#! /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 utils import fr as fr_utils 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 str_enum from utils.enums import DataType, Likelihood, ParamTag from utils.enums import PriorsCateg, StatCateg, Texture from utils.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=6.9, 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' ) 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) # Setup Golemfit. 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)) # 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 try: for f in glob.glob(prefix + '*'): 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()