diff options
Diffstat (limited to 'utils/gf.py')
| -rw-r--r-- | utils/gf.py | 22 |
1 files changed, 13 insertions, 9 deletions
diff --git a/utils/gf.py b/utils/gf.py index 59d1033..17ac029 100644 --- a/utils/gf.py +++ b/utils/gf.py @@ -16,6 +16,7 @@ import GolemFitPy as gf from utils.enums import DataType, SteeringCateg from utils.misc import enum_parse, thread_factors +from utils.param import ParamSet def fit_flags(llh_paramset): @@ -23,9 +24,6 @@ def fit_flags(llh_paramset): # False means it's not fixed in minimization 'astroFlavorAngle1' : True, 'astroFlavorAngle2' : True, - 'astroENorm' : True, - 'astroMuNorm' : True, - 'astroTauNorm' : True, 'convNorm' : True, 'promptNorm' : True, 'muonNorm' : True, @@ -44,9 +42,14 @@ def fit_flags(llh_paramset): 'astroDeltaGammaSec' : True } flags = gf.FitParametersFlag() + gf_nuisance = [] for param in llh_paramset: - flags.__setattr__(param.name, False) - return flags + if param.name in default_flags: + print 'Setting param {0:<15} to float in the ' \ + 'minimisation'.format(param.name) + flags.__setattr__(param.name, False) + gf_nuisance.append(param) + return flags, ParamSet(gf_nuisance) def steering_params(args): @@ -68,7 +71,7 @@ def set_up_as(fitter, params): print 'Injecting the model', params asimov_params = gf.FitParameters(gf.sampleTag.HESE) for parm in params: - asimov_params.__setattr__(parm.name, parm.value) + asimov_params.__setattr__(parm.name, float(parm.value)) fitter.SetupAsimov(asimov_params) @@ -84,7 +87,7 @@ def setup_fitter(args, asimov_paramset): def get_llh(fitter, params): fitparams = gf.FitParameters(gf.sampleTag.HESE) for parm in params: - fitparams.__setattr__(parm.name, parm.value) + fitparams.__setattr__(parm.name, float(parm.value)) llh = -fitter.EvalLLH(fitparams) return llh @@ -93,9 +96,10 @@ def get_llh_freq(fitter, params): print 'setting to {0}'.format(params) fitparams = gf.FitParameters(gf.sampleTag.HESE) for parm in params: - fitparams.__setattr__(parm.name, parm.value) + fitparams.__setattr__(parm.name, float(parm.value)) fitter.SetFitParametersSeed(fitparams) - return -fitter.MinLLH().likelihood + llh = -fitter.MinLLH().likelihood + return llh def data_distributions(fitter): |