diff options
Diffstat (limited to 'sens.py')
| -rwxr-xr-x | sens.py | 35 |
1 files changed, 14 insertions, 21 deletions
@@ -23,8 +23,9 @@ from utils import gf as gf_utils from utils import llh as llh_utils from utils import misc as misc_utils from utils import mn as mn_utils -from utils.enums import Likelihood, ParamTag -from utils.enums import PriorsCateg, SensitivityCateg, StatCateg, Texture +from utils.enums import str_enum +from utils.enums import DataType, Likelihood, ParamTag +from utils.enums import PriorsCateg, StatCateg, Texture from utils.param import Param, ParamSet @@ -77,30 +78,23 @@ def get_paramsets(args, nuisance_paramset): for parm in llh_paramset: parm.value = args.__getattribute__(parm.name) - tag = ParamTag.MMANGLES - llh_paramset.extend([ - Param(name='np_s_12^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag), - Param(name='np_c_13^4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{c}_{13}^4', tag=tag), - Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^2', tag=tag), - Param(name='np_dcp', value=np.pi, ranges=[0., 2*np.pi], std=0.2, tex=r'\tilde{\delta_{CP}}', tag=tag) - ]) - boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension] tag = ParamTag.SCALE llh_paramset.append( Param( name='logLam', value=np.mean(boundaries), ranges=boundaries, std=3, - tex=r'{\rm log}_{10}\left (\Lambda^{-1}'+get_units(args.dimension)+r'\right )', + tex=r'{\rm log}_{10}\left (\Lambda^{-1}' + \ + misc_utils.get_units(args.dimension)+r'\right )', tag=tag ) ) llh_paramset = ParamSet(llh_paramset) tag = ParamTag.BESTFIT - if args.data in [DataType.ASIMOV, DataType.REALISATION]: - flavour_angles = fr_to_angles(args.injected_ratio) + if args.data is not DataType.REAL: + flavour_angles = fr_utils.fr_to_angles(args.injected_ratio) else: - flavour_angles = fr_to_angles([1, 1, 1]) + flavour_angles = fr_utils.fr_to_angles([1, 1, 1]) asimov_paramset.extend(gf_nuisance) asimov_paramset.extend([ @@ -136,8 +130,9 @@ def process_args(args): else: args.eval_segment = int(args.eval_segment) - if args.stat_method is StatCateg.FREQUENTIST and \ - args.likelihood is Likelihood.GOLEMFIT: + if args.stat_method is StatCateg.BAYESIAN: + args.likelihood = Likelihood.GOLEMFIT + elif args.stat_method is StatCateg.FREQUENTIST: args.likelihood = Likelihood.GF_FREQ if args.texture is Texture.NONE: @@ -191,9 +186,7 @@ def main(): # Scale and BSM mixings will be fixed. scale_prm = llh_paramset.from_tag(ParamTag.SCALE)[0] - base_mn_pset = llh_paramset.from_tag( - [ParamTag.SCALE, ParamTag.MMANGLES], invert=True - ) + base_mn_pset = llh_paramset.from_tag(ParamTag.SCALE, invert=True) # Array of scales to scan over. boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension] @@ -228,8 +221,8 @@ def main(): scale_prm.value = scale if args.stat_method is StatCateg.BAYESIAN: - identifier = 'b{0}_{1}_sce{2}_sca{3}'.format( - args.eval_segment, args.segments, int(args.texture), scale + identifier = 'b{0}_{1}_{2}_sca{3}'.format( + args.eval_segment, args.segments, str_enum(args.texture), scale ) try: stat = mn_utils.mn_evidence( |