"""
    @organization: ScipyFitting module contains FitArrange , ScipyFit,
    Parameter classes.All listed classes work together to perform a 
    simple fit with scipy optimizer.
"""

import numpy 
from scipy import optimize

from AbstractFitEngine import FitEngine, SansAssembly,FitAbort

class fitresult(object):
    """
        Storing fit result
    """
    def __init__(self, model=None, paramList=None):
        self.calls     = None
        self.fitness   = None
        self.chisqr    = None
        self.pvec      = None
        self.cov       = None
        self.info      = None
        self.mesg      = None
        self.success   = None
        self.stderr    = None
        self.parameters = None
        self.model = model
        self.paramList = paramList
     
    def set_model(self, model):
        self.model = model
        
    def __str__(self):
        if self.pvec == None and self.model is None and self.paramList is None:
            return "No results"
        n = len(self.model.parameterset)

        result_param = zip(xrange(n), self.model.parameterset)
        L = ["P%-3d  %s......|.....%s"%(p[0], p[1], p[1].value) for p in result_param if p[1].name in self.paramList ]
        L.append("=== goodness of fit: %s"%(str(self.fitness)))
        return "\n".join(L)
    
    def print_summary(self):
        print self   

class ScipyFit(FitEngine):
    """ 
        ScipyFit performs the Fit.This class can be used as follow:
        #Do the fit SCIPY
        create an engine: engine = ScipyFit()
        Use data must be of type plottable
        Use a sans model
        
        Add data with a dictionnary of FitArrangeDict 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.In case of Scipyfit set_param is called in
             fit () automatically.
        engine.set_param( model,"M1", {'A':2,'B':4})
        
        Add model with a dictionnary of FitArrangeDict{} 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)
    """
    def __init__(self):
        """
            Creates a dictionary (self.fitArrangeDict={})of FitArrange elements
            with Uid as keys
        """
        self.fitArrangeDict={}
        self.paramList=[]
    #def fit(self, *args, **kw):
    #    return profile(self._fit, *args, **kw)

    def fit(self, q=None, handler=None, curr_thread=None):
       
        fitproblem=[]
        for id ,fproblem in self.fitArrangeDict.iteritems():
            if fproblem.get_to_fit()==1:
                fitproblem.append(fproblem)
        if len(fitproblem)>1 : 
            msg = "Scipy can't fit more than a single fit problem at a time."
            raise RuntimeError, msg
            return
        elif len(fitproblem)==0 : 
            raise RuntimeError, "No Assembly scheduled for Scipy fitting."
            return
    
        listdata=[]
        model = fitproblem[0].get_model()
        listdata = fitproblem[0].get_data()
        # Concatenate dList set (contains one or more data)before fitting
        data = listdata
        self.curr_thread= curr_thread
        result = fitresult(model=model, paramList=self.paramList)
        if handler is not None:
            handler.set_result(result=result)
        #try:
        functor = SansAssembly(self.paramList, model, data, handler=handler,
                                fitresult=result,curr_thread= self.curr_thread)
       
       
        out, cov_x, info, mesg, success = optimize.leastsq(functor,
                                                model.getParams(self.paramList),
                                                    full_output=1, warning=True)
        
        chisqr = functor.chisq(out)
        
        if cov_x is not None and numpy.isfinite(cov_x).all():
            stderr = numpy.sqrt(numpy.diag(cov_x))
        else:
            stderr = None
        if not (numpy.isnan(out).any()) or ( cov_x !=None) :
                result.fitness = chisqr
                result.stderr  = stderr
                result.pvec = out
                result.success = success
                print result
                if q is not  None:
                    #print "went here"
                    q.put(result)
                    #print "get q scipy fit enfine",q.get()
                    return q
                return result
        else:  
            raise ValueError, "SVD did not converge"+str(success)
    


def profile(fn, *args, **kw):
    import cProfile, pstats, os
    global call_result
    def call():
        global call_result
        call_result = fn(*args, **kw)
    cProfile.runctx('call()', dict(call=call), {}, 'profile.out')
    stats = pstats.Stats('profile.out')
    #stats.sort_stats('time')
    stats.sort_stats('calls')
    stats.print_stats()
    os.unlink('profile.out')
    return call_result