Mon Apr 16 16:59:28 CDT 2018

1 files changed, 0 insertions, 141 deletions

diff --git a/sens.py b/sens.py
deleted file mode 100755
index 8b41a4a..0000000
--- a/sens.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#! /usr/bin/env python
-# author : S. Mandalia
-#          s.p.mandalia@qmul.ac.uk
-#
-# date   : April 10, 2018
-
-"""
-HESE BSM flavour ratio analysis script
-"""
-
-from __future__ import absolute_import, division
-
-import numpy as np
-import matplotlib as mpl
-mpl.use('Agg')
-from matplotlib import pyplot as plt
-from matplotlib import rc
-
-import GolemFitPy as gf
-import pymultinest
-
-import fr
-from utils import gf as gf_utils
-from utils import likelihood as llh_utils
-from utils import misc as misc_utils
-from utils.enums import Likelihood, ParamTag
-from utils.plot import plot_BSM_angles_limit
-
-rc('text', usetex=False)
-rc('font', **{'family':'serif', 'serif':['Computer Modern'], 'size':18})
-
-
-RUN = False
-
-
-z = 0.
-scenarios = [
-    [np.sin(np.pi/2.)**2, z, z, z],
-    [z, np.cos(np.pi/2.)**4, z, z],
-    [z, z, np.sin(np.pi/2.)**2, z],
-]
-xticks = [r'$\mathcal{O}_{12}$', r'$\mathcal{O}_{13}$', r'$\mathcal{O}_{23}$']
-
-
-def main():
-    args = fr.parse_args()
-    fr.process_args(args)
-    misc_utils.print_args(args)
-
-    bins = 10
-
-    asimov_paramset, mcmc_paramset = fr.get_paramsets(args)
-
-    sc_range = mcmc_paramset.from_tag(ParamTag.SCALE)[0].ranges
-    scan_scales = np.linspace(sc_range[0], sc_range[1], bins)
-    print 'scan_scales', scan_scales
-
-    p = mcmc_paramset.from_tag([ParamTag.SCALE, ParamTag.MMANGLES], invert=True)
-    n_params = len(p)
-    prior_ranges = p.seeds
-
-    outfile = './sens'
-    if RUN:
-        if args.likelihood is Likelihood.GOLEMFIT:
-            fitter = gf_utils.setup_fitter(args, asimov_paramset)
-        elif args.likelihood is Likelihood.GAUSSIAN:
-            fitter = None
-
-        def CubePrior(cube, ndim, nparams):
-            # default are uniform priors
-            return ;
-
-        data = np.zeros((len(scenarios), bins, 2))
-        mm_angles = mcmc_paramset.from_tag(ParamTag.MMANGLES)
-        sc_angles = mcmc_paramset.from_tag(ParamTag.SCALE)[0]
-        for idx, scen in enumerate(scenarios):
-            scales = []
-            evidences = []
-            for yidx, an in enumerate(mm_angles):
-                an.value = scen[yidx]
-            for sc in scan_scales:
-                sc_angles.value = sc
-                def lnProb(cube, ndim, nparams):
-                    for i in range(ndim):
-                        prange = prior_ranges[i][1] - prior_ranges[i][0]
-                        p[i].value = prange*cube[i] + prior_ranges[i][0]
-                    for name in p.names:
-                        mcmc_paramset[name].value = p[name].value
-                    theta = mcmc_paramset.values
-                    # print 'theta', theta
-                    # print 'mcmc_paramset', mcmc_paramset
-                    return llh_utils.triangle_llh(
-                        theta=theta,
-                        args=args,
-                        asimov_paramset=asimov_paramset,
-                        mcmc_paramset=mcmc_paramset,
-                        fitter=fitter
-                    )
-                # TODO(shivesh)
-                prefix = 'mnrun_{0:.0E}'.format(np.power(10, sc)) + '_' + misc_utils.gen_outfile_name(args)[2:]
-                print 'begin running evidence calculation for {0}'.format(prefix)
-                result = pymultinest.run(
-                    LogLikelihood=lnProb,
-                    Prior=CubePrior,
-                    n_dims=n_params,
-                    importance_nested_sampling=True,
-                    n_live_points=args.bayes_live_points,
-                    evidence_tolerance=args.bayes_tolerance,
-                    outputfiles_basename=prefix,
-                    resume=False,
-                    verbose=True
-                )
-
-                analyzer = pymultinest.Analyzer(outputfiles_basename=prefix, n_params=n_params)
-                a_lnZ = analyzer.get_stats()['global evidence']
-                print 'Evidence = {0}'.format(a_lnZ)
-                scales.append(sc)
-                evidences.append(a_lnZ)
-
-            for i, d in enumerate(evidences):
-                data[idx][i][0] = scales[i]
-                data[idx][i][1] = d
-
-        np.save(outfile + '.npy', data)
-
-    plot_BSM_angles_limit(
-        infile=outfile+'.npy',
-        outfile=outfile,
-        xticks=xticks,
-        outformat=['png'],
-        args=args,
-        bayesian=True
-    )
-
-
-main.__doc__ = __doc__
-
-
-if __name__ == '__main__':
-    main()
-