Parameters#

class gammapy.modeling.Parameters(parameters=None)[source]#

Bases: collections.abc.Sequence

Parameters container.

  • List of Parameter objects.

  • Covariance matrix.

Parameters
parameterslist of Parameter

List of parameters.

Attributes Summary

free_parameters

List of free parameters.

max

Parameter maxima as a numpy.ndarray.

min

Parameter minima as a numpy.ndarray.

names

List of parameter names.

norm_parameters

List of norm parameters.

prior

types

Parameter types.

unique_parameters

Unique parameters as a Parameters object.

value

Parameter values as a numpy.ndarray.

Methods Summary

autoscale()

Autoscale all parameters.

check_limits()

Check parameter limits and emit a warning.

copy()

Deep copy.

count(value)

freeze_all()

Freeze all parameters.

from_dict(data)

from_stack(parameters_list)

Create Parameters by stacking a list of other Parameters objects.

index(val)

Get position index for a given parameter.

prior_stat_sum()

restore_status([restore_values])

Context manager to restore status.

select([name, type, frozen])

Create a mask of models, true if all conditions are verified.

set_parameter_factors(factors)

Set factor of all parameters.

to_dict()

to_table()

Convert parameter attributes to Table.

unfreeze_all()

Unfreeze all parameters (even those frozen by default).

Attributes Documentation

free_parameters#

List of free parameters.

max#

Parameter maxima as a numpy.ndarray.

min#

Parameter minima as a numpy.ndarray.

names#

List of parameter names.

norm_parameters#

List of norm parameters.

prior#
types#

Parameter types.

unique_parameters#

Unique parameters as a Parameters object.

value#

Parameter values as a numpy.ndarray.

Methods Documentation

autoscale()[source]#

Autoscale all parameters.

See autoscale().

check_limits()[source]#

Check parameter limits and emit a warning.

copy()[source]#

Deep copy.

count(value) integer -- return number of occurrences of value#
freeze_all()[source]#

Freeze all parameters.

classmethod from_dict(data)[source]#
classmethod from_stack(parameters_list)[source]#

Create Parameters by stacking a list of other Parameters objects.

Parameters
parameters_listlist of Parameters

List of Parameters objects.

index(val)[source]#

Get position index for a given parameter.

The input can be a parameter object, parameter name (str) or if a parameter index (int) is passed in, it is simply returned.

prior_stat_sum()[source]#
restore_status(restore_values=True)[source]#

Context manager to restore status.

A copy of the values is made on enter, and those values are restored on exit.

Parameters
restore_valuesbool, optional

Restore values if True, otherwise restore only frozen status. Default is None.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> pwl = PowerLawSpectralModel(index=2)
>>> with pwl.parameters.restore_status():
>>>     pwl.parameters["index"].value = 3
>>> print(pwl.parameters["index"].value)
select(name=None, type=None, frozen=None)[source]#

Create a mask of models, true if all conditions are verified.

Parameters
namestr or list, optional

Name of the parameter. Default is None.

type{None, “spatial”, “spectral”, “temporal”}

Type of models. Default is None.

frozenbool, optional

Select frozen parameters if True, exclude them if False. Default is None.

Returns
parametersParameters

Selected parameters.

set_parameter_factors(factors)[source]#

Set factor of all parameters.

Used in the optimizer interface.

to_dict()[source]#
to_table()[source]#

Convert parameter attributes to Table.

unfreeze_all()[source]#

Unfreeze all parameters (even those frozen by default).