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Represents the system as the continuous 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`. Continuous-time ZerosPolesGain systems inherit additional functionality from the lti class.

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:

Continuous-time Linear Time Invariant system in zeros, poles, gain form.

See Also

StateSpace
TransferFunction
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
)
See :

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GitHub : /scipy/signal/_ltisys.py#1081
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