FittingWithOutlierRemoval#
- 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 orniter
is reached.- Parameters:
- fitter
Fitter
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
).- outlier_func
callable()
A function for outlier removal. If this accepts an
axis
parameter like thenumpy
functions, the appropriate value will be supplied automatically when fitting model sets (unless overridden inoutlier_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.- niter
int
, optional Maximum number of iterations.
- outlier_kwargs
dict
, optional Keyword arguments for outlier_func.
- fitter
- Attributes:
- fit_info
dict
The
fit_info
(if any) from the last iteration of the wrappedfitter
during the most recent fit. An entry is also added with the keywordniter
that records the actual number of fitting iterations performed (as opposed to the user-specified maximum).
- fit_info
Methods Summary
__call__
(model, x, y[, z, weights])- Parameters:
Methods Documentation
- __call__(model, x, y, z=None, weights=None, **kwargs)[source]#
- Parameters:
- model
FittableModel
An analytic model which will be fit to the provided data. This also contains the initial guess for an optimization algorithm.
- xarray_like
Input coordinates.
- yarray_like
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.
- kwargs
dict
, optional Keyword arguments to be passed to the fitter.
- model
- Returns:
- fitted_model
FittableModel
Fitted model after outlier removal.
- mask
numpy.ndarray
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).
- fitted_model