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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
|
# author : S. Mandalia
# s.p.mandalia@qmul.ac.uk
#
# date : April 19, 2018
"""
Param class and functions for the BSM flavour ratio analysis
"""
from __future__ import absolute_import, division
import sys
from collections import Sequence
from copy import deepcopy
import numpy as np
from utils.plot import get_units
from utils.fr import fr_to_angles
from utils.enums import Likelihood, ParamTag, PriorsCateg
class Param(object):
"""Parameter class to store parameters."""
def __init__(self, name, value, ranges, prior=None, seed=None, std=None,
tex=None, tag=None):
self._prior = None
self._seed = None
self._ranges = None
self._tex = None
self._tag = None
self.name = name
self.value = value
self.nominal_value = deepcopy(value)
self.prior = prior
self.ranges = ranges
self.seed = seed
self.std = std
self.tex = tex
self.tag = tag
@property
def ranges(self):
return tuple(self._ranges)
@ranges.setter
def ranges(self, values):
self._ranges = [val for val in values]
@property
def prior(self):
return self._prior
@prior.setter
def prior(self, value):
if value is None:
self._prior = PriorsCateg.UNIFORM
else:
assert value in PriorsCateg
self._prior = value
@property
def seed(self):
if self._seed is None: return self.ranges
return tuple(self._seed)
@seed.setter
def seed(self, values):
if values is None: return
self._seed = [val for val in values]
@property
def tex(self):
return r'{0}'.format(self._tex)
@tex.setter
def tex(self, t):
self._tex = t if t is not None else r'{\rm %s}' % self.name
@property
def tag(self):
return self._tag
@tag.setter
def tag(self, t):
if t is None: self._tag = ParamTag.NONE
else:
assert t in ParamTag
self._tag = t
class ParamSet(Sequence):
"""Container class for a set of parameters."""
def __init__(self, *args):
param_sequence = []
for arg in args:
try:
param_sequence.extend(arg)
except TypeError:
param_sequence.append(arg)
if len(param_sequence) != 0:
# Disallow duplicated params
all_names = [p.name for p in param_sequence]
unique_names = set(all_names)
if len(unique_names) != len(all_names):
duplicates = set([x for x in all_names if all_names.count(x) > 1])
raise ValueError('Duplicate definitions found for param(s): ' +
', '.join(str(e) for e in duplicates))
# Elements of list must be Param type
assert all([isinstance(x, Param) for x in param_sequence]), \
'All params must be of type "Param"'
self._params = param_sequence
def __len__(self):
return len(self._params)
def __getitem__(self, i):
if isinstance(i, int):
return self._params[i]
elif isinstance(i, basestring):
return self._by_name[i]
def __getattr__(self, attr):
try:
return super(ParamSet, self).__getattribute__(attr)
except AttributeError:
t, v, tb = sys.exc_info()
try:
return self[attr]
except KeyError:
raise t, v, tb
def __iter__(self):
return iter(self._params)
def __str__(self):
o = '\n'
for obj in self._params:
o += '== {0:<15} = {1:<15}, tag={2:<15}\n'.format(
obj.name, obj.value, obj.tag
)
return o
@property
def _by_name(self):
return {obj.name: obj for obj in self._params}
@property
def names(self):
return tuple([obj.name for obj in self._params])
@property
def labels(self):
return tuple([obj.tex for obj in self._params])
@property
def values(self):
return tuple([obj.value for obj in self._params])
@property
def nominal_values(self):
return tuple([obj.nominal_value for obj in self._params])
@property
def seeds(self):
return tuple([obj.seed for obj in self._params])
@property
def ranges(self):
return tuple([obj.ranges for obj in self._params])
@property
def stds(self):
return tuple([obj.std for obj in self._params])
@property
def tags(self):
return tuple([obj.tag for obj in self._params])
@property
def params(self):
return self._params
def to_dict(self):
return {obj.name: obj.value for obj in self._params}
def from_tag(self, tag, values=False, index=False, invert=False):
if values and index: assert 0
tag = np.atleast_1d(tag)
if not invert:
ps = [(idx, obj) for idx, obj in enumerate(self._params)
if obj.tag in tag]
else:
ps = [(idx, obj) for idx, obj in enumerate(self._params)
if obj.tag not in tag]
if values:
return tuple([io[1].value for io in ps])
elif index:
return tuple([io[0] for io in ps])
else:
return ParamSet([io[1] for io in ps])
def remove_params(self, params):
rm_paramset = []
for parm in self.params:
if parm.name not in params.names:
rm_paramset.append(parm)
return ParamSet(rm_paramset)
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)]
)
if args.likelihood in [Likelihood.GOLEMFIT, Likelihood.GF_FREQ]:
gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)]
asimov_paramset.extend(gf_nuisance)
llh_paramset.extend(gf_nuisance)
for parm in llh_paramset:
parm.value = args.__getattribute__(parm.name)
tag = ParamTag.BESTFIT
flavour_angles = fr_to_angles(args.measured_ratio)
asimov_paramset.extend([
Param(name='astroFlavorAngle1', value=flavour_angles[0], ranges=[0., 1.], std=0.2, tag=tag),
Param(name='astroFlavorAngle2', value=flavour_angles[1], ranges=[-1., 1.], std=0.2, tag=tag),
])
asimov_paramset = ParamSet(asimov_paramset)
if not args.fix_mixing and not args.fix_mixing_almost:
tag = ParamTag.MMANGLES
llh_paramset.extend([
Param(name='np_s_12^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag),
Param(name='np_c_13^4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{c}_{13}^4', tag=tag),
Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^2', tag=tag),
Param(name='np_dcp', value=np.pi, ranges=[0., 2*np.pi], std=0.2, tex=r'\tilde{\delta_{CP}}', tag=tag)
])
if args.fix_mixing_almost:
tag = ParamTag.MMANGLES
llh_paramset.extend([
Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^4', tag=tag)
])
if not args.fix_scale:
tag = ParamTag.SCALE
if hasattr(args, 'dimension'):
llh_paramset.append(
Param(name='logLam', value=np.log10(args.scale), ranges=np.log10(args.scale_region), std=3,
tex=r'{\rm log}_{10}\left (\Lambda^{-1}'+get_units(args.dimension)+r'\right )', tag=tag)
)
elif hasattr(args, 'dimensions'):
llh_paramset.append(
Param(name='logLam', value=np.log10(args.scale), ranges=np.log10(args.scale_region), std=3,
tex=r'{\rm log}_{10}\left (\Lambda^{-1} / GeV^{-d+4}\right )', tag=tag)
)
if not args.fix_source_ratio:
tag = ParamTag.SRCANGLES
llh_paramset.extend([
Param(name='s_phi4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'sin^4(\phi)', tag=tag),
Param(name='c_2psi', value=0.5, ranges=[0., 1.], std=0.2, tex=r'cos(2\psi)', tag=tag)
])
llh_paramset = ParamSet(llh_paramset)
return asimov_paramset, llh_paramset
|
