IterFit

class sherpa.fit.IterFit(data, model, stat, method, itermethod_opts=None)[source] [edit on github]

Bases: object

Support iterative fitting schemes.

This class is highly coupled to Fit.

Changed in version 4.17.0: Several internal fields have been removed as they are now handled by the IterCallback class and the changes to the _get_callback routine.

Methods Summary

`fit`(statfunc, pars, parmins, parmaxes[, ...])
`sigmarej`(statfunc, pars, parmins, parmaxes)	Exclude points that are significately far away from the best fit.

Methods Documentation

fit(statfunc, pars, parmins, parmaxes, statargs=(), statkwargs=None)[source] [edit on github]

sigmarej(statfunc, pars, parmins, parmaxes, statargs=(), statkwargs=None, cache=True)[source] [edit on github]

Exclude points that are significately far away from the best fit.

The sigmarej scheme is based on the IRAF sfit function [3], where after a fit data points are excluded if the value of (data-model) / error exceeds a threshold, and the data re-fit. This removal of data points continues until the fit has converged or a maximum number of iterations has been reached. The error removal can be asymmetric, since there are separate options for the lower and upper limits.

Raises:: sherpa.utils.err.FitErr – This exception is raised if the statistic is not supported. This method can only be used with Chi-Square statistics with errors.

Notes

The following keys are looked for in the itermethod_opts dictionary:

Key	Type	Description
maxiters	int > 0	The maximum number of iterations.
lrej	number > 0	The number of sigma below the model to reject.
hrej	number > 0	The number of sigma above the model to reject.
grow	int >= 0	If greater than zero, also remove this many data points to either side of the identified element.

References