def recon_from_config(Config,
recon_section,
parray,
theory,
lmax,
grad_cut=None,
pol=False,
u_equals_l=True):
min_ell = fmaps.minimum_ell(parray.shape, parray.wcs)
lb = ellbounds_from_config(Config, recon_section, min_ell)
tellmin = lb['tellminY']
tellmax = lb['tellmaxY']
pellmin = lb['pellminY']
pellmax = lb['pellmaxY']
kellmin = lb['kellmin']
kellmax = lb['kellmax']
lxmap_dat, lymap_dat, modlmap_dat, angmap_dat, lx_dat, ly_dat = fmaps.get_ft_attributes_enmap(
parray.shape, parray.wcs)
template_dat = fmaps.simple_flipper_template_from_enmap(
parray.shape, parray.wcs)
nT = parray.nT
nP = parray.nP
kbeam_dat = parray.lbeam
fMaskCMB_T = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=tellmin,
lmax=tellmax)
fMaskCMB_P = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=pellmin,
lmax=pellmax)
fMask = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=kellmin,
lmax=kellmax)
with oio.nostdout():
from alhazen.quadraticEstimator import Estimator
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
noiseY2dTEB=[nT, nP, nP],
fmaskX2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeam_dat,
kBeamY=kbeam_dat,
doCurl=False,
TOnly=not (pol),
halo=True,
uEqualsL=u_equals_l,
gradCut=grad_cut,
bigell=lmax)
return tellmin, tellmax, pellmin, pellmax, kellmin, kellmax, qest
def _init_qests(self):
mlist = ['e','s','r']
self.qest = {}
tellminY = 500
tellmaxY = 3000
pellminY = 500
pellmaxY = 3000
tellminX = 500
tellmaxX = 3000
pellminX = 500
pellmaxX = 3000
kellmin = 80
kellmax = 3000
self.kellmin = kellmin
self.kellmax = kellmax
for m in mlist:
lxmap_dat,lymap_dat,modlmap_dat,angmap_dat,lx_dat,ly_dat = fmaps.get_ft_attributes_enmap(self.shape[m],self.wcs[m])
nT = modlmap_dat.copy()*0.
nP = modlmap_dat.copy()*0.
lbeam = modlmap_dat.copy()*0.+1.
template_dat = fmaps.simple_flipper_template_from_enmap(self.shape[m],self.wcs[m])
fMaskCMB_TX = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=tellminX,lmax=tellmaxX)
fMaskCMB_TY = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=tellminY,lmax=tellmaxY)
fMaskCMB_PX = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=pellminX,lmax=pellmaxX)
fMaskCMB_PY = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=pellminY,lmax=pellmaxY)
fMask = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=kellmin,lmax=kellmax)
with io.nostdout():
self.qest[m] = Estimator(template_dat,
self.theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT,nP,nP],
noiseY2dTEB=[nT,nP,nP],
fmaskX2dTEB=[fMaskCMB_TX,fMaskCMB_PX,fMaskCMB_PX],
fmaskY2dTEB=[fMaskCMB_TY,fMaskCMB_PY,fMaskCMB_PY],
fmaskKappa=fMask,
kBeamX = lbeam,
kBeamY = lbeam,
doCurl=False,
TOnly=True,
halo=True,
uEqualsL=True,
gradCut=None,verbose=False,
bigell=self.lmax)
) * 0. + gGenT.power / beamTemplate[:, :]**2. / TCMB**2.
filt_noiseE = fot.copy(
) * 0. + gGenP1.power / beamTemplate[:, :]**2. / TCMB**2.
filt_noiseB = fot.copy(
) * 0. + gGenP2.power / beamTemplate[:, :]**2. / TCMB**2.
if k == 0:
qest = Estimator(lensedTLm,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[filt_noiseT, filt_noiseE, filt_noiseB],
noiseY2dTEB=[filt_noiseT, filt_noiseE, filt_noiseB],
fmaskX2dTEB=[fMaskCMB] * 3,
fmaskY2dTEB=[fMaskCMB] * 3,
fmaskKappa=fMask,
doCurl=False,
TOnly=tonly,
halo=True,
gradCut=cmbellmax,
verbose=True,
loadPickledNormAndFilters=loadFile,
savePickledNormAndFilters=saveFile)
print "Reconstructing", i, " ..."
qest.updateTEB_X(fot, foe, fob, alreadyFTed=True)
qest.updateTEB_Y(alreadyFTed=True)
for j, polComb in enumerate(polCombList):
kappa = qest.getKappa(polComb)
kellmax = 4000
fMaskCMB = fmaps.fourierMask(lx,
ly,
modLMap,
lmin=cmbellmin,
lmax=cmbellmax)
fMask = fmaps.fourierMask(lx, ly, modLMap, lmin=kellmin, lmax=kellmax)
qest = Estimator(smap,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[filt_noiseT, filt_noiseE, filt_noiseB],
noiseY2dTEB=[filt_noiseT, filt_noiseE, filt_noiseB],
fmaskX2dTEB=[fMaskCMB] * 3,
fmaskY2dTEB=[fMaskCMB] * 3,
fmaskKappa=fMask,
doCurl=False,
TOnly=True,
halo=True,
gradCut=gradCut,
verbose=False,
loadPickledNormAndFilters=None,
savePickledNormAndFilters=None)
lensedMapX = stamp.copy() * win
lensedMapY = stamp.copy() * win
try:
fotX = np.nan_to_num(
fft(lensedMapX, axes=[-2, -1]) / beamTemplate[:, :])
except:
lmax=pellmax)
fMask = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=kellmin,
lmax=kellmax)
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
noiseY2dTEB=[nT, nP, nP],
fmaskX2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeampass,
kBeamY=kbeampass,
doCurl=False,
TOnly=not (pol),
halo=True,
uEqualsL=True,
gradCut=gradCut,
verbose=False,
bigell=lmax)
clkk2d = theory.gCl('kk', modlmap_dat)
for k, index in enumerate(my_tasks):
if rank == 0:
print(
("Rank ", rank, " doing job ", k + 1, " / ", len(my_tasks), "..."))
lxMap,lyMap,modLMap,thetaMap,lx,ly = fmaps.getFTAttributesFromLiteMap(templateMap)
print "Making white noise..."
nT,nP = fmaps.whiteNoise2D([noiseT,noiseP],beamArcmin,modLMap,TCMB=TCMB)
fMask = fmaps.fourierMask(lx,ly,modLMap,lmin=cmbellmin,lmax=cmbellmax)
fMaskK = fmaps.fourierMask(lx,ly,modLMap,lmin=kellmin,lmax=kellmax)
qest = Estimator(templateMap,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT,nP,nP],
noiseY2dTEB=[nT,nP,nP],
fmaskX2dTEB=[fMask]*3,
fmaskY2dTEB=[fMask]*3,
fmaskKappa=fMaskK,
doCurl=False,
TOnly=len(polCombList)==1,
halo=True,
gradCut=10000,
verbose=True,
loadPickledNormAndFilters=loadFile,
savePickledNormAndFilters=saveFile)
modLMap = qest.N.modLMap
bin_edges = np.arange(2,kellmax,10)
pl = Plotter(scaleY='log',scaleX='log')
pl.add(ellkk,4.*Clkk/2./np.pi)
# CHECK THAT NORM MATCHES HU/OK
ly_dat,
modlmap_dat,
lmin=kellmin,
lmax=kellmax)
with io.nostdout():
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
noiseY2dTEB=[nT, nP, nP],
fmaskX2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeampass,
kBeamY=kbeampass,
doCurl=False,
TOnly=not (pol),
halo=True,
uEqualsL=True,
gradCut=10000,
verbose=False,
loadPickledNormAndFilters=None,
savePickledNormAndFilters=None,
bigell=9000)
taper_percent = 14.0
pad_percent = 2.0
Ny, Nx = shape_dat
taper = fmaps.cosineWindow(Ny,
Nx,
lmin=kellmin,
lmax=kellmax)
if deconvolve_beam:
kbeampass = None
else:
kbeampass = kbeam_dat
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
noiseY2dTEB=[nT, nP, nP],
fmaskX2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeampass,
kBeamY=kbeampass,
doCurl=False,
TOnly=not (pol),
halo=True,
gradCut=gradCut,
verbose=False,
loadPickledNormAndFilters=None,
savePickledNormAndFilters=None)
if maxlike:
qest_maxlike = Estimator(
template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
nT,nP,nP = fmaps.whiteNoise2D([1.0,1.0,1.0],1.0,modLMap,TCMB = TCMB)
gradCut = 2000
cmbellmin = 200
cmbellmax = 8000
kellmin = 100
kellmax = 8000
fMaskCMB = fmaps.fourierMask(lx,ly,modLMap,lmin=cmbellmin,lmax=cmbellmax)
fMask = fmaps.fourierMask(lx,ly,modLMap,lmin=kellmin,lmax=kellmax)
qest = Estimator(templateLM,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT,nP,nP],
noiseY2dTEB=[nT,nP,nP],
fmaskX2dTEB=[fMaskCMB]*3,
fmaskY2dTEB=[fMaskCMB]*3,
fmaskKappa=fMask,
doCurl=False,
TOnly=not(pol),
halo=True,
gradCut=gradCut,verbose=True,
loadPickledNormAndFilters=None,
savePickledNormAndFilters=None)
# bin_edges = np.arange(kellmin,kellmax,20)
# b = bin2D(modLMap,bin_edges)
# ls,Nls = b.bin(qest.N.Nlkk['EB'])
# pl = Plotter(scaleY='log')
# pl.add(bin_edges,theory.gCl("kk",bin_edges))
# pl.add(ls,Nls)
nT = parray_dat.nT
nP = parray_dat.nP
kbeam_dat = parray_dat.lbeam
kbeampass = kbeam_dat
fMaskCMB_T = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=tellmin,lmax=tellmax)
fMaskCMB_P = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=pellmin,lmax=pellmax)
fMask = fmaps.fourierMask(lx_dat,ly_dat,modlmap_dat,lmin=kellmin,lmax=kellmax)
with io.nostdout():
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT,nP,nP],
noiseY2dTEB=[nT,nP,nP],
fmaskX2dTEB=[fMaskCMB_T,fMaskCMB_P,fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T,fMaskCMB_P,fMaskCMB_P],
fmaskKappa=fMask,
kBeamX = kbeampass,
kBeamY = kbeampass,
doCurl=False,
TOnly=not(pol),
halo=True,
uEqualsL=True,
gradCut=args.grad_cut,bigell=lmax)
covseed = 0
def l(cseed,kseed,returnk=False,index=None):
cname = fout_dir+"lensed_covseed_"+str(args.covseed).zfill(3)+"_cmbseed_"+str(cseed).zfill(5)+"_kseed_"+str(kseed).zfill(5)+".hdf"
if unlensed:
seedroot = (covseed)*Nsets*Nsims
