- scipy.signal.freqresp(system, w=None, n=10000)¶
Calculate the frequency response of a continuous-time system.
system : an instance of the lti class or a tuple describing the system.
The following gives the number of elements in the tuple and the interpretation:
- 1 (instance of lti)
- 2 (num, den)
- 3 (zeros, poles, gain)
- 4 (A, B, C, D)
w : array_like, optional
Array of frequencies (in rad/s). 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.
w : 1D ndarray
Frequency array [rad/s]
H : 1D ndarray
Array of complex magnitude values
If (num, den) is passed in for system, coefficients for both the numerator and denominator should be specified in descending exponent order (e.g. s^2 + 3s + 5 would be represented as [1, 3, 5]).
Generating the Nyquist plot of a transfer function
>>> from scipy import signal >>> import matplotlib.pyplot as plt
Transfer function: H(s) = 5 / (s-1)^3
>>> s1 = signal.ZerosPolesGain(, [1, 1, 1], )
>>> w, H = signal.freqresp(s1)
>>> plt.figure() >>> plt.plot(H.real, H.imag, "b") >>> plt.plot(H.real, -H.imag, "r") >>> plt.show()