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#! /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 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
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)
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)
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=misc_utils.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=misc_utils.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=[1, 2, 0],
help='Set the source flavour ratios'
)
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=misc_utils.parse_bool, default='True',
help='Make sensitivity plot x vs limit'
)
parser.add_argument(
'--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())
def main():
args = parse_args()
process_args(args)
misc_utils.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]
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
# 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):
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:
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'
)
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)
argsc = deepcopy(args)
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):
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][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}/{2}/'.format(
*map(misc_utils.parse_enum, [args.stat_method, args.data]), prefix
)
if args.plot_x:
for idim, dim in enumerate(args.dimensions):
argsc.dimension = dim
plot_utils.plot_x(
data = data,
outfile = baseoutfile + '/hese_x',
outformat = ['png', 'pdf'],
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__
if __name__ == '__main__':
main()
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