1 files changed, 0 insertions, 109 deletions
diff --git a/utils/multinest.py b/utils/multinest.py
deleted file mode 100644
index fdd87cd..0000000
--- a/utils/multinest.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# author : S. Mandalia
-#          s.p.mandalia@qmul.ac.uk
-#
-# date   : April 19, 2018
-
-"""
-Useful functions to use MultiNest for the BSM flavour ratio analysis
-"""
-
-from __future__ import absolute_import, division
-
-from functools import partial
-
-import numpy as np
-
-from pymultinest import analyse, run
-
-from utils import likelihood
-from utils.misc import gen_identifier, make_dir, solve_ratio
-
-
-def CubePrior(cube, ndim, n_params):
-    pass
-
-
-def lnProb(cube, ndim, n_params, mn_paramset, llh_paramset, asimov_paramset,
-           args, fitter):
-    if ndim != len(mn_paramset):
-        raise AssertionError(
-            'Length of MultiNest scan paramset is not the same as the input '
-            'params\ncube={0}\nmn_paramset]{1}'.format(cube, mn_paramset)
-        )
-    pranges = mn_paramset.seeds
-    for i in xrange(ndim):
-        mn_paramset[i].value = (pranges[i][1]-pranges[i][0])*cube[i] + pranges[i][0]
-    for pm in mn_paramset.names:
-        llh_paramset[pm].value = mn_paramset[pm].value
-    theta = llh_paramset.values
-    # print 'llh_paramset', llh_paramset
-    llh = likelihood.ln_prob(
-        theta=theta,
-        args=args,
-        asimov_paramset=asimov_paramset,
-        llh_paramset=llh_paramset,
-        fitter=fitter
-    )
-    # print 'llh', llh
-    return llh
-
-
-def mn_argparse(parser):
-    parser.add_argument(
-        '--mn-live-points', type=int, default=3000,
-        help='Number of live points for MultiNest evaluations'
-    )
-    parser.add_argument(
-        '--mn-tolerance', type=float, default=0.01,
-        help='Tolerance for MultiNest'
-    )
-    parser.add_argument(
-        '--mn-output', type=str, default='./mnrun/',
-        help='Folder to store MultiNest evaluations'
-    )
-
-
-def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, fitter,
-                identifier='mn'):
-    """Run the MultiNest algorithm to calculate the evidence."""
-    n_params = len(mn_paramset)
-
-    for n in mn_paramset.names:
-        llh_paramset[n].value = mn_paramset[n].value
-
-    lnProbEval = partial(
-        lnProb,
-        mn_paramset     = mn_paramset,
-        llh_paramset    = llh_paramset,
-        asimov_paramset = asimov_paramset,
-        args            = args,
-        fitter          = fitter
-    )
-
-    # prefix = './mnrun/DIM{0}/{1}_{2}_{3:>010}_'.format(
-    #     args.dimension, args.likelihood, gen_identifier(args), np.random.randint(0, 2**30)
-    # )
-    llh = '{0}'.format(args.likelihood).split('.')[1]
-    data = '{0}'.format(args.data).split('.')[1]
-    sr1, sr2, sr3 = solve_ratio(args.source_ratio)
-    prefix = './mnrun/DIM{0}/{1}/{2}/s{3}{4}{5}/{6}'.format(
-        args.dimension, data, llh, sr1, sr2, sr3, identifier
-    )
-    make_dir(prefix)
-    print 'Running evidence calculation for {0}'.format(prefix)
-    run(
-        LogLikelihood              = lnProbEval,
-        Prior                      = CubePrior,
-        n_dims                     = n_params,
-        n_live_points              = args.mn_live_points,
-        evidence_tolerance         = args.mn_tolerance,
-        outputfiles_basename       = prefix,
-        importance_nested_sampling = True,
-        resume                     = False,
-        verbose                    = True
-    )
-
-    analyser = analyse.Analyzer(
-        outputfiles_basename=prefix, n_params=n_params
-    )
-    return analyser.get_stats()['global evidence']