LevMarLSQFitter#
- class astropy.modeling.fitting.LevMarLSQFitter(calc_uncertainties=False)[source]#
Bases:
_NonLinearLSQFitter
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
Notes
The
fit_info
dictionary contains the values returned byscipy.optimize.leastsq
for the most recent fit, including the values from theinfodict
dictionary it returns. See thescipy.optimize.leastsq
documentation for details on the meaning of these values. Note that thex
return value is not included (as it is instead the parameter values of the returned model). Additionally, one additional element offit_info
is computed whenever a model is fit, with the key ‘param_cov’. The corresponding value is the covariance matrix of the parameters as a 2D numpy array. The order of the matrix elements matches the order of the parameters in the fitted model (i.e., the same order asmodel.param_names
).- Attributes:
- fit_info
dict
The
scipy.optimize.leastsq
result for the most recent fit (see notes).
- fit_info
Attributes Summary
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:
- model
FittableModel
model to fit to x, y, z
- x
array
input coordinates
- y
array
input coordinates
- z
array
, optional input coordinates
- weights
array
, 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, chooseweights=None
.- maxiter
int
maximum number of iterations
- acc
float
Relative error desired in the approximate solution
- epsilon
float
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.
- equivalencies
list
orNone
, 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
- model
- Returns:
- model_copy
FittableModel
a copy of the input model with parameters set by the fitter
- model_copy