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general_gaussian(M, p, sig, sym=True)

Notes

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

$$(2 \log(2))^{1/(2 p)} \sigma$$

Parameters

M : int

Number of points in the output window. If zero or less, an empty array is returned.

p : float

Shape parameter. p = 1 is identical to gaussian , p = 0.5 is the same shape as the Laplace distribution.

sig : float

The standard deviation, sigma.

sym : bool, optional

When True (default), generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis.

Returns

w : ndarray

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.

Examples

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()
... 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]")
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.signal.windows._windows.general_gaussian scipy.signal.windows._windows.get_window scipy.signal.general_gaussian

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GitHub : /scipy/signal/windows/_windows.py#1279
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