""" Unit tests for fitting module @author Gervaise Alina """ import unittest from sans.fit.AbstractFitEngine import Model import math class testFitModule(unittest.TestCase): """ test fitting """ def test1(self): """ Fit 1 data (testdata_line.txt)and 1 model(lineModel) """ #load data from DataLoader.loader import Loader data = Loader().load("testdata_line.txt") #Importing the Fit module from sans.fit.Fitting import Fit fitter = Fit('scipy') # Receives the type of model for the fitting from sans.models.LineModel import LineModel model1 = LineModel() model1.name = "M1" model = Model(model1) #fit with scipy test pars1= ['param1','param2'] fitter.set_data(data,1) try:fitter.set_model(model,1,pars1) except ValueError,msg: print "ValueError was raised: "+str(msg) #assert str(msg)=="wrong paramter %s used to set model %s. Choose\ # parameter name within %s"%('param1', model.model.name,str(model.model.getParamList())) else: raise AssertError,"No error raised for scipy fitting with wrong parameters name to fit" pars1= ['A','B'] fitter.set_model(model,1,pars1) fitter.select_problem_for_fit(id=1,value=1) result1 = fitter.fit() self.assert_(result1) self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] ) self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1]) self.assertTrue( result1.fitness/len(data.x) < 2 ) #fit with park test fitter = Fit('park') fitter.set_data(data,1) fitter.set_model(model,1,pars1) fitter.select_problem_for_fit(id=1,value=1) result2 = fitter.fit() self.assert_(result2) self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) self.assertTrue( result2.fitness/len(data.x) < 2) # compare fit result result for scipy and park self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) self.assertAlmostEquals( result1.fitness, result2.fitness/len(data.x),1 ) def test2(self): """ fit 2 data and 2 model with no constrainst""" #load data from DataLoader.loader import Loader l = Loader() data1=l.load("testdata_line.txt") data2=l.load("testdata_line1.txt") #Importing the Fit module from sans.fit.Fitting import Fit fitter = Fit('scipy') # Receives the type of model for the fitting from sans.models.LineModel import LineModel model11 = LineModel() model11.name= "M1" model22 = LineModel() model11.name= "M2" model1 = Model(model11) model2 = Model(model22) #fit with scipy test pars1= ['A','B'] fitter.set_data(data1,1) fitter.set_model(model1,1,pars1) fitter.select_problem_for_fit(id=1,value=0) fitter.set_data(data2,2) fitter.set_model(model2,2,pars1) fitter.select_problem_for_fit(id=2,value=0) try: result1 = fitter.fit() except RuntimeError,msg: assert str(msg)=="No Assembly scheduled for Scipy fitting." else: raise AssertError,"No error raised for scipy fitting with no model" fitter.select_problem_for_fit(id=1,value=1) fitter.select_problem_for_fit(id=2,value=1) try: result1 = fitter.fit() except RuntimeError,msg: assert str(msg)=="Scipy can't fit more than a single fit problem at a time." else: raise AssertError,"No error raised for scipy fitting with more than 2 models" #fit with park test fitter = Fit('park') fitter.set_data(data1,1) fitter.set_model(model1,1,pars1) fitter.set_data(data2,2) fitter.set_model(model2,2,pars1) fitter.select_problem_for_fit(id=1,value=1) fitter.select_problem_for_fit(id=2,value=1) result2 = fitter.fit() self.assert_(result2) self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) self.assertTrue( result2.fitness/(len(data1.x)+len(data2.x)) < 2) def test3(self): """ fit 2 data and 2 model with 1 constrainst""" #load data from DataLoader.loader import Loader l = Loader() data1= l.load("testdata_line.txt") data2= l.load("testdata_cst.txt") # Receives the type of model for the fitting from sans.models.LineModel import LineModel model11 = LineModel() model11.name= "line" model11.setParam("A", 1.0) model11.setParam("B",1.0) from sans.models.Constant import Constant model22 = Constant() model22.name= "cst" model22.setParam("value", 1.0) model1 = Model(model11) model2 = Model(model22) model1.set(A=4) model1.set(B=3) # Constraint the constant value to be equal to parameter B (the real value is 2.5) model2.set(value='line.B') #fit with scipy test pars1= ['A','B'] pars2= ['value'] #Importing the Fit module from sans.fit.Fitting import Fit fitter = Fit('park') fitter.set_data(data1,1) fitter.set_model(model1,1,pars1) fitter.set_data(data2,2,smearer=None) fitter.set_model(model2,2,pars2) fitter.select_problem_for_fit(id=1,value=1) fitter.select_problem_for_fit(id=2,value=1) result2 = fitter.fit() self.assert_(result2) self.assertTrue( math.fabs(result2.pvec[0]-4.0)/3. <= result2.stderr[0]) self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3. <= result2.stderr[1]) self.assertTrue( result2.fitness/(len(data1.x)+len(data2.x)) < 2) def test4(self): """ fit 2 data concatenates with limited range of x and one model """ #load data from DataLoader.loader import Loader l = Loader() data1 = l.load("testdata_line.txt") data2 = l.load("testdata_line1.txt") # Receives the type of model for the fitting from sans.models.LineModel import LineModel model1 = LineModel() model1.name= "M1" model1.setParam("A", 1.0) model1.setParam("B",1.0) model = Model(model1) #fit with scipy test pars1= ['A','B'] #Importing the Fit module from sans.fit.Fitting import Fit fitter = Fit('scipy') fitter.set_data(data1,1,qmin=0, qmax=7) fitter.set_model(model,1,pars1) fitter.set_data(data2,1,qmin=1,qmax=10) fitter.select_problem_for_fit(id=1,value=1) result1 = fitter.fit() self.assert_(result1) self.assertTrue( math.fabs(result1.pvec[0]-4)/3 <= result1.stderr[0] ) self.assertTrue( math.fabs(result1.pvec[1]-2.5)/3 <= result1.stderr[1]) self.assertTrue( result1.fitness/len(data1.x) < 2 ) #fit with park test fitter = Fit('park') fitter.set_data(data1,1,qmin=0, qmax=7) fitter.set_model(model,1,pars1) fitter.set_data(data2,1,qmin=1,qmax=10) fitter.select_problem_for_fit(id=1,value=1) result2 = fitter.fit() self.assert_(result2) self.assertTrue( math.fabs(result2.pvec[0]-4)/3 <= result2.stderr[0] ) self.assertTrue( math.fabs(result2.pvec[1]-2.5)/3 <= result2.stderr[1] ) self.assertTrue( result2.fitness/len(data1.x) < 2) # compare fit result result for scipy and park self.assertAlmostEquals( result1.pvec[0], result2.pvec[0] ) self.assertAlmostEquals( result1.pvec[1],result2.pvec[1] ) self.assertAlmostEquals( result1.stderr[0],result2.stderr[0] ) self.assertAlmostEquals( result1.stderr[1],result2.stderr[1] ) self.assertTrue( result2.fitness/(len(data2.x)+len(data1.x)) < 2 )