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).