fpg(st1, st2, dt=0.01, fmin=1.0, fmax=10.0, nf=100, w0=6, norm='global', st2_isref=True, a=10.0, k=1.0)[source]

Frequency-dependent Phase Goodness-of-Fit

See also

[Kristekova2009], Eq.(16)

  • st1 signal 1 of two signals to compare, type numpy.ndarray with shape (number of components, number of time samples) or (number of timesamples, ) for single component data
  • st2 signal 2 of two signals to compare, type and shape as st1
  • dt time step between two samples in st1 and st2
  • fmin minimal frequency to be analyzed
  • fmax maximal frequency to be analyzed
  • nf number of frequencies (will be chosen with logarithmic spacing)
  • w0 parameter for the wavelet, tradeoff between time and frequency resolution
  • norm ‘global’ or ‘local’ normalization of the misfit
  • st2_isref (bool) True if st2 is a reference signal, False if none is a reference
  • a Maximum value of Goodness-of-Fit for perfect agreement
  • k sensitivity of Goodness-of-Fit to the misfit

frequency dependent Phase Goodness-of-Fit, type numpy.ndarray with shape (nf,) for single component data and (number of components, nf) for multicomponent data