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)))
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
if year == 40:
llh = data_multi.init40(energy=True)
mc = cache.load(filename_pickle + "IC40/mc.pickle")
extra = cache.load(filename_pickle + "IC40/dpsi.pickle")
if year == 59:
llh = data_multi.init59(energy=True)
mc = cache.load(filename_pickle + "IC59/mc.pickle")
extra = cache.load(filename_pickle + "IC59/dpsi.pickle")
if year == 79:
llh = data_multi.init79(energy=True)
mc = cache.load(filename_pickle + "IC79/mc.pickle")
extra = cache.load(filename_pickle + "IC79/dpsi.pickle")
elif year == 86:
llh = data_multi.init86I(energy=True)
mc = cache.load(filename_pickle + "IC86I/mc.pickle")
extra = cache.load(filename_pickle + "IC86I/dpsi.pickle")
dpsi = extra['dpsi']
print(llh)
# datatest
#Currently don't need to remake these plots. but DONT DELETE
colors = ['b', 'g', 'y', 'r']
gamma = np.linspace(1., 2.7, 4)
# fig_energy, (ax1, ax2) = plt.subplots(ncols=2)
# ax1.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Pseudo-Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# ax1.legend(loc="best")
# ax1.set_title("Reconstructed Energy - IC{}".format(str(year)))
# ax1.set_xlabel("logE")
# ax1.set_ylabel("Relative Abundance")
# ax1.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# ax2.set_title("Zoomed In")
# ax2.set_xlabel("logE")
# ax2.set_xlim(4,10)
# ax2.set_ylim(1e-5,1)
# ax2.hist([llh.exp["logE"]] + [mc["logE"] for i in gamma],
# weights=[np.ones(len(llh.exp))]
# + [mc["ow"] * mc["trueE"]**(-g) for g in gamma],
# label=["Pseudo-Data"] + [r"$\gamma={0:.1f}$".format(g) for g in gamma], color= ['k'] + [colors[g] for g in range(len(gamma))],
# histtype="step", bins=100, log=True, normed=True, cumulative=-1)
# fig_energy.savefig(filename_plots + 'energy_hists_IC{}.pdf'.format(str(year)))
fig_ratio, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
dec = np.arcsin(mc["sinDec"])
angdist = np.degrees(dpsi)
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], 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(1.1774), 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.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)))