residual_dmp_osc_same_t0

TRXASprefitpack.res.residual_dmp_osc_same_t0(x0: ndarray, num_comp: int, irf: str, t: Sequence[ndarray] | None = None, intensity: Sequence[ndarray] | None = None, eps: Sequence[ndarray] | None = None) → ndarray

scipy.optimize.least_squares compatible gradient of vector residual function for fitting multiple set of time delay scan with the sum of convolution of damped oscillation and instrumental response function

Parameters:

• x0 –

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

  • 1st: fwhm_(G/L)

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

  • \(2+N_{dset}+i\): time constant of each damped oscillation

  • \(2+N_{dset}+N_{osc}+i\): period of each damped oscillation

  if irf == ‘pv’:

  • 1st and 2nd: fwhm_G, fwhm_L

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

  • \(3+N_{dset}+i\): time constant of each damped oscillation

  • \(3+N_{dset}+N_{osc}+i\): period of each damped oscillation

• num_comp – number of damped oscillation 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