Design of deconvolution filters¶
The estimation of the measurand in the analysis of dynamic measurements typically corresponds to a deconvolution problem. Therefore, a digital filter can be designed whose input is the measured system output signal and whose output is an estimate of the measurand. This module implements methods for the design of such filters given an array of frequency response values with associated uncertainties for the measurement system.
This module contains functions to carry out a leastsquares fit of a digital filter to the reciprocal of a given complex frequency response.
This module contains the following functions:
 LSFIR: Leastsquares 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
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.

PyDynamic.deconvolution.fit_filter.
LSFIR
(H, N, tau, f, Fs, Wt=None)[source]¶ Leastsquares fit of a digital FIR filter to the reciprocal of a given frequency response.
Parameters:  H (np.ndarray of shape (M,) and dtype complex) – frequency response values
 N (int) – FIR filter order
 tau (float) – delay of filter
 f (np.ndarray of shape (M,)) – frequencies
 Fs (float) – sampling frequency of digital filter
 Wt (np.ndarray of shape (M,)  optional) – vector of weights
Returns: bFIR – filter coefficients
Return type: np.ndarray of shape (N,)
References
 Elster and Link [Elster2008]

PyDynamic.deconvolution.fit_filter.
LSFIR_unc
(H, UH, N, tau, f, Fs, wt=None, verbose=True, trunc_svd_tol=None)[source]¶ Design of FIR filter as fit to reciprocal of frequency response values with uncertainty
Leastsquares fit of a digital FIR filter to the reciprocal of a frequency response for which associated uncertainties are given for its real and imaginary part. Uncertainties are propagated using a truncated svd and linear matrix propagation.
Parameters:  H (np.ndarray of shape (M,)) – frequency response values
 UH (np.ndarray of shape (2M,2M)) – uncertainties associated with the real and imaginary part
 N (int) – FIR filter order
 tau (float) – delay of filter
 f (np.ndarray of shape (M,)) – frequencies
 Fs (float) – sampling frequency of digital filter
 wt (np.ndarray of shape (2M,)  optional) – array of weights for a weighted leastsquares method
 verbose (bool, optional) – whether to print statements to the command line
 trunc_svd_tol (float) – lower bound for singular values to be considered for pseudoinverse
Returns:  b (np.ndarray of shape (N+1,)) – filter coefficients of shape
 Ub (np.ndarray of shape (N+1,N+1)) – uncertainties associated with b
References
 Elster and Link [Elster2008]

PyDynamic.deconvolution.fit_filter.
LSIIR
(Hvals, Nb, Na, f, Fs, tau, justFit=False, verbose=True)[source]¶ Design of a stable IIR filter as fit to reciprocal of frequency response values
Leastsquares fit of a digital IIR filter to the reciprocal of a given set of frequency response values using the equationerror method and stabilization by pole mapping and introduction of a time delay.
Parameters:  Hvals (np.ndarray of shape (M,) and dtype complex) – frequency response values.
 Nb (int) – order of IIR numerator polynomial.
 Na (int) – order of IIR denominator polynomial.
 f (np.ndarray of shape (M,)) – frequencies corresponding to Hvals
 Fs (float) – sampling frequency for digital IIR filter.
 tau (float) – initial estimate of time delay for filter stabilization.
 justFit (bool) – if True then no stabilization is carried out.
 verbose (bool) – If True print some more detail about input parameters.
Returns:  b, a (np.ndarray) – IIR filter coefficients
 tau (int) – time delay (in samples)
References
 Eichstädt, Elster, Esward, Hessling [Eichst2010]

PyDynamic.deconvolution.fit_filter.
LSIIR_unc
(H, UH, Nb, Na, f, Fs, tau=0)[source]¶ Design of stabel IIR filter as fit to reciprocal of given frequency response with uncertainty
Leastsquares fit of a digital IIR filter to the reciprocal of a given set of frequency response values with given associated uncertainty. Propagation of uncertainties is carried out using the Monte Carlo method.
Parameters:  H (np.ndarray of shape (M,) and dtype complex) – frequency response values.
 UH (np.ndarray of shape (2M,2M)) – uncertainties associated with real and imaginary part of H
 Nb (int) – order of IIR numerator polynomial.
 Na (int) – order of IIR denominator polynomial.
 f (np.ndarray of shape (M,)) – frequencies corresponding to H
 Fs (float) – sampling frequency for digital IIR filter.
 tau (float) – initial estimate of time delay for filter stabilization.
Returns:  b,a (np.ndarray) – IIR filter coefficients
 tau (int) – time delay (in samples)
 Uba (np.ndarray of shape (Nb+Na+1, Nb+Na+1)) – uncertainties associated with [a[1:],b]
References
 Eichstädt, Elster, Esward and Hessling [Eichst2010]

PyDynamic.deconvolution.fit_filter.
LSFIR_uncMC
(H, UH, N, tau, f, Fs, verbose=True)[source]¶ Design of FIR filter as fit to reciprocal of frequency response values with uncertainty
Leastsquares fit of a FIR filter to the reciprocal of a frequency response for which associated uncertainties are given for its real and imaginary parts. Uncertainties are propagated using a Monte Carlo method. This method may help in cases where the weighting matrix or the Jacobian are illconditioned, resulting in false uncertainties associated with the filter coefficients.
Parameters:  H (np.ndarray of shape (M,) and dtype complex) – frequency response values
 UH (np.ndarray of shape (2M,2M)) – uncertainties associated with the real and imaginary part of H
 N (int) – FIR filter order
 tau (int) – time delay of filter in samples
 f (np.ndarray of shape (M,)) – frequencies corresponding to H
 Fs (float) – sampling frequency of digital filter
 verbose (bool, optional) – whether to print statements to the command line
Returns:  b (np.ndarray of shape (N+1,)) – filter coefficients of shape
 Ub (np.ndarray of shape (N+1, N+1)) – uncertainties associated with b
References
 Elster and Link [Elster2008]