LogUniformPrior#

class gammapy.modeling.models.LogUniformPrior(**kwargs)[source]#

LogUniform Prior.

Equivalent to a uniform prior on the log of the parameter

Parameters:

Attributes Summary

`covariance`
`default_parameters`
`frozen`	Frozen status of a model, True if all parameters are frozen.
`max`
`min`
`parameters`	Prior parameters as a `PriorParameters` object.
`parameters_unique_names`
`tag`
`type`
`weight`	Weight mulitplied to the prior when evaluated.

Methods Summary

`__call__`(value)	Call evaluate method.
`copy`(**kwargs)	Deep copy.
`evaluate`(value, min, max)	Evaluate the likelihood penalization term (hence -2*).
`freeze`()	Freeze all parameters.
`from_dict`(data, **kwargs)	Get prior parameters from dictionary.
`from_parameters`(parameters, **kwargs)	Create model from parameter list.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another.
`to_dict`([full_output])	Create dictionary for YAML serialisation.
`unfreeze`()	Restore parameters frozen status to default.

Attributes Documentation

Methods Documentation

static evaluate(value, min, max)[source]#: Evaluate the likelihood penalization term (hence -2*). Note that this is currently a different scaling that the Uniform or Gaussian priors. With current implementation the TS of a source with/without LogUniform prior would be different… TBD

classmethod from_dict(data, **kwargs)#: Get prior parameters from dictionary.

classmethod from_parameters(parameters, **kwargs)#

Create model from parameter list.

Parameters:

Returns:

reassign(datasets_names, new_datasets_names)#

Reassign a model from one dataset to another.

Parameters:

datasets_namesstr or list: Name of the datasets where the model is currently defined.
new_datasets_namesstr or list: Name of the datasets where the model should be defined instead. If multiple names are given the two list must have the save length, as the reassignment is element-wise.

Returns: