How to use NanoComp - 10 common examples

<style>
            table, th, td {
                text-align: left;
                padding: 2px;
                /* border: 1px solid black;
                border-collapse: collapse; */
            }
            h2 {
                line-height: 0pt;
            }
            </style>
            <title>NanoComp Report</title>
        """
    html_content = ["\n\n<h1>NanoComp report</h1>"]
    html_content.append("<h2>Summary statistics</h2>")
    html_content.append(utils.stats2html(path + "NanoStats.txt"))
    html_content.append('\n<br>\n<br>\n<br>\n<br>')
    html_content.append("<h2>Plots</h2>")
    for plot in plots:
        html_content.append("\n<h3>" + plot.title + "</h3>\n" + plot.encode())
        html_content.append('\n<br>\n<br>\n<br>\n<br>')
    html_body = '\n'.join(html_content) + ""
    html_str = html_head + html_body
    with open(path + "NanoComp-report.html", "w") as html_file:
        html_file.write(html_str)
    return path + "NanoComp-report.html"

plot=settings["plot"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "duration" in df:
        plots.extend(
            compplots.compare_sequencing_speed(
                df=df,
                figformat=settings["format"],
                path=settings["path"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "percentIdentity" in df:
        plots.extend(
            compplots.violin_or_box_plot(
                df=df[df["percentIdentity"] > np.percentile(df["percentIdentity"], 1)],
                y="percentIdentity",
                figformat=settings["format"],
                path=settings["path"],
                y_name="Percent reference identity",
                plot=settings["plot"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "start_time" in df:
        plots.extend(
            compplots.compare_cumulative_yields(
                df=df,
                path=settings["path"],
                title=settings["title"],
                palette=settings["colors"])

title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.violin_or_box_plot(
            df=df[df["length_filter"]],
            y="lengths",
            figformat=settings["format"],
            path=settings["path"],
            y_name="Read length",
            plot=settings["plot"],
            title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.violin_or_box_plot(
            df=df[df["length_filter"]],
            y="log length",
            figformat=settings["format"],
            path=settings["path"],
            y_name="Log-transformed read length",
            plot=settings["plot"],
            log=True,
            title=settings["title"],
            palette=settings["colors"])
    )
    if "quals" in df:
        plots.extend(
            compplots.violin_or_box_plot(
                df=df,
                y="quals",
                figformat=settings["format"],

)
    plots.extend(
        compplots.violin_or_box_plot(
            df=df[df["length_filter"]],
            y="log length",
            figformat=settings["format"],
            path=settings["path"],
            y_name="Log-transformed read length",
            plot=settings["plot"],
            log=True,
            title=settings["title"],
            palette=settings["colors"])
    )
    if "quals" in df:
        plots.extend(
            compplots.violin_or_box_plot(
                df=df,
                y="quals",
                figformat=settings["format"],
                path=settings["path"],
                y_name="Average base call quality score",
                plot=settings["plot"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "duration" in df:
        plots.extend(
            compplots.compare_sequencing_speed(
                df=df,
                figformat=settings["format"],
                path=settings["path"],
                title=settings["title"],

df=df,
            figformat=settings["format"],
            path=settings["path"],
            title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.n50_barplot(
            df=df,
            figformat=settings["format"],
            path=settings["path"],
            title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.violin_or_box_plot(
            df=df[df["length_filter"]],
            y="lengths",
            figformat=settings["format"],
            path=settings["path"],
            y_name="Read length",
            plot=settings["plot"],
            title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.violin_or_box_plot(
            df=df[df["length_filter"]],
            y="log length",
            figformat=settings["format"],
            path=settings["path"],
            y_name="Log-transformed read length",

def main():
    '''
    Organization function
    -setups logging
    -gets inputdata
    -calls plotting function
    '''
    settings, args = utils.get_args()
    try:
        utils.make_output_dir(args.outdir)
        utils.init_logs(args)
        args.format = utils.check_valid_format(args.format)
        sources = {
            "fastq": args.fastq,
            "bam": args.bam,
            "cram": args.cram,
            "summary": args.summary,
            "fasta": args.fasta,
            "ubam": args.ubam,
        }
        if args.split_runs:
            split_dict = utils.validate_split_runs_file(args.split_runs)
        if args.pickle:
            from nanoget import combine_dfs

def make_plots(df, settings):
    utils.plot_settings(dict(), dpi=settings["dpi"])
    df["log length"] = np.log10(df["lengths"])
    plots = []
    plots.extend(
        compplots.output_barplot(
            df=df,
            figformat=settings["format"],
            path=settings["path"],
            title=settings["title"],
            palette=settings["colors"])
    )
    plots.extend(
        compplots.n50_barplot(
            df=df,
            figformat=settings["format"],
            path=settings["path"],
            title=settings["title"],

readtype=args.readtype,
                names=args.names,
                barcoded=args.barcoded,
                combine="track")
        datadf, settings = filter_and_transform_data(datadf, vars(args))
        if args.raw:
            datadf.to_csv(settings["path"] + "NanoComp-data.tsv.gz",
                          sep="\t",
                          index=False,
                          compression="gzip")
        if args.store:
            pickle.dump(
                obj=datadf,
                file=open(settings["path"] + "NanoComp-data.pickle", 'wb'))
        if args.split_runs:
            utils.change_identifiers(datadf, split_dict)
        if args.barcoded:
            datadf["dataset"] = datadf["barcode"]
        identifiers = list(datadf["dataset"].unique())
        write_stats(
            datadfs=[datadf[datadf["dataset"] == i] for i in identifiers],
            outputfile=settings["path"] + "NanoStats.txt",
            names=identifiers)
        if args.plot != 'false':
            plots = make_plots(datadf, settings)
            make_report(plots, settings["path"])
        logging.info("Succesfully processed all input.")
    except Exception as e:
        logging.error(e, exc_info=True)
        raise

y_name="Percent reference identity",
                plot=settings["plot"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "start_time" in df:
        plots.extend(
            compplots.compare_cumulative_yields(
                df=df,
                path=settings["path"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "channelIDs" in df:
        plots.append(
            compplots.active_pores_over_time(
                df=df,
                path=settings["path"],
                palette=settings["colors"],
                title=settings["title"]
            )
        )
    plots.extend(
        compplots.overlay_histogram(
            df=df,
            path=settings["path"],
            palette=settings["colors"]
        )
    )
    return plots

)
    if "percentIdentity" in df:
        plots.extend(
            compplots.violin_or_box_plot(
                df=df[df["percentIdentity"] > np.percentile(df["percentIdentity"], 1)],
                y="percentIdentity",
                figformat=settings["format"],
                path=settings["path"],
                y_name="Percent reference identity",
                plot=settings["plot"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "start_time" in df:
        plots.extend(
            compplots.compare_cumulative_yields(
                df=df,
                path=settings["path"],
                title=settings["title"],
                palette=settings["colors"])
        )
    if "channelIDs" in df:
        plots.append(
            compplots.active_pores_over_time(
                df=df,
                path=settings["path"],
                palette=settings["colors"],
                title=settings["title"]
            )
        )
    plots.extend(
        compplots.overlay_histogram(