source: sasview/park_integration/ScipyFitting.py @ b24bf4e

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalccostrafo411magnetic_scattrelease-4.1.1release-4.1.2release-4.2.2release_4.0.1ticket-1009ticket-1094-headlessticket-1242-2d-resolutionticket-1243ticket-1249ticket885unittest-saveload
Last change on this file since b24bf4e was f8ce013, checked in by Gervaise Alina <gervyh@…>, 16 years ago

set_data modified

  • Property mode set to 100644
File size: 4.3 KB
Line 
1"""
2    @organization: ScipyFitting module contains FitArrange , ScipyFit,
3    Parameter classes.All listed classes work together to perform a
4    simple fit with scipy optimizer.
5"""
6#import scipy.linalg
7import numpy 
8from sans.guitools.plottables import Data1D
9from Loader import Load
10from scipy import optimize
11
12from AbstractFitEngine import FitEngine, sansAssembly
13from AbstractFitEngine import FitArrange,Data
14class fitresult:
15    """
16        Storing fit result
17    """
18    calls     = None
19    fitness   = None
20    chisqr    = None
21    pvec      = None
22    cov       = None
23    info      = None
24    mesg      = None
25    success   = None
26    stderr    = None
27    parameters= None
28   
29class ScipyFit(FitEngine):
30    """
31        ScipyFit performs the Fit.This class can be used as follow:
32        #Do the fit SCIPY
33        create an engine: engine = ScipyFit()
34        Use data must be of type plottable
35        Use a sans model
36       
37        Add data with a dictionnary of FitArrangeDict where Uid is a key and data
38        is saved in FitArrange object.
39        engine.set_data(data,Uid)
40       
41        Set model parameter "M1"= model.name add {model.parameter.name:value}.
42        @note: Set_param() if used must always preceded set_model()
43             for the fit to be performed.In case of Scipyfit set_param is called in
44             fit () automatically.
45        engine.set_param( model,"M1", {'A':2,'B':4})
46       
47        Add model with a dictionnary of FitArrangeDict{} where Uid is a key and model
48        is save in FitArrange object.
49        engine.set_model(model,Uid)
50       
51        engine.fit return chisqr,[model.parameter 1,2,..],[[err1....][..err2...]]
52        chisqr1, out1, cov1=engine.fit({model.parameter.name:value},qmin,qmax)
53    """
54    def __init__(self):
55        """
56            Creates a dictionary (self.fitArrangeDict={})of FitArrange elements
57            with Uid as keys
58        """
59        self.fitArrangeDict={}
60        self.paramList=[]
61    def fit(self,qmin=None, qmax=None):
62        # Protect against simultanous fitting attempts
63        #if len(self.fitArrangeDict)>1:
64        #    raise RuntimeError, "Scipy can't fit more than a single fit problem at a time."
65        # fitproblem contains first fitArrange object(one model and a list of data)
66        #list of fitproblem
67        fitproblem=[]
68        for id ,fproblem in self.fitArrangeDict.iteritems():
69            print "ScipyFitting:fproblem.get_to_fit() ",fproblem.get_to_fit()
70            if fproblem.get_to_fit()==1:
71                fitproblem.append(fproblem)
72        if len(fitproblem)>1 : 
73            raise RuntimeError, "Scipy can't fit more than a single fit problem at a time."
74            return
75        elif len(fitproblem)==0 : 
76            raise RuntimeError, "No Assembly scheduled for Scipy fitting."
77            return
78   
79        listdata=[]
80        model = fitproblem[0].get_model()
81        listdata = fitproblem[0].get_data()
82        # Concatenate dList set (contains one or more data)before fitting
83        #data=self._concatenateData( listdata)
84        data=listdata
85        #Assign a fit range is not boundaries were given
86        if not hasattr(data, 'image'):
87            if qmin==None:
88                qmin= min(data.x)
89            if qmax==None:
90                qmax= max(data.x) 
91        else:
92            if qmin==None:
93                qmin= numpy.min(data.image)
94            if qmax==None:
95                qmax= numpy.max(data.image) 
96        functor= sansAssembly(self.paramList,model,data)
97        out, cov_x, info, mesg, success = optimize.leastsq(functor,model.getParams(self.paramList), full_output=1, warning=True)
98        chisqr = functor.chisq(out)
99       
100        if cov_x is not None and numpy.isfinite(cov_x).all():
101            stderr = numpy.sqrt(numpy.diag(cov_x))
102        else:
103            stderr=None
104        if not (numpy.isnan(out).any()) or ( cov_x !=None) :
105                result = fitresult()
106                result.fitness = chisqr
107                result.stderr  = stderr
108                result.pvec = out
109                result.success =success
110               
111                return result
112        else: 
113            raise ValueError, "SVD did not converge"+str(success)
114       
115       
116             
117           
118     
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