def test2(self): """ fit 2 data and 2 model with no constrainst""" #load data l = Loader() data1=l.load("testdata_line.txt") data1.name = data1.filename data2=l.load("testdata_line1.txt") data2.name = data2.filename #Importing the Fit module fitter = Fit() # Receives the type of model for the fitting model11 = LineModel() model11.name= "M1" model22 = LineModel() model11.name= "M2" model1 = Model(model11,data1) model2 = Model(model22,data2) 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(handler=FitHandler()) except RuntimeError,msg: assert str(msg)=="Nothing to fit"
def test2(self): """ fit 2 data and 2 model with no constrainst""" #load data l = Loader() data1 = l.load("testdata_line.txt") data1.name = data1.filename data2 = l.load("testdata_line1.txt") data2.name = data2.filename #Importing the Fit module fitter = Fit('bumps') # Receives the type of model for the fitting model11 = LineModel() model11.name = "M1" model22 = LineModel() model11.name = "M2" model1 = Model(model11, data1) model2 = Model(model22, data2) 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(handler=FitHandler()) except RuntimeError, msg: assert str(msg) == "Nothing to fit"
def fit_single(self, isdream=False): fitter = Fit() data = Loader().load("testdata_line.txt") data.name = data.filename fitter.set_data(data,1) # Receives the type of model for the fitting model1 = LineModel() model1.name = "M1" model = Model(model1,data) pars1= ['A','B'] fitter.set_model(model,1,pars1) fitter.select_problem_for_fit(id=1,value=1) result1, = fitter.fit(handler=FitHandler()) # The target values were generated from the following statements p,s,fx = result1.pvec, result1.stderr, result1.fitness #print "p0,p1,s0,s1,fx = %g, %g, %g, %g, %g"%(p[0],p[1],s[0],s[1],fx) p0,p1,s0,s1,fx_ = 3.68353, 2.61004, 0.336186, 0.105244, 1.20189 if isdream: # Dream is not a minimizer: just check that the fit is within # uncertainty self.assertTrue( abs(p[0]-p0) <= s0 ) self.assertTrue( abs(p[1]-p1) <= s1 ) else: self.assertTrue( abs(p[0]-p0) <= 1e-5 ) self.assertTrue( abs(p[1]-p1) <= 1e-5 ) self.assertTrue( abs(fx-fx_) <= 1e-5 )
def test4(self): """ fit 2 data concatenates with limited range of x and one model """ #load data l = Loader() data1 = l.load("testdata_line.txt") data1.name = data1.filename data2 = l.load("testdata_line1.txt") data2.name = data2.filename # Receives the type of model for the fitting model1 = LineModel() model1.name= "M1" model1.setParam("A", 1.0) model1.setParam("B",1.0) model = Model(model1,data1) pars1= ['A','B'] #Importing the Fit module fitter = Fit() 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(handler=FitHandler()) 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)
def fit_single(self, fitter_name, isdream=False): fitter = Fit(fitter_name) data = Loader().load("testdata_line.txt") data.name = data.filename fitter.set_data(data, 1) # Receives the type of model for the fitting model1 = LineModel() model1.name = "M1" model = Model(model1, data) pars1 = ['A', 'B'] fitter.set_model(model, 1, pars1) fitter.select_problem_for_fit(id=1, value=1) result1, = fitter.fit(handler=FitHandler()) # The target values were generated from the following statements p, s, fx = result1.pvec, result1.stderr, result1.fitness #print "p0,p1,s0,s1,fx = %g, %g, %g, %g, %g"%(p[0],p[1],s[0],s[1],fx) p0, p1, s0, s1, fx_ = 3.68353, 2.61004, 0.336186, 0.105244, 1.20189 if isdream: # Dream is not a minimizer: just check that the fit is within # uncertainty self.assertTrue(abs(p[0] - p0) <= s0) self.assertTrue(abs(p[1] - p1) <= s1) else: self.assertTrue(abs(p[0] - p0) <= 1e-5) self.assertTrue(abs(p[1] - p1) <= 1e-5) self.assertTrue(abs(fx - fx_) <= 1e-5)
def test4(self): """ fit 2 data concatenates with limited range of x and one model """ #load data l = Loader() data1 = l.load("testdata_line.txt") data1.name = data1.filename data2 = l.load("testdata_line1.txt") data2.name = data2.filename # Receives the type of model for the fitting model1 = LineModel() model1.name = "M1" model1.setParam("A", 1.0) model1.setParam("B", 1.0) model = Model(model1, data1) pars1 = ['A', 'B'] #Importing the Fit module fitter = Fit('bumps') 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(handler=FitHandler()) 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)
def test_bad_pars(self): fitter = Fit() data = Loader().load("testdata_line.txt") data.name = data.filename fitter.set_data(data,1) model1 = LineModel() model1.name = "M1" model = Model(model1, data) pars1= ['param1','param2'] try: fitter.set_model(model,1,pars1) except ValueError,exc: #print "ValueError was correctly raised: "+str(msg) assert str(exc).startswith('parameter param1')
def test_bad_pars(self): fitter = Fit('bumps') data = Loader().load("testdata_line.txt") data.name = data.filename fitter.set_data(data, 1) model1 = LineModel() model1.name = "M1" model = Model(model1, data) pars1 = ['param1', 'param2'] try: fitter.set_model(model, 1, pars1) except ValueError, exc: #print "ValueError was correctly raised: "+str(msg) assert str(exc).startswith('parameter param1')
def test_constraints(self): """ fit 2 data and 2 model with 1 constrainst""" #load data l = Loader() data1= l.load("testdata_line.txt") data1.name = data1.filename data2= l.load("testdata_cst.txt") data2.name = data2.filename # Receives the type of model for the fitting model11 = LineModel() model11.name= "line" model11.setParam("A", 1.0) model11.setParam("B",1.0) model22 = Constant() model22.name= "cst" model22.setParam("value", 1.0) model1 = Model(model11,data1) model2 = Model(model22,data2) 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') pars1= ['A','B'] pars2= ['value'] #Importing the Fit module fitter = Fit() fitter.set_data(data1,1) fitter.set_model(model1,1,pars1) fitter.set_data(data2,2,smearer=None) fitter.set_model(model2,2,pars2,constraints=[("value","line.B")]) fitter.select_problem_for_fit(id=1,value=1) fitter.select_problem_for_fit(id=2,value=1) R1,R2 = fitter.fit(handler=FitHandler()) self.assertTrue( math.fabs(R1.pvec[0]-4.0)/3. <= R1.stderr[0]) self.assertTrue( math.fabs(R1.pvec[1]-2.5)/3. <= R1.stderr[1]) self.assertTrue( R1.fitness/(len(data1.x)+len(data2.x)) < 2)
def test_constraints(self): """ fit 2 data and 2 model with 1 constrainst""" #load data l = Loader() data1 = l.load("testdata_line.txt") data1.name = data1.filename data2 = l.load("testdata_cst.txt") data2.name = data2.filename # Receives the type of model for the fitting model11 = LineModel() model11.name = "line" model11.setParam("A", 1.0) model11.setParam("B", 1.0) model22 = Constant() model22.name = "cst" model22.setParam("value", 1.0) model1 = Model(model11, data1) model2 = Model(model22, data2) 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') pars1 = ['A', 'B'] pars2 = ['value'] #Importing the Fit module fitter = Fit('bumps') fitter.set_data(data1, 1) fitter.set_model(model1, 1, pars1) fitter.set_data(data2, 2, smearer=None) fitter.set_model(model2, 2, pars2, constraints=[("value", "line.B")]) fitter.select_problem_for_fit(id=1, value=1) fitter.select_problem_for_fit(id=2, value=1) R1, R2 = fitter.fit(handler=FitHandler()) self.assertTrue(math.fabs(R1.pvec[0] - 4.0) / 3. <= R1.stderr[0]) self.assertTrue(math.fabs(R1.pvec[1] - 2.5) / 3. <= R1.stderr[1]) self.assertTrue(R1.fitness / (len(data1.x) + len(data2.x)) < 2)