Merge branch 'features/wind_farms_and_clusters' into dev

Author: SabineHaas

windpowerlib/power_output.py

@@ -9,6 +9,7 @@
 
 import numpy as np
 import pandas as pd
+from windpowerlib import tools
 
 
 def power_coefficient_curve(wind_speed, power_coefficient_curve_wind_speeds,

windpowerlib/tools.py

@@ -71,3 +71,53 @@ def linear_interpolation_extrapolation(df, target_height):
     return ((df[heights_sorted[1]] - df[heights_sorted[0]]) /
             (heights_sorted[1] - heights_sorted[0]) *
             (target_height - heights_sorted[0]) + df[heights_sorted[0]])
+
+
+def gaussian_distribution(function_variable, standard_deviation, mean=0):
+    r"""
+    Normal distribution or gaussian distribution.
+
+    Parameters
+    ----------
+    function_variable : Float
+        Variable of the gaussian distribution.
+    standard_deviation : Float
+        Standard deviation of the gaussian distribution.
+    mean : Float
+        Defines the offset of the gaussian distribution function. Default: 0.
+
+    Returns
+    -------
+    pandas.Series or numpy.array
+        Wind speed at hub height. Data type depends on type of `wind_speed`.
+
+    Notes
+    -----
+    The following equation is used [1]_:
+
+    .. math:: f(x) = \frac{1}{\sigma \sqrt{2 \pi}} exp
+                     \left[ -\frac{(x-\mu)^2}{2 \sigma^2}  \right]
+
+    with:
+        # TODO: add variables
+
+    References
+    ----------
+    .. [1] Berendsen, H.: "A Student's Guide to Data and Error Analysis".
+             New York, Cambridge University Press, 2011, p. 37
+    
+    # TODO: add references
+
+    """
+    return (1 / (standard_deviation * np.sqrt(2 * np.pi)) *
+            np.exp(-(function_variable - mean)**2 /
+                   (2 * standard_deviation**2)))
+
+
+def estimate_turbulence_intensity(height, roughness_length):
+    """
+    Calculate turbulence intensity.
+
+    """
+    # TODO: Search other possibilities for TI.
+    return 1 / (np.log(height / roughness_length))