LMLSQFitter

class astropy.modeling.fitting.LMLSQFitter(calc_uncertainties=False)

Bases: astropy.modeling.fitting._NLLSQFitter

scipy.optimize.least_squares Levenberg-Marquardt algorithm and least squares statistic.

Parameters

calc_uncertaintiesbool

If the covarience matrix should be computed and set in the fit_info. 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)

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.

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.

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]]