diff options
Diffstat (limited to 'fr.py')
| -rwxr-xr-x | fr.py | 64 |
1 files changed, 32 insertions, 32 deletions
@@ -21,10 +21,10 @@ from utils import gf as gf_utils from utils import likelihood as llh_utils from utils import mcmc as mcmc_utils from utils import misc as misc_utils +from utils import plot as plot_utils from utils.enums import EnergyDependance, Likelihood, MCMCSeedType, ParamTag from utils.fr import MASS_EIGENVALUES, normalise_fr, fr_to_angles from utils.misc import enum_parse, Param, ParamSet -from utils.plot import plot_argparse, chainer_plot def define_nuisance(): @@ -89,7 +89,8 @@ def get_paramsets(args): lL_range = (logLam-scale_region, logLam+scale_region) tag = ParamTag.SCALE mcmc_paramset.append( - Param(name='logLam', value=logLam, ranges=lL_range, std=3, tex=r'{\rm log}_{10}\Lambda', tag=tag) + Param(name='logLam', value=logLam, ranges=lL_range, std=3, + tex=r'{\rm log}_{10}\Lambda'+plot_utils.get_units(args.dimension), tag=tag) ) if not args.fix_source_ratio: tag = ParamTag.SRCANGLES @@ -98,7 +99,6 @@ def get_paramsets(args): Param(name='c_2psi', value=0.5, ranges=[0., 1.], std=0.2, tex=r'cos(2\psi)', tag=tag) ]) mcmc_paramset = ParamSet(mcmc_paramset) - # TODO(shivesh): unify return asimov_paramset, mcmc_paramset @@ -110,9 +110,9 @@ def process_args(args): raise NotImplementedError( '--fix-mixing and --fix-mixing-almost cannot be used together' ) - if args.bayes_factor and args.fix_scale: + if args.run_bayes_factor and args.fix_scale: raise NotImplementedError( - '--bayes-factor and --fix-scale cannot be used together' + '--run-bayes-factor and --fix-scale cannot be used together' ) args.measured_ratio = normalise_fr(args.measured_ratio) @@ -172,14 +172,18 @@ def parse_args(): help='Binning for spectral energy dependance' ) parser.add_argument( - '--bayes-factor', type=misc_utils.parse_bool, default='False', + '--run-bayes-factor', type=misc_utils.parse_bool, default='False', help='Make the bayes factor plot for the scale' ) parser.add_argument( - '--bayes-bins', type=int, default=100, + '--bayes-bins', type=int, default=10, help='Number of bins for the Bayes factor plot' ) parser.add_argument( + '--bayes-live-points', type=int, default=400, + help='Number of live points for MultiNest evaluations' + ) + parser.add_argument( '--bayes-output', type=str, default='./mnrun/', help='Folder to store MultiNest evaluations' ) @@ -233,9 +237,9 @@ def parse_args(): ) llh_utils.likelihood_argparse(parser) mcmc_utils.mcmc_argparse(parser) - nuisance_argparse(parser) gf_utils.gf_argparse(parser) - plot_argparse(parser) + plot_utils.plot_argparse(parser) + nuisance_argparse(parser) return parser.parse_args() @@ -252,12 +256,7 @@ def main(): if args.run_mcmc: if args.likelihood is Likelihood.GOLEMFIT: - datapaths = gf.DataPaths() - sparams = gf_utils.steering_params(args) - npp = gf.NewPhysicsParams() - fitter = gf.GolemFit(datapaths, sparams, npp) - gf_utils.set_up_as(fitter, asimov_paramset) - # fitter.WriteCompact() + fitter = gf_utils.setup_fitter(args, asimov_paramset) else: fitter = None @@ -289,7 +288,7 @@ def main(): ) mcmc_utils.save_chains(samples, outfile) - chainer_plot( + plot_utils.chainer_plot( infile = outfile+'.npy', outfile = outfile[:5]+outfile[5:].replace('data', 'plots'), outformat = ['pdf'], @@ -297,18 +296,14 @@ def main(): mcmc_paramset = mcmc_paramset ) - if args.bayes_factor: + if args.run_bayes_factor: # TODO(shivesh) import pymultinest from pymultinest.solve import solve from pymultinest.watch import ProgressPrinter if not args.run_mcmc and args.likelihood is Likelihood.GOLEMFIT: - datapaths = gf.DataPaths() - sparams = gf_utils.steering_params(args) - npp = gf.NewPhysicsParams() - fitter = gf.GolemFit(datapaths, sparams, npp) - gf_utils.set_up_as(fitter, asimov_paramset) + fitter = gf_utils.setup_fitter(args, asimov_paramset) else: fitter = None @@ -318,9 +313,8 @@ def main(): ) p = mcmc_paramset.from_tag(ParamTag.SCALE, invert=True) - prior_ranges = p.ranges n_params = len(p) - hypo_paramset = asimov_paramset + prior_ranges = p.ranges def CubePrior(cube, ndim, nparams): # default are uniform priors @@ -333,7 +327,7 @@ def main(): for i in range(ndim): prange = prior_ranges[i][1] - prior_ranges[i][0] theta[i] = prange*cube[i] + prior_ranges[i][0] - theta_ = np.concatenate([theta, [s]]) + theta_ = np.array(theta.tolist() + [s]) return llh_utils.triangle_llh( theta=theta_, args=args, @@ -342,25 +336,31 @@ def main(): fitter=fitter ) - prefix = 'mnrun_{0:.0E}'.format(np.power(10, s)) + prefix = 'mnrun_{0:.0E}'.format(np.power(10, s)) + '_' + outfile[2:] print 'begin running evidence calculation for {0}'.format(prefix) result = pymultinest.run( - LogLikelihood=lnProb, Prior=CubePrior, n_dims=n_params, - outputfiles_basename=prefix#, verbose=True + LogLikelihood=lnProb, + Prior=CubePrior, + n_dims=n_params, + n_live_points=args.bayes_live_points, + outputfiles_basename=prefix, + # resume=False ) - print 'end running evidence calculation for {0}'.format(prefix) - print 'begin analysis for {0}'.format(prefix) analyzer = pymultinest.Analyzer(outputfiles_basename=prefix, n_params=n_params) a_lnZ = analyzer.get_stats()['global evidence'] - print 'end analysis for {0}'.format(prefix) print 'Evidence = {0}'.format(a_lnZ) arr.append([s, a_lnZ]) - out = args.bayes_output+'fr_evidence_'+outfile[2:]+'.npy' + out = args.bayes_output+'fr_evidence_'+outfile[2:]+'.npy' misc_utils.make_dir(out) np.save(out, np.array(arr)) + b_out = args.bayes_output+'fr_evidence_'+outfile[2:] + plot_utils.bayes_factor_plot( + infile=b_out+'.npy', outfile=b_out, outformat=['png'], args=args + ) + print "DONE!" |