`scipy.signal._ltisys:ZerosPolesGain`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_ltisys.py:1137

Signature

class ZerosPolesGain(*system, dt = None)

Members

Summary

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

Extended Summary

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 gain, $z$ are the zeros and $p$ are the poles. ZerosPolesGain systems inherit additional functionality from the lti, respectively the dlti classes, depending on which system representation is used.

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:

1: lti or dlti system: (StateSpace, TransferFunction or ZerosPolesGain)
3: array_like: (zeros, poles, gain)

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 ZerosPolesGain system representation (such as the A, B, C, 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.

Examples

Construct the transfer function :math:`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 :math:`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
)

Aliases

scipy.signal.ZerosPolesGain