threeML.plugin_prototype module

Define the interface for a plugin class.

class threeML.plugin_prototype.PluginPrototype(name: str, nuisance_parameters: Dict[str, Parameter])

Bases: object

exclude_from_fit(flag=False)

This can be used to explude a plug in from the fit :param flag: True or Fase (default) :return:

abstract get_log_like() → float

Return the value of the log-likelihood with the current values for the parameters

get_name() → str

get_number_of_data_points() → int

This returns the number of data points that are used to evaluate the likelihood. For binned measurements, this is the number of active bins used in the fit. For unbinned measurements, this would be the number of photons/particles that are evaluated on the likelihood

abstract inner_fit()

This is used for the profile likelihood. Keeping fixed all parameters in the LikelihoodModel, this method minimize the logLike over the remaining nuisance parameters, i.e., the parameters belonging only to the model for this particular detector. If there are no nuisance parameters, simply return the logLike value.

property name: str

Returns the name of this instance

Returns:

a string (this is enforced to be a valid python identifier)

property nuisance_parameters: Dict[str, Parameter]

Returns a dictionary containing the nuisance parameters for this dataset

Returns:

a dictionary

abstract set_model(likelihood_model_instance: Model)

Set the model to be used in the joint minimization. Must be a LikelihoodModel instance.

property tag

Gets/sets the tag for this instance, as (independent variable, start, [end])

update_nuisance_parameters(new_nuisance_parameters: Dict[str, Parameter]) → None