#OUTPUT FILES figout = PdfPages('ninv'+dd+uu+'_poly'+str(npt)+'_'+gom+ss+'.pdf') fout = open("nalpha"+dd+uu+"_poly"+str(npt)+"_"+gom+ss+".txt","w") fouts = open("nstd"+dd+uu+"_poly"+str(npt)+"_"+gom+ss+".txt","w") #============================================================================== ksp = makeksp(0.15e-4,200.0,1000.0) psp = readcamb("dr9_full.ztf", ns, 8.0, sigma8_lin, omega_b, omega_c, ksp) print 'sigmaR =', sigmaR(ksp, psp, 8.0) psp_c = psp*bias**2. nk = np.size(ksp) #spline no wiggle transfer function onto same k-grid as P(k), then apodize tspnw = spllog(ksp, tnw, knw) pc = apodize(tspnw, psp_c, ksp, ns, signl_ap) kcovdex = np.squeeze(np.where(ksp < 1000.0/max(r))) ksp_c = ksp[kcovdex] pc = pc[kcovdex] if(gom == 'gc'): npc = np.size(kcovdex) stream = calc_stream(ksp_c,npc,beta,par3,'exp') pvol = [par0,par1,par2] inttot = volint(pvol,pc,npc,stream,nbar,mesh) cov = cov00(ksp_c, inttot, kr, r) + par4 print "Fitting using the Gaussian Covariance matrix..." covinv = np.linalg.inv(cov[sub1:sub2,sub1:sub2]) if(gom == 'mc'): cov = mcov00("red_norec.txt","red_norec_avg.xi")
ksp = makeksp(0.15e-4,200.0,1000.0) psp = readcamb("dr9_full.ztf", ns, 8.0, sigma8_lin, omega_b, omega_c, ksp) print 'sigmaR =', sigmaR(ksp, psp, 8.0) k, tt = np.loadtxt(infile[0],unpack=True) dex = np.squeeze(np.where(k > 0.)) k = k[dex] psp = spllog(k, psp, ksp) nk = np.size(k) #spline no wiggle transfer function onto same k-grid as P(k), then apodize tspnw = spllog(k, tnw, knw) #DIVIDE data by pspnw (BAO-less P(k)), pk_mod should already be correctly #normalized pspnw, pk_mod = apodize(tspnw, psp, k, ns, snl) #UNCOMMENT if want model with RSD #rsd = calc_stream(k, nk, beta, 3.0, 'exp') #pk_mod = pk_mod*rsd #np.savetxt("pk_mod_rsd.txt",zip(k,pk_mod)) cov = mcov00("red_norec.txt","red_norec_avg.pk",pspnw) print "Fitting using the mock Covariance matrix..." covinv = np.linalg.inv(cov[sub1:sub2,sub1:sub2]) perr = np.zeros(nk) for i in range(0,nk): perr[i] = math.sqrt(cov[i,i]) xib0 = np.zeros((npt+1,nk))