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# scipy.signal.freqz

`scipy.signal.``freqz`(b, a=1, worN=None, whole=False, plot=None)

  Compute the frequency response of a digital filter.

  Given the M-order numerator b and N-order denominator a of a digital filter, compute its frequency response:

                  jw               -jw               -jwM
         jw    B(e  )  b[0] + b[1]e    + .... + b[M]e
      H(e  ) = ---- = -----------------------------------
                  jw               -jw               -jwN
               A(e  )  a[0] + a[1]e    + .... + a[N]e

  Parameters: | b : array_like numerator of a linear filter a : array_like denominator of a linear filter worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If whole is True, compute frequencies from 0 to 2*pi radians/sample. plot : callable A callable that takes two arguments. If given, the return parameters w and h are passed to plot. Useful for plotting the frequency response inside `freqz`.
  Returns: | w : ndarray The normalized frequencies at which h was computed, in radians/sample. h : ndarray The frequency response, as complex numbers.

  See also

  `sosfreqz`

  Notes

  Using Matplotlib’s “plot” function as the callable for plot produces unexpected results, this plots the real part of the complex transfer function, not the magnitude. Try `lambda w, h: plot(w, abs(h))`.

  Examples

      >>> from scipy import signal
      >>> b = signal.firwin(80, 0.5, window=('kaiser', 8))
      >>> w, h = signal.freqz(b)

      >>> import matplotlib.pyplot as plt
      >>> fig = plt.figure()
      >>> plt.title('Digital filter frequency response')
      >>> ax1 = fig.add_subplot(111)

      >>> plt.plot(w, 20 * np.log10(abs(h)), 'b')
      >>> plt.ylabel('Amplitude [dB]', color='b')
      >>> plt.xlabel('Frequency [rad/sample]')

      >>> ax2 = ax1.twinx()
      >>> angles = np.unwrap(np.angle(h))
      >>> plt.plot(w, angles, 'g')
      >>> plt.ylabel('Angle (radians)', color='g')
      >>> plt.grid()
      >>> plt.axis('tight')
      >>> plt.show()

  (Source code)