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# 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 args.likelihood is Likelihood.GOLEMFIT:
params.frequentist = False;
elif args.likelihood is Likelihood.GF_FREQ:
params.frequentist = True;
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.sampleToLoad = gf.sampleTag.MagicTau
params.use_legacy_selfveto_calculation = False
# params.spline_hole_ice = False
# params.spline_dom_efficiency = False
params.spline_hole_ice = True
params.spline_dom_efficiency = True
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'
)
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