IterativeFitter

IterativeFitter = class IterativeFitter(BaseFitter)

IterativeFitter(xdata, model, maxIter=None, tolerance=0.0001, verbose=1, **kwargs) Base class with methods common to all iterative fitters. Author: Do Kester. Attributes ---------- tolerance : float When absolute and relative steps in subsequent chisq steps are less than tolerance, the fitter stops. Default = 0.0001 maxIter : int When the number of iterations gets larger than maxiter the fitter stops with a ConvergenceError Default = 1000 * nparams iter : int (read only) iteration counter ntrans : int (read only) number of transforms verbose : int information per iteration. 0 : silent 1 : base information (default) 2 : report about every 100th iteration 3 : report about every ietration tooLarge : int When the length parameter array is too large to make a Hessian. To avert OutOfMemory. Default = 100 plotter : Plotter Iteration plotter class. Default = IterationPlotter plotIter : int Produce a plot for every plotIter-th iteration. Default = 0 (no plotting) Raises ------ ConvergenceError Something went wrong during the convergence if the fit.

Method resolution order:: IterativeFitter; BaseFitter; builtins.object

Constructor:

IterativeFitter( xdata, model, maxIter=None, tolerance=0.0001, verbose=1, **kwargs ): Create a new iterative fitter, providing xdatas and model. This is a base class. It collects stuff common to all iterative fitters. It does not work by itself. Parameters ---------- xdata : array_like array of independent input values model : Model the model function to be fitted tolerance : float When absolute and relative steps in subsequent chisq steps are less than tolerance, the fitter stops. Default = 0.01 maxIter : None or int When the number of iterations gets larger than maxiter the fitter stops with a ConvergenceError Default = 1000 * nparams verbose : int 0 : silent 1 : report result 2 : report every 100th iteration 3 : report every iteration kwargs for BaseFitter map, keep, fixedScale

Methods defined here:

doPlot( param, force=False ): Plot intermediate result. Parameters ---------- param : array_like of the model force : bool do the plot

fit( ydata, weights=None, keep=None, **kwargs ): Return model parameters fitted to the data. It will calculate the hessian matrix and chisq. Parameters ---------- ydata : array_like the data vector to be fitted weights : array_like weights pertaining to the data keep : dict of {int:float} dictionary of indices (int) to be kept at a fixed value (float) kwargs : passed to the fitter Raises ------ ConvergenceError if it stops when the tolerance has not yet been reached.

fitprolog( ydata, weights=None, accuracy=None, keep=None ): Prolog for all iterative Fitters. 1. Sets up plotting (if requested) 2. Sets self.iter and self.ntrans to 0 3. Checks data/weighs for Nans 4. Makes fitIndex. Parameters ---------- ydata : array_like the data vector to be fitted weights : array_like weights pertaining to the data accuracy : float or array_like accuracy of (individual) data keep : dict of {int:float} dictionary of indices (int) to be kept at a fixed value (float) Returns ------- fitIndex : ndarray of int Indices of the parameters that need fitting

report( verbose, param, chi, more=None, force=False ): Report on intermediate results.

setParameters( params ): Initialize the parameters of the model A little superfluous: see {link Model#setParameters} Parameters ---------- params : array_like initial parameters

Methods inherited from BaseFitter: