Design of deconvolution filters¶
Deprecated since version 1.2.71: The module deconvolution will be combined with the module identification and
renamed to model_estimation in the next major release 2.0.0. From then on you
should only use the new module Model estimation instead. The
functions LSFIR()
, LSFIR_unc()
, LSIIR()
, LSIIR_unc()
,
LSFIR_uncMC()
are then prefixed with an “inv” for “inverse”, indicating the
treatment of the reciprocal of frequency response values. Please use the new
function names (e.g. PyDynamic.model_estimation.fit_filter.invLSIIR_unc()
)
starting from version 1.4.1. The old function names without preceding “inv” will
only be preserved until the release prior to version 2.0.0.
The PyDynamic.deconvolution.fit_filter
module implements methods for the
design of digital deconvolution filters by least-squares fitting to the reciprocal of
a given frequency response with associated uncertainties.
This module for now still contains the following functions:
LSFIR()
: Least-squares fit of a digital FIR filter to the reciprocal of a given frequency response.LSFIR_unc()
: Design of FIR filter as fit to reciprocal of frequency response values with uncertaintyLSFIR_uncMC()
: Design of FIR filter as fit to reciprocal of frequency response values with uncertainty via Monte CarloLSIIR()
: Design of a stable IIR filter as fit to reciprocal of frequency response valuesLSIIR_unc()
: Design of a stable IIR filter as fit to reciprocal of frequency response values with uncertainty