residual_decay_same_t0

TRXASprefitpack.res.residual_decay_same_t0(x0: ndarray, base: bool, irf: str, t: Optional[Sequence[ndarray]] = None, intensity: Optional[Sequence[ndarray]] = None, eps: Optional[Sequence[ndarray]] = None) → ndarray

scipy.optimize.least_squares compatible vector residual function for fitting multiple set of time delay scan with the sum of convolution of exponential decay and instrumental response function Set Time Zero of every time dset in same dataset same

Parameters

• x0 –

  initial parameter, if irf == ‘g’,’c’:

  • 1st: fwhm_(G/L)

  • 2nd to \(2+N_{dset}\): time zero of each data set

  • \(2+N_{dset}\) to \(2+N_{dset}+N_{\tau}\): time constant of each decay component

  if irf == ‘pv’:

  • 1st and 2nd: fwhm_G, fwhm_L

  • 3rd to \(3+N_{dset}\): time zero of each data set

  • \(3+N_{dset}\) to \(3+N_{dset}+N_{\tau}\): time constant of each decay component

• num_comp – number of exponential decay component (except base)

• base – whether or not include baseline (i.e. very long lifetime component)

• irf –

  shape of instrumental response function

  • ’g’: normalized gaussian distribution,

  • ’c’: normalized cauchy distribution,

  • ’pv’: pseudo voigt profile \((1-\eta)g(f) + \eta c(f)\)

  For pseudo voigt profile, the mixing parameter \(\eta(f_G, f_L)\) and uniform fwhm paramter \(f(f_G, f_L)\) are calculated by calc_eta and calc_fwhm routine

• t – time points for each data set

• intensity – sequence of intensity of datasets

• eps – sequence of estimated error of datasets

Returns

Residual vector