StaticResult

class TRXASprefitpack.driver.StaticResult

Bases: dict

Represent results for fitting static driver routine

model

model used for fitting

voigt: sum of voigt function, edge function and base funtion

thy: sum of voigt broadened theoretical lineshape spectrum, edge function and base function

Type:

{‘thy’, ‘voigt’}

thy_peak

theoretical calculated peak position and intensity [model: thy]

Type:

sequence of np.ndarray

policy

policy to match discrepancy between theoretical spectrum and experimental static spectrum.

Type:

{‘shift’, ‘scale’, ‘both’}

e

energy range

Type:

np.ndarray

intensity

intensity of static spectrum

Type:

np.ndarray

eps

estimated error of static spectrum

Type:

np.ndarray

fit

fitting curve for data (n,)

Type:

np.ndarray

fit_comp

curve for each voigt component and edge

Type:

np.ndarray

base

fitting curve for baseline

Type:

np.ndaray

res

residual curve (data-fit) for static spectrum (n,)

Type:

np.ndarray

edge

type of edge function, if edge is None then edge function is not included in the fitting model

‘g’: gaussian type edge function

‘l’: lorenzian type edge function

Type:

{‘g’, ‘l’}

base_order

order of baseline function if base_order is None then baseline is not included in the fitting model

Type:

int

param_name

name of parameter

Type:

np.ndarray

n_voigt

number of voigt component

Type:

int

n_edge

number of edge component

Type:

int

x

best parameter

Type:

np.ndarray

bounds

boundary of each parameter

Type:

sequence of tuple

c

best weight of each voigt component and edge of data

Type:

np.ndarray

chi2

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

red_chi2

total reduced chi squared value of fitting

Type:

float

nfev

total number of function evaluation

Type:

int

n_param

total number of effective parameter

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}