Tue Apr 24 11:22:19 CDT 2018

1 files changed, 94 insertions, 242 deletions

diff --git a/sens.py b/sens.py
index 578528c..980ef3f 100755
--- a/sens.py
+++ b/sens.py
@@ -16,6 +16,7 @@ from functools import partial
 
 import numpy as np
 import numpy.ma as ma
+from scipy.optimize import minimize
 
 from utils import fr as fr_utils
 from utils import gf as gf_utils
@@ -23,32 +24,44 @@ from utils import likelihood as llh_utils
 from utils import misc as misc_utils
 from utils import plot as plot_utils
 from utils.enums import EnergyDependance, Likelihood, ParamTag
-from utils.enums import SensitivityCateg, StatCateg
-from utils.fr import estimate_scale, normalise_fr
-from utils.misc import enum_parse, parse_bool
+from utils.enums import PriorsCateg, SensitivityCateg, StatCateg
 from utils.param import Param, ParamSet, get_paramsets
 
 from utils import multinest as mn_utils
 
 
 def define_nuisance():
-    """Define the nuisance parameters to scan over with default values,
-    ranges and sigma.
-    """
+    """Define the nuisance parameters."""
+    tag = ParamTag.SM_ANGLES
+    g_prior = PriorsCateg.GAUSSIAN
+    nuisance = [
+        Param(name='s_12_2', value=0.307,          seed=[0.29, 0.31], ranges=[0., 1.],      std=0.013,   tex=r's_{12}^2', prior=g_prior, tag=tag),
+        Param(name='c_13_4', value=1-(0.02206)**2, seed=[0.998, 1.0], ranges=[0., 1.],      std=0.00147, tex=r'c_{13}^4', prior=g_prior, tag=tag),
+        Param(name='s_23_2', value=0.538,          seed=[0.46, 0.61], ranges=[0., 1.],      std=0.069,   tex=r's_{23}^2', prior=g_prior, tag=tag),
+        Param(name='dcp',    value=4.08404,        seed=[0, 2*np.pi], 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 = ParamSet(
+    nuisance.extend([
         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=[1. , 3. ], ranges=[-5., 5. ], std=0.1,  tag=tag)
-    )
-    return nuisance
+    ])
+    return ParamSet(nuisance)
 
 
 def nuisance_argparse(parser):
-    nuisance_paramset = define_nuisance()
-    for parm in nuisance_paramset:
+    nuisance = define_nuisance()
+    for parm in nuisance:
         parser.add_argument(
             '--'+parm.name, type=float, default=parm.value,
             help=parm.name+' to inject'
@@ -68,9 +81,9 @@ def process_args(args):
             '--mn-run and --fix-scale cannot be used together'
         )
 
-    args.measured_ratio = normalise_fr(args.measured_ratio)
+    args.measured_ratio = fr_utils.normalise_fr(args.measured_ratio)
     if args.fix_source_ratio:
-        args.source_ratio = normalise_fr(args.source_ratio)
+        args.source_ratio = fr_utils.normalise_fr(args.source_ratio)
 
     if args.energy_dependance is EnergyDependance.SPECTRAL:
         args.binning = np.logspace(
@@ -78,7 +91,7 @@ def process_args(args):
         )
 
     if not args.fix_scale:
-        args.scale = estimate_scale(args)
+        args.scale = fr_utils.estimate_scale(args)
         args.scale_region = (args.scale/args.scale_region, args.scale*args.scale_region)
 
     if args.mn_eval_bin.lower() == 'all':
@@ -86,6 +99,10 @@ def process_args(args):
     else:
         args.mn_eval_bin = int(args.mn_eval_bin)
 
+    if args.stat_method is StatCateg.FREQUENTIST and \
+       args.likelihood is Likelihood.GOLEMFIT::
+        args.likelihood = Likelihood.GF_FREQ
+
 
 def parse_args(args=None):
     """Parse command line arguments"""
@@ -107,14 +124,19 @@ def parse_args(args=None):
     )
     parser.add_argument(
         '--run-method', default='full',
-        type=partial(enum_parse, c=SensitivityCateg), choices=SensitivityCateg,
+        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(enum_parse, c=StatCateg), choices=StatCateg,
+        type=partial(misc_utils.enum_parse, c=StatCateg), choices=StatCateg,
         help='Statistical method to employ'
     )
+    parser.add_argument(
+        '--plot-statistic', type=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)
@@ -131,29 +153,31 @@ def main():
     if args.seed is not None:
         np.random.seed(args.seed)
 
-    asimov_paramset, llh_paramset = get_paramsets(args)
+    asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance())
     scale = llh_paramset.from_tag(ParamTag.SCALE)[0]
     outfile = misc_utils.gen_outfile_name(args)
     print '== {0:<25} = {1}'.format('outfile', outfile)
 
     mmangles = llh_paramset.from_tag(ParamTag.MMANGLES)
     if args.run_method is SensitivityCateg.FULL:
-        mn_paramset_arr = [llh_paramset.from_tag(ParamTag.SCALE, invert=True)]
+        st_paramset_arr = [llh_paramset.from_tag(ParamTag.SCALE, invert=True)]
     elif args.run_method is SensitivityCateg.FIXED_ANGLE or \
             args.run_method is SensitivityCateg.CORR_ANGLE:
         nscale_pmset = llh_paramset.from_tag(ParamTag.SCALE, invert=True)
-        mn_paramset_arr = []
+        st_paramset_arr = []
         for x in xrange(3):
-            mn_paramset_arr.append(
+            st_paramset_arr.append(
                 ParamSet([prms for prms in nscale_pmset
                           if mmangles[x].name != prms.name])
             )
 
-    out = args.outfile+'/{0}/{1}/fr_evidence'.format(args.stat_method, args.run_method) \
+    out = args.outfile+'/{0}/{1}/fr_stat'.format(args.stat_method, args.run_method) \
         + misc_utils.gen_identifier(args)
     if args.mn_run:
         if args.likelihood is Likelihood.GOLEMFIT:
             fitter = gf_utils.setup_fitter(args, asimov_paramset)
+            if args.StatCateg is StatCateg.FREQUENTIST:
+                fitter.SetFitParametersFlag(gf_utils.fit_flags(llh_paramset)
         else: fitter = None
 
         scan_scales = np.linspace(
@@ -170,14 +194,14 @@ def main():
         else: eval_dim = 1
 
         if args.run_method is SensitivityCateg.FULL:
-            evidence_arr = np.full((eval_dim, 2), np.nan)
+            statistic_arr = np.full((eval_dim, 2), np.nan)
         elif args.run_method is SensitivityCateg.FIXED_ANGLE:
-            evidence_arr = np.full((len(mn_paramset_arr), eval_dim, 2), np.nan)
+            statistic_arr = np.full((len(st_paramset_arr), eval_dim, 2), np.nan)
         elif args.run_method is SensitivityCateg.CORR_ANGLE:
-            evidence_arr = np.full((len(mn_paramset_arr), eval_dim, eval_dim, 3), np.nan)
+            statistic_arr = np.full((len(st_paramset_arr), eval_dim, eval_dim, 3), np.nan)
 
 
-        for idx_scen, mn_paramset in enumerate(mn_paramset_arr):
+        for idx_scen, mn_paramset in enumerate(st_paramset_arr):
             print '|||| SCENARIO = {0}'.format(idx_scen)
             for x in mmangles: x.value = 0.
             if args.run_method is SensitivityCateg.FIXED_ANGLE:
@@ -204,38 +228,60 @@ def main():
 
                     print '|||| SCALE = {0:.0E}'.format(np.power(10, sc))
                     scale.value = sc
-                    try:
-                        a_lnZ = mn_utils.mn_evidence(
-                            mn_paramset     = mn_paramset,
-                            llh_paramset    = llh_paramset,
-                            asimov_paramset = asimov_paramset,
-                            args            = args,
-                            fitter          = fitter
-                        )
-                    except:
-                        print 'Failed run, continuing'
-                        continue
-                    print '## Evidence = {0}'.format(a_lnZ)
+                    if args.stat_method is StatCateg.BAYESIAN:
+                        try:
+                            stat = mn_utils.mn_evidence(
+                                mn_paramset     = mn_paramset,
+                                llh_paramset    = llh_paramset,
+                                asimov_paramset = asimov_paramset,
+                                args            = args,
+                                fitter          = fitter
+                            )
+                        except:
+                            print 'Failed run, continuing'
+                            continue
+                        print '## Evidence = {0}'.format(stat)
+                    elif args.stat_method is StatCateg.FREQUENTIST:
+                        llh_paramset_freq = [x for parm in llh_paramset if
+                                             x.name not in asimov_paramset.names]
+                        def fn(x):
+                            for idx, parm in enumerate(llh_paramset_freq):
+                                parm.value = x[idx]
+                            theta = llh_paramset_freq.values
+                            try:
+                                llh = llh_utils.ln_prob(
+                                    theta=theta, args=args, asimov_paramset=asimov_paramset,
+                                    mcmc_paramset=mcmc_paramset_freq, fitter=fitter
+                                )
+                            except:
+                                print 'Failed run, continuing'
+                                return np.inf
+                            return -llh
+                        
+                        x0 = np.average(llh_paramset_freq.values, axis=1)
+                        res = minimize(fun=fn, x0=x0, method='L-BFGS-B',
+                                       bounds=llh_paramset.seed, fitter=fitter)
+                        stat = -fn(res.x)
                     if args.run_method is SensitivityCateg.FULL:
-                        evidence_arr[idx_sc] = np.array([sc, a_lnZ])
+                        statistic_arr[idx_sc] = np.array([sc, stat])
                     elif args.run_method is SensitivityCateg.FIXED_ANGLE:
-                        evidence_arr[idx_scen][idx_sc] = np.array([sc, a_lnZ])
+                        statistic_arr[idx_scen][idx_sc] = np.array([sc, stat])
                     elif args.run_method is SensitivityCateg.CORR_ANGLE:
-                        evidence_arr[idx_scen][idx_an][idx_sc] = np.array([an, sc, a_lnZ])
+                        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', np.array(evidence_arr))
+        np.save(out+'.npy', np.array(statistic_arr))
 
-    if args.plot_multinest:
-        if args.mn_run: raw = evidence_arr
+    if args.plot_statistic:
+        if args.mn_run: raw = statistic_arr
         else: raw = np.load(out+'.npy')
         data = ma.masked_invalid(raw, 0)
 
         basename = os.path.dirname(out) + '/mnrun/' + os.path.basename(out)
         baseoutfile = basename[:5]+basename[5:].replace('data', 'plots')
         if args.run_method is SensitivityCateg.FULL:
-            plot_utils.plot_multinest(
+            plot_utils.plot_statistic(
                 data        = data,
                 outfile     = baseoutfile,
                 outformat   = ['png'],
@@ -243,13 +289,13 @@ def main():
                 scale_param = scale
             )
         elif args.run_method is SensitivityCateg.FIXED_ANGLE:
-            for idx_scen in xrange(len(mn_paramset_arr)):
+            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'$\mathcal{O}_{12}=\frac{1}{2}$'
                 elif idx_scen == 1: label = r'$\mathcal{O}_{13}=\frac{1}{2}$'
                 elif idx_scen == 2: label = r'$\mathcal{O}_{23}=\frac{1}{2}$'
-                plot_utils.plot_multinest(
+                plot_utils.plot_statistic(
                     data        = data[idx_scen],
                     outfile     = outfile,
                     outformat   = ['png'],
@@ -258,7 +304,7 @@ def main():
                     label       = label
                 )
         elif args.run_method is SensitivityCateg.CORR_ANGLE:
-            for idx_scen in xrange(len(mn_paramset_arr)):
+            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'$\mathcal{O}_{12}='
@@ -268,7 +314,7 @@ def main():
                     print '|||| ANGLE = {0:<04.2}'.format(an)
                     outfile = basescenoutfile + '_ANGLE{0}'.format(idx_an)
                     label += r'{0:<04.2}$'.format(an)
-                    plot_utils.plot_multinest(
+                    plot_utils.plot_statistic(
                         data        = data[idx_scen][idx_an][:,1:],
                         outfile     = outfile,
                         outformat   = ['png'],
@@ -277,200 +323,6 @@ def main():
                         label       = label
                     )
 
-    # dirname = os.path.dirname(out)
-    # plot_utils.bayes_factor_plot(
-    #     dirname=dirname, outfile=out, outformat=['png'], args=args
-    # )
-
-    # out = args.angles_lim_output+'/fr_an_evidence' + misc_utils.gen_identifier(args)
-    # if args.run_angles_limit:
-    #     import pymultinest
-
-    #     scenarios = [
-    #         [np.sin(np.pi/2.)**2, 0, 0, 0],
-    #         [0, np.cos(np.pi/2.)**4, 0, 0],
-    #         [0, 0, np.sin(np.pi/2.)**2, 0],
-    #     ]
-    #     p = llh_paramset.from_tag([ParamTag.SCALE, ParamTag.MMANGLES], invert=True)
-    #     n_params = len(p)
-    #     prior_ranges = p.seeds
-
-    #     if not args.run_llh and args.likelihood is Likelihood.GOLEMFIT:
-    #         fitter = gf_utils.setup_fitter(args, asimov_paramset)
-    #     else: fitter = None
-
-    #     def CubePrior(cube, ndim, nparams):
-    #         # default are uniform priors
-    #         return ;
-
-    #     if args.bayes_eval_bin is not None:
-    #         data = np.zeros((len(scenarios), 1, 2))
-    #     else: data = np.zeros((len(scenarios), args.bayes_bins, 2))
-    #     mm_angles = llh_paramset.from_tag(ParamTag.MMANGLES)
-    #     sc_angles = llh_paramset.from_tag(ParamTag.SCALE)[0]
-    #     for idx, scen in enumerate(scenarios):
-    #         scales, evidences = [], []
-    #         for yidx, an in enumerate(mm_angles):
-    #             an.value = scen[yidx]
-    #         for s_idx, sc in enumerate(scan_scales):
-    #             if args.bayes_eval_bin is not None:
-    #                 if s_idx in args.bayes_eval_bin:
-    #                     if idx == 0:
-    #                         out += '_scale_{0:.0E}'.format(np.power(10, sc))
-    #                 else: continue
-
-    #             print '== SCALE = {0:.0E}'.format(np.power(10, sc))
-    #             sc_angles.value = sc
-    #             def lnProb(cube, ndim, nparams):
-    #                 for i in range(ndim):
-    #                     prange = prior_ranges[i][1] - prior_ranges[i][0]
-    #                     p[i].value = prange*cube[i] + prior_ranges[i][0]
-    #                 for name in p.names:
-    #                     mcmc_paramset[name].value = p[name].value
-    #                 theta = mcmc_paramset.values
-    #                 # print 'theta', theta
-    #                 # print 'mcmc_paramset', mcmc_paramset
-    #                 return llh_utils.triangle_llh(
-    #                     theta=theta,
-    #                     args=args,
-    #                     asimov_paramset=asimov_paramset,
-    #                     mcmc_paramset=mcmc_paramset,
-    #                     fitter=fitter
-    #                 )
-    #             prefix = 'mnrun/' + os.path.basename(out) + '_'
-    #             if args.bayes_eval_bin is not None:
-    #                 prefix += '{0}_{1}_'.format(idx, s_idx)
-    #             print 'begin running evidence calculation for {0}'.format(prefix)
-    #             result = pymultinest.run(
-    #                 LogLikelihood=lnProb,
-    #                 Prior=CubePrior,
-    #                 n_dims=n_params,
-    #                 importance_nested_sampling=True,
-    #                 n_live_points=args.bayes_live_points,
-    #                 evidence_tolerance=args.bayes_tolerance,
-    #                 outputfiles_basename=prefix,
-    #                 resume=False,
-    #                 verbose=True
-    #             )
-
-    #             analyzer = pymultinest.Analyzer(outputfiles_basename=prefix, n_params=n_params)
-    #             a_lnZ = analyzer.get_stats()['global evidence']
-    #             print 'Evidence = {0}'.format(a_lnZ)
-    #             scales.append(sc)
-    #             evidences.append(a_lnZ)
-
-    #         for i, d in enumerate(evidences):
-    #             data[idx][i][0] = scales[i]
-    #             data[idx][i][1] = d
-
-    #     misc_utils.make_dir(out)
-    #     print 'saving to {0}.npy'.format(out)
-    #     np.save(out+'.npy', np.array(data))
-
-    # dirname = os.path.dirname(out)
-    # plot_utils.plot_BSM_angles_limit(
-    #     dirname=dirname, outfile=outfile, outformat=['png'],
-    #     args=args, bayesian=True
-    # )
-
-    # out = args.angles_corr_output+'/fr_co_evidence' + misc_utils.gen_identifier(args)
-    # if args.run_angles_correlation:
-    #     if args.bayes_eval_bin is None: assert 0
-    #     import pymultinest
-
-    #     scenarios = [
-    #         [1, 0, 0, 0],
-    #         [0, 1, 0, 0],
-    #         [0, 0, 1, 0],
-    #     ]
-    #     nuisance = mcmc_paramset.from_tag(ParamTag.NUISANCE)
-    #     mm_angles = mcmc_paramset.from_tag(ParamTag.MMANGLES)
-    #     sc_angles = mcmc_paramset.from_tag(ParamTag.SCALE)[0]
-
-    #     if not args.run_mcmc and args.likelihood is Likelihood.GOLEMFIT:
-    #         fitter = gf_utils.setup_fitter(args, asimov_paramset)
-    #     else: fitter = None
-
-    #     def CubePrior(cube, ndim, nparams):
-    #         # default are uniform priors
-    #         return ;
-
-    #     scan_angles = np.linspace(0, 1, args.bayes_bins)
-
-    #     if args.bayes_eval_bin is not None:
-    #         data = np.zeros((len(scenarios), 1, 1, 3))
-    #     else: data = np.zeros((len(scenarios), args.bayes_bins, args.bayes_bins, 3))
-    #     for idx, scen in enumerate(scenarios):
-    #         for an in mm_angles:
-    #             an.value = 0
-    #         keep = mcmc_paramset.from_tag(ParamTag.MMANGLES)[idx]
-    #         q = ParamSet(nuisance.params + [x for x in mm_angles if x.name != keep.name])
-    #         n_params = len(q)
-    #         prior_ranges = q.seeds
-
-    #         scales, angles, evidences = [], [], []
-    #         for s_idx, sc in enumerate(scan_scales):
-    #             for a_idx, an in enumerate(scan_angles):
-    #                 index = s_idx*args.bayes_bins + a_idx
-    #                 if args.bayes_eval_bin is not None:
-    #                     if index in args.bayes_eval_bin:
-    #                         if idx == 0:
-    #                             out += '_idx_{0}'.format(index)
-    #                     else: continue
-
-    #                 print '== SCALE = {0:.0E}'.format(np.power(10, sc))
-    #                 print '== ANGLE = {0:.2f}'.format(an)
-    #                 sc_angles.value = sc
-    #                 keep.value = an
-    #                 def lnProb(cube, ndim, nparams):
-    #                     for i in range(ndim-1):
-    #                         prange = prior_ranges[i][1] - prior_ranges[i][0]
-    #                         q[i].value = prange*cube[i] + prior_ranges[i][0]
-    #                     for name in q.names:
-    #                         mcmc_paramset[name].value = q[name].value
-    #                     theta = mcmc_paramset.values
-    #                     # print 'theta', theta
-    #                     # print 'mcmc_paramset', mcmc_paramset
-    #                     return llh_utils.triangle_llh(
-    #                         theta=theta,
-    #                         args=args,
-    #                         asimov_paramset=asimov_paramset,
-    #                         mcmc_paramset=mcmc_paramset,
-    #                         fitter=fitter
-    #                     )
-    #                 prefix = 'mnrun/' + os.path.basename(out) + '_'
-    #                 if args.bayes_eval_bin is not None:
-    #                     prefix += '{0}_{1}_{2}'.format(idx, s_idx, a_idx)
-
-    #                 print 'begin running evidence calculation for {0}'.format(prefix)
-    #                 result = pymultinest.run(
-    #                     LogLikelihood=lnProb,
-    #                     Prior=CubePrior,
-    #                     n_dims=n_params,
-    #                     importance_nested_sampling=True,
-    #                     n_live_points=args.bayes_live_points,
-    #                     evidence_tolerance=args.bayes_tolerance,
-    #                     outputfiles_basename=prefix,
-    #                     resume=False,
-    #                     verbose=True
-    #                 )
-
-    #                 analyzer = pymultinest.Analyzer(outputfiles_basename=prefix, n_params=n_params)
-    #                 a_lnZ = analyzer.get_stats()['global evidence']
-    #                 print 'Evidence = {0}'.format(a_lnZ)
-    #                 scales.append(sc)
-    #                 angles.append(an)
-    #                 evidences.append(a_lnZ)
-
-    #             for i, d in enumerate(evidences):
-    #                 data[idx][i][i][0] = scales[i]
-    #                 data[idx][i][i][1] = angles[i]
-    #                 data[idx][i][i][2] = d
-
-    #     misc_utils.make_dir(out)
-    #     print 'saving to {0}.npy'.format(out)
-    #     np.save(out+'.npy', np.array(data))
-
 
 main.__doc__ = __doc__