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

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)

vals = []
    total = 0
    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)

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)

minV = v
        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=matplotlib.cm.get_cmap(options.colormap), norm=matplotlib.colors.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)

for f in files:
        h = [0] * options.bins
        for v in values[f]:
            i = min(int((v - minV) / binWidth), options.bins - 1)
            h[i] = h[i] + 1
        hists[f] = h

    width = binWidth / float(len(files)) * .8
    offset = binWidth * .1
    center = []
    for j in range(0, options.bins):
        center.append(binWidth * j + offset)

    fig, ax = helpers.openFigure(options)
    for i, f in enumerate(files):
        c = helpers.getColor(options, i, len(files))
        plt.bar(center, hists[f], width=width, label=helpers.getLabel(f, i, options), color=c)
        for j in range(options.bins):
            center[j] += width
    helpers.closeFigure(fig, ax, options)

value = float(v[options.column]) / options.norm
        vals.append(value)
        vlabels.append(str(value) + "%")
        total += value
        xs.append(x)
        ts.append(t)
        x = x + s
        t = t + s

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

    fig, ax = helpers.openFigure(options)
    if not options.vertical:
        rects = plt.barh(xs, vals, height=options.width)
        for i, rect in enumerate(rects):
            if options.showValues:
                width = rect.get_width()
                ax.text(width + options.valuesOffset, rect.get_y() +
                        rect.get_height() / 2., vlabels[i], va='center', ha='left')
            rect.set_color(colors[i])
            rect.set_edgecolor('k')
        plt.ylim(0, x)
        plt.yticks(ts, labels)
    else:
        rects = plt.bar(xs, vals, width=options.width)
        for i, rect in enumerate(rects):

for f in files:
        h = [0] * options.bins
        for v in values[f]:
            i = min(int((v - minV) / binWidth), options.bins - 1)
            h[i] = h[i] + 1
        hists[f] = h

    width = binWidth / float(len(files)) * .8
    offset = binWidth * .1
    center = []
    for j in range(0, options.bins):
        center.append(binWidth * j + offset)

    fig, ax = helpers.openFigure(options)
    for i, f in enumerate(files):
        c = helpers.getColor(options, i, len(files))
        l = helpers.getLabel(f, i, options)
        plt.bar(center, hists[f], width=width, label=l, color=c)
        for j in range(0, options.bins):
            center[j] = center[j] + width
    helpers.closeFigure(fig, ax, options)