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Module « scipy.signal »

Fonction general_gaussian - module scipy.signal

Signature de la fonction general_gaussian

def general_gaussian(*args, **kwargs) 

Description

general_gaussian.__doc__

Return a window with a generalized Gaussian shape.

    .. warning:: scipy.signal.general_gaussian is deprecated,
                 use scipy.signal.windows.general_gaussian instead.

    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]")

    

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