if args.make_plot:
if args.limit_or_detect == 'detect':
far = 0.05
dr_list = [0.95, 0.68]
bu.OSupperLimit(psr, GCGnoiseInv, HnD, optimalStat, far, dr_list)
else:
ul_list = [0.95, 0.90]
bu.OSupperLimit(psr, GCGnoiseInv, HnD, optimalStat, ul_list)
bu.OScrossPower(angSep, optimalStat[3], optimalStat[4])
if args.LMAX != 0:
anisOptStat = utils.AnisOptStat(psr,
GCGnoiseInv,
CorrCoeff,
args.LMAX,
gam_gwb=gam_bkgrd)
print "\n The ML coefficients of an l={0} search are {1}\n".format(
args.LMAX, anisOptStat[0] / np.sqrt(4.0 * np.pi))
print "\n The error-bars from the inverse Fisher matrix are {0}\n".format(
np.sqrt(np.diag(anisOptStat[1])) / np.sqrt(4.0 * np.pi))
print "\n The Fisher information is {0}\n".format(anisOptStat[2])
print "\n The ML coefficients of an l={0} search are {1}\n".format(
args.LMAX, anisOptStat[0])
print "\n The full covariance matrix is {0}\n".format(anisOptStat[1])
np.save('mlcoeff_lmax{0}'.format(args.LMAX), anisOptStat[0])
