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

mdata = [
            self.mdata['covdist'],
            self.mdata['covdist_q40'],
            self.mdata['covdist_q40_botgc'],
            self.mdata['covdist_q40_topgc'],
            self.mdata['covdist_cpg'],
            self.mdata['covdist_cpg_q40'],
            self.mdata['covdist_cpg_q40_botgc'],
            self.mdata['covdist_cpg_q40_topgc']]

        self.add_section(
            name = 'Cumulative Base Coverage',
            anchor = 'biscuit-coverage-base',
            description = "This plot shows the cummulative base coverage. High and low GC content region are the top and bottom 10% 100bp window in GC content.",
            helptext = "Q40 means only reads mapped with mapping quality (Q) greater than or equal to 40 are considered.",
            plot = linegraph.plot(mdata, {'id':'biscuit_coverage_base',
                'title': 'BISCUIT: Cumulative Base Coverage',
                'xLabelFormat':'{value}X',
                'xlab': 'Sequencing Depth',
                'data_labels': [
                    {'name': 'All', 'ylab':'Million Bases'}, 
                    {'name': 'Q40', 'ylab':'Million Bases (Q40)'}, 
                    {'name': 'Q40 low GC', 'ylab':'Million Low-GC Bases (Q40)'}, 
                    {'name': 'Q40 high GC', 'ylab':'Million High-GC Bases (Q40)'}, 
                    {'name': 'CpG (all)', 'ylab':'Million CpGs'}, 
                    {'name': 'CpG Q40', 'ylab':'Million CpGs (Q40)'}, 
                    {'name': 'CpG Q40 low GC', 'ylab':'Million Low-GC CpGs (Q40)'}, 
                    {'name': 'CpG Q40 high GC', 'ylab':'Million High-GC CpGs (Q40)'}, 
                ]})
        )

        for sid, dd in mdata[0].items():

log.info("Too many chromosomes found: %s, limiting to %s" % (len(chroms), max_chroms))
                chroms = chroms[:max_chroms]
            pconfig = {
                'id': 'goleft_indexcov-roc-plot',
                'title': 'goleft indexcov: ROC - genome coverage per scaled depth by chromosome',
                'xlab': 'Scaled coverage',
                'ylab': 'Proportion of regions covered',
                'ymin': 0, 'ymax': 1.0,
                'xmin': 0, 'xmax': 1.5,
                'data_labels': [{"name": self._short_chrom(c)} for c in chroms]}
            self.add_section (
                name = 'Scaled coverage ROC plot',
                anchor = 'goleft_indexcov-roc',
                description = 'Coverage (ROC) plot that shows genome coverage at at given (scaled) depth.',
                helptext = helptext,
                plot = linegraph.plot([data[c] for c in chroms], pconfig)
            )
            return True
        else:
            return False

(see the <a href="http://qualimap.bioinfo.cipf.es/doc_html/index.html">Qualimap 2 documentation</a>), where the `TLEN` (or
        'observed Template LENgth') field contains 'the number of bases from the
        leftmost mapped base to the rightmost mapped base'
        (<a href="https://samtools.github.io/hts-specs/">SAM
        format specification</a>). Note that because it is defined in terms of
        alignment to a reference sequence, the value of the `TLEN` field may
        differ from the insert size due to factors such as alignment clipping,
        alignment errors, or structural variation or splicing in a gap between
        reads from the same fragment.'''
        self.add_section (
            name = 'Insert size histogram',
            anchor = 'qualimap-insert-size-histogram',
            description = 'Distribution of estimated insert sizes of mapped reads.',
            helptext = insert_size_helptext,
            plot = linegraph.plot(self.qualimap_bamqc_insert_size_hist, {
                'id': 'qualimap_insert_size',
                'title': 'Qualimap BamQC: Insert size histogram',
                'ylab': 'Fraction of reads',
                'xlab': 'Insert Size (bp)',
                'ymin': 0,
                'xmin': 0,
                'tt_label': '<b>{point.x} bp</b>: {point.y}',
            })
        )

    # Section 4 - GC-content distribution
    if len(self.qualimap_bamqc_gc_content_dist) &gt; 0:
        gc_content_helptext = '''
        GC bias is the difference between the guanine-cytosine content
        (GC-content) of a set of sequencing reads and the GC-content of the DNA
        or RNA in the original sample. It is a well-known issue with sequencing

# Calculate the coverage rates for this range of coverages
            rates_within_threshs[s_name] = _calculate_bases_within_thresholds(hist, total, depth_range)
            # Add requested coverage levels to the General Statistics table
            for c in self.covs:
                if int(c) in rates_within_threshs[s_name]:
                    self.general_stats_data[s_name]['{}_x_pc'.format(c)] = rates_within_threshs[s_name][int(c)]
                else:
                    self.general_stats_data[s_name]['{}_x_pc'.format(c)] = 0

        # Section 1 - BamQC Coverage Histogram
        self.add_section (
            name = 'Coverage histogram',
            anchor = 'qualimap-coverage-histogram',
            description = 'Distribution of the number of locations in the reference genome with a given depth of coverage.',
            helptext = coverage_histogram_helptext,
            plot = linegraph.plot(self.qualimap_bamqc_coverage_hist, {
                'id': 'qualimap_coverage_histogram',
                'title': 'Qualimap BamQC: Coverage histogram',
                'ylab': 'Genome bin counts',
                'xlab': 'Coverage (X)',
                'ymin': 0,
                'xmin': 0,
                'xmax': max_x,
                'xDecimals': False,
                'tt_label': '<b>{point.x}X</b>: {point.y}',
            })
        )
        # Section 2 - BamQC cumulative coverage genome fraction
        self.add_section (
            name = 'Cumulative genome coverage',
            anchor = 'qualimap-cumulative-genome-fraction-coverage',
            description = 'Percentage of the reference genome with at least the given depth of coverage.',

'xmax': 100,
            'tt_label': '<b>{point.x}%</b>: {point.y:.3f}'
        }
        if len(extra_series) == 1:
            desc += ' The dotted line represents a pre-calculated GC distribution for the reference genome.'
            lg_config['extra_series'] = extra_series
        elif len(extra_series) &gt; 1:
            desc += ' Each dotted line represents a pre-calculated GC distribution for a specific reference genome.'
            lg_config['extra_series'] = extra_series

        self.add_section (
            name = 'GC content distribution',
            anchor = 'qualimap-gc-distribution',
            description = desc,
            helptext = gc_content_helptext,
            plot = linegraph.plot(self.qualimap_bamqc_gc_content_dist, lg_config)
        )

pconfig = {
            'id': 'cutadapt_plot',
            'title': 'Cutadapt: Lengths of Trimmed Sequences',
            'ylab': 'Counts',
            'xlab': 'Length Trimmed (bp)',
            'xDecimals': False,
            'ymin': 0,
            'tt_label': '<b>{point.x} bp trimmed</b>: {point.y:.0f}',
            'data_labels': [{'name': 'Counts', 'ylab': 'Count'},
                            {'name': 'Obs/Exp', 'ylab': 'Observed / Expected'}]
        }

        self.add_section(
            description = description,
            plot = linegraph.plot([self.cutadapt_length_counts, self.cutadapt_length_obsexp], pconfig)
        )

def chart_align_isize(self):

        pd_isize = {}
        for sid, dd in self.mdata['align_isize'].items():
            pd_isize[sid] = dd['I']

        self.add_section(
            name = 'Insert Size Distribution',
            anchor = 'biscuit-isize',
            description = "This plot shows the distribution of insert size.",
            plot = linegraph.plot(pd_isize, 
                {'id':'biscuit_isize', 'title': 'BISCUIT: Insert Size Distribution',
                 'ymin': 0, 'yLabelFormat': '{value}%', 
                 'smooth_points': 500,       # limit number of points / smooth data
                 'tt_label': '<strong>Q{point.x}:</strong> {point.y:.2f}% of reads', 
                 'ylab': '% Mapped Reads', 'xlab': 'Insert Size'}))

}
        )

    plot_params = {
            'id': 'bbmap-' + file_type + '_plot',
            'title': 'BBTools: ' + plot_args['plot_title'],
            'xlab': 'Percent identity',
            'ylab': 'Read count',
            'data_labels': [
                {'name': 'Reads', 'ylab': 'Read count'},
                {'name': 'Bases', 'ylab': 'Number of bases'},
            ]

    }
    plot_params.update(plot_args['plot_params'])
    plot = linegraph.plot(
        plot_data,
        plot_params
    )

    return plot

## cytosine retention distribution
        mdata_meth = self.mdata['retention_dist']
        mdata = self.mdata['retention_dist_byread']

        pd = [
            mdata_meth,
            dict([(sid, dd['CA']) for sid, dd in mdata.items()]),
            dict([(sid, dd['CC']) for sid, dd in mdata.items()]),
            dict([(sid, dd['CG']) for sid, dd in mdata.items()]),
            dict([(sid, dd['CT']) for sid, dd in mdata.items()]),
        ]
        self.add_section(
            name = 'Number of Retention Distribution',
            anchor = 'biscuit-retention-read',
            description = "This plot shows the distribution of the number of retained cytosine in each read, up to 10.",
            plot = linegraph.plot(pd, {
                'id': 'biscuit_retention_read_cpa', 
                'xlab': 'Number of Retention within Read',
                'title': 'BISCUIT: Retention Distribution',
                'data_labels': [
                    {'name': 'CpG retention', 'ylab': 'Fraction of cytosine in CpG context', 'xlab': 'Retention Level (%)'},
                    {'name': 'Within-read CpA', 'ylab': 'Number of Reads'},
                    {'name': 'Within-read CpC', 'ylab': 'Number of Reads'},
                    {'name': 'Within-read CpG', 'ylab': 'Number of Reads'},
                    {'name': 'Within-read CpT', 'ylab': 'Number of Reads'},
                ]})