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NotesParametersReturnsBackRef

Notes

Parameters

wp, ws : float or array like, shape (2,)

Passband and stopband edge frequencies. Possible values are scalars (for lowpass and highpass filters) or ranges (for bandpass and bandstop filters). For digital filters, these are in the same units as fs. By default, fs is 2 half-cycles/sample, so these are normalized from 0 to 1, where 1 is the Nyquist frequency. 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). Note, that for bandpass and bandstop filters passband must lie strictly inside stopband or vice versa.

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.

ftype : str, optional

The type of IIR filter to design:

output : {'ba', 'zpk', 'sos'}, optional

Filter form of the output:

fs : float, optional

The sampling frequency of the digital system.

Returns

b, a : ndarray, ndarray

Numerator (b) and denominator (a) polynomials of the IIR filter. Only returned if output='ba'.

z, p, k : ndarray, ndarray, float

Zeros, poles, and system gain of the IIR filter transfer function. Only returned if output='zpk'.

sos : ndarray

Second-order sections representation of the IIR filter. Only returned if output='sos'.

See Also

bessel

butter

Filter design using order and critical points

buttord

Find order and critical points from passband and stopband spec

cheb1ord

cheb2ord

cheby1

cheby2

ellip

ellipord

iirfilter

General filter design using order and critical frequencies

Examples

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

wp = 0.2
ws = 0.3
gpass = 1
gstop = 40

system = signal.iirdesign(wp, ws, gpass, gstop)
w, h = signal.freqz(*system)

fig, ax1 = plt.subplots()
ax1.set_title('Digital filter frequency response')
ax1.plot(w, 20 * np.log10(abs(h)), 'b')
ax1.set_ylabel('Amplitude [dB]', color='b')
ax1.set_xlabel('Frequency [rad/sample]')
ax1.grid(True)
ax1.set_ylim([-120, 20])
ax2 = ax1.twinx()
angles = np.unwrap(np.angle(h))
ax2.plot(w, angles, 'g')
ax2.set_ylabel('Angle (radians)', color='g')
ax2.grid(True)
ax2.axis('tight')
ax2.set_ylim([-6, 1])
nticks = 8
ax1.yaxis.set_major_locator(matplotlib.ticker.LinearLocator(nticks))
ax2.yaxis.set_major_locator(matplotlib.ticker.LinearLocator(nticks))

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