"""
    @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:
                if p._getname()in self.paramList and not p.iscomputed():
                    p.status = 'fitted' # make it a fitted parameter
                            #iscomputed  paramter with string inside
               
            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, qmin=None, qmax=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
        # fitmc(fitness,localfit,n,handler):
        #Run a monte carlo fit.
        #This procedure maps a local optimizer across a set of n initial points.
        #The initial parameter value defined by the fitness parameters defines
        #one initial point.  The remainder are randomly generated within the
        #bounds of the problem.
        #localfit is the local optimizer to use.  It should be a bounded
        #optimizer following the `park.fitmc.LocalFit` interface.
        #handler accepts updates to the current best set of fit parameters.
        # See `park.fitresult.FitHandler` for details.
        fitter = FitMC(localfit=localfit)
        #result = fit.fit(self.problem,
        #             fitter=fitter,
        #            handler= GuiUpdate(window))
        result = fit.fit(self.problem,
                         fitter=fitter,
                         handler= fitresult.ConsoleUpdate(improvement_delta=0.1))
        #handler = fitresult.ConsoleUpdate(improvement_delta=0.1)
        #models=self.problem
        #service=None
        #if models is None: raise RuntimeError('fit expected a list of models')
        #from park.fit import LocalQueue,FitJob
        #if service is None: service = LocalQueue()
        #if fitter is None: fitter = fitmc.FitMC()
        #if handler is None: handler = fitresult.FitHandler()
    
        #objective = assembly.Assembly(models) if isinstance(models,list) else models
        #job = FitJob(self.problem,fitter,handler)
        #service.start(job)
        #import wx
        #while not self.job.handler.done:
        #    time.sleep(interval)
        #    wx.Yield()
        #result=service.job.handler.result
        
        if result !=None:
            return result
        else:
            raise ValueError, "SVD did not converge"