How to use the jcvi.graphics.base.savefig function in jcvi

SH = None
            draw_tree(ax, tree, rmargin=rmargin, reroot=False, \
                supportcolor="r", SH=SH, **kwargs)

        root.set_xlim(0, 1)
        root.set_ylim(0, 1)
        root.set_axis_off()

        format = iopts.format if iopts else "pdf"
        dpi = iopts.dpi if iopts else 300
        if n == 1:
            image_name = f.rsplit(".", 1)[0] + "." + format
        else:
            image_name = "trees{0}.{1}".format(x, format)
        image_name = op.join(outdir, image_name)
        savefig(image_name, dpi=dpi, iopts=iopts)
        plt.clf()

%prog qc postgenomic-s.tsv

    Plot basic statistics of a given sample:
    Age, Gender, Ethnicity, Cohort, Chemistry
    """
    p = OptionParser(heritability.__doc__)
    opts, args, iopts = p.set_image_options(args, figsize="10x6")

    if len(args) != 1:
        sys.exit(not p.print_help())

    tsvfile, = args
    df = pd.read_csv(tsvfile, sep="\t")
    composite_qc(df, size=(iopts.w, iopts.h))
    outfile = tsvfile.rsplit(".", 1)[0] + ".qc.pdf"
    savefig(outfile)

MZ = list(get_pairs(mz))
    DZ = list(get_pairs(dz))

    print(len(MZ), "monozygotic twins")
    print(len(DZ), "dizygotic twins")

    df = pd.read_csv(combined, sep="\t", index_col=0)
    df["Sample name"] = np.array(df["Sample name"], dtype=np.str)
    gender = extract_trait(df, "Sample name", "hli_calc_gender")
    sameGenderMZ = list(filter_same_gender(MZ, gender))
    sameGenderDZ = list(filter_same_gender(DZ, gender))

    composite(df, sameGenderMZ, sameGenderDZ, size=(iopts.w, iopts.h))
    logging.getLogger().setLevel(logging.CRITICAL)
    savefig("heritability.pdf")

p = axC.pcolormesh(C, cmap=default_cm)
    axA.set_xlim(0, len(gA))
    axC.set_xlim(0, len(gC))

    x, y, w, h = .35, .1, .3, .05
    ax_colorbar = fig.add_axes([x, y, w, h])
    fig.colorbar(p, cax=ax_colorbar, orientation='horizontal')
    root.text(x - d, y + h / 2, "RPKM", ha="right", va="center")

    root.set_xlim(0, 1)
    root.set_ylim(0, 1)
    for x in (axA, axC, root):
        x.set_axis_off()

    image_name = "napusf4b." + iopts.format
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)

sbed = subset_bed(sbed, sseqids)

    fig = plt.figure(1, (iopts.w, iopts.h))
    root = fig.add_axes([0, 0, 1, 1])  # the whole canvas
    ax = fig.add_axes([.1, .1, .8, .8])  # the dot plot

    dotplot(anchorfile, qbed, sbed, fig, root, ax,
            vmin=opts.vmin, vmax=opts.vmax, is_self=is_self,
            synteny=opts.synteny, cmap_text=opts.cmaptext, cmap=iopts.cmap,
            genomenames=opts.genomenames, sample_number=opts.sample_number,
            minfont=opts.minfont, palette=palette, sep=(not opts.nosep),
            title=opts.title, stdpf=(not opts.nostdpf), chpf=(not opts.nochpf))

    image_name = opts.outfile or \
            (op.splitext(anchorfile)[0] + "." + opts.format)
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)
    fig.clear()

root.text(sum(xx) / 2, ypos - pad, "34,115bp", **fontprop)
    xx = markers[1], begs[1]
    root.plot(xx, (ypos, ypos), "-", lw=2, color=lsg)
    root.text(sum(xx) / 2, ypos - pad, "81,276bp", **fontprop)

    root.plot((ends[0], begs[1]), (y, y), ":", lw=2, color=lsg)
    root.text(sum(markers) / 2, ypos - 3 * pad, r"$\textit{Estimated gap size: 96,433bp}$",
                                  color="r", ha="center", va="center")

    labels = ((.05, .95, 'A'), (.05, .6, 'B'), (.05, .27, 'C'))
    panel_labels(root, labels)
    normalize_axes(root)

    pf = "estimategaps"
    image_name = pf + "." + iopts.format
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)

root.plot((xpos, xpos), (ymax, .63), ":o", **kwargs)
    root.plot((xpos, xmin), (ymax, ymin), ":o", **kwargs)
    root.plot((xpos, xmax), (ymax, ymin), ":o", **kwargs)
    RoundRect(root, (.06, .17), .92, .35, fill=False, lw=2, ec=light)

    # Panels
    root.text(.05, .95, "a", size=20, fontweight="bold")
    root.text(.1, .45, "b", size=20, fontweight="bold")

    root.set_xlim(0, 1)
    root.set_ylim(0, 1)
    root.set_axis_off()

    pf = "cotton"
    image_name = pf + "." + iopts.format
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)

line, = ax.plot(x, y, '-', color=c, lw=2, alpha=.5)
        lines.append(line)
        legends.append(legend)

    leg = ax.legend(lines, legends, shadow=True, fancybox=True)
    leg.get_frame().set_alpha(.5)

    ylabel = "Average depth per {0}Kb".format(size / bins / 1000)
    ax.set_xlim(0, size)
    ax.set_ylim(0, opts.ymax)
    ax.set_xlabel(ctg)
    ax.set_ylabel(ylabel)
    set_human_base_axis(ax)

    figname ="{0}.{1}.pdf".format(fastafile, ctg)
    savefig(figname, dpi=iopts.dpi, iopts=iopts)

# Delete unused axes
    for ax in axes[len(loci):]:
        ax.set_axis_off()

    root = fig.add_axes([0, 0, 1, 1])
    pad = .03
    if not opts.nopanels:
        panel_labels(root, ((pad / 2, 1 - pad, "A"), (.5 + pad, 1 - pad, "B"),
                        (pad / 2, 2 / 3. - pad / 2, "C"), (.5 + pad, 2 / 3. - pad / 2, "D"),
                        (pad / 2, 1 / 3. , "E"), (.5 + pad, 1 / 3. , "F"),
                        ))
    normalize_axes(root)

    image_name = "allelefreq." + iopts.format
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)

)
    tx = df["BinStart"]
    A, B, label = fit_power_law(tx, df["LinkDensity"])
    ty = A * tx ** B
    ax.plot(tx, ty, "r:", lw=3, label=label)
    ax.legend()
    if opts.title:
        ax.set_title(markup(opts.title))
    ax.set_xlabel("Link size (bp)")
    ax.set_ylabel("Density (\# of links per bp)")
    ax.set_xscale("log", nonposx="clip")
    ax.set_yscale("log", nonposy="clip")
    ax.xaxis.set_major_formatter(human_base_formatter)

    image_name = pf + "." + opts.format
    savefig(image_name, dpi=iopts.dpi, iopts=iopts)