def get_weighter(open_hdf_file, simtype, nfiles=1):
'''
please first open the file by doing
open_hdf_file = pandas.HDFStore(file, 'r')
'''
if simtype is 'nugen':
weighter = simweights.NuGenWeighter(open_hdf_file, nfiles=nfiles)
elif simtype is 'corsika':
weighter = simweights.CorsikaWeighter(open_hdf_file, nfiles=nfiles)
else:
raise NotImplementedError(
'Simi type {} is not implemented'.format(simtype))
return weighter
def find_weighter(fileobj: pd.HDFStore, nfiles: int) -> simweights.Weighter:
"""
Try to automatically determine which type of file this is and return the correct weighter
"""
try:
return simweights.CorsikaWeighter(fileobj)
except RuntimeError:
pass
try:
return simweights.CorsikaWeighter(fileobj, nfiles=nfiles)
except AttributeError:
pass
try:
return simweights.NuGenWeighter(fileobj, nfiles=nfiles)
except AttributeError:
pass
try:
return simweights.GenieWeighter(fileobj)
except AttributeError:
pass
raise RuntimeError(
f"Could not find a suitable weighter for file object `{fileobj.filename}`"
)
files = args.input_files
primary_energy = np.asarray([])
weights = np.asarray([])
# weighter = None
def power_law(energy):
# https://pos.sissa.it/301/1005/pdf
return 1.01e-18 * np.power(energy/1e5,-2.19)
cr_model = nuflux.makeFlux('H3a_SIBYLL23C')
for file in files:
file_in = pd.HDFStore(file, 'r')
loc_weighter = simweights.NuGenWeighter(file_in, nfiles=10)
# if weighter is None:
# weighter = loc_weighter
# else:
# weighter += loc_weighter
local_primary_energy = loc_weighter.get_column('PolyplopiaPrimary', 'energy')
# local_weights = loc_weighter.get_weights(power_law)
local_weights = loc_weighter.get_weights(cr_model)
primary_energy = np.concatenate((primary_energy, local_primary_energy))
weights = np.concatenate((weights, local_weights))
file_in.close()
for f in filelist:
print("Reading", f)
inFile_nugen = dataio.I3File(f)
while inFile_nugen.more():
frame = inFile_nugen.pop_physics()
if "FilterMask" in frame:
if frame["FilterMask"]["MuonFilter_13"].condition_passed:
MCmuonEnergy_nugen = np.append(
MCmuonEnergy_nugen,
get_most_energetic_muon(frame["MMCTrackList"]))
for k in weight_keys:
I3MCWeightDict[k].append(frame["I3MCWeightDict"][k])
nfiles = len(filelist)
wobj = simweights.NuGenWeighter({"I3MCWeightDict": I3MCWeightDict},
nfiles=nfiles)
# check that what we got matches what is in OneWeight
np.testing.assert_allclose(
wobj.get_weights(1),
np.array(I3MCWeightDict["OneWeight"]) /
(0.5 * I3MCWeightDict["NEvents"][0] * nfiles),
)
conventional = nuflux.makeFlux("honda2006")
conventional.knee_reweighting_model = "gaisserH3a_elbert"
weights_simweights = wobj.get_weights(conventional)
erange = wobj.surface.get_energy_range(None)
czrange = wobj.surface.get_cos_zenith_range(None)
print(wobj.surface)
import pandas as pd
import pylab as plt
import simweights
# load the hdf5 file that we just created using pandas
hdffile = pd.HDFStore("Level2_IC86.2016_NuMu.021217.hdf5", "r")
# instantiate the weighter object by passing the pandas file to it
weighter = simweights.NuGenWeighter(hdffile, nfiles=10)
# create an function to represent the IceCube northern track limit
# Note that the units are GeV^-1 * cm^-2 * sr^-1 * s^-1 per particle type
def northern_track(energy):
return 1.44e-18 / 2 * (energy / 1e5)**-2.2
# get the weights by passing the flux to the weighter
weights = weights = weighter.get_weights(northern_track)
# print some info about the weighting object
print(weighter.tostring(northern_track))
# create equal spaced bins in log space
bins = plt.geomspace(1e2, 1e8, 50)
# get energy of the primary cosmic-ray from `PolyplopiaPrimary`
primary_energy = weighter.get_column("PolyplopiaPrimary", "energy")
# histogram the primary energy with the weights
import numpy as np
import pylab as plt
import tables
import simweights
# start-example1
# load hdf5 file
f = tables.open_file("Level2_IC86.2016_NuMu.021217.N100.hdf5", "r")
# create weighter object
w = simweights.NuGenWeighter(f, nfiles=10)
# create the energy and zenith bins
energy_bins = np.geomspace(1e2, 1e8, 25)
zenith_bins = [-1, -0.5, 0, 0.5, 1]
# calculate the effective area in energy and zenith bins with all of the events in the sample
effective_area = w.effective_area(energy_bins, zenith_bins)
# make some labels for the different zenith bins
zenith_labels = [
"120° < Zenith < 180°",
"\u200790° < Zenith < 120°",
"\u200760° < Zenith < \u200790°",
"\u2007\u20070° < Zenith < \u200760°",
]
# for each zenith bin plot effective area as a function of energy
for i, zenith_slice in enumerate(effective_area):
plt.step(energy_bins, np.r_[0, zenith_slice], label=zenith_labels[i])
# end-example1