code: def create_normal(mu=0., sigma=1., mu_err=0.,
                    sigma_err=1., seed=None, **kwargs):
      
  
      random = np.random.RandomState(seed)
      return create_random(
          magf=random.normal, magf_params={"loc": mu, "scale": sigma},
          errf=random.normal, errf_params={"loc": mu_err, "scale": sigma_err},
          **kwargs)
docs: Generate a data with magnitudes that follows a Gaussian
       distribution. Also their errors are gaussian.
  
      Parameters
      ----------
  
      mu : float (default=0)
          Mean of the gaussian distribution of magnitudes
      sigma : float (default=1)
          Standar deviation of the gaussian distribution of magnitude errors
      mu_err : float (default=0)
          Mean of the gaussian distribution of magnitudes
      sigma_err : float (default=1)
          Standar deviation of the gaussian distribution of magnitude errorrs
      seed : {None, int, array_like}, optional
          Random seed used to initialize the pseudo-random number generator.
          Can be any integer between 0 and 2**32 - 1 inclusive, an
          array (or other sequence) of such integers, or None (the default).
          If seed is None, then RandomState will try to read data from
          /dev/urandom (or the Windows analogue) if available or seed from
          the clock otherwise.
      kwargs : optional
          extra arguments for create_random.
  
      Returns
      -------
  
      data
          A Data object with a random lightcurves.
  
      Examples
      --------
  
      .. code-block:: pycon
  
          >>> ds = create_normal(0, 1, 0, .0008, seed=42)
          >>> ds
          Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
          >>> ds.data.B
          LightCurve(time[10000], magnitude[10000], error[10000])
          >>> ds.data.B.time
          array([  0.00000000e+00,   1.00010001e-04,   2.00020002e-04, ...,
                   9.99799980e-01,   9.99899990e-01,   1.00000000e+00])