def METHOD_NAME(hs_image, ax, axis, line, imnorm=False, linenorm=False, dat='', smooth=None):
    if dat=='':
        spectra = hs_image.spectra
        wavelength = hs_image.wavelength
    elif dat=='trans':
        data = hs_image.data_t
        wavelength = hs_image.wavelength_t
    line_spectra = hs_image.get_line_spectra(axis, line, dat)
    if smooth != None: line_spectra = gaussian_filter(line_spectra, smooth)
    if linenorm == True:
        for i in range(line_spectra.shape[0]):
            line_spectra[i] = old_div(line_spectra[i], line_spectra[i].max())
    if imnorm == True:
        minimum = line_spectra.min()
        maximum = line_spectra.max()
    else:
        minimum = data.min() * (data.min()>=0.0)
        maximum = data.max() * (data.max()>=0.0)
    #lev_exp = np.linspace(np.log10(minimum), np.log10(maximum), 200)
    #levs = np.power(10, lev_exp)
    #norm = LogNorm(minimum, maximum)
    levs = np.linspace(minimum, maximum, 200)
    norm = cm.colors.Normalize(vmax=maximum, vmin=minimum)
    X, Y = np.meshgrid(hs_image.x, wavelength, indexing='ij')
    ax.contourf(X, Y, line_spectra, levs, norm=norm, cmap=cm.CMRmap)
    if axis=='x': label = 'y={0:.2f} nm'.format(hs_image.x[line])
    if axis=='y': label = 'x={0:.2f} nm'.format(hs_image.y[line])
    ax.text(0.95, 0.95, label, va='top', ha='right',
            transform=ax.transAxes, color='white', fontsize='small')
    ax.tick_params(axis='both', which='major', labelsize='small')
    ax.set_xlim(hs_image.x.min(), hs_image.x.max())
    ax.set_ylim(wavelength.min(), wavelength.max())
    _format_image_plot(ax)
    return line_spectr