def plot_Pgg(self,
EGeV,
P,
axis=0,
plot_all=False,
filename=None,
plot_one=True,
EcritLabel='',
y0=1e-3,
y1=1.1,
loc=3):
"""
Plot the transfer function
Arguments
---------
EGeV: m-dim array with energies in GeV
P: nxm-dim array with transfer function
kwargs
------
axis: int, axis with B-field realisations (if more than one, only used if n > 1)
plot_all: bool, if true, plot all realizations of transfer function
plot_one: bool, if true, plot one realization of transfer function
y0: float, min of y-axis of main plot
y1: float, max of y-axis of main plot
loc: int, location of legend
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(111)
ax2 = fig.add_axes([0.2, 0.2, 0.4, 0.4]) # left bottom width height
try:
self.scenario.index('ICM')
Ecrit = self.EcritAve()
except ValueError:
Ecrit = Ecrit_GeV(self.m, self.kwargs['n'], self.kwargs['B'],
self.g)
imin = np.argmin(np.abs(EGeV - Ecrit))
for i, a in enumerate([ax, ax2]):
a.set_xscale('log')
a.set_yscale('log')
if len(P.shape) > 1:
MedCon = median_contours(P, axis=axis)
a.fill_between(EGeV,
MedCon['conf_95'][0],
y2=MedCon['conf_95'][1],
color=plt.cm.Greens(0.8))
a.fill_between(EGeV,
MedCon['conf_68'][0],
y2=MedCon['conf_68'][1],
color=plt.cm.Greens(0.5))
if plot_one:
a.plot(EGeV,
P[0],
ls='-',
color='0.',
label='$P_{\gamma\gamma}$, one realization',
lw=2)
if plot_all:
for i in range(1, P.shape[axis]):
a.plot(EGeV, P[i], ls=':', color='0.', lw=1)
ymin = ceil(np.log10(MedCon['median'][imin])) - 0.3
ymax = ceil(np.log10(MedCon['median'][imin]))
else:
a.plot(EGeV, P, ls='-', color='0.', label='$P_{\gamma\gamma}$')
ymin = ceil(np.log10(P[imin])) - 0.3
ymax = ceil(np.log10(P[imin]))
a.axvline(Ecrit,
ls='--',
color='0.',
lw=1.,
label=r'$E_\mathrm{{crit}}^\mathrm{{{0:s}}}$'.format(
EcritLabel))
a.plot(EGeV,
np.exp(-1. * self.ebl_norm *
self.tau.opt_depth_array(self.z, EGeV / 1e3)[0]),
ls='-',
color='red',
lw=2.,
label=r'$\exp(-\tau)$')
if not i:
a.legend(loc=loc, fontsize='small')
v = a.axis([EGeV[0], EGeV[-1], y0, y1])
ax.set_xlabel("Energy (GeV)")
ax.set_ylabel("Photon survival probability")
else:
xmin = np.log10(Ecrit) - 0.5
xmax = np.log10(Ecrit) + 0.5
a.axis([10.**xmin, 10.**xmax, 10.**ymin, 10.**ymax])
if not filename == None:
plt.savefig(filename + '.pdf', format='pdf')
plt.savefig(filename + '.png', format='png')
plt.show()
return ax, ax2
Pu = np.zeros((cc.nsim, EGeV.shape[0]))
Pa = np.zeros((cc.nsim, EGeV.shape[0]))
# calculate the mixing, nsim > 0 only if ICM or IGM are included
for i in range(cc.nsim):
try:
cc.scenario.index('Jet')
new_angles = False
except ValueError:
new_angles = True
# calculate the mixing for all energies. If new_angles = True,
# new random angles will be generated for each random realizatioin
Pt[i], Pu[i], Pa[i] = cc.calc_conversion(EGeV, new_angles=new_angles)
# calculate the median and 68% and 95% confidence contours
MedCon = median_contours(Pt + Pu)
# plot the results
plt.figure()
ax = plt.subplot(111)
ax.set_xscale("log")
ax.set_yscale("log")
# plot the contours if we are dealing with many realizations
if cc.nsim > 1:
plt.fill_between(EGeV,
MedCon['conf_95'][0],
y2=MedCon['conf_95'][1],
color=plt.cm.Greens(0.8))
plt.fill_between(EGeV,
MedCon['conf_68'][0],
def plot_Pgg(self, EGeV, P, axis = 0, plot_all = False, filename = None, plot_one = True, EcritLabel = '', y0 = 1e-3, y1 = 1.1, loc = 3):
"""
Plot the transfer function
Arguments
---------
EGeV: m-dim array with energies in GeV
P: nxm-dim array with transfer function
kwargs
------
axis: int, axis with B-field realisations (if more than one, only used if n > 1)
plot_all: bool, if true, plot all realizations of transfer function
plot_one: bool, if true, plot one realization of transfer function
y0: float, min of y-axis of main plot
y1: float, max of y-axis of main plot
loc: int, location of legend
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(111)
ax2 = fig.add_axes([0.2, 0.2, 0.4, 0.4]) # left bottom width height
try:
self.scenario.index('ICM')
Ecrit = self.EcritAve()
except ValueError:
Ecrit = Ecrit_GeV(self.m,self.kwargs['n'],self.kwargs['B'],self.g)
imin = np.argmin(np.abs(EGeV - Ecrit))
for i,a in enumerate([ax,ax2]):
a.set_xscale('log')
a.set_yscale('log')
if len(P.shape) > 1:
MedCon = median_contours(P, axis = axis)
a.fill_between(EGeV,MedCon['conf_95'][0],y2 = MedCon['conf_95'][1], color = plt.cm.Greens(0.8))
a.fill_between(EGeV,MedCon['conf_68'][0],y2 = MedCon['conf_68'][1], color = plt.cm.Greens(0.5))
if plot_one:
a.plot(EGeV,P[0], ls = '-', color = '0.', label = '$P_{\gamma\gamma}$, one realization', lw = 2)
if plot_all:
for i in range(1,P.shape[axis]):
a.plot(EGeV,P[i], ls = ':', color = '0.', lw = 1)
ymin = ceil(np.log10(MedCon['median'][imin])) - 0.3
ymax = ceil(np.log10(MedCon['median'][imin]))
else:
a.plot(EGeV,P, ls = '-', color = '0.', label = '$P_{\gamma\gamma}$')
ymin = ceil(np.log10(P[imin])) - 0.3
ymax = ceil(np.log10(P[imin]))
a.axvline(Ecrit,
ls = '--', color = '0.',lw = 1.,
label = r'$E_\mathrm{{crit}}^\mathrm{{{0:s}}}$'.format(EcritLabel)
)
a.plot(EGeV,np.exp(-1. * self.ebl_norm * self.tau.opt_depth_array(self.z,EGeV / 1e3)[0]), ls = '-', color = 'red', lw = 2.,label = r'$\exp(-\tau)$')
if not i:
a.legend(loc = loc, fontsize = 'small')
v = a.axis([EGeV[0],EGeV[-1],y0,y1])
ax.set_xlabel("Energy (GeV)")
ax.set_ylabel("Photon survival probability")
else:
xmin = np.log10(Ecrit) - 0.5
xmax = np.log10(Ecrit) + 0.5
a.axis([10.**xmin,10.**xmax,10.**ymin,10.**ymax])
if not filename == None:
plt.savefig(filename + '.pdf', format = 'pdf')
plt.savefig(filename + '.png', format = 'png')
plt.show()
return ax,ax2
Pu = np.zeros((cc.nsim,EGeV.shape[0]))
Pa = np.zeros((cc.nsim,EGeV.shape[0]))
# calculate the mixing, nsim > 0 only if ICM or IGM are included
for i in range(cc.nsim):
try:
cc.scenario.index('Jet')
new_angles = False
except ValueError:
new_angles = True
# calculate the mixing for all energies. If new_angles = True,
# new random angles will be generated for each random realizatioin
Pt[i],Pu[i],Pa[i] = cc.calc_conversion(EGeV, new_angles = new_angles)
# calculate the median and 68% and 95% confidence contours
MedCon = median_contours(Pt + Pu)
# plot the results
plt.figure()
ax = plt.subplot(111)
ax.set_xscale("log")
ax.set_yscale("log")
# plot the contours if we are dealing with many realizations
if cc.nsim > 1:
plt.fill_between(EGeV,MedCon['conf_95'][0],y2 = MedCon['conf_95'][1], color = plt.cm.Greens(0.8))
plt.fill_between(EGeV,MedCon['conf_68'][0],y2 = MedCon['conf_68'][1], color = plt.cm.Greens(0.5))
plt.plot(EGeV,MedCon['median'], ls = '-', color = 'gold', label = 'median')
label = 'one realization'
else:
label = 'w/ ALPs'
