`scipy.signal._ltisys:dstep`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_ltisys.py:3556

Signature

def dstep(system, x0 = None, t = None, n = None)

Summary

Step response of discrete-time system.

Parameters

system : dlti | tuple

An instance of the LTI class dlti or a tuple describing the system. The number of elements in the tuple determine the interpretation. I.e.:

system: Instance of LTI class dlti. Note that derived instances, such as instances of TransferFunction, ZerosPolesGain, or StateSpace, are allowed as well.
(num, den, dt): Rational polynomial as described in TransferFunction. The coefficients of the polynomials should be specified in descending exponent order, e.g., z² + 3z + 5 would be represented as [1, 3, 5].
(zeros, poles, gain, dt): Zeros, poles, gain form as described in ZerosPolesGain.
(A, B, C, D, dt): State-space form as described in StateSpace.

x0 : array_like, optional

Initial state-vector. Defaults to zero.

t : array_like, optional

Time points. Computed if not given.

n : int, optional

The number of time points to compute (if t is not given).

Returns

tout : ndarray: Output time points, as a 1-D array.
yout : tuple of ndarray: Step response of system. Each element of the tuple represents the output of the system based on a step response to each input.

Examples

The following example illustrates how to create a digital Butterworth filer and plot its step response:

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
dt = 1  # sampling interval is one => time unit is sample number
bb, aa = signal.butter(3, 0.25, fs=1/dt)
t, y = signal.dstep((bb, aa, dt), n=25)
fig0, ax0 = plt.subplots()

✓

ax0.step(t, np.squeeze(y), '.-', where='post')
ax0.set_title(r"Step Response of a $3^\text{rd}$ Order Butterworth Filter")
ax0.set(xlabel='Sample number', ylabel='Amplitude', ylim=(0, 1.1*np.max(y)))

✗

[Text(0.5, 0, 'Sample number'), Text(0, 0.5, 'Amplitude'), (0.0, 1.2222222222222223)]

ax0.grid()
plt.show()

✓

Aliases

scipy.signal.dstep