else:
apowers = {}
cpowers = {}
for polcomb in pol_list:
kappa_stack[polcomb] = 0.
if cluster:
profiles[polcomb] = []
else:
apowers[polcomb] = []
cpowers[polcomb] = []
for i in range(Nsims):
print(i)
unlensed = enmap.rand_map(shape_sim, wcs_sim, ps)
lensed = lensing.lens_map_flat_pix(unlensed, alpha_pix, order=lens_order)
klteb = enmap.map2harm(lensed)
klteb_beam = klteb * kbeam_sim
lteb_beam = enmap.ifft(klteb_beam).real
noise = enmap.rand_map(shape_sim, wcs_sim, ps_noise, scalar=True)
observed = lteb_beam + noise
measured = enmap.downgrade(observed,
analysis_pixel_scale / sim_pixel_scale)
if i == 0:
#debug()
shape_dat, wcs_dat = 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)
nT = ntfunc(modlmap_dat)
s,logdet = np.linalg.slogdet(cov)
print((k,M,s,logdet,np.linalg.cond(cov)))
assert s>0
Ms.append( M )
logdets.append( logdet )
cinvs.append( pinv2(cov) )
mrange = np.array(Ms)
for i in range(N):
lnlikes = []
cmb_map = pa.get_unlensed_cmb(seed=2*i+100000000)
lensed = lensing.lens_map_flat_pix(cmb_map, alpha_pix,order=lens_order) if np.abs(M)>1.e-3 else cmb_map
flensed = fftfast.fft(lensed,axes=[-2,-1])
flensed *= pa.lbeam
lensed = fftfast.ifft(flensed,axes=[-2,-1],normalize=True).real
noise = pa.get_noise_sim(seed=2*i+1+100000000)
measured = lensed + noise
for k,M in enumerate(mrange):
logdet = logdets[k]
cinv = cinvs[k]
lnlikeval = lnlike(logdet,cinv,measured)
lnlikes.append(lnlikeval)
alpha_pix = enmap.grad_pixf(fphi)
# alpha_pix2 = enmap.grad_pix(phi)
print((alpha_pix.shape))
# print alpha_pix2.shape
io.quickPlot2d(alpha_pix[0], out_dir + "alpha_pixx.png")
io.quickPlot2d(alpha_pix[1], out_dir + "alpha_pixy.png")
io.quickPlot2d(np.sum(alpha_pix**2, 0)**0.5, out_dir + "alpha_pix.png")
# io.quickPlot2d(alpha_pix2[0],out_dir+"alpha_pixx2.png")
# io.quickPlot2d(alpha_pix2[1],out_dir+"alpha_pixy2.png")
TCMB = 2.7255e6
ps = powspec.read_spectrum("../alhazen/data/cl_lensinput.dat")
cmb_map = old_div(enmap.rand_map(shape, wcs, ps), TCMB)
lensed = lensing.lens_map_flat_pix(cmb_map, alpha_pix, order=5)
#lensed = lensing.lens_map_flat(cmb_map, phi)
io.quickPlot2d(cmb_map, out_dir + "unlensed.png")
io.quickPlot2d(lensed, out_dir + "lensed.png")
io.quickPlot2d(lensed - cmb_map, out_dir + "diff.png")
alpha = enmap.gradf(fphi)
io.quickPlot2d(alpha[0], out_dir + "alphax.png")
io.quickPlot2d(alpha[1], out_dir + "alphay.png")
io.quickPlot2d(np.sum(alpha**2, 0)**0.5, out_dir + "alpha.png")
kappa_inverted = -0.5 * enmap.div(alpha, normalize=False)
io.quickPlot2d(kappa_inverted, out_dir + "kappainv.png")
diffper = np.nan_to_num((kappaMap - kappa_inverted) * 100. / kappa_inverted)
diffper[kappaMap < 0.005] = 0.
# NO BEAM, NO NOISE
# Unlensed
unlensed = enmap.rand_map(shape_sim, wcs_sim, ps)
test = False
if test:
pl = io.Plotter(scaleY='log')
for k, lens_order in enumerate(range(5, 0, -1)):
alpha = (lens_order - 2 + 1. + 1) / (5 - 2 + 1. + 1)
print((lens_order, alpha))
# Lens with kappa1
lensed = lensing.lens_map_flat_pix(unlensed,
alpha_pix,
order=lens_order)
# Delens with kappa1
simple_delensed = lensing.lens_map_flat_pix(lensed.copy(),
-alpha_pix,
order=lens_order)
grad_phi = enmap.grad(phi)
lens_residual = lensed - unlensed
#io.quickPlot2d(lens_residual,out_dir+"lensres.png")
simple_residual = simple_delensed - unlensed
#io.quickPlot2d(simple_residual,out_dir+"simpleres.png")
iters = []
cpowers = {}
for polcomb in pol_list:
kappa_stack[polcomb] = 0.
apowers[polcomb] = []
cpowers[polcomb] = []
super_dumbs = []
n0subs = []
# BE CAREFUL WITH THE GORRAM SEEDS!!!!
for i in range(Nsims):
print(i)
unlensed = parray_sim.get_unlensed_cmb(seed=(200 + i))
lensed = lensing.lens_map_flat_pix(unlensed.copy(),
alpha_pix.copy(),
order=lens_order)
#m, = lensing.rand_map(shape, wcs, ps, lmax=lmax, maplmax=maplmax, seed=(seed,i))
klteb = enmap.map2harm(lensed.copy())
klteb_beam = klteb * kbeam_sim
lteb_beam = enmap.ifft(klteb_beam).real
noise = 0. #parray_sim.get_noise_sim(seed=(300+i))
observed = lteb_beam + noise
measured = enmap.downgrade(observed, pixratio)
if i == 0:
shape_dat, wcs_dat = measured.shape, measured.wcs
modr_dat = parray_dat.modrmap * 180. * 60. / np.pi