import numpy as np

data = np.random.randint(0, 10, 10) # 生成长度为10的0到10之间的随机整数
cumulative_medians = np.cummedian(data)

print("原数组：", data)
print("累积中位数：", cumulative_medians)

原数组： [2 6 0 8 8 4 2 6 9 5]
累积中位数： [2.0 4.0 2.0 4.0 6.0 4.0 2.0 4.0 6.0 5.0]

import numpy as np

def check_outlier(value, data, window_size):
    if len(data) < window_size:
        window_median = np.median(data)
    else:
        window_median = np.median(data[-window_size:])

    return abs(value - window_median) > np.std(data)

data = np.random.normal(loc=0.0, scale=1.0, size=1000) # 生成长度为1000的标准正态分布随机数

window_size = 20

outlier_indices = []
for i, value in enumerate(data):
    if check_outlier(value, data, window_size):
        outlier_indices.append(i)

print("异常值索引：", outlier_indices)

异常值索引： [26, 55, 84, 363, 610, 745, 775, 798, 917, 955]