def add_contour(filename, ls, cmap='cool', chop=False):
f = open(filename, 'rb')
data = pickle.load(f)
f.close()
this_chi = np.array(data["chi2s"])
evs = [0.5, 1.0, 3.0, 10.0]
count = 0
for ev in evs:
which_sliver = get_loc(ev, msqs)
temp = np.zeros(shape=(2, len(theta24s), len(theta34s)))
for t24 in range(len(theta24s)):
for t34 in range(len(theta34s)):
temp[0][t24][t34] = this_chi[t24][t34][which_sliver[0]]
temp[1][t24][t34] = this_chi[t24][t34][which_sliver[1]]
final_chi = get_closest(ev,
[msqs[which_sliver[0]], msqs[which_sliver[1]]],
temp)
if chop:
# need to chop off the right side :frown:
for i_x in range(len(scale_x)):
for i_y in range(len(scale_y[i_x])):
if final_chi[i_x][i_y] < 0 or final_chi[i_x][i_y] > 1000:
final_chi[i_x][i_y] = None
if scale_x[i_x][i_x] > 6e-2:
final_chi[i_x][i_y] = None
ct = plt.contour(scale_x,
scale_y,
final_chi.transpose(),
levels=chis_l,
cmap=cmap,
linestyles=ls)
ct.collections[0].set_color(get_color(count + 1,
len(evs) + 1, "magma"))
suffix = "Joint" if chop else "Cascades"
ct.collections[0].set_label("{}, {:.1f}".format(suffix, ev) +
r"eV$^{2}$")
count += 1
return ct
plt.xlabel(r"$\left|U_{\mu 4}\right|^{2}=\sin^{2}\theta_{24}$")
plt.ylabel(r"$\left| U_{\tau 4}\right|^{2}=\sin^{2}\theta_{34}\cdot\cos^{2}\theta_{24}$")
# plt.ylabel(r"$\sin^{2}2\theta_{34}$",size=14)
#plt.plot(t24s, scaled_minos_34, color='k', label="Minos")
#plt.plot(super_k[0], super_k[1], color=get_color(1,4,cmap="magma"), label="Super K")
#plt.plot(deepcore[0], deepcore[1], color=get_color(2,4,"magma"), label="DeepCore")
#plt.plot(antares[0], antares[1], color=get_color(3,4,"magma"), label="KM3NeT/ORCA NO")
ct = plt.contour(scale_x, scale_y, chis.transpose(), levels=chis_l, cmap='cool',linestyles='dashed')
print(np.nanmin(chis))
mindices = np.nanargmin(chis)
x,y = np.unravel_index(mindices, shape=np.shape(chis))
print(x,y)
if ev==4.64:
plt.scatter(np.sin(theta24s[x])**2, (np.cos(theta24s[x])**2)*(np.sin(theta34s[y])**2), s=80, marker=(5,1), color=get_color(color_count, len(evs)+1, "magma"))
print(theta24s[x], theta34s[y])
#set_lbls(ct, [str(ev)+r"eV$^{2}$"], get_color(color_count, len(evs), "viridis"))
ct.collections[0].set_color(get_color(color_count, len(evs)+1, "magma"))
ct.collections[0].set_label("{}".format(ev) + r" eV$^{2}$")
#plt.vlines(x=scaled_minos_24, ymin=0, ymax=scaled_minos_34[-1], color='k')
plt.xlim([5e-3,0.2])
plt.xscale('log')
plt.yscale('log')
plt.ylim([9e-3, 5e-1])
plt.tight_layout()
#chis = chis - np.nanmin(chis)
if count == 5:
plt.pcolormesh(scale_x,
scale_y,
chis.transpose(),
vmin=0,
vmax=25,
cmap='PuBu')
count += 1
if msqs[msq] == 1.0 and theta24s[th24] == 0.3826:
p1 = (0.853393, 0.00693782)
p2 = (0.87203, 0.00814776)
plt.plot([p1[0], p2[0]], [p1[1], p2[1]],
color=get_color(2 + msq, 5, 'magma'),
ls='-')
ct = plt.contour(scale_x,
scale_y,
chis.transpose(),
levels=chis_l,
linestyles=linestyle)
ct.collections[0].set_color(get_color(2 + msq, 5, 'magma'))
ct.collections[0].set_label("{:.1f} eV".format(msqs[msq]) +
r"$^{2}$, $\theta_{24}=$" +
"{:.2f}".format(theta24s[th24]))
plt.plot(scale_x[minloc[0]][minloc[0]],
scale_y[minloc[1]][minloc[1]],
marker="*",
e_grid = np.logspace(log10(emin), log10(emax), npoints)
evaled = {}
evaled_pg = {}
mag = 1
counter = 0
for cid in stuff.keys():
cast = int(cid)
n_nucl = hg.Z_A(cast)[1] #number of nucleons
evaled[cast] = [hg.nucleus_flux(cast, e*n_nucl) for e in e_grid]
evaled_pg[cast] = [pg.nucleus_flux(cast, e*n_nucl) for e in e_grid]
plt.plot(e_grid, evaled[cast]*(e_grid**mag), label=stuff[cid], color=get_color(counter, len(list(stuff.keys()))-1), ls='-')
# plt.plot(e_grid, evaled_pg[cast]*(e_grid**mag), color=get_color(counter, len(list(stuff.keys()))-1), ls='--')
counter+=1
plt.xscale('log')
plt.yscale('log')
#plt.ylim([10**-1,10**4])
plt.xlabel(r"$E_{kinetic}$ [GeV/nucleon]", size=16)
plt.ylabel(r"E $dN/dE$", size=16)
plt.legend(prop={'size':14})
plt.tight_layout()
plt.show()
if i34 == 0:
ls = 'solid'
elif i34 == 1:
ls = 'dotted'
else:
ls = ''
elif i24 == 1:
if i34 == 0:
ls = '-.'
elif i34 == 1:
ls = (0, (5, 10))
else:
ls = 'dashdot'
for ims in range(len(msqs)):
color = get_color(ims, 4, "magma")
llh_cut = [chis[i14][i24][i34][ims] for i14 in range(len(th14s))]
label = r"$\theta_{24}$: " + "{:.2f}, ".format(th24s[i24])
label += r"$\theta_{34}$: " + "{:.2f}, ".format(th34s[i34])
label += r"$\Delta m_{41}^{2}$: " + "{:.2f}".format(msqs[ims])
plt.plot(np.sin(th14s)**2, llh_cut, ls=ls, color=color)
title_1 = r"\textbf{Mixing Angles: }"
title_2 = r"\textbf{Mass-Squared: }"
#title_2 = r"$ \Delta m_{41}^{2}:$"
all_labels = [title_1]
plt.gca().add_line(plt.Line2D([], [], color="none", label=title_1))
plt.plot([], [],
color='black',
# plt.figure(figsize=(9.5/1.5,9.34/1.5))
plt.xscale('log')
#plt.title("At {:.2f} eV".format(ev if interp else msqs[which_sliver[0]]) +r"$^{2}$")
# plt.yscale('log')
plt.ylim([0.00, 0.30])
plt.xlim([1e-3, 0.5])
plt.xlabel(r"$\left|U_{\mu 4}\right|^{2}=\sin^{2}\theta_{24}$")
plt.ylabel(
r"$\left| U_{\tau 4}\right|^{2}=\sin^{2}\theta_{34}\cdot\cos^{2}\theta_{24}$"
)
# plt.ylabel(r"$\sin^{2}2\theta_{34}$",size=14)
#plt.plot(t24s, scaled_minos_34, color='k', label="Minos")
plt.plot(super_k[0],
super_k[1],
color=get_color(1, 4, cmap="magma"),
label="Super K")
plt.plot(deepcore[0],
deepcore[1],
color=get_color(2, 4, "magma"),
label="DeepCore")
#plt.plot(antares[0], antares[1], color=get_color(3,4,"magma"), label="KM3NeT/ORCA NO")
new_color = np.array((255, 157, 0)) / 256.
ct = add_contour(
"/home/bsmithers/software/data/hg_sib/0.0/joint_likelihood_0.0_0.0_0.0_0.0.dat",
'--', 'cool', True)
ct.collections[0].set_label("Joint Fit")
ct.collections[0].set_color(new_color)
ct = add_contour(
#plt.plot(yellow_right_interp, msqs)
plt.gca().add_line(plt.Line2D([], [], color="none", label=title_2))
ct = plt.contour(si2(theta24s),
msqs,
chis.transpose(),
levels=[-2 * log(0.10)],
cmap='Oranges_r')
ct8 = plt.contour(si2(t24_8),
msqs_8,
chis8.transpose(),
levels=[-2 * log(0.10)],
cmap='Oranges_r')
all_labels += [title_2, "1 year", "8 years"]
ct.collections[0].set_label("1 year")
ct.collections[0].set_color(get_color(2, 4, 'Oranges'))
ct8.collections[0].set_label("8 years")
ct8.collections[0].set_color(get_color(4, 4, 'Oranges'))
plt.xlim([0.001, 1])
plt.ylim([0.01, 10])
#set_lbls(ct)
plt.xscale('log')
plt.yscale('log')
# plt.title(r"90% CL Sensitivity from Tracks", size=14)
#plt.text(2,25, "Smithers Preliminary", color="r",size=14)
plt.ylabel(r"$\Delta m_{14}^{2}$ [eV$^{2}$]", size=18)
plt.xlabel(r"$\sin^{2}(2\theta_{24})$", size=18)
def reorderLegend(ax=None, order=None):
handles, labels = ax.get_legend_handles_labels()
mu_flux = np.sum(mu_flux, 1) * z_width
mubar_flux = np.sum(mubar_flux, 1) * z_width
return (mu_flux, mubar_flux)
e_flux, mu_flux, tau_flux = get_flux(filename)
pe_flux, pmu_flux, ptau_flux = get_flux(
os.path.join(folders[1], config["mceq_flux"]))
if not contour:
plt.plot(energies,
e_flux,
label=r"HG2012 $\nu_{e}$",
ls='-',
color=get_color(0, 3, 'plasma'))
plt.plot(energies,
mu_flux,
label=r"HG2012 $\nu_{\mu}$",
ls='-',
color=get_color(1, 3, 'plasma'))
plt.plot(energies,
tau_flux,
label=r"HG2012 $\nu_{\tau}$",
ls='-',
color=get_color(2, 3, 'plasma'))
plt.plot(energies,
pe_flux,
label=r"Poly $\nu_{e}$",
ls='--',
cbar = plt.colorbar()
cbar.set_label("New Fudge")
plt.show()
if not do_fudge:
e_range = np.logspace(2, 8, 1000)
a_edges = np.linspace(-1, 1, 11)
e_edges = np.logspace(2, 8, 21)
for flav in flavors:
fig = plt.figure()
i_count = 0
for fit in master_fit.fits[flav]:
ys = fit(e_range)
plt.plot(e_range, ys, color=get_color(i_count, 9))
i_count += 1
plt.title(flav, size=16)
plt.xscale('log')
plt.yscale('log')
plt.xlim([1e2, 1e8])
#plt.ylim([1e-2,1e3])
plt.show()
values = np.zeros(shape=(20, 10))
# now the integrated area plot
for i_e in range(len(e_edges) - 1):
for i_a in range(len(a_edges) - 1):
values[i_e][i_a] = log10(
dblquad(lambda e, a: master_fit(e, a, flav),
a=a_edges[i_a],