def PlotEigenvaluesEnergy(Emin,Emax,xRi,body,param,plot_path = "../plots/"):
plt.figure()
param.Refresh()
fM2 = no.flavorM2(param)
ordmag = np.log10(Emax)-np.log10(Emin)
npoints = 1000.0*ordmag
# begin energy scale #
if(Emax/param.MeV <= 500.0) :
scale = param.MeV
scalen = "[MeV]"
elif(Emax/param.GeV <= 500.0) :
scale = param.GeV
scalen = "[GeV]"
else :
scale = param.TeV
scalen = "[TeV]"
# end energy scale #
Estep = (Emax-Emin)/npoints
Enu = np.arange(Emin,Emax,Estep)/scale
EE = map(lambda E : no.Eeigenvals(E*scale, xRi, body, fM2, param),Enu)
plt.xlabel(r"$E_\nu \mathrm{"+scalen+"}$")
plt.ylabel(r"$\mathrm{E[eV]}$")
plt.plot(Enu,EE,color = 'black')
#plt.loglog()
plt.semilogx()
plt.savefig(plot_path+"PlotEnergyEigenvaluesEnergy.png")
def PlotRKProbabilityTime(param):
""" Plots the CPU time in second to calculate the probability.
@type param : physicsconstants
@param param : set of physical parameters used to make the plot.
@rtype : plot
@return : generates the spectra plot
"""
import hotshot as hs
import hotshot.stats as hstat
import re
prof = hs.Profile("RKprob.prof")
E = np.arange(1.0,1000.0,50.0)
Sun = bd.Sun()
Ri = 0.01*Sun.Radius*param.km
Rf = Sun.Radius*param.km
track = Sun.track(Ri,Ri,Rf)
fM2 = no.flavorM2(param)
EE = 1.0*param.GeV
benchtime = prof.runcall(no.AvgNeuProb_RK,1,EE,track,Sun,fM2,param)
prof.close()
print benchtime
stat = hstat.load("RKprob.prof")
stat.strip_dirs()
stat.dump_stats('RKprob.stat')
#re.match('calls',stat.print_stats(0))
stat.print_stats()
def PlotEigenvaluesRho(E,body,param):
plt.figure()
param.Refresh()
fM2 = no.flavorM2(param)
if(E/param.MeV <= 500.0) :
scale = param.MeV
scalen = "MeV"
elif(E/param.GeV <= 500.0) :
scale = param.GeV
scalen = "GeV"
else :
scale = param.TeV
scalen = "TeV"
R = np.arange(1.0,0.01,-0.001)
Rho = map(lambda r : body.rdensity(r), R)
EE = map(lambda x : no.Eeigenvals(E, x, body, fM2, param),R)
plt.xlabel(r"$\rho \mathrm{[g/cm^{-3}]}$")
plt.ylabel(r"$\mathrm{E[eV]}$")
plt.plot(Rho,EE,color = 'black')
#plt.loglog()
plt.semilogx()
#plt.ylim(1.0e-14,1.0e-12)
filename = "PlotEnergyEigenvaluesRho_E_"+str(E/scale)+"_"+scalen+".png"
plt.savefig(plot_path + filename)
def PlotEigenvaluesRadius(E,body,param,plot_path = "../plots/"):
plt.figure()
R = np.arange(0.01,1.0,0.01)
param.Refresh()
fM2 = no.flavorM2(param)
EE = map(lambda x : no.Eeigenvals(E, x, body, fM2, param),R)
plt.xlabel(r"$\frac{R}{R_\odot}$")
plt.ylabel(r"$\mathrm{E[eV]}$")
plt.plot(R,EE,color = 'black')
plt.semilogy()
plt.savefig(plot_path+"PlotEnergyEigenvaluesRadius.png")
def NeuOsc3g(a,b,E,track,body,param): """ Formalae for standard 3-flavor neutrino oscillation for constant density case. # Using evolution operator formalism in constant density. # E : neutrino energy [eV] # track : body track # body : body where neutrino propagates # a : initial neutrino flavor (0 : e, 1 : mu, 2 : tau) # b : final neutrino flavor (0 : e, 1 : mu, 2 : tau) """ fM2 = no.flavorM2(param) return no.OscProbConstantDensity(a,b,E,track,body,fM2,param)
def PlotNeuOscProb(ineu, fneu, Ri, Enumin, Enumax, body, param, rkcomp):
"""Plots P(neu_ineu -> neu_fneu) as a function of the Energy
# ineu,fneu : 0 (electron), 1 (muon), 2 (tau)
# Ri : neutrino production point in the body
# Enumin : minimum neutrino energy
# Enumax : maximum neutrino energy
# body : body where the neutrino propagates
# param : physics parameter set
# rkcomp : toggle RK validation
# If rkcomp = True, will use RK to compute the probability and compare
"""
plt.figure()
ordmag = np.log10(Enumax) - np.log10(Enumin)
npoints = 1000.0 * ordmag
# Figuring out best energy scale
try:
if (Enumax / param[0].MeV <= 500.0):
scale = param[0].MeV
scalen = "MeV"
elif (Enumax / param[0].GeV <= 500.0):
scale = param[0].GeV
scalen = "GeV"
else:
scale = param[0].TeV
scalen = "TeV"
except (TypeError, AttributeError):
if (Enumax / param.MeV <= 500.0):
scale = param.MeV
scalen = "MeV"
elif (Enumax / param.GeV <= 500.0):
scale = param.GeV
scalen = "GeV"
else:
scale = param.TeV
scalen = "TeV"
# Figuring out ylabel
if (ineu == 0):
ineulabel = "e"
sineulabel = "e"
elif (ineu == 1):
ineulabel = "\mu"
sineulabel = "mu"
elif (ineu == 2):
ineulabel = "\\tau"
sineulabel = "tau"
elif (ineu > 2):
ineulabel = "s"
sineulabel = "s"
if (fneu == 0):
fneulabel = "e"
sfneulabel = "e"
elif (fneu == 1):
fneulabel = "\mu"
sfneulabel = "mu"
elif (fneu == 2):
fneulabel = "\\tau"
sfneulabel = "tau"
elif (fneu > 2):
fneulabel = "s"
sfneulabel = "s"
# RK points
ERKstep = (np.log10(Enumax) - np.log10(Enumin)) / (10.0)
ERK = np.arange(np.log10(Enumin), np.log10(Enumax), ERKstep)
ERK = map(lambda E: (10**E) / scale, ERK)
Estep = (Enumax - Enumin) / npoints
Enu = np.arange(Enumin, Enumax, Estep) / scale
# Generating plot
try:
pid = [0] * len(param)
for i, p in enumerate(param):
p.Refresh()
PMNS = no.mixmatrix(p)
fM2 = no.flavorM2(p)
Rf = body.Radius * p.km
track = body.track(Ri, Ri, Rf)
Pe = map(
lambda E: no.AdiabaticProbability(ineu, fneu, E * scale, Ri,
body, PMNS, fM2, p), Enu)
plt.xlabel(r"$\mathrm{E}_\nu\mathrm{[" + scalen + "]}$")
ylabel = "$ P(\\nu_{" + ineulabel + "} \\rightarrow \\nu_{" + fneulabel + "}) $"
plt.ylabel(r"" + ylabel)
pid[i] = plt.plot(Enu,
Pe,
color='black',
label=r"" + p.name,
linestyle=p.linestyle)
if rkcomp:
PeRK = map(
lambda E: no.AvgNeuProb_RK_STD(ineu, fneu, E * scale, p),
ERK)
#PeRK = map(lambda E : no.probneu(ineu,fneu,E*scale,track,body,fM2,p),ERK)
for i, e in enumerate(ERK):
plt.plot([e], [PeRK[i]],
p.markerstyle,
color='red',
markersize=12,
markeredgecolor='red',
markeredgewidth=1,
label='_nolegend_')
except (TypeError, AttributeError):
param.Refresh()
PMNS = no.mixmatrix(param)
fM2 = no.flavorM2(param)
Rf = body.Radius * param.km
track = body.track(Ri, Ri, Rf)
Pe = map(
lambda E: no.AdiabaticProbability(ineu, fneu, E * scale, Ri, body,
PMNS, fM2, param), Enu)
plt.xlabel(r"$\mathrm{E}_\nu\mathrm{[" + scalen + "]}$")
ylabel = "$ P(\\nu_{" + ineulabel + "} \\rightarrow \\nu_{" + fneulabel + "}) $"
plt.ylabel(r"" + ylabel)
plt.plot(Enu, Pe, color='black', linestyle=param.linestyle)
if rkcomp:
PeRK = map(
lambda E: no.probneu(ineu, fneu, E * scale, track, body, fM2,
param), ERK)
for i, e in enumerate(ERK):
plt.plot([e], [PeRK[i]],
param.markerstyle,
color='red',
markersize=12,
markeredgecolor='red',
markeredgewidth=1,
label='_nolegend_')
plt.legend(loc=0)
plt.semilogx()
plt.axis([Enumin / scale, Enumax / scale, 0.0, 1.0])
path = "../plots/"
filename = path + "PlotNeuOscProb_Emin_" + str(
Enumin / scale) + "_" + scalen + "_Emax_" + str(
Enumax / scale
) + "_" + scalen + "_nui_" + sineulabel + "_nuf_" + sfneulabel + ".png"
plt.savefig(filename)
PlotNewIcecubeLimit(pc)
quit()
PlotCompareProbabilitiesMC(pcc, pc)
quit()
#PlotFluxAtDetector(pc,pc)
PlotDMMC_MTonDetector(pcc, pc)
quit()
#PlotOscProb(Enumin,Enumax,[pc,pcc])
PlotSingleNeuCompositionCompare(E, Sun, pcc, pc)
#PlotOscProb(Enumin,Enumax,[pc,pcc])
quit()
PMNS = no.mixmatrix(pc)
fM2 = no.flavorM2(pc)
Ri = 0.05 * Sun.Radius * pcc.km
Rf = Sun.Radius * pcc.km
# #
# ##PlotDMFluxSpectraMultiCh(100,Sun,pcc)
E = 10 * pcc.MeV #1012773328388.837
# ##E = 1.0*pcc.TeV#10.0*pcc.TeV
# ##prob = [no.AdiabaticProbability(i,i,E,Ri,Sun,PMNS,fM2,pcc) for i in range(5)]
# #
#track = Sun.track(Ri,Ri,Rf)
# #
# #print no.AvgNeuProb_RK(1,E,track,Sun,fM2,pcc)
#
# def singlepoint():
# no.AvgNeuProb_RK(1,E,track,Sun,fM2,pcc)
def PlotSingleNeuCompositionCompare(E,body,param,sparam = PC.PhysicsConstants()):
""" Plots the composition of a single mass neutrino state.
E : neutrino energy [eV]
body : body with the asociated density profile.
param : set of physical parameters used to make the plot. param can be a list.
sparam : standard parameters
"""
fig = plt.figure()
ax = plt.subplot(111)
mpl.rcParams['axes.labelsize'] = "x-large"
mpl.rcParams['xtick.labelsize'] = "x-large"
mpl.rcParams['legend.fontsize'] = "small"
mpl.rcParams['font.size'] = 18
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
linestyles = ['--','-.',':','-','-']
#B Initializing variables
param.Refresh()
fM2 = no.flavorM2(param)
sparam.Refresh()
fM2STD = no.flavorM2(sparam)
R = np.arange(1.0,0.01,-0.001)
Rho = map(lambda r : body.rdensity(r), R)
#E Initializing variables
#B Estimating Energy Scale
if(E/param.MeV <= 500.0) :
scale = param.MeV
scalen = "MeV"
elif(E/param.GeV <= 500.0) :
scale = param.GeV
scalen = "GeV"
else :
scale = param.TeV
scalen = "TeV"
#E Estimating Energy Scale
#B Adding title
#tit = "Energy : "+str(E/scale)+" "+scalen+ " Parameters :"#+" $\\th_{12}$ = " + str(param.th12) + " $\\th_{23}$ = " + str(param.th23) + " $\\th_{13}$ = "+str(param.th13)
#atit = []
#[[ atit.append(" $\\theta_{"+str(j)+str(i)+"}$ = "+format(param.th[j][i],'.4f')) for i in range(1,param.numneu+1) if i>j] for j in range(1,param.numneu+1) ]
#[[ atit.append(" $\\Delta m^2_{"+str(i)+str(j)+"}$ = "+format(param.dm2[j][i],'.4f')) for i in range(1,param.numneu+1) if i>j and j == 1] for j in range(1,param.numneu+1) ]
##[[ atit.append(" $\\Delta m^2_{"+str(j)+str(i)+"}$ = "+format(param.dm2[j][i],'.4f')) for i in range(1,param.numneu+1) if i>j and j == 1] for j in range(1,param.numneu+1) ]
#for i in range(len(atit)):
# tit = tit + atit[i]
#plt.suptitle(tit,horizontalalignment='center')
#E Adding title
##B PLOTTING MASS BASIS AS FUNCTION OF FLAVOR BASIS
for i in [1]:
#fig.add_subplot(2,param.numneu,i+1)
flavor = False
NeuComp = map(lambda x : no.NeuComposition(i,E, x, body, fM2, param,flavor),R)
plt.xlabel(r"$\rho \mathrm{[g/cm^{3}]}$")
pp = []
for k in range(param.numneu):
kNeuComp = map(lambda x: x[k],NeuComp)
# log interpolator
rholog = gt.LogSpaceEnergies(float(Rho[0]),float(Rho[-1]),100)
#print rholog , float(Rho[0]),float(Rho[-1])
rholog[-1] = Rho[-1]
inter_neu = interpolate.interp1d(Rho,kNeuComp)
logkNeuComp = map(inter_neu,rholog)
if k == 3:
#pp.append(plt.plot(Rho,kNeuComp,'o-',color = 'r',markevery = 10,markeredgewidth = 0.0, ms = 2.0))
pp.append(plt.plot(rholog,logkNeuComp,'x-',color = 'r',markevery = 10,markeredgewidth = 0.0, ms = 2.0,aa = True,solid_joinstyle = 'bevel'))
elif k == 4:
pp.append(plt.plot(Rho,kNeuComp,'o-',color = 'r',markevery = 10,markeredgewidth = 0.0, ms = 2.0))
else :
pp.append(plt.plot(Rho,kNeuComp,linestyle = linestyles[k] ,color = 'r'))
if i<=2 :
NeuCompSTD = map(lambda x : no.NeuComposition(i,E, x, body, fM2STD, sparam,flavor),R)
for k in range(sparam.numneu):
kNeuCompSTD = map(lambda x: x[k],NeuCompSTD)
plt.plot(Rho,kNeuCompSTD, color = 'k', linestyle = linestyles[k])
#Solar density
#ps = plt.vlines(150, 0.0, 1.0, linestyle = "dashed", label = r"$\rho_S$")
#B plt format
plt.title(r"Composition of $\nu_"+str(i+1)+"$")
plt.semilogx()
plt.ylim(0.0,1.0)
plt.xlim(1.0,150.0)
plt.yticks(np.arange(0.0,1.1,0.1))
xtt = [1.0,5.0,10.0,30.0,100.0]#,150.0]
#plt.xticks(xtt)
ax.set_xticks(xtt)
ax.set_xticklabels(['$1$','$5$','$10$','$30$','$100$'])#,'$\\rho_\\odot = 150$'])
#plt.xscale()
#B LEGEND
plots = []
for e in pp :
plots.append(e[0])
#plots.append(ps)
leg = ["$\\nu_e$","$\\nu_\mu$","$\\nu_\\tau$"]
ss = ["$\\nu_{s"+str(i)+"}$" for i in np.arange(1,param.numneu-3+1,1)]
if ss != []:
leg.extend(ss)
leg = plt.legend(plots,leg,loc = 6,fancybox=True,bbox_to_anchor = (0.05, 0.75))
leg.get_frame().set_alpha(0.25)
#E LEGEND
#E plt format
#B EXTRA LEGEND
box1t = osb.TextArea("STD", textprops=dict(color="k"))
box1d = osb.DrawingArea(60, 20, 0, 0)
el1 = ptc.Ellipse((10, 10), width=5, height=5, angle=0, fc="k", edgecolor = 'none')
box1d.add_artist(el1)
box2t = osb.TextArea("2+3", textprops=dict(color="k"))
box2d = osb.DrawingArea(60, 20, 0, 0)
el2 = ptc.Ellipse((10, 10), width=5, height=5, angle=0, fc="r", edgecolor = 'none')
box2d.add_artist(el2)
box1 = osb.HPacker(children=[box1t, box1d],
align="center",
pad=0, sep=1)
box2 = osb.HPacker(children=[box2t, box2d],
align="center",
pad=0, sep=5)
anchored_box1 = osb.AnchoredOffsetbox(loc=9,
child=box1, pad=5.0,
frameon=False,
#bbox_to_anchor=(0., 1.02),
#bbox_transform=ax.transAxes,
borderpad=0.,
)
anchored_box2 = osb.AnchoredOffsetbox(loc=9,
child=box2, pad=6.0,
frameon=False,
#bbox_to_anchor=(0., 1.02),
#bbox_transform=ax.transAxes,
borderpad=0.,
)
ax.add_artist(anchored_box1)
ax.add_artist(anchored_box2)
#E EXTRA LEGEND
##E PLOTTING MASS BASIS AS FUNCTION OF FLAVOR BASIS
#plt.suptitle("*Dashed colored lines are 3-flavor standard oscillations.", x = 0.15, y = 0.03)
path = "../plots/"
filename = "PlotNeuComposition_E_"+str(E/scale)+"_"+scalen+"_FIG2.eps"
plt.savefig(path + filename, dpi = 1200)
def PlotNeuCompositionCompare(E,body,param,sparam = PC.PhysicsConstants(),plot_path = "../plots/",xlim = None, fmt = "png"):
""" Plots the composition of neutrinos as a function of mass states, flavors, and density. Compares with STD.
E : neutrino energy [eV]
body : body with the asociated density profile.
param : set of physical parameters used to make the plot. param can be a list.
sparam : standard parameters
"""
fig = plt.figure(figsize = (4*param.numneu,8))
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
#B Initializing variables
param.Refresh()
fM2 = no.flavorM2(param)
fM2STD = no.flavorM2(sparam)
R = np.arange(1.0,0.01,-0.001)
Rho = map(lambda r : body.rdensity(r), R)
#E Initializing variables
#B Estimating Energy Scale
if(E/param.MeV <= 500.0) :
scale = param.MeV
scalen = "MeV"
elif(E/param.GeV <= 500.0) :
scale = param.GeV
scalen = "GeV"
else :
scale = param.TeV
scalen = "TeV"
#E Estimating Energy Scale
#B Adding title
tit = "Energy : "+str(E/scale)+" "+scalen+ " Parameters :"#+" $\\th_{12}$ = " + str(param.th12) + " $\\th_{23}$ = " + str(param.th23) + " $\\th_{13}$ = "+str(param.th13)
atit = []
try:
[[ atit.append(" $\\theta_{"+str(j)+str(i)+"}$ = "+format(param.th[j][i],'.4f')) for i in range(1,param.numneu+1) if i>j] for j in range(1,param.numneu+1) ]
[ atit.append(" $\\Delta m^2_{"+str(j)+str(1)+"}$ = "+format(param.dm2[1][j],'.3e')) for j in range(2,param.numneu+1) ]
except TypeError:
[[ atit.append(" $\\theta_{"+str(j)+str(i)+"}$ = "+"{:.4}".format(param.th[j][i])) for i in range(1,param.numneu+1) if i>j] for j in range(1,param.numneu+1) ]
[ atit.append(" $\\Delta m^2_{"+str(j)+str(1)+"}$ = "+"{:.3}".format(param.dm2[1][j])) for j in range(2,param.numneu+1) ]
for i in range(len(atit)):
tit = tit + atit[i]
plt.suptitle(tit,horizontalalignment='center')
#E Adding title
##B PLOTTING MASS BASIS AS FUNCTION OF FLAVOR BASIS
for i in np.arange(0,param.numneu,1):
fig.add_subplot(2,param.numneu,i+1)
flavor = False
NeuComp = map(lambda x : no.NeuComposition(i,E, x, body, fM2, param,flavor),R)
plt.xlabel(r"$\rho \mathrm{[g/cm^{3}]}$")
pp = []
for k in range(param.numneu):
kNeuComp = map(lambda x: x[k],NeuComp)
pp.append(plt.plot(Rho,kNeuComp, color = colors[k],lw = 3))
if i<=2 :
NeuCompSTD = map(lambda x : no.NeuComposition(i,E, x, body, fM2STD, sparam,flavor),R)
for k in range(sparam.numneu):
kNeuCompSTD = map(lambda x: x[k],NeuCompSTD)
plt.plot(Rho,kNeuCompSTD, color = colors[k], linestyle = 'dashed',lw = 3)
#Solar density
ps = plt.vlines(150, 0.0, 1.0, linestyle = "dashed", label = r"$\rho_S$")
#B plt format
plt.title(r"Composition of $\nu_"+str(i+1)+"$")
plt.semilogx()
plt.ylim(0.0,1.0)
plt.xlim(0.0,1000.0)
plt.yticks(np.arange(0.0,1.1,0.1))
#plt.xscale()
if xlim == None :
plt.xlim(Rho[0],Rho[-1])
else :
plt.xlim(xlim)
if i == param.numneu - 1 :
plots = []
for e in pp :
plots.append(e[0])
plots.append(ps)
leg = ["$\\nu_e$","$\\nu_\mu$","$\\nu_\\tau$"]
ss = ["$\\nu_{s"+str(i)+"}$" for i in np.arange(1,param.numneu-3+1,1)]
if ss != []:
leg.extend(ss)
#leg.append("$\\rho_\odot$")
plt.legend(plots,leg,bbox_to_anchor = (1.5, 0.90))
fig.subplots_adjust(left=0.05, right=0.85,hspace = 0.35)#,wspace = 0.25, top = 0.95, bottom = 0.05)
#E plt format
##E PLOTTING MASS BASIS AS FUNCTION OF FLAVOR BASIS
##B PLOTTING FLAVOR BASIS AS FUNCTION OF MASS BASIS
for i in np.arange(param.numneu,2*param.numneu,1):
fig.add_subplot(2,param.numneu,i+1)
flavor = True
NeuComp = map(lambda x : no.NeuComposition(i-param.numneu,E, x, body, fM2, param,flavor),R)
plt.xlabel(r"$\rho \mathrm{[g/cm^{-3}]}$")
pp = []
for k in range(param.numneu):
kNeuComp = map(lambda x: x[k],NeuComp)
pp.append(plt.plot(Rho,kNeuComp, color = colors[k],lw = 3))
if i<=2+param.numneu :
NeuCompSTD = map(lambda x : no.NeuComposition(i-param.numneu,E, x, body, fM2STD, sparam,flavor),R)
for k in range(sparam.numneu):
kNeuCompSTD = map(lambda x: x[k],NeuCompSTD)
plt.plot(Rho,kNeuCompSTD, color = colors[k], linestyle = 'dashed',lw = 3)
#Solar density
ps = plt.vlines(150, 0.0, 1.0, linestyle = "dashed", label = r"$\rho_S$")
#B plt format
if i == param.numneu :
plt.title(r"Composition of $\nu_e$")
elif i == param.numneu + 1:
plt.title(r"Composition of $\nu_\mu$")
elif i == param.numneu + 2:
plt.title(r"Composition of $\nu_\tau$")
else :
plt.title(r"Composition of $\nu_{s"+str(i+1-param.numneu-3)+"}$")
plt.semilogx()
plt.ylim(0.0,1.0)
plt.yticks(np.arange(0.0,1.1,0.1))
if xlim == None :
plt.xlim(Rho[0],Rho[-1])
else :
plt.xlim(xlim)
#plt.xscale()
if i == 2*param.numneu - 1 :
plots = []
for e in pp :
plots.append(e[0])
plots.append(ps)
leg = ["$\\nu_{"+str(i)+"}$" for i in np.arange(1,param.numneu+1,1)]
#leg.append("$\\rho_\odot$")
plt.legend(plots,leg,bbox_to_anchor = (1.5, 0.90))
fig.subplots_adjust(left=0.05, right=0.85)#, top = 0.85, bottom = 0.05)
##E PLOTTING FLAVOR BASIS AS FUNCTION OF MASS BASIS
plt.suptitle("*Dashed colored lines are 3-flavor standard oscillations.", x = 0.15, y = 0.03)
filename = "PlotNeuComposition_E_"+str(E/scale)+"_"+scalen+"_"+param.name+"."+fmt
plt.savefig(plot_path + filename)
def PlotNeuOscProbSun(ineu,Enumin,Enumax,param,rkcomp,plot_path = "../plots/"):
"""Plots P(neu_ineu -> neu_fneu) as a function of the Energy from an initial flavor state (ineu) to all final flavor states (fneu) on the sun
# ineu,fneu : 0 (electron), 1 (muon), 2 (tau)
# Enumin : minimum neutrino energy [eV]
# Enumax : maximum neutrino energy [eV]
# param : physics parameter set list [param_1,param_2,...,param_n]
# rkcomp : toggle RK validation [boolean]
# plot_path : path to save plot [string]
# If rkcomp = True, will use RK to compute the probability and compare
"""
body = bd.Sun()
fig = plt.figure(figsize = (4*3+2,6))
ordmag = np.log10(Enumax)-np.log10(Enumin)
npoints = 1000.0*ordmag
# Figuring out best energy scale
try:
if(Enumax/param[0].MeV <= 500.0) :
scale = param[0].MeV
scalen = "MeV"
elif(Enumax/param[0].GeV <= 500.0) :
scale = param[0].GeV
scalen = "GeV"
else :
scale = param[0].TeV
scalen = "TeV"
except (TypeError,AttributeError):
if(Enumax/param.MeV <= 500.0) :
scale = param.MeV
scalen = "MeV"
elif(Enumax/param.GeV <= 500.0) :
scale = param.GeV
scalen = "GeV"
else :
scale = param.TeV
scalen = "TeV"
neulabel = {0 : "e",1 : "\\mu", 2 : "\\tau",3 : "{s_1}",4 : "{s_2}"}
sneulabel = {0 : "e",1 : "mu", 2 : "tau",3 : "s1",4 : "s2"}
# RK points
ERKstep = (np.log10(Enumax)-np.log10(Enumin))/(20.0)
ERK = np.arange(np.log10(Enumin),np.log10(Enumax),ERKstep)
ERK = map(lambda E : (10**E)/scale,ERK)
Estep = (Enumax-Enumin)/npoints
Enu = np.arange(Enumin,Enumax,Estep)/scale
# Generating plot
for fneu in [0,1,2]:
fig.add_subplot(1,3,fneu+1)
try:
for i,p in enumerate(param):
p.Refresh()
PMNS = no.mixmatrix(p)
fM2 = no.flavorM2(p)
Ri = 0.01*body.Radius*p.km
Rf = body.Radius*p.km
track = body.track(Ri,Ri,Rf)
Pe = map(lambda E : no.AdiabaticProbability(ineu,fneu,E*scale,Ri,body,PMNS,fM2,p),Enu)
plt.xlabel(r"$\mathrm{E}_\nu\mathrm{["+scalen+"]}$")
plt.ylabel("$ P(\\nu_{"+neulabel[ineu]+"} \\rightarrow \\nu_{"+neulabel[fneu]+"}) $")
plt.plot(Enu,Pe,color = 'black',linestyle = p.linestyle,label='$ P_{\\mathrm{Adb.}-\\mathrm{'+p.name+'}}(\\nu_'+neulabel[ineu]+'\\rightarrow \\nu_'+neulabel[fneu]+')$')
if rkcomp :
PRK = map(lambda E : no.AvgNeuProb_RK_STD(ineu,fneu,E*scale,p),ERK)
plt.plot(ERK,PRK,p.markerstyle,color=p.colorstyle,markersize=6,markeredgecolor=p.colorstyle,markeredgewidth=1,label='$ P_{\\mathrm{RK}-\\mathrm{'+p.name+'}}(\\nu_'+neulabel[ineu]+'\\rightarrow \\nu_'+neulabel[fneu]+')$')
except (TypeError,AttributeError):
param.Refresh()
PMNS = no.mixmatrix(param)
fM2 = no.flavorM2(param)
Ri = 0.01*body.Radius*param.km
Rf = body.Radius*param.km
track = body.track(Ri,Ri,Rf)
Pe = map(lambda E : no.AdiabaticProbability(ineu,fneu,E*scale,Ri,body,PMNS,fM2,param),Enu)
plt.xlabel(r"$\mathrm{E}_\nu\mathrm{["+scalen+"]}$")
plt.ylabel("$ P(\\nu_{"+neulabel[ineu]+"} \\rightarrow \\nu_{"+neulabel[fneu]+"}) $")
plt.plot(Enu,Pe,color = 'black',linestyle = param.linestyle,label='$ P_{\\mathrm{Adb.}-\\mathrm{'+param.name+'}}(\\nu_'+neulabel[ineu]+'\\rightarrow \\nu_'+neulabel[fneu]+')$')
if rkcomp :
PRK = map(lambda E : no.probneu(ineu,fneu,E*scale,track,body,fM2,param),ERK)
plt.plot(ERK,PRK,param.markerstyle,color=param.colorstyle,markersize=6,markeredgecolor=param.colorstyle,markeredgewidth=1,label='$ P_{\\mathrm{RK}-\\mathrm{'+param.name+'}}(\\nu_'+neulabel[ineu]+'\\rightarrow \\nu_'+neulabel[fneu]+')$')
plt.semilogx()
plt.axis([Enumin/scale,Enumax/scale,0.0,1.0])
try:
plt.suptitle(param[0].neutype+" oscillations probabilities")
except TypeError:
plt.suptitle(param.neutype+" oscillations probabilities")
fig.subplots_adjust(left=0.05, right=0.85,wspace = 0.35, top = 0.85, bottom = 0.15)
mpl.rcParams['legend.fontsize'] = "small"
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,fancybox = True)
# path = "../plots/"
try:
filename = plot_path+"PlotNeuOscProbSun_Emin_"+str(Enumin/scale)+"_"+scalen+"_Emax_"+str(Enumax/scale)+"_"+scalen+"_nui_"+neulabel[ineu]+"_"+param[0].neutype+".png"
except TypeError:
filename = plot_path+"PlotNeuOscProbSun_Emin_"+str(Enumin/scale)+"_"+scalen+"_Emax_"+str(Enumax/scale)+"_"+scalen+"_nui_"+neulabel[ineu]+"_"+param.neutype+".png"
plt.savefig(filename)