Represents the system as the transfer function $H(s)=\sum_{i=0}^N b[N-i] s^i / \sum_{j=0}^M a[M-j] s^j$
, where $b$
are elements of the numerator :None:None:`num`, $a$
are elements of the denominator :None:None:`den`, and N == len(b) - 1
, M == len(a) - 1
. Continuous-time TransferFunction
systems inherit additional functionality from the lti
class.
Changing the value of properties that are not part of the TransferFunction
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.
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]
)
The TransferFunction
class can be instantiated with 1 or 2 arguments. The following gives the number of input arguments and their interpretation:
1:
ltisystem: (StateSpace,TransferFunctionorZerosPolesGain)2: array_like: (numerator, denominator)
Continuous-time Linear Time Invariant system in transfer function form.
Construct the transfer function $H(s) = \frac{s^2 + 3s + 3}{s^2 + 2s + 1}$ :
>>> from scipy import signal
>>> num = [1, 3, 3]
... den = [1, 2, 1]
>>> signal.TransferFunction(num, den) TransferFunctionContinuous( array([ 1., 3., 3.]), array([ 1., 2., 1.]), dt: None )See :
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