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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
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):
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()
params.quiet = False
params.fastmode = True
params.simToLoad= steering_categ.name.lower()
params.spline_dom_efficiency = False
params.spline_hole_ice = False
params.spline_anisotrophy = False
params.evalThreads = args.threads
# params.evalThreads = thread_factors(args.threads)[1]
params.diffuse_fit_type = gf.DiffuseFitType.SinglePowerLaw
return params
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, float(parm.value))
fitter.SetupAsimov(asimov_params)
def setup_fitter(args, asimov_paramset):
datapaths = gf.DataPaths()
sparams = steering_params(args)
npp = gf.NewPhysicsParams()
fitter = gf.GolemFit(datapaths, sparams, npp)
set_up_as(fitter, asimov_paramset)
return fitter
def get_llh(fitter, params):
fitparams = gf.FitParameters(gf.sampleTag.HESE)
for parm in params:
fitparams.__setattr__(parm.name, float(parm.value))
llh = -fitter.EvalLLH(fitparams)
return llh
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, float(parm.value))
fitter.SetFitParametersSeed(fitparams)
llh = -fitter.MinLLH().likelihood
return llh
def data_distributions(fitter):
hdat = fitter.GetDataDistribution()
binedges = np.asarray([fitter.GetZenithBinsData(), fitter.GetEnergyBinsData()])
return hdat, binedges
def gf_argparse(parser):
parser.add_argument(
'--data', default='real', 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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