def get_aeff_flavor(self,flavor,aeff_egy_par,aeff_coszen_par):
'''
Creates the 2d aeff file from the parameterized aeff
vs. energy .dat file, an input to the parametric settings file.
'''
aeff_file = aeff_egy_par[flavor]
aeff_arr = np.loadtxt(open_resource(aeff_file)).T
# interpolate
aeff_func = interp1d(aeff_arr[0], aeff_arr[1], kind='linear',
bounds_error=False, fill_value=0)
czcen = get_bin_centers(self.czbins)
ecen = get_bin_centers(self.ebins)
# Get 1D array interpolated values at bin centers, assume no cz dep
aeff1d = aeff_func(ecen)
# Make this into a 2D array:
aeff2d = np.reshape(np.repeat(aeff1d, len(czcen)), (len(ecen), len(czcen)))
# Now add cz-dependence, assuming nu and nu_bar has same dependence:
cz_dep = eval(aeff_coszen_par[flavor.strip('_bar')])(czcen)
# Normalize:
cz_dep *= len(cz_dep)/np.sum(cz_dep)
return (aeff2d*cz_dep)
def __init__(self, tables, smooth=0.05, **params):
logging.info("Loading atmospheric flux table %s" %tables)
#Load the data table
table = np.loadtxt(open_resource(tables)).T
#columns in Honda files are in the same order
cols = ['energy']+primaries
flux_dict = dict(zip(cols, table))
for key in flux_dict.iterkeys():
#There are 20 lines per zenith range
flux_dict[key] = np.array(np.split(flux_dict[key], 20))
if not key=='energy':
flux_dict[key] = flux_dict[key].T
#Set the zenith and energy range
flux_dict['energy'] = flux_dict['energy'][0]
flux_dict['coszen'] = np.linspace(0.95, -0.95, 20)
#Now get a spline representation of the flux table.
logging.debug('Make spline representation of flux')
# do this in log of energy and log of flux (more stable)
logE, C = np.meshgrid(np.log10(flux_dict['energy']), flux_dict['coszen'])
self.spline_dict = {}
for nutype in primaries:
#Get the logarithmic flux
log_flux = np.log10(flux_dict[nutype]).T
#Get a spline representation
spline = bisplrep(logE, C, log_flux, s=smooth)
#and store
self.spline_dict[nutype] = spline
def __init__(self, flux_file=None, smooth=0.05, **params):
logging.info("Loading atmospheric flux table %s" % flux_file)
#Load the data table
table = np.loadtxt(open_resource(flux_file)).T
#columns in Honda files are in the same order
cols = ['energy'] + primaries
flux_dict = dict(zip(cols, table))
for key in flux_dict.iterkeys():
#There are 20 lines per zenith range
flux_dict[key] = np.array(np.split(flux_dict[key], 20))
if not key == 'energy':
flux_dict[key] = flux_dict[key].T
#Set the zenith and energy range
flux_dict['energy'] = flux_dict['energy'][0]
flux_dict['coszen'] = np.linspace(0.95, -0.95, 20)
#Now get a spline representation of the flux table.
logging.debug('Make spline representation of flux')
# do this in log of energy and log of flux (more stable)
logE, C = np.meshgrid(np.log10(flux_dict['energy']),
flux_dict['coszen'])
self.spline_dict = {}
for nutype in primaries:
#Get the logarithmic flux
log_flux = np.log10(flux_dict[nutype]).T
#Get a spline representation
spline = bisplrep(logE, C, log_flux, s=smooth)
#and store
self.spline_dict[nutype] = spline
def get_aeff_flavor(self, flavor, aeff_egy_par, aeff_coszen_par):
'''
Creates the 2d aeff file from the parameterized aeff
vs. energy .dat file, an input to the parametric settings file.
'''
aeff_file = aeff_egy_par[flavor]
aeff_arr = np.loadtxt(open_resource(aeff_file)).T
# interpolate
aeff_func = interp1d(aeff_arr[0],
aeff_arr[1],
kind='linear',
bounds_error=False,
fill_value=0)
czcen = get_bin_centers(self.czbins)
ecen = get_bin_centers(self.ebins)
# Get 1D array interpolated values at bin centers, assume no cz dep
aeff1d = aeff_func(ecen)
# Correct for final energy bin, since interpolation does not
# extend to JUST right outside the final bin:
if aeff1d[-1] == 0.0: aeff1d[-1] = aeff1d[-2]
# Make this into a 2D array:
aeff2d = np.reshape(np.repeat(aeff1d, len(czcen)),
(len(ecen), len(czcen)))
# Now add cz-dependence, assuming nu and nu_bar has same dependence:
cz_dep = eval(aeff_coszen_par[flavor.strip('_bar')])(czcen)
# Normalize:
cz_dep *= len(cz_dep) / np.sum(cz_dep)
return (aeff2d * cz_dep)
from pisa.flux.HondaFluxService import primaries
from pisa.utils.plot import show_map, delta_map, ratio_map
from pisa.resources.resources import open_resource
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('-IP','--IP_flux_file',type=str,help="JSON file containing IP flux values to work with")
parser.add_argument('-SI','--SI_flux_file',type=str,help="JSON file containing standard interpolation flux values to work with")
args = parser.parse_args()
titles = {}
titles['nue'] = r'$\nu_e$'
titles['nue_bar'] = r'$\bar{\nu}_e$'
titles['numu'] = r'$\nu_{\mu}$'
titles['numu_bar'] = r'$\bar{\nu}_{\mu}$'
HondaFluxTable = np.loadtxt(open_resource('/Users/steven/IceCube/PISA/pisa/pisa/resources/flux/spl-2015-solmax-aa.d')).T
cols = ['energy']+primaries
flux_dict = dict(zip(cols, HondaFluxTable))
for key in flux_dict.iterkeys():
#There are 20 lines per zenith range
flux_dict[key] = np.array(np.split(flux_dict[key], 20))
new_array = []
for i in range(0,len(flux_dict[key])):
new_array.append(flux_dict[key][len(flux_dict[key])-1-i])
flux_dict[key] = np.array(new_array)
if not key=='energy':
flux_dict[key] = flux_dict[key].T
flux_dict['energy'] = flux_dict['energy'][0]
flux_dict['coszen'] = np.linspace(0.95, -0.95, 20)
