source: sasview/park_integration/src/sans/fit/ScipyFitting.py @ aac129aa

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

import numpy 
import sys


from sans.fit.AbstractFitEngine import FitEngine
from sans.fit.AbstractFitEngine import SansAssembly
from sans.fit.AbstractFitEngine import FitAbort
from sans.fit.AbstractFitEngine import Model
from sans.fit.AbstractFitEngine import FResult 

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.fit_arrange_dict={})of FitArrange elements
        with Uid as keys
        """
        FitEngine.__init__(self)
        self.fit_arrange_dict = {}
        self.param_list = []
        self.curr_thread = None
    #def fit(self, *args, **kw):
    #    return profile(self._fit, *args, **kw)

    def fit(self, msg_q=None,
            q=None, handler=None, curr_thread=None, 
            ftol=1.49012e-8, reset_flag=False):
        """
        """
        fitproblem = []
        for fproblem in self.fit_arrange_dict.itervalues():
            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
        model = fitproblem[0].get_model()
        if reset_flag:
            # reset the initial value; useful for batch
            for name in fitproblem[0].pars:
                ind = fitproblem[0].pars.index(name)
                model.model.setParam(name, fitproblem[0].vals[ind])
        listdata = []
        listdata = fitproblem[0].get_data()
        # Concatenate dList set (contains one or more data)before fitting
        data = listdata
       
        self.curr_thread = curr_thread
        ftol = ftol
        
        # Check the initial value if it is within range
        self._check_param_range(model)
        
        result = FResult(model=model, data=data, param_list=self.param_list)
        result.pars = fitproblem[0].pars
        result.fitter_id = self.fitter_id
        if handler is not None:
            handler.set_result(result=result)
        try:
            # This import must be here; otherwise it will be confused when more
            # than one thread exist.
            from scipy import optimize
            
            functor = SansAssembly(paramlist=self.param_list, 
                                   model=model, 
                                   data=data,
                                    handler=handler,
                                    fitresult=result,
                                     curr_thread=curr_thread,
                                     msg_q=msg_q)
            out, cov_x, _, mesg, success = optimize.leastsq(functor,
                                            model.get_params(self.param_list),
                                                    ftol=ftol,
                                                    full_output=1)

        except KeyboardInterrupt:
            msg = "Fitting: Terminated!!!"
            handler.error(msg)
            raise KeyboardInterrupt, msg #<= more stable
            #less stable below
            """
            if hasattr(sys, 'last_type') and sys.last_type == KeyboardInterrupt:
                if handler is not None:
                    msg = "Fitting: Terminated!!!"
                    handler.error(msg)
                    result = handler.get_result()
                    return result
            else:
                raise 
            """
        except:
            raise
        chisqr = functor.chisq()

        if cov_x is not None and numpy.isfinite(cov_x).all():
            stderr = numpy.sqrt(numpy.diag(cov_x))
        else:
            stderr = []
            
        result.index = data.idx
        result.fitness = chisqr
        result.stderr  = stderr
        result.pvec = out
        result.success = success
        result.theory = functor.theory
        if handler is not None:
            handler.set_result(result=result)
            handler.update_fit(last=True)
        if q is not None:
            q.put(result)
            return q
        if success < 1 or success > 5:
            result.fitness = None
        return [result]

        
    def _check_param_range(self, model):
        """
        Check parameter range and set the initial value inside 
        if it is out of range.
        
        : model: park model object
        """
        is_outofbound = False
        # loop through parameterset
        for p in model.parameterset:        
            param_name = p.get_name()
            # proceed only if the parameter name is in the list of fitting
            if param_name in self.param_list:
                # if the range was defined, check the range
                if numpy.isfinite(p.range[0]):
                    if p.value <= p.range[0]: 
                        # 10 % backing up from the border if not zero
                        # for Scipy engine to work properly.
                        shift = self._get_zero_shift(p.range[0])
                        new_value = p.range[0] + shift
                        p.value =  new_value
                        is_outofbound = True
                if numpy.isfinite(p.range[1]):
                    if p.value >= p.range[1]:
                        shift = self._get_zero_shift(p.range[1])
                        # 10 % backing up from the border if not zero
                        # for Scipy engine to work properly.
                        new_value = p.range[1] - shift
                        # Check one more time if the new value goes below
                        # the low bound, If so, re-evaluate the value 
                        # with the mean of the range.
                        if numpy.isfinite(p.range[0]):
                            if new_value < p.range[0]:
                                new_value = (p.range[0] + p.range[1]) / 2.0
                        # Todo: 
                        # Need to think about when both min and max are same.
                        p.value =  new_value
                        is_outofbound = True
                        
        return is_outofbound
    
    def _get_zero_shift(self, range):
        """
        Get 10% shift of the param value = 0 based on the range value
        
        : param range: min or max value of the bounds
        """
        if range == 0:
            shift = 0.1
        else:
            shift = 0.1 * range
            
        return shift
    
    
#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