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#! /usr/bin/env python
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : November 26, 2018
"""
HESE flavour ratio contour
"""
from __future__ import absolute_import, division
import os
import argparse
from copy import deepcopy
from functools import partial
import numpy as np
from utils import fr as fr_utils
from utils import gf as gf_utils
from utils import llh as llh_utils
from utils import misc as misc_utils
from utils import mcmc as mcmc_utils
from utils import plot as plot_utils
from utils.enums import str_enum
from utils.enums import DataType, Likelihood, MCMCSeedType, ParamTag
from utils.enums import PriorsCateg
from utils.param import Param, ParamSet
def define_nuisance():
"""Define the nuisance parameters."""
nuisance = []
tag = ParamTag.NUISANCE
lg_prior = PriorsCateg.LIMITEDGAUSS
nuisance.extend([
Param(name='convNorm', value=1., seed=[0.5, 2. ], ranges=[0.1, 10.], std=0.4, prior=lg_prior, tag=tag),
Param(name='promptNorm', value=0., seed=[0., 6. ], ranges=[0., 20.], std=2.4, prior=lg_prior, tag=tag),
# Param(name='convNorm', value=1., seed=[0.5, 2. ], ranges=[0.1, 10.], std=0.4, tag=tag),
# Param(name='promptNorm', value=0., seed=[0., 6. ], ranges=[0., 20.], std=2.4, tag=tag),
Param(name='muonNorm', value=1., seed=[0.1, 2. ], ranges=[0., 10.], std=0.1, tag=tag),
Param(name='astroNorm', value=6.9, seed=[0., 5. ], ranges=[0., 20.], std=1.5, tag=tag),
Param(name='astroDeltaGamma', value=2.5, seed=[2.4, 3. ], ranges=[-5., 5. ], std=0.1, tag=tag),
# Param(name='CRDeltaGamma', value=0., seed=[-0.1, 0.1 ], ranges=[-1., 1. ], std=0.1, tag=tag),
# Param(name='NeutrinoAntineutrinoRatio', value=1., seed=[0.8, 1.2 ], ranges=[0., 2. ], std=0.1, tag=tag),
# Param(name='anisotropyScale', value=1., seed=[0.8, 1.2 ], ranges=[0., 2. ], std=0.1, tag=tag),
# Param(name='domEfficiency', value=0.99, seed=[0.8, 1.2 ], ranges=[0.8, 1.2 ], std=0.1, tag=tag),
# Param(name='holeiceForward', value=0., seed=[-0.8, 0.8 ], ranges=[-4.42, 1.58 ], std=0.1, tag=tag),
# Param(name='piKRatio', value=1.0, seed=[0.8, 1.2 ], ranges=[0., 2. ], std=0.1, tag=tag)
])
return ParamSet(nuisance)
def get_paramsets(args, nuisance_paramset):
"""Make the paramsets for generating the Asmimov MC sample and also running
the MCMC.
"""
asimov_paramset = []
llh_paramset = []
gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)]
llh_paramset.extend(gf_nuisance)
for parm in llh_paramset:
parm.value = args.__getattribute__(parm.name)
llh_paramset = ParamSet(llh_paramset)
if args.data is not DataType.REAL:
flavour_angles = fr_utils.fr_to_angles(args.injected_ratio)
else:
flavour_angles = fr_utils.fr_to_angles([1, 1, 1])
tag = ParamTag.BESTFIT
asimov_paramset.extend(gf_nuisance)
asimov_paramset.extend([
Param(name='astroFlavorAngle1', value=flavour_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag),
Param(name='astroFlavorAngle2', value=flavour_angles[1], ranges=[-1., 1.], std=0.2, tag=tag),
])
asimov_paramset = ParamSet(asimov_paramset)
return asimov_paramset, llh_paramset
def nuisance_argparse(parser):
nuisance = define_nuisance()
for parm in nuisance:
parser.add_argument(
'--'+parm.name, type=float, default=parm.value,
help=parm.name+' to inject'
)
def process_args(args):
"""Process the input args."""
if args.data is not DataType.REAL:
args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio)
args.likelihood = Likelihood.GOLEMFIT
args.mcmc_threads = misc_utils.thread_factors(args.threads)[0]
args.threads = misc_utils.thread_factors(args.threads)[1]
def parse_args(args=None):
"""Parse command line arguments"""
parser = argparse.ArgumentParser(
description="BSM flavour ratio analysis",
formatter_class=misc_utils.SortingHelpFormatter,
)
parser.add_argument(
'--injected-ratio', type=float, nargs=3, default=[1, 1, 1],
help='Set the central value for the injected flavour ratio at IceCube'
)
parser.add_argument(
'--seed', type=misc_utils.seed_parse, default='26',
help='Set the random seed value'
)
parser.add_argument(
'--threads', type=misc_utils.thread_type, default='1',
help='Set the number of threads to use (int or "max")'
)
parser.add_argument(
'--datadir', type=str, default='./untitled',
help='Path to output results'
)
gf_utils.gf_argparse(parser)
mcmc_utils.mcmc_argparse(parser)
nuisance_argparse(parser)
if args is None: return parser.parse_args()
else: return parser.parse_args(args.split())
def gen_identifier(args):
f = '_{0}'.format(str_enum(args.data))
if args.data is not DataType.REAL:
f += '_INJ_{0}'.format(misc_utils.solve_ratio(args.injected_ratio))
return f
def gen_figtext(args, asimov_paramset):
f = ''
if args.data is DataType.REAL:
f += 'IceCube Preliminary'
else:
f += '_INJ_{0}'.format(misc_utils.solve_ratio(args.injected_ratio))
for param in asimov_paramset:
f += '\nInjected {0:20s} = {1:.3f}'.format(
param.name, param.nominal_value
)
return f
def triangle_llh(theta, args, hypo_paramset):
"""Log likelihood function for a given theta."""
if len(theta) != len(hypo_paramset):
raise AssertionError(
'Dimensions of scan is not the same as the input '
'params\ntheta={0}\nparamset]{1}'.format(theta, hypo_paramset)
)
for idx, param in enumerate(hypo_paramset):
param.value = theta[idx]
if args.likelihood is Likelihood.GOLEMFIT:
llh = gf_utils.get_llh(hypo_paramset)
elif args.likelihood is Likelihood.GF_FREQ:
llh = gf_utils.get_llh_freq(hypo_paramset)
return llh
def ln_prob(theta, args, hypo_paramset):
dc_hypo_paramset = deepcopy(hypo_paramset)
lp = llh_utils.lnprior(theta, paramset=dc_hypo_paramset)
if not np.isfinite(lp):
return -np.inf
return lp + triangle_llh(
theta,
args = args,
hypo_paramset = dc_hypo_paramset,
)
def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, hypo_paramset = get_paramsets(args, define_nuisance())
hypo_paramset.extend(asimov_paramset.from_tag(ParamTag.BESTFIT))
prefix = ''
outfile = args.datadir + '/contour' + prefix + gen_identifier(args)
print '== {0:<25} = {1}'.format('outfile', outfile)
print 'asimov_paramset', asimov_paramset
print 'hypo_paramset', hypo_paramset
if args.run_mcmc:
gf_utils.setup_fitter(args, asimov_paramset)
ln_prob_eval = partial(
ln_prob,
hypo_paramset = hypo_paramset,
args = args,
)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(
hypo_paramset, nwalkers=args.nwalkers
)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(
hypo_paramset, nwalkers=args.nwalkers
)
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = ln_prob_eval,
ndim = len(hypo_paramset),
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
args = args,
threads = args.mcmc_threads
)
mcmc_utils.save_chains(samples, outfile)
of = outfile[:5]+outfile[5:].replace('data', 'plots')+'_posterior'
plot_utils.chainer_plot(
infile = outfile+'.npy',
outfile = of,
outformat = ['png'],
args = args,
llh_paramset = hypo_paramset,
fig_text = gen_figtext(args, hypo_paramset)
)
print "DONE!"
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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