import numpy as np
import pandas as pd

def Tv_As():

    subregion_adj = pd.read_csv('subregion_adj.csv',header = None)
    yanlinks = pd.read_csv('yanlinks.csv')

    unique_values, unique_indices = np.unique(yanlinks[:, 10], return_index=True)

    Asb = 0  # 计算平均相似性
    for i in unique_values:
        wu = np.where(subregion_adj[int(i) - 1, :] == 1)  # wu是元组
        smrjj_divide_smrjj_ = 0
        # 0726

        wu_1 = wu[0]

        for j in wu_1:

            selected_values_list = [yanlinks[yanlinks[:, 10] == j + 1][:, 5]]

            # 主区域邻接的一个区域速度均值与方差
            selected_values = np.array(selected_values_list)
            average = np.mean(selected_values)
            variance = np.var(selected_values)

            # 计算主区域的速度均值与方差
            selected_values1 = yanlinks[yanlinks[:, 10] == i][:, 5]
            average1 = np.mean(selected_values1)
            variance1 = np.var(selected_values1)

            smrjj = 2 * variance1  # jj情况下的smrjj
            smrjj_ = variance + variance1 + (average - average1) ** 2
            smrjj_divide_smrjj_one = smrjj / smrjj_
            smrjj_divide_smrjj_ += smrjj_divide_smrjj_one

        num_elements = len(wu[0])  # 计算分母NE
        Asb_one = smrjj_divide_smrjj_ / num_elements
        Asb += Asb_one

    print('Asb=', Asb)

    Tvb = 0
    for i in unique_values:
        selected_values = yanlinks[yanlinks[:, 10] == i][:, 5]
        variance = np.var(selected_values)
        Tvb += variance

    print('Tvb=', Tvb)

    return Asb, Tvb