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), "..."))
unlensed = parray_sim.get_unlensed_cmb(seed=index, scalar=False)
luteb, dummy = sverif_cmb.add_power("unlensed", unlensed)
kappa = parray_sim.get_kappa(ktype="grf", vary=False, seed=index + 1000000)
phi, fphi = lt.kappa_to_phi(kappa, parray_sim.modlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
lensed = lensing.lens_map(unlensed.copy(), grad_phi, order=lens_order)
llteb, dummy = sverif_cmb.add_power("lensed", lensed)
pdelensed = lensing.delens_map(lensed,
grad_phi,
nstep=delens_steps,
order=lens_order)
lpteb, dummy = sverif_cmb.add_power("pdelensed", pdelensed)
qest.updateTEB_X(llteb[0], llteb[1], llteb[2], alreadyFTed=True)
qest.updateTEB_Y(alreadyFTed=True)
with io.nostdout():
rawkappa_TT = qest.getKappa("TT").real
rawkappa_EB = qest.getKappa("EB").real
grad_cut = None
clkk = theory.gCl("kk", fine_ells)
clkk.resize((1, 1, clkk.size))
kappa_map = enmap.rand_map(shape_sim[-2:], wcs_sim, cov=clkk, scalar=True)
pkk = fmaps.get_simple_power_enmap(kappa_map)
debug_edges = np.arange(2, 12000, 80)
dbinner = stats.bin2D(modlmap_sim, debug_edges)
cents, bclkk = dbinner.bin(pkk)
clkk.resize((clkk.shape[-1]))
pl = io.Plotter(scaleY='log', scaleX='log')
pl.add(fine_ells, clkk)
pl.add(cents, bclkk)
pl.done(out_dir + "clkk.png")
phi, fphi = lt.kappa_to_phi(kappa_map, modlmap_sim, return_fphi=True)
io.quickPlot2d(kappa_map, out_dir + "kappa_map.png")
io.quickPlot2d(phi, out_dir + "phi.png")
alpha_pix = enmap.grad_pixf(fphi)
# === SIM AND RECON LOOP ===
kappa_stack = {}
if cluster:
profiles = {}
else:
apowers = {}
cpowers = {}
for polcomb in pol_list:
kappa_stack[polcomb] = 0.
if cluster:
model_uniform_kappa = 0.02 #02
else:
clkk = theory.gCl("kk", fine_ells)
clkk.resize((1, 1, clkk.size))
kappa_map = enmap.rand_map(shape_sim[-2:], wcs_sim, cov=clkk, scalar=True)
if debug:
pkk = fmaps.get_simple_power_enmap(kappa_map)
debug_edges = np.arange(kellmin, 8000, 80)
dbinner = stats.bin2D(modlmap_sim, debug_edges)
cents, bclkk = dbinner.bin(pkk)
clkk.resize((clkk.shape[-1]))
pl = io.Plotter(scaleY='log', scaleX='log')
pl.add(fine_ells, clkk)
pl.add(cents, bclkk)
pl.done(out_dir + "clkk.png")
phi, fphi = lt.kappa_to_phi(kappa_map, modlmap_sim, return_fphi=True)
io.quickPlot2d(kappa_map, out_dir + "kappa_map.png")
io.quickPlot2d(phi, out_dir + "phi.png")
alpha_pix = enmap.grad_pixf(fphi)
# === EXPERIMENT ===
if deconvolve_beam:
ntfunc = cmb.get_noise_func(beam_arcmin,
noise_T_uK_arcmin,
ellmin=tellmin,
ellmax=tellmax,
TCMB=TCMB)
npfunc = cmb.get_noise_func(beam_arcmin,
noise_P_uK_arcmin,
ellmin=pellmin,
noise_T = 1.0
pa = fmaps.PatchArray(shape,wcs,dimensionless=False,skip_real=False)
pa.add_theory(cc,theory,lmax)
pa.add_gaussian_beam(1.0)
pa.add_white_noise_with_atm(noise_T,0.,0,1,0,1)
Npoints = 60
mrange = np.linspace(1,3,Npoints)*1.e14
trueM = 2e14
kappa = nfwkappa(trueM)
phi, fphi = lt.kappa_to_phi(kappa,pa.modlmap,return_fphi=True)
#grad_phi = enmap.grad(phi)
alpha_pix = enmap.grad_pixf(fphi)
N = 10000
Nfor = N
totlikes = 0.
allike = 1.
Ms = []
logdets = []
cinvs = []
TCMB = 1. #2.7255e6
from scipy.linalg import pinv2
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeampass,
kBeamY=kbeampass,
doCurl=False,
TOnly=not (pol),
halo=True,
uEqualsL=False,
gradCut=gradCut,
verbose=False,
bigell=lmax,
lEqualsU=False)
kappa = get_nfw(5.e14)
#+ parray_sim.get_grf_kappa(seed=10,skip_update=False)
phi, fphi = lt.kappa_to_phi(kappa, parray_sim.modlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
kappa_model = kappa.copy() / 5.
#kappa_model = kappa.copy()# /5.
for k, index in enumerate(my_tasks):
if rank == 0:
print(
("Rank ", rank, " doing job ", k + 1, " / ", len(my_tasks), "..."))
unlensed = parray_sim.get_unlensed_cmb(seed=index, scalar=False)
luteb, dummy = sverif_cmb.add_power("unlensed", unlensed)
lensed = lensing.lens_map(unlensed.copy(), grad_phi, order=lens_order)
lensed += parray_dat.get_noise_sim(seed=index + 100000)
llteb, dummy = sverif_cmb.add_power("lensed", lensed)
pixratio = analysis_resolution/Config.getfloat(sim_section,"pixel_arcmin")
px_dat = analysis_resolution
lens_order = Config.getint(sim_section,"lens_order")
k = -1
for index in my_tasks:
k += 1
if k==0:
from alhazen.halos import nfw_kappa
kappa = nfw_kappa(cluster_mass,parray_sim.modrmap,cc)
#kappa = parray_sim.get_grf_kappa(seed=1)
phi, fphi = lt.kappa_to_phi(kappa,parray_sim.modlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
if rank==0: print(("Generating unlensed CMB for ", k, "..."))
unlensed = parray_sim.get_unlensed_cmb(seed=index)
if rank==0: print("Lensing...")
lensed = unlensed if nolens else lensing.lens_map(unlensed.copy(), grad_phi, order=lens_order, mode="spline", border="cyclic", trans=False, deriv=False, h=1e-7)
#lensed = lensing.lens_map_flat(unlensed.copy(), phi, order=lens_order)
if rank==0: print("Downsampling...")
cmb = lensed if abs(pixratio-1.)<1.e-3 else resample.resample_fft(lensed,shape_dat)
cmb = enmap.ndmap(cmb,wcs_dat)
if rank==0: print("Adding noise...")
flensed = fftfast.fft(cmb,axes=[-2,-1])
flensed *= parray_dat.lbeam
lensedt = fftfast.ifft(flensed,axes=[-2,-1],normalize=True).real