scipy.signal.dfreqresp¶

scipy.signal.
dfreqresp
(system, w=None, n=10000, whole=False)[source]¶ Calculate the frequency response of a discretetime system.
 Parameters
 systeman instance of the
dlti
class or a tuple describing the system. The following gives the number of elements in the tuple and the interpretation:
1 (instance of
dlti
)2 (numerator, denominator, dt)
3 (zeros, poles, gain, dt)
4 (A, B, C, D, dt)
 warray_like, optional
Array of frequencies (in radians/sample). 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.
 wholebool, optional
Normally, if ‘w’ is not given, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upperhalf of unitcircle). If whole is True, compute frequencies from 0 to 2*pi radians/sample.
 systeman instance of the
 Returns
 w1D ndarray
Frequency array [radians/sample]
 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.z^2 + 3z + 5
would be represented as[1, 3, 5]
).New in version 0.18.0.
Examples
Generating the Nyquist plot of a transfer function
>>> from scipy import signal >>> import matplotlib.pyplot as plt
Transfer function: H(z) = 1 / (z^2 + 2z + 3)
>>> sys = signal.TransferFunction([1], [1, 2, 3], dt=0.05)
>>> w, H = signal.dfreqresp(sys)
>>> plt.figure() >>> plt.plot(H.real, H.imag, "b") >>> plt.plot(H.real, H.imag, "r") >>> plt.show()