diff options
Diffstat (limited to 'plot_sens_sourcescan.py')
| -rwxr-xr-x | plot_sens_sourcescan.py | 411 |
1 files changed, 0 insertions, 411 deletions
diff --git a/plot_sens_sourcescan.py b/plot_sens_sourcescan.py deleted file mode 100755 index 130817d..0000000 --- a/plot_sens_sourcescan.py +++ /dev/null @@ -1,411 +0,0 @@ -#! /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-bins', type=int, default=5, - help='Binning in source flavour space' - ) - 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) - binning = np.linspace(0, 1, args.source_bins) - grid = np.dstack(np.meshgrid(binning, binning)).reshape( - args.source_bins*args.source_bins, 2 - ) - source_ratios = [] - for x in grid: - if x[0]+x[1] > 1: - continue - source_ratios.append([x[0], x[1], 1-x[0]-x[1]]) - args.source_ratios = source_ratios - n_source_ratios = map(fr_utils.normalise_fr, source_ratios) - - srcs = len(n_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(n_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/sourcescan/{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 - print 'data.shape', data.shape - if args.plot_statistic: - assert 0 - 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/sourcescan'.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(n_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_substantial', - # outformat = ['png', 'pdf'], - # args = args, - # ) - # plot_utils.plot_sens_fixed_angle( - # data = data, - # outfile = baseoutfile + '/FIXED_ANGLE', - # outformat = ['png'], - # args = args, - # ) - plot_utils.plot_source_ternary_1D( - data = data, - outfile = baseoutfile + '/source_ternary', - 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() |