Represents the system as the discrete-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`. Discrete-time ZerosPolesGain
systems inherit additional functionality from the dlti
class.
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.
The ZerosPolesGain
class can be instantiated with 1 or 3 arguments. The following gives the number of input arguments and their interpretation:
1:
dltisystem: (StateSpace,TransferFunctionorZerosPolesGain)3: array_like: (zeros, poles, gain)
Sampling time [s] of the discrete-time systems. Defaults to :None:None:`True` (unspecified sampling time). Must be specified as a keyword argument, for example, dt=0.1
.
Discrete-time Linear Time Invariant system in zeros, poles, gain form.
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 )
Construct the transfer function $H(s) = \frac{5(z - 1)(z - 2)}{(z - 3)(z - 4)}$ 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 )See :
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