我目前正在運(yùn)行test_matrix_speed()以查看我的search_and_book_availability功能有多快。使用 PyCharm 分析器，我可以看到每個(gè)search_and_book_availability函數(shù)調(diào)用的平均速度為 0.001 毫秒。擁有 Numba@jit(nopython=True)裝飾器對(duì)該函數(shù)的性能沒(méi)有影響。這是因?yàn)闆](méi)有任何改進(jìn)并且 Numpy 在這里運(yùn)行得盡可能快嗎？（我不在乎generate_searches函數(shù)的速度）這是我正在運(yùn)行的代碼import randomimport numpy as npfrom numba import jitdef generate_searches(number, sim_start, sim_end):    searches = []    for i in range(number):        start_slot = random.randint(sim_start, sim_end - 1)        end_slot = random.randint(start_slot + 1, sim_end)        searches.append((start_slot, end_slot))    return searches@jit(nopython=True)def search_and_book_availability(matrix, search_start, search_end):    search_slice = matrix[:, search_start:search_end]    output = np.where(np.sum(search_slice, axis=1) == 0)[0]    number_of_bookable_vecs = output.size    if number_of_bookable_vecs > 0:        if number_of_bookable_vecs == 1:            id_to_book = output[0]        else:            id_to_book = np.random.choice(output)        matrix[id_to_book, search_start:search_end] = 1        return True    else:        return Falsedef test_matrix_speed():    shape = (10, 1440)    matrix = np.zeros(shape)    sim_start = 0    sim_end = 1440    searches = generate_searches(1000000, sim_start, sim_end)    for i in searches:        search_start = i[0]        search_end = i[1]        availability = search_and_book_availability(matrix, search_start, search_end)