lti — SciPy v1.15.3 Manual (original) (raw)

scipy.signal.

class scipy.signal.lti(*system)[source]#

Continuous-time linear time invariant system base class.

Parameters:

*systemarguments

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.

Notes

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

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, callsys = sys.to_zpk() before accessing/changing the zeros, poles or gain.

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 )

Attributes:

dt

Return the sampling time of the system, None for lti systems.

poles

Poles of the system.

zeros

Zeros of the system.

Methods