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taylor(M, nbar=4, sll=30, norm=True, sym=True)

The Taylor window taper function approximates the Dolph-Chebyshev window's constant sidelobe level for a parameterized number of near-in sidelobes, but then allows a taper beyond .

The SAR (synthetic aperature radar) community commonly uses Taylor weighting for image formation processing because it provides strong, selectable sidelobe suppression with minimum broadening of the mainlobe .

Parameters

M : int

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

nbar : int, optional

Number of nearly constant level sidelobes adjacent to the mainlobe.

sll : float, optional

Desired suppression of sidelobe level in decibels (dB) relative to the DC gain of the mainlobe. This should be a positive number.

norm : bool, optional

When True (default), divides the window by the largest (middle) value for odd-length windows or the value that would occur between the two repeated middle values for even-length windows such that all values are less than or equal to 1. When False the DC gain will remain at 1 (0 dB) and the sidelobes will be :None:None:`sll` dB down.

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

out : array

The window. When :None:None:`norm` is True (default), the maximum value is normalized to 1 (though the value 1 does not appear if M is even and :None:None:`sym` is True).

Return a Taylor window.

See Also

bartlett
blackman
chebwin
hamming
hanning
kaiser

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.taylor(51, nbar=20, sll=100, norm=False)
... plt.plot(window)
... plt.title("Taylor window (100 dB)")
... 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("Frequency response of the Taylor window (100 dB)")
... 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.get_window scipy.signal.windows._windows.taylor

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GitHub : /scipy/signal/windows/_windows.py#1623
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