[26e4a24] | 1 | """ |
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| 2 | Unit tests for dispersion functionality of |
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| 3 | C++ model classes |
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| 4 | """ |
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| 5 | |
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| 6 | import unittest, math, numpy |
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| 7 | |
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| 8 | class TestCylinder(unittest.TestCase): |
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| 9 | """ |
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| 10 | Testing C++ Cylinder model |
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| 11 | """ |
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| 12 | def setUp(self): |
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| 13 | from sans.models.CylinderModel import CylinderModel |
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| 14 | self.model= CylinderModel() |
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| 15 | |
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| 16 | self.model.setParam('scale', 1.0) |
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| 17 | self.model.setParam('radius', 20.0) |
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| 18 | self.model.setParam('length', 400.0) |
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[ca4c150] | 19 | self.model.setParam('sldCyl', 4.e-6) |
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| 20 | self.model.setParam('sldSolv', 1.e-6) |
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[26e4a24] | 21 | self.model.setParam('background', 0.0) |
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| 22 | self.model.setParam('cyl_theta', 0.0) |
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[62827da] | 23 | self.model.setParam('cyl_phi', 90.0) |
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[26e4a24] | 24 | |
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| 25 | def test_simple(self): |
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| 26 | self.assertAlmostEqual(self.model.run(0.001), 450.355, 3) |
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| 27 | self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3) |
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| 28 | |
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| 29 | def test_constant(self): |
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| 30 | from sans.models.dispersion_models import DispersionModel |
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| 31 | disp = DispersionModel() |
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| 32 | self.model.setParam('scale', 10.0) |
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| 33 | self.model.set_dispersion('radius', disp) |
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[876192b2] | 34 | self.model.dispersion['radius']['width'] = 5.0/20.0 |
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[26e4a24] | 35 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 36 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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[dfa8832] | 37 | print "constant",self.model.run(0.001), self.model.dispersion |
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| 38 | self.assertAlmostEqual(self.model.run(0.001), 1.021051*4527.47250339, 3) |
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| 39 | self.assertAlmostEqual(self.model.runXY([0.001, 0.001]), 1.021048*4546.997777604715, 2) |
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[26e4a24] | 40 | |
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| 41 | def test_gaussian(self): |
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| 42 | from sans.models.dispersion_models import GaussianDispersion |
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| 43 | disp = GaussianDispersion() |
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| 44 | self.model.set_dispersion('radius', disp) |
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[876192b2] | 45 | self.model.dispersion['radius']['width'] = 5.0/20.0 |
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[26e4a24] | 46 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 47 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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[26e4a24] | 48 | self.model.setParam('scale', 10.0) |
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| 49 | |
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[dfa8832] | 50 | self.assertAlmostEqual(self.model.run(0.001), 1.1804794*4723.32213339, 3) |
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| 51 | self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 1.180454*4743.56, 2) |
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[26e4a24] | 52 | |
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| 53 | def test_clone(self): |
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| 54 | from sans.models.dispersion_models import GaussianDispersion |
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| 55 | disp = GaussianDispersion() |
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| 56 | self.model.set_dispersion('radius', disp) |
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[876192b2] | 57 | self.model.dispersion['radius']['width'] = 5.0/20.0 |
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[26e4a24] | 58 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 59 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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[26e4a24] | 60 | self.model.setParam('scale', 10.0) |
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| 61 | |
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| 62 | new_model = self.model.clone() |
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| 63 | print "gaussian",self.model.run(0.001) |
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[dfa8832] | 64 | self.assertAlmostEqual(new_model.run(0.001), 1.1804794*4723.32213339, 3) |
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| 65 | self.assertAlmostEqual(new_model.runXY([0.001,0.001]), 1.180454*4743.56, 2) |
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[26e4a24] | 66 | |
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| 67 | def test_schulz_zero(self): |
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| 68 | from sans.models.dispersion_models import SchulzDispersion |
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| 69 | disp = SchulzDispersion() |
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| 70 | self.model.set_dispersion('radius', disp) |
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[876192b2] | 71 | self.model.dispersion['radius']['width'] = 5.0/20.0 |
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[26e4a24] | 72 | #self.model.dispersion['radius']['width'] = 0.0 |
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| 73 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 74 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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| 75 | self.model.setParam('scale', 1.0) |
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| 76 | #self.model.setParam('scale', 10.0) |
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[26e4a24] | 77 | print "schulz",self.model.run(0.001), self.model.dispersion |
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[876192b2] | 78 | self.assertAlmostEqual(self.model.run(0.001), 542.23568, 3) |
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| 79 | self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 544.54864, 3) |
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[26e4a24] | 80 | |
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| 81 | def test_lognormal_zero(self): |
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| 82 | from sans.models.dispersion_models import LogNormalDispersion |
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| 83 | disp = LogNormalDispersion() |
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| 84 | self.model.set_dispersion('radius', disp) |
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[876192b2] | 85 | self.model.dispersion['radius']['width'] = 5.0/20.0 |
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[26e4a24] | 86 | #self.model.dispersion['radius']['width'] = 0.0 |
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| 87 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 88 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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| 89 | self.model.setParam('scale', 1.0) |
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| 90 | #self.model.setParam('scale', 10.0) |
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[26e4a24] | 91 | print "model dispersion",self.model.dispersion |
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| 92 | print "lognormal",self.model.run(0.001) |
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[c6cc2910] | 93 | self.assertAlmostEqual(self.model.run(0.001), 554.41257, 3) |
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| 94 | self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 556.77560, 3) |
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[26e4a24] | 95 | |
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| 96 | def test_gaussian_zero(self): |
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| 97 | from sans.models.dispersion_models import GaussianDispersion |
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| 98 | disp = GaussianDispersion() |
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| 99 | self.model.set_dispersion('radius', disp) |
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| 100 | self.model.dispersion['radius']['width'] = 0.0 |
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| 101 | self.model.dispersion['radius']['npts'] = 100 |
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[876192b2] | 102 | self.model.dispersion['radius']['nsigmas'] = 2.0 |
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[26e4a24] | 103 | self.model.setParam('scale', 1.0) |
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| 104 | |
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| 105 | self.assertAlmostEqual(self.model.run(0.001), 450.355, 3) |
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| 106 | self.assertAlmostEqual(self.model.runXY([0.001,0.001]), 452.299, 3) |
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| 107 | |
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| 108 | def test_array(self): |
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| 109 | """ |
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| 110 | Perform complete rotational average and |
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| 111 | compare to 1D |
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| 112 | """ |
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| 113 | from sans.models.dispersion_models import ArrayDispersion |
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| 114 | disp_ph = ArrayDispersion() |
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| 115 | disp_th = ArrayDispersion() |
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| 116 | |
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| 117 | values_ph = numpy.zeros(100) |
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| 118 | values_th = numpy.zeros(100) |
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| 119 | weights = numpy.zeros(100) |
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| 120 | for i in range(100): |
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[ca4c150] | 121 | values_ph[i]=(360/99.0*i) |
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| 122 | values_th[i]=(180/99.0*i) |
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[26e4a24] | 123 | weights[i]=(1.0) |
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| 124 | |
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| 125 | disp_ph.set_weights(values_ph, weights) |
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| 126 | disp_th.set_weights(values_th, weights) |
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| 127 | |
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| 128 | self.model.set_dispersion('cyl_theta', disp_th) |
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| 129 | self.model.set_dispersion('cyl_phi', disp_ph) |
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| 130 | |
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| 131 | val_1d = self.model.run(math.sqrt(0.0002)) |
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| 132 | val_2d = self.model.runXY([0.01,0.01]) |
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| 133 | |
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| 134 | self.assertTrue(math.fabs(val_1d-val_2d)/val_1d < 0.02) |
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| 135 | |
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| 136 | |
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| 137 | if __name__ == '__main__': |
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| 138 | unittest.main() |
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| 139 | |
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