code: def general_gaussian(M, p, sig, sym=True):
      r"""Return a window with a generalized Gaussian shape.
  
      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 `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:
  
      >>> 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]")
  
      """
      if _len_guards(M):
          return np.ones(M)
      M, needs_trunc = _extend(M, sym)
  
      n = np.arange(0, M) - (M - 1.0) / 2.0
      w = np.exp(-0.5 * np.abs(n / sig) ** (2 * p))
  
      return _truncate(w, needs_trunc)
  
imports: import operator
  import warnings
  import numpy
  from scipy import linalg, special, fft
label_desc: Use image node_id 22 for calling a global function with example usage: general_gaussian(M, p, sig, sym) and returns: _truncate, np