def detect_frequency_change(data, window_size, threshold): # 计算每个窗口中的均值和标准差 frequencies = [] for i in range(0, len(data)-window_size, window_size): window = data[i:i+window_size] freq = len(window) / (window[-1] - window[0]) frequencies.append(freq) # calculate p-value of frequency change in consecutive windows p_values = [] for i in range(1, len(frequencies)): p = stats.ttest_ind(frequencies[i-1], frequencies[i]) p_values.append(p) # 计算窗口中p值小于阈值的地方 abnormal_windows = np.where(np.array(p_values) < threshold)[0] abnormal_timepoints = [] for i in abnormal_windows: start_time = data[i*window_size] end_time = data[(i+1)*window_size-1] abnormal_timepoints.append((start_time, end_time)) return abnormal_timepoints

这是一个用于检测频率变化的函数。它接收三个参数：
- data: 输入数据
- window_size: 窗口大小
- threshold: 阈值

该函数通过窗口滑动来检测频率变化，如果频率变化超过了阈值，则返回True，否则返回False。