reogranise into a python package

1 files changed, 296 insertions, 0 deletions

diff --git a/scripts/sens.py b/scripts/sens.py
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+++ b/scripts/sens.py
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+#! /usr/bin/env python
+# author : S. Mandalia
+#          s.p.mandalia@qmul.ac.uk
+#
+# date   : March 17, 2018
+
+"""
+HESE BSM flavour ratio analysis script
+"""
+
+from __future__ import absolute_import, division
+
+import os
+import argparse
+from functools import partial
+
+import glob
+
+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
+from utils import llh as llh_utils
+from utils import misc as misc_utils
+from utils import mn as mn_utils
+from utils.enums import str_enum
+from utils.enums import DataType, Likelihood, ParamTag
+from utils.enums import PriorsCateg, StatCateg, Texture
+from utils.param import Param, ParamSet
+
+
+def define_nuisance():
+    """Define the nuisance parameters."""
+    tag = ParamTag.SM_ANGLES
+    nuisance = []
+    g_prior = PriorsCateg.GAUSSIAN
+    lg_prior = PriorsCateg.LIMITEDGAUSS
+    e = 1e-9
+    nuisance.extend([
+        Param(name='s_12_2', value=0.307,            seed=[0.26, 0.35],     ranges=[0., 1.],      std=0.013,   tex=r's_{12}^2', prior=lg_prior,  tag=tag),
+        Param(name='c_13_4', value=(1-(0.02206))**2, seed=[0.950, 0.961],   ranges=[0., 1.],      std=0.00147, tex=r'c_{13}^4', prior=lg_prior,  tag=tag),
+        Param(name='s_23_2', value=0.538,            seed=[0.31, 0.75],     ranges=[0., 1.],      std=0.069,   tex=r's_{23}^2', prior=lg_prior,  tag=tag),
+        Param(name='dcp',    value=4.08404,          seed=[0+e, 2*np.pi-e], ranges=[0., 2*np.pi], std=2.0,     tex=r'\delta_{CP}', tag=tag),
+        Param(
+            name='m21_2', value=7.40E-23, seed=[7.2E-23, 7.6E-23], ranges=[6.80E-23, 8.02E-23],
+            std=2.1E-24, tex=r'\Delta m_{21}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag
+        ),
+        Param(
+            name='m3x_2', value=2.494E-21, seed=[2.46E-21, 2.53E-21], ranges=[2.399E-21, 2.593E-21],
+            std=3.3E-23, tex=r'\Delta m_{3x}^2{\rm GeV}^{-2}', prior=g_prior, tag=tag
+        )
+    ])
+    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=8.0, 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 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)
+
+    boundaries = fr_utils.SCALE_BOUNDARIES[args.dimension]
+    tag = ParamTag.SCALE
+    llh_paramset.append(
+        Param(
+            name='logLam', value=np.mean(boundaries), ranges=boundaries, std=3,
+            tex=r'{\rm log}_{10}\left (\Lambda^{-1}' + \
+                misc_utils.get_units(args.dimension)+r'\right )',
+            tag=tag
+        )
+    )
+    llh_paramset = ParamSet(llh_paramset)
+
+    tag = ParamTag.BESTFIT
+    if args.data is not DataType.REAL:
+        flavour_angles = fr_utils.fr_to_angles(args.injected_ratio)
+    else:
+        flavour_angles = fr_utils.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:
+        parser.add_argument(
+            '--'+parm.name, type=float, default=parm.value,
+            help=parm.name+' to inject'
+        )
+
+
+def process_args(args):
+    """Process the input args."""
+    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)
+
+    args.binning = np.logspace(
+        np.log10(args.binning[0]), np.log10(args.binning[1]), args.binning[2]+1
+    )
+
+    if args.eval_segment.lower() == 'all':
+        args.eval_segment = None
+    else:
+        args.eval_segment = int(args.eval_segment)
+
+    if args.stat_method is StatCateg.BAYESIAN:
+        args.likelihood = Likelihood.GOLEMFIT
+    elif args.stat_method is StatCateg.FREQUENTIST:
+        args.likelihood = Likelihood.GF_FREQ
+
+    if args.texture is Texture.NONE:
+        raise ValueError('Must assume a BSM texture')
+
+
+def parse_args(args=None):
+    """Parse command line arguments"""
+    parser = argparse.ArgumentParser(
+        description="BSM flavour ratio analysis",
+        formatter_class=misc_utils.SortingHelpFormatter,
+    )
+    parser.add_argument(
+        '--seed', type=misc_utils.seed_parse, default='25',
+        help='Set the random seed value'
+    )
+    parser.add_argument(
+        '--threads', type=misc_utils.thread_type, default='1',
+        help='Set the number of threads to use (int or "max")'
+    )
+    parser.add_argument(
+        '--datadir', type=str, default='./untitled',
+        help='Path to store chains'
+    )
+    parser.add_argument(
+        '--segments', type=int, default=10,
+        help='Number of new physics scales to evaluate'
+    )
+    parser.add_argument(
+        '--eval-segment', type=str, default='all',
+        help='Which point to evalaute'
+    )
+    parser.add_argument(
+        '--overwrite', type=misc_utils.parse_bool, default='False',
+        help='Overwrite chains'
+    )
+    fr_utils.fr_argparse(parser)
+    gf_utils.gf_argparse(parser)
+    llh_utils.llh_argparse(parser)
+    mn_utils.mn_argparse(parser)
+    nuisance_argparse(parser)
+    if args is None: return parser.parse_args()
+    else: return parser.parse_args(args.split())
+
+
+def main():
+    args = parse_args()
+    process_args(args)
+    misc_utils.print_args(args)
+
+    if args.seed is not None:
+        np.random.seed(args.seed)
+
+    asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance())
+
+    # 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, invert=True)
+
+    # 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])
+
+    # Evaluate just one point (job), or all points.
+    if args.eval_segment is None:
+        eval_dim = args.segments
+    else: eval_dim = 1
+
+    outfile = args.datadir + '/{0}/{1}/fr_stat'.format(
+        *map(misc_utils.parse_enum, [args.stat_method, args.data])
+    ) + misc_utils.gen_identifier(args)
+
+    if not args.overwrite and os.path.isfile(outfile+'.npy'):
+        print 'FILE EXISTS {0}'.format(outfile+'.npy')
+        print 'Exiting...'
+        return
+
+    # Setup Golemfit.
+    if args.run_mn:
+        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))
+
+        if not args.overwrite and os.path.isfile(outfile+'.npy'):
+            print 'FILE EXISTS {0}'.format(outfile+'.npy')
+            t = np.load(outfile+'.npy')
+            if np.any(~np.isfinite(t)):
+                print 'nan found, rerunning...'
+                pass
+            else:
+                print 'Exiting...'
+                return
+
+        # Lower scale boundary for first (NULL) point and set the scale param.
+        reset_range = None
+        if scale < scale_prm.ranges[0]:
+            reset_range = scale_prm.ranges
+            scale_prm.ranges = (scale, scale_prm.ranges[1])
+        scale_prm.value = scale
+
+        identifier = 'b{0}_{1}_{2}_sca{3}'.format(
+            args.eval_segment, args.segments, str_enum(args.texture), scale
+        )
+        llh = '{0}'.format(args.likelihood).split('.')[1]
+        data = '{0}'.format(args.data).split('.')[1]
+        src_string = misc_utils.solve_ratio(args.source_ratio)
+        prefix = args.mn_output + '/DIM{0}/{1}/{2}/s{3}/{4}'.format(
+            args.dimension, data, llh, src_string, identifier
+        )
+        try:
+            stat = mn_utils.mn_evidence(
+                mn_paramset     = base_mn_pset,
+                llh_paramset    = llh_paramset,
+                asimov_paramset = asimov_paramset,
+                args            = args,
+                prefix          = prefix
+            )
+        except:
+            print 'Failed run'
+            raise
+        print '## Evidence = {0}'.format(stat)
+
+        if args.eval_segment is not None:
+            stat_arr[0] = np.array([scale, stat])
+        else:
+            stat_arr[idx_sc] = np.array([scale, stat])
+
+        # Cleanup.
+        if reset_range is not None:
+            scale_prm.ranges = reset_range
+
+        if args.run_mn and not args.debug:
+            try:
+                for f in glob.glob(prefix + '*'):
+                    print 'cleaning file {0}'.format(f)
+                    os.remove(f)
+            except:
+                print 'got error trying to cleanup, continuing'
+                pass
+
+    misc_utils.make_dir(outfile)
+    print 'Saving to {0}'.format(outfile+'.npy')
+    np.save(outfile+'.npy', stat_arr)
+
+
+main.__doc__ = __doc__
+
+
+if __name__ == '__main__':
+    main()