Plot element data (parameter sets).

        If the parameter *ax* is not set, then a new figure will be created
        with a corresponding axes.


        Parameters
        ----------
        cid : int or :py:class:`numpy.ndarray`
            if *cid* is an int, then treat it as the id of the parameter set
            stored in self.parman. Otherwise, expect it to be the data to plot.
            At the moment no checks are made that the data fits the grid.
        ax : matplotlib.Axes, optional
            plot to this axes object, if provided
        alpha_cid : int, optional
            if given, use the corresponding dataset in self.parman as the alpha
            channel. No checks are made if all values of this data set lie
            between 0 and 1 (0 being fully transparent, and 1 being opaque).
        xmin : float, optional
            minimal x limit to plot
        xmax : float, optional
            maximal x limit to plot
        zmin : float, optional
            minimal z limit to plot
        zmax : float, optional
            maximial z limit to plot
        converter : function, optional
            if given, then use this function to convert the data into another
            representation. The given function must work with a numpy array.
            Default: None
        norm : norm object, optional
            the norm object for matplotlib plotting can be provided here
        cmap_name : string, optional
            name of the colorbar to use. Default is "viridis". To reverse
            colors, use the _r version "viridis_r"
        cbposition : ?
            ?
        cblabel : string, optional
            colorbar label
        cbsegments : int, optional
            ?
        cbnrticks : int, optional
            ?
        over : color, optional
            color to use for values above the current cb-limit. Default: ?
        under :
            color to use for values below the current cb-limit. Default: ?
        bad :
            color to use for nan-values. Default: ?
        plot_colorbar : bool, optional
            if true, plot a colorbar next to the plot
        title : string, optional
            plot title string
        xlabel : string, optional
            Set xlabel of the resulting plot
        ylabel : string, optional
            Set ylabel of the resulting plot
        no_elecs : bool, optional
            If True, plot no electrodes
        rasterize: bool, optional
            if True, rasterize the plot. Default: False

        Returns
        -------
        fig:

        ax:

        cnorm:

        cmap:

        cb: colorbar instance, optional
            only of plot_colorbar is True
        scalarMap:
            use to create custom colorbars

def plot_elements_to_ax(self, cid, ax=None, **kwargs):
        """Plot element data (parameter sets).

        If the parameter *ax* is not set, then a new figure will be created
        with a corresponding axes.


        Parameters
        ----------
        cid : int or :py:class:`numpy.ndarray`
            if *cid* is an int, then treat it as the id of the parameter set
            stored in self.parman. Otherwise, expect it to be the data to plot.
            At the moment no checks are made that the data fits the grid.
        ax : matplotlib.Axes, optional
            plot to this axes object, if provided
        alpha_cid : int, optional
            if given, use the corresponding dataset in self.parman as the alpha
            channel. No checks are made if all values of this data set lie
            between 0 and 1 (0 being fully transparent, and 1 being opaque).
        xmin : float, optional
            minimal x limit to plot
        xmax : float, optional
            maximal x limit to plot
        zmin : float, optional
            minimal z limit to plot
        zmax : float, optional
            maximial z limit to plot
        converter : function, optional
            if given, then use this function to convert the data into another
            representation. The given function must work with a numpy array.
            Default: None
        norm : norm object, optional
            the norm object for matplotlib plotting can be provided here
        cmap_name : string, optional
            name of the colorbar to use. Default is "viridis". To reverse
            colors, use the _r version "viridis_r"
        cbposition : ?
            ?
        cblabel : string, optional
            colorbar label
        cbsegments : int, optional
            ?
        cbnrticks : int, optional
            ?
        over : color, optional
            color to use for values above the current cb-limit. Default: ?
        under :
            color to use for values below the current cb-limit. Default: ?
        bad :
            color to use for nan-values. Default: ?
        plot_colorbar : bool, optional
            if true, plot a colorbar next to the plot
        title : string, optional
            plot title string
        xlabel : string, optional
            Set xlabel of the resulting plot
        ylabel : string, optional
            Set ylabel of the resulting plot
        no_elecs : bool, optional
            If True, plot no electrodes
        rasterize: bool, optional
            if True, rasterize the plot. Default: False

        Returns
        -------
        fig:

        ax:

        cnorm:

        cmap:

        cb: colorbar instance, optional
            only of plot_colorbar is True
        scalarMap:
            use to create custom colorbars

        """

        rasterize = kwargs.get('rasterize', False)
        xmin = kwargs.get('xmin', self.grid.grid['x'].min())
        xmax = kwargs.get('xmax', self.grid.grid['x'].max())
        zmin = kwargs.get('zmin', self.grid.grid['z'].min())
        zmax = kwargs.get('zmax', self.grid.grid['z'].max())

        # try to create a suitable default figure size
        if ax is None:
            # 15 cm
            sizex = 15 / 2.54
            sizez = sizex * (np.abs(zmax - zmin) / np.abs(xmax - xmin) * 1.1)
            # add 1 inch to accommodate colorbar
            sizez += 1.3
            fig, ax = plt.subplots(figsize=(sizex, sizez))
        else:
            fig = ax.get_figure()
            sizex, sizez = fig.get_size_inches()

        # get data
        if isinstance(cid, int):
            subdata = self.parman.parsets[cid]
        else:
            subdata = cid

        if 'converter' in kwargs:
            subdata = kwargs['converter'](subdata)

        # color map
        cmap_name = kwargs.get('cmap_name', 'viridis')
        cmap = mpl.cm.get_cmap(
            cmap_name,
            kwargs.get('cbsegments', None)
        )
        over = kwargs.get('over', 'orange')
        under = kwargs.get('under', 'mediumblue')
        bad = kwargs.get('bad', 'white')
        cmap.set_over(over)
        cmap.set_under(under)
        cmap.set_bad(bad)

        # normalize data
        data_min = kwargs.get('cbmin', subdata.min())
        data_max = kwargs.get('cbmax', subdata.max())
        if(data_min is not None and data_max is not None and
           data_min == data_max):
            data_min -= 1
            data_max += 1
        cnorm = mpl.colors.Normalize(vmin=data_min, vmax=data_max)
        scalarMap = mpl.cm.ScalarMappable(norm=cnorm, cmap=cmap)
        fcolors = scalarMap.to_rgba(subdata)
        scalarMap.set_array(subdata)

        # if applicable, apply alpha values
        alpha_cid = kwargs.get('cid_alpha', None)
        if isinstance(alpha_cid, int):
            print('applying alpha')
            alpha = self.parman.parsets[alpha_cid]
            # make sure this data set is normalized between 0 and 1
            if np.nanmin(alpha) < 0 or np.nanmax(alpha) > 1:
                raise Exception(
                    'alpha data set must be normalized between 0 and 1'
                )
            fcolors[:, 3] = alpha

        all_xz = []
        for x, z in zip(self.grid.grid['x'], self.grid.grid['z']):
            tmp = np.vstack((x, z)).T
            all_xz.append(tmp)

        norm = kwargs.get('norm', None)

        collection = mpl.collections.PolyCollection(
            all_xz,
            edgecolor=fcolors,
            facecolor=fcolors,
            linewidth=0.0,
            cmap=cmap,
            norm=norm,
            rasterized=rasterize,
        )
        collection.set_cmap(cmap)
        ax.add_collection(collection)
        no_elecs = kwargs.get('no_elecs', False)
        if self.grid.electrodes is not None and no_elecs is not True:
            ax.scatter(
                self.grid.electrodes[:, 1],
                self.grid.electrodes[:, 2],
                color=self.grid.props['electrode_color'],
                # clip_on=False,
            )

        ax.set_xlim(xmin, xmax)
        ax.set_ylim(zmin, zmax)
        ax.set_xlabel(kwargs.get('xlabel', 'x'))
        ax.set_ylabel(kwargs.get('zlabel', 'z'))
        ax.set_aspect('equal')
        ax.set_title(
            kwargs.get('title', '')
        )

        if kwargs.get('plot_colorbar', False):
            divider = make_axes_locatable(ax)
            cbposition = kwargs.get('cbposition', 'vertical')
            if cbposition == 'horizontal':
                ax_cb = divider.new_vertical(
                    size=0.1, pad=0.4, pack_start=True
                )
            elif cbposition == 'vertical':
                ax_cb = divider.new_horizontal(
                    size=0.1, pad=0.4,
                )
            else:
                raise Exception('cbposition not recognized')

            ax.get_figure().add_axes(ax_cb)

            cb = fig.colorbar(
                scalarMap,
                cax=ax_cb,
                orientation=cbposition,
                label=kwargs.get('cblabel', ''),
                ticks=mpl.ticker.MaxNLocator(kwargs.get('cbnrticks', 3)),
                format=kwargs.get('cbformat', None),
                extend='both',
            )

            return fig, ax, cnorm, cmap, cb, scalarMap

        return fig, ax, cnorm, cmap, scalarMap