python中布尔值可以比较吗,python怎么设置缺失值

目前我只能对numpy.correlate发表评论。它是一个强大的工具。我用它有两个目的。首先是在另一个模式中找到一个模式：import numpy as np

import matplotlib.pyplot as plt

some_data = np.random.uniform(0,1,size=100)

subset = some_data[42:50]

mean = np.mean(some_data)

some_data_normalised = some_data - mean

subset_normalised = subset - mean

correlated = np.correlate(some_data_normalised, subset_normalised)

max_index = np.argmax(correlated) # 42 !

第二个用途(以及如何解释结果)是用于频率检测：hz_a = np.cos(np.linspace(0,np.pi*6,100))

hz_b = np.cos(np.linspace(0,np.pi*4,100))

f, axarr = plt.subplots(2, sharex=True)

axarr[0].plot(hz_a)

axarr[0].plot(hz_b)

axarr[0].grid(True)

hz_a_autocorrelation = np.correlate(hz_a,hz_a,'same')[round(len(hz_a)/2):]

hz_b_autocorrelation = np.correlate(hz_b,hz_b,'same')[round(len(hz_b)/2):]

axarr[1].plot(hz_a_autocorrelation)

axarr[1].plot(hz_b_autocorrelation)

axarr[1].grid(True)

plt.show()

找到第二个峰的指数。从这里你可以找到频率。first_min_index = np.argmin(hz_a_autocorrelation)

second_max_index = np.argmax(hz_a_autocorrelation[first_min_index:])

frequency = 1/second_max_index