iirnotch#
- scipy.signal.iirnotch(w0, Q, fs=2.0)[source]#
- 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:
- w0float
- Frequency to remove from a signal. If fs is specified, this is in the same units as fs. By default, it is a normalized scalar that must satisfy - 0 < w0 < 1, with- w0 = 1corresponding to half of the sampling frequency.
- Qfloat
- Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth - bwrelative to its center frequency,- Q = w0/bw.
- fsfloat, optional
- The sampling frequency of the digital system. - Added in version 1.2.0. 
 
- Returns:
- b, andarray, ndarray
- Numerator ( - b) and denominator (- a) polynomials of the IIR filter.
 
 - See also - Notes - Added in version 0.19.0. - References [1]- Sophocles J. Orfanidis, “Introduction To Signal Processing”, Prentice-Hall, 1996 - Examples - Design and plot filter to remove the 60 Hz component from a signal sampled at 200 Hz, using a quality factor Q = 30 - >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> import numpy as np - >>> fs = 200.0 # Sample frequency (Hz) >>> f0 = 60.0 # Frequency to be removed from signal (Hz) >>> Q = 30.0 # Quality factor >>> # Design notch filter >>> b, a = signal.iirnotch(f0, Q, fs) - >>> # Frequency response >>> freq, h = signal.freqz(b, a, fs=fs) >>> # 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(True) >>> ax[1].plot(freq, np.unwrap(np.angle(h))*180/np.pi, color='green') >>> ax[1].set_ylabel("Phase [deg]", 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(True) >>> plt.show() 