reogranise into a python package

1 files changed, 0 insertions, 295 deletions

diff --git a/plot_sens.py b/plot_sens.py
deleted file mode 100755
index bd8c72b..0000000
--- a/plot_sens.py
+++ /dev/null
@@ -1,295 +0,0 @@
-#! /usr/bin/env python
-# author : S. Mandalia
-#          s.p.mandalia@qmul.ac.uk
-#
-# date   : April 28, 2018
-
-"""
-HESE BSM flavour ratio analysis plotting script
-"""
-
-from __future__ import absolute_import, division
-
-import os
-import argparse
-from functools import partial
-from copy import deepcopy
-
-import numpy as np
-import numpy.ma as ma
-
-from utils import fr as fr_utils
-from utils import llh as llh_utils
-from utils import plot as plot_utils
-from utils.enums import DataType, Texture
-from utils.misc import enum_parse, parse_bool, parse_enum, print_args
-from utils.misc import gen_identifier, SortingHelpFormatter
-from utils.param import Param, ParamSet
-
-
-MASK_X = (0.3, 0.7)
-
-
-def process_args(args):
-    """Process the input args."""
-    if args.data is not DataType.REAL:
-        args.injected_ratio = fr_utils.normalise_fr(args.injected_ratio)
-
-    # Anon points
-    anon = []
-    if args.dimensions[0] == 3:
-        anon.append([0.825, 0.845])
-        anon.append([0.865, 0.875])
-        anon.append([0.875, 0.885])
-        anon.append([0.905, 0.915])
-        anon.append([0.925, 0.935])
-    if args.dimensions[0] == 4:
-        anon.append([0.165, 0.175])
-        anon.append([0.805, 0.825])
-        anon.append([0.835, 0.845])
-        anon.append([0.855, 0.885])
-        anon.append([0.965, 0.975])
-    if args.dimensions[0] == 5:
-        anon.append([0.895, 0.905])
-        anon.append([0.955, 0.965])
-    if args.dimensions[0] == 6:
-        anon.append([0.115, 0.125])
-        anon.append([0.855, 0.865])
-    if args.dimensions[0] == 7:
-        # anon.append([0.815, 0.835])
-        anon.append([0.875, 0.885])
-    if args.dimensions[0] == 8:
-        anon.append([0.915, 0.935])
-        anon.append([0.875, 0.895])
-        anon.append([0.845, 0.855])
-
-    if args.source_ratios is not None:
-        if args.x_segments is not None:
-            raise ValueError('Cannot do both --source-ratios and --x-segments')
-        if len(args.source_ratios) % 3 != 0:
-            raise ValueError(
-                'Invalid source ratios input {0}'.format(args.source_ratios)
-            )
-
-        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)
-    elif args.x_segments is not None:
-        x_array = np.linspace(0, 1, args.x_segments)
-        sources = []
-        for x in x_array:
-            if x >= MASK_X[0] and x <= MASK_X[1]: continue
-            skip = False
-            for a in anon:
-                if (a[1] > x) & (x > a[0]):
-                    print 'Skipping src', x
-                    skip = True
-                    break
-            if skip: continue
-            sources.append([x, 1-x, 0])
-        args.source_ratios = sources
-    else:
-        raise ValueError('Must supply either --source-ratios or --x-segments')
-
-    args.dimensions = np.sort(args.dimensions)
-
-
-def parse_args(args=None):
-    """Parse command line arguments"""
-    parser = argparse.ArgumentParser(
-        description="HESE BSM flavour ratio analysis plotting script",
-        formatter_class=SortingHelpFormatter,
-    )
-    parser.add_argument(
-        '--datadir', type=str,
-        default='/data/user/smandalia/flavour_ratio/data/sensitivity',
-        help='Path to data directory'
-    )
-    parser.add_argument(
-        '--segments', type=int, default=10,
-        help='Number of new physics scales to evaluate'
-    )
-    parser.add_argument(
-        '--split-jobs', type=parse_bool, default='True',
-        help='Did the jobs get split'
-    )
-    parser.add_argument(
-        '--dimensions', type=int, nargs='*', default=[3, 6],
-        help='Set the new physics dimensions to consider'
-    )
-    parser.add_argument(
-        '--source-ratios', type=int, nargs='*', default=None,
-        required=False, help='Set the source flavour ratios'
-    )
-    parser.add_argument(
-        '--x-segments', type=int, default=None,
-        required=False, help='Number of segments in x'
-    )
-    parser.add_argument(
-        '--texture', type=partial(enum_parse, c=Texture),
-        default='none', choices=Texture, help='Set the BSM mixing texture'
-    )
-    parser.add_argument(
-        '--data', default='asimov', type=partial(enum_parse, c=DataType),
-        choices=DataType, help='select datatype'
-    )
-    parser.add_argument(
-        '--plot-x', type=parse_bool, default='False',
-        help='Make sensitivity plot x vs limit'
-    )
-    parser.add_argument(
-        '--plot-table', type=parse_bool, default='False',
-        help='Make sensitivity table plot'
-    )
-    parser.add_argument(
-        '--plot-statistic', type=parse_bool, default='False',
-        help='Plot MultiNest evidence or LLH value'
-    )
-    llh_utils.llh_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)
-    print_args(args)
-
-    dims = len(args.dimensions)
-    srcs = len(args.source_ratios)
-    if args.texture is Texture.NONE:
-        textures = [Texture.OEU, Texture.OET, Texture.OUT]
-        if args.plot_table:
-            textures = [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'
-    argsc = deepcopy(args)
-    for idim, dim in enumerate(args.dimensions):
-        argsc.dimension = dim
-
-        datadir = args.datadir + '/DIM{0}'.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
-
-            for itex, texture in enumerate(textures):
-                argsc.texture = texture
-
-                base_infile = datadir + '/{0}/{1}/'.format(
-                    *map(parse_enum, [args.stat_method, args.data])
-                ) + r'{0}/fr_stat'.format(prefix) + 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:
-                            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(
-                                infile+'.npy'
-                            )
-                            # raise
-                            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)
-
-    print 'data', data
-    if args.plot_statistic:
-        print 'Plotting statistic'
-
-        for idim, dim in enumerate(args.dimensions):
-            argsc.dimension = 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
-                for itex, texture in enumerate(textures):
-                    argsc.texture = texture
-
-                    base_infile = args.datadir + '/DIM{0}/{1}/{2}/'.format(
-                        dim, *map(parse_enum, [args.stat_method, args.data])
-                    ) + r'{0}/fr_stat'.format(prefix) + gen_identifier(argsc)
-                    basename = os.path.dirname(base_infile)
-                    outfile = basename[:5]+basename[5:].replace('data', 'plots')
-                    outfile += '/' + os.path.basename(base_infile)
-
-                    label = r'$\text{Texture}=' + plot_utils.texture_label(texture)[1:]
-                    plot_utils.plot_statistic(
-                        data        = data[idim][isrc][itex],
-                        outfile     = outfile,
-                        outformat   = ['png'],
-                        args        = argsc,
-                        scale_param = scale,
-                        label       = label
-                    )
-
-    basename = args.datadir[:5]+args.datadir[5:].replace('data', 'plots')
-    baseoutfile = basename + '/{0}/{1}/'.format(
-        *map(parse_enum, [args.stat_method, args.data])
-    ) + r'{0}'.format(prefix)
-
-    argsc = deepcopy(args)
-    if args.plot_x:
-        for idim, dim in enumerate(args.dimensions):
-            print '|||| DIM = {0}'.format(dim)
-            argsc.dimension = dim
-            plot_utils.plot_x(
-                data      = data[idim],
-                outfile   = baseoutfile + '/hese_x_DIM{0}'.format(dim),
-                outformat = ['png', 'pdf'],
-                args      = argsc,
-                normalise = True
-            )
-
-    if args.plot_table:
-        plot_utils.plot_table_sens(
-            data      = data,
-            outfile   = baseoutfile + '/hese_table',
-            outformat = ['png', 'pdf'],
-            args      = args,
-            show_lvatmo = True
-        )
-
-
-main.__doc__ = __doc__
-
-
-if __name__ == '__main__':
-    main()