ameter.  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 `sym` is True).

Notes
-----
The generalized Gaussian window is defined as

.. math::  w(n) = e^{ -\frac{1}{2}\left|\frac{n}{\sigma}\right|^{2p} }

the half-power point is at

.. math::  (2 \log(2))^{1/(2 p)} \sigma

Examples
--------
Plot the window and its frequency response:

>>> import numpy as np
>>> 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]")

r