`scipy.signal._ltisys:dlti.bode`

Signature

def bode(self, w = None, n = 100)

Calculate Bode magnitude and phase data of a discrete-time system.

w : array_like, optional: Array of frequencies normalized to the Nyquist frequency being π, i.e., having unit radiant / sample. Magnitude and phase data is calculated for every value in this array. If not given, a reasonable set will be calculated.
n : int, optional: Number of frequency points to compute if w is not given. The n frequencies are logarithmically spaced in an interval chosen to include the influence of the poles and zeros of the system. Defaults to 100.

w : 1D ndarray: Array of frequencies normalized to the Nyquist frequency being np.pi/dt with dt being the sampling interval of the system parameter. The unit is rad/s assuming dt is in seconds.
mag : 1D ndarray: Magnitude array in dB
phase : 1D ndarray: Phase array in degrees

from scipy import signal
import matplotlib.pyplot as plt

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Construct the transfer function :math:`H(z) = \frac{1}{z^2 + 2z + 3}` with sampling time 0.5s:

sys = signal.TransferFunction([1], [1, 2, 3], dt=0.5)

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Equivalent: signal.dbode(sys)

w, mag, phase = sys.bode()

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plt.figure()
plt.semilogx(w, mag)    # Bode magnitude plot
plt.figure()
plt.semilogx(w, phase)  # Bode phase plot

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[<matplotlib.lines.Line2D object at 0x7fa8dd20f4d0>]

plt.show()

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