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)
lbinner_sim = stats.bin2D(modlmap_sim, lbin_edges)
sverif_cmb = SpectrumVerification(mpibox,
theory,
shape_sim,
wcs_sim,
lbinner=lbinner_sim,
pol=True)
sverif_kappa = SpectrumVerification(mpibox,
theory,
shape_dat,
wcs_dat,
lbinner=lbinner_dat,
pol=False)
template_dat = fmaps.simple_flipper_template_from_enmap(shape_dat, wcs_dat)
nT = parray_dat.nT
nP = parray_dat.nP
if rank == 0: io.quickPlot2d(nT, out_dir + "nt.png")
kbeam_dat = parray_dat.lbeam
kbeampass = kbeam_dat
if rank == 0: io.quickPlot2d(kbeampass, out_dir + "kbeam.png")
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,
beam = 0.
noiseT = 0.
# cosmology
cc = Cosmology(lmax=lmax_global, pickling=True) #,dimensionless=False)
theory = cc.theory
ellrange = np.arange(0, lmax_global, 1)
clkk = theory.gCl('kk', ellrange)
lcltt = theory.lCl('TT', ellrange)
ucltt = theory.uCl('TT', ellrange)
# quadratic estimator
deg = 5.
px = 0.5
shape, wcs = enmap.get_enmap_patch(deg * 60., px, proj="car", pol=False)
template = fmaps.simple_flipper_template_from_enmap(shape, wcs)
lxmap_dat, lymap_dat, modlmap_dat, angmap_dat, lx_dat, ly_dat = fmaps.get_ft_attributes_enmap(
shape, wcs)
lbin_edges = np.arange(kellmin, kellmax, 200)
lbinner_dat = stats.bin2D(modlmap_dat, lbin_edges)
fmask = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=kellmin,
lmax=kellmax)
nlgen = qe.NlGenerator(template, theory)
nTX, nPX, nTY, nPY = nlgen.updateNoise(beamX=beam,
noiseTX=noiseT,
noisePX=0.,
tellminX=tellmin,
tellmaxX=tellmax,
Nsims = int(50/numcores)
irange = list(range(rank*Nsims,(rank+1)*Nsims))
for i,k in enumerate(irange):
print(i)
measured = enmap.read_map(mapfile(k))
measured = measured[0]/TCMB
if i==0:
shape_dat = measured.shape
wcs_dat = measured.wcs
# === ESTIMATOR ===
template_dat = fmaps.simple_flipper_template_from_enmap(measured.shape,measured.wcs)
lxmap_dat,lymap_dat,modlmap_dat,angmap_dat,lx_dat,ly_dat = fmaps.get_ft_attributes_enmap(shape_dat,wcs_dat)
taper_percent = 15.0
Ny,Nx = shape_dat
taper = fmaps.cosineWindow(Ny,Nx,lenApodY=taper_percent*min(Ny,Nx)/100.,lenApodX=taper_percent*min(Ny,Nx)/100.,padY=0,padX=0)
tapered = measured*taper
# io.quickPlot2d(tapered,"map.png")
w2 = np.mean(taper**2.)
w4 = np.mean(taper**4.)
debug_edges = np.arange(400,8000,150)
dbinner_dat = stats.bin2D(modlmap_dat,debug_edges)