How to use the sumolib.visualization.helpers.openFigure function in sumolib

options, remaining_args = optParser.parse_args(args=args)

    if options.input is None:
        print("Error: an input file must be given")
        sys.exit(1)

    minV = 0
    maxV = 0
    if options.columns is not None:
        options.columns = [int(i) for i in options.columns.split(",")]
    nums = readValues(options.input, options.verbose, options.columns)
    for f in nums:
        maxV = max(maxV, len(nums[f]))
    ts = range(minV, maxV + 1)

    fig, ax = helpers.openFigure(options)
    for i in nums:
        v = nums[i]
        ci = i
        if options.columns is not None:
            ci = options.columns.index(i)
        c = helpers.getColor(options, ci, len(nums))
        l = helpers.getLabel(str(i), ci, options)
        plt.plot(ts[0:len(v)], v, label=l, color=c)
    helpers.closeFigure(fig, ax, options)

for line in fd:
        v = line.strip().split(";")
        if len(v) < 2:
            continue
        labels.append(v[0].replace("\\n", "\n"))
        vals.append(float(v[1]))
        total += float(v[1])

    if options.revert:
        labels.reverse()
        vals.reverse()
    colors = []
    for i, e in enumerate(labels):
        colors.append(helpers.getColor(options, i, len(labels)))

    fig, ax = helpers.openFigure(options)
    if options.nolabels:
        labels = None
    shadow = options.shadow
    if options.percentage:
        def autopct(p):
            return '{:.1f}%'.format(p)
        # autopct = lambda p: '{:.1f}%'.format(p)
    else:
        def autopct(p):
            return '{:.0f}'.format(p * total / 100)
        # autopct = lambda p: '{:.0f}'.format(p * total / 100)
    patches, texts, autotexts = plt.pie(
        vals, labels=labels, autopct=autopct, colors=colors, shadow=shadow, startangle=options.startangle)
    helpers.closeFigure(fig, ax, options)

options, remaining_args = optParser.parse_args(args=args)

    if options.summary is None:
        print("Error: at least one summary file must be given")
        sys.exit(1)

    minV = 0
    maxV = 0
    files = options.summary.split(",")
    nums = readValues(files, options.verbose, options.measure)
    times = readValues(files, options.verbose, "time")
    for f in files:
        maxV = max(maxV, len(nums[f]))
    ts = range(minV, maxV + 1)

    fig, ax = helpers.openFigure(options)
    for i, f in enumerate(files):
        v = sumolib.output.toList(nums[f], options.measure)
        t = sumolib.output.toList(times[f], "time")
        c = helpers.getColor(options, i, len(files))
        l = helpers.getLabel(f, i, options)
        plt.plot(t, v, label=l, color=c)
    helpers.closeFigure(fig, ax, options)

options, _ = optParser.parse_args(args=args)

    if options.summary is None:
        print("Error: at least one summary file must be given")
        sys.exit(1)

    minV = 0
    maxV = 0
    files = options.summary.split(",")
    nums = readValues(files, options.verbose, options.measure)
    times = readValues(files, options.verbose, "time")
    for f in files:
        maxV = max(maxV, len(nums[f]))
    range(minV, maxV + 1)

    fig, ax = helpers.openFigure(options)
    for i, f in enumerate(files):
        v = sumolib.output.toList(nums[f], options.measure)
        t = sumolib.output.toList(times[f], "time")
        c = helpers.getColor(options, i, len(files))
        plt.plot(t, v, label=helpers.getLabel(f, i, options), color=c)
    helpers.closeFigure(fig, ax, options)

return 1
    if options.selection is None:
        print("Error: a selection to load must be given.")
        return 1
    if options.verbose:
        print("Reading network from '%s'" % options.net)
    net = sumolib.net.readNet(options.net)
    selection = sumolib.files.selection.read(options.selection)

    colors = {}
    widths = {}
    for e in selection["edge"]:
        colors[e] = options.selectedColor
        widths[e] = options.selectedWidth

    fig, ax = helpers.openFigure(options)
    ax.set_aspect("equal", None, 'C')
    helpers.plotNet(net, colors, widths, options)
    options.nolegend = True
    helpers.closeFigure(fig, ax, options)

if options.widthMax is not None:
            maxWidthValue = options.widthMax
        if options.widthMin is not None:
            minWidthValue = options.widthMin
        if options.logWidths:
            helpers.logNormalise(widths, options.colorMax)
        else:
            helpers.linNormalise(widths, minWidthValue, maxWidthValue)
        for e in widths:
            widths[e] = options.minWidth + widths[e] * \
                (options.maxWidth - options.minWidth)
        if options.verbose:
            print("Width values are between %s and %s" %
                  (minWidthValue, maxWidthValue))

        fig, ax = helpers.openFigure(options)
        ax.set_aspect("equal", None, 'C')
        helpers.plotNet(net, colors, widths, options)

        # drawing the legend, at least for the colors
        norm = matplotlib.colors.LogNorm if options.logColors else matplotlib.colors.Normalize
        sm = plt.cm.ScalarMappable(cmap=matplotlib.cm.get_cmap(options.colormap),
                                   norm=norm(vmin=minColorValue, vmax=maxColorValue))

        # "fake up the array of the scalar mappable. Urgh..."
        # (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
        sm._A = []
        plt.colorbar(sm)

        # Should we also save the figure to a file / list of files (comma
        # separated)?
        if options.output:

if maxV is None or maxV < v:
            maxV = v
        speeds[e] = v
    if options.minV is not None:
        minV = options.minV
    if options.maxV is not None:
        maxV = options.maxV
    # if options.logColors:
#    helpers.logNormalise(colors, maxColorValue)
#  else:
#    helpers.linNormalise(colors, minColorValue, maxColorValue)

    helpers.linNormalise(speeds, minV, maxV)
    for e in speeds:
        speeds[e] = helpers.getColor(options, speeds[e], 1.)
    fig, ax = helpers.openFigure(options)
    ax.set_aspect("equal", None, 'C')
    helpers.plotNet(net, speeds, {}, options)

    # drawing the legend, at least for the colors
    print("%s -> %s" % (minV, maxV))
    sm = matplotlib.cm.ScalarMappable(
        cmap=get_cmap(options.colormap), norm=plt.normalize(vmin=minV, vmax=maxV))
    # "fake up the array of the scalar mappable. Urgh..." (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
    sm._A = []
    plt.colorbar(sm)
    options.nolegend = True
    helpers.closeFigure(fig, ax, options)

if options.widthMax is not None:
            maxWidthValue = options.widthMax
        if options.widthMin is not None:
            minWidthValue = options.widthMin
        if options.logWidths:
            helpers.logNormalise(widths, options.colorMax)
        else:
            helpers.linNormalise(widths, minWidthValue, maxWidthValue)
        for e in widths:
            widths[e] = options.minWidth + widths[e] * \
                (options.maxWidth - options.minWidth)
        if options.verbose:
            print("Width values are between %s and %s" %
                  (minWidthValue, maxWidthValue))

        fig, ax = helpers.openFigure(options)
        ax.set_aspect("equal", None, 'C')
        helpers.plotNet(net, colors, widths, options)

        # drawing the legend, at least for the colors
        norm = matplotlib.colors.LogNorm if options.logColors else matplotlib.colors.Normalize
        sm = plt.cm.ScalarMappable(cmap=matplotlib.cm.get_cmap(options.colormap),
                                   norm=norm(vmin=minColorValue, vmax=maxColorValue))

        # "fake up the array of the scalar mappable. Urgh..."
        # (pelson, http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots)
        sm._A = []
        color_bar = plt.colorbar(sm)
        color_bar.set_label(options.colorBarLabel)

        # Should we also save the figure to a file / list of files (comma
        # separated)?