From 128470d9296a7c8d39aa8defa00f99f5ca5c36fd Mon Sep 17 00:00:00 2001
From: shivesh <s.p.mandalia@qmul.ac.uk>
Date: Mon, 19 Mar 2018 12:00:12 -0500
Subject: refactor

---
 utils/gf.py | 105 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 utils/gf.py

(limited to 'utils/gf.py')

diff --git a/utils/gf.py b/utils/gf.py
new file mode 100644
index 0000000..766c161
--- /dev/null
+++ b/utils/gf.py
@@ -0,0 +1,105 @@
+# author : S. Mandalia
+#          s.p.mandalia@qmul.ac.uk
+#
+# date   : March 17, 2018
+
+"""
+Useful GolemFit wrappers for the BSM flavour ratio analysis
+"""
+
+from __future__ import absolute_import, division
+
+import argparse
+from functools import partial
+
+import GolemFitPy as gf
+
+from utils.enums import *
+from utils.misc import enum_keys, enum_parse
+
+
+def data_distributions(fitter):
+    hdat = fitter.GetDataDistribution()
+    binedges = np.asarray([fitter.GetZenithBinsData(), fitter.GetEnergyBinsData()])
+    return hdat, binedges
+
+
+def fit_flags(fitflag_categ):
+    flags = gf.FitParametersFlag()
+    if fitflag_categ is FitFlagCateg.xs:
+        # False means it's not fixed in minimization
+        flags.NeutrinoAntineutrinoRatio = False
+    return flags
+
+
+def fit_params(fit_categ):
+    params = gf.FitParameters()
+    params.astroNorm = 7.5
+    params.astroDeltaGamma = 0.9
+    if fit_categ is FitCateg.hesespl:
+        params.astroNormSec = 0
+    elif fit_categ is FitCateg.hesedpl:
+        params.astroNormSec=7.0
+    elif fit_categ is FitCateg.zpspl:
+        # zero prompt, single powerlaw
+        params.promptNorm = 0
+        params.astroNormSec = 0
+    elif fit_categ is FitCateg.zpdpl:
+        # zero prompt, double powerlaw
+        params.promptNorm = 0
+        params.astroNormSec=7.0
+    elif fit_categ is FitCateg.nunubar2:
+        params.NeutrinoAntineutrinoRatio = 2
+
+
+def fit_priors(fitpriors_categ):
+    priors = gf.Priors()
+    if fitpriors_categ == FitPriorsCateg.xs:
+        priors.promptNormCenter = 1
+        priors.promptNormWidth = 3
+        priors.astroDeltaGammaCenter = 0
+        priors.astroDeltaGammaWidth = 1
+    return priors
+
+
+def gen_steering_params(steering_categ, quiet=False):
+    params = gf.SteeringParams()
+    if quiet: params.quiet = True
+    params.fastmode = False
+    params.do_HESE_reshuffle = False
+    params.numc_tag = steering_categ.name
+    params.baseline_astro_spectral_index = -2.
+    if steering_categ is SteeringCateg.LONGLIFE:
+        params.years = [999]
+        params.numc_tag = 'std_half1'
+    if steering_categ is SteeringCateg.DPL:
+        params.diffuse_fit_type = gf.DiffuseFitType.DoublePowerLaw
+        params.numc_tag = 'std_half1'
+    return params
+
+
+def gf_argparse(parser):
+    parser.add_argument(
+        '--data', default='real', type=partial(enum_parse, c=DataType),
+        choices=enum_keys(DataType), help='select datatype'
+    )
+    parser.add_argument(
+        '--ast', default='baseline', type=partial(enum_parse, c=SteeringCateg),
+        choices=enum_keys(SteeringCateg),
+        help='use asimov/fake dataset with specific steering'
+    )
+    parser.add_argument(
+        '--aft', default='hesespl', type=partial(enum_parse, c=FitCateg),
+        choices=enum_keys(FitCateg),
+        help='use asimov/fake dataset with specific Fit'
+    )
+    parser.add_argument(
+        '--axs', default='nom', type=partial(enum_parse, c=XSCateg),
+        choices=enum_keys(XSCateg),
+        help='use asimov/fake dataset with xs scaling'
+    )
+    parser.add_argument(
+        '--priors', default='uniform', type=partial(enum_parse, c=Priors),
+        choices=enum_keys(Priors), help='Bayesian priors'
+    )
+
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