TransientResult

class TRXASprefitpack.driver.TransientResult

Bases: dict

Represent results for fitting driver routine

model

model used for fitting

decay: sum of the convolution of exponential decay and instrumental response function

dmp_osc: sum of the convolution of damped oscillation and instrumental response function

both: sum of decay and dmp_osc model

Type:

{‘decay’, ‘dmp_osc’, ‘both’}

same_t0

whether or not time zero set to same for every time delay scan in same dataset

Type:

bool

name_of_dset

name of each dataset

Type:

sequence of str

t

time range for each dataset

Type:

sequence of np.ndarray

intensity

sequence of datasets of intensity of time delay scan

Type:

sequence of np.ndarray

eps

sequence of datasets for estimated error of time delay scan

Type:

sequence of np.ndarray

fit

fitting curve for each data set

Type:

sequence of np.ndarray

fit_decay

decay part of fitting curve for each data set [model = ‘both’]

Type:

sequence of np.ndarray

fit_osc

oscillation part of fitting curve for each data set [model = ‘both’]

Type:

sequence of np.ndarray

res

residual curve (data-fit) for each data set

Type:

sequence of np.ndarray

irf

shape of instrument response function

‘g’: gaussian instrumental response function

‘c’: cauchy (lorenzian) instrumental response function

‘pv’: pseudo voigt instrumental response function (linear combination of gaussian and lorenzian function)

Type:

{‘g’, ‘c’, ‘pv’}

fwhm

unifrom fwhm parameter for pseudo voigt function \(((1-\eta)*g(t, {fwhm})+\eta*c{t, {fwhm}})\)

Type:

float

eta

mixing parameter for pseudo voigt function \(((1-\eta)*g(t, {fwhm})+\eta*c{t, {fwhm}})\)

Type:

float

param_name

name of parameter

Type:

np.ndarray

n_decay

number of decay components (except baseline feature)

Type:

int

n_osc

number of damped oscillation components

Type:

int

x

best parameter

Type:

np.ndarray

bounds

boundary of each parameter

Type:

sequence of tuple

base

whether or not use baseline feature in fitting process

Type:

bool

c

best weight of each component of each datasets

Type:

sequence of np.ndarray

phase

phase factor of each oscillation component of each datasets [mode = ‘dmp_osc’, ‘both’]

Type:

sequence of np.ndarray

chi2

total chi squared value of fitting

Type:

float

aic

Akaike Information Criterion statistic: \(N\log(\chi^2/N)+2N_{parm}\)

Type:

float

bic

Bayesian Information Criterion statistic: \(N\log(\chi^2/N)+N_{parm}\log(N)\)

Type:

float

chi2_ind

chi squared value of individual time delay scan

Type:

np.ndarray

red_chi2

total reduced chi squared value of fitting

Type:

float

red_chi2_ind

reduced chi squared value of individul time delay scan

Type:

np.ndarray

nfev

total number of function evaluation

Type:

int

n_param

total number of effective parameter

Type:

int

n_param_ind

number of parameter which affects fitting quality of indiviual time delay scan

Type:

int

num_pts

total number of data points

Type:

int

jac

jacobian of objective function at optimal point

Type:

np.ndarray

cov

covariance matrix (i.e. inverse of \(J^T J\))

Type:

np.ndarray

cov_scaled

scaled covariance matrix (i.e. \(\chi^2_{red} \cdot {cov}\))

Type:

np.ndarray

corr

parameter correlation matrix

Type:

np.ndarray

x_eps

estimated error of parameter (i.e. square root of diagonal element of conv_scaled)

Type:

np.ndarray

method_glb

method of global optimization used in fitting process

Type:

{‘ampgo’, ‘basinhopping’}

message_glb

messages from global optimization process

Type:

str

method_lsq

method of local optimization for least_squares minimization (refinement of global optimization solution)

Type:

{‘trf’, ‘dogbox’, ‘lm’}

success_lsq

whether or not local least square optimization is successed

Type:

bool

message_lsq

messages from local least square optimization process

Type:

str

status

status of optimization process

0 : normal termination

-1 : least square optimization process is failed

Type:

{0, -1}