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#! /usr/bin/env python
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : March 17, 2018
"""
Sample SM points for (x, 1-x, 0) only assuming unitarity
"""
from __future__ import absolute_import, division
import argparse
from copy import deepcopy
from functools import partial
import numpy as np
from utils import fr as fr_utils
from utils import llh 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 MCMCSeedType, ParamTag, PriorsCateg
from utils.param import Param, ParamSet
def define_nuisance():
"""Define the nuisance parameters."""
tag = ParamTag.SM_ANGLES
nuisance = []
g_prior = PriorsCateg.GAUSSIAN
lg_prior = PriorsCateg.LIMITEDGAUSS
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', 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', 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', 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),
])
tag = ParamTag.SRCANGLES
nuisance.extend([
Param(name='astroX', value=0.5, seed=[0., 1.], ranges=[0., 1.], std=0.1, tex=r'x', 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 = []
hypo_paramset = []
hypo_paramset.extend(
[x for x in nuisance_paramset.from_tag((
ParamTag.SM_ANGLES, ParamTag.SRCANGLES
))]
)
for parm in hypo_paramset:
parm.value = args.__getattribute__(parm.name)
hypo_paramset = ParamSet(hypo_paramset)
tag = ParamTag.BESTFIT
asimov_paramset.extend([
Param(name='astroFlavorAngle1', value=0., ranges=[ 0., 1.], std=0.2, tag=tag),
Param(name='astroFlavorAngle2', value=0., ranges=[-1., 1.], std=0.2, tag=tag),
])
asimov_paramset = ParamSet(asimov_paramset)
return asimov_paramset, hypo_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."""
pass
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='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 store chains'
)
mcmc_utils.mcmc_argparse(parser)
nuisance_argparse(parser)
misc_utils.remove_option(parser, 'injected_ratio')
misc_utils.remove_option(parser, 'plot_angles')
misc_utils.remove_option(parser, 'plot_elements')
if args is None: return parser.parse_args()
else: return parser.parse_args(args.split())
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]
return 1. # Flat 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())
prefix = ''
outfile = args.datadir + '/mc_unitarity' + prefix
print '== {0:<25} = {1}'.format('outfile', outfile)
print 'asimov_paramset', asimov_paramset
print 'hypo_paramset', hypo_paramset
if args.run_mcmc:
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.threads
)
nsamples = len(samples)
srcs = [fr_utils.normalise_fr((x, 1-x, 0)) for x in samples.T[-1]]
mmxs = map(fr_utils.angles_to_u, samples.T[:-1].T)
frs = np.array(
[fr_utils.u_to_fr(srcs[i], mmxs[i]) for i in xrange(nsamples)]
)
mcmc_utils.save_chains(frs, outfile)
print "DONE!"
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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