def geneateNcos_thetaFile(year):
Nzenith_bin = 100
N_coszenith = np.zeros((len(Defaults.map_logE_center), Nzenith_bin))
for file in [os.path.join(icecube_data_dir, year + '-events.txt')]:
AtmBG_file = np.loadtxt(file)
_index_map_logE = np.searchsorted(Defaults.map_logE_edge,
AtmBG_file[:, 1]) - 1
_index_map_logE[_index_map_logE ==
-1] = 0 # group logE < 2 events to bin 0
for i in np.arange(len(Defaults.map_logE_center)):
""" use healpy convention: theta = 180 - zenith_IceCube """
N_coszenith_i, cosZenithBinEdges =\
np.histogram(np.cos(np.pi - AtmBG_file[:, 6][_index_map_logE == i] * np.pi / 180.), Nzenith_bin, (-1, 1))
N_coszenith[i] += N_coszenith_i
cosZenithBinCenters = (cosZenithBinEdges[0:-1] +
cosZenithBinEdges[1:]) / 2.
figs = FigureDict()
figs.plot_yvals('N_coszenith',
cosZenithBinCenters,
N_coszenith,
xlabel=r'$\cos\,\theta$',
ylabel='Number of counts in 2010-2012 data',
figsize=(8, 6))
for i in np.arange(Defaults.NEbin):
np.savetxt(
os.path.join(irf_dir,
'Ncos_theta_' + year + '_' + str(i) + '.txt'),
np.column_stack((cosZenithBinCenters, N_coszenith[i])))
def test_PowerSpectrum():
ns_atm = NeutrinoSample()
ns_atm.inputData(bgpath)
gs = GALAXY_LIBRARY.get_sample('analy')
if MAKE_TEST_PLOTS:
figs = FigureDict()
color = [
'r', 'orange', 'limegreen', 'skyblue', 'mediumslateblue', 'purple',
'grey'
]
figs.plot_cl('PowerSpectrum_atm',
Defaults.ell,
ns_atm.getPowerSpectrum(),
xlabel="l",
ylavel=r'$C_{l}$',
figsize=(8, 6),
colors=color)
figs.plot('PowerSpectrum_atm',
Defaults.ell,
gs.analyCL[0:3 * Defaults.NSIDE],
color='k',
linestyle='--',
lw=2)
figs.save_all(testfigpath, 'pdf')
def atmBG_coszenith(energyBin=0):
eg = EventGenerator()
N_coszenith = eg.atm_gen.coszenith()[energyBin]
recovered_values = eg.atmBG_coszenith(int(np.sum(N_coszenith[:, 1])),
energyBin)
index = np.where(np.abs(recovered_values) > 1)
if len(index) > 1:
print(index, recovered_values[index])
if MAKE_TEST_PLOTS:
figs = FigureDict()
figkey = 'N_coszenith' + str(energyBin)
o_dict = figs.setup_figure(figkey,
xlabel=r'$\cos\,\theta$',
ylabel='Number of counts',
figsize=(8, 6))
fig = o_dict['fig']
axes = o_dict['axes']
axes.plot(N_coszenith[:, 0], N_coszenith[:, 1], lw=2, label='data')
axes.hist(recovered_values, N_coszenith[:, 0], label='mock')
fig.legend()
figs.save_all(testfigpath, 'pdf')
def astroEvent_galaxy(f_diff=1.):
gs = GALAXY_LIBRARY.get_sample('analy')
eg = EventGenerator(year='IC86-2012', astroModel='observed_numu_fraction')
N_astro_north_obs = np.random.poisson(eg.nevts * 1 *
eg.f_astro_north_truth)
N_astro_north_exp = [
N_astro_north_obs[i] /
np.sum(eg._astro_gen.prob_reject()[i] * gs.density)
for i in range(Defaults.NEbin)
]
astro_map = eg.astroEvent_galaxy(np.array(N_astro_north_exp), gs.density)
file_utils.write_maps_to_fits(astro_map, astropath)
if MAKE_TEST_PLOTS:
figs = FigureDict()
figs.mollview_maps('astro', astro_map)
figs.save_all(testfigpath, 'pdf')
def test_CrossCorrelation():
ns_astro = NeutrinoSample()
ns_astro.inputData(astropath)
gs = GALAXY_LIBRARY.get_sample('analy')
w_cross = ns_astro.getCrossCorrelation(gs.overdensityalm)
if MAKE_TEST_PLOTS:
figs = FigureDict()
color = [
'r', 'orange', 'limegreen', 'skyblue', 'mediumslateblue', 'purple',
'grey'
]
o_dict = figs.setup_figure('Wcross',
xlabel="l",
ylabel=r'$w$',
figsize=(6, 8))
axes = o_dict['axes']
for i in range(Defaults.NEbin):
axes.plot(Defaults.ell,
gs.analyCL[0:3 * Defaults.NSIDE] * 10**(i * 2),
color='k',
lw=2)
w_cross[i] *= 10**(i * 2)
figs.plot_cl('Wcross',
Defaults.ell,
np.abs(w_cross),
xlabel="l",
ylabel=r'$C_{l}$',
colors=color,
ymin=1e-7,
ymax=1e10,
lw=3)
figs.save_all(testfigpath, 'pdf')
def atmBG():
eg = EventGenerator()
eventmap = eg.atmEvent(1.)
eventmap2 = np.zeros((Defaults.NEbin, Defaults.NPIXEL))
file_utils.write_maps_to_fits(eventmap, bgpath)
for i in range(Defaults.NEbin):
eventmap2[i] = eventmap[i]
eventmap2[i][Defaults.idx_muon] = hp.UNSEEN
mask = np.zeros(Defaults.NPIXEL)
mask[Defaults.idx_muon] = 1.
for i in range(Defaults.NEbin):
test = np.ma.masked_array(eventmap[i], mask=mask)
print(test.sum())
if MAKE_TEST_PLOTS:
figs = FigureDict()
figs.mollview_maps('eventmap_atm', eventmap2)
figs.save_all(testfigpath, 'pdf')
import os
import numpy as np
import healpy as hp
from scipy import stats
from KIPAC import nuXgal
from KIPAC.nuXgal import EventGenerator
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import hp_utils
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
gg_cl_map = nuXgal.Map.create_from_cl(galaxy_galaxy_cl_path)
cls_od = gg_cl_map.cl()[0]
gg_overdensity = hp.sphtfunc.synfast(cls_od, Defaults.NSIDE)
gg_density = np.clip(gg_overdensity + 1, 0., np.inf)
gg_density /= gg_density.sum()
cl_density = hp.sphtfunc.anafast(gg_density)
n_events = np.logspace(1, 7, 13)
import os
import numpy as np
import healpy as hp
from KIPAC import nuXgal
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import hp_utils
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR, 'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR, 'eventmap_atm{i}.fits')
syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(syn_astro_counts_path, aeff_path, Defaults.NEbin)
syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(syn_atm_counts_path, aeff_path, Defaults.NEbin)
gg_od_map = nuXgal.Map.create_from_overdensity_maps(galaxy_od_path)
gg_cl_map = nuXgal.Map.create_from_cl(galaxy_galaxy_cl_path)
nevts_2000 = 2000.
nevts_10000 = 10000.
return hist
def plot_density_complex(hist):
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
axes.set_xlabel(r'$\mathcal{R}$')
axes.set_ylabel(r'$\mathcal{I}$')
extent = (hist[1][0], hist[1][-1], hist[2][0], hist[2][-1])
img = axes.imshow(hist[0], extent=extent, interpolation='none')
cbar = plt.colorbar(img)
return dict(fig=fig, axes=axes, img=img, cbar=cbar)
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_atm{i}.fits')
#syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(syn_astro_counts_path, aeff_path, Defaults.NEbin)
#syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(syn_atm_counts_path, aeff_path, Defaults.NEbin)
gg_od_map = nuXgal.Map.create_from_overdensity_maps(galaxy_od_path)
import os
import numpy as np
import healpy as hp
from scipy import stats
from KIPAC import nuXgal
from KIPAC.nuXgal import EventGenerator
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import hp_utils
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_atm{i}.fits')
syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_astro_counts_path, aeff_path, Defaults.NEbin)
syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_atm_counts_path, aeff_path, Defaults.NEbin)
import os
import numpy as np
import healpy as hp
from KIPAC import nuXgal
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import hp_utils
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR, 'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR, 'eventmap_atm{i}.fits')
#syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(syn_astro_counts_path, aeff_path, Defaults.NEbin)
#syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(syn_atm_counts_path, aeff_path, Defaults.NEbin)
gg_od_map = nuXgal.Map.create_from_overdensity_maps(galaxy_od_path)
gg_cl_map = nuXgal.Map.create_from_cl(galaxy_galaxy_cl_path)
mean_density = 1.
import os
import numpy as np
import healpy as hp
from scipy import stats
from KIPAC import nuXgal
from KIPAC.nuXgal import EventGenerator
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import hp_utils
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_atm{i}.fits')
syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_astro_counts_path, aeff_path, Defaults.NEbin)
syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_atm_counts_path, aeff_path, Defaults.NEbin)
import os
import numpy as np
import healpy as hp
from KIPAC import nuXgal
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_atm{i}.fits')
syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_astro_counts_path, aeff_path, Defaults.NEbin)
syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_atm_counts_path, aeff_path, Defaults.NEbin)
gg_od_map = nuXgal.Map.create_from_overdensity_maps(galaxy_od_path)
gg_cl_map = nuXgal.Map.create_from_cl(galaxy_galaxy_cl_path)
mean_density = 1.
import os
import numpy as np
import healpy as hp
from KIPAC import nuXgal
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import Utilityfunc
import matplotlib.pyplot as plt
eg = nuXgal.EventGenerator()
figs = FigureDict()
galaxy_od_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR,
'galaxySampleOverdensity.fits')
galaxy_galaxy_cl_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'Cl_ggRM.dat')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
syn_astro_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_astro{i}.fits')
syn_atm_counts_path = os.path.join(Defaults.NUXGAL_SYNTHETICDATA_DIR,
'eventmap_atm{i}.fits')
syn_map_astr = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_astro_counts_path, aeff_path, Defaults.NEbin)
syn_map_atm = nuXgal.Map.create_from_counts_and_exposure_maps(
syn_atm_counts_path, aeff_path, Defaults.NEbin)
gg_od_map = nuXgal.Map.create_from_overdensity_maps(galaxy_od_path)
gg_cl_map = nuXgal.Map.create_from_cl(galaxy_galaxy_cl_path)
import os
import numpy as np
import healpy as hp
from KIPAC import nuXgal
from KIPAC.nuXgal import Defaults
from KIPAC.nuXgal import FigureDict
from KIPAC.nuXgal import file_utils
from KIPAC.nuXgal import Utilityfunc
from KIPAC.nuXgal.Generator import AtmGenerator, AstroGenerator
import matplotlib.pyplot as plt
figs = FigureDict()
coszenith_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'N_coszenith{i}.txt')
aeff_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'Aeff{i}.fits')
nevents_path = os.path.join(Defaults.NUXGAL_IRF_DIR, 'eventNumber_Ebin_perIC86year.txt')
gg_sample_path = os.path.join(Defaults.NUXGAL_ANCIL_DIR, 'galaxySampleOverdensity.fits')
aeff = file_utils.read_maps_from_fits(aeff_path, Defaults.NEbin)
cosz = file_utils.read_cosz_from_txt(coszenith_path, Defaults.NEbin)
nevts = np.loadtxt(nevents_path)
gg_overdensity = hp.fitsfunc.read_map(gg_sample_path)
gg_density = 1. + gg_overdensity
gg_density /= gg_density.sum()