`scipy.signal._ltisys:StateSpace`

source: /scipy/signal/_ltisys.py :1225

Signature

class StateSpace ( * system , ** kwargs )

Members

Summary

Linear Time Invariant system in state-space form.

Extended Summary

Represents the system as the continuous-time, first order differential equation $\overset{x}{˙} = A x + B u$ or the discrete-time difference equation $x [k + 1] = A x [k] + B u [k]$ . StateSpace systems inherit additional functionality from the lti, respectively the dlti classes, depending on which system representation is used.

Parameters

*system: arguments

The StateSpace class can be instantiated with 1 or 4 arguments. The following gives the number of input arguments and their interpretation:

1: lti or dlti system: (StateSpace, TransferFunction or ZerosPolesGain)
4: array_like: (A, B, C, D)

dt: float, optional

Sampling time [s] of the discrete-time systems. Defaults to None (continuous-time). Must be specified as a keyword argument, for example, dt=0.1.

Notes

Changing the value of properties that are not part of the StateSpace system representation (such as zeros or poles) is very inefficient and may lead to numerical inaccuracies. It is better to convert to the specific system representation first. For example, call sys = sys.to_zpk() before accessing/changing the zeros, poles or gain.

Array API Standard Support

StateSpace has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ⛔                     ⛔                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

from scipy import signal
import numpy as np
a = np.array([[0, 1], [0, 0]])
b = np.array([[0], [1]])
c = np.array([[1, 0]])
d = np.array([[0]])

✓

sys = signal.StateSpace(a, b, c, d)
print(sys)

✓

sys.to_discrete(0.1)

✓

a = np.array([[1, 0.1], [0, 1]])
b = np.array([[0.005], [0.1]])

✓

signal.StateSpace(a, b, c, d, dt=0.1)

✓

Aliases

scipy.signal.StateSpace