1 | """ |
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2 | Unit tests for fitting module |
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3 | @author M. Doucet |
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4 | """ |
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5 | import unittest |
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6 | import math |
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7 | |
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8 | import numpy |
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9 | from sas.fit.AbstractFitEngine import Model |
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10 | from sas.fit.BumpsFitting import BumpsFit as Fit |
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11 | from sas.dataloader.loader import Loader |
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12 | from sas.models.qsmearing import smear_selection |
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13 | from sas.models.CylinderModel import CylinderModel |
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14 | from sas.models.SphereModel import SphereModel |
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15 | |
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16 | class testFitModule(unittest.TestCase): |
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17 | """ test fitting """ |
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18 | |
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19 | def test_without_resolution(self): |
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20 | """ Simple cylinder model fit """ |
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21 | |
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22 | out=Loader().load("cyl_400_20.txt") |
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23 | # This data file has not error, add them |
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24 | #out.dy = out.y |
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25 | |
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26 | fitter = Fit() |
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27 | fitter.set_data(out,1) |
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28 | |
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29 | # Receives the type of model for the fitting |
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30 | model1 = CylinderModel() |
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31 | model1.setParam("scale", 1.0) |
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32 | model1.setParam("radius",18) |
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33 | model1.setParam("length", 397) |
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34 | model1.setParam("sldCyl",3e-006 ) |
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35 | model1.setParam("sldSolv",0.0 ) |
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36 | model1.setParam("background", 0.0) |
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37 | model = Model(model1) |
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38 | pars1 =['length','radius','scale'] |
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39 | fitter.set_model(model,1,pars1) |
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40 | |
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41 | # What the hell is this line for? |
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42 | fitter.select_problem_for_fit(id=1,value=1) |
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43 | result1, = fitter.fit() |
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44 | #print "result1",result1 |
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45 | |
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46 | self.assert_(result1) |
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47 | self.assertTrue(len(result1.pvec) > 0) |
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48 | self.assertTrue(len(result1.stderr) > 0) |
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49 | |
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50 | self.assertTrue( math.fabs(result1.pvec[0]-400.0)/3.0 < result1.stderr[0] ) |
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51 | self.assertTrue( math.fabs(result1.pvec[1]-20.0)/3.0 < result1.stderr[1] ) |
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52 | self.assertTrue( math.fabs(result1.pvec[2]-1)/3.0 < result1.stderr[2] ) |
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53 | self.assertTrue( result1.fitness < 1.0 ) |
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54 | |
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55 | def test_dispersion(self): |
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56 | """ |
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57 | Cylinder fit with dispersion |
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58 | """ |
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59 | alg = 'lm' |
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60 | from bumps import fitters |
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61 | fitters.FIT_DEFAULT = alg |
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62 | #fitters.FIT_OPTIONS[alg].options.update(opts) |
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63 | fitters.FIT_OPTIONS[alg].options.update(monitors=[]) |
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64 | self._dispersion(fitter = Fit()) |
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65 | |
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66 | def _dispersion(self, fitter): |
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67 | # Load data |
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68 | # This data is for a cylinder with |
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69 | # length=400, radius=20, radius disp=5, scale=1e-10 |
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70 | out=Loader().load("cyl_400_20_disp5r.txt") |
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71 | out.dy = numpy.zeros(len(out.y)) |
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72 | for i in range(len(out.y)): |
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73 | out.dy[i] = math.sqrt(out.y[i]) |
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74 | |
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75 | # Receives the type of model for the fitting |
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76 | model1 = CylinderModel() |
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77 | model1.setParam("scale", 10.0) |
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78 | model1.setParam("radius",18) |
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79 | model1.setParam("length", 397) |
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80 | model1.setParam("sldCyl",3e-006 ) |
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81 | model1.setParam("sldSolv",0.0 ) |
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82 | model1.setParam("background", 0.0) |
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83 | |
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84 | # Dispersion parameters |
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85 | model1.dispersion['radius']['width'] = 0.25 |
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86 | model1.dispersion['radius']['npts'] = 50 |
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87 | |
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88 | model = Model(model1) |
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89 | |
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90 | pars1 =['length','radius','scale','radius.width'] |
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91 | fitter.set_data(out,1) |
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92 | fitter.set_model(model,1,pars1) |
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93 | fitter.select_problem_for_fit(id=1,value=1) |
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94 | #import time; T0 = time.time() |
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95 | result1, = fitter.fit() |
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96 | |
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97 | self.assert_(result1) |
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98 | self.assertTrue(len(result1.pvec)>0) |
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99 | self.assertTrue(len(result1.stderr)>0) |
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100 | |
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101 | #print [z for z in zip(result1.param_list,result1.pvec,result1.stderr)] |
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102 | self.assertTrue( math.fabs(result1.pvec[0]-399.8)/3.0 < result1.stderr[0] ) |
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103 | self.assertTrue( math.fabs(result1.pvec[1]-17.5)/3.0 < result1.stderr[1] ) |
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104 | self.assertTrue( math.fabs(result1.pvec[2]-11.1)/3.0 < result1.stderr[2] ) |
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105 | self.assertTrue( math.fabs(result1.pvec[3]-0.276)/3.0 < result1.stderr[3] ) |
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106 | self.assertTrue( result1.fitness < 1.0 ) |
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107 | |
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108 | |
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109 | class smear_testdata(unittest.TestCase): |
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110 | """ |
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111 | Test fitting with the smearing operations |
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112 | The output of the fits should be compated to fits |
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113 | done with IGOR for the same models and data sets. |
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114 | """ |
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115 | def setUp(self): |
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116 | data = Loader().load("latex_smeared.xml") |
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117 | self.data_res = data[0] |
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118 | self.data_slit = data[1] |
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119 | |
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120 | self.sphere = SphereModel() |
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121 | self.sphere.setParam('background', 0) |
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122 | self.sphere.setParam('radius', 5000.0) |
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123 | self.sphere.setParam('scale', 0.4) |
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124 | self.sphere.setParam('sldSolv',0) |
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125 | self.sphere.setParam('sldSph',1e-6) |
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126 | #self.sphere.setParam('radius.npts', 30) |
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127 | #self.sphere.setParam('radius.width',50) |
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128 | |
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129 | def test_reso(self): |
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130 | |
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131 | # Let the data module find out what smearing the |
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132 | # data needs |
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133 | smear = smear_selection(self.data_res) |
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134 | #self.assertEqual(smear.__class__.__name__, 'QSmearer') |
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135 | #self.assertEqual(smear.__class__.__name__, 'PySmearer') |
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136 | |
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137 | # Fit |
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138 | fitter = Fit() |
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139 | |
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140 | # Data: right now this is the only way to set the smearer object |
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141 | # We should improve that and have a way to get access to the |
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142 | # data for a given fit. |
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143 | fitter.set_data(self.data_res,1) |
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144 | fitter.fit_arrange_dict[1].data_list[0].smearer = smear |
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145 | |
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146 | # Model: maybe there's a better way to do this. |
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147 | # Ideally we should have to create a new model from our sas model. |
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148 | fitter.set_model(Model(self.sphere),1, ['radius','scale', 'background']) |
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149 | |
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150 | # Why do we have to do this...? |
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151 | fitter.select_problem_for_fit(id=1,value=1) |
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152 | |
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153 | # Perform the fit (might take a while) |
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154 | result1, = fitter.fit() |
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155 | |
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156 | #print "v",result1.pvec |
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157 | #print "dv",result1.stderr |
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158 | #print "chisq(v)",result1.fitness |
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159 | |
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160 | self.assertTrue( math.fabs(result1.pvec[0]-5000) < 20 ) |
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161 | self.assertTrue( math.fabs(result1.pvec[1]-0.48) < 0.02 ) |
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162 | self.assertTrue( math.fabs(result1.pvec[2]-0.060) < 0.002 ) |
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163 | |
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164 | |
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165 | def test_slit(self): |
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166 | smear = smear_selection(self.data_slit) |
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167 | #self.assertEqual(smear.__class__.__name__, 'SlitSmearer') |
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168 | #self.assertEqual(smear.__class__.__name__, 'PySmearer') |
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169 | |
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170 | fitter = Fit() |
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171 | |
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172 | # Data: right now this is the only way to set the smearer object |
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173 | # We should improve that and have a way to get access to the |
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174 | # data for a given fit. |
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175 | fitter.set_data(self.data_slit,1) |
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176 | fitter.fit_arrange_dict[1].data_list[0].smearer = smear |
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177 | fitter.fit_arrange_dict[1].data_list[0].qmax = 0.003 |
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178 | |
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179 | # Model |
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180 | fitter.set_model(Model(self.sphere),1, ['radius','scale']) |
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181 | fitter.select_problem_for_fit(id=1,value=1) |
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182 | |
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183 | result1, = fitter.fit() |
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184 | |
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185 | #print "v",result1.pvec |
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186 | #print "dv",result1.stderr |
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187 | #print "chisq(v)",result1.fitness |
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188 | |
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189 | numpy.testing.assert_allclose(result1.pvec, [2323.466,0.22137], rtol=0.001) |
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190 | |
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191 | if __name__ == '__main__': |
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192 | unittest.main() |
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