Notes

lti instances do not exist directly. Instead, lti creates an instance of one of its subclasses: StateSpace , TransferFunction or ZerosPolesGain .

If (numerator, denominator) is passed in for *system , coefficients for both the numerator and denominator should be specified in descending exponent order (e.g., s^2 + 3s + 5 would be represented as [1, 3, 5] ).

Changing the value of properties that are not directly part of the current system representation (such as the zeros of a StateSpace system) 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.

Parameters

*system : arguments

The lti class can be instantiated with either 2, 3 or 4 arguments. The following gives the number of arguments and the corresponding continuous-time subclass that is created:

• 2: TransferFunction : (numerator, denominator)

• 3: ZerosPolesGain : (zeros, poles, gain)

• 4: StateSpace : (A, B, C, D)

Each argument can be an array or a sequence.

Continuous-time linear time invariant system base class.

See Also

StateSpace

TransferFunction

ZerosPolesGain

dlti

Examples

>>> from scipy import signal

>>> signal.lti(1, 2, 3, 4)
StateSpaceContinuous(
array([[1]]),
array([[2]]),
array([[3]]),
array([[4]]),
dt: None
)

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

>>> signal.lti([1, 2], [3, 4], 5)
ZerosPolesGainContinuous(
array([1, 2]),
array([3, 4]),
5,
dt: None
)

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

>>> signal.lti([3, 4], [1, 2])
TransferFunctionContinuous(
array([3., 4.]),
array([1., 2.]),
dt: None
)

See :

Back References

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

scipy.signal._ltisys.LinearTimeInvariant.__init__ scipy.signal._ltisys.lti.__init__ scipy.signal._ltisys.dlti scipy.signal._ltisys.lsim scipy.signal._ltisys.TransferFunctionContinuous scipy.signal._ltisys.StateSpace scipy.signal._filter_design.lp2bs scipy.signal._ltisys.lti scipy.signal._ltisys.freqresp scipy.signal._filter_design.lp2bp scipy.signal._ltisys.step2 scipy.signal._ltisys.TransferFunction scipy.signal._ltisys.impulse scipy.signal._ltisys.bode scipy.signal._ltisys.StateSpaceContinuous scipy.signal._lti_conversion.cont2discrete scipy.signal._filter_design.bilinear scipy.signal._ltisys.impulse2 scipy.signal._ltisys.lsim2 scipy.signal._ltisys.step scipy.signal._filter_design.lp2hp scipy.signal._filter_design.lp2lp scipy.signal._ltisys.ZerosPolesGainContinuous scipy.signal._ltisys.dlti.__init__ scipy.signal._ltisys.ZerosPolesGain scipy.signal._filter_design.bilinear_zpk

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

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