code: def get_priors(like):
      
  
      npar = len(like.params())
  
      vals = np.ones(npar)
      errs = np.ones(npar)
      has_prior = np.array([False] * npar)
  
      for i, p in enumerate(like.params()):
  
          prior = like[i].log_prior()
  
          if prior is None:
              continue
  
          par_names = pyLike.StringVector()
          prior.getParamNames(par_names)
  
          if not 'Mean' in par_names:
              raise Exception('Failed to find Mean in prior parameters.')
  
          if not 'Sigma' in par_names:
              raise Exception('Failed to find Sigma in prior parameters.')
  
          for t in par_names:
  
              if t == 'Mean':
                  vals[i] = prior.parameter(t).getValue()
  
              if t == 'Sigma':
                  errs[i] = prior.parameter(t).getValue()
  
          has_prior[i] = True
  
      return vals, errs, has_prior
docs: Extract priors from a likelihood object.