A2

Single run multi group sequential fit This is the flow of information in mujpy to produce the sequential fit by
dofit_singlerun_multigroup_sequential
Back to Fit Types

dashboard, containing a dict, is a json file

• key "model_guess" contains a list, like that of A1 fits, plus
  • component dicts, which contains "pardicts", a list of
    • parameter dicts, contains extra keys
      • "function_multi", a list of as many strings as groups, to evaluate a distinct parameter value vs p = Minuit parameter array
      • "error_propagation_multi", a list of as many strings as groups, to evaluate a distinct parameter-error value vs p = Minuit parameter array

aux int2min produces the minuit list of guess parameter values by scanning the model components, as in A1 fits

aux int2_multigroup_method_key takes care of passing the right parameter to the right component method. It produces a list of [method, keys], one per method, where

• method is the python function from mucomponents mumodel that calculates the component, and
• keys is a list of lists of lambda functions, typically key_as_lambda = eval('lambda p:'+pardict["function_multi"][l]) , for the l-th group, or key_as_lambda = eval('lambda p:'+pardict["function"]) to be used as
  • f = method(t,*argv), where t is the 1-d time array, argv is a list of the dashboard parameter values for that component, and f is an array with the same shape as t
  • argv is calculated in _add_multigroup_ as [[key(p) for key in groups_key] for groups_key in keys], assuming that p is the array of parameter values passed by Minuit at this iteration

The convention for A2 sequential fits is that

• if "flag": "~" or "!" the parameter is a Minuit parameter, and "function" is empty, the key is in the form "p[k]" with k the index of the parameter
• if "flag": "=" the parameter is missing and the key is specified either by "function": "p[k]", with k the index of a previous parameter as written by the user, with reference to internal, dashboard parameter indexing, or by "function_multi", same syntax.
• indices are recomputed by aux translate, since the dashboard parameters with "flag":"=" de-synchronize dashboard and Minuit indices.

aux mint2int translates back best fit values to component parameters

mumodel _load_data_ preloads time, asymmetry and asymmetry errors, methods and key lists, alpha. The last is deprecated, only needed for linear "da" corrections.

mumodel _chisquare_ is the cost function.

mumodel _add_single_ assigns the correct parameters to each component, calculated from the Minuit array of parameters

• the_model._load_data_(time,asymmetry,...),
• m = Minuit(the_model._chisquare_,names,*values)
• m.errors = errors, etc
• m.migrad()

Minuit migrad() method calls

• the_model._chisquare_, which in turns calls
• the_model._add_single_(x,*argv), where argv is the Minuit array of parameters. Their assignment to component methods is performed
  • scanning the method list
    • scanning the keys list of each method and evaluating each key

Back to Fit Types

Single run multi group global fit This is the flow of information in mujpy to produce the global fit by
dofit_singlerun_multigroup_userpardicts
Back to Fit Types

The dashboard, is a json file containing a dict a new key is introduced

• "userpardicts_guess", a list of dicts pardict, each containing the follwing keys
  • "name"
  • "value"
  • "flag"
  • "error"
  • "limits"

besides the usual

• key "model_guess", which contains a list, in the order of their appearance in the model-name string, of
  • component dicts, with a key "pardicts", a list of
    • parameter dicts, with usual keys, among which
      • "flag", can only be "=", referred either to "function" or, if it exists, to
      • "function_multi", a list of as many strings as groups, to evaluate a distinct parameter value vs p = Minuit parameter array
      • "error_propagation_multi", a list of as many strings as groups, to evaluate a distinct parameter-error value vs p = Minuit parameter array

aux int2min_multigroup produces the Minuit list of guess parameter values by scanning the userpardicts list (different from the sequential case!)

aux int2_multigroup_method_key takes care of passing the right parameter to the right component method. It produces a list of [method, keys], like that of the sequential case, above.

aux mint2int_multigroup is invoked by summary_global for translating back best fit values to component parameters

mumodel _load_data_multigroup_ preloads time, asymmetry and asymmetry errors, methods and key lists.

mumodel _chisquare_ is the cost function.

mumodel _add_multigroup_ assigns the correct parameters to each component, calculated from the Minuit array of parameters

• the_model._load_data_multigroup_(time,asymmetry,...),
• m = Minuit(the_model._chisquare_,names,*values)
• m.errors = errors, etc
• m.migrad()

Minuit migrad() method calls

• the_model._chisquare_, which in turns calls
• the_model._add_multigroup_(x,*argv), where argv is the Minuit array of parameters. Their assignment to component methods is performed
  • scanning the method list
    • scanning the keys list of each method

Back to Fit Types