Parameters

class gammapy.utils.fitting.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(val)
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.spectrum.models import PowerLaw
pwl = PowerLaw(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(val)[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.