def _load_likelihoods(self,
include_likelihoods=None,
exclude_likelihoods=None):
if include_likelihoods is not None and exclude_likelihoods is not None:
raise ValueError("include_likelihoods and exclude_likelihoods "
"should not be specified simultaneously.")
for fn in self._fast_likelihoods_yaml:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
with open(self._get_likelihood_path(fn), 'r') as f:
L = FastLikelihood.load(f)
self.fast_likelihoods[fn] = L
for fn in self._likelihoods_yaml:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
if self.eft != 'SMEFT' and fn in ['likelihood_ewpt.yaml',
'likelihood_zlfv.yaml',]:
continue
with open(self._get_likelihood_path(fn), 'r') as f:
L = Likelihood.load(f)
self.likelihoods[fn] = L
for name, observables in self._custom_likelihoods_dict.items():
L = CustomLikelihood(self, observables)
self.custom_likelihoods['custom_' + name] = L
def _load_likelihoods(self,
include_likelihoods=None,
exclude_likelihoods=None):
if include_likelihoods is not None and exclude_likelihoods is not None:
raise ValueError("include_likelihoods and exclude_likelihoods "
"should not be specified simultaneously.")
for argument_name, argument in [
('exclude_likelihoods', exclude_likelihoods),
('include_likelihoods', include_likelihoods)
]:
if argument:
unknown_likelihoods = set(argument) - set(
self._fast_likelihoods_yaml + self._likelihoods_yaml)
if unknown_likelihoods:
raise ValueError(
"{} contains unknown likelihoods: {}".format(
argument_name, unknown_likelihoods))
# load ckm parameters for given CKM scheme
par_ckm_file = 'par_ckm_{}.yaml'.format(self._ckm_scheme_name)
with open(self._get_yaml_path(par_ckm_file), 'r') as f:
par_ckm_dict = flavio.io.yaml.load_include(f)
for fn in self._fast_likelihoods_yaml:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
with open(self._get_yaml_path(fn), 'r') as f:
yaml_dict = flavio.io.yaml.load_include(f)
if not self.fix_ckm:
yaml_dict['par_obj'] = par_ckm_dict
L = FastLikelihood.load_dict(yaml_dict)
self.fast_likelihoods[fn] = L
for fn in self._likelihoods_yaml:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
if self.eft != 'SMEFT' and fn in [
'likelihood_ewpt.yaml',
'likelihood_eeww.yaml',
'likelihood_zlfv.yaml',
'likelihood_higgs.yaml',
]:
continue
with open(self._get_yaml_path(fn), 'r') as f:
L = Likelihood.load(f)
self.likelihoods[fn] = L
for name, observables in self._custom_likelihoods_dict.items():
L = CustomLikelihood(self, observables)
self.custom_likelihoods['custom_' + name] = L
def _load_likelihoods(self,
include_likelihoods=None,
exclude_likelihoods=None):
if include_likelihoods is not None and exclude_likelihoods is not None:
raise ValueError("include_likelihoods and exclude_likelihoods "
"should not be specified simultaneously.")
for fn in [
'fast_likelihood_quarks.yaml', 'fast_likelihood_leptons.yaml'
]:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
with open(get_datapath('smelli', 'data/yaml/' + fn), 'r') as f:
L = FastLikelihood.load(f)
self.fast_likelihoods[fn] = L
for fn in [
'likelihood_ewpt.yaml',
'likelihood_lept.yaml',
'likelihood_rd_rds.yaml',
'likelihood_lfu_fccc.yaml',
'likelihood_lfu_fcnc.yaml',
'likelihood_bcpv.yaml',
'likelihood_bqnunu.yaml',
'likelihood_lfv.yaml',
'likelihood_zlfv.yaml',
]:
if include_likelihoods is not None and fn not in include_likelihoods:
continue
if exclude_likelihoods is not None and fn in exclude_likelihoods:
continue
if self.eft != 'SMEFT' and fn in [
'likelihood_ewpt.yaml',
'likelihood_zlfv.yaml',
]:
continue
with open(get_datapath('smelli', 'data/yaml/' + fn), 'r') as f:
L = Likelihood.load(f)
self.likelihoods[fn] = L