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| author | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-04-10 22:28:20 +0100 |
|---|---|---|
| committer | shivesh <s.p.mandalia@qmul.ac.uk> | 2019-04-10 22:28:20 +0100 |
| commit | 32c333652da8beb1758d8852d8b67d4eff78b657 (patch) | |
| tree | e79e16369671ef757a9b10cfdcb052b388a0b9b5 | |
| parent | ab3c4ac2bfcdae65767f5402cf66d251bb08cadd (diff) | |
| download | GolemFlavor-32c333652da8beb1758d8852d8b67d4eff78b657.tar.gz GolemFlavor-32c333652da8beb1758d8852d8b67d4eff78b657.zip | |
refactor sensitivity/limit scripts
| -rwxr-xr-x | plot_sens.py | 397 | ||||
| -rwxr-xr-x | sens.py | 420 | ||||
| -rw-r--r-- | submitter/sens_dag.py | 180 | ||||
| -rw-r--r-- | submitter/sens_submit.sub | 12 | ||||
| -rw-r--r-- | utils/enums.py | 33 | ||||
| -rw-r--r-- | utils/fr.py | 140 | ||||
| -rw-r--r-- | utils/gf.py | 41 | ||||
| -rw-r--r-- | utils/llh.py | 104 | ||||
| -rw-r--r-- | utils/misc.py | 101 | ||||
| -rw-r--r-- | utils/mn.py | 13 | ||||
| -rw-r--r-- | utils/param.py | 66 | ||||
| -rw-r--r-- | utils/plot.py | 161 |
12 files changed, 512 insertions, 1156 deletions
diff --git a/plot_sens.py b/plot_sens.py index f91b5f2..f7179d8 100755 --- a/plot_sens.py +++ b/plot_sens.py @@ -19,98 +19,30 @@ 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' - ) +from utils.param import Param, ParamSet 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' - ) + if args.data is not DataType.REAL: + args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio) - 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 len(args.source_ratios) % 3 != 0: + raise ValueError( + 'Invalid source ratios input {0}'.format(args.source_ratios) ) - if args.split_jobs and args.run_method is SensitivityCateg.FULL: - raise NotImplementedError( - 'split_jobs and run_method not implemented' - ) + 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) 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""" @@ -119,53 +51,47 @@ def parse_args(args=None): formatter_class=misc_utils.SortingHelpFormatter, ) parser.add_argument( - '--infile', type=str, default='./untitled', - help='Path to input dir' + '--datadir', type=str, + default='/data/user/smandalia/flavour_ratio/data/sensitivity', + help='Path to data directory' ) 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' + '--segments', type=int, default=10, + help='Number of new physics scales to evaluate' ) parser.add_argument( - '--stat-method', default='bayesian', - type=partial(misc_utils.enum_parse, c=StatCateg), choices=StatCateg, - help='Statistical method to employ' + '--split-jobs', type=misc_utils.parse_bool, default='True', + help='Did the jobs get split' ) parser.add_argument( - '--sens-bins', type=int, default=10, - help='Number of bins for the Bayes factor plot' + '--dimensions', type=int, nargs='*', default=[3, 6], + help='Set the new physics dimensions to consider' ) parser.add_argument( - '--split-jobs', type=misc_utils.parse_bool, default='False', - help='Did the jobs get split' + '--source-ratios', type=int, nargs='*', default=[1, 2, 0], + help='Set the source flavour ratios for the case when you want to fix it' ) parser.add_argument( - '--plot', type=misc_utils.parse_bool, default='True', - help='Make sensitivity plots' + '--texture', type=partial(enum_parse, c=Texture), + default='none', choices=Texture, help='Set the BSM mixing texture' ) parser.add_argument( - '--plot-statistic', type=misc_utils.parse_bool, default='False', - help='Plot MultiNest evidence or LLH value' + '--data', default='asimov', type=partial(enum_parse, c=DataType), + choices=DataType, help='select datatype' ) - 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' + '--plot-x', type=misc_utils.parse_bool, default='True', + help='Make sensitivity plot x vs limit' ) parser.add_argument( - '--source-ratios', type=int, nargs='*', default=[1, 2, 0], - help='Set the source flavour ratios for the case when you want to fix it' + '--plot-table', type=misc_utils.parse_bool, default='True', + help='Make sensitivity table plot' ) + parser.add_argument( + '--plot-statistic', type=misc_utils.parse_bool, default='False', + help='Plot MultiNest evidence or LLH value' + ) + llh_utils.likelihood_argparse(parser) if args is None: return parser.parse_args() else: return parser.parse_args(args.split()) @@ -173,99 +99,70 @@ def parse_args(args=None): 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 - ) + if args.texture is Texture.NONE: + textures = [Texture.OEU, Texture.OET, Texture.OUT] + else: + textures = [args.texture] + texs = len(textures) + + prefix = '' + # prefix = 'noprior' + + # Initialise data structure. + statistic_arr = np.full((dims, srcs, texs, args.segments, 2), 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 + + # Array of scales to scan over. + boundaries = fr_utils.SCALE_BOUNDARIES[argsc.dimension] + eval_scales = np.linspace( + boundaries[0], boundaries[1], args.segments-1 ) - scan_scales = np.concatenate([[-100.], scan_scales]) + eval_scales = np.concatenate([[-100.], eval_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)) + for itex, texture in enumerate(textures): + argc.texture = texture + + base_infile = args.datadir + '/{0}/{1}/{2}/fr_stat'.format( + *map(misc_utils.parse_enum, [args.stat_method, args.data]), + prefix + ) + misc_utils.gen_identifier(argsc) + + print '== {0:<25} = {1}'.format('base_infile', base_infile) + + if args.split_jobs: + for idx_sc, scale in enumerate(eval_scales): + infile = base_infile + '_scale_{0:.0E}'.format( + np.power(10, scale) + ) 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] + print 'Loading from {0}'.format(infile+'.npy') + statistic_arr[idim][isrc][itex][idx_sc] = \ + np.load(infile+'.npy')[:,0] except: - print 'Unable to load file {0}'.format(filename+'.npy') + print 'Unable to load file {0}'.format( + infile+'.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 + else: + print 'Loading from {0}'.format(base_infile+'.npy') + try: + statistic_arr[idim][isrc][itex] = \ + np.load(base_infile+'.npy') + except: + print 'Unable to load file {0}'.format( + base_infile+'.npy' + ) + continue data = ma.masked_invalid(statistic_arr) @@ -273,114 +170,64 @@ def main(): if args.plot_statistic: print 'Plotting statistic' - argsc = deepcopy(args) 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 - 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) + + # Array of scales to scan over. + boundaries = fr_utils.SCALE_BOUNDARIES[argsc.dimension] + eval_scales = np.linspace( + boundaries[0], boundaries[1], args.segments-1 + ) + eval_scales = np.concatenate([[-100.], eval_scales]) 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 + for itex, texture in enumerate(textures): + argc.texture = texture + + base_infile = args.datadir + '/{0}/{1}/{2}/fr_stat'.format( + *map(misc_utils.parse_enum, [args.stat_method, args.data]), + prefix + ) + misc_utils.gen_identifier(argsc) + basename = os.path.dirname(base_infile) + outfile = basename[:5]+basename[5:].replace('data', 'plots') + outfile += '/' + os.path.basename(base_infile) + + label = plot_utils.texture_label(texture)[:-1]+r'=\pi/4$' plot_utils.plot_statistic( - data = data[idim][isrc], + data = data[idim][isrc][itex], outfile = outfile, outformat = ['png'], args = argsc, scale_param = scale, + label = label ) - if args.run_method in fixed_angle_categ: - for idx_scen in xrange(len(st_paramset_arr)): - print '|||| SCENARIO = {0}'.format(idx_scen) - outfile = baseoutfile + '_SCEN{0}'.format(idx_scen) - if idx_scen == 0: label = r'$\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]) - ) + basename = args.datadir[:5]+args.datadir[5:].replace('data', 'plots') + baseoutfile = basename + '/{0}/{1}/{2}/'.format( + *map(misc_utils.parse_enum, [args.stat_method, args.data]), prefix + ) - 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, - # ) - elif args.run_method in corr_angles_categ: - plot_utils.plot_sens_corr_angle( + if args.plot_x: + for idim, dim in enumerate(args.dimensions): + argsc = deepcopy(args) + argsc.dimension = dim + plot_utils.plot_x( data = data, - outfile = baseoutfile + '/CORR_ANGLE', + outfile = baseoutfile + '/hese_x', outformat = ['png', 'pdf'], - args = args, + args = argsc, ) + if args.plot_table: + plot_utils.plot_table_sens( + data = data, + outfile = baseoutfile + '/hese_table', + outformat = ['png', 'pdf'], + args = args, + ) + main.__doc__ = __doc__ @@ -22,12 +22,10 @@ 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 +from utils.enums import Likelihood, ParamTag +from utils.enums import PriorsCateg, SensitivityCateg, StatCateg, Texture +from utils.param import Param, ParamSet def define_nuisance(): @@ -58,20 +56,62 @@ def define_nuisance(): 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='convNorm', value=1., seed=[0.5, 2. ], ranges=[0. , 50.], std=0.3, tag=tag), - # Param(name='promptNorm', value=0., seed=[0. , 6. ], ranges=[0. , 50.], std=0.05, tag=tag), - # Param(name='muonNorm', value=1., seed=[0.1, 2. ], ranges=[0. , 50.], std=0.1, tag=tag), - # Param(name='astroNorm', value=6.9, seed=[0.1, 10.], ranges=[0. , 50.], std=0.1, tag=tag), - # Param(name='astroDeltaGamma', value=2.5, seed=[2.4, 3. ], ranges=[-5., 5. ], std=0.1, tag=tag) - # Param(name='convNorm', value=1., seed=[0.5, 2. ], ranges=[0.5, 2. ], std=0.3, tag=tag), - # Param(name='promptNorm', value=0., seed=[0. , 6. ], ranges=[0. , 6. ], std=0.05, tag=tag), - # Param(name='muonNorm', value=1., seed=[0.1, 2. ], ranges=[0.1, 2. ], std=0.1, tag=tag), - # Param(name='astroNorm', value=6.9, seed=[0.1, 10.], ranges=[0.1, 10.], std=0.1, tag=tag), - # Param(name='astroDeltaGamma', value=2.5, seed=[2.4, 3. ], ranges=[2.4, 3. ], std=0.1, 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 = [] + + gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)] + + llh_paramset.extend( + [x for x in nuisance_paramset.from_tag(ParamTag.SM_ANGLES)] + ) + llh_paramset.extend(gf_nuisance) + + for parm in llh_paramset: + parm.value = args.__getattribute__(parm.name) + + tag = ParamTag.MMANGLES + llh_paramset.extend([ + Param(name='np_s_12^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag), + Param(name='np_c_13^4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{c}_{13}^4', tag=tag), + Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^2', tag=tag), + Param(name='np_dcp', value=np.pi, ranges=[0., 2*np.pi], std=0.2, tex=r'\tilde{\delta_{CP}}', tag=tag) + ]) + + 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}'+get_units(args.dimension)+r'\right )', + tag=tag + ) + ) + llh_paramset = ParamSet(llh_paramset) + + tag = ParamTag.BESTFIT + if args.data in [DataType.ASIMOV, DataType.REALISATION]: + flavour_angles = fr_to_angles(args.injected_ratio) + else: + flavour_angles = fr_to_angles([1, 1, 1]) + + asimov_paramset.extend(gf_nuisance) + 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: @@ -83,43 +123,25 @@ def nuisance_argparse(parser): 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.sens_run and args.fix_scale: - raise NotImplementedError( - '--sens-run and --fix-scale cannot be used together' - ) - - args.measured_ratio = fr_utils.normalise_fr(args.measured_ratio) - if args.fix_source_ratio: - args.source_ratio = fr_utils.normalise_fr(args.source_ratio) + args.source_ratio = fr_utils.normalise_fr(args.source_ratio) + if args.data is not DataType.REAL: + args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio) - 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 not args.fix_scale: - args.scale, args.scale_region = fr_utils.estimate_scale(args) + args.binning = np.logspace( + np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1 + ) - if args.sens_eval_bin.lower() == 'all': - args.sens_eval_bin = None + if args.eval_segment.lower() == 'all': + args.eval_segment = None else: - args.sens_eval_bin = int(args.sens_eval_bin) - - if args.sens_eval_bin is not None and args.plot_statistic: - print 'Cannot make plot when specific scale bin is chosen' - args.plot_statistic = False + args.eval_segment = int(args.eval_segment) if args.stat_method is StatCateg.FREQUENTIST and \ args.likelihood is Likelihood.GOLEMFIT: args.likelihood = Likelihood.GF_FREQ - args.burnin = False + if args.texture is Texture.NONE: + raise ValueError('Must assume a BSM texture') def parse_args(args=None): @@ -137,39 +159,20 @@ def parse_args(args=None): help='Set the number of threads to use (int or "max")' ) parser.add_argument( - '--outfile', type=str, default='./untitled', - help='Path to output chains' - ) - parser.add_argument( - '--sens-run', type=misc_utils.parse_bool, default='True', - help='Generate sensitivities' - ) - 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' + '--datadir', type=str, default='./untitled', + help='Path to store chains' ) parser.add_argument( - '--sens-bins', type=int, default=10, - help='Number of bins for the Bayes factor plot' + '--segments', type=int, default=10, + help='Number of new physics scales to evaluate' ) parser.add_argument( - '--sens-eval-bin', type=str, default='all', - help='Which bin to evalaute for Bayes factor plot' - ) - parser.add_argument( - '--plot-statistic', type=misc_utils.parse_bool, default='False', - help='Plot MultiNest evidence or LLH value' + '--eval-segment', type=str, default='all', + help='Which point to evalaute' ) fr_utils.fr_argparse(parser) gf_utils.gf_argparse(parser) - llh_utils.likelihood_argparse(parser) + llh_utils.llh_argparse(parser) mn_utils.mn_argparse(parser) nuisance_argparse(parser) if args is None: return parser.parse_args() @@ -186,235 +189,72 @@ def main(): 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]) - ) - - scan_scales = np.linspace( - np.log10(args.scale_region[0]), np.log10(args.scale_region[1]), args.sens_bins + # Scale and BSM mixings will be fixed. + scale_prm = llh_paramset.from_tag(ParamTag.SCALE)[0] + base_mn_pset = llh_paramset.from_tag( + [ParamTag.SCALE, ParamTag.MMANGLES], invert=True ) - scan_scales = np.concatenate([[-100.], scan_scales]) - # scan_scales = np.array([-100.]) - - 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_scales', scan_scales - print 'scan_angles', scan_angles + # Array of scales to scan over. + boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension] + eval_scales = np.linspace(boundaries[0], boundaries[1], args.segments-1) + eval_scales = np.concatenate([[-100.], eval_scales]) - if args.sens_eval_bin is None: - eval_dim = args.sens_bins + # Evaluate just one point (job), or all points. + if args.eval_segment is None: + eval_dim = args.segments else: eval_dim = 1 - out = args.outfile+'/{0}/{1}/{2}/fr_stat'.format( - *map(misc_utils.parse_enum, [args.stat_method, args.run_method, args.data]) + outfile = args.datadir + '/{0}/{1}/fr_stat'.format( + *map(misc_utils.parse_enum, [args.stat_method, args.data]) ) + misc_utils.gen_identifier(args) - if args.sens_run: - if args.likelihood in [Likelihood.GOLEMFIT, Likelihood.GF_FREQ]: - fitter = gf_utils.setup_fitter(args, asimov_paramset) - if args.stat_method is StatCateg.FREQUENTIST: - flags, gf_nuisance = gf_utils.fit_flags(llh_paramset) - llh_paramset = llh_paramset.remove_params(gf_nuisance) - asimov_paramset = asimov_paramset.remove_params(gf_nuisance) - st_paramset_arr = [x.remove_params(gf_nuisance) - for x in st_paramset_arr] - fitter.SetFitParametersFlag(flags) - else: fitter = None - - if args.run_method is SensitivityCateg.FULL: - statistic_arr = np.full((eval_dim, 2), np.nan) - elif args.run_method in fixed_angle_categ: - statistic_arr = np.full((len(st_paramset_arr), eval_dim, 2), np.nan) - elif args.run_method in corr_angles_categ: - statistic_arr = np.full( - (len(st_paramset_arr), eval_dim, eval_dim, 3), np.nan - ) - for idx_scen, sens_paramset in enumerate(st_paramset_arr): - print '|||| SCENARIO = {0}'.format(idx_scen) - if args.run_method in fixed_angle_categ: - for x in mmangles: x.value = 0.+1e-9 - if idx_scen == 0 or idx_scen == 2: - mmangles[idx_scen].value = np.sin(np.pi/4.)**2 - """s_12^2 or s_23^2""" - mmangles[1].value = 1. - """c_13^4""" - elif idx_scen == 1: - mmangles[idx_scen].value = np.cos(np.pi/4.)**4 - """c_13^4""" - - for idx_an, an in enumerate(scan_angles): - if args.run_method in corr_angles_categ: - for x in mmangles: x.value = 0.+1e-9 - angle = np.arcsin(np.sqrt(an))/2. - if idx_scen == 0 or idx_scen == 2: - mmangles[idx_scen].value = np.sin(angle)**2 - """s_12^2 or s_23^2""" - mmangles[1].value = 1. - """c_13^4""" - elif idx_scen == 1: - mmangles[idx_scen].value = np.cos(angle)**4 - """c_13^4""" - - for idx_sc, sc in enumerate(scan_scales): - if args.sens_eval_bin is not None: - if args.run_method in corr_angles_categ: - index = idx_an*args.sens_bins + idx_sc - else: index = idx_sc - if index == args.sens_eval_bin: - if idx_scen == 0: - out += '_scale_{0:.0E}'.format(np.power(10, sc)) - if args.run_method in corr_angles_categ: - out += '_angle_{0:<04.2}'.format(an) - else: continue - - sc_param = llh_paramset.from_tag(ParamTag.SCALE)[0] - if sc < sc_param.ranges[0]: - sc_param.ranges = (sc, sc_param.ranges[1]) - - if idx_sc == 0 or args.sens_eval_bin is not None: - print '|||| ANGLE = {0:<04.2}'.format(float(an)) - print '|||| SCALE = {0:.0E}'.format(np.power(10, sc)) - scale.value = sc - if args.stat_method is StatCateg.BAYESIAN: - identifier = 'b{0}_{1}_sce{2}_sca{3}_an{4}'.format( - args.sens_eval_bin, args.sens_bins, idx_scen, sc, idx_an - ) - try: - stat = mn_utils.mn_evidence( - mn_paramset = sens_paramset, - llh_paramset = llh_paramset, - asimov_paramset = asimov_paramset, - args = args, - fitter = fitter, - identifier = identifier - ) - except: - print 'Failed run, continuing' - # raise - continue - print '## Evidence = {0}'.format(stat) - elif args.stat_method is StatCateg.FREQUENTIST: - raise NotImplementedError('Still needs testing') - def fn(x): - # TODO(shivesh): should be seed or ranges? - # Force prior ranges to be inside "seed" - for el in x: - if el < 0 or el > 1: return np.inf - pranges = sens_paramset.seeds - for i, name in enumerate(sens_paramset.names): - llh_paramset[name].value = \ - (pranges[i][1]-pranges[i][0])*x[i] + pranges[i][0] - theta = llh_paramset.values - llh = llh_utils.ln_prob( - theta=theta, args=args, asimov_paramset=asimov_paramset, - llh_paramset=llh_paramset, fitter=fitter - ) - # print 'llh_paramset', llh_paramset - # print '-llh', -llh - return -llh - - n_params = len(sens_paramset) - bounds = np.dstack([np.zeros(n_params), np.ones(n_params)])[0] - x0_arr = np.linspace(0+1e-3, 1-1e-3, 3) - stat = -np.inf - try: - for x0_v in x0_arr: - x0 = np.full(n_params, x0_v) - # res = minimize(fun=fn, x0=x0, method='Powell', tol=1e-3) - res = minimize(fun=fn, x0=x0, method='Nelder-Mead', tol=1e-3) - # res = minimize(fun=fn, x0=x0, method='L-BFGS-B', tol=1e-3, bounds=bounds) - # res = minimize(fun=fn, x0=x0, method='BFGS', tol=1e-3) - s = -fn(res.x) - if s > stat: - stat = s - except AssertionError: - # print 'Failed run, continuing' - raise - continue - print '=== final llh', stat - if args.run_method is SensitivityCateg.FULL: - statistic_arr[idx_sc] = np.array([sc, stat]) - elif args.run_method in fixed_angle_categ: - if args.sens_eval_bin is not None: - statistic_arr[idx_scen][0] = np.array([sc, stat]) - else: - statistic_arr[idx_scen][idx_sc] = np.array([sc, stat]) - elif args.run_method in corr_angles_categ: - if args.sens_eval_bin is not None: - statistic_arr[idx_scen][0][0] = np.array([an, sc, stat]) - else: - statistic_arr[idx_scen][idx_an][idx_sc] = np.array([an, sc, stat]) - - misc_utils.make_dir(out) - print 'Saving to {0}'.format(out+'.npy') - np.save(out+'.npy', statistic_arr) - - if args.plot_statistic: - print 'Plotting statistic' - if args.sens_run: raw = statistic_arr - else: raw = np.load(out+'.npy') - data = ma.masked_invalid(raw) - - basename = os.path.dirname(out) + '/statrun/' + os.path.basename(out) - baseoutfile = basename[:5]+basename[5:].replace('data', 'plots') - if args.run_method is SensitivityCateg.FULL: - plot_utils.plot_statistic( - data = data, - outfile = baseoutfile, - outformat = ['png'], - args = args, - scale_param = scale + # Setup Golemfit. + gf_utils.setup_fitter(args, asimov_paramset) + + # Initialise data structure. + stat_arr = np.full((eval_dim, 2), np.nan) + + for idx_sc, scale in enumerate(eval_scales): + if args.eval_segment is not None: + if idx_sc == args.eval_segment: + outfile += '_scale_{0:.0E}'.format(np.power(10, scale)) + else: continue + print '|||| SCALE = {0:.0E}'.format(np.power(10, scale)) + + # Lower scale boundary for first (NULL) point and set the scale param. + if scale < scale_prm.ranges[0]: + scale_prm.ranges = (scale, scale_prm.ranges[1]) + scale_prm.value = scale + + if args.stat_method is StatCateg.BAYESIAN: + identifier = 'b{0}_{1}_sce{2}_sca{3}'.format( + args.eval_segment, args.segments, int(args.texture), scale ) - elif 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$' - plot_utils.plot_statistic( - data = data[idx_scen], - outfile = outfile, - outformat = ['png'], - args = args, - scale_param = scale, - label = label + try: + stat = mn_utils.mn_evidence( + mn_paramset = base_mn_pset, + llh_paramset = llh_paramset, + asimov_paramset = asimov_paramset, + args = args, + identifier = identifier ) - elif args.run_method in corr_angles_categ: - for idx_scen in xrange(len(st_paramset_arr)): - print '|||| SCENARIO = {0}'.format(idx_scen) - basescenoutfile = baseoutfile + '_SCEN{0}'.format(idx_scen) - if idx_scen == 0: label = r'$\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[idx_scen][index][:,1:], - outfile = outfile, - outformat = ['png'], - args = args, - scale_param = scale, - label = _label - ) + except: + print 'Failed run, continuing' + # raise + continue + print '## Evidence = {0}'.format(stat) + elif args.stat_method is StatCateg.FREQUENTIST: + raise NotImplementedError('Still needs testing') + + if args.eval_segment is not None: + stat_arr[0] = np.array([scale, stat]) + else: + stat_arr[idx_sc] = np.array([scale, stat]) + + misc_utils.make_dir(outfile) + print 'Saving to {0}'.format(outfile+'.npy') + np.save(outfile+'.npy', stat_arr) main.__doc__ = __doc__ diff --git a/submitter/sens_dag.py b/submitter/sens_dag.py index 2046efb..e60477c 100644 --- a/submitter/sens_dag.py +++ b/submitter/sens_dag.py @@ -3,34 +3,36 @@ import os import numpy as np -full_scan_mfr = [ - # (1, 1, 1), (1, 2, 0) +sources = [ + (1, 2, 0), + (1, 0, 0), + (0, 1, 0), ] -fix_sfr_mfr = [ - (1, 1, 1, 1, 2, 0), - (1, 1, 1, 1, 0, 0), - (1, 1, 1, 0, 1, 0), - # (1, 1, 1, 0, 0, 1), - # (1, 1, 0, 1, 2, 0), - # (1, 1, 0, 1, 0, 0), - # (1, 1, 0, 0, 1, 0), - # (1, 0, 0, 1, 0, 0), - # (0, 1, 0, 0, 1, 0), - # (1, 2, 0, 1, 2, 0), - # (1, 2, 0, 0, 1, 0), +dims = [ + 3, 4, 5, 6, 7, 8 ] +textures = [ + 'OEU', 'OET', 'OUT' +] + +datadir = '/data/user/smandalia/flavour_ratio/data/sensitivity' +scratchdir = '/scratch/smandalia/flavour_ratio' + +prefix = '' +# prefix = '_noprior' + +golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' +condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub' + GLOBAL_PARAMS = {} # Bayes Factor -sens_eval_bin = 'true' # set to 'all' to run normally GLOBAL_PARAMS.update(dict( - sens_run = 'True', - run_method = 'fixed_angle', # full, fixed_angle, corr_angle - stat_method = 'bayesian', - sens_bins = 10, - seed = None + stat_method = 'bayesian', + segments = 10, + seed = 26 )) # MultiNest @@ -40,31 +42,14 @@ GLOBAL_PARAMS.update(dict( # mn_live_points = 300, # mn_tolerance = 0.1, mn_tolerance = 0.3, - mn_output = './mnrun' + mn_output = scratchdir + '/mnrun' )) # FR -# dimension = [6] -# dimension = [3, 6] -dimension = [3, 4, 5, 6, 7, 8] GLOBAL_PARAMS.update(dict( - threads = 1, - binning = '6e4 1e7 20', - no_bsm = 'False', - scale_region = "1E10", - energy_dependance = 'spectral', - spectral_index = -2, - fix_mixing = 'None', - fix_mixing_almost = 'False', - fold_index = 'True', - save_measured_fr = 'False', - output_measured_fr = './frs/' -)) - -# Likelihood -GLOBAL_PARAMS.update(dict( - likelihood = 'golemfit', - sigma_ratio = '0.01' + threads = 4, + binning = '6e4 1e7 20', + no_bsm = 'False' )) # GolemFit @@ -73,95 +58,32 @@ GLOBAL_PARAMS.update(dict( data = 'real' )) -# Plot -GLOBAL_PARAMS.update(dict( - plot_statistic = 'True' -)) - -outfile = 'dagman_FR_SENS_{0}_{1}_{2}_{3}'.format( - GLOBAL_PARAMS['stat_method'], GLOBAL_PARAMS['run_method'], - GLOBAL_PARAMS['likelihood'], GLOBAL_PARAMS['data'] +dagfile = 'dagman_SENS_{0}_{1}'.format( + GLOBAL_PARAMS['stat_method'], GLOBAL_PARAMS['data'] ) -# outfile += '_seed2' -# outfile += '_tol03' -# outfile += '_NULL' -# outfile += '_prior' -# outfile += '_strictprior' -# outfile += '_noprior' -outfile += '.submit' -golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' -condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub' +dagfile += prefix + '.submit' -if sens_eval_bin.lower() != 'all': - if GLOBAL_PARAMS['run_method'].lower() == 'corr_angle': - raise NotImplementedError - sens_runs = GLOBAL_PARAMS['sens_bins']**2 - else: - sens_runs = GLOBAL_PARAMS['sens_bins'] + 1 -else: sens_runs = 1 - -with open(outfile, 'w') as f: +with open(dagfile, 'w') as f: job_number = 1 - for dim in dimension: - print 'dimension', dim - outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}'.format( - GLOBAL_PARAMS['likelihood'], dim - ) - for frs in fix_sfr_mfr: - print 'frs', frs - output = outchain_head + '/fix_ifr/' - if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': - output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) - # output += 'seed2/' - # output += 'mn_noverlap/' - # output += 'tol_03/' - # output += 'prior/' - # output += 'strictprior/' - # output += 'noprior/' - for r in xrange(sens_runs): - print 'run', r - f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) - f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) - f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) - f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, True)) - f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, frs[3])) - f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, frs[4])) - f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, frs[5])) - if sens_eval_bin.lower() != 'all': - f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r)) - else: - f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all')) - for key in GLOBAL_PARAMS.iterkeys(): - f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) - f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, output)) - job_number += 1 - # break - - # for frs in full_scan_mfr: - # print 'frs', frs - # output = outchain_head + '/full/' - # if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': - # output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) - # for r in xrange(sens_runs): - # print 'run', r - # f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) - # f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - # f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) - # f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) - # f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - # f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, False)) - # f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, 0)) - # if sens_eval_bin.lower() != 'all': - # f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r)) - # else: - # f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all')) - # for key in GLOBAL_PARAMS.iterkeys(): - # f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) - # f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, output)) - # job_number += 1 + for dim in dims: + print 'dims', dim + of_d = datadir + '/DIM{0}/{1}'.format(dim, prefix) + for src in sources: + print 'source flavour', src + for tex in textures: + print 'texture', tex + for r in xrange(GLOBAL_PARAMS['segments']): + print 'run', r + f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) + f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) + f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, src[0])) + f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, src[1])) + f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, src[2])) + f.write('VARS\tjob{0}\ttexture="{1}"\n'.format(job_number, tex)) + f.write('VARS\tjob{0}\teval_segment="{1}"\n'.format(job_number, r)) + for key in GLOBAL_PARAMS.iterkeys(): + f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) + f.write('VARS\tjob{0}\tdatadir="{1}"\n'.format(job_number, of_d)) + job_number += 1 - print 'dag file = {0}'.format(outfile) + print 'dag file = {0}'.format(dagfile) diff --git a/submitter/sens_submit.sub b/submitter/sens_submit.sub index 6c92337..5a2c670 100644 --- a/submitter/sens_submit.sub +++ b/submitter/sens_submit.sub @@ -1,10 +1,10 @@ Executable = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/sens.py -Arguments = "--ast $(ast) --data $(data) --dimension $(dimension) --fix-mixing $(fix_mixing) --fix-source-ratio $(fix_source_ratio) --likelihood $(likelihood) --measured-ratio $(mr0) $(mr1) $(mr2) --no-bsm $(no_bsm) --outfile $(outfile) --scale-region $(scale_region) --seed $(seed) --sigma-ratio $(sigma_ratio) --source-ratio $(sr0) $(sr1) $(sr2) --threads $(threads) --energy-dependance $(energy_dependance) --spectral-index $(spectral_index) --binning $(binning) --fix-mixing-almost $(fix_mixing_almost) --sens-run $(sens_run) --run-method $(run_method) --stat-method $(stat_method) --sens-bins $(sens_bins) --sens-eval-bin $(sens_eval_bin) --mn-live-points $(mn_live_points) --mn-tolerance $(mn_tolerance) --mn-output $(mn_output) --plot-statistic $(plot_statistic) --fold-index $(fold_index) --save-measured-fr $(save_measured_fr) --output-measured-fr=$(output_measured_fr)" +Arguments = "--ast $(ast) --data $(data) --dimension $(dimension) --no-bsm $(no_bsm) --datadir $(datadir) --seed $(seed) --source-ratio $(sr0) $(sr1) $(sr2) --threads $(threads) --binning $(binning) --texture $(texture) --segments $(segments) --eval-segment $(eval_segment) --stat-method $(stat_method) --mn-live-points $(mn_live_points) --mn-tolerance $(mn_tolerance) --mn-output $(mn_output)" # All logs will go to a single file -log = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).log -output = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).out -error = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).err +log = /scratch/smandalia/flavour_ratio/submitter/logs/job_$(Cluster).log +output = /scratch/smandalia/flavour_ratio/submitter/logs/job_$(Cluster).out +error = /scratch/smandalia/flavour_ratio/submitter/logs/job_$(Cluster).err getenv = True # environment = "X509_USER_PROXY=x509up_u14830" @@ -14,10 +14,10 @@ getenv = True # but do not try to copy outputs back (see: https://htcondor-wiki.cs.wisc.edu/index.cgi/tktview?tn=3081) # +TransferOutput="" -Transfer_output_files = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/metaouts/ +# Transfer_output_files = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/metaouts/ request_memory = 3GB -request_cpus = 1 +request_cpus = 4 Universe = vanilla Notification = never diff --git a/utils/enums.py b/utils/enums.py index 441a16f..e85158d 100644 --- a/utils/enums.py +++ b/utils/enums.py @@ -20,23 +20,9 @@ class DataType(Enum): REALISATION = 3 -class EnergyDependance(Enum): - MONO = 1 - SPECTRAL = 2 - - class Likelihood(Enum): - FLAT = 1 - GAUSSIAN = 2 - GOLEMFIT = 3 - GF_FREQ = 4 - - -class MixingScenario(Enum): - T12 = 1 - T13 = 2 - T23 = 3 - NONE = 4 + GOLEMFIT = 1 + GF_FREQ = 2 class ParamTag(Enum): @@ -60,14 +46,6 @@ class MCMCSeedType(Enum): GAUSSIAN = 2 -class SensitivityCateg(Enum): - FULL = 1 - FIXED_ANGLE = 2 - CORR_ANGLE = 3 - FIXED_ONE_ANGLE = 4 - CORR_ONE_ANGLE = 5 - - class StatCateg(Enum): BAYESIAN = 1 FREQUENTIST = 2 @@ -79,6 +57,7 @@ class SteeringCateg(Enum): class Texture(Enum): - OEU = 0 - OET = 1 - OUT = 2 + OEU = 1 + OET = 2 + OUT = 3 + NONE = 4 diff --git a/utils/fr.py b/utils/fr.py index 1d12fe5..b8eba44 100644 --- a/utils/fr.py +++ b/utils/fr.py @@ -13,7 +13,7 @@ from functools import partial import numpy as np -from utils.enums import EnergyDependance, MixingScenario +from utils.enums import Texture from utils.misc import enum_parse, parse_bool import mpmath as mp @@ -40,7 +40,17 @@ ASIN = mp.asin EXP = mp.exp MASS_EIGENVALUES = [7.40E-23, 2.515E-21] -"""SM mass eigenvalues""" +"""SM mass eigenvalues.""" + +SCALE_BOUNDARIES = { + 3 : (-32, -20), + 4 : (-40, -24), + 5 : (-48, -27), + 6 : (-56, -30), + 7 : (-64, -33), + 8 : (-72, -36) +} +"""Boundaries to scan the NP scale for each dimension.""" def determinant(x): @@ -244,38 +254,13 @@ def normalise_fr(fr): return np.array(fr) / float(np.sum(fr)) -def estimate_scale(args): - """Estimate the scale at which new physics will enter.""" - try: m_eign = args.m3x_2 - except: m_eign = MASS_EIGENVALUES[1] - if hasattr(args, 'scale'): - if args.scale != 0: - scale = args.scale - scale_region = (scale/args.scale_region, scale*args.scale_region) - return scale, scale_region - if args.energy_dependance is EnergyDependance.MONO: - scale = np.power( - 10, np.round(np.log10(m_eign/args.energy)) - \ - np.log10(args.energy**(args.dimension-3)) - ) - scale_region = (scale/args.scale_region, scale*args.scale_region) - elif args.energy_dependance is EnergyDependance.SPECTRAL: - lower_s = (m_eign/args.binning[-1]) / (args.binning[-1]**(args.dimension-3)) - upper_s = (m_eign/args.binning[0]) / (args.binning[0]**(args.dimension-3)) - scale = np.power(10, np.average(np.log10([lower_s, upper_s]))) - diff = upper_s / lower_s - scale_region = (lower_s/np.power(10, args.dimension), upper_s*diff*np.power(10, args.dimension)) - scale_region = [np.power(10, np.round(np.log10(x))) for x in scale_region] - return scale, scale_region - - def fr_argparse(parser): parser.add_argument( - '--measured-ratio', type=float, nargs=3, default=[1, 1, 1], - help='Set the central value for the measured flavour ratio at IceCube' + '--injected-ratio', type=float, nargs=3, required=False, + help='Injected ratio if not using data' ) parser.add_argument( - '--source-ratio', type=float, nargs=3, default=[2, 1, 0], + '--source-ratio', type=float, nargs=3, default=[1, 2, 0], help='Set the source flavour ratio for the case when you want to fix it' ) parser.add_argument( @@ -287,51 +272,13 @@ def fr_argparse(parser): help='Set the new physics dimension to consider' ) parser.add_argument( - '--energy-dependance', default='spectral', type=partial(enum_parse, c=EnergyDependance), - choices=EnergyDependance, - help='Type of energy dependance to use in the BSM fit' - ) - parser.add_argument( - '--spectral-index', default=-2, type=float, - help='Spectral index for spectral energy dependance' - ) - parser.add_argument( - '--fold-index', default='True', type=parse_bool, - help='Fold in the spectral index when using GolemFit' + '--texture', type=partial(enum_parse, c=Texture), + default='none', choices=Texture, help='Set the BSM mixing texture' ) parser.add_argument( '--binning', default=[6e4, 1e7, 20], type=float, nargs=3, help='Binning for spectral energy dependance' ) - parser.add_argument( - '--fix-source-ratio', type=parse_bool, default='False', - help='Fix the source flavour ratio' - ) - parser.add_argument( - '--fix-mixing', type=partial(enum_parse, c=MixingScenario), - default='None', choices=MixingScenario, - help='Fix all mixing parameters to choice of maximal mixing' - ) - parser.add_argument( - '--fix-mixing-almost', type=parse_bool, default='False', - help='Fix all mixing parameters except s23' - ) - parser.add_argument( - '--fix-scale', type=parse_bool, default='False', - help='Fix the new physics scale' - ) - parser.add_argument( - '--scale', type=float, default=0, - help='Set the new physics scale' - ) - parser.add_argument( - '--scale-region', type=float, default=1e10, - help='Set the size of the box to scan for the scale' - ) - parser.add_argument( - '--energy', type=float, default=1000, - help='Set the energy scale' - ) def fr_to_angles(ratios): @@ -363,8 +310,7 @@ NUFIT_U = angles_to_u((0.307, (1-0.02195)**2, 0.565, 3.97935)) def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, - sm_u=NUFIT_U, no_bsm=False, fix_mixing=MixingScenario.NONE, - fix_mixing_almost=False, fix_scale=False, scale=None, + sm_u=NUFIT_U, no_bsm=False, texture=Texture.NONE, check_uni=True, epsilon=1e-7): """Construct the BSM mixing matrix from the BSM parameters. @@ -388,17 +334,8 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, no_bsm : bool Turn off BSM behaviour - fix_mixing : MixingScenario - Fix the BSM mixing angles - - fix_mixing_almost : bool - Fix the BSM mixing angles except one - - fix_scale : bool - Fix the BSM scale - - scale : float - Used with fix_scale - scale at which to fix + texture : Texture + BSM mixing texture check_uni : bool Check the resulting BSM mixing matrix is unitary @@ -422,33 +359,20 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, 'got\n{0}'.format(sm_u) ) - if fix_mixing is not MixingScenario.NONE and fix_mixing_almost: - raise NotImplementedError( - '--fix-mixing and --fix-mixing-almost cannot be used together' - ) - if not isinstance(theta, (list, tuple)): theta = [theta] - if fix_mixing is MixingScenario.T12: - s12_2, c13_4, s23_2, dcp, sc2 = 0.5, 1.0, 0.0, 0., theta - elif fix_mixing is MixingScenario.T13: - s12_2, c13_4, s23_2, dcp, sc2 = 0.0, 0.25, 0.0, 0., theta - elif fix_mixing is MixingScenario.T23: - s12_2, c13_4, s23_2, dcp, sc2 = 0.0, 1.0, 0.5, 0., theta - elif fix_mixing_almost: - s12_2, c13_4, dcp = 0.5, 1.0-1E-6, 0. - s23_2, sc2 = theta - elif fix_scale: - s12_2, c13_4, s23_2, dcp = theta - sc2 = scale + z = 0.+1e-9 + if texture is Texture.OEU: + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = 0.5, 1.0, z, z, theta + elif texture is Texture.OET: + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 0.25, z, z, theta + elif texture is Texture.OUT: + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = z, 1.0, 0.5, z, theta else: - s12_2, c13_4, s23_2, dcp, sc2 = theta + np_s12_2, np_c13_4, np_s23_2, np_dcp, sc2 = theta - if len(theta) != 0: - sc2 = np.power(10., sc2) - else: - sc2 = scale + sc2 = np.power(10., sc2) sc1 = sc2 / 100. mass_matrix = np.array( @@ -458,11 +382,11 @@ def params_to_BSMu(theta, dim, energy, mass_eigenvalues=MASS_EIGENVALUES, if no_bsm: eg_vector = cardano_eqn(sm_ham) else: - new_physics_u = angles_to_u((s12_2, c13_4, s23_2, dcp)) - scale_matrix = np.array( + NP_U = angles_to_u((np_s12_2, np_c13_4, np_s23_2, np_dcp)) + SC_U = np.array( [[0, 0, 0], [0, sc1, 0], [0, 0, sc2]] ) - bsm_term = (energy**(dim-3)) * np.dot(new_physics_u, np.dot(scale_matrix, new_physics_u.conj().T)) + bsm_term = (energy**(dim-3)) * np.dot(NP_U, np.dot(SC_U, NP_U.conj().T)) bsm_ham = sm_ham + bsm_term eg_vector = cardano_eqn(bsm_ham) diff --git a/utils/gf.py b/utils/gf.py index 13d5728..bc004bd 100644 --- a/utils/gf.py +++ b/utils/gf.py @@ -24,6 +24,9 @@ from utils.misc import enum_parse, parse_bool, thread_factors from utils.param import ParamSet +FITTER = None + + def fit_flags(llh_paramset): default_flags = { # False means it's not fixed in minimization @@ -74,9 +77,6 @@ def steering_params(args): elif args.likelihood is Likelihood.GF_FREQ: params.frequentist = True; - # For Tianlu - # params.years = [999] - if hasattr(args, 'binning'): params.minFitEnergy = args.binning[0] # GeV params.maxFitEnergy = args.binning[-1] # GeV @@ -95,59 +95,61 @@ def steering_params(args): return params -def setup_asimov(fitter, params): +def setup_asimov(params): print 'Injecting the model', params asimov_params = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: asimov_params.__setattr__(parm.name, float(parm.value)) - fitter.SetupAsimov(asimov_params) + FITTER.SetupAsimov(asimov_params) -def setup_realisation(fitter, params, seed): +def setup_realisation(params, seed): print 'Injecting the model', params asimov_params = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: asimov_params.__setattr__(parm.name, float(parm.value)) - fitter.Swallow(fitter.SpitRealization(asimov_params, seed)) + FITTER.Swallow(FITTER.SpitRealization(asimov_params, seed)) def setup_fitter(args, asimov_paramset): + global FITTER datapaths = gf.DataPaths() sparams = steering_params(args) npp = gf.NewPhysicsParams() - fitter = gf.GolemFit(datapaths, sparams, npp) + FITTER = gf.GolemFit(datapaths, sparams, npp) if args.data is DataType.ASIMOV: - setup_asimov(fitter, asimov_paramset) + setup_asimov(FITTER, asimov_paramset) elif args.data is DataType.REALISATION: seed = args.seed if args.seed is not None else 1 - setup_realisation(fitter, asimov_paramset, seed) + setup_realisation(FITTER, asimov_paramset, seed) elif args.data is DataType.REAL: print 'Using MagicTau DATA' - return fitter -def get_llh(fitter, params): +def get_llh(params): # print 'params', params fitparams = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: fitparams.__setattr__(parm.name, float(parm.value)) - llh = -fitter.EvalLLH(fitparams) + llh = -FITTER.EvalLLH(fitparams) return llh -def get_llh_freq(fitter, params): +def get_llh_freq(params): print 'setting to {0}'.format(params) fitparams = gf.FitParameters(gf.sampleTag.MagicTau) for parm in params: fitparams.__setattr__(parm.name, float(parm.value)) - fitter.SetFitParametersSeed(fitparams) - llh = -fitter.MinLLH().likelihood + FITTER.SetFitParametersSeed(fitparams) + llh = -FITTER.MinLLH().likelihood return llh -def data_distributions(fitter): - hdat = fitter.GetDataDistribution() - binedges = np.asarray([fitter.GetZenithBinsData(), fitter.GetEnergyBinsData()]) +def data_distributions(): + hdat = FITTER.GetDataDistribution() + binedges = np.asarray( + [FITTER.GetZenithBinsData(), FITTER.GetEnergyBinsData()] + ) return hdat, binedges @@ -164,4 +166,3 @@ def gf_argparse(parser): choices=SteeringCateg, help='use asimov/fake dataset with specific steering' ) - diff --git a/utils/llh.py b/utils/llh.py index 0c1e97d..93587b9 100644 --- a/utils/llh.py +++ b/utils/llh.py @@ -17,7 +17,7 @@ from scipy.stats import multivariate_normal, truncnorm from utils import fr as fr_utils from utils import gf as gf_utils -from utils.enums import EnergyDependance, Likelihood, ParamTag, PriorsCateg +from utils.enums import Likelihood, ParamTag, PriorsCateg from utils.misc import enum_parse, gen_identifier, parse_bool @@ -34,22 +34,11 @@ def multi_gaussian(fr, fr_bf, sigma, offset=-320): return np.log(multivariate_normal.pdf(fr, mean=fr_bf, cov=cov_fr)) + offset -def likelihood_argparse(parser): +def llh_argparse(parser): parser.add_argument( - '--likelihood', default='gaussian', type=partial(enum_parse, c=Likelihood), - choices=Likelihood, help='likelihood contour' - ) - parser.add_argument( - '--sigma-ratio', type=float, default=0.01, - help='Set the 1 sigma for the measured flavour ratio for a gaussian LLH' - ) - parser.add_argument( - '--save-measured-fr', type=parse_bool, default='False', - help='Output the measured flavour ratios' - ) - parser.add_argument( - '--output-measured-fr', type=str, default='./frs', - help='Output of the measured flavour ratios' + '--stat-method', default='bayesian', + type=partial(misc_utils.enum_parse, c=StatCateg), choices=StatCateg, + help='Statistical method to employ' ) @@ -82,7 +71,7 @@ def lnprior(theta, paramset): return prior -def triangle_llh(theta, args, asimov_paramset, llh_paramset, fitter): +def triangle_llh(theta, args, asimov_paramset, llh_paramset): """Log likelihood function for a given theta.""" if len(theta) != len(llh_paramset): raise AssertionError( @@ -95,31 +84,14 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset, fitter): for param in llh_paramset.from_tag(ParamTag.NUISANCE): hypo_paramset[param.name].value = param.value - if args.energy_dependance is EnergyDependance.SPECTRAL: - bin_centers = np.sqrt(args.binning[:-1]*args.binning[1:]) - bin_width = np.abs(np.diff(args.binning)) - if args.likelihood in [Likelihood.GOLEMFIT, Likelihood.GF_FREQ] \ - and args.fold_index: - args.spectral_index = -hypo_paramset['astroDeltaGamma'].value - - if args.fix_source_ratio: - if args.energy_dependance is EnergyDependance.MONO: - source_flux = args.source_ratio - elif args.energy_dependance is EnergyDependance.SPECTRAL: - source_flux = np.array( - [fr * np.power(bin_centers, args.spectral_index) - for fr in args.source_ratio] - ).T - else: - if args.energy_dependance is EnergyDependance.MONO: - source_flux = fr_utils.angles_to_fr( - llh_paramset.from_tag(ParamTag.SRCANGLES, values=True) - ) - elif args.energy_dependance is EnergyDependance.SPECTRAL: - source_flux = np.array( - [fr * np.power(bin_centers, args.spectral_index) - for fr in fr_utils.angles_to_fr(theta[-2:])] - ).T + bin_centers = np.sqrt(args.binning[:-1]*args.binning[1:]) + bin_width = np.abs(np.diff(args.binning)) + spectral_index = -hypo_paramset['astroDeltaGamma'].value + + source_flux = np.array( + [fr * np.power(bin_centers, spectral_index) + for fr in args.source_ratio] + ).T bsm_angles = llh_paramset.from_tag( [ParamTag.SCALE, ParamTag.MMANGLES], values=True @@ -139,21 +111,7 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset, fitter): if args.no_bsm: fr = fr_utils.u_to_fr(source_flux, np.array(sm_u, dtype=np.complex256)) - elif args.energy_dependance is EnergyDependance.MONO: - u = fr_utils.params_to_BSMu( - theta = bsm_angles, - dim = args.dimension, - energy = args.energy, - mass_eigenvalues = mass_eigenvalues, - sm_u = sm_u, - no_bsm = args.no_bsm, - fix_mixing = args.fix_mixing, - fix_mixing_almost = args.fix_mixing_almost, - fix_scale = args.fix_scale, - scale = args.scale - ) - fr = fr_utils.u_to_fr(source_flux, u) - elif args.energy_dependance is EnergyDependance.SPECTRAL: + else: mf_perbin = [] for i_sf, sf_perbin in enumerate(source_flux): u = fr_utils.params_to_BSMu( @@ -163,10 +121,7 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset, fitter): mass_eigenvalues = mass_eigenvalues, sm_u = sm_u, no_bsm = args.no_bsm, - fix_mixing = args.fix_mixing, - fix_mixing_almost = args.fix_mixing_almost, - fix_scale = args.fix_scale, - scale = args.scale + texture = args.texture, ) fr = fr_utils.u_to_fr(sf_perbin, u) mf_perbin.append(fr) @@ -181,35 +136,18 @@ def triangle_llh(theta, args, asimov_paramset, llh_paramset, fitter): for idx, param in enumerate(hypo_paramset.from_tag(ParamTag.BESTFIT)): param.value = flavour_angles[idx] - if args.likelihood is Likelihood.FLAT: - llh = 1. - elif args.likelihood is Likelihood.GAUSSIAN: - fr_bf = args.measured_ratio - llh = multi_gaussian(fr, fr_bf, args.sigma_ratio) - elif args.likelihood is Likelihood.GOLEMFIT: - llh = gf_utils.get_llh(fitter, hypo_paramset) + if args.likelihood is Likelihood.GOLEMFIT: + llh = gf_utils.get_llh(hypo_paramset) elif args.likelihood is Likelihood.GF_FREQ: - llh = gf_utils.get_llh_freq(fitter, hypo_paramset) - - if args.save_measured_fr and args.burnin is False: - n = gen_identifier(args) + '.txt' - with open(args.output_measured_fr + n, 'a') as f: - f.write(r'{0:.3f} {1:.3f} {2:.3f}'.format( - float(fr[0]), float(fr[1]), float(fr[2]) - )) - for p in llh_paramset: - f.write(r' {0:.3f}'.format(p.value)) - f.write(' LLH = {0:.3f}'.format(llh)) - f.write('\n') - + llh = gf_utils.get_llh_freq(hypo_paramset) return llh -def ln_prob(theta, args, asimov_paramset, llh_paramset, fitter): +def ln_prob(theta, args, asimov_paramset, llh_paramset): lp = lnprior(theta, paramset=llh_paramset) if not np.isfinite(lp): return -np.inf return lp + triangle_llh( theta, args=args, asimov_paramset=asimov_paramset, - llh_paramset=llh_paramset, fitter=fitter + llh_paramset=llh_paramset ) diff --git a/utils/misc.py b/utils/misc.py index 26bd828..36d5330 100644 --- a/utils/misc.py +++ b/utils/misc.py @@ -19,7 +19,8 @@ from operator import attrgetter import numpy as np -from utils.enums import Likelihood, MixingScenario +from utils.enums import str_enum +from utils.enums import Likelihood, Texture class SortingHelpFormatter(argparse.HelpFormatter): @@ -31,30 +32,20 @@ class SortingHelpFormatter(argparse.HelpFormatter): def solve_ratio(fr): denominator = reduce(gcd, fr) - return [int(x/denominator) for x in fr] + f = [int(x/denominator) for x in fr] + if f[0] > 1E3 or f[1] > 1E3 or f[2] > 1E3: + return '{0:.2f}_{1:.2f}_{2:.2f}'.format(fr[0], fr[1], fr[2]) + else: + return '{0}_{1}_{2}'.format(f[0], f[1], f[2]) def gen_identifier(args): f = '_DIM{0}'.format(args.dimension) - mr1, mr2, mr3 = solve_ratio(args.measured_ratio) - if args.fix_source_ratio: - sr1, sr2, sr3 = solve_ratio(args.source_ratio) - f += '_sfr_{0:G}_{1:G}_{2:G}_mfr_{3:G}_{4:G}_{5:G}'.format( - sr1, sr2, sr3, mr1, mr2, mr3 - ) - else: - f += '_mfr_{3:03d}_{4:03d}_{5:03d}'.format(mr1, mr2, mr3) - - if args.fix_mixing is not MixingScenario.NONE: - f += '_{0}'.format(args.fix_mixing) - elif args.fix_mixing_almost: - f += '_fix_mixing_almost' - elif args.fix_scale: - f += '_fix_scale_{0}'.format(args.scale) - - if args.likelihood is Likelihood.FLAT: f += '_flat' - elif args.likelihood is Likelihood.GAUSSIAN: - f += '_sigma_{0:03d}'.format(int(args.sigma_ratio*1000)) + f += '_sfr_' + solve_ratio(args.source_ratio) + if args.data in [DataType.ASIMOV, DataType.REALISATION]: + f += '_mfr_' + solve_ratio(args.injected_ratio) + if args.Texture is not Texture.NONE: + f += '_{0}'.format(str_enum(args.texture)) return f @@ -163,3 +154,71 @@ def thread_factors(t): if t%x == 0: return (x, int(t/x)) + +def centers(x): + return (x[:-1]+x[1:])*0.5 + + +def get_units(dimension): + if dimension == 3: return r' / \:{\rm GeV}' + if dimension == 4: return r'' + if dimension == 5: return r' / \:{rm GeV}^{-1}' + if dimension == 6: return r' / \:{rm GeV}^{-2}' + if dimension == 7: return r' / \:{rm GeV}^{-3}' + if dimension == 8: return r' / \:{rm GeV}^{-4}' + + +def calc_nbins(x): + n = (np.max(x) - np.min(x)) / (2 * len(x)**(-1./3) * (np.percentile(x, 75) - np.percentile(x, 25))) + return np.floor(n) + + +def calc_bins(x): + nbins = calc_nbins(x) + return np.linspace(np.min(x), np.max(x)+2, num=nbins+1) + + +def most_likely(arr): + """Return the densest region given a 1D array of data.""" + binning = calc_bins(arr) + harr = np.histogram(arr, binning)[0] + return centers(binning)[np.argmax(harr)] + + +def interval(arr, percentile=68.): + """Returns the *percentile* shortest interval around the mode.""" + center = most_likely(arr) + sarr = sorted(arr) + delta = np.abs(sarr - center) + curr_low = np.argmin(delta) + curr_up = curr_low + npoints = len(sarr) + while curr_up - curr_low < percentile/100.*npoints: + if curr_low == 0: + curr_up += 1 + elif curr_up == npoints-1: + curr_low -= 1 + elif sarr[curr_up]-sarr[curr_low-1] < sarr[curr_up+1]-sarr[curr_low]: + curr_low -= 1 + elif sarr[curr_up]-sarr[curr_low-1] > sarr[curr_up+1]-sarr[curr_low]: + curr_up += 1 + elif (curr_up - curr_low) % 2: + # they are equal so step half of the time up and down + curr_low -= 1 + else: + curr_up += 1 + return sarr[curr_low], center, sarr[curr_up] + + +def flat_angles_to_u(x): + """Convert from angles to mixing elements.""" + return abs(angles_to_u(x)).astype(np.float32).flatten().tolist() + + +def myround(x, base=2, up=False, down=False): + if up == down and up is True: assert 0 + if up: return int(base * np.round(float(x)/base-0.5)) + elif down: return int(base * np.round(float(x)/base+0.5)) + else: int(base * np.round(float(x)/base)) + + diff --git a/utils/mn.py b/utils/mn.py index 6582c80..c60d316 100644 --- a/utils/mn.py +++ b/utils/mn.py @@ -24,7 +24,7 @@ def CubePrior(cube, ndim, n_params): def lnProb(cube, ndim, n_params, mn_paramset, llh_paramset, asimov_paramset, - args, fitter): + args): if ndim != len(mn_paramset): raise AssertionError( 'Length of MultiNest scan paramset is not the same as the input ' @@ -41,7 +41,6 @@ def lnProb(cube, ndim, n_params, mn_paramset, llh_paramset, asimov_paramset, args=args, asimov_paramset=asimov_paramset, llh_paramset=llh_paramset, - fitter=fitter ) return llh @@ -61,13 +60,14 @@ def mn_argparse(parser): ) -def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, fitter, +def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, identifier='mn'): """Run the MultiNest algorithm to calculate the evidence.""" n_params = len(mn_paramset) for n in mn_paramset.names: llh_paramset[n].value = mn_paramset[n].value + print 'llh_paramset', llh_paramset lnProbEval = partial( lnProb, @@ -75,14 +75,13 @@ def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, fitter, llh_paramset = llh_paramset, asimov_paramset = asimov_paramset, args = args, - fitter = fitter ) llh = '{0}'.format(args.likelihood).split('.')[1] data = '{0}'.format(args.data).split('.')[1] - sr1, sr2, sr3 = solve_ratio(args.source_ratio) - prefix = './mnrun/DIM{0}/{1}/{2}/s{3}{4}{5}/{6}'.format( - args.dimension, data, llh, sr1, sr2, sr3, identifier + src_string = solve_ratio(args.source_ratio) + prefix = './mnrun/DIM{0}/{1}/{2}/s{3}/{4}'.format( + args.dimension, data, llh, src_string, identifier ) make_dir(prefix) print 'Running evidence calculation for {0}'.format(prefix) diff --git a/utils/param.py b/utils/param.py index 572b65a..558018e 100644 --- a/utils/param.py +++ b/utils/param.py @@ -16,9 +16,8 @@ from copy import deepcopy import numpy as np -from utils.plot import get_units from utils.fr import fr_to_angles -from utils.enums import DataType, Likelihood, MixingScenario, ParamTag, PriorsCateg +from utils.enums import DataType, Likelihood, ParamTag, PriorsCateg class Param(object): @@ -219,66 +218,3 @@ class ParamSet(Sequence): elif isinstance(p, ParamSet): param_sequence.extend(p.params) return ParamSet(param_sequence) - - -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)] - ) - if args.likelihood in [Likelihood.GOLEMFIT, Likelihood.GF_FREQ]: - gf_nuisance = [x for x in nuisance_paramset.from_tag(ParamTag.NUISANCE)] - asimov_paramset.extend(gf_nuisance) - llh_paramset.extend(gf_nuisance) - for parm in llh_paramset: - parm.value = args.__getattribute__(parm.name) - tag = ParamTag.BESTFIT - try: - flavour_angles = fr_to_angles(args.measured_ratio) - except: - flavour_angles = fr_to_angles(args.injected_ratio) - 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) - - if hasattr(args, 'fix_source_ratio'): - if args.fix_mixing is MixingScenario.NONE and not args.fix_mixing_almost: - tag = ParamTag.MMANGLES - llh_paramset.extend([ - Param(name='np_s_12^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{12}^2', tag=tag), - Param(name='np_c_13^4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{c}_{13}^4', tag=tag), - Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^2', tag=tag), - Param(name='np_dcp', value=np.pi, ranges=[0., 2*np.pi], std=0.2, tex=r'\tilde{\delta_{CP}}', tag=tag) - ]) - if args.fix_mixing_almost: - tag = ParamTag.MMANGLES - llh_paramset.extend([ - Param(name='np_s_23^2', value=0.5, ranges=[0., 1.], std=0.2, tex=r'\tilde{s}_{23}^4', tag=tag) - ]) - if not args.fix_scale: - tag = ParamTag.SCALE - if hasattr(args, 'dimension'): - llh_paramset.append( - Param(name='logLam', value=np.log10(args.scale), ranges=np.log10(args.scale_region), std=3, - tex=r'{\rm log}_{10}\left (\Lambda^{-1}'+get_units(args.dimension)+r'\right )', tag=tag) - ) - elif hasattr(args, 'dimensions'): - llh_paramset.append( - Param(name='logLam', value=np.log10(args.scale), ranges=np.log10(args.scale_region), std=3, - tex=r'{\rm log}_{10}\left (\Lambda^{-1} / GeV^{-d+4}\right )', tag=tag) - ) - if not args.fix_source_ratio: - tag = ParamTag.SRCANGLES - llh_paramset.extend([ - Param(name='s_phi4', value=0.5, ranges=[0., 1.], std=0.2, tex=r'sin^4(\phi)', tag=tag), - Param(name='c_2psi', value=0.5, ranges=[-1., 1.], std=0.2, tex=r'cos(2\psi)', tag=tag) - ]) - llh_paramset = ParamSet(llh_paramset) - return asimov_paramset, llh_paramset diff --git a/utils/plot.py b/utils/plot.py index 6161cfb..29ef5dc 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -19,12 +19,13 @@ from scipy.interpolate import splev, splprep from scipy.ndimage.filters import gaussian_filter import matplotlib as mpl +import matplotlib.patches as patches +import matplotlib.gridspec as gridspec mpl.use('Agg') + from matplotlib import rc from matplotlib import pyplot as plt from matplotlib.offsetbox import AnchoredText -import matplotlib.patches as patches -import matplotlib.gridspec as gridspec from matplotlib.lines import Line2D from matplotlib.patches import Patch @@ -32,22 +33,19 @@ from getdist import plots, mcsamples import ternary from ternary.heatmapping import polygon_generator -# print ternary.__file__ -# assert 0 import shapely.geometry as geometry from utils import misc as misc_utils from utils.enums import DataType, EnergyDependance, str_enum -from utils.enums import Likelihood, MixingScenario, ParamTag, StatCateg -from utils.enums import Texture +from utils.enums import Likelihood, ParamTag, StatCateg, Texture from utils.fr import angles_to_u, angles_to_fr -bayes_K = 1. # Substantial degree of belief. -# bayes_K = 3/2. # Strong degree of belief. -# bayes_K = 2. # Very strong degree of belief -# bayes_K = 5/2. # Decisive degree of belief +BAYES_K = 1. # Substantial degree of belief. +# BAYES_K = 3/2. # Strong degree of belief. +# BAYES_K = 2. # Very strong degree of belief +# BAYES_K = 5/2. # Decisive degree of belief if os.path.isfile('./plot_llh/paper.mplstyle'): @@ -58,64 +56,16 @@ if 'submitter' in socket.gethostname(): rc('text', usetex=False) -def centers(x): - return (x[:-1]+x[1:])*0.5 - - -def get_units(dimension): - if dimension == 3: return r' / \:{\rm GeV}' - if dimension == 4: return r'' - if dimension == 5: return r' / \:{rm GeV}^{-1}' - if dimension == 6: return r' / \:{rm GeV}^{-2}' - if dimension == 7: return r' / \:{rm GeV}^{-3}' - if dimension == 8: return r' / \:{rm GeV}^{-4}' - - -def calc_nbins(x): - n = (np.max(x) - np.min(x)) / (2 * len(x)**(-1./3) * (np.percentile(x, 75) - np.percentile(x, 25))) - return np.floor(n) - - -def calc_bins(x): - nbins = calc_nbins(x) - return np.linspace(np.min(x), np.max(x)+2, num=nbins+1) - - -def most_likely(arr): - """Return the densest region given a 1D array of data.""" - binning = calc_bins(arr) - harr = np.histogram(arr, binning)[0] - return centers(binning)[np.argmax(harr)] - - -def interval(arr, percentile=68.): - """Returns the *percentile* shortest interval around the mode.""" - center = most_likely(arr) - sarr = sorted(arr) - delta = np.abs(sarr - center) - curr_low = np.argmin(delta) - curr_up = curr_low - npoints = len(sarr) - while curr_up - curr_low < percentile/100.*npoints: - if curr_low == 0: - curr_up += 1 - elif curr_up == npoints-1: - curr_low -= 1 - elif sarr[curr_up]-sarr[curr_low-1] < sarr[curr_up+1]-sarr[curr_low]: - curr_low -= 1 - elif sarr[curr_up]-sarr[curr_low-1] > sarr[curr_up+1]-sarr[curr_low]: - curr_up += 1 - elif (curr_up - curr_low) % 2: - # they are equal so step half of the time up and down - curr_low -= 1 - else: - curr_up += 1 - return sarr[curr_low], center, sarr[curr_up] - - -def flat_angles_to_u(x): - """Convert from angles to mixing elements.""" - return abs(angles_to_u(x)).astype(np.float32).flatten().tolist() +def gen_figtext(args): + """Generate the figure text.""" + t = '' + t += 'Source flavour ratio = [{0}]'.format(solve_ratio(args.source_ratio)) + if args.data in [DataType.ASIMOV, DataType.REALISATION]: + t += '\nIC injected flavour ratio = [{0}]'.format( + solve_ratio(args.injected_ratio) + ) + t += '\nDimension = {0}'.format(args.dimension) + return t def plot_Tchain(Tchain, axes_labels, ranges): @@ -139,39 +89,6 @@ def plot_Tchain(Tchain, axes_labels, ranges): return g -def gen_figtext(args): - """Generate the figure text.""" - t = '' - mr1, mr2, mr3 = misc_utils.solve_ratio(args.measured_ratio) - if args.fix_source_ratio: - sr1, sr2, sr3 = misc_utils.solve_ratio(args.source_ratio) - t += 'Source flavour ratio = [{0}, {1}, {2}]'.format(sr1, sr2, sr3) - if args.data in [DataType.ASIMOV, DataType.REALISATION]: - t += '\nIC observed flavour ratio = [{0}, {1}, {2}]'.format( - mr1, mr2, mr3 - ) - t += '\nDimension = {0}'.format(args.dimension) - else: - if args.data in [DataType.ASIMOV, DataType.REALISATION]: - t += 'IC observed flavour ratio = [{0}, {1}, ' \ - '{2}]\nDimension = {3}'.format( - mr1, mr2, mr3, args.dimension, int(args.energy) - ) - if args.fix_scale: - t += 'Scale = {0}'.format(args.scale) - if args.likelihood is Likelihood.GAUSSIAN: - t += '\nSigma = {0:.3f}'.format(args.sigma_ratio) - if args.energy_dependance is EnergyDependance.SPECTRAL: - if not args.fold_index: - t += '\nSpectral Index = {0}'.format(int(args.spectral_index)) - t += '\nBinning = [{0}, {1}] TeV - {2} bins'.format( - int(args.binning[0]/1e3), int(args.binning[-1]/1e3), len(args.binning)-1 - ) - elif args.energy_dependance is EnergyDependance.MONO: - t += '\nEnergy = {0} TeV'.format(int(args.energy/1e3)) - return t - - def chainer_plot(infile, outfile, outformat, args, llh_paramset, fig_text=None): """Make the triangle plot.""" if hasattr(args, 'plot_elements'): @@ -287,13 +204,6 @@ def chainer_plot(infile, outfile, outformat, args, llh_paramset, fig_text=None): g.export(outfile+'_elements.'+of) -def myround(x, base=2, up=False, down=False): - if up == down and up is True: assert 0 - if up: return int(base * np.round(float(x)/base-0.5)) - elif down: return int(base * np.round(float(x)/base+0.5)) - else: int(base * np.round(float(x)/base)) - - def plot_statistic(data, outfile, outformat, args, scale_param, label=None): """Make MultiNest factor or LLH value plot.""" print 'Making Statistic plot' @@ -337,7 +247,7 @@ def plot_statistic(data, outfile, outformat, args, scale_param, label=None): ax.plot(scales_rm, reduced_ev) - ax.axhline(y=np.log(10**(bayes_K)), color='red', alpha=1., linewidth=1.3) + ax.axhline(y=np.log(10**(BAYES_K)), color='red', alpha=1., linewidth=1.3) for ymaj in ax.yaxis.get_majorticklocs(): ax.axhline(y=ymaj, ls=':', color='gray', alpha=0.3, linewidth=1) @@ -394,7 +304,7 @@ def plot_sens_full(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic - null) - al = scales[reduced_ev > np.log(10**(bayes_K))] # Strong degree of belief + al = scales[reduced_ev > np.log(10**(BAYES_K))] # Strong degree of belief # al = scales[reduced_ev > 0.4] # Testing elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic - null) @@ -437,8 +347,8 @@ def plot_sens_full(data, outfile, outformat, args): fig.savefig(outfile+'.'+of, bbox_inches='tight', dpi=150) -def plot_sens_fixed_angle_pretty(data, outfile, outformat, args): - print 'Making FIXED_ANGLE_PRETTY sensitivity plot' +def plot_table_sens(data, outfile, outformat, args): + print 'Making TABLE sensitivity plot' argsc = deepcopy(args) dims = len(data) LV_atmo_90pc_limits = { @@ -598,14 +508,14 @@ def plot_sens_fixed_angle_pretty(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic_rm - null) - al = scales_rm[reduced_ev > np.log(10**(bayes_K))] + al = scales_rm[reduced_ev > np.log(10**(BAYES_K))] elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic_rm - null) al = scales_rm[reduced_ev > 2.71] # 90% CL for 1 DOF via Wilks if len(al) == 0: print 'No points for DIM {0} FRS {1}!'.format(dim, src) continue - if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: + if reduced_ev[-1] < np.log(10**(BAYES_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} FRS{1}!'.format(dim, src) continue @@ -739,14 +649,14 @@ def plot_sens_fixed_angle(data, outfile, outformat, args): null = statistic[min_idx] if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic - null) - al = scales[reduced_ev > np.log(10**(bayes_K))] # Strong degree of belief + al = scales[reduced_ev > np.log(10**(BAYES_K))] # Strong degree of belief elif args.stat_method is StatCateg.FREQUENTIST: reduced_ev = -2*(statistic - null) al = scales[reduced_ev > 2.71] # 90% CL for 1 DOF via Wilks if len(al) == 0: print 'No points for DIM {0} FRS {1}!'.format(dim, src) continue - if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: + if reduced_ev[-1] < np.log(10**(BAYES_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} FRS{1}!'.format(dim, src) continue @@ -903,7 +813,7 @@ def plot_sens_corr_angle(data, outfile, outformat, args): print sep_arrays if args.stat_method is StatCateg.BAYESIAN: - reduced_pdat_mask = (sep_arrays[2] > np.log(10**(bayes_K))) # Strong degree of belief + reduced_pdat_mask = (sep_arrays[2] > np.log(10**(BAYES_K))) # Strong degree of belief elif args.stat_method is StatCateg.FREQUENTIST: reduced_pdat_mask = (sep_arrays[2] > 4.61) # 90% CL for 2 DOFS via Wilks reduced_pdat = sep_arrays.T[reduced_pdat_mask].T @@ -1175,7 +1085,7 @@ def plot_source_ternary(data, outfile, outformat, args): for idim, dim in enumerate(args.dimensions): print '|||| DIM = {0}, {1}'.format(idim, dim) for isce in xrange(r_data.shape[1]): - print '|||| SCEN = {0}'.format(str_enum(Texture(isce))) + print '|||| SCEN = {0}'.format(str_enum(MixingScenario(isce+1))) fig = plt.figure(figsize=(8, 8)) ax = fig.add_subplot(111) tax = get_tax(ax, scale=nsrcs) @@ -1204,14 +1114,14 @@ def plot_source_ternary(data, outfile, outformat, args): if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic_rm - null) print 'reduced_ev', reduced_ev - al = scales_rm[reduced_ev > np.log(10**(bayes_K))] + al = scales_rm[reduced_ev > np.log(10**(BAYES_K))] else: assert 0 if len(al) == 0: print 'No points for DIM {0} FRS {1}!'.format(dim, src) interp_dict[src_sc] = -60 continue - if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: + if reduced_ev[-1] < np.log(10**(BAYES_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} FRS{1}!'.format(dim, src) interp_dict[src_sc] = -60 @@ -1244,8 +1154,9 @@ def texture_label(x): raise AssertionError -def plot_source_ternary_1D(data, outfile, outformat, args): +def plot_x(data, outfile, outformat, args): """Ternary plot in the source flavour space for each operator texture.""" + print 'Making X sensitivity plot' sources = args.source_ratios x_1D = [] i_src_1D = [] @@ -1279,7 +1190,7 @@ def plot_source_ternary_1D(data, outfile, outformat, args): ax.set_ylabel(r'${\rm New\:Physics\:Scale}\:[\:{\rm log}_{10} (\Lambda_{d=6}^{-1}\:/\:{\rm GeV}^{-2})\: ]$', fontsize=18) ax.set_xlim(0, 1) for isce in xrange(r_data.shape[1]): - print '|||| SCEN = {0}'.format(str_enum(Texture(isce))) + print '|||| SCEN = {0}'.format(str_enum(MixingScenario(isce+1))) H = np.full(len(x_1D), np.nan) for ix, x in enumerate(x_1D): print '|||| X = {0}'.format(x) @@ -1303,13 +1214,13 @@ def plot_source_ternary_1D(data, outfile, outformat, args): if args.stat_method is StatCateg.BAYESIAN: reduced_ev = -(statistic_rm - null) print 'reduced_ev', reduced_ev - al = scales_rm[reduced_ev > np.log(10**(bayes_K))] + al = scales_rm[reduced_ev > np.log(10**(BAYES_K))] else: assert 0 if len(al) == 0: print 'No points for DIM {0} X {1}!'.format(dim, x) continue - if reduced_ev[-1] < np.log(10**(bayes_K)) - 0.1: + if reduced_ev[-1] < np.log(10**(BAYES_K)) - 0.1: print 'Peaked contour does not exclude large scales! For ' \ 'DIM {0} X {1}!'.format(dim, x) continue @@ -1320,7 +1231,7 @@ def plot_source_ternary_1D(data, outfile, outformat, args): H = ma.masked_invalid(H) H_0 = np.concatenate([[H[0]], H]) ax.step(be, H_0, linewidth=2, - label=texture_label(Texture(isce)), linestyle='-', + label=texture_label(MixingScenario(isce+1)), linestyle='-', drawstyle='steps-pre') print 'H', H print |