Fitting Models to Data

This module provides wrappers, called Fitters, around some Numpy and Scipy fitting functions. All Fitters can be called as functions. They take an instance of FittableModel as input and modify its parameters attribute. The idea is to make this extensible and allow users to easily add other fitters.

Linear fitting is done using Numpy’s numpy.linalg.lstsq function. There are currently two non-linear fitters which use scipy.optimize.leastsq and scipy.optimize.fmin_slsqp.

The rules for passing input to fitters are:

• Non-linear fitters currently work only with single models (not model sets).
• The linear fitter can fit a single input to multiple model sets creating multiple fitted models. This may require specifying the model_set_axis argument just as used when evaluating models; this may be required for the fitter to know how to broadcast the input data.
• The LinearLSQFitter currently works only with simple (not compound) models.
• The current fitters work only with models that have a single output (including bivariate functions such as Chebyshev2D but not compound models that map x, y -> x', y').

Plugin Fitters

Fitters defined outside of astropy’s core can be inserted into the astropy.modeling.fitting namespace through the use of entry points. Entry points are references to importable objects. A tutorial on defining entry points can be found in setuptools’ documentation. Plugin fitters are required to extend from the Fitter base class. For the fitter to be discovered and inserted into astropy.modeling.fitting the entry points must be inserted into the astropy.modeling entry point group

setup(
      # ...
      entry_points = {'astropy.modeling': 'PluginFitterName = fitter_module:PlugFitterClass'}
)

This would allow users to import the PlugFitterName through astropy.modeling.fitting by

from astropy.modeling.fitting import PlugFitterName

One project which uses this functionality is Saba, which insert its SherpaFitter class and thus allows astropy users to use Sherpa’s fitting routine.