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#! /usr/bin/env python
"""
Sample points for a specific scenario
"""
from __future__ import absolute_import, division
import sys
sys.path.extend(['.', '../'])
import argparse
from functools import partial
import numpy as np
from scipy.stats import multivariate_normal
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.param import Param, ParamSet, get_paramsets
from utils.enums import MixingScenario, MCMCSeedType
def triangle_llh(theta, args):
"""-Log likelihood function for a given theta."""
fr = fr_utils.angles_to_fr(theta)
fr_bf = args.measured_ratio
cov_fr = np.identity(3) * args.sigma_ratio
return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr))
def ln_prior(theta):
"""Priors on theta."""
sphi4, c2psi = theta
# Flavour ratio bounds
if 0. <= sphi4 <= 1.0 and -1.0 <= c2psi <= 1.0:
pass
else: return -np.inf
return 0.
def ln_prob(theta, args):
"""Prob function for mcmc."""
lp = ln_prior(theta)
if not np.isfinite(lp):
return -np.inf
return lp + triangle_llh(theta, args)
def process_args(args):
"""Process the input args."""
if args.fix_mixing is MixingScenario.NONE:
raise AssertionError('Must set a mixing scenario using --fix-mixing')
if not args.fix_source_ratio:
raise AssertionError('Must set source ratio using --fix-source-ratio')
args.source_ratio = fr_utils.normalise_fr(args.source_ratio)
if args.fix_mixing is MixingScenario.T12:
s12_2, c13_4, s23_2, dcp = 0.5, 1.0, 0.0, 0.
elif args.fix_mixing is MixingScenario.T13:
s12_2, c13_4, s23_2, dcp = 0.0, 0.25, 0.0, 0.
elif args.fix_mixing is MixingScenario.T23:
s12_2, c13_4, s23_2, dcp = 0.0, 1.0, 0.5, 0.
mm = np.array(
fr_utils.angles_to_u((s12_2, c13_4, s23_2, dcp)), dtype=np.complex256
)
args.measured_ratio = fr_utils.u_to_fr(args.source_ratio, mm)
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 chains'
)
parser.add_argument(
'--plot-statistic', type=misc_utils.parse_bool, default='False',
help='Plot MultiNest evidence or LLH value'
)
fr_utils.fr_argparse(parser)
llh_utils.likelihood_argparse(parser)
mcmc_utils.mcmc_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)
paramset = ParamSet([
Param(name='sphi4', value=0.5, ranges=[0.0, 1.0], std=0.2),
Param(name='c2psi', value=0.0, ranges=[-1.0, 1.0], std=0.2)
])
outfile = misc_utils.gen_outfile_name(args)
print '== {0:<25} = {1}'.format('outfile', outfile)
if args.run_mcmc:
ndim = len(paramset)
print paramset
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(paramset, nwalkers=args.nwalkers)
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = partial(ln_prob, args=args),
ndim = ndim,
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
threads = args.threads
)
fr_chains = np.array(map(fr_utils.angles_to_fr, samples), dtype=float)
mcmc_utils.save_chains(fr_chains, outfile)
print "DONE!"
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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