iir_design_helper¶
Basic IIR Bilinear Transform-Based Digital Filter Design Helper
Copyright (c) March 2017, Mark Wickert All rights reserved.
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- sk_dsp_comm.iir_design_helper.IIR_bpf(f_stop1, f_pass1, f_pass2, f_stop2, Ripple_pass, Atten_stop, fs=1.0, ftype='butter', status=True)[source]¶
Design an IIR bandpass filter using scipy.signal.iirdesign. The filter order is determined based on f_pass Hz, f_stop Hz, and the desired stopband attenuation d_stop in dB, all relative to a sampling rate of fs Hz.
- Parameters:
- f_stop1ndarray of the numerator coefficients
- f_passndarray of the denominator coefficients
- Ripple_pass
- Atten_stop
- fssampling rate in Hz
- ftypeAnalog prototype from ‘butter’ ‘cheby1’, ‘cheby2’,
‘ellip’, and ‘bessel’
- Returns:
- bndarray of the numerator coefficients
- andarray of the denominator coefficients
- sos2D ndarray of second-order section coefficients
Examples
>>> fs = 48000 >>> f_pass = 8000 >>> f_stop = 5000 >>> b_but,a_but,sos_but = IIR_hpf(f_stop,f_pass,0.5,60,fs,'butter') >>> b_cheb1,a_cheb1,sos_cheb1 = IIR_hpf(f_stop,f_pass,0.5,60,fs,'cheby1') >>> b_cheb2,a_cheb2,sos_cheb2 = IIR_hpf(f_stop,f_pass,0.5,60,fs,'cheby2') >>> b_elli,a_elli,sos_elli = IIR_hpf(f_stop,f_pass,0.5,60,fs,'ellip')
Mark Wickert October 2016
- sk_dsp_comm.iir_design_helper.IIR_bsf(f_pass1, f_stop1, f_stop2, f_pass2, Ripple_pass, Atten_stop, fs=1.0, ftype='butter', status=True)[source]¶
Design an IIR bandstop filter using scipy.signal.iirdesign. The filter order is determined based on f_pass Hz, f_stop Hz, and the desired stopband attenuation d_stop in dB, all relative to a sampling rate of fs Hz.
Mark Wickert October 2016
- sk_dsp_comm.iir_design_helper.IIR_hpf(f_stop, f_pass, Ripple_pass, Atten_stop, fs=1.0, ftype='butter', status=True)[source]¶
Design an IIR highpass filter using scipy.signal.iirdesign. The filter order is determined based on f_pass Hz, f_stop Hz, and the desired stopband attenuation d_stop in dB, all relative to a sampling rate of fs Hz.
- Parameters:
- f_stop
- f_pass
- Ripple_pass
- Atten_stop
- fssampling rate in Hz
- ftypeAnalog prototype from ‘butter’ ‘cheby1’, ‘cheby2’,
‘ellip’, and ‘bessel’
- Returns:
- bndarray of the numerator coefficients
- andarray of the denominator coefficients
- sos2D ndarray of second-order section coefficients
Examples
>>> fs = 48000 >>> f_pass = 8000 >>> f_stop = 5000 >>> b_but,a_but,sos_but = IIR_hpf(f_stop,f_pass,0.5,60,fs,'butter') >>> b_cheb1,a_cheb1,sos_cheb1 = IIR_hpf(f_stop,f_pass,0.5,60,fs,'cheby1') >>> b_cheb2,a_cheb2,sos_cheb2 = IIR_hpf(f_stop,f_pass,0.5,60,fs,'cheby2') >>> b_elli,a_elli,sos_elli = IIR_hpf(f_stop,f_pass,0.5,60,fs,'ellip')
Mark Wickert October 2016
- sk_dsp_comm.iir_design_helper.IIR_lpf(f_pass, f_stop, Ripple_pass, Atten_stop, fs=1.0, ftype='butter', status=True)[source]¶
Design an IIR lowpass filter using scipy.signal.iirdesign. The filter order is determined based on f_pass Hz, f_stop Hz, and the desired stopband attenuation d_stop in dB, all relative to a sampling rate of fs Hz.
- Parameters:
- f_passPassband critical frequency in Hz
- f_stopStopband critical frequency in Hz
- Ripple_passFilter gain in dB at f_pass
- Atten_stopFilter attenuation in dB at f_stop
- fsSampling rate in Hz
- ftypeAnalog prototype from ‘butter’ ‘cheby1’, ‘cheby2’,
‘ellip’, and ‘bessel’
- Returns:
- bndarray of the numerator coefficients
- andarray of the denominator coefficients
- sos2D ndarray of second-order section coefficients
Notes
Additionally a text string telling the user the filter order is written to the console, e.g., IIR cheby1 order = 8.
Examples
>>> fs = 48000 >>> f_pass = 5000 >>> f_stop = 8000 >>> b_but,a_but,sos_but = IIR_lpf(f_pass,f_stop,0.5,60,fs,'butter') >>> b_cheb1,a_cheb1,sos_cheb1 = IIR_lpf(f_pass,f_stop,0.5,60,fs,'cheby1') >>> b_cheb2,a_cheb2,sos_cheb2 = IIR_lpf(f_pass,f_stop,0.5,60,fs,'cheby2') >>> b_elli,a_elli,sos_elli = IIR_lpf(f_pass,f_stop,0.5,60,fs,'ellip')
Mark Wickert October 2016
- sk_dsp_comm.iir_design_helper.freqz_cas(sos, w)[source]¶
Cascade frequency response
Mark Wickert October 2016
- sk_dsp_comm.iir_design_helper.freqz_resp_cas_list(sos, mode='dB', fs=1.0, n_pts=1024, fsize=(6, 4))[source]¶
A method for displaying cascade digital filter form frequency response magnitude, phase, and group delay. A plot is produced using matplotlib
freq_resp(self,mode = ‘dB’,Npts = 1024)
A method for displaying the filter frequency response magnitude, phase, and group delay. A plot is produced using matplotlib
freqz_resp(b,a=[1],mode = ‘dB’,Npts = 1024,fsize=(6,4))
b = ndarray of numerator coefficients a = ndarray of denominator coefficents
- mode = display mode: ‘dB’ magnitude, ‘phase’ in radians, or
‘groupdelay_s’ in samples and ‘groupdelay_t’ in sec, all versus frequency in Hz
Npts = number of points to plot; default is 1024
fsize = figure size; defult is (6,4) inches
Mark Wickert, January 2015
- sk_dsp_comm.iir_design_helper.freqz_resp_list(b, a=array([1]), mode='dB', fs=1.0, Npts=1024, fsize=(6, 4))[source]¶
A method for displaying digital filter frequency response magnitude, phase, and group delay. A plot is produced using matplotlib
freq_resp(self,mode = ‘dB’,Npts = 1024)
A method for displaying the filter frequency response magnitude, phase, and group delay. A plot is produced using matplotlib
freqz_resp(b,a=[1],mode = ‘dB’,Npts = 1024,fsize=(6,4))
b = ndarray of numerator coefficients a = ndarray of denominator coefficents
- mode = display mode: ‘dB’ magnitude, ‘phase’ in radians, or
‘groupdelay_s’ in samples and ‘groupdelay_t’ in sec, all versus frequency in Hz
Npts = number of points to plot; default is 1024
fsize = figure size; defult is (6,4) inches
Mark Wickert, January 2015
- sk_dsp_comm.iir_design_helper.sos_zplane(sos, auto_scale=True, size=2, tol=0.001)[source]¶
Create an z-plane pole-zero plot.
Create an z-plane pole-zero plot using the numerator and denominator z-domain system function coefficient ndarrays b and a respectively. Assume descending powers of z.
- Parameters:
- sosndarray of the sos coefficients
- auto_scalebool (default True)
- sizeplot radius maximum when scale = False
- Returns:
- (M,N)tuple of zero and pole counts + plot window
Notes
This function tries to identify repeated poles and zeros and will place the multiplicity number above and to the right of the pole or zero. The difficulty is setting the tolerance for this detection. Currently it is set at 1e-3 via the function signal.unique_roots.
Examples
>>> # Here the plot is generated using auto_scale >>> sos_zplane(sos) >>> # Here the plot is generated using manual scaling >>> sos_zplane(sos,False,1.5)
