1 files changed, 0 insertions, 156 deletions
diff --git a/utils/gf.py b/utils/gf.py
deleted file mode 100644
index de21cc5..0000000
--- a/utils/gf.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# author : S. Mandalia
-#          s.p.mandalia@qmul.ac.uk
-#
-# date   : March 17, 2018
-
-"""
-Useful GolemFit wrappers for the BSM flavour ratio analysis
-"""
-
-from __future__ import absolute_import, division
-
-from functools import partial
-
-import numpy as np
-
-try:
-    import GolemFitPy as gf
-except:
-    print 'Running without GolemFit'
-    pass
-
-from utils.enums import DataType, Likelihood, SteeringCateg
-from utils.misc import enum_parse, parse_bool, thread_factors
-from utils.param import ParamSet
-
-
-FITTER = None
-
-
-def fit_flags(llh_paramset):
-    default_flags = {
-        # False means it's not fixed in minimization
-        'astroFlavorAngle1'         : True,
-        'astroFlavorAngle2'         : True,
-        'convNorm'                  : True,
-        'promptNorm'                : True,
-        'muonNorm'                  : True,
-        'astroNorm'                 : True,
-        'astroParticleBalance'      : True,
-        # 'astroDeltaGamma'           : True,
-        'cutoffEnergy'              : True,
-        'CRDeltaGamma'              : True,
-        'piKRatio'                  : True,
-        'NeutrinoAntineutrinoRatio' : True,
-        'darkNorm'                  : True,
-        'domEfficiency'             : True,
-        'holeiceForward'            : True,
-        'anisotropyScale'           : True,
-        'astroNormSec'              : True,
-        'astroDeltaGammaSec'        : True
-    }
-    flags = gf.FitParametersFlag()
-    gf_nuisance = []
-    for param in llh_paramset:
-        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):
-    steering_categ = args.ast
-    params = gf.SteeringParams(gf.sampleTag.MagicTau)
-    params.quiet = False
-    if args.debug:
-        params.fastmode = False
-    else:
-        params.fastmode = True
-    params.simToLoad= steering_categ.name.lower()
-    params.evalThreads = args.threads
-
-    if hasattr(args, 'binning'):
-        params.minFitEnergy = args.binning[0]  # GeV
-        params.maxFitEnergy = args.binning[-1] # GeV
-    else:
-        params.minFitEnergy = 6E4 # GeV
-        params.maxFitEnergy = 1E7 # GeV
-    params.load_data_from_text_file = False
-    params.do_HESE_reshuffle=False
-    params.use_legacy_selfveto_calculation = False
-
-    return params
-
-
-def setup_asimov(params):
-    print 'Injecting the model', params
-    asimov_params = gf.FitParameters(gf.sampleTag.MagicTau)
-    for parm in params:
-        asimov_params.__setattr__(parm.name, float(parm.value))
-    FITTER.SetupAsimov(asimov_params)
-
-
-def setup_realisation(params, seed):
-    print 'Injecting the model', params
-    asimov_params = gf.FitParameters(gf.sampleTag.MagicTau)
-    for parm in params:
-        asimov_params.__setattr__(parm.name, float(parm.value))
-    FITTER.Swallow(FITTER.SpitRealization(asimov_params, seed))
-
-
-def setup_fitter(args, asimov_paramset):
-    global FITTER
-    datapaths = gf.DataPaths()
-    sparams = steering_params(args)
-    npp = gf.NewPhysicsParams()
-    FITTER = gf.GolemFit(datapaths, sparams, npp)
-    if args.data is DataType.ASIMOV:
-        setup_asimov(FITTER, asimov_paramset)
-    elif args.data is DataType.REALISATION:
-        seed = args.seed if args.seed is not None else 1
-        setup_realisation(FITTER, asimov_paramset, seed)
-    elif args.data is DataType.REAL:
-        print 'Using MagicTau DATA'
-
-
-def get_llh(params):
-    fitparams = gf.FitParameters(gf.sampleTag.MagicTau)
-    for parm in params:
-        fitparams.__setattr__(parm.name, float(parm.value))
-    llh = -FITTER.EvalLLH(fitparams)
-    return llh
-
-
-def get_llh_freq(params):
-    print 'setting to {0}'.format(params)
-    fitparams = gf.FitParameters(gf.sampleTag.MagicTau)
-    for parm in params:
-        fitparams.__setattr__(parm.name, float(parm.value))
-    FITTER.SetFitParametersSeed(fitparams)
-    llh = -FITTER.MinLLH().likelihood
-    return llh
-
-
-def data_distributions():
-    hdat = FITTER.GetDataDistribution()
-    binedges = np.asarray(
-        [FITTER.GetZenithBinsData(), FITTER.GetEnergyBinsData()]
-    )
-    return hdat, binedges
-
-
-def gf_argparse(parser):
-    parser.add_argument(
-        '--debug', default='False', type=parse_bool, help='Run without fastmode'
-    )
-    parser.add_argument(
-        '--data', default='asimov', type=partial(enum_parse, c=DataType),
-        choices=DataType, help='select datatype'
-    )
-    parser.add_argument(
-        '--ast', default='p2_0', type=partial(enum_parse, c=SteeringCateg),
-        choices=SteeringCateg,
-        help='use asimov/fake dataset with specific steering'
-    )