iircomb(w0, Q, ftype='notch', fs=2.0)
A notching comb 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.
A peaking comb filter is a band-pass filter with a narrow bandwidth (high quality factor). It rejects components outside a narrow frequency band.
For implementation details, see . The TF implementation of the comb filter is numerically stable even at higher orders due to the use of a single repeated pole, which won't suffer from precision loss.
Frequency to attenuate (notching) or boost (peaking). If :None:None:`fs` is specified, this is in the same units as :None:None:`fs`. By default, it is a normalized scalar that must satisfy 0 < w0 < 1
, with w0 = 1
corresponding to half of the sampling frequency.
Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth bw
relative to its center frequency, Q = w0/bw
.
The type of comb filter generated by the function. If 'notch', then it returns a filter with notches at frequencies 0
, w0
, 2 * w0
, etc. If 'peak', then it returns a filter with peaks at frequencies 0.5 * w0
, 1.5 * w0
, 2.5 * w0`
, etc. Default is 'notch'.
The sampling frequency of the signal. Default is 2.0.
If :None:None:`w0` is less than or equal to 0 or greater than or equal to fs/2
, if :None:None:`fs` is not divisible by :None:None:`w0`, if :None:None:`ftype` is not 'notch' or 'peak'
Numerator ( b
) and denominator ( a
) polynomials of the IIR filter.
Design IIR notching or peaking digital comb filter.
Design and plot notching comb filter at 20 Hz for a signal sampled at 200 Hz, using quality factor Q = 30
>>> from scipy import signal
... import matplotlib.pyplot as plt
>>> fs = 200.0 # Sample frequency (Hz)
... f0 = 20.0 # Frequency to be removed from signal (Hz)
... Q = 30.0 # Quality factor
... # Design notching comb filter
... b, a = signal.iircomb(f0, Q, ftype='notch', fs=fs)
>>> # Frequency response
... freq, h = signal.freqz(b, a, fs=fs)
... response = abs(h)
... # To avoid divide by zero when graphing
... response[response == 0] = 1e-20
... # Plot
... fig, ax = plt.subplots(2, 1, figsize=(8, 6))
... ax[0].plot(freq, 20*np.log10(abs(response)), 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([-30, 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()
Design and plot peaking comb filter at 250 Hz for a signal sampled at 1000 Hz, using quality factor Q = 30
>>> fs = 1000.0 # Sample frequency (Hz)
... f0 = 250.0 # Frequency to be retained (Hz)
... Q = 30.0 # Quality factor
... # Design peaking filter
... b, a = signal.iircomb(f0, Q, ftype='peak', fs=fs)
>>> # Frequency response
... freq, h = signal.freqz(b, a, fs=fs)
... response = abs(h)
... # To avoid divide by zero when graphing
... response[response == 0] = 1e-20
... # Plot
... fig, ax = plt.subplots(2, 1, figsize=(8, 6))
... ax[0].plot(freq, 20*np.log10(np.maximum(abs(h), 1e-5)), color='blue')
... ax[0].set_title("Frequency Response")
... ax[0].set_ylabel("Amplitude (dB)", color='blue')
... ax[0].set_xlim([0, 500])
... ax[0].set_ylim([-80, 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, 500])
... ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
... ax[1].set_ylim([-90, 90])
... ax[1].grid()
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._filter_design.iircomb
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