code: def gauss_distribution(function_variable, standard_deviation, mean=0):
      r
      return (1 / (standard_deviation * np.sqrt(2 * np.pi)) *
              np.exp(-(function_variable - mean)**2 /
                     (2 * standard_deviation**2)))
docs: r"""
      Gauss distribution.
  
      The Gauss distribution is used in the function
      :py:func:`~.power_curves.smooth_power_curve` for power curve smoothing.
  
      Parameters
      ----------
      function_variable : float
          Variable of the gaussian distribution.
      standard_deviation : float
          Standard deviation of the Gauss distribution.
      mean : Float
          Defines the offset of the Gauss distribution. Default: 0.
  
      Returns
      -------
      pandas.Series or numpy.array
          Wind speed at hub height. Data type depends on the 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:
          :math:`\sigma`: standard deviation, :math:`\mu`: mean
  
      References
      ----------
      .. [1] Berendsen, H.: "A Student's Guide to Data and Error Analysis".
               New York, Cambridge University Press, 2011, p. 37