def prepare():
##### In this Block, the model is introduced
## e.g. Introducing the folder where the data is
## or introducing the names of the primaries to consider,
## so that there spectra can be found and read
#####
model_dir = os.path.split(os.path.realpath(__file__))[0]
model_name = model_dir.split('/')[-1]
primaries = np.array(['muon', 'bottom'], dtype=np.dtype('U32'))
#####
##### The idea of the 'model'-mode is to do the interpolation of the spectra once
## and then save the 'interpolator', that the spectrum can then easily be sampled
## by loading the object and run the .__call__ method.
## To this end, we store the interpolators in a dictionary and save this
## dictionary with the dill-module.
## To get the energies at which we need to sample the spectra we make
## use of the get_logEnergies-method which imports the global logEnergies array
#####
dump_dict = dict()
logEnergies = get_logEnergies()
for idx_prim, primary in enumerate(primaries):
fname = os.path.join(model_dir,
'data/{:s}_50-100_spectrum.dat'.format(primary))
dump_dict_key = 'spec_interp_{:s}'.format(primary)
dump_dict_value = load_from_spectrum(fname, logEnergies)
dump_dict.update({dump_dict_key: dump_dict_value})
with open(os.path.join(model_dir, '{}.obj'.format(model_name)),
'wb') as dump_file:
dill.dump(dump_dict, dump_file)
def prepare():
dump_dict = dict()
frac_read = np.genfromtxt(os.path.join(model_dir, 'data/Fractions.dat'),
unpack=True,
usecols=(0, 4, 5, 6, 7, 8, 9, 10, 11, 12),
dtype=np.float64)
primaries = np.array([
'top', 'higgs', 'zzstar', 'wwstar', 'bottom', 'tau', 'charm', 'gluon',
'gamma'
],
dtype=np.dtype('a32'))
#primaries = np.array(['top', 'higgs'], dtype=np.dtype('a32'))
dump_dict.update({'channels': primaries})
for idx_prim, primary in enumerate(primaries):
dump_dict_key = 'frac_interp_{:s}'.format(primary)
dump_dict_value = logLinearInterpolator(frac_read[0, :],
frac_read[idx_prim + 1, :],
0,
logspace_wanted=False)
dump_dict.update({dump_dict_key: dump_dict_value})
fname = os.path.join(model_dir, 'data/{:s}_EW.dat'.format(primary))
dump_dict_key = 'spec_interp_{:s}'.format(primary)
dump_dict_value = load_from_spectrum(fname, logEnergies)
dump_dict.update({dump_dict_key: dump_dict_value})
with open(os.path.join(model_dir, '{}.obj'.format(model_name)),
'wb') as dump_file:
dill.dump(dump_dict, dump_file)
def prepare():
model_dir = os.path.split(os.path.realpath(__file__))[0]
model_name = model_dir.split('/')[-1]
primaries = np.array(['onlyZ', 'WandZ'], dtype=np.dtype('a16'))
dump_dict = dict()
for idx_prim, primary in enumerate(primaries):
fname = os.path.join(model_dir,
'data/susy3_right_{:s}.dat'.format(primary))
dump_dict_key = 'spec_interp_right_{:s}'.format(primary)
dump_dict_value = load_from_spectrum(fname, logEnergies, **DarkOptions)
dump_dict.update({dump_dict_key: dump_dict_value})
with open(os.path.join(model_dir, '{}.obj'.format(model_name)),
'wb') as dump_file:
dill.dump(dump_dict, dump_file)
def prepare():
import itertools
import dill
import numpy as np
import sys
import os
if os.environ['DARKAGES_BASE']:
sys.path.insert(0, os.environ['DARKAGES_BASE'] )
import DarkAges
from DarkAges import logEnergies, DarkOptions
from DarkAges.recipes import load_from_spectrum
##### In this Block, the model is introduced
## e.g. Introducing the folder where the data is
## or introducing the names of the primaries to consider,
## so that there spectra can be found and read
#####
model_dir = os.path.split(os.path.realpath(__file__))[0]
model_name = model_dir.split('/')[-1]
primaries = np.array(['muon','bottom'], dtype=np.dtype('a32'))
#####
##### The idea of the 'model'-mode is to do the interpolation of the spectra once
## and then save the 'interpolator', that the spectrum can then easily be sampled
## by loading the object and run the .__call__ method.
## To this end, we store the interpolators in a dictionary and save this
## dictionary with the dill-module
#####
dump_dict = dict()
for idx_prim, primary in enumerate(primaries):
fname = os.path.join(model_dir, 'data/{:s}_50-100_spectrum.dat'.format(primary))
dump_dict_key = 'spec_interp_{:s}'.format(primary)
dump_dict_value = load_from_spectrum(fname, logEnergies, **DarkOptions)
dump_dict.update({dump_dict_key:dump_dict_value})
with open(os.path.join(model_dir, '{}.obj'.format(model_name)),'wb') as dump_file:
dill.dump(dump_dict, dump_file)