code: def plotNLL_v_Flux(nll, fluxType, nstep=25, xlims=None):
      
      import matplotlib.pyplot as plt
  
      if xlims is None:
          xmin = nll.interp.xmin
          xmax = nll.interp.xmax
      else:
          xmin = xlims[0]
          xmax = xlims[1]
  
      y1 = nll.interp(xmin)
      y2 = nll.interp(xmax)
  
      ymin = min(y1, y2, 0.0)
      ymax = max(y1, y2, 0.5)
  
      xvals = np.linspace(xmin, xmax, nstep)
      yvals = nll.interp(xvals)
  
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ax.set_xlim((xmin, xmax))
      ax.set_ylim((ymin, ymax))
  
      ax.set_xlabel(NORM_LABEL[fluxType])
      ax.set_ylabel(r'$-\Delta \log\mathcal{L}$')
      ax.plot(xvals, yvals)
      return fig, ax
docs: Plot the (negative) log-likelihood as a function of normalization
  
      nll   : a LnLFN object
      nstep : Number of steps to plot 
      xlims : x-axis limits, if None, take tem from the nll object
  
      returns fig,ax, which are matplotlib figure and axes objects