scipy.signal.

bilinear#

scipy.signal.bilinear(b, a, fs=1.0)[source]#

Return a digital IIR filter from an analog one using a bilinear transform.

Transform a set of poles and zeros from the analog s-plane to the digital z-plane using Tustin’s method, which substitutes 2*fs*(z-1) / (z+1) for s, maintaining the shape of the frequency response.

Parameters:

barray_like: Numerator of the analog filter transfer function.
aarray_like: Denominator of the analog filter transfer function.
fsfloat: Sample rate, as ordinary frequency (e.g., hertz). No prewarping is done in this function.

Returns:

bndarray: Numerator of the transformed digital filter transfer function.
andarray: Denominator of the transformed digital filter transfer function.

See also

lp2lp, lp2hp, lp2bp, lp2bs
bilinear_zpk

Examples

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

>>> fs = 100
>>> bf = 2 * np.pi * np.array([7, 13])
>>> filts = signal.lti(*signal.butter(4, bf, btype='bandpass',
...                                   analog=True))
>>> filtz = signal.lti(*signal.bilinear(filts.num, filts.den, fs))
>>> wz, hz = signal.freqz(filtz.num, filtz.den)
>>> ws, hs = signal.freqs(filts.num, filts.den, worN=fs*wz)

>>> plt.semilogx(wz*fs/(2*np.pi), 20*np.log10(np.abs(hz).clip(1e-15)),
...              label=r'$|H_z(e^{j \omega})|$')
>>> plt.semilogx(wz*fs/(2*np.pi), 20*np.log10(np.abs(hs).clip(1e-15)),
...              label=r'$|H(j \omega)|$')
>>> plt.legend()
>>> plt.xlabel('Frequency [Hz]')
>>> plt.ylabel('Magnitude [dB]')
>>> plt.grid(True)

../../_images/scipy-signal-bilinear-1.png