How to use the multiqc.plots.scatter.plot function in multiqc

'title': 'Peddy: Sex Check',
            'xlab': 'Sex From Ped',
            'ylab': 'Sex Het Ratio',
            'categories': ["Female", "Male", "Unknown"]
        }

        self.add_section(
            name = 'Sex Check',
            description = "Predicted sex against heterozygosity ratio",
            helptext = """
            Higher values of Sex Het Ratio suggests the sample is female, low values suggest male.

            See [the main peddy documentation](http://peddy.readthedocs.io/en/latest/#sex-check) for more details about the `het_check` command.
            """,
            anchor='peddy-sexcheck-plot',
            plot=scatter.plot(data, pconfig)
        )

}

        pconfig = {
            'id': 'peddy_pca_plot',
            'title': 'Peddy: PCA Plot',
            'xlab': 'PC1',
            'ylab': 'PC2',
            'marker_size': 5,
            'marker_line_width': 0
        }

        if len(data) > 0:
            self.add_section (
                name = 'PCA Plot',
                anchor = 'peddy-pca-plot',
                plot = scatter.plot(data, pconfig)
            )

pconfig['sortRows'] = pconfig.get('sortRows', False)
            headers = mod['config'].get('headers')
            self.add_section( plot = table.plot(mod['data'], headers, pconfig) )
            self.write_data_file( mod['data'], "multiqc_{}".format(modname.lower().replace(' ', '_')) )

        # Bar plot
        elif mod['config'].get('plot_type') == 'bargraph':
            self.add_section( plot = bargraph.plot(mod['data'], mod['config'].get('categories'), pconfig) )

        # Line plot
        elif mod['config'].get('plot_type') == 'linegraph':
            self.add_section( plot = linegraph.plot(mod['data'], pconfig) )

        # Scatter plot
        elif mod['config'].get('plot_type') == 'scatter':
            self.add_section( plot = scatter.plot(mod['data'], pconfig) )

        # Heatmap
        elif mod['config'].get('plot_type') == 'heatmap':
            self.add_section( plot = heatmap.plot(mod['data'], mod['config'].get('xcats'), mod['config'].get('ycats'), pconfig) )

        # Beeswarm plot
        elif mod['config'].get('plot_type') == 'beeswarm':
            self.add_section( plot = beeswarm.plot(mod['data'], pconfig) )

        # Raw HTML
        elif mod['config'].get('plot_type') == 'html':
            self.add_section( content = mod['data'] )

        # Raw image file as html
        elif mod['config'].get('plot_type') == 'image':
            self.add_section( content = mod['data'] )

'id': 'peddy_het_check_plot',
            'title': 'Peddy: Het Check',
            'xlab': 'median depth',
            'ylab': 'proportion het calls',
        }

        self.add_section (
            name = 'Het Check',
            description = "Proportion of sites that were heterozygous against median depth.",
            helptext = """
            A high proportion of heterozygous sites suggests contamination, a low proportion suggests consanguinity.
            
            See [the main peddy documentation](https://peddy.readthedocs.io/en/latest/output.html#het-check) for more details about the `het_check` command.
            """,
            anchor = 'peddy-hetcheck-plot',
            plot = scatter.plot(data, pconfig)
        )

data = self.ignore_samples(data)

        if data:
            log.info("Found goleft indexcov bin reports for %s samples" % (len(data)))
            pconfig = {
                'id': 'goleft_indexcov-bin-plot',
                'title': 'goleft indexcov: Problematic low and non-uniform coverage bins',
                'xlab': 'Proportion of bins with depth < 0.15',
                'ylab': 'Proportion of bins with depth outside of (0.85, 1.15)',
                'yCeiling': 1.0, 'yFloor': 0.0, 'xCeiling': 1.0, 'xFloor': 0.0}
            self.add_section (
                name = 'Problem coverage bins',
                anchor = 'goleft_indexcov-bin',
                description = 'This plot identifies problematic samples using binned coverage distributions.',
                helptext = helptext,
                plot = scatter.plot(data, pconfig)
            )
            return True
        else:
            return False

pconfig = {
            'id': 'slamdunk_pca',
            'title': 'Slamdunk: PCA',
            'xlab': 'PC1',
            'ylab': 'PC2',
            'tt_label': 'PC1 {point.x:.2f}: PC2 {point.y:.2f}'
        }

        self.add_section (
            name = 'PCA (T>C based)',
            anchor = 'slamdunk_PCA',
            description = """This plot shows the principal components of samples based
                        on the distribution of reads with T>C conversions within UTRs
                        (see the <a href="http://t-neumann.github.io/slamdunk/docs.html#summary">slamdunk docs</a>).""",
            plot = scatter.plot(self.PCA_data, pconfig)
        )

}
            data = dict()
            for s_name in self.deeptools_plotPCAData:
                try:
                    data[s_name] = {'x': self.deeptools_plotPCAData[s_name][1], 'y': self.deeptools_plotPCAData[s_name][2]}
                except KeyError:
                    pass
            if len(data) == 0:
                log.debug('No valid data for PCA plot')
                return None

            self.add_section(
                name="PCA plot",
                anchor="deeptools_pca",
                description="PCA plot with the top two principal components calculated based on genome-wide distribution of sequence reads",
                plot=scatter.plot(data, config)
            )

        return len(self.deeptools_plotPCAData)