def METHOD_NAME(hs_image, ax, wl, polarisation=1, wlnorm=True, rescale_axes=False,
                    smoothing=None, contour_lines=None, mult=1.,
                    loc='upper right', xlabels=True, ylabels=True, threshold=None,
                    img_kwargs={}, contour_kwargs={'colors':'k'}, **kwargs):
    """Plots the hyperspectral image at the selected wavelength on a given axis."""
    image = hs_image.get_image(wl, polarisation, **kwargs)
    if wlnorm:
        minimum, maximum = (image.min(), image.max())
    else:
        wavelength, spectra = hs_image.get_spectra(polarisation)
        minimum, maximum = (spectra.min(), spectra.max())
    if threshold is not None:
        maximum = threshold*maximum + (1-threshold)*minimum
    x, unit = scale_axes(hs_image.x)
    y, unit = scale_axes(hs_image.y)
    if rescale_axes:
        x -= x.mean()
        y -= y.mean()
    image *= mult
    if smoothing is not None:
        image = gaussian_filter(image, smoothing)
    img = _plot_image(x, y, image, ax, **img_kwargs)
    if contour_lines is not None:
        ax.contour(x, y, image, contour_lines, **contour_kwargs)
    if xlabels: xlabel = '$x$ (%s)'%unit
    else: xlabel = None
    if ylabels: ylabel = '$y$ (%s)'%unit
    else: ylabel = None
    _format_image_plot(ax, xlabel=xlabel, ylabel=ylabel)
    tx, ty = locs[loc]
    ax.text(tx, ty, str(wl)+' nm', va='top', ha='right',
            transform=ax.transAxes, color='white', fontsize='small', fontweight='bold')
    return img