code: def prior_transform(self, unit_coords, priors, prior_args=[]):
      
      theta = []
      for i, (u, p) in enumerate(zip(unit_coords, priors)):
          func = p.unit_transform
          try:
              kwargs = prior_args[i]
          except(IndexError):
              kwargs = {}
          theta.append(func(u, **kwargs))
      return theta
docs: An example of one way to use the `Prior` objects below to go from unit
      cube to parameter space, for nested sampling.  This takes and returns a
      list instead of an array, to accomodate possible vector parameters.  Thus
      one will need something like ``theta_array=np.concatenate(*theta)``
  
      :param unit_coords:
          Coordinates on the unit prior hyper-cube.  Iterable.
  
      :param priors:
          A list of `Prior` objects, iterable of same length as `unit_coords`.
  
      :param prior_args: (optional)
          A list of dictionaries of prior function keyword arguments.
  
      :returns theta:
          A list of parameter values corresponding to the given coordinates on
          the prior unit hypercube.