scipy 1.8.0 Pypi GitHub Homepage
Other Docs
NotesParametersBackRef

Represents the system as the continuous- or discrete-time transfer function $H(s)=k \prod_i (s - z[i]) / \prod_j (s - p[j])$ , where $k$ is the :None:None:`gain`, $z$ are the zeros and $p$ are the :None:None:`poles`. ZerosPolesGain systems inherit additional functionality from the lti , respectively the dlti classes, depending on which system representation is used.

Notes

Changing the value of properties that are not part of the ZerosPolesGain system representation (such as the :None:None:`A`, :None:None:`B`, :None:None:`C`, :None:None:`D` state-space matrices) 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_ss() before accessing/changing the A, B, C, D system matrices.

Parameters

*system : arguments

The ZerosPolesGain class can be instantiated with 1 or 3 arguments. The following gives the number of input arguments and their interpretation:

dt: float, optional :

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

Linear Time Invariant system class in zeros, poles, gain form.

See Also

StateSpace
TransferFunction
dlti
lti
zpk2sos
zpk2ss
zpk2tf

Examples

Construct the transfer function $H(s) = \frac{5(s - 1)(s - 2)}{(s - 3)(s - 4)}$ :

>>> from scipy import signal
>>> signal.ZerosPolesGain([1, 2], [3, 4], 5)
ZerosPolesGainContinuous(
array([1, 2]),
array([3, 4]),
5,
dt: None
)

Construct the transfer function $H(z) = \frac{5(z - 1)(z - 2)}{(z - 3)(z - 4)}$ with a sampling time of 0.1 seconds:

>>> signal.ZerosPolesGain([1, 2], [3, 4], 5, dt=0.1)
ZerosPolesGainDiscrete(
array([1, 2]),
array([3, 4]),
5,
dt: 0.1
)
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.signal._ltisys.TransferFunctionContinuous scipy.signal._ltisys.ZerosPolesGainContinuous.to_discrete scipy.signal._ltisys.TransferFunctionDiscrete scipy.signal._ltisys.LinearTimeInvariant._as_zpk scipy.signal._ltisys.dlti scipy.signal._ltisys.StateSpace scipy.signal._ltisys.StateSpace.to_zpk scipy.signal._ltisys.lti scipy.signal._ltisys.ZerosPolesGainDiscrete scipy.signal._ltisys.freqresp scipy.signal._ltisys.ZerosPolesGainContinuous scipy.signal._ltisys.ZerosPolesGain._copy scipy.signal._ltisys.StateSpaceContinuous scipy.signal._ltisys.TransferFunction.to_zpk scipy.signal._ltisys.ZerosPolesGain scipy.signal._ltisys.TransferFunction scipy.signal._ltisys.StateSpaceDiscrete scipy.signal._ltisys.ZerosPolesGain.__repr__

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


GitHub : /scipy/signal/_ltisys.py#879
type: <class 'type'>
Commit: