tf2zpk(b, a)
If some values of b
are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted.
The b
and a
arrays are interpreted as coefficients for positive, descending powers of the transfer function variable. So the inputs $b = [b_0, b_1, ..., b_M]$
and $a =[a_0, a_1, ..., a_N]$
can represent an analog filter of the form:
or a discrete-time filter of the form:
$$H(z) = \frac {b_0 z^M + b_1 z^{(M-1)} + \cdots + b_M} {a_0 z^N + a_1 z^{(N-1)} + \cdots + a_N}$$This "positive powers" form is found more commonly in controls engineering. If :None:None:`M`
and :None:None:`N`
are equal (which is true for all filters generated by the bilinear transform), then this happens to be equivalent to the "negative powers" discrete-time form preferred in DSP:
Although this is true for common filters, remember that this is not true in the general case. If :None:None:`M`
and :None:None:`N`
are not equal, the discrete-time transfer function coefficients must first be converted to the "positive powers" form before finding the poles and zeros.
Numerator polynomial coefficients.
Denominator polynomial coefficients.
Zeros of the transfer function.
Poles of the transfer function.
System gain.
Return zero, pole, gain (z, p, k) representation from a numerator, denominator representation of a linear filter.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._ltisys.TransferFunctionContinuous
scipy.signal._ltisys.TransferFunction
scipy.signal._signaltools.filtfilt
scipy.signal._ltisys.TransferFunctionDiscrete
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