TRXASprefitpack.res package

Module contents

TRXASprefitpack.res Package

res: subpackage for resdiual function for fitting time delay scan data or static spectrum data

copyright

2021-2022 by pistack (Junho Lee).

license

LGPL3.

Functions

set_bound_t0(t0, fwhm)

Setting bound for time zero

set_bound_tau(tau, fwhm)

Setting bound for lifetime constant

set_bound_e0(e0, fwhm_G, fwhm_L)

Setting bound for peak position and edge position

residual_decay(x0, base, irf[, t, ...])

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

res_grad_decay(x0, num_comp, base, irf[, ...])

scipy.optimize.minimize compatible scalar residual and its gradient function for fitting multiple set of time delay scan with the sum of convolution of exponential decay and instrumental response function

residual_dmp_osc(x0, num_comp, irf[, t, ...])

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

res_grad_dmp_osc(x0, num_comp, irf[, ...])

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

residual_both(x0, num_comp, num_comp_osc, ...)

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

res_grad_both(x0, num_comp, num_comp_osc, ...)

scipy.optimize.minimize compatible scalar residual and its gradient function for fitting multiple set of time delay scan with the sum of convolution of (sum of exponential decay damped oscillation) and instrumental response function

residual_decay_same_t0(x0, base, irf[, t, ...])

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

res_grad_decay_same_t0(x0, num_comp, base, irf)

scipy.optimize.minimize compatible scalar residual and its gradient function for fitting multiple set of time delay scan with the sum of convolution of exponential decay and instrumental response function

residual_dmp_osc_same_t0(x0, num_comp, irf)

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

res_grad_dmp_osc_same_t0(x0, num_comp, irf)

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

residual_both_same_t0(x0, num_comp, ...[, ...])

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

res_grad_both_same_t0(x0, num_comp, ...[, ...])

scipy.optimize.minimize compatible scalar residual and its gradient function for fitting multiple set of time delay scan with the sum of convolution of (sum of exponential decay damped oscillation) and instrumental response function

residual_voigt(x0, num_voigt[, edge, ...])

scipy.optimize.least_squares compatible vector residual function for fitting static spectrum with the sum of voigt function, edge function base function

res_grad_voigt(x0, num_voigt[, edge, ...])

scipy.optimize.minimizer compatible scalar residual function and its gradient for fitting static spectrum with the sum of voigt function, edge function base function

residual_thy(x0, policy, thy_peak[, edge, ...])

residaul_thy scipy.optimize.least_squares compatible vector residual function for fitting static spectrum with the sum of voigt broadend theoretical spectrum, edge function base function

res_grad_thy(x0, policy, thy_peak[, edge, ...])

scipy.optimize.minimize compatible scalar residual function and its gradient for fitting static spectrum with the sum of voigt broadend theoretical spectrum, edge function base function