def plot_pseudosection(df, plot_key, spacing=1, ctypes=None, dd_merge=False,
                       cb=False, **kwargs):
    """Create a pseudosection plot for a given measurement

    Parameters
    ----------
    df: dataframe
        measurement dataframe, one measurement frame (i.e., only one frequency
        etc)
    key:
        which key to colorcode
    spacing: float, optional
        assumed electrode spacing
    ctypes: list of strings
        which configurations to plot, default: dd
    dd_merge: bool, optional
        ?
    cb: bool, optional
        ?

    """
    grid = None

    pseudo_d_functions = {
        'dd': _pseudodepths_dd_simple,
        'schlumberger': _pseudodepths_schlumberger,
        'wenner': _pseudodepths_wenner,
    }

    titles = {
        'dd': 'dipole-dipole configurations',
        'schlumberger': 'Schlumberger configurations',
        'wenner': 'Wenner configurations',
    }

    # for now sort data and only plot dipole-dipole
    only_types = ctypes or ['dd', ]
    if 'schlumberger' in only_types:
        raise Exception('plotting of pseudosections not implemented for ' +
                        'Schlumberger configurations!')

    configs = df[['a', 'b', 'm', 'n']].values
    results = fT.filter(
        configs,
        settings={'only_types': only_types, }, )
    values = df[plot_key].values

    plot_objects = []
    for key in sorted(results.keys()):
        print('plotting: ', key)
        if key == 'not_sorted':
            continue
        index_dict = results[key]
        # it is possible that we want to generate multiple plots for one
        # type of measurement, i.e., to separate skips of dipole-dipole
        # measurements. Therefore we generate two lists:
        # 1) list of list of indices to plot
        # 2) corresponding labels
        if key == 'dd' and not dd_merge:
            plot_list = []
            labels_add = []
            for skip in sorted(index_dict.keys()):
                plot_list.append(index_dict[skip])
                labels_add.append(' - skip {0}'.format(skip))
        else:
            # merge all indices
            plot_list = [np.hstack(index_dict.values()), ]
            print('schlumberger', plot_list)
            labels_add = ['', ]

        # generate plots
        for indices, label_add in zip(plot_list, labels_add):
            if len(indices) == 0:
                continue

            ddc = configs[indices]
            plot_data = values[indices]
            px, pz = pseudo_d_functions[key](ddc, spacing, grid)
            # we need at least four points for a spatial interpolation, I
            # think...
            if px.size <= 4:
                continue

            # take 200 points for the new grid in every direction. Could be
            # adapted to the actual ratio
            xg = np.linspace(px.min(), px.max(), 200)
            zg = np.linspace(pz.min(), pz.max(), 200)

            x, z = np.meshgrid(xg, zg)

            cmap_name = kwargs.get('cmap_name', 'jet')
            cmap = mpl.cm.get_cmap(cmap_name)

            # normalize data
            data_min = kwargs.get('cbmin', plot_data.min())
            data_max = kwargs.get('cbmax', plot_data.max())
            cnorm = mpl.colors.Normalize(vmin=data_min, vmax=data_max)
            scalarMap = mpl.cm.ScalarMappable(norm=cnorm, cmap=cmap)
            fcolors = scalarMap.to_rgba(plot_data)

            try:
                image = si.griddata(
                    (px, pz),
                    fcolors,
                    (x, z),
                    method='linear', )
            except siq.QhullError as e:
                print('Ex', e)
                continue

            cmap = mpl.cm.get_cmap('jet_r')

            data_ratio = np.abs(px.max() - px.min()) / np.abs(pz.min())

            fig_size_y = 15 / data_ratio + 6 / 2.54
            fig = plt.figure(figsize=(15, fig_size_y))

            fig_top = 1 / 2.54 / fig_size_y
            fig_left = 2 / 2.54 / 15
            fig_right = 1 / 2.54 / 15
            if cb:
                fig_bottom = 3 / 2.54 / fig_size_y
            else:
                fig_bottom = 0.05

            ax = fig.add_axes([
                fig_left, fig_bottom + fig_top * 2, 1 - fig_left - fig_right,
                1 - fig_top - fig_bottom - fig_top * 2
            ])

            im = ax.imshow(
                image[::-1],
                extent=(xg.min(), xg.max(), zg.min(), zg.max()),
                interpolation='none',
                aspect='auto',
                # vmin=10,
                # vmax=300,
                cmap=cmap, )
            ax.set_ylim(pz.min(), 0)

            # colorbar
            if cb:
                print('plotting colorbar')
                # the colorbar has 3 cm on the bottom
                ax_cb = fig.add_axes([
                    fig_left * 4, fig_top * 2,
                    1 - fig_left * 4 - fig_right * 4, fig_bottom - fig_top * 2
                ])
                # from mpl_toolkits.axes_grid1 import make_axes_locatable
                # divider = make_axes_locatable(ax)
                # ax_cb = divider.append_axes("bottom", "5%", pad="3%")
                # (ax_cb, kw) = mpl.colorbar.make_axes_gridspec(
                #     ax,
                #     orientation='horizontal',
                #     fraction=fig_bottom,
                #     pad=0.3,
                #     shrink=0.9,
                #     # location='bottom',
                # )
                cb = mpl.colorbar.ColorbarBase(
                    ax=ax_cb,
                    cmap=cmap,
                    norm=cnorm,
                    orientation='horizontal',
                    # **kw
                )
                cb.set_label('cb label')
            else:
                fig_bottom = 0.05

            # 1cm on top

            # # 3 cm on bottom for colorbar
            # fig.subplots_adjust(
            #     top=1 - fig_top,
            #     bottom=fig_bottom,
            # )

            ax.set_title(titles[key] + label_add)
            ax.set_aspect('equal')
            ax.set_xlabel('x [m]')
            ax.set_ylabel('x [z]')
            plot_objects.append((fig, ax, im))

    return plot_objects
Create a pseudosection plot for a given measurement

    Parameters
    ----------
    df: dataframe
        measurement dataframe, one measurement frame (i.e., only one frequency
        etc)
    key:
        which key to colorcode
    spacing: float, optional
        assumed electrode spacing
    ctypes: list of strings
        which configurations to plot, default: dd
    dd_merge: bool, optional
        ?
    cb: bool, optional
        ?