def test_cylinder_fit(self):
""" Simple cylinder model fit """
out = Loader().load("cyl_400_20.txt")
fitter = Fit('bumps')
# Receives the type of model for the fitting
from sas.models.CylinderModel import CylinderModel
model = CylinderModel()
model.setParam('sldCyl', 1)
model.setParam('sldSolv', 0)
model.setParam('scale', 1e-10)
pars1 = ['length', 'radius', 'scale']
fitter.set_data(out, 1)
fitter.set_model(model, 1, pars1, constraints=())
fitter.select_problem_for_fit(id=1, value=1)
result1, = fitter.fit()
#print result1
#print result1.__dict__
self.assert_(result1)
self.assertTrue(len(result1.pvec) > 0 or len(result1.pvec) == 0)
self.assertTrue(len(result1.stderr) > 0 or len(result1.stderr) == 0)
self.assertTrue(
math.fabs(result1.pvec[0] - 400.0) / 3.0 < result1.stderr[0])
self.assertTrue(
math.fabs(result1.pvec[1] - 20.0) / 3.0 < result1.stderr[1])
self.assertTrue(
math.fabs(result1.pvec[2] - 9.0e-12) / 3.0 < result1.stderr[2])
self.assertTrue(result1.fitness < 1.0)
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 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 test_slit(self):
smear = smear_selection(self.data_slit)
self.assertEqual(smear.__class__.__name__, 'SlitSmearer')
fitter = Fit('bumps')
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_slit, 1)
fitter._engine.fit_arrange_dict[1].data_list[0].smearer = smear
fitter._engine.fit_arrange_dict[1].data_list[0].qmax = 0.003
# Model
fitter.set_model(Model(self.sphere), 1, ['radius', 'scale'])
fitter.select_problem_for_fit(id=1, value=1)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
self.assertTrue(math.fabs(result1.pvec[0] - 2340) < 20)
self.assertTrue(math.fabs(result1.pvec[1] - 0.010) < 0.002)
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_reso(self):
# Let the data module find out what smearing the
# data needs
smear = smear_selection(self.data_res)
self.assertEqual(smear.__class__.__name__, 'QSmearer')
# Fit
fitter = Fit('bumps')
# Data: right now this is the only way to set the smearer object
# We should improve that and have a way to get access to the
# data for a given fit.
fitter.set_data(self.data_res, 1)
fitter._engine.fit_arrange_dict[1].data_list[0].smearer = smear
# Model: maybe there's a better way to do this.
# Ideally we should have to create a new model from our sas model.
fitter.set_model(Model(self.sphere), 1,
['radius', 'scale', 'background'])
# Why do we have to do this...?
fitter.select_problem_for_fit(id=1, value=1)
# Perform the fit (might take a while)
result1, = fitter.fit()
#print "v",result1.pvec
#print "dv",result1.stderr
#print "chisq(v)",result1.fitness
self.assertTrue(math.fabs(result1.pvec[0] - 5000) < 20)
self.assertTrue(math.fabs(result1.pvec[1] - 0.48) < 0.02)
self.assertTrue(math.fabs(result1.pvec[2] - 0.060) < 0.002)
def _fit(self, name="bumps"):
""" return fit result """
fitter = Fit(name)
fitter.set_data(self.data,1)
fitter.set_model(self.model,1,self.pars1)
fitter.select_problem_for_fit(id=1,value=1)
result1, = fitter.fit()
self.assert_(result1)
self.assertTrue(len(result1.pvec)>0 or len(result1.pvec)==0 )
self.assertTrue(len(result1.stderr)> 0 or len(result1.stderr)==0)
self.assertTrue( math.fabs(result1.pvec[0]-400.0)/3.0 < result1.stderr[0] )
self.assertTrue( math.fabs(result1.pvec[1]-20.0)/3.0 < result1.stderr[1] )
self.assertTrue( math.fabs(result1.pvec[2]-1.0)/3.0 < result1.stderr[2] )
self.assertTrue( result1.fitness < 1.0 )
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 test_cyl_times_square(self):
""" Simple cylinder model fit """
out = Loader().load("cyl_400_20.txt")
data = Data1D(x=out.x, y=out.y, dx=out.dx, dy=out.dy)
# Receives the type of model for the fitting
model1 = MultiplicationModel(CylinderModel(), SquareWellStructure())
model1.setParam('background', 0.0)
model1.setParam('sldCyl', 3e-006)
model1.setParam('sldSolv', 0.0)
model1.setParam('length', 420)
model1.setParam('radius', 40)
model1.setParam('scale_factor', 2)
model1.setParam('volfraction', 0.04)
model1.setParam('welldepth', 1.5)
model1.setParam('wellwidth', 1.2)
model = Model(model1)
pars1 = ['length', 'radius', 'scale_factor']
fitter = Fit('bumps')
fitter.set_data(data, 1)
fitter.set_model(model, 1, pars1)
fitter.select_problem_for_fit(id=1, value=1)
result1, = fitter.fit()
self.assert_(result1)
self.assertTrue(len(result1.pvec) >= 0)
self.assertTrue(len(result1.stderr) >= 0)
#print "results",list(zip(result1.pvec, result1.stderr))
self.assertTrue(
math.fabs(result1.pvec[0] - 612) / 3.0 <= result1.stderr[0])
self.assertTrue(
math.fabs(result1.pvec[1] - 20.3) / 3.0 <= result1.stderr[1])
self.assertTrue(
math.fabs(result1.pvec[2] - 25) / 3.0 <= result1.stderr[2])
self.assertTrue(result1.fitness / len(data.x) < 1.0)
def test_without_resolution(self):
""" Simple cylinder model fit """
out = Loader().load("cyl_400_20.txt")
# This data file has not error, add them
#out.dy = out.y
fitter = Fit('bumps')
fitter.set_data(out, 1)
# Receives the type of model for the fitting
model1 = CylinderModel()
model1.setParam("scale", 1.0)
model1.setParam("radius", 18)
model1.setParam("length", 397)
model1.setParam("sldCyl", 3e-006)
model1.setParam("sldSolv", 0.0)
model1.setParam("background", 0.0)
model = Model(model1)
pars1 = ['length', 'radius', 'scale']
fitter.set_model(model, 1, pars1)
# What the hell is this line for?
fitter.select_problem_for_fit(id=1, value=1)
result1, = fitter.fit()
#print "result1",result1
self.assert_(result1)
self.assertTrue(len(result1.pvec) > 0)
self.assertTrue(len(result1.stderr) > 0)
self.assertTrue(
math.fabs(result1.pvec[0] - 400.0) / 3.0 < result1.stderr[0])
self.assertTrue(
math.fabs(result1.pvec[1] - 20.0) / 3.0 < result1.stderr[1])
self.assertTrue(
math.fabs(result1.pvec[2] - 1) / 3.0 < result1.stderr[2])
self.assertTrue(result1.fitness < 1.0)
def _reset_helper(self, path=None, engine="bumps", npts=NPTS):
"""
Set value to fitter engine and prepare inputs for map function
"""
for i in range(npts):
data = Loader().load(path)
fitter = Fit(engine)
#create model
model = CylinderModel()
model.setParam('scale', 1.0)
model.setParam('radius', 20.0)
model.setParam('length', 400.0)
model.setParam('sldCyl', 4e-006)
model.setParam('sldSolv', 1e-006)
model.setParam('background', 0.0)
for param in model.dispersion.keys():
model.set_dispersion(param, self.polydisp['gaussian']())
model.setParam('cyl_phi.width', 10)
model.setParam('cyl_phi.npts', 3)
model.setParam('cyl_theta.nsigmas', 10)
# for 2 data cyl_theta = 60.0 [deg] cyl_phi= 60.0 [deg]
fitter.set_model(model, i, self.param_to_fit,
self.list_of_constraints)
#smear data
current_smearer = smear_selection(data, model)
import cPickle
p = cPickle.dumps(current_smearer)
sm = cPickle.loads(p)
fitter.set_data(data=data,
id=i,
smearer=current_smearer,
qmin=self.qmin,
qmax=self.qmax)
fitter.select_problem_for_fit(id=i, value=1)
self.list_of_fitter.append(copy.deepcopy(fitter))
self.list_of_function.append('fit')
self.list_of_mapper.append(classMapper)