def aeff(self):
"""
Plot and tabulate Aeff.
"""
import colormaps as cmaps
plt.register_cmap(name='viridis', cmap=cmaps.viridis)
plt.set_cmap(cmaps.viridis)
logEmin, logEmax = 2., 9.
dlogE = 0.1
n_bins_E = (logEmax - logEmin) / dlogE
dcz = 0.01
dOmega = 2 * pi * dcz
n_bins_cz = 2 / dcz
nu = self.nu
nu.cz = -np.sin(nu.trueDec)
w_aeff = 1 / (1e4 * np.log(10)) * nu.ow / nu.trueE / dOmega / dlogE
h_aeff = hl.hist(
(nu.trueE, nu.cz),
w_aeff,
bins=(n_bins_E, n_bins_cz),
range=((10**logEmin, 10**logEmax), (-1, 1)),
log=(True, False),
)
misc.tex_mpl_rc(True)
fig = getfig(aspect=4 / 3., width=5)
ax = fig.add_subplot(111)
fig.subplots_adjust(bottom=.15, left=.15)
result = hl.plot2d(ax,
h_aeff,
cbar=True,
log=True,
vmin=5e-6,
vmax=1e4,
zmin=5e-6)
result['colorbar'].set_label(r'effective area $[\text{m}^2]$')
ax.set_xlabel('neutrino energy [GeV]')
ax.set_ylabel(r'$\cos(\text{zenith})$')
plot_dir = misc.ensure_dir('{0}/plots'.format(self.mode_dir))
savingfig(fig, plot_dir, 'aeff')
bins = h_aeff.bins
filename = '{}/aeff.txt'.format(plot_dir)
prush('-> {} ...'.format(filename))
with open(filename, 'w') as f:
pr = lambda *a, **kw: print(*a, file=f, **kw)
pr('# {:>11}{:>13}{:>16}{:>16}{:>16}'.format(
'E_min[GeV]', 'E_max[GeV]', 'cos(zenith)_min',
'cos(zenith)_max', 'Aeff[m^2]'))
for (Emin, Emax) in izip(bins[0][:-1], bins[0][1:]):
for (czmin, czmax) in izip(bins[1][:-1], bins[1][1:]):
pr('{:13.3e}{:13.3e}{:+16.2f}{:+16.2f}{:16.3e}'.format(
Emin, Emax, czmin, czmax,
h_aeff.get_value(1.001 * Emin, 1e-3 + czmin)))
import matplotlib as mpl
mpl.use('pdf')
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.stats as stats
from scipy.stats import chi2
from icecube.umdtools import cache,misc
from icecube import icetray, dataclasses, histlite
from skylab import statistics
fitfun = statistics.delta_chi2
##Make plots prettier
misc.tex_mpl_rc()
w=4
propsmall = mpl.font_manager.FontProperties (size='small')
propxsmall = mpl.font_manager.FontProperties (size='x-small')
###This script imports the sensitivities from the submitter and plots them.###
#picklefolder = '/data/user/brelethford/Data/SwiftBAT70m/pickle/'
## Define fcn to read in background trials previously logged ##
def getBckg(datafolder):
files = [cache.load(datafolder+file) for file in os.listdir(datafolder) if file.endswith('.array')]
n_inj=[]
nsources=[]
TS=[]
beta=(0.5) #For background ts
TS_beta=[] #Calculated from the total TS median after we get all the TS.
def sd(self):
"""Plot sensitivity and discovery potential."""
misc.tex_mpl_rc()
sig_info = self.sig_info
fig = getfig(aspect=16 / 10., width=6)
ax = fig.add_subplot(111)
nfig = getfig(aspect=16 / 10., width=6)
nax = nfig.add_subplot(111)
rfig = getfig(aspect=16 / 10., width=6)
rax = rfig.add_subplot(111)
curves = {}
for n_sigma in (0, 5):
if n_sigma == 0:
thing = 'Sensitivity'
CL = 0.9
ls = '--'
else:
thing = 'Disc. Pot.'
CL = 0.5
ls = '-'
color = 'b'
alpha = .8
label = r'$E^{{-2}}$ {}'.format(thing)
x = bk.get_best(sig_info, n_sigma, CL, 2)
sin_dec = np.array([np.cos(-k / 180. * pi) for k in sorted(x)])
curve = np.array([x[k][0] for k in sorted(x)])
ncurve = np.array([x[k][1] for k in sorted(x)])
ax.semilogy(sin_dec,
curve,
label=label,
ls=ls,
color=color,
alpha=alpha,
lw=2)
nax.plot(sin_dec,
ncurve,
label=label,
ls=ls,
color=color,
alpha=alpha,
lw=2)
curves[n_sigma] = sin_dec, curve
x, y = np.genfromtxt('{}/etc/orig_sens.txt'.format(self.root_dir)).T
label = r'$E^{{-2}}$ Sensitivity (original)'.format()
ax.semilogy(x,
1e-3 * y,
label=label,
ls='--',
color='k',
alpha=.8,
lw=1)
rax.plot(curves[0][0],
curves[0][1] / np.interp(curves[0][0], x, 1e-3 * y),
ls='--',
color='k',
lw=1,
label='Sensitivity ratio')
x, y = np.genfromtxt('{}/etc/orig_disc.txt'.format(self.root_dir)).T
label = r'$E^{{-2}}$ Disc. Pot. (original)'.format()
ax.semilogy(x,
1e-3 * y,
label=label,
ls='-',
color='k',
alpha=.8,
lw=1)
rax.plot(curves[5][0],
curves[5][1] / np.interp(curves[5][0], x, 1e-3 * y),
ls='-',
color='k',
lw=1,
label='Disc. Pot. ratio')
ax.set_xlabel(r'$\sin(\delta)$')
nax.set_xlabel(r'$\sin(\delta)$')
rax.set_xlabel(r'$\sin(\delta)$')
ax.set_ylabel(r'$E^2 '
'\cdot (E/100\,\mathrm{TeV})^{\gamma-2}'
'\cdot dN/dE\,\,\,'
'[\mathrm{TeV}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}]$')
nax.set_ylabel(r'$n_\mathrm{inj}$')
nax.set_ylabel(r'ratio')
ax.grid()
legend = ax.legend(loc='upper right',
prop=propsmall,
handlelength=4,
ncol=2)
frame = legend.get_frame()
frame.set_linewidth(0)
nax.grid()
legend = nax.legend(loc='upper right',
prop=propsmall,
handlelength=4,
ncol=2)
frame = legend.get_frame()
frame.set_linewidth(0)
rax.set_ylim(0.5, 1.5)
rax.grid()
legend = rax.legend(loc='best', prop=propsmall, handlelength=4, ncol=2)
legend.get_frame().set_linewidth(0)
fig.subplots_adjust(bottom=.14, top=.91, right=.97)
nfig.subplots_adjust(bottom=.14, top=.91, right=.97)
rfig.subplots_adjust(bottom=.14, top=.91, right=.97)
plot_dir = misc.ensure_dir('{0}/plots'.format(self.mode_dir))
savingfig(fig, plot_dir, 'sensdisc')
savingfig(nfig, plot_dir, 'sensdisc_ninj')
savingfig(rfig, plot_dir, 'sensdisc_ratio')
