[ca6d914] | 1 | """ |
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| 2 | Unit tests for fitting module |
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[2eaaf1a] | 3 | @author Gervaise Alina |
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[ca6d914] | 4 | """ |
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| 5 | import unittest |
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[6fe5100] | 6 | import math |
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[2eaaf1a] | 7 | |
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[1e3169c] | 8 | from sans.fit.AbstractFitEngine import Model |
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[6c00702] | 9 | from sans.dataloader.loader import Loader |
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| 10 | from sans.fit.Fitting import Fit |
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| 11 | from sans.models.LineModel import LineModel |
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| 12 | from sans.models.Constant import Constant |
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| 13 | |
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[ca6d914] | 14 | class testFitModule(unittest.TestCase): |
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| 15 | """ test fitting """ |
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[2eaaf1a] | 16 | |
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[ca6d914] | 17 | def test1(self): |
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[8bbab51] | 18 | """ Fit 1 data (testdata_line.txt)and 1 model(lineModel) """ |
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[ca6d914] | 19 | #load data |
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[1e3169c] | 20 | data = Loader().load("testdata_line.txt") |
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[da5d8e8] | 21 | data.name = data.filename |
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[ca6d914] | 22 | #Importing the Fit module |
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[6fe5100] | 23 | from bumps import fitters |
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| 24 | fitters.FIT_DEFAULT = 'dream' |
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| 25 | print fitters.FIT_OPTIONS['dream'].__dict__ |
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| 26 | fitter = Fit('bumps') |
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[ca6d914] | 27 | # Receives the type of model for the fitting |
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| 28 | model1 = LineModel() |
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| 29 | model1.name = "M1" |
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[6c00702] | 30 | model = Model(model1,data) |
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[ca6d914] | 31 | #fit with scipy test |
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| 32 | |
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| 33 | pars1= ['param1','param2'] |
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| 34 | fitter.set_data(data,1) |
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| 35 | try:fitter.set_model(model,1,pars1) |
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[6c00702] | 36 | except ValueError,exc: |
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| 37 | #print "ValueError was correctly raised: "+str(msg) |
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| 38 | assert str(exc).startswith('wrong parameter') |
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| 39 | else: raise AssertionError("No error raised for scipy fitting with wrong parameters name to fit") |
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[ca6d914] | 40 | pars1= ['A','B'] |
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| 41 | fitter.set_model(model,1,pars1) |
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[dd8ee14] | 42 | fitter.select_problem_for_fit(id=1,value=1) |
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[da5d8e8] | 43 | result1, = fitter.fit() |
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[6c00702] | 44 | |
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[da5d8e8] | 45 | self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] ) |
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| 46 | self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1]) |
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| 47 | self.assertTrue( result1.fitness/len(data.x) < 2 ) |
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[6c00702] | 48 | |
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[6fe5100] | 49 | return |
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[ca6d914] | 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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[dd8ee14] | 54 | fitter.select_problem_for_fit(id=1,value=1) |
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[da5d8e8] | 55 | result2, = fitter.fit() |
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[ca6d914] | 56 | |
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| 57 | self.assert_(result2) |
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[2eaaf1a] | 58 | self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) |
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| 59 | self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) |
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| 60 | self.assertTrue( result2.fitness/len(data.x) < 2) |
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[ca6d914] | 61 | # compare fit result result for scipy and park |
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| 62 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 63 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 64 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 65 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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[2eaaf1a] | 66 | self.assertAlmostEquals( result1.fitness, |
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| 67 | result2.fitness/len(data.x),1 ) |
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| 68 | |
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[ca6d914] | 69 | |
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| 70 | def test2(self): |
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| 71 | """ fit 2 data and 2 model with no constrainst""" |
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| 72 | #load data |
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| 73 | l = Loader() |
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[1e3169c] | 74 | data1=l.load("testdata_line.txt") |
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[da5d8e8] | 75 | data1.name = data1.filename |
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[1e3169c] | 76 | |
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| 77 | data2=l.load("testdata_line1.txt") |
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[da5d8e8] | 78 | data2.name = data2.filename |
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[1e3169c] | 79 | |
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[ca6d914] | 80 | #Importing the Fit module |
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| 81 | fitter = Fit('scipy') |
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| 82 | # Receives the type of model for the fitting |
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| 83 | model11 = LineModel() |
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| 84 | model11.name= "M1" |
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| 85 | model22 = LineModel() |
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| 86 | model11.name= "M2" |
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[1e3169c] | 87 | |
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[da5d8e8] | 88 | model1 = Model(model11,data1) |
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| 89 | model2 = Model(model22,data2) |
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[ca6d914] | 90 | #fit with scipy test |
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| 91 | pars1= ['A','B'] |
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| 92 | fitter.set_data(data1,1) |
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| 93 | fitter.set_model(model1,1,pars1) |
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[dd8ee14] | 94 | fitter.select_problem_for_fit(id=1,value=0) |
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[ca6d914] | 95 | fitter.set_data(data2,2) |
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| 96 | fitter.set_model(model2,2,pars1) |
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[dd8ee14] | 97 | fitter.select_problem_for_fit(id=2,value=0) |
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[8bbab51] | 98 | |
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[6c00702] | 99 | try: result1, = fitter.fit() |
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[8bbab51] | 100 | except RuntimeError,msg: |
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| 101 | assert str(msg)=="No Assembly scheduled for Scipy fitting." |
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[da5d8e8] | 102 | else: raise AssertionError,"No error raised for scipy fitting with no model" |
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[dd8ee14] | 103 | fitter.select_problem_for_fit(id=1,value=1) |
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| 104 | fitter.select_problem_for_fit(id=2,value=1) |
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[6c00702] | 105 | try: result1, = fitter.fit() |
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[ca6d914] | 106 | except RuntimeError,msg: |
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| 107 | assert str(msg)=="Scipy can't fit more than a single fit problem at a time." |
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[da5d8e8] | 108 | else: raise AssertionError,"No error raised for scipy fitting with more than 2 models" |
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[6fe5100] | 109 | |
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| 110 | return |
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[ca6d914] | 111 | #fit with park test |
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| 112 | fitter = Fit('park') |
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| 113 | fitter.set_data(data1,1) |
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| 114 | fitter.set_model(model1,1,pars1) |
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| 115 | fitter.set_data(data2,2) |
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| 116 | fitter.set_model(model2,2,pars1) |
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[dd8ee14] | 117 | fitter.select_problem_for_fit(id=1,value=1) |
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| 118 | fitter.select_problem_for_fit(id=2,value=1) |
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[da5d8e8] | 119 | R1,R2 = fitter.fit() |
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[ca6d914] | 120 | |
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[da5d8e8] | 121 | self.assertTrue( math.fabs(R1.pvec[0]-4)/3 <= R1.stderr[0] ) |
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| 122 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3 <= R1.stderr[1] ) |
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| 123 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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[ca6d914] | 124 | |
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| 125 | |
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| 126 | def test3(self): |
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| 127 | """ fit 2 data and 2 model with 1 constrainst""" |
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[6fe5100] | 128 | return |
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[ca6d914] | 129 | #load data |
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| 130 | l = Loader() |
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[1e3169c] | 131 | data1= l.load("testdata_line.txt") |
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[da5d8e8] | 132 | data1.name = data1.filename |
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[1e3169c] | 133 | data2= l.load("testdata_cst.txt") |
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[da5d8e8] | 134 | data2.name = data2.filename |
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[1e3169c] | 135 | |
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[ca6d914] | 136 | # Receives the type of model for the fitting |
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| 137 | model11 = LineModel() |
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| 138 | model11.name= "line" |
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[2eaaf1a] | 139 | model11.setParam("A", 1.0) |
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| 140 | model11.setParam("B",1.0) |
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| 141 | |
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[ca6d914] | 142 | model22 = Constant() |
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| 143 | model22.name= "cst" |
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[2eaaf1a] | 144 | model22.setParam("value", 1.0) |
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[8bbab51] | 145 | |
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[da5d8e8] | 146 | model1 = Model(model11,data1) |
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| 147 | model2 = Model(model22,data2) |
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[8bbab51] | 148 | model1.set(A=4) |
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| 149 | model1.set(B=3) |
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[1e3169c] | 150 | # Constraint the constant value to be equal to parameter B (the real value is 2.5) |
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[8bbab51] | 151 | model2.set(value='line.B') |
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[ca6d914] | 152 | #fit with scipy test |
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| 153 | pars1= ['A','B'] |
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| 154 | pars2= ['value'] |
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| 155 | |
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[1e3169c] | 156 | #Importing the Fit module |
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| 157 | fitter = Fit('park') |
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[ca6d914] | 158 | fitter.set_data(data1,1) |
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| 159 | fitter.set_model(model1,1,pars1) |
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[8296ff5] | 160 | fitter.set_data(data2,2,smearer=None) |
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[ca6d914] | 161 | fitter.set_model(model2,2,pars2) |
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[dd8ee14] | 162 | fitter.select_problem_for_fit(id=1,value=1) |
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| 163 | fitter.select_problem_for_fit(id=2,value=1) |
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[ca6d914] | 164 | |
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[da5d8e8] | 165 | R1,R2 = fitter.fit() |
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| 166 | self.assertTrue( math.fabs(R1.pvec[0]-4.0)/3. <= R1.stderr[0]) |
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| 167 | self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3. <= R1.stderr[1]) |
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| 168 | self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2) |
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[ca6d914] | 169 | |
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| 170 | |
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| 171 | def test4(self): |
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| 172 | """ fit 2 data concatenates with limited range of x and one model """ |
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| 173 | #load data |
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| 174 | l = Loader() |
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[1e3169c] | 175 | data1 = l.load("testdata_line.txt") |
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[da5d8e8] | 176 | data1.name = data1.filename |
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[1e3169c] | 177 | data2 = l.load("testdata_line1.txt") |
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[da5d8e8] | 178 | data2.name = data2.filename |
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| 179 | |
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[ca6d914] | 180 | # Receives the type of model for the fitting |
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| 181 | model1 = LineModel() |
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| 182 | model1.name= "M1" |
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[2eaaf1a] | 183 | model1.setParam("A", 1.0) |
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| 184 | model1.setParam("B",1.0) |
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[da5d8e8] | 185 | model = Model(model1,data1) |
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[1e3169c] | 186 | |
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[ca6d914] | 187 | #fit with scipy test |
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| 188 | pars1= ['A','B'] |
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[1e3169c] | 189 | #Importing the Fit module |
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| 190 | fitter = Fit('scipy') |
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[ca6d914] | 191 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 192 | fitter.set_model(model,1,pars1) |
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| 193 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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[dd8ee14] | 194 | fitter.select_problem_for_fit(id=1,value=1) |
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[ca6d914] | 195 | |
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[6c00702] | 196 | result1, = fitter.fit() |
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[6fe5100] | 197 | #print(result1) |
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[ca6d914] | 198 | self.assert_(result1) |
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[916a15f] | 199 | |
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[2eaaf1a] | 200 | self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] ) |
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| 201 | self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1]) |
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| 202 | self.assertTrue( result1.fitness/len(data1.x) < 2 ) |
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[6fe5100] | 203 | |
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| 204 | return |
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[ca6d914] | 205 | #fit with park test |
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| 206 | fitter = Fit('park') |
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| 207 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 208 | fitter.set_model(model,1,pars1) |
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| 209 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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[dd8ee14] | 210 | fitter.select_problem_for_fit(id=1,value=1) |
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[da5d8e8] | 211 | result2, = fitter.fit() |
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[ca6d914] | 212 | |
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| 213 | self.assert_(result2) |
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[2eaaf1a] | 214 | self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) |
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| 215 | self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) |
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| 216 | self.assertTrue( result2.fitness/len(data1.x) < 2) |
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[ca6d914] | 217 | # compare fit result result for scipy and park |
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| 218 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 219 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 220 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 221 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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[1e3169c] | 222 | self.assertTrue( result2.fitness/(len(data2.x)+len(data1.x)) < 2 ) |
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[6fe5100] | 223 | |
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| 224 | |
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| 225 | if __name__ == "__main__": |
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| 226 | unittest.main() |
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[2eaaf1a] | 227 | |
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