#! /usr/bin/env python # author : S. Mandalia # s.p.mandalia@qmul.ac.uk # # date : April 28, 2018 """ HESE BSM flavour ratio analysis plotting script """ from __future__ import absolute_import, division import os import argparse from functools import partial from copy import deepcopy import numpy as np import numpy.ma as ma 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 plot as plot_utils from utils.enums import EnergyDependance, Likelihood, MixingScenario, ParamTag from utils.enums import PriorsCateg, SensitivityCateg, StatCateg from utils.param import Param, ParamSet, get_paramsets from utils import mn as mn_utils def define_nuisance(): """Define the nuisance parameters.""" tag = ParamTag.SM_ANGLES g_prior = PriorsCateg.GAUSSIAN lg_prior = PriorsCateg.LIMITEDGAUSS e = 1e-9 nuisance = [ 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 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.""" if args.fix_mixing is not MixingScenario.NONE and args.fix_scale: raise NotImplementedError('Fixed mixing and scale not implemented') if args.fix_mixing is not MixingScenario.NONE and args.fix_mixing_almost: raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) if args.fix_scale: raise NotImplementedError( '--fix-scale not implemented' ) args.measured_ratio = fr_utils.normalise_fr(args.measured_ratio) if args.fix_source_ratio: assert len(args.source_ratios) % 3 == 0 srs = [args.source_ratios[3*x:3*x+3] for x in range(int(len(args.source_ratios)/3))] args.source_ratios = map(fr_utils.normalise_fr, srs) if args.energy_dependance is EnergyDependance.SPECTRAL: args.binning = np.logspace( np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1 ) if args.split_jobs and args.run_method is SensitivityCateg.FULL: raise NotImplementedError( 'split_jobs and run_method not implemented' ) args.dimensions = np.sort(args.dimensions) args_copy = deepcopy(args) scale_regions = [] for dim in args.dimensions: args_copy.dimension = dim _, scale_region = fr_utils.estimate_scale(args_copy) scale_regions.append(scale_region) args.scale_region = [np.min(scale_regions), np.max(scale_regions)] args.scale = np.power(10., np.average(np.log10(args.scale_region))) def parse_args(args=None): """Parse command line arguments""" parser = argparse.ArgumentParser( description="HESE BSM flavour ratio analysis plotting script", formatter_class=misc_utils.SortingHelpFormatter, ) parser.add_argument( '--infile', type=str, default='./untitled', help='Path to input dir' ) parser.add_argument( '--run-method', default='full', type=partial(misc_utils.enum_parse, c=SensitivityCateg), choices=SensitivityCateg, help='Choose which type of sensivity plot to make' ) parser.add_argument( '--stat-method', default='bayesian', type=partial(misc_utils.enum_parse, c=StatCateg), choices=StatCateg, help='Statistical method to employ' ) parser.add_argument( '--sens-bins', type=int, default=10, help='Number of bins for the Bayes factor plot' ) parser.add_argument( '--split-jobs', type=misc_utils.parse_bool, default='False', help='Did the jobs get split' ) parser.add_argument( '--plot', type=misc_utils.parse_bool, default='True', help='Make sensitivity plots' ) parser.add_argument( '--plot-statistic', type=misc_utils.parse_bool, default='False', help='Plot MultiNest evidence or LLH value' ) fr_utils.fr_argparse(parser) gf_utils.gf_argparse(parser) llh_utils.likelihood_argparse(parser) mn_utils.mn_argparse(parser) nuisance_argparse(parser) misc_utils.remove_option(parser, 'dimension') misc_utils.remove_option(parser, 'source_ratio') misc_utils.remove_option(parser, 'scale') misc_utils.remove_option(parser, 'scale_region') parser.add_argument( '--dimensions', type=int, nargs='*', default=[3, 6], help='Set the new physics dimensions to consider' ) parser.add_argument( '--source-ratios', type=int, nargs='*', default=[2, 1, 0], help='Set the source flavour ratios for the case when you want to fix it' ) if args is None: return parser.parse_args() else: return parser.parse_args(args.split()) def main(): args = parse_args() process_args(args) args.scale = 0 misc_utils.print_args(args) asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance()) scale = llh_paramset.from_tag(ParamTag.SCALE)[0] mmangles = llh_paramset.from_tag(ParamTag.MMANGLES) if args.run_method is SensitivityCateg.FULL: st_paramset_arr = [llh_paramset.from_tag(ParamTag.SCALE, invert=True)] elif args.run_method in [SensitivityCateg.FIXED_ANGLE, SensitivityCateg.CORR_ANGLE]: nscale_pmset = llh_paramset.from_tag([ParamTag.SCALE, ParamTag.MMANGLES], invert=True) st_paramset_arr = [nscale_pmset] * 3 elif args.run_method in [SensitivityCateg.FIXED_ONE_ANGLE, SensitivityCateg.CORR_ONE_ANGLE]: nscale_pmset = llh_paramset.from_tag(ParamTag.SCALE, invert=True) st_paramset_arr = [] for x in xrange(3): st_paramset_arr.append( ParamSet([prms for prms in nscale_pmset if mmangles[x].name != prms.name]) ) corr_angles_categ = [SensitivityCateg.CORR_ANGLE, SensitivityCateg.CORR_ONE_ANGLE] fixed_angle_categ = [SensitivityCateg.FIXED_ANGLE, SensitivityCateg.FIXED_ONE_ANGLE] if args.run_method in corr_angles_categ: scan_angles = np.linspace(0+1e-9, 1-1e-9, args.sens_bins) else: scan_angles = np.array([0]) print 'scan_angles', scan_angles dims = len(args.dimensions) srcs = len(args.source_ratios) if args.run_method is SensitivityCateg.FULL: statistic_arr = np.full((dims, srcs, args.sens_bins, 2), np.nan) elif args.run_method in fixed_angle_categ: statistic_arr = np.full((dims, srcs, len(st_paramset_arr), args.sens_bins, 2), np.nan) elif args.run_method in corr_angles_categ: statistic_arr = np.full( (dims, srcs, len(st_paramset_arr), args.sens_bins, args.sens_bins, 3), np.nan ) print 'Loading data' for idim, dim in enumerate(args.dimensions): argsc = deepcopy(args) argsc.dimension = dim _, scale_region = fr_utils.estimate_scale(argsc) argsc.scale_region = scale_region scan_scales = np.linspace( np.log10(scale_region[0]), np.log10(scale_region[1]), args.sens_bins ) scan_scales = np.concatenate([[-100.], scan_scales]) for isrc, src in enumerate(args.source_ratios): argsc.source_ratio = src infile = args.infile if args.likelihood is Likelihood.GOLEMFIT: infile += '/golemfit/' elif args.likelihood is Likelihood.GAUSSIAN: infile += '/gaussian/' if args.likelihood is Likelihood.GAUSSIAN: infile += '{0}/'.format(str(args.sigma_ratio).replace('.', '_')) infile += '/DIM{0}/fix_ifr/{1}/{2}/{3}/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/prior/{1}/{2}/{3}/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/{1}/{2}/{3}/old/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/seed2/{1}/{2}/{3}/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/100TeV/{1}/{2}/{3}/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/strictprior/{1}/{2}/{3}/fr_stat'.format( # infile += '/DIM{0}/fix_ifr/noprior/{1}/{2}/{3}/fr_stat'.format( dim, *map(misc_utils.parse_enum, [args.stat_method, args.run_method, args.data]) ) + misc_utils.gen_identifier(argsc) print '== {0:<25} = {1}'.format('infile', infile) if args.split_jobs: for idx_an, an in enumerate(scan_angles): for idx_sc, sc in enumerate(scan_scales): filename = infile + '_scale_{0:.0E}'.format(np.power(10, sc)) try: if args.run_method in fixed_angle_categ: print 'Loading from {0}'.format(filename+'.npy') statistic_arr[idim][isrc][:,idx_sc] = np.load(filename+'.npy')[:,0] if args.run_method in corr_angles_categ: filename += '_angle_{0:<04.2}'.format(an) print 'Loading from {0}'.format(filename+'.npy') statistic_arr[idim][isrc][:,idx_an,idx_sc] = np.load(filename+'.npy')[:,0,0] except: print 'Unable to load file {0}'.format(filename+'.npy') continue else: print 'Loading from {0}'.format(infile+'.npy') try: statistic_arr[idim][isrc] = np.load(infile+'.npy') except: print 'Unable to load file {0}'.format(infile+'.npy') continue data = ma.masked_invalid(statistic_arr) print 'data', data if args.plot_statistic: print 'Plotting statistic' argsc = deepcopy(args) for idim, dim in enumerate(args.dimensions): argsc.dimension = dim _, scale_region = fr_utils.estimate_scale(argsc) argsc.scale_region = scale_region base_infile = args.infile if args.likelihood is Likelihood.GOLEMFIT: base_infile += '/golemfit/' elif args.likelihood is Likelihood.GAUSSIAN: base_infile += '/gaussian/' if args.likelihood is Likelihood.GAUSSIAN: base_infile += '{0}/'.format(str(args.sigma_ratio).replace('.', '_')) base_infile += '/DIM{0}/fix_ifr'.format(dim) # base_infile += '/DIM{0}/fix_ifr/prior'.format(dim) # base_infile += '/DIM{0}/fix_ifr/seed2'.format(dim) # base_infile += '/DIM{0}/fix_ifr/100TeV'.format(dim) # base_infile += '/DIM{0}/fix_ifr/strictprior'.format(dim) # base_infile += '/DIM{0}/fix_ifr/noprior'.format(dim) for isrc, src in enumerate(args.source_ratios): argsc.source_ratio = src infile = base_infile +'/{0}/{1}/{2}/fr_stat'.format( # infile = base_infile +'/{0}/{1}/{2}/old/fr_stat'.format( *map(misc_utils.parse_enum, [args.stat_method, args.run_method, args.data]) ) + misc_utils.gen_identifier(argsc) basename = os.path.dirname(infile) baseoutfile = basename[:5]+basename[5:].replace('data', 'plots') baseoutfile += '/' + os.path.basename(infile) if args.run_method is SensitivityCateg.FULL: outfile = baseoutfile plot_utils.plot_statistic( data = data[idim][isrc], outfile = outfile, outformat = ['png'], args = argsc, scale_param = scale, ) if args.run_method in fixed_angle_categ: for idx_scen in xrange(len(st_paramset_arr)): print '|||| SCENARIO = {0}'.format(idx_scen) outfile = baseoutfile + '_SCEN{0}'.format(idx_scen) if idx_scen == 0: label = r'$\mathcal{O}_{12}=\pi/4$' elif idx_scen == 1: label = r'$\mathcal{O}_{13}=\pi/4$' elif idx_scen == 2: label = r'$\mathcal{O}_{23}=\pi/4$' plot_utils.plot_statistic( data = data[idim][isrc][idx_scen], outfile = outfile, outformat = ['png'], args = argsc, scale_param = scale, label = label ) elif args.run_method in corr_angles_categ: for idx_scen in xrange(len(st_paramset_arr)): print '|||| SCENARIO = {0}'.format(idx_scen) basescenoutfile = baseoutfile + '_SCEN{0}'.format(idx_scen) if idx_scen == 0: label = r'$\mathcal{O}_{12}=' elif idx_scen == 1: label = r'$\mathcal{O}_{13}=' elif idx_scen == 2: label = r'$\mathcal{O}_{23}=' for idx_an, an in enumerate(scan_angles): print '|||| ANGLE = {0:<04.2}'.format(float(an)) outfile = basescenoutfile + '_ANGLE{0}'.format(idx_an) _label = label + r'{0:<04.2}$'.format(an) plot_utils.plot_statistic( data = data[idim][isrc][idx_scen][idx_an][:,1:], outfile = outfile, outformat = ['png'], args = argsc, scale_param = scale, label = _label ) if args.plot: print 'Plotting sensitivities' basename = args.infile[:5]+args.infile[5:].replace('data', 'plots') baseoutfile = basename + '/{0}/{1}/{2}/'.format( *map(misc_utils.parse_enum, [args.likelihood, args.stat_method, args.data]) ) if args.run_method is SensitivityCateg.FULL: plot_utils.plot_sens_full( data = data, outfile = baseoutfile + '/FULL', outformat = ['png', 'pdf'], args = args, ) elif args.run_method in fixed_angle_categ: plot_utils.plot_sens_fixed_angle_pretty( data = data, outfile = baseoutfile + '/fixed_angle_pretty', outformat = ['png', 'pdf'], args = args, ) # plot_utils.plot_sens_fixed_angle( # data = data, # outfile = baseoutfile + '/FIXED_ANGLE', # outformat = ['png'], # args = args, # ) elif args.run_method in corr_angles_categ: plot_utils.plot_sens_corr_angle( data = data, outfile = baseoutfile + '/CORR_ANGLE', outformat = ['png', 'pdf'], args = args, ) main.__doc__ = __doc__ if __name__ == '__main__': main()