# 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 uncertainty

• LSFIR_uncMC(): Design of FIR filter as fit to reciprocal of frequency response values with uncertainty via Monte Carlo

• LSIIR(): Design of a stable IIR filter as fit to reciprocal of frequency response values

• LSIIR_unc(): Design of a stable IIR filter as fit to reciprocal of frequency response values with uncertainty