add test scripts for Golem LV and NSI

1 files changed, 1 insertions, 69 deletions

diff --git a/utils/gf.py b/utils/gf.py
index eaa9450..3fb063b 100644
--- a/utils/gf.py
+++ b/utils/gf.py
@@ -15,7 +15,7 @@ from functools import partial
 
 import GolemFitPy as gf
 
-from utils.enums import *
+from utils.enums import DataType, SteeringCateg
 from utils.misc import enum_parse, thread_factors
 
 
@@ -70,60 +70,6 @@ def data_distributions(fitter):
     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),
@@ -134,18 +80,4 @@ def gf_argparse(parser):
         choices=SteeringCateg,
         help='use asimov/fake dataset with specific steering'
     )
-    parser.add_argument(
-        '--aft', default='hesespl', type=partial(enum_parse, c=FitCateg),
-        choices=FitCateg,
-        help='use asimov/fake dataset with specific Fit'
-    )
-    parser.add_argument(
-        '--axs', default='nom', type=partial(enum_parse, c=XSCateg),
-        choices=XSCateg,
-        help='use asimov/fake dataset with xs scaling'
-    )
-    parser.add_argument(
-        '--priors', default='uniform', type=partial(enum_parse, c=Priors),
-        choices=Priors, help='Bayesian priors'
-    )