1 | """ |
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2 | Unit tests for fitting module |
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3 | """ |
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4 | import unittest |
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5 | from sans.fit.AbstractFitEngine import Model |
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6 | import math |
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7 | import numpy |
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8 | from sans.fit.Fitting import Fit |
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9 | from DataLoader.loader import Loader |
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10 | |
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11 | class testFitModule(unittest.TestCase): |
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12 | """ test fitting """ |
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13 | |
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14 | def test_scipy(self): |
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15 | """ Simple cylinder model fit (scipy) """ |
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16 | |
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17 | out=Loader().load("cyl_400_20.txt") |
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18 | # This data file has not error, add them |
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19 | out.dy = out.y |
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20 | |
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21 | fitter = Fit('scipy') |
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22 | fitter.set_data(out,1) |
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23 | |
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24 | # Receives the type of model for the fitting |
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25 | from sans.models.CylinderModel import CylinderModel |
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26 | model1 = CylinderModel() |
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27 | model1.setParam('contrast', 1) |
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28 | model = Model(model1) |
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29 | model.set(scale=1e-10) |
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30 | pars1 =['length','radius','scale'] |
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31 | fitter.set_model(model,1,pars1) |
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32 | |
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33 | # What the hell is this line for? |
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34 | fitter.select_problem_for_fit(Uid=1,value=1) |
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35 | result1 = fitter.fit() |
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36 | |
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37 | self.assert_(result1) |
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38 | self.assertTrue(len(result1.pvec)>0 or len(result1.pvec)==0 ) |
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39 | self.assertTrue(len(result1.stderr)> 0 or len(result1.stderr)==0) |
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40 | |
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41 | self.assertTrue( math.fabs(result1.pvec[0]-400.0)/3.0 < result1.stderr[0] ) |
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42 | self.assertTrue( math.fabs(result1.pvec[1]-20.0)/3.0 < result1.stderr[1] ) |
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43 | self.assertTrue( math.fabs(result1.pvec[2]-9.0e-12)/3.0 < result1.stderr[2] ) |
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44 | self.assertTrue( result1.fitness < 1.0 ) |
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45 | |
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46 | def test_scipy_dispersion(self): |
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47 | """ |
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48 | Cylinder fit with dispersion |
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49 | """ |
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50 | # Load data |
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51 | # This data is for a cylinder with |
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52 | # length=400, radius=20, radius disp=5, scale=1e-10 |
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53 | out=Loader().load("cyl_400_20_disp5r.txt") |
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54 | out.dy = numpy.zeros(len(out.y)) |
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55 | for i in range(len(out.y)): |
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56 | out.dy[i] = math.sqrt(out.y[i]) |
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57 | |
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58 | # Set up the fit |
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59 | fitter = Fit('scipy') |
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60 | # Receives the type of model for the fitting |
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61 | from sans.models.CylinderModel import CylinderModel |
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62 | model1 = CylinderModel() |
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63 | model1.setParam('contrast', 1) |
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64 | |
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65 | # Dispersion parameters |
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66 | model1.dispersion['radius']['width'] = 0.001 |
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67 | model1.dispersion['radius']['npts'] = 50 |
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68 | |
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69 | model = Model(model1) |
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70 | |
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71 | pars1 =['length','radius','scale','radius.width'] |
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72 | fitter.set_data(out,1) |
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73 | model.set(scale=1e-10) |
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74 | fitter.set_model(model,1,pars1) |
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75 | fitter.select_problem_for_fit(Uid=1,value=1) |
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76 | result1 = fitter.fit() |
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77 | |
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78 | self.assert_(result1) |
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79 | self.assertTrue(len(result1.pvec)>0 or len(result1.pvec)==0 ) |
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80 | self.assertTrue(len(result1.stderr)> 0 or len(result1.stderr)==0) |
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81 | |
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82 | self.assertTrue( math.fabs(result1.pvec[0]-400.0)/3.0 < result1.stderr[0] ) |
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83 | self.assertTrue( math.fabs(result1.pvec[1]-20.0)/3.0 < result1.stderr[1] ) |
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84 | self.assertTrue( math.fabs(result1.pvec[2]-1.0e-10)/3.0 < result1.stderr[2] ) |
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85 | self.assertTrue( math.fabs(result1.pvec[3]-5.0)/3.0 < result1.stderr[3] ) |
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86 | self.assertTrue( result1.fitness < 1.0 ) |
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87 | |
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88 | |
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89 | class smear_testdata(unittest.TestCase): |
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90 | """ |
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91 | Test fitting with the smearing operations |
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92 | The output of the fits should be compated to fits |
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93 | done with IGOR for the same models and data sets. |
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94 | """ |
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95 | def setUp(self): |
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96 | print "TEST DONE WITHOUT PROPER OUTPUT CHECK:" |
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97 | print " ---> TEST NEEDS TO BE COMPLETED" |
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98 | from sans.models.SphereModel import SphereModel |
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99 | data = Loader().load("latex_smeared.xml") |
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100 | self.data_res = data[0] |
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101 | self.data_slit = data[1] |
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102 | |
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103 | self.sphere = SphereModel() |
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104 | self.sphere.setParam('radius', 5000.0) |
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105 | self.sphere.setParam('scale', 1.0e-13) |
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106 | self.sphere.setParam('radius.npts', 30) |
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107 | self.sphere.setParam('radius.width',500) |
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108 | |
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109 | def test_reso(self): |
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110 | from DataLoader.qsmearing import smear_selection |
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111 | |
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112 | # Let the data module find out what smearing the |
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113 | # data needs |
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114 | smear = smear_selection(self.data_res) |
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115 | self.assertEqual(smear.__class__.__name__, 'QSmearer') |
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116 | |
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117 | # Fit |
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118 | fitter = Fit('scipy') |
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119 | |
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120 | # Data: right now this is the only way to set the smearer object |
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121 | # We should improve that and have a way to get access to the |
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122 | # data for a given fit. |
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123 | fitter.set_data(self.data_res,1) |
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124 | fitter._engine.fitArrangeDict[1].dList[0].smearer = smear |
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125 | |
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126 | # Model: maybe there's a better way to do this. |
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127 | # Ideally we should have to create a new model from our sans model. |
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128 | fitter.set_model(Model(self.sphere),1, ['radius','scale']) |
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129 | |
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130 | # Why do we have to do this...? |
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131 | fitter.select_problem_for_fit(Uid=1,value=1) |
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132 | |
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133 | # Perform the fit (might take a while) |
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134 | result1 = fitter.fit() |
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135 | |
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136 | # Replace this with proper test once we know what the |
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137 | # result should be |
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138 | print result1.pvec |
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139 | print result1.stderr |
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140 | |
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141 | def test_slit(self): |
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142 | from DataLoader.qsmearing import smear_selection |
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143 | |
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144 | smear = smear_selection(self.data_slit) |
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145 | self.assertEqual(smear.__class__.__name__, 'SlitSmearer') |
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146 | |
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147 | # Fit |
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148 | fitter = Fit('scipy') |
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149 | |
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150 | # Data: right now this is the only way to set the smearer object |
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151 | # We should improve that and have a way to get access to the |
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152 | # data for a given fit. |
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153 | fitter.set_data(self.data_slit,1) |
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154 | fitter._engine.fitArrangeDict[1].dList[0].smearer = smear |
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155 | fitter._engine.fitArrangeDict[1].dList[0].qmax = 0.003 |
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156 | |
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157 | # Model |
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158 | fitter.set_model(Model(self.sphere),1, ['radius','scale']) |
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159 | fitter.select_problem_for_fit(Uid=1,value=1) |
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160 | |
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161 | result1 = fitter.fit() |
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162 | |
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163 | # Replace this with proper test once we know what the |
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164 | # result should be |
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165 | print result1.pvec |
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166 | print result1.stderr |
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167 | |
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168 | |
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169 | |
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170 | if __name__ == '__main__': |
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171 | unittest.main() |
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