plot sensitivity center distribution for all configurations in
        config.dat.  The centers of mass are colored by the data given in
        volt_file.

def plot_sens_center(self, frequency=2):
        """
        plot sensitivity center distribution for all configurations in
        config.dat.  The centers of mass are colored by the data given in
        volt_file.
        """
        try:
            colors = np.loadtxt(self.volt_file, skiprows=1)
        except IOError:
            print('IOError opening {0}'.format(volt_file))
            exit()

        # check for 1-dimensionality
        if(len(colors.shape) > 1):
            print('Artificial or Multi frequency data')
            colors = colors[:, frequency].flatten()

        colors = colors[~np.isnan(colors)]

        elem.load_elem_file(self.elem_file)
        elem.load_elec_file(self.elec_file)
        nr_elements = len(elem.element_type_list[0])
        elem.element_data = np.zeros((nr_elements, 1)) * np.nan

        elem.plt_opt.title = ' '
        elem.plt_opt.reverse = True
        elem.plt_opt.cbmin = -1
        elem.plt_opt.cbmax = 1
        elem.plt_opt.cblabel = self.cblabel
        elem.plt_opt.xlabel = 'x (m)'
        elem.plt_opt.ylabel = 'z (m)'

        fig = plt.figure(figsize=(5, 7))
        ax = fig.add_subplot(111)
        ax, pm, cb = elem.plot_element_data_to_ax(0, ax, scale='linear',
                                                  no_cb=True)
        ax.scatter(self.sens_centers[:, 0], self.sens_centers[:, 1], c=colors,
                   s=100, edgecolors='none')

        cb_pos = mpl_get_cb_bound_next_to_plot(ax)
        ax1 = fig.add_axes(cb_pos, frame_on=True)
        cmap = mpl.cm.jet_r
        norm = mpl.colors.Normalize(vmin=np.nanmin(colors),
                                    vmax=np.nanmax(colors))
        mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm,
                                  orientation='vertical')

        fig.savefig(self.output_file, bbox_inches='tight', dpi=300)