2.3.3.3.126. NXfit¶
Status:
base class, extends NXprocess
Description:
Description of a fit procedure.
Symbols:
The symbols used in the schema to specify e.g. dimensions of arrays.
dimRank: Rank of the dependent and independent data arrays (for multidimensional/multivariate fit.)
- Groups cited:
Structure:
label: (optional) NX_CHAR
Human-readable label for this fit procedure.
figure_of_meritMETRIC: (optional) NX_NUMBER {units=NX_UNITLESS}
Figure-of-merit to determine the goodness of fit, i.e., how well the peak mode ...
Figure-of-merit to determine the goodness of fit, i.e., how well the peak model fits the measured observations.
This value (which is a single number) is oftenused to guide adjustments to the fitting parameters in the peak fitting process.
@metric: (optional) NX_CHAR
Metric used to determine the goodness of fit. Examples include: ...
Metric used to determine the goodness of fit. Examples include: - \(\chi^2\), the squared sum of the sigma-weighted residuals - reduced \(\chi^2\):, \(\chi^2\): per degree of freedom - \(R^2\), the coefficient of determination
data: (optional) NXdata
Input data and results of the fit.
input_dependent: (optional) NX_NUMBER (Rank: dimRank) {units=NX_ANY} ⤆
Position values along one or more data dimensions (to hold the ...
Position values along one or more data dimensions (to hold the values for the independent variable for this fit procedure).
input_independent: (optional) NX_NUMBER (Rank: dimRank) {units=NX_ANY} ⤆
Independent input axis for this fit procedure.
envelope: (optional) NX_NUMBER (Rank: dimRank) {units=NX_ANY} ⤆
residual: (optional) NX_NUMBER (Rank: dimRank) {units=NX_ANY} ⤆
Difference between the envelope and the input_independent data to be fitted.
peakPEAK: (optional) NXpeak
One peak of the peak model. ...
One peak of the peak model. If there is no characteristic name for each peak component, is envisioned that peaks are labeled as peak_0, peak_1, and so on.
total_area: (optional) NX_NUMBER {units=NX_ANY} ⤆
Total area under the curve (can also be used for the total area minus any ...
Total area under the curve (can also be used for the total area minus any background values).
relative_sensitivity_factor: (optional) NX_NUMBER {units=NX_UNITLESS}
Relative sensitivity for this peak, to be used for quantification in ...
Relative sensitivity for this peak, to be used for quantification in an NXprocess.
As an example, in X-ray spectroscopy could depend on the energy scale (see position), the ionization cross section, and the element probed.
relative_area: (optional) NX_NUMBER {units=NX_ANY}
Relative area of this peak compared to other peaks. ...
Relative area of this peak compared to other peaks.
The relative area can simply be derived by dividing the total_area by the total area of all peaks or by a more complicated method (e.g., by additionally dividing by the relative sensitivity factors). Details shall be given in global_fit_function.
backgroundBACKGROUND: (optional) NXfit_background
One fitted background (functional form, position, and intensities) of the peak ...
One fitted background (functional form, position, and intensities) of the peak fit. If there is no characteristic name for each peak component, it is envisioned that backgrounds are labeled as background_0, background_1, and so on.
global_fit_function: (optional) NXfit_function
Function used to describe the overall fit to the data, taking into account the ...
Function used to describe the overall fit to the data, taking into account the parameters of the individual NXpeak and NXfit_background components.
Oftentimes, if the peaks and fit backgrounds are defined independently (i.e, ...
Oftentimes, if the peaks and fit backgrounds are defined independently (i.e, with their own parameter sets), the resulting global fit is a function of the form \(model = peak_1(p_1) + peak2(p_2) + backgr(p_3).\), where each \(p_x\) describes the set of parameters for one peak/background.
error_function: (optional) NXfit_function
Function used to optimize the parameters during peak fitting.
description: (optional) NX_CHAR ⤆
Description of the method used to optimize the parameters during peak fittin ...
Description of the method used to optimize the parameters during peak fitting. Examples: - least squares - non-linear least squares - Levenberg-Marquardt algorithm (damped least-squares) - linear regression - Bayesian linear regression
For the optimization, the formula is any optimization process on the `global ...
For the optimization, the formula is any optimization process on the global_fit_function given above. As an example, for a linear least squared algorithm on independent components, the formula of the error_function would be \(LLS(peak_1(p_1) + peak2(p_2) + backgr(p_3))\), where each \(p_x\) describes the set of parameters for one peak/background.
It is however also possible to supply more involved formulas (e.g., in the case of constrained fits).
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