source: sasview/park_integration/ParkFitting.py @ 2fe03d5

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Last change on this file since 2fe03d5 was f6a9248, checked in by Gervaise Alina <gervyh@…>, 16 years ago

print statement remove

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1"""
2    @organization: ParkFitting module contains SansParameter,Model,Data
3    FitArrange, ParkFit,Parameter classes.All listed classes work together to perform a
4    simple fit with park optimizer.
5"""
6import time
7import numpy
8import park
9from park import fit,fitresult
10from park import assembly
11from park.fitmc import FitSimplex, FitMC
12
13#from Loader import Load
14from AbstractFitEngine import FitEngine
15
16
17class ParkFit(FitEngine):
18    """
19        ParkFit performs the Fit.This class can be used as follow:
20        #Do the fit Park
21        create an engine: engine = ParkFit()
22        Use data must be of type plottable
23        Use a sans model
24       
25        Add data with a dictionnary of FitArrangeList where Uid is a key and data
26        is saved in FitArrange object.
27        engine.set_data(data,Uid)
28       
29        Set model parameter "M1"= model.name add {model.parameter.name:value}.
30        @note: Set_param() if used must always preceded set_model()
31             for the fit to be performed.
32        engine.set_param( model,"M1", {'A':2,'B':4})
33       
34        Add model with a dictionnary of FitArrangeList{} where Uid is a key and model
35        is save in FitArrange object.
36        engine.set_model(model,Uid)
37       
38        engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]]
39        chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax)
40        @note: {model.parameter.name:value} is ignored in fit function since
41        the user should make sure to call set_param himself.
42    """
43    def __init__(self):
44        """
45            Creates a dictionary (self.fitArrangeList={})of FitArrange elements
46            with Uid as keys
47        """
48        self.fitArrangeDict={}
49        self.paramList=[]
50       
51    def createAssembly(self):
52        """
53        Extract sansmodel and sansdata from self.FitArrangelist ={Uid:FitArrange}
54        Create parkmodel and park data ,form a list couple of parkmodel and parkdata
55        create an assembly self.problem=  park.Assembly([(parkmodel,parkdata)])
56        """
57        mylist=[]
58        listmodel=[]
59        i=0
60        fitproblems=[]
61        for id ,fproblem in self.fitArrangeDict.iteritems():
62            if fproblem.get_to_fit()==1:
63                fitproblems.append(fproblem)
64               
65        if len(fitproblems)==0 : 
66            raise RuntimeError, "No Assembly scheduled for Park fitting."
67            return
68        for item in fitproblems:
69            parkmodel = item.get_model()
70            for p in parkmodel.parameterset:
71                if p._getname()in self.paramList and not p.iscomputed():
72                    p.status = 'fitted' # make it a fitted parameter
73                            #iscomputed  paramter with string inside
74               
75            i+=1
76            Ldata=item.get_data()
77            #parkdata=self._concatenateData(Ldata)
78            parkdata=Ldata
79            fitness=(parkmodel,parkdata)
80            mylist.append(fitness)
81       
82        self.problem =  park.Assembly(mylist)
83       
84   
85    def fit(self,handler=None):
86        """
87            Performs fit with park.fit module.It can  perform fit with one model
88            and a set of data, more than two fit of  one model and sets of data or
89            fit with more than two model associated with their set of data and constraints
90           
91           
92            @param pars: Dictionary of parameter names for the model and their values.
93            @param qmin: The minimum value of data's range to be fit
94            @param qmax: The maximum value of data's range to be fit
95            @note:all parameter are ignored most of the time.Are just there to keep ScipyFit
96            and ParkFit interface the same.
97            @return result.fitness: Value of the goodness of fit metric
98            @return result.pvec: list of parameter with the best value found during fitting
99            @return result.cov: Covariance matrix
100        """
101        self.createAssembly()
102   
103        localfit = FitSimplex()
104        localfit.ftol = 1e-8
105       
106        # See `park.fitresult.FitHandler` for details.
107        fitter = FitMC(localfit=localfit, start_points=1)
108        if handler == None:
109            #print "no handler"
110            handler= fitresult.ConsoleUpdate(improvement_delta=0.1)
111        #print "park handler", handler
112       
113           
114        result = fit.fit(self.problem,
115                         fitter=fitter,
116                         handler= handler)
117        self.problem.all_results(result)
118        if result !=None:
119            return result
120        else:
121            raise ValueError, "SVD did not converge"
122           
123
124       
125   
126   
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