numpy.blackman

numpy.blackman(M)

返回 Blackman 窗.

Blackman 窗是一个锥形,通过使用余弦求和的前三项形成.它被设计为具有接近最小的可能的泄漏.它接近最优,仅比 Kaiser 窗稍差.

参数:

Mint

输出窗口中的点数.如果小于或等于零,则返回一个空数组.

返回:

outndarray

窗,最大值归一化为 1(仅当样本数为奇数时才出现值 1).

参见

bartlett , hamming , hanning , kaiser

注释

Blackman 窗函数定义为

\[w(n) = 0.42 - 0.5 \cos(2\pi n/M) + 0.08 \cos(4\pi n/M)\]

大多数关于 Blackman 窗函数的引用都来自信号处理文献,它被用作平滑值的众多窗函数之一.它也被称为apodization(意思是"去除足部",即平滑采样信号开始和结束处的不连续性)或锥形函数.它被称为"接近最优"的锥形函数,几乎与 kaiser 窗函数一样好(在某些方面).

参考文献

Blackman, R.B. 和 Tukey, J.W., (1958) The measurement of power spectra, Dover Publications, New York.

Oppenheim, A.V., 和 R.W. Schafer. Discrete-Time Signal Processing. Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.

示例

Try it in your browser!

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> np.blackman(12)
array([-1.38777878e-17,   3.26064346e-02,   1.59903635e-01, # may vary
        4.14397981e-01,   7.36045180e-01,   9.67046769e-01,
        9.67046769e-01,   7.36045180e-01,   4.14397981e-01,
        1.59903635e-01,   3.26064346e-02,  -1.38777878e-17])

绘制窗函数和频率响应.

import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.blackman(51)
plt.plot(window)
plt.title("Blackman window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show()  # doctest: +SKIP

plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
with np.errstate(divide='ignore', invalid='ignore'):
    response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of Blackman window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()

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