[ca6d914] | 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.guitools.plottables import Theory1D |
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| 6 | from sans.guitools.plottables import Data1D |
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| 7 | from sans.fit.AbstractFitEngine import Data, Model |
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| 8 | import math |
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| 9 | class testFitModule(unittest.TestCase): |
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| 10 | """ test fitting """ |
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| 11 | def test1(self): |
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| 12 | """ Fit 1 data (testdata_line.txt)and 1 model(lineModel) """ |
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| 13 | #load data |
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| 14 | from sans.fit.Loader import Load |
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| 15 | load = Load() |
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| 16 | load.set_filename("testdata_line.txt") |
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| 17 | load.set_values() |
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| 18 | data1 = Data1D(x=[], y=[],dx=None, dy=None) |
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| 19 | load.load_data(data1) |
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| 20 | #Importing the Fit module |
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| 21 | from sans.fit.Fitting import Fit |
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| 22 | fitter = Fit('scipy') |
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| 23 | # Receives the type of model for the fitting |
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| 24 | from sans.guitools.LineModel import LineModel |
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| 25 | model1 = LineModel() |
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| 26 | model1.name = "M1" |
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| 27 | data = Data(sans_data=data1 ) |
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| 28 | model = Model(model1) |
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| 29 | #fit with scipy test |
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| 30 | |
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| 31 | pars1= ['param1','param2'] |
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| 32 | fitter.set_data(data,1) |
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| 33 | try:fitter.set_model(model,1,pars1) |
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| 34 | except ValueError,msg: |
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| 35 | assert str(msg)=="wrong paramter %s used to set model %s. Choose\ |
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| 36 | parameter name within %s"%('param1', model.model.name,str(model.model.getParamList())) |
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| 37 | else: raise AssertError,"No error raised for scipy fitting with wrong parameters name to fit" |
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| 38 | pars1= ['A','B'] |
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| 39 | fitter.set_model(model,1,pars1) |
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| 40 | result1 = fitter.fit() |
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| 41 | self.assert_(result1) |
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| 42 | |
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| 43 | self.assertTrue( ( math.fabs(result1.pvec[0]-4)/3 == result1.stderr[0] ) or |
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| 44 | ( math.fabs(result1.pvec[0]-4)/3 < result1.stderr[0]) ) |
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| 45 | |
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| 46 | self.assertTrue( ( math.fabs(result1.pvec[1]-2.5)/3 == result1.stderr[1]) or |
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| 47 | ( math.fabs(result1.pvec[1]-2.5)/3 < result1.stderr[1] ) ) |
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| 48 | self.assertTrue( result1.fitness/49 < 2 ) |
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| 49 | |
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| 50 | #fit with park test |
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| 51 | fitter = Fit('park') |
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| 52 | fitter.set_data(data,1) |
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| 53 | fitter.set_model(model,1,pars1) |
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| 54 | result2 = fitter.fit() |
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| 55 | |
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| 56 | self.assert_(result2) |
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| 57 | |
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| 58 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 59 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 60 | |
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| 61 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 62 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 63 | self.assertTrue(result2.fitness/49 < 2) |
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| 64 | # compare fit result result for scipy and park |
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| 65 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 66 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 67 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 68 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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| 69 | self.assertAlmostEquals( result1.fitness,result2.fitness ) |
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| 70 | |
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| 71 | def test2(self): |
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| 72 | """ fit 2 data and 2 model with no constrainst""" |
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| 73 | #load data |
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| 74 | from DataLoader.loader import Loader |
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| 75 | l = Loader() |
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| 76 | out=l.load("testdata_line.txt") |
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| 77 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 78 | out=l.load("testdata_line1.txt") |
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| 79 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 80 | #Importing the Fit module |
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| 81 | from sans.fit.Fitting import Fit |
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| 82 | fitter = Fit('scipy') |
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| 83 | # Receives the type of model for the fitting |
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| 84 | from sans.guitools.LineModel import LineModel |
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| 85 | model11 = LineModel() |
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| 86 | model11.name= "M1" |
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| 87 | model22 = LineModel() |
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| 88 | model11.name= "M2" |
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| 89 | data1 = Data(sans_data=data11 ) |
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| 90 | data2 = Data(sans_data=data22 ) |
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| 91 | model1 = Model(model11) |
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| 92 | model2 = Model(model22) |
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| 93 | #fit with scipy test |
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| 94 | pars1= ['A','B'] |
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| 95 | fitter.set_data(data1,1) |
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| 96 | fitter.set_model(model1,1,pars1) |
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| 97 | fitter.set_data(data2,2) |
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| 98 | fitter.set_model(model2,2,pars1) |
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| 99 | try: result1 = fitter.fit() |
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| 100 | except RuntimeError,msg: |
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| 101 | assert str(msg)=="Scipy can't fit more than a single fit problem at a time." |
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| 102 | else: raise AssertError,"No error raised for scipy fitting with more than 2 models" |
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| 103 | |
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| 104 | #fit with park test |
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| 105 | fitter = Fit('park') |
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| 106 | fitter.set_data(data1,1) |
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| 107 | fitter.set_model(model1,1,pars1) |
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| 108 | fitter.set_data(data2,2) |
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| 109 | fitter.set_model(model2,2,pars1) |
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| 110 | result2 = fitter.fit() |
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| 111 | |
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| 112 | self.assert_(result2) |
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| 113 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 114 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 115 | |
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| 116 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 117 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 118 | self.assertTrue(result2.fitness/49 < 2) |
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| 119 | |
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| 120 | |
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| 121 | def test3(self): |
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| 122 | """ fit 2 data and 2 model with 1 constrainst""" |
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| 123 | #load data |
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| 124 | from DataLoader.loader import Loader |
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| 125 | l = Loader() |
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| 126 | out=l.load("testdata_line.txt") |
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| 127 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 128 | out=l.load("testdata_cst.txt") |
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| 129 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 130 | #Importing the Fit module |
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| 131 | from sans.fit.Fitting import Fit |
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| 132 | fitter = Fit('park') |
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| 133 | # Receives the type of model for the fitting |
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| 134 | from sans.guitools.LineModel import LineModel |
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| 135 | from sans.models.Constant import Constant |
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| 136 | model11 = LineModel() |
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| 137 | model11.name= "line" |
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| 138 | model22 = Constant() |
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| 139 | model22.name= "cst" |
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| 140 | model22.setParam('value','line.A') |
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| 141 | |
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| 142 | data1 = Data(sans_data=data11 ) |
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| 143 | data2 = Data(sans_data=data22 ) |
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| 144 | model1 = Model(model11) |
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| 145 | model2 = Model(model22) |
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| 146 | #fit with scipy test |
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| 147 | pars1= ['A','B'] |
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| 148 | pars2= ['value'] |
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| 149 | |
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| 150 | fitter.set_data(data1,1) |
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| 151 | fitter.set_model(model1,1,pars1) |
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| 152 | fitter.set_data(data2,2) |
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| 153 | fitter.set_model(model2,2,pars2) |
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| 154 | |
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| 155 | result2 = fitter.fit() |
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| 156 | self.assert_(result2) |
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| 157 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 158 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 159 | |
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| 160 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 161 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 162 | self.assertTrue(result2.fitness/49 < 2) |
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| 163 | |
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| 164 | |
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| 165 | def test4(self): |
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| 166 | """ fit 2 data concatenates with limited range of x and one model """ |
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| 167 | #load data |
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| 168 | from DataLoader.loader import Loader |
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| 169 | l = Loader() |
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| 170 | out=l.load("testdata_line.txt") |
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| 171 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 172 | out=l.load("testdata_line1.txt") |
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| 173 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 174 | #Importing the Fit module |
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| 175 | from sans.fit.Fitting import Fit |
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| 176 | fitter = Fit('scipy') |
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| 177 | # Receives the type of model for the fitting |
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| 178 | from sans.guitools.LineModel import LineModel |
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| 179 | model1 = LineModel() |
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| 180 | model1.name= "M1" |
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| 181 | |
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| 182 | data1 = Data(sans_data=data11 ) |
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| 183 | data2 = Data(sans_data=data22 ) |
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| 184 | model = Model(model1) |
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| 185 | |
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| 186 | #fit with scipy test |
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| 187 | pars1= ['A','B'] |
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| 188 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 189 | fitter.set_model(model,1,pars1) |
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| 190 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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| 191 | |
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| 192 | result1 = fitter.fit() |
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| 193 | self.assert_(result1) |
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| 194 | |
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| 195 | self.assertTrue( ( math.fabs(result1.pvec[0]-4)/3 == result1.stderr[0] ) or |
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| 196 | ( math.fabs(result1.pvec[0]-4)/3 < result1.stderr[0]) ) |
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| 197 | |
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| 198 | self.assertTrue( ( math.fabs(result1.pvec[1]-2.5)/3 == result1.stderr[1]) or |
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| 199 | ( math.fabs(result1.pvec[1]-2.5)/3 < result1.stderr[1] ) ) |
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| 200 | self.assertTrue( result1.fitness/49 < 2 ) |
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| 201 | |
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| 202 | |
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| 203 | #fit with park test |
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| 204 | fitter = Fit('park') |
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| 205 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 206 | fitter.set_model(model,1,pars1) |
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| 207 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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| 208 | |
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| 209 | result2 = fitter.fit() |
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| 210 | |
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| 211 | self.assert_(result2) |
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| 212 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 213 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 214 | |
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| 215 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 216 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 217 | self.assertTrue(result2.fitness/49 < 2) |
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| 218 | # compare fit result result for scipy and park |
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| 219 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 220 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 221 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 222 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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| 223 | self.assertAlmostEquals( result1.fitness,result2.fitness ) |
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| 224 | |
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