Fitting Tutorial with BOOTSTRAPPING

This notebook tutorial runs in only one mass bin at the time

Define Waves for Fit (and input initial values of minuit and fitted parameters).*

In this example (as expected by the defined amplitude)\ each wave is defined by (epsilon.l.m) and each parameter has a real and imaginary part.\ i.e a epsilon=-1, l=1 (P wave), m=1 will produce Vs(r.-1.1.1) and Vs(i.-1.1.1) names.\ (In this example the imaginary part of the P-wave is kept fixed at 0 value\ in the fit)

Read data and montecarlos (accepted and generated) samples

Binning of the data/monte-carlo and define amplitude (function) to fit*

Here the user difine number of bins, variable to be binned and range

Check that bins have enough number of events for fit

BOOSTTRAPPING

Fitting with Minuit and Extended LogLikelihood

Look at other possibilities through pypwa (use the ?pwa command\ or see https://pypwa.jlab.org or https://github.com/JeffersonLab/PyPWA)

Plot Total Intensities

Plot intensities for each wave

Plot Waves amplitudes (real, imaginary)