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#! /usr/bin/env python
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : March 17, 2018
"""
HESE BSM flavour ratio MCMC analysis script
"""
from __future__ import absolute_import, division
import argparse
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 likelihood as llh_utils
from utils import mcmc as mcmc_utils
from utils import misc as misc_utils
from utils import plot as plot_utils
from utils.enums import EnergyDependance, Likelihood, MixingScenario
from utils.enums import MCMCSeedType, ParamTag, PriorsCateg
from utils.param import Param, ParamSet, get_paramsets
def define_nuisance():
"""Define the nuisance parameters."""
tag = ParamTag.SM_ANGLES
nuisance = []
g_prior = PriorsCateg.GAUSSIAN
hg_prior = PriorsCateg.HALFGAUSS
e = 1e-9
nuisance.extend([
Param(name='s_12_2', value=0.307, seed=[0.26, 0.35], ranges=[0., 1.], std=0.013, tex=r's_{12}^2', prior=g_prior, tag=tag),
Param(name='c_13_4', value=(1-(0.02206))**2, seed=[0.950, 0.961], ranges=[0., 1.], std=0.00147, tex=r'c_{13}^4', prior=g_prior, tag=tag),
Param(name='s_23_2', value=0.538, seed=[0.31, 0.75], ranges=[0., 1.], std=0.069, tex=r's_{23}^2', prior=g_prior, tag=tag),
Param(name='dcp', value=4.08404, seed=[0+e, 2*np.pi-e], ranges=[0., 2*np.pi], std=2.0, tex=r'\delta_{CP}', tag=tag),
Param(
name='m21_2', value=7.40E-23, seed=[7.2E-23, 7.6E-23], ranges=[6.80E-23, 8.02E-23],
std=2.1E-24, tex=r'\Delta m_{21}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag
),
Param(
name='m3x_2', value=2.494E-21, seed=[2.46E-21, 2.53E-21], ranges=[2.399E-21, 2.593E-21],
std=3.3E-23, tex=r'\Delta m_{3x}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag
)
])
tag = ParamTag.NUISANCE
nuisance.extend([
Param(name='convNorm', value=1., seed=[0.5, 2. ], ranges=[0. , 50.], std=0.3, tag=tag),
Param(name='promptNorm', value=0., seed=[0. , 6. ], ranges=[0. , 50.], std=0.05, tag=tag),
Param(name='muonNorm', value=1., seed=[0.1, 2. ], ranges=[0. , 50.], std=0.1, tag=tag),
Param(name='astroNorm', value=6.9, seed=[0.1, 10.], ranges=[0. , 50.], std=0.1, tag=tag),
Param(name='astroDeltaGamma', value=2.5, seed=[1. , 3. ], ranges=[-5., 5. ], std=0.1, tag=tag)
])
return ParamSet(nuisance)
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.fix_mixing is not MixingScenario.NONE and args.fix_scale:
raise NotImplementedError('Fixed mixing and scale not implemented')
if args.fix_mixing is not MixingScenario.NONE and args.fix_mixing_almost:
raise NotImplementedError(
'--fix-mixing and --fix-mixing-almost cannot be used together'
)
args.measured_ratio = fr_utils.normalise_fr(args.measured_ratio)
if args.fix_source_ratio:
args.source_ratio = fr_utils.normalise_fr(args.source_ratio)
if args.energy_dependance is EnergyDependance.SPECTRAL:
args.binning = np.logspace(
np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1
)
if not args.fix_scale:
args.scale, args.scale_region = fr_utils.estimate_scale(args)
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(
'--seed', type=misc_utils.seed_parse, default='25',
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(
'--outfile', type=str, default='./untitled',
help='Path to output results'
)
fr_utils.fr_argparse(parser)
gf_utils.gf_argparse(parser)
llh_utils.likelihood_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 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, llh_paramset = get_paramsets(args, define_nuisance())
outfile = misc_utils.gen_outfile_name(args)
print '== {0:<25} = {1}'.format('outfile', outfile)
if args.run_mcmc:
if args.likelihood is Likelihood.GOLEMFIT:
fitter = gf_utils.setup_fitter(args, asimov_paramset)
else: fitter = None
ln_prob = partial(
llh_utils.ln_prob, args=args, fitter=fitter,
asimov_paramset=asimov_paramset, llh_paramset=llh_paramset
)
ndim = len(llh_paramset)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(
llh_paramset, nwalkers=args.nwalkers
)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(
llh_paramset, nwalkers=args.nwalkers
)
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = ln_prob,
ndim = ndim,
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
threads = 1
# TODO(shivesh): broken because you cannot pickle a GolemFitPy object
# threads = misc_utils.thread_factors(args.threads)[0]
)
mcmc_utils.save_chains(samples, outfile)
plot_utils.chainer_plot(
infile = outfile+'.npy',
outfile = outfile[:5]+outfile[5:].replace('data', 'plots'),
outformat = ['pdf'],
args = args,
llh_paramset = llh_paramset
)
print "DONE!"
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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