class astropy.modeling.fitting.FittingWithOutlierRemoval(fitter, outlier_func, niter=3, **outlier_kwargs)[source]#

Bases: object

This class combines an outlier removal technique with a fitting procedure. Basically, given a maximum number of iterations niter, outliers are removed and fitting is performed for each iteration, until no new outliers are found or niter is reached.


An instance of any Astropy fitter, i.e., LinearLSQFitter, LevMarLSQFitter, SLSQPLSQFitter, SimplexLSQFitter, JointFitter. For model set fitting, this must understand masked input data (as indicated by the fitter class attribute supports_masked_input).


A function for outlier removal. If this accepts an axis parameter like the numpy functions, the appropriate value will be supplied automatically when fitting model sets (unless overridden in outlier_kwargs), to find outliers for each model separately; otherwise, the same filtering must be performed in a loop over models, which is almost an order of magnitude slower.

niterint, optional

Maximum number of iterations.

outlier_kwargsdict, optional

Keyword arguments for outlier_func.


The fit_info (if any) from the last iteration of the wrapped fitter during the most recent fit. An entry is also added with the keyword niter that records the actual number of fitting iterations performed (as opposed to the user-specified maximum).

Methods Summary

__call__(model, x, y[, z, weights])


Methods Documentation

__call__(model, x, y, z=None, weights=None, **kwargs)[source]#

An analytic model which will be fit to the provided data. This also contains the initial guess for an optimization algorithm.


Input coordinates.


Data measurements (1D case) or input coordinates (2D case).

zarray_like, optional

Data measurements (2D case).

weightsarray_like, optional

Weights to be passed to the fitter.

kwargsdict, optional

Keyword arguments to be passed to the fitter.


Fitted model after outlier removal.


Boolean mask array, identifying which points were used in the final fitting iteration (False) and which were found to be outliers or were masked in the input (True).