def METHOD_NAME(name_demo):
    nb = nb_data(name_demo)
    v = [0]*nb
    value = [0]*nb
    value2 = [0]*nb
    min_v = [0]*nb
    max_v = [0]*nb
    fig, ax = plt.subplots(2, sharex='col', sharey='row')
    with open('benchmark/'+name_demo+'.csv') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count_marks = 0
        for row in csv_reader:
            line_count_marks += 1
    BDM_marks = [0] * line_count_marks
    with open('benchmark/'+name_demo+'.csv') as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=',')
        line_count = 0
        for row in csv_reader:
            v[line_count] = row[0]
            v[line_count] = v[line_count][:7]
            value[line_count] = float(row[1])/10**9
            value2[line_count] = float(row[2])
            min_v[line_count] = float(row[3])/10**9
            max_v[line_count] = float(row[4])/10**9
            BDM_marks[line_count] = (value[0] / value[line_count]) *1000
            line_count += 1
    average = np.average(BDM_marks)
    print("BDM_marks", name_demo, ':', average)
    xlabels = v
    ax[0].METHOD_NAME(range(nb), value, 'bo-')
    ax[0].METHOD_NAME(range(nb), min_v, 'b--')
    ax[0].METHOD_NAME(range(nb), max_v, 'b--')
    ax[0].set_ylabel("CPU Time in second")
    ax[0].set_xlabel("Version")
    ax[1].set_ylabel("Memory")
    ax[1].METHOD_NAME(range(nb), value2, 'ro-')
    ax[0].set_title(name_demo)
    ax[1].set_xticks(range(nb))
    ax[1].set_xticklabels(xlabels, rotation=90)
    plt.tight_layout()
    plt.savefig('benchmark/'+name_demo+'.png')
    return average