lxMap, lyMap, modLMap, thetaMap, lx, ly = fmaps.getFTAttributesFromLiteMap(
lensedTLm)
beamTemplate = fmaps.makeTemplate(l, beamells, modLMap)
fMaskCMB = fmaps.fourierMask(lx,
ly,
modLMap,
lmin=cmbellmin,
lmax=cmbellmax)
fMask = fmaps.fourierMask(lx, ly, modLMap, lmin=kellmin, lmax=kellmax)
ellNoise = np.arange(0, modLMap.max())
Ntt = ellNoise * 0. + whiteNoiseT
Npp = ellNoise * 0. + whiteNoiseP
Ntt[0] = 0.
Npp[0] = 0.
gGenT = fmaps.GRFGen(lensedTLm.copy(), ellNoise, Ntt, bufferFactor=1)
gGenP1 = fmaps.GRFGen(lensedTLm.copy(), ellNoise, Npp, bufferFactor=1)
gGenP2 = fmaps.GRFGen(lensedTLm.copy(), ellNoise, Npp, bufferFactor=1)
if noiseT > 1.e-3:
lensedTLm.data = lensedTLm.data + gGenT.getMap(stepFilterEll=None)
if noiseP > 1.e-3:
lensedQLm.data = lensedQLm.data + gGenP1.getMap(stepFilterEll=None)
if noiseP > 1.e-3:
lensedULm.data = lensedULm.data + gGenP2.getMap(stepFilterEll=None)
# pl = Plotter()
# pl.plot2d(lensedTLm.data)
# pl.done("map.png")
# sys.exit()
tht_fwhm = np.deg2rad(fwhm / 60.)
l = np.arange(0., 10000.)
beamells = np.exp(-(tht_fwhm**2.) * (l**2.) / (8. * np.log(2.)))
beamTemplate = fmaps.makeTemplate(l, beamells, modLMap)
noiseT = 0.1
noiseP = np.sqrt(2.) * noiseT
whiteNoiseT = (np.pi / (180. * 60))**2. * noiseT**2. / TCMB**2.
whiteNoiseP = (np.pi / (180. * 60))**2. * noiseP**2. / TCMB**2.
ellNoise = np.arange(0, modLMap.max())
Ntt = ellNoise * 0. + np.nan_to_num(whiteNoiseT)
Npp = ellNoise * 0. + np.nan_to_num(whiteNoiseP)
Ntt[0] = 0.
Npp[0] = 0.
gGenT = fmaps.GRFGen(templateLM, ellNoise, Ntt, bufferFactor=1)
gGenP1 = fmaps.GRFGen(templateLM, ellNoise, Npp, bufferFactor=1)
gGenP2 = fmaps.GRFGen(templateLM, ellNoise, Npp, bufferFactor=1)
theory = cc.theory
fot = np.zeros((templateLM.Ny, templateLM.Nx)) + 0.j
filt_noiseT = fot.copy() * 0. + np.nan_to_num(
gGenT.power / beamTemplate[:, :]**2.)
filt_noiseE = fot.copy() * 0. + np.nan_to_num(
gGenP1.power / beamTemplate[:, :]**2.)
filt_noiseB = fot.copy() * 0. + np.nan_to_num(
gGenP2.power / beamTemplate[:, :]**2.)
gradCut = 2000
cmbellmin = 20
cmbellmax = 8000
l = np.arange(0., 10000.)
beamells = np.exp(-(tht_fwhm**2.) * (l**2.) / (8. * np.log(2.)))
beamTemplate = fmaps.makeTemplate(l, beamells, modLMap)
#print beamTemplate.shape
noiseT = 42.0
noiseP = np.sqrt(2.) * noiseT
whiteNoiseT = (np.pi / (180. * 60))**2. * noiseT**2. / TCMB**2.
whiteNoiseP = (np.pi / (180. * 60))**2. * noiseP**2. / TCMB**2.
ellNoise = np.arange(0, modLMap.max())
Ntt = ellNoise * 0. + np.nan_to_num(whiteNoiseT)
Npp = ellNoise * 0. + np.nan_to_num(whiteNoiseP)
Ntt[0] = 0.
Npp[0] = 0.
gGenT = fmaps.GRFGen(smap, ellNoise, Ntt, bufferFactor=1)
gGenP1 = fmaps.GRFGen(smap, ellNoise, Npp, bufferFactor=1)
gGenP2 = fmaps.GRFGen(smap, ellNoise, Npp, bufferFactor=1)
theory = cc.theory
fot = np.zeros((smap.Ny, smap.Nx)) + 0.j
filt_noiseT = fot.copy() * 0. + np.nan_to_num(
gGenT.power / beamTemplate[:, :]**2.)
filt_noiseE = fot.copy() * 0. + np.nan_to_num(
gGenP1.power / beamTemplate[:, :]**2.)
filt_noiseB = fot.copy() * 0. + np.nan_to_num(
gGenP2.power / beamTemplate[:, :]**2.)
gradCut = 2000
cmbellmin = 20
cmbellmax = 4000
def getDLnMCMB(ells,Nls,clusterCosmology,log10Moverh,z,concentration,arcStamp,pxStamp,arc_upto,bin_width,expectedSN,Nclusters=1000,numSims=30,saveId=None,numPoints=1000,nsigma=8.,overdensity=500.,critical=True,atClusterZ=True):
import flipper.liteMap as lm
if saveId is not None: from orphics.tools.output import Plotter
M = 10.**log10Moverh
cc = clusterCosmology
stepfilter_ellmax = max(ells)
lmap = lm.makeEmptyCEATemplate(raSizeDeg=arcStamp/60., decSizeDeg=arcStamp/60.,pixScaleXarcmin=pxStamp,pixScaleYarcmin=pxStamp)
xMap,yMap,modRMap,xx,xy = fmaps.getRealAttributes(lmap)
lxMap,lyMap,modLMap,thetaMap,lx,ly = fmaps.getFTAttributesFromLiteMap(lmap)
kappaMap,retR500 = NFWkappa(cc,M,concentration,z,modRMap*180.*60./np.pi,winAtLens,overdensity,critical,atClusterZ)
finetheta = np.arange(0.01,arc_upto,0.01)
finekappa,retR500 = NFWkappa(cc,M,concentration,z,finetheta,winAtLens,overdensity,critical,atClusterZ)
kappaMap = fmaps.stepFunctionFilterLiteMap(kappaMap,modLMap,stepfilter_ellmax)
generator = fmaps.GRFGen(lmap,ells,Nls)
bin_edges = np.arange(0.,arc_upto,bin_width)
binner = bin2D(modRMap*180.*60./np.pi, bin_edges)
centers, thprof = binner.bin(kappaMap)
if saveId is not None:
pl = Plotter()
pl.plot2d(kappaMap)
pl.done("output/"+saveId+"kappa.png")
expectedSNGauss = expectedSN*np.sqrt(numSims)
sigma = 1./expectedSNGauss
amplitudeRange = np.linspace(1.-nsigma*sigma,1.+nsigma*sigma,numPoints)
lnLikes = 0.
bigStamp = 0.
for i in range(numSims):
profiles,totstamp = getProfiles(generator,stepfilter_ellmax,kappaMap,binner,Nclusters)
bigStamp += totstamp
stats = getStats(profiles)
if i==0 and (saveId is not None):
pl = Plotter()
pl.add(centers,thprof,lw=2,color='black')
pl.add(finetheta,finekappa,lw=2,color='black',ls="--")
pl.addErr(centers,stats['mean'],yerr=stats['errmean'],lw=2)
pl._ax.set_ylim(-0.01,0.3)
pl.done("output/"+saveId+"profile.png")
pl = Plotter()
pl.plot2d(totstamp)
pl.done("output/"+saveId+"totstamp.png")
Likes = getAmplitudeLikelihood(stats['mean'],stats['covmean'],amplitudeRange,thprof)
lnLikes += np.log(Likes)
width = amplitudeRange[1]-amplitudeRange[0]
Likes = np.exp(lnLikes)
Likes = Likes / (Likes.sum()*width) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,Likes,p0=[1.0,0.5])[0]
sn = ampBest/ampErr/np.sqrt(numSims)
snAll = ampBest/ampErr
if snAll<5.: print "WARNING: ", saveId, " run with mass ", M , " and redshift ", z , " has overall S/N<5. \
Consider re-running with a greater numSims, otherwise estimate of per Ncluster S/N will be noisy."
if saveId is not None:
Fit = np.array([np.exp(-0.5*(x-ampBest)**2./ampErr**2.) for x in amplitudeRange])
Fit = Fit / (Fit.sum()*width) #normalize
pl = Plotter()
pl.add(amplitudeRange,Likes,label="like")
pl.add(amplitudeRange,Fit,label="fit")
pl.legendOn(loc = 'lower left')
pl.done("output/"+saveId+"like.png")
pl = Plotter()
pl.plot2d(bigStamp/numSims)
pl.done("output/"+saveId+"bigstamp.png")
np.savetxt("data/"+saveId+"_m"+str(log10Moverh)+"_z"+str(z)+".txt",np.array([log10Moverh,z,1./sn]))
return 1./sn