used.
+    instance of the :class:`~.modelchain.ModelChain` class is
+    'power_coefficient_curve'. If the parameter `density_correction` is True
+    the density corrected power curve (See
+    :py:func:`~.power_curve_density_correction`) is used.
 
     Parameters
     ----------
@@ -32,7 +33,7 @@ def power_coefficient_curve(wind_speed, cp_values,
         Rotor diameter in m.
     density : pandas.Series or numpy.array
         Density of air at hub height in kg/m³.
-    density_corr : boolean
+    density_correction : boolean
         If the parameter is True the density corrected power curve is used for
         the calculation of the turbine power output. Default: False.
 
@@ -66,20 +67,21 @@ def power_coefficient_curve(wind_speed, cp_values,
             Wirtschaftlichkeit". 4. Auflage, Springer-Verlag, 2008, p. 542
 
     """
-    if density_corr is False:
+    if density_correction is False:
         cp_time_series = np.interp(wind_speed, cp_values.index, cp_values,
                                    left=0, right=0)
         power_output = (1 / 8 * density * rotor_diameter ** 2 * np.pi
                         * np.power(wind_speed, 3) * cp_time_series)
-    elif density_corr is True:
+    elif density_correction is True:
         p_values = (1 / 8 * 1.225 * rotor_diameter ** 2 * np.pi *
                     np.power(cp_values.index, 3) * cp_values)
         p_values = pd.Series(np.array(p_values), index=cp_values.index)
-        power_output = _p_curve_density_corr(wind_speed, p_values, density)
+        power_output = power_curve_density_correction(wind_speed, p_values,
+                                                     density)
     else:
-        raise TypeError("'{0}' is an invalid type.".format(type(
-                        density_corr)) + "`density_corr` must be Boolean " +
-                        "(True or False).")
+        raise TypeError("'{0}' is an invalid type. ".format(type(
+                        density_correction)) + "`density_corr` 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):
@@ -90,14 +92,14 @@ def power_coefficient_curve(wind_speed, cp_values,
     return power_output
 
 
-def power_curve(wind_speed, p_values, density=None, density_corr=False):
+def power_curve(wind_speed, p_values, density=None, density_correction=False):
     r"""
     Calculates the turbine power output using a power curve.
 
     This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class is 'p_values'. If
-    the parameter `density_corr` is True the density corrected power curve
-    (:py:func:`~._p_curve_density_corr`) is used.
+    instance of the :class:`~.modelchain.ModelChain` class is 'power_curve'. If
+    the parameter `density_correction` is True the density corrected power
+    curve (See :py:func:`~.power_curve_density_correction`) is used.
 
     Parameters
     ----------
@@ -109,7 +111,7 @@ def power_curve(wind_speed, p_values, density=None, density_corr=False):
     density : pandas.Series or numpy.array
         Density of air at hub height in kg/m³. This parameter is needed
         if `density_corr` is True. Default: None.
-    density_corr : boolean
+    density_correction : boolean
         If the parameter is True the density corrected power curve is used for
         the calculation of the turbine power output. In this case `density`
         cannot be None. Default: False.
@@ -126,15 +128,16 @@ def power_curve(wind_speed, p_values, density=None, density_corr=False):
     and below the minimum wind speed given in the power curve is zero.
 
     """
-    if density_corr is False:
+    if density_correction is False:
         power_output = np.interp(wind_speed, p_values.index, p_values,
                                  left=0, right=0)
-    elif density_corr is True:
-        power_output = _p_curve_density_corr(wind_speed, p_values, density)
+    elif density_correction is True:
+        power_output = power_curve_density_correction(wind_speed, p_values,
+                                                      density)
     else:
-        raise TypeError("'{0}' is an invalid type.".format(type(
-                        density_corr)) + "`density_corr` must be Boolean " +
-                        "(True or False).")
+        raise TypeError("'{0}' is an invalid type. ".format(type(
+                        density_correction)) + "`density_corr` 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,
@@ -144,13 +147,13 @@ def power_curve(wind_speed, p_values, density=None, density_corr=False):
     return power_output
 
 
-def _p_curve_density_corr(wind_speed, p_values, density):
+def power_curve_density_correction(wind_speed, p_values, density):
     r"""
     Calculates the turbine power output using a density corrected power curve.
 
     This function is carried out when the parameter `power_output_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class is 'p_values' and
-    the parameter `density_corr` is True.
+    instance of the :class:`~.modelchain.ModelChain` class is 'power_curve'
+    and the parameter `density_correction` is True.
 
     Parameters
     ----------
@@ -209,8 +212,8 @@ def _p_curve_density_corr(wind_speed, p_values, density):
     """
     if density is None:
         raise TypeError("`density` is None. For the calculation with a " +
-                        "density corrected power curve density at hub height" +
-                        "is needed.")
+                        "density corrected power curve density at hub " +
+                        "height is needed.")
     return [(np.interp(wind_speed[i],
                        p_values.index * (1.225 / density[i])**(
                            np.interp(p_values.index, [7.5, 12.5], [1/3, 2/3])),

windpowerlib/wind_speed.py

@@ -18,9 +18,8 @@ def logarithmic_profile(wind_speed, wind_speed_height, hub_height,
 
     The logarithmic height equation is used. There is the possibility of
     including the height of the surrounding obstacles in the calculation. This
-    function is carried out when the parameter `wind_model` of an instance of
-    the :class:`~.modelchain.ModelChain` class is 'logarithmic' or
-    'logarithmic_closest'.
+    function is carried out when the parameter `wind_speed_model` of an
+    instance of the :class:`~.modelchain.ModelChain` class is 'logarithmic'.
 
     Parameters
     ----------
@@ -76,9 +75,9 @@ def logarithmic_profile(wind_speed, wind_speed_height, hub_height,
 
     """
     if 0.7 * obstacle_height > wind_speed_height:
-        raise ValueError("To take an obstacle height of {0} m".format(
-                         obstacle_height) + " into consideration, wind" +
-                         " speed data of a greater height is needed.")
+        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 wind_speed is np.array
     if (isinstance(wind_speed, np.ndarray) and
             isinstance(roughness_length, pd.Series)):
@@ -96,7 +95,7 @@ def hellman(wind_speed, wind_speed_height, hub_height,
     Calculates the wind speed at hub height using the hellman equation.
 
     It is assumed that the wind profile follows a power law. This function is
-    carried out when the parameter `wind_model` of an instance of
+    carried out when the parameter `wind_speed_model` of an instance of
     the :class:`~.modelchain.ModelChain` class is 'hellman'.
 
     Parameters