12. Fitting and Modeling Data¶
A key motivation for Larch is to provide easy and robust ways to model data and perform complex fits of data to models. Data modeling and fitting can be messy and challenging tasks, so a major factor in Larch’s design was to make this as simple as possible. This chapter discusses the basic concepts for building models, setting up and performing fits, and inspecting the results.
The concepts presented here focus on modeling and fitting of general
spectra and data. Of course, Larch can provides other, specific functions
for doing fits, such as the EXAFS procedures
_xafs.feffit(). Many of these concepts (and the underlying fitting
algorithms) are used for those other functions as well.
- 12.1. Fitting Overview
- 12.2. Parameters
minimize()and objective Functions
- 12.4. Fit Results and Outputs
- 12.5. Some Builtin Line-shape Functions
- 12.6. Fit Examples
- 12.7. Simplified Peak Fitting with
- 12.8. Advanced Confidence Intervals and Chi-square maps