Parameters

class gammapy.modeling.Parameters(parameters=None, covariance=None)

Bases: collections.abc.Sequence

Parameters container.

• List of Parameter objects.

• Covariance matrix.

Parameters

parameterslist of Parameter

List of parameters

covariancendarray, optional

Parameters covariance matrix. Order of values as specified by parameters.

Attributes Summary

correlation	Correlation matrix (numpy.ndarray).
covariance	Covariance matrix (numpy.ndarray).
free_parameters	List of free parameters
names	List of parameter names
restore_values	Context manager to restore values.
scipy_mvn
unique_parameters	Unique parameters (Parameters).
values	Parameter values (numpy.ndarray).

Methods Summary

autoscale(self[, method])	Autoscale all parameters.
copy(self)	A deep copy
count(self, value)
error(self, parname)	Get parameter error.
freeze_all(self)	Freeze all parameters
from_dict(data)
from_stack(parameters_list)	Create Parameters by stacking a list of other Parameters objects.
from_values([values, covariance])	Create Parameters from values.
get_subcovariance(self, parameters)	Get sub-covariance matrix
index(self, value[, start, stop])	Raises ValueError if the value is not present.
link(self, par, other_par)	Create link to other parameter
set_covariance_factors(self, matrix)	Set covariance from factor covariance matrix.
set_error(self, \*\*kwargs)	Set errors on parameters.
set_parameter_factors(self, factors)	Set factor of all parameters.
set_subcovariance(self, parameters)	Set sub-covariance matrix
to_dict(self)
to_table(self)	Convert parameter attributes to Table.
update_from_dict(self, data)

Attributes Documentation

correlation

Correlation matrix (numpy.ndarray).

Correlation \(C\) is related to covariance \(\Sigma\) via:

\[C_{ij} = \frac{ \Sigma_{ij} }{ \sqrt{\Sigma_{ii} \Sigma_{jj}} }\]

covariance

Covariance matrix (numpy.ndarray).

free_parameters

List of free parameters

names

List of parameter names

restore_values

Context manager to restore values.

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

Examples

from gammapy.modeling.models import PowerLawSpectralModel
pwl = PowerLawSpectralModel(index=2)
with pwl.parameters.restore_values:
    pwl.parameters["index"].value = 3
print(pwl.parameters["index"].value)

scipy_mvn

unique_parameters

Unique parameters (Parameters).

values

Parameter values (numpy.ndarray).

Methods Documentation

autoscale(self, method='scale10')

Autoscale all parameters.

See autoscale()

Parameters

method{‘factor1’, ‘scale10’}

Method to apply

copy(self)

A deep copy

count(self, value)

error(self, parname)

Get parameter error.

Parameters

parnamestr, int

Parameter name or index

freeze_all(self)

Freeze all parameters

classmethod from_dict(data)

classmethod from_stack(parameters_list)

Create Parameters by stacking a list of other Parameters objects.

Parameters

parameters_listlist of Parameters

List of Parameters objects

classmethod from_values(values=None, covariance=None)

Create Parameters from values.

TODO: document.

get_subcovariance(self, parameters)

Get sub-covariance matrix

Parameters

parametersParameters

Sub list of parameters.

Returns

covariancendarray

Sub-covariance.

index(self, value, start=0, stop=None)

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

link(self, par, other_par)

Create link to other parameter

set_covariance_factors(self, matrix)

Set covariance from factor covariance matrix.

Used in the optimizer interface.

set_error(self, **kwargs)

Set errors on parameters.

Pass parameter errors as keyword arguments, similar to how parameter values are passed in other places.

Usually parameter errors come via a fit and a covariance matrix. This method is only used to make models from previously published results, e.g. in gammapy.catalog.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> model = PowerLawSpectralModel(amplitude="4.2e-11 cm-2 s-1 TeV-1", index=2.7)
>>> model.parameters.set_error(amplitude="0.6-11 cm-2 s-1 TeV-1", index=0.2)

set_parameter_factors(self, factors)

Set factor of all parameters.

Used in the optimizer interface.

set_subcovariance(self, parameters)

Set sub-covariance matrix

Parameters

parametersParameters

Sub list of parameters.

to_dict(self)

to_table(self)

Convert parameter attributes to Table.

update_from_dict(self, data)