def METHOD_NAME():
    """
    Compares the time performance of pandas and Vaex and generates a plot. This scripts takes several minutes to run.
    This results shoud shown that vaex and pandas provide similar performances in term if speed.
    Returns
    -------
    None.
    """
    time_list, timeC_list, lines_list = [], [], []
    time_list_va, timeC_list_va, lines_list_va = [], [], []
    wmin = 1000
    steps = 5
    wmax_arr = np.geomspace(10, 1000, steps)
    initial_engine = config[
        "DATAFRAME_ENGINE"
    ]  # To make sure dataframe engine not changed after running this test
    pb = ProgressBar(N=2 * steps)
    for i, engine in enumerate(["vaex", "pandas"]):
        config["DATAFRAME_ENGINE"] = engine
        for j, w_range in enumerate(wmax_arr):
            t0 = time.time()
            s = calc_spectrum(
                wmin,
                wmin + w_range,  # cm-1
                molecule="H2O",
                isotope="1,2,3",
                pressure=1.01325,  # bar
                Tgas=1000,
                mole_fraction=0.1,
                databank="hitemp",  # or 'hitemp'
                wstep="auto",
                cutoff=1e-28,
                verbose=0,
            )
            t1 = time.time()
            if engine == "vaex":
                timeC_list_va.append(s.conditions["calculation_time"])
                lines_list_va.append(s.conditions["lines_calculated"])
                time_list_va.append(t1 - t0)
                # lines_list_va.append(s.conditions['lines_calculated']+s.conditions['lines_cutoff'])
            else:
                timeC_list.append(s.conditions["calculation_time"])
                lines_list.append(s.conditions["lines_calculated"])
                time_list.append(t1 - t0)
                # lines_list.append(s.conditions['lines_calculated']+s.conditions['lines_cutoff'])
            pb.update(i * steps + (j + 1))
    plt.figure()
    plt.plot(lines_list, time_list, "k", label="pandas total")
    plt.plot(lines_list, timeC_list, "k--", label="pandas computation")
    plt.plot(lines_list_va, time_list_va, "r", label="vaex total")
    plt.plot(lines_list_va, timeC_list_va, "r--", label="vaex computation")
    plt.ylabel("Time [s]")
    plt.xlabel("Number of lines")
    plt.legend()
    config["DATAFRAME_ENGINE"] = initial_engine