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# scipy.signal.iirnotch

`scipy.signal.``iirnotch`(w0, Q)

  Design second-order IIR notch digital filter.

  A notch filter is a band-stop filter with a narrow bandwidth (high quality factor). It rejects a narrow frequency band and leaves the rest of the spectrum little changed.

  Parameters: | w0 : float Normalized frequency to remove from a signal. It is a scalar that must satisfy `0 < w0 < 1`, with `w0 = 1` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth `bw` relative to its center frequency, `Q = w0/bw`.
  Returns: | b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter.

  See also

  `iirpeak`

  Notes

  References

  [R239] | Sophocles J. Orfanidis, “Introduction To Signal Processing”, Prentice-Hall, 1996

  Examples

  Design and plot filter to remove the 60Hz component from a signal sampled at 200Hz, using a quality factor Q = 30

      >>> from scipy import signal
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt

      >>> fs = 200.0  # Sample frequency (Hz)
      >>> f0 = 60.0  # Frequency to be removed from signal (Hz)
      >>> Q = 30.0  # Quality factor
      >>> w0 = f0/(fs/2)  # Normalized Frequency
      >>> # Design notch filter
      >>> b, a = signal.iirnotch(w0, Q)

      >>> # Frequency response
      >>> w, h = signal.freqz(b, a)
      >>> # Generate frequency axis
      >>> freq = w*fs/(2*np.pi)
      >>> # Plot
      >>> fig, ax = plt.subplots(2, 1, figsize=(8, 6))
      >>> ax[0].plot(freq, 20*np.log10(abs(h)), color='blue')
      >>> ax[0].set_title("Frequency Response")
      >>> ax[0].set_ylabel("Amplitude (dB)", color='blue')
      >>> ax[0].set_xlim([0, 100])
      >>> ax[0].set_ylim([-25, 10])
      >>> ax[0].grid()
      >>> ax[1].plot(freq, np.unwrap(np.angle(h))*180/np.pi, color='green')
      >>> ax[1].set_ylabel("Angle (degrees)", color='green')
      >>> ax[1].set_xlabel("Frequency (Hz)")
      >>> ax[1].set_xlim([0, 100])
      >>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
      >>> ax[1].set_ylim([-90, 90])
      >>> ax[1].grid()
      >>> plt.show()

  (Source code)