def METHOD_NAME(fobs,
           sigma,
           fcalc,
           alpha,
           beta,
           epsilon,
           centric,
           out,
           limit=5.0,
           steps=1000,
           plot_title="Outlier plot"):
  fobs_a    = flex.double( [fobs] )
  fcalc_a   = flex.double( [fcalc] )
  epsilon_a = flex.double( [epsilon] )
  alpha_a   = flex.double( [alpha] )
  beta_a    = flex.double( [beta] )
  centric_a = flex.bool  ( [centric] )
  p_calc = scaling.likelihood_ratio_outlier_test(
    fobs_a,
    None,
    fcalc_a,
    epsilon_a,
    centric_a,
    alpha_a,
    beta_a)
  print(file=out)
  print("#Input parameters: ", file=out)
  print("#Title        : ", plot_title, file=out)
  print("#F-calc       : ", fcalc, file=out)
  print("#F-obs        : ", fobs, file=out)
  print("#epsilon      : ", epsilon, file=out)
  print("#alpha        : ", alpha, file=out)
  print("#beta         : ", beta, file=out)
  print("#centric      : ", centric, file=out)
  mode = p_calc.posterior_mode()[0]
  snd_der = math.sqrt(1.0/ math.fabs( p_calc.posterior_mode_snd_der()[0] )  )
  print("#A Gaussian approximation of the likelihood function", file=out)
  print("#could be constructed as follows with: ", file=out)
  print("# exp[-(fobs-mode)**2/(2*stdev**2)] /(sqrt(2 pi) stdev)", file=out)
  print("#with", file=out)
  print("#mode         = ", mode, file=out)
  print("#stdev        = ", snd_der, file=out)
  print(file=out)
  print("#The log likelihood values for the mode and ", file=out)
  print("#observed values are", file=out)
  print("#Log[P(fobs)] : ",  p_calc.log_likelihood()[0], file=out)
  print("#Log[P(mode)] : ",  p_calc.posterior_mode_log_likelihood()[0], file=out)
  print("#Their difference is:", file=out)
  print("#delta        : ",  p_calc.log_likelihood()[0]-p_calc.posterior_mode_log_likelihood()[0], file=out)
  print("#", file=out)
  mean_fobs = p_calc.mean_fobs()
  print("#mean f_obs   : ", mean_fobs[0], "   (first moment)", file=out)
  low_limit = mode-snd_der*limit
  if low_limit<0:
    low_limit=0
  high_limit = mode+limit*snd_der
  if fobs < low_limit:
    low_limit = fobs-2.0*snd_der
    if low_limit<0:
      low_limit=0
  if fobs > high_limit:
    high_limit = fobs+2.0*snd_der
  fobs_a = flex.double( range(steps) )*(
    high_limit-low_limit)/float(steps)+low_limit
  fcalc_a   = flex.double( [fcalc]*steps )
  epsilon_a = flex.double( [epsilon]*steps )
  alpha_a   = flex.double( [alpha]*steps )
  beta_a    = flex.double( [beta]*steps )
  centric_a = flex.bool  ( [centric]*steps )
  p_calc = scaling.likelihood_ratio_outlier_test(
    fobs_a,
    None,
    fcalc_a,
    epsilon_a,
    centric_a,
    alpha_a,
    beta_a)
  ll = p_calc.log_likelihood()    #-p_calc.posterior_mode_log_likelihood()
  ll = flex.exp( ll )
  if (sigma is None) or (sigma <=0 ):
    sigma=fobs/30.0
  obs_gauss = (fobs_a - fobs)/float(sigma)
  obs_gauss = flex.exp( -obs_gauss*obs_gauss/2.0 ) /(
    math.sqrt(2.0*math.pi*sigma*sigma))
  max_ll = flex.max( ll )*1.10
  truncate_mask = flex.bool( obs_gauss >= max_ll )
  obs_gauss = obs_gauss.set_selected( truncate_mask, max_ll )
  ccp4_loggraph_plot = data_plots.plot_data(
    plot_title=plot_title,
    x_label = 'Fobs',
    y_label = 'P(Fobs)',
    x_data = fobs_a,
    y_data = ll,
    y_legend = 'P(Fobs|Fcalc,alpha,beta)',
    comments = 'Fobs=%5.2f, sigma=%5.2f, Fcalc=%5.2f'%(fobs,sigma,fcalc) )
  ccp4_loggraph_plot.add_data(
    y_data = obs_gauss,
    y_legend = "P(Fobs|<Fobs>,sigma)"
    )
  data_plots.plot_data_loggraph(ccp4_loggraph_plot,out)