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Download GUM2DFT

NameInitiates file downloadGUM2DFT
TypePython
Date01.06.2015
deconvolution result with uncertainty
Result of the application of GUM2DFT for the deconvolution of a hydrophone measurement.

Background

The Fourier transform and its counterpart for discrete time signals, the DFT, are common tools in measurement science and application. Although almost every scientific software package offers ready-to-use implementations of the DFT, the propagation of uncertainties in line with GUM is typically neglected. This is of particular importance in dynamic metrology, when input estimation is carried out by deconvolution in the frequency domain. To this end, we present the new open-source software tool GUM2DFT, which utilizes closed formulas for the efficient propagation of uncertainties for the application of the DFT, inverse DFT and input estimation in the frequency domain. It handles different frequency domain representations, accounts for autocorrelation and takes advantage of the symmetry inherent in the DFT result for real-valued time domain signals.

The corresponding scientific publication is

S. Eichstädt and V. Wilkens "Opens external link in new windowGUM2DFT -- A software tool for uncertainty evaluation of transient signals in the frequency domain". Meas. Sci. Technol. 27(5), 055001, 2016

For questions and remarks please contact Opens window for sending emailSascha Eichstädt.

Methods

GUM_DFTcalculation of DFT(x) and the associated covariance matrix
GUM_iDFTcalculation of iDFT(F) and the associated covariance matrix
DFT2AmpPhasecalculation of amplitude and phase from F and the assoc. covariance matrix
AmpPhase2DFTcalculation of real and imag part from ampl and phase and the assoc. covariance matrix
Time2AmpPhasecalculation of amplitude and phase from x and the assoc covariance matrix
AmpPhase2Timecalculation of x from amplitude and phase and the assoc. covariance matrix
GUMdeconvcalculation of deconvolution X=Y/H with assoc. covariance matrix (DFT domain)


A detailled example of using the software can be downloaded here:
Initiates file downloaddownload

The corresponding IPython notebook and more examples are part of the software download.