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

# Specifying the categories of the bargraph
        keys = OrderedDict()
        keys = ['AC', 'AG', 'AT', 'CG', 'CT', 'GT', 'Ts', 'Tv']

        pconfig = {
            'id': 'vcftools_tstv_summary',
            'title': 'VCFTools: TsTv Summary',
            'ylab': 'Counts',
        }

        self.add_section(
            name = 'TsTv Summary',
            anchor = 'vcftools-tstv-summary',
            description = "Plot of `TSTV-SUMMARY` - count of different types of transition and transversion SNPs.",
            plot = bargraph.plot(self.vcftools_tstv_summary,keys,pconfig)
        )

        return len(self.vcftools_tstv_summary)

""" Make the HighCharts HTML to plot the alignment rates """

        # Specify the order of the different possible categories
        keys = OrderedDict()
        keys['pseudoaligned_reads'] = { 'color': '#437bb1', 'name': 'Pseudoaligned' }
        keys['not_pseudoaligned_reads'] =   { 'color': '#b1084c', 'name': 'Not aligned' }

        # Config for the plot
        config = {
            'id': 'kallisto_alignment',
            'title': 'Kallisto: Alignment Scores',
            'ylab': '# Reads',
            'cpswitch_counts_label': 'Number of Reads'
        }

        return bargraph.plot(self.kallisto_data, keys, config)

keys['bad_reads_with_bad_overlap'] =     { 'name': 'Bad Overlap' }
        keys['bad_reads_with_bad_read_length'] = { 'name': 'Bad Read Length' }
        keys['bad_reads_with_low_quality'] =     { 'name': 'Low Quality' }
        keys['bad_reads_with_polyX'] =           { 'name': 'PolyX' }
        keys['bad_reads_with_reads_in_bubble'] = { 'name': 'Reads In Bubble' }
        keys['bad_reads_with_too_many_N'] =      { 'name': 'Too many N' }

        # Config for the plot
        pconfig = {
            'id': 'afterqc_bad_reads_plot',
            'title': 'AfterQC: Filtered Reads',
            'ylab': '# Reads',
            'cpswitch_counts_label': 'Number of Reads',
            'hide_zero_cats': False,
        }
        return bargraph.plot(self.afterqc_data, keys, pconfig)

cats_pec['singleton_m2'] = {'name': 'Singleton R2'}

            if self.__any_collapsed:
                cats_pec['full-length_cp'] = {'name': 'Full-length Collapsed Pairs'}
                cats_pec['truncated_cp'] = {'name': 'Truncated Collapsed Pairs'}

        cats_pec['discarded_m1'] = {'name': 'Discarded R1'}

        if self.__any_paired:
            cats_pec['discarded_m2'] = {'name': 'Discarded R2'}

        self.add_section(
            name='Retained and Discarded Paired-End Collapsed',
            anchor='adapter_removal_retained_plot',
            description='The number of retained and discarded reads.',
            plot=bargraph.plot(self.adapter_removal_data, cats_pec, pconfig)
        )

# kingdom: 
        keys['metazoa'] = {'color': 'green', 'name': 'Metazoa'}
        keys['fungi'] = {'color': 'purple', 'name': 'Fungi'}
        # others
        keys['unclassified'] = {'color': 'grey', 'name': 'Unclassified'}
        keys['others'] = {'color': 'blue', 'name': 'Others'}
        # subkingdom
        #keys['viridiplantae'] = {'color': 'yellow', 'name': 'Viridiplantae'}
        #keys['dikarya'] = {'color': 'brown', 'name': 'dikarya'}

        self.add_section(
            name = 'Taxonomy by kingdom',
            anchor = 'taxonomy',
            description = 'The following barplots summarizes the kraken analysis for each sample. ',
            helptext = "",
            plot = bargraph.plot(data, keys, pconfig))

def chart_align_strand(self):

        # mapping strand distribution
        pd1 = {}
        pd2 = {}
        for sid, dd in self.mdata['align_strand'].items():
            pd1[sid] = dd['read1']
            pd2[sid] = dd['read2']

        self.add_section(
            name='Mapping Strand Distribution',
            anchor='biscuit-strands',
            description = "This plot shows the distribution of strand of mapping and strand of bisulfite conversion.",
            helptext="Most bisulfite libraries has read 1 goes to parent `++` or `--` and read 2 goes to daughter/synthesized `+-` or `-+`. PBAT or most single-cell/low input libraries typically don't observe this rule.",
            plot = bargraph.plot([pd1, pd2], 
                [OrderedDict([
                    ('++', {'name':'++: Waston-Aligned, Waston-Bisulfite Conversion', 'color': '#F53855'}),
                    ('+-', {'name':'+-: Waston-Aligned, Crick-Bisulfite Conversion', 'color': '#E37B40'}),
                    ('-+', {'name':'-+: Crick-Aligned, Waston-Bisulfite Conversion', 'color': '#46B29D'}),
                    ('--', {'name':'--: Crick-Aligned, Crick-Bisulfite Conversion', 'color': '#324D5C'}),]),
                OrderedDict([
                    ('++', {'name':'++: Waston-Aligned, Waston-Bisulfite Conversion', 'color': '#F53855'}),
                    ('+-', {'name':'+-: Waston-Aligned, Crick-Bisulfite Conversion', 'color': '#E37B40'}),
                    ('-+', {'name':'-+: Crick-Aligned, Waston-Bisulfite Conversion', 'color': '#46B29D'}),
                    ('--', {'name':'--: Crick-Aligned, Crick-Bisulfite Conversion', 'color': '#324D5C'})])], 
                {'id':'biscuit_strands',
                 'title':'BISCUIT: Mapping Strand Distribution',
                 'ylab':'Number of Reads',
                 'cpswitch_c_active': True,
                 'cpswitch_counts_label': '# Reads',
                 'data_labels': [

keys = OrderedDict()
        keys['reads_mirna']      =   { 'color': '#33a02c', 'name': 'miRNA' }
        keys['reads_rrna']       =   { 'color': '#ff7f00', 'name': 'rRNA' }
        keys['reads_trna']       =   { 'color': '#1f78b4', 'name': 'tRNA' }
        keys['reads_artifact']   =   { 'color': '#fb9a99', 'name': 'Artifact' }
        keys['reads_unknown']    =   { 'color': '#d9d9d9', 'name': 'Unknown' }

        # Config for the plot
        config = {
            'id': 'mirtrace_rna_categories_plot',
            'title': 'miRTrace: RNA Categories',
            'ylab': '# Reads',
            'cpswitch_counts_label': 'Number of Reads'
        }

        return bargraph.plot(self.summary_data, keys, config)

pconfig = {
            'id': 'rseqc_junction_annotation_junctions_plot',
            'title': 'RSeQC: Splicing Junctions',
            'ylab': '% Junctions',
            'cpswitch_c_active': False,
            'data_labels': [ 'Junctions', 'Events' ]
        }
        self.add_section (
            name = 'Junction Annotation',
            anchor = 'rseqc_junction_annotation',
            description = '<a href="http://rseqc.sourceforge.net/#junction-annotation-py">Junction annotation</a>' \
                " compares detected splice junctions to" \
                " a reference gene model. An RNA read can be spliced 2" \
                " or more times, each time is called a splicing event.",
            plot = bargraph.plot([self.junction_annotation_data, self.junction_annotation_data], keys, pconfig)
        )

    # Return number of samples found
    return len(self.junction_annotation_data)

for sample in self.sortmerna:
            for key in self.sortmerna[sample]:
                if not key in ["total", "rRNA", "non_rRNA"] and not "_pct" in key:
                    metrics.add(key)

        for key in metrics:
            keys[key] = { 'name': key.replace("_count","") }

        # Config for the plot
        pconfig = {
            'id': 'sortmerna-detailed-plot',
            'title': 'SortMeRNA: Hit Counts',
            'ylab': 'Reads'
        }

        self.add_section( plot = bargraph.plot(self.sortmerna, keys, pconfig) )

keys['N_unmapped'] =     { 'color': '#7f0000', 'name': 'Unmapped' }

        # Config for the plot
        pconfig = {
            'id': 'star_gene_counts',
            'title': 'STAR: Gene Counts',
            'ylab': '# Reads',
            'cpswitch_counts_label': 'Number of Reads',
            'data_labels': ['Unstranded','Same Stranded','Reverse Stranded']
        }
        datasets = [
            self.star_genecounts_unstranded,
            self.star_genecounts_first_strand,
            self.star_genecounts_second_strand
        ]
        return bargraph.plot(datasets, [keys,keys,keys,keys], pconfig)