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#! /usr/bin/env python
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : April 25, 2019
"""
Sample points for a specific scenario
"""
from __future__ import absolute_import, division
import argparse
from copy import deepcopy
from functools import partial
import numpy as np
from golemflavor import fr as fr_utils
from golemflavor import llh as llh_utils
from golemflavor import mcmc as mcmc_utils
from golemflavor import misc as misc_utils
from golemflavor import plot as plot_utils
from golemflavor.enums import MCMCSeedType, ParamTag, PriorsCateg, Texture
from golemflavor.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', prior=lg_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=lg_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=lg_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
)
])
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 = []
llh_paramset.extend(
[x for x in nuisance_paramset.from_tag(ParamTag.SM_ANGLES)]
)
for parm in llh_paramset:
parm.value = args.__getattribute__(parm.name)
boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension]
tag = ParamTag.SCALE
llh_paramset.append(
Param(
name='logLam', value=np.mean(boundaries), ranges=boundaries, std=3,
tex=r'{\rm log}_{10}\left (\Lambda^{-1}' + \
misc_utils.get_units(args.dimension)+r'\right )',
tag=tag
)
)
llh_paramset = ParamSet(llh_paramset)
tag = ParamTag.BESTFIT
flavor_angles = fr_utils.fr_to_angles([1, 1, 1])
asimov_paramset.extend([
Param(name='astroFlavorAngle1', value=flavor_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag),
Param(name='astroFlavorAngle2', value=flavor_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.texture is Texture.NONE:
raise ValueError('Must assume a BSM texture')
args.source_ratio = fr_utils.normalize_fr(args.source_ratio)
args.binning = np.logspace(
np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1
)
def parse_args(args=None):
"""Parse command line arguments"""
parser = argparse.ArgumentParser(
description="BSM flavor 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(
'--spectral-index', type=float, default='-2',
help='Astro spectral index'
)
parser.add_argument(
'--datadir', type=str, default='./untitled',
help='Path to store chains'
)
fr_utils.fr_argparse(parser)
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 gen_identifier(args):
f = '_DIM{0}'.format(args.dimension)
f += '_SRC_' + misc_utils.solve_ratio(args.source_ratio)
f += '_{0}'.format(misc_utils.str_enum(args.texture))
return f
def triangle_llh(theta, args, llh_paramset):
"""Log likelihood function for a given theta."""
if len(theta) != len(llh_paramset):
raise AssertionError(
'Dimensions of scan is not the same as the input '
'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset)
)
for idx, param in enumerate(llh_paramset):
param.value = theta[idx]
return 1. # Flat LLH
def ln_prob(theta, args, llh_paramset):
dc_llh_paramset = deepcopy(llh_paramset)
lp = llh_utils.lnprior(theta, paramset=dc_llh_paramset)
if not np.isfinite(lp):
return -np.inf
return lp + triangle_llh(
theta,
args = args,
llh_paramset = dc_llh_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, llh_paramset = get_paramsets(args, define_nuisance())
prefix = ''
outfile = args.datadir + '/mc_texture' + prefix + gen_identifier(args)
print('== {0:<25} = {1}'.format('outfile', outfile))
print('asimov_paramset', asimov_paramset)
print('llh_paramset', llh_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
ln_prob,
llh_paramset = llh_paramset,
args = args,
)
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_eval,
ndim = len(llh_paramset),
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
threads = args.threads
)
frs = np.array(
map(lambda x: fr_utils.flux_averaged_BSMu(
x, args, args.spectral_index, llh_paramset
), samples),
dtype=float
)
frs_scale = np.vstack((frs.T, samples[:-1].T)).T
mcmc_utils.save_chains(frs_scale, outfile)
print("DONE!")
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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