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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
import argparse
import socket
from functools import partial
import GolemFitPy as gf
from utils.enums import *
from utils.misc import enum_parse, thread_factors
def steering_params(args):
steering_categ = args.ast
params = gf.SteeringParams()
if 'cobalt0' in socket.gethostname().split('.')[0]:
params.quiet = False
else:
params.quiet = True
# TODO(shivesh): figure out right number for missid
params.track_to_shower_missid = 0.3
params.fastmode = True
# params.fastmode = False
# params.readCompact = True
params.readCompact = False
params.do_HESE_reshuffle = False
params.sampleToLoad = gf.sampleTag.HESE
params.simToLoad= steering_categ.name.lower()
params.evalThreads = args.threads
# params.evalThreads = thread_factors(args.threads)[1]
params.baseline_astro_spectral_index = -2.
params.spline_hole_ice = True
params.spline_dom_efficiency = True
if steering_categ == SteeringCateg.LONGLIFE:
params.years = [999]
params.simToLoad= 'p2_0'
elif steering_categ == SteeringCateg.DPL:
params.diffuse_fit_type = gf.DiffuseFitType.DoublePowerLaw
params.simToLoad= 'p2_0'
return params
def set_up_as(fitter, params):
print 'Injecting the model', params
asimov_params = gf.FitParameters()
for parm in params:
asimov_params.__setattr__(parm.name, parm.value)
fitter.SetupAsimov(asimov_params)
def get_llh(fitter, params):
fitparams = gf.FitParameters()
for parm in params:
fitparams.__setattr__(parm.name, parm.value)
return fitter.EvalLLH(fitparams)
def data_distributions(fitter):
hdat = fitter.GetDataDistribution()
binedges = np.asarray([fitter.GetZenithBinsData(), fitter.GetEnergyBinsData()])
return hdat, binedges
def fit_flags(fitflag_categ):
flags = gf.FitParametersFlag()
if fitflag_categ is FitFlagCateg.xs:
# False means it's not fixed in minimization
flags.NeutrinoAntineutrinoRatio = False
return flags
def fit_params(fit_categ):
params = gf.FitParameters()
params.astroNorm = 7.5
params.astroDeltaGamma = 0.9
if fit_categ is FitCateg.hesespl:
params.astroNormSec = 0
elif fit_categ is FitCateg.hesedpl:
params.astroNormSec=7.0
elif fit_categ is FitCateg.zpspl:
# zero prompt, single powerlaw
params.promptNorm = 0
params.astroNormSec = 0
elif fit_categ is FitCateg.zpdpl:
# zero prompt, double powerlaw
params.promptNorm = 0
params.astroNormSec=7.0
elif fit_categ is FitCateg.nunubar2:
params.NeutrinoAntineutrinoRatio = 2
def fit_priors(fitpriors_categ):
priors = gf.Priors()
if fitpriors_categ == FitPriorsCateg.xs:
priors.promptNormCenter = 1
priors.promptNormWidth = 3
priors.astroDeltaGammaCenter = 0
priors.astroDeltaGammaWidth = 1
return priors
def gen_steering_params(steering_categ, quiet=False):
params = gf.SteeringParams()
if quiet: params.quiet = True
params.fastmode = False
params.do_HESE_reshuffle = False
params.numc_tag = steering_categ.name
params.baseline_astro_spectral_index = -2.
if steering_categ is SteeringCateg.LONGLIFE:
params.years = [999]
params.numc_tag = 'std_half1'
if steering_categ is SteeringCateg.DPL:
params.diffuse_fit_type = gf.DiffuseFitType.DoublePowerLaw
params.numc_tag = 'std_half1'
return params
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'
)
parser.add_argument(
'--aft', default='hesespl', type=partial(enum_parse, c=FitCateg),
choices=FitCateg,
help='use asimov/fake dataset with specific Fit'
)
parser.add_argument(
'--axs', default='nom', type=partial(enum_parse, c=XSCateg),
choices=XSCateg,
help='use asimov/fake dataset with xs scaling'
)
parser.add_argument(
'--priors', default='uniform', type=partial(enum_parse, c=Priors),
choices=Priors, help='Bayesian priors'
)
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