source: sasview/test/park_integration/test/utest_fit_smeared.py @ 35ec279

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Last change on this file since 35ec279 was 35ec279, checked in by krzywon, 9 years ago

Completed the SANS to SAS conversion on the tests. src.sas is left.

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File size: 7.2 KB
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1"""
2    Unit tests for fitting module
3    @author M. Doucet
4"""
5import unittest
6import math
7
8import numpy
9from sas.fit.AbstractFitEngine import Model
10from sas.fit.Fitting import Fit
11from sas.dataloader.loader import Loader
12from sas.models.qsmearing import smear_selection
13from sas.models.CylinderModel import CylinderModel
14from sas.models.SphereModel import SphereModel
15
16class testFitModule(unittest.TestCase):
17    """ test fitting """
18   
19    def test_scipy(self):
20        """ Simple cylinder model fit (scipy)  """
21       
22        out=Loader().load("cyl_400_20.txt")
23        # This data file has not error, add them
24        #out.dy = out.y
25       
26        fitter = Fit('scipy')
27        fitter.set_data(out,1)
28       
29        # Receives the type of model for the fitting
30        model1  = CylinderModel()
31        model1.setParam("scale", 1.0)
32        model1.setParam("radius",18)
33        model1.setParam("length", 397)
34        model1.setParam("sldCyl",3e-006 )
35        model1.setParam("sldSolv",0.0 )
36        model1.setParam("background", 0.0)
37        model = Model(model1)
38        pars1 =['length','radius','scale']
39        fitter.set_model(model,1,pars1)
40       
41        # What the hell is this line for?
42        fitter.select_problem_for_fit(id=1,value=1)
43        result1, = fitter.fit()
44        #print "result1",result1
45
46        self.assert_(result1)
47        self.assertTrue(len(result1.pvec) > 0)
48        self.assertTrue(len(result1.stderr) > 0)
49       
50        self.assertTrue( math.fabs(result1.pvec[0]-400.0)/3.0 < result1.stderr[0] )
51        self.assertTrue( math.fabs(result1.pvec[1]-20.0)/3.0  < result1.stderr[1] )
52        self.assertTrue( math.fabs(result1.pvec[2]-1)/3.0   < result1.stderr[2] )
53        self.assertTrue( result1.fitness < 1.0 )
54
55    def test_park_dispersion(self):
56        """
57            Cylinder fit with dispersion
58        """
59        self._dispersion(fitter = Fit('park'))
60
61    def test_bumps_dispersion(self):
62        """
63            Cylinder fit with dispersion
64        """
65        alg = 'lm'
66        from bumps import fitters
67        fitters.FIT_DEFAULT = alg
68        #fitters.FIT_OPTIONS[alg].options.update(opts)
69        fitters.FIT_OPTIONS[alg].options.update(monitors=[])
70        self._dispersion(fitter = Fit('bumps'))
71
72    def test_scipy_dispersion(self):
73        """
74            Cylinder fit with dispersion
75        """
76        self._dispersion(fitter = Fit('scipy'))
77
78    def _dispersion(self, fitter):
79        # Load data
80        # This data is for a cylinder with
81        #   length=400, radius=20, radius disp=5, scale=1e-10
82        out=Loader().load("cyl_400_20_disp5r.txt")
83        out.dy = numpy.zeros(len(out.y))
84        for i in range(len(out.y)):
85            out.dy[i] = math.sqrt(out.y[i])
86       
87        # Receives the type of model for the fitting
88        model1  = CylinderModel()
89        model1.setParam("scale", 10.0)
90        model1.setParam("radius",18)
91        model1.setParam("length", 397)
92        model1.setParam("sldCyl",3e-006 )
93        model1.setParam("sldSolv",0.0 )
94        model1.setParam("background", 0.0)
95
96        # Dispersion parameters
97        model1.dispersion['radius']['width'] = 0.25
98        model1.dispersion['radius']['npts'] = 50
99
100        model = Model(model1)
101
102        pars1 =['length','radius','scale','radius.width']
103        fitter.set_data(out,1)
104        fitter.set_model(model,1,pars1)
105        fitter.select_problem_for_fit(id=1,value=1)
106        #import time; T0 = time.time()
107        result1, = fitter.fit()
108        #print "time",time.time()-T0,fitter._engine.__class__.__name__
109       
110        self.assert_(result1)
111        self.assertTrue(len(result1.pvec)>0)
112        self.assertTrue(len(result1.stderr)>0)
113
114        #print [z for z in zip(result1.param_list,result1.pvec,result1.stderr)]
115        self.assertTrue( math.fabs(result1.pvec[0]-399.8)/3.0 < result1.stderr[0] )
116        self.assertTrue( math.fabs(result1.pvec[1]-17.5)/3.0  < result1.stderr[1] )
117        self.assertTrue( math.fabs(result1.pvec[2]-11.1)/3.0   < result1.stderr[2] )
118        self.assertTrue( math.fabs(result1.pvec[3]-0.276)/3.0   < result1.stderr[3] )
119        self.assertTrue( result1.fitness < 1.0 )
120       
121       
122class smear_testdata(unittest.TestCase):
123    """
124        Test fitting with the smearing operations
125        The output of the fits should be compated to fits
126        done with IGOR for the same models and data sets.
127    """
128    def setUp(self):
129        data = Loader().load("latex_smeared.xml")
130        self.data_res = data[0]
131        self.data_slit = data[1]
132       
133        self.sphere = SphereModel()
134        self.sphere.setParam('background', 0)
135        self.sphere.setParam('radius', 5000.0)
136        self.sphere.setParam('scale', 0.4)
137        self.sphere.setParam('sldSolv',0)
138        self.sphere.setParam('sldSph',1e-6)
139        #self.sphere.setParam('radius.npts', 30)
140        #self.sphere.setParam('radius.width',50)
141
142    def test_reso(self):
143
144        # Let the data module find out what smearing the
145        # data needs
146        smear = smear_selection(self.data_res)
147        self.assertEqual(smear.__class__.__name__, 'QSmearer')
148
149        # Fit
150        fitter = Fit('scipy')
151       
152        # Data: right now this is the only way to set the smearer object
153        # We should improve that and have a way to get access to the
154        # data for a given fit.
155        fitter.set_data(self.data_res,1)
156        fitter._engine.fit_arrange_dict[1].data_list[0].smearer = smear
157
158        # Model: maybe there's a better way to do this.
159        # Ideally we should have to create a new model from our sas model.
160        fitter.set_model(Model(self.sphere),1, ['radius','scale', 'background'])
161       
162        # Why do we have to do this...?
163        fitter.select_problem_for_fit(id=1,value=1)
164
165        # Perform the fit (might take a while)
166        result1, = fitter.fit()
167       
168        #print "v",result1.pvec
169        #print "dv",result1.stderr
170        #print "chisq(v)",result1.fitness
171
172        self.assertTrue( math.fabs(result1.pvec[0]-5000) < 20 )
173        self.assertTrue( math.fabs(result1.pvec[1]-0.48) < 0.02 )
174        self.assertTrue( math.fabs(result1.pvec[2]-0.060)  < 0.002 )
175
176
177    def test_slit(self):
178        smear = smear_selection(self.data_slit)
179        self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
180
181        fitter = Fit('scipy')
182       
183        # Data: right now this is the only way to set the smearer object
184        # We should improve that and have a way to get access to the
185        # data for a given fit.
186        fitter.set_data(self.data_slit,1)
187        fitter._engine.fit_arrange_dict[1].data_list[0].smearer = smear
188        fitter._engine.fit_arrange_dict[1].data_list[0].qmax = 0.003
189       
190        # Model
191        fitter.set_model(Model(self.sphere),1, ['radius','scale'])
192        fitter.select_problem_for_fit(id=1,value=1)
193       
194        result1, = fitter.fit()
195       
196        #print "v",result1.pvec
197        #print "dv",result1.stderr
198        #print "chisq(v)",result1.fitness
199       
200        self.assertTrue( math.fabs(result1.pvec[0]-2340) < 20 )
201        self.assertTrue( math.fabs(result1.pvec[1]-0.010) < 0.002 )
202
203if __name__ == '__main__':
204    unittest.main()
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