scipy.signal.dfreqresp#
- scipy.signal.dfreqresp(system, w=None, n=10000, whole=False)[source]#
Calculate the frequency response of a discrete-time 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 (upper-half of unit-circle). 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
Construct the transfer function \(H(z) = \frac{1}{z^2 + 2z + 3}\) with a sampling time of 0.05 seconds:
>>> 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()