First version

TestEsti.py

+from pylab import *
+import astropy.io.fits as fits
+import xqml
+import numpy as np
+import healpy as hp
+import matplotlib.pyplot as plt
+
+
+nside = 8
+lmax = 3*nside-1
+Slmax = 3*nside-1
+deltal = 1
+clth = read_cl( 'planck_base_planck_2015_TTlowP.fits')
+lth = arange(2,lmax+1)
+
+ellbins = arange(2,lmax+2,deltal)
+ellbins[-1] = lmax+1
+
+P, Q, ell, ellval = xqml.GetBinningMatrix(ellbins, lmax)
+nbins=len(ellbins)-1
+
+
+#create mask
+t,p=hp.pix2ang( nside, range(hp.nside2npix(nside)))
+mask = np.ones(hp.nside2npix(nside),bool)
+mask[abs(90-rad2deg(t)) < 10] = False
+
+ip=arange(hp.nside2npix(nside))
+ipok=ip[mask]
+npix=len(ipok)
+
+fwhm = 0.5
+bl=gauss_beam(deg2rad(fwhm), lmax=Slmax+1)
+
+allStoke, der, ind = xqml.getstokes(polar=True,temp=False,EBTB=False)
+nder=len(der)
+
+muKarcmin = 0.1
+
+
+
+###################################### Compute ds_dcb ######################################
+Pl, S = xqml.compute_ds_dcb(ellbins,nside,ipok,bl,clth,Slmax,polar=True,temp=False, EBTB=False, pixwining=True, timing=True, MC=False)
+Pl = Pl.reshape((nder)*(np.shape(Pl)[1]), 2*npix, 2*npix)
+
+
+###################################### Construct El, Wb ######################################
+pixvar = 2*xqml.muKarcmin2var(muKarcmin, nside)
+varmap = ones((2*npix))*pixvar
+
+NoiseVar = np.diag(varmap)
+
+C = S + NoiseVar
+
+invC = inv(C)
+#invS = inv(S)
+
+El = xqml.El(invC, invC, Pl)
+Wb = xqml.CrossWindowFunction(El, Pl)
+invW = inv(Wb)
+
+
+###################################### Compute spectra ######################################
+cmb = hp.synfast( clth, nside, fwhm=deg2rad(fwhm), pixwin=True, new=True, verbose=False)
+noise = (randn(len(varmap)) * varmap**.5).reshape(2,-1)
+
+dm = cmb[1:,mask] + noise
+
+yl = xqml.yQuadEstimator(dm.ravel(), dm.ravel(), El)
+cl = xqml.ClQuadEstimator(invW, yl).reshape(nder,-1)
+
+plot( clth.transpose()[:lmax+1,:])
+plot( ellval, cl.transpose())
+
+
+G = xqml.CrossGisherMatrix(El, C)
+V = xqml.CovAB(invW, G)
+
+
+###################################### Construct MC ######################################
+
+allcl = []
+for n in range(100):
+	progress_bar( n, 100)
+	cmb = hp.synfast( clth, nside, fwhm=deg2rad(fwhm), pixwin=True, new=True, verbose=False)
+	dm = cmb[1:,mask] + (randn(2*npix)*sqrt(varmap)).reshape( 2, npix)
+	yl = xqml.yQuadEstimator(dm.ravel(), dm.ravel(), El)
+	allcl.append( xqml.ClQuadEstimator(invW, yl).reshape(nder,-1))
+
+fits.writeto( "allcl_Slmax%d.fits" % Slmax, array(allcl))
+
+figure()
+subplot( 2,1,1)
+plot( lth, clth.transpose()[lth,1:3], 'k')
+plot( ellval, mean( allcl,0).transpose(), 'r')
+plot( ellval, mean( allcl,0).transpose() + std( allcl,0).transpose(), 'r--')
+plot( ellval, mean( allcl,0).transpose() - std( allcl,0).transpose(), 'r--')
+subplot( 2,1,2)
+cosmic = sqrt( 2./(2*lth+1))/mean(mask) * clth[1:3,lth]
+plot( lth, cosmic.transpose(), 'k')
+plot( ellval, std( allcl,0).transpose(), 'r')
+plot( ellval, sqrt(diag(V)).reshape(nder,-1).transpose(), 'b')
+
+
+
+
+
+