Move labels to bars in vertical graph.

    #    print("\t_node__"+edge['Start'] + " -> _node__"+edge['End'] + "[label=\""+str(edge['Correlation'])+"\"];")

    print("}")

# Taken from: http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/

def autolabel(rects, ax, labels):

    # Get y-axis height to calculate label position from.

    (y_bottom, y_top) = ax.get_ylim()

    y_height = y_top - y_bottom

    for i, rect in enumerate(rects):

        height = rect.get_height()

        color = "black"

        # Fraction of axis height taken up by this rectangle

        p_height = (height / y_height)

        # If we can fit the label above the column, do that;

        # otherwise, put it inside the column.

        if p_height > 0.50: # arbitrary; 95% looked good to me.

            label_position = 0.01

            color = "white"

        else:

            label_position = height + (y_height * 0.01)

        ax.text(rect.get_x() + rect.get_width()/2., label_position,

                labels[i],

                ha='center', va='bottom', rotation=90, color=color)

def _plot_critical_regions(df,hdb):

    """

    plot regions, sorted by latency criticality

    for region in sorted(hdb, key = lambda x: -x['Correlation']):

        print("%-10f %10s -> %10s"%(region['Correlation'], region['Start'], region['End']), file=sys.stderr)

    relevant = sorted([x for x in hdb if x['Correlation'] > 0], key = lambda x: -x['Correlation'])

    relevant = sorted([x for x in hdb if x['Correlation'] > 0], key = lambda x: -x['Correlation'], reverse=True)

    x = np.arange(len(relevant))

    bars = plt.bar(x, list(map(lambda x: x['Correlation'], relevant)))

    correlations = list(map(lambda x: x['Correlation'], relevant))

    ticks = list(map(lambda x: "%s-%s" % (x['Start'][:-2], x['End'][:-2]), relevant))

    fig, ax = plt.subplots()

    rects = ax.bar(x, correlations, align="center", tick_label="")

    autolabel(rects, ax, ticks)

    # TODO: find a more elegant solution for the label text

    plt.xticks(x, list(map(lambda x: "%s-%s"%(x['Start'][:-2], x['End'][:-2]) , relevant)), rotation=90)

    plt.tight_layout()

    plt.savefig("latency-criticality.png")

    plt.savefig("latency-criticality.pdf")

    plt.close()

def analyse(df, config):

    hb = []

    for event1 in df: