def _obstable_tree(self):
if not self._obstable_tree_cache:
llh = self.likelihood
info = copy(llh.obstable_sm)
for flh_name, flh in llh.fast_likelihoods.items():
# loop over fast likelihoods: they only have a single "measurement"
m = flh.pseudo_measurement
ml = flh.full_measurement_likelihood
pred = ml.get_predictions_par(self.par_dict_np, self.w)
for i, obs in enumerate(flh.observables):
info[obs]['theory'] = pred[obs]
ll_central = info[obs]['ll_central']
ll_sm = info[obs]['ll_sm']
ll = m.get_logprobability_single(obs, pred[obs])
# DeltaChi2 is -2*DeltaLogLikelihood
info[obs]['pull exp.'] = pull(-2 * (ll - ll_central), dof=1)
s = -1 if ll > ll_sm else 1
info[obs]['pull SM'] = s * pull(-2 * (ll - ll_sm), dof=1)
for lh_name, lh in llh.likelihoods.items():
# loop over "normal" likelihoods
ml = lh.measurement_likelihood
pred = ml.get_predictions_par(self.par_dict_np, self.w)
for i, obs in enumerate(lh.observables):
info[obs]['theory'] = pred[obs]
ll_central = info[obs]['ll_central']
ll_sm = info[obs]['ll_sm']
p_comb = info[obs]['exp. PDF']
ll = p_comb.logpdf([pred[obs]])
info[obs]['pull exp.'] = pull(-2 * (ll - ll_central), dof=1)
s = -1 if ll > ll_sm else 1
info[obs]['pull SM'] = s * pull(-2 * (ll - ll_sm), dof=1)
self._obstable_tree_cache = info
return self._obstable_tree_cache
def _obstable_tree(self):
if not self._obstable_tree_cache:
info = tree() # nested dict
pull_dof = 1
llh = self.likelihood
for flh_name, flh in llh.fast_likelihoods.items():
# loop over fast likelihoods: they only have a single "measurement"
m = flh.pseudo_measurement
ml = flh.full_measurement_likelihood
pred = ml.get_predictions_par(llh.par_dict, self.w)
sm_cov = flh.sm_covariance.get(force=False)
_, exp_cov = flh.exp_covariance.get(force=False)
inspire_dict = self._get_inspire_dict(flh.observables, ml)
for i, obs in enumerate(flh.observables):
info[obs]['lh_name'] = flh_name
info[obs]['name'] = obs if isinstance(obs, str) else obs[0]
info[obs]['theory'] = pred[obs]
info[obs]['th. unc.'] = np.sqrt(sm_cov[i, i])
info[obs]['experiment'] = m.get_central(obs)
info[obs]['exp. unc.'] = np.sqrt(exp_cov[i, i])
info[obs]['exp. PDF'] = NormalDistribution(
m.get_central(obs), np.sqrt(exp_cov[i, i]))
info[obs]['inspire'] = sorted(set(inspire_dict[obs]))
ll_central = m.get_logprobability_single(
obs, m.get_central(obs))
ll = m.get_logprobability_single(obs, pred[obs])
# DeltaChi2 is -2*DeltaLogLikelihood
info[obs]['pull'] = pull(-2 * (ll - ll_central),
dof=pull_dof)
for lh_name, lh in llh.likelihoods.items():
# loop over "normal" likelihoods
ml = lh.measurement_likelihood
pred = ml.get_predictions_par(llh.par_dict, self.w)
inspire_dict = self._get_inspire_dict(lh.observables, ml)
for i, obs in enumerate(lh.observables):
obs_dict = flavio.Observable.argument_format(obs, 'dict')
obs_name = obs_dict.pop('name')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
p_comb = flavio.combine_measurements(
obs_name,
include_measurements=ml.get_measurements,
**obs_dict)
info[obs]['experiment'] = p_comb.central_value
info[obs]['exp. unc.'] = max(p_comb.error_left,
p_comb.error_right)
info[obs]['exp. PDF'] = p_comb
info[obs]['inspire'] = sorted(set(inspire_dict[obs]))
info[obs]['theory'] = pred[obs]
info[obs]['th. unc.'] = 0
info[obs]['lh_name'] = lh_name
info[obs]['name'] = obs if isinstance(obs, str) else obs[0]
ll = p_comb.logpdf([pred[obs]]) - p_comb.logpdf(
[p_comb.central_value])
info[obs]['pull'] = pull(-2 * ll, dof=pull_dof)
self._obstable_tree_cache = info
return self._obstable_tree_cache
def predictions(filename):
f = open(filename, 'at', buffering=1)
chiSM = chi2()
obscalc = [('<Rmue>(B+->Kll)', 1.0, 6.0), ('<Rmue>(B0->K*ll)', 0.045, 1.1),
('<Rmue>(B0->K*ll)', 1.1, 6.0)]
for w in wcs:
f.write(w.__name__ + '\n=================\n')
chi = lambda cr, ci: chi2(w(cr, ci))
m = Minuit(chi,
cr=0,
ci=0,
error_cr=0.01,
error_ci=0.01,
errordef=1,
print_level=0)
m.migrad()
f.write('\tBest fit: ' + str(m.values[0]) + ' + ' + str(m.values[1]) +
'i\n')
chibf = m.fval
f.write('\tPull (sqrt): ' + str(sqrt(chiSM - chibf)) + '\n')
f.write('\tPull (sigma): ' + str(pull(chiSM - chibf, 2)) + r' \sigma' +
'\n')
f.write('\tChi2/dof: ' + str(chibf / (nobs - 2)) + '\n')
#m.minos()
xr_centr = m.values[0]
xi_centr = m.values[1]
wcObj = w(xr_centr, xi_centr)
cont = m.mncontour('cr', 'ci', numpoints=40)[2]
for o in range(0, len(obscalc)):
obs_centr = flavio.np_prediction(obscalc[o][0], wcObj,
*obscalc[o][1:])
obs_max = obs_min = obs_centr
for i in range(0, len(cont)):
wcObj = w(*cont[i])
obs_max = max(
obs_max,
flavio.np_prediction(obscalc[o][0], wcObj,
*obscalc[o][1:]))
obs_min = min(
obs_min,
flavio.np_prediction(obscalc[o][0], wcObj,
*obscalc[o][1:]))
f.write('\t' + str(obscalc[o]) + ': ' + str(obs_centr) + ' + ' +
str(obs_max - obs_centr) + ' - ' +
str(obs_centr - obs_min) + '\n')
f.write('\n\n')
f.close()