def plotNLL_v_Flux(nll, fluxType, nstep=25, xlims=None):
    """ 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
    """
    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