bundles / scipy 1.17.1 / scipy / signal / _filter_design / iirdesign
function
scipy.signal._filter_design:iirdesign
Signature
def iirdesign ( wp , ws , gpass , gstop , analog = False , ftype = ellip , output = ba , fs = None ) Summary
Complete IIR digital and analog filter design.
Extended Summary
Given passband and stopband frequencies and gains, construct an analog or digital IIR filter of minimum order for a given basic type. Return the output in numerator, denominator ('ba'), pole-zero ('zpk') or second order sections ('sos') form.
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,fsis 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,
wpandwsare angular frequencies (e.g., rad/s). Note, that for bandpass and bandstop filters passband must lie strictly inside stopband or vice versa. Also note that the cutoff at the band edges for IIR filters is defined as half-power, so -3dB, not half-amplitude (-6dB) like forscipy.signal.fiwin.gpass: floatThe maximum loss in the passband (dB).
gstop: floatThe minimum attenuation in the stopband (dB).
analog: bool, optionalWhen True, return an analog filter, otherwise a digital filter is returned.
ftype: str, optionalThe type of IIR filter to design:
Butterworth'butter'
Chebyshev I'cheby1'
Chebyshev II'cheby2'
Cauer/elliptic: 'ellip'
output: {'ba', 'zpk', 'sos'}, optionalFilter form of the output:
second-order sections (recommended): 'sos'
numerator/denominator (default)'ba'
pole-zero'zpk'
In general the second-order sections ('sos') form is recommended because inferring the coefficients for the numerator/denominator form ('ba') suffers from numerical instabilities. For reasons of backward compatibility the default form is the numerator/denominator form ('ba'), where the 'b' and the 'a' in 'ba' refer to the commonly used names of the coefficients used.
Note: Using the second-order sections form ('sos') is sometimes associated with additional computational costs: for data-intense use cases it is therefore recommended to also investigate the numerator/denominator form ('ba').
fs: float, optionalThe sampling frequency of the digital system.
Returns
b, a: ndarray, ndarrayNumerator (b) and denominator (a) polynomials of the IIR filter. Only returned if
output='ba'.z, p, k: ndarray, ndarray, floatZeros, poles, and system gain of the IIR filter transfer function. Only returned if
output='zpk'.sos: ndarraySecond-order sections representation of the IIR filter. Only returned if
output='sos'.
Notes
The 'sos' output parameter was added in 0.16.0.
Array API Standard Support
iirdesign has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.
==================== ==================== ==================== Library CPU GPU ==================== ==================== ==================== NumPy ✅ n/a CuPy n/a ✅ PyTorch ✅ ✅ JAX ✅ ✅ Dask ✅ n/a ==================== ==================== ====================
See
dev-arrayapifor more information.
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() phase = np.unwrap(np.angle(h))✓
ax2.plot(w, phase, 'g') ax2.set_ylabel('Phase [rad]', 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))✓
See also
Aliases
-
scipy.signal.iirdesign