legnoFG = '$\epsilon=1$, No FG: r < {0:5.2f} (95% CL)'.format(upperlimit(chain_nofg_r,'r'))
legend([bla, a, b03, d03],[legnoFG, legA, legB03, legD03], frameon=False, title='QUBIC 2 years '+config+site,fontsize=13)
savefig('all_limits_qubic_dl_beta_r.png', transparent=False)
############ Lensing floor ?
dl_lensing = ic.get_Dlbb_fromlib(lll, 0, camblib)
fsky=0.8
deltal=100
nsig=2
svar_lensing = np.sqrt(2./(2*lll+1)/fsky/deltal)*dl_lensing*nsig
svar_lensing_5percent = np.sqrt(2./(2*lll+1)/0.05/deltal)*dl_lensing*nsig
svar_lensing_1percent = np.sqrt(2./(2*lll+1)/0.01/deltal)*dl_lensing*nsig
dl_01 = ic.get_Dlbb_fromlib(lll, 0.01, camblib)-dl_lensing
dl_001 = ic.get_Dlbb_fromlib(lll, 0.001, camblib)-dl_lensing
dl_005 = ic.get_Dlbb_fromlib(lll, 0.005, camblib)-dl_lensing
clf()
xlim(0,150)
yscale('log')
ylim(1e-6,0.02)
['150, 220'],
'm',
0.01,
[qubic_duration, qubic_duration],
[theepsilon, theepsilon],
camblib=camblib, dustParams=Planck_in_Bicep_pars)
ellvals = (ellmin + ellmax)/2
specbin = np.reshape(bla[3], ((3,len(ellvals))))
specbinerr= np.reshape(bla[4], ((3,len(ellvals))))
noise_errors = np.reshape(np.sqrt(np.diag(bla[1])), (3,len(ellvals)))
ther = 0.05
thecl = ic.get_Dlbb_fromlib(lll, ther, camblib)
dllensing = spectrum*lll*(lll+1)/2/pi
lbins = np.zeros(2*len(ellvals))
errs = np.zeros((3,2*len(ellvals)))
for i in xrange(len(ellvals)):
lbins[i*2] = ellmin[i]
lbins[i*2+1] = ellmax[i]
errs[:,i*2] = noise_errors[:,i]
errs[:,i*2+1] = noise_errors[:,i]
clf()
yscale('log')
#xscale('log')
xlim(0,300)
