def NIST_runner(dataset, method='least_squares', chi_atol=1e-5,
val_rtol=1e-2, err_rtol=5e-3):
NIST_dataset = ReadNistData(dataset)
x, y = (NIST_dataset['x'], NIST_dataset['y'])
if dataset == 'Nelson':
y = np.log(y)
params = NIST_dataset['start']
fitfunc = NIST_Models[dataset][0]
model = Model(params, fitfunc)
objective = Objective(model, (x, y))
fitter = CurveFitter(objective)
result = fitter.fit(method=method)
assert_allclose(objective.chisqr(),
NIST_dataset['sum_squares'],
atol=chi_atol)
certval = NIST_dataset['cert_values']
assert_allclose(result.x, certval, rtol=val_rtol)
if 'stderr' in result:
certerr = NIST_dataset['cert_stderr']
assert_allclose(result.stderr, certerr, rtol=err_rtol)
def NIST_runner(
dataset,
method="least_squares",
chi_atol=1e-5,
val_rtol=1e-2,
err_rtol=6e-3,
):
NIST_dataset = ReadNistData(dataset)
x, y = (NIST_dataset["x"], NIST_dataset["y"])
if dataset == "Nelson":
y = np.log(y)
params = NIST_dataset["start"]
fitfunc = NIST_Models[dataset][0]
model = Model(params, fitfunc)
objective = Objective(model, (x, y))
fitter = CurveFitter(objective)
result = fitter.fit(method=method)
assert_allclose(objective.chisqr(),
NIST_dataset["sum_squares"],
atol=chi_atol)
certval = NIST_dataset["cert_values"]
assert_allclose(result.x, certval, rtol=val_rtol)
if "stderr" in result:
certerr = NIST_dataset["cert_stderr"]
assert_allclose(result.stderr, certerr, rtol=err_rtol)
def test_parallel_objective(self):
# check that a parallel objective works without issue
# (it could be possible that parallel evaluation fails at a higher
# level in e.g. emcee or in scipy.optimize.differential_evolution)
model = self.model361
model.threads = 2
objective = Objective(
model,
(self.qvals361, self.rvals361, self.evals361),
transform=Transform("logY"),
)
p0 = np.array(objective.varying_parameters())
cov = objective.covar()
walkers = np.random.multivariate_normal(np.atleast_1d(p0),
np.atleast_2d(cov),
size=(100))
map_logl = np.array(list(map(objective.logl, walkers)))
map_chi2 = np.array(list(map(objective.chisqr, walkers)))
wf = Wrapper_fn2(model.model, p0)
map_mod = np.array(list(map(wf, walkers)))
with MapWrapper(2) as g:
mapw_mod = g(wf, walkers)
mapw_logl = g(objective.logl, walkers)
mapw_chi2 = g(objective.chisqr, walkers)
assert_allclose(mapw_logl, map_logl)
assert_allclose(mapw_chi2, map_chi2)
assert_allclose(mapw_mod, map_mod)
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 _update_analysis_objects(self):
use_weights = self.use_weights.value == 'Yes'
self.objective = Objective(self.model,
self.dataset,
transform=self.transform,
use_weights=use_weights)
self._curvefitter = None
def setup_method(self, tmpdir):
self.path = os.path.dirname(os.path.abspath(__file__))
self.tmpdir = tmpdir.strpath
theoretical = np.loadtxt(os.path.join(self.path, "gauss_data.txt"))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
# these best weighted values and uncertainties obtained with Igor
self.best_weighted = [-0.00246095, 19.5299, -8.28446e-2, 1.24692]
self.best_weighted_errors = [
0.0220313708486,
1.12879436221,
0.0447659158681,
0.0412022938883,
]
self.best_weighted_chisqr = 77.6040960351
self.best_unweighted = [
-0.10584111872702096,
19.240347049328989,
0.0092623066070940396,
1.501362314145845,
]
self.best_unweighted_errors = [
0.34246565477,
0.689820935208,
0.0411243173041,
0.0693429375282,
]
self.best_unweighted_chisqr = 497.102084956
self.p0 = np.array([0.1, 20.0, 0.1, 0.1])
self.names = ["bkg", "A", "x0", "width"]
self.bounds = [(-1, 1), (0, 30), (-5.0, 5.0), (0.001, 2)]
self.params = Parameters(name="gauss_params")
for p, name, bound in zip(self.p0, self.names, self.bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
self.params.append(param)
self.model = Model(self.params, fitfunc=gauss)
self.data = Data1D((xvals, yvals, evals))
self.objective = Objective(self.model, self.data)
return 0
def test_run():
print("\n\n\n")
from refnx.dataset import Data1D
from refnx.dataset import ReflectDataset
import refnx
import data_in
data = data_in.data_in('d2o/29553_54.dat')
# dataset = data # ...
data = Data1D(data)
from make_egg import bsla_thesis
# air = SLD(0)
# air = air(0,0)
bt = bsla_thesis()
bt.interface_protein_solvent.setp(vary=True, bounds=(11, 40))
bt.protein_length.setp(vary=True, bounds=(25, 55))
bt.number_of_water_molecules.setp(vary=True, bounds=(1, 10000))
bt.interface_width_air_solvent.setp(vary=True, bounds=(0.001, 30))
#bt.interface_width_protein_solvent.setp(vary=True, bounds=(0, 5))
bt.sld_of_protein.setp(vary=True, bounds=(1.92, 6.21)) # *(10**(-6))
bt.d2o_to_h2o_ratio.setp(vary=True, bounds=(0, 1))
# if isinstance(bt, Component):
# print("it is comp")
# if isinstance(bt, Structure):
# print("it is")
#print(bt.parameters)
# d2o = 1.9185/0.3
# h2o = -0.1635/0.3
# solvent = SLD((bt.d2o_to_h2o_ratio.value*d2o + (1-bt.d2o_to_h2o_ratio.value)*h2o))
# solvent = solvent(0,0)
# from refnx.reflect import Structure
# structure = air|bt|solvent
# structure.name = "bsla"
from refnx.reflect import ReflectModel
model = ReflectModel(bt) #structure)
from refnx.analysis import Transform, CurveFitter, Objective
objective = Objective(model, data)
fitter = CurveFitter(objective)
fitter.fit('differential_evolution')
import matplotlib.pyplot as plt
#%matplotlib notebook
# plt.plot(*bt.sld_profile())
objective.plot()
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Q')
plt.ylabel('Reflectivity')
plt.legend()
print(bt)
plt.show()
def setup():
# load the data.
DATASET_NAME = os.path.join(refnx.__path__[0],
'analysis',
'test',
'c_PLP0011859_q.txt')
# load the data
data = ReflectDataset(DATASET_NAME)
# the materials we're using
si = SLD(2.07, name='Si')
sio2 = SLD(3.47, name='SiO2')
film = SLD(2, name='film')
d2o = SLD(6.36, name='d2o')
structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3)
structure[1].thick.setp(vary=True, bounds=(15., 50.))
structure[1].rough.setp(vary=True, bounds=(1., 6.))
structure[2].thick.setp(vary=True, bounds=(200, 300))
structure[2].sld.real.setp(vary=True, bounds=(0.1, 3))
structure[2].rough.setp(vary=True, bounds=(1, 6))
model = ReflectModel(structure, bkg=9e-6, scale=1.)
model.bkg.setp(vary=True, bounds=(1e-8, 1e-5))
model.scale.setp(vary=True, bounds=(0.9, 1.1))
model.threads = 1
# fit on a logR scale, but use weighting
objective = Objective(model, data, transform=Transform('logY'),
use_weights=True)
return objective
def setup(self):
pth = os.path.dirname(os.path.abspath(refnx.reflect.__file__))
e361 = RD(os.path.join(pth, 'test', 'e361r.txt'))
sio2 = SLD(3.47, name='SiO2')
si = SLD(2.07, name='Si')
d2o = SLD(6.36, name='D2O')
polymer = SLD(1, name='polymer')
# e361 is an older dataset, but well characterised
structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)
model361 = ReflectModel(structure361, bkg=2e-5)
model361.scale.vary = True
model361.bkg.vary = True
model361.scale.range(0.1, 2)
model361.bkg.range(0, 5e-5)
model361.dq = 5.
# d2o
structure361[-1].sld.real.vary = True
structure361[-1].sld.real.range(6, 6.36)
structure361[1].thick.vary = True
structure361[1].thick.range(5, 20)
structure361[2].thick.vary = True
structure361[2].thick.range(100, 220)
structure361[2].sld.real.vary = True
structure361[2].sld.real.range(0.2, 1.5)
e361.x_err = None
objective = Objective(model361, e361)
self.fitter = CurveFitter(objective, nwalkers=200)
self.fitter.initialise('jitter')
def setup(self):
# Reproducible results!
np.random.seed(123)
m_true = -0.9594
b_true = 4.294
f_true = 0.534
m_ls = -1.1040757010910947
b_ls = 5.4405552502319505
# Generate some synthetic data from the model.
N = 50
x = np.sort(10 * np.random.rand(N))
y_err = 0.1 + 0.5 * np.random.rand(N)
y = m_true * x + b_true
y += np.abs(f_true * y) * np.random.randn(N)
y += y_err * np.random.randn(N)
data = Data1D(data=(x, y, y_err))
p = Parameter(b_ls, 'b', vary=True, bounds=(-100, 100))
p |= Parameter(m_ls, 'm', vary=True, bounds=(-100, 100))
model = Model(p, fitfunc=line)
self.objective = Objective(model, data)
self.mcfitter = CurveFitter(self.objective)
self.mcfitter_t = CurveFitter(self.objective, ntemps=20)
self.mcfitter.initialise('prior')
self.mcfitter_t.initialise('prior')
def test_code_fragment(self):
e361 = ReflectDataset(os.path.join(self.pth, "e361r.txt"))
si = SLD(2.07, name="Si")
sio2 = SLD(3.47, name="SiO2")
d2o = SLD(6.36, name="D2O")
polymer = SLD(1, name="polymer")
# e361 is an older dataset, but well characterised
self.structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)
self.model361 = ReflectModel(self.structure361, bkg=2e-5)
self.model361.scale.vary = True
self.model361.bkg.vary = True
self.model361.scale.range(0.1, 2)
self.model361.bkg.range(0, 5e-5)
# d2o
self.structure361[-1].sld.real.vary = True
self.structure361[-1].sld.real.range(6, 6.36)
self.structure361[1].thick.vary = True
self.structure361[1].thick.range(5, 20)
self.structure361[2].thick.vary = True
self.structure361[2].thick.range(100, 220)
self.structure361[2].sld.real.vary = True
self.structure361[2].sld.real.range(0.2, 1.5)
objective = Objective(self.model361, e361, transform=Transform("logY"))
objective2 = eval(repr(objective))
assert_allclose(objective2.chisqr(), objective.chisqr())
exec(repr(objective))
exec(code_fragment(objective))
# artificially link the two thicknesses together
# check that we can reproduce the objective from the repr
self.structure361[2].thick.constraint = self.structure361[1].thick
fragment = code_fragment(objective)
fragment = fragment + "\nobj = objective()\nresult = obj.chisqr()"
d = {}
# need to provide the globals dictionary to exec, so it can see imports
# e.g. https://bit.ly/2RFOF7i (from stackoverflow)
exec(fragment, globals(), d)
assert_allclose(d["result"], objective.chisqr())
def test_reflectivity_emcee(self):
model = self.model361
model.dq = 5.
objective = Objective(model,
(self.qvals361, self.rvals361, self.evals361),
transform=Transform('logY'))
fitter = CurveFitter(objective, nwalkers=100)
assert_(len(objective.generative().shape) == 1)
assert_(len(objective.residuals().shape) == 1)
res = fitter.fit('least_squares')
res_mcmc = fitter.sample(steps=5, nthin=10, random_state=1,
verbose=False)
mcmc_val = [mcmc_result.median for mcmc_result in res_mcmc]
assert_allclose(mcmc_val, res.x, rtol=0.05)
def test_reflectivity_fit(self):
# a smoke test to make sure the reflectivity fit proceeds
model = self.model361
objective = Objective(model,
(self.qvals361, self.rvals361, self.evals361),
transform=Transform('logY'))
fitter = CurveFitter(objective)
with np.errstate(invalid='raise'):
fitter.fit('differential_evolution')
def setup_method(self):
# Choose the "true" parameters.
# Reproducible results!
np.random.seed(123)
self.m_true = -0.9594
self.b_true = 4.294
self.f_true = 0.534
self.m_ls = -1.1040757010910947
self.b_ls = 5.4405552502319505
# Generate some synthetic data from the model.
N = 50
x = np.sort(10 * np.random.rand(N))
y_err = 0.1 + 0.5 * np.random.rand(N)
y = self.m_true * x + self.b_true
y += np.abs(self.f_true * y) * np.random.randn(N)
y += y_err * np.random.randn(N)
self.data = Data1D(data=(x, y, y_err))
self.p = Parameter(self.b_ls, 'b') | Parameter(self.m_ls, 'm')
self.model = Model(self.p, fitfunc=line)
self.objective = Objective(self.model, self.data)
# want b and m
self.p[0].vary = True
self.p[1].vary = True
mod = np.array([4.78166609, 4.42364699, 4.16404064, 3.50343504,
3.4257084, 2.93594347, 2.92035638, 2.67533842,
2.28136038, 2.19772983, 1.99295496, 1.93748334,
1.87484436, 1.65161016, 1.44613461, 1.11128101,
1.04584535, 0.86055984, 0.76913963, 0.73906649,
0.73331407, 0.68350418, 0.65216599, 0.59838566,
0.13070299, 0.10749131, -0.01010195, -0.10010155,
-0.29495372, -0.42817431, -0.43122391, -0.64637715,
-1.30560686, -1.32626428, -1.44835768, -1.52589881,
-1.56371158, -2.12048349, -2.24899179, -2.50292682,
-2.53576659, -2.55797996, -2.60870542, -2.7074727,
-3.93781479, -4.12415366, -4.42313742, -4.98368609,
-5.38782395, -5.44077086])
self.mod = mod
def __init__(self, file_path, layers, predicted_slds, predicted_depths, xray):
"""Initialises the Model class by creating a refnx model with given predicted values.
Args:
file_path (string): a path to the file with the data to construct the model for.
layers (int): the number of layers for the model predicted by the classifier.
predicted_slds (ndarray): an array of predicted SLDs for each layer.
predicted_depths (ndarray): an array of predicted depths for each layer.
xray (Boolean): whether the model should use a neutron or x-ray probe.
"""
self.structure = SLD(0, name='Air') #Model starts with air.
if xray: #Use x-ray probe
for i in range(layers):
density = predicted_slds[i] / XRayGenerator.density_constant
SLD_layer = MaterialSLD(XRayGenerator.material, density, probe='x-ray', wavelength=XRayGenerator.wavelength, name='Layer {}'.format(i+1))
layer = SLD_layer(thick=predicted_depths[i], rough=Model.roughness)
layer.density.setp(bounds=XRayGenerator.density_bounds, vary=True)
layer.thick.setp(bounds=ImageGenerator.depth_bounds, vary=True)
layer.rough.setp(bounds=Model.rough_bounds, vary=True)
self.structure = self.structure | layer #Next comes each layer.
#Then substrate
si_substrate = MaterialSLD(XRayGenerator.material, XRayGenerator.substrate_density, probe='x-ray', name='Si Substrate')(thick=0, rough=Model.roughness)
else: #Use neutron probe
for i in range(layers):
layer = SLD(predicted_slds[i], name='Layer {}'.format(i+1))(thick=predicted_depths[i], rough=Model.roughness)
layer.sld.real.setp(bounds=ImageGenerator.sld_neutron_bounds, vary=True)
layer.thick.setp(bounds=ImageGenerator.depth_bounds, vary=True)
layer.rough.setp(bounds=Model.rough_bounds, vary=True)
self.structure = self.structure | layer #Next comes each layer.
#Then substrate
si_substrate = SLD(Model.si_sld, name='Si Substrate')(thick=0, rough=Model.roughness)
si_substrate.rough.setp(bounds=Model.rough_bounds, vary=True)
self.structure = self.structure | si_substrate
data = self.__load_data(file_path) #Pre-process and load given dataset.
self.model = ReflectModel(self.structure, scale=Model.scale, dq=Model.dq, bkg=Model.bkg)
self.objective = Objective(self.model, data)
def test_residuals(self):
# weighted, with and without transform
assert_almost_equal(self.objective.residuals(),
(self.data.y - self.mod) / self.data.y_err)
objective = Objective(self.model, self.data,
transform=Transform('lin'))
assert_almost_equal(objective.residuals(),
(self.data.y - self.mod) / self.data.y_err)
# unweighted, with and without transform
objective = Objective(self.model, self.data, use_weights=False)
assert_almost_equal(objective.residuals(),
self.data.y - self.mod)
objective = Objective(self.model, self.data, use_weights=False,
transform=Transform('lin'))
assert_almost_equal(objective.residuals(),
self.data.y - self.mod)
def test_modelvals_degenerate_layers(self):
# try fitting dataset with a deposited layer split into two degenerate
# layers
fname = os.path.join(self.pth, "c_PLP0011859_q.txt")
dataset = ReflectDataset(fname)
sio2 = SLD(3.47, name="SiO2")
si = SLD(2.07, name="Si")
d2o = SLD(6.36, name="D2O")
polymer = SLD(2.0, name="polymer")
sio2_l = sio2(30, 3)
polymer_l = polymer(125, 3)
structure = si | sio2_l | polymer_l | polymer_l | d2o(0, 3)
polymer_l.thick.setp(value=125, vary=True, bounds=(0, 250))
polymer_l.rough.setp(value=4, vary=True, bounds=(0, 8))
structure[-1].rough.setp(vary=True, bounds=(0, 6))
sio2_l.rough.setp(value=3.16, vary=True, bounds=(0, 8))
model = ReflectModel(structure, bkg=2e-6)
objective = Objective(model,
dataset,
use_weights=False,
transform=Transform("logY"))
model.scale.setp(vary=True, bounds=(0, 2))
model.bkg.setp(vary=True, bounds=(0, 8e-6))
slabs = structure.slabs()
assert_equal(slabs[2, 0:2], slabs[3, 0:2])
assert_equal(slabs[2, 3], slabs[3, 3])
assert_equal(slabs[1, 3], sio2_l.rough.value)
f = CurveFitter(objective)
f.fit(method="differential_evolution", seed=1, maxiter=3)
slabs = structure.slabs()
assert_equal(slabs[2, 0:2], slabs[3, 0:2])
assert_equal(slabs[2, 3], slabs[3, 3])
def make_model(names, bs, thicks, roughs, fig_i, data, show=False, mcmc=False):
no_layers = len(bs)
layers = []
for i in range(no_layers):
names.append('layer' + str(i))
for i in range(no_layers):
sld = SLD(bs[i], name=names[i])
layers.append(sld(thicks[i], roughs[i]))
layers[0].thick.setp(vary=True, bounds=(thicks[i] - 1, thicks[i] + 1))
layers[0].sld.real.setp(vary=True, bounds=(bs[i] - 1, bs[i] + 1))
for layer in layers[1:]:
layer.thick.setp(vary=True, bounds=(thicks[i] - 1, thicks[i] + 1))
layer.sld.real.setp(vary=True, bounds=(bs[i] - 1, bs[i] + 1))
layer.rough.setp(vary=True, bounds=(0, 5))
structure = layers[0]
for layer in layers[1:]:
structure |= layer
print(structure)
model = ReflectModel(structure, bkg=3e-6, dq=5.0)
#model.scale.setp(bounds=(0.6, 1.2), vary=True)
#model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)
objective = Objective(model, data, transform=Transform('logY'))
fitter = CurveFitter(objective)
if mcmc:
fitter.sample(1000)
process_chain(objective, fitter.chain, nburn=300, nthin=100)
else:
fitter.fit('differential_evolution')
print(objective.parameters)
if show:
plt.figure(fig_i)
plt.plot(*structure.sld_profile())
plt.ylabel('SLD /$10^{-6} \AA^{-2}$')
plt.xlabel('distance / $\AA$')
return structure, fitter, objective, fig_i + 1
def test_resolution_speed_comparator(self):
fname = os.path.join(self.pth, "c_PLP0011859_q.txt")
dataset = ReflectDataset(fname)
sio2 = SLD(3.47, name="SiO2")
si = SLD(2.07, name="Si")
d2o = SLD(6.36, name="D2O")
polymer = SLD(2.0, name="polymer")
sio2_l = sio2(30, 3)
polymer_l = polymer(125, 3)
dx = dataset.x_err
structure = si | sio2_l | polymer_l | polymer_l | d2o(0, 3)
model = ReflectModel(structure, bkg=2e-6, dq_type="constant")
objective = Objective(model,
dataset,
use_weights=False,
transform=Transform("logY"))
# check that choose_resolution_approach doesn't change state
# of model
fastest_method = choose_dq_type(objective)
assert model.dq_type == "constant"
assert_equal(dx, objective.data.x_err)
# check that the comparison worked
const_time = time.time()
for i in range(1000):
objective.generative()
const_time = time.time() - const_time
model.dq_type = "pointwise"
point_time = time.time()
for i in range(1000):
objective.generative()
point_time = time.time() - point_time
if fastest_method == "pointwise":
assert point_time < const_time
elif fastest_method == "constant":
assert const_time < point_time
# check that we could use the function to setup a reflectmodel
ReflectModel(structure, bkg=2e-6, dq_type=choose_dq_type(objective))
def make_model(names, bs, thicks, roughs, fig_i, data, show=False, mcmc=False):
extent = sum(
thicks[:, 0]) # (float or Parameter) – Total extent of spline region
vs = array(
bs
)[:,
0] #(Sequence of float/Parameter) – the real part of the SLD values of each of the knots.
dz = cum_sum(
array(thicks[:, 0])
) #(Sequence of float/Parameter) – the lateral offset between successive knots.
print(dz)
name = "number of nots " + str(len(names)) #(str) – Name of component
component = Spline(extent, vs, dz, name)
front = SLD(0)
front = front(0, 0)
back = SLD(0)
back = back(0, 0)
structure = front | component | back
model = ReflectModel(structure, bkg=3e-6, dq=5.0)
objective = Objective(model, data, transform=Transform('logY'))
fitter = CurveFitter(objective)
fitter.fit('differential_evolution')
return structure, fitter, objective, fig_i + 1
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)
class TestFitterGauss(object):
# Test CurveFitter with a noisy gaussian, weighted and unweighted, to see
# if the parameters and uncertainties come out correct
@pytest.fixture(autouse=True)
def setup_method(self, tmpdir):
self.path = os.path.dirname(os.path.abspath(__file__))
self.tmpdir = tmpdir.strpath
theoretical = np.loadtxt(os.path.join(self.path, "gauss_data.txt"))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
# these best weighted values and uncertainties obtained with Igor
self.best_weighted = [-0.00246095, 19.5299, -8.28446e-2, 1.24692]
self.best_weighted_errors = [
0.0220313708486,
1.12879436221,
0.0447659158681,
0.0412022938883,
]
self.best_weighted_chisqr = 77.6040960351
self.best_unweighted = [
-0.10584111872702096,
19.240347049328989,
0.0092623066070940396,
1.501362314145845,
]
self.best_unweighted_errors = [
0.34246565477,
0.689820935208,
0.0411243173041,
0.0693429375282,
]
self.best_unweighted_chisqr = 497.102084956
self.p0 = np.array([0.1, 20.0, 0.1, 0.1])
self.names = ["bkg", "A", "x0", "width"]
self.bounds = [(-1, 1), (0, 30), (-5.0, 5.0), (0.001, 2)]
self.params = Parameters(name="gauss_params")
for p, name, bound in zip(self.p0, self.names, self.bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
self.params.append(param)
self.model = Model(self.params, fitfunc=gauss)
self.data = Data1D((xvals, yvals, evals))
self.objective = Objective(self.model, self.data)
return 0
def test_pickle(self):
# tests if a CurveFitter can be pickled/unpickled.
f = CurveFitter(self.objective)
pkl = pickle.dumps(f)
g = pickle.loads(pkl)
g._check_vars_unchanged()
def test_best_weighted(self):
assert_equal(len(self.objective.varying_parameters()), 4)
self.objective.setp(self.p0)
f = CurveFitter(self.objective, nwalkers=100)
res = f.fit("least_squares", jac="3-point")
output = res.x
assert_almost_equal(output, self.best_weighted, 3)
assert_almost_equal(self.objective.chisqr(), self.best_weighted_chisqr,
5)
# compare the residuals
res = (self.data.y - self.model(self.data.x)) / self.data.y_err
assert_equal(self.objective.residuals(), res)
# compare objective.covar to the best_weighted_errors
uncertainties = [param.stderr for param in self.params]
assert_allclose(uncertainties, self.best_weighted_errors, rtol=0.005)
# we're also going to try the checkpointing here.
checkpoint = os.path.join(self.tmpdir, "checkpoint.txt")
# compare samples to best_weighted_errors
np.random.seed(1)
f.sample(steps=201, random_state=1, verbose=False, f=checkpoint)
process_chain(self.objective, f.chain, nburn=50, nthin=10)
uncertainties = [param.stderr for param in self.params]
assert_allclose(uncertainties, self.best_weighted_errors, rtol=0.07)
# test that the checkpoint worked
check_array = np.loadtxt(checkpoint)
check_array = check_array.reshape(201, f._nwalkers, f.nvary)
assert_allclose(check_array, f.chain)
# test loading the checkpoint
chain = load_chain(checkpoint)
assert_allclose(chain, f.chain)
f.initialise("jitter")
f.sample(steps=2, nthin=4, f=checkpoint, verbose=False)
assert_equal(f.chain.shape[0], 2)
# we should be able to produce 2 * 100 steps from the generator
g = self.objective.pgen(ngen=20000000000)
s = [i for i, a in enumerate(g)]
assert_equal(np.max(s), 200 - 1)
g = self.objective.pgen(ngen=200)
pvec = next(g)
assert_equal(pvec.size, len(self.objective.parameters.flattened()))
# check that all the parameters are returned via pgen, not only those
# being varied.
self.params[0].vary = False
f = CurveFitter(self.objective, nwalkers=100)
f.initialise("jitter")
f.sample(steps=2, nthin=4, f=checkpoint, verbose=False)
g = self.objective.pgen(ngen=100)
pvec = next(g)
assert_equal(pvec.size, len(self.objective.parameters.flattened()))
# the following test won't work because of emcee/gh226.
# chain = load_chain(checkpoint)
# assert_(chain.shape == f.chain.shape)
# assert_allclose(chain, f.chain)
# try reproducing best fit with parallel tempering
self.params[0].vary = True
f = CurveFitter(self.objective, nwalkers=100, ntemps=10)
f.fit("differential_evolution", seed=1)
f.sample(steps=201, random_state=1, verbose=False)
process_chain(self.objective, f.chain, nburn=50, nthin=15)
print(self.params[0].chain.shape, self.params[0].chain)
uncertainties = [param.stderr for param in self.params]
assert_allclose(uncertainties, self.best_weighted_errors, rtol=0.07)
def test_best_unweighted(self):
self.objective.weighted = False
f = CurveFitter(self.objective, nwalkers=100)
res = f.fit()
output = res.x
assert_almost_equal(self.objective.chisqr(),
self.best_unweighted_chisqr)
assert_almost_equal(output, self.best_unweighted, 5)
# compare the residuals
res = self.data.y - self.model(self.data.x)
assert_equal(self.objective.residuals(), res)
# compare objective._covar to the best_unweighted_errors
uncertainties = np.array([param.stderr for param in self.params])
assert_almost_equal(uncertainties, self.best_unweighted_errors, 3)
# the samples won't compare to the covariance matrix...
# f.sample(nsteps=150, nburn=20, nthin=30, random_state=1)
# uncertainties = [param.stderr for param in self.params]
# assert_allclose(uncertainties, self.best_unweighted_errors,
# rtol=0.15)
def test_all_minimisers(self):
"""test minimisers against the Gaussian fit"""
f = CurveFitter(self.objective)
methods = ["differential_evolution", "L-BFGS-B", "least_squares"]
if hasattr(sciopt, "shgo"):
methods.append("shgo")
if hasattr(sciopt, "dual_annealing"):
methods.append("dual_annealing")
for method in methods:
self.objective.setp(self.p0)
res = f.fit(method=method)
assert_almost_equal(res.x, self.best_weighted, 3)
# smoke test to check that we can use nlpost
self.objective.setp(self.p0)
logp0 = self.objective.logp()
# check that probabilities are calculated correctly
assert_allclose(
self.objective.logpost(),
self.objective.logp() + self.objective.logl(),
)
assert_allclose(self.objective.nlpost(), -self.objective.logpost())
assert_allclose(self.objective.nlpost(self.p0),
-self.objective.logpost(self.p0))
# if the priors are all uniform then the only difference between
# logpost and logl is a constant. A minimiser should converge on the
# same answer. The following tests examine that.
# The test works for dual_annealing, but not for differential
# evolution, not sure why that is.
self.objective.setp(self.p0)
res1 = f.fit(method="dual_annealing", seed=1)
assert_almost_equal(res1.x, self.best_weighted, 3)
nll1 = self.objective.nll()
nlpost1 = self.objective.nlpost()
self.objective.setp(self.p0)
res2 = f.fit(method="dual_annealing", target="nlpost", seed=1)
assert_almost_equal(res2.x, self.best_weighted, 3)
nll2 = self.objective.nll()
nlpost2 = self.objective.nlpost()
assert_allclose(nlpost1, nlpost2, atol=0.001)
assert_allclose(nll1, nll2, atol=0.001)
# these two priors are calculated for different parameter values
# (before and after the fit) they should be the same because all
# the parameters have uniform priors.
assert_almost_equal(self.objective.logp(), logp0)
def test_pymc3_sample(self):
# test sampling with pymc3
try:
import pymc3 as pm
from refnx.analysis import pymc3_model
except (ModuleNotFoundError, ImportError, AttributeError):
# can't run test if pymc3/theano not installed
return
with pymc3_model(self.objective):
s = pm.NUTS()
pm.sample(
200,
tune=100,
step=s,
discard_tuned_samples=True,
compute_convergence_checks=False,
random_seed=1,
)
def getObjective(data, thicknesses, slds, layerNames, logpExtra=None):
air = SLD(0, name="air layer")
airSlab = air(10, 0)
sio2 = SLD(10, name="bottem layer")
sio2Slab = sio2(10, 0)
# print(" ... ",slds[0].startPoint)
if len(layerNames) >= 1:
i = 0
sld1 = SLD(float(slds[i].startPoint), name=layerNames[i])
sld1Slab = sld1(thicknesses[i].startPoint, thicknesses[i].roughness)
sld1Slab.thick.setp(vary=thicknesses[i].vary,
bounds=(thicknesses[i].lower,
thicknesses[i].upper))
sld1Slab.sld.real.setp(vary=slds[i].vary,
bounds=(slds[i].lower, slds[i].upper))
else:
print(layerNames, " : variable 'layerNames' is empty")
if len(layerNames) >= 2:
i = 1
sld2 = SLD(slds[i].startPoint, name=layerNames[i])
sld2Slab = sld2(thicknesses[i].startPoint, thicknesses[i].roughness)
sld2Slab.thick.setp(vary=thicknesses[i].vary,
bounds=(thicknesses[i].lower,
thicknesses[i].upper))
sld2Slab.sld.real.setp(vary=slds[i].vary,
bounds=(slds[i].lower, slds[i].upper))
if len(layerNames) >= 3:
i = 2
sld3 = SLD(slds[i].startPoint, name=layerNames[i])
sld3Slab = sld3(thicknesses[i].startPoint, thicknesses[i].roughness)
sld3Slab.thick.setp(vary=thicknesses[i].vary,
bounds=(thicknesses[i].lower,
thicknesses[i].upper))
sld3Slab.sld.real.setp(vary=slds[i].vary,
bounds=(slds[i].lower, slds[i].upper))
if len(layerNames) >= 4:
i = 3
sld4 = SLD(slds[i].startPoint, name=layerNames[i])
sld4Slab = sld4(thicknesses[i].startPoint, thicknesses[i].roughness)
sld4Slab.thick.setp(vary=thicknesses[i].vary,
bounds=(thicknesses[i].lower,
thicknesses[i].upper))
sld4Slab.sld.real.setp(vary=slds[i].vary,
bounds=(slds[i].lower, slds[i].upper))
if len(layerNames) == 1:
structure = airSlab | sld1Slab | sio2Slab
if len(layerNames) == 2:
structure = airSlab | sld1Slab | sld2Slab | sio2Slab
if len(layerNames) == 3:
structure = airSlab | sld1Slab | sld2Slab | sld3Slab | sio2Slab
if len(layerNames) == 4:
structure = airSlab | sld1Slab | sld2Slab | sld3Slab | sld4Slab | sio2Slab
model = ReflectModel(structure, bkg=3e-6, dq=5.0)
objective = Objective(model,
data,
transform=Transform('logY'),
logp_extra=logpExtra)
return objective, structure
class Motofit(object):
"""
An interactive slab modeller (Jupyter/ipywidgets based) for Neutron and
X-ray reflectometry data.
The interactive modeller is designed to be used in a Jupyter notebook.
>>> # specify that plots are in a separate graph window
>>> %matplotlib qt
>>> # alternately if you want the graph to be embedded in the notebook use
>>> # %matplotlib notebook
>>> from refnx.reflect import Motofit
>>> # create an instance of the modeller
>>> app = Motofit()
>>> # display it in the notebook by calling the object with a datafile.
>>> app('dataset1.txt')
>>> # lets fit a different dataset
>>> app2 = Motofit()
>>> app2('dataset2.txt')
The `Motofit` instance has several useful attributes that can be used in
other cells. For example, one can access the `objective` and `curvefitter`
attributes for more advanced fitting functionality than is available in the
GUI. A `code` attribute can be used to retrieve a Python code fragment that
can be used as a basis for developing more complicated models, such as
interparameter constraints, global fitting, etc.
Attributes
----------
dataset: :class:`refnx.dataset.Data1D`
The dataset associated with the modeller
model: :class:`refnx.reflect.ReflectModel`
Calculates a theoretical model, from an interfacial structure
(`model.Structure`).
objective: :class:`refnx.analysis.Objective`
The Objective that allows one to compare the model against the data.
fig: :class:`matplotlib.figure.Figure`
Graph displaying the data.
"""
def __init__(self):
# attributes for the graph
# for the graph
self.qmin = 0.005
self.qmax = 0.5
self.qpnt = 1000
self.fig = None
self.ax_data = None
self.ax_residual = None
self.ax_sld = None
# gridspecs specify how the plots are laid out. Gridspec1 is when the
# residuals plot is displayed. Gridspec2 is when it's not visible
self._gridspec1 = gridspec.GridSpec(2,
2,
height_ratios=[5, 1],
width_ratios=[1, 1],
hspace=0.01)
self._gridspec2 = gridspec.GridSpec(1, 2)
self.theoretical_plot = None
self.theoretical_plot_sld = None
# attributes for a user dataset
self.dataset = None
self.objective = None
self._curvefitter = None
self.data_plot = None
self.residuals_plot = None
self.data_plot_sld = None
self.dataset_name = widgets.Text(description="dataset:")
self.dataset_name.disabled = True
self.chisqr = widgets.FloatText(description="chi-squared:")
self.chisqr.disabled = True
# fronting
slab0 = Slab(0, 0, 0)
slab1 = Slab(25, 3.47, 3)
slab2 = Slab(0, 2.07, 3)
structure = slab0 | slab1 | slab2
rename_params(structure)
self.model = ReflectModel(structure)
structure = slab0 | slab1 | slab2
self.model = ReflectModel(structure)
# give some default parameter limits
self.model.scale.bounds = (0.1, 2)
self.model.bkg.bounds = (1e-8, 2e-5)
self.model.dq.bounds = (0, 20)
for slab in self.model.structure:
slab.thick.bounds = (0, 2 * slab.thick.value)
slab.sld.real.bounds = (0, 2 * slab.sld.real.value)
slab.sld.imag.bounds = (0, 2 * slab.sld.imag.value)
slab.rough.bounds = (0, 2 * slab.rough.value)
# the main GUI widget
self.display_box = widgets.VBox()
self.tab = widgets.Tab()
self.tab.set_title(0, "Model")
self.tab.set_title(1, "Limits")
self.tab.set_title(2, "Options")
self.tab.observe(self._on_tab_changed, names="selected_index")
# an output area for messages.
self.output = widgets.Output()
# options tab
self.plot_type = widgets.Dropdown(
options=["lin", "logY", "YX4", "YX2"],
value="lin",
description="Plot Type:",
disabled=False,
)
self.plot_type.observe(self._on_plot_type_changed, names="value")
self.use_weights = widgets.RadioButtons(
options=["Yes", "No"],
value="Yes",
description="use dataset weights?",
style={"description_width": "initial"},
)
self.use_weights.observe(self._on_use_weights_changed, names="value")
self.transform = Transform("lin")
self.display_residuals = widgets.Checkbox(
value=False, description="Display residuals")
self.display_residuals.observe(self._on_display_residuals_changed,
names="value")
self.model_view = None
self.set_model(self.model)
def save_model(self, *args, f=None):
"""
Serialise a model to a pickle file.
If `f` is not specified then the file name is constructed from the
current dataset name; if there is no current dataset then the filename
is constructed from the current time. These constructed filenames will
be in the current working directory, for a specific save location `f`
must be provided.
This method is only intended to be used to serialise models created by
this interactive Jupyter widget modeller.
Parameters
----------
f: file like or str, optional
File to save model to.
"""
if f is None:
f = "model_" + datetime.datetime.now().isoformat() + ".pkl"
if self.dataset is not None:
f = "model_" + self.dataset.name + ".pkl"
with possibly_open_file(f) as g:
pickle.dump(self.model, g)
def load_model(self, *args, f=None):
"""
Load a serialised model.
If `f` is not specified then an attempt will be made to find a model
corresponding to the current dataset name,
`'model_' + self.dataset.name + '.pkl'`. If there is no current
dataset then the most recent model will be loaded.
This method is only intended to be used to deserialise models created
by this interactive Jupyter widget modeller, and will not successfully
load complicated ReflectModel created outside of the interactive
modeller.
Parameters
----------
f: file like or str, optional
pickle file to load model from.
"""
if f is None and self.dataset is not None:
# try and load the model corresponding to the current dataset
f = "model_" + self.dataset.name + ".pkl"
elif f is None:
# load the most recent model file
files = list(filter(os.path.isfile, glob.glob("model_*.pkl")))
files.sort(key=lambda x: os.path.getmtime(x))
files.reverse()
if len(files):
f = files[0]
if f is None:
self._print("No model file is specified/available.")
return
try:
with possibly_open_file(f, "rb") as g:
reflect_model = pickle.load(g)
self.set_model(reflect_model)
except (RuntimeError, FileNotFoundError) as exc:
# RuntimeError if the file isn't a ReflectModel
# FileNotFoundError if the specified file name wasn't found
self._print(repr(exc), repr(f))
def set_model(self, model):
"""
Change the `refnx.reflect.ReflectModel` associated with the `Motofit`
instance.
Parameters
----------
model: refnx.reflect.ReflectModel
"""
if not isinstance(model, ReflectModel):
raise RuntimeError("`model` was not an instance of ReflectModel")
if self.model_view is not None:
self.model_view.unobserve_all()
# figure out if the reflect_model is a different instance. If it is
# then the objective has to be updated.
if model is not self.model:
self.model = model
self._update_analysis_objects()
self.model = model
self.model_view = ReflectModelView(self.model)
self.model_view.observe(self.update_model, names=["view_changed"])
self.model_view.observe(self.redraw, names=["view_redraw"])
# observe when the number of varying parameters changed. This
# invalidates a curvefitter, and a new one has to be produced.
self.model_view.observe(self._on_num_varying_changed,
names=["num_varying"])
self.model_view.do_fit_button.on_click(self.do_fit)
self.model_view.to_code_button.on_click(self._to_code)
self.model_view.save_model_button.on_click(self.save_model)
self.model_view.load_model_button.on_click(self.load_model)
self.redraw(None)
def update_model(self, change):
"""
Updates the plots when the parameters change
Parameters
----------
change
"""
if not self.fig:
return
q = np.linspace(self.qmin, self.qmax, self.qpnt)
theoretical = self.model.model(q)
yt, _ = self.transform(q, theoretical)
sld_profile = self.model.structure.sld_profile()
z, sld = sld_profile
if self.theoretical_plot is not None:
self.theoretical_plot.set_data(q, yt)
self.theoretical_plot_sld.set_data(z, sld)
self.ax_sld.relim()
self.ax_sld.autoscale_view()
if self.dataset is not None:
# if there's a dataset loaded then residuals_plot
# should exist
residuals = self.objective.residuals()
self.chisqr.value = np.sum(residuals**2)
self.residuals_plot.set_data(self.dataset.x, residuals)
self.ax_residual.relim()
self.ax_residual.autoscale_view()
self.fig.canvas.draw()
def _on_num_varying_changed(self, change):
# observe when the number of varying parameters changed. This
# invalidates a curvefitter, and a new one has to be produced.
if change["new"] != change["old"]:
self._curvefitter = None
def _update_analysis_objects(self):
use_weights = self.use_weights.value == "Yes"
self.objective = Objective(
self.model,
self.dataset,
transform=self.transform,
use_weights=use_weights,
)
self._curvefitter = None
def __call__(self, data=None, model=None):
"""
Display the `Motofit` GUI in a Jupyter notebook cell.
Parameters
----------
data: refnx.dataset.Data1D
The dataset to associate with the `Motofit` instance.
model: refnx.reflect.ReflectModel or str or file-like
A model to associate with the data.
If `model` is a `str` or `file`-like then the `load_model` method
will be used to try and load the model from file. This assumes that
the file is a pickle of a `ReflectModel`
"""
# the theoretical model
# display the main graph
import matplotlib.pyplot as plt
self.fig = plt.figure(figsize=(9, 4))
# grid specs depending on whether the residuals are displayed
if self.display_residuals.value:
d_gs = self._gridspec1[0, 0]
sld_gs = self._gridspec1[:, 1]
else:
d_gs = self._gridspec2[0, 0]
sld_gs = self._gridspec2[0, 1]
self.ax_data = self.fig.add_subplot(d_gs)
self.ax_data.set_xlabel(r"$Q/\AA^{-1}$")
self.ax_data.set_ylabel("Reflectivity")
self.ax_data.grid(True, color="b", linestyle="--", linewidth=0.1)
self.ax_sld = self.fig.add_subplot(sld_gs)
self.ax_sld.set_ylabel(r"$\rho/10^{-6}\AA^{-2}$")
self.ax_sld.set_xlabel(r"$z/\AA$")
self.ax_residual = self.fig.add_subplot(self._gridspec1[1, 0],
sharex=self.ax_data)
self.ax_residual.set_xlabel(r"$Q/\AA^{-1}$")
self.ax_residual.grid(True, color="b", linestyle="--", linewidth=0.1)
self.ax_residual.set_visible(self.display_residuals.value)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self.fig.tight_layout()
q = np.linspace(self.qmin, self.qmax, self.qpnt)
theoretical = self.model.model(q)
yt, _ = self.transform(q, theoretical)
self.theoretical_plot = self.ax_data.plot(q, yt, zorder=2)[0]
self.ax_data.set_yscale("log")
z, sld = self.model.structure.sld_profile()
self.theoretical_plot_sld = self.ax_sld.plot(z, sld)[0]
# the figure has been reset, so remove ref to the data_plot,
# residual_plot
self.data_plot = None
self.residuals_plot = None
self.dataset = None
if data is not None:
self.load_data(data)
if isinstance(model, ReflectModel):
self.set_model(model)
return self.display_box
elif model is not None:
self.load_model(model)
return self.display_box
self.redraw(None)
return self.display_box
def load_data(self, data):
"""
Load a dataset into the `Motofit` instance.
Parameters
----------
data: refnx.dataset.Data1D, or str, or file-like
"""
if isinstance(data, ReflectDataset):
self.dataset = data
else:
self.dataset = ReflectDataset(data)
self.dataset_name.value = self.dataset.name
# loading a dataset changes the objective and curvefitter
self._update_analysis_objects()
self.qmin = np.min(self.dataset.x)
self.qmax = np.max(self.dataset.x)
if self.fig is not None:
yt, et = self.transform(self.dataset.x, self.dataset.y)
if self.data_plot is None:
(self.data_plot, ) = self.ax_data.plot(
self.dataset.x,
yt,
label=self.dataset.name,
ms=2,
marker="o",
ls="",
zorder=1,
)
self.data_plot.set_label(self.dataset.name)
self.ax_data.legend()
# no need to calculate residuals here, that'll be updated in
# the redraw method
(self.residuals_plot, ) = self.ax_residual.plot(self.dataset.x)
else:
self.data_plot.set_xdata(self.dataset.x)
self.data_plot.set_ydata(yt)
# calculate theoretical model over same range as data
# use redraw over update_model because it ensures chi2 widget gets
# displayed
self.redraw(None)
self.ax_data.relim()
self.ax_data.autoscale_view()
self.ax_residual.relim()
self.ax_residual.autoscale_view()
self.fig.canvas.draw()
def redraw(self, change):
"""
Redraw the Jupyter GUI associated with the `Motofit` instance.
"""
self._update_display_box(self.display_box)
self.update_model(None)
@property
def curvefitter(self):
"""
class:`CurveFitter` : Object for fitting the data based on the
objective.
"""
if self.objective is not None and self._curvefitter is None:
self._curvefitter = CurveFitter(self.objective)
return self._curvefitter
def _print(self, string):
"""
Print to the output widget
"""
from IPython.display import clear_output
with self.output:
clear_output()
print(string)
def do_fit(self, *args):
"""
Ask the Motofit object to perform a fit (differential evolution).
Parameters
----------
change
Notes
-----
After performing the fit the Jupyter display is updated.
"""
if self.dataset is None:
return
if not self.model.parameters.varying_parameters():
self._print("No parameters are being varied")
return
try:
logp = self.objective.logp()
if not np.isfinite(logp):
self._print("One of your parameter values lies outside its"
" bounds. Please adjust the value, or the bounds.")
return
except ZeroDivisionError:
self._print("One parameter has equal lower and upper bounds."
" Either alter the bounds, or don't let that"
" parameter vary.")
return
def callback(xk, convergence):
self.chisqr.value = self.objective.chisqr(xk)
self.curvefitter.fit("differential_evolution", callback=callback)
# need to update the widgets as the model will be updated.
# this also redraws GUI.
# self.model_view.refresh()
self.set_model(self.model)
self._print(str(self.objective))
def _to_code(self, change=None):
self._print(self.code)
@property
def code(self):
"""
str : A Python code fragment capable of fitting the data.
Executable Python code fragment for the GUI model.
"""
if self.objective is None:
self._update_analysis_objects()
return to_code(self.objective)
def _on_tab_changed(self, change):
pass
def _on_plot_type_changed(self, change):
"""
User would like to plot and fit as logR/linR/RQ4/RQ2, etc
"""
self.transform = Transform(change["new"])
if self.objective is not None:
self.objective.transform = self.transform
if self.dataset is not None:
yt, _ = self.transform(self.dataset.x, self.dataset.y)
self.data_plot.set_xdata(self.dataset.x)
self.data_plot.set_ydata(yt)
self.update_model(None)
# probably have to change LHS axis of the data plot when
# going between different plot types.
if change["new"] == "logY":
self.ax_data.set_yscale("linear")
else:
self.ax_data.set_yscale("log")
self.ax_data.relim()
self.ax_data.autoscale_view()
self.fig.canvas.draw()
def _on_use_weights_changed(self, change):
self._update_analysis_objects()
self.update_model(None)
def _on_display_residuals_changed(self, change):
import matplotlib.pyplot as plt
if change["new"]:
self.ax_residual.set_visible(True)
self.ax_data.set_position(self._gridspec1[0, 0].get_position(
self.fig))
self.ax_sld.set_position(self._gridspec1[:,
1].get_position(self.fig))
plt.setp(self.ax_data.get_xticklabels(), visible=False)
else:
self.ax_residual.set_visible(False)
self.ax_data.set_position(self._gridspec2[:, 0].get_position(
self.fig))
self.ax_sld.set_position(self._gridspec2[:,
1].get_position(self.fig))
plt.setp(self.ax_data.get_xticklabels(), visible=True)
@property
def _options_box(self):
return widgets.VBox(
[self.plot_type, self.use_weights, self.display_residuals])
def _update_display_box(self, box):
"""
Redraw the Jupyter GUI associated with the `Motofit` instance
"""
vbox_widgets = []
if self.dataset is not None:
vbox_widgets.append(widgets.HBox([self.dataset_name, self.chisqr]))
self.tab.children = [
self.model_view.model_box,
self.model_view.limits_box,
self._options_box,
]
vbox_widgets.append(self.tab)
vbox_widgets.append(self.output)
box.children = tuple(vbox_widgets)
class TestCurveFitter(object):
def setup_method(self):
# Reproducible results!
np.random.seed(123)
self.m_true = -0.9594
self.b_true = 4.294
self.f_true = 0.534
self.m_ls = -1.1040757010910947
self.b_ls = 5.4405552502319505
# Generate some synthetic data from the model.
N = 50
x = np.sort(10 * np.random.rand(N))
y_err = 0.1 + 0.5 * np.random.rand(N)
y = self.m_true * x + self.b_true
y += np.abs(self.f_true * y) * np.random.randn(N)
y += y_err * np.random.randn(N)
self.data = Data1D(data=(x, y, y_err))
self.p = Parameter(self.b_ls, "b", vary=True, bounds=(-100, 100))
self.p |= Parameter(self.m_ls, "m", vary=True, bounds=(-100, 100))
self.model = Model(self.p, fitfunc=line)
self.objective = Objective(self.model, self.data)
assert_(len(self.objective.varying_parameters()) == 2)
mod = np.array([
4.78166609,
4.42364699,
4.16404064,
3.50343504,
3.4257084,
2.93594347,
2.92035638,
2.67533842,
2.28136038,
2.19772983,
1.99295496,
1.93748334,
1.87484436,
1.65161016,
1.44613461,
1.11128101,
1.04584535,
0.86055984,
0.76913963,
0.73906649,
0.73331407,
0.68350418,
0.65216599,
0.59838566,
0.13070299,
0.10749131,
-0.01010195,
-0.10010155,
-0.29495372,
-0.42817431,
-0.43122391,
-0.64637715,
-1.30560686,
-1.32626428,
-1.44835768,
-1.52589881,
-1.56371158,
-2.12048349,
-2.24899179,
-2.50292682,
-2.53576659,
-2.55797996,
-2.60870542,
-2.7074727,
-3.93781479,
-4.12415366,
-4.42313742,
-4.98368609,
-5.38782395,
-5.44077086,
])
self.mod = mod
self.mcfitter = CurveFitter(self.objective)
def test_bounds_list(self):
bnds = bounds_list(self.p)
assert_allclose(bnds, [(-100, 100), (-100, 100)])
# try making a Parameter bound a normal distribution, then get an
# approximation to box bounds
self.p[0].bounds = PDF(norm(0, 1))
assert_allclose(bounds_list(self.p),
[norm(0, 1).ppf([0.005, 0.995]), (-100, 100)])
def test_constraints(self):
# constraints should work during fitting
self.p[0].value = 5.4
self.p[1].constraint = -0.203 * self.p[0]
assert_equal(self.p[1].value, self.p[0].value * -0.203)
res = self.mcfitter.fit()
assert_(res.success)
assert_equal(len(self.objective.varying_parameters()), 1)
# lnsigma is parameters[0]
assert_(self.p[0] is self.objective.parameters.flattened()[0])
assert_(self.p[1] is self.objective.parameters.flattened()[1])
assert_almost_equal(self.p[0].value, res.x[0])
assert_almost_equal(self.p[1].value, self.p[0].value * -0.203)
# check that constraints work during sampling
# the CurveFitter has to be set up again if you change how the
# parameters are being fitted.
mcfitter = CurveFitter(self.objective)
assert_(mcfitter.nvary == 1)
mcfitter.sample(5)
assert_equal(self.p[1].value, self.p[0].value * -0.203)
# the constrained parameters should have a chain
assert_(self.p[0].chain is not None)
assert_(self.p[1].chain is not None)
assert_allclose(self.p[1].chain, self.p[0].chain * -0.203)
def test_mcmc(self):
self.mcfitter.sample(steps=50, nthin=1, verbose=False)
assert_equal(self.mcfitter.nvary, 2)
# smoke test for corner plot
self.mcfitter.objective.corner()
# we're not doing Parallel Tempering here.
assert_(self.mcfitter._ntemps == -1)
assert_(isinstance(self.mcfitter.sampler, emcee.EnsembleSampler))
# should be able to multithread
mcfitter = CurveFitter(self.objective, nwalkers=50)
res = mcfitter.sample(steps=33, nthin=2, verbose=False, pool=2)
# check that the autocorrelation function at least runs
acfs = mcfitter.acf(nburn=10)
assert_equal(acfs.shape[-1], mcfitter.nvary)
# check the standalone autocorrelation calculator
acfs2 = autocorrelation_chain(mcfitter.chain, nburn=10)
assert_equal(acfs, acfs2)
# check integrated_time
integrated_time(acfs2, tol=5)
# check chain shape
assert_equal(mcfitter.chain.shape, (33, 50, 2))
# assert_equal(mcfitter._lastpos, mcfitter.chain[:, -1, :])
assert_equal(res[0].chain.shape, (33, 50))
# if the number of parameters changes there should be an Exception
# raised
from pytest import raises
with raises(RuntimeError):
self.p[0].vary = False
self.mcfitter.sample(1)
# can fix by making the sampler again
self.mcfitter.make_sampler()
self.mcfitter.sample(1)
def test_random_seed(self):
# check that MCMC sampling is reproducible
self.mcfitter.sample(steps=2, random_state=1)
# get a starting pos
starting_pos = self.mcfitter._state.coords
# is sampling reproducible
self.mcfitter.reset()
self.mcfitter.initialise(pos=starting_pos)
self.mcfitter.sample(3, random_state=1, pool=1)
chain1 = np.copy(self.mcfitter.chain)
self.mcfitter.reset()
self.mcfitter.initialise(pos=starting_pos)
self.mcfitter.sample(3, random_state=1, pool=1)
chain2 = np.copy(self.mcfitter.chain)
assert_equal(chain1, chain2)
def test_mcmc_pt(self):
# smoke test for parallel tempering
x = np.array(self.objective.parameters)
mcfitter = CurveFitter(self.objective, ntemps=10, nwalkers=50)
assert_equal(mcfitter.sampler.ntemps, 10)
# check that the parallel sampling works
# and that chain shape is correct
res = mcfitter.sample(steps=5, nthin=2, verbose=False, pool=-1)
assert_equal(mcfitter.chain.shape, (5, 10, 50, 2))
assert_equal(res[0].chain.shape, (5, 50))
assert_equal(mcfitter.chain[:, 0, :, 0], res[0].chain)
assert_equal(mcfitter.chain[:, 0, :, 1], res[1].chain)
chain = np.copy(mcfitter.chain)
# the sampler should store the probability
assert_equal(mcfitter.logpost.shape, (5, 10, 50))
assert_allclose(mcfitter.logpost, mcfitter.sampler._ptchain.logP)
logprobs = mcfitter.logpost
highest_prob_loc = np.argmax(logprobs[:, 0])
idx = np.unravel_index(highest_prob_loc, logprobs[:, 0].shape)
idx = list(idx)
idx.insert(1, 0)
idx = tuple(idx)
assert_equal(idx, mcfitter.index_max_prob)
pvals = mcfitter.chain[idx]
assert_allclose(logprobs[idx], self.objective.logpost(pvals))
# try resetting the chain
mcfitter.reset()
# test for reproducible operation
self.objective.setp(x)
mcfitter = CurveFitter(self.objective, ntemps=10, nwalkers=50)
mcfitter.initialise("jitter", random_state=1)
mcfitter.sample(steps=5, nthin=2, verbose=False, random_state=2)
chain = np.copy(mcfitter.chain)
self.objective.setp(x)
mcfitter = CurveFitter(self.objective, ntemps=10, nwalkers=50)
mcfitter.initialise("jitter", random_state=1)
mcfitter.sample(steps=5, nthin=2, verbose=False, random_state=2)
chain2 = np.copy(mcfitter.chain)
assert_allclose(chain2, chain)
def test_mcmc_init(self):
# smoke test for sampler initialisation
# TODO check that the initialisation worked.
# reproducible initialisation with random_state dependents
self.mcfitter.initialise("prior", random_state=1)
starting_pos = np.copy(self.mcfitter._state.coords)
self.mcfitter.initialise("prior", random_state=1)
starting_pos2 = self.mcfitter._state.coords
assert_equal(starting_pos, starting_pos2)
self.mcfitter.initialise("jitter", random_state=1)
starting_pos = np.copy(self.mcfitter._state.coords)
self.mcfitter.initialise("jitter", random_state=1)
starting_pos2 = self.mcfitter._state.coords
assert_equal(starting_pos, starting_pos2)
mcfitter = CurveFitter(self.objective, nwalkers=100)
mcfitter.initialise("covar")
assert_equal(mcfitter._state.coords.shape, (100, 2))
mcfitter.initialise("prior")
assert_equal(mcfitter._state.coords.shape, (100, 2))
mcfitter.initialise("jitter")
assert_equal(mcfitter._state.coords.shape, (100, 2))
# initialise with last position
mcfitter.sample(steps=1)
chain = mcfitter.chain
mcfitter.initialise(pos=chain[-1])
assert_equal(mcfitter._state.coords.shape, (100, 2))
# initialise with chain
mcfitter.sample(steps=2)
chain = mcfitter.chain
mcfitter.initialise(pos=chain)
assert_equal(mcfitter._state.coords, chain[-1])
# initialise with chain if it's never been run before
mcfitter = CurveFitter(self.objective, nwalkers=100)
mcfitter.initialise(chain)
# initialise for Parallel tempering
mcfitter = CurveFitter(self.objective, ntemps=20, nwalkers=100)
mcfitter.initialise("covar")
assert_equal(mcfitter._state.coords.shape, (20, 100, 2))
mcfitter.initialise("prior")
assert_equal(mcfitter._state.coords.shape, (20, 100, 2))
mcfitter.initialise("jitter")
assert_equal(mcfitter._state.coords.shape, (20, 100, 2))
# initialise with last position
mcfitter.sample(steps=1)
chain = mcfitter.chain
mcfitter.initialise(pos=chain[-1])
assert_equal(mcfitter._state.coords.shape, (20, 100, 2))
# initialise with chain
mcfitter.sample(steps=2)
chain = mcfitter.chain
mcfitter.initialise(pos=np.copy(chain))
assert_equal(mcfitter._state.coords, chain[-1])
# initialise with chain if it's never been run before
mcfitter = CurveFitter(self.objective, nwalkers=100, ntemps=20)
mcfitter.initialise(chain)
def test_fit_smoke(self):
# smoke tests to check that fit runs
def callback(xk):
return
def callback2(xk, **kws):
return
# L-BFGS-B
res0 = self.mcfitter.fit(callback=callback)
assert_almost_equal(res0.x, [self.b_ls, self.m_ls], 6)
res0 = self.mcfitter.fit()
res0 = self.mcfitter.fit(verbose=False)
res0 = self.mcfitter.fit(verbose=False, callback=callback)
# least_squares
res1 = self.mcfitter.fit(method="least_squares")
assert_almost_equal(res1.x, [self.b_ls, self.m_ls], 6)
# least_squares doesn't accept a callback. As well as testing that
# least_squares works, it checks that providing a callback doesn't
# trip the fitter up.
res1 = self.mcfitter.fit(method="least_squares", callback=callback)
assert_almost_equal(res1.x, [self.b_ls, self.m_ls], 6)
# need full bounds for differential_evolution
self.p[0].range(3, 7)
self.p[1].range(-2, 0)
res2 = self.mcfitter.fit(
method="differential_evolution",
seed=1,
popsize=10,
maxiter=100,
callback=callback2,
)
assert_almost_equal(res2.x, [self.b_ls, self.m_ls], 6)
# check that the res object has covar and stderr
assert_("covar" in res0)
assert_("stderr" in res0)
def test_NIST(self):
# Run all the NIST standard tests with leastsq
for model in NIST_Models:
try:
NIST_runner(model)
except Exception:
print(model)
raise
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)
n = l.reshape(timesteps, sim.layers.shape[1]-layers_to_cut, sim.layers.shape[2])
data_dir = '../data/reflectometry2/dspc_{}/'.format(surface_pressure)
dataset = ReflectDataset(os.path.join(data_dir, '{}{}.dat'.format(contrast, surface_pressure)))
refy = np.zeros((n.shape[0], dataset.x.size))
sldy = []
chi = np.zeros((n.shape[0]))
print(n.shape[0])
for i in range(n.shape[0]):
sim.av_layers = n[i, :, :]
model = ReflectModel(sim)
model.scale.setp(1, vary=True, bounds=(0.00000001, np.inf))
model.bkg.setp(dataset.y[-1], vary=False)
objective = Objective(model, dataset, transform=Transform('YX4'))
fitter = CurveFitter(objective)
res = fitter.fit()
refy[i] = model(dataset.x, x_err=dataset.x_err)*(dataset.x)**4
sldy.append(sim.sld_profile()[1])
chi[i] = objective.chisqr()
all_chi = np.append(all_chi, objective.chisqr())
if i == 0:
ax1.errorbar(dataset.x,
dataset.y*(dataset.x)**4 * 10**(ci-1),
yerr=dataset.y_err*(
dataset.x)**4 * 10**(ci-1),
linestyle='', marker='o',
color=sns.color_palette()[ci])
if i % 5 == 0:
ax1.plot(dataset.x,
d2o = SLD(6.36, name='d2o')
structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3)
structure[1].thick.setp(vary=True, bounds=(15., 50.))
structure[1].rough.setp(vary=True, bounds=(1., 6.))
structure[2].thick.setp(vary=True, bounds=(200, 300))
structure[2].sld.real.setp(vary=True, bounds=(0.1, 3))
structure[2].rough.setp(vary=True, bounds=(1, 6))
model = ReflectModel(structure, bkg=9e-6, scale=1.)
model.bkg.setp(vary=True, bounds=(1e-8, 1e-5))
model.scale.setp(vary=True, bounds=(0.9, 1.1))
# fit on a logR scale, but use weighting
objective = Objective(model,
data,
transform=Transform('logY'),
use_weights=True)
# create the fit instance
fitter = CurveFitter(objective)
# do the fit
res = fitter.fit(method='differential_evolution')
# see the fit results
print(objective)
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
ax.scatter(data.x, data.y, label=DATASET_NAME)
ax.semilogy()
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
hold_phih=True,
)
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]),
