reogranise into a python package

1 files changed, 0 insertions, 111 deletions

diff --git a/utils/llh.py b/utils/llh.py
deleted file mode 100644
index 5a0eea7..0000000
--- a/utils/llh.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# author : S. Mandalia
-#          s.p.mandalia@qmul.ac.uk
-#
-# date   : April 04, 2018
-
-"""
-Likelihood functions for the BSM flavour ratio analysis
-"""
-
-from __future__ import absolute_import, division
-
-from copy import deepcopy
-from functools import partial
-
-import numpy as np
-import scipy
-from scipy.stats import multivariate_normal, truncnorm
-
-from utils import fr as fr_utils
-from utils import gf as gf_utils
-from utils.enums import Likelihood, ParamTag, PriorsCateg, StatCateg
-from utils.misc import enum_parse, gen_identifier, parse_bool
-
-
-def GaussianBoundedRV(loc=0., sigma=1., lower=-np.inf, upper=np.inf):
-    """Normalised gaussian bounded between lower and upper values"""
-    low, up = (lower - loc) / sigma, (upper - loc) / sigma
-    g = scipy.stats.truncnorm(loc=loc, scale=sigma, a=low, b=up)
-    return g
-
-
-def multi_gaussian(fr, fr_bf, sigma, offset=-320):
-    """Multivariate gaussian likelihood."""
-    cov_fr = np.identity(3) * sigma
-    return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr)) + offset
-
-
-def llh_argparse(parser):
-    parser.add_argument(
-        '--stat-method', default='bayesian',
-        type=partial(enum_parse, c=StatCateg), choices=StatCateg,
-        help='Statistical method to employ'
-    )
-
-
-def lnprior(theta, paramset):
-    """Priors on theta."""
-    if len(theta) != len(paramset):
-        raise AssertionError(
-            'Length of MCMC scan is not the same as the input '
-            'params\ntheta={0}\nparamset={1}'.format(theta, paramset)
-        )
-    for idx, param in enumerate(paramset):
-        param.value = theta[idx]
-    ranges = paramset.ranges
-    for value, range in zip(theta, ranges):
-        if range[0] <= value <= range[1]:
-            pass
-        else: return -np.inf
-
-    prior = 0
-    for param in paramset:
-        if param.prior is PriorsCateg.GAUSSIAN:
-            prior += GaussianBoundedRV(
-                loc=param.nominal_value, sigma=param.std
-            ).logpdf(param.value)
-        elif param.prior is PriorsCateg.LIMITEDGAUSS:
-            prior += GaussianBoundedRV(
-                loc=param.nominal_value, sigma=param.std,
-                lower=param.ranges[0], upper=param.ranges[1]
-            ).logpdf(param.value)
-    return prior
-
-
-def triangle_llh(theta, args, asimov_paramset, llh_paramset):
-    """Log likelihood function for a given theta."""
-    if len(theta) != len(llh_paramset):
-        raise AssertionError(
-            'Length of MCMC scan is not the same as the input '
-            'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset)
-        )
-    hypo_paramset = asimov_paramset
-    for param in llh_paramset.from_tag(ParamTag.NUISANCE):
-        hypo_paramset[param.name].value = param.value
-
-    spectral_index = -hypo_paramset['astroDeltaGamma'].value
-    # Assigning llh_paramset values from theta happens in this function.
-    fr = fr_utils.flux_averaged_BSMu(theta, args, spectral_index, llh_paramset)
-
-    flavour_angles = fr_utils.fr_to_angles(fr)
-    # print 'flavour_angles', map(float, flavour_angles)
-    for idx, param in enumerate(hypo_paramset.from_tag(ParamTag.BESTFIT)):
-        param.value = flavour_angles[idx]
-
-    if args.likelihood is Likelihood.GOLEMFIT:
-        llh = gf_utils.get_llh(hypo_paramset)
-    elif args.likelihood is Likelihood.GF_FREQ:
-        llh = gf_utils.get_llh_freq(hypo_paramset)
-    return llh
-
-
-def ln_prob(theta, args, asimov_paramset, llh_paramset):
-    dc_asimov_paramset = deepcopy(asimov_paramset)
-    dc_llh_paramset = deepcopy(llh_paramset)
-    lp = lnprior(theta, paramset=dc_llh_paramset)
-    if not np.isfinite(lp):
-        return -np.inf
-    return lp + triangle_llh(
-        theta, args=args, asimov_paramset=dc_asimov_paramset,
-        llh_paramset=dc_llh_paramset
-    )