| 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 danse.common.plottools.plottables import Data1D,Theory1D |
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| 8 | from sans.fit.AbstractFitEngine import Data, Model,FitData1D |
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| 9 | import math |
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| 10 | class testFitModule(unittest.TestCase): |
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| 11 | def smalltest(self): |
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| 12 | |
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| 13 | import numpy |
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| 14 | x=[1,22,3] |
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| 15 | y=[5,6,7,8] |
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| 16 | array= numpy.zeros(len(x), len(y)) |
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| 17 | |
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| 18 | """ test fitting """ |
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| 19 | def test1(self): |
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| 20 | """ Fit 1 data (testdata_line.txt)and 1 model(lineModel) """ |
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| 21 | #load data |
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| 22 | from DataLoader.loader import Loader |
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| 23 | data1 = Loader().load("testdata_line.txt") |
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| 24 | #Importing the Fit module |
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| 25 | from sans.fit.Fitting import Fit |
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| 26 | fitter = Fit('scipy') |
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| 27 | # Receives the type of model for the fitting |
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| 28 | from sans.models.LineModel import LineModel |
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| 29 | model1 = LineModel() |
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| 30 | model1.name = "M1" |
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| 31 | #data = Data(sans_data=data1 ) |
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| 32 | #data1.smearer=None |
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| 33 | data = FitData1D(data1 ) |
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| 34 | model = Model(model1) |
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| 35 | #fit with scipy test |
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| 36 | |
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| 37 | pars1= ['param1','param2'] |
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| 38 | fitter.set_data(data,1) |
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| 39 | try:fitter.set_model(model,1,pars1) |
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| 40 | except ValueError,msg: |
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| 41 | assert str(msg)=="wrong paramter %s used to set model %s. Choose\ |
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| 42 | parameter name within %s"%('param1', model.model.name,str(model.model.getParamList())) |
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| 43 | else: raise AssertError,"No error raised for scipy fitting with wrong parameters name to fit" |
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| 44 | pars1= ['A','B'] |
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| 45 | fitter.set_model(model,1,pars1) |
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| 46 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 47 | result1 = fitter.fit() |
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| 48 | self.assert_(result1) |
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| 49 | |
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| 50 | self.assertTrue( ( math.fabs(result1.pvec[0]-4)/3 == result1.stderr[0] ) or |
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| 51 | ( math.fabs(result1.pvec[0]-4)/3 < result1.stderr[0]) ) |
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| 52 | |
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| 53 | self.assertTrue( ( math.fabs(result1.pvec[1]-2.5)/3 == result1.stderr[1]) or |
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| 54 | ( math.fabs(result1.pvec[1]-2.5)/3 < result1.stderr[1] ) ) |
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| 55 | self.assertTrue( result1.fitness/49 < 2 ) |
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| 56 | |
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| 57 | #fit with park test |
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| 58 | fitter = Fit('park') |
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| 59 | fitter.set_data(data,1) |
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| 60 | fitter.set_model(model,1,pars1) |
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| 61 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 62 | result2 = fitter.fit() |
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| 63 | |
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| 64 | self.assert_(result2) |
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| 65 | |
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| 66 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 67 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 68 | |
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| 69 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 70 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 71 | self.assertTrue(result2.fitness/49 < 2) |
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| 72 | # compare fit result result for scipy and park |
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| 73 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 74 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 75 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 76 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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| 77 | self.assertAlmostEquals( result1.fitness,result2.fitness ) |
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| 78 | |
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| 79 | def test2(self): |
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| 80 | """ fit 2 data and 2 model with no constrainst""" |
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| 81 | #load data |
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| 82 | from DataLoader.loader import Loader |
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| 83 | l = Loader() |
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| 84 | out=l.load("testdata_line.txt") |
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| 85 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 86 | out=l.load("testdata_line1.txt") |
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| 87 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 88 | #Importing the Fit module |
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| 89 | from sans.fit.Fitting import Fit |
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| 90 | fitter = Fit('scipy') |
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| 91 | # Receives the type of model for the fitting |
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| 92 | from sans.models.LineModel import LineModel |
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| 93 | model11 = LineModel() |
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| 94 | model11.name= "M1" |
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| 95 | model22 = LineModel() |
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| 96 | model11.name= "M2" |
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| 97 | data11.smearer=None |
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| 98 | data22.smearer=None |
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| 99 | data1 = FitData1D(data11 ) |
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| 100 | data2 = FitData1D(data22 ) |
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| 101 | |
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| 102 | model1 = Model(model11) |
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| 103 | model2 = Model(model22) |
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| 104 | #fit with scipy test |
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| 105 | pars1= ['A','B'] |
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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.select_problem_for_fit(Uid=1,value=0) |
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| 109 | fitter.set_data(data2,2) |
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| 110 | fitter.set_model(model2,2,pars1) |
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| 111 | fitter.select_problem_for_fit(Uid=2,value=0) |
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| 112 | |
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| 113 | try: result1 = fitter.fit() |
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| 114 | except RuntimeError,msg: |
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| 115 | assert str(msg)=="No Assembly scheduled for Scipy fitting." |
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| 116 | else: raise AssertError,"No error raised for scipy fitting with no model" |
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| 117 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 118 | fitter.select_problem_for_fit(Uid=2,value=1) |
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| 119 | try: result1 = fitter.fit() |
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| 120 | except RuntimeError,msg: |
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| 121 | assert str(msg)=="Scipy can't fit more than a single fit problem at a time." |
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| 122 | else: raise AssertError,"No error raised for scipy fitting with more than 2 models" |
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| 123 | |
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| 124 | #fit with park test |
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| 125 | fitter = Fit('park') |
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| 126 | fitter.set_data(data1,1) |
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| 127 | fitter.set_model(model1,1,pars1) |
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| 128 | fitter.set_data(data2,2) |
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| 129 | fitter.set_model(model2,2,pars1) |
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| 130 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 131 | fitter.select_problem_for_fit(Uid=2,value=1) |
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| 132 | result2 = fitter.fit() |
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| 133 | |
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| 134 | self.assert_(result2) |
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| 135 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 136 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 137 | |
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| 138 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 139 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 140 | self.assertTrue(result2.fitness/49 < 2) |
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| 141 | |
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| 142 | |
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| 143 | def test3(self): |
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| 144 | """ fit 2 data and 2 model with 1 constrainst""" |
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| 145 | #load data |
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| 146 | from DataLoader.loader import Loader |
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| 147 | l = Loader() |
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| 148 | out=l.load("testdata_line.txt") |
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| 149 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 150 | out=l.load("testdata_cst.txt") |
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| 151 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 152 | #Importing the Fit module |
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| 153 | from sans.fit.Fitting import Fit |
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| 154 | fitter = Fit('park') |
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| 155 | # Receives the type of model for the fitting |
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| 156 | from sans.models.LineModel import LineModel |
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| 157 | from sans.models.Constant import Constant |
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| 158 | model11 = LineModel() |
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| 159 | model11.name= "line" |
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| 160 | |
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| 161 | model22 = Constant() |
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| 162 | model22.name= "cst" |
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| 163 | # Constrain the constant value to be equal to parameter B (the real value is 2.5) |
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| 164 | |
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| 165 | #data1 = Data(sans_data=data11 ) |
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| 166 | #data2 = Data(sans_data=data22 ) |
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| 167 | data11.smearer= None |
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| 168 | data22.smearer= None |
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| 169 | data1 = FitData1D(data11 ) |
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| 170 | data2 = FitData1D(data22 ) |
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| 171 | model1 = Model(model11) |
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| 172 | model2 = Model(model22) |
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| 173 | model1.set(A=4) |
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| 174 | model1.set(B=3) |
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| 175 | model2.set(value='line.B') |
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| 176 | #fit with scipy test |
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| 177 | pars1= ['A','B'] |
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| 178 | pars2= ['value'] |
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| 179 | |
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| 180 | fitter.set_data(data1,1) |
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| 181 | fitter.set_model(model1,1,pars1) |
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| 182 | fitter.set_data(data2,2,smearer=None) |
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| 183 | fitter.set_model(model2,2,pars2) |
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| 184 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 185 | fitter.select_problem_for_fit(Uid=2,value=1) |
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| 186 | |
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| 187 | result2 = fitter.fit() |
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| 188 | self.assert_(result2) |
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| 189 | self.assertTrue( ( math.fabs(result2.pvec[0]-4.0)/3. < result2.stderr[0]) ) |
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| 190 | |
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| 191 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3. < result2.stderr[1]) ) |
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| 192 | self.assertTrue(result2.fitness/49 < 2) |
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| 193 | |
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| 194 | |
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| 195 | def test4(self): |
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| 196 | """ fit 2 data concatenates with limited range of x and one model """ |
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| 197 | #load data |
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| 198 | from DataLoader.loader import Loader |
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| 199 | l = Loader() |
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| 200 | out=l.load("testdata_line.txt") |
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| 201 | data11 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 202 | out=l.load("testdata_line1.txt") |
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| 203 | data22 = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.y) |
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| 204 | #Importing the Fit module |
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| 205 | from sans.fit.Fitting import Fit |
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| 206 | fitter = Fit('scipy') |
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| 207 | # Receives the type of model for the fitting |
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| 208 | from sans.models.LineModel import LineModel |
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| 209 | model1 = LineModel() |
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| 210 | model1.name= "M1" |
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| 211 | |
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| 212 | data11.smearer=None |
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| 213 | data22.smearer=None |
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| 214 | data1 = FitData1D(data11 ) |
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| 215 | data2 = FitData1D(data22 ) |
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| 216 | model = Model(model1) |
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| 217 | |
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| 218 | #fit with scipy test |
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| 219 | pars1= ['A','B'] |
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| 220 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 221 | fitter.set_model(model,1,pars1) |
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| 222 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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| 223 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 224 | |
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| 225 | result1 = fitter.fit() |
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| 226 | self.assert_(result1) |
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| 227 | |
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| 228 | self.assertTrue( ( math.fabs(result1.pvec[0]-4)/3 == result1.stderr[0] ) or |
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| 229 | ( math.fabs(result1.pvec[0]-4)/3 < result1.stderr[0]) ) |
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| 230 | |
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| 231 | self.assertTrue( ( math.fabs(result1.pvec[1]-2.5)/3 == result1.stderr[1]) or |
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| 232 | ( math.fabs(result1.pvec[1]-2.5)/3 < result1.stderr[1] ) ) |
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| 233 | self.assertTrue( result1.fitness/49 < 2 ) |
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| 234 | |
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| 235 | |
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| 236 | #fit with park test |
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| 237 | fitter = Fit('park') |
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| 238 | fitter.set_data(data1,1,qmin=0, qmax=7) |
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| 239 | fitter.set_model(model,1,pars1) |
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| 240 | fitter.set_data(data2,1,qmin=1,qmax=10) |
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| 241 | fitter.select_problem_for_fit(Uid=1,value=1) |
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| 242 | result2 = fitter.fit() |
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| 243 | |
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| 244 | self.assert_(result2) |
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| 245 | self.assertTrue( ( math.fabs(result2.pvec[0]-4)/3 == result2.stderr[0] ) or |
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| 246 | ( math.fabs(result2.pvec[0]-4)/3 < result2.stderr[0]) ) |
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| 247 | |
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| 248 | self.assertTrue( ( math.fabs(result2.pvec[1]-2.5)/3 == result2.stderr[1] ) or |
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| 249 | ( math.fabs(result2.pvec[1]-2.5)/3 < result2.stderr[1]) ) |
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| 250 | self.assertTrue(result2.fitness/49 < 2) |
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| 251 | # compare fit result result for scipy and park |
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| 252 | self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) |
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| 253 | self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) |
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| 254 | self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) |
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| 255 | self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) |
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| 256 | self.assertAlmostEquals( result1.fitness,result2.fitness ) |
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| 257 | |
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