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# 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
import numpy as np
from utils.plot import get_units
from utils.fr import fr_to_angles
from utils.enums import Likelihood, ParamTag
class Param(object):
"""Parameter class to store parameters."""
def __init__(self, name, value, ranges, seed=None, std=None, tex=None, tag=None):
self._seed = None
self._ranges = None
self._tex = None
self._tag = None
self.name = name
self.value = value
self.seed = seed
self.ranges = ranges
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 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 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 get_paramsets(args):
"""Make the paramsets for generating the Asmimov MC sample and also running
the MCMC.
"""
asimov_paramset = []
llh_paramset = []
if args.likelihood == Likelihood.GOLEMFIT:
nuisance_paramset = define_nuisance()
asimov_paramset.extend(nuisance_paramset.params)
llh_paramset.extend(nuisance_paramset.params)
for parm in nuisance_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
e = 1e-10
llh_paramset.extend([
Param(name='s_12^2', value=0.5, ranges=[0.+e, 1.-e], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag),
Param(name='c_13^4', value=0.5, ranges=[0.+e, 1.-e], std=0.2, tex=r'\tilde{c}_{13}^4', tag=tag),
Param(name='s_23^2', value=0.5, ranges=[0.+e, 1.-e], std=0.2, tex=r'\tilde{s}_{23}^4', tag=tag),
Param(name='dcp', value=np.pi, ranges=[0.+e, 2*np.pi-e], std=0.2, tex=r'\tilde{\delta_{CP}}', tag=tag)
])
if args.fix_mixing_almost:
tag = ParamTag.MMANGLES
llh_paramset.extend([
Param(name='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
llh_paramset.append(
Param(name='logLam', value=np.log10(args.scale), ranges=np.log10(args.scale_region), std=3,
tex=r'{\rm log}_{10}\Lambda^{-1}'+get_units(args.dimension), 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
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