How to use the snakemake.report.data_uri function in snakemake

def make_picard_summary_plots(inputfiles, ncol=4):
    d = {}
    TOOLS = "pan,box_zoom,wheel_zoom,box_select,lasso_select,reset,save,hover"
    for (metrics_file, hist_file) in zip(inputfiles[0::2], inputfiles[1::2]):
        df_met = _read_metrics(metrics_file)
        df_hist = _read_metrics(hist_file)
        p1 = df_met.plot_metrics(tools=TOOLS)
        key = os.path.splitext(metrics_file)[0]
        if df_met.label not in d:
            d[df_met.label] = {}
            d[df_met.label][key] = {}
            d[df_met.label][key]['uri'] = [data_uri(metrics_file)]
            d[df_met.label][key]['file'] = [metrics_file]
        if df_hist is not None:
            p2 = df_hist.plot_hist(tools=TOOLS)
            d[df_met.label][key]['uri'].append(data_uri(hist_file))
            d[df_met.label][key]['file'].append(hist_file)
        else:
            p2 = []
        plist = p1 + p2
        gp = gridplot([plist[i:i+ncol] for i in range(0, len(plist), ncol)])
        d[df_met.label][key]['fig'] = gp
    return d

def make_cutadapt_summary_plot(inputfile):
    df_summary = pd.read_csv(inputfile)
    df_summary["sample"] = df_summary["sample"].astype("str")
    TOOLS = "pan,wheel_zoom,box_zoom,box_select,reset,save"
    df_pivot = df_summary[["sample", "read1_pct", "read2_pct"]].pivot_table(index="sample").stack().reset_index([0,1])
    df_pivot.columns = ["Sample", "Level", "Percent"]
    fig = Scatter(df_pivot, x="Sample", y="Percent",
                  color="Level", legend="top_right",
                  title="Cutadapt metrics")
    return {'fig': fig, 'uri': data_uri(inputfile), 'file': inputfile}

def make_qualimap_plots(qmglobals=None, coverage_per_contig=None,
                        **kwargs):
    """Make qualimap summary plots"""
    retval = {'fig': {'coverage_per_contig': None, 'globals': None},
              'file': {'coverage_per_contig': coverage_per_contig,
                       'globals': qmglobals},
              'uri': {'coverage_per_contig': data_uri(coverage_per_contig),
                      'globals': data_uri(qmglobals)}}
    # Globals
    if qmglobals is not None:
        df_all = pd.read_csv(qmglobals)
        df_all['number of unique reads'] = df_all['number of mapped reads']\
            - df_all['number of duplicated reads']
        plot_config = {'x': 'Sample',
                       'y': ['number of reads',
                             'number of mapped reads',
                             'number of duplicated reads',
                             'number of unique reads'],
                       'df': df_all, 'groups': ['Sample'],
                       'y_range': [0, max(df_all['number of reads'])],
                       'xaxis': {'axis_label': "sample",
                                 'major_label_orientation': np.pi / 3,
                                 'axis_label_text_font_size': '10pt'},

tooltips=[{'type': HoverTool, 'tips': [
                              ('Sample', '@samples'),
                              ('ExonMap', '@ExonMap'), ]}],
                          title="Tags mapping to exons", **plot_config)
    # Fraction reads mapping to the 10% right-most end
    p2 = make_scatterplot(y='three_prime_map', x='samples',
                          source=source,
                          tooltips=[{
                              'type': HoverTool, 'tips': [
                                  ('Sample', '@samples'),
                                  ('ThreePrimeMap', '@three_prime_map'), ]}],
                          title="Reads mapping to 3' end", **plot_config)
    p2.x_range = p1.x_range

    return {'fig': gridplot([[p1, p2]]),
            'uri': [data_uri(rd_file), data_uri(gc_file)],
            'file': [rd_file, gc_file]}

def make_qualimap_plots(qmglobals=None, coverage_per_contig=None,
                        **kwargs):
    """Make qualimap summary plots"""
    retval = {'fig': {'coverage_per_contig': None, 'globals': None},
              'file': {'coverage_per_contig': coverage_per_contig,
                       'globals': qmglobals},
              'uri': {'coverage_per_contig': data_uri(coverage_per_contig),
                      'globals': data_uri(qmglobals)}}
    # Globals
    if qmglobals is not None:
        df_all = pd.read_csv(qmglobals)
        df_all['number of unique reads'] = df_all['number of mapped reads']\
            - df_all['number of duplicated reads']
        plot_config = {'x': 'Sample',
                       'y': ['number of reads',
                             'number of mapped reads',
                             'number of duplicated reads',
                             'number of unique reads'],
                       'df': df_all, 'groups': ['Sample'],
                       'y_range': [0, max(df_all['number of reads'])],
                       'xaxis': {'axis_label': "sample",
                                 'major_label_orientation': np.pi / 3,
                                 'axis_label_text_font_size': '10pt'},
                       'yaxis': {'axis_label': "count",

# p6.yaxis.axis_label = "Count"

    df_qc = None
    if do_qc:
        # QC summary table
        d = {'samples': samples,
             'read_filter': df['Number_of_input_reads'] < min_reads,
             'map_filter': df['Uniquely_mapped_reads_PCT'] < min_map,
             'mismatch_filter': df['mismatch_sum'] > 1.0, }
        d['filter'] = d['read_filter'] | d['map_filter'] | d['mismatch_filter']
        df_qc = pd.DataFrame(data=d, index=df.samples)

    return {'fig': VBox(children=[gridplot([[p1, p2, p3]]),
                                  HBox(children=[gridplot([[p4, p5]])])]),
            'table': table, 'qctable': df_qc,
            'uri': data_uri(inputfile),
            'file': inputfile}

Args:
      inutfile (str): input file name
      qc (dict): qc parameter values
      ncol (int): number of columns in returned gridplot
      share_x_range (bool): share x range between plots
      kwargs: keyword argument passed to plots

    Returns:
      gp (py:class:`~bokeh.models.plots.GridPlot`): gridplot object

    """
    retval = {'fig': None,
              'file': inputfile,
              'table': None,
              'uri': data_uri(inputfile)}
    if inputfile is None:
        return retval
    df = pd.read_csv(inputfile, index_col=0)

    columns = [
        TableColumn(field="samples", title="Sample"),
        TableColumn(field="Number_of_input_reads",
                    title="Number of input reads"),
        TableColumn(field="Uniquely_mapped_reads_PCT",
                    title="Uniquely mapped reads (%)"),
        TableColumn(field="Mismatch_rate_per_base__PCT",
                    title="Mismatch rate per base (%)"),
        TableColumn(field="Insertion_rate_per_base",
                    title="Insertion rate per base (%)"),
        TableColumn(field="Deletion_rate_per_base",
                    title="Deletion rate per base (%)"),

def make_picard_summary_plots(inputfiles, ncol=4):
    d = {}
    TOOLS = "pan,box_zoom,wheel_zoom,box_select,lasso_select,reset,save,hover"
    for (metrics_file, hist_file) in zip(inputfiles[0::2], inputfiles[1::2]):
        df_met = _read_metrics(metrics_file)
        df_hist = _read_metrics(hist_file)
        p1 = df_met.plot_metrics(tools=TOOLS)
        key = os.path.splitext(metrics_file)[0]
        if df_met.label not in d:
            d[df_met.label] = {}
            d[df_met.label][key] = {}
            d[df_met.label][key]['uri'] = [data_uri(metrics_file)]
            d[df_met.label][key]['file'] = [metrics_file]
        if df_hist is not None:
            p2 = df_hist.plot_hist(tools=TOOLS)
            d[df_met.label][key]['uri'].append(data_uri(hist_file))
            d[df_met.label][key]['file'].append(hist_file)
        else:
            p2 = []
        plist = p1 + p2
        gp = gridplot([plist[i:i+ncol] for i in range(0, len(plist), ncol)])
        d[df_met.label][key]['fig'] = gp
    return d