How to use the mpld3.plugins.clear function in mpld3

if idf.opt['plotLegend']:
                handles, labels = ax.get_legend_handles_labels()
                if idf.opt['outputAs'] == 'html':
                    #TODO: show legend properly (see mpld3 bug #274)
                    #leg = fig.legend(handles, labels, loc='upper right', fancybox=True, fontsize=10, title='')
                    leg = axes[0].legend(handles, labels, loc='upper right', fancybox=True, fontsize=10, title='', prop={'size': 8})
                else:
                    leg = plt.figlegend(handles, labels, loc='upper right', fancybox=True,
                            fontsize=font_size, title='', prop={'size': font_size-3})
                    leg.draggable()

            fig.subplots_adjust(hspace=2)
            fig.set_tight_layout(True)

            if idf.opt['outputAs'] == 'html':
                plugins.clear(fig)
                plugins.connect(fig, plugins.Reset(), plugins.BoxZoom(), plugins.Zoom(enabled=False),
                                plugins.MousePosition(fontsize=14, fmt=".5g"))
                figures.append(mpld3.fig_to_html(fig))
            elif idf.opt['outputAs'] == 'interactive':
                plt.show(block=False)
            elif idf.opt['outputAs'] == 'pdf':
                pp.savefig(plt.gcf())
            elif idf.opt['outputAs'] == 'tikz':
                from matplotlib2tikz import save as tikz_save
                tikz_save('{}_{}_{}.tex'.format(filename,
                    group['dataset'][0]['title'].replace('_','-'), ds // idf.model.num_dofs),
                    figureheight = '\\figureheight', figurewidth = '\\figurewidth', show_info=False)

        if idf.opt['outputAs'] == 'html':
            path = os.path.dirname(os.path.abspath(__file__))
            template_environment = Environment(autoescape=False,

_plot_event(options, event_info, fig, axes, gs, xlim)

        ### we only need to adapt the xoom for one axis object - as we share the x
        zoom_x = [float(x) for x in options.zoom_x.split(',')]
        xlim = axes[0].get_xlim()
        xdiff = xlim[1] - xlim[0]
        axes[0].set_xlim([xlim[0] + (zoom_x[0] * xdiff), xlim[0] + (zoom_x[1] * xdiff)])

        for ax in axes:
            vax.clean_axis(ax)

        plt.tight_layout()
        ### save plot into file
        if options.format == 'd3':
            out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.html' % (options.confidence, gene.name, event_tag, log_tag))
            plugins.clear(fig)
            plugins.connect(fig, plugins.Zoom(enabled=True))
            mpld3.save_html(fig, open(out_fname, 'w'))
        else:
            if options.test_result > 0:
                out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' % (options.confidence, gene.name, event_tag, log_tag, options.format))
            else:
                out_fname = os.path.join(dirname, 'plots', 'gene_overview_C%i_%s%s%s.%s' % (options.confidence, gene.name, event_tag, log_tag, options.format))
            plt.savefig(out_fname, format=options.format, bbox_inches='tight')
        plt.close(fig)

highlight_scaffold_ids : d[scaffold_id] -> color
            Scaffolds in genome to highlight.
        link_scaffold_ids : list of scaffold pairs
            Pairs of scaffolds to link together.
        mean_signature : float
          Mean tetranucleotide signature of genome.
        td_dist : d[length][percentile] -> critical value
          TD distribution.
        percentiles_to_plot : iterable
          Percentile values to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width, self.options.height)

        axes_hist = self.fig.add_subplot(121)
        axes_scatter = self.fig.add_subplot(122)

        self.plot_on_axes(self.fig,
                          genome_scaffold_stats,
                          highlight_scaffold_ids,
                          link_scaffold_ids,
                          mean_signature, td_dist, percentiles_to_plot,
                          axes_hist, axes_scatter, True)

        self.fig.tight_layout(pad=1, w_pad=1)

gc_dist : d[gc][length][percentile] -> critical value
          GC distribution.
        td_dist : d[length][percentile] -> critical value
          TD distribution.
        gc_perc : float
          GC percentile value to mark on plot.
        td_perc : float
          TD percentile value to mark on plot.
        cov_perc : float
            Mean percent deviation to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width, self.options.height)
        
        # create subplots depending on availability of coverage information
        if len(genome_stats.mean_coverage) >= 1:
            # note: the ordering here is specific and ensures
            # proper linked brushing
            axes_gc_dist = self.fig.add_subplot(241)
            axes_tetra_dist = self.fig.add_subplot(242)
            axes_coverage_dist = self.fig.add_subplot(243)
            axes_td_cov = self.fig.add_subplot(246)
            axes_pc1_cov = self.fig.add_subplot(247)
            axes_tetra_pc1_pc3 = self.fig.add_subplot(248)  
            axes_gc_coverage = self.fig.add_subplot(245)

   @property
   def fig(self):
      """ Returns figure with plugins attached."""
      mpld3.plugins.clear(self._fig)
      for p in self._plugins.values():
         if p in LABELPLUGINS:
            for idx, line in enumerate(self.lines):
               mpld3.plugins.connect(self._fig, p(*self.lines))            

         else:
            mpld3.plugins.connect(self._fig, p())
     #    except TypeError:
     #       mpld3.plugins.connect(self._fig, p(*self.lines))            
      return self._fig

genome_scaffold_stats: d[scaffold_id] -> namedtuple of scaffold stats
          Statistics for scaffolds in genome.
        highlight_scaffold_ids : d[scaffold_id] -> color
            Scaffolds in genome to highlight.
        link_scaffold_ids : list of scaffold pairs
            Pairs of scaffolds to link together.
        mean_coverage : float
          Mean coverage profile of genome.
        cov_corrs : iterable
          Coverage correlation values to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width, self.options.height)

        axes_hist = self.fig.add_subplot(121)
        axes_scatter = self.fig.add_subplot(122)

        self.plot_on_axes(self.fig, genome_scaffold_stats,
                          highlight_scaffold_ids,
                          link_scaffold_ids,
                          mean_coverage, cov_corrs,
                          axes_hist, axes_scatter, True)

        self.fig.tight_layout(pad=1, w_pad=1)
        self.draw()

genome_scaffold_stats: d[scaffold_id] -> namedtuple of scaffold stats
          Statistics for scaffolds in genome.
        highlight_scaffold_ids : d[scaffold_id] -> color
            Scaffolds in genome to highlight.
        link_scaffold_ids : list of scaffold pairs
            Pairs of scaffolds to link together.
        mean_coverage : list of float
          Mean coverage profile of genome.
        cov_percs : iterable
          Coverage percentile values to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width, self.options.height)

        axes_hist = self.fig.add_subplot(121)
        axes_scatter = self.fig.add_subplot(122)

        self.plot_on_axes(self.fig, genome_scaffold_stats,
                          highlight_scaffold_ids,
                          link_scaffold_ids,
                          mean_coverage, cov_percs,
                          axes_hist, axes_scatter, True)

        self.fig.tight_layout(pad=1, w_pad=1)
        self.draw()

gc_dist : d[gc][length][percentile] -> critical value
          GC distribution.
        td_dist : d[length][percentile] -> critical value
          TD distribution.
        gc_perc : float
          GC percentile value to mark on plot.
        td_perc : float
          TD percentile value to mark on plot.
        cov_perc : float
            Mean percent deviation to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width*(2.0/3), self.options.height)

        # create subplots depending on availability of coverage information
        if len(genome_stats.mean_coverage) >= 1:
            axes_hist_GC = self.fig.add_subplot(321)
            axes_scatter_GC = self.fig.add_subplot(322)
            axes_hist_TD = self.fig.add_subplot(323)
            axes_scatter_TD = self.fig.add_subplot(324)
            axes_hist_cov_perc = self.fig.add_subplot(325)
            axes_scatter_cov_perc = self.fig.add_subplot(326)
        else:
            axes_hist_GC = self.fig.add_subplot(221)
            axes_scatter_GC = self.fig.add_subplot(222)

highlight_scaffold_ids : d[scaffold_id] -> color
            Scaffolds in genome to highlight.
        link_scaffold_ids : list of scaffold pairs
            Pairs of scaffolds to link together.
        mean_gc : float
          Mean GC of genome.
        gc_dist : d[gc][length][percentile] -> critical value
          GC distribution.
        percentiles_to_plot : iterable
          Percentile values to mark on plot.
        """

        # Set size of figure
        self.fig.clear()

        mpld3.plugins.clear(self.fig)
        mpld3.plugins.connect(self.fig, mpld3.plugins.Reset(), mpld3.plugins.BoxZoom(), mpld3.plugins.Zoom())
        mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=12, fmt='.1f'))

        self.fig.set_size_inches(self.options.width, self.options.height)

        axes_hist = self.fig.add_subplot(121)
        axes_scatter = self.fig.add_subplot(122)

        self.plot_on_axes(self.fig, genome_scaffold_stats,
                          highlight_scaffold_ids,
                          link_scaffold_ids,
                          mean_gc, gc_dist, percentiles_to_plot,
                          axes_hist, axes_scatter, True)

        self.fig.tight_layout(pad=1, w_pad=1)
        self.draw()