SLSQPLSQFitter

class astropy.modeling.fitting.SLSQPLSQFitter

Bases: Fitter

Sequential Least Squares Programming (SLSQP) optimization algorithm and least squares statistic.

Raises:

ModelLinearityError

A linear model is passed to a nonlinear fitter

Notes

See also the SLSQP optimizer.

Attributes Summary

supported_constraints

Methods Summary

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

Attributes Documentation

supported_constraints = ['bounds', 'eqcons', 'ineqcons', 'fixed', 'tied']

Methods Documentation

__call__(model, x, y, z=None, weights=None, **kwargs)

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.

kwargspython:dict

optional keyword arguments to be passed to the optimizer or the statistic

verblevelpython:int

0-silent 1-print summary upon completion, 2-print summary after each iteration

maxiterpython:int

maximum number of iterations

epsilonpython:float

the step size for finite-difference derivative estimates

accpython:float

Requested accuracy

equivalenciespython:list or python: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:

fpspython:list

parameters returned by the fitter

argspython:list

[model, [other_args], [input coordinates]] other_args may include weights or any other quantities specific for a statistic

Notes

The list of arguments (args) is set in the __call__ method. Fitters may overwrite this method, e.g. when statistic functions require other arguments.