Parameters¶

class gammapy.modeling.Parameters(parameters=None, covariance=None, apply_autoscale=True)[source]¶

Bases: object

List of Parameter.

Holds covariance matrix.

Parameters:	parameters : list of `Parameter` List of parameters covariance : `ndarray`, optional Parameters covariance matrix. Order of values as specified by `parameters`. apply_autoscale : bool, optional Flag for optimizers, if True parameters are autoscaled before the fit, see `autoscale`

Attributes Summary

`correlation`	Correlation matrix (`numpy.ndarray`).
`free_parameters`	List of free parameters
`names`	List of parameter names
`parameters`	List of `Parameter`.
`restore_values`	Context manager to restore values.

Methods Summary

`autoscale`(self[, method])	Autoscale all parameters.
`copy`(self)	A deep copy
`covariance_to_table`(self)	Convert covariance matrix to `Table`.
`error`(self, parname)	Get parameter error.
`freeze_all`(self)	Freeze all parameters
`from_dict`(data)
`set_covariance_factors`(self, matrix)	Set covariance from factor covariance matrix.
`set_error`(self, parname, err)	Set parameter error.
`set_parameter_errors`(self, errors)	Set uncorrelated parameters errors.
`set_parameter_factors`(self, factors)	Set factor of all parameters.
`to_dict`(self)
`to_table`(self)	Convert parameter attributes to `Table`.

Attributes Documentation

correlation¶

Correlation matrix (numpy.ndarray).

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

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

free_parameters¶: List of free parameters

names¶: List of parameter names

parameters¶: List of Parameter.

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)

Methods Documentation

autoscale(self, method='scale10')[source]¶

Autoscale all parameters.

See autoscale()

Parameters:	method : {‘factor1’, ‘scale10’} Method to apply

copy(self)[source]¶: A deep copy

covariance_to_table(self)[source]¶: Convert covariance matrix to Table.

error(self, parname)[source]¶

Get parameter error.

Parameters:	parname : str, int Parameter name or index

freeze_all(self)[source]¶: Freeze all parameters

classmethod from_dict(data)[source]¶

set_covariance_factors(self, matrix)[source]¶

Set covariance from factor covariance matrix.

Used in the optimizer interface.

set_error(self, parname, err)[source]¶

Set parameter error.

Parameters:	parname : str, int Parameter name or index err : float or Quantity Parameter error

set_parameter_errors(self, errors)[source]¶

Set uncorrelated parameters errors.

Parameters:	errors : dict of `Quantity` Dict of parameter errors.

set_parameter_factors(self, factors)[source]¶

Set factor of all parameters.

Used in the optimizer interface.

to_dict(self)[source]¶

to_table(self)[source]¶: Convert parameter attributes to Table.