scipy.signal.freqresp#
- scipy.signal.freqresp(system, w=None, n=10000)[source]#
Calculate the frequency response of a continuous-time system.
- Parameters
- systeman 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)
- warray_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.
- nint, 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.
- systeman instance of the
- Returns
- w1D ndarray
Frequency array [rad/s]
- H1D ndarray
Array of complex magnitude values
Notes
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]
).Examples
Generating the Nyquist plot of a transfer function
>>> from scipy import signal >>> import matplotlib.pyplot as plt
Construct the transfer function \(H(s) = \frac{5}{(s-1)^3}\):
>>> s1 = signal.ZerosPolesGain([], [1, 1, 1], [5])
>>> w, H = signal.freqresp(s1)
>>> plt.figure() >>> plt.plot(H.real, H.imag, "b") >>> plt.plot(H.real, -H.imag, "r") >>> plt.show()