1 files changed, 91 insertions, 0 deletions
diff --git a/utils/likelihood.py b/utils/likelihood.py
new file mode 100644
index 0000000..5c9af51
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+++ b/utils/likelihood.py
@@ -0,0 +1,91 @@
+# 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
+
+import argparse
+from functools import partial
+
+import numpy as np
+from scipy.stats import multivariate_normal
+
+import GolemFitPy as gf
+
+from utils import fr as fr_utils
+from utils import gf as gf_utils
+from utils.enums import Likelihood, ParamTag
+from utils.misc import enum_parse
+
+
+def gaussian_llh(fr, fr_bf, sigma):
+    """Multivariate gaussian likelihood."""
+    cov_fr = np.identity(3) * sigma
+    return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr))
+
+
+def likelihood_argparse(parser):
+    parser.add_argument(
+        '--likelihood', default='gaussian', type=partial(enum_parse, c=Likelihood),
+        choices=Likelihood, help='likelihood contour'
+    )
+
+
+def lnprior(theta, paramset):
+    """Priors on theta."""
+    ranges = paramset.ranges
+    for value, range in zip(theta, ranges):
+        if range[0] <= value <= range[1]:
+            pass
+        else: return -np.inf
+    return 0.
+
+
+def triangle_llh(theta, args, asimov_paramset, mcmc_paramset, fitter):
+    """-Log likelihood function for a given theta."""
+    if len(theta) != len(mcmc_paramset):
+        raise AssertionError(
+            'Length of MCMC scan is not the same as the input '
+            'params\ntheta={0}\nmcmc_paramset]{1}'.format(theta, mcmc_paramset)
+        )
+    for idx, param in enumerate(mcmc_paramset):
+        param.value = theta[idx]
+    hypo_paramset = asimov_paramset
+    for param in mcmc_paramset.from_tag(ParamTag.NUISANCE):
+        hypo_paramset[param.name].value = param.value
+
+    if args.fix_source_ratio:
+        fr1, fr2, fr3 = args.source_ratio
+    else:
+        fr1, fr2, fr3 = fr_utils.angles_to_fr(
+            mcmc_paramset.from_tag(ParamTag.SRCANGLES, values=True)
+        )
+    bsm_angles = mcmc_paramset.from_tag(
+        [ParamTag.SCALE, ParamTag.MMANGLES], values=True
+    )
+
+    u = fr_utils.params_to_BSMu(
+        theta      = bsm_angles,
+        dim        = args.dimension,
+        energy     = args.energy,
+        no_bsm     = args.no_bsm,
+        fix_mixing = args.fix_mixing,
+        fix_scale  = args.fix_scale,
+        scale      = args.scale
+    )
+    fr = fr_utils.u_to_fr((fr1, fr2, fr3), u)
+    for idx, param in enumerate(hypo_paramset.from_tag(ParamTag.BESTFIT)):
+        param.value = fr[idx]
+
+    if args.likelihood is Likelihood.FLAT:
+        return 1.
+    elif args.likelihood is Likelihood.GAUSSIAN:
+        fr_bf = args.measured_ratio
+        return gaussian_llh(fr, fr_bf, args.sigma_ratio)
+    elif args.likelihood is Likelihood.GOLEMFIT:
+        return gf_utils.get_llh(fitter, hypo_paramset)