code: def create_uniform(low=0., high=1., mu_err=0., sigma_err=1.,
                     seed=None, **kwargs):
      
      random = np.random.RandomState(seed)
      return create_random(
          magf=random.uniform, magf_params={"low": low, "high": high},
          errf=random.normal, errf_params={"loc": mu_err, "scale": sigma_err},
          **kwargs)
docs: Generate a data with magnitudes that follows a uniform
       distribution; the error instead are gaussian.
  
      Parameters
      ----------
  
      low : float, optional
          Lower boundary of the output interval.  All values generated will be
          greater than or equal to low.  The default value is 0.
      high : float, optional
          Upper boundary of the output interval.  All values generated will be
          less than high.  The default value is 1.0.
      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 = synthetic.create_uniform(1, 2, 0, .0008, 42)
          >>> ds
          Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
          >>> ds.data.B.magnitude
          array([ 1.37454012,  1.95071431,  1.73199394, ...,  1.94670792,
                  1.39748799,  1.2171404 ])