general_gaussian(*args, **kwargs)
use scipy.signal.windows.general_gaussian instead.
The generalized Gaussian window is defined as
$$w(n) = e^{ -\frac{1}{2}\left|\frac{n}{\sigma}\right|^{2p} }$$the half-power point is at
$$(2 \log(2))^{1/(2 p)} \sigma$$Number of points in the output window. If zero or less, an empty array is returned.
Shape parameter. p = 1 is identical to gaussian
, p = 0.5 is the same shape as the Laplace distribution.
The standard deviation, sigma.
When True (default), generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis.
The window, with the maximum value normalized to 1 (though the value 1 does not appear if M is even and :None:None:`sym` is True).
Return a window with a generalized Gaussian shape.
Plot the window and its frequency response:
>>> from scipy import signal
... from scipy.fft import fft, fftshift
... import matplotlib.pyplot as plt
>>> window = signal.windows.general_gaussian(51, p=1.5, sig=7)
... plt.plot(window)
... plt.title(r"Generalized Gaussian window (p=1.5, $\sigma$=7)")
... plt.ylabel("Amplitude")
... plt.xlabel("Sample")
>>> plt.figure()See :
... A = fft(window, 2048) / (len(window)/2.0)
... freq = np.linspace(-0.5, 0.5, len(A))
... response = 20 * np.log10(np.abs(fftshift(A / abs(A).max())))
... plt.plot(freq, response)
... plt.axis([-0.5, 0.5, -120, 0])
... plt.title(r"Freq. resp. of the gen. Gaussian "
... r"window (p=1.5, $\sigma$=7)")
... plt.ylabel("Normalized magnitude [dB]")
... plt.xlabel("Normalized frequency [cycles per sample]")
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Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
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