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)