1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
|
#! /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 os
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 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 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.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=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
)
# Param(name='s_12_2', value=0.307, ranges=[0., 1.], std=20, tex=r's_{12}^2', prior=g_prior, tag=tag),
# Param(name='c_13_4', value=(1-(0.02206))**2, ranges=[0., 1.], std=20, tex=r'c_{13}^4', prior=g_prior, tag=tag),
# Param(name='s_23_2', value=0.538, ranges=[0., 1.], std=20, tex=r's_{23}^2', prior=g_prior, tag=tag),
# Param(name='dcp', value=4.08404, ranges=[0., 2*np.pi], std=20, tex=r'\delta_{CP}', tag=tag),
# Param(
# name='m21_2', value=7.40E-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, 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=[0.1, 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 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)
try:
gf_utils.gf_argparse(parser)
except: pass
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
print 'asimov_paramset', asimov_paramset
print 'llh_paramset', llh_paramset
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
)
if args.save_measured_fr:
n = misc_utils.gen_identifier(args) + '.txt'
f = args.output_measured_fr + n
if os.path.isfile(f):
open(f, 'w').close()
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = ln_prob,
ndim = ndim,
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
args = args,
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()
|
