def setup_method(self):
self.pth = os.path.dirname(os.path.abspath(__file__))
self.si = SLD(2.07, name='Si')
self.sio2 = SLD(3.47, name='SiO2')
self.d2o = SLD(6.36, name='d2o')
self.h2o = SLD(-0.56, name='h2o')
self.cm3 = SLD(3.5, name='cm3')
self.polymer = SLD(2, name='polymer')
self.sio2_l = self.sio2(40, 3)
self.polymer_l = self.polymer(200, 3)
self.structure = (self.si | self.sio2_l | self.polymer_l
| self.d2o(0, 3))
fname = os.path.join(self.pth, 'c_PLP0011859_q.txt')
self.dataset = ReflectDataset(fname)
self.model = ReflectModel(self.structure, bkg=2e-7)
self.objective = Objective(self.model,
self.dataset,
use_weights=False,
transform=Transform('logY'))
self.global_objective = GlobalObjective([self.objective])
def test_multipledataset_corefinement(self):
# test corefinement of three datasets
data361 = ReflectDataset(os.path.join(self.pth, 'e361r.txt'))
data365 = ReflectDataset(os.path.join(self.pth, 'e365r.txt'))
data366 = ReflectDataset(os.path.join(self.pth, 'e366r.txt'))
si = SLD(2.07, name='Si')
sio2 = SLD(3.47, name='SiO2')
d2o = SLD(6.36, name='d2o')
h2o = SLD(-0.56, name='h2o')
cm3 = SLD(3.47, name='cm3')
polymer = SLD(1, name='polymer')
structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)
structure365 = si | structure361[1] | structure361[2] | cm3(0, 3)
structure366 = si | structure361[1] | structure361[2] | h2o(0, 3)
structure365[-1].rough = structure361[-1].rough
structure366[-1].rough = structure361[-1].rough
structure361[1].thick.setp(vary=True, bounds=(0, 20))
structure361[2].thick.setp(value=200., bounds=(200., 250.), vary=True)
structure361[2].sld.real.setp(vary=True, bounds=(0, 2))
structure361[2].vfsolv.setp(value=5., bounds=(0., 100.), vary=True)
model361 = ReflectModel(structure361, bkg=2e-5)
model365 = ReflectModel(structure365, bkg=2e-5)
model366 = ReflectModel(structure366, bkg=2e-5)
model361.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
model365.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
model366.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
objective361 = Objective(model361, data361)
objective365 = Objective(model365, data365)
objective366 = Objective(model366, data366)
global_objective = GlobalObjective(
[objective361, objective365, objective366])
# are the right numbers of parameters varying?
assert_equal(len(global_objective.varying_parameters()), 7)
# can we set the parameters?
global_objective.setp(np.array([1e-5, 10, 212, 1, 10, 1e-5, 1e-5]))
f = CurveFitter(global_objective)
f.fit()
indiv_chisqr = np.sum(
[objective.chisqr() for objective in global_objective.objectives])
# the overall chi2 should be sum of individual chi2
global_chisqr = global_objective.chisqr()
assert_almost_equal(global_chisqr, indiv_chisqr)
# now check that the parameters were held in common correctly.
slabs361 = structure361.slabs()
slabs365 = structure365.slabs()
slabs366 = structure366.slabs()
assert_equal(slabs365[0:2, 0:5], slabs361[0:2, 0:5])
assert_equal(slabs366[0:2, 0:5], slabs361[0:2, 0:5])
assert_equal(slabs365[-1, 3], slabs361[-1, 3])
assert_equal(slabs366[-1, 3], slabs361[-1, 3])
# check that the residuals are the correct lengths
res361 = objective361.residuals()
res365 = objective365.residuals()
res366 = objective366.residuals()
res_global = global_objective.residuals()
assert_allclose(res_global[0:len(res361)], res361, rtol=1e-5)
assert_allclose(res_global[len(res361):len(res361) + len(res365)],
res365,
rtol=1e-5)
assert_allclose(res_global[len(res361) + len(res365):],
res366,
rtol=1e-5)
repr(global_objective)
class TestGlobalFitting(object):
def setup_method(self):
self.pth = os.path.dirname(os.path.abspath(__file__))
self.si = SLD(2.07, name='Si')
self.sio2 = SLD(3.47, name='SiO2')
self.d2o = SLD(6.36, name='d2o')
self.h2o = SLD(-0.56, name='h2o')
self.cm3 = SLD(3.5, name='cm3')
self.polymer = SLD(2, name='polymer')
self.sio2_l = self.sio2(40, 3)
self.polymer_l = self.polymer(200, 3)
self.structure = (self.si | self.sio2_l | self.polymer_l
| self.d2o(0, 3))
fname = os.path.join(self.pth, 'c_PLP0011859_q.txt')
self.dataset = ReflectDataset(fname)
self.model = ReflectModel(self.structure, bkg=2e-7)
self.objective = Objective(self.model,
self.dataset,
use_weights=False,
transform=Transform('logY'))
self.global_objective = GlobalObjective([self.objective])
def test_residuals_length(self):
# the residuals should be the same length as the data
residuals = self.global_objective.residuals()
assert_equal(residuals.size, len(self.dataset))
def test_globalfitting(self):
# smoke test for can the global fitting run?
# also tests that global fitting gives same output as
# normal fitting (for a single dataset)
self.model.scale.setp(vary=True, bounds=(0.1, 2))
self.model.bkg.setp(vary=True, bounds=(1e-10, 8e-6))
self.structure[-1].rough.setp(vary=True, bounds=(0.2, 6))
self.sio2_l.thick.setp(vary=True, bounds=(0.2, 80))
self.polymer_l.thick.setp(bounds=(0.01, 400), vary=True)
self.polymer_l.sld.real.setp(vary=True, bounds=(0.01, 4))
self.objective.transform = Transform('logY')
starting = np.array(self.objective.parameters)
with np.errstate(invalid='raise'):
g = CurveFitter(self.global_objective)
res_g = g.fit()
# need the same starting point
self.objective.setp(starting)
f = CurveFitter(self.objective)
res_f = f.fit()
# individual and global should give the same fit.
assert_almost_equal(res_g.x, res_f.x)
def test_multipledataset_corefinement(self):
# test corefinement of three datasets
data361 = ReflectDataset(os.path.join(self.pth, 'e361r.txt'))
data365 = ReflectDataset(os.path.join(self.pth, 'e365r.txt'))
data366 = ReflectDataset(os.path.join(self.pth, 'e366r.txt'))
si = SLD(2.07, name='Si')
sio2 = SLD(3.47, name='SiO2')
d2o = SLD(6.36, name='d2o')
h2o = SLD(-0.56, name='h2o')
cm3 = SLD(3.47, name='cm3')
polymer = SLD(1, name='polymer')
structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)
structure365 = si | structure361[1] | structure361[2] | cm3(0, 3)
structure366 = si | structure361[1] | structure361[2] | h2o(0, 3)
structure365[-1].rough = structure361[-1].rough
structure366[-1].rough = structure361[-1].rough
structure361[1].thick.setp(vary=True, bounds=(0, 20))
structure361[2].thick.setp(value=200., bounds=(200., 250.), vary=True)
structure361[2].sld.real.setp(vary=True, bounds=(0, 2))
structure361[2].vfsolv.setp(value=5., bounds=(0., 100.), vary=True)
model361 = ReflectModel(structure361, bkg=2e-5)
model365 = ReflectModel(structure365, bkg=2e-5)
model366 = ReflectModel(structure366, bkg=2e-5)
model361.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
model365.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
model366.bkg.setp(vary=True, bounds=(1e-6, 5e-5))
objective361 = Objective(model361, data361)
objective365 = Objective(model365, data365)
objective366 = Objective(model366, data366)
global_objective = GlobalObjective(
[objective361, objective365, objective366])
# are the right numbers of parameters varying?
assert_equal(len(global_objective.varying_parameters()), 7)
# can we set the parameters?
global_objective.setp(np.array([1e-5, 10, 212, 1, 10, 1e-5, 1e-5]))
f = CurveFitter(global_objective)
f.fit()
indiv_chisqr = np.sum(
[objective.chisqr() for objective in global_objective.objectives])
# the overall chi2 should be sum of individual chi2
global_chisqr = global_objective.chisqr()
assert_almost_equal(global_chisqr, indiv_chisqr)
# now check that the parameters were held in common correctly.
slabs361 = structure361.slabs()
slabs365 = structure365.slabs()
slabs366 = structure366.slabs()
assert_equal(slabs365[0:2, 0:5], slabs361[0:2, 0:5])
assert_equal(slabs366[0:2, 0:5], slabs361[0:2, 0:5])
assert_equal(slabs365[-1, 3], slabs361[-1, 3])
assert_equal(slabs366[-1, 3], slabs361[-1, 3])
# check that the residuals are the correct lengths
res361 = objective361.residuals()
res365 = objective365.residuals()
res366 = objective366.residuals()
res_global = global_objective.residuals()
assert_allclose(res_global[0:len(res361)], res361, rtol=1e-5)
assert_allclose(res_global[len(res361):len(res361) + len(res365)],
res365,
rtol=1e-5)
assert_allclose(res_global[len(res361) + len(res365):],
res366,
rtol=1e-5)
repr(global_objective)
models = []
t = len(cont)
for i in range(t):
models.append(ReflectModel(structures[i]))
models[i].scale.setp(vary=True, bounds=(0.005, 10))
models[i].bkg.setp(datasets[i].y[-1], vary=True, bounds=(1e-4, 1e-10))
objectives = []
t = len(cont)
for i in range(t):
objectives.append(
Objective(models[i], datasets[i], transform=Transform("YX4")))
global_objective = GlobalObjective(objectives)
chain = refnx.analysis.load_chain("{}_chain.txt".format(anal_dir))
pchain = refnx.analysis.process_chain(global_objective, chain)
para_labels = [
'_scale_{}_{}'.format(sp, cont[0]), '_bkg_{}_{}'.format(sp, cont[0]),
'-d_h_{}'.format(sp), '-d_t_{}'.format(sp), '_rough_{}'.format(sp),
'_scale_{}_{}'.format(sp, cont[1]), '_bkg_{}_{}'.format(sp, cont[1]),
'_scale_{}_{}'.format(sp, cont[2]), '_bkg_{}_{}'.format(sp, cont[2]),
'_scale_{}_{}'.format(sp, cont[3]), '_bkg_{}_{}'.format(sp, cont[3]),
'_scale_{}_{}'.format(sp, cont[4]), '_bkg_{}_{}'.format(sp, cont[4]),
'_scale_{}_{}'.format(sp, cont[5]), '_bkg_{}_{}'.format(sp, cont[5]),
'_scale_{}_{}'.format(sp, cont[6]), '_bkg_{}_{}'.format(sp, cont[6])
]
def getObjective(data, nLayers, bs_contrast_layer=None,
contrast_layer=None,
limits = None, doMCMC=False,
logpExtra=None, onlyStructure=False,
both=False, globalObjective=False):
if globalObjective:
if bs_contrast_layer is None:
bs_contrast_layer = 6
if contrast_layer is None:
contrast_layer = 1
# print("data, nLayers, bs_contrast_layer=None,\n contrast_layer=None,\nlimits = None, doMCMC=False,\nlogpExtra=None, onlyStructure=False,\nboth=False, globalObjective=False: ",
# data, nLayers, bs_contrast_layer,
# contrast_layer,
# limits, doMCMC,
# logpExtra, onlyStructure,
# both, globalObjective)
air = SLD(0,name="air layer")
airSlab = air(10,0)
sio2 = SLD(10,name="bottem layer")
sio2Slab = sio2(10,0)
if limits is None:
limits = [350,50,4,6]
# maxThick = 350
# lowerThick = 50
# upperThick = maxThick - nLayers*lowerThick
# lowerB = 4
# upperB = 6
maxThick = float(limits[0])
lowerThick = limits[1]
upperThick = maxThick - nLayers*lowerThick
lowerB = limits[2]
upperB = limits[3]
if globalObjective:
thick_contrast_layer=Parameter(maxThick/nLayers,
"layer1 thickness")
rough_contrast_layer=Parameter(0,
"layer0/contrast roughness")
sldcontrastA=SLD(5,name="contrast A layer")
sldcontrastASlab= sldcontrastA(thick_contrast_layer,rough_contrast_layer)
sldcontrastASlab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sldcontrastASlab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
sldcontrastB=SLD(5,name="contrast B layer")
sldcontrastBSlab = sldcontrastB(thick_contrast_layer,rough_contrast_layer)
sldcontrastBSlab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sldcontrastBSlab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
if nLayers>=1 and not globalObjective:
sld1 = SLD(5,name="first layer")
sld1Slab = sld1(maxThick/nLayers,0)
sld1Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sld1Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
if nLayers>=2:
sld2 = SLD(5,name="second layer")
sld2Slab = sld2(maxThick/nLayers,0)
sld2Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sld2Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
if nLayers>=3:
sld3 = SLD(5,name="third layer")
sld3Slab = sld3(maxThick/nLayers,0)
sld3Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sld3Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
if nLayers>=4:
sld4 = SLD(5,name="forth layer")
sld4Slab = sld4(maxThick/nLayers,0)
sld4Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
sld4Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
# if nLayers>=1:
# sld1Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
# sld1Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
# if nLayers>=2:
# sld2Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
# sld2Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
# if nLayers>=3:
# sld3Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
# sld3Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
# if nLayers>=4:
# sld4Slab.thick.setp(vary=True, bounds=(lowerThick,upperThick))
# sld4Slab.sld.real.setp(vary=True, bounds=(lowerB,upperB))
if globalObjective and contrast_layer==1:
if nLayers==1:
structure1 = airSlab|sldcontrastASlab|sio2Slab
structure2 = airSlab|sldcontrastBSlab|sio2Slab
if nLayers==2:
structure1 = airSlab|sldcontrastASlab|sld2Slab|sio2Slab
structure2 = airSlab|sldcontrastBSlab|sld2Slab|sio2Slab
if nLayers==3:
structure1 = airSlab|sldcontrastASlab|sld2Slab|sld3Slab|sio2Slab
structure2 = airSlab|sldcontrastBSlab|sld2Slab|sld3Slab|sio2Slab
if nLayers==4:
structure1 = airSlab|sldcontrastASlab|sld2Slab|sld3Slab|sld4Slab|sio2Slab
structure2 = airSlab|sldcontrastBSlab|sld2Slab|sld3Slab|sld4Slab|sio2Slab
if onlyStructure:
returns = structure1,structure2
elif both:
model1 = ReflectModel(structure1, bkg=3e-6, dq=5.0)
model1.scale.setp(bounds=(0.85, 1.2), vary=True)
model1.bkg.setp(bounds=(1e-9, 9e-6), vary=True)
objective1 = Objective(model1, data[0],
transform=Transform('logY'),
logp_extra=logpExtra)
model2 = ReflectModel(structure2, bkg=3e-6, dq=5.0)
model2.scale.setp(bounds=(0.85, 1.2), vary=True)
model2.bkg.setp(bounds=(1e-9, 9e-6), vary=True)
objective2 = Objective(model2, data[1],
transform=Transform('logY'),
logp_extra=logpExtra)
returns = GlobalObjective([objective1, objective2]), structure1, structure2
print("GlobalObjective and 2 structures")
else:
model1 = ReflectModel(structure1, bkg=3e-6, dq=5.0)
model1.scale.setp(bounds=(0.85, 1.2), vary=True)
model1.bkg.setp(bounds=(1e-9, 9e-6), vary=True)
objective1 = Objective(model1, data[0],
transform=Transform('logY'),
logp_extra=logpExtra)
model2 = ReflectModel(structure2, bkg=3e-6, dq=5.0)
model2.scale.setp(bounds=(0.85, 1.2), vary=True)
model2.bkg.setp(bounds=(1e-9, 9e-6), vary=True)
objective2 = Objective(model2, data[1],
transform=Transform('logY'),
logp_extra=logpExtra)
returns = GlobalObjective([objective1, objective2])
elif not globalObjective:
if nLayers==1:
structure = airSlab|sld1Slab|sio2Slab
if nLayers==2:
structure = airSlab|sld1Slab|sld2Slab|sio2Slab
if nLayers==3:
structure = airSlab|sld1Slab|sld2Slab|sld3Slab|sio2Slab
if nLayers==4:
structure = airSlab|sld1Slab|sld2Slab|sld3Slab|sld4Slab|sio2Slab
if onlyStructure:
returns = structure
elif both:
model = ReflectModel(structure, bkg=3e-6, dq=5.0)
objective = Objective(model, data,
transform=Transform('logY'),
logp_extra=logpExtra)
returns = objective, structure
else:
model = ReflectModel(structure, bkg=3e-6, dq=5.0)
objective = Objective(model, data, transform=Transform('logY'),logp_extra=logpExtra)
returns = objective
else:
print("error contrast layer not at sld1Slab ie contrast_layer!=0")
# print(returns)
return returns
model_lipid3 = ReflectModel(structure_lipid3)
model_lipid3.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid3.bkg.setp(dataset3.y[-1], vary=False)
model_lipid4 = ReflectModel(structure_lipid4)
model_lipid4.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid4.bkg.setp(dataset4.y[-1], vary=False)
models = [model_lipid1, model_lipid2, model_lipid3, model_lipid4]
objective1 = Objective(model_lipid1, dataset1, transform=Transform('YX4'))
objective2 = Objective(model_lipid2, dataset2, transform=Transform('YX4'))
objective3 = Objective(model_lipid3, dataset3, transform=Transform('YX4'))
objective4 = Objective(model_lipid4, dataset4, transform=Transform('YX4'))
global_objective = GlobalObjective(
[objective1, objective2, objective3, objective4])
# The chain is read in by refnx, and processed to assigned it to the global objective.
# In[8]:
chain = refnx.analysis.load_chain('{}/{}/chain.txt'.format(
analysis_dir, lipid))
processed_chain = refnx.analysis.process_chain(global_objective, chain)
# The global objective is printed to check it is accurate.
# In[9]:
print(global_objective)
for i in range(t):
models.append(ReflectModel(structures[i]))
models[i].scale.setp(vary=True, bounds=(0.005, 10))
models[i].bkg.setp(datasets[i].y[-1], vary=False)
objectives = []
if neutron:
t = len(cont)
else:
t = sp.size
for i in range(t):
objectives.append(
Objective(models[i], datasets[i], transform=Transform("YX4")))
global_objective = GlobalObjective(objectives)
fitter = CurveFitter(global_objective)
np.random.seed(1)
res = fitter.fit("differential_evolution")
print(global_objective)
fitter.sample(200, random_state=1)
fitter.sampler.reset()
res = fitter.sample(
1000,
nthin=1,
random_state=1,
f="{}/{}_chain.txt".format(anal_dir, label),
)
model_lipid4 = ReflectModel(structure_lipid4)
model_lipid4.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid4.bkg.setp(dataset4.y[-1], vary=False)
models = [model_lipid1, model_lipid2, model_lipid3, model_lipid4]
# The global objective fitting object is defined and the fitting and MCMC performed.
# In[ ]:
objective1 = Objective(model_lipid1, dataset1, transform=Transform('YX4'))
objective2 = Objective(model_lipid2, dataset2, transform=Transform('YX4'))
objective3 = Objective(model_lipid3, dataset3, transform=Transform('YX4'))
objective4 = Objective(model_lipid4, dataset4, transform=Transform('YX4'))
global_objective = GlobalObjective(
[objective1, objective2, objective3, objective4])
# ## Fitting
#
# The differential evolution algorithm is used to find optimal parameters, before the MCMC algorithm probes the parameter space for 1000 steps.
# In[ ]:
fitter = CurveFitter(global_objective)
res = fitter.fit('differential_evolution', seed=1)
fitter.sample(200, random_state=1)
fitter.sampler.reset()
res = fitter.sample(1000,
nthin=1,
random_state=1,
model_lipid1 = ReflectModel(structure_lipid_1)
model_lipid1.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid1.bkg.setp(dataset1.y[-2], vary=False)
model_lipid2 = ReflectModel(structure_lipid_2)
model_lipid2.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid2.bkg.setp(dataset2.y[-2], vary=False)
models = [model_lipid1, model_lipid2]
structures = [structure_lipid_1, structure_lipid_2]
# building the global objective
objective_n1 = Objective(model_lipid1, dataset1, transform=Transform('YX4'))
objective_n2 = Objective(model_lipid2, dataset2, transform=Transform('YX4'))
global_objective = GlobalObjective([objective_n1, objective_n2])
# The chain is read in by refnx, and processed to assigned it to the global objective.
# In[ ]:
chain = refnx.analysis.load_chain('{}/{}/{}_chain_neutron.txt'.format(
analysis_dir, lipid, sp))
processed_chain = refnx.analysis.process_chain(global_objective, chain)
# The global objective is printed to check it is accurate.
# In[ ]:
print(global_objective)
model_lipid_1.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid_1.bkg.setp(dataset_1.y[-2], vary=False)
model_lipid_2 = ReflectModel(structure_lipid_2)
model_lipid_2.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid_2.bkg.setp(dataset_2.y[-2], vary=False)
# The global objective fitting object is defined and the fitting and MCMC performed.
# In[ ]:
# building the global objective
objective_n1 = Objective(model_lipid_1, dataset_1, transform=Transform('YX4'))
objective_n2 = Objective(model_lipid_2, dataset_2, transform=Transform('YX4'))
global_objective = GlobalObjective([objective_n1, objective_n2])
# ## Fitting
#
# The differential evolution algorithm is used to find optimal parameters, before the MCMC algorithm probes the parameter space for 1000 steps.
# In[ ]:
# A differential evolution algorithm is used to obtain an best fit
fitter = CurveFitter(global_objective)
# A seed is used to ensure reproduciblity
res = fitter.fit('differential_evolution', seed=1)
# The first 200*200 samples are binned
fitter.sample(200, random_state=1)
fitter.sampler.reset()
# The collection is across 5000*200 samples
