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 dlticlass 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 + 5would be represented as- [1, 3, 5]).- Added 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() 