< Mujpy.Multigroup | Index | Mujpy.PositiveParameters >
Policy for saving fits:
- single or sequential fits have
- one set of values and errors for each Minuit parameter,
- transform them back into component parameter values by min2int
- store component parameter values and errors in dashboard["model_result"]
- save the augmented dashboard to the json file (mufit.save_fit)
- writes component parameters etc in a csv file (mufit.prepare_csv and aux.write_csv)
- single run or sequential run multigroup userpardicts fits (one chi2 for several groups, single run) have:
- user parameters guesses (dashboard["userpardicts_guess"], a list of dicts) that initialize Minuit parameters
- The result of a fit is an equivalent single set of Minuit (user) parameters, that determine the component parameters for all groups, based on int2_multigroup_method
- store their optimized values in dashboard["userpardicts_results"]; these are global
- save the augmented dashboard (mufit.save_fit_multigroup)
- write the same user parameter values and errors in a csv file (mufit.prepare_csv_multigroup and aux.write_csv)
- the same "function" evaluation, for common parameters
- specific "function_multi" evaluation (as many "function" strings as groups), for group-specific parameters
- can transform them back to component parameter values by min2int_multigroup, using "function" and "function_multi" evaluation
- skip storing component parameter best fit values (evaluated) into dashboard["model_result"]: evaluated by mucomponents using int2_multigroup_method
- at this stage json files are
- just guess if no "..result" is present,
- single run fits is they have a "model_result" key (including calib)
- multigroup fits if they have a "userpardicts_result" key
< Mujpy.Multigroup | Index | Mujpy.PositiveParameters >
