source: sasview/test/park_integration/test/test_fit_line.py @ 6fe5100

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Last change on this file since 6fe5100 was 6fe5100, checked in by pkienzle, 10 years ago

Bumps first pass. Fitting works but no pretty pictures

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Line 
1"""
2    Unit tests for fitting module
3    @author Gervaise Alina
4"""
5import unittest
6import math
7
8from sans.fit.AbstractFitEngine import Model
9from sans.dataloader.loader import Loader
10from sans.fit.Fitting import Fit
11from sans.models.LineModel import LineModel
12from sans.models.Constant import Constant
13
14class testFitModule(unittest.TestCase):
15    """ test fitting """
16   
17    def test1(self):
18        """ Fit 1 data (testdata_line.txt)and 1 model(lineModel) """
19        #load data
20        data = Loader().load("testdata_line.txt")
21        data.name = data.filename
22        #Importing the Fit module
23        from bumps import fitters
24        fitters.FIT_DEFAULT = 'dream'
25        print fitters.FIT_OPTIONS['dream'].__dict__
26        fitter = Fit('bumps')
27        # Receives the type of model for the fitting
28        model1  = LineModel()
29        model1.name = "M1"
30        model = Model(model1,data)
31        #fit with scipy test
32       
33        pars1= ['param1','param2']
34        fitter.set_data(data,1)
35        try:fitter.set_model(model,1,pars1)
36        except ValueError,exc:
37            #print "ValueError was correctly raised: "+str(msg)
38            assert str(exc).startswith('wrong parameter')
39        else: raise AssertionError("No error raised for scipy fitting with wrong parameters name to fit")
40        pars1= ['A','B']
41        fitter.set_model(model,1,pars1)
42        fitter.select_problem_for_fit(id=1,value=1)
43        result1, = fitter.fit()
44
45        self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] )
46        self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1])
47        self.assertTrue( result1.fitness/len(data.x) < 2 )
48
49        return
50        #fit with park test
51        fitter = Fit('park')
52        fitter.set_data(data,1)
53        fitter.set_model(model,1,pars1)
54        fitter.select_problem_for_fit(id=1,value=1)
55        result2, = fitter.fit()
56       
57        self.assert_(result2)
58        self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) 
59        self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] )
60        self.assertTrue( result2.fitness/len(data.x) < 2)
61        # compare fit result result for scipy and park
62        self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] )
63        self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] )
64        self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] )
65        self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] )
66        self.assertAlmostEquals( result1.fitness,
67                                 result2.fitness/len(data.x),1 )
68       
69       
70    def test2(self):
71        """ fit 2 data and 2 model with no constrainst"""
72        #load data
73        l = Loader()
74        data1=l.load("testdata_line.txt")
75        data1.name = data1.filename
76     
77        data2=l.load("testdata_line1.txt")
78        data2.name = data2.filename
79     
80        #Importing the Fit module
81        fitter = Fit('scipy')
82        # Receives the type of model for the fitting
83        model11  = LineModel()
84        model11.name= "M1"
85        model22  = LineModel()
86        model11.name= "M2"
87     
88        model1 = Model(model11,data1)
89        model2 = Model(model22,data2)
90        #fit with scipy test
91        pars1= ['A','B']
92        fitter.set_data(data1,1)
93        fitter.set_model(model1,1,pars1)
94        fitter.select_problem_for_fit(id=1,value=0)
95        fitter.set_data(data2,2)
96        fitter.set_model(model2,2,pars1)
97        fitter.select_problem_for_fit(id=2,value=0)
98       
99        try: result1, = fitter.fit()
100        except RuntimeError,msg:
101           assert str(msg)=="No Assembly scheduled for Scipy fitting."
102        else: raise AssertionError,"No error raised for scipy fitting with no model"
103        fitter.select_problem_for_fit(id=1,value=1)
104        fitter.select_problem_for_fit(id=2,value=1)
105        try: result1, = fitter.fit()
106        except RuntimeError,msg:
107           assert str(msg)=="Scipy can't fit more than a single fit problem at a time."
108        else: raise AssertionError,"No error raised for scipy fitting with more than 2 models"
109
110        return
111        #fit with park test
112        fitter = Fit('park')
113        fitter.set_data(data1,1)
114        fitter.set_model(model1,1,pars1)
115        fitter.set_data(data2,2)
116        fitter.set_model(model2,2,pars1)
117        fitter.select_problem_for_fit(id=1,value=1)
118        fitter.select_problem_for_fit(id=2,value=1)
119        R1,R2 = fitter.fit()
120       
121        self.assertTrue( math.fabs(R1.pvec[0]-4)/3 <= R1.stderr[0] )
122        self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3 <= R1.stderr[1] )
123        self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2)
124       
125       
126    def test3(self):
127        """ fit 2 data and 2 model with 1 constrainst"""
128        return
129        #load data
130        l = Loader()
131        data1= l.load("testdata_line.txt")
132        data1.name = data1.filename
133        data2= l.load("testdata_cst.txt")
134        data2.name = data2.filename
135       
136        # Receives the type of model for the fitting
137        model11  = LineModel()
138        model11.name= "line"
139        model11.setParam("A", 1.0)
140        model11.setParam("B",1.0)
141       
142        model22  = Constant()
143        model22.name= "cst"
144        model22.setParam("value", 1.0)
145       
146        model1 = Model(model11,data1)
147        model2 = Model(model22,data2)
148        model1.set(A=4)
149        model1.set(B=3)
150        # Constraint the constant value to be equal to parameter B (the real value is 2.5)
151        model2.set(value='line.B')
152        #fit with scipy test
153        pars1= ['A','B']
154        pars2= ['value']
155       
156        #Importing the Fit module
157        fitter = Fit('park')
158        fitter.set_data(data1,1)
159        fitter.set_model(model1,1,pars1)
160        fitter.set_data(data2,2,smearer=None)
161        fitter.set_model(model2,2,pars2)
162        fitter.select_problem_for_fit(id=1,value=1)
163        fitter.select_problem_for_fit(id=2,value=1)
164       
165        R1,R2 = fitter.fit()
166        self.assertTrue( math.fabs(R1.pvec[0]-4.0)/3. <= R1.stderr[0])
167        self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3. <= R1.stderr[1])
168        self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2)
169       
170       
171    def test4(self):
172        """ fit 2 data concatenates with limited range of x and  one model """
173            #load data
174        l = Loader()
175        data1 = l.load("testdata_line.txt")
176        data1.name = data1.filename
177        data2 = l.load("testdata_line1.txt")
178        data2.name = data2.filename
179
180        # Receives the type of model for the fitting
181        model1  = LineModel()
182        model1.name= "M1"
183        model1.setParam("A", 1.0)
184        model1.setParam("B",1.0)
185        model = Model(model1,data1)
186     
187        #fit with scipy test
188        pars1= ['A','B']
189        #Importing the Fit module
190        fitter = Fit('scipy')
191        fitter.set_data(data1,1,qmin=0, qmax=7)
192        fitter.set_model(model,1,pars1)
193        fitter.set_data(data2,1,qmin=1,qmax=10)
194        fitter.select_problem_for_fit(id=1,value=1)
195       
196        result1, = fitter.fit()
197        #print(result1)
198        self.assert_(result1)
199
200        self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] )
201        self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1])
202        self.assertTrue( result1.fitness/len(data1.x) < 2 )
203
204        return
205        #fit with park test
206        fitter = Fit('park')
207        fitter.set_data(data1,1,qmin=0, qmax=7)
208        fitter.set_model(model,1,pars1)
209        fitter.set_data(data2,1,qmin=1,qmax=10)
210        fitter.select_problem_for_fit(id=1,value=1)
211        result2, = fitter.fit()
212       
213        self.assert_(result2)
214        self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] )
215        self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] )
216        self.assertTrue( result2.fitness/len(data1.x) < 2)
217        # compare fit result result for scipy and park
218        self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] )
219        self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] )
220        self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] )
221        self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] )
222        self.assertTrue( result2.fitness/(len(data2.x)+len(data1.x)) < 2 )
223
224
225if __name__ == "__main__":
226    unittest.main()
227   
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