Fitting

Here can be find the functions to load and fit experimental data. EPRAYA has 5 methods for EPR data fitting: Nelder-Mead, Genetic Algorithm, Metrópolis, Least Squares and the use of the ADAM Algorithm.

Data load functions

EPRAYA has different forms of loading data, based in the np.load function. The choosing between this functions depends on the user and the possibility of use the python package tkinter.

Name	Description
epraya.Sload	Basic function for data load of counts and fields.
epraya.Splot	Plotting function for field and count data.
epraya.Sfilter	Applys a Savitzky-Golay filter to the count data, finds the resonant fields and peak to peak width, and generates the plots of the filtered data, its first and second integrals.
epraya.Overseer	Interactive function for spectrum analysis of the experimental data, applying Savitzky-Golay filter to the data, finding it’s resonant fields, peak to peak distance and first and second integral.
epraya.Sload1	Basic function for data load of counts and fields in Seek.
epraya.Spmanipulation	Applys a Savitzky-Golay filter to the count data, finds the resonant fields and peak to peak width, and generates the plots of the filtered data, its first and second integrals, using the values of the sliders.
epraya.Seek	Function for data loading and initial analysis, based on tkinter.
epraya.Termal	Function to analyze the change in peak to peak distance, resonant field position, first and second integral of EPR spectrum data with the temperature.

Fitting functions

This methods can be use directly or with the wrap function Fitting, however, the Briggs function for the ADAM method requieres the use of the JAX framework of EPRAYA, described in Jax implementation.

Name	Description
epraya.Fitting	Wrap function for the data fitting process.
epraya.Nelder	Implementation of the Nelder Mead algorithm for fitting data.
epraya.Nelder1	Implementation of the Nelder Mead algorithm for fitting data for a single system.
epraya.Nelder2	Implementation of the Nelder Mead algorithm for fitting data for multisystems.
epraya.Genio	Fitting function for the experimental data using the genetic algorithm.
epraya.Genio1	Fitting function for the experimental data using the genetic algorithm for a single system.
epraya.Genio2	Fitting function for the experimental data using the genetic algorithm for multysystems.
epraya.Metro	Fitting adjutsment of the experimental data using a modified Metrópolis-Simulated annealing approach.
epraya.Metro1	Fitting adjutsment of the experimental data using a modified Metrópolis-Simulated annealing approach for a single system.
epraya.Metro2	Fitting adjutsment of the experimental data using a modified Metrópolis-Simulated annealing approach for multiple systems.
epraya.LSquare	Fitting adjutsment of the experimental data using the scipy.optimize.least_squares method.
epraya.LSquare1	Fitting adjutsment of the experimental data using the scipy.optimize.least_squares method. This case is for simple systems.
epraya.LSquare2	Fitting adjutsment of the experimental data using the scipy.optimize.least_squares method. This case is for multiple systems.
epraya.Briggs	Wrap function for the ADAM fitting method.