# cupyx.scipy.signal.windows.taylor#

cupyx.scipy.signal.windows.taylor(M, nbar=4, sll=30, norm=True, sym=True)[source]#

Return a Taylor window. 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 [2]. The SAR (synthetic aperture radar) community commonly uses Taylor weighting for image formation processing because it provides strong, selectable sidelobe suppression with minimum broadening of the mainlobe [1].

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 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 – The window. When norm is True (default), the maximum value is normalized to 1 (though the value 1 does not appear if M is even and sym is True).

Return type:

array

References

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]”)