diff options
| author | shivesh <s.p.mandalia@qmul.ac.uk> | 2018-05-14 16:45:57 -0500 |
|---|---|---|
| committer | shivesh <s.p.mandalia@qmul.ac.uk> | 2018-05-14 16:45:57 -0500 |
| commit | e32bf7123fe6abb0e1319c02d49c1a33c4380a6e (patch) | |
| tree | 2908ee005191d2cc591b14128383d90402ec270b /utils/gf.py | |
| parent | c2c427177af69b0a08874a4466c95f2f6aa94c44 (diff) | |
| download | GolemFlavor-e32bf7123fe6abb0e1319c02d49c1a33c4380a6e.tar.gz GolemFlavor-e32bf7123fe6abb0e1319c02d49c1a33c4380a6e.zip | |
fix bug in energy spectrum
Diffstat (limited to 'utils/gf.py')
| -rw-r--r-- | utils/gf.py | 14 |
1 files changed, 6 insertions, 8 deletions
diff --git a/utils/gf.py b/utils/gf.py index 7ded152..cc093e1 100644 --- a/utils/gf.py +++ b/utils/gf.py @@ -55,7 +55,6 @@ def fit_flags(llh_paramset): def steering_params(args): steering_categ = args.ast - # params = gf.SteeringParams(gf.sampleTag.HESE) params = gf.SteeringParams(gf.sampleTag.MagicTau) params.quiet = False params.fastmode = True @@ -66,14 +65,13 @@ def steering_params(args): # For Tianlu # params.years = [999] - params.minFitEnergy = 1.0e5 # GeV + # params.minFitEnergy = 1.0e5 # GeV return params def set_up_as(fitter, params): print 'Injecting the model', params - # asimov_params = gf.FitParameters(gf.sampleTag.HESE) asimov_params = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: asimov_params.__setattr__(parm.name, float(parm.value)) @@ -85,13 +83,14 @@ def setup_fitter(args, asimov_paramset): sparams = steering_params(args) npp = gf.NewPhysicsParams() fitter = gf.GolemFit(datapaths, sparams, npp) - # comment to use data - # set_up_as(fitter, asimov_paramset) + if args.data is DataType.ASIMOV: + set_up_as(fitter, asimov_paramset) + elif args.data is DataType.REAL: + print 'Using MagicTau DATA' return fitter def get_llh(fitter, params): - # fitparams = gf.FitParameters(gf.sampleTag.HESE) fitparams = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: fitparams.__setattr__(parm.name, float(parm.value)) @@ -101,7 +100,6 @@ def get_llh(fitter, params): def get_llh_freq(fitter, params): print 'setting to {0}'.format(params) - # fitparams = gf.FitParameters(gf.sampleTag.HESE) fitparams = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: fitparams.__setattr__(parm.name, float(parm.value)) @@ -118,7 +116,7 @@ def data_distributions(fitter): def gf_argparse(parser): parser.add_argument( - '--data', default='real', type=partial(enum_parse, c=DataType), + '--data', default='asimov', type=partial(enum_parse, c=DataType), choices=DataType, help='select datatype' ) parser.add_argument( |