numpy.blackman#

numpy.blackman(M)[源代码]#

返回 Blackman 窗.

Blackman 窗是通过使用余弦求和的前三个项形成的锥形.它被设计为具有尽可能接近最小的泄漏.它接近最佳,仅比 Kaiser 窗略差.

参数:
Mint

输出窗口中的点数. 如果为零或更小,则返回一个空数组.

返回:
outndarray

该窗口的最大值已归一化为 1(仅当样本数为奇数时,值才为 1).

参见

bartlett , hamming , hanning , kaiser

注释

Blackman 窗口定义为

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

对 Blackman 窗口的大部分引用都来自信号处理文献,在这些文献中,它被用作众多窗口函数之一,用于平滑值.它也被称为保迹函数(apodization)(意思是“去除脚”,即平滑采样信号开始和结束时的不连续性)或锥形函数. 它被称为“接近最优”的锥形函数,(通过某些衡量标准)几乎与 kaiser 窗口一样好.

参考

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

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

示例

>>> 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])

绘制窗口和频率响应.

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
../../_images/numpy-blackman-1_00_00.png 
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()
../../_images/numpy-blackman-1_01_00.png
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