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 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, 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('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)
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 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)
