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| author | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-09-20 09:53:10 -0500 |
|---|---|---|
| committer | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-09-20 09:53:10 -0500 |
| commit | e29c4b027ea3e67950de28b9c9165dcdb6bc0776 (patch) | |
| tree | 23919013173075d11051bb4a90a4a3f3fd937050 /utils | |
| parent | 1edf1e62497024aee542fea5a1d251750c36b170 (diff) | |
| download | GolemFlavor-e29c4b027ea3e67950de28b9c9165dcdb6bc0776.tar.gz GolemFlavor-e29c4b027ea3e67950de28b9c9165dcdb6bc0776.zip | |
update golemfit
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/gf.py | 12 | ||||
| -rw-r--r-- | utils/mn.py | 5 |
2 files changed, 6 insertions, 11 deletions
diff --git a/utils/gf.py b/utils/gf.py index d0c62ca..de21cc5 100644 --- a/utils/gf.py +++ b/utils/gf.py @@ -71,11 +71,6 @@ def steering_params(args): params.simToLoad= steering_categ.name.lower() params.evalThreads = args.threads - if args.likelihood is Likelihood.GOLEMFIT: - params.frequentist = False; - elif args.likelihood is Likelihood.GF_FREQ: - params.frequentist = True; - if hasattr(args, 'binning'): params.minFitEnergy = args.binning[0] # GeV params.maxFitEnergy = args.binning[-1] # GeV @@ -83,13 +78,8 @@ def steering_params(args): params.minFitEnergy = 6E4 # GeV params.maxFitEnergy = 1E7 # GeV params.load_data_from_text_file = False - - params.sampleToLoad = gf.sampleTag.MagicTau + params.do_HESE_reshuffle=False params.use_legacy_selfveto_calculation = False - # params.spline_hole_ice = False - # params.spline_dom_efficiency = False - params.spline_hole_ice = True - params.spline_dom_efficiency = True return params diff --git a/utils/mn.py b/utils/mn.py index ac42858..e3a4a09 100644 --- a/utils/mn.py +++ b/utils/mn.py @@ -55,6 +55,10 @@ def mn_argparse(parser): help='Tolerance for MultiNest' ) parser.add_argument( + '--mn-efficiency', type=float, default=0.3, + help='Sampling efficiency for MultiNest' + ) + parser.add_argument( '--mn-output', type=str, default='./mnrun/', help='Folder to store MultiNest evaluations' ) @@ -88,6 +92,7 @@ def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, prefix='mn'): n_dims = n_params, n_live_points = args.mn_live_points, evidence_tolerance = args.mn_tolerance, + sampling_efficiency = args.mn_efficiency, outputfiles_basename = prefix, importance_nested_sampling = True, # resume = False, |