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 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 _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(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 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 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 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 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 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 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 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_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 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 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 __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 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 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
def test_multidimensionality(self):
# Check that ND data can be used with an objective/model/data
# (or at least it doesn't stand in the way)
rng = np.random.default_rng()
x = rng.uniform(size=100).reshape(50, 2)
desired = line_ND(x, self.p)
assert desired.shape == (50, 2)
data = Data1D((x, desired))
model = Model(self.p, fitfunc=line_ND)
y = model(x)
assert_allclose(y, desired)
objective = Objective(model, data)
assert_allclose(objective.chisqr(), 0)
assert_allclose(objective.generative(), desired)
assert_allclose(objective.residuals(), 0)
assert objective.residuals().shape == (50, 2)
objective.logl()
objective.logpost()
covar = objective.covar()
assert covar.shape == (2, 2)
lipid_2.thick_heads.constraint = lipid_1.thick_heads
structure_lipid_2[-1].rough.constraint = structure_lipid_1[-1].rough
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.
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]),
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,
model_lipid2 = ReflectModel(structure_lipid2)
model_lipid2.scale.setp(vary=True, bounds=(0.005, 10))
model_lipid2.bkg.setp(dataset2.y[-1], vary=False)
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)
def test_covar(self):
# checks objective.covar against optimize.least_squares covariance.
path = os.path.dirname(os.path.abspath(__file__))
theoretical = np.loadtxt(os.path.join(path, 'gauss_data.txt'))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
p0 = np.array([0.1, 20., 0.1, 0.1])
names = ['bkg', 'A', 'x0', 'width']
bounds = [(-1, 1), (0, 30), (-5., 5.), (0.001, 2)]
params = Parameters(name="gauss_params")
for p, name, bound in zip(p0, names, bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
params.append(param)
model = Model(params, fitfunc=gauss)
data = Data1D((xvals, yvals, evals))
objective = Objective(model, data)
# first calculate least_squares jac/hess/covariance matrices
res = least_squares(objective.residuals,
np.array(params),
jac='3-point')
hess_least_squares = np.matmul(res.jac.T, res.jac)
covar_least_squares = np.linalg.inv(hess_least_squares)
# now calculate corresponding matrices by hand, to see if the approach
# concurs with least_squares
objective.setp(res.x)
_pvals = np.array(res.x)
def residuals_scaler(vals):
return np.squeeze(objective.residuals(_pvals * vals))
jac = approx_derivative(residuals_scaler, np.ones_like(_pvals))
hess = np.matmul(jac.T, jac)
covar = np.linalg.inv(hess)
covar = covar * np.atleast_2d(_pvals) * np.atleast_2d(_pvals).T
assert_allclose(covar, covar_least_squares)
# check that objective.covar corresponds to the least_squares
# covariance matrix
objective.setp(res.x)
_pvals = np.array(res.x)
covar_objective = objective.covar()
assert_allclose(covar_objective, covar_least_squares)
# now see what happens with a parameter that has no effect on residuals
param = Parameter(1.234, name='dummy')
param.vary = True
params.append(param)
from pytest import raises
with raises(LinAlgError):
objective.covar()
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, 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
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