def analysis(self, _posterior):
"""Create an eos.Analysis object for the named posterior."""
if _posterior not in self._posteriors:
raise RuntimeError(
'Cannot create analysis for unknown posterior: \'{}\''.format(
_posterior))
posterior = self._posteriors[_posterior]
prior = []
for p in posterior['prior']:
prior.extend(self._priors[p]['parameters'])
likelihood = []
manual_constraints = {}
for lh in posterior['likelihood']:
likelihood.extend(self._likelihoods[lh]['constraints'] if
'constraints' in self._likelihoods[lh] else [])
manual_constraints.update(
self._likelihoods[lh]['manual_constraints']
if 'manual_constraints' in self._likelihoods[lh] else {})
global_options = posterior[
'global_options'] if 'global_options' in posterior else None
return eos.Analysis(prior,
likelihood,
global_options,
manual_constraints=manual_constraints)
def analysis(self, _posterior):
"""Create an eos.Analysis object for the named posterior."""
if _posterior not in self._posteriors:
raise RuntimeError(
'Cannot create analysis for unknown posterior: \'{}\''.format(
_posterior))
posterior = self._posteriors[_posterior]
prior = []
for p in posterior['prior']:
prior.extend(self._priors[p]['parameters'])
likelihood = []
manual_constraints = {}
for lh in posterior['likelihood']:
LH_ALLOWED_KEYS = {'name', 'constraints', 'manual_constraints'}
for key in self._likelihoods[lh]:
if key not in LH_ALLOWED_KEYS:
raise KeyError(
f"Unsupported key in 'likelihoods['{lh}']': {key}")
if 'constraints' not in self._likelihoods[
lh] and 'manual_constraints' not in self._likelihoods[lh]:
raise KeyError(
f'Missing entry in \'likelihoods[\'{lh}\']\': neither \'constraints\' nor \'manual_constraints\' is provided'
)
likelihood.extend(self._likelihoods[lh]['constraints'] if
'constraints' in self._likelihoods[lh] else [])
manual_constraints.update(
self._likelihoods[lh]['manual_constraints']
if 'manual_constraints' in self._likelihoods[lh] else {})
global_options = posterior[
'global_options'] if 'global_options' in posterior else {}
fixed_parameters = posterior[
'fixed_parameters'] if 'fixed_parameters' in posterior else {}
return eos.Analysis(prior,
likelihood,
global_options,
manual_constraints=manual_constraints,
fixed_parameters=fixed_parameters)
def clone(self):
"""Returns an independent instance of eos.Analysis."""
return eos.Analysis(**self.init_args)
def test_parameter_scaling(self):
analysis_args = {
'global_options': {
'form-factors': 'BSZ2015',
'model': 'CKM'
},
'priors': [{
'parameter': 'CKM::abs(V_cb)',
'min': 38e-3,
'max': 45e-3,
'type': 'uniform'
}, {
'parameter': 'B->D::alpha^f+_0@BSZ2015',
'min': 0.0,
'max': 1.0,
'type': 'uniform'
}, {
'parameter': 'B->D::alpha^f+_1@BSZ2015',
'min': -4.0,
'max': -1.0,
'type': 'uniform'
}, {
'parameter': 'B->D::alpha^f+_2@BSZ2015',
'min': +4.0,
'max': +6.0,
'type': 'uniform'
}, {
'parameter': 'B->D::alpha^f0_1@BSZ2015',
'min': -1.0,
'max': +2.0,
'type': 'uniform'
}, {
'parameter': 'B->D::alpha^f0_2@BSZ2015',
'min': -2.0,
'max': 0.0,
'type': 'uniform'
}],
'likelihood': [
'B->D::f_++f_0@HPQCD:2015A', 'B->D::f_++f_0@FNAL+MILC:2015B',
'B^0->D^+e^-nu::BRs@Belle:2015A',
'B^0->D^+mu^-nu::BRs@Belle:2015A'
]
}
# Test analysis definition
analysis = eos.Analysis(**analysis_args)
# Test analysis optimization
bfp = analysis.optimize()
# Test analysis optimization from a random point
bfp = analysis.optimize(start_point='random',
rng=np.random.mtrand.RandomState(123))
# Test parameter scaling
point = bfp.point
self.assertTrue(np.max(analysis._par_to_x(point)) < 1.0)
self.assertTrue(np.min(analysis._par_to_x(point)) > -1.0)
self.assertTrue(
np.max(analysis._x_to_par(analysis._par_to_x(point)) -
point) < 1e-10)