ModelBase#

class gammapy.modeling.models.ModelBase[source]#

Model base class.

Attributes Summary

`covariance`
`frozen`	Frozen status of a model, True if all parameters are frozen.
`parameters`	Parameters as a `Parameters` object.
`parameters_unique_names`
`type`

Methods Summary

`copy`(**kwargs)	Deep copy.
`freeze`()	Freeze all parameters.
`from_dict`(data, **kwargs)
`from_parameters`(parameters, **kwargs)	Create model from parameter list.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another.
`sample_parameters_from_covariance`([...])	Create parameters samples from covariance using multivariate normal distribution.
`to_dict`([full_output])	Create dictionary for YAML serialisation.
`unfreeze`()	Restore parameters frozen status to default.

Attributes Documentation

Methods Documentation

classmethod from_parameters(parameters, **kwargs)[source]#

Create model from parameter list.

Parameters:

Returns:

reassign(datasets_names, new_datasets_names)[source]#

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:

sample_parameters_from_covariance(n_samples=1000, random_state=42, free_only=True)#

Create parameters samples from covariance using multivariate normal distribution.

Parameters:

n_samplesint, optional: Number of samples to generate. Default is 1000.
random_state{int, ‘random-seed’, ‘global-rng’, RandomState}, optional: Defines random number generator initialisation. Passed to get_random_state. Default is 42.
free_onlybool, optional: If True, sample only free parameters (default).

Returns:

to_dict(full_output=False)[source]#: Create dictionary for YAML serialisation.