diff options
Diffstat (limited to 'scripts/sens.py')
| -rw-r--r-- | scripts/sens.py | 296 |
1 files changed, 296 insertions, 0 deletions
diff --git a/scripts/sens.py b/scripts/sens.py new file mode 100644 index 0000000..6368159 --- /dev/null +++ b/scripts/sens.py @@ -0,0 +1,296 @@ +#! /usr/bin/env python +# author : S. Mandalia +# s.p.mandalia@qmul.ac.uk +# +# date : March 17, 2018 + +""" +HESE BSM flavour ratio analysis script +""" + +from __future__ import absolute_import, division + +import os +import argparse +from functools import partial + +import glob + +import numpy as np +import numpy.ma as ma +from scipy.optimize import minimize + +from golemflavor import fr as fr_utils +from golemflavor import gf as gf_utils +from golemflavor import llh as llh_utils +from golemflavor import misc as misc_utils +from golemflavor import mn as mn_utils +from golemflavor.enums import str_enum +from golemflavor.enums import DataType, Likelihood, ParamTag +from golemflavor.enums import PriorsCateg, StatCateg, Texture +from golemflavor.param import Param, ParamSet + + +def define_nuisance(): + """Define the nuisance parameters.""" + tag = ParamTag.SM_ANGLES + nuisance = [] + g_prior = PriorsCateg.GAUSSIAN + lg_prior = PriorsCateg.LIMITEDGAUSS + e = 1e-9 + nuisance.extend([ + Param(name='s_12_2', value=0.307, seed=[0.26, 0.35], ranges=[0., 1.], std=0.013, tex=r's_{12}^2', prior=lg_prior, tag=tag), + Param(name='c_13_4', value=(1-(0.02206))**2, seed=[0.950, 0.961], ranges=[0., 1.], std=0.00147, tex=r'c_{13}^4', prior=lg_prior, tag=tag), + Param(name='s_23_2', value=0.538, seed=[0.31, 0.75], ranges=[0., 1.], std=0.069, tex=r's_{23}^2', prior=lg_prior, tag=tag), + Param(name='dcp', value=4.08404, seed=[0+e, 2*np.pi-e], ranges=[0., 2*np.pi], std=2.0, tex=r'\delta_{CP}', tag=tag), + Param( + name='m21_2', value=7.40E-23, seed=[7.2E-23, 7.6E-23], ranges=[6.80E-23, 8.02E-23], + std=2.1E-24, tex=r'\Delta m_{21}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag + ), + Param( + name='m3x_2', value=2.494E-21, seed=[2.46E-21, 2.53E-21], ranges=[2.399E-21, 2.593E-21], + std=3.3E-23, tex=r'\Delta m_{3x}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag + ) + ]) + tag = ParamTag.NUISANCE + nuisance.extend([ + Param(name='convNorm', value=1., seed=[0.5, 2. ], ranges=[0.1, 10.], std=0.4, prior=lg_prior, tag=tag), + Param(name='promptNorm', value=0., seed=[0. , 6. ], ranges=[0. , 20.], std=2.4, prior=lg_prior, tag=tag), + Param(name='muonNorm', value=1., seed=[0.1, 2. ], ranges=[0. , 10.], std=0.1, tag=tag), + Param(name='astroNorm', value=8.0, seed=[0., 5. ], ranges=[0. , 20.], std=1.5, tag=tag), + Param(name='astroDeltaGamma', value=2.5, seed=[2.4, 3. ], ranges=[-5., 5. ], std=0.1, tag=tag) + ]) + return ParamSet(nuisance) + + +def get_paramsets(args, nuisance_paramset): + """Make the paramsets for generating the Asmimov MC sample and also running + the MCMC. + """ + asimov_paramset = [] + llh_paramset = [] + + gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)] + + llh_paramset.extend( + [x for x in nuisance_paramset.from_tag(ParamTag.SM_ANGLES)] + ) + llh_paramset.extend(gf_nuisance) + + for parm in llh_paramset: + parm.value = args.__getattribute__(parm.name) + + 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}' + \ + misc_utils.get_units(args.dimension)+r'\right )', + tag=tag + ) + ) + llh_paramset = ParamSet(llh_paramset) + + tag = ParamTag.BESTFIT + if args.data is not DataType.REAL: + flavour_angles = fr_utils.fr_to_angles(args.injected_ratio) + else: + flavour_angles = fr_utils.fr_to_angles([1, 1, 1]) + + asimov_paramset.extend(gf_nuisance) + asimov_paramset.extend([ + Param(name='astroFlavorAngle1', value=flavour_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag), + Param(name='astroFlavorAngle2', value=flavour_angles[1], ranges=[-1., 1.], std=0.2, tag=tag), + ]) + asimov_paramset = ParamSet(asimov_paramset) + + return asimov_paramset, llh_paramset + + +def nuisance_argparse(parser): + nuisance = define_nuisance() + for parm in nuisance: + parser.add_argument( + '--'+parm.name, type=float, default=parm.value, + help=parm.name+' to inject' + ) + + +def process_args(args): + """Process the input args.""" + args.source_ratio = fr_utils.normalise_fr(args.source_ratio) + if args.data is not DataType.REAL: + args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio) + + args.binning = np.logspace( + np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1 + ) + + if args.eval_segment.lower() == 'all': + args.eval_segment = None + else: + args.eval_segment = int(args.eval_segment) + + 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: + raise ValueError('Must assume a BSM texture') + + +def parse_args(args=None): + """Parse command line arguments""" + parser = argparse.ArgumentParser( + description="BSM flavour ratio analysis", + formatter_class=misc_utils.SortingHelpFormatter, + ) + parser.add_argument( + '--seed', type=misc_utils.seed_parse, default='25', + help='Set the random seed value' + ) + parser.add_argument( + '--threads', type=misc_utils.thread_type, default='1', + help='Set the number of threads to use (int or "max")' + ) + parser.add_argument( + '--datadir', type=str, default='./untitled', + help='Path to store chains' + ) + parser.add_argument( + '--segments', type=int, default=10, + help='Number of new physics scales to evaluate' + ) + parser.add_argument( + '--eval-segment', type=str, default='all', + help='Which point to evalaute' + ) + parser.add_argument( + '--overwrite', type=misc_utils.parse_bool, default='False', + help='Overwrite chains' + ) + fr_utils.fr_argparse(parser) + gf_utils.gf_argparse(parser) + llh_utils.llh_argparse(parser) + mn_utils.mn_argparse(parser) + nuisance_argparse(parser) + if args is None: return parser.parse_args() + else: return parser.parse_args(args.split()) + + +def main(): + args = parse_args() + process_args(args) + misc_utils.print_args(args) + + if args.seed is not None: + np.random.seed(args.seed) + + asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance()) + + # 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, invert=True) + + # Array of scales to scan over. + boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension] + eval_scales = np.linspace(boundaries[0], boundaries[1], args.segments-1) + eval_scales = np.concatenate([[-100.], eval_scales]) + + # Evaluate just one point (job), or all points. + if args.eval_segment is None: + eval_dim = args.segments + else: eval_dim = 1 + + outfile = args.datadir + '/{0}/{1}/fr_stat'.format( + *map(misc_utils.parse_enum, [args.stat_method, args.data]) + ) + misc_utils.gen_identifier(args) + + if not args.overwrite and os.path.isfile(outfile+'.npy'): + print 'FILE EXISTS {0}'.format(outfile+'.npy') + print 'Exiting...' + return + + # Setup Golemfit. + if args.run_mn: + gf_utils.setup_fitter(args, asimov_paramset) + + # Initialise data structure. + stat_arr = np.full((eval_dim, 2), np.nan) + + for idx_sc, scale in enumerate(eval_scales): + if args.eval_segment is not None: + if idx_sc == args.eval_segment: + outfile += '_scale_{0:.0E}'.format(np.power(10, scale)) + else: continue + print '|||| SCALE = {0:.0E}'.format(np.power(10, scale)) + + if not args.overwrite and os.path.isfile(outfile+'.npy'): + print 'FILE EXISTS {0}'.format(outfile+'.npy') + t = np.load(outfile+'.npy') + if np.any(~np.isfinite(t)): + print 'nan found, rerunning...' + pass + else: + print 'Exiting...' + return + + # Lower scale boundary for first (NULL) point and set the scale param. + reset_range = None + if scale < scale_prm.ranges[0]: + reset_range = scale_prm.ranges + scale_prm.ranges = (scale, scale_prm.ranges[1]) + scale_prm.value = scale + + identifier = 'b{0}_{1}_{2}_sca{3}'.format( + args.eval_segment, args.segments, str_enum(args.texture), scale + ) + llh = '{0}'.format(args.likelihood).split('.')[1] + data = '{0}'.format(args.data).split('.')[1] + src_string = misc_utils.solve_ratio(args.source_ratio) + prefix = args.mn_output + '/DIM{0}/{1}/{2}/s{3}/{4}'.format( + args.dimension, data, llh, src_string, identifier + ) + try: + stat = mn_utils.mn_evidence( + mn_paramset = base_mn_pset, + llh_paramset = llh_paramset, + asimov_paramset = asimov_paramset, + args = args, + prefix = prefix + ) + except: + print 'Failed run' + raise + print '## Evidence = {0}'.format(stat) + + if args.eval_segment is not None: + stat_arr[0] = np.array([scale, stat]) + else: + stat_arr[idx_sc] = np.array([scale, stat]) + + # Cleanup. + if reset_range is not None: + scale_prm.ranges = reset_range + + if args.run_mn and not args.debug: + try: + for f in glob.glob(prefix + '*'): + print 'cleaning file {0}'.format(f) + os.remove(f) + except: + print 'got error trying to cleanup, continuing' + pass + + misc_utils.make_dir(outfile) + print 'Saving to {0}'.format(outfile+'.npy') + np.save(outfile+'.npy', stat_arr) + + +main.__doc__ = __doc__ + + +if __name__ == '__main__': + main() |