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

""" Make the HTSeq Count assignment rates plot """
        cats = OrderedDict()
        cats['assigned'] =      { 'name': 'Assigned' }
        cats['ambiguous'] =     { 'name': 'Ambiguous' }
        cats['alignment_not_unique'] = { 'name': 'Alignment Not Unique' }
        cats['no_feature'] =    { 'name': 'No Feature' }
        cats['too_low_aQual'] = { 'name': 'Too Low aQual' }
        cats['not_aligned'] =   { 'name': 'Not Aligned' }
        config = {
            'id': 'htseq_assignment_plot',
            'title': 'HTSeq: Count Assignments',
            'ylab': '# Reads',
            'hide_zero_cats': False,
            'cpswitch_counts_label': 'Number of Reads'
        }
        return bargraph.plot(self.htseq_data, cats, config)

for sid, dd in self.mdata['align_mapq'].items():
            pd[sid] = {'OAligned':0, 'SAligned':0, 'UAligned':1}
            for mapq, cnt in dd.items():
                if mapq == 'unmapped':
                    pd[sid]['UAligned'] += int(cnt)
                elif int(mapq) >= 40:
                    pd[sid]['OAligned'] += int(cnt)
                else:
                    pd[sid]['SAligned'] += int(cnt)

        self.add_section(
            name = 'Mapping Summary',
            anchor = 'biscuit-mapping',
            description = 'This shows the fraction of optimally aligned reads, which is defined by mapQ >= 40.',
            helptext = 'A good library should have high fraction of reads optimally aligned. Suboptimally aligned reads include both nonunique alignments and imperfect alignments.',
            plot = bargraph.plot(pd, OrderedDict([
                ('OAligned', {'name':'Optimally Aligned Reads'}),
                ('SAligned', {'name':'Suboptimally Aligned Reads'}),
                ('UAligned', {'name':'Unaligned Reads'})
            ]), {'id':'biscuit_mapping_summary',
                 'title':'BISCUIT: Mapping Summary',
                 'ylab':'Number of Reads',
                 'cpswitch_counts_label': '# Reads'
            })
        )

        # Mapping quality together in one plot
        total = {}
        for sid, dd in self.mdata['align_mapq'].items():
            total[sid] = sum([int(cnt) for _, cnt in dd.items() if _ != "unmapped"])

        pd_mapping = {}

# Config for the plot
        pconfig = {
            'id': 'picard_rrbs_converted_bases_plot',
            'title': 'Picard: Converted Bases',
            'ylab': '# CpG Bases',
            'cpswitch_counts_label': 'Number of Bases',
            'data_labels': [
                {'name': 'CpG', 'ylab': '# CpG Bases'},
                {'name': 'Non-CpG', 'ylab': '# Non-CpG Bases'}
            ]
        }

        self.add_section (
            name = 'RRBS Converted Bases',
            anchor = 'picard-rrbssummary-convertedbases',
            plot = bargraph.plot([pdata_cpg, pdata_noncpg], [keys, keys], pconfig)
        )


        # Make the bar plot of processed reads
        pdata = dict()
        for s_name in self.picard_rrbs_metrics.keys():
            pdata[s_name] = dict()
            pdata[s_name]['with_no_cpg'] = self.picard_rrbs_metrics[s_name]['READS_WITH_NO_CPG']
            pdata[s_name]['ignored_short'] = self.picard_rrbs_metrics[s_name]['READS_IGNORED_SHORT']
            pdata[s_name]['ignored_mismatches'] = self.picard_rrbs_metrics[s_name]['READS_IGNORED_MISMATCHES']
            pdata[s_name]['not_ignored'] = (
                self.picard_rrbs_metrics[s_name]['READS_ALIGNED'] -
                pdata[s_name]['with_no_cpg'] -
                pdata[s_name]['ignored_short'] -
                pdata[s_name]['ignored_mismatches']
            )

config = {
            'id': 'seqyclean-discarded-reads-plot',
            'title': 'SeqyClean: Discarded Reads',
            'ylab': 'Number of Reads'
        }
        keys = [
            'SEDiscByContam', # single end
            'SEDiscByLength',
            'PE1DiscByContam', # paired end
            'PE1DiscByLength',
            'PE2DiscByContam',
            'PE2DiscByLength',
            'DiscByContam', # 454 data
            'DiscByLength'
        ]
        return bargraph.plot(self.seqyclean_data, self._clean_keys(keys), config)

data = {}
        for name in self.sequana_data.keys():
            data[name] = {
                'polyA':
                    self.sequana_data[name]["polyA"]
            }
        pconfig = {
            "title": "polyA",
            "logswitch": True,
        }
        self.add_section(
            name = 'Number of polyA',
            anchor = 'poylA',
            description = 'polyA',
            helptext="",
            plot = bargraph.plot(data, None, pconfig))

data[name] = {'failed_qc_pct': self.sequana_data[name]["failed_qc_pct"]}

        pconfig = {
            "title": "Failed QC (%)",
            "percentages": True,
            "min": 0,
            "max":100,
            "logswitch": False,
        }

        total = sum([data[name]['failed_qc_pct'] for name in self.sequana_data.keys()])
        if total == 0:
            plot = None
            description = "Failed QC (none found)." 
        else:
            plot = bargraph.plot(data, None, pconfig)
            description = "Failed QC (%)." 

        self.add_section(
            name = 'Failed QC (%)',
            anchor = 'failed_qc',
            description = 'Failed QC',
            helptext = "",
            plot = plot)

pconfig = {
            "title": "CCS reads",
            "percentages": False,
            "min": 0,
            #"max":100,
            "format": '{0:.2f}',
            "logswitch": False,
        }

        self.add_section(
            name = 'CCS reads',
            anchor = 'ccs_reads',
            description = 'The following barplots summarizes the number of CCS reads.',
            helptext = "",
            plot = bargraph.plot(data, None, pconfig))

"percentages": True,
            "min": 0,
            "max":100,
            "logswitch": False,
        }

        keys = OrderedDict()
        keys['fwd'] = {'color': '#437bb1', 'name': 'Mapped'}
        keys['rev'] = {'color': '#b1084c', 'name': 'Unmapped'}

        self.add_section(
            name = 'Forward/Reverse',
            anchor = 'fwd_rev',
            description = 'Forward reverse',
            helptext = "",
            plot = bargraph.plot(data, keys, pconfig))

data[name] = {
                'mean': self.sequana_data[name]["mean_length"],
            }
        pconfig = {
                "title": "Mean CCS read length",
            "percentages": False,
            "min": 100,
            "logswitch": True,
        }

        self.add_section(
            name = 'Mean CCS read length',
            anchor = 'mean_ccs_read_length',
            description = 'Mean CCS length of the reads',
            helptext = "",
            plot = bargraph.plot(data, None, pconfig))