numpy.
kaiser
返回凯撒窗口。
凯撒窗是一种使用贝塞尔函数形成的锥度。
输出窗口中的点数。如果小于或等于零，则返回空数组。
窗口的形状参数。
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
hamming
hanning
笔记
凯撒窗口定义为
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具有
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在哪里？ 是修正的零阶贝塞尔函数。
凯撒是以吉姆·凯撒命名的，他发现了基于贝塞尔函数的DPSS窗口的简单近似。Kaiser窗是一个非常好的近似数字长圆形序列，或Sleepian窗，这是一个转换，最大化的能量在窗口主瓣相对于总能量。
皇帝可以通过改变beta参数来近似许多其他窗口。
贝塔
窗口形状
0
矩形
5
类似于吊床
6
类似于汉宁
8.6
类似于黑人
beta值为14可能是一个很好的起点。请注意，当beta变大时，窗口会缩小，因此样本的数量需要足够大，以对越来越窄的尖峰进行采样，否则nan会返回。
大多数对Kaiser窗口的引用来自信号处理文献，它被用作平滑值的许多窗口函数之一。它也被称为变迹（意思是“消除脚”，即在采样信号的开始和结束处平滑不连续）或渐缩函数。
工具书类
J.F.Kaiser，《数字滤波器》——第7章，载于《数字计算机系统分析》，编辑：F.F.Kuo和J.F.Kaiser，第218-285页。约翰威利父子，纽约，（1966）。
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177-178页。
维基百科，“窗口函数”，https://en.wikipedia.org/wiki/window_function
实例
>>> import matplotlib.pyplot as plt >>> np.kaiser(12, 14) array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary 2.29737120e-01, 5.99885316e-01, 9.45674898e-01, 9.45674898e-01, 5.99885316e-01, 2.29737120e-01, 4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
绘制窗口和频率响应：
>>> from numpy.fft import fft, fftshift >>> window = np.kaiser(51, 14) >>> plt.plot(window) [<matplotlib.lines.Line2D object at 0x...>] >>> plt.title("Kaiser window") Text(0.5, 1.0, 'Kaiser 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 Kaiser window") Text(0.5, 1.0, 'Frequency response of Kaiser 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') (-0.5, 0.5, -100.0, ...) # may vary >>> plt.show()