Merge branch 'features/tools' into features/tests

Conflicts:
	doc/modules.rst
	windpowerlib/density.py
	windpowerlib/modelchain.py

Author: SabineHaas

doc/modules.rst

@@ -105,6 +105,7 @@ Additional functions used in the windpowerlib.
    :toctree: temp/
 
    tools.smallest_difference
+   tools.linear_extra_interpolation
 
 
 Example

windpowerlib/density.py

@@ -59,53 +59,6 @@ def temperature_gradient(temp_air, temp_height, hub_height):
     return temp_air - 0.0065 * (hub_height - temp_height)
 
 
-def temperature_interpol(temp_air_1, temp_air_2,
-                         temp_air_height_1, temp_air_height_2, hub_height):
-    r"""
-    Calculates the temperature at hub height by inter- or extrapolation.
-
-    This function is carried out when the parameter `temperature_model` of an
-    instance of the :class:`~.modelchain.ModelChain` class
-    is 'interpolation'.
-
-    Parameters
-    ----------
-    temp_air_1 : pandas.Series or numpy.array
-        Air temperature.
-    temp_air_2 : pandas.Series or numpy.array
-        Second air temperature for interpolation.
-    temp_air_height_1 : float
-        Height for which the parameter `temp_air_1` applies.
-    temp_air_height_2 : float
-        Height for which the parameter `temp_air_2` applies.
-    hub_height : float
-        Hub height of wind turbine in m.
-
-    Returns
-    -------
-    pandas.Series or numpy.array
-        Temperature at hub height.
-
-    Notes
-    -----
-
-    The following equation is used:
-
-    .. math:: T_{hub} = (T_2 - T_1) / (h_2 - h_1) * (h_{hub} - h_1) + T_1
-
-    with:
-        T: temperature, h: height
-
-    Assumptions:
-
-    * linear temperature gradient
-
-    """
-    return ((temp_air_2 - temp_air_1) /
-            (temp_air_height_2 - temp_air_height_1) *
-            (hub_height - temp_air_height_1) + temp_air_1)
-
-
 def rho_barometric(pressure, pressure_height, hub_height, temp_hub):
     r"""
     Calculates the density of air at hub height using the barometric height

windpowerlib/modelchain.py

@@ -31,10 +31,6 @@ class ModelChain(object):
         Parameter to define which model to use to calculate the density of air
         at hub height. Valid options are 'barometric' and 'ideal_gas'.
         Default: 'barometric'.
-    temperature_model : string
-        Parameter to define which model to use to calculate the temperature at
-        hub height. Valid options are 'gradient' and 'interpolation'.
-        Default: 'gradient'.
     power_output_model : string
         Parameter to define which model to use to calculate the turbine power
         output. Valid options are 'cp_values' and 'p_values'.
@@ -63,10 +59,6 @@ class ModelChain(object):
         Parameter to define which model to use to calculate the density of air
         at hub height. Valid options are 'barometric' and 'ideal_gas'.
         Default: 'barometric'.
-    temperature_model : string
-        Parameter to define which model to use to calculate the temperature at
-        hub height. Valid options are 'gradient' and 'interpolation'.
-        Default: 'gradient'.
     power_output_model : string
         Parameter to define which model to use to calculate the turbine power
         output. Valid options are 'cp_values' and 'p_values'.
@@ -90,8 +82,7 @@ class ModelChain(object):
     ...    'd_rotor': 127,
     ...    'wind_conv_type': 'ENERCON E 126 7500'}
     >>> e126 = wind_turbine.WindTurbine(**enerconE126)
-    >>> modelchain_data = {'rho_model': 'ideal_gas',
-    ...    'temperature_model': 'interpolation'}
+    >>> modelchain_data = {'rho_model': 'ideal_gas'}
     >>> e126_md = modelchain.ModelChain(e126, **modelchain_data)
     >>> print(e126.d_rotor)
     127
@@ -102,7 +93,6 @@ def __init__(self, wind_turbine,
                  obstacle_height=0,
                  wind_model='logarithmic',
                  rho_model='barometric',
-                 temperature_model='gradient',
                  power_output_model='p_values',
                  density_corr=False,
                  hellman_exp=None):
@@ -111,7 +101,6 @@ def __init__(self, wind_turbine,
         self.obstacle_height = obstacle_height
         self.wind_model = wind_model
         self.rho_model = rho_model
-        self.temperature_model = temperature_model
         self.power_output_model = power_output_model
         self.density_corr = density_corr
         self.hellman_exp = hellman_exp
@@ -123,16 +112,13 @@ def rho_hub(self, weather, data_height):
 
         The density is calculated using the method specified by the parameter
         `rho_model`. Previous to the calculation of density the temperature at
-        hub height is calculated using the method specified by the parameter
-        `temperature_model`.
+        hub height is calculated using a linear temperature gradient.
 
         Parameters
         ----------
         weather : DataFrame or Dictionary
             Containing columns or keys with timeseries for temperature
-            `temp_air` in K and pressure `pressure` in Pa, as well as
-            optionally the temperature `temp_air_2` in K at a different height
-            for interpolation.
+            `temp_air` in K and pressure `pressure` in Pa.
             If a Dictionary is used the data inside the dictionary has to be of
             the types pandas.Series or numpy.array.
         data_height : Dictionary
@@ -161,25 +147,11 @@ def rho_hub(self, weather, data_height):
             logging.debug('Using given temperature at hub height.')
             temp_hub = temp_air_closest
         # Calculation of temperature in K at hub height.
-        elif self.temperature_model == 'gradient':
+        else:
             logging.debug('Calculating temperature using a temp. gradient.')
             temp_hub = density.temperature_gradient(
                 temp_air_closest, temp_air_height,
                 self.wind_turbine.hub_height)
-        elif self.temperature_model == 'interpolation':
-            try:
-                logging.debug('Calculating temperature using interpolation.')
-                temp_hub = density.temperature_interpol(
-                    weather['temp_air'], weather['temp_air_2'],
-                    data_height['temp_air'], data_height['temp_air_2'],
-                    self.wind_turbine.hub_height)
-            except TypeError:
-                raise KeyError("No 'temp_air_2' in `weather`, but needed " +
-                               "for interpolation.")
-        else:
-            raise ValueError("'{0}' is an invalid value.".format(
-                             self.temperature_model) + "`temperature_model` " +
-                             "must be 'gradient' or 'interpolation'.")
         # Calculation of density in kg/m³ at hub height
         if self.rho_model == 'barometric':
             logging.debug('Calculating density using barometric height eq.')
@@ -333,8 +305,8 @@ def run_model(self, weather, data_height):
             Containing columns or keys with the timeseries for wind speed
             `v_wind` in m/s, roughness length `z0` in m, temperature
             `temp_air` in K and pressure `pressure` in Pa, as well as
-            optionally wind speed `v_wind_2` in m/s and temperature
-            `temp_air_2` in K at different height for interpolation.
+            optionally wind speed `v_wind_2` in m/s in K at different height
+            for interpolation.
             If a Dictionary is used the data inside the dictionary has to be of
             the types pandas.Series or numpy.array.
         data_height : Dictionary

windpowerlib/tools.py

@@ -4,6 +4,9 @@
 
 """
 
+import numpy as np
+
+
 __copyright__ = "Copyright oemof developer group"
 __license__ = "GPLv3"
 
@@ -28,14 +31,14 @@ def smallest_difference(value_1, value_2, comp_value, corresp_1, corresp_2):
         Second value for comparison.
     comp_value : float
         Comparative value.
-    corresp_1 : float
+    corresp_1 : pd.Series or np.array or float
         Corresponding value to `value_1`.
-    corresp_2 : float
+    corresp_2 : pd.Series or np.array or float
         Corresponding value to `value_2`.
 
     Returns
     -------
-    Tuple(float, float)
+    Tuple(float, pd.Series or np.array or float)
         Value closer to comparing value as float and its corresponding value as
         float.
 
@@ -54,3 +57,67 @@ def smallest_difference(value_1, value_2, comp_value, corresp_1, corresp_2):
     else:
         corresp_value = corresp_2
     return (closest_value, corresp_value)
+
+
+def linear_extra_interpolation(data_frame, requested_height, column_name):
+    r"""
+    Inter- or extrapolates between the values of a data frame.
+
+    This function can for example be used for the interpolation of a wind
+    speed, density or temperature.
+
+    Parameters
+    ----------
+    data_frame : DataFrame
+        Indices are the values between which will be interpolated or from which
+        will be extrapolated, the corresponding values are in the column
+        specified by `column_name` and they can be floats, pd.Series or
+        np.arrays.
+    requested_height : float
+        Height for which the interpolation takes place (e.g. hub height of wind
+        turbine).
+    column_name : string
+        Name of the column in the DataFrame `data_frame` that contains the
+        correponding values.
+
+    Returns
+    -------
+    interpolant : pandas.Series, numpy.array or float
+        Result of the interpolation (e.g. density at hub height).
+
+    Notes
+    -----
+
+    For the interpolation np.interp() is used and the following equation is
+    used for extrapolation:
+
+    .. math:: interpolant = (value_2 - value_1) / (height_2 - height_1) *
+              (height_{requested} - height_1) + value_1
+
+    with:
+        :math:`height_2`: largest/smallest value in data frame,
+        :math:`height_1`: second largest/smallest value in data frame,
+        :math:`value_2`: corresponding value to `height_2`,
+        :math:`value_1`: correponding value to `height_1`,
+        :math:`height_{requested}` : Height for which the interpolation takes
+        place
+
+    """
+    if requested_height > max(data_frame.index):
+        height_2 = max(data_frame.index)
+        value_2 = data_frame[column_name][height_2]
+        height_1 = sorted(data_frame.index)[-2]  # Second largest number
+        value_1 = data_frame[column_name][height_1]
+        interpolant = ((value_2 - value_1) / (height_2 - height_1) *
+                       (requested_height - height_1) + value_1)
+    elif requested_height < min(data_frame.index):
+        height_2 = min(data_frame.index)
+        value_2 = data_frame[column_name][height_2]
+        height_1 = sorted(data_frame.index)[1]  # Second smallest number
+        value_1 = data_frame[column_name][height_1]
+        interpolant = ((value_2 - value_1) / (height_2 - height_1) *
+                       (requested_height - height_1) + value_1)
+    else:
+        interpolant = np.interp(requested_height, data_frame.index,
+                                data_frame[column_name])
+    return interpolant