Parameters¶

class gammapy.utils.fitting.Parameters(parameters, 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

`names`	List of parameter names
`parameters`	List of `Parameter`.

Methods Summary

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

Attributes Documentation

names¶: List of parameter names

parameters¶: List of Parameter.

Methods Documentation

autoscale(method='scale10')[source]¶

Autoscale all parameters.

See autoscale()

Parameters:

method : {‘factor1’, ‘scale10’}

Method to apply

copy()[source]¶: A deep copy

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

error(parname)[source]¶

Get parameter error.

Parameters:

parname : str, int

Parameter name or index

classmethod from_dict(val)[source]¶

set_covariance_factors(matrix)[source]¶

Set covariance from factor covariance matrix.

Used in the optimiser interface.

set_error(parname, err)[source]¶

Set parameter error.

Parameters:

parname : str, int

Parameter name or index

err : float or Quantity

Parameter error

set_parameter_errors(errors)[source]¶

Set uncorrelated parameters errors.

Parameters:

errors : dict of Quantity

Dict of parameter errors.

set_parameter_factors(factors)[source]¶

Set factor of all parameters.

Used in the optimiser interface.

to_dict()[source]¶

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