def TF_OSupperLimit(psr,
fqs,
Tspan,
F,
GCGnoiseInv,
ORF,
OSsmbhb,
ul_list=None,
far=None,
drlist=None,
tex=True,
nlims=70):
if tex == True:
plt.rcParams['text.usetex'] = True
gam_bkgrd = np.linspace(0.01, 6.99, nlims)
optStatList = []
ct = 0
for indx in gam_bkgrd:
optStatList.append(
utils.TFoptStat(psr, fqs, Tspan, F, GCGnoiseInv, ORF,
gam_gwb=indx)[:3])
ct += 1
print "Finished {0}% of optimal-statistic upper-limit calculations...".format(
100.0 * ct / (1.0 * nlims))
optStatList = np.array(optStatList)
fig, ax = plt.subplots()
stylelist = ['solid', 'dashed', 'dotted']
if ul_list is not None:
for ii in range(len(ul_list)):
ax.plot(
gam_bkgrd,
np.sqrt(optStatList[:, 0] + optStatList[:, 1] * np.sqrt(2.0) *
(ss.erfcinv(2.0 * (1. - ul_list[ii])))),
linestyle=stylelist[ii],
color='black',
linewidth=3.0,
label='{0}$\%$ upper-limit'.format(ul_list[ii] * 100))
plt.hlines(y=np.sqrt(OSsmbhb[0] + OSsmbhb[1] * np.sqrt(2.0) *
(ss.erfcinv(2.0 * (1. - ul_list[ii])))),
xmin=gam_bkgrd.min(),
xmax=13. / 3.,
linewidth=3.0,
linestyle='solid',
color='red')
plt.vlines(x=13. / 3.,
ymin=0.0,
ymax=np.sqrt(OSsmbhb[0] + OSsmbhb[1] * np.sqrt(2.0) *
(ss.erfcinv(2.0 * (1. - ul_list[ii])))),
linewidth=3.0,
linestyle='solid',
color='red')
else:
for ii in range(len(drlist)):
ax.plot(
gam_bkgrd,
np.sqrt(
optStatList[:, 1] * np.sqrt(2.0) *
(ss.erfcinv(2.0 * far) - ss.erfcinv(2.0 * drlist[ii]))),
linestyle=stylelist[ii],
color='black',
linewidth=3.0,
label='$A$ ({0}$\%$ FAR, {1}$\%$ DR)'.format(
far * 100, drlist[ii] * 100))
plt.hlines(y=np.sqrt(
OSsmbhb * np.sqrt(2.0) *
(ss.erfcinv(2.0 * far) - ss.erfcinv(2.0 * drlist[ii]))),
xmin=gam_bkgrd.min(),
xmax=13. / 3.,
linewidth=3.0,
linestyle='solid',
color='red')
plt.vlines(
x=13. / 3.,
ymin=0.0,
ymax=np.sqrt(
OSsmbhb * np.sqrt(2.0) *
(ss.erfcinv(2.0 * far) - ss.erfcinv(2.0 * drlist[ii]))),
linewidth=3.0,
linestyle='solid',
color='red')
ax.set_yscale('log')
ax.set_xlabel(r'$\gamma\equiv 3-2\alpha$', fontsize=20)
ax.set_ylabel(r'$A$', fontsize=20)
ax.minorticks_on()
plt.tick_params(labelsize=18)
plt.grid(which='major')
plt.grid(which='minor')
plt.title('Optimal-statistic bounds')
plt.legend(loc='lower left', shadow=True, frameon=True, prop={'size': 15})
plt.show()
