source: sasview/park_integration/Fitting.py @ fca90f82

"""
Class Fit contains ScipyFit and ParkFit methods declaration
allows to create instance of type ScipyFit or ParkFit to perform either
a park fit or a scipy fit.
"""

from scipy import optimize
from ScipyFitting import ScipyFit
from ParkFitting import ParkFit


class Fit:
    """ 
    Wrap class that allows to select the fitting type.this class 
    can be used as follow : ::
    
        from sans.fit.Fitting import Fit
        fitter= Fit()
        fitter.fit_engine('scipy') or fitter.fit_engine('park')
        engine = fitter.returnEngine()
        engine.set_data(data,Uid)
        engine.set_param( model,model.name, pars)
        engine.set_model(model,Uid)
        
        chisqr1, out1, cov1=engine.fit(pars,qmin,qmax)
        
    """  
    def __init__(self, engine='scipy'):
        """
        """
        #self._engine will contain an instance of ScipyFit or ParkFit
        self._engine = None
        self.set_engine(engine)
          
    def set_engine(self,word):
        """
        Select the type of Fit 
        
        :param word: the keyword to select the fit type 
        
        :raise: if the user does not enter 'scipy' or 'park',
             a valueError is raised 
             
        """
        if word=="scipy":
            self._engine=ScipyFit()
        elif word=="park":
            self._engine=ParkFit()
        else:
            raise ValueError, "enter the keyword scipy or park"

    def fit(self, q=None, handler=None, curr_thread=None):
        """Perform the fit """
        try:
            return self._engine.fit(q,handler, curr_thread= curr_thread)
        except:
            raise
    
    def set_model(self,model,Uid,pars=[],constraints=[]):
        """
        store a model model to fit at the position Uid of the fit engine
        """
        self._engine.set_model(model,Uid,pars,constraints)
   
   
    def set_data(self,data,Uid,smearer=None,qmin=None, qmax=None):
        """
        Store data to fit at the psotion Uid of the fit engine
        
        :param data: data to fit
        :param smearer: smearerobject to smear data
        :param qmin: the minimum q range to fit 
        :param qmax: the minimum q range to fit
        
        """
        self._engine.set_data(data,Uid,smearer,qmin, qmax)
        
        
    def get_model(self,Uid):
        """ return list of data"""
        self._engine.get_model(Uid)


    def remove_Fit_Problem(self,Uid):
        """remove fitarrange in Uid"""
        self._engine.remove_Fit_Problem(Uid)
        
        
    def select_problem_for_fit(self,Uid,value):
        """
        select a couple of model and data at the Uid position in dictionary
        and set in self.selected value to value
        
        :param value: the value to allow fitting.
             can only have the value one or zero
        """
        self._engine.select_problem_for_fit(Uid,value)
        
        
    def get_problem_to_fit(self,Uid):
        """
        return the self.selected value of the fit problem of Uid
           
        :param Uid: the Uid of the problem
        
        """
        return self._engine.get_problem_to_fit(Uid)