add test scripts for Golem LV and NSI

3 files changed, 12 insertions, 94 deletions

diff --git a/utils/enums.py b/utils/enums.py
index bce3da2..45f164d 100644
--- a/utils/enums.py
+++ b/utils/enums.py
@@ -16,19 +16,6 @@ class DataType(Enum):
     ASMIMOV = 3
 
 
-class FitCateg(Enum):
-    HESESPL  = 1
-    HESEDPL  = 2
-    ZPSPL    = 3
-    ZPDPL    = 4
-    NUNUBAR2 = 5
-
-
-class FitFlagCateg(Enum):
-    DEFAULT = 1
-    XS      = 2
-
-
 class FitPriorsCateg(Enum):
     DEFAULT = 1
     XS      = 2
@@ -49,10 +36,9 @@ class ParamTag(Enum):
     NONE      = 6
 
 
-class Priors(Enum):
-    UNIFORM    = 1
-    LOGUNIFORM = 2
-    JEFFREYS   = 3
+class MCMCSeedType(Enum):
+    UNIFORM  = 1
+    GAUSSIAN = 2
 
 
 class SteeringCateg(Enum):
@@ -69,10 +55,3 @@ class SteeringCateg(Enum):
     LONGLIFE              = 10
     DPL                   = 11
 
-class XSCateg(Enum):
-    HALF    = 1
-    NOM     = 2
-    TWICE   = 3
-    TWICE01 = 4
-    TWICE02 = 5
-
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'
-    )
 
diff --git a/utils/mcmc.py b/utils/mcmc.py
index d2036da..0b78f1e 100644
--- a/utils/mcmc.py
+++ b/utils/mcmc.py
@@ -10,13 +10,15 @@ Useful functions to use an MCMC for the BSM flavour ratio analysis
 from __future__ import absolute_import, division
 
 import argparse
+from functools import partial
 
 import emcee
 import tqdm
 
 import numpy as np
 
-from utils.misc import make_dir, parse_bool
+from utils.enums import MCMCSeedType
+from utils.misc import enum_parse, make_dir, parse_bool
 
 
 def mcmc(p0, triangle_llh, lnprior, ndim, nwalkers, burnin, nsteps, ntemps=1, threads=1):
@@ -65,6 +67,11 @@ def mcmc_argparse(parser):
         '--nsteps', type=int, default=2000,
         help='Number of steps to run'
     )
+    parser.add_argument(
+        '--mcmc-seed-type', default='uniform',
+        type=partial(enum_parse, c=MCMCSeedType), choices=MCMCSeedType,
+        help='Type of distrbution to make the initial MCMC seed'
+    )
 
 
 def flat_seed(paramset, ntemps, nwalkers):