How to use the dtaidistance.util.prepare_directory function in dtaidistance

def plot_margins(serie, weights, clfs, importances=None):
    global directory
    if directory is None:
        directory = prepare_directory()
    from sklearn import tree
    feature_names = ["f{} ({}, {})".format(i // 2, i, '-' if (i % 2) == 0 else '+') for i in range(2 * len(serie))]
    out_str = io.StringIO()
    for clf in clfs:
        tree.export_graphviz(clf, out_file=out_str, feature_names=feature_names)
        print("\n\n", file=out_str)
    with open(str(directory / "tree.dot"), "w") as ofile:
        print(out_str.getvalue(), file=ofile)
    dtww.plot_margins(serie, weights, filename=str(directory / "margins.png"), importances=importances)

def plot_series(s, l, idx=None):
    global directory
    if directory is None:
        directory = prepare_directory()
    import matplotlib.pyplot as plt
    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
    fig1, ax1 = plt.subplots(nrows=len(s), ncols=1)
    fig2, ax2 = plt.subplots(nrows=1, ncols=1)
    for i, si in enumerate(s):
        if i == idx:
            color = colors[0]
        else:
            color = colors[int(1 + l[i])]
        ax1[i].plot(si, color=color)
        ax2.plot(si, color=color)
    fig1.savefig(str(directory / "series1.png"))
    fig2.savefig(str(directory / "series2.png"))

def test_distance1():
    directory = prepare_directory()

    s1 = np.array([0., 0, 1, 2, 1, 0, 1, 0, 0, 2, 1, 0, 0])
    s2 = np.array([0., 1, 2, 3, 1, 10, 1, 0, 2, 1, 0, 0, 0])
    d, paths = dtw.warping_paths(s1, s2)
    # print(d, "\n", paths)
    dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp1.png")

    weights = np.full((len(s1), 8), np.inf)
    weights[:, 2:4] = 0.0
    weights[4:7, 2:4] = 10.0
    weights[:, 4:6] = 0.0
    weights[4:7, 4:6] = 10.0
    d, paths = dtww.warping_paths(s1, s2, weights)
    # print(d, "\n", paths)
    dtwvis.plot_warpingpaths(s1, s2, paths, filename=directory / "temp2.png")