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bundles / scipy latest / scipy / signal / _lti_conversion / abcd_normalize

function

scipy.signal._lti_conversion:abcd_normalize

source: /scipy/signal/_lti_conversion.py :117

Signature

def   abcd_normalize ( A = None ,  B = None ,  C = None ,  D = None )

Summary

Check state-space matrices compatibility and ensure they are 2d arrays.

Extended Summary

Converts input matrices into two-dimensional arrays as needed. Then the dimensions n, q, p are determined by investigating the non-zero entries of the array shapes. If a parameter is None, or has shape (0, 0), it is set to a zero-array of compatible shape. Finally, it is verified that all parameter shapes are compatible to each other. If that fails, a ValueError is raised. Note that the dimensions n, q, p are allowed to be zero.

Parameters

A: array_like, optional

Two-dimensional array of shape (n, n).

B: array_like, optional

Two-dimensional array of shape (n, p).

C: array_like, optional

Two-dimensional array of shape (q, n).

D: array_like, optional

Two-dimensional array of shape (q, p).

Returns

A, B, C, D : array

State-space matrices as two-dimensional arrays.

Raises

: ValueError

If the dimensions n, q, or p could not be determined or if the shapes are incompatible with each other.

Notes

The tutorial_signal_state_space_representation section of the user_guide presents the corresponding definitions of continuous-time and disrcete time state space systems.

Array API Standard Support

abcd_normalize 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

The following example demonstrates that the passed lists are converted into two-dimensional arrays: 
from scipy.signal import abcd_normalize
AA, BB, CC, DD = abcd_normalize(A=[[1, 2], [3, 4]], B=[[-1], [5]],
                                C=[[4, 5]], D=2.5)
AA.shape, BB.shape, CC.shape, DD.shape
✓
In the following, the missing parameter C is assumed to be an array of zeros with shape (1, 2): 
from scipy.signal import abcd_normalize
AA, BB, CC, DD = abcd_normalize(A=[[1, 2], [3, 4]], B=[[-1], [5]], D=2.5)
AA.shape, BB.shape, CC.shape, DD.shape
CC
✓

See also

StateSpace

Linear Time Invariant system in state-space form.

cont2discrete

Transform a continuous to a discrete state-space system.

dlti

Discrete-time linear time invariant system base class.

ss2tf

State-space to transfer function.

ss2zpk

State-space representation to zero-pole-gain representation.

tf2ss

Transfer function to state-space representation.

Aliases

  • scipy.signal.abcd_normalize

Referenced by

This package