import numpy as np

def make_rand_matrix(side=20): # 制作随机矩阵，用于测试

    a = np.random.random((side,side))

    for i in range(0,side):

        for j in range(0,side):

            if a[i,j]>0.3:

                a[i,j] = 1

            else:

                a[i,j] = 0

    return a

def get_min_std_matrix(A): # 制作最分布均匀矩阵

    sum_ = sum(sum(A))

    [x_side, y_side] = A.shape

    B = np.zeros((x_side,y_side))

    long_ = int(x_side*y_side/sum_)

    cnt = 0

    for i in range(0,x_side):

        for j in range(0,y_side):

            cnt = cnt + 1

            if cnt%long_ == 0:

                B[i,j] = 1

    return B

def get_max_std_matrix(A): # 制作分布最不均匀矩阵

    sum_ = sum(sum(A))

    [x_side, y_side] = A.shape

    B = np.zeros((x_side,y_side))

    cnt = 0

    for i in range(0,x_side):

        for j in range(0,y_side):

            B[i,j] = 1

            cnt = cnt + 1

            if cnt >= sum_:

                break

    return B

def get_rand_std(rand_matrix): # 输入矩阵为0,1矩阵，计算矩阵分布均匀程度

    sum_ = sum(sum(rand_matrix))

    [x_side, y_side] = rand_matrix.shape

    fit_x_side = int(x_side/10)

    fit_y_side = int(y_side/10)

    location_p = []

    for i in range(0, x_side-10, int(fit_x_side/2)): # 让每个元素被扫描两次（规则自己随便定，保证元素都被扫描到就行）

        for j in range(0,y_side-10, int(fit_y_side/2)):

            temp_matrix = rand_matrix[i:i+10,j:j+10]

            cnt_p = sum(sum(temp_matrix))

            location_p.append(cnt_p)

    std = np.std(location_p)

    return std

def get_stand_std(A): # 将均匀程度分布在0,1之间，1表示分布最均匀

    if sum(sum(A)) == 0:

        print('不得传入全0矩阵')

        exit()

    A_max = get_max_std_matrix(A)

    A_min = get_min_std_matrix(A)

    A_std = get_rand_std(A)

    A_max_std = get_rand_std(A_max)

    A_min_std = get_rand_std(A_min)

    if A_std < A_min_std:

        return 1

    else:

        return max(0,(A_max_std-A_std)/(A_max_std-A_min_std))

if __name__ == "__main__":

    # 当矩阵为0或者1元素过少，计算分布没有意义

    A = make_rand_matrix(100) # numpy产生随机0,1矩阵均匀程度在0.9~1之间

    print(get_stand_std(A))