scipy.signal.cheb2ord

scipy.signal.cheb2ord(wp, ws, gpass, gstop, analog=False)

Chebyshev type II filter order selection.

Return the order of the lowest order digital or analog Chebyshev Type II filter that loses no more than gpass dB in the passband and has at least gstop dB attenuation in the stopband.

Parameters:

wp, ws : float

Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (wp and ws are thus in half-cycles / sample.) For example:

• Lowpass: wp = 0.2, ws = 0.3
• Highpass: wp = 0.3, ws = 0.2
• Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6]
• Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5]

For analog filters, wp and ws are angular frequencies (e.g. rad/s).

gpass : float

The maximum loss in the passband (dB).

gstop : float

The minimum attenuation in the stopband (dB).

analog : bool, optional

When True, return an analog filter, otherwise a digital filter is returned.

Returns:

ord : int

The lowest order for a Chebyshev type II filter that meets specs.

wn : ndarray or float

The Chebyshev natural frequency (the “3dB frequency”) for use with cheby2 to give filter results.

See also

cheby2

Filter design using order and critical points

buttord

Find order and critical points from passband and stopband spec

cheb1ord, ellipord

iirfilter

General filter design using order and critical frequencies

iirdesign

General filter design using passband and stopband spec

Examples

Design a digital bandstop filter which rejects -60 dB from 0.2*(fs/2) to 0.5*(fs/2), while staying within 3 dB below 0.1*(fs/2) or above 0.6*(fs/2). Plot its frequency response, showing the passband and stopband constraints in gray.

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

>>> N, Wn = signal.cheb2ord([0.1, 0.6], [0.2, 0.5], 3, 60)
>>> b, a = signal.cheby2(N, 60, Wn, 'stop')
>>> w, h = signal.freqz(b, a)
>>> plt.plot(w / np.pi, 20 * np.log10(abs(h)))
>>> plt.xscale('log')
>>> plt.title('Chebyshev II bandstop filter fit to constraints')
>>> plt.xlabel('Normalized frequency')
>>> plt.ylabel('Amplitude [dB]')
>>> plt.grid(which='both', axis='both')
>>> plt.fill([.01, .1, .1, .01], [-3,  -3, -99, -99], '0.9', lw=0) # stop
>>> plt.fill([.2,  .2, .5,  .5], [ 9, -60, -60,   9], '0.9', lw=0) # pass
>>> plt.fill([.6,  .6,  2,   2], [-99, -3,  -3, -99], '0.9', lw=0) # stop
>>> plt.axis([0.06, 1, -80, 3])
>>> plt.show()

(Source code)