source: sasview/park_integration/ParkFitting.py @ d8a2e31

"""
    @organization: ParkFitting module contains SansParameter,Model,Data
    FitArrange, ParkFit,Parameter classes.All listed classes work together to perform a 
    simple fit with park optimizer.
"""
import time
import numpy
import park
from park import fit,fitresult
from park import assembly
from park.fitmc import FitSimplex, FitMC

#from Loader import Load
from AbstractFitEngine import FitEngine


class ParkFit(FitEngine):
    """ 
        ParkFit performs the Fit.This class can be used as follow:
        #Do the fit Park
        create an engine: engine = ParkFit()
        Use data must be of type plottable
        Use a sans model
        
        Add data with a dictionnary of FitArrangeList where Uid is a key and data
        is saved in FitArrange object.
        engine.set_data(data,Uid)
        
        Set model parameter "M1"= model.name add {model.parameter.name:value}.
        @note: Set_param() if used must always preceded set_model()
             for the fit to be performed.
        engine.set_param( model,"M1", {'A':2,'B':4})
        
        Add model with a dictionnary of FitArrangeList{} where Uid is a key and model
        is save in FitArrange object.
        engine.set_model(model,Uid)
        
        engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]]
        chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax)
        @note: {model.parameter.name:value} is ignored in fit function since 
        the user should make sure to call set_param himself.
    """
    def __init__(self):
        """
            Creates a dictionary (self.fitArrangeList={})of FitArrange elements
            with Uid as keys
        """
        self.fitArrangeDict={}
        self.paramList=[]
        
    def createAssembly(self):
        """
        Extract sansmodel and sansdata from self.FitArrangelist ={Uid:FitArrange}
        Create parkmodel and park data ,form a list couple of parkmodel and parkdata
        create an assembly self.problem=  park.Assembly([(parkmodel,parkdata)])
        """
        mylist=[]
        listmodel=[]
        i=0
        fitproblems=[]
        for id ,fproblem in self.fitArrangeDict.iteritems():
            if fproblem.get_to_fit()==1:
                fitproblems.append(fproblem)
                
        if len(fitproblems)==0 : 
            raise RuntimeError, "No Assembly scheduled for Park fitting."
            return
        for item in fitproblems:
            parkmodel = item.get_model()
            for p in parkmodel.parameterset:
                ## does not allow status change for constraint parameters
                if p.status!= 'computed':
                    if p._getname()in item.pars:
                        ## make parameters selected for fit will be between boundaries
                        p.set( p.range )
                                
                    else:
                        p.status= 'fixed'
             
            i+=1
            Ldata=item.get_data()
            #parkdata=self._concatenateData(Ldata)
            parkdata=Ldata
            fitness=(parkmodel,parkdata)
            mylist.append(fitness)
        
        self.problem =  park.Assembly(mylist)
        
    def fit(self, *args, **kw):
        return profile(self._fit, *args, **kw)
    
    def _fit(self,handler=None, curr_thread= None):
        """
            Performs fit with park.fit module.It can  perform fit with one model
            and a set of data, more than two fit of  one model and sets of data or 
            fit with more than two model associated with their set of data and constraints
            
            
            @param pars: Dictionary of parameter names for the model and their values.
            @param qmin: The minimum value of data's range to be fit
            @param qmax: The maximum value of data's range to be fit
            @note:all parameter are ignored most of the time.Are just there to keep ScipyFit
            and ParkFit interface the same.
            @return result.fitness: Value of the goodness of fit metric
            @return result.pvec: list of parameter with the best value found during fitting
            @return result.cov: Covariance matrix
        """
        self.createAssembly()
    
        localfit = FitSimplex()
        localfit.ftol = 1e-8
        
        # See `park.fitresult.FitHandler` for details.
        fitter = FitMC(localfit=localfit, start_points=1)
        if handler == None:
            handler= fitresult.ConsoleUpdate(improvement_delta=0.1)
      
            
        result = fit.fit(self.problem,
                         fitter=fitter,
                         handler= handler)
        self.problem.all_results(result)
        if result !=None:
            return result
        else:
            raise ValueError, "SVD did not converge"