cupyx.scipy.signal.sosfreqz(sos, worN=512, whole=False, fs=6.283185307179586)[source]#

Compute the frequency response of a digital filter in SOS format.

Given sos, an array with shape (n, 6) of second order sections of a digital filter, compute the frequency response of the system function:

       B0(z)   B1(z)         B{n-1}(z)
H(z) = ----- * ----- * ... * ---------
       A0(z)   A1(z)         A{n-1}(z)

for z = exp(omega*1j), where B{k}(z) and A{k}(z) are numerator and denominator of the transfer function of the k-th second order section.

  • sos (array_like) – Array of second-order filter coefficients, must have shape (n_sections, 6). Each row corresponds to a second-order section, with the first three columns providing the numerator coefficients and the last three providing the denominator coefficients.

  • worN ({None, int, array_like}, optional) –

    If a single integer, then compute at that many frequencies (default is N=512). Using a number that is fast for FFT computations can result in faster computations (see Notes of freqz).

    If an array_like, compute the response at the frequencies given (must be 1-D). These are in the same units as fs.

  • whole (bool, optional) – Normally, frequencies are computed from 0 to the Nyquist frequency, fs/2 (upper-half of unit-circle). If whole is True, compute frequencies from 0 to fs.

  • fs (float, optional) –

    The sampling frequency of the digital system. Defaults to 2*pi radians/sample (so w is from 0 to pi).

    New in version 1.2.0.


  • w (ndarray) – The frequencies at which h was computed, in the same units as fs. By default, w is normalized to the range [0, pi) (radians/sample).

  • h (ndarray) – The frequency response, as complex numbers.