source: sasview/sansmodels/src/sans/models/test/utest_other_dispersity.py @ bdc25e2

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Last change on this file since bdc25e2 was ca4c150, checked in by Jae Cho <jhjcho@…>, 14 years ago

update nsigmas value due to the default changes from 2.5 to 3.0
  • Property mode set to 100644
File size: 5.8 KB
Line 
1"""
2    Unit tests for dispersion functionality of
3    C++ model classes
4"""
5
6import unittest, math, numpy
7
8class TestCylinder(unittest.TestCase):
9    """
10        Testing C++ Cylinder model
11    """
12    def setUp(self):
13        from sans.models.CylinderModel import CylinderModel
14        self.model= CylinderModel()
15       
16        self.model.setParam('scale', 1.0)
17        self.model.setParam('radius', 20.0)
18        self.model.setParam('length', 400.0)
19        self.model.setParam('sldCyl', 4.e-6)
20        self.model.setParam('sldSolv', 1.e-6)
21        self.model.setParam('background', 0.0)
22        self.model.setParam('cyl_theta', 0.0)
23        self.model.setParam('cyl_phi', 0.0)
24       
25    def test_simple(self):
26        self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
27        self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
28       
29    def test_constant(self):
30        from sans.models.dispersion_models import DispersionModel
31        disp = DispersionModel()
32        self.model.setParam('scale', 10.0)
33        self.model.set_dispersion('radius', disp)
34        self.model.dispersion['radius']['width'] = 5.0
35        self.model.dispersion['radius']['npts'] = 100
36        self.model.dispersion['radius']['nsigmas'] = 2.5
37        print "constant",self.model.run(0.001), self.model.dispersion
38        self.assertAlmostEqual(self.model.run(0.001), 1.021051*4527.47250339, 3)
39        self.assertAlmostEqual(self.model.runXY([0.001, 0.001]), 1.021048*4546.997777604715, 2)
40       
41    def test_gaussian(self):
42        from sans.models.dispersion_models import GaussianDispersion
43        disp = GaussianDispersion()
44        self.model.set_dispersion('radius', disp)
45        self.model.dispersion['radius']['width'] = 5.0
46        self.model.dispersion['radius']['npts'] = 100
47        self.model.dispersion['radius']['nsigmas'] = 2.5
48        self.model.setParam('scale', 10.0)
49       
50        self.assertAlmostEqual(self.model.run(0.001), 1.1804794*4723.32213339, 3)
51        self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 1.180454*4743.56, 2)
52       
53    def test_clone(self):
54        from sans.models.dispersion_models import GaussianDispersion
55        disp = GaussianDispersion()
56        self.model.set_dispersion('radius', disp)
57        self.model.dispersion['radius']['width'] = 5.0
58        self.model.dispersion['radius']['npts'] = 100
59        self.model.dispersion['radius']['nsigmas'] = 2.5
60        self.model.setParam('scale', 10.0)
61       
62        new_model = self.model.clone()
63        print "gaussian",self.model.run(0.001)
64        self.assertAlmostEqual(new_model.run(0.001), 1.1804794*4723.32213339, 3)
65        self.assertAlmostEqual(new_model.runXY([0.001,0.001]), 1.180454*4743.56, 2)
66       
67    def test_schulz_zero(self):
68        from sans.models.dispersion_models import SchulzDispersion
69        disp = SchulzDispersion()
70        self.model.set_dispersion('radius', disp)
71        self.model.dispersion['radius']['width'] = 5.0
72        #self.model.dispersion['radius']['width'] = 0.0
73        self.model.dispersion['radius']['npts'] = 100
74        self.model.dispersion['radius']['nsigmas'] = 2.5
75        #self.model.setParam('scale', 1.0)
76        self.model.setParam('scale', 10.0)
77        print "schulz",self.model.run(0.001), self.model.dispersion
78        self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
79        self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
80       
81    def test_lognormal_zero(self):
82        from sans.models.dispersion_models import LogNormalDispersion
83        disp = LogNormalDispersion()
84        self.model.set_dispersion('radius', disp)
85        self.model.dispersion['radius']['width'] = 5.0
86        #self.model.dispersion['radius']['width'] = 0.0
87        self.model.dispersion['radius']['npts'] = 100
88        self.model.dispersion['radius']['nsigmas'] = 2.5
89        #self.model.setParam('scale', 1.0)
90        self.model.setParam('scale', 10.0)
91        print "model dispersion",self.model.dispersion
92        print "lognormal",self.model.run(0.001)
93        self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
94        self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
95       
96    def test_gaussian_zero(self):
97        from sans.models.dispersion_models import GaussianDispersion
98        disp = GaussianDispersion()
99        self.model.set_dispersion('radius', disp)
100        self.model.dispersion['radius']['width'] = 0.0
101        self.model.dispersion['radius']['npts'] = 100
102        self.model.dispersion['radius']['nsigmas'] = 2.5
103        self.model.setParam('scale', 1.0)
104       
105        self.assertAlmostEqual(self.model.run(0.001), 450.355, 3)
106        self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3)
107       
108    def test_array(self):
109        """
110            Perform complete rotational average and
111            compare to 1D
112        """
113        from sans.models.dispersion_models import ArrayDispersion
114        disp_ph = ArrayDispersion()
115        disp_th = ArrayDispersion()
116       
117        values_ph = numpy.zeros(100)
118        values_th = numpy.zeros(100)
119        weights   = numpy.zeros(100)
120        for i in range(100):
121            values_ph[i]=(360/99.0*i)
122            values_th[i]=(180/99.0*i)
123            weights[i]=(1.0)
124       
125        disp_ph.set_weights(values_ph, weights)
126        disp_th.set_weights(values_th, weights)
127       
128        self.model.set_dispersion('cyl_theta', disp_th)
129        self.model.set_dispersion('cyl_phi', disp_ph)
130       
131        val_1d = self.model.run(math.sqrt(0.0002))
132        val_2d = self.model.runXY([0.01,0.01]) 
133       
134        self.assertTrue(math.fabs(val_1d-val_2d)/val_1d < 0.02)
135       
136
137if __name__ == '__main__':
138    unittest.main()
139   
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