code: def hsepd_manual(sigma1, sigma2, xi, beta,
                   sim=None, obs=None, node=None, skip_nan=False):
      
      sim, obs = prepare_arrays(sim, obs, node, skip_nan)
      return _hsepd_manual(sigma1, sigma2, xi, beta, sim, obs)
docs: Calculate the mean of the logarithmised probability densities of the
      'heteroskedastic skewed exponential power distribution.
  
      The following examples are taken from the documentation of function
      |hsepd_pdf|, which is used by function |hsepd_manual|.  The first
      one deals with a heteroscedastic normal distribution:
  
      >>> from hydpy import round_
      >>> from hydpy import hsepd_manual
      >>> round_(hsepd_manual(sigma1=0.2, sigma2=0.2,
      ...                     xi=1.0, beta=0.0,
      ...                     sim=numpy.arange(10.0, 41.0),
      ...                     obs=numpy.full(31, 25.0)))
      -3.682842
  
      The second one is supposed to show to small zero probability density
      values are set to 1e-200 before calculating their logarithm (which
      means that the lowest possible value returned by function
      |hsepd_manual| is approximately -460):
  
      >>> round_(hsepd_manual(sigma1=0.2, sigma2=0.0,
      ...                     xi=1.0, beta=-0.99,
      ...                     sim=numpy.arange(10.0, 41.0),
      ...                     obs=numpy.full(31, 25.0)))
      -209.539335