DogBoxLSQFitter#

class astropy.modeling.fitting.DogBoxLSQFitter(calc_uncertainties=False, use_min_max_bounds=False)[source]#

Bases: _NLLSQFitter

DogBox algorithm and least squares statistic.

Parameters:

calc_uncertaintiesbool: If the covarience matrix should be computed and set in the fit_info. Default: False
use_min_max_bounds: bool: If the set parameter bounds for a model will be enforced each given parameter while fitting via a simple min/max condition. A True setting will replicate how LevMarLSQFitter enforces bounds. Default: False

Attributes:

fit_info: A scipy.optimize.OptimizeResult class which contains all of the most recent fit information

Attributes Summary

supported_constraints

The constraint types supported by this fitter type.

Methods Summary

`__call__`(model, x, y[, z, weights, maxiter, ...])	Fit data to this model.
`objective_function`(fps, *args)	Function to minimize.

Attributes Documentation

supported_constraints = ['fixed', 'tied', 'bounds']#: The constraint types supported by this fitter type.

Methods Documentation

__call__(model, x, y, z=None, weights=None, maxiter=100, acc=1e-07, epsilon=1.4901161193847656e-08, estimate_jacobian=False, filter_non_finite=False)#

Fit data to this model.

Parameters:

modelFittableModel: model to fit to x, y, z
xarray: input coordinates
yarray: input coordinates
zarray, optional: input coordinates
weightsarray, optional: Weights for fitting. For data with Gaussian uncertainties, the weights should be 1/sigma.

Changed in version 5.3: Calculate parameter covariances while accounting for weights as “absolute” inverse uncertainties. To recover the old behavior, choose weights=None.
maxiterint: maximum number of iterations
accfloat: Relative error desired in the approximate solution
epsilonfloat: A suitable step length for the forward-difference approximation of the Jacobian (if model.fjac=None). If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.
estimate_jacobianbool: If False (default) and if the model has a fit_deriv method, it will be used. Otherwise the Jacobian will be estimated. If True, the Jacobian will be estimated in any case.
equivalencieslist or None, optional, keyword-only: List of additional equivalencies that are should be applied in case x, y and/or z have units. Default is None.
filter_non_finitebool, optional: Whether or not to filter data with non-finite values. Default is False

Returns:

model_copyFittableModel: a copy of the input model with parameters set by the fitter

objective_function(fps, *args)#

Function to minimize.

Parameters:

fpslist: parameters returned by the fitter
argslist: [model, [weights], [input coordinates]]