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#! /usr/bin/env python
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : February 24, 2019
"""
HESE BSM Flavour Figure 2
"""
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 misc as misc_utils
from utils import plot as plot_utils
from utils.enums import str_enum
from utils.enums import Likelihood, ParamTag, PriorsCateg
from utils.param import Param, ParamSet
from matplotlib import pyplot as plt
from pymultinest import Analyzer
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='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)
])
return ParamSet(nuisance)
def get_paramsets(args, nuisance_paramset):
paramset = []
if args.likelihood in [Likelihood.GOLEMFIT, Likelihood.GF_FREQ]:
gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)]
paramset.extend(gf_nuisance)
tag = ParamTag.BESTFIT
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),
])
paramset = ParamSet(paramset)
return paramset
def process_args(args):
"""Process the input args."""
if args.likelihood is not Likelihood.GOLEMFIT \
and args.likelihood is not Likelihood.GF_FREQ:
raise AssertionError(
'Likelihood method {0} not supported for this '
'script!\nChoose either GOLEMFIT or GF_FREQ'.format(
str_enum(args.likelihood)
)
)
def parse_args(args=None):
"""Parse command line arguments"""
parser = argparse.ArgumentParser(
description="HESE BSM Flavour Figure 2",
formatter_class=misc_utils.SortingHelpFormatter,
)
parser.add_argument(
'--likelihood', default='golemfit',
type=partial(misc_utils.enum_parse, c=Likelihood),
choices=Likelihood, help='likelihood contour'
)
parser.add_argument(
'--contour-dir', type=str,
help='Path to directory containing MultiNest runs'
)
parser.add_argument(
'--outfile', type=str, default='./untitled',
help='Output path'
)
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)
paramset = get_paramsets(args, define_nuisance())
n_params = len(paramset)
print n_params
# Data
data_path = '{0}/{1}/real'.format(
args.contour_dir, str_enum(args.likelihood).lower()
)
prefix = '{0}/_{1}_REAL_mn_'.format(data_path, str_enum(args.likelihood))
analyser = Analyzer(
outputfiles_basename=prefix, n_params=n_params
)
print analyser
pranges = paramset.ranges
bf = analyser.get_best_fit()['parameters']
for i in xrange(len(bf)):
bf[i] = (pranges[i][1]-pranges[i][0])*bf[i] + pranges[i][0]
print 'bestfit = ', bf
print 'bestfit log_likelihood', analyser.get_best_fit()['log_likelihood']
print
print '{0:50} = {1}'.format('global evidence', analyser.get_stats()['global evidence'])
print
chains = analyser.get_data()[:,2:]
for x in chains:
for i in xrange(len(x)):
x[i] = (pranges[i][1]-pranges[i][0])*x[i] + pranges[i][0]
llh = -0.5 * analyser.get_data()[:,1]
flavour_angles = chains[:,-2:]
flavour_ratios = np.array(
map(fr_utils.angles_to_fr, flavour_angles)
)
nbins = 25
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
tax = plot_utils.get_tax(ax, scale=nbins)
plot_utils.flavour_contour(
frs = flavour_ratios,
ax = ax,
nbins = nbins,
coverage = 99,
linewidth = 2,
color = 'green'
)
plot_utils.flavour_contour(
frs = flavour_ratios,
ax = ax,
nbins = nbins,
coverage = 90,
linewidth = 2,
color = 'blue'
)
plot_utils.flavour_contour(
frs = flavour_ratios,
ax = ax,
nbins = nbins,
coverage = 68,
linewidth = 2,
color = 'red'
)
ax.legend()
fig.savefig('test.png', bbox_inches='tight', dpi=150)
print "DONE!"
main.__doc__ = __doc__
if __name__ == '__main__':
main()
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