numpy.
hamming
把吊床窗户还给我。
汉明窗是用加权余弦形成的锥形窗。
输出窗口中的点数。如果小于或等于零，则返回空数组。
最大值规范化为1的窗口（只有当样本数为奇数时，才会显示值1）。
参见
bartlett
blackman
hanning
kaiser
笔记
汉明窗定义为
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Type H <return> for immediate help. ... l.14 ...��（\frac{2\pi{n}}{M-1}\右）\end{split} [1] (./math.aux) ) (see the transcript file for additional information) Output written on math.dvi (1 page, 416 bytes). Transcript written on math.log.
哈明是以R.W.Hamming的名字命名的，他是J.W.Tukey的合伙人，在Blackman和Tukey中有描述。建议在时间域内平滑截断的自方差函数。对汉明窗的大多数引用来自信号处理文献，它被用作平滑值的许多窗函数之一。它也被称为变迹（意思是“去除脚”，即平滑采样信号开始和结束时的不连续性）或渐缩函数。
工具书类
Blackman，R.B.和Tukey，J.W.，（1958）功率谱测量，多佛出版社，纽约。
E.R.Kanasewich，“地球物理学中的时序分析”，阿尔伯塔大学出版社，1975年，第109-110页。
维基百科，“窗口函数”，https://en.wikipedia.org/wiki/window_function
W.H.Press、B.P.Flannery、S.A.Teukolsky和W.T.Vetterling，“数字食谱”，剑桥大学出版社，1986年，第425页。
实例
>>> np.hamming(12) array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594, # may vary 0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909, 0.15302337, 0.08 ])
绘制窗口和频率响应：
>>> import matplotlib.pyplot as plt >>> from numpy.fft import fft, fftshift >>> window = np.hamming(51) >>> plt.plot(window) [<matplotlib.lines.Line2D object at 0x...>] >>> plt.title("Hamming window") Text(0.5, 1.0, 'Hamming window') >>> plt.ylabel("Amplitude") Text(0, 0.5, 'Amplitude') >>> plt.xlabel("Sample") Text(0.5, 0, 'Sample') >>> plt.show()
>>> plt.figure() <Figure size 640x480 with 0 Axes> >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [<matplotlib.lines.Line2D object at 0x...>] >>> plt.title("Frequency response of Hamming window") Text(0.5, 1.0, 'Frequency response of Hamming window') >>> plt.ylabel("Magnitude [dB]") Text(0, 0.5, 'Magnitude [dB]') >>> plt.xlabel("Normalized frequency [cycles per sample]") Text(0.5, 0, 'Normalized frequency [cycles per sample]') >>> plt.axis('tight') ... >>> plt.show()