FitterBase

class pyMaCh3._pyMaCh3.fitter.FitterBase

Bases: pybind11_object

Methods Summary

`add_sample_PDF`(self, sample)	This function adds a sample PDF object to the analysis framework.
`add_syst_object`(self, cov)	This function adds a Covariance object to the analysis framework.
`drag_race`(self[, NLaps])	Calculates the required time for each sample or covariance object in a drag race simulation.
`get_name`(self)	The name of the algorithm, you should override this with something like.
`get_step_scale_from_LLH_scan`(self)	LLH scan is good first estimate of step scale, this will get the rough estimates for the step scales based on running an LLH scan
`run`(self)	The implementation of the fitter, you should override this with your own desired fitting algorithm
`run_2d_LLH_scan`(self)	Perform a 2D likelihood scan.
`run_LLH_scan`(self)	Perform a 1D likelihood scan
`run_sigma_var`(self)	Perform a 2D and 1D sigma var for all samples.

Methods Documentation

add_sample_PDF(self: pyMaCh3._pyMaCh3.fitter.FitterBase, sample: samplePDFBase) → None: This function adds a sample PDF object to the analysis framework. The sample PDF object will be utilized in fitting procedures or likelihood scans. :param sample: A sample PDF object derived from samplePDFBase.

add_syst_object(self: pyMaCh3._pyMaCh3.fitter.FitterBase, cov: covarianceBase) → None: This function adds a Covariance object to the analysis framework. The Covariance object will be utilized in fitting procedures or likelihood scans. :param cov: A Covariance object derived from covarianceBase.

drag_race(self: pyMaCh3._pyMaCh3.fitter.FitterBase, NLaps: int = 100) → None: Calculates the required time for each sample or covariance object in a drag race simulation. Inspired by Dan’s feature :param NLaps: number of laps, every part of Fitter will be tested with given number of laps and you will get total and average time

The name of the algorithm, you should override this with something like:

return 'mySuperCoolAlgoName'

get_step_scale_from_LLH_scan(self: pyMaCh3._pyMaCh3.fitter.FitterBase) → None: LLH scan is good first estimate of step scale, this will get the rough estimates for the step scales based on running an LLH scan

run(self: pyMaCh3._pyMaCh3.fitter.FitterBase) → None: The implementation of the fitter, you should override this with your own desired fitting algorithm

run_2d_LLH_scan(self: pyMaCh3._pyMaCh3.fitter.FitterBase) → None: Perform a 2D likelihood scan. :param warning: This operation may take a significant amount of time, especially for complex models.

run_LLH_scan(self: pyMaCh3._pyMaCh3.fitter.FitterBase) → None: Perform a 1D likelihood scan

run_sigma_var(self: pyMaCh3._pyMaCh3.fitter.FitterBase) → None: Perform a 2D and 1D sigma var for all samples. :param warning: Code uses TH2Poly