GaussianPrior#

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

Bases: Prior

One-dimensional Gaussian Prior.

Parameters:
mufloat, optional

Mean of the Gaussian distribution. Default is 0.

sigmafloat, optional

Standard deviation of the Gaussian distribution. Default is 1.

Attributes Summary

covariance

default_parameters

frozen

Frozen status of a model, True if all parameters are frozen.

mu

Parameter of a Prior.

parameters

Prior parameters as a PriorParameters object.

parameters_unique_names

sigma

Parameter of a Prior.

tag

type

weight

Weight multiplied to the prior when evaluated.

Methods Summary

__call__(value)

Call evaluate method.

copy(**kwargs)

Deep copy.

evaluate(value, mu, sigma)

Evaluate the Gaussian prior.

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

covariance#
default_parameters = <gammapy.modeling.parameter.PriorParameters object>#
frozen#

Frozen status of a model, True if all parameters are frozen.

mu#

Parameter of a Prior.

A prior is a probability density function of a model parameter and can take different forms, including but not limited to Gaussian distributions and uniform distributions. The prior includes information or knowledge about the dataset or the parameters of the fit.

Parameters:
namestr

Name.

valuefloat or Quantity

Value.

unitUnit or str, optional

Unit. Default is “”.

Examples

For a usage example see Priors tutorial.

parameters#

Prior parameters as a PriorParameters object.

parameters_unique_names#
sigma#

Parameter of a Prior.

A prior is a probability density function of a model parameter and can take different forms, including but not limited to Gaussian distributions and uniform distributions. The prior includes information or knowledge about the dataset or the parameters of the fit.

Parameters:
namestr

Name.

valuefloat or Quantity

Value.

unitUnit or str, optional

Unit. Default is “”.

Examples

For a usage example see Priors tutorial.

tag = ['GaussianPrior']#
type#
weight#

Weight multiplied to the prior when evaluated.

Methods Documentation

__call__(value)#

Call evaluate method.

copy(**kwargs)#

Deep copy.

static evaluate(value, mu, sigma)[source]#

Evaluate the Gaussian prior.

freeze()#

Freeze all parameters.

classmethod from_dict(data, **kwargs)#

Get prior parameters from dictionary.

classmethod from_parameters(parameters, **kwargs)#

Create model from parameter list.

Parameters:
parametersParameters

Parameters for init.

Returns:
modelModel

Model instance.

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:
modelModel

Reassigned model.

to_dict(full_output=False)#

Create dictionary for YAML serialisation.

unfreeze()#

Restore parameters frozen status to default.