StaticResult¶
- class TRXASprefitpack.driver.StaticResult[source]¶
Bases:
dictRepresent 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 (jac.T @ jac))
- Type
np.ndarray
- cov_scaled¶
scaled covariance matrix (i.e. red_chi2 * 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
{‘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}