residual_decay_same_t0

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

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

  • 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

  • tau_mask (sequence of boolean np.ndarray) – whether or not include jth time constant in ith dataset fitting (tau_mask[i][j]) If base is True, size of tau_mask[i] should be num_tau+1.

  • t – time points for each data set

  • intensity – sequence of intensity of datasets

  • eps – sequence of estimated error of datasets

Returns:

Residual vector