def cutout_rec(shape, wcs, feed_dict, cmask, kmask, map1_k, map2_k):
""" cutout lensing reconstruction """
feed_dict['X'] = map1_k
feed_dict['Y'] = map2_k
# unnormalized lensing map in fourier space
ukappa_k = s.unnormalized_quadratic_estimator(shape,
wcs,
feed_dict,
"hu_ok",
"TT",
xmask=cmask,
ymask=cmask)
# normaliztion
norm_k = s.A_l(shape,
wcs,
feed_dict,
"hu_ok",
"TT",
xmask=cmask,
ymask=cmask,
kmask=kmask)
# noise
noise_2d = s.N_l_from_A_l_optimal(shape, wcs, norm_k)
# normalized Fourier space CMB lensing convergence map
kappa_k = norm_k * ukappa_k
# real space CMB lensing convergence map
kappa = enmap.ifft(kappa_k, normalize='phys')
return kappa, noise_2d
def get_nlkk_single(estimator,
modlmap,
tnoise,
enoise,
bnoise,
tmask,
pmask,
pols=['TT', 'TE', 'EE', 'EB', 'TB']):
import symlens as s
feed_dict = get_feed(lcltt, lclte, lclee, lclbb, tnoise, enoise, bnoise)
alpha_estimator = estimator
beta_estimator = estimator
npols = len(pols)
masks = {'T': tmask, 'E': pmask, 'B': pmask}
bin_edges = np.arange(40, 2500, 40)
binner = stats.bin2D(modlmap, bin_edges)
cents = binner.centers
nls = np.zeros((npols, npols, cents.size))
Als = []
for i in range(npols):
a, b = pols[i]
Als.append(
s.A_l(shape,
wcs,
feed_dict,
alpha_estimator,
pols[i],
xmask=masks[a],
ymask=masks[b]))
for i in range(npols):
for j in range(i, npols):
print(pols[i], 'x', pols[j])
alpha_XY = pols[i]
beta_XY = pols[j]
a, b = alpha_XY
c, d = beta_XY
if i == j:
xmask = masks[a]
ymask = masks[b]
else:
xmask = masks['T']
ymask = masks['T']
nl = s.N_l_cross(shape,
wcs,
feed_dict,
alpha_estimator,
alpha_XY,
beta_estimator,
beta_XY,
xmask=xmask,
ymask=ymask,
Aalpha=Als[i],
Abeta=Als[j],
field_names_alpha=None,
field_names_beta=None)
cents, nl1d = binner.bin(nl)
nls[i, j] = nl1d.copy()
if i != j: nls[j, i] = nl1d.copy()
ncoadd = get_mv(nls)
return cents, nls, ncoadd
def test_shear():
from orphics import lensing, io, cosmology, maps
deg = 20.
px = 2.0
tellmin = 30
tellmax = 3500
kellmin = 10
kellmax = 3000
bin_width = 20
beam_arcmin = 1.4
noise_uk_arcmin = 7.0
theory = cosmology.default_theory(lpad=30000)
shape, wcs = s.rect_geometry(width_deg=deg, px_res_arcmin=px)
flsims = lensing.FlatLensingSims(shape, wcs, theory, beam_arcmin,
noise_uk_arcmin)
kbeam = flsims.kbeam
modlmap = enmap.modlmap(shape, wcs)
fc = maps.FourierCalc(shape, wcs)
n2d = (noise_uk_arcmin * np.pi / 180. / 60.)**2. / flsims.kbeam**2.
tmask = s.mask_kspace(shape, wcs, lmin=tellmin, lmax=tellmax)
kmask = s.mask_kspace(shape, wcs, lmin=kellmin, lmax=kellmax)
bin_edges = np.arange(kellmin, kellmax, bin_width)
binner = s.bin2D(modlmap, bin_edges)
i = 0
unlensed, kappa, lensed, beamed, noise_map, observed = flsims.get_sim(
seed_cmb=(i, 1),
seed_kappa=(i, 2),
seed_noise=(i, 3),
lens_order=5,
return_intermediate=True)
_, kmap, _ = fc.power2d(observed)
pii2d, kinput, _ = fc.power2d(kappa)
feed_dict = {}
cltt = theory.lCl('TT', modlmap)
feed_dict['uC_T_T'] = theory.lCl('TT', modlmap)
feed_dict['tC_T_T'] = (cltt + n2d)
feed_dict['X'] = kmap / kbeam
feed_dict['Y'] = kmap / kbeam
ells = np.arange(0, 10000, 1)
ucltt = theory.lCl('TT', ells)
feed_dict['duC_T_T'] = s.interp(ells,
np.gradient(np.log(ucltt),
np.log(ells)))(modlmap)
sAl = s.A_l(shape, wcs, feed_dict, "shear", "TT", xmask=tmask, ymask=tmask)
sNl = s.N_l(shape,
wcs,
feed_dict,
"shear",
"TT",
xmask=tmask,
ymask=tmask,
Al=sAl)
sukappa = s.unnormalized_quadratic_estimator(shape,
wcs,
feed_dict,
"shear",
"TT",
xmask=tmask,
ymask=tmask)
snkappa = sAl * sukappa
pir2d3 = fc.f2power(snkappa, kinput)
cents, pir1d3 = binner.bin(pir2d3)
cents, pii1d = binner.bin(pii2d)
cents, prr1d = binner.bin(fc.f2power(snkappa, snkappa))
cents, Nlkk3 = binner.bin(sNl)
pl = io.Plotter(xyscale='loglog')
pl.add(ells, theory.gCl('kk', ells))
pl.add(cents, pii1d, color='k', lw=3)
pl.add(cents, pir1d3, label='shear')
pl.add(cents, prr1d)
pl.add(cents, Nlkk3, ls=":")
pl._ax.set_xlim(10, 3500)
pl.done("ncomp.png")
def test_lens_recon():
from orphics import lensing, io, cosmology, maps
from enlib import bench
deg = 10.
px = 2.0
tellmin = 100
tellmax = 3000
kellmin = 40
kellmax = 3000
grad_cut = None
bin_width = 80
beam_arcmin = 0.01
noise_uk_arcmin = 0.01
theory = cosmology.default_theory(lpad=30000)
shape, wcs = s.rect_geometry(width_deg=deg, px_res_arcmin=px)
flsims = lensing.FlatLensingSims(shape, wcs, theory, beam_arcmin,
noise_uk_arcmin)
kbeam = flsims.kbeam
modlmap = enmap.modlmap(shape, wcs)
fc = maps.FourierCalc(shape, wcs)
n2d = (noise_uk_arcmin * np.pi / 180. / 60.)**2. / flsims.kbeam**2.
tmask = s.mask_kspace(shape, wcs, lmin=tellmin, lmax=tellmax)
kmask = s.mask_kspace(shape, wcs, lmin=kellmin, lmax=kellmax)
with bench.show("orphics init"):
qest = lensing.qest(shape,
wcs,
theory,
noise2d=n2d,
kmask=tmask,
kmask_K=kmask,
pol=False,
grad_cut=grad_cut,
unlensed_equals_lensed=True,
bigell=30000)
bin_edges = np.arange(kellmin, kellmax, bin_width)
binner = s.bin2D(modlmap, bin_edges)
i = 0
unlensed, kappa, lensed, beamed, noise_map, observed = flsims.get_sim(
seed_cmb=(i, 1),
seed_kappa=(i, 2),
seed_noise=(i, 3),
lens_order=5,
return_intermediate=True)
kmap = enmap.fft(observed, normalize="phys")
# _,kmap,_ = fc.power2d(observed)
with bench.show("orphics"):
kkappa = qest.kappa_from_map("TT",
kmap / kbeam,
alreadyFTed=True,
returnFt=True)
pir2d, kinput = fc.f1power(kappa, kkappa)
pii2d = fc.f2power(kinput, kinput)
prr2d = fc.f2power(kkappa, kkappa)
cents, pir1d = binner.bin(pir2d)
cents, pii1d = binner.bin(pii2d)
cents, prr1d = binner.bin(prr2d)
feed_dict = {}
cltt = theory.lCl('TT', modlmap)
feed_dict['uC_T_T'] = theory.lCl('TT', modlmap)
feed_dict['tC_T_T'] = cltt + n2d
feed_dict['X'] = kmap / kbeam
feed_dict['Y'] = kmap / kbeam
with bench.show("symlens init"):
Al = s.A_l(shape,
wcs,
feed_dict,
"hdv",
"TT",
xmask=tmask,
ymask=tmask)
Nl = s.N_l_from_A_l_optimal(shape, wcs, Al)
with bench.show("symlens"):
ukappa = s.unnormalized_quadratic_estimator(shape,
wcs,
feed_dict,
"hdv",
"TT",
xmask=tmask,
ymask=tmask)
nkappa = Al * ukappa
pir2d2 = fc.f2power(nkappa, kinput)
cents, pir1d2 = binner.bin(pir2d2)
cents, Nlkk = binner.bin(qest.N.Nlkk['TT'])
cents, Nlkk2 = binner.bin(Nl)
pl = io.Plotter(xyscale='linlog')
pl.add(cents, pii1d, color='k', lw=3)
pl.add(cents, pir1d, label='orphics')
pl.add(cents, pir1d2, label='hdv symlens')
pl.add(cents, Nlkk, ls="--", label='orphics')
pl.add(cents, Nlkk2, ls="-.", label='symlens')
pl.done("ncomp.png")