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Diffstat (limited to 'scripts/mc_texture.py')
| -rwxr-xr-x | scripts/mc_texture.py | 233 |
1 files changed, 233 insertions, 0 deletions
diff --git a/scripts/mc_texture.py b/scripts/mc_texture.py new file mode 100755 index 0000000..b61855e --- /dev/null +++ b/scripts/mc_texture.py @@ -0,0 +1,233 @@ +#! /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 utils import fr as fr_utils +from utils import llh as llh_utils +from utils import mcmc as mcmc_utils +from utils import misc as misc_utils +from utils import plot as plot_utils +from utils.enums import MCMCSeedType, ParamTag, PriorsCateg, 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 + ) + ]) + 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 + flavour_angles = fr_utils.fr_to_angles([1, 1, 1]) + 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.""" + if args.texture is Texture.NONE: + raise ValueError('Must assume a BSM texture') + args.source_ratio = fr_utils.normalise_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 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( + '--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, + args = args, + 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() |