#! /usr/bin/env python # author : S. Mandalia # s.p.mandalia@qmul.ac.uk # # date : November 26, 2018 """ HESE flavour ratio contour """ from __future__ import absolute_import, division import os import argparse from functools import partial import numpy as np 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 mcmc as mcmc_utils from utils import plot as plot_utils from utils.enums import str_enum from utils.enums import DataType, Likelihood, MCMCSeedType, ParamTag, PriorsCateg from utils.param import Param, ParamSet, get_paramsets from pymultinest import Analyzer, run def define_nuisance(): """Define the nuisance parameters.""" nuisance = [] tag = ParamTag.NUISANCE lg_prior = PriorsCateg.LIMITEDGAUSS 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='convNorm', value=1., seed=[0.5, 2. ], ranges=[0.1, 10.], std=0.4, tag=tag), Param(name='promptNorm', value=0., seed=[0., 6. ], ranges=[0., 20.], std=2.4, 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), Param(name='CRDeltaGamma', value=0., seed=[-0.1, 0.1 ], ranges=[-1., 1. ], std=0.1, tag=tag), Param(name='NeutrinoAntineutrinoRatio', value=1., seed=[0.8, 1.2 ], ranges=[0., 2. ], std=0.1, tag=tag), Param(name='anisotropyScale', value=1., seed=[0.8, 1.2 ], ranges=[0., 2. ], std=0.1, tag=tag), Param(name='domEfficiency', value=0.99, seed=[0.8, 1.2 ], ranges=[0.8, 1.2 ], std=0.1, tag=tag), Param(name='holeiceForward', value=0., seed=[-0.8, 0.8 ], ranges=[-4.42, 1.58 ], std=0.1, tag=tag), Param(name='piKRatio', value=1.0, seed=[0.8, 1.2 ], ranges=[0., 2. ], 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.likelihood is not Likelihood.GOLEMFIT \ and args.likelihood is not Likelihood.GF_FREQ: raise AssertionError( 'Likelihood method {0} not supported for this ' 'script!\nChoose either GOLEMFIT or GF_FREQ'.format( str_enum(args.likelihood) ) ) 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( '--injected-ratio', type=float, nargs=3, default=[1, 1, 1], help='Set the central value for the injected flavour ratio at IceCube' ) 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( '--outfile', type=str, default='./untitled', help='Path to output results' ) try: gf_utils.gf_argparse(parser) except: pass llh_utils.likelihood_argparse(parser) mcmc_utils.mcmc_argparse(parser) nuisance_argparse(parser) misc_utils.remove_option(parser, 'sigma_ratio') if args is None: return parser.parse_args() else: return parser.parse_args(args.split()) def gen_identifier(args): f = '_{0}_{1}'.format(*map(str_enum, (args.likelihood, args.data))) if args.data is not DataType.REAL: ir1, ir2, ir3 = misc_utils.solve_ratio(args.injected_ratio) f += '_INJ_{0:03d}_{1:03d}_{2:03d}'.format(ir1, ir2, ir3) return f def gen_figtext(args, asimov_paramset): f = '' if args.data is DataType.REAL: f += 'IceCube Preliminary' else: ir1, ir2, ir3 = misc_utils.solve_ratio(args.injected_ratio) f += 'Injected ratio = [{0}, {1}, {2}]'.format(ir1, ir2, ir3) for param in asimov_paramset: f += '\nInjected {0:20s} = {1:.3f}'.format( param.name, param.nominal_value ) return f def triangle_llh(theta, args, hypo_paramset, fitter): """Log likelihood function for a given theta.""" if len(theta) != len(hypo_paramset): raise AssertionError( 'Dimensions of scan is not the same as the input ' 'params\ntheta={0}\nparamset]{1}'.format(theta, hypo_paramset) ) for idx, param in enumerate(hypo_paramset): param.value = theta[idx] if args.likelihood is Likelihood.GOLEMFIT: llh = gf_utils.get_llh(fitter, hypo_paramset) elif args.likelihood is Likelihood.GF_FREQ: llh = gf_utils.get_llh_freq(fitter, hypo_paramset) return llh def ln_prob(theta, args, hypo_paramset, fitter): lp = llh_utils.lnprior(theta, paramset=hypo_paramset) if not np.isfinite(lp): return -np.inf return lp + triangle_llh( theta, args = args, hypo_paramset = hypo_paramset, fitter = fitter ) 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, hypo_paramset = get_paramsets(args, define_nuisance()) hypo_paramset.extend(asimov_paramset.from_tag(ParamTag.BESTFIT)) outfile = args.outfile + gen_identifier(args) print '== {0:<25} = {1}'.format('outfile', outfile) n_params = len(hypo_paramset) outfile = outfile + '_emcee_' print 'asimov_paramset', asimov_paramset print 'hypo_paramset', hypo_paramset if args.run_mcmc: fitter = gf_utils.setup_fitter(args, asimov_paramset) ln_prob_eval = partial( ln_prob, hypo_paramset = hypo_paramset, args = args, fitter = fitter ) if args.mcmc_seed_type == MCMCSeedType.UNIFORM: p0 = mcmc_utils.flat_seed( hypo_paramset, nwalkers=args.nwalkers ) elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN: p0 = mcmc_utils.gaussian_seed( hypo_paramset, nwalkers=args.nwalkers ) samples = mcmc_utils.mcmc( p0 = p0, ln_prob = ln_prob_eval, ndim = n_params, nwalkers = args.nwalkers, burnin = args.burnin, nsteps = args.nsteps, args = args, threads = 1 # TODO(shivesh): broken because you cannot pickle a GolemFitPy object # threads = misc_utils.thread_factors(args.threads)[0] ) mcmc_utils.save_chains(samples, outfile) of = outfile[:5]+outfile[5:].replace('data', 'plots')+'_posterior' plot_utils.chainer_plot( infile = outfile+'.npy', outfile = of, outformat = ['png'], args = args, llh_paramset = hypo_paramset, fig_text = gen_figtext(args, hypo_paramset) ) print "DONE!" main.__doc__ = __doc__ if __name__ == '__main__': main()