#! /usr/bin/env python # author : S. Mandalia # s.p.mandalia@qmul.ac.uk # # date : April 25, 2019 """ Sample points for a specific scenario """ from __future__ import absolute_import, division import argparse from copy import deepcopy from functools import partial import numpy as np from golemflavor import fr as fr_utils from golemflavor import llh as llh_utils from golemflavor import mcmc as mcmc_utils from golemflavor import misc as misc_utils from golemflavor import plot as plot_utils from golemflavor.enums import MCMCSeedType, ParamTag, PriorsCateg, 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 ) ]) 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 = [] llh_paramset.extend( [x for x in nuisance_paramset.from_tag(ParamTag.SM_ANGLES)] ) 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 flavor_angles = fr_utils.fr_to_angles([1, 1, 1]) asimov_paramset.extend([ Param(name='astroFlavorAngle1', value=flavor_angles[0], ranges=[ 0., 1.], std=0.2, tag=tag), Param(name='astroFlavorAngle2', value=flavor_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.""" if args.texture is Texture.NONE: raise ValueError('Must assume a BSM texture') args.source_ratio = fr_utils.normalize_fr(args.source_ratio) args.binning = np.logspace( np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1 ) def parse_args(args=None): """Parse command line arguments""" parser = argparse.ArgumentParser( description="BSM flavor 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( '--spectral-index', type=float, default='-2', help='Astro spectral index' ) parser.add_argument( '--datadir', type=str, default='./untitled', help='Path to store chains' ) fr_utils.fr_argparse(parser) mcmc_utils.mcmc_argparse(parser) nuisance_argparse(parser) misc_utils.remove_option(parser, 'injected_ratio') misc_utils.remove_option(parser, 'plot_angles') misc_utils.remove_option(parser, 'plot_elements') if args is None: return parser.parse_args() else: return parser.parse_args(args.split()) def gen_identifier(args): f = '_DIM{0}'.format(args.dimension) f += '_SRC_' + misc_utils.solve_ratio(args.source_ratio) f += '_{0}'.format(misc_utils.str_enum(args.texture)) return f def triangle_llh(theta, args, llh_paramset): """Log likelihood function for a given theta.""" if len(theta) != len(llh_paramset): raise AssertionError( 'Dimensions of scan is not the same as the input ' 'params\ntheta={0}\nparamset]{1}'.format(theta, llh_paramset) ) for idx, param in enumerate(llh_paramset): param.value = theta[idx] return 1. # Flat LLH def ln_prob(theta, args, llh_paramset): dc_llh_paramset = deepcopy(llh_paramset) lp = llh_utils.lnprior(theta, paramset=dc_llh_paramset) if not np.isfinite(lp): return -np.inf return lp + triangle_llh( theta, args = args, llh_paramset = dc_llh_paramset, ) 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()) prefix = '' outfile = args.datadir + '/mc_texture' + prefix + gen_identifier(args) print('== {0:<25} = {1}'.format('outfile', outfile)) print('asimov_paramset', asimov_paramset) print('llh_paramset', llh_paramset) if args.run_mcmc: ln_prob_eval = partial( ln_prob, llh_paramset = llh_paramset, args = args, ) if args.mcmc_seed_type == MCMCSeedType.UNIFORM: p0 = mcmc_utils.flat_seed( llh_paramset, nwalkers=args.nwalkers ) elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN: p0 = mcmc_utils.gaussian_seed( llh_paramset, nwalkers=args.nwalkers ) samples = mcmc_utils.mcmc( p0 = p0, ln_prob = ln_prob_eval, ndim = len(llh_paramset), nwalkers = args.nwalkers, burnin = args.burnin, nsteps = args.nsteps, threads = args.threads ) frs = np.array( map(lambda x: fr_utils.flux_averaged_BSMu( x, args, args.spectral_index, llh_paramset ), samples), dtype=float ) frs_scale = np.vstack((frs.T, samples[:-1].T)).T mcmc_utils.save_chains(frs_scale, outfile) print("DONE!") main.__doc__ = __doc__ if __name__ == '__main__': main()