def test_equa_from_dir(self):
random.seed(0)
t1 = dataclasses.I3Time()
t1.set_utc_cal_date(2000, 1, 1, 0, 0, 0, 0.0)
mjd1 = t1.mod_julian_day_double
t1.set_utc_cal_date(2030, 1, 1, 0, 0, 0, 0.0)
mjd2 = t1.mod_julian_day_double
for n in range(1000):
t1.set_mod_julian_time_double(random.uniform(mjd1, mjd2))
d = dataclasses.I3Direction(
random.uniform(0, math.pi),
random.uniform(0, 2 * math.pi),
)
eq = astro.I3GetEquatorialFromDirection(d, t1)
dprime = astro.I3GetDirectionFromEquatorial(eq, t1)
self.assert_almost_equal(d.zenith, dprime.zenith, 5e-6)
self.assert_almost_equal(d.azimuth * math.sin(d.zenith),
dprime.azimuth * math.sin(d.zenith), 6e-6)
self.assert_less(
astro.angular_distance(d.azimuth, math.pi / 2 - d.zenith,
dprime.azimuth,
math.pi / 2 - dprime.zenith), 6e-6)
def test_dir_from_equa(self):
random.seed(0)
t1 = dataclasses.I3Time()
t1.set_utc_cal_date(2000, 1, 1, 0, 0, 0, 0.0)
mjd1 = t1.mod_julian_day_double
t1.set_utc_cal_date(2030, 1, 1, 0, 0, 0, 0.0)
mjd2 = t1.mod_julian_day_double
for n in range(1000):
t1.set_mod_julian_time_double(random.uniform(mjd1, mjd2))
eq = astro.I3Equatorial(random.uniform(0, 2 * math.pi),
random.uniform(-math.pi / 2, math.pi / 2))
d = astro.I3GetDirectionFromEquatorial(eq, t1)
eqprime = astro.I3GetEquatorialFromDirection(d, t1)
self.assert_almost_equal(eq.ra * math.cos(eq.dec),
eqprime.ra * math.cos(eqprime.dec), 4e-6)
self.assert_almost_equal(eq.dec, eqprime.dec, 4e-6)
self.assert_less(
astro.angular_distance(eq.ra, eq.dec, eqprime.ra, eqprime.dec),
5e-6)
def oversample(tmp_weight, tmp_nu_type,
tmp_energy_true, tmp_energy_reco,
tmp_zenith_true, tmp_zenith_reco,
tmp_azimuth_true, tmp_azimuth_reco,
nOversampling):
stime = time.mktime(time.strptime("1/1/2022/00/00/00", '%m/%d/%Y/%H/%M/%S'))
etime = time.mktime(time.strptime("1/1/2023/00/00/00", '%m/%d/%Y/%H/%M/%S'))
oversampled_psi_true = np.zeros((nOversampling,len(tmp_weight)))
oversampled_psi_reco = np.zeros((nOversampling,len(tmp_weight)))
oversampled_RA_reco = np.zeros((nOversampling,len(tmp_weight)))
oversampled_Dec_reco = np.zeros((nOversampling,len(tmp_weight)))
for i in range(nOversampling):
eventTime = stime + random.random() * (etime - stime)
eventTime = apTime(eventTime,format='unix').mjd
RA_true, DEC_true = astro.dir_to_equa(tmp_zenith_true,tmp_azimuth_true,eventTime)
RA_reco, DEC_reco = astro.dir_to_equa(tmp_zenith_reco,tmp_azimuth_reco,eventTime)
oversampled_psi_true[i] = astro.angular_distance(RA_true,DEC_true,GC_Pos[0],GC_Pos[1])
oversampled_psi_reco[i] = astro.angular_distance(RA_reco,DEC_reco,GC_Pos[0],GC_Pos[1])
oversampled_RA_reco[i] = RA_reco
oversampled_Dec_reco[i] = DEC_reco
# append n versions
oversampled_weight = np.tile(tmp_weight/nOversampling,nOversampling)
oversampled_nu_type = np.tile(tmp_nu_type,nOversampling)
oversampled_energy_true = np.tile(tmp_energy_true,nOversampling)
oversampled_energy_reco = np.tile(tmp_energy_reco,nOversampling)
return oversampled_weight, oversampled_nu_type, oversampled_energy_true, oversampled_energy_reco, oversampled_psi_true.flatten(), oversampled_psi_reco.flatten(), oversampled_RA_reco.flatten() , oversampled_Dec_reco.flatten()
def test_gal_from_sg(self):
random.seed(0)
for n in range(10000):
sg = astro.I3SuperGalactic(random.uniform(0,2*math.pi),
random.uniform(-math.pi/2,math.pi/2),
)
gal = astro.I3GetGalacticFromSuperGalactic(sg)
sgprime = astro.I3GetSuperGalacticFromGalactic(gal)
self.assert_almost_equal(math.cos(sg.b)*sg.l,math.cos(sgprime.b)*sgprime.l,1e-9)
self.assert_almost_equal(sg.b,sgprime.b,1e-10)
self.assert_less(astro.angular_distance(sg.l,sg.b,sgprime.l,sgprime.b),1e-10)
def test_sg_from_equa(self):
random.seed(0)
for n in range(10000):
eq = astro.I3Equatorial(random.uniform(0,2* math.pi),
random.uniform(-math.pi/2,math.pi/2),
)
sg = astro.I3GetSuperGalacticFromEquatorial(eq)
eqprime = astro.I3GetEquatorialFromSuperGalactic(sg)
self.assert_almost_equal(math.cos(eq.dec)*eq.ra ,math.cos(eqprime.dec)*eqprime.ra,1e-9)
self.assert_almost_equal(eq.dec,eqprime.dec,1e-10)
self.assert_less(astro.angular_distance(eq.ra,eq.dec,eqprime.ra,eqprime.dec),1e-10)
def test_equa_from_gal(self):
random.seed(0)
for n in range(10000):
gal = astro.I3Galactic(random.uniform(0,2*math.pi),
random.uniform(-math.pi/2,math.pi/2),
)
eq = astro.I3GetEquatorialFromGalactic(gal)
galprime = astro.I3GetGalacticFromEquatorial(eq)
self.assert_almost_equal(math.cos(gal.b)*gal.l,math.cos(galprime.b)*galprime.l,1e-9)
self.assert_almost_equal(gal.b,galprime.b,1e-10)
self.assert_less(astro.angular_distance(gal.l,gal.b,galprime.l,galprime.b),1e-10)
def plot_error(year):
#The following currently devoted to making energy and angular error plots for a year of data.
# init mc
mc = np.load(filename_pickle + "IC{}_MC.npy".format(year))
dpsi = [
astro.angular_distance(mc['trueRa'][i], mc['trueDec'][i], mc['ra'][i],
np.arcsin(mc['sinDec'][i]))
for i in range(len(mc))
]
mc_corr = np.load(filename_pickle + "IC{}_corrected_MC.npy".format(year))
dpsi_corr = [
astro.angular_distance(mc_corr['trueRa'][i],
mc_corr['trueDec'][i], mc_corr['ra'][i],
np.arcsin(mc_corr['sinDec'][i]))
for i in range(len(mc_corr))
]
colors = ['g'] #['b','g','y','r']
colors_corr = ['r'] #['b','g','y','r']
gamma = np.array([2.]) #np.linspace(1., 2.7, 4)
fig_angular_error, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
dec = np.arcsin(mc["sinDec"])
dec_corr = np.arcsin(mc_corr["sinDec"])
angdist = np.degrees(dpsi)
angdist_corr = np.degrees(dpsi_corr)
ax1.hist([np.log10(np.degrees(mc["sigma"])) for i in gamma],
label=[r"$\sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma],
linestyle='dashed',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.hist(
[np.log10(angdist) for i in gamma],
label=[r"$\Delta \psi$ - $\gamma={0:.1f}$".format(g) for g in gamma],
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
linestyle='solid',
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.hist([np.log10(np.degrees(mc_corr["sigma"])) for i in gamma],
label=[r"$\sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma],
linestyle='dashdot',
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.hist([np.log10(angdist_corr) for i in gamma],
label=[
r"'Corrected' $\Delta \psi$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
linestyle='dotted',
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
ax1.set_title("Reco MC Angular Error Check - IC{}".format(str(year)))
ax1.set_xlabel(r"log$\sigma_{ang}$ (degrees)")
ax1.set_ylabel("Relative Abundance")
ax1.set_ylim(0, 1.5)
ax2.hist([(np.degrees(mc["sigma"])) for i in gamma],
label=[r"$\sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma],
linestyle='dashed',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.hist(
[(angdist) for i in gamma],
label=[r"$\Delta \psi$ - $\gamma={0:.1f}$".format(g) for g in gamma],
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
linestyle='solid',
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.hist([(np.degrees(mc_corr["sigma"])) for i in gamma],
label=[
r"'corrected' $\sigma$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
linestyle='dashdot',
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.hist(
[(angdist_corr) for i in gamma],
label=[r"$\Delta \psi$ - $\gamma={0:.1f}$".format(g) for g in gamma],
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
linestyle='dotted',
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
normed=True)
ax2.legend(loc="upper right")
ax2.set_xlim(0, 5)
ax2.set_ylim(0, 3.5)
ax2.set_xlabel(r"$\sigma_{ang}$ (degrees)")
fig_angular_error.savefig(filename_plots +
'angular_error_hists_IC{}.pdf'.format(str(year)))
def plotpull2d(year):
#The following currently devoted to making energy and angular error plots for a year of data.
# init likelihood class
mc = np.load(filename_pickle + "IC{}_MC.npy".format(year))
dpsi = [
astro.angular_distance(mc['trueRa'][i], mc['trueDec'][i], mc['ra'][i],
np.arcsin(mc['sinDec'][i]))
for i in range(len(mc))
]
mc_corr = np.load(filename_pickle + "IC{}_corrected_MC.npy".format(year))
dpsi_corr = [
astro.angular_distance(mc_corr['trueRa'][i],
mc_corr['trueDec'][i], mc_corr['ra'][i],
np.arcsin(mc_corr['sinDec'][i]))
for i in range(len(mc_corr))
]
colors = ['g'] #['b','g','y','r']
colors_corr = ['r'] #['b','g','y','r']
gamma = np.array([2.]) #np.linspace(1., 2.7, 4)
fig_pull, (ax) = plt.subplots(ncols=1, figsize=(10, 5))
dec = np.arcsin(mc["sinDec"])
angdist = np.degrees(dpsi)
dec_corr = np.arcsin(mc_corr["sinDec"])
angdist_corr = np.degrees(dpsi_corr)
#medians = [misc.weighted_median(np.log10(np.degrees(mc["sigma"])/(angdist)),mc["ow"] * mc["trueE"]**(-g)) for g in gamma]
#for g in range(len(gamma)):
# ax1.axvline(medians[g], color=colors[g], linestyle = 'dotted', alpha = 0.3)
#ax1.hist([np.log10(np.degrees(mc_corr["sigma"])/(angdist_corr)) for i in gamma], label = [r"'Corrected' $\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma], linestyle = 'dotted',
# weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma], color=[colors_corr[g] for g in range(len(gamma))],
# histtype="step", bins=100, normed=True)
#medians = [misc.weighted_median(np.log10(np.degrees(mc_corr["sigma"])/(angdist_corr)),mc["ow"] * mc["trueE"]**(-g)) for g in gamma]
#for g in range(len(gamma)):
# ax1.axvline(medians[g], color=colors_corr[g], marker='+',alpha = 0.3)
#ax1.set_title(r"Reco MC $\Delta \psi / \sigma$ Check - IC{}".format(str(year)))
#ax1.set_xlabel(r"log$\Delta \psi / \sigma_{ang}$")
#ax1.set_ylabel("Relative Abundance")
#ax1.set_ylim(0,1.5)
#ax1.set_xlim(-2.5,2.5)
#ax1.axvline(x=0, color='k')
#ax1.axvline(x=np.log10(1./1.1774), color='k')
#set gamma
g = 2.0
pull = np.log10(np.degrees(mc["sigma"]) / (angdist))
#Need to calc the median of pull for each energyrange:
pullrange = (-4, 4)
erange = (3, 8)
ebins = 50
pullbins = 200
eedges = np.linspace(erange[0], erange[1], ebins + 1)
ezones = zip(eedges[:-1], eedges[1:])
emid = [np.median(ezone) for ezone in ezones]
emasks = [
(np.log10(mc["trueE"]) > ezone[0]) & (np.log10(mc["trueE"]) < ezone[1])
for ezone in ezones
]
medians = [
misc.weighted_median(pull[emask],
mc["ow"][emask] * mc["trueE"][emask]**(-g))
for emask in emasks
]
#pull_corr = [(np.degrees(mc_corr["sigma"]))/(angdist_corr) for i in gamma]
hpull = histlite.hist((np.log10(mc['trueE']), pull),
weights=mc["ow"] * mc["trueE"]**(-g),
bins=(ebins, pullbins),
range=(erange, pullrange),
log=(False, False))
histlite.plot2d(
ax,
hpull.normalize([-1]),
label=[r"$\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g)],
cbar=True,
cmap='jet',
color=colors[0])
ax.scatter(emid, medians, color='white')
ax.axhline(y=np.log10(1.1774), color='white', linestyle='dashed')
#ax.hist([(np.degrees(mc_corr["sigma"]))/(angdist_corr) for i in gamma], label = [r"'Corrected' $\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g) for g in gamma], linestyle = 'dotted',
# weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma], color=[colors_corr[g] for g in range(len(gamma))],
# histtype="step", bins=1000, range = (0,5), normed=True)
ax.set_title("Pull for IC{}".format(str(year)))
ax.legend(loc="upper right")
ax.set_xlim(3, 8)
ax.set_xlabel("log(trueE[GeV])")
ax.set_ylim(-2, 2)
ax.set_ylabel(r"log($\Delta \psi / \sigma_{ang}$)")
fig_pull.savefig(filename_plots + 'opp_pull_IC{}.pdf'.format(str(year)))
def plot_ratio(year):
#The following currently devoted to making energy and angular error plots for a year of data.
# init likelihood class
mc = np.load(filename_pickle + "IC{}_MC.npy".format(year))
dpsi = [
astro.angular_distance(mc['trueRa'][i], mc['trueDec'][i], mc['ra'][i],
np.arcsin(mc['sinDec'][i]))
for i in range(len(mc))
]
mc_corr = np.load(filename_pickle + "IC{}_corrected_MC.npy".format(year))
dpsi_corr = [
astro.angular_distance(mc_corr['trueRa'][i],
mc_corr['trueDec'][i], mc_corr['ra'][i],
np.arcsin(mc_corr['sinDec'][i]))
for i in range(len(mc_corr))
]
colors = ['g'] #['b','g','y','r']
colors_corr = ['r'] #['b','g','y','r']
gamma = np.array([2.]) #np.linspace(1., 2.7, 4)
fig_ratio, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
dec = np.arcsin(mc["sinDec"])
angdist = np.degrees(dpsi)
dec_corr = np.arcsin(mc_corr["sinDec"])
angdist_corr = np.degrees(dpsi_corr)
ax1.hist([np.log10(np.degrees(mc["sigma"]) / (angdist)) for i in gamma],
label=[
r"$\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
linestyle='solid',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
medians = [
misc.weighted_median(np.log10(np.degrees(mc["sigma"]) / (angdist)),
mc["ow"] * mc["trueE"]**(-g)) for g in gamma
]
for g in range(len(gamma)):
ax1.axvline(medians[g], color=colors[g], linestyle='dotted', alpha=0.3)
ax1.hist(
[
np.log10(np.degrees(mc_corr["sigma"]) / (angdist_corr))
for i in gamma
],
label=[
r"'Corrected' $\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
linestyle='dotted',
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=100,
normed=True)
medians = [
misc.weighted_median(
np.log10(np.degrees(mc_corr["sigma"]) / (angdist_corr)),
mc["ow"] * mc["trueE"]**(-g)) for g in gamma
]
for g in range(len(gamma)):
ax1.axvline(medians[g], color=colors_corr[g], marker='+', alpha=0.3)
ax1.set_title(r"Reco MC $\Delta \psi / \sigma$ Check - IC{}".format(
str(year)))
ax1.set_xlabel(r"log$\Delta \psi / \sigma_{ang}$")
ax1.set_ylabel("Relative Abundance")
ax1.set_ylim(0, 1.5)
ax1.set_xlim(-2.5, 2.5)
#ax1.axvline(x=0, color='k')
ax1.axvline(x=np.log10(1. / 1.1774), color='k')
ax2.set_xlim(-5, 5)
ax2.hist([(np.degrees(mc["sigma"])) / (angdist) for i in gamma],
label=[
r"$\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
linestyle='solid',
weights=[mc["ow"] * mc["trueE"]**(-g) for g in gamma],
color=[colors[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
range=(0, 5),
normed=True)
ax2.hist(
[(np.degrees(mc_corr["sigma"])) / (angdist_corr) for i in gamma],
label=[
r"'Corrected' $\Delta \psi / \sigma$ - $\gamma={0:.1f}$".format(g)
for g in gamma
],
linestyle='dotted',
weights=[mc_corr["ow"] * mc_corr["trueE"]**(-g) for g in gamma],
color=[colors_corr[g] for g in range(len(gamma))],
histtype="step",
bins=1000,
range=(0, 5),
normed=True)
ax2.legend(loc="upper right")
ax2.set_xlim(0, 5)
ax2.set_ylim(0, 3.5)
ax2.set_xlabel(r"$\Delta \psi / \sigma_{ang}$")
fig_ratio.savefig(filename_plots +
'dpsi_sigma_ratio_IC{}.pdf'.format(str(year)))